Search Results (3,348)

The increasing prevalence of common cold viruses and bacteria in daily life has heightened interest in sterilization lamp technologies. Compared with traditional mercury-based ultraviolet (UV) lamps, modern UV lamps offer advantages including extended operational lifespan, high energy efficiency, compact form factor, and the absence of hazardous materials, rendering them both safer and environmentally sustainable. In particular, UV-C LED lamps, which emit at short wavelengths, are capable of disrupting the molecular structure of DNA or RNA in microbial cells, thereby inhibiting cellular replication and achieving effective disinfection and sterilization. Conventional UV-C LED sterilization lamp driver circuits frequently employ a two-stage architecture, which requires a large number of components, occupies substantial physical space, and exhibits reduced efficiency due to multiple stages of power conversion. To address these limitations, this paper proposes a UV-C LED sterilization lamp driver circuit for an AC voltage supply, employing boundary conduction mode (BCM) control with integrated power factor correction (PFC). The proposed single-stage, single-switch topology combines a buck PFC converter and a flyback converter while recovering transformer leakage energy to further improve efficiency. Compared with conventional two-stage designs, the proposed circuit reduces the number of power switches and components, thereby lowering manufacturing cost and enhancing overall energy conversion efficiency. The operating principles of the proposed driver circuit are analyzed, and a prototype is developed for a 110 V AC input with an output specification of 10.8 W (90 V/0.12 A). Experimental results demonstrate that the prototype achieves an efficiency exceeding 92%, a power factor of 0.91, an output voltage ripple of 1.298%, and an output current ripple of 4.44%. Full article

Injection strategies play a crucial role in determining hydrogen engine performance. The diversity of these strategies and the limited number of comparative studies highlight the need for further investigation. This study focuses on the analysis, parameter selection, and comparison of single early and late direct injection, single injection with ignition occurring during injection (the so-called jet-guided operation), and dual injection in a hydrogen spark-ignition engine. The applicability and effectiveness of these injection strategies are assessed using contour maps, with ignition timing and start of injection as coordinates representing equal levels of key engine parameters. Based on this approach, injection and ignition settings are selected for a range of engine operating modes. Simulations of engine performance under different load conditions are carried out using the selected parameters for each strategy. The results indicate that the highest indicated thermal efficiencies are achieved with single late injection, while the lowest occur with dual injection. At the same time, both dual injection and jet-guided operation provide advantages in terms of knock suppression, peak pressure reduction, and reduced nitrogen oxide emissions. Full article

In situ leaching represents an efficient and safe method for uranium mining, where a suboptimal well flow rate distribution leads to solution imbalances between wells, forming stagnant zones that increase operational costs. This study examines a real technological block from the Budenovskoye deposit, applying reactive transport modeling to optimize well flow rates and reduce operational time and reagent consumption. A reactive transport model was developed based on mass conservation and Darcy’s laws coupled with chemical kinetics describing sulfuric acid interactions with uranium minerals ($U{O}_2$ and $U{O}_3$). The model simulated a technological block with 4 production and 18 injection wells arranged in hexagonal cells over 511–542 days to achieve 90% uranium recovery. Six approaches for well flow rate redistribution were compared, based on different weighting factor calculation methods: advanced traditional, linear distance, squared distance, quadrilateral area, and two streamline-based approaches utilizing the minimum and average time of flight. The squared distance method achieved the highest efficiency, reducing operational costs by 5.7% through improved flow redistribution. The streamline-based methods performed comparably and offer potential advantages for heterogeneous conditions by automatically identifying hydraulic connections. The reactive transport modeling approach successfully demonstrated that multi-criteria optimization methods can improve ISL efficiency by 3.9%–5.7% while reducing operational costs. Full article

In the context of global industrial chain decarbonization, the perpetuation of corporate green innovation has emerged as a linchpin for sustaining a competitive advantage in the pursuit of environmental sustainability. Employing a panel data framework, this investigation analyzes A-share listed firms in China from 2011 to 2023. In terms of supply chain perspectives, this study utilizes fixed effects models, mediation analysis, and moderation analysis to empirically examine how downstream enterprises’ digital transformation affects the sustainability of upstream enterprises’ green innovation, while deconstructing the “capability–motivation” dual pathway underlying such sustainability. The key findings are as follows: (1) downstream digital transformation significantly strengthens upstream green innovation persistence through both capability reinforcement and motivation amplification, with a notably stronger impact on the latter; (2) mechanism tests show that capability improvement primarily arises from knowledge spillovers and enhanced supply–demand coordination efficiency, while motivation enhancement stems from intensified market competition and greater responsiveness to tax incentives; and (3) supply chain structural characteristics exert critical moderating effects. This research elucidates the operational logic and boundary conditions of supply chain digital coordination in driving green innovation persistence, contributing to theoretical frameworks while offering actionable insights for policymaking and corporate strategic optimization in sustainable supply chain management. Full article

The flaring of associated gas in oil and gas operations contributes significantly to greenhouse gas emissions and represents a loss of valuable hydrocarbon resources. While amine absorption is widely applied for acid gas removal, the use of supercritical carbon dioxide (sc-CO₂) for flare gas treatment remains largely unexplored, despite its proven selectivity for hydrocarbons in other industries such as natural product extraction and polymer processing. Conventional flare gas treatment methods face trade-offs: amine absorption achieves high acid gas removal efficiency but offers limited selectivity for heavier hydrocarbons, whereas sc-CO₂ extraction enables efficient recovery of higher hydrocarbons but does not fully remove acid gases. This study addresses these gaps by evaluating three two-stage flare gas treatment configurations—dual-stage amine absorption, dual-stage sc-CO₂ absorption, and a hybrid of sc-CO₂ followed by amine absorption—using Aspen HYSYS V12.1 simulations, with recycling processes considered in each case. The dual-stage sc-CO₂ process achieved nearly complete hydrocarbon recovery (100%) and complete H₂S removal, but CO₂ remained at elevated concentrations in the treated gas. The dual-stage amine process completely removed CO₂ and H₂S, though with higher energy demand for solvent regeneration. The hybrid configuration combined the advantages of both approaches, achieving complete H₂S removal, 100% hexane recovery, 95.02% methane recovery, and a drastic reduction in CO₂ concentration (to 0.0012 mole fraction). These results demonstrate that integrating sc-CO₂ with amine absorption resolves the trade-off between hydrocarbon selectivity and acid gas removal, establishing a technically viable pathway for flare gas utilization with potential application in gas-to-liquids (GTL) and carbon management strategies Full article

Micro-hydropower technologies are increasingly attracting attention due to their potential to contribute to sustainable energy generation. With the growing global demand for electricity, it is essential to research and innovate in the development of devices capable of harnessing hydroelectric potential through such technologies. In this context, the Archimedes screw generator (ASG) stands out as a device that potentially offers significant advantages for micro-hydropower generation. This study aimed, through a simplified yet effective method, to analyze and determine the simultaneous effects of the number of blades, inclination angle, and flow rate on the torque, mechanical power, and efficiency of an ASG. Computational Fluid Dynamics (CFD) was employed to obtain the torque and perform the hydrodynamic analysis of the devices, in order to compare the results of the optimal geometric and operational characteristics with previous studies. This proposal also helps guide future work in the preliminary design and evaluation of ASGs, considering the geometric and flow conditions that take full advantage of the available water resources. Under the specific conditions analyzed, the most efficient generator featured three blades, a 20° inclination, and an inlet flow rate of 24.5 L/s, achieving a mechanical power output of 117 W with an efficiency of 71%. Full article

Agriculture and its allied activities contribute to the primary sector in India and act as the basis for the country’s economy. Available agricultural landholdings are scattered as multiple plots across the country. Land fragmentation has led to problems achieving economies of scale and economies of scope; lower productivity, efficiency, and modernization; loss of biodiversity; and little scope for mechanization and technology. FPOs are small clusters of farmers who collaborate to enhance their bargaining strength through collective procurement, processing, and marketing efforts. To enhance the performance of FPOs at the grassroots level, the engagement of cluster-based business organizations (CBBOs) is vital. Millet FPOs are similar to voluntary farmer groups that are involved in the cultivation and promotion of millets. IIMR-promoted millet FPOs were selected purposively for the present study as they are involved in millet cultivation and farming. A total of 450 millet farmers from 15 FPOs and 3 states were randomly chosen for this action research study. The present research identified 10 key factors and collected farmers’ opinions toward member participation in millet FPOs using interpretive structural modeling. The ISM approach provided a clear understanding of how the selected factors interconnect hierarchically with each other as foundational drivers and dependent outcomes. The results from the MICMAC analysis demonstrated that foundational interventions, such as post-harvest technology availability (V2) and knowledge transfer by KVKs (V5), directly support higher-level objectives. Intermediate factors like economies of scale (V1) and market and credit linkages (V3) transform these services into operational advantages, while the outcome factors of business planning (V8), FPO branding (V7), and bargaining power (V9) emerge as dependent variables. The model demonstrates that V2 catalyzes improvements across the production, market, and institutional domains, cascading through intermediate enablers (V1, V4, V5, V6) to strengthen outcomes (V3, V7, V8, V9, V10). This hierarchy demonstrates that investing in post-harvest technology and complementary extension services is critical for building resilient millet FPOs and enhancing member participation. Full article

Food industry generates substantial waste, raising economic and environmental concerns. Green Chemistry (GC) highlights the extraction of nutritional and bioactive compounds as a key strategy for waste valorization, driving interest in sustainable methods to recover valuable compounds efficiently. This review evaluates the sustainability of widely used emerging extraction technologies—Microwave-, Ultrasound- and Enzyme-Assisted, as well as Supercritical Fluid Extraction—and their alignment with GC principles for agri-food waste valorization. It first outlines the principles, key parameters, and main advantages and limitations of each technique. Subsequently, sustainability is then assessed in selected studies using the Analytical GREEnness Metric Approach (AGREEprep). By calculating the greenness score (GS), this metric quantifies the adherence of extraction processes to sustainability standards. The analysis reveals variations within the same extraction method, influenced by solvent choice and operating conditions, as well as differences across the techniques, highlighting the importance of process design in achieving green and efficient valorization. Full article

Associative learning is a fundamental neural mechanism in human memory and cognition. It has attracted considerable attention in neuromorphic system design owing to its multimodal integration, fault tolerance, and energy efficiency. However, prior studies mostly focused on single inputs, with limited attention to multibit pairs or recall under non-orthogonal input patterns. To address these issues, this study proposes a bidirectional associative learning system using paired multibit inputs. It employs a synapse–neuron structure based on spiking neural networks (SNNs) that emulate biological learning, with simple circuits supporting synaptic operations and pattern evaluation. Importantly, the update and read functions were designed by drawing inspiration from the operational characteristics of emerging synaptic devices, thereby ensuring future compatibility with device-level implementations. The proposed system was verified through Cadence-based simulations using CMOS neurons and Verilog-A synapses. The results show that all patterns are reliably recalled under intact synaptic conditions, and most patterns are still robustly recalled under biologically plausible conditions such as partial synapse loss or noisy initial synaptic weight states. Moreover, by avoiding massive data converters and relying only on basic digital gates, the proposed design achieves associative learning with a simple structure. This provides an advantage for future extension to large-scale arrays. Full article

This study investigates the role of Modern Management Accounting (MMA)—which integrates Strategic Management Accounting (SMA) and Strategic Customer Knowledge (SCK)—in driving Sustainable Competitive Advantage (SCA) and Business Sustainability (BS) in Thai green hotels. Business Sustainability is conceptualized as the achievement of balanced outcomes across economic performance, social responsibility, and environmental stewardship. It addresses a theoretical debate by testing two competing SCA models: a disaggregated model (which separates SCA into Customer Experience Advantage (CEA) and Operational Efficiency Advantage (OEA)) and a holistic model (which treats SCA as a unified construct). Data from 115 certified green hotels were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results revealed a critical distinction between the models. In the disaggregated model, SMA and SCK contributed to both CEA and OEA, but only OEA directly enhanced BS and served as a partial mediator in the relationships from both SMA and SCK to BS, whereas CEA showed no significant mediating effects. Conversely, the holistic model demonstrated that overall SCA served as a partial mediator in the relationships from both SMA and SCK to BS, while also exerting a strong direct effect on BS. The study concludes that achieving business sustainability requires a holistic SCA that integrates both operational efficiency and customer experience, offering a comprehensive framework for strategic management in the hotel industry. These findings underscore the strategic imperative for hotel managers to cultivate an integrated competitive advantage, where superior customer experiences and operational excellence are synergistically managed, to ensure long-term business sustainability. Full article

Objectives: To predict the progression of knee osteoarthritis (OA), a deep convolutional neural network model was developed and applied to basic images and clinical data. Design: A vision transformer-based model was trained using 5565 knee radiographs as baseline images from the osteoarthritis initiative (OAI), including 578 testing images. Each knee had a corresponding Kellgren and Lawrence (KL) stage after 48 months of follow-up. Another 274 cases from the Far Eastern Memorial Hospital were used for external validation. The data included a combination of single/pairing images and full/essential clinical factors. Area under the receiver operating characteristics (AUROC), accuracy, sensitivity, specificity, odds ratio, and ability to discriminate surgical candidates were applied to evaluate model performance. Results: In cases with OA progression, the AUROC for identifying surgical candidates was 0.844, 0.804, 0.766, and 0.718 in the combination of a single image with essential factors, single image with full factors, pairing images with essential factors, and pairing images with full factors, respectively. In OAI testing using the simplest input, AUROC of identifying OA progression was 0.808, with 74.1% accuracy, 91.8% sensitivity, and 71% specificity. In external validation, AUROC of identifying OA progression was 0.709, with 71.2% accuracy, 72.2% sensitivity, and 70.3% specificity. Positive model prediction had an odds ratio of 23.87 (CI: 11.24~50.67) in OAI and 5.92 (CI: 3.50~10.03) in external validation. Conclusions: Our model provides reliable prediction results for knee OA cases with the advantages of simplicity and flexibility. The model performance was excellent in progression cases, potentially making early intervention in OA patients more efficient. Full article

The hydrogen economy stands at the forefront of the global energy transition, and additive manufacturing (AM) is increasingly recognized as a critical enabler of this transformation. AM offers unique capabilities for improving the performance and durability of hydrogen energy components through rapid prototyping, topology optimization, functional integration of cooling channels, and the fabrication of intricate, hierarchical, structured pores with precisely controlled connectivity. These features facilitate efficient heat and mass transfer, thereby improving hydrogen production, storage, and utilization efficiency. Furthermore, AM’s multi-material and functionally graded printing capability holds promise for producing components with tailored properties to mitigate hydrogen embrittlement, significantly extending operational lifespan. Collectively, these advances suggest that AM could lower manufacturing costs for hydrogen-related systems while improving performance and reliability. However, the current literature provides limited evidence on the integrated techno-economic advantages of AM in hydrogen applications, posing a significant barrier to large-scale industrial adoption. At present, the technological readiness level (TRL) of AM-based hydrogen components is estimated to be 4–5, reflecting laboratory-scale progress but underscoring the need for further development, validation and industrial-scale demonstration before commercialization can be realized. Full article

Very-high-energy electron (VHEE) beams, ranging from 50 to 300 or 400 MeV, are the subject of intense research investigation, with considerable interest concerning applications in radiation therapy due to their accurate energy deposition into large and deep-seated tissues, sharp beam edges, high sparing properties, and minimal radiation effects on normal tissues. The very-high-energy electron beam, which ranges from 50 to 400 MeV, and Ultra-High-Energy Electron beams up to 1–2 GeV, are considered extremely effective for human tumor therapy while avoiding the spatial requirements and cost of proton and heavy ion facilities. Many research laboratories have developed advanced testing infrastructures with VHEE beams in Europe, the USA, Japan, and other countries. These facilities aim to accelerate the transition to clinical application, following extensive simulations for beam transport that support preclinical trials and imminent clinical deployment. However, the clinical implementation of VHEE for FLASH radiation therapy requires advances in several areas, including the development of compact, stable, and efficient accelerators; the definition of sophisticated treatment plans; and the establishment of clinically validated protocols. In addition, the perspective of VHEE for accessing ultra-high dose rate (UHDR) dosimetry presents a promising procedure for the practical integration of FLASH radiotherapy for deep tumors, enhancing normal tissue sparing while maintaining the inherent dosimetry advantages. However, it has been proven that a strong effort is necessary to improve the main operational accelerator conditions, ensuring a stable beam over time and across space, as well as compact infrastructure to support the clinical implementation of VHEE for FLASH cancer treatment. VHEE-accessing ultra-high dose rate (UHDR) perspective dosimetry is integrated with FLASH radiotherapy and well-prepared cancer treatment tools that provide an advantage in modern oncology regimes. This study explores technological progress and the evolution of electron accelerator beam energy technology, as simulated by the ASTRA code, for developing VHEE and UHEE beams aimed at medical applications. FLUKA code simulations of electron beam provide dose distribution plots and the range for various energies inside the phantom of PMMA. Full article

Accurate load profile identification is crucial for effective and sustainable power system planning. This study proposes a characterization methodology based on clustering techniques applied to consumption data from medium- and low-voltage users, as well as distribution transformers from an electric utility. Three algorithms—K-means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and Gaussian Mixture Models (GMM)—were implemented and compared in terms of their ability to form representative strata using variables such as observation count, projected energy, load factor ($LF$), and characteristic power levels. The methodology includes data cleaning, normalization, dimensionality reduction, and quality metric analysis to ensure cluster consistency. Results were benchmarked against a prior study conducted by Empresa Eléctrica Regional Centro Sur C.A. (EERCS). Among the evaluated algorithms, GMM demonstrated superior performance in modeling irregular consumption patterns and probabilistically assigning observations, resulting in more coherent and representative segmentations. The resulting clusters exhibited an average $LF$ of 58.82%, indicating balanced demand distribution and operational consistency across the groups. Compared to alternative clustering techniques, GMM demonstrated advantages in capturing heterogeneous consumption patterns, adapting to irregular load behaviors, and identifying emerging user segments such as induction-cooking households. These characteristics arise from its probabilistic nature, which provides greater flexibility in cluster formation and robustness in the presence of variability. Therefore, the findings highlight the suitability of GMM for real-world applications where representativeness, efficiency, and cluster stability are essential. The proposed methodology supports improved transformer sizing, more precise technical loss assessments, and better demand forecasting. Periodic application and integration with predictive models and smart grid technologies are recommended to enhance strategic and operational decision-making, ultimately supporting the transition toward smarter and more resilient power distribution systems. Full article

Due to their low elasticity modulus, significant fatigue strength, and good formability, titanium and titanium alloys have shown a continuous growth trend in various fields of application. However, the passivation film on the surface of titanium and titanium alloys may dissolve, leading to corrosion under certain environmental conditions. Surface modification of these materials has become an indispensable and critical step in meeting the requirements of various operating conditions of material performance. Compared to other surface treatment techniques, plasma surface treatment has advantages such as high efficiency, wide applicability, environmental friendliness, flexibility and controllability, and low-temperature treatment. This article focuses on the topic of plasma surface modification technology for titanium and titanium alloys and highlights the key limitations of Plasma chemical heat treatment, Physical Vapor Deposition (PVD), plasma-enhanced chemical vapor deposition (PECVD), Plasma immersion ion implantation (PIII), and plasma spraying (PS). The current research status of surface modification methods in improving the surface properties of titanium and titanium alloys and the prospects of surface modification technology for titanium alloys are also discussed. Full article