Activities performed in this experiment.
\r\n\tGlobalization does not represent a pure and generous process for humanity or other species, but rather it implies social exclusion and also provokes situations of vulnerability in groups of people, forced exclusion, and apartheid: poor job opportunities, lack of access to education, worse socio-sanitary conditions. Specifically, it can be said that social segregation entails the apartheid of social groups of different ages, genders, and ethnicities; these groups live a reality manifested through the deepening of poverty, in terms of increased vulnerability of the poor and groups with little economic, social, cultural, labor and health stability.
\r\n\r\n\tThis book aims to talk about some topics that are neglected in the discourses of academic communities and political elites. The inequality process is deeply rooted among humans and is part of many people's lives in the form of modern apartheid, gender segregation, lack of health access, and cultural gap. All those structural inequality processes are the product of the biopower perpetuated and produced in the macrosystem, exosystem, mesosystem, and microsystem. For many people from the academy, the information-consuming public, and the society in general, it is a problem to talk about these processes, since they have either lost interest or have normalized the structural and social inequity. For this reason, we see it as transcendental to explain how this situation occurs from the most internal fibers to the most evident processes, intending to make it more visible and thus expose the situation for possible solutions.
",isbn:"978-1-83768-406-9",printIsbn:"978-1-83768-405-2",pdfIsbn:"978-1-83768-407-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"cefab077e403fd1695fb2946e7914942",bookSignature:"Ph.D. Yaroslava Robles-Bykbaev",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11473.jpg",keywords:"Wage Gap, Gender Segregation, Fundamental Human Rights, Health Access, Social Inequity Processes, Modern Apartheid, Resilience, Cultural Gaps, Globalization, Geopolitics of Social Inequality, Public Policies, Social Vulnerability",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 15th 2022",dateEndSecondStepPublish:"July 13th 2022",dateEndThirdStepPublish:"September 11th 2022",dateEndFourthStepPublish:"November 30th 2022",dateEndFifthStepPublish:"January 29th 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"13 days",secondStepPassed:!1,areRegistrationsClosed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Dr. Bykbaev is a member of the UNESCO Chair of Politecnica Salesiana University. 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From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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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"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.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:"63641",title:"Design and Estimation of an AUV Portable Intelligent Rescue System Based on Attitude Recognition Algorithm",doi:"10.5772/intechopen.79980",slug:"design-and-estimation-of-an-auv-portable-intelligent-rescue-system-based-on-attitude-recognition-alg",body:'\nSignificant research is being conducted on the development of autonomy for underwater robotic vehicles, which are widely employed in many fields of application such as oceanographic, marine archeology [1], military organizations, and cable tracking and inspection [2]. The advent of underwater robotic vehicles has significantly reduced the dangers in deep sea exploration. Two kinds of robotic vehicles used in marine research are remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs). The main difference between the two is that ROVs are connected to the ship by communication cables whereas AUVs operate independently from the ship. AUVs operate without an umbilical; therefore, AUVs are able to conduct a larger range of work area. In this research, AUV intelligent rescue system (AIRS) can minimize the loss of losing or unpredictable catastrophic failure of AUVs caused by emergency conditions.
\nIn recent years, the micro-electro-mechanical system (MEMS) has almost become a vital technology for modern society because of its small volume, low power consumption, low cost, and ease of integration into systems or modification [3]. The MEMS technology creates entirely innovative kinds of products, such as gyroscope sensor in camera-shake detection systems [4], multi-axis inertial motion sensors for smartphone-based navigation [5], and rehabilitation systems based on inertial measurement units (IMUs) [6]. Additionally, the integration of global positioning system and inertial navigation system (GPS/INS) is usually employed to measure the position of AUVs [7, 8]. Unfortunately, small errors in the measurement of initial data are double integrated into larger errors progressively in attitude data, and such errors increase without bound. Error reduction calibration for initialization of INS is paramount for the systematic parameter, like scale factor, bias, and misalignment of the axes. As the situation depicted above, a calibration method is investigated and adopted in this research. There are many researches utilizing Kalman filter (KF) [9], complementary filter (CF) [10], adaptive Kalman filter (AKF) [11], or extended Kalman filter (EKF) [12] to fuse the gyroscope and accelerometer together, taking advantages of their individual strength. In this research, we use EKF to filter IMU outputs with a balance of noise canceling and adaptability simultaneously used in sensing attitude algorithm for AIRS.
\nThe sensing algorithm with EKF for sensing the attitude of AUV, while employing the backpropagation network (BPN) to classify motion data that are formed in the AUV. Before classify the data, there is a problem. The problem is compounded by the fact that our system is computationally complex due to values estimation of AUV and causes the high dimension that is called “curse of dimensionality,” which donates the drastic rise of computational complexity and the classification error. Therefore, the step of feature extraction (FE) is an important procedure in data classification [26]. Further, we feed the data into eural network to distinguish every AUV motion in each operating period. The neural network consists of multiple artificial neurons to receive inputs, and process them to obtain an output [13]. By repeating amendments to the model weights, the neural network makes central processing unit (CPU) more logical to calculate the nonlinear systems of AUV motion [14]. The neural network has been widely used because of a number of advantages, including estimating which variables are important in classification, detecting possible interactions between predictor variables, and constructing the prediction model with a high accuracy rate. Furthermore, the neural network has emerged as an important tool in classification which has been investigated in many different important applications [15]. In this study, neural network classifier (NNC) is used in the calculation of nonlinear systems AUV attitude data to detect a catastrophic failure of an AUV. Based on the multilayer feed-forward backpropagation algorithm as NNC, we proposed an effective activity recognition method using 10-DOF sensor module.
\nThe concept of AIRS is inspired from the vehicle safety device: airbags have a flexible fabric bag that can protect and restrict the occupant from being thrown away during a crash accident [16, 17]. Wang and Dragcevic designed the airbags, which fire via a small pyrotechnic charge to increase motorcyclists’ safety protections when riding a motorcycle [18, 19]. The airbags are applied to not only fall-protection devices but also automatic inflatable life buoy by the inflatable method of a gas cylinder [20, 21, 22, 23]. We extended the airbag systems to load on the AUV with CO2 cylinder to implement the AIRS. In this research, we developed a malformed detection algorithm for AUV with both EKF and BPN, and the implementation of the airbag system loaded on AUV is underway.
\nIn this section, we introduce the hardware components of an AIRS and its working principles. The hardware of AIRS contains the sensing module and a double-trigger inflator. The 11-DOF sensing module combines the 9-DOF inertial sensor, barometric pressure sensor, and temperature sensor to collect the motion data and the depth of the underwater vehicle. The inflator is used with gas cylinders as the double trigger mechanism, which can be triggered not only by electricity but also by water damage when the waterproof cabin is severely broken. Furthermore, we used the commercial software LabVIEW™ to analyze the relation between buoyancy force and inflator device in water, to confirm the inflator specifications (Figure 1).
\nThe structure of the intelligent rescue system.
Master control module uses ATmega2560 with the Arduino program written to read motion data of sensor. Ten-axis motion sensor combined with the accelerometer, gyroscope, magnetometer, and the barometer to acquire attitude angle data through the inter-integrated circuit (I2C) is adopted. The communication module uses Bluetooth transmit data wirelessly to the processing software. The block diagram of the 11-DOF sensing module is shown in Figure 2.
\nThe block diagram of the sensing module.
In case of failure and sinking of AUVs underwater, we designed an airbag system to increase the buoyancy and reduce the overall density of the AUVs. AIRS is installed on top of AUV, an airbag inflated in a fault condition to force the AUVs to surface up. In this section, we are going to introduce how to manufacture an inflator mechanism of the AIRS for the AUV, including the specification analysis and the mechanism design.
\nThe AIRS mechanism is installed on top of the AUV and dives down into the ocean. One should note that the hydrostatic pressure exerted by a liquid increases with increase in ocean depth, which influences the specification of the cylinder. Not only that, the full displacement and weight of the AUV also affect the size of the airbag and cylinder. These interrelated factors cause the different performance of the airbag inflation time and rescue time. To analyze the inflator specification more accurately and conveniently, we have engineered the specification calculator for the inflator to ensure that the airbag can be inflated and can bring the AUV to surface successfully. From the required buoyancy and the depth of the location, the calculator analyzes the required gas cylinders and airbag volume.
\nFrom the result of the calculation illustrated in Figure 3(b), it takes 68.1 seconds to make nylon airbags fill with CO2 and provides 42.1 N buoyancy for AUV. The design of added buoyancy can bring 7.47 kgw of underwater vehicle to the surface (assume that the full load displacement is fixed). With this technology, we can install the appropriate number of gas cylinders and airbags in accordance with different displacements of AUV accurately and conveniently.
\nThe result of calculation specification analysis. (a) The front panel of the specification calculator. (b) The growth rate between the water depth and the airbag volume.
After the above analysis by the calculator, we found out the appropriate gas cylinder and airbag, and designed the corresponding mechanism parts for the selected gas cylinder and airbag. We are going to elaborate on the design concept of the inflator mechanism in the following. Figure 4 illustrates a double-trigger inflator including a servomotor, horn, spring, spring case, crowbar-striker, water-soluble PVA fiber, release button, and CO2 cylinder. The designed double-trigger inflator can be triggered not only by electricity but also by water damage when the waterproof cabin is severely broken.
\nAfter the above analysis by the calculator, we found out the appropriate gas cylinder and airbag, and design the corresponding mechanism parts for the selected gas cylinder and airbag.
\nThe design diagram of a double-trigger inflator. (a) before release. (b) release by servo motor. (c) release by spring.
There are two operating mechanisms to trigger the inflator. To begin with the electricity trigger mechanism as shown in Figure 4(b), we assume that the waterproof cabin is watertight; after detecting the failure of AUVs, servo motor rotates for pushing the crowbar-striker, and the crowbar-striker punches the release button, which needs 35 N normal force to release into the airbag through the air tube. We chose MG995 for servomotor, which has product size 40.7 × 19.7 × 42.9 mm and weight 55 g; MG995 is sufficient to move the horn for the release button with its maximum torque 127 N-cm.
\nBesides, if the AUV is severely impacted and causes cabin seepage, so that the power system is damaged, the AUV cannot resurface by the thruster or the electricity-trigger inflator. Hence, we designed the water-trigger inflator for this kind of case. When the cabin is broken, water melts the water-soluble PVA fiber and releases the spring case as shown in Figure 4(c). Within the spring case, the music wire is selected to spring; the true maximum load of spring is 27.985 N, which is not enough to press the release button. Thus, we extended the striker into a crowbar-striker, and amplified the spring force by the lever principle to release the push button. The final part is the airbag, which is made of nylon 6/6 and folded under a cover. The airbag weighs 150 g and the value of tensile strength is 809.3 atm, which can withstand the pressure caused by gas cylinders.
\nThe method and the procedure of data processing and activity recognition are described in Section 3. The former includes the signal calibration and extended Kalman filter algorithm; the latter is constructed of FE and BPN classifier.
\nThe 10-DOF sensing module including the 9-DOF IMU (three accelerometers, three gyroscopes, three magnetometers), barometer, and thermometer integrated with a microcontroller (Arduino Mega2560). The six-axis inertial sensor (MPU6050), which is a complete triple-axis gyroscope and triple-axis accelerometer inertial sensing system, is the most suitable sensor for stabilization and attitude measurement. MPU6050 contains the digital motion processor (DMP) which performs the motion processing algorithm itself. However, the horizontal attitude (yaw) is not a possible measurement. We used a magnetometer (HMC5883L) with fusion algorithm accordingly for eliminating the gyroscope offset to recognize the AUV activity context more reliably. The barometer (BMP180) is used in AIRS to detect the depth of the AUV and control the pressure of the airbag for supplying the perfect buoyancy. In addition, BMP180 also includes a temperature sensor, which does not only measure the underwater temperature but also thermal compensation for the MPU6050. Although the development of the MEMS technology has made a great progress, the IMUs are still difficult to collect precise data in the presence of various errors. Calibration is the process of comparing the measurement outputs with known reference data and determining the coefficients that drive the output to agree with the reference data over a range of output. We calibrated the sensor in terms of its thermal noise, bias, and scale factor.
\nA quaternion-based EKF is proposed in this section for determining the attitude of the AUV from the outputs of IMU. Attitude estimation is a very important part of the AIRS. If the initial data are double integrated into larger errors without bound, it brings on the misclassification of ANN algorithm with wrong attitude values. For the sake of this error, we used a series of measurements and observed over time for signal processing. The main advantage of the KF is its ability to provide the quality of the estimate, but the KF only applies to linear and Gaussian models. The EKF conversely is a nonlinear version of the KF which linearizes about an estimate of the current mean and covariance. In view of this, we chose the EKF to filter IMU outputs with a balance of noise canceling and adaptability simultaneously, used in sensing attitude algorithm for AIRS. We proposed the EKF fusing with the accelerometer, gyroscope, and magnetometer integrated with sensor calibration (SC). SCEKF results in an improvement of the orientation accuracy from IMU. A flow-chart of structure performed by the proposed SCEKF is capsuled in Figure 5.
\nOverview of the SCEKF structure.
The first step of EKF predicts a current state and covariance matrix at time \n
However, in view of the nonlinear process of state transition \n
where the innovation covariance matrix \n
where the measurement covariance matrix of magnetometers \n
AUV motions are defined by the six degrees of freedom, including heave, surge, sway, pitch, roll, and yaw, and they are coupling by the vehicle shape, trends, and current interaction. Therefore, the attitude data of AUV is of high dimension and very complex. The value of FE is to reduce the dimension of the large measurement data and prevent program operation to run out of memories. The characteristics of a data segment are to keep the most meaningful features and remove the redundant data. Therefore, the FE methods have been applied for activity detection from accelerometer data [24, 25]. İn order to extract features easily, the continuous measurement data of sensors are divided into many overlapping segments of which each is 20 seconds long, as illustrated in Figure 6. A sliding window technique cuts the sensors’ data into 20 seconds (550 samples) in each short-term window with 50% overlapping. As previous studies found that the effectiveness of FE on windows with 50% overlap is an effective window size [24].
\nAttitude data with a long-term window, which consists of many overlapping short-term windows.
In this chapter, we adopted principal component analysis (PCA) as the feature selection procedure to lower the dimension of the original features. Feature extraction is highly subjective in nature, it depends on applications. Here, we introduced the following features that are beneficial to the classification of AUV failure detection, and used these features to discriminate the type of AUV activity: (1) min, max; (2) mean; (3) interquartile range (IQR); (4) root mean square (RMS); (5) standard deviation (STD); (6) root mean square error (RMSE); (7) signal magnitude area (SMA); (8) signal vector magnitude (SVM); and (9) averaged acceleration energy (AEE).
\nFinally, this feature extraction by the PCA is fed into the short-term classifier and long-term classifier sequentially, and then output the AUV conditions. In order to detect the AUV condition accurately, reliably, stably, and robustly, we divided the recognition system into three classifiers which are based on BPN. BPN is considered the workhorse of ANNs and is the multilayer perceptron (MLP) based on a feed-forward algorithm. The hidden layers, between input and output layers, use the error backpropagation (BP) algorithm to compute the nonlinear relationship in supervised learning as shown in Figure 7. The main features of BPN are as follows: high learning accuracy, fast response, and ability to process the nonlinear problems.
\nThe topology of the BPN classifier.
In this chapter, a three-layer BPN is used for classifying AUV failure conditions. The topology of the BPN classifier is shown in Figure 7 [26]. The input layer has R neurons, equal to the dimension of the feature vectors \n
In the BPN, the intermediate quantity \n
The discrepancy \n
Then, the weights are adjusted to find the partial derivative \n
Let us discuss each of partial derivatives in turn. In the last term of Eq. (15) is the derivative of the net \n
Next, the derivative of the activation \n
Last, we consider the derivative of \n
Finally, we substitute these results Eqs. (15)–(17) back into original Eq. (17) to find the weight change \n
We can find the weight change \n
The BPN algorithm approach to recognize and intelligently detect failures is based on changes in weight values \n
To validate the sensor calibration and fusion method proposed in this chapter, this section covers the results obtained from the 10-DOF sensor module with operating system of Windows 8.1, 2.20 GHz CPU, 8 GB memory, and experiments are performed with the Arduino program described in Section 2.1. The system was fixed on an anti-vibration table to minimize interference. The proposed SCEKF algorithm was used to estimate accelerometer and orientation with 27-Hz updating rate.
\nAs the calculation process of sensor algorithm, we used the rotary platform with outputting the quantitative and stable signal to observe the relative signal output. The first process is fixing IMU at the center and along the rotation axis of the platform and fixing it by valves. After installation, setting the required rate or angle of rotation as a reference data by the user interface, we measured the output corresponding to the reference data. The performance of the orientation before/after SCEKF algorithm is presented in Figure 8(a–c), respectively; it produced reasonable output values within the expected ranges. It is seen that each component of Euler orientation (i.e., roll, pitch, and yaw) is within 0·1 degree after SCEKF processing in the static test.
\nThe calibration results from the static condition produced by the SCEKF processing. (a) Roll angle calibration.(b) Pitch angle calibration. (c) Yaw angle calibration.
\nFigure 9 shows the whole system of the TrenchRoDer equipped with the proposed AIRS. The whole equipment includes the TrenchRoDer, a camera, an AIRS, a Bluetooth box, and a floating board. We put a camera in the waterproof bags and tied them to a fixed plate in order to observe the situation when the airbag is inflated. The function of the floating board is to protect the Bluetooth box from immersing into the water.
\nThe TrenchRoDer equipped with the proposed AIRS.
In order to construct a robust underwater vehicle fault attitude database, we simulated the underwater vehicle motion model with different situations in the water environment, which can provide the particular case of attitude data, such as if the AUVs suffered a crash or a disabled propeller. Moreover, we built the fault simulator GUI interface for a more convenient operation, in other words, the AIRS starts with the fault simulator GUI interface, after inputting the required AUV’s dimensions, hydrodynamic coefficients, buoyancy center, and inertia coefficients. Next, the simulator calculates the different AUV motion data under different cases through the Matlab™ program as shown in Figure 10. The above results from simulator will combine with the experimental data as the database for the training and verification of BPN classifier. Last but not least, the real AUV motion signal is set as the testing dataset to ensure the establishment of the AIRS. The modular modeling equation of AUVs is selected from [28]. The modeling method of Prestero’s which id without considering the sea conditions of emission can help us to generate the dynamic models of AUVs quickly and conveniently. The dynamics model and kinematics model of AUVs are established by analysis of the force working on AUVs moving underwater, based on the theorem of the momentum of the rotation around the buoyancy center and the theorem of the motion of mass center. The motion in 6-DOF of the AUVs is determined. And then, the attitude data of AUVs at any instant are determined.
\nThe AUV’s fault simulator diagram.
Since we simulated the AUV motion in the underwater environment without considering the influence of currents and waves, we did the wave maker experiment in NCKU’s ship model towing tank to observe the changes and effects on the AUV motion data for different wave heights. By experimenting with simulation, we can be closer to the state of the real ocean environment. The experiment of the AUVs affected by wave maker in NCKU’s ship model towing tank is illustrated in Figure 11.
\nThe wave maker of the NCKU’s ship model towing tank.
In this chapter, we focused on 10 conditions of AUV, which are listed in Table 1.
\nNumber | \nActivity description | \n
---|---|
1 | \nFunctional condition after horizontal sinking | \n
2 | \nMalfunction after horizontal sinking | \n
3 | \nFunctional condition after vertical sinking | \n
4 | \nMalfunction after vertical sinking | \n
5 | \nFunctional condition after ramped sinking | \n
6 | \nMalfunction after ramped sinking | \n
7 | \nFunctional condition after turbulence | \n
8 | \nMalfunction after turbulence | \n
9 | \nFunctional condition after collision | \n
10 | \nMalfunction after collision | \n
Activities performed in this experiment.
There are two types of these AUV conditions. One of them is failure situation and the other is functional condition and both include five motion status of the AUV. We carried out 20 experiments for capturing data and verifying classifier. The data from 17 experiments were adopted in the training program of the recognition scheme; the data obtained from the other experiments were used for testing the recognition performance. Note that, since the sampling frequency is 27.5 Hz, the total number of the short-term and long-term samplings for each activity of each experiment is 550 and 2200, respectively, which means 20 seconds per short-term window and 80 seconds per long-term window. The feature extraction of this chapter was based on 50% overlapping windows using 550 samples of window sizes to avoid information loss at the boundary of a single window. The dimension of a feature vector was 45 (an accelerometer × 3 axes × 9 features + a gyroscope × 3 axes × 6 features). Figure 12 illustrates the first 2200 data of accelerations and Euler orientations collected from the first experiment. The selected features of sensor’s data enabled effective recognition of the conditions and were suggested for BPN training procedure. A computation program adopted the input features and activated the feature classifier learning procedure with the BP algorithm, and outputted the results to short-term classifier. Then, an AUV condition was distinguished by a long-term classifier, of which the input is from the short-term classifier to raise the accuracy of failure recognition. The number of neurons in each hidden layer is 4, 6, and 7 for the feature classifier, short-term classifier, and long-term classifier, respectively, and the number of epochs is 700 for each neural training. The BPN classifier was trained on the training data set and tested on the test set which are from the experiment values. The classifier was created by neural network toolbox of MATLABTM for practical implementation and for validating the proposed model.
\nThe accelerations and Euler orientation of the first experiment.
After building up our prediction algorithm, we apply our chosen prediction algorithm on our new test set which is from the real signal of AUV, in order to have an idea about the algorithm’s performance on unseen data. The confusion matrix measured in the real AUV test is shown in Table 2, which recorded the results from 20 times experiments on each condition of AUV. We have implemented in two different ways under MATLABTM environment. In the first, we conducted in our proposed classifier system with feature extraction and the results indicate that the AUV failure detection on the average 97% of the time, and a successful functional condition accuracy of 93% is achieved. Second, we chose a classifier in ANN learning algorithm without feature extraction for comparing with the classifier that we proposed. The performance indicated that the ANN classifier without feature extraction performs poorer than our proposed classifier. From the confusion matrix, we can know that the malfunction and functional conditions are not easy to be confused. However, the motions within functional condition may be misclassified between each other, because these activities contain similar amplitude peaks and waveforms at the AUV.
\n20 times of testing experiments | \n|||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Classified Type | \n1 | \n2 | \n3 | \n4 | \n5 | \n6 | \n7 | \n8 | \n9 | \n10 | \nRecognition rate (%) | \nDetection accuracy (%) | \n|
1 | \n14/13 | \n0/0 | \n1/2 | \n0/0 | \n5/6 | \n0/0 | \n0/0 | \n0/0 | \n0/0 | \n0/1 | \n70/65 | \nMalfunction | \n97/92 | \n
2 | \n1/2 | \n16/14 | \n0/0 | \n0/0 | \n0/0 | \n1/2 | \n0/0 | \n0/1 | \n2/3 | \n2/4 | \n80/70 | \nFunctional condition | \n93/88 | \n
3 | \n1/1 | \n0/0 | \n15/13 | \n0/0 | \n0/1 | \n0/0 | \n0/0 | \n0/0 | \n1/1 | \n0/0 | \n75/65 | \n\n | \n |
4 | \n0/0 | \n0/0 | \n0/0 | \n18/16 | \n0/0 | \n2/2 | \n0/1 | \n0/0 | \n0/0 | \n0/0 | \n90/80 | \n\n | \n |
5 | \n4/3 | \n0/0 | \n2/4 | \n0/1 | \n15/13 | \n0/0 | \n0/0 | \n0/0 | \n0/1 | \n0/0 | \n75/65 | \n\n | \n |
6 | \n0/1 | \n1/1 | \n0/0 | \n2/3 | \n0/0 | \n16/15 | \n0/0 | \n3/4 | \n0/0 | \n0/0 | \n80/75 | \n\n | \n |
7 | \n0/0 | \n0/0 | \n0/0 | \n0/0 | \n0/0 | \n0/0 | \n17/15 | \n1/2 | \n0/0 | \n0/0 | \n85/75 | \n\n | \n |
8 | \n0/0 | \n0/0 | \n0/0 | \n0/0 | \n0/0 | \n1/1 | \n3/4 | \n16/13 | \n0/0 | \n0/0 | \n80/65 | \n\n | \n |
9 | \n0/0 | \n1/2 | \n2/1 | \n0/0 | \n0/2 | \n0/0 | \n0/0 | \n0/0 | \n16/14 | \n1/2 | \n80/70 | \n\n | \n |
10 | \n0/0 | \n2/3 | \n0/0 | \n0/0 | \n0/0 | \n0/0 | \n0/0 | \n0/0 | \n1/1 | \n17/14 | \n85/70 | \n\n | \n |
Confusion matrix for all the testing experiments.
\n* Proposed classifier with feature extraction in BP algorithm/ANN classifier without feature extraction.
The main objective of this work was to develop an intelligent AUV rescue system (AIRS) for detecting failure to minimize loss of an autonomous underwater vehicle (AUV). We combined three main ideas to construct the AIRS, including sensors algorithm, classifier conducting, and airbag system. Complex data acquisition was done by the 10-DOF sensor module with sensor calibration and extended Kalman filter (SCEKF), where Euler orientation fused with gravity and magnetic field are state variables, can benefit to get the precise attitude from the AUV. After SCEKF processed, we extracted the features of these signals from the 10-DOF sensor module and selected these features by principal component analysis (PCA) method. The results were incorporated with feature classifier, short-term classifier, and long-term classifier in order to recognize 10 types of AUV conditions. According to the experimental test in Section 3, we have shown that the 20 experimental data sets are categorized into “malfunction” or “functional condition” categories. The outcomes of the proposed classification with features extracted, whose failure detection accuracy is 97%, were more accurate than those of the ANN without features extracted. These results confirmed that the technology of the AIRS was feasible and that the proposed methods were accurate. Furthermore, we designed an inflatable mechanism, which fills CO2 in the airbags to generate buoyancy for AUV during failure detection.
\nThe attitude estimation and classification applied in the underwater environment are a new field. Considering future work, we will try to extend more condition types of AUV for classifying more complex situations and accomplish the airbag system for setting on the AUV. We can have the ability to construct a variety of different models for the AUV’s fault simulation, such as underwater turbulence or underwater creatures’ interference. Providing a more comprehensive AIRS, to not have to do the experiment in the real underwater environment, also can improve the performance and convenience of installation to the AIRS. With this technology, we can install the appropriate number of gas cylinders and airbags in accordance with the different displacements of AUV to avoid the loss of AUV; this can even be used in rescuing vessels to reduce shipwreck in the future, thereby minimizing loss of life and property. This study will have outstanding contributions for the next generation of underwater vehicles. We are looking forward to the application of the SIRS being used widely in the future.
\nThe authors hereby extend sincere thanks to the Ministry of Science and Technology (MOST) and Fisheries Agency for their financial support of this research, whose project codes are MOST 106-2221-E-006-121, MOST 107-2218-E-006-031, MOST 107-2218-E-110-004-and 107AS-14.2.7-FA-F1(3) and 107農科-14.2.7-漁-F1(3). We thank to the generous patronage of the MOST and Fisheries Agency that this study has been smoothly performed.
\nBacteria, fungi (yeasts and molds), mycobacteria, prions, protozoa, and viruses are common pathogens infecting humans and animals. They typically exist within the host or in the environment. It has been observed that these microorganisms exhibit a notable difference in the natural survivability in the environment, as well as susceptibility to chemical and physical inactivation. For example, under ambient and dried conditions, human coronaviruses seem to lose their infectivity in a matter of several hours to several days [1], whereas endospores and prions may remain infectious for years to decades or even indefinitely [2, 3].
As more and more data have become available regarding the survivability and susceptibility of pathogens to microbicides, it has been observed that the pathogens seem to demonstrate an order of susceptibility to chemical and physical inactivation. E. H. Spaulding first proposed a classification system for the sterilization and disinfection of medical instruments based on the infection risk in 1939 [4]. On the basis of this classification, the concept of a hierarchy of pathogen susceptibility was proposed, in which microorganisms are placed into several groups and ranked from least susceptible to most susceptible. In this hierarchy concept, bacterial spores were ranked the least susceptible, followed by mycobacteria, non-enveloped viruses, fungi, vegetative bacteria, and enveloped viruses. The susceptibility hierarchy was also believed to be related to the biochemical and biophysical characteristics of a pathogen [5, 6].
This hierarchy concept has been slightly modified and expanded over the years. For example, prions were added and considered less susceptible to inactivation by microbicides than bacterial spores; small non-enveloped viruses were considered less susceptible than large non-enveloped viruses; and the order between mycobacteria and small non-enveloped viruses was sometimes reversed (Figure 1) [7, 8, 9, 10]. Additionally, it has been suggested that the hierarchy concept may be applied either “vertically” (i.e., ranking of susceptibility
Proposed hierarchy of susceptibility of pathogens to microbicides. Note: slightly different versions of the hierarchy concept have been proposed in the literature. Mycobacteria have been placed above small non-enveloped viruses, and molds have been placed above large non-enveloped viruses in certain versions. In some versions, the small and large non-enveloped viruses are combined; and yeasts and molds may be combined.
The hierarchy concept has been quite useful for enabling scientists to better understand the innate difference among various types of pathogens. In the case of newly emerged pathogens, especially, the hierarchy concept has helped stakeholders design and implement a disinfection strategy swiftly with a reasonable level of confidence. The concept also helps the contaminant control for food, pharmaceutical, and biopharmaceutical products, as it is impractical to test every possible contaminating pathogen, and a robust infectivity assay system may be lacking for certain pathogens (e.g., hepatitis E virus).
Despite its usefulness, the hierarchy concept should be interpreted with caution, as it may oversimply the differences and trending of pathogen susceptibilities. Further examination and refinement of the concept may be necessary; and several important questions should be answered. For example, how often do exceptions to the hierarchy occur and what are the underlying reasons? Could a trending be specific to a given type of chemistry? Is the hierarchy the same between susceptibility to both chemical and physical inactivation? Why do pathogens in the same group, or even the same family or genus, sometimes exhibit striking differences in susceptibility? Is there a way to identify and separate reliable/consistent trending versus blurred/variable trending? A deeper look at the efficacy data for various types of microbicidal actives, especially for non-enveloped viruses, may help stakeholders understand the scope, reliability, and limitation of the hierarchy concept so that it can be best utilized.
This chapter reviews the inactivation efficacy data from the literature against non-enveloped viruses for several commonly used types of chemistries, either in formulated or unformulated form, in an effort to generate a separate relative order of susceptibility among these non-enveloped viruses for each type of chemistry and to differentiate consistent versus variable trending. Physical inactivation approaches are not covered in this chapter, although a significant degree of variation also exists for physical treatments. It is not clear that the physical inactivation approaches, in general, are governed by the same hierarchy to susceptibility as is observed for chemical inactivation approaches [12].
Currently, there are a total of 21 families of viruses (including enveloped and non-enveloped) identified for humans [13], which represent only a small part of the entire paradigm of viruses in nature, whose host ranges extend from vertebrates to plants to bacteria. The most common families of non-enveloped viruses for humans and animals include
Family | Example virus | Abbreviation | Genus | Genome | Size (nm) |
---|---|---|---|---|---|
Adenovirus type 2 | AdV-2 | ds DNA | 70–90 | ||
Adenovirus type 5 | AdV-5 | ds DNA | 70–90 | ||
Adenovirus type 8 | AdV-8 | ds DNA | 70–90 | ||
Human astrovirus | HAstV | ss RNA | 28–35 | ||
Feline calicivirus | FCV | ss RNA | 28–40 | ||
Human norovirus | HuNoV | ss RNA | 28–40 | ||
Murine norovirus | MNV | ss RNA | 28–40 | ||
Tulane virus | TuV | ss RNA | 28–40 | ||
Porcine circovirus | PCV | ss DNA | ∼17 | ||
Hepatitis E virus | HEV | ss DNA | 32–34 | ||
Human papillomavirus | HPV | ds DNA | 50–60 | ||
Bovine parvovirus | BPV | ss DNA | 20–28 | ||
Canine parvovirus | CPV | ss DNA | 20–25 | ||
Human parvovirus B19 | B19V | ss DNA | 23–26 | ||
Minute virus of mice | MVM (MMV) | ss DNA | 20–25 | ||
Porcine parvovirus | PPV | ss DNA | 20–25 | ||
Bovine enterovirus | BEV | ss RNA | 30–32 | ||
Coxsackievirus | Cox | ss RNA | 30–32 | ||
Echovirus 11 | Echo11 | ss RNA | 30–32 | ||
Encephalomyocarditis virus | EMCV | ss RNA | 30–32 | ||
Enterovirus 71 | EV-71 | ss RNA | 30–32 | ||
Enterovirus D68 | EV-D68 | ss RNA | 30–32 | ||
Foot and mouth disease virus | FMDV | ss RNA | 30–32 | ||
Hepatitis A virus | HAV | ss RNA | 30–32 | ||
Poliovirus type 1 | PV1 | ss RNA | 30–32 | ||
Rhinovirus | RV | ss RNA | 30–32 | ||
Seneca Valley virus | SVV | ss RNA | 30–32 | ||
Bovine polyomavirus | BPyV | ds DNA | 40–50 | ||
Simian virus 40 | SV40 | ds DNA | 40–50 | ||
Bluetongue virus | BTV | ds RNA | 60–80 | ||
Reovirus type 3 | REO-3 | ds RNA | 60–80 | ||
Rotavirus | Rota | ds RNA | 60–80 |
Common families of human and animal non-enveloped viruses.
Among these, the
It is worth noting that viruses are typically classified taxonomically on the basis of virion properties (size, shape, envelope, physical, and chemical properties, etc.), genome organization, replication mechanism, antigenic properties, and biological properties [13, 14, 15]. The final classification is a combined consideration of these properties. However, the stability and susceptibility to inactivation of a virus may not relate to all of these properties and, as such, may not always align with the taxonomic classification system. For example, the susceptibility of a virus to surfactants may primarily be related to the envelope of the virion and not related to the genome structure or mode of replication.
The susceptibilities of non-enveloped viruses to chemicals have been found to be highly variable and somewhat hard to predict, since they do not always agree with the hierarchy concept. For example, according to the hierarchy concept as modified by Sattar [8], small non-enveloped viruses should be less susceptible than large non-enveloped viruses. Additionally, if there is a fixed hierarchy, all small non-enveloped viruses should either display similar levels of susceptibility or should demonstrate a definitive trend of relative susceptibility, regardless of the type of microbicide. Based on the literature, neither of these predictions appear to hold in every case. The relative order of susceptibility seems chemistry-dependent; and sometimes viruses within the same family or even genus have been found to exhibit unequivocal differences in their susceptibilities (reviewed in [16]). Any trending or hierarchy, therefore, must be reviewed in the context of the type of chemistry, and it should not be assumed that non-enveloped viruses within the same family or genus will always display similar susceptibilities to a given microbicide.
Viral inactivation may be achieved by chemical and/or physical methods. The subset of chemicals commonly used for inactivation of non-enveloped viruses includes alcohols, oxidizers, halogen compounds, quaternary ammonium compounds, phenolics, aldehydes, acids, and alkalines [17, 18, 19]. These differ with respect to efficacy, stability, toxicity, material or surface compatibility, cost, and sensitivity to organic soil load. Soil load is a term used to signify an organic matrix used to challenge the inactivating efficacy of a microbicide. It is intended to mimic secretions or excretions in which the virus would be released from an infected person or animal. Some chemistries (e.g., sodium hypochlorite, phenolics, and aldehydes) are mostly used for environmental or medical device disinfection. Other chemistries (e.g., ethanol) are more commonly used for hand hygiene, while some others (e.g., quaternary ammonium compounds) may be used for both environmental disinfection and skin antisepsis (Table 2).
Class | Chemical | Typical conc. | Usage | Mechanism of viral inactivation | Sensitivity to soil load |
---|---|---|---|---|---|
Alcohols | Ethanol | 50–95% | Disinfection; Antisepsis | Protein denaturation | + |
Isopropanol | 70–90% | Disinfection | Protein denaturation | + | |
Oxidizers | Sodium hypochlorite | 0.01–0.5% | Disinfection | Protein/genome damage | ++ |
Chlorine dioxide | 0.1–1 mg/L | Disinfection; Water treatment | Protein/genome damage | — | |
Hydrogen peroxide | 0.1–10% | Disinfection; Antisepsis | Lipid/protein/genome damage | + | |
Hypochlorous acid | 0.002–0.1% | Disinfection; Water treatment | Protein/genome damage | ++ | |
Peracetic acid | 0.01–1% | Disinfection; Sterilization | Protein denaturation | — | |
Povidone-iodine | 0.02–8% | Disinfection; Antisepsis | Protein/genome damage | ++ | |
Chlorohexidine | 0.02–0.2% | Antisepsis | Protein denaturation | + | |
QAC | BKC, DDAC, etc. | 0.01–0.2% | Disinfection | Lipid/protein damage | + |
Low pH | Acids | ≤ pH 4 | Sanitization; Biomanufacturing | Capsid/protein damage | — |
High pH | NaOH, etc. | ≥ pH 10 | Disinfection; Tissue processing | Capsid/genome damage | — |
Aldehydes | Glutaraldehyde | 0.02–2% | HLD; Sterilization | Crosslinking/protein & genome damage | — |
Formaldehyde | 0.1–5% | Disinfection/Preservation | Alkylating/protein & genome damage | — | |
OPA | 0.02–2% | HLD; Sterilization | Crosslinking/protein damage | — | |
Phenolics | Phenylphenol, etc. | 0.05–5% | Disinfection | Protein damage | — |
Common types of chemistries used for non-enveloped viral inactivation.
Abbreviations used: BKC, benzalkonium chloride; Conc, concentration; DDAC, didecyldimethylammonium chloride; HLD, high-level disinfection; NaOH, sodium hydroxide; OPA, ortho-phthaldehyde; QAC, quaternary ammonium compounds.
The virucidal efficacy of a product is not only determined by the type and concentration of the chemical, but is also heavily influenced by the formulation, pH, exposure (contact or dwell) time, organic soil load, temperature, and surface characteristics (as applicable), etc. [10, 20, 21, 22]. Given the differences between various testing methods, as well as the intrinsic variability of viral infectivity (titration) assays, a general conclusion on the efficacy of a particular type of active ingredient will be enhanced if the efficacy is derived from multiple sets of data and under various application conditions (such as the concentration of the microbicidal active(s), contact time, formulation matrix (as applicable), and organic soil load, etc.) Additionally, in order best to explore the relative ranking of susceptibility between viruses, or the lack thereof, efficacy data from side-by-side studies wherein the same test methodologies and conditions were used would be preferable. Care should be taken when comparing data from different studies, especially if the formulations, test methods, and test conditions were different.
Alcohols, primarily ethanol and isopropanol, are widely used for hand hygiene and environmental disinfection, and their efficacies against bacteria and viruses have been extensively studied [23, 24, 25]. Ethanol at a concentration of 70–90% and isopropanol at 70% have been broadly shown to be effective against enveloped viruses; however, their efficacies against non-enveloped viruses are much more variable.
The trending of the degree of susceptibility of non-enveloped viruses to ethanol and isopropanol is generally clearer and more consistent than it is for many other types of chemistries, thanks to the large amount of data in the literature. The relative ranking of susceptibility of non-enveloped viruses seems to differ between ethanol and isopropanol; and the ranking does not appear to align well with the classical virological taxonomy.
For ethanol, parvoviruses and the polyomavirus simian virus 40 have low susceptibility, while rotavirus (a reovirus) is susceptible (Table 3). Viruses in the
Virusa | Method | Soil/Matrixb | Log10 Reduction after | References | |||
---|---|---|---|---|---|---|---|
30 s | 1 min | 5 min | 10 min | ||||
PPV | Stainless steel | Erythrocytes + BSA | 0.3 | 0.6 | [26] | ||
MVM | Stainless steel | Erythrocytes + BSA | 0.3 | 0.7 | [26] | ||
HEV71 | Suspension test | Medium | < 1 | [27] | |||
HAV | Suspension test | Medium | 0.4 | [28] | |||
HAV | Suspension test | 20% fecal | 0.4 | [28] | |||
HuNoV | Suspension test | 20% stool | <0.5 | [29] | |||
TuV | Suspension test | Medium | <0.5 | [30] | |||
PV1 | Suspension test | 20% fecal | 0.3 | [28] | |||
PV1 | Suspension test | Medium | 0.4 | [31] | |||
PV1 | Glass | Medium | 2.3 | 1.0 | 5.0 | [31] | |
PV1 | Stainless steel | Erythrocytes + BSA | 2.1 | 1.8 | [26] | ||
PV1 | Suspension test | Medium | 4 | [28] | |||
FCV | Suspension test | Medium | 1.7 | 2.2 | [30] | ||
AdV-8 | Suspension test | Medium | 1.9 | [33] | |||
AdV-5 | Stainless steel | Erythrocytes + BSA | 2.4 | >4.1 | [26] | ||
AdV-5 | Stainless steel | Medium | ∼5 | [34] | |||
MNV | Suspension test | Medium | 5 | [30] | |||
Rotavirus | Suspension test | Medium | > 3.1 | [28] | |||
CPV | Stainless steel | Medium | 0.1 | [36] | |||
SV40 | Suspension test | Medium | <1 | [37] | |||
PV1 | Glass | Medium | 2.9 | 2.9 | 5.4 | [31] | |
TuV | Suspension test | Medium | <0.5 | [30] | |||
FCV | Suspension test | Medium | <0.5 | [30] | |||
HEV71 | Suspension test | Medium | <1 | [27] | |||
PV1 | Suspension test | medium | <1 | [37] | |||
PV1 | Glass | Medium | 1.2 | 1.3 | 1.0 | [31] | |
AdV-5 | Stainless steel | Medium | ∼1 | [34] | |||
AdV-8 | Suspension test | Medium | 2.0 | [33] | |||
MNV | Suspension test | Medium | 1.8 | 3.1 | [30] | ||
SV40 | Suspension test | Medium | >4 | [37] | |||
Rotavirus | Suspension test | Medium | > 4 | [42] |
Efficacy of alcohols against non-enveloped viruses.
See Table 1 for abbreviations used for viruses.
BSA, bovine serum albumin; medium, culture medium; RT, room temperature.
Entries in purple font indicate results from undiluted or diluted formulations with the indicated microbicidal active ingredients.
Interestingly, the above order of susceptibility does not appear to hold the same for isopropanol (Table 3). For example, the polyomavirus simian virus 40 is much more susceptible to isopropanol than many other non-enveloped viruses; and poliovirus appears to display a lower susceptibility, similar to that of hepatitis A virus and human enterovirus 71. Murine norovirus is still more susceptible than feline calicivirus to isopropanol, but not as susceptible as simian virus 40 or rotavirus. The apparent difference between adenovirus 5 and adenovirus 8 that has been observed for ethanol has not been observed for isopropanol.
An oxidizer or oxidizing agent is a chemical that has the ability to oxidize other molecules, i.e., to accept their electrons. Common oxidizing agents used for disinfection, sterilization, or antisepsis include hydrogen peroxide, peracetic acid, ozone, and halogen-containing compounds such as sodium hypochlorite (bleach), hypochlorous acid, povidone-iodine, chlorohexidine, and chlorine dioxide, etc. These compounds can react with and alter the proteins and nucleic acids of non-enveloped viruses and render them noninfectious. Oxidizers comprise a large group of chemicals, and the relative order of susceptibility of non-enveloped viruses to oxidizers seems to vary by specific type of active ingredient (Table 4).
Virusa | Method | Soil/Matrixb | Log10 Reduction after | References | |||
---|---|---|---|---|---|---|---|
≤ 1 min | 2 min | 5 min | 10 min | ||||
FCV | Suspension test | Medium | 3 | [29] | |||
FCV | Suspension test | 20% stool | 0.5 | [29] | |||
MNV | Suspension test | Medium | 3 | [29] | |||
MNV | Suspension test | 20% stool | 0.0 | [29] | |||
CPV | Stainless steel | 90% plasma | < 1 | [43] | |||
CPV | Stainless steel | 5% serum | 5 | [43] | |||
HAV | Stainless steel | 5% serum | 5 | [43] | |||
HAV | Stainless steel | 90% plasma | <1 | 5 | [43] | ||
HAV | Suspension test | PBS/20% fecal | 4 | [28] | |||
PV1 | Suspension test | PBS/20% fecal | 4 | [28] | |||
PPV | Stainless steel | Erythrocytes + BSA | 0.6 | 1.0 | [26] | ||
MVM | Stainless steel | Erythrocytes + BSA | 3.0 | 4.4 | [26] | ||
PV1 | Stainless steel | Erythrocytes + BSA | 2.8 | 4.5 | [26] | ||
AdV-5 | Stainless steel | Erythrocytes + BSA | 4 | [26] | |||
PV1 | Glass | Medium | 0.4 | 0.9 | [16] | ||
RV14 | Glass | Medium | >4.9 | [16] | |||
PPV | Stainless steel | Erythrocytes + BSA | 0.5 | [26] | |||
MVM | Stainless steel | Erythrocytes + BSA | 1.5 | [26] | |||
PV1 | Stainless steel | Erythrocytes + BSA | 3.9 | [26] | |||
AdV-5 | Stainless steel | Erythrocytes + BSA | 2.3 | [26] | |||
MNV | Suspension test | Medium | ∼3 | [52] | |||
HAV | Suspension test | Medium | ∼3 | [53] | |||
PV | Suspension test | Medium | >3 | [53] | |||
CPV | Stainless steel | BSA | 1.6 | [34] | |||
MVM | Stainless steel | BSA | 2.3-2.9 | [34] | |||
PPV | Stainless steel | BSA | 3.8-5.5 | [34] | |||
AdV-5 | Stainless steel | BSA | 4.9-5.8 | [34] |
Efficacy of oxidizers against non-enveloped viruses.
See Table 1 for abbreviations used for viruses.
BSA, bovine serum albumin; PBS, phosphate buffered saline; medium, culture medium; RT, room temperature.
Viral-inoculated lettuce was washed with PAA solution for a defined period of time.
Entries in purple font indicate results from undiluted original or diluted formulations with microbicidal active ingredients.
Parvoviruses are generally among the least susceptible viruses to various types of oxidizers, including sodium hypochlorite, hydrogen peroxide, and peracetic acid. However, for sodium hypochlorite, minute virus of mice appears to be more susceptible than porcine parvovirus and canine parvovirus. All picornaviruses appear to exhibit a similar degree of susceptibility to sodium hypochlorite; but within the family of
The trending for hydrogen peroxide seems more complex than that for sodium hypochlorite. For example, there seems a higher level of variability within the
For peracetic acid, hepatitis A virus also seems less susceptible than poliovirus. Both feline calicivirus and murine norovirus are susceptible to peracetic acid and so is adenovirus.
Quaternary ammonium compounds (QAC) are widely used as active ingredients for disinfectants. Among the advantages of QAC are good stability, dual function of disinfection and cleaning, surface activity, low toxicity, and lack of odor, etc. The potential limitation in the microbicidal efficacy and possible effect in promoting antimicrobial resistance of QAC have also been discussed in the literature [54, 55].
Quaternary ammonium compounds are generally efficacious on most vegetative bacteria and enveloped viruses. Their efficacies against non-enveloped viruses, however, are generally much weaker. Nevertheless, several non-enveloped viruses, such as rotavirus, rhinovirus, and coxsackievirus A11, have been shown to be susceptible to QAC. The susceptibility levels among the
Virusa | Method | Soil/matrixb | Log10 reduction after | References | |||
---|---|---|---|---|---|---|---|
30 s | 1 min | 10 min | 60 min | ||||
PPV | Stainless steel | Erythrocytes + BSA | 0.4 | [26] | |||
MVM | Stainless steel | Erythrocytes + BSA | 0.5 | [26] | |||
PV1 | Stainless steel | Erythrocytes + BSA | 0.5 | [26] | |||
AdV-5 | Stainless steel | Erythrocytes + BSA | 1.8 | [26] | |||
AdV-8 | Suspension test | Medium | 1.0-1.8 | [57] | |||
AdV-5 | Suspension test | Medium | 3.7-5.3 | [57] | |||
TuV | Suspension test | Medium | <0.5 | [30] | |||
PV1 | Suspension test | BSA/yeast extract | 0.0 | [58] | |||
AdV-25 | Suspension test | BSA/yeast extract | 0.3 | [58] | |||
Cox A11 | Suspension test | BSA/yeast extract | >5.1 | [58] | |||
FCV | Suspension test | Medium | <0.5 | [29] | |||
MNV | Suspension test | Medium | <0.5 | [29] | |||
Rhinovirus | Glass | Medium | >3.0 | >3.3 | [16] |
Efficacy of QAC against non-enveloped viruses.
See Table 1 for abbreviations used for viruses.
BSA, bovine serum albumin; medium, culture medium; QAC, quaternary ammonium compound.
Entries in purple font indicate results from original or diluted formulations with microbicidal active ingredients.
Acids and alkalines, either used alone or in combination with other active ingredients in formulated products, can be an effective means for viral inactivation. Acids may be used for disinfection, sanitization, textile or face mask pretreatment, or viral clearance during biopharmaceutical manufacturing. Alkalines may also be used for disinfection, sanitization, and viral clearance during biopharmaceutical manufacturing and can be effective against even the least susceptible of pathogens, the prions [58].
It has been widely reported that a low-pH treatment (typically at pH 4 and below) can effectively inactivate most enveloped viruses, although some enveloped viruses, such as bovine viral diarrhea virus, still exhibit a relatively low susceptibility to this treatment pH [22]. The range of susceptibilities of non-enveloped viruses to low pH seems quite scattered and often goes against the “conventional wisdom” that non-enveloped viruses are not susceptible to acidic pH (Table 6). For instance, in the family of
Virusa | Method | Soil/Matrixb | Log10 Reduction after | References | |||
---|---|---|---|---|---|---|---|
20 min | 30 min | 45 min | 1–2 hr | ||||
REO-3 | Suspension test | Medium | 1–3 | [59] | |||
PCV | Suspension test | Medium | >3 | [60] | |||
MVM | Suspension test | Medium | <1 | [61] | |||
MNV | Suspension test | Medium | <0.5 | [30] | |||
TuV | Suspension test | Medium | <0.5 | [30] | |||
PARV4 | Suspension test | Medium | 2–3 | [61] | |||
B19V | Suspension test | Medium | > 4 | [61] | |||
FCV | Suspension test | Medium | 6.3 | [30] | |||
FCV | Suspension test | Medium | >5 | [62] | |||
PV | Suspension test | Medium | <1 | [63] | |||
PV | Suspension test | Medium | <1 | [64] | |||
HAV | Suspension test | Medium | <1 | [64] | |||
MNV | Suspension test | Medium | <0.5 | [30] | |||
TuV | Suspension test | Medium | <0.5 | [30] | |||
Cox A9 | Suspension test | Medium | <1 | [65] | |||
FCV | Suspension test | Medium | ∼3 | [30] | |||
FCV | Suspension test | Medium | ∼4.7 | [62] | |||
RV | Suspension test | Medium | >3 | [65] | |||
FMDV | Suspension test | Medium | >3 | [65] | |||
MVM | Suspension test | Medium | <1 | [66] | |||
EV71 | Suspension test | Medium | <1 | [67] | |||
EV-D68 | Suspension test | Medium | ∼4–5 | <5 | [67] | ||
B19V | Suspension test | Medium | [66] |
Efficacy of low pH against non-enveloped viruses.
The
Feline calicivirus and murine norovirus in the family
Viruses, both enveloped and non-enveloped, are generally susceptible to high pH. At an environment of pH 12 or above, most if not all non-enveloped viruses would be inactivated, with extent depending both on temperature and contact time. Reovirus, simian virus 40, hepatitis A virus, canine parvovirus, poliovirus, murine norovirus, and Tulane virus seem to be less susceptible than minute virus of mice, feline calicivirus, adenovirus, rotavirus, and foot-and-mouth disease virus. It may be worth noting that the order of susceptibility to high pH seems to be in discord with the hierarchy concept by the greatest degree: in this case, an enveloped virus, bovine viral diarrhea virus, seems to be less susceptible than most, if not all, non-enveloped viruses [22]; parvoviruses are not necessarily less susceptible than many other non-enveloped viruses; and the size of the viral particle does not seem to matter much with regard to the degree of susceptibility (Table 7).
Virusa | Method | Soil/Matrixb | Log10 Reduction after | References | |||
---|---|---|---|---|---|---|---|
≤ 1 min | 10 min | 30 min | 1 hr | ||||
MNV | Suspension test | Medium | ∼2 | [30] | |||
TuV | Suspension test | Medium | ∼2.2 | [30] | |||
FCV | Suspension test | Medium | >5.5 | [30] | |||
REO-3 | Suspension test | Medium | 3 | [68] | |||
Cox B | Suspension test | Medium | 5 | [69] | |||
Echo 11 | Suspension test | Medium | 6 | [68] | |||
BVDV | Suspension test | Medium | 2.5 | [70] | |||
HAV | Suspension test | Medium | 2.7 | [59] | |||
SV40 | Suspension test | Medium | 3.9 | [70] | |||
HAV | Stainless steel | 5% serum | 3.0 | [43] | |||
HAV | Stainless steel | 90% plasma | 3.6 | [43] | |||
CPV | Stainless steel | 5% serum | 3.5 | [43] | |||
CPV | Stainless steel | 90% plasma | 5.2 | [43] | |||
MVM | Suspension test | Medium | >4.7 | [71] | |||
MVM | Suspension test | Medium | >4 | [66] | |||
CPV | Suspension test | Medium | 5.6 | [70] | |||
PV | Suspension test | Medium | 5.9 | [70] | |||
AdV-2 | Suspension test | Medium | >6.9 | [70] | |||
AdV-5 | Suspension test | Medium | >6 | [72] | |||
HAV | suspension test | Medium | 2.4 | [59] | |||
PV | suspension test | Medium | 4.1 | [63] | |||
Avian Reo | Suspension test | Medium | 4 | [73] | |||
PV | Suspension test | Medium | 5.1 | [73] | |||
Bovine Rota | Suspension test | Medium | >6 | [73] |
Efficacy of high pH against non-enveloped viruses.
Entries in purple font indicate results from undiluted or diluted formulations with microbicidal active ingredients.
Aldehydes, such as glutaraldehyde, formaldehyde, and
Virusa | Method | Soil/Matrixb | Log10 Reduction after | References | |||
---|---|---|---|---|---|---|---|
5 min | 10 min | 30 min | 60 min | ||||
HAV | Suspension test | Medium | 3.0 | [75] | |||
PPV | Stainless steel | BSA | 1.7–2.8 | [34] | |||
MVM | Stainless steel | BSA | 2.5–3.3 | [34] | |||
PV1 | Suspension test | Medium | >3 | [76] | |||
AdV-5 | Stainless steel | BSA | 4.9–6.3 | [34] | |||
PPV | Stainless steel | Erythrocytes + BSA | 3.6 | [26] | |||
MVM | Stainless steel | Erythrocytes + BSA | >4.4 | [26] | |||
AdV-5 | Suspension test | Medium | >5.0 | [77] | |||
Ortho-phthaldehyde, 0.55% | |||||||
PPV | Stainless steel | Erythrocytes + BSA | 3.6 | [26] | |||
MVM | Stainless steel | Erythrocytes + BSA | >4. | [26] |
Efficacy of aldehydes against non-enveloped viruses.
See Table 1 for abbreviations used for viruses.
BSA, bovine serum albumin; medium, culture medium; RT, room temperature.
Entries in purple font indicate results from original or diluted formulations with microbicidal active ingredients.
In the simplified hierarchy of susceptibility of pathogens to microbicides concept, small non-enveloped viruses are considered less susceptible than large non-enveloped viruses, and both groups of non-enveloped viruses are believed to be less susceptible than enveloped viruses. The hierarchy concept also assumes that the ranking applies to all types of microbicidal actives. Additionally, the hierarchy concept can generally lead to common notions that viruses that share similar virological properties (e.g., same family or genus of virus) may be expected to display similar degrees of susceptibility and that the smaller a virus is, the less susceptible it will be to microbicides in general.
These generalizations are correct, to a degree. For example, most enveloped viruses are indeed more susceptible than non-enveloped viruses to chemical inactivation. It should be noted though that exceptions to the hierarchy concept do exist, e.g., especially in the case of viral susceptibility to acids and alkalines [22], and exceptions are not uncommon for certain other chemistries. The hierarchy concept was never applied specifically to physical inactivation approaches, nor should it be. The evidence for heat inactivation, UV inactivation, and gamma irradiation indicates differing rankings of susceptibility to these modalities. Envelope status and particle size do not, in each case, relate to susceptibility for inactivation by these physical approaches [22, 78, 79, 80].
The validity of the hierarchy concept
The accuracy and usefulness of a hierarchy concept can be improved if the model is broken into separate chemistries for non-enveloped viruses, since many viruses do exhibit a reliable and consistent trend of susceptibility for a specific type of chemical. Table 9 and Figure 2 provide a summary of the relative order of susceptibility for selected non-enveloped viruses under specific types of chemistry.
Chemical | Lower susceptibility | Medium susceptibility | Higher susceptibility |
---|---|---|---|
Ethanol | Animal parvovirus | Poliovirus | Murine norovirus |
Simian virus 40 | Foot and mouth disease virus | Rhinovirus | |
Hepatitis A virus | Human norovirus | Adenovirus 5 | |
Enterovirus 71 | Feline calicivirus | Rotavirus | |
Adenovirus 2, 8 | |||
Isopropanol | Animal parvovirus | Adenovirus 5, 8 | Simian virus 40 |
Hepatitis A virus | Murine norovirus | Rotavirus | |
Enterovirus 71 | |||
Poliovirus | |||
Feline calicivirus | |||
NaOCl | Porcine parvovirus | Minute virus of mice | Feline calicivirus |
Hepatitis A virus | Hepatitis A virus | Adenovirus | |
Poliovirus | Rotavirus | ||
Enterovirus 71 | |||
Murine norovirus | |||
H2O2 | Animal parvovirus | Poliovirus | Rhinovirus |
Hepatitis A virus | Murine norovirus | Feline calicivirus | |
Adenovirus | Rotavirus | ||
PAA | Animal parvovirus | Poliovirus | Feline calicivirus |
Hepatitis A virus | Murine norovirus | ||
Adenovirus | |||
QAC | Animal parvovirus | Feline calicivirus | Rotavirus |
Poliovirus | Murine norovirus | Rhinovirus | |
Adenovirus 8, 25 | Adenovirus 5 | Coxsackievirus A11 | |
Low pH | Minute virus of mice | Human parvovirus 4 | Feline calicivirus |
Hepatitis A virus | Rhinovirus | ||
Poliovirus | Foot and mouth disease virus | ||
Enterovirus 71 | Enterovirus EV-D68 | ||
Coxsackievirus A9 | Human parvovirus B19 | ||
Murine norovirus | |||
Rotavirus | |||
Reovirus | |||
High pH | Bovine viral diarrhea virus | Reovirus | Murine minute virus |
Simian virus 40 | Feline calicivirus | ||
Hepatitis A virus | Adenovirus | ||
Canine parvovirus | Rotavirus | ||
Poliovirus | Foot and mouth disease virus | ||
Murine norovirus | |||
Tulane virus | |||
Aldehydes | Porcine parvovirus | Minute virus of mice | Poliovirus |
Hepatitis A virus | |||
Feline calicivirus | |||
Adenovirus | |||
Reovirus | |||
Rotavirus |
Relative order of susceptibility of non-enveloped viruses to chemical inactivation.
Abbreviations used: H2O2, hydrogen peroxide; NaOCl, sodium hypochlorite; PAA, peracetic acid; QAC, quaternary ammonium compound.
Relative order of susceptibility of non-enveloped viruses per microbicidal chemistry. Note: various types of adenoviruses exhibit different degrees of susceptibility to ethanol and quaternary ammonium compounds.
The Spaulding concept of the hierarchy of susceptibility of pathogens to microbicidal inactivation, along with its modifications, has been widely influential. Multiple industries as well as regulatory agencies have adopted or referenced this concept to various degrees [9, 10, 81, 82]. The concept does provide a good tool for understanding the innate differences and trending of susceptibility among various types of pathogens. For the most part, the hierarchy is insightful and valuable. It is particularly helpful when a pathogen is newly emerged, and limited or no knowledge is yet available regarding its level of susceptibility to microbicides [83, 84]. In fact, the United States Environmental Protection Agency (U.S. EPA) and Centers for Disease Control and Prevention (U.S. CDC) use the hierarchy concept as the basis of the Emerging Viral Pathogen Guidance for Antimicrobial Pesticides and public hygiene [10, 82, 85, 86] specifically to deal with just such a possibility.
It should be cautioned, however, that the hierarchy concept is largely oversimplified and by no means perfect [87]. For viruses, although enveloped viruses are usually more susceptible than non-enveloped viruses, certain enveloped viruses such as bovine viral diarrhea virus can be less susceptible than some non-enveloped viruses (e.g., feline calicivirus) under certain chemistries (e.g., low pH and high pH).
The accuracy and applicability of the hierarchy concept are more complex and limited among non-enveloped viruses. The trending is highly dependent on the type of chemistry; and the size of the virion is not always a primary determinant of viral susceptibility among non-enveloped viruses. If a clearer and more consistent trending can be identified among non-enveloped viruses, albeit only specific to a given type of chemistry, the knowledge should be useful.
To generalize an order of susceptibility, for a specific chemistry, data from side-by-side studies wherein viruses are evaluated concurrently by the same test method and under the same conditions should, ideally, be used. When results from different studies are used, caution should be taken to exclude conditional or case-specific differences that result from the test methodology and/or condition. For instance, a surface (carrier) test may give different log10 reduction results than a suspension test of the same microbicide or formulation under certain situations [88]. For example, the data of Kindermann et al. [47] and Tyler et al. [31] indicate that sodium hypochlorite causes a higher log10 reduction value (LRV) when tested in a suspension test than in a surface test. On the other hand, glutaraldehyde has been found to cause similar log reduction in either methodology, while hydrogen peroxide causes higher LRV in the surface test, which is thought to be likely related to the consumption of hydrogen peroxide by the protein in the virus-suspending solution [31].
The organic soil load in which the challenge virus is suspended prior to inoculation can also impact the viral inactivation outcome, especially for oxidizers, alcohols, and QAC. It would be inaccurate or even misleading if a result from a light organic load (e.g., 5% animal serum or phosphate-buffered saline) were to be directly compared with a test that used a heavier organic load (e.g., 90% blood or 20% fecal suspension). Tung
Other testing conditions may also affect the reduction results. For instance, a higher contact temperature may work in the favor of the virucide under investigation, which may result in a higher log reduction. Nemoto et al. [56] reported that a 0.125% glutaraldehyde solution completely inactivated rotavirus after 10 min under ambient temperature, but not when evaluated on ice. The pH and other components in the product formulation could also affect the viral reduction outcome, presumably by activating the chemical and/or by a synergistic or additive effect between the pH and the active chemical [22, 39, 89]. The efficacy of formulated versus non-formulated microbicides may differ even within the same type and concentration of active(s). For example, formulated QAC and ethanol products have been reported to exhibit strong activities against certain non-enveloped viruses albeit the efficacy may be weaker for non-formulated solutions [45, 54, 90, 91]. Therefore, the formulation of the microbicidal active must be considered. The viral stock (i.e., inoculum) preparation method and the challenge viral titer may also affect the reported viral reduction efficacy. For example, purified virus may be more susceptible than crude virus preparations [49]; viral clumps can make the virus less susceptible [92]; and a higher viral challenge titer could make the chemical harder to achieve an expected log10 reduction. Sometimes, viruses propagated in different host cell types may behave differently. It would therefore be ideal if all studies could use a standardized viral preparation and infectivity assay protocol. This is, of course, practically challenging. Last, but not least, the method for preparing the microbicide and the verification of the active concentration might also differ from lab to lab, thus potentially influencing the efficacy results obtained.
Despite these practically hard-to-avoid differences in test methodology and conditions, some generalizations on the pattern of susceptibility among non-enveloped viruses can still be made with confidence. For instance, it is quite apparent that the
The family
Different types of adenoviruses seem to exhibit varying degrees of susceptibility to ethanol and QAC. For example, adenovirus type 5 appears to be notably more susceptible to ethanol than are adenovirus types 2 and 8. In general, however, adenoviruses are more susceptible than many other non-enveloped viruses. Considering that adenovirus type 5 is listed as one of the allowable challenge viruses for a generic or “broad-spectrum” virucidal efficacy claim (i.e., a product that is effective for adenovirus type 5 may be considered effective against all viruses) [97, 98], this practice may not represent a challenge and lead to an insufficient safety margin, which is not supported by the published data.
Parvoviruses are among the smallest of non-enveloped viruses. The animal parvoviruses (e.g., minute virus of mice, porcine parvovirus, bovine parvovirus, canine parvovirus, etc.) are considered to exhibit very low susceptibility to chemical inactivation [99] and are commonly used as a worst-case model for viral inactivation studies. This literature review generally supports this notion, although it should be noted that the animal parvoviruses do not appear to represent a worst-case challenge for high-pH inactivation, and porcine parvovirus seems less susceptible than minute virus of mice at times. Additionally, human parvovirus B19 seems especially susceptible to acid treatment [100].
It has been observed that the particle size of a virus is not an exclusive or even a primary determinant of susceptibility to microbicides for non-enveloped viruses, albeit this characteristic may play a role. There are numerous reports demonstrating that larger non-enveloped viruses, such as adenoviruses and reoviruses, are less susceptible than some of the smaller non-enveloped viruses for certain chemistries. Interestingly though, rotavirus, a large non-enveloped virus, indeed seems to be the most susceptible among non-enveloped viruses, except to low pH.
The mechanisms underlying the large variation in susceptibility among non-enveloped viruses and the chemistry dependency are not always clear, but they could presumably be related to the physicochemical properties of the virus as well as the mechanisms of action of the chemical inactivants. For alcohols, for instance, it has been proposed that the hydrophobicity or hydrophilicity of the viral particles is an important determinant of susceptibility [101]. Poliovirus, which is hydrophilic, is more susceptible to ethanol than it is to isopropyl alcohol. This is attributed to the fact that ethanol is more hydrophilic than isopropanol. In comparison, the hydrophobic simian virus 40 is susceptible to isopropanol but not to ethanol [101]. Enterovirus 71 (EV71) and enterovirus EV-D68 (EV-D68) are both enteroviruses in the family
A review of the relative order of susceptibility for non-enveloped viruses under each chemistry reveals that the order for some chemicals (e.g. aldehydes) seems to fit the traditional hierarchy concept well (e.g., parvoviruses are less susceptible than larger viruses); but the order for some other chemistries (e.g., low pH) does not seem to agree with the concept as well.
The variability in viral susceptibility to physical treatments is not covered in this chapter; however, a marked degree of variation also exists for physical treatments, both within non-enveloped viruses and between enveloped and non-enveloped viruses [12, 16, 21, 49]. A comparison of the order of susceptibility of viruses to chemical versus physical treatments and an exploration of the underlying mechanisms would be interesting and revealing.
This chapter reviewed the literature on chemical inactivation of non-enveloped viruses, with an emphasis on the relative difference and trending of susceptibility among some relevant (from a public health perspective) non-enveloped viruses under each type of chemistry. The traditional concept of a hierarchy of susceptibility to microbicides provides a useful tool in understanding and predicting the susceptibility of a pathogen; however, the concept tends to be oversimplified. The order of susceptibility among non-enveloped viruses depends on the type of chemistry, and there is no universal order that holds true for all types of chemistries. Picornaviruses and caliciviruses exhibit a particularly high degree of intrafamily variation, and the order may even be reversed between viruses, depending on the chemistry. Additionally, larger non-enveloped viruses are not always more susceptible than some of the smaller non-enveloped viruses. It may be inappropriate to consider adenovirus type 5 as a worst-case non-enveloped virus; and even the animal parvoviruses, universally considered among the least susceptible to chemical inactivation, do not actually represent the least susceptible virus type for certain chemistries.
The author thanks Drs. Raymond Nims and M. Khalid Ijaz for the critical review of the manuscript and discussion.
The author declares no conflict of interest.
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