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The aim of this research study is to mitigate shading impact on solar photovoltaic array. Photovoltaic (PV) array when getting shaded not only results in appreciable power loss but also exhibits multiple power peaks. Due to these multiple power peaks, the maximum power point tracking (MPPT) controllers’ performance will be affected, as most of the times it ends up in tracking the local maximum power peak and not the global power peak.

The PV panels in an PV array when getting shaded even partially would result in huge power loss. The pattern of shading also plays a crucial role, as it renders a cascaded impact on the overall power output because the cells/panels are connected in series and are parallel. Therefore, during shading, intelligent schemes are needed to appropriately connect and discard the unhealthy and healthy panels in right place with right combination. This research proposes one such scheme to mitigate the shading impact.

To mitigate the shading impact and also to have a smooth power-voltage (P-V) curve, a new series inducing switching scheme is introduced. The proposed scheme not only mitigates the shading impact and enhances the output power but also smoothens the P-V curve that facilitates the MPPTs to track the P-V appropriately.

The research findings are inventive in nature and not copied work. The reference works and the inspirations have been duly cited and credited.

The efficacy of antiscaling treatments under simulated flow conditions was studied by chronoamperometric technique. The effect of temperature and concentration on the scale forming behaviour of different compounds were also studied under the simulated flow conditions. In order to simulate the flow conditions a rotating disc electrode technique was employed. The mechanism of antiscaling behaviour of different chemicals was studied through electrochemical impedance spectroscopy. It was found that the flow velocity affected the efficiency of antiscalants. Polymer based compounds follow the growth modification adsorption mechanism, while compounds like EDTA and phosphonate follow nucleation modification absorption/chemisorption mechanism. Temperature and concentration of the scale forming compounds have a significant role in the scaling process, particularly at the low concentrations.

Introduction: Cost competitiveness, customer focus, and sustainability compliance are essential for new-age firms to survive and succeed in the VUCA market environment. This study examines how automobile corporations have improved cost competitiveness, productivity, and product quality.

Purpose: This study examines the importance of cost competitiveness, customer focus, and sustainability compliance for the long-term survival of organisations in VUCA markets, looking at the practical efforts made by automobile corporations to enhance cost competitiveness, productivity, and quality.

Methodology: The study utilises a comprehensive analysis of the strategies and initiatives implemented by the selected automobile companies. It involves a review of relevant literature, case studies, financial data analysis, and interviews with key industry experts, providing a holistic understanding of the actions taken by these organisations to achieve their goals.

Findings: The study reveals that cost competitiveness, customer focus, and sustainability compliance are critical factors for the long-term survival and success of organisations in the automotive industry. The analysed automobile companies have undertaken practical efforts to improve cost competitiveness, enhance productivity, and ensure high-quality products, enabling them to navigate the challenges and maintain a competitive edge.

Significance: The findings of this study contribute to a deeper understanding of the importance of cost competitiveness, customer focus, and sustainability compliance in the automotive industry. It highlights the need for organisations to constantly monitor both qualitative and quantitative profit to avoid complacency and ensure long-term efficiency. The study’s insights are relevant to businesses operating in other sectors, as they face similar challenges in the VUCA market environment.

The energy generation process through photovoltaic (PV) panels is contingent upon uncontrollable variables such as wind patterns, cloud cover, temperatures, solar irradiance intensity and duration of exposure. Fluctuations in these variables can lead to interruptions in power generation and losses in output. This study aims to establish a measurement setup that enables monitoring, tracking and prediction of the generated energy in a PV energy system to ensure overall system security and stability. Toward this goal, data pertaining to the PV energy system is measured and recorded in real-time independently of location. Subsequently, the recorded data is used for power prediction.

Data obtained from the experimental setup include voltage and current values of the PV panel, battery and load; temperature readings of the solar panel surface, environment and the battery; and measurements of humidity, pressure and radiation values in the panel’s environment. These data were monitored and recorded in real-time through a computer interface and mobile interface enabling remote access. For prediction purposes, machine learning methods, including the gradient boosting regressor (GBR), support vector machine (SVM) and k-nearest neighbors (k-NN) algorithms, have been selected. The resulting outputs have been interpreted through graphical representations. For the numerical interpretation of the obtained predictive data, performance measurement criteria such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R-squared (R²) have been used.

It has been determined that the most successful prediction model is k-NN, whereas the prediction model with the lowest performance is SVM. According to the accuracy performance comparison conducted on the test data, k-NN exhibits the highest accuracy rate of 82%, whereas the accuracy rate for the GBR algorithm is 80%, and the accuracy rate for the SVM algorithm is 72%.

The experimental setup used in this study, including the measurement and monitoring apparatus, has been specifically designed for this research. The system is capable of remote monitoring both through a computer interface and a custom-developed mobile application. Measurements were conducted on the Karabük University campus, thereby revealing the energy potential of the Karabük province. This system serves as an exemplary study and can be deployed to any desired location for remote monitoring. Numerous methods and techniques exist for power prediction. In this study, contemporary machine learning techniques, which are pertinent to power prediction, have been used, and their performances are presented comparatively.

The purpose of this paper is to identify the antecedents of continued usage intentions in the connected devices ecosystem in health care by analyzing the users' and physicians' expectations in a new ecosystem where one prefers to connect digitally rather than physically.

This is a unique study in which data was collected from 242 doctors and 215 end-users to gauge the expectations from the connected devices in health care. Further, these responses were hypothesised using UTAUT-2 and ECT theories to analyze general users’ and professional users’ or doctors’ expectations for continued usage in connected devices ecosystem in the health-care ecosystem.

Performance expectancy, social influence, facilitating conditions and price value emerged as significant predictors of satisfaction in both user groups. But habit and hedonic motivation reflected an insignificant impact on user satisfaction. Surprisingly, effort expectancy emerged as a significant factor for end-user satisfaction, and this became insignificant for professional user satisfaction. Satisfaction was positively related to continued usage for both user groups, and app quality has a positive impact on all the predictors.

To the best of the authors’ knowledge, this is the first comparative study to understand the factors which influence consumer behavior leading to a holistic model and can be imbibed for creating a better customer experience in an era where we are more comfortable connecting digitally rather than physically.

This study has used the Unified Theory of Acceptance and Use of Technology-2 model and expectation confirmation theory to analyze the key factors influencing the intentions for continued usage of devices in the Internet of Medical Devices setup.

Industry 4.0 accelerates the performance of supply chains, in particular, the reduction in supply chain cost (SCC) and improvement in supply chain flexibility (SCF). The aim of this study is to examine the role of Industry 4.0 on SCC and SCF, using network theory to explain the interrelationships.

Data were collected from 182 manufacturing firms in Turkey. The partial least square structural equation modelling (PLS-SEM) was employed in testing the research hypotheses.

The results showed that Industry 4.0 positively affects SCC; however, no direct relationship was found between Industry 4.0 and SCF. Moreover, SCC was found to have a positive impact on SCF, while SCC was found to mediate the relationship between Industry 4.0 and SCF. An additional finding was that customer integration (CI) moderates the relationship between Industry 4.0 and SCC; however, CI does not moderate the relationship between Industry 4.0 and SCF.

The research validates the role of Industry 4.0 on supply chain processes and thus provides valuable insights into supply chain practitioners and decision-makers interested in Industry 4.0 for supply chain management.

In view of the limited number of studies, this study empirically contributes to the literature on the relationships among Industry 4.0, SCC, SCF and CI.

The Denial of Service (DoS) attack is a category of intrusion that devours various services and resources of the organization by the dispersal of unusable traffic, so that reliable users are not capable of getting benefit from the services. In general, the DoS attackers preserve their independence by collaborating several victim machines and following authentic network traffic, which makes it more complex to detect the attack. Thus, these issues and demerits faced by existing DoS attack recognition schemes in cloud are specified as a major challenge to inventing a new attack recognition method.

This paper aims to detect DoS attack detection scheme, termed as sine cosine anti coronavirus optimization (SCACVO)-driven deep maxout network (DMN). The recorded log file is considered in this method for the attack detection process. Significant features are chosen based on Pearson correlation in the feature selection phase. The over sampling scheme is applied in the data augmentation phase, and then the attack detection is done using DMN. The DMN is trained by the SCACVO algorithm, which is formed by combining sine cosine optimization and anti-corona virus optimization techniques.

The SCACVO-based DMN offers maximum testing accuracy, true positive rate and true negative rate of 0.9412, 0.9541 and 0.9178, respectively.

The DoS attack detection using the proposed model is accurate and improves the effectiveness of the detection.

The purpose of the study is to examine the influence of the corn biofuel on the Jet engine. Each tests were carried out in a small gas turbine setup. The performance characteristics of thrust, thrust-specific fuel consumption, exhaust gas temperature and emission characteristics of Carbon monoxide(CO), Carbon dioxide (CO₂), Oxygen (O₂), Unburned hydrocarbons (UHC) and Nitrogen of oxides (NO) emissions were measured and compared with Jet-A fuel to find the suitability of the biofuel used.

Upgrading and using biofuels in aviation sector have been emerging as a fruitful method to diminish the CO emission into the atmosphere. This research paper explores the possibility of using nanoparticles-enriched bio-oil as a fuel for jet engines. The biofuel taken is corn oil and the added nanoparticles are Al₂O₃.

The biofuel blends used are B0 (100% Jet-A fuel), B10 (10 % corn oil biofuel + 90% Jet-A fuel), B20 (20% corn oil biofuel + 80% Jet-A fuel) and B30 (30% corn oil biofuel + 70% Jet-A fuel). All fuel blends were mixed with the moderate dosage level of 30 ppm. All tests were conducted at different rpm as 50,000, 60,000, 70,000 and 80,000 rpm.

The results proved that within the lower limit, use of biofuel increased the performance characteristics and reduced the emission characteristics except the emission of NO. The moderate-level biofuel with Jet-A fuel showed the equally better performance to the neat Jet-A fuel.

Purpose: The purpose of this study is to identify and understand the obstacles hindering social entrepreneurship as a business model. Methodology/Approach: We performed an exploratory analysis structured in three stages. First, we used the Delphi method to identify the main difficulties with the collaboration of 20 social entrepreneurship experts. We then analyzed how these experts and a group of 21 social entrepreneurs rated the importance of the difficulties that had been identified. Finally, we performed a comparative analysis of both groups' ratings and found significant differences between their perceptions. Findings: Experts and social entrepreneurs agree on identifying financial difficulties as the main obstacles. They all highlight the lack of financial resources and difficulties in the sustainability and independence of the venture in the long term. However, while the experts recognize that human resources' lack of skills is another important obstacle, the social entrepreneurs give more importance to external factors, such as resistance to social change or lack of knowledge and understanding of the social entrepreneurship concept. Practical Implications: The decision to seek the collaboration of two different groups is enriching, as the results show that their perceptions of the barriers facing social entrepreneurship do not always coincide. Originality/Value of Chapter: It is a chapter focused exclusively on deepening the knowledge of the obstacles to social entrepreneurship, which tries not only to identify them but also to offer the vision of experts in social entrepreneurship as well as of social entrepreneurs themselves.