Electron configurations of Magnesium
\r\n\tThis book aims to cover recent developments and novel components in gasification technologies that suit the requirements related to CO2 reduction, syngas conversion, hydrogen production, renewable usage, and reliability as an economic process. The conventional gasification process is inherently expensive due to the toxicity and explosiveness of syngas in addition to the difficulty of the impurities removal process. Many novel ideas and processes have tried to overcome these inherent limitations. This book hopes to provide more insights on the future of the utility of gasification technologies at this climate-conscious time.
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Dr. Yun is the vice-president of IAE and is the holder of more than 30 gasification-related patents.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"144925",title:"Dr.",name:"Yongseung",middleName:null,surname:"Yun",slug:"yongseung-yun",fullName:"Yongseung Yun",profilePictureURL:"https://mts.intechopen.com/storage/users/144925/images/system/144925.jpg",biography:"Dr. Yongseung Yun majored in Chemical Engineering and received his Ph.D. degree at the University of Utah, USA, in 1990. He obtained his M.A. from KAIST, Korea, in 1981 and his B.A. from the Yonsei University, Korea, in 1979. He currently works as vice president at the Institute for Advanced Engineering in Korea.\nHe has been working on gasification technology development since 1994, starting from coal gasification to municipal solid wastes gasification, and petroleum coke gasification. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"46861",title:"The Influence of Alloy Element on Magnesium for Electronic Devices Applications – A Review",doi:"10.5772/58460",slug:"the-influence-of-alloy-element-on-magnesium-for-electronic-devices-applications-a-review",body:'This chapter presents a concise feature on past, present and the fast emerging future trends of the effective use of magnesium alloys in electronic industries. Magnesium’s furthermost potential places on all types of handheld electronic devices, from mobile phones to MP3 players, cameras to smartphones, especially with the emission and interference suppression requirements of electronic devices. A lot of effort has been made to understand some properties required in electronic behavior of magnesium alloys and to have a look on their potential for further use.
To understanding some fundamental mechanism involved in any metallic material processing it is important, for example do not stop thinking about the atomic structure, chemical bonding energy and also electron distribution of the element magnesium (Table 1).
Element | \n\t\t\tAtomic number | \n\t\t\tElectron configuration | \n\t\t
Magnesium | \n\t\t\t12 | \n\t\t\t1s22s22p63s2\n\t\t\t | \n\t\t
Electron configurations of Magnesium
The accurate knowledge of electron configuration is particularly related with elastic properties of phases in magnesium alloy (elastic constants, the bulk moduli, shear moduli, Young\'s moduli and Poisson\'s ratio). Many of these phases are ductile. Revision involving the states density (DOS), the number of occupied orbitals (Mulliken electron occupation number) and charge density difference support the understanding of the mechanism of structural stability and mechanical properties [1].
Magnesium is the lightest of all metals used as the basis for structural alloys. The requirement to reduce the weight of automobile components as a result in part of the introduction of legislation limiting emission has triggered renewed interest in magnesium. A wider use of magnesium base alloys necessitates several parallel programs. These can be classified as alloy development, process development, improvement and design considerations [2].
Magnesium alloys are employed as a replacement for plastics when require adequate thermal conductivity properties. This makes magnesium alloys a better choice in electronic appliances to dissipate heat generated by electronic circuits. These alloys are utilized within TVs, LCDs and PC casings. There are many other better magnesium alloy properties. Magnesium alloys are less resistant to cutting, which makes them easier for rapid machining. The Mg alloys are superior at electromagnetic interference (EMI) shielding and often used in casings for mobile phones. Magnesium alloys are easily recyclable, ductile and can absorb vibration well. Magnesium alloys also have excellent resistance to corrosion. The alloys resist dents and are less likely to give dents to other metals [3-5].
Magnesium is the perfect material for applications where weight saving is a precedence, as it has the lowest density of all structural metals. Almost as light in weight as plastic, magnesium has the advantage of greater strength and rigidity, along with inherent EMI shielding and full recyclability. The magnesium benefits are: the lightest of all structural metals; excellent stiffness & strength-to-weight ratio; exceptional EMI shielding properties; high electrical and thermal conductivity; withstands high operating temperatures; cost effective versus many engineered thermoplastic materials; high dimensional accuracy and stability; exceptional thin-wall capability; good environmental corrosion resistance; good finishing characteristics; fully recyclable, all of these, ideal for electronic devices [6-8].
There are several magnesium compositions which are currently being explored. Some of the alloys are at more developed research stages than others, and each alloy has been tailored for specific applications as previously mentioned. Pure magnesium has been used, as well as other elements including Zn, Mn, Al, Ca, Li, Zr, Y, and rare earth metals (RE). These within the Mg matrix create different mechanical and physical properties. If the alloying element can accomplish the metallurgical principle of developing solid solution, then solid solution strengthening can be achieved (Table 2).
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
AZ91 | \n\t\t\t9.0%Al, 0.7%Zn, 0.2%Mn | \n\t\t\tGeneral casting alloy | \n\t\t\tGood castability, good mechanical properties at T<150 0C | \n\t\t
AZ31 | \n\t\t\t3.0%Al, 1.0%Zn, 0.13%Mn | \n\t\t\tWrought magnesium products | \n\t\t\tGood extrusion alloy | \n\t\t
AM60 | \n\t\t\t6.0%Al, 0.15%Mn | \n\t\t\tHigh pressure die-casting alloy | \n\t\t\tBetter toughness and ductility than AZ91. Automotive structural applications | \n\t\t
AM50 | \n\t\t\tMg-Al system | \n\t\t\tGeneral casting alloy | \n\t\t\tGood strength, ductility, energy absorption properties and castability | \n\t\t
AM20 | \n\t\t\tMg-Al system | \n\t\t\tCasting alloy | \n\t\t\tHigh ductility, toughness, poor die-castability | \n\t\t
AE42 | \n\t\t\tMg-4at%Al-2at%rare earths | \n\t\t\tGeneral casting alloy | \n\t\t\tLow castability, good creep behavior | \n\t\t
AE44 | \n\t\t\tMg-Al-rare earth (RE) system | \n\t\t\tGeneral casting alloy | \n\t\t\tBetter creep behavior and castability than AE42 | \n\t\t
ZE41 | \n\t\t\t4.2%Zn, 1.2%RE, 0.7%Zr | \n\t\t\tCasting alloy | \n\t\t\tRE addition improves creep strength at superior temperatures. Pressure tight. | \n\t\t
AS21 | \n\t\t\tMg-Al-Si system | \n\t\t\tCasting alloy | \n\t\t\tFor use at temperatures in excess of 1200C | \n\t\t
AS41 | \n\t\t\t4.2%Al, 1.0%Si | \n\t\t\tGeneral casting alloy | \n\t\t\tBetter creep resistance than AZ91 at elevated temperatures, lower strength | \n\t\t
AJ62 | \n\t\t\tMg-Al-Sr system | \n\t\t\tHigh pressure die-casting (HPDC) | \n\t\t\tSuperior castability, corrosion resistance and creep behavior; good thermal and mechanical strength | \n\t\t
MRI 153M | \n\t\t\tMg-Al-Ca-Sr system | \n\t\t\tCasting alloy | \n\t\t\tFor high temperatures applications up to 150 0C | \n\t\t
MRI230D | \n\t\t\tMg-Al-Ca-Sr system | \n\t\t\tCasting alloy | \n\t\t\tFor high temperatures applications up to 190 0C | \n\t\t
Commercial magnesium alloys and their applications [9]
Solid Solutions are generally stronger than pure metals due to the enhanced crystal structure and the introduction of crystalline defects (for example, dislocations) inside the matrix, but generally, with lower electric conductivity compared to the pure metal. This process is known as solid-solution strengthening. The formation of inter-matrix phases improves the strength of the alloy and is referred to as dispersion strengthening. This is the common practice when forming an alloy. Typically, one metal will have larger atoms relative to other constituents within the material and form a ductile phase (matrix) that consists of the major volume for the alloy. The second metal added to the alloy consists of smaller atoms that are usually both stronger and harder relative to the mechanical properties [10 – 15].
When two metals are mixed together to obtaining an alloy, the resulting material is dispersion strengthened (exist grain boundaries between two or more phases). When dispersion strengthening occurs, micro-scaled precipitates usually form within the grain boundaries, which further strengthen the material as the precipitates prevent slipping of the dislocations or other crystal defects within the grain or phase. Such action induced by heat treatment within the manufacturing process is referred to as precipitation hardening [10 – 15].
Refined magnesium almost always has trace amounts of other impurity elements. These impurities result from the natural composition of magnesium found within the earth, as well as the casting and refining processes used. The degree of impurities after the refining process is dependent upon the efficiency of the refining process itself. Elements currently found within the magnesium include copper (Cu), beryllium (Be), nickel (Ni), and iron (Fe). Standards for characteristic element inclusion in Magnesium are: 4 ppm Be, 100–300 ppm Cu, 35–50 ppm Fe, and 20–50 ppm Ni (weight percent). Other elements may be existent and are referred to as general alloying elements. Always the amount of these impurities should be controlled for any application, especially if pure magnesium is desired [13, 15 – 19].
Magnesium-zirconium-aluminum alloys are utilized in relatively low volume applications where they are processed by sand or lost-wax casting, or wrought products by extrusion or forging. The Mg-Al-Zn group of alloys is the most common alloying elements for room temperature applications. Cerium and zirconium (without aluminium) are used for elevated temperatures and form the Mg-Zn-Zr group (Table 2). Cerium is added to improve strength at the temperatures of 260°C to 370°C. Aluminium is the most effective ingredient in improving results. As little as 2% to 10% aluminium with minor additions of zinc and manganese increases strength and hardness, at the expense of less ductility, without harming weldability and making the alloy receptive to heat treatment [9].
This low density of magnesium and alloys has stimulated the use in helicopter parts, auto parts and portable electronic appliance parts. Magnesium alloys are utilized in many engineering applications where having light weight is a significant advantage. Magnesium alloys have always been attractive to designers due to their low density, only two thirds that of aluminum. This has been a major factor in the widespread use of magnesium alloy castings and wrought products [20-22].
A further requirement in recent years due to superior corrosion performance and dramatic improvements have been demonstrated for new magnesium alloys. Ultra-light magnesium alloys with excellent specific properties potentially met the present demands for lighter and reliable construction. There is a fast emerging trend towards using the lightweight materials and structures principally for electronic devices. The second principal magnesium application field are in electronic devices; see as example in Table 3 for electronic rain sensor housing.
Magnesium Alloy | \n\t\t\tAZ91D | \n\t\t\tAM60Bt | \n\t\t
Alloy Element (%) | \n\t\t\t\n\t\t\t | \n\t\t |
Aluminum | \n\t\t\t8.3-9.7 | \n\t\t\t5.5-6.5 | \n\t\t
Manganese | \n\t\t\t0.15-0.5 | \n\t\t\t0.24-0.6 | \n\t\t
Zinc | \n\t\t\t0.35-1.0 | \n\t\t\t0.22 (max) | \n\t\t
Silicon (max) | \n\t\t\t0.1 | \n\t\t\t0.1 | \n\t\t
Copper (max) | \n\t\t\t0.03 | \n\t\t\t0.01 | \n\t\t
Nickel (max) | \n\t\t\t0.002 | \n\t\t\t0.002 | \n\t\t
Iron (max) | \n\t\t\t0.005 | \n\t\t\t0.005 | \n\t\t
Other Metallic (max) | \n\t\t\t0.02 | \n\t\t\t0.02 | \n\t\t
Magnesium | \n\t\t\tremainder | \n\t\t\tRemainder | \n\t\t
Magnesium Alloys for Electronic Rain Sensor Housing
The AZ91D is a high-purity alloy which has excellent corrosion resistance. As mentioned before, due to low weight, good mechanical and electrical properties, magnesium is widely used for manufacturing of mobile phones, laptop computers, cameras, and other electronic components. Since its too low mechanical strength, pure magnesium must be alloyed with other elements, which confer improved properties. The tendency has been for an increase in the use of magnesium die castings and examples are computer housings and mobile telephone cases where lightness, suitability for thin wall casting and the characteristic of electromagnetic shielding are particular advantages [10, 12, 15, 16].
Magnesium alloys containing more than 1.5% Al are susceptible to stress corrosion and must be stress relieved after welding. Iron, copper and nickel are considered impurities to be limited because they degrade the corrosion resistance of magnesium alloys. Zinc combined with aluminium overcomes detrimental corrosive effects of iron and nickel impurities that may be present in magnesium alloys. The higher the Zn content (over 1%) the higher the hot shortness, causing weld cracking. Manganese improves yield strength and the saltwater resistance of magnesium alloys [10, 12, 15, 16].
Particularly for electronic devices there is alertness due to the some disadvantages of Mg alloys: high reactivity in the molten state, galvanic corrosion resistance, fire hazard, inferior fatigue and creep. The design of the Mg alloy parts is important for adequate drainage, to prevent the accumulation of corrosive substances, such as water/moisture. Iron, nickel and copper reduce the corrosion resistance of Mg alloys. Thus, during the processing of hot/molten Mg alloys, the metal must be shielded by inert gas or flux to overcome fire risk. In machining process of Mg alloys, the fire hazard/risk can be eliminated by avoiding fine cuts, dull tools, and high speeds, using proper tool design to avoid heat buildup, avoiding the accumulation of chips and dust on machines and cloths, and using coolants [10, 12, 15, 16].
Substantial research is still needed on magnesium processing, alloy development, joining, surface treatment, corrosion resistance and mechanical properties improvement to achieve future goals in all requests areas and actually, the amount of greenhouse gases [17]. Production and application technologies must be cost effective for magnesium alloys to make magnesium alloys an economically viable alternative for the automotive industry. For example the automobile contained around 20 kg of magnesium in powertrain and during its peak production consumption of magnesium reached 42,000 ton per year [6].
The global magnesium consumption is projected to rise in 2015 more than 1.3 Mt. Its single largest use, die-cast alloys for the automotive industry, is expected to show the fastest rate of about 10%p.a., underpinned largely by Chinese vehicle production. The use of magnesium cast alloys in computers, communications and consumer electronics, principally for injection molded housings, and uses for the desulphurization of steel, is also expected to grow strongly [23].
The major step for improving the corrosion resistance of magnesium alloys was the introduction of high purity alloys. Alloying can further improve the general corrosion behavior, but it does not change galvanic corrosion problems if magnesium is in contact with another metal and an electrolyte. The galvanic corrosion problem can only be solved by proper coating systems. Different coating methods are used to increase the corrosion resistance of magnesium alloys. Problems with contact corrosion can be minimized, on the one hand, by useful measures and, on the other hand, by an appropriate choice of material couple or the use of protective coatings. Aluminum coating is also used for magnesium alloys using aluminum vapor deposition and finish treatment by resin coating. The Al coating provides high corrosion resistance for magnesium alloys with various metallic sheens [7, 13, 15, 16].
Lightweight, strong and durable, magnesium alloy parts are expanding their sphere of influence into professional grade high-end appliance and electronics products. Manufacturers who are looking for ways to improve quality and reliability are fine tuning product capabilities by adding the precision and versatility of magnesium components. Magnesium is chosen for top of-the-line products that offer superior staying power, and rugged structural and mechanical properties that are unmatched by other materials. Magnesium’s inherent ability to shield internal technology from electromagnetic interference and radio frequency interference (EMI/RFI) make it the ideal light metal for housing electronics.
Stand mixer has long been considered the industry standard in countertop kitchen appliances that chefs, cooks and bakers everywhere count on to mix and blend their favorite recipes. The mixer’s direct drive transmission with all-metal construction is housed in magnesium. Every part of the stand mixer, from its magnesium housed transmission, to its professional-level motor, to its spiral dough hook is equipped for high performance. Product engineers,(for example, from
For this type of equipment, in a high-load and high-temperature application, magnesium was chosen over cast aluminum because of its ability to be molded, since cast aluminum or cast zinc would require secondary machining.
Studies have shown that one of the most important factors in determining corrosion of a magnesium implant is dependent on the purity of the material. It has been well noted that Fe, Ni, and Cu are three critical corrosion elements, which when present within magnesium significantly increase the rate of corrosion. These metals are harmful because of their low solid-solubility limits and because they serve as active cathodic sites within the material itself. Cathodic sites are regions within the material where reduction reactions occur. These regions are essential to the formation of a corrosion cell, and are generally involved with absorbing electrons produced from oxidizing materials (anodic site). At cathodic sites, nearly no material serving as the cathode is lost [13, 15, 16].
The solid-solubility limit of a material is the extent to which an alloying element will dissolve in base materials without forming a different phase. Generally, the higher the solid solubility limit the more likely the alloying element can homogenously disperse within a material. Whereas, low solid-solubility limits will more readily form separate phases within the material. For example, an alloy of Mg-Cu would more likely be separated into two different phases, a copper phase and a magnesium phase. This can be seen in classical cases of pitting corrosion formation.
Magnesium materials exposed to atmospheric conditions will develop a thin gray layer on its surface, which is partially protective. Ions in general are considered to be corrosive if they have the ability to breakdown the protective layer on the surface of magnesium, such as chlorides and oxidizing salts. Passivating elements like chromates, vanadates, and phosphates have been used to retard corrosion because of their ability to form a passive surface film [10-13]
Magnesium is a metal that readily corrodes in the presence of water. Atmospheric conditions contain certain levels of water content in the form of humidity, and the corrosion of magnesium alloys increases with relative humidity. The corrosion of magnesium increases significantly when exposed to pure water. This is especially true to evaluate the corrosion behavior of implantable medical devices manufactured from magnesium based materials. There are several different types of alloying elements used in magnesium based materials in an attempt to control their corrosion properties and feasibility for biomaterial implant. Elements like Mn, Cu, Al, Ca, Zr, Gd, and Zn have all been explored [11-14].
Magnesium alloy AZ91D is used, for example, by Phillips Magnesium Injection Molding, Eau Claire, Wisconsin, to form 15 different parts (power input mount housing; manifold cover; docking station battery door; RAM access, CMOS, PCMCIA, DVD, computer battery and hard drive doors; bezel for display screen frame and cover for back of display screen; the computer chassis and cover; and the docking station chassis and cover) used in the maintenance support device laptop, made by VT Miltope, Hope Hull, Alabama, has high-performance computing.
Magnesium’s advantages include high strength, stiffness, durability, and superior impact resistance, making the laptop parts 20 times stronger than typical thermoplastics. The magnesium alloy housings and enclosures provide effective EMI shielding without using fillers, with an applied conversion coating. Magnesium’s EMI shielding ability is critical during military field operations, since the magnesium parts protect the laptop from radiated and conducted emissions, electromagnetic pulses and radiation hazards, withstanding extreme temperatures, solar radiation, shock, transportation vibration, altitude, rain, humidity, sand, dust, and salt fog [24-26].
Some especial consumer camcorder is housed in a magnesium alloy frame, making the hand-held unit much lighter and easier to hold. The magnesium frame enclosure houses a host of high-tech electronics, and includes features such as a backlit 3D button enabling easy switching from 2D to 3D mode, 5 times optical zoom, the ability to access the battery port and SD card slot while mounted on a tripod, and sports a mottled finish for an easier grip and high-end look.
Another good example is a professional video camera – magnesium and aluminum co-star as die-cast precision components for the out-of-studio camera’s base, chassis, outer panels and internal sub-assemblies. The electronic engineers required lightweight, high-strength components to protect the advanced CCD image sensors, digital video processing and digital control systems inside. Magnesium industries was chosen these demands by die casting the portable video camera’s housing components using magnesium AZ91D and aluminum 380 alloys for the camera’s chassis, base, case panels and related parts [25-26].
Magnesium castings form the front-end chassis, frame, handle, and right and left cover panels and base, while aluminum takes the back-end role of left, right and top cover panels and back frame. An ingenious internal magnesium/front and aluminum/rear stop block system enables a miniature camera to slide into and out of the main video camera’s shell, allowing versatile options for televising in-studio or remotely.
The Integrated Imaging Capsule allows the virtual plug-in mini-camera to nest inside the studio camera head or the portable camera head, delivering equally precise advanced imaging from each camera version. The die-cast rigid magnesium chassis and case panels provide the internal electronics with built-in EMI/RFI shielding. Inside, the chassis is center-gated, with walls cast to 1.016 mm. Outside panels are cast to net shape and some parts receive secondary machining, including: CNC hole drilling and tapping; minimum vibratory noise; hand cleaning; coating; final powder coat or wet paint [25-26].
Since the 90 years the electronics industries try to obtain a new type of clean magnesium alloy known as eco-magnesium alloy [27-28]. Finally in 2012, one of these real advances was developed for LG by the Korea Institute of Industrial Technology (KITECH) with funding from the Korean government, for parts being produced for all of its mobile phones.
In special LG trusts on magnesium parts for its mobile phones due to its lightness and hardness, but sought out a way to mass-produce the magnesium parts using an environmentally responsible method. The die casting process used to produce Eco-Magnesium components virtually eliminates use of damaging sulfur-hexafluoride (SF6) cover gases. As a result, LG plans a reduction in greenhouse gas emissions by a factor of approximately 24,000 during the die casting process without affecting product quality [29, 30].
Technical texts of LG indicate that is considering the use of Eco-Magnesium as soon as possible to other portable devices, such as laptops and tablets. The potential environmental benefits for expanding Eco-Magnesium use to additional electronic devices are substantial, given the company’s projections for massive reductions in greenhouse gas emissions from their mobile phone production alone. Globally, manufacturers in key industries are seeking environmentally sound ways to produce and form magnesium alloys. Magnesium’s spectrum of sustainable product possibilities is expected to broaden and grow, as new forming and processing technologies enable the use of magnesium parts in a host of new high-performance products [29, 30].
Within the last fifteen years several heavy magnesium parts have been assembled in passenger cars, such as gearbox housings and crankcases. Reasons are the new heat resistant alloys and the growing duty of automotive constructors to reduce the vehicles weight and in that way CO2 emission (regulation). The rising quantity of magnesium in automobiles will result in an increasing amount of post-consumer scrap. So far magnesium recycling for magnesium alloy production is done only for clean scrap. Clean in this regard means “free from impurities”, contaminations and sorted according to a chemical composition or a single phase alloy [25-28].
For automotive post-consumer scrap the materials quality strongly depends on the way end-of-life vehicles are treated. Up to date automotive manufacturers have no uniform recycling system for end-of-life vehicles. By proper dismantling of old cars, the bigger magnesium components could be collected and possibly treated similar to class 1 scrap.
So far the biggest part of old cars has been shredded and it is probable that this practice will be continued for reasons of economy. The magnesium fraction can be segregated from the non-magnetic shredder fraction via sink float separation and eddy current separation. A further separation according to chemical composition is claimed to be feasible. Still another problem is unsolved. Due to coatings on magnesium components or aluminium alloy contaminations it is expected that magnesium post-consumer scrap will be enriched with Cu, Fe and Ni during remelting [30].
Eco-conscious design innovations require materials that support and facilitate optimal energy efficiency and longer product life cycles. Magnesium alloys achieve this while being fully reusable and recyclable. From Light Emitting Diode (LED) lighting to lighter vehicles that reduce an engine’s load, to housing longer-life batteries in computers and electric personal transport, magnesium is turning into the designer’s most versatile material choice to improve product functionality and develop user mobility with greater energy efficiency and environmental benefits.
A light-emitting diode (LED) is a two-lead semiconductor light source that resembles a basic pn-junction diode, except that an LED also emits light. When a LED\'s anode lead has a voltage that is more positive than its cathode lead by at least the LED\'s forward voltage drop, current flows and electrons are able to recombine with holes within the device, releasing energy in the form of photons. This effect is called electroluminescence, and the color of the light (corresponding to the energy of the photon) is determined by the energy band gap of the semiconductor [31, 32].
The light’s entire housing is constructed of magnesium alloy AZ91D by AltusLumen, Hong Kong, China [31, 32]. The portable LED is a convenient rechargeable lithium-ion battery light with intelligent power management, featuring a three-dimensional hinge that adjusts to any lighting angle. The ultra-bright and efficient runs for several hours on a single charge, and may be charged by solar panel with an optional solar charger because magnesium conducts heat generated by the LED light away from the LED bulb, extending its usable life.
In addition, only magnesium provides the strength required to produce the unique feature of a three-dimensional swivel hinge. The LED light component is screwed into the magnesium housing, with upper and lower housings joined by a metal pin, force fitting upper and lower housings together. The magnesium housing is cast via injection. After parts are injected, CNC machining removes extra material. Holes are then drilled into the housing for mounting and the magnesium is powder coat painted. The pocket-size portable light follows the company’s guiding sustainability principles that include energy efficiency, using renewable energy, and choosing materials that are recycled and recyclable (AZ91D).
Magnesium is one of the few materials that can be continuously recycled without degradation. Magnesium is easier to recycle and its value makes it an attractive material to recycle. Another company also chooses magnesium alloy housing for its energy efficient LED warm-white and pure-white frosted soft flood lights (LEDtronics, Inc., Torrance, California) and maintains high shock and vibration resistance.
Thermal management performed using the magnesium housing offers the best heat sinks, even better than copper. The long-life LED flood light is used for signage, architectural and landscape lighting, security, aviation, industrial equipment, medical, and theatrical lighting. The magnesium swivel hinge and foldable tripod stand make the TRI-L LED portable light a hands free, go anywhere device. The Parabolic Aluminized Reflector (PAR) 38 Series LED flood light features a magnesium alloy body in a solid-state design that facilitates energy savings of up to 85 percent compared to incandescent flood lights [33].
In the modern market, small and portable electronics are trending increasing. The demand for compact devices that can be easily transported is successful and magnesium is often a key component in meeting this demand. Many magnesium alloys being used to replace plastics are just as light, but they are much stronger and more durable.
Magnesium is also better in regards to heat transfer and dissipation as well as its ability to shield electromagnetic and radio frequency interference. Many electronics require parts or casings with multifarious shapes which are possible with magnesium. Camera, cell phone, laptop and portable media device lodgings are all common applications in addition to arms of hard drives [33, 34].
Electroplated plastics might look like metals but, because the underlying plastic substrate is thermally non-conductive, they do not feel like metals.
This simple matter of aesthetics often disappoints consumers when it is realized, when touched, that an outwardly metallic looking item clearly isn’t what it was thought to be. Because it is a conductive metal, AM-lite® does have a “metallic feel” and so is more attractive to customers. AM-lite® has a much higher stiffness, yield strength and creep strength than unreinforced plastics thus allowing improved thin section light weight designs. The alloy’s electrical and thermal conductivity, and EMS shielding, is also important for the design of consumer electronics such as mobile phones and laptop computers [34].
In the first two decades of the twenty-first century, two big deadly epidemics posed a global public health challenge. Infectious disease known as serious acute respiratory syndrome (SARS) cases first appeared in 2002, and a novel coronavirus (SARS-CoV-2) was reported as the etiologic agent in coronavirus disease 2019 (COVID-19), with the start of a new outbreak at the end of 2019. SARS spread to five continents, prompting the World Health Organization (WHO) to declare the outbreak was caused by a novel pathogen, a member of the coronavirus family that had never been seen before in human history [1]. In 2019, a mysterious pneumonia outbreak occurred in Wuhan, China, which the WHO classified as a pandemic in March 2020 [2]. Globally, cases have been recorded in over 20 nations, regions, or territories across five continents [3].
The world is in the midst of a time of recurrent crises, and conventional crisis management models are struggling to cope with today’s dynamic crises. The new crisis management system should be converted from a passive crisis response to a dominant crisis management system. It is essential to develop a modernization management system for effective crisis response, which should include the immediate implementation of basic preventive measures against emergencies, as well as accurate and rapid diagnosis for containment and clinical management. Furthermore, new developments in disease-related applied research and technology would be needed to slow the COVID-19 pandemic’s spread.
The fields of medicine, research and development, and public health are all being transformed by artificial intelligence (AI) [4]. AI has taken over some routine tasks in the last decade, and its effect on repetitive tasks has already begun. We have all witnessed the information revolution, which in just a few decades has totally transformed the way people operate. The AI era has also resulted in the creation of intelligent advanced solutions for different aspects of life: AI can be used to optimize quantitative activities on a wide scale; it can be used to measure and practice a planned action or project under various conditions; and it can be used to assist in job optimization processes in various industries.
AI has reached a crucial juncture in its growth and implementation. Artificial neural networks, machine learning, and deep learning are examples of AI systems that have made substantial progress. In several tasks, AI algorithms have been able to mimic or even outperform the human brain. Machine learning, as opposed to traditional statistical analysis methods that use a predetermined equation as a model, can account for all interactions among variations and integrate new data to update algorithms [5]. Due to their important information processing properties in terms of nonlinearity, high levels of parallelism, noise and fault tolerance, as well as learning, generalization, and adaptive capabilities, AI systems are advantageous [6]. AI is not only a tool for assisting humans with all types of technological and mental tasks, but also an extension of their senses and abilities.
There is an immediate need for safety assurance and cost efficiency in the management of public health crises as a result of the recent global epidemic. Public health surveillance has benefited greatly as a result of recent AI advancements. There is an increasing body of knowledge in the field of AI-enabled and AI-enhanced public health monitoring research [7]. AI is becoming increasingly important in evidence-based approaches to efficiently respond to public health emergencies.
Public health emergencies are a subset of public emergencies that are related to health incidents and have an inclusion and exclusion arrangement with public emergencies. The Emergency of Public Health (Emergency of Public Health) is described as “mainly including infectious diseases, mass diseases of unknown origin, food safety and occupational hazards, animal epidemics, and other events that seriously affect public health and life safety” in the “China National Overall Emergency Plan for Public Emergencies” promulgated on January 8, 2006.
The recovery phase in public health crisis management refers to the stage during which the crisis is gradually alleviated and eliminated. The flow of factors that trigger disasters has slowed, and public health emergencies have been effectively addressed. The government’s goal at this time is to reduce the impact of public health crises, contribute to social and economic recovery, summarize crisis management flaws, and improve the experience of managing public health emergencies in crisis.
It is critical to use AI and other technologies to promote the resumption of work and development in order to ensure sustainable and stable economic growth. Intelligent network systems have been used in China [8] to carry out online workplace, online teaching, and other activities. Companies must “not close” during the epidemic, and schools must “suspend classes without suspension.” During the nationwide “war epidemic,” AI networks such as WeLink, DingTalk, and Tencent Conference were widely popularized in order to minimize the losses incurred by shutdowns and output shutdowns, which played a positive role in reducing crowd gathering and reducing the risk of cross-infection while going out [9]. On the other hand, using a big data platform to analyze the migration and traffic situation in each region, as well as AI technology to prevent the epidemic from resuming, and to genuinely achieve safe resumption of work and development. Manually processing these data makes it difficult to ensure the data’s validity and timeliness. Experts use AI to assist them, collect crisis-related data, and determine the type of crisis [10]. Moreover, AI calculates the severity of the crisis’ effects and analyzes the causes of the crisis. Furthermore, AI allows for early detection of a problem, allowing for more time to deal with it.
The period in which the crisis will break out in the crisis management of public health crises is referred to as the preparatory stage of crisis management for public health emergencies. Since the onset of public health emergencies is uncertain and unpredictable, it is important to track and alert them. For this stage, improving the ability to monitor and respond to public health emergencies is the main focus of the government’s work. The preparatory stage of crisis management for public health emergencies consists of two parts: crisis early warning, crisis training and exercises [10].
Early notice of public health crises is a vital task in the planning stage of disaster prevention. When a crisis occurs, successful early warning will significantly speed up the organization’s response time. To develop an infectious disease outbreak early warning system, governments of various countries currently depend primarily on conventional surveillance methods (collaboration of medical institutions at all levels, disease prevention and control centers, and influenza-like case monitoring sentinel hospitals, and medical institutions diagnose and record clinically diagnosed and confirmed cases of influenza). However, there are some disadvantages of this monitoring system: the data collected is from a single source, and there is no comparison or correction of data from other sources; the data acquisition process of daily sampling and weekly summary, the data results are comparatively lagging; the monitoring consumes a lot of manpower and material resources, and the monitoring covers the entire country; the monitoring consumes a lot of manpower and material resources, and the monitoring covers the entire country. The accuracy of the data would be affected by an error in any node in the network [10]. The use of AI to perform infectious disease forecasting and early warning work, as well as monitoring social media, online news posts, and government reports for signs of infectious disease outbreaks, can significantly assist relevant government agencies in keeping track of the epidemic, rationally allocating medical resources, and improving advances. The cost of national disease prediction and infection prevention and control is reduced by the success rate of prevention. By scanning foreign language news stories, animal and plant disease reports, and various official statements, the AI system provided alerts to its customers, recognizing the first foreign alert of the epidemic at an early stage. Machine learning has been used to track, locate and report on infectious spread. It provides alerts to a wide range of clients, including health care, government, industry, and public health organizations. It also serves as an alert about the existence of a new coronavirus [11]. In several aspects of the global battle against the epidemic, AI has already played a valuable but fragmented role. Screening, contact tracing, contact alerts, diagnosis, automatic deliveries, and laboratory drug discovery are only a few of the applications. AI has already played a useful but fragmented role in many aspects of the global fight against the epidemic. It has been widely used in screening, contact tracing, contact alerts, diagnosis, automated deliveries, and laboratory drug discovery [11]. It also predicts whether or not a person is infectious in advance, as well as the seriousness of the infection. By doing some general data analysis, one can significantly reduce waiting time, determine whether or not one has come into contact with virus carriers, and prevent the virus from spreading. Systematic planning and drills are an important way to enhance emergency response in the event of a disaster. Knowledge map technology can be used in public health emergency training to combine and link all of the information in the knowledge base and create a bottom map that covers all knowledge and records the connections between knowledge and knowledge, significantly increasing the scope and depth of training. The use of AI technology to perform public health emergency simulation exercises, build public health emergency simulation scenarios, deduce public health emergency handling protocols, and summarize the effects of public health emergency crisis exercises. It will help the government assess the epidemic’s condition, develop decision-making and deployment capabilities for epidemic prevention and control, and test a range of mature response plans in simulation, setting the groundwork for potential rapid response and precise policy implementation in actual combat in the future [18].
Is it a Cough or a Covid? COVID-19 Detection Using Artificial Intelligence from Cough Sounds.
Increased disease screening and early warning capabilities can help to dramatically delay the spread and effect of a disease. Recent progress in developing deep learning AI models to classify cough sounds as a COVID-19 prescreening tool has shown early promise. Cough-based diagnosis is a non-invasive, cost-effective, and scalable method of diagnosing COVID-19 that, if approved, could be a game-changer in the battle against the virus. Cough sounds have recently been tested as a preliminary diagnostic or a prescreening technique for Covid-19 identification in asymptomatic individuals by AI researchers [11]. This is advantageous because the virus can trigger subtle changes in the body that can be identified by complex algorithms combining audio signal processing and machine learning, even though no symptoms are present. This technology may also be more efficient than the standard strategy of prescreening for COVID-19 based on temperature, especially in asymptomatic patients.
AI expands data access. AI’s predictive ability is based on the volume and variety of data available; optimizing emerging tools requires extensive data access across the healthcare ecosystem. To prevent gaming of findings and prejudice, data scientists must commit to robust research over several parameters. AI allows for more concentrated collaboration. Thousands of inputs must be incorporated by scientists and technologists in collaboration with clinical specialist physicians, including lab results, vital signs, drug administration, prescription doses and durations, length of stay in hospital, and patient and hospital demographics, to name a few. Clinicians can participate in the validation process and feature engineering for each organ- or condition-specific version of an AI surveillance system so that the solution can produce customized, actionable risk scores that clinicians will use. Moreover, transparency in clinical surveillance is aided by AI. These surveillance solutions can enable clinicians to apply their own clinical judgment to the performance by offering a visual representation of how and why AI made the predictions. Any AI-enabled tool should do the same thing to promote clinician buy-in and the requisite change management for widespread adoption.
The government’s approaches or policies to combat the outbreak are unquestionably important in effectively controlling the virus’s spread. AI can be used to help them make the best decision possible.
Key parameters that define the characteristics of the spread, such as the transmission rate, incubation time, population density in the region, and so on, can be used to create a simulated model that mimics the actual environment of pandemics.
Following the development of the environment simulator, Reinforcement Learning can be used to determine the best strategy for achieving our aim of preventing virus spread while minimizing economic costs.
A simple compartmental model in epidemiology, known as the SIR model, is commonly used to simulate the spread of disease [12].
where
The SIR model is a fundamental model for studying individual flow between compartments, assuming that all individuals within a compartment are homogeneous. An Agent-Based Model is developed to simulate the behavior of heterogeneous individuals, taking into account their characteristics [13, 14]. We may, for example, identify various types of agents, such as individuals, families, businesses, and governments, and then enable them to communicate with one another. Each type of agent may have different attributes, such as age, location of the person, location of the house/business, and wealth of the agents. Different activities, such as going to work, going home, or making business connections, can be simulated at different times.
Different social, epidemiological, and economic parameters, such as individual mobility, incubation, transmission, recovery time, income, and GDP, must be specifically defined by domain experts, using empirical evidence, or designed by the author to simulate the attributes and actions of agents. During simulation, the economic impact and pandemic statistics, such as individual wealth and the number of active cases, can be generated for evaluation.
Reinforcement Learning (RL) is an area of machine learning that focuses on learning strategies or sequential decisions in order to optimize long-term reward in the defined environment.
A basic reinforcement learning model is shown in Figure 1 [15]. It involves an Agent who interacts with the environment in each time steps by taking different actions. At time
Basic reinforcement learning model.
To train the RL model and learn the optimal policy, one way is to use Monte Carlo Tree Search, which is a searching algorithm to determine the best moves. It repeated the process of “Selecting ➔ Expanding ➔ Simulating ➔ Updating” to update the nodes in a tree (Figure 2) [16]. Each node in the tree represents the action we can take, with a node value which can be the probability of winning or the expected reward. At “Selecting” stage, we select the path by the value of the node until we reach the leaf node at the end of the branch. At the leaf node, we “Expand” by randomly choosing the action from the action space. Then, we “Simulate” the complete rollout, until the terminal state and obtain the final cumulative reward. The reward will then be backpropagated to update the values in each node along the path.
Phases of the Monte Carlo tree search algorithm. A search tree, rooted at the current state, is grown through repeated application of the above four phases.
RL, in combination with the Agent-Based Model and the SIR model, will help the government make the best decision possible to combat the pandemic [17]. The state and the environment in RL can be simulated by the Agent-Based Model and SIR Model.
The reward function can be designed based on the pandemic statistics and economic statistics generated from the Agent-Based Model. The trained RL model will be able to advise the government on the best course of action to take at various stages of the pandemic and scenarios in order to contain the pandemic with the least amount of economic effect. The agent is to be trained with empirical demographic data, pandemic data and economic data in pandemic time to simulate the impacts of policies conditioned with the predicted pandemic data. An Action space can be defined. Social distancing, lockdown, company and school closures, wearing a face mask, doing nothing, public hygiene promotion and so on. The impact of policies can be highly dimensioned vectors and subject to execution error. However, all these attributes can be inputs of the simulations and the prediction error can be reduced by more data input.
With the numerous simulations, government can have a full profile of impacts of policies to be taken in different scenarios by making assumption on the parameters of the effectiveness of policy, say the shut-down of schools reduce the younger age group infection by {38.2%, 50%, 61.8%}, and the impact of this reduction can be propagated to other age groups through a trained Boltzmann Machine which depict the dynamics of infection rate between the age groups. The fidelity of the simulations is correlated with the complexity of model and number of data used to train the model.
The reward functions can deviate across regions; however the reward function can be designed based on the pandemic statistics and economic statistics generated from the Agent-Based model. The trained RL model would allow the government to select the optimal policy and evaluate the drawbacks before the policy is implemented.
Many AI technologies, such as robots assisting in hospital transportation, have accelerated as a result of the epidemic’s social isolation. One of the most contentious examples is contact tracing. Many countries around the world have successfully developed contact tracing systems, allowing them to efficiently monitor the epidemic’s spread. This strategy, however, is seen as an infringement of privacy in the United States, Europe, and other countries. Although it has a bright future, there are challenges in the areas of privacy, data processing, ethics, and social issues. When it comes to medical information, these questions must be seriously addressed in the light of public health or personal health. During a public health crisis, the government must strike a balance between citizens’ rights and the need for effective prevention and control measures to efficiently control disease transmission until the outbreak is over, and then return to normal.
No one wants to replicate the epidemic’s mistakes. AI will be used in the future to prevent epidemics from arising and spreading. Hospitals will make effective use of sensors and wearable devices to collect outbreak data and report possible hazards in a timely manner, allowing them to properly respond to the crisis and avoid losing control again. Inevitably, privacy concerns would arise in the former. There are some notable inconsistencies between privacy rights and the requirements of machine learning. Although privacy security necessitates as little data sharing as possible, machine learning necessitates as much data as possible. Many countries are concerned that misuse of contact tracing could compromise privacy, so they have developed and implemented a variety of privacy security technologies. AI techniques, processes, and technology are being used to develop health care and programs. The good news is that they can coexist, and AI is a double-edged sword that can help to foster global governance and cultural change.
Deep learning has the ability to process multidimensional data at high speeds while also facilitating the recognition of unique features, making it one of AI’s most far-reaching applications. Deep learning and deep neural networks have already been widely used in a variety of medical applications, including medical image recognition, drug design, decision support, and predictive analytics, to deliver accurate and rapid algorithmic interpretation [18]. A straightforward blueprint for how AI will be infused into health care as a result of the pandemic. The most accurate insights into health and disease can come from all of the world’s results. AI will assist us in being adequately prepared for the next pandemic, efficiently responding to public health monitoring and emergencies, and advancing global healthcare systems.
In order to enhance the timeliness and accuracy of outbreak detection and early warning approaches, public health researchers continuously analyze and explore sensor data and indicators to and from the physical world, including health, environmental, social, financial, and economic aspects, among others. Deep learning has been used to identify multiple infectious disease outbreaks. A dynamic neural network model was created to predict the probability of infectious disease outbreaks in the United States, such as Zika virus (ZIKV). Decision-makings can easily modify the risk of an indicator, the risk classification system, and the forecast window for prediction based on their own unique needs [19]. Support vector machine (SVM), gradient boosting machine, and random forest (RF) were applied to simulate the global distribution of infectious diseases. To train the models, multidimensional and multidisciplinary datasets were qualified and quantified, such as social variables, incident medical records, high-risk areas, and cyberspace data. The suitability of the temperature has been stated to have the best discriminatory power among variables, and random forest (RF) is known to obtain the highest area under curve (AUC) value [20]. Each bootstrap sample was fitted with an unpruned decision tree. The risk maps were accurate in over 80% of the observed risk ranks falling within the 80% prediction interval, according to random bootstrap samples drawn from the results [21]. The use of data from the cyberspace, such as keyword google searches, Key Opinion Leaders’ blogs, and social media networking messages, has taken significant effort. Machine learning has been used for sentiment analysis and text classification from social media data for surveillance purposes. In India, a social media-based early warning system for mosquito-borne disease has been proposed [18].
There are a number of major effects: For instance, there was no intelligent big data research in the past. The network accounts of various hospitals could not be compared after a single patient was diagnosed with an infectious disease. Now, using AI’s dynamic perception, the device may display an outbreak or cluster of infectious disease under uncommon conditions in real time via case reports. Second, the use of AI technology to evaluate the infectious case’s time, space, and meteorological factors may have an effect on the local agricultural product market and economic conditions. Third, disease patterns can be forecast and early alerts for key ties can be issued using infectious disease data and local environmental monitoring. While AI’s dynamic models of infectious diseases are consistent, the neural network model of experts must be introduced because infectious diseases have different epidemics in different regions.
The AI industry should concentrate on core technology research and development in order to address technological challenges. Overall, the application of AI technology for disease prevention and control is still in its early stages of growth. Furthermore, AI also has an inexplicability that prevents it from being fully incorporated into the epidemiological system. The use of AI technology in disease prevention has been hampered by the lack of timely data collection and integration capabilities. As a result, play a bigger role in command. Epidemic modeling can be used to perform theoretical research on interpretability and improve the processing of large multi-dimensional data to this end.
AI has been commonly used in the medical field as a result of continuous optimization of medical data and algorithm models. AI has achieved a great improvement in work efficiency in a subversive way, particularly during this special time of the new crown epidemic, and has spawned new demands. The use of AI has demonstrated quick landings, a wide range of effects, and major effects. However, AI in disease prevention and control is still in its early stages of research, and there are still flaws and issues in many areas. The use of AI in disease prevention and control should be thoroughly investigated, and a set of creative and reliable AI approaches should be used to aid in the detection and treatment of epidemics, as well as to minimize the risk of staff cross-infection. Improve disease management and control effectiveness, and provide strong scientific and technical support for winning the fight against epidemic prevention and control.
AI models have been applied to detect outbreaks of infectious diseases. Researchers have a long history of successfully developing a global outbreak surveillance approach using Internet-based approaches. Internet-based disease tracking approaches have provided a real-time alternative to conventional indicator-based public health disease surveillance [22, 23]. Internet-based monitoring systems use a range of open-source Internet data, including online news and social media, as well as other Internet-based data sources, to detect early warning signals of threats to public health. AI techniques have played a significant role in a series of data processing and analysis activities. AI techniques have recently become popular for completing tasks in highly dynamic, complex, and data-rich environments. In the modern age of public health approaches, it is critical and important. Machine learning and deep learning as AI core technologies are among the most important, methodologically, for fundamental and increasing interests, intense research activities in the interdisciplinary field of AI. Despite the impressive list of achievements already achieved, AI technologies in the sense of public health and public health monitoring are still in their early stages of growth, with a lot of potentials yet to be realized. Outbreak identification, early warning, trend prediction, and public health evidence-based approaches effectively response modeling and assessment are among the core tasks of public health surveillance and response, particularly in light of the current COVID-19 pandemic.
By pinpointing specific demographics or geographies where population health issues exist, AI and machine learning can help to target and precisely implement education and treatment programs and reduce spending waste. AI enables computers to mimic the cognitive function of human minds, and machine learning gives computers the ability to learn without being explicitly programmed. By using AI and machine learning to review vast sets of real-time data, health experts can identify at-risk populations for any number of diseases, from diabetes to heart disease. Throughout the coronavirus pandemic, the industry has witnessed the power of clinical surveillance. With a broad array of discrete tests that can identify a COVID-19 infection, health systems and public health authorities have needed a way to interpret and track the patients with infections.
Data is at the heart of clinical surveillance. When data is combined with evidence-based clinical decision support, a single source of reality can be created that connects the disease’s related symptoms, allowing for the discovery of how quickly a disease is progressing and what lab tests reveal. Keeping up with the latest advances in medical terminology and the related diagnosis and procedure codes is critical for recognizing clinical patterns as well as securing support, funding and reimbursement. Many health systems have transitioned to finding out the patterns in COVID-19 and better predicting respiratory and organ failures associated with the virus, despite being reluctant to implement technology in the past.
When the pandemic struck, healthcare providers immediately shifted their focus to include COVID-19 updates in their clinical surveillance activities. Hospitals and healthcare systems have been able to proactively monitor patient status for earlier interventions and broaden data flow in significant ways with a centralized, global view of COVID-19 cases coupled with real-time alerting. Age, where the disease was possibly contracted, if the patient was examined, and how long the patient was in the ICU are only a few of the important patient measurements that have been monitored. Patients’ pre-existing conditions were taken into account during surveillance. This data trail assists providers in developing a constantly evolving coronavirus profile and provides key data points for reporting to state and local governments and public health agencies. Clinical monitoring now brings together information from various areas of the hospital and clinics into a centralized view of COVID care, such as lab results, patient data, co-morbidities, mortality, and drugs, since there are no other ways to put together seemingly fragmented information.
COVID-19 puts people at risk of sepsis, so they wanted to identify those who were most at risk. Many AI-powered fast-tracking techniques were put to the test. This health epidemic shows what can be done to anticipate and avoid a variety of chronic health concerns. This technology can then be used to save lives and money in cases where prevention has proven to be ineffective. To achieve those savings, it is necessary to refine the use of AI for clinical surveillance; 2) extend access to everything from electronic health records (EHR) to knowledge that exists outside of direct clinical settings, ranging from the omics to social determinants of health; and 3) differentiate AI hype from solutions that offer proven, actionable insights for specific clinical concerns.
Though COVID-19 appeared to be a test ground for machine learning and AI, the industry had been focusing on harnessing technology’s power for healthcare-associated infections (HAI) for some time. According to publicly available reports, HAIs cost the US healthcare system up to $45 billion a year [24]. On any given day, about one out of every 31 patients will be infected with at least one HAI [25]. One example is
Rules-based systems are less effective for these “edge” scenarios, as researchers know, since each new data feature necessitates a new rule. AI at warp speed will help hospitals and communities respond to complex cases like COVID-19, C. diff, and even sepsis until clusters, outbreaks, or critical medical emergencies worsen. Clinical surveillance based on AI can monitor when relevant factors arise in a specific way and understand how timing plays a role in interactions. Time is difficult to incorporate, but recognizing when the white blood cell count has increased or decreased, for example, is crucial to make reliable C. diff predictions.
These types of forecasts can make a huge difference in clinical emergencies like brain injury, heart arrest, and respiratory failure in healthcare organizations all over the world - cases where minutes can mean the difference between life and death. Clinical surveillance with AI has the ability to provide next-generation decision-support resources that incorporate powerful technology, public health’s preventive emphasis, and clinicians’ diagnosis and treatment expertise. As a result, surveillance has the potential to play a key role in achieving the quality and cost goals that our industry has long pursued.
Overall, the use of AI technology for disease prevention and control is still in its early stages of investigation; there is an inexplicability about AI that prevents it from being effectively incorporated into the epidemiological system, and data collection and integration capacity building is still lacking. Due to lag and other problems, AI technology has been largely restricted from playing a larger role in epidemics prevention and control. To that end, disease modeling should be used for theoretical interpretability analysis, and large multi-dimensional data processing capacities should be enhanced to compensate for the corresponding technological flaws. However, it is important to understand and acknowledge the weaknesses and potentially major prejudices associated with public health big data, and there is still space for improvement. To comply with social ethics and norms, intellectual properties in algorithm methodologies and interpretability, as well as privacy security, should be given serious consideration. AI-enabled and –enhanced evidence-based public health monitoring and response, as seen in various AI applications in the medical sector, has real potential, but there are major challenges ahead.
We would like to thank Dr. Zheng Xiang from the University of Hong Kong for his support in the literature review.
The authors declare no conflict of interest.
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In this work, we are interested in the Neuromate robot. The latter uses the procedure of stereotaxic surgery but with better planning, greater precision and simpler execution. The Neuromate robot allows in particular the registration with intraoperative images (ventriculographies, and especially angiographies) in order to perfect the planning. In this book, we focus on the contact force measurement system required for the effectiveness of the stimulation between the robot probe and the patient’s head and thus ensure the safety of the patient. A force sensor is integrated upstream of the wrist, the pressure sensor is part of a silicon matrix that has been bonded to a metal plate at 70°C. The study was carried out under the software COMSOL Multiphysics, ideally suited for the simulation of applications (Microelectromechanical systems) “MEMS”. After electromechanical stationary survey, deflection of the quadrant when the pressure difference across the membrane was 25 kPa, as expected, the deviation was expected to be greatest at the center of the membrane. The proposed sensor structure is a suitable selection for MEMS capacitive pressure sensors.",book:{id:"6865",slug:"becoming-human-with-humanoid-from-physical-interaction-to-social-intelligence",title:"Becoming Human with Humanoid",fullTitle:"Becoming Human with Humanoid - From Physical Interaction to Social Intelligence"},signatures:"Hacene Ameddah",authors:[{id:"302678",title:"Dr.",name:"Hacene",middleName:null,surname:"Ameddah",slug:"hacene-ameddah",fullName:"Hacene Ameddah"}]},{id:"70653",title:"Living and Interacting with Robots: Engaging Users in the Development of a Mobile Robot",slug:"living-and-interacting-with-robots-engaging-users-in-the-development-of-a-mobile-robot",totalDownloads:794,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Mobile robots such as Aldebaran’s humanoid Pepper currently find their way into society. Many research projects already try to match humanoid robots with humans by letting them assist, e.g., in geriatric care or simply for purposes of keeping company or entertainment. However, many of these projects deal with acceptance issues that come with a new type of interaction between humans and robots. These issues partly originate from different types of robot locomotion, limited human-like behaviour as well as limited functionalities in general. At the same time, animal-type robots—quadrupeds such as Boston Dynamic’s WildCat—and underactuated robots are on the rise and present social scientists with new challenges such as the concept of uncanny valley. The possible positive aspects of the unusual cooperations and interactions, however, are mostly pushed into the background. This paper describes an approach of a project at a research institution in Germany that aims at developing a setting of human–robot-interaction and collaboration that engages the designated users in the whole process.",book:{id:"6865",slug:"becoming-human-with-humanoid-from-physical-interaction-to-social-intelligence",title:"Becoming Human with Humanoid",fullTitle:"Becoming Human with Humanoid - From Physical Interaction to Social Intelligence"},signatures:"Valerie Varney, Christoph Henke and Daniela Janssen",authors:[{id:"299092",title:"Dr.",name:"Valerie",middleName:null,surname:"Varney (neé Stehling)",slug:"valerie-varney-(nee-stehling)",fullName:"Valerie Varney (neé Stehling)"},{id:"299578",title:"Dr.",name:"Daniela",middleName:null,surname:"Janssen",slug:"daniela-janssen",fullName:"Daniela Janssen"},{id:"299580",title:"MSc.",name:"Christoph",middleName:null,surname:"Henke",slug:"christoph-henke",fullName:"Christoph Henke"}]},{id:"66784",title:"Physical Interaction and Control of Robotic Systems Using Hardware-in-the-Loop Simulation",slug:"physical-interaction-and-control-of-robotic-systems-using-hardware-in-the-loop-simulation",totalDownloads:966,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Robotic systems used in industries and other complex applications need huge investment, and testing of them under robust conditions are highly challenging. 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He received his Ph.D. in Molecular Biology with his thesis “Genetic variability of the tick-borne encephalitis virus in natural foci of Novosibirsk city and its suburbs.” His primary field is molecular virology with research emphasis on vector-borne viruses, especially tick-borne encephalitis virus, Kemerovo virus and Omsk hemorrhagic fever virus, rabies virus, molecular genetics, biology, and epidemiology of virus pathogens.",institutionString:"Russian Academy of Sciences",institution:{name:"Russian Academy of Sciences",country:{name:"Russia"}}},{id:"310962",title:"Dr.",name:"Amlan",middleName:"Kumar",surname:"Patra",slug:"amlan-patra",fullName:"Amlan Patra",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/310962/images/system/310962.jpg",biography:"Amlan K. Patra, FRSB, obtained a Ph.D. in Animal Nutrition from Indian Veterinary Research Institute, India, in 2002. He is currently an associate professor at West Bengal University of Animal and Fishery Sciences. He has more than twenty years of research and teaching experience. He held previous positions at the American Institute for Goat Research, The Ohio State University, Columbus, USA, and Free University of Berlin, Germany. His research focuses on animal nutrition, particularly ruminants and poultry nutrition, gastrointestinal electrophysiology, meta-analysis and modeling in nutrition, and livestock–environment interaction. He has authored around 175 articles in journals, book chapters, and proceedings. Dr. Patra serves on the editorial boards of several reputed journals.",institutionString:null,institution:{name:"West Bengal University of Animal and Fishery Sciences",country:{name:"India"}}},{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.png",biography:"László Babinszky is Professor Emeritus, Department of Animal Nutrition Physiology, University of Debrecen, Hungary. He has also worked in the Department of Animal Nutrition, University of Wageningen, Netherlands; the Institute for Livestock Feeding and Nutrition (IVVO), Lelystad, Netherlands; the Agricultural University of Vienna (BOKU); the Institute for Animal Breeding and Nutrition, Austria; and the Oscar Kellner Research Institute for Animal Nutrition, Rostock, Germany. In 1992, Dr. Babinszky obtained a Ph.D. in Animal Nutrition from the University of Wageningen. His main research areas are swine and poultry nutrition. He has authored more than 300 publications (papers, book chapters) and edited four books and fourteen international conference proceedings.",institutionString:"University of Debrecen",institution:{name:"University of Debrecen",country:{name:"Hungary"}}},{id:"201830",title:"Dr.",name:"Fernando",middleName:"Sanchez",surname:"Davila",slug:"fernando-davila",fullName:"Fernando Davila",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/201830/images/5017_n.jpg",biography:"I am a professor at UANL since 1988. My research lines are the development of reproductive techniques in small ruminants. We also conducted research on sexual and social behavior in males.\nI am Mexican and study my professional career as an engineer in agriculture and animal science at UANL. Then take a masters degree in science in Germany (Animal breeding). Take a doctorate in animal science at the UANL.",institutionString:null,institution:{name:"Universidad Autónoma de Nuevo León",country:{name:"Mexico"}}},{id:"309250",title:"Dr.",name:"Miguel",middleName:null,surname:"Quaresma",slug:"miguel-quaresma",fullName:"Miguel Quaresma",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/309250/images/9059_n.jpg",biography:"Miguel Nuno Pinheiro Quaresma was born on May 26, 1974 in Dili, Timor Island. He is married with two children: a boy and a girl, and he is a resident in Vila Real, Portugal. He graduated in Veterinary Medicine in August 1998 and obtained his Ph.D. degree in Veterinary Sciences -Clinical Area in February 2015, both from the University of Trás-os-Montes e Alto Douro. He is currently enrolled in the Alternative Residency of the European College of Animal Reproduction. He works as a Senior Clinician at the Veterinary Teaching Hospital of UTAD (HVUTAD) with a role in clinical activity in the area of livestock and equine species as well as to support teaching and research in related areas. He teaches as an Invited Professor in Reproduction Medicine I and II of the Master\\'s in Veterinary Medicine degree at UTAD. Currently, he holds the position of Chairman of the Portuguese Buiatrics Association. He is a member of the Consultive Group on Production Animals of the OMV. He has 19 publications in indexed international journals (ISIS), as well as over 60 publications and oral presentations in both Portuguese and international journals and congresses.",institutionString:"University of Trás-os-Montes and Alto Douro",institution:{name:"University of Trás-os-Montes and Alto Douro",country:{name:"Portugal"}}},{id:"38652",title:"Prof.",name:"Rita",middleName:null,surname:"Payan-Carreira",slug:"rita-payan-carreira",fullName:"Rita Payan-Carreira",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRiFPQA0/Profile_Picture_1614601496313",biography:"Rita Payan Carreira earned her Veterinary Degree from the Faculty of Veterinary Medicine in Lisbon, Portugal, in 1985. She obtained her Ph.D. in Veterinary Sciences from the University of Trás-os-Montes e Alto Douro, Portugal. After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. She is also a frequent referee for various journals.",institutionString:null,institution:{name:"University of Évora",country:{name:"Portugal"}}},{id:"283019",title:"Dr.",name:"Oudessa",middleName:null,surname:"Kerro Dego",slug:"oudessa-kerro-dego",fullName:"Oudessa Kerro Dego",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/283019/images/system/283019.png",biography:"Dr. Kerro Dego is a veterinary microbiologist with training in veterinary medicine, microbiology, and anatomic pathology. Dr. Kerro Dego is an assistant professor of dairy health in the department of animal science, the University of Tennessee, Institute of Agriculture, Knoxville, Tennessee. He received his D.V.M. (1997), M.S. (2002), and Ph.D. (2008) degrees in Veterinary Medicine, Animal Pathology and Veterinary Microbiology from College of Veterinary Medicine, Addis Ababa University, Ethiopia; College of Veterinary Medicine, Utrecht University, the Netherlands and Western College of Veterinary Medicine, University of Saskatchewan, Canada respectively. He did his Postdoctoral training in microbial pathogenesis (2009 - 2015) in the Department of Animal Science, the University of Tennessee, Institute of Agriculture, Knoxville, Tennessee. Dr. Kerro Dego’s research focuses on the prevention and control of infectious diseases of farm animals, particularly mastitis, improving dairy food safety, and mitigation of antimicrobial resistance. Dr. Kerro Dego has extensive experience in studying the pathogenesis of bacterial infections, identification of virulence factors, and vaccine development and efficacy testing against major bacterial mastitis pathogens. Dr. Kerro Dego conducted numerous controlled experimental and field vaccine efficacy studies, vaccination, and evaluation of immunological responses in several species of animals, including rodents (mice) and large animals (bovine and ovine).",institutionString:"University of Tennessee at Knoxville",institution:{name:"University of Tennessee at Knoxville",country:{name:"United States of America"}}},{id:"251314",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Gardón",slug:"juan-carlos-gardon",fullName:"Juan Carlos Gardón",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/251314/images/system/251314.jpeg",biography:"Juan Carlos Gardón Poggi received University degree from the Faculty of Agrarian Science in Argentina, in 1983. Also he received Masters Degree and PhD from Córdoba University, Spain. He is currently a Professor at the Catholic University of Valencia San Vicente Mártir, at the Department of Medicine and Animal Surgery. He teaches diverse courses in the field of Animal Reproduction and he is the Director of the Veterinary Farm. He also participates in academic postgraduate activities at the Veterinary Faculty of Murcia University, Spain. His research areas include animal physiology, physiology and biotechnology of reproduction either in males or females, the study of gametes under in vitro conditions and the use of ultrasound as a complement to physiological studies and development of applied biotechnologies. Routinely, he supervises students preparing their doctoral, master thesis or final degree projects.",institutionString:"Catholic University of Valencia San Vicente Mártir, Spain",institution:null},{id:"125292",title:"Dr.",name:"Katy",middleName:null,surname:"Satué Ambrojo",slug:"katy-satue-ambrojo",fullName:"Katy Satué Ambrojo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/125292/images/system/125292.jpeg",biography:"Katy Satué Ambrojo received her Veterinary Medicine degree, Master degree in Equine Technology and doctorate in Veterinary Medicine from the Faculty of Veterinary, CEU-Cardenal Herrera University in Valencia, Spain. She is a Full Professor at the Department of Medicine and Animal Surgery at the same University. She developed her research activity in the field of Endocrinology, Hematology, Biochemistry and Immunology of horses. She is a scientific reviewer of several international journals : American Journal of Obstetrics and Gynecology, Comparative Clinical Pathology, Veterinary Clinical Pathology, Journal of Equine Veterinary Science, Reproduction in Domestic Animals, Research Veterinary Science, Brazilian Journal of Medical and Biological Research, Livestock Production Science and Theriogenology. Since 2014, she has been the Head of the Clinical Analysis Laboratory of the Hospital Clínico Veterinario from the Faculty of Veterinary, CEU-Cardenal Herrera University.",institutionString:"CEU-Cardenal Herrera University",institution:{name:"CEU Cardinal Herrera University",country:{name:"Spain"}}},{id:"309529",title:"Dr.",name:"Albert",middleName:null,surname:"Rizvanov",slug:"albert-rizvanov",fullName:"Albert Rizvanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/309529/images/9189_n.jpg",biography:'Albert A. Rizvanov is a Professor and Director of the Center for Precision and Regenerative Medicine at the Institute of Fundamental Medicine and Biology, Kazan Federal University (KFU), Russia. He is the Head of the Center of Excellence “Regenerative Medicine” and Vice-Director of Strategic Academic Unit \\"Translational 7P Medicine\\". Albert completed his Ph.D. at the University of Nevada, Reno, USA and Dr.Sci. at KFU. He is a corresponding member of the Tatarstan Academy of Sciences, Russian Federation. Albert is an author of more than 300 peer-reviewed journal articles and 22 patents. He has supervised 11 Ph.D. and 2 Dr.Sci. dissertations. Albert is the Head of the Dissertation Committee on Biochemistry, Microbiology, and Genetics at KFU.\nORCID https://orcid.org/0000-0002-9427-5739\nWebsite https://kpfu.ru/Albert.Rizvanov?p_lang=2',institutionString:"Kazan Federal University",institution:{name:"Kazan Federal University",country:{name:"Russia"}}},{id:"210551",title:"Dr.",name:"Arbab",middleName:null,surname:"Sikandar",slug:"arbab-sikandar",fullName:"Arbab Sikandar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210551/images/system/210551.jpg",biography:"Dr. Arbab Sikandar, PhD, M. Phil, DVM was born on April 05, 1981. He is currently working at the College of Veterinary & Animal Sciences as an Assistant Professor. He previously worked as a lecturer at the same University. \nHe is a Member/Secretory of Ethics committee (No. CVAS-9377 dated 18-04-18), Member of the QEC committee CVAS, Jhang (Regr/Gen/69/873, dated 26-10-2017), Member, Board of studies of Department of Basic Sciences (No. CVAS. 2851 Dated. 12-04-13, and No. CVAS, 9024 dated 20/11/17), Member of Academic Committee, CVAS, Jhang (No. CVAS/2004, Dated, 25-08-12), Member of the technical committee (No. CVAS/ 4085, dated 20,03, 2010 till 2016).\n\nDr. Arbab Sikandar contributed in five days hands-on-training on Histopathology at the Department of Pathology, UVAS from 12-16 June 2017. He received a Certificate of appreciation for contributions for Popularization of Science and Technology in the Society on 17-11-15. He was the resource person in the lecture series- ‘scientific writing’ at the Department of Anatomy and Histology, UVAS, Lahore on 29th October 2015. He won a full fellowship as a principal candidate for the year 2015 in the field of Agriculture, EICA, Egypt with ref. to the Notification No. 12(11) ACS/Egypt/2014 from 10 July 2015 to 25th September 2015.; he received a grant of Rs. 55000/- as research incentives from Director, Advanced Studies and Research, UVAS, Lahore upon publications of research papers in IF Journals (DR/215, dated 19-5-2014.. He obtained his PhD by winning a HEC Pakistan indigenous Scholarship, ‘Ph.D. fellowship for 5000 scholars – Phase II’ (2av1-147), 17-6/HEC/HRD/IS-II/12, November 15, 2012. \n\nDr. Sikandar is a member of numerous societies: Registered Veterinary Medical Practitioner (life member) and Registered Veterinary Medical Faculty of Pakistan Veterinary Medical Council. The Registration code of PVMC is RVMP/4298 and RVMF/ 0102.; Life member of the University of Veterinary and Animal Sciences, Lahore, Alumni Association with S# 664, dated: 6-4-12. ; Member 'Vets Care Organization Pakistan” with Reference No. VCO-605-149, dated 05-04-06. :Member 'Vet Crescent” (Society of Animal Health and Production), UVAS, Lahore.",institutionString:"University of Veterinary & Animal Science",institution:{name:"University of Veterinary and Animal Sciences",country:{name:"Pakistan"}}},{id:"311663",title:"Dr.",name:"Prasanna",middleName:null,surname:"Pal",slug:"prasanna-pal",fullName:"Prasanna Pal",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311663/images/13261_n.jpg",biography:null,institutionString:null,institution:{name:"National Dairy Research Institute",country:{name:"India"}}},{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",biography:"Catrin Rutland is an Associate Professor of Anatomy and Developmental Genetics at the University of Nottingham, UK. She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. Dr. Rutland has also written popular science books for the public. https://orcid.org/0000-0002-2009-4898. www.nottingham.ac.uk/vet/people/catrin.rutland",institutionString:null,institution:{name:"University of Nottingham",country:{name:"United Kingdom"}}},{id:"283315",title:"Prof.",name:"Samir",middleName:null,surname:"El-Gendy",slug:"samir-el-gendy",fullName:"Samir El-Gendy",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRduYQAS/Profile_Picture_1606215849748",biography:"Samir El-Gendy is a Professor of anatomy and embryology at the faculty of veterinary medicine, Alexandria University, Egypt. Samir obtained his PhD in veterinary science in 2007 from the faculty of veterinary medicine, Alexandria University and has been a professor since 2017. Samir is an author on 24 articles at Scopus and 12 articles within local journals and 2 books/book chapters. His research focuses on applied anatomy, imaging techniques and computed tomography. 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