Articles

This study presents a physics-based parametric optimization framework for thermoelastic energy harvesting from cyclic temperature variations using a cylindrical AISI 4340 steel rod. The model couples thermoelastic force and displacement, Euler buckling and yielding constraints, lumped-capacitance heat transfer, and mass-power multi-objective optimization. The feasible design space was evaluated as a function of rod diameter, length, temperature change, and load-transfer parameter. The results show that Euler buckling governs the practically relevant portion of the design space at moderate temperature changes, whereas yielding becomes increasingly restrictive at higher thermal loads. The Pareto-optimal solutions exhibit a clear mass-power scaling law, with the analytical prediction P_avg ∝ m^2/3 closely reproduced by the numerical fit (n = 0.66717, R² = 0.999999). Under the baseline condition λ = 0.50, the maximum-power design produces approximately 0.16624 W, while a distance-to-ideal criterion identifies a substantially lighter compromise design with m = 223.0 kg and P_avg = 0.09231 W. Refined optimization gives λ_opt = 0.473 and P_max = 0.170454 W, indicating a power penalty of only 2.47% when the simpler λ = 0.50 condition is used. Grid-refinement tests show changes below 0.006% in maximum power and below 0.5% in the compromise-design mass between the 250 × 250 and 500 × 500 grids. The results establish compact scaling and optimization rules for balancing structural stability, thermal response, mass, and average mechanical power in thermoelastic energy-harvesting elements.

This aimed to examine the societal perception of new religious movements' televangelism in Dunkwa-On-Offin, Ghana's Central Region. The study used a sequential explanatory mixed-methods design, using the mixed methods approach to research. The population of this study comprised Women, Regular churchgoers, believers, and those listening to televangelism services by new religious movements, as well as clergy, lay leaders, and other male worshippers. Random and purposive sampling techniques were used to select 200 women and 20 men from House of Power Ministry International and GILGAL Pentecostal Prayer Ministry International as the respondents for the study. The instruments used for data collection were questionnaires and interview guides. IBM SPSS statistics software version 23 was used to analyse the quantitative data, while qualitative data was analysed thematically with an interpretative lens. The study has revealed that society perceives the televangelism movements as helpful and encouraging but could also create trouble in fundamental Christian worship principles. The study also indicates that televangelists emphasise material things rather than spiritual matters. Other concerns included that televangelism brings about the separation of family and friends because they always inform members that their friends and family are evil. It is recommended that there is the need to change or improve the credibility perception among Christians in Ghana about televangelism. Televangelists must exhibit accountability and integrity to their worshippers by providing adequate information to encourage viewers to support their ministries.

We study a dynamic model of the relationship between two people where the states depend on the “power” in the relationship. We perform a comprehensive analysis of stability of the system, and determine a set of conditions under which stable relationships are possible. In particular, stable relationships can occur if both people are dominant, but the sum of dominances is below a bound determined by the model’s parameters. Stable relationships can also occur if one person is dominant and the other is submissive, provided the level of dominance exceeds the level of submissiveness but not beyond a threshold. We also conclude that a stable relationship is not possible if both people are submissive. While our model is motivated by a social or romantic relationship, it can also be applied to professional or business relationships, diplomatic relationships between nations, and certain biological interactions between organisms and between automated agents or robots.

High-Performance Computing (HPC) is indispensable for large-scale scientific simulations, but achieving optimal performance on modern supercomputers is increasingly challenging. As HPC systems scale toward exascale, they face escalating complexity in hardware, software, and workloads. Traditional optimization methods (manual tuning and heuristic algorithms) struggle to cope with dynamic workloads and intricate system behaviors. Artificial Intelligence (AI) techniques offer a promising approach to address these challenges. This article provides an overview of AI-powered optimization in HPC, focusing on how machine learning and related AI methods enhance performance, efficiency, and scalability of scientific simulations. We survey key AI techniques applied to HPC optimization including machine learning for performance modeling, deep reinforcement learning for resource management, and AI-driven surrogate models for accelerating simulations and illustrate their impact through case studies in domains such as job scheduling and fluid dynamics. We discuss the practical applications of these techniques, highlighting reported performance gains (e.g., substantial reductions in simulation run time and improved resource utilization). We also examine the challenges in integrating AI with HPC (such as training overhead, data movement, and reliability concerns) and outline future directions for research. The convergence of AI and HPC is poised to produce “smart” simulation workflows that intelligently adapt and optimize in real time, pushing the frontiers of scientific computing.

The recent development of Mobile System-on-Chip (SoC) has completely changed the functionality of the contemporary smartphones, wearables, and IoT devices through enhanced computational strength but with stringent power and heat constraints. Mobile applications are becoming more complex (e.g., real-time computer vision, high-resolution imaging, on-device artificial intelligence, etc.) and SoCs have to provide high throughput, good work load distribution, and responsive multitasking without sacrificing battery life. To overcome these issues, modern SoCs are based on heterogeneous multicore designs, with big. LITTLE designs, special purpose accelerators, like NPUs and ISPs, and memory arranged hierarchies and backed with high bandwidth buses. Also, more sophisticated power-management approaches like Dynamic Voltage and Frequency Scaling (DVFS), clock gating, and power gating provide fine-tuning energy management in the active and idle conditions. Most recently, scheduling and thermal sensitivity resource allocation using AI has become a promising approach in increasing real-time refinement and flexibility. This paper gives a detailed discussion of performance-driven and power-efficient design methods in mobile SoCs, including architectural innovations, workload optimization, and built-in AI-assisted energy management. The discussion identifies major research trends, system-level developments, and new challenges defining next-generation mobile computing systems.

This charge-ahead paper suggests that transitioning the automotive industry towards a zero-carbon ecosystem from material to end-of-life can be accomplished through disruptive zero-carbon manufacturing in the broad area of all-electric vehicle production technology. To accomplish zero carbon emission automotive manufacturing in the vehicle assembly domain, future paradigms must converge on the decoupling of carbon dioxide emissions from automobile manufacturing and use the design, processing, and manufacturing conditions. The envisioned zero carbon emission vehicle manufacturing domain consists of two complementary components: (a) making more efficient use of energy and (b) reducing carbon in energy use. This paper presents the status of key scientific and technological advancements to bring the manufacturing model of today to a zero-carbon ecosystem for the entire automotive industry of tomorrow. This paper suggests the groundbreaking application of dynamic and distributed predictive scheduling algorithms and open sensing and visualization technology to meet the zero carbon emission vehicle manufacturing goals. Power-aware high-performance computing clusters have recently become a viable solution for sustainable production. Advances in scalable and self-adaptive monitoring, predictive analytics, timeline-based machine learning, and digital replica of cyber-physical systems are also seen co-evolving in the zero carbon manufacturing future. These methods are inspired by initiatives to decouple gross domestic product growth and energy-related carbon dioxide emissions. Stakeholders could co-design and implement shared roadmaps to transition the automotive manufacturing sector with relevant societal and environmental benefits. The automated mobility sector offers a program, an industry-leading example of transforming an automotive production facility to carbon neutrality status. The conclusions from this paper challenge automotive manufacturers to engage in industry offsetting and carbon tax programs to drive continuous improvement and circular vehicle flows via a multi-directional zero-carbon smart grid.

The growing complexity and variability in healthcare delivery and costs within Medicare Advantage (MA) and Medicare Supplement (Medigap) plans present significant challenges for improving health outcomes and managing expenditures. Neural networks, a subset of artificial intelligence (AI), have shown considerable promise in optimizing healthcare processes, particularly in predictive modeling, personalized treatment recommendations, and risk stratification. This paper explores the application of neural networks in enhancing health outcomes within the context of Medicare Advantage and Supplement plans. We review how deep learning models can be leveraged to predict patient risk, optimize resource allocation, and identify at-risk populations for preventive interventions. Additionally, we discuss the potential for neural networks to improve claims processing, reduce fraud, and streamline administrative burdens. By integrating various data sources, including medical records, claims data, and demographic information, neural networks enable more accurate and efficient decision-making processes. Ultimately, this approach can lead to better patient care, reduced healthcare costs, and improved satisfaction for beneficiaries of these programs. The paper concludes by highlighting the current limitations, ethical considerations, and future directions for AI adoption in the Medicare Advantage and Supplement sectors.

The bottom-up or informal placemaking process is “illegal”, practised only by the “poor” and “unintellectual” classes. The author argues that various classes practise informal placemaking regardless of their economic status or educational level. Studying the spatial manifestation of informal placemaking helps to understand the concrete needs of the residents and reconsider their role in identifying places. Aiming to comprehend informal placemaking and its importance, this study investigates informal placemaking in Sheraton Residence, Heliopolis, Cairo. The area is chosen for its formality, socio-economic and cultural patterns. Cases of informal placemaking in Sheraton Residence are described and analysed to pinpoint their role in creating a sense of place, territorial identity and comprehending their meaning. A comparison with cases in informal low-income housing is conducted to construct diversities and similarities. The study encourages the re-thinking of the public role in placemaking as a place of negotiation and belonging. We can improve their urban quality by re-thinking informal placemaking and looking at how people use the place. The general impression that only informal housing and low-educated classes practise informal placemaking is incorrect.

Heavy-duty powertrain systems resource multiple sensors collecting streams of data during production. Sensor data streams, originating from various production machines, simultaneously encounter the challenging situation of limited resilience to sensor quality issues in terms of sensors being non-linear, outdated, spawning false alarms, or going offline. Alleviating this critical situation requires a reinforcement technology to assure resilience, with both verified lineage-based approaches and a observe-and-repair mechanism of limited quality. Moreover, legislation advances aimed to guarantee higher future operational safety put additional pressure on the overall manufacturing sensor monitoring frame. For heavy-duty powertrain systems harnessed for combustion engines, an integrated technology based on automatic data lineage and quality governance detection of production machine sensor data streams resource the frame using a unified provenance model and ontology supporting the explicit definition of sensor quality rules. Provenance models automatic integration form the basis that enables the monitoring of multiple machine resources. Used quality rules enable the detection of bad-quality sensor segments while, in parallel, additional quality rules based on observed data control supported by human intervention capabilities repair gaps during production. The proposed automated technology provides an additional layer of resilience that improves the reliability of sensor data stream quality and contributes to the adherence of operational safety future rules. The implementation case study focuses on sensors deployed in powertrain systems production that modern industries must follow to ensure next-generation production safety levels.

This paper includes the impacts of the Internet of Things (IoT), Big Data, and other emerging technologies in the vehicle production sector, digital connectivity, and sustainable transport system. Automated and intelligent transportation for safe, efficient, and sustainable transport systems will be stressed. Key factors to promote automated or connected vehicles including connected environment, integration of all transport modes, advanced cooperative systems, and policy enforcement will be discussed. This paper contains the Axiomatic Categorisation Framework (AFS) for the dynamic alignment in a collection of disparate functions in cyber-physical systems (CPS). Developed system is enhanced for breaking the rules within autonomous vehicles (AV). It means the human personal injury is inevitable while the vehicle does not do any rules. Especially in complicated traffic situations, many of the constraints are mutually exclusive, and there is no way to obey all of them at a time. Also, there is no way to ensure that the self-driving vehicle has priority in all situations [1]. Public distrust in AV systems has to be increased and the investment in this technology has to slow down. Instead, a human driver should be partially responsible for operation. The development of a driver-behavior assistant (DBA) system is proposed, which should be able to break the rules for the distances of such slow development. It is intended to be effective in non-deterministic situations while maintaining the safety of the AV and those involved in the event. A driver's actions would not only be acceptable as a driving strategy but also would be predictable, and therefore other road users could unambiguously react.

This paper shows how Big Data Analytics (BDA) and Artificial Intelligence (AI) automation facilitate regulatory compliance in Finance. Regulatory compliance is essential in helping institutions to mitigate reputational, litigation, and financial risk. Existing literature reveals several preconditions for compliance. However, much of the literature has adopted an internal view of compliance without considering external regulatory frameworks. This research draws on the cognitive model of regulation that looks at regulatory compliance as a social construct. It uses a triangulation research method comprising literature review, interview of trade compliance experts, and questionnaire survey of compliance practitioners to understand how regulation affects compliance and what role ICTs play in implementing compliance. The findings of this study present a regulatory compliance framework comprising four cognitive stages and a conceptual regulatory compliance system that presents how BDA and AI automation are applied to mitigate regulatory complexity and enhance regulatory compliance. The conceptual regulatory compliance system shows how BDA and AI enable institutions to dynamically assess regulatory risk, automatically monitor compliance, and intelligently predict risk violations mitigating regulatory complexity and preventing producing unnecessary documents. It provides theoretical contributions to understanding regulatory evolution and compliance and practical implications for understanding how regulation evolves to be more complicated and elements of a regulatory compliance system mitigate proliferating regulations. Additionally, it provides avenues for future research into the relationship between competing regulatory mandates and how institutions cope with that. Regulations are important for ensuring compliance and governance in finance and to curb systemic risk. Complying with regulations is difficult due to their growing volume, complexity, and fragmentation. Institutions use large-scale Information and Communication Technologies (ICTs), such as Big Data Analytics (BDA) and Artificial Intelligence (AI) automation, to monitor compliance and mitigate regulatory complexity. However, less is known about how firms comply with regulation. Most literature does not thoroughly investigate regulatory elements nor explicitly relate them to compliance.

Green cloud computing is part of endeavors to develop sustainable data center ecosystems and, more importantly, nurtures a mindful alignment between environmental considerations and our cloud computing practices. This view is reinforced with the requirements of resource and energy minimization, as well as clean computing. This paper surveys the current practices, strategies, and significant aspects involved in moving towards green cloud computing, providing energy-efficient data centers. The energy efficiency criteria call for unified strategies in power-proportional components, big data storage, server systems, and power supply units to save holistic energy. In addition, there are significant challenges in moving towards green cloud computing for service providers and data center operators. We address various energy-conscious resource management technologies and discuss the importance of developing innovative, effective green management solutions. Data centers are ubiquitous but inherently more conspicuous to begin to see the urgency of making them sustainable in our ecological environment. With this in mind, this paper encapsulates the multidimensional issues and increased complexities of bringing up green solutions in cloud computing practices and provides guidance and potential strategies. We outline, realign, and insist on adopting strategies in practice not only from the technical aspect but also in strengthening partnerships and investigating strategies to further dissect challenges, converge solutions, and consider our impact in even more areas of study.