Search Results (23,326)

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

HgCdSe has recently been proposed as a potential alternative material to HgCdTe for fabricating high-performance infrared detectors. This work presents a study on the growth of high-crystalline-quality HgCdSe materials on GaSb (211)B substrates via molecular beam epitaxy and demonstration of the first prototype HgCdSe-based mid-wave infrared detectors. By optimizing the MBE growth parameters, and especially the thermal cleaning process of the GaSb substrate surface prior to epitaxial growth, high-quality HgCdSe material was achieved with a record XRD full width at half maximum of ~65 arcsec. At a temperature of 77 K, the mid-wave infrared HgCdSe n-type material demonstrated a minority carrier lifetime of ~1.19 µs, background electron concentration of ~2.2 × 10¹⁷ cm⁻³, and electron mobility of ~1.6 × 10⁴ cm²/Vs. The fabricated mid-wave infrared HgCdSe photoconductor presented a cut-off wavelength of 4.2 µm, a peak responsivity of ~40 V/W, and a peak detectivity of ~1.2 × 10⁹ cmHz^1/2/W at 77 K. Due to the relatively high background electron concentration, the detector performance is lower than that of state-of-the-art low-doped HgCdTe counterparts. However, these preliminary results indicate the great potential of HgCdSe materials for achieving next-generation IR detectors on large-area substrates with features of lower cost and larger array format size. Full article

Today, blockchain technologies are a separate, rapidly developing area. With rapid development, they open up a number of scientific problems. One of these problems is the problem of reliability, which is primarily associated with the use of cryptographic primitives. The threat of the emergence of quantum computers is now widely discussed, in connection with which the direction of post-quantum cryptography is actively developing. Nevertheless, the most popular blockchain platforms (such as Bitcoin and Ethereum) use asymmetric cryptography based on elliptic curves. Here, cryptographic primitives for blockchain systems are divided into four groups according to their functionality: keyless, single-key, dual-key, and hybrid. The main attention in the work is paid to the most significant cryptographic primitives for blockchain systems: keyless and single-key. This manuscript discusses possible scenarios in which, during practical implementation, the mathematical foundations embedded in the algorithms for generating a digital signature and encrypting data using algorithms based on elliptic curves are violated. In this case, vulnerabilities arise that can lead to the compromise of a private key or a substitution of a digital signature. We consider cases of vulnerabilities in a blockchain system due to incorrect use of a cryptographic primitive, describe the problem, formulate the problem statement, and assess its complexity for each case. For each case, strict calculations of the maximum computational costs are given when the conditions of the case under consideration are met. Among other things, we present a new version of the encryption algorithm for data stored in blockchain systems or transmitted between blockchain systems using elliptic curves. This algorithm is not the main blockchain algorithm and is not included in the core of modern blockchain systems. This algorithm allows the use of the same keys that system users have in order to store sensitive user data in an open blockchain database in encrypted form. At the same time, possible vulnerabilities that may arise from incorrect implementation of this algorithm are considered. The scenarios formulated in the article can be used to test the reliability of both newly created blockchain platforms and to study long-existing ones. Full article

This mini-review emphasizes the potential of biomass-derived materials as sustainable components for next-generation electrochemical energy storage systems. Biomass obtained from abundant and renewable natural resources can be transformed into carbonaceous materials. These materials typically possess hierarchical porosities, adjustable surface functionalities, and inherent heteroatom doping. These physical and chemical characteristics provide the structural and chemical flexibility needed for various electrochemical applications. Additionally, biomass-derived materials offer a cost-effective and eco-friendly alternative to traditional components, promoting green chemistry and circular resource utilization. This review provides a systematic overview of synthesis methods, structural design strategies, and material engineering approaches for their use in lithium-ion batteries (LIBs), lithium–sulfur batteries (LSBs), and supercapacitors (SCs). It also highlights key challenges in these systems, such as the severe volume expansion of anode materials in LIBs and the shuttle effect in LSBs and discusses how biomass-derived carbon can help address these issues. Full article

Effective in situ H₂S removal is essential for the utilization of small, remote natural gas wells, where centralized treatment is often unfeasible. This study presents an integrated environmental–economic assessment of two such processes, LO-CAT^® and triazine-based absorption, using a scenario-based framework. Environmental impacts were assessed via the Waste Reduction Algorithm (WAR), considering both Potential Environmental Impact (PEI) generation and output across eight categories, while economic performance was analyzed based on equipment, chemical, energy, environmental treatment, and labor costs. Results show that the triazine-based process offers superior environmental performance due to lower toxic emissions, whereas LO-CAT^® demonstrates better economic viability at higher gas flow rates and H₂S concentrations. An integrated assessment combining monetized environmental impacts with economic costs reveals that the triazine-based process becomes competitive only if environmental impacts are priced above specific thresholds. This study contributes a practical evaluation framework and scenario-based dataset that support sustainable process selection for decentralized sour gas treatment applications. Full article

Recent advancements in generative artificial intelligence have significantly progressed the field of text-to-music generation, enabling users to create music from natural language descriptions. Despite the success of various models, such as MusicLM, MusicGen, and AudioLDM, the current approaches struggle to capture fine-grained genre-specific characteristics, precisely control musical attributes, and handle underrepresented cultural data. This paper introduces a novel, lightweight fine-tuning method for the AudioLDM framework using low-rank adaptation (LoRA). By updating only selected attention and projection layers, the proposed method enables efficient adaptation to musical genres with limited data and computational cost. The proposed method enhances controllability over key musical parameters such as rhythm, emotion, and timbre. At the same time, it maintains the overall quality of music generation. This paper represents the first application of LoRA in AudioLDM, offering a scalable solution for fine-grained, genre-aware music generation and customization. The experimental results demonstrate that the proposed method improves the semantic alignment and statistical similarity compared with the baseline. The contrastive language–audio pretraining score increased by 0.0498, indicating enhanced text-music consistency. The kernel audio distance score decreased by 0.8349, reflecting improved similarity to real music distributions. The mean opinion score ranged from 3.5 to 3.8, confirming the perceptual quality of the generated music. Full article

To fully accommodate renewable and derivative energy sources in mine energy systems under supply and demand uncertainties, this paper proposes an optimized electricity–heat scheduling method for mining areas that incorporates Power-to-Gas (P2G) technology and Conditional Value-at-Risk (CVaR). First, to address uncertainties on both the supply and demand sides, a P2G unit is introduced, and a Latin hypercube sampling technique based on Cholesky decomposition is employed to generate wind–solar-load sample matrices that capture source–load correlations, which are subsequently used to construct representative scenarios. Second, a stochastic optimization scheduling model is developed for the mine electricity–heat energy system, aiming to minimize the total scheduling cost comprising day-ahead scheduling cost, expected reserve adjustment cost, and CVaR. Finally, a case study on a typical mine electricity–heat energy system is conducted to validate the effectiveness of the proposed method in terms of operational cost reduction and system reliability. The results demonstrate a 1.4% reduction in the total operating cost, achieving a balance between economic efficiency and system security. Full article

Electricity distribution systems face increasing challenges due to demand growth, regulatory requirements, and the integration of distributed generation. In this context, distribution companies must make strategic and well-supported investment decisions, particularly in asset reinforcement actions such as reconductoring. This paper presents a multi-stage methodology to optimize reconductoring investments under load forecasting uncertainties. The approach combines a decomposition strategy with Monte Carlo simulation to capture demand variability. By discretizing a lognormal probability density function and selecting the largest loads in the network, the methodology balances computational feasibility with modeling accuracy. The optimization model employs exhaustive search techniques independently for each network branch, ensuring precise and consistent investment decisions. Tests conducted on the IEEE 123-bus feeder consider both operational and regulatory constraints from the Brazilian context. Results show that uncertainty-aware planning leads to a narrow investment range—between USD 55,108 and USD 66,504—highlighting the necessity of reconductoring regardless of demand scenarios. A comparative analysis of representative cases reveals consistent interventions, changes in conductor selection, and schedule adjustments based on load conditions. The proposed methodology enables flexible, cost-effective, and regulation-compliant investment planning, offering valuable insights for utilities seeking to enhance network reliability and performance while managing demand uncertainties. Full article

The rapid development of the modern power system puts forward high requirements for economic dispatch, and the defects of the traditional centralized economic dispatch method with low security and poor optimization effect have been difficult to adapt to the development of power system. Therefore, finding an economic dispatch method that reduces electricity generation costs and CO₂ emissions is important. This study establishes a multi-region distributed optimization model and combines the multi-region distributed optimization model with a multi-gradient optimization algorithm to propose a multi-region distributed multi-gradient whale optimization algorithm (MRDMGWOA). In this study, MRDMGWOA is simulated on the IEEE 39 system and 118 system, and its performance is compared with other heuristic algorithms. The results show that: (1) in the IEEE 39 system, MRDMGWOA reduces the power generation cost and CO₂ emission by 17% and 22%, respectively, and reduces the computation time by 16.14 s compared with the centralized optimization; (2) in the IEEE 118 system, the two metrics are further optimized, with a 20% and 17% reduction in the cost and emission, respectively, and an improvement in the computational efficiency by 45.46 s; (3) in the spacing, hypervolume, and Euclidian metrics evaluation, MRDMGWOA outperforms other algorithms; (4) compared with the existing DMOGWO and DMOMFO, the computation time of MRDMGWOA is reduced by 177.49 s and 124.15 s, respectively, and the scheduling scheme obtained by MRDMGWOA is more optimal than DMOGWO and DMOMFO. Full article

Some of nuclear power’s primary detractors are the unique environmental challenges and impacts of radioactive wastes generated during fuel cycle operations. Key benefits of spent fuel reprocessing (SFR) are reductions in primary high active waste (HAW) masses, volumes, and lengths of radiotoxicity at the expense of secondary waste generation and high capital and operational costs. By employing advanced waste management and resource recovery concepts in SFR beyond the existing standard PUREX process, such as minor actinide and fission product partitioning, these challenges could be mitigated, alongside further reductions in HAW volumes, masses, and duration of radiotoxicity. This work assesses various current and proposed SFR and fuel cycle options as base cases, with further options for fission product partitioning of the high heat radionuclides (HHRs), rare earths, and platinum group metals investigated. A focus on primary waste outputs and the additional energy that could be generated by the reprocessing of high-burnup PWR fuel from Gen III(+) reactors using a simple fuel cycle model is used; the effects of 5- and 10-year spent fuel cooling times before reprocessing are explored. We demonstrate that longer cooling times are preferable in all cases except where short-lived isotope recovery may be desired, and that the partitioning of high-heat fission products (Cs and Sr) could allow for the reclassification of traditional raffinates to intermediate level waste. Highly active waste volume reductions approaching 50% vs. PUREX raffinate could be achieved in single-target partitioning of the inactive and low-activity rare earth elements, and the need for geological disposal could potentially be mitigated completely if HHRs are separated and utilised. Full article

This theoretical study rigorously investigates how generative artificial intelligence reshapes decision-making in social entrepreneurship by modeling the opportunity search process through the lens of optimal stopping theory. Social entrepreneurs often face high uncertainty and resource constraints, requiring them to strategically balance the cost of continued searching with the chance of identifying socially impactful opportunities. This study develops a formal model that captures two core mechanisms of generative AI: reducing search costs and increasing the probability of mission-aligned opportunity success. The theoretical analysis yields three key findings. First, generative AI accelerates the optimal stopping point, allowing social entrepreneurs to act more quickly on high-potential opportunities by lowering cognitive and resource burdens. Second, the influence of increased success probability outweighs that of reduced search costs, underscoring the strategic importance of insight quality over efficiency in socially embedded contexts. Third, the benefits of generative AI are amplified in uncertain environments, where it helps navigate complexity and mitigate information asymmetry. These insights contribute to a deeper conceptual understanding of how intelligent technologies transform the cognitive and strategic dimensions of social entrepreneurship, and they offer empirically testable propositions for future research at the intersection of AI, innovation, and mission-driven opportunity pursuit. Full article

The design of robots is highly dependent on their applications. For agricultural robots, terrain, weather, and crop diversity need to be considered, and work efficiency, cost, and reliability must be evaluated. These factors are important to determine the design of agricultural robots. In this study, we identified the constraint factors of agricultural robots from the perspectives of navigation, movement, control, cost, and reliability. The orthogonal defect classification (ODC) method was used to classify and grade these factors and explore the relationships among these factors. Based on the results, the design rules of agricultural robots were created, and an automatic production knowledge base of agricultural robot design was constructed. The results contribute to the automatic generation of the design framework of agricultural robots under specific environments to effectively improve the design level and quality of agricultural robots and popularize agricultural robots. Full article

Industrial catalysts are accelerating the global transition toward renewable energy, serving as enablers for innovative technologies that enhance efficiency, lower costs, and improve environmental sustainability. This review explores the pivotal roles of industrial catalysts in hydrogen production, biofuel generation, and biomass conversion, highlighting their transformative impact on renewable energy systems. Precious-metal-based electrocatalysts such as ruthenium (Ru), iridium (Ir), and platinum (Pt) demonstrate high efficiency but face challenges due to their cost and stability. Alternatives like nickel-cobalt oxide (NiCo₂O₄) and Ti₃C₂ MXene materials show promise in addressing these limitations, enabling cost-effective and scalable hydrogen production. Additionally, nickel-based catalysts supported on alumina optimize SMR, reducing coke formation and improving efficiency. In biofuel production, heterogeneous catalysts play a crucial role in converting biomass into valuable fuels. Co-based bimetallic catalysts enhance hydrodeoxygenation (HDO) processes, improving the yield of biofuels like dimethylfuran (DMF) and γ-valerolactone (GVL). Innovative materials such as biochar, red mud, and metal–organic frameworks (MOFs) facilitate sustainable waste-to-fuel conversion and biodiesel production, offering environmental and economic benefits. Power-to-X technologies, which convert renewable electricity into chemical energy carriers like hydrogen and synthetic fuels, rely on advanced catalysts to improve reaction rates, selectivity, and energy efficiency. Innovations in non-precious metal catalysts, nanostructured materials, and defect-engineered catalysts provide solutions for sustainable energy systems. These advancements promise to enhance efficiency, reduce environmental footprints, and ensure the viability of renewable energy technologies. Full article

The Union of the Comoros, located in the Indian Ocean, faces persistent energy challenges due to its geographic isolation, heavy dependence on imported fossil fuels, and underdeveloped electricity infrastructure. This study investigates the techno-economic optimization of a hybrid microgrid designed to supply electricity to a rural village in Grande Comore. The proposed system integrates photovoltaic (PV) panels, wind turbines, a diesel generator, and battery storage. Detailed modeling and simulation were conducted using HOMER Energy, accompanied by a sensitivity analysis on solar irradiance, wind speed, and diesel price. The results indicate that the optimal configuration consists solely of PV and battery storage, meeting 100% of the annual electricity demand with a competitive levelized cost of energy (LCOE) of 0.563 USD/kWh and zero greenhouse gas emissions. Solar PV contributes over 99% of the total energy production, while wind and diesel components remain unused under optimal conditions. Furthermore, the system generates a substantial energy surplus of 63.7%, which could be leveraged for community applications such as water pumping, public lighting, or future system expansion. This study highlights the technical viability, economic competitiveness, and environmental sustainability of 100% solar microgrids for non-interconnected island territories. The approach provides a practical and replicable decision-support framework for decentralized energy planning in remote and vulnerable regions. Full article

This study investigates indoor and outdoor air quality monitoring in a student dormitory located in northern Milan (Italy) using low-cost sensors. This research compares two monitoring periods in June and October 2024 to examine common PM_2.5 vertical patterns and differences at the building level, as well as their influence on the indoor spaces at the corresponding positions. In each period, around 30 sensors were installed at various heights and orientations across indoor and outdoor spots for 2 weeks to capture spatial variations around the building. Meanwhile, qualitative surveys on occupation presence, satisfaction, and well-being were distributed in selected rooms. The analysis of PM_2.5 data reveals that the building’s lower floors tended to have slightly higher outdoor PM_2.5 concentrations, while the upper floors generally had lower PM_2.5 indoor/outdoor (I/O) ratios, with the top-floor rooms often below 1. High outdoor humidity reduced PM infiltration, but when outdoor PM fell below 20 µg/m³ in these two periods, indoor sources became dominant, especially on the lower floors. Air pressure I/O differences had minimal impact on PM_2.5 I/O ratios, though slightly positive indoor pressure might help prevent indoor PM infiltration. Lower ventilation in Period-2 possibly contributed to more reported symptoms, especially in rooms with higher PM from shared kitchens. While outdoor air quality affects IAQ, occupant behavior—especially window opening and ventilation management—remains crucial in minimizing indoor pollutants. Users can also manage exposure by ventilating at night based on comfort and avoiding periods of high outdoor PM. Full article