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Antibodies are produced by the immune system in response to invasion of the body by ‘foreign’ material. Molecules larger than 1000 molecular weight including proteins, polysaccharides and lipids are immunogenic. Recognition of the foreign substance by receptors on the surface of B lymphocytes in the blood stimulates the cell to multiply and to produce antibodies capable of binding specifically to the compound. Each lymphocyte clone produces only one antibody, but many different lymphocytes (and thus antibodies) may be stimulated by a single immunogen.

The purpose of this paper is to present the use of artificial immune systems (AISs) to solve constrained design optimization problems for active magnetic bearings (AMBs).

This research applies the AIS approach, more specifically, a representative clonal selection-based AIS called CLONALG, to the single-objective structural design optimization of AMBs. In addition, when compared with a genetic algorithm (GA) developed in the previous work, the CLONALG fails to produce best solutions when a nearly zero feasible ratio occurs in an AMB design problem. Therefore, an AIS called ARISCO (AIS for constrained optimization) is proposed to address the above issue.

A total of six AMB design cases are solved by the GA, CLONALG, and ARISCO. Based on the simulation results, in terms of solution quality, the ARISCO is shown to have better overall performance than the CLONALG and GA. In particular, when solving a problem with a nearly zero feasible ratio, the ARISCO and GA perform equally and both outperform the CLONALG.

In summary, the contributions of this paper include: this research applies the AIS approach, more precisely, the CLONALG, to the single-objective structural design optimization of AMBs; the ARISCO overall produces better AMB designs than the CLONALG and a GA developed in the previous work; in situations where a nearly zero feasible ratio occurs, the ARISCO and GA perform equally, and they both outperform the CLONALG.

The study examines health care inequities in viral load testing among hepatitis C (HCV) antibody-positive patients. The analysis predicts whether individual and census tract sociodemographic characteristics impact the likelihood of viral load testing.

This a study of 26,218 HCV antibody-positive patients in Orange County, California, from 2010 to 2020. The case data were matched with the 2017 American Community Survey to help understand the role of neighborhood socioeconomic characteristics in testing for viral load. Multivariable logistic regression was used to predict the probability of ever testing for HCV viral load.

Thirty-six percent of antibody-positive persons were never viral load tested. The results show inequalities in viral load testing by sociodemographic factors. The following groups were less likely to ever test for viral load than their counterparts: (1) individuals under 65 years old, (2) females, (3) residents of census tracts with lower levels of health insurance enrollment, (4) residents of census tracts with lower levels of government health insurance, and (5) residents of census tracts with a higher proportion of non-white residents.

This is a secondary database from public health department reports. Using census tract data raises the issue of the ecological fallacy. Detailed medical records were not available. The results of this study emphasize the social inequality in viral load testing for HCV. These groups are less likely to be treated and cured, and may spread the disease to others.

This chapter is unique as it combines routinely collected public health department data with census tract level data to examine social inequities associated with lower rates of HCV viral load testing.

Nature and occurrence of food-borne pathogens in raw and processed food products evolved greatly in the past few years due to new modes of transmission and resistance build-up against sundry micro-/macro-environmental conditions. Assurance of food health and safety thus gained immense importance, for which bio-sensing technology proved very promising in the detection and quantification of food-borne pathogens. Considering the importance, different studies have been performed, and different biosensors have been developed. This study aims to summarize the different biosensors used for the deduction of food-borne pathogens.

The present review highlights different biosensors developed apropos to food matrices, factors governing their selection, their potential and applicability. The paper discusses some related key challenges and constraints and also focuses on the needs and future research prospects in this field.

The shift in consumers’ and industries’ perceptions directed the further approach to achieve portable, user and environmental friendly biosensing techniques. Despite of these developments, it was still observed that the comparison among the different biosensors and their categories proved tedious on a single platform; since the food matrices tested, pathogen detected or diagnosed, time of detection, etc., varied greatly and very few products have been commercially launched. Conclusively, a challenge lies in front of food scientists and researchers to maintain pace and develop techniques for efficiently catering to the needs of the food industry.

Biosensors deduction limit varied with the food matrix, type of organism, material of biosensors’ surface, etc. The food matrix itself consists of complex substances, and various types of food are available in nature. Considering the diversity of food there is a need to develop a universal biosensor that can be used for all the food matrices for a pathogen. Further research is needed to develop a pathogen-specific biosensor that can be used for all the food products that may have accuracy to eliminate the traditional method of deduction.

The present paper summarized and categorized the different types of biosensors developed for food-borne pathogens.

There are several infectious agents in the environment that can cause persistent infections in the host. They usually cause their symptoms shortly after first infection and later persist as silent viruses and bacteria within the body. However, these chronic infections may play an important role in the pathogenesis of schizophrenia and Tourette's syndrome (TS). We investigated the distribution of different neurotrophic infectious agents in TS, schizophrenia and controls. A total of 93 individuals were included (schizophrenic patients, Tourette patients and controls). We evaluated antibodies against cytomegalovirus (CMV), herpes-simplex virus (HSV), Epstein-Barr virus, Toxoplasma, Mycoplasma and Chlamydia trachomatis/pneumoniae. By comparing schizophrenia and TS, we found a higher prevalence of HSV (P=0.017) and CMV (P=0.017) antibodies in schizophrenic patients. Considering the relationship between schizophrenia, TS and healthy controls, we showed that there are associations for Chlamydia trachomatis (P=0.007), HSV (P=0.027) and CMV (P=0.029). When all measured viruses, bacteria and protozoa were combined, schizophrenic patients had a higher rate of antibodies to infectious agents than TS patients (P=0.049). Tourette and schizophrenic patients show a different vulnerability to infectious agents. Schizophrenic patients were found to have a higher susceptibility to viral infections than individuals with TS. This finding might point to a modification in special immune parameters in these diseases.

In the development of electromagnetic devices, multiobjective topology optimisation is effective to obtain diverse design candidates for production models. However, multiobjective topology optimisation has not widely been performed because it is difficult to obtain resultant shapes for engineering realisation due to large search spaces. The purpose of this paper is to present a new multiobjective topology optimisation method.

This paper presents a new multiobjective topology optimisation method in which the Immune Algorithm is modified for multiobjecrive optimisation and a shape modification process based on spatial filtering is employed.

The present method shows that better Pareto solutions can be found in comparison with the conventional methods.

A new effective multiobjective topology optimisation is presented. This method enables to diverse design candidates for production models.

The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day (TOD) electricity tariff, to reduce electricity bill.

Two optimization models for charging plans respectively with fixed and stochastic trip travel times are developed, to minimize the electricity costs of daily operation of an electric bus. The charging time is taken as the optimization variable. The TOD electricity tariff is considered, and the energy consumption model is developed based on real operation data. An optimal charging plan provides charging times at bus idle times in operation hours during the whole day (charging time is 0 if the bus is not get charged at idle time) which ensure the regular operation of every trip served by this bus.

The electricity costs of the bus route can be reduced by applying the optimal charging plans.

This paper produces a viable option for transit agencies to reduce their operation costs.

This paper aims to review the potential application of surface plasmon resonance (SPR) in diagnosis of dengue virus (DENV-2) E-protein and the development of SPR to become an alternative DENV sensor.

In this review, the existing standard laboratory techniques to diagnosis of DENV are discussed, together with their drawbacks. To overcome these drawbacks, SPR has been aimed to be a valuable optical biosensor for identification of antibodies to the DENV antigen. The review also includes the future studies on three-dimensional poly(amidoamine) (PAMAM) dendrimer-surface-assembled monolayer (SAM)-Au multilayer thin films, which are envisaged to have high potential sensitive and selective detection ability toward target E-proteins.

Application of SPR in diagnosis of DENV emerged over recent years. A wide range of immobilized biorecognition molecules have been developed to combine with SPR as an effective sensor. The detection limit, sensitivity and selectivity of SPR sensing in DENV have been enhanced from time to time, until the present.

The main purpose of this review is to provide authors with up-to-date and useful information on sensing DENV using SPR and to introduce a novel three-dimensional PAMAM-SAM-Au multilayer thin films for future research on SPR sensing applications.

The purpose of this study is to develop an intelligent methodology to find out an optimal feasible assembly sequence while considering the assembly predicates.

This proposed study is carried out by using two artificial immune system-based models, namely, Bone Marrow Model and Negative Selection Algorithms, to achieve the following objectives: to obtain the possible number of assembly sequences; to obtain the feasible assembly sequences while considering different assembly predicates; and to obtain an optimal feasible assembly sequence.

Proposed bone-marrow model determines the possible assembly sequences to ease the intricacy of the problem formulation. Further evaluation has been carried out through negative-selection censoring and monitoring models. These developed models reduce the overall computational time to determine the optimal feasible assembly sequence.

In this paper, the novel and efficient strategies based on artificial immune system have been developed and proposed to obtain all valid assembly sequences and optimized assembly sequence for a given assembled product using assembly attributes. The introduced methodology has proven its effectiveness in achieving optimal assembly sequence with less computational time.