Search Results (2,360)

The increase in population and demand for the various needs of citizens increases the interaction with the geo-environment. Thus, the rate of natural events affecting daily human life increases. Such an event occurred on a rock cliff in a densely populated area in İstanbul (Türkiye). More than four rock blocks (approximately 3–5 m³) belonging to the Paleozoic sequence of İstanbul, composed of nodular limestone with sandy-clay interlayers, detached and fell. The blocks traveled along a path of approximately 60 m and stopped by crushing a couple of buildings downslope. The path was rough and contained various surface conditions (e.g., bedrock, talus, and plants). This study was initiated by the examination of the dimensions of failed rock blocks, their paths, and topographic conditions. Unmanned vehicles (drones) facilitated the generation of 3D numerical models of topographic changes on the site. Quantifying discontinuity properties (such as persistence, spacing, roughness, etc.) and defining weathering properties comprises the second stage, along with sampling. Based on digital topographic data and field observations, cross-sections were defined by means of possible rockfall areas within the area of potentially unstable blocks. Numerical analysis and rockfall analysis were conducted along these critical sections. Interpretation of laboratory data and results obtained from numerical studies leads to an understanding of the mechanism of the recent rockfall event and demonstrates the most critical areas to be considered and reinforced. The research comprises proposing appropriate reinforcement techniques due to the strong Turkish regulations along the “Bosphorus Waterfront Protected Zone”. The study advises pre-cleaning of potentially unstable blocks after a fence production on paths where rocks could fall, and rock anchors in some localities with varying lengths. The latest part of the research covers the re-assessment of mitigation processes with numerical models, which shows that the factor of safety increased to the desired levels. The reinforcement applications at the site match well with the proposed prevention methods. Full article

Background: Repetitive peripheral magnetic stimulation (rPMS) delivers magnetic pulses to peripheral nerves and muscles, producing afferent input that can modulate corticospinal excitability (CSE). While the effects of rPMS on upper-limb muscles have been explored, its short-term effects on lower-limb CSE remain less understood. This study aimed to investigate the short-term effects of rPMS on CSE in the tibialis anterior (TA) muscle among healthy individuals. Methods: Twenty participants completed a repeated- measure, pre-post study. rPMS was applied to the non-dominant TA muscle at 10% above motor threshold for 15 min. CSE was assessed using transcranial magnetic stimulation (TMS), with measurements of motor evoked potential (MEP) amplitude, latency, and duration recorded at baseline, immediately after, 30 min, and 60 min post-stimulation. All analyses were conducted on clean datasets following removal of artifact-related outliers. Results: MEP amplitude showed a significant main effect of Side (p = 0.005), with greater values on the stimulated compared to the non-stimulated side. No significant main effects were found for Time (p = 0.351) or for the Side × Time interaction (p = 0.900). Descriptively, the largest increase in amplitude on the stimulated side was observed at 30 min post-stimulation (12% above baseline). MEP latency and duration showed no significant main or interaction effects. Conclusions: In conclusion, a single rPMS session applied to the TA produced a modest, side-specific increase in CSE lasting up to 60 min, as reflected in MEP amplitude. However, the absence of a significant time effect and perhaps non-optimized stimulation parameters limit the interpretation of sustained neuromodulatory effects. Future studies should examine optimal stimulation parameters and explore underlying mechanisms using measures such as the cortical silent period and interhemispheric inhibition. Full article

Generalized aquifers are widely used in various fields, such as groundwater use, mine water prevention and control, and geothermal energy. This paper presents a transformer-model-based automatic aquifer generalization method using borehole logs in scenarios with scarce experimental parameters. Relying only on basic borehole data, the method used an agent-assisted approach to extract and clean key lithological and coordinate information, which was then fused using a dual embedding mechanism. The model leveraged multi-head self-attention to calculate attention weights between the target stratum and its adjacent strata, capturing the potential contextual correlations in aquifer potential across strata. The resulting deep feature vectors from the transformer’s encoder were fed into a classification head to predict aquifer potential labels. Evaluation results demonstrated a model accuracy of 0.86, significantly outperforming the random classification baseline in precision, recall, the F1-score, and the kappa coefficient. Full article

As shale gas development extends into deeper formations, the unclear brittle-ductile transition (BDT) mechanism and low fracturing efficiency have emerged as critical bottlenecks, posing challenges to the sustainable and economical utilization of this clean energy resource. This study, focusing on the Liangshang Formation shale of Sichuan Basin’s Pingye-1 Well, pioneers a paradigm shift by identifying Poisson’s ratio ($\mathsf{\nu }$) as the master variable governing this transition. Triaxial tests reveal that $\mathsf{\nu }$ systematically increases with depth, directly regulating the failure mode shift from brittle fracture to ductile flow. Building on this, we innovatively propose the Poisson’s Ratio-regulated Energy-based Brittleness Index (PNE-BI) model. This model achieves a decoupled diagnosis of BDT by quantifying how ν intrinsically orchestrates the energy redistribution between elastic storage and plastic dissipation, utilizing ν as the sole governing variable to regulate energy weighting for rapid and accurate distinction between brittle, transitional, and ductile states. Experiments confirm the $\mathsf{\nu }$-dominated energy evolution: Low $\mathsf{\nu }$ rocks favor elastic energy accumulation, while high $\mathsf{\nu }$ rocks (>0.22) exhibit a dramatic 1520% surge in plastic dissipation, dominating energy consumption (35.9%) and confirming that $\mathsf{\nu }$ enhances ductility by reducing intergranular sliding barriers. Compared to traditional multi-variable models, the PNE-BI model utilizes $\mathsf{\nu }$ values readily obtained from conventional well logs, providing a transformative field-ready tool that significantly reduces the experimental footprint and promotes resource efficiency. It guides toughened fracturing fluid design in ductile zones to suppress premature closure and optimizes injection rates in brittle zones to prevent fracture runaway, thereby enhancing operational longevity and minimizing environmental impact. This work offers a groundbreaking and sustainable solution for boosting the efficiency of mid-deep shale gas development, contributing directly to more responsible and cleaner energy extraction. Full article

Automated optical inspection (AOI) technologies are widely used in PCB and semiconductor manufacturing to improve accuracy and reduce human error during quality inspection. While existing AOI systems can perform defect detection, they often rely on pre-defined camera positions and lack flexibility for interactive inspection, especially when the operator needs to visually verify solder pad conditions or examine specific layout regions. This study focuses on the front-end optical positioning and inspection stage of the AOI workflow, providing an automated mechanism to link digitally generated layout reports from EDA layout tools with real PCB inspection tasks. The proposed system operates on component-placement reports exported by EDA layout environments and uses them to automatically guide the camera to the corresponding PCB coordinates. Since PCB design reports may vary in format and structure across EDA tools, this study proposes a vision-based extraction approach that employs Hough transform-based region detection and a CNN-based digit recognizer to recover component coordinates from visually rendered design data. A dual-axis sliding platform is driven through a hierarchical control architecture, where coarse positioning is performed via TB6600 stepper control and Bluetooth-based communication, while fine alignment is achieved through a non-contact, gesture-based interface designed for clean-room operation. A high-resolution autofocus camera subsequently displays the magnified solder pads on a large screen for operator verification. Experimental results show that the proposed platform provides accurate, repeatable, and intuitive optical positioning, improving inspection efficiency while maintaining operator ergonomics and system modularity. Rather than replacing defect-classification AOI systems, this work complements them by serving as a positioning-assisted inspection module for interactive and semi-automated PCB quality evaluation. Full article

Transparent polymeric materials such as poly(methyl methacrylate) (PMMA), polycarbonate (PC), and polyethylene terephthalate (PET) are widely used as glass alternatives in algal bioreactors, where optical clarity and mechanical stability are crucial. However, their long-term use is limited by surface degradation processes. Photodegradation, hydrolysis, and biofilm accumulation can reduce light transmission in the 400–700 nm range essential for photosynthesis. This study examined the aging of PMMA, PC, and PET under bioreactor conditions. Samples were exposed for 70 days to illumination, culture medium, and aquatic environments. Changes in their optical transmittance, surface roughness, and wettability were analyzed. All polymers exhibited measurable surface degradation, characterized by an average 15% loss in transparency, significant increases in surface roughness, and reduced contact angles. PMMA demonstrated the highest optical stability, maintaining strong transmission in key blue and red spectral regions, while PET performed the worst, showing low initial clarity and the steepest decline. The most severe surface degradation occurred in areas exposed to the receding liquid interface, highlighting the need for targeted cleaning and/or a reduction in the size of the liquid–vapor transition zone. Overall, the results identify PMMA and recycled PMMA (PMMA_R) as durable, cost-effective materials for transparent bioreactor walls. Full article

Forming tools adjustable by tensile elastic deformations offer opportunities for improved process control and reduced wear in high-volume metal forming processes such as ironing. However, the lack of tensile and fatigue data for hardened cold-work tool steels limits their broader adoption. This study investigates the mechanical performance of three tool steels—Vanadis^®4 Extra SuperClean, Vancron^® SuperClean, and Caldie^®—through uniaxial tensile and fatigue testing, supplemented by destructive static and fatigue/wear tests on specimens representative of an adjustable ironing punch. Non-coated specimens exhibited ultimate tensile strengths above 2700 MPa with approximately 2% plastic strain, while coated specimens fractured in a brittle manner between 1600–1900 MPa. Fatigue life at stress ranges between 1450–1750 MPa varied from several thousand to over four million cycles, with crack initiation linked to non-metallic inclusions and precipitates 10–30 $\mathsf{\mu }$m in size. Finite element simulations accurately linked failure observed in uniaxial tests to the component-level tests, confirming that first principal stress is a reliable predictor for punch failure. All punch specimens withstood ${10}^6$ cycles at diameter changes up to 140 $\mathsf{\mu }$m (4‰), with coated punches exhibiting minimal wear and non-coated ones showing localized surface damage. The findings support material and coating selection for adjustable forming tools and highlight opportunities for further optimization. Full article

Script-based computer-aided design tools offer accessible and customizable environments, but their broader adoption is limited by the cognitive and computational difficulty of describing curved, irregular, or free-form geometries through code. This study addresses this challenge by contributing a unified, open-source framework that enables concept-to-model transformation through 2D point-based representations. Unlike previous ad hoc methods, this framework systematically integrates an interactive point-cloud modeling layer with modular systems for curve construction, point generation, transformation, sequencing, and formatting, together with script-based rendering functions. This framework allows users to generate geometrically valid models without navigating the heavy geometric calculations, strict syntax requirements, and debugging demands typical of script-based workflows. Structured case studies demonstrate the underlying workflow across mechanical, artistic, and handcrafted forms, contributing empirical evidence of its applicability to diverse tasks ranging from mechanical component modeling to cultural heritage digitization and reverse engineering. Comparative analysis demonstrates that the framework reduces user-facing code volume by over 97% compared to traditional scripting and provides a lightweight, noise-free alternative to traditional hardware-based reverse engineering by allowing users to define clean geometry from the outset. The findings confirm that the framework generates fabrication-ready outputs—including volumetric models and vector representations—suitable for various manufacturing contexts. All systems and rendering functions are made publicly available, enabling the entire pipeline to be performed using free tools. By establishing a practical and reproducible basis for point-based modeling, this study contributes to the advancement of computational design practice and supports the wider adoption of script-based design workflows. Full article

Manual window cleaning in high-rise urban buildings is labor-intensive, risky, and resource-inefficient. This study addresses these challenges by investigating a resource-aware mechatronic architecture through the design, development, and experimental validation of a modular Automated Window Cleaning System (AWCS). Unlike conventional open-loop solutions, the AWCS integrates mechanical scrubbing with a closed-loop fluid management system, featuring precise dispensing and vacuum-assisted recovery. The system is governed by a deterministic finite state machine implemented on an ESP32 microcontroller, enabling low-latency IoT connectivity and autonomous operation. Two implementation variants—integrated and retrofit—were validated to ensure structural adaptability. Experimental results across 30 cycles demonstrate a cleaning efficiency of ~2 min/m², a water consumption of <150 mL/m² (representing a >95% reduction compared to manual methods), and an optical cleaning efficacy of 96.9% ± 1.4%. Safety protocols were substantiated through a calculated mechanical safety factor of 6.12 for retrofit applications. This research establishes the AWCS as a sustainable, safe, and scalable solution for autonomous building maintenance, contributing to the advancement of resource-circular domestic robotics and smart home automation. Full article

Reducing dependency on chemical pesticides is a core ambition of the European Green Deal, yet adoption of low-input practices remains uneven. This systematic review synthesizes evidence on the behavioural determinants of European farmers’ knowledge, attitudes, and practices (KAP) regarding sustainable pesticide use and evaluates the role of digital tools in facilitating Integrated Pest Management (IPM). Following PRISMA 2020 guidelines, we analysed 65 peer-reviewed articles published between 2011 and 2025, which were identified through Scopus and Web of Science. The synthesis reveals that while pro-environmental attitudes drive the intention to change, actual behaviour is frequently inhibited by loss aversion, ‘clean field’ social norms, and perceived economic risks. Digital tools—specifically Decision Support Systems (DSSs) and precision technologies—demonstrate technical potential to reduce pesticide loads but are constrained by the same behavioural barriers: a lack of trust in models, perceived complexity, and costs. Consequently, we propose a Psycho-Digital Integration Framework which posits that digital innovation acts as a catalyst only when embedded in systemic enablers—specifically green insurance schemes and independent advisory networks. These mechanisms are critical to redistribute perceived agricultural risk and bridge the gap between technical potential and behavioral adoption. Full article

The development of energy systems toward a decarbonized economy is increasingly constrained not only by technological challenges, but also by deficiencies in the organization, coordination, and governability of sustainable financing. This study aims to substantiate an integrated conceptual model and a multi-level governance framework for the sustainable financing mechanism of energy system development under decarbonization, ensuring the alignment of financial instruments with transition strategies, performance indicators, and feedback mechanisms. The methodology combines a bibliometric analysis of Scopus-indexed journal publications with an examination of international statistical and analytical data produced by leading global organizations, complemented by systemic, institutional, and comparative analytical approaches. The bibliometric analysis was conducted in 2025 and covered peer-reviewed articles published during 2017–2025, while empirical financial indicators were synthesized for the most recent available period of 2022–2024 using comparable time-series data reported by international institutions. The results indicate that despite global energy investments reaching approximately $3 trillion in 2024—nearly $2 trillion of which was allocated to clean energy technologies—a persistent annual financing gap for climate change mitigation in the energy sector remains. Moreover, to remain consistent with the Net Zero trajectory, investments in clean energy must increase by approximately 1.7 times by 2030. The synthesis of contemporary research and empirical evidence reveals a predominance of studies focused on individual green and transition finance instruments, accompanied by persistent fragmentation between financial flows, governance structures, and measurable decarbonization outcomes. To address this gap, the paper proposes a conceptual model that interprets sustainable finance as a governed system rather than a collection of isolated instruments, together with a multi-level governance framework integrating strategic (policy), sectoral, and project-level decision-making with systems of key performance indicators, monitoring, and feedback. The findings demonstrate that the effectiveness of sustainable financing critically depends on the coherence between financial instruments, governance architectures, and decarbonization objectives, which ultimately determines the capacity to translate mobilized capital into tangible energy infrastructure modernization and measurable emissions reductions. The proposed approach provides a practical foundation for improving energy transition policies and investment strategies at both national and supranational levels. Full article

Polyethylene terephthalate (PET) recycling technology has developed into a mature system, providing a key paradigm for the circular utilization of polyester waste. Its pathways are primarily divided into mechanical recycling and chemical recycling. Mechanical recycling converts waste PET into rPET through physical processes such as efficient sorting, deep cleaning, and melt extrusion. However, the resulting product often faces issues of decreased intrinsic viscosity and thermal oxidative degradation. Chemical recycling, particularly depolymerization techniques like saccharification, hydrolysis, and methanolysis, can reduce PET waste back to monomers. After purification, these monomers can be repolymerized into virgin-quality PET, achieving a closed-loop cycle. However, this approach faces challenges related to cost and process complexity. Against this backdrop, this paper further explores potential recycling methods for polyethylene terephthalate-1,4-cyclohexanedimethyleneterephthalate (PETG). This paper argues that the experience of PET recycling provides a crucial foundation for addressing PETG challenges but is not a direct solution. Future development directions include: developing intelligent sorting technologies, creating highly efficient selective catalysts to optimize depolymerization reactions, and other initiatives. These measures are essential for establishing an efficient recycling system for complex polyester waste. Full article

With the advancement of the “dual carbon” goal, the power system is accelerating its transition towards a clean and low-carbon structure, with a continuous increase in the penetration rate of renewable energy generation (REG). However, the volatility and uncertainty of REG output pose severe challenges to power grid operation. Traditional distribution networks face immense pressure in terms of scheduling flexibility and power supply reliability. Active distribution networks (ADNs), by integrating energy storage systems (ESSs), soft open points (SOPs), and demand response (DR), have become key to enhancing the system’s adaptability to high-penetration renewable energy. This work proposes a DR-aware scheduling strategy for ESS-integrated flexible distribution networks, constructing a bi-level optimization model: the upper-level introduces a price-based DR mechanism, comprehensively considering net load fluctuation, user satisfaction with electricity purchase cost, and power consumption comfort; the lower-level coordinates SOP and ESS scheduling to achieve the dual goals of grid stability and economic efficiency. The non-dominated sorting genetic algorithm III (NSGA-III) is adopted to solve the model, and case verification is conducted on the standard 33-node system. The results show that the proposed method not only improves the economic efficiency of grid operation but also effectively reduces net load fluctuation (peak–valley difference decreases from 2.020 MW to 1.377 MW, a reduction of 31.8%) and enhances voltage stability (voltage deviation drops from 0.254 p.u. to 0.082 p.u., a reduction of 67.7%). This demonstrates the effectiveness of the scheduling strategy in scenarios with renewable energy integration, providing a theoretical basis for the optimal operation of ADNs. Full article

As a future abundant and environmentally friendly clean energy source, the decomposition process of natural gas hydrates is significantly regulated by reservoir structural characteristics. Improper extraction can easily trigger geological hazards, yet current research on the coupling mechanism between wellbore microstructure and decomposition remains incomplete. To elucidate the regulatory role of reservoir structural characteristics, this study employed a self-developed physical simulation system to conduct triaxial creep experiments. It compared the mechanical response and decomposition dynamics of sediments under layered and homogeneous hydrate distribution patterns, while simultaneously monitoring gas production and formation displacement parameters. Results indicate that layered distribution significantly influences overall sediment creep behavior and failure patterns: low-saturation sublayers dominate the creep softening–hardening mechanism, while strain evolution at different timescales and long-term bearing capacity are controlled by low- and high-saturation sublayers, respectively. Creep cohesion and internal friction angle exhibit distinct differences between the two distribution patterns, with the influence mechanisms of relevant mechanical indicators closely related to the roles of sublayers with varying saturations. The study also uncovers the intrinsic link between gas production and stratigraphic subsidence during hydrate decomposition, clarifying the core mechanism by which reservoir structures influence decomposition stability through regulating mechanical responses. The methodologies and conclusions of this research provide scientific support for predicting the long-term stability of natural gas hydrate reservoirs and enabling safe, efficient extraction, while laying the groundwork for the systematic development of comprehensive hydrate technologies. Full article

This paper discusses an on-device artificial intelligence (AI) solution for real-time, privacy-preserving fraud detection in smart financial environments, ensuring privacy-preserving consumer transactions. We suggest a distributed, on-device fraud detection solution that uses federated learning (FL) to improve privacy while detecting fraudulent transactions efficiently across decentralized smart environments. In this work, we used several models, including reinforcement learning (RL) agent and Random Forest, and we tested their performance using several measures like accuracy, precision, recall, and F-score, ensuring their applicability to smart environments with resource constraints. The recommended mechanism also uses t-Distributed Stochastic Neighbor Embedding (t-SNE) and Principal Component Analysis (PCA) to reduce dimensions of data, visualize the results, and evaluate the success rate of transactions classified as fraudulent and non-fraudulent. In our methodology, we applied data collection, data preprocessing, and cleaning, and we evaluated the metrics of selected models to allocate resources effectively and support decision-making processes in edge-based fraud detection systems within smart environments. Full article