Optimized shape parameters of the proposed antenna.
\r\n\tDigital images can be easily distorted by noise during the acquisition, processing, and transmission. Noise level is an important parameter to consider in image processing algorithms, including denoising, compression, feature extraction, motion estimation, optical flow, segmentation, super-resolution, and image quality assessment. Their performance depends on the accuracy of the noise level estimate.
\r\n\r\n\tImage denoising is an important stage to improve the accuracy of many image processing techniques, such as image segmentation and recognition. Image segmentation is another important stage in computer vision applications. Many methodologies utilize both stages in a unique algorithm to solve the problem of the segmentation of noisy images to provide better classification and recognition compared to algorithms that independently use these two stages.
\r\n\tThe goal of this book will be to collect original research chapters that develop or apply new theories and/or hardware or software to process the acquired noisy images to solve the problem of Segmentation of noisy images in the field of medical imaging, remote sensing, engineering, and other research applications.
The fourth generation and Wi-MAX technologies require high data rates and longer range so that the end users can enjoy the quality service. In order to accomplish this, wireless communication systems have to be pushed to the physical limits of the radio channels [1]. As “a key to gigabit wireless” multiple-input multiple-output (MIMO) technology as a diversity scheme made a great breakthrough for raising the performance with high-speed transmission rates, high-quality mobile communication services without the need of any additional frequency spectrum or power [2, 3]. The vast potential of MIMO techniques is manifested by rapid espousal into the wireless standards, such as LTE (Long Term Evolution), UMTS, and Wi-MAX [4, 5, 6, 7].
LTE standard is used for high speed and better quality communication of mobile phones and data terminals that can integrate MIMO technologies for reducing fading phenomenon, increasing channel capacity, and could be incorporated into handheld mobile applications [8, 9]. The most challenging task in MIMO systems is to enforce multiple antennas on handheld terminals as small as mobile handsets. Now-a-days, the slimness of mobile phone has been tremendously increased therefore, to keeping the volume of an antenna small, became a difficult task. The antennas are required to be small, designed with in the small volume and yet their functioning has to be maintained to achieve high gain, wide bandwidth, and low correlation coefficient. The use of inductors and capacitors are, to match of the resonance frequency, tuning of the frequency bands, and to increase the electrical length, but simultaneously reduces the bandwidth and the efficiency of the antenna due to these chip elements. Some of the literature available to make antenna electrically small size, and to obtain wider impedance bandwidth [10, 11]. A dual electrically small MIMO antenna system for 4G terminals was presented by Sharawi et al. which covered the band of 760–886 MHz [10]. Zhang et al. presented a wideband LTE MIMO antenna in mobile handset operating at 740 MHz [11]. The matching level and central frequency are tuned by the shunt capacitor of the port and the series inductor. Shen et al. introduce a wideband diversity antenna which operates in a very wide bandwidth of 1200 MHz starting from 1700 to 2900 MHz [12]. A compact Tri-band MIMO/diversity antenna for mobile was proposed which covers LTE band (765–787 MHz), PCS 1900 (1850–1920 MHz) and Wi-Max (3050–3650 MHz) [13]. The above-proposed antennas are complex in terms of fabrication (loaded with chip components like inductors and capacitors) and in design.
Figure 1(a) illustrates the specific geometry of the proposed antenna, consisting of two symmetrical back to back monopole antenna elements, which are printed on the upper corners of the mobile circuit board (FR4 substrate with εr = 4.4 and tan δ = 0.02). The two antenna elements are considered in this study which are placed near to the corner of mobile phone. The dimension of the substrate is chosen as 120 × 60 × 0.8 mm3. On the upper surface of the substrate, the main rectangular ground of dimension 104 × 60 mm2 is disposed. The volume of the single antenna element is 16 × 21 × 7 mm3 which are mounted at the top corner of the mobile circuit board. The enlarged view of the single unfolded antenna element is given Figure 1(b) whereas Figure 1(c) shows the detailed dimensions of the proposed slot configuration on the ground plane. Detailed dimensions of the unfolded metal strip are shown in Figure 1(d). All optimized parameters are shown in Table 1.
(a) Configuration of the proposed antenna (b) detail dimensions of the unfolded proposed antenna (c) detail dimensions of the slot cut on the ground (d) detail dimensions of the unfolded metal strip inserted between the antennas.
Parameter | Value (mm) | Parameter | Value (mm) | Parameter | Value (mm) |
---|---|---|---|---|---|
Lg | 120 | L6 | 1 | W6 | 1.5 |
Wg | 60 | L7 | 4 | W7 | 18.5 |
L1 | 14 | L8 | 1 | La | 10 |
W1 | 21 | L9 | 2 | Lq | 2 |
L2 | 8.5 | W2 | 7 | Lp | 27 |
L3 | 5 | W3 | 18 | Wp | 17 |
L4 | 6 | W4 | 20.5 | x | 3 |
L5 | 7 | W5 | 9 | y | 9 |
Lx | 16 | Ly | 7 | Lt | 16 |
Wx | 5 | Wy | 1 |
Optimized shape parameters of the proposed antenna.
All the simulations are carried out on finite element method (FEM) based high-frequency structure simulator (HFSS). Figure 2 shows the optimized
The main driven element which is directly fed by a microstrip line is designed to resonate at 937 MHz simultaneously higher modes provide another resonance centered at 1787 MHz. A parasitic element is used which resonates at 2.55 GHz. To achieve the LTE700 frequency band the vertical plate is added to the main radiating element due to which the electrical length is increased correspondingly the lower operating frequency is shifted from 937 to 762 MHz as shown in Figure 3.
Effect of the vertical plate and the parasitic element on the
Figure 4 shows the variation of
Individual effects of the ground slot and metal strip on
Some key parameters are optimized for proper bandwidth and impedance matching at lower frequency side as well as higher frequency side. Figure 6 shows the effect of ‘W1’ on
Effect of length ‘W1’ on the
Effect of length ‘Wx’ on the
Figure 8 shows surface current distributions are shown at 775, 1660, 1810, and 2545 MHz when antenna 1 is excited and antenna 2 is matched terminated. These figures validate the role of slot structure and the metal strip as it is clearly observed that most of the current is present nearby the excited antenna and is trapped in and around the slot structure, thus preventing the flow of current into the second antenna. In this way, the two antennas are less coupled to each other which are the basic requirement of MIMO antenna array.
Surface current distributions when the Antenna1 is excited and antenna 2 is matched.
The CST MWS is used to plot the 3D far field radiation patterns. The 3D far-field radiation patterns are shown in Figure 9. In the case of MIMO antenna system, only one port is excited while keeping other port matched terminated with the 50 Ω load. The radiation patterns of Antenna 1 and Antenna 2 are almost mirror images of each other over all the operating frequency bands. That means they are covering the complementary space regions and indicating that the proposed MIMO antenna has good pattern diversity characteristics.
3D far field radiation patterns at different frequencies (a) 775 MHz, (b) 1660 MHz, (c) 1810 MHz, and (d) 2545 MHz.
The diversity performance of the proposed MIMO antenna is evaluated by the MEG, correlation coefficient, and diversity gain.
The MEG values determined MIMO antenna system and given in Table 2. The values for MEG1 and MEG2 are almost identical (less than 3 dB difference) and the ratio of MEG1 with MEG2 is close to 1 which satisfies the equality criterion for the two antennas. Figure 10 shows the computed MEG for Antenna 1 when assuming
Mean effective gain | Frequency (GHz) | ||
---|---|---|---|
0.77 | 1.66 | 2.54 | |
MEG1 | −4.3533 | −4.8742 | −6.0447 |
MEG2 | −4.3625 | −4.8624 | −6.0210 |
MEG1/MEG2 | 0.9978 | 1.0024 | 1.0039 |
MEG at different frequencies.
Variation of MEG of antenna 1 with XPR computed.
To evaluate the performance of the proposed MIMO antenna, key performance parameter is ECC. Figure 11 shows the simulated ECC for the proposed MIMO antenna. The ECC is obtained well below 0.25 for all the operation bands which are practically acceptable.
Variation of ECC with frequency.
The effectiveness of diversity is given in terms of diversity gain. Diversity gain is calculated using equation (1):
where,
The effect of mobile phone configuration (all the major metallic components) is studied for the proposed antenna by keeping the mobile phone antenna at top of the mobile circuit board in talk mode (SAM head and hand). Further, three commonly user style is considered to analyze the user’s proximity. Figure 12(a) shows talk mode which includes SAM head and PDA hand. Further, to study the data mode (PDA hand) and read mode (dual hand) in addition to the talk mode, the simulation set-up is created in computer simulation technology microwave studio (CST MWS) [14]. Figure 12(a) shows two layers head tissue model (fluid and cells) and antenna along with mobile phone configuration (LCD, battery, buttons, speaker, camera, microphones, connectors, and housing) in Talk mode. Figure 12(b) shows the hand tissue model and antenna locations for the Data mode. The hand model and holding rules are exactly the same as in the Talk mode, the only difference is that there is no human head model in Data mode. Figure 12(c) shows read mode. The user’s body *“SAM head and PDA hand (Talk mode)”; “PDA hand (Data mode)” + and position of the mobile phone (antenna with the mobile environment) are in accordance with the cellular telecommunication industry association (CTIA) [15]. Generally, there are three commonly used ways in which users’ use their mobile phone i.e. Talk mode, Data mode, and Read mode. In the simulations, human head consists of two layers namely, fluid and cells whereas fluid is confined within the cells and hand model consists of only one layer. The dielectric properties of the human tissue are used in this study can be found in [15]. However, the placement of multi-antenna systems over mobile circuit board is symmetrical with respect to the other mobile circuitry. Hence, any hand of the user (left or right will not cause any difference. In the simulations, we have considered right hand to hold the mobile phone (as most people do). Further, the position of the antenna over mobile circuit board is considered at top and bottom for each case of user proximity. The antenna placed near to the human ear is considered as top position while near to the human mouth is considered as a bottom position of the antenna.
User proximity (a) SAM head and PDA hand, (b) PDA hand, and (c) dual hand.
The next important parameters channel capacity loss (CCL) employed to characterize equality of a multi-antenna array. Thus, the CCL is also investigated in free space and user proximity for top and bottom position antenna array. It is computed using
where,
here,
The calculated values of CCL for the multi-antenna array in free space and user proximity are given in Table 3. In the free space, the measured CCL is in close agreement with simulated one. Moreover, calculated CCL in user proximity provides high value for the bottom-placed antenna in comparison to the top placed antenna array this is due to high correlation between multi-antenna arrays. For the good MIMO antenna performance, CCL should be less than 0.4 bits/s/Hz. From the Table 3, it is depicted that the values are well below 0.4 bits/s/Hz in free space as well as in user proximity.
Frequency (GHz)/Users condition | Antenna at top | Antenna at bottom | ||
---|---|---|---|---|
0.777 | Free space | Simulated | 0.37 | 0.29 |
Measured | 0.35 | 0.35 | ||
Talk mode | 0.38 | 0.28 | ||
Data mode | 0.3 | 0.25 | ||
Read mode | 0.28 | 0.2 | ||
1.9 | Free space | Simulated | 0.27 | 0.21 |
Measured | 0.21 | 0.25 | ||
Talk mode | 0.24 | 0.29 | ||
Data mode | 0.2 | 0.19 | ||
Read mode | 0.28 | 0.19 | ||
2.1 | Free space | Simulated | 0.29 | 0.21 |
Measured | 0.21 | 0.29 | ||
Talk mode | 0.14 | 0.13 | ||
Data mode | 0.12 | 0.12 | ||
Read mode | 0.14 | 0.12 | ||
2.5 | Free space | Simulated | 0.2 | 0.21 |
Measured | 0.24 | 0.21 | ||
Talk mode | 0.19 | 0.15 | ||
Data mode | 0.17 | 0.17 | ||
Read mode | 0.14 | 0.18 |
Variation of CCL in free space and user proximity.
The effect of radiation from the antenna in human tissues can be evaluated by specific absorption rate (SAR) [15]. The Cellular Telecommunication Industry Association (CTIA) standard is used to calculate the SAR of multi-antenna systems and simulation setup is shown in Figure 13. The American standard federal communication commission (FCC) postulate 1.6 W/kg average 1 g tissues, while the European standard postulate 2 W/kg average over 10 g tissues. The stimulating power for SAR calculation is 24 dBm at a lower frequency (0.777 GHz) and 21 dBm for higher frequency (1.9, 2.1, and 2.5 GHz). The calculated values of SAR are given in Table 4. As per the simulation setup, the distance between head phantom and the bottom-placed antenna is more, result in lower SAR while top located antenna provides larger SAR values. It is interestingly noted that lower SAR values are achieved for both standards because of the plastic box cover the mobile phone antenna. It can also be observed that SAR values of Ant. 1 and Ant. 2 is slightly different for top and bottom placed elements due to non-planar phantom. The calculated values of SAR follow the defined standard.
SAR setup according to CTIA.
Antenna | |||||
---|---|---|---|---|---|
Freq. | 0.777 GHz | 1.9 GHz | 2.1 GHz | 2.5 GHz | |
Top position | |||||
Ant. 1 (W/kg) | FCC | 0.6 | 0.7 | 0.78 | 0.49 |
European | 0.29 | 0.31 | 0.43 | 0.21 | |
Ant. 2 (W/kg) | FCC | 0.7 | 1.2 | 0.95 | 0.68 |
European | 0.42 | 0.37 | 0.64 | 0.29 | |
Bottom position | |||||
Ant. 1 (W/kg) | FCC | 0.38 | 0.39 | 0.24 | 0.33 |
European | 0.38 | 0.29 | 0.28 | 0.15 | |
Ant. 2 (W/kg) | FCC | 0.42 | 0.38 | 0.37 | 0.24 |
European | 0.3 | 0.29 | 0.27 | 0.21 |
SAR values for the head phantom.
In this chapter, a quad-band monopole MIMO antenna covering LTE13/14, GSM1800, PCS1900, and LTE2600 is presented. The proposed antenna consists of an antenna and parasitic plate which resonates at 762, 1787, and 2550 MHz with good return loss. By introducing the vertical plate shifting of lower frequency from 937 to 762 MHz is observed. Enhancement of bandwidth and isolation is observed by cutting slot on the ground and by placing the metal strip in between the antennas. With these features, as well as compact and simple configuration, the proposed MIMO antenna is appropriate for the mobile handsets. The study of user proximity confirms that the proposed antenna suitable for mobile handsets.
COVID-19 pandemic has affected people’s daily life dramatically since December 2019, since it was firstly discovered in Wuhan, China, in December 2019. The pandemic has already spread out to the entire world since March 2020. More than 211 million cases and 4.42 million deaths have been reported and confirmed all over the world. The COVID-19 pandemic has already become one of the deadliest pandemics in human history.
Globally, residents in long-term facilities have experienced disproportionately high morbidity and mortality from COVID-19. Across Europe, almost all countries have struggled to protect vulnerable people in long-term care facilities, no matter their relaxed or strict strategies. Despite the difficulties arising from differences in definitions, in almost all countries where there have been deaths linked to COVID-19, a substantial proportion of those deaths were among care home residents [1]. Based on the same report from International Long Term Care Policy Network, the current average of the share of all COVID-19 deaths that were care home residents is 46% (data from 21 countries). Take an example of May 2020, in Belgium, 51% of the country’s 9,052 fatal COVID-19 cases were reported from long-term care facilities [2]; there were 7,469 reported cases from long-term care facilities in France, including 66% from nursing homes and 34% from other facilities [3]; Germany had reported 22,071 infections related to institutions caring for the elderly (long-term care facilities and nursing homes), disabled people, homeless people, migrants, or those in prison [4]. In the US, COVID-19 also has been shown to affect the elderly (aged 65 years and older particularly severely, with this demographic having the highest COVID-19-associated hospitalization rate around 600 per 100,000 cases based on the date in 2020 [5].
Given that older adults experience a greater number and severity of chronic diseases and disabilities, it is inevitable that COVID-19 will disproportionately affect this population [6]. Thus, it is not surprising that elderly people residing in long-term care facilities, such as nursing homes, care homes, have the greatest susceptibility to COVID-19, as well as the poorest outcomes from infections. This Chapter would like to overview the insight and impact of COVID-19 on the care homes in the UK, US, and Australia, and provide possible implications for the long-term care market post-pandemic.
The UK government publishes daily statistics on COVID-19 related cases and deaths. The detailed reports about care homes across the UK were published in different ways in each separate sub-section.
In England, the Office of National Statistics (ONS) started publishing data on all care home resident deaths on April 11th, 2020.29,511 care home residents in England died from COVID-19 or related causes up until July 23rd, 2021, which is 22% of all England COVID-related deaths. The lower percentage of deaths in 2021, compared with 2020, also relatively shows the effectiveness of the vaccines.
In Scotland, the Care Inspectorate Scotland (CIS) has reported weekly data on confirmed COVID-19 related death of care home residents since May 25th, 2020. There are 10,505 COVID-19 related deaths in Scotland overall as the date of August 16th, 2021, 3,317 care home deaths (32%) are included. However, even there’s a lower number of deaths in Scotland, the percentage of COVID-related nursing home deaths in 2021 is higher than that in 2020. The elderlies living in care homes (nursing homes) are still the group that has been hit the most.
In Wales, Care inspectorate Wales (CIW) reported weekly data on notifications of deaths of care home residents. A total of 1,932 care home residents’ COVID-19 related deaths have been reported to the CIW, since the first confirmation on March 16th, 2020. There are 24% of overall COVID-related deaths in Wales happened in care homes (nursing homes).
The Department of Health of Northern Ireland released daily statistics on COVID-19 cases and deaths since April 19th, 2020. By the date of August 26th, 2021, there are 2,337 COVID-19 related deaths in Northern Ireland. Of those, 439 occurred in residential/care homes, and 1,859 were confirmed in hospitals [7]. During Fall 2020, 437 care home residents died from suspected or confirmed COVID-19, i.e., around 81% of all COVID-19 care home resident deaths occurred within the care home [8] . Therefore, the total care home resident deaths make up 14% of all COVID-19 deaths in Northern Ireland (Table 1).
Total deaths (2021) | Care home deaths (2021) | % | Total deaths (2020) | Care home deaths (2020) | % | Total death percentage | |
---|---|---|---|---|---|---|---|
UK Total | 65,276 | 12,353 | 19% | 89,243 | 22,836 | 26% | 23% |
England | 57,137 | 10,263 | 18% | 75,782 | 19,248 | 25% | 22% |
Scotland | 3,762 | 1,320 | 35% | 6,686 | 1,997 | 30% | 32% |
Wales | 3,002 | 663 | 22% | 4,945 | 1,269 | 26% | 24% |
Northern Ireland | 1,255 | 107 | 9% | 1,830 | 322 | 18% | 14% |
The COVID-19 statistics about care homes in the UK (July 2021).
Source: Office of National Statistics; Care Inspectorate Scotland; Care Inspectorate Wales; Department of Health of Northern Ireland.
The UK care home market is a relatively fragmented sector. The structures of care homes are from large corporate operators providing in excess of 10,000 beds to sole traders with one or two homes. Most of the care homes are operated by charities and other not-for-profit organizations. The care home industry, as a whole, is a vital social service. The independent (non-public sector) operators, such as the 10 largest for-profit providers, dominated a quarter of the market. Smaller groups made up around 38% of capacity, with the remainder owned by operators with one or two homes [9]. According to the Grant Thornton report in 2018, in recent years, a two-tier market has emerged based on payment methods, including public pay and self-paying. The operators whose income is mainly from public supports, such as local authority or NHS usually achieves significantly lower profits than those with self-paying.
Like many other countries, the UK’s age structure is shifting towards older ages. In 1999, around 15.8% of people were 65 years and over, this increased to 18.5% people in 2019 and is projected to reach around 23.9% people by 2039 (Figure 1) [10].
Percentage of Population by Age Group in the UK. Source: Office for National Statistics (January 2021).
The increasing number and percentage of an aging population will be a large group requiring access to care services, such as residential care, care home services, and nursing homes. However, although the growth rate in the 65+ population is forecast to accelerate over the next decade, the absolute number of long-term residential accommodations has declined by 4.4% between 2001 and 2016 [9]. In 2017, 14.8% (estimated) of 85 years and older were in elderly residential accommodation, compared with 25.2% [9].
There are two types of payments for elderlies, one is from local authorities (i.e., public payment), the other one is self-pay. According to Grant Thornton’s research in 2018, around 25% of care homes have over 75% of residents placed by local authorities. Average fees covered by local authorities were 10% less than the total cost of providing care, approximately a 200 million pounds to 300 million pounds shortfall in funding. To counteract this, self-funders have to pay around 40% more out of pocket to care homes, compared with the UK council-funded patients. Even though, the self-pay market is still growing at a fast rate.
The biggest challenges of care homes in the UK, before the COVID-19 pandemic, are three aspects: decreasing potential demand due to the switching payment methods; increasing population size with limited bed numbers; declining qualified staff and nurses in care homes. These challenges, under the outbreak of COVID-19, become much tougher and more difficult to deal with.
Before the COVID-19 pandemic, the potential demand for care homes have already decreased due to the switching payment methods. Self-funders become the new focus of the market after several care home closures that focused on local authority-funded payers [9]. Without the support from the local authority, not every single elderly, who is eligible for residential care homes, has sufficient ability to self-support his/her stay and other further expenses. After the pandemic, due to the high numbers of confirmed cases and deaths, a proportion of potential consumers would start to question care homes and reconsider their options after retirement. The demand for care homes may have an even sharp decline during the post-pandemic period.
The residential care home industry has experienced dramatic changes due to the pandemic. With the drastic hit from COVID-19, residential care homes would have to face many potential problems, such as social distancing bed arrangement, more inputs on disinfection supplies, etc. The capacity of the care homes dropped because of the limitation of room and space, to protect patients and follow the pandemic safety rule. Piling inputs on disinfection supplies may cause declining investment in other aspects (such as staffing, daily operation supplies, etc.), which possibly affect the daily routine or even the operation model. The percentage of net earnings1 among care home providers falls from 32.8% in 2006/07 to 25.2% in 2016/17, which has been attributed to increased food and property costs, the freezing of local authority fee rates in real terms, and higher wage costs before COVID-19 [9]. After the pandemic, the care homes may face an even lower percentage of net earnings due to the increasing investment in preventive and disinfection supplies and higher hiring costs for qualified staff.
Hiring a qualified and satisfying staff was one of the biggest challenges for the care homes in the UK before the pandemic. It is estimated that 1.22 million social care workers will be needed between 2016 and 2036 [9]. The increase of national living wage (NLW) and national minimum wage (NMW) has raised the real wages and the original costs of each care home. However, the pandemic completely changed the story of nursing education, which may turn the staffing situation in UK in a different direction. According to the data from the Universities and Colleges Admission Services (UCAS), there has been a total 37,635 nursing students accepted onto the course in 2020, comparing 30,395 in 2019. It includes 29,740 students placed in England, 4,785 in Scotland, 1,985 in Wales, and 1,125 in Northern Ireland. There was a significant fall in nursing students in England in 2017 due to the removal of the NHS bursary. This 2020 number shows a new faith and understanding of the nursing world during pandemic. Even though, the Health Foundation warned the government’s pledge for 50,000 more nurses by 2025 would be insufficient to meet growing demand, especially during the coronavirus pandemic. The pandemic may still have potential to shrink this gap by encouraging more people to pursue careers in medical field.
The COVID-19 pandemic in the United States has already confirmed more than 39.1 million cases, with more than 639,000 deaths, which make the United States the most of any country, and the 24 highest per capita worldwide. COVID-19 has been shown to affect the elderly (aged 65 years and older) particularly severely, with this demographic having the highest COVID-19-related hospitalization rate in the US of around 600 per 100,000 cases [5]. Given that older adults experience a greater number and severity of chronic diseases and disabilities, it is inevitable that COVID-19 will disproportionately affect this population [6]. Thus, it is not surprising that elderly people residing in long-term care facilities, such as nursing homes, have the greatest susceptibility to COVID-19, as well as the poorest outcomes from infections.
Using state and federal data, the COVID Tracking Project (CTP) estimates that as of March 2021, about 8% of people who love in US long-term care facilities have died of COVID-19 (nearly 1 in 12), and nearly 1 in 10 for nursing homes alone. According to CTP, throughout the COVID-19 pandemic, a third of all US COVID-related deaths were long-term-care facility deaths, which include nursing homes, assisted living, and other long-term care facilities.
Nursing homes in the US are aiming to provide care to those with permanent disabilities rather than to ‘cure’ [11]. According to the Centers for Disease Control and Prevention 2016 statistics, there are approximately 15,600 nursing homes in the US, with around 1.7 million beds and over 1.3 million residents. There are also 12,200 home health agencies, providing care in the patient’s own home and serving 4.5 million patients each year. Other types of long-term care contain hospice care agencies, residential care communities, and adult day service communities, the latter two of which provide able patients assistance (Table 2).
Nursing homes | Home health agencies | Hospice care agencies | Residential care communities | Adult day services centers | |
---|---|---|---|---|---|
Number of facilities | 15,600 | 12,200 | 4,300 | 28,900 | 4,600 |
Number of beds | 1,700,000 | N/A | N/A | 996,100 | N/A |
Number of residents | 1,300,000 | 4,500,000 | 1,400,000 | 811,500 | 286,300 |
Statistics about Long-Term Care Facilities in the US (2016).
Source: National Center for Health Statistics, CDC, 2019.
The nursing home industry contains a relatively large number of nursing homes, with fees being affected by numerous factors, including consumer demand and government regulations. As a result, it is a highly competitive industry [12].
The COVID-19 pandemic may affect consumers’ preferences regarding the long-term care facilities they choose. Before the substantial rise in COVID-19 cases among the general population in July 2020, nursing homes and assisted living facilities made up the majority of US COVID-19 cases, with a fatality count of more than 50,000, comprising over 43% of the total deaths in June [18]. This high death rate and the following struggle to ensure patient safety and transmission control in nursing homes may deter potential service users from selecting residential care. This scenario may lead to a demand shifting on the long-term care services market, from nursing homes to home care.
The conduct of nursing homes relates to various external factors, which impact the behavior of long-term care providers, such as government regulations, policies, and property rights (for-profit or not-for-profit). And not all patients were always treated and cared for equally.
Throughout the pandemic, all residential care facilities were following the same government COVID-19 regulations and protocols to the best of their ability, such as maintaining social distancing rules in a public area, separating and quarantining COVID-19 patients, emphasizing strict facial mask rules, etc [13]. All the regulations and rules caused another layer of the financial burden for most residential care facilities. Some small facilities, or those that lack strong financial support, may struggle to implement these changes, and therefore must face tough competition from other nursing homes with greater financial means, as well as from other substitutes for long-term care.
To assess the overall performance of the long-term care industry, several measures are usually examined, including expenditure on long-term care, private insurance for long-term care, the price of nursing home care, and the relative use of nursing homes, home healthcare, and hospice services [12]. Long-term care facility costs comprise the largest share of annual direct spending by those receiving Medicare beneficiaries as a result of age (>65 years) or disability status [14]. Long-term care expenses, such as nursing homes and assisted living facilities, are the number one category for out-of-pocket payment, followed by home healthcare [15]. In 2020, nursing homes cost an averagely of $6,844 monthly, or $82,128 per year, which is higher than the median household income in the US. Given the scale of the financial costs, the additional risk of COVID- 19 infection, and the restrictions on visiting could turn potential consumers away from all this type of long-term care, especially with inexpensive other forms of care. For example, home healthcare costs an average of roughly $4,000 a month [16].
Before the pandemic, nursing homes were usually the prioritized choice for elderlies, because of trustworthy performance, regarding the quality of care, group living, and partial insurance coverage [17]. However, even prior to 2020, the average annual growth in demand for home healthcare was greater than that of nursing home facilities (Table 3) [18]. With the additional influence of the pandemic, it is reasonable to observe that more people will start to choose home healthcare over nursing home care as the most appropriate, safe, and affordable option for their later lives.
NHE, billions | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 |
---|---|---|---|---|---|---|---|
Nursing care facilities and continuing care retirement communities | 134.9 | 140.0 | 145.0 | 147.4 | 149.2 | 152.6 | 158.8 |
Home health care | 67.3 | 71.0 | 73.8 | 77.1 | 80.0 | 83.6 | 88.8 |
Annual Growth, % | |||||||
Nursing care facilities and continuing care retirement communities | 3.5 | 3.8 | 3.6 | 1.6 | 1.3 | 2.3 | 2.7 |
Home health care | 8.3 | 5.5 | 4.0 | 4.4 | 3.7 | 4.5 | 6.3 |
National Health Expenditure (NHE) amounts and annual growth for nursing home and home health care.
Source: Centers for Medicare and Medicaid Services (CMS), Office of Actuary, National Health Statistics Group.
The COVID-19 pandemic was confirmed to have reached Australia on January 25th, 2020. Until now, there were 56,565 positive cases and 1,019 deaths confirmed in Australia. The Australian Government Department of Health reported positive cases and COVID-associated deaths on daily basis. The elderly account for a staggering 697 of those deaths. 61% of deaths occurred in residential aged care facilities. People who had picked up the infection in the nursing home where they were supposed to feel safe. Table 4 here illustrated the positive cases overall and in residential aged care facilities for each subarea in Australia.
Total cases | Total deaths | Total cases in residential aged care facilities | Total deaths in residential aged care facilities | Percentage deaths in residential aged care facilities | |
---|---|---|---|---|---|
Australia | 85,629 | 1,162 | 2,338 | 710 | 61.15% |
Australian Capital Territory (ACT) | 742 | 3 | — | — | — |
New South Wales (NSW) | 51,986 | 295 | 345 | 53 | 17.97% |
Northern Territory | 204 | — | — | — | — |
Queensland | 2,016 | 7 | 1 | 1 | 14.29% |
South Australia | 898 | 4 | — | — | — |
Tasmania | 235 | 13 | 1 | 1 | 7.69% |
Victoria | 28,456 | 831 | 1,991 | 655 | 78.82% |
Western Australia | 1,093 | 9 | — | — | — |
COVID-19 in Australia (09/19/2021).
Source: Australia Government Department of Health (09/19/2021).
In the ACT, Northern Territory, South Australia, and West Australia, there’re no active reported positive cases or death available in nursing homes. In other subareas, the number of positive cases and deaths is not as high as UK or US, but the percentage still may prove the severity of the outbreak at the residential aged care facilities. In Victoria, almost 80% of COVID-related deaths are from residential aged care facilities (Figure 2).
COVID-19 in Australia’s nursing homes (September 2021).
Aged Care in Australia is the provision of services to meet the unique requirements and needs of elderlies in Australia, which includes in-home care, residential care in aged care (nursing) homes, and short-term care such as respite care [19]. The aged care industry provides older Australians with a range of different services, allowing them to access appropriate and affordable care when and where they need it. In Australia, many aged care services are government-subsidized to help keep costs manageable and affordable [20]. Australians, who are eligible for government subsidies, will still be expected to contribute to the cost of services if they can afford to.
The Australian aged care sector is large and complex. According to the Aged Care Financing Authority, the aged care industry is one of Australia’s largest service industries, including over 350,000 employees, over one million consumers, and around 2,000 service providers. The aged care industry is essential to elderlies in providing a variety of care options for their later stages of life. In 2016/17, aged care services made up $17.8 billion of Commonwealth expenditure, which is a significant public involvement [21].
There are over 2,000 aged care services in Australia, supplying three different types of aged care services: home and community care (HCC); home care; and residential care. HCC provides entry-level basic support for older people who need assistance to live independently. Home care contains 4 levels of care provided to elderlies living at home, ranging from basic to high-care needs. Residential care provides accommodation and support for those who choose to live within residential care facilities, which is the one we focus on in this chapter. In 2013/14, residential care facilities generated 82% (14.8b) of total revenue and received 73% ($9.8b) of total Commonwealth funding to the aged care sector, while residential aged care places represented only 18% (189,283) of total aged care places (Table 5).
HCC | Home care | Residential care | |
---|---|---|---|
Number of providers | 1,676 | 504 | 1,016 |
Number of places | 775,959 | 66,149 | 189,283 |
Commonwealth funding | $1,701m | $1,271m | $9,814m |
Total revenue | $1.8b | $1.3b | $14.8b |
A combination of for-profit, not-for-profit, and government service providers contributed to these three types of aged care services. Across all three types, not-for-profit providers supplied most aged care services, with the market share of these providers ranging from 52% in residential care to 74% in HCC [21].
Looking at the recent patterns and prominent factors, the growth of the aged care industry is a given without the hit of COVID-19. Apparently, the pandemic warned people to make a smart choice about their aged life. The aged care facilities, especially the residential aged care facilities, face quite big challenges before and after the COVID-19 pandemic.
The first challenge is the changes of demands of the Baby Boomers. As Baby Boomers move into old age, this trend is set father greater momentum over the next three decades. Baby Boomers are a sizable cohort, accounting for nearly 5.6 million Australians [22]. The proportion of people aged 65 years or over is projected to increase from 13% in 2010 to 23% by June 2050 (Figure 3) [23].
Estimates about the percentage of elderlies by 2050. Source: Australian Government [
Given that the number of seniors is continuously climbing and potential consumers are now exercising more control over the care packages and facilities, policymakers and service providers should focus on creating a sustainable system that supports the increasing volume of the elderly [24]. Compared with the new generations, Baby Boomer tends to be cautious and rational on consumption behavior. With the hit from COVID-19, it is possible to switch Baby Boomers’ decision-making process and affect the demand of aged care facilities. Aged care service providers can consider investing in technologies such as Augmented Reality, Virtual Reality, Artificial Intelligence, Video Calling, CRM solutions and more to suit the varying demands of seniors.
The second challenge is the lack of skilled staffs and how the aged care facilities attract them. The Aged care sector is heavily dependent on the skills of the workforce. From the population perspective, for every couple of retirement age, there were 15 people in the working-age population in 1970. This number dropped to 10 by 2010 and is projected to decline to 5 by 2050 [25]. Apparently, the number of skilled workers leaving this industry is rising. Increasing workload and stress, less career growth opportunities, extended working hours and dissatisfaction from the remuneration are some of the main reasons responsible for the drain of skilled employees [24].
The COVID-19 pandemic is possible to make this situation even worse. According to a survey of the Australian nursing, midwifery, and care worker workforce, which was completed by Rosemary Bryant AO Research Centre in May 2021, around half of the survey respondents were moderately or extremely concerned about having adequate staff (53.18%), the welfare of their colleagues (52.15%) and having the right skills mix in the workplace (51.43%). The workload for nurses has significantly increased since the pandemic. During the pandemic, working at an aged care facilities means the highest levels of workplace demand, role conflict, work-life conflict, and lowest job satisfaction, as well as the lowest clarity compared to other workplace groups [26].
Another problem is to attract qualified and skilled workers. Recruitment of a skilled workforce such as certified care givers and nurses for the aged care facilities is also a big challenge as well. Under situations with high workloads and low job satisfaction during the pandemic, attracting a qualified workforce becomes even harder. The aged care service provider may invest in some high-quality technology solutions and create a robust infrastructure to manage the situation. For example, they can offer the remote working options or proper transport facilities by arranging private vehicles or an appropriate bus facility [24].
Starting from the early months of the COVID-19 pandemic, the long-term care facilities have experienced an extremely tough period all over the world. Suddenly, the nursing home industry, which has been long operated out of public view, finds itself at the center of scrutiny. During a crisis like this pandemic, it becomes especially critical to preserve the necessary functions for long-term care facilities. It is essential for aged care centers to maintain their size, the number of beds, recourses, manage external emergency circumstances, and also boost the potential demands. Based on our findings from the UK, the US, and Australia, we can say that the COVID-19 outbreak will likely push potential or existing residents and their families to question and reconsider whether the residential long-term care facilities, such as nursing homes, senior centers, or aged care facilities, are the best or safest options for them to be.
The pandemic challenged and changed the way of operations for almost all the long-term care facilities all over the world. Even the number of elderlies is rising in the future, the diverse format of retirement life care and the decreasing trend of public financial supports have already dropped the demand for residential long-term care facilities before the COVID-19 pandemic. Due to the rising number of confirmed COVID cases and deaths in nursing homes, not only the potential patients, but also the qualified staffs and workers may have second thoughts about continuing their works in nursing homes. Meanwhile, the entire long-term care industry has been lacking qualified and skilled staffs and nurses all the time even without the pandemic. Although, in some countries (such as UK), potential students who may dedicate their will to become a nurse or font-line medical workers after seeing and understanding the meaning and importance of medical-related work. Unfortunately, this is not happening in most of the countries.
After the COVID-19, for potential elderlies or demanders who may need residential care, there’re many other new options that can provide sufficient and affordable care. All options for later-life care, including assisted living facilities, home healthcare, and community-based care, need to be reviewed and re-evaluated after COVID-19. As the major financial supporters of long-term care, each government may also need to consider how to adjust the residential aged care facilities and adapt them into the new business norm after COVID-19. And also, potentially speaking, home- and community-based care are highly possible to become the new dominants for the post-COVID long-term care industry.
The authors declare no conflict of interest.
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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. 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Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. 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Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression"},{id:"15",title:"Chemical Biology",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors"},{id:"17",title:"Metabolism",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation"},{id:"18",title:"Proteomics",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:null},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/108314",hash:"",query:{},params:{id:"108314"},fullPath:"/profiles/108314",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()