Search Results (430)

In contemporary debates on the United Nations Sustainable Development Goals, there is growing recognition that artificial intelligence (AI) may contribute meaningfully to SDG 2 (Zero Hunger), particularly by enhancing the efficiency of food aid distribution and resource allocation. However, such optimism must be critically situated within the broader institutional and ethical contexts in which AI operates. This study argues that the effectiveness of AI in conflict-affected settings is contingent not only on technical capacity but also on governance structures, ethical safeguards, and institutional trust, dimensions closely aligned with SDG 16 (Peace, Justice, and Strong Institutions). Using the Gaza Strip as a case study, this article demonstrates that AI-driven food assistance mechanisms may inadvertently reinforce structural vulnerabilities. Specifically, algorithmic targeting of aid risks deepening dependency, exacerbating digital exclusion, and weakening already fragile governance systems. The absence of robust data accountability frameworks further complicates these dynamics, raising concerns regarding transparency, fairness, and long-term sustainability. The findings caution against privileging technical efficiency at the expense of socio-political stability. Rather, they highlight that the sustainability of AI interventions in humanitarian contexts fundamentally depends on the credibility and legitimacy of institutions. Accordingly, this study proposes a conceptual model for AI in hunger relief and digital humanitarianism that integrates technical innovation with institutional accountability and social trust. This study presents a narrative review informed by structural searching that examines the influence of AI on food security interventions in fragile contexts. This analysis applies a combined ethical governance and sustainability lens to assess current applications and risks. This research advances a broader analytical framework that moves beyond purely technical interpretations of AI, emphasizing its role as a socio-political tool, through identifying five key pillars for sustainable AI governance: data sovereignty, algorithmic accountability, inclusive system design, community-led governance, and market integrity. Full article

A dam deformation prediction method based on interpretable component decomposition and integration is proposed to address the problems of weak interpretability, difficult identification of key factors, and insufficient accuracy in the prediction model of deformation monitoring values of concrete dams due to multiple factors such as environmental loads and time factors. This method first strips the temporal component from the original sequence to obtain the castration sequence. Furthermore, complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose and reconstruct it into environmental load components and residual terms. In the process of deformation prediction, based on the characteristics of each deformation component, logarithmic functions, bidirectional long short-term memory (BiLSTM) networks optimized by The Black-Winged Kite Algorithm (BKA), and cloud models are used to fit and predict the temporal components, environmental load components, and residual terms, and the final prediction results are obtained through integration. At the same time, the SHAP (SHapley Additive exPlanations) method is introduced to quantify the contribution of input factors to enhance the interpretability of the model. Case study shows that the model outperforms the comparison model in both prediction accuracy and trend tracking ability, effectively improving the reliability of prediction results and significantly increasing the interpretability of deformation prediction, providing a more reliable analysis technique for dam deformation safety monitoring. Full article

High-voltage bushings, the fault-prone key electrical components of transformers, are critical for real-time and high-accuracy fault monitoring and management. Intelligent fault detection via infrared images is plagued by low classification accuracy due to massive interference from similar tubular objects and small target characteristics. This study proposes a lightweight deep learning model, MTrip–YOLO, an improved YOLO11n integrated with Mamba-2 (Structured State Space Duality, SSD) and Triplet Attention, to achieve efficient fault monitoring in complex backgrounds. The training and validation dataset comprises open-source images, on-site data from a substation, and field-collected infrared images, categorized into four types: normal bushings, poor contact, oil shortage, and high dielectric loss faults. Mamba-2 captures the long-range global context of infrared features with its linear-complexity long-range modeling capability to enhance feature extraction, while Triplet Attention suppresses complex background radiation noise through cross-dimensional interaction without dimensionality reduction, enabling the model to focus on small targets and accurately classify bushings from morphologically similar strip-shaped objects. Experimental results show that MTrip–YOLO achieves a top mAP50 of 91.6% and a minimal parameter count of 1.9 M, outperforming Faster R-CNN, RT-DETR, and YOLO26n across all evaluated metrics and being potentially suitable for edge deployment on UAV-mounted or handheld infrared platforms, pending hardware validation on embedded computing devices. Ablation experiments verify the independent contributions of Mamba-2 (0.8027% mAP50 improvement) and Triplet Attention (0.89327% mAP50 improvement), with a synergistic effect from their combination. MTrip–YOLO provides a potential edge-deployable solution for high-voltage bushing fault monitoring, offering important application value for the intelligent operation and maintenance of substations. Full article

Background: Ceramide (Cer) dysregulation in content and composition is linked to various skin conditions, particularly sensitive and dry skin. Existing ceramide quantification methods often lack efficiency, sensitivity, or comprehensive analytical capabilities. This study aimed to adopt an optimized LC-MS/MS platform to ensure the acquisition of reliable and accurate ceramide quantitative data, thereby providing robust methodological support for an in-depth investigation of the differences in ceramide profiles among different dry skin subtypes. Methods: Stratum corneum samples were collected via tape stripping from 93 adult female volunteers, who were stratified into sensitive dry skin, non-sensitive dry skin, and normal skin groups based on clinical assessments. Cer metabolomics was analyzed via targeted metabolomics using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Results: Quantitative analysis of ceramide content in different groups revealed significantly elevated levels of ultra-long-chain ceramides and the atypical Cer (d17:1/24:0) in the SD group, alongside relatively lower levels of shorter-chain ceramides. The NSD group, in contrast, was predominantly enriched in shorter-chain ceramides. Statistical analysis showed statistically significant differences in the levels of Cer (d18:1/24:0), Cer (d18:1/24:1), and Cer (d17:1/24:0) between the SD group and the N group. The UPLC-MS/MS method exhibits a wide linear range and high recovery. Conclusions: This method offers a reliable tool for the quantitative analysis of ceramides in dermatological, physiological, and pathological research. The findings not only underscore the profound heterogeneity in lipid metabolism underlying different dry skin subtypes but also provide a molecular rationale linking aberrant ceramide chain lengths to compromised barrier integrity and heightened inflammatory susceptibility. The partially validated analytical platform and the specific ceramide signatures revealed herein offer valuable tools and insights for advancing the mechanistic understanding, diagnosis, and targeted intervention of sensitive dry skin. Full article

High-resolution remote sensing imagery encompasses complex land cover types and rich textural details, whilst temporal variations often manifest as subtle feature differences and unstable structural patterns. This renders traditional change detection methods ineffective at accurately characterizing genuine alterations, frequently leading to underdetection, false positives, and ambiguous boundaries. To address these challenges, this paper proposes a Transformer-Based Multi-level Adaptive Fusion Network. It is built upon the DeepLabV3+ encoder–decoder framework, in which a shared-weight ResNet-101 is adopted as the backbone for dual-temporal feature extraction, with the final residual block of layer 4 cropped to extract deeper semantic features at a higher spatial resolution. The Adaptive Window–Attention Feature Fusion Module (AWAFM) adaptively models local and global differences across temporal phases, enhancing sensitivity to genuine changes. The Dual Strip Pool Fusion Module (DSPFM) enhances sensitivity to directional structural variations through horizontal and vertical strip pooling. The Progressive Multi-Scale Feature Fusion Module (PMFFM) progressively aggregates deep and shallow features via semantic residual transmission. To further suppress misleading suppression caused by complex textures, the Transformer-Enhanced Reverse Attention Fusion Module (TRAFM) explicitly models long-range dependencies, effectively mitigating false change responses. On the LEVIR-CD dataset, it achieves state-of-the-art performance, with a PA and an IoU of 92.36% and 90.13%, respectively. On the SYSU-CD dataset, PA and IoU reach 88.96% and 86.15%, demonstrating TMAFNet’s stability and superiority in scenarios involving complex ground surface disturbances, weak textural variations, and large-scale structural changes. Full article

Coherent optical communication serves as the backbone of long-haul, high-capacity optical networks, where polarization-insensitive 90° optical hybrids (OHs) are crucial for system simplification and robustness. This work presents a polarization-insensitive 90° OH based on asymmetric double-strip silicon nitride waveguides, designed for dual-polarization quadrature phase-shift keying (DP-QPSK) systems. The device consists of a cascaded polarization-insensitive structure incorporating one 1 × 2 and three 2 × 2 multimode interference (MMI) couplers, interconnected by four 90° bent waveguides. Optimized via 3D finite-difference time-domain (FDTD) simulations, the 1 × 2 MMI coupler exhibits insertion losses below 0.06 dB (TE) and 0.09 dB (TM), while each 2 × 2 MMI coupler shows insertion losses under 0.2/0.4 dB, amplitude imbalance below 0.05/0.18 dB, and phase error within ±0.5°/±1.5° for the TE/TM modes, respectively. Based on these components, the full device achieves polarization-insensitive operation across a 100 nm bandwidth (1500–1600 nm), with a phase error within ±1°, insertion loss below 0.3 dB (TE) and 0.5 dB (TM), and common-mode rejection ratio better than −40 dB (TE) and −30 dB (TM). Furthermore, the design demonstrates high fabrication tolerance, maintaining performance under manufacturing deviations of ±2 μm in MMI length and ±20 nm in waveguide spacing. This work provides a promising polarization-insensitive OH design and a viable route toward cost-effective mass production of next-generation high-speed coherent systems. Full article

Coal is the primary energy source in China and has long dominated energy consumption, serving as both the cornerstone for safeguarding national energy security and the backbone of stable energy supply. Despite the gradual improvement in the level of fully mechanized and intelligent mining in recent years, as well as the remarkable progress achieved in safe and efficient mining technologies, significant challenges are still encountered in the horizontal slicing mining of steeply inclined coal seams. This study was conducted against the engineering backdrop of the steeply inclined extra-thick coal seam in the Yimen Coal Mine, Sichuan Province. A combination of theoretical analysis, FLAC3D numerical simulation, and on-site monitoring was employed to investigate the support technology for mining roadways. Considering the geological occurrence conditions, roadway dimensions, and service life, the bolt (cable) + steel strip + metal mesh system was selected as the basic support method, with shed supports supplemented for reinforcement in areas with special geological structures or fractured surrounding rock. A non-uniform roadway support technology for horizontal slicing mining of steeply inclined extra-thick coal seams was proposed. The optimal support parameters of the roadways were determined through numerical simulation, and favorable support effects were verified by field measurements. Full article

Electrical imaging logging images play a crucial role in petroleum exploration; however, in practical applications, blank strips frequently appear due to instrument malfunctions or data transmission failures, severely compromising geological interpretation and hydrocarbon evaluation. Existing image inpainting methods have limited adaptability to blank strips at different depth scales and exhibit blurred high-resolution geological textures. To address these issues, this paper proposes a blank strip filling method that integrates Arbitrary Kernel Convolution (AKConv) with the Aggregated Contextual-Transformations Generative Adversarial Network (AOT-GAN). Specifically, the adaptive sampling mechanism of AKConv is incorporated into the generator network of AOT-GAN, enabling the model—to effectively capture long-range contextual information and adaptively handle blank strips of varying scales and shapes through multi-scale feature fusion. Experimental results on real oilfield datasets demonstrate that the proposed method achieves significant improvements in PSNR, SSIM, and MAE, exhibiting superior structural preservation and texture sharpness—especially in restoring deep and large-scale blank strips. Furthermore, visual comparisons confirm the method’s superior performance in recovering key geological features, such as bedding continuity and fracture structures, thus providing an effective approach for electrical imaging logging image restoration. Full article

The harvesting of energy from movements is one of the purposes of triboelectric nanogenerators (TENGs). Among the various devices designed to perform this function, floors are one of the primary ones, as they do not need to be individually fitted to each subject and can be manufactured and installed on a large scale. This work classifies previously published TENG-based floors based on their materials, electrical performance in terms of the voltage, current, and power they produce, and their application in different fields. The materials used have been correlated with other important aspects for floors, such as weather or flame resistance, sustainability, recyclability or biodegradability of materials, and price. The synthesis of the variety of TENG-based floor models, which incorporate novel materials, hybrid technologies, or various functionalities, among other characteristics, can enrich and inspire the reader to enhance the performance of future floor designs based on the triboelectric effect. In addition, a novel triboelectric floor design made of nitrile butadiene rubber (NBR) and fluorine kautschuk material is presented, along with the electrical power generated when tribolayers are in contact. For the three floor strips measuring 40 cm long × 4 cm wide and 1 mm thick, electrical current and voltage output was measured, achieving nearly 0.1 W (20 V & 4.5 mA) of electrical power generation. Full article

Anode-less battery architectures, which eliminate the host anode material, have attracted considerable attention as a promising approach to increase energy density, simplify cell manufacturing, and improve safety in next-generation energy storage systems. This review provides a structured and integrative overview on the current research landscape of anode-less cells, spanning both liquid- and solid-electrolyte technologies. It first introduces the fundamental principles, key advantages, and inherent challenges of the anode-less concept. Advanced characterization techniques, including electrochemical, interfacial, morphological, and operando approaches, are then discussed as essential tools for probing metal plating/stripping behavior and degradation mechanisms. The core of the review examines how system design governs performance, addressing strategies for liquid electrolytes, including current collector design, electrolyte formulation, and deposition control, as well as solid electrolytes, with an emphasis on interfacial engineering, fundamental limitations, and extensions to Na- and K-based batteries. By integrating insights across these systems, the review identifies critical challenges, including unstable solid-electrolyte interphases, dendrite formation, and interfacial contact loss. Finally, a development pyramid is introduced as a conceptual framework linking fundamental research to practical implementation, outlining key priorities from interface control and full-cell compatibility to long-term reliability while also highlighting industrial pathways toward hybrid and fully solid-state anode-less batteries. Full article

To address the shortage of natural sand and the unclear mechanism of lithology’s influence on the application of manufactured sand, this study explores the applicability of neutral rock manufactured sand in asphalt mixtures. Taking neutral diabase manufactured sand as the research object, a series of tests including the Marshall test, water stability test, high- and low-temperature stability test, and surface free energy (SFE) test were conducted to systematically analyze the effects of aggregate lithology on the volumetric indicators, road performance, and interface adhesion of asphalt mixtures. Additionally, the improvement effect of cement as an anti-stripping agent was verified. The results show that lithology of manufactured sand significantly regulates the performance of asphalt mixtures. In terms of volumetric indicators, the limestone manufactured sand mixture has the smallest void ratio (3.81%), while the diabase manufactured sand mixture has the largest (5.81%), requiring an appropriate increase in the mixing ratio of diabase manufactured sand to optimize the compaction effect. For water stability, the short-term performance ranks as diabase ≈ limestone > granite, and the long-term durability ranks as limestone > diabase > granite. A least-squares linear regression model demonstrated that the polar component of aggregate surface free energy exhibits a strong positive correlation with asphalt–aggregate adhesion work (R² = 0.92), which quantitatively explains variations in the 48 h immersed Marshall residual stability ratio among different lithologies. Regarding high-temperature stability, the order is diabase > limestone > granite. Thanks to its low crushing value and strong angularity, the diabase manufactured sand mixture achieves a dynamic stability of 12,629 times/mm at 60 °C, showing the best rutting resistance. In terms of low-temperature performance, the diabase manufactured sand mixture exhibits the optimal initial crack resistance (maximum flexural strain of 2757 με) and long-term durability (strain attenuation rate of 11.7% after 30 cycles), while the granite manufactured sand mixture fails to meet the design requirements. Adding 1.5%~2.0% cement can significantly improve the adhesion between manufactured sand and asphalt, with more obvious enhancement effects on granite and diabase, thereby optimizing water stability and high-temperature stability. The research results provide theoretical support and technical reference for the scientific selection and engineering application of fine aggregates in asphalt pavements. Full article

Diabetes, projected to affect over 1.3 billion people by 2050, presents significant healthcare burdens and environmental challenges, necessitating innovative and sustainable solutions to manage complications effectively. This study applies life cycle assessment to evaluate the environmental impacts of two semiconductor-enabled diabetes care devices: (1) a single-use urine-based C-peptide measurement strip aligned with the reduce strategy and (2) a reusable smart wound dressing for chronic wound monitoring under the reuse strategy. Integrating green electricity reduced the total lifecycle global warming potential by 16.2% for the urine strip and 0.4% for the smart wound dressing. The results emphasize the importance of tailored design strategies, showing that the impact of green integrated circuits is substantial for single-use reduce systems, while long-term treatments benefit more from reuse strategies paired with durable, complex designs that extend component lifespan and limit new manufacturing burdens. Full article

With the conclusion of proton–proton collision data-taking in 2025, the ATLAS experiment has now integrated a luminosity exceeding 300 ${\mathrm{fb}}^{-1}$ during the Run 3 period, which began in July 2022 following Long Shutdown 2 (LS2). During LS2, a series of detector upgrades were implemented, including the installation of the New Small Wheel (NSW) in the innermost stations of the Muon Spectrometer end-caps. The ATLAS Muon Spectrometer, the largest muon system ever built at a collider, now comprises both established gaseous detectors—Monitored Drift Tubes, Thin Gap Chambers, and Resistive Plate Chambers—and newer detectors like Micromegas and small-strip TGCs in the NSW. These new systems are now in stable operation following an extensive phase of construction and commissioning, providing enhanced muon tracking and trigger capabilities. This presentation covers the performance of the muon system, focusing on the stability of the established detectors over time, their ability to handle increasing luminosity and associated irradiation levels, and studies on detector aging. Emphasis will be placed on the NSW upgrade, including the strategies adopted for alignment, track reconstruction, and trigger. The performance results presented in this contribution are based on Run 3 data collected up to 2024. Full article

With the rapid evolution of remote sensing earth observation technology, high-resolution object detection is crucial in military and civilian domains but faces challenges from expansive views and complex backgrounds. Small objects are particularly challenging due to their low pixel coverage, poor textures, and susceptibility to drastic illumination changes and background clutter. To address these problems, this paper proposes MDCA-YOLO for oriented object detection. A Dual-Branch Perception Module (DBPM) is designed utilizing a synergistic mechanism of large-kernel and strip convolutions to establish long-range dependencies, accurately capturing geometric features of tiny objects even in the absence of local details; Multi-Adaptive Selection Fusion (MASF) is proposed to address cross-scale feature loss by adaptively enhancing feature response while suppressing background noise; furthermore, a reconstructed decoupled detection head, CoordAttOBB, significantly improves angle regression accuracy while reducing complexity. Experimental results on the DIOR-R dataset show MDCA-YOLO surpasses YOLO11s, improving mAP50 and mAP50:95 by 2.5% and 2.7%, respectively, effectively proving the algorithm’s superiority in remote sensing tasks. Full article

The soybean–maize strip intercropping system enhances soybean yield while maintaining maize production, improving nitrogen use efficiency, and fostering intercropping mutualism. However, vigorous weed growth in warm and humid regions competes for nitrogen, while elevated soil temperatures accelerate nitrification, promoting nitrogen loss, especially during the peak nitrogen demand period of maize. Plastic film mulching, which conserves moisture, regulates temperature, and suppresses weeds, can improve the soil environment. A two-year field experiment was conducted with polyethylene (PE) films of various thicknesses (0.01, 0.014, 0.02 millimeters) and colors (black, white, silver-black) with an un-mulched control plot. Soil nitrogen content, microbial diversity, soil properties, and crop productivity were analyzed. The results indicated that plastic film mulching significantly altered soil nutrient availability and rhizosphere microbial community structures, while simultaneously enhancing crop productivity. The 0.014 mm black and white films performed best, showing a positive association with enhanced nitrogen transformation indices, which coincided with increased available nitrogen, biomass, and crop yield. However, long-term soil nutrient depletion remains a risk, suggesting the need for strategies like organic fertilizers or crop rotation to maintain soil fertility and ecological sustainability. Full article