Search Results (67,436)

This study addresses the problem of actively stabilizing the longitudinal body inclination of a tracked mobile platform operating over uneven terrain. A novel drive system architecture is proposed that combines conventional track traction electric drives with an inertial body-stabilization drive based on a flywheel mounted on the pitch axis between the chassis and the body module. The main contribution of the proposed approach is the coordinated control of the traction drives and the inertial actuator based on a unified dynamic model of the platform. A quadratic performance criterion is formulated, and a coordinated optimal control law is synthesized to limit body angular oscillations while accounting for actuator energy consumption. Simulation results for motion over step-like and random terrain irregularities, as well as under external moment disturbances, demonstrate a significant reduction in both peak and root-mean-square pitch-angle deviations relative to configurations without an inertial actuator and with local body stabilization. The results obtained confirm the potential and effectiveness of inertial stabilization drives as part of coordinated drive control systems for tracked mobile platforms intended for special-purpose applications, and indicate prospects for their use in advanced terrestrial robotic platforms and future space robotic systems operating in challenging environments. Full article

Pharmaceutical industries require a continuous heat supply to sustain around-the-clock operations such as sterilization. While fossil-fuel systems ensure reliability, they increase emissions and fuel dependence. Integrating a small-scale parabolic trough collector (PTC) with concrete thermal energy storage (C-TES) enables continuous and stable solar heat delivery, offering a flexible solution for pharmaceutical manufacturing. This study investigates the integration of PTC and C-TES to provide continuous heat supply using 12 representative days of the year based on weather data for Makkah City obtained from the Renewable Resource Atlas (RRA) developed by the King Abdullah City for Atomic and Renewable Energy (K.A.CARE). Model validation was performed using experimental PTC–C-TES charging data and a simplified C-TES module model. The results show that the C-TES system successfully maintained operating temperatures between 120 °C and 310 °C. Demand coverage was identified as a key design parameter. Full demand coverage requires approximately 73 PTC units and 1600 C-TES modules, representing increases of about 4.5 and 5 times compared with the 25% coverage case. Techno-economic analysis indicates that the levelized cost of heat (LCOH) reaches an optimum of approximately 89.7 USD/MWh at 25% coverage, while overall efficiency peaks at about 41%. The results indicate that a moderate solar contribution of around 25% provides the optimal balance between cost and operational flexibility. Full article

With the sharp increase in winter heating demand in northern China, the carbon emissions of combined heat and power (CHP) units remain high. This paper proposes a low-carbon optimal scheduling model for the system, considering the dynamic carbon-green certificate coupling and the multi-source energy supply of carbon capture and storage (CCS). Firstly, we analyze the thermal and electrical demand characteristics of the installed CCS and optimize its supply mode, and propose the corresponding low-carbon operation strategy for the CHP-CCS unit. Secondly, a dynamic coupling mechanism of carbon-green certificates with the acquisition volume of green certificates and the trading volume of carbon emission rights as the interaction medium should be constructed. The transmission effect of the historical trading volume on the current period should be achieved through dynamic prices, and a low-carbon economic scheduling model with the goal of minimizing operating costs should be established. Again, for the source-load uncertainty, by integrating the entropy weight method and the information gap decision theory, an IES optimization scheduling model based on the information gap decision theory method (IGDT) is established. Finally, through multi-scenario case simulation verification, the results confirmed that the proposed model can effectively improve the economy and low-carbon performance of the system. Full article

The Triple-angle Ionospheric PhotoMeter (Tri-IPM), an airglow and aurora monitoring payload onboard the Fengyun-3E (FY-3E) satellite, is designed for high-sensitivity observations of far-ultraviolet airglow during twilight from the ionosphere‒thermosphere system. This compact, nadir-viewing instrument features three probes (A, B, and C) oriented at 0°, −30°, and 30° relative to the nadir direction, enabling multiangle detection of OI 135.6 nm and N₂ Lyman–Birge–Hopfield (LBH) band (147.5–162.5 nm) emissions. With a spatial resolution of ~30 km × 14 km and a responsivity exceeding 2 counts/s/R, the Tri-IPM achieves high-precision measurements while maintaining a red-leak suppression ratio of ~10⁹ to minimize spectral contamination. This paper presents the design principles, ground calibration, and preliminary on-orbit performance of the Tri-IPM. On-orbit tests demonstrate excellent agreement between the observed airglow radiances, their spatial distributions, and the solar zenith angle dependencies of the theoretical models. Furthermore, the results exhibit strong consistency with observations from the Global-scale Observations of the Limb and Disk (GOLD) mission, validating the instrument’s reliability. By providing high-sensitivity, high-resolution global observations of far-ultraviolet (FUV) twilight airglow, the Tri-IPM advances research on ionospheric–thermospheric dynamics and enhances space weather monitoring capabilities. Its integrated on-orbit calibration ensures long-term data accuracy, making it a valuable tool for both scientific studies and operational space environment surveillance. Full article

Ground-based remote sensing of seismic and geophysical displacements remains a major challenge due to environmental hazards, signal attenuation, and practical deployment limitations of traditional seismometers. In this study, we present a detailed design, implementation, and performance evaluation of a Moiré-based apparatus for remote ground displacement measurement. The system operates by detecting fringe shifts formed between a fixed and a displaced grating, with displacement magnified through controlled angular superposition. We systematically assess each component of the system, including telescope optics, imaging sensors, and grating configurations, to optimize spatial resolution, contrast, and robustness under varying environmental conditions. A digital approach for fringe generation was employed, allowing controlled magnification and improved sensitivity without the need for physical alignment of dual gratings. Indoor experiments under low-turbulence conditions validated the system’s capability to detect displacements as small as $50\mathsf{\mu }\mathrm{m}$. Subsequent outdoor trials at different distances demonstrated successful measurement of both square-wave and seismic-like displacements despite increased atmospheric turbulence and wind. The results confirm the system’s ability to perform real-time, long-range, non-contact displacement monitoring with high accuracy and resilience to environmental variability. This study establishes a foundation for the application of Moiré-based sensing in challenging field conditions, including volcanic and seismic zones. Full article

The accurate forecasting of CO${}_2$ emissions from power stations is critical for effective climate policy and the transition to sustainable energy systems. However, the complexity of power generation processes and the high dimensionality of operational data present significant challenges to traditional modeling approaches. This paper introduces a novel multi-stage framework that integrates advanced feature selection with explainable machine learning (XAI) to deliver high-accuracy forecasts of power station CO${}_2$ emissions while maintaining full model transparency. The proposed methodology comprises a three-stage feature selection process—combining filter, wrapper, and embedded methods—to systematically identify the most influential emission drivers from a large set of potential variables. The selected features are then used to train a suite of machine learning models, including XGBoost, Random Forest, LSTM, and SVR. The best-performing model, XGBoost, achieved a Root Mean Square Error (RMSE) of 28.5, a Mean Absolute Error (MAE) of 19.8, and a coefficient of determination (R²) of 0.96 on a real-world dataset. To address the “black-box” nature of these models, we employ SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to interpret the model’s predictions, providing granular insights into the key factors driving emissions. The results demonstrate that the proposed framework not only outperforms state-of-the-art forecasting models but also offers a clear, interpretable, and actionable tool for policymakers and plant operators to support CO${}_2$ reduction strategies. The novelty of this work lies in its unique combination of a multi-stage feature selection pipeline and a comprehensive XAI-based analysis, providing a robust and transparent solution for a critical environmental challenge. Full article

Sodium-ion batteries (SIBs) are regarded as an important complementary technology to lithium-ion batteries due to their abundant resources and low cost, demonstrating broad application prospects, especially in large-scale energy storage. As a core component of SIBs, the cathode material directly determines key performance indicators such as energy density, cycling stability, and rate capability. Currently, the main cathode material systems under extensive research include transition metal oxides, polyanionic compounds, and Prussian blue analogues (PBAs), each exhibiting distinct characteristics in terms of crystal structure and electrochemical performance. Transition metal oxides have attracted significant research interest owing to their high specific capacity, while polyanionic compounds are known for their excellent structural stability and operating voltage. PBAs, on the other hand, have gained considerable attention due to their open framework structure and simple synthesis process. In recent years, modification strategies such as nanostructure engineering, surface coating, and elemental doping have significantly enhanced the electrochemical performance of these cathode materials. Future research should focus on addressing critical scientific challenges, including low intrinsic electronic conductivity and poor interfacial stability, while also exploring novel composite cathode material systems to facilitate the practical application of sodium-ion batteries. Full article

The construction sector contributes approximately 39% of global carbon emissions, with embodied carbon—emissions from material extraction, manufacturing, transportation, and construction—representing a systematically underestimated yet increasingly critical component of building life cycle environmental impacts. Traditional Life Cycle Assessment (LCA) methods suffer from static database dependencies, delayed feedback cycles, and limited integration with active construction decision-making, creating a fundamental gap between environmental assessment and construction operations. This paper presents the Digital Twin-Enabled Life Cycle Assessment Framework (DT-LCAF), a dynamic construction-phase embodied carbon accounting system aligned with the EN 15978 standard (stages A1–A5) that integrates Building Information Modeling (BIM), Internet of Things (IoT) sensor networks, and machine learning designed to support real-time sustainability decision-making during smart building construction, with computational performance validated through the offline processing of historical datasets. The framework introduces two enabling mechanisms: (1) a Multi-Scale Carbon Prediction Network (MSCPN) employing hierarchical graph attention networks to capture material interdependencies across component, system, and building scales; and (2) a Reinforcement Learning-based Carbon Optimization Engine (RL-COE) that generates constraint-aware recommendations for material substitution, supplier selection, and construction sequencing while respecting structural, economic, and temporal constraints. Experimental evaluation employs two complementary validation strategies using proxy embodied carbon labels (not ground-truth construction measurements): embodied carbon prediction accuracy is assessed using proxy carbon labels derived from the CBECS dataset (5900 commercial buildings) combined with the ICE Database v3.0 emission factors, achieving a 10.24% MAPE, representing a 23.7% improvement over the best-performing baseline in predicting these proxy estimates; temporal responsiveness and streaming data ingestion capabilities are validated using the Building Data Genome Project 2 (1636 buildings, 3053 m). The RL-COE optimization engine demonstrates an 18.4% mean carbon reduction rate within the proxy label framework across building types while maintaining cost and schedule feasibility. A BIM-based case study illustrates the framework’s construction-phase update loop, showing how embodied carbon estimates evolve dynamically as construction progresses. The limitations regarding the proxy-based nature of embodied carbon labels and the absence of ground-truth construction-phase measurements are explicitly discussed. The framework contributes to smart city sustainability by enabling scalable, data-driven embodied carbon intelligence across building portfolios. All quantitative results are based on proxy embodied carbon estimates derived from building characteristics and standard emission factor databases, rather than measured project data. The reported performance therefore demonstrates a proof-of-concept within the proxy system, and real-project, measurement-based validation remains future work. Full article

In order to cope with the challenge of large-scale integration of renewable energy to the balance of power supply and demand, and give full play to the potential of flexible regulation of iron and steel enterprises, a source load coordination optimization scheduling model considering the flexible coordination of iron and steel production and energy storage is proposed. Firstly, the multi-unit coupling adjustable capacity model of electric arc furnace (EAF), air separation unit (ASU), rolling mill and captive power plant is established, and the regulation characteristics and coupling relationship between different production units are clarified. Secondly, a day-ahead and intra-day two-stage scheduling framework is proposed. In the intra-day stage, the energy storage system is introduced to mitigate the fluctuation in wind power, and the mixed integer linear programming method is adopted to minimize the total operating cost of the system. Finally, an example is given to verify the effectiveness of the model. Case studies demonstrate that the proposed approach effectively reduces load variability and enhances operational stability. After the introduction of energy storage, the power standard deviation of EAFs and ASUs decreases by 29.6% and 28%, respectively, and the operational continuity of the rolling process is improved. Although the initial wind curtailment level in the test system is relatively low, the proposed strategy further mitigates peak curtailment and improves renewable accommodation capability. In addition, moderate operational cost savings are achieved. Full article

Modern supply chains operate as highly interconnected networks characterized by decentralization, data silos, and increasing sustainability constraints. Although Graph Neural Networks (GNNs) have demonstrated strong capability in modeling relational dependencies in such systems, their deployment is often restricted by limited inter-organizational data sharing. Federated learning (FL) enables collaborative model training without exposing proprietary data; however, existing federated approaches rarely integrate graph structure and sustainability objectives within a unified framework. This study proposes a Sustainability-Aware Federated Graph Attention Network (FedGAT) for decentralized supply chain process modeling. The framework combines Graph Attention Networks with federated optimization and introduces an emission-weighted attention modulation mechanism that embeds environmental considerations directly into the message-passing process. A multi-tier synthetic supply chain benchmark is constructed to evaluate the approach under realistic governance and data-locality constraints. Experiments are conducted across multiple random seeds, graph scales (up to 500 nodes), and client partition settings. Results show that while centralized graph learning achieves the lowest prediction error, the proposed sustainability-aware federated model maintains statistically indistinguishable predictive performance compared to standard federated baselines (paired sign test p = 1.000), while systematically reducing attention allocated to high-emission transport links. A structured label sensitivity analysis confirms that performance gains are not attributable to circular label construction. Furthermore, a λ-ablation study demonstrates a smooth and controllable trade-off between predictive accuracy and sustainability alignment through a single governance parameter. These findings establish the feasibility of privacy-preserving, sustainability-modulated graph learning for decentralized supply chain analytics and provides a principled foundation for environmentally aligned AI deployment in multi-enterprise networks. Full article

Five-membered monoheterocycles, either isolated or embedded in more complex systems, are ubiquitous structural motifs in nature and hence privileged targets of synthetic chemistry. Among a plethora of methodologies used for their assembly, [3+2] annulation strategies keep attracting particular interest among chemists, partly because of some significant characteristics from both the operative and the environmental viewpoints. Herein, the extensive use of conjugated nitroolefins as twofold electrophilic, two-carbon components of [3+2] MIRC (Michael-Initiated Ring Closure) annulations is reviewed as a practical and mechanistic update covering the last decade (2015–2025). Full article

The widespread adoption of renewable energy generation and diversified end-user equipment has significantly enhanced user benefits, attracting sustained attention from the research community. As energy systems become increasingly decentralized, traditional centralized optimization methods struggle to effectively capture the interactions among multiple agents. Achieving efficient interaction between diversified energy devices and load demands has emerged as a key challenge in current research. This study first outlines the system operation architecture and the involved game-theoretic agents, clarifying the roles of all participating entities. Subsequently, optimization models are established for the Virtual Power Plant (VPP) and the user aggregator, respectively, incorporating an integrated electro-thermal demand response mechanism under multi-device scenarios. By analyzing the Stackelberg game between the VPP and end-users, the existence of a unique equilibrium solution for this game is demonstrated. Simulations are conducted on the MATLAB R2021b platform using the YALMIP 20210331 toolbox and the CPLEX solver, with heuristic algorithms applied to further optimize the results. The proposed model effectively balances the interests of both parties while maintaining robust privacy protection for critical data. Full article

Energy efficiency is a challenging research topic in robotics, since it can reduce operating costs and increase production sustainability. In this paper, we present a strategy for energy-efficient trajectory planning in redundant robotic systems. The proposed approach aims at optimizing the solution of inverse kinematics at each of the waypoints that define the considered task, so as to minimize the energy consumption. The approach is validated with simulations and bespoke experiments on two different robotic systems with seven and eight degrees of freedom (DOFs). Two test cases are considered, i.e., a point-to-point motion and a pick-and-place task. The experimental results quantify the energy saving capabilities of the proposed approach up to 82.54% and 94.28% with the seven-DOF and eight-DOF robots, respectively, with respect to reference cases. Full article

This paper presents a transient performance comparison of a flat-plate solar collector (FPSC) and a sun-tracked parabolic trough collector (PTC) with a double U-tube receiver. Both collectors were modeled using in-house transient mathematical models and validated against experimental data obtained from a dedicated test stand. After validation, annual simulations were conducted for Kraków, Poland, using hourly meteorological data from the PVGIS database. The analysis focused on the long-term thermal and economic performance of both collector types under identical boundary conditions. The electricity demand of the tracking system was included using a constant motor power assumption. A simplified techno-economic evaluation was performed using the Levelized Cost of Heat (LCOH), accounting for investment costs, operating and maintenance expenses, auxiliary electricity consumption, system degradation, and cost escalation over a 20-year lifetime. For a comparable aperture area, the calculated LCOH amounted to 0.096 EUR/kWh for the sun-tracked PTC and 0.041 EUR/kWh for the stationary FPSC. The results indicate that, despite higher thermal performance, the examined PTC configuration is not economically competitive for low-temperature heat production under the assumed cost structure, mainly due to its higher investment cost. Full article

Thin, stratiform ore bodies pose persistent challenges for conventional underground mining due to limited thickness, high ore-grade dilution, and restricted operating space. This study introduces a morphology-based scoring framework for assessing the suitability of ore deposits for the Narrow Reef Mining Equipment method—an ultra-low-profile mechanized technique designed for stoping width up to 1.7 m and inclination up to 22°. A dataset comprising 178 ore deposits/mines was evaluated using integrated geological, morphological, and economic criteria. The results demonstrate that NRE suitability is primarily controlled by ore morphology, which is governed by the genetic model. The highest compatibility is associated with stratiform mineralization formed in layered mafic–ultramafic intrusions (e.g., Bushveld Complex, Great Dyke) and sediment-hosted stratiform copper and gold deposits developed along laterally extensive depositional or redox-controlled interfaces (e.g., Kupferschiefer, Witwatersrand). Although genetic origin defines deposit-scale suitability, secondary geological disturbances—post-genetic tectonism and hydrothermal overprinting—restrict NRE applicability to individual ore bodies within otherwise favourable deposits. By formalizing ore body dip and thickness into standardized efficiency and suitability classes, the proposed scoring system provides a reproducible early-stage geological screening methodology for evaluating NRE applicability during initial mine project development. Economic evaluation based on data from the Unki Mine provides operational validation of the proposed scoring framework and demonstrates that NRE increases monthly output at reduced stoping widths while maintaining ore grades and improving operational safety compared to conventional methods. Full article