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The service sector is the key driver of a country’s economic growth. The quality of service is more important for the survival of any organization. It is the interactive process by which the organization understands the customer and satisfies their needs. The main purpose of this study is to identify the factors influencing service quality in ocean freight forwarding and to study the association between the factors.

This research uses a deductive approach, which understands the theory first and collects the data. A questionnaire is designed to collect the data. The sampling technique used is two-stage sampling. First, the freight forwarders are selected and then the customers, importers and exporters are selected randomly. Likert scales are used to measure quality factors such as tangibility, reliability, responsibility, value, empathy and assurance. The association of factors is empirically evaluated. The SPSS tool is used for the correlation analysis.

An extensive review of the literature has been done to study and identify these service quality factors influencing customer satisfaction and loyalty. The result of this extensive literature review revealed that tangibility, responsiveness, reliability, trust, empathy and value are the service quality. It has been proved that there exists a significant association between the service quality factors and is positively related to the customer satisfaction.

Some studies have examined the freight forwarders’ service quality, but not specifically related to any dimension. This study attempts to bring together the five dimensions of SERVQUAL scale and the value dimension evaluating the cost, freight charges, safety and security criteria in the industry and examines the association between the quality factors and customer satisfaction.

The recommended public healthcare infrastructure and human resources are essential to deliver healthcare services, particularly in tribal areas, as the country’s tribal population depends mainly on the public healthcare system for their medical needs. India has a substantial share of the tribal population, accounting for approximately 9% of the total population. The paper reviews the state of public healthcare infrastructure and human resources in tribal areas for a period that spreads over a decade.

The paper relied on data from the Government of India’s Rural Health Statistics (RHS) reports (2011–2012 and 2021–2022). From these reports, data on the physical infrastructure and human resources in the tribal areas were extracted. The extracted data were compiled and analyzed using Microsoft Excel.

The analysis showed that the improvement in public healthcare infrastructure and human resource situation in tribal areas of the country was not commensurate with the tribal population growth seen in the last decade. As a result, the average population covered by a health facility was greater than the prescribed norms in the tribal-dominated geographies. The health worker-population ratio at the primary healthcare level was also higher than the national norms. However, there was a substantial improvement in the doctor-population ratio at the primary health center level. In comparison to tribal-lean states, tribal-dominated states faced the concurrent challenge of a growing population and strained healthcare facilities and human resources. As a result, the healthcare infrastructure and human resource gap continued in the tribal-dominated states of the country. The gaps in health infrastructure and human resources in tribal-dominated states must be addressed as a priority under the health infrastructure strengthening efforts to ensure that the tribal population receives and has access to quality health care from publicly funded health facilities, leading to improved health outcomes in the tribal population and the achievement of the sustainable development goals (SDGs).

We have not come across any paper that has carried out pancountry analysis of healthcare infrastructure and human resources in tribal areas.

Brief introduction on the importance and the need for plastic analysis methods were presented in the beginning section of this review. The plastic method for analysis was considered to be the more advanced method of analysis because of its ability to represent the true behaviour of the steel structures. Then in the following section, a literature analysis has been carried out on the previous investigations done on steel plates, steel beams and steel frames by other authors. The behaviour of them under different types of loading were presented and are under the investigation of innovative new analysis methods.

Structure member connections also have the potential for plastic failure. In this study, the authors have highlighted a few topics to be discussed. The three topics in this study are T-end plate connections to a square hollow section, semi-rigid connections and cold-formed steel storage racks with spine bracings using speed-lock connections. Connection is one of the important parts of a structure that ensures the integrity of the structure. Finally, in this technical paper, the authors introduce some topics related to seismic action. Application of the Theory of Plastic Mechanism Control in seismic design is studied in the beginning. At the end, its in-depth application for moment resisting frames-eccentrically braced frames dual systems is investigated.

When this study involves the design of a plastic structure, the design criteria must involve the ultimate load rather than the yield stress. As the steel behaves in the plastic range, it means the capacity of the steel has reached the ultimate load. Ultimate load design and load factor design are the methods in the range of plastic analysis. After the steel capacity has reached beyond the yield stress, it fulfills the requirement in this method. The plastic analysis method offers a consistent and logical approach to structural analysis. It provides an economical solution in terms of steel weight, as the sections designed using this method are smaller compared with elastic design methods.

The plastic method is the primary approach used in the analysis and design of statically indeterminate frame structures.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Denial-of-service (DoS) attacks develop unauthorized entry to various network services and user information by building traffic that creates multiple requests simultaneously making the system unavailable to users. Protection of internet services requires effective DoS attack detection to keep an eye on traffic passing across protected networks, freeing the protected internet servers from surveillance threats and ensuring they can focus on offering high-quality services with the fewest response times possible.

This paper aims to develop a hybrid optimization-based deep learning model to precisely detect DoS attacks.

The designed Aquila deer hunting optimization-enabled deep belief network technique achieved improved performance with an accuracy of 92.8%, a true positive rate of 92.8% and a true negative rate of 93.6.

The introduced detection approach effectively detects DoS attacks available on the internet.

Ocean transportation is not only the cheapest and the best mode of bulk transport but also the most polluting form of transportation. The International Maritime Organization (IMO) has set strict targets to cut down carbon dioxide (CO₂) emissions, following which several initiatives have been taken by the shipping industry to embrace new technologies that can make the industry greener. Significant investments have been made into research and development (R&D) to develop alternative marine fuels. This chapter explores the feasibility of setting up a Biomass Recycling Facility (BRF) in the Tirupur–Tuticorin region in Tamil Nadu. The region was chosen because Tirupur being a textile valley generates tonnes of textile wastes every year. It can become good feedstock for biofuel generation, and it is also near Tuticorin Port, which is one of the major ports in Tamil Nadu. On an average, every year 1,000 vessels of medium and large size call at this port. There is a high probability that a BRF established in the vicinity can generate and supply bioethanol for the ships calling at Tuticorin Port. It is apparent from the findings of the study that the feedstock generated by textile industry alone may not be sufficient to meet the huge volumes of biofuel requirements of vessels, more over considerable investments into infrastructure and technology are required. But the study points out that still it could become a viable option because of the government support and favourable Foreign Direct Investment (FDI) policies. The growing demand for biofuel and the increasing price in the world market can become an added advantage.

This systematic review aims to synthesize the literature reporting the motives, sociodemographic, attitude/behavior and impacts of fake news during the COVID-19 pandemic, targeting the general population worldwide.

A systematic review approach was adopted based on PRISMA, targeting articles published in five databases from January 2020 to November 2021. The screening resulted in 46 eligible papers.

Results indicate low level of awareness, knowledge, media/health literacy, low trust in science/scientists and entertainment/socialization to be the main motivating drivers for fake news dissemination, whereas the phenomenon is more prominent among those with low socio-economic status, and males. Negative impacts were reported due to fake news dissemination, especially violation to precautionary measures, negative affections, and low trust in government/news, with many believing that others are more susceptible to fake news than themselves.

Considering the pandemic is still on-going and the deleterious consequences of fake news, there is a need for cohort-based interventions from the concerned authorities.

The systematic review covers a wide timeline of 23 months (i.e. up to end of 2022) targeting five well-known databases, hence articles examined are deemed extensive and comprehensive. The review specifically focused on the general population with results revealing interesting motives, sociodemographic profiles, attitude and impact of this phenomenon during the COVID-19 pandemic.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2022-0082.

Software reliability growth models (SRGMs) are used to assess and predict reliability of a software system. Many of these models are effective in predicting future failures unless the software evolves.

This objective of this paper is to identify the best path for rectifying the BFT (bug fixing time) and BFR (bug fixing rate). Moreover, the flexible software project has been examined while materializing the BFR. To enhance the BFR, the traceability of bug is lessened by the version tag virtue in every software deliverable component. The release time of software build is optimized with the utilization of mathematical optimization mechanisms like ‘software reliability growth’ and ‘non-homogeneous Poisson process methods.’

In current market scenario, this is most essential. The automation and variation of build is also resolved in this contribution. Here, the software, which is developed, is free from the bugs or defects and enhances the quality of software by increasing the BFR.

In current market scenario, this is most essential. The automation and variation of build is also resolved in this contribution. Here, the software, which is developed, is free from the bugs or defects and enhances the quality of software by increasing the BFR.

This study aims to examine the relationship between corporate social responsibility (CSR) and job pursuit intention (JPI), and the role of job seekers’ perception on employer prosocial orientation, value congruence and employer attractiveness in this relationship. CSR is measured based on internal and external CSR.

By adopting quantitative approach, data was obtained through survey questionnaire from 420 bachelor’s degree university fresh graduates from five universities in Malaysia who are actively seeking for jobs. Data was analysed using structural equation modelling technique.

Research findings show that internal and external CSR positively impact job seekers’ perception of employer prosocial orientation. Job seekers’ perception towards employer prosocial orientation has a significant positive impact on value congruence. Value congruence has a significant positive influence on employer attractiveness. Finally, employer attractiveness has a significant positive impact on JPI.

The findings are useful for human resources management. Organisations (employers) should focus on effective internal and external CSR practices through a prosocial orientation approach to attract the best talents and create a strong position in the job market.

This study extends the Signalling Theory and P-O Fit theory by applying them to an entirely different context of CSR and JPI, by incorporated the holistic job seekers’ psychological processes of the recruitment signals (internal and external CSR), signalling process and person-organisation fit (perception on employer prosocial orientation, value congruence and employer attractiveness) thoroughly.

This paper tactics to implement the attack detection in medical Internet of things (IoT) devices using improved deep learning architecture for accomplishing the concept bring your own device (BYOD). Here, a simulation-based hospital environment is modeled where many IoT devices or medical equipment are communicated with each other. The node or the device, which is creating the attack are recognized with the support of attribute collection. The dataset pertaining to the attack detection in medical IoT is gathered from each node that is considered as features. These features are subjected to a deep belief network (DBN), which is a part of deep learning algorithm. Despite the existing DBN, the number of hidden neurons of DBN is tuned or optimized correctly with the help of a hybrid meta-heuristic algorithm by merging grasshopper optimization algorithm (GOA) and spider monkey optimization (SMO) in order to enhance the accuracy of detection. The hybrid algorithm is termed as local leader phase-based GOA (LLP-GOA). The DBN is used to train the nodes by creating the data library with attack details, thus maintaining accurate detection during testing.

This paper has presented novel attack detection in medical IoT devices using improved deep learning architecture as BYOD. With this, this paper aims to show the high convergence and better performance in detecting attacks in the hospital network.

From the analysis, the overall performance analysis of the proposed LLP-GOA-based DBN in terms of accuracy was 0.25% better than particle swarm optimization (PSO)-DBN, 0.15% enhanced than grey wolf algorithm (GWO)-DBN, 0.26% enhanced than SMO-DBN and 0.43% enhanced than GOA-DBN. Similarly, the accuracy of the proposed LLP-GOA-DBN model was 13% better than support vector machine (SVM), 5.4% enhanced than k-nearest neighbor (KNN), 8.7% finer than neural network (NN) and 3.5% enhanced than DBN.

This paper adopts a hybrid algorithm termed as LLP-GOA for the accurate detection of attacks in medical IoT for improving the enhanced security in healthcare sector using the optimized deep learning. This is the first work which utilizes LLP-GOA algorithm for improving the performance of DBN for enhancing the security in the healthcare sector.