Search Results (4,337)

Amidst the ongoing global energy crisis, environmental deterioration, and the exacerbation of climate change, the development of renewable energy, particularly solar energy, has become a central topic in the global energy transition. This study investigates a solar photovoltaic thermal (PVT) heat pump system that utilizes an expanded honeycomb-channel PVT module to enhance the comprehensive utilization efficiency of solar energy. A simulation platform for the solar PVT heat pump system was established using Aspen Plus software (V12), and the system’s performance impact mechanisms and engineering applications were researched. The results indicate that solar irradiance and the circulating water temperature within the PVT module are the primary factors affecting system performance: for every 100 W/m² increase in solar irradiance, the coefficient of performance for heating (COP_h) increases by 13.7%, the thermoelectric comprehensive performance coefficient (COP_co) increases by 14.9%, and the electrical efficiency of the PVT array decreases by 0.05%; for every 1 °C increase in circulating water temperature, the COP_h and COP_co increase by 11.8% and 12.3%, respectively, and the electrical efficiency of the PVT array decreases by 0.03%. In practical application, the system achieves an annual heating capacity of 24,000 GJ and electricity generation of 1.1 million kWh, with average annual COP_h and COP_co values of 5.30 and 7.60, respectively. The Life Cycle Cost (LCC) is 13.2% lower than that of the air-source heat pump system, the dynamic investment payback period is 4–6 years, and the annual carbon emissions are reduced by 94.6%, demonstrating significant economic and environmental benefits. This research provides an effective solution for the efficient and comprehensive utilization of solar energy, utilizing the low-global-warming-potential refrigerant R290, and is particularly suitable for combined heat and power applications in regions with high solar irradiance. Full article

The incorporation of Artificial Intelligence (AI) into medical services in Saudi Arabia offers a substantial opportunity. Despite the increasing integration of AI techniques such as machine learning, natural language processing, and predictive analytics, there persists an issue in the thorough comprehension of their applications, advantages, and issues within the Saudi healthcare framework. This study aims to perform a thorough systematic literature review (SLR) to assess the current status of AI in Saudi healthcare, determine its alignment with Vision 2030, and suggest practical recommendations for future research and policy. In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, 699 studies were initially obtained from electronic databases, with 24 studies selected after the application of established inclusion and exclusion criteria. The results indicated that AI has been effectively utilised in disease prediction, diagnosis, therapy optimisation, patient monitoring, and resource allocation, resulting in notable advancements in diagnostic accuracy, operational efficiency, and patient outcomes. Nonetheless, limitations to adoption, such as ethical issues, legislative complexities, data protection issues, and shortages in worker skills, were also recognised. This review emphasises the necessity for strong ethical frameworks, regulatory control, and capacity-building efforts to guarantee the responsible and fair implementation of AI in healthcare. Recommendations encompass the creation of national AI ethics and governance frameworks, investment in AI education and training initiatives, and the formulation of modular AI solutions to guarantee scalability and cost-effectiveness. This breakthrough enables Saudi Arabia to realise its Vision 2030 objectives, establishing the Kingdom as a global leader in AI-driven healthcare innovation. Full article

This study optimizes fertilization schemes through the emergy analysis of different nutrient reduction treatments in maize cropping ecosystems in Xinjiang, thereby providing technical support for improving chemical fertilizer use efficiency and maintaining the stability of farmland ecosystems. The study was conducted in 2024 at Huaxing Farm in Changji Hui Autonomous Prefecture, Xinjiang Uyghur Autonomous Region. The experiment used the local conventional nitrogen and phosphorus fertilization rates as the control treatment N0P0 (applying P 183 kg·hm⁻² and N 246 kg·hm⁻²), with eight different N and P nutrient reduction treatments: N0P1 (10% reduction in P only), N0P2 (20% reduction in P only), N1P0 (10% reduction in N only), N2P0 (20% N reduction), N1P1 (10% N and P reduction), N1P2 (10% N and 20% P reduction), N2P1 (20% N and 10% P reduction), and N2P2 (20% N and P reduction). Each treatment was replicated three times. Based on biomass data of maize plant components under different fertilization treatments, emergy analysis of farmland ecosystems and integration of economic benefit indicators led to the optimization of an optimal fertilization scheme. Results indicate that the N0P1 treatment performed optimally: maize plant biomass reached 251.09 g, significantly higher than other treatments. The N0P1 treatment exhibited the highest energy output (3.23 × 10¹⁶ sej·hm⁻²), the highest net energy yield ratio (EYR) of 1.45, and an energy sustainability index (ESI) of 3.34, representing a high level. It also delivered the highest economic benefit, with a net profit of 8571.95 CNY·hm⁻² and a production–investment ratio of 1.71. In conclusion, the N0P1 treatment (10% reduction in phosphorus alone) demonstrated superior performance in biomass yield, energy utilization efficiency, ecological sustainability, and economic benefits, making it the optimal fertilization strategy for maize fields in this region. Full article

This case study in Foz do Iguaçu, Brazil, addresses the urban problem of the degradation of road corridors used by public transport, affecting the accessibility, safety, and efficiency of urban mobility. To address this issue, a multi-criteria methodology based on MIVES (Integrated Value Model for Sustainable Assessments) was applied, combined with the AHP (Analytic Hierarchy Process) method, allowing the evaluation of 20 key urban roads using a hierarchical set of indicators linked to infrastructure, accessibility, and mobility. The assessment was operationalized through the Transport Infrastructure Condition Index (TICI), which yielded results ranging from 0.32 to 0.88, reflecting significant contrasts in the road’s upkeep and maintenance conditions. The lowest scores were associated with deficiencies in universal accessibility, cycling infrastructure, signage, and adaptations for people with reduced mobility, highlighting structural limitations in sustainability and urban inclusion. The model facilitates the prioritization of road interventions based on urgency and criticality, becoming a useful tool for guiding public investment decisions. Its comprehensive approach and replicability make it a valuable methodological alternative for other Latin American contexts, where pressure to improve urban services coexists with budgetary constraints, contributing to more efficient and sustainable strategic planning of public transportation. Full article

High-precision calibration of inertial measurement units for automotive safety systems combines fixed automated chamber cycles with semi-manual loading, alignment, and transfer. Motion waste and ergonomic constraints can therefore dominate throughput and cycle time stability. This study redesigns a production calibration workstation using time-and-motion analysis, operator observation, and structured root-cause analysis based on the Ishikawa diagram and the five whys. Three interventions were implemented and validated with pre- and post-measurements: bundled handling that consolidates full-set transfers and reduces non-value-adding motions; a fixture and material handling redesign with a manual lifting aid to reduce physical load and enable reliable single-operator operation; and a modular workstation layout that supports the phased addition of chambers. Total cycle time decreased from 4475 s to 1230 s, a 72 percent reduction, and weekly output rose from 800 to 4500 units without additional staffing or significant automation investment. Overall equipment efficiency improved from 75.3 percent to 85.2 percent, while the quality rate remained at 98.8 percent. Full article

This study aimed to analyze the impact of the digital economy and artificial intelligence (AI) on GDP growth in 10 developed and developing countries during the period 2010–2024. It was based on the hypothesis that increased digitalization and AI investments promote sustainable economic growth by improving national productivity and efficiency, in accordance with modern technological growth theory, which links digital innovation to economic development. The study used tablet data comprising 150 observations, which were analyzed using fixed- and random-effects models, controlling for traditional variables such as employment, human capital, and investment. The results showed that the Digitalization Indicators (DIGI) had a significant positive impact on growth (fixed: 0.003479, p < 0.01; random: 0.003325, p < 0.01), and that investment in AI also had a significant positive impact (fixed: 0.063695, p < 0.05; random: 0.066548, p < 0.05). In contrast, workforce size had a limited impact, while education and human capital emerged as key drivers of sustainable growth (Constant: 0.003257, p < 0.01; Random: 0.003264, p < 0.01). The inclusion of dummy variables further differentiated between developed and developing countries in the random-effects model, reinforcing the economic interpretation of the findings. The study suggests that integrating digitalization, education, and investment in artificial intelligence is an effective strategy for promoting sustainable economic growth, while emphasizing the importance of workforce skills development to maximize its impact. Full article

Agricultural robotics and artificial intelligence (AI) are becoming essential to building more sustainable, efficient, and resilient food systems. As climate change, food security pressures, and labour shortages intensify, the integration of intelligent technologies in agriculture has gained strategic importance. This systematic review provides a consolidated assessment of AI and robotics research in agriculture from 2000 to 2025, identifying major trends, methodological trajectories, and underexplored domains. A structured search was conducted in the Scopus database—which was selected for its broad coverage of engineering, computer science, and agricultural technology—and records were screened using predefined inclusion and exclusion criteria across title, abstract, keywords, and eligibility levels. The final dataset was analysed through descriptive statistics and science-mapping techniques (VOSviewer, SciMAT). Out of 4894 retrieved records, 3673 studies met the eligibility criteria and were included. As with all bibliometric reviews, the synthesis reflects the scope of indexed publications and available metadata, and potential selection bias was mitigated through a multi-stage screening workflow. The analysis revealed four dominant research themes: deep-learning-based perception, UAV-enabled remote sensing, data-driven decision systems, and precision agriculture. Several strategically relevant but underdeveloped areas also emerged, including soft manipulation, multimodal sensing, sim-to-real transfer, and adaptive autonomy. Geographical patterns highlight a strong concentration of research in China and India, reflecting agricultural scale and investment dynamics. Overall, the field appears technologically mature in perception and aerial sensing but remains limited in physical interaction, uncertainty-aware control, and long-term autonomous operation. These gaps indicate concrete opportunities for advancing next-generation AI-driven robotic systems in agriculture. Funding sources are reported in the full manuscript. Full article

This study presents a comparative analysis of the potential for combined use of wind and solar energy in nine key coastal settlements on the Bulgarian Black Sea coast—Shabla, Balchik, Varna, Byala, Obzor, Nesebar, Burgas, Primorsko, and Tsarevo—selected for their diverse geographical and meteorological characteristics. The study evaluates the feasibility of implementing hybrid renewable energy systems by analyzing the average annual solar radiation and wind velocity for each location. A methodology based on physical and technical parameters is applied to determine the required installed capacity of photovoltaic systems to meet the average annual household electricity consumption of 6000 kWh. Concurrently, wind energy potential is assessed through theoretical and practical models using two turbine sizes (3 m and 6 m in diameter), which represent small-scale residential wind applications. Full article

This work presents the design, modeling, and experimental validation of an innovative, highly autonomous, and economically viable photovoltaic solar cooker, integrating a robust battery storage system. The system combines 1200 Wp photovoltaic panels, a control block with DC/DC power converters and digital control for intelligent energy management, and a thermally insulated heating plate equipped with two resistors. The objective of the system is to reduce dependence on conventional fuels while overcoming the limitations of existing solar cookers, particularly insufficient cooking temperatures, the need for continuous solar orientation, and significant thermal losses. The optimization of thermal insulation using a ceramic fiber and glass wool configuration significantly reduces heat losses and increases the thermal efficiency to 64%, nearly double that of the non-insulated case (34%). This improvement enables cooking temperatures of 100–122 °C, heating element surface temperatures of 185–464 °C, and fast cooking times ranging from 20 to 58 min, depending on the prepared dish. Thermal modeling takes into account sheet metal, strengths, and food. The experimental results show excellent agreement between simulation and measurements (deviation < 5%), and high converter efficiencies (84–97%). The integration of the batteries guarantees an autonomy of 6 to 12 days and a very low depth of discharge (1–3%), allowing continuous cooking even without direct solar radiation. Crucially, the techno-economic analysis confirmed the system’s strong market competitiveness. Despite an Initial Investment Cost (CAPEX) of USD 1141.2, the high performance and low operational expenditure lead to a highly favorable Return on Investment (ROI) of only 4.31 years. Compared to existing conventional and solar cookers, the developed system offers superior energy efficiency and optimized cooking times, and demonstrates rapid profitability. This makes it a sustainable, reliable, and energy-efficient home solution, representing a major technological leap for domestic cooking in rural areas. Full article

In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives. This paper addresses constrained multi-objective optimization (MOO) with an application to portfolio optimization for minimizing risk and maximizing return. To this end, and to address existing gaps, we propose a novel reinforcement learning (RL)-guided non-dominated sorting genetic algorithm II (NSGA-II) enhanced with gray relational coefficients (GRC), termed RL-NSGA-II-GRC, which combines an RL agent controller and GRC-based selection to improve the convergence and diversity of the Pareto-optimal fronts. The agent adapts key evolutionary parameters online using population-level metrics of hypervolume, feasibility, and diversity, while the GRC-enhanced tournament operator ranks parents via a unified score simultaneously considering dominance rank, crowding distance, and geometric proximity to ideal reference. We evaluate the framework on the Kursawe and CONSTR benchmark problems and on a NASDAQ portfolio optimization application. On the benchmarks, RL-NSGA-II-GRC achieves convergence metric improvements of about 5.8% and 4.4% over the original NSGA-II, while preserving a well-distributed set of non-dominated solutions. In the portfolio application, the method produces a smooth and densely populated efficient frontier that supports the identification of the maximum Sharpe ratio portfolio (with annualized Sharpe ratio = 1.92), as well as utility-optimal portfolios for different risk-aversion levels. The main contributions of this work are three-fold: (1) we propose an RL-NSGA-II-GRC method that integrates an RL agent into the evolutionary framework to adaptively control key parameters using generational feedback; (2) we design a GRC-enhanced binary tournament selection operator that provides a comprehensive performance indicator to efficiently guide the search toward the Pareto-optimal front; (3) we demonstrate, on benchmark MOO problems and a NASDAQ portfolio case study, that the proposed method delivers improved convergence and well-populated efficient frontiers that support actionable investment insights. Full article

This study examines the impact of tax avoidance practices on investment efficiency in Egypt, with particular emphasis on the moderating role of earnings management by exploring whether these tactics reflect managerial opportunism or serve as a mechanism to ease financial constraints. We employ panel data regression to analyze a sample of 58 non-financial firms listed on the Egyptian Exchange (EGX) over the period 2017–2024, yielding 464 firm-year observations. Data are collected from official corporate websites, EGX, and Egypt for Information Dissemination (EGID). Grounded in agency theory, signaling theory, and pecking order theory, this study reveals how conflicts of interest and information asymmetry between managers and stakeholders lead to managerial opportunism. The findings show that tax avoidance undermines the investment efficiency in the Egyptian market. Earnings manipulation further intensified this effect due to the financial statements’ opacity. A closer examination reveals that earnings management exacerbates overinvestment by masking managerial decisions. Conversely, for financially constrained firms with a tendency to underinvest, tax avoidance and earnings management may contribute to improved efficiency by generating internal liquidity and alleviating external financing constraints. These results provide valuable insights for regulators, highlighting that policy should be directed against managerial opportunism and improving transparency, instead of focusing solely on curbing tax avoidance. From an investor perspective, they should closely monitor and understand the tax-planning strategies to ensure they enhance the firm’s value. Full article

The accelerating global transition to electric mobility demands data-driven infrastructure planning that balances technical, economic, and spatial considerations. This study develops a scenario-based demand and economic modeling framework to estimate electric vehicle (EV) charging infrastructure needs across Abu Dhabi’s urban and rural regions through 2050. Two adoption pathways, Progressive and Thriving, were constructed to capture contrasting policy and technological trajectories consistent with the UAE’s Net Zero 2050 targets. The model integrates regional travel behavior, energy consumption (0.23–0.26 kWh/km), and differentiated charging patterns to project EV penetration, charging demand, and economic feasibility. Results indicate that EV stocks may reach 750,000 (Progressive) and 1.1 million (Thriving) by 2050. The Thriving scenario, while demanding greater capital investment (≈108 million AED), yields higher utilization, improved spatial equity (Gini = 0.27), and stronger long-term returns compared to the Progressive case. Only 17.6% of communities currently meet infrastructure readiness thresholds, emphasizing the need for coordinated grid expansion and equitable deployment strategies. Findings provide a quantitative basis for balancing economic efficiency, spatial equity, and policy ambition in the design of sustainable EV charging networks for emerging low-carbon cities. Full article

Genetic resources are becoming increasingly important in aquatic species, especially in sectors such as aquaculture and biomedical research. These advancements, however, lack standardized methodology to consistently improve efficient use of gametes for fertilization and to eliminate male variation during spawning. This study provides a conceptual basis for generalizable quality control in artificial spawning of aquatic species by using interdisciplinary, industrial-scale tools to calculate a fertilization unit (e.g., the amount of sperm required to reliably fertilize the eggs produced by a female). Blue catfish (Ictalurus furcatus), zebrafish (Danio rerio), and eastern oysters (Crassostrea virginica) were used as diverse representative species. Comparisons among aquatic species were reviewed, fertilization units were defined, and a sensitivity analysis was performed to assess how deviations from the fertilization unit could affect artificial spawning efficiency. Overall, reproductive strategy (e.g., gamete biology) and production setting significantly influenced the fertilization unit. Employing a fertilization unit decreased “wasted” sperm and reduced male variability during spawning. Furthermore, fertilization efficiency dropped significantly when sperm use strayed from the fertilization unit, declining with both underuse and overuse, especially in oysters and catfish. Standardizing gamete use in aquatic species is essential for economic planning and achieving commercial-scale production, especially when investing in selectively bred or cryopreserved sperm. Full article

In the rapidly evolving digital economy, the expansion of business-to-business e-commerce ecosystems has compelled traditional industries to integrate into digital supply chains to achieve sustainable development. Industrial e-commerce is no longer limited to online transactions but extends to the digital transformation of backend operations. Drawing upon the perspective of the digital business ecosystem, this study investigates how digital supply chain integration, manifested through digital transformation, impacts energy efficiency. By utilizing a panel fixed effects model and advanced text mining techniques on a dataset of 721 listed firms in the resource-intensive sectors of China spanning from 2011 to 2023, this research constructs a novel index to quantify corporate digital maturity based on semantic analysis. The empirical results demonstrate that digital transformation significantly enhances energy efficiency by facilitating optimized resource allocation and data-driven decision making required by modern digital markets. Mechanism analysis reveals that green innovation functions as a pivotal mediator that bridges the gap between digital investments and environmental performance. Furthermore, this relationship is found to be contingent upon corporate social responsibility strategies, ownership structures, and the scale of the firm. This study contributes to the electronic commerce literature by elucidating how traditional manufacturers can leverage digital technologies and green innovation to navigate the twin transition of digitalization and sustainability, offering theoretical implications for platform governance in industrial sectors. Full article

This study investigates the weak-form efficiency of South Africa’s summer grain spot markets, focusing on white maize, yellow maize, sunflower, and soybean. Using daily log return data from 2007 to 2025, we apply autocorrelation, Portmanteau (Q), and heteroskedasticity-robust Lo–MacKinlay variance ratio tests, along with Bai–Perron structural break analysis, Pesaran–Timmermann directional accuracy tests, and mean return per trade calculations. Results reveal significant short-term serial dependence and mean-reverting behaviour across all commodities, indicating partial predictability and deviations from weak-form efficiency. Structural break analysis identifies multiple regimes within the price series, showing that market dynamics are not constant over time. Directional accuracy and MRP results indicate that while some predictability exists, the economic gains from exploiting past prices are small and likely insufficient to overcome trading frictions. These findings suggest that price adjustments are gradual rather than instantaneous, reflecting structural and operational market frictions such as limited liquidity, low adoption of electronic trading, and constrained transparency. Enhancing digital trading platforms, improving real-time price reporting, and investing in storage and logistics could strengthen price discovery and reduce transaction costs. The study provides insights into emerging agricultural markets and highlights the importance of considering market structure when evaluating efficiency. Full article