Search Results (4,406)

Precision manufacturing requires handling multi-physics coupling during processing, where digital twin and AI technologies enable rapid robot programming under customized requirements. However, heterogeneous data sources, diverse domain models, and rapidly changing demands pose significant challenges to digital twin system integration. To overcome these limitations, this paper proposes a digital twin modeling strategy based on a metamodel and a virtual–real fusion architecture, which unifies models between the virtual and physical domains. Within this framework, subsystems achieve rapid integration through ontology-driven knowledge configuration, while ROS provides the execution environment for establishing robot manufacturing digital twin scenarios. A case study of a robot shaping system demonstrates that the proposed architecture effectively addresses heterogeneous data association, model interaction, and application customization, thereby enhancing the adaptability and intelligence of precision manufacturing processes. Full article

In the shift toward sustainable energy, there is a strong demand for efficient and durable energy storage solutions. Supercapacitors, in particular, are a promising technology, but they require high-performance materials that can be produced using simple, eco-friendly methods. This has led researchers to investigate new materials and composites that can deliver high energy and power densities, along with long-term stability. Herein, we report a green synthesis approach to create a composite material consisting of reduced graphene oxide and silver nanoparticles (rGO/Ag). The method uses ascorbic acid, a natural compound found in fruits and vegetables, as a non-toxic agent to simultaneously reduce graphene oxide and silver nitrate. To enhance electrochemical performance, the incorporation of silver nanoparticles into the rGO structures is optimized. In this study, different molar concentrations of silver nitrate (1.0, 0.10, and 0.01 M) are used to control silver nanoparticle loading during the synthesis and reduction process. A correlation between silver concentration, defect density in rGO, and the resulting capacitive behavior was assessed by systematically varying the silver molarity. The synthesized materials exhibited excellent performance as supercapacitor electrodes in a three-electrode configuration, with the rGO/Ag 1.0 M composite showing the best performance, reaching a maximum specific capacitance of 392 Fg⁻¹ at 5 mVs⁻¹. Furthermore, the performance of this optimized electrode material was investigated in a two-electrode configuration as a coin cell device, which demonstrates a maximum areal-specific capacitance of 22.63 mFcm⁻² and a gravimetric capacitance of 19.00 Fg⁻¹, which is within the range of commercially viable devices and a significant enhancement, outperforming low-level graphene-based devices. Full article

The present study aims to develop 3D-structured adsorbents for carbon capture with the utilization of coal ash after its conversion into zeolites. For this purpose, printing paste mixtures with a viscosity of 800 Pa·s were developed based on an environmentally friendly and safe polymer binder filled with coal ash zeolite with the addition of bentonite as a filler. The optimal consistency of the printing mixtures for preserving the shape and dimensions of the 3D-printed structures was established. Various model configurations of the macrostructure of 3D adsorbents were developed, and the optimal settings of the extruding system for their printing were established. After calcination, the resulting 3D structures were studied using instrumental analysis techniques, investigating the influence of 3D structuring on the phase composition, surface characteristics, and adsorption capacity for CO₂ capture in comparison with the initial powder coal ash zeolite adsorbents. The role of compensating cations in terms of the adsorption ability of powders in 3D-printed adsorbents was investigated. The current study offers an innovative and previously unexplored approach to a more expedient and practically significant utilization of aluminosilicate solid waste and, in particular, coal ash, through their 3D structuring and outlines a new research and technological direction in the development of economically advantageous, technologically feasible, and environmentally friendly 3D adsorbents. Full article

With the rapid development of artificial intelligence technology in the field of intelligent manufacturing, convolutional neural networks (CNNs) have shown excellent performance and generalization capabilities in industrial applications. However, the huge computational and resource requirements of CNNs have brought great obstacles to their deployment on low-end hardware platforms. To address this issue, this paper proposes a scalable CNN accelerator that can operate on low-performance Field-Programmable Gate Arrays (FPGAs), which is aimed at tackling the challenge of efficiently running complex neural network models on resource-constrained hardware platforms. This study specifically optimizes depthwise separable convolution and the squeeze-and-excite module to improve their computational efficiency. The proposed accelerator allows for the flexible adjustment of hardware resource consumption and computational speed through configurable parameters, making it adaptable to FPGAs with varying performance and different application requirements. By fully exploiting the characteristics of depthwise separable convolution, the accelerator optimizes the convolution computation process, enabling flexible and independent module stackings at different stages of computation. This results in an optimized balance between hardware resource consumption and computation time. Compared to ARM CPUs, the proposed approach yields at least a 1.47× performance improvement, and compared to other FPGA solutions, it saves over 90% of Digital Signal Processors (DSPs). Additionally, the optimized computational flow significantly reduces the accelerator’s reliance on internal caches, minimizing data latency and further improving overall processing efficiency. Full article

As states within the United States respond to future grid development goals, there is a growing demand for reliable and resilient nighttime generation that can be addressed by low-cost, long-duration energy storage solutions. This report studies the potential of including concentrating solar power (CSP) in the technology mix to support California’s goals as defined in Senate Bill 100. A joint agency report study that determined potential pathways to achieve the renewable portfolio standard set by the bill did not include CSP, and our work provides information that could be used as a follow-up. This study uses a capacity expansion model configured to have nodal spatial fidelity in California and balancing-area fidelity in the Western Interconnection outside of California. The authors discovered that by applying current technology cost projections CSP fulfills nearly 15% of the annual load while representing just 6% of total installed capacity in 2045, replacing approximately 30 GWe of wind, solar PV, and standalone batteries compared to a scenario without CSP included. The deployment of CSP in the results is sensitive to the technology’s cost, which highlights the importance of meeting cost targets in 2030 and beyond to enable the technology’s potential contribution to California’s carbon reduction goals. Full article

Geothermal energy has been recognized as a promising renewable resource for sustainable power generation; however, the efficiency of conventional geothermal power plants has remained relatively low, and high investment costs have limited their competitiveness with other renewable technologies. In this context, the present study introduced an innovative geothermal electricity generation system aimed at enhancing energy efficiency, cost-effectiveness, and sustainability. Unlike traditional configurations, the system raised the geothermal source temperature passively by employing advanced heat transfer mechanisms, eliminating the need for additional energy input. Comprehensive energy, exergy, and exergoeconomic analyses were carried out, revealing a net power output of 43,210 kW and an energy efficiency of 30.03%, notably surpassing the conventional Kalina cycle’s typical 10.30–19.48% range. The system’s annual electricity generation was 11,138.53 MWh, with an initial investment of USD 3.04 million and a short payback period of 3.20 years. A comparative assessment confirmed its superior thermoeconomic performance. In addition to its technoeconomic advantages, the environmental performance of the proposed configuration was quantified. A streamlined life cycle assessment (LCA) was performed with a functional unit of 1 MWh of net electricity. The proposed system exhibited a carbon footprint of 20–60 kg CO₂ eq MWh⁻¹ (baseline: 45 kg CO₂ eq MWh⁻¹), corresponding to annual emissions of 0.22–0.67 kt CO₂ eq for the simulated output of 11,138.53 MWh. Compared with coal- and gas-fired plants of the same capacity, avoided emissions of approximately 8.6 kt and 5.0 kt CO₂ eq per year were achieved. The water footprint was determined as ≈0.10 m³ MWh⁻¹ (≈1114 m³ yr⁻¹), which was substantially lower than the values reported for fossil technologies. These findings confirmed that the proposed system offered a sustainable alternative to conventional geothermal and fossil-based electricity generation. Multi-objective optimization using NSGA-II was carried out to maximize energy and exergy efficiencies while minimizing total cost. Key parameters such as turbine inlet temperature (459–460 K) and ammonia concentration were tuned for performance stability. A sensitivity analysis identified the heat exchanger, the first condenser (Condenser 1), and two separators (Separator 1, Separator 2) as influential on both performance and cost. The exergoeconomic results indicated Separator 1, Separator 2, and the turbine as primary locations of exergy destruction. With an LCOE of 0.026 USD/kWh, the system emerged as a cost-effective and scalable solution for sustainable geothermal power production without auxiliary energy demand. Full article

Microbial electrosynthesis (MES) has emerged as a promising bio-electrochemical technology for sustainable CO₂ conversion into valuable organic compounds since it uses living electroactive microbes to directly convert CO₂ into value-added products. This review synthesizes advancements in MES from 2010 to 2025, focusing on the electrode materials, microbial communities, reactor engineering, performance trends, techno-economic evaluations, and future challenges, especially on the results reported between 2020 and 2025, thus highlighting that MES technology is now a technology to be reckoned with in the spectrum of biofuel technology production. While the current productivity and scalability of microbial electrochemical systems (MESs) remain limited compared to conventional CO₂ conversion technologies, MES offers distinct advantages, including process simplicity, as it operates under ambient conditions without the need for high pressures or temperatures; modularity, allowing reactors to be stacked or scaled incrementally to match varying throughput requirements; and seamless integration with circular economy strategies, enabling the direct valorization of waste streams, wastewater, or renewable electricity into valuable multi-carbon products. These features position MES as a promising platform for sustainable and adaptable CO₂ utilization, particularly in decentralized or resource-constrained settings. Recent innovations in electrode materials, such as conductive polymers and metal–organic frameworks, have enhanced electron transfer efficiency and microbial attachment, leading to improved MES performance. The development of diverse microbial consortia has expanded the range of products achievable through MES, with studies highlighting the importance of microbial interactions and metabolic pathways in product formation. Advancements in reactor design, including continuous-flow systems and membrane-less configurations, have addressed scalability issues, enhancing mass transfer and system stability. Performance metrics, such as the current densities and product yields, have improved due to exceptionally high product selectivity and surface-area-normalized production compared to abiotic systems, demonstrating the potential of MES for industrial applications. Techno-economic analyses indicate that while MES offers promising economic prospects, challenges related to cost-effective electrode materials and system integration remain. Future research should focus on optimizing microbial communities, developing advanced electrode materials, and designing scalable reactors to overcome the existing limitations. Addressing these challenges will be crucial for the commercialization of MES as a viable technology for sustainable chemical production. Microbial electrosynthesis (MES) offers a novel route to biofuels by directly converting CO₂ and renewable electricity into energy carriers, bypassing the costly biomass feedstocks required in conventional pathways. With advances in electrode materials, reactor engineering, and microbial performance, MES could achieve cost-competitive, carbon-neutral fuels, positioning it as a critical complement to future biofuel technologies. Full article

In global economic integration and rapid urbanization, the equilibrium between resource utilization efficiency and ecological preservation is confronted with significant challenges. Emerging contaminants have further exacerbated environmental pressures and posed threats to the ecosystem and human health. Metal–organic frameworks (MOFs) have emerged as a prominent area of research in ecological remediation, owing to their distinctive porous configuration, substantial specific surface area, and exceptional chemical stability. The Materials Institute Lavoisier (MIL) series (e.g., MIL-53, MIL-88, MIL-100, MIL-101, and MIL-125) has been shown to effectively promote the separation and migration of photogenerated carriers and significantly enhance the degradation of organic contaminants. This property renders it highly promising for the photocatalytic degradation of emerging contaminants. This paper provides a concise overview of the classification, synthesis methods, modification strategies, and application effects of MIL series MOFs in the removal of emerging contaminants. The advantages and limitations of MIL series MOFs in environmental remediation are further analyzed. Particularly, we offer insights and support for innovative strategies in the treatment of emerging contaminants, including POPs, PPCPs, VOCs, and microplastics, contributing to technological innovation and development in environmental remediation. Future development of MOFs includes the optimization of the performance of the MILs, reducing the high synthesis costs of MILs, applying MILs in real-environment scenarios, and accurate detection of degradation products of environmental pollutants. Full article

The main objective of this work is to provide a comprehensive explanation of the Register Transfer Level (RTL) to Graphic Data System II (GDSII) flow for designing custom Field-Programmable Gate Array (FPGA) architectures at the 130 nm technology node using the SKY130 Process Design Kit (PDK). By leveraging open-source tools—specifically OpenLane and OpenFPGA—this study details the methodology and implementation steps required to generate a GDSII layout of a custom FPGA. OpenLane offers an integrated RTL-to-GDSII flow by combining multiple Electronic Design Automation (EDA) tools, while OpenFPGA enables the construction of flexible and customizable FPGA architectures. The article covers key aspects of the RTL-to-GDSII workflow, including RTL file configuration, the utilization of configuration variables for physical design, hierarchical chip design, macro and core implementation, chip-level integration, and gate-level simulation. Experimental results validate the proposed workflow, showcasing the successful transformation from RTL to GDSII. The findings of this research provide valuable insights for researchers and engineers in the FPGA design field, advancing the state of the art in FPGA architecture development. Full article

Robotic Process Automation (RPA) is a software-based technology that uses configurable algorithmic software agents (bots) to replicate manual user activities across digital systems. It represents an evolution from earlier workflow scripting tools, and is distinguished by its ability to be used without requiring substantial IT infrastructure modifications or extensive programming knowledge. In the banking and insurance sectors, organizations face increasing pressure to adopt modern technologies that streamline operations and reduce costs while complying with strict regulatory requirements. Robotic Process Automation (RPA) has emerged as a viable and cost-effective solution, enabling automation of repetitive and rule-based tasks without requiring major changes to legacy IT systems. This paper conducts a literature review to examine the current use cases of RPA technologies in banking and insurance, analyzing how these technologies are employed to enhance corporate efficiency and performance. The review draws from recent academic publications and case studies between 2017 and 2025, identifying core implementation areas such as customer onboarding, claims processing, compliance reporting, and underwriting automation. The results highlight substantial improvements in processing speed, error reduction, and resource optimization, along with evolving metrics for measuring effectiveness. The study concludes by identifying key success factors, performance measurement approaches, and challenges in RPA implementation, offering insights for both practitioners and researchers aiming to understand the role of automation in financial services transformation. Full article

The monitoring of safety and health in lithium-ion batteries (LIBs) presents a significant challenge. Ultrasonic detection techniques fulfil the requirements for high sensitivity and non-destructive evaluation in the safety assessment of these batteries. This study concentrates on the application of electromagnetic acoustic transducer (EMAT) technology for non-destructive battery testing, utilizing non-contact electromagnetic coupling to generate and receive ultrasonic waves. This method addresses the limitations associated with conventional piezoelectric ultrasonic coupling media, thereby facilitating highly reliable assessment of the internal condition of batteries. Specifically, this paper independently designs an EMAT featuring a Halbach magnet array and a butterfly coil. Based on this design, optimization is performed, and the amplitude of the received signal is increased fourfold compared to the pre-optimization configuration. The optimized transducer is employed to evaluate a set of retired batteries with a nominal capacity of 270 Ah. Experimental results demonstrate that batteries exhibiting capacities below 240 Ah produced average signal amplitudes more than 40% lower than those of batteries with higher capacities. This technology provides a non-contact, disassembly-free approach for rapid performance evaluation of batteries and demonstrates potential for effective application in sorting retired battery units. Full article

The rapid evolution of autonomous vehicle technologies has amplified the need for crash detection that operates robustly under complex traffic conditions with minimal latency. We propose a hybrid temporal hierarchy that augments a Region-based Convolutional Neural Network (R-CNN) with an adaptive time-variant Kalman filter (with total-variation prior), a Hidden Markov Model (HMM) for state stabilization, and a lightweight Artificial Neural Network (ANN) for learned temporal refinement, enabling real-time crash detection from monocular video. Evaluated on simulated traffic in CARLA and real-world driving in Istanbul, the full temporal stack achieves the best precision–recall balance, yielding 83.47% F1 offline and 82.57% in real time (corresponding to 94.5% and 91.2% detection accuracy, respectively). Ablations are consistent and interpretable: removing the HMM reduces F1 by 1.85–2.16 percentage points (pp), whereas removing the ANN has a larger impact of 2.94–4.58 pp, indicating that the ANN provides the largest marginal gains—especially under real-time constraints. The transition from offline to real time incurs a modest overall loss (−0.90 pp F1), driven more by recall than precision. Compared to strong single-frame baselines, YOLOv10 attains 82.16% F1 and a real-time Transformer detector reaches 82.41% F1, while our full temporal stack remains slightly ahead in real time and offers a more favorable precision–recall trade-off. Notably, integrating the ANN into the HMM-based pipeline improves accuracy by 2.2%, while the time-variant Kalman configuration reduces detection lag by approximately 0.5 s—an improvement that directly addresses the human reaction time gap. Under identical conditions, the best RCNN-based configuration yields AP@0.50 ≈ 0.79 with an end-to-end latency of 119 ± 21 ms per frame (~8–9 FPS). Overall, coupling deep learning with probabilistic reasoning yields additive temporal benefits and advances deployable, camera-only crash detection that is cost-efficient and scalable for intelligent transportation systems. Full article

Absolute linear encoders have emerged as a core technical enabler in the fields of high-end manufacturing and precision displacement measurement, owing to their inherent advantages such as the elimination of the need for homing operations and the retention of position data even upon power failure. However, there remains a notable scarcity of comprehensive review materials that can provide systematic guidance for practitioners engaged in the field of absolute linear encoder measurement technology. The present study aims to address this gap by offering a practical reference to professionals in this domain. In this research, we first systematically delineate the three fundamental categories of measurement principles underlying absolute linear encoders. Subsequently, we analyze the evolutionary trajectory of coding technologies, encompassing the design logics and application characteristics of quasi-absolute coding (including non-embedded and embedded variants) as well as absolute coding (covering multi-track and single-track configurations). Furthermore, we summarize the primary error sources that influence measurement accuracy and explore the operational mechanisms of various types of errors. This study clarifies the key technical pathways and existing challenges associated with absolute linear encoders, thereby providing practitioners in relevant fields with a decision-making guide for technology selection and insights into future development directions. Moving forward, efforts should focus on achieving breakthroughs in critical technologies such as high fault-tolerant coding design, integrated manufacturing, and error compensation, so as to advance the development of absolute linear encoders toward higher precision, miniaturization, cost reduction, and enhanced reliability. Full article

In the event of power outages caused by natural disasters, accidents, or other emergencies, outdoor emergency lighting systems play a critical role in providing illumination to maintain spatial orientation, facilitate evacuation procedures, and help individuals avoid hazardous areas or locate safe shelters. Compared to traditional lighting technologies, LED-based outdoor emergency lighting offers several advantages, including compact size, long operational lifespan, low energy consumption, high safety, resistance to breakage, and the absence of chemical residue or pollution. These characteristics align with contemporary trends in environmental sustainability and energy efficiency. This study proposes a novel LED driver circuit architecture for outdoor emergency lighting applications. The primary circuit topology is based on an improved buck-boost converter integrated with a flyback converter, forming a hybrid buck-boost-flyback configuration. The proposed circuit is capable of recycling the energy stored in the transformer’s leakage inductance, thereby enhancing overall power conversion efficiency. A 12 W (20 V/0.6 A) prototype LED driver circuit was designed and implemented to validate the performance of the proposed system. Experimental measurements, including waveform analysis and efficiency evaluation, demonstrate that the driver circuit achieves a high efficiency exceeding 91%. These results confirm the practical feasibility and effectiveness of the proposed electronic driver for LED-based outdoor emergency lighting applications. Full article

The growing volume of decommissioned wind turbine blades (WTBs) poses substantial challenges for end-of-life (EoL) material management, particularly within the composite repurposing and recycling strategies. This study investigates the repurposing of EoL WTB segments in a full-scale demonstrator for a photovoltaic (PV) floating platform. The design process is supported by a calibrated numerical model replicating the structure’s behaviour under representative operating conditions. The prototype reached Technology Readiness Level 6 (TRL 6) through laboratory-scale wave basin testing, under irregular wave conditions with heights up to 0.22 m. Structural assessment validates deformation limits and identifies critical zones using composite failure criteria. A comparison between two configurations underscores the importance of load continuity and effective load distribution. Additionally, a life cycle assessment (LCA) evaluates environmental impact of the repurposed solution. Results indicate that the demonstrator’s footprint is comparable to those of conventional PV-floating installations reported in the literature. Furthermore, overall sustainability can be significantly enhanced by reducing transport distances associated with repurposed components. The findings support the structural feasibility and environmental value of second-life applications for composite WTB segments, offering a circular and scalable pathway for their integration into aquatic infrastructures. Full article