Search Results (1,651)

This review explicitly focuses on agricultural attachments and executing components that interact directly with soil and crops, rather than the tractor vehicle itself. Operating within complex and variable farmland media environments, the key components of agricultural machinery have long been constrained by bottlenecks such as high-energy draught resistance, severe solid–liquid interfacial adhesion, and intense abrasive wear. Bionic functional surfaces, based on the coupling of micro-geometric morphology and surface-interface physical chemistry, provide a scientific approach to overcoming traditional tribological limitations by reconstructing the contact mechanics and fluid dynamics boundaries at the interface. This paper presents a comprehensive review of the latest research progress regarding bionic functional surfaces in the fields of friction reduction, wear resistance, and anti-adhesion in agricultural machinery. The article systematically categorises typical biological prototypes, such as soil-burrowing animals, aquatic organisms, and plant leaves, alongside their multidimensional feature extraction methods. It provides an in-depth analysis of core interaction mechanisms, ranging from static air cushion effects and dynamic wetting evolution to active electro-osmotic soil detachment, interfacial stress redistribution, and microscopic wear debris capture. Furthermore, it evaluates the efficacy of cross-scale coupled numerical simulation technologies in resolving interfacial interactions. At the engineering application level, this review extensively discusses the field performance of bionic structures in typical operational scenarios, including draught reduction in tillage and land preparation, blockage prevention in seed-metering channels, and low-damage harvesting in agricultural machinery. Finally, countermeasures are proposed to address the fatigue degradation of bionic surfaces under alternating field loads and the barriers to the large-scale fabrication of large-sized components. The paper further highlights the development trend towards the deep integration of bionic tribology with digital twins and intelligent wear-state perception technologies, aiming to provide systematic underlying theoretical and technical references for the research and development of the next generation of intelligent agricultural equipment characterised by low energy consumption and a prolonged service life. Full article

Against the backdrop of natural gas energy measurements and pipeline interconnectivity, the supply–demand imbalance has become increasingly prominent in multi-source gas pipeline networks. Existing pipeline scheduling studies mostly focus solely on economic optimization or simple gas quality constraints, while rarely quantifying user satisfaction and integrating it with operational profit within a systematic multi-objective framework, leaving a critical research gap for refined scheduling under energy metering modes. This paper first develops a quantitative user satisfaction function incorporating calorific value and methane content indicators and further establishes a novel multi-objective operational scheduling model coupled with gas quality limitations, which simultaneously maximizes network operating profit and user gas supply satisfaction. The ε-constraint method combined with the GAMS/ANTIGONE solver is adopted to address the constructed Mixed-Integer Nonlinear Programming (MINLP) model. Taking a typical long-distance pipeline in China as the engineering case, a series of Pareto-optimal solutions is obtained. The results show that user satisfaction ranges from 99.70% to 99.77% and operating profit varies from 1403.07 × 10⁴ to 1752.44 × 10⁴ CNY. The derived Pareto frontier quantitatively reveals the inherent trade-off mechanism between user satisfaction and operating profit. The case results demonstrate the applicability of the proposed framework in this specific pipeline scenario, rather than claiming universal validity. It is acknowledged that model validation is currently limited to only one single long-distance pipeline case, with no additional case studies and no comparison with historical operation data conducted in this work. Different from conventional single-objective or simplified gas quality-optimization methods, this study enriches scheduling scheme alternatives and provides theoretical support and a practical decision-making reference for multi-source pipeline operational optimization under energy metering. Full article

Current building energy benchmarking relies on annual Energy Use Intensity (EUI), which cannot detect temporal operational anomalies—such as after-hours equipment operation or irregular scheduling—that represent actionable efficiency opportunities. We demonstrate that 64.7% of ENERGY STAR-certifiable buildings exhibit temporal irregularities invisible to annual EUI. To capture these hidden patterns, we propose a hierarchical, three-level evaluation framework that pairs EUI with the zero-shot prediction error (CVRMSE) from a population-trained Transformer model (TransformerWithGaussian-L, pre-trained on 900,000 simulated buildings). Applied to 611 real buildings from the Building Data Genome Project 2 (9,247,992 observation–prediction pairs), we show that EUI and CVRMSE are near-orthogonal (r = −0.029), confirming they measure fundamentally distinct performance dimensions. The framework proceeds through three diagnostic levels: (L1) EUI × CVRMSE quadrant classification, (L2) decomposition of prediction error into inherent variability versus genuine atypicality (R² = 0.700), and (L3) NMBE directional analysis identifying over- versus under-consuming buildings. Requiring only hourly metered energy and geographic coordinates, this framework enables temporal pattern diagnostics applicable to large building portfolios. Full article

Smart meters increasingly operate as grid-edge sensing and communication nodes, extending their role beyond conventional digital billing by generating records for local energy trading. In such settings, smart meter-derived records may support coordination, participant interaction, validation, billing, and settlement across different trading architectures. Once these records leave the metering edge, their security and privacy risks depend on how they are routed, reused, protected, and interpreted across centralized, transactive, and peer-to-peer trading workflows. In this review, we examine smart meter-based energy trading through a record-centric and framework-oriented lens. We first clarify the role of smart meters and smart meter-derived records, then compare three representative trading frameworks in terms of data-path structure, coordination pattern, trust organization, and validation or settlement positioning. Building on the comparison, we identify three lifecycle-based layers of issues: record integrity and temporal consistency, insecure transmission and interface access security, and confidentiality and privacy exposure. We also review existing mitigation mechanisms and remaining limitations for each issue layer. We conclude that future work should prioritize lifecycle-wide record governance, temporal continuity, privacy–accountability co-design, and deployable protection across hybrid trading environments. Full article

Large language models (LLMs) are increasingly considered for deployment on edge and limited-resource systems, where local inference can reduce latency, improve privacy, and decrease dependence on cloud infrastructure. While prior studies have evaluated either task accuracy or hardware efficiency in isolation, few benchmarks combine generation-based response-quality evaluation with real-device power measurements on a representative limited-resource platform. This study addresses that gap by benchmarking twelve compact and mid-scale open-weight LLMs (sub-1B to 8B parameters), evaluating generation-based accuracy on a desktop platform and measuring deployment efficiency—throughput, power consumption, and energy use—on an NVIDIA Jetson Orin Nano Super; the accuracy–efficiency trade-off is thus established at the model-configuration level. Unlike prior Jetson-based evaluations relying solely on internal telemetry, this work pairs generation-compatible lm-eval accuracy tasks with a dual power-measurement setup that combines internal tegrastats rail readings with external board-level input power measured using a digital multimeter and explicitly compares GPU-accelerated and CPU-only inference modes. GPU-accelerated inference provided a clear advantage, increasing median throughput from 7.12 to 18.13 tok/s and improving external-meter energy efficiency from 0.453 to 0.823 tok/J, despite higher mean input power. Sub-1B models offered the best throughput and energy efficiency, whereas 7–8B models achieved stronger accuracy at a substantially higher energy cost per generated token. These results demonstrate that edge LLM deployment requires multi-objective evaluation balancing accuracy, throughput, power consumption, and energy efficiency. Full article

Accurately simulating fault rupture dynamics is critical for aftershock prediction but remains computationally prohibitive due to the multiscale nature of earthquake processes. While Fourier Neural Operators (FNOs) offer a promising framework for seismic simulation, their direct application to rupture dynamics is hindered by spectral bias from global processing and resolution loss from uniform downsampling. To overcome these limitations, this paper introduces a novel dual-stage FNO architecture explicitly designed for multiscale rupture simulation. The architecture decouples the problem into a first stage for efficient low-frequency wave propagation in the non-fault zone and a second stage for resolving meter-scale nonlinear rupture dynamics within the fault zone. Then, we propose a physics-based sampling strategy that maintains high resolution in the critical fault zone while coarsening the non-fault zone based on wave-propagation criteria, coupled with an interpolation scheme that enforces conservation of mass, momentum, and energy. Evaluated on the SCEC TPV101 benchmark, our integrated framework achieves a 92.4% reduction in model parameters and a 2.34× speedup in training time compared to a baseline FNO approach, while also reducing the NRMSE in fault zones by 80.1%. Furthermore, the model demonstrates robust generalization to unseen geological parameters. Full article

Accurate knowledge of the thermal transmittance (U-value) of existing building envelopes is essential for reliable energy performance assessment and the planning of energy-efficient refurbishment measures. However, in practice, the material composition of existing walls is often unknown, and installing measurement devices may be restricted due to limited accessibility, the risk of structural damage, or varying on-site boundary conditions. Although several in situ methods for determining the U-value have been proposed in the literature, systematic comparisons of their performance under real environmental conditions remain limited. This lack of comparative evaluation makes it difficult to select the most appropriate method under specific practical constraints. To address this gap, this study presents a comprehensive experimental comparison of four in situ U-value measurement methods applied simultaneously to the same building element under identical real boundary conditions, providing new insights into their accuracy, uncertainty, and practical applicability. In this study, four in situ techniques commonly used to determine the thermal transmittance (U-value) were tested on a double-leaf brick wall at the University of Stuttgart: heat flow meter (HFM), infrared thermography (IRT), infrared thermometer (IRTM), and thermometric method (THM). The measurements were carried out over several days under real boundary conditions, during which air temperature, surface temperature, and heat flux were recorded at regular intervals. The results show that all four techniques can be reliably used under real boundary conditions, with the measured U-values lying within a comparable range. Differences among the methods were observed, largely due to their varying sensitivity to environmental influences and sensor placement. A comparison between the upper and lower parts of the wall indicated that its thermal response is non-uniform, and the observed deviations can be attributed to its inhomogeneous structure. By outlining the strengths and limitations of each technique and comparing their measurement outcomes, this study provides practical guidance for selecting suitable approaches for in situ U-value determination. Furthermore, the findings support future efforts to refine thermal evaluation methods and improve energy performance in existing buildings. Full article

Low-voltage distribution networks (LVDNs) in urban and semi urban areas face increasing challenges in maintaining supply reliability due to limited automation, growing demand, and high penetration of distributed energy resources (DERs). This paper proposes a coordinated framework for supply restoration, demand management, and DER control using a novel multi parameter optimization framework with a priority-based objective function and real-time smart meter data. The approach integrates essential and non-essential load separation, smart field devices, and coordinated control strategies to enable adaptive network operation under normal and contingency conditions. The framework is evaluated using a modified IEEE 33 bus-based LVDN model with data derived from real smart meter measurements from a Sri Lankan LVDN. Results demonstrate that restoration time is reduced from approximately 2.5 h to a few minutes through automated switching. In addition, selective curtailment of non-essential loads improves service continuity, while coordinated DER control mitigates voltage rise under high photovoltaic penetration. A quantitative comparison confirms significant reductions in energy not served (ENS) and associated economic costs based on Public Utilities Commission of Sri Lanka values. The proposed framework provides a practical and scalable solution for enhancing reliability and enabling advanced distribution management in modern LVDNs. Full article

Electricity theft remains a critical challenge that destabilizes power systems, causes significant financial losses, and disrupts the grid, particularly in developing countries. This study presents a machine learning framework integrating an ANN and advanced performance metrics to accurately detect fraud consumption patterns in a smart meter dataset. The method achieves strong categorization between normal and abnormal conduct by simulating temporal behavior across seasons, applying feature extraction to high-resolution energy signals, and assessing performance using RMSE, MAE, and R². The experimental results demonstrate that intelligent algorithms significantly improve theft-detection accuracy; reduce losses, especially NTLs; and provide a scalable foundation for future smart-grid security. Full article

The Indonesian government should seriously consider the use of renewable energy, given the natural potential that can still be utilized as an environmentally friendly power source. The utilization of renewable energy can be achieved by harnessing available natural resources. Pico hydro power plants (PLTPHs) can serve as an alternative electricity generator for use in Indonesia due to the existing natural potential. The output from this power plant can be utilized directly or stored in batteries. Directly measuring the generator’s performance on-site is deemed less effective. Therefore, a monitoring system is introduced as a solution to allow remote monitoring and display parameters such as voltage, current, frequency, and power of the generator online. This system is designed to display the micro hydro generator’s output parameter data on the Blynk application. The display on the Blynk application can be monitored via a connected mobile phone. Testing of the monitoring system was carried out by comparing two sets of measurements: one through the PZEM-004T sensor system and the other through a kWh meter (Kilowatt-hour meter). For the AC output from the battery with a 12-watt lamp load (tested 4 times), the reading error values obtained were a voltage reading error of 0.2%, a current reading error of 19.4%, a frequency reading error of 0.67%, and a power reading error of 18.2%. Full article

In this paper, a supervised deep reinforcement learning (SDRL)-based positioning scheme is proposed for indoor wireless localization. The proposed scheme formulates the positioning problem as a Markov decision process and introduces a target-aware reward design based on the artificial potential field (APF) to alleviate the sparse reward problem commonly encountered in search-based reinforcement learning. In the proposed scheme, supervision is provided at the reward level by incorporating the target position into the reward design, rather than at the action level via expert demonstrations. A multi-scale action set with 49 candidates is further adopted to provide a favorable trade-off between estimation accuracy and search efficiency. An anchor-based environment construction strategy is developed by selecting the four strongest reference points (RPs) and transforming their coordinates with respect to the strongest RP. Simulation results show that the proposed scheme achieves a mean absolute error (MAE) below 0.8 m and success rates above 99.1% within 1 m and 99.2% within 2 m under the default Bluetooth Low Energy setting, while the convex-valid rate of the anchor-based environment exceeds 99.5%. Compared with existing methods, the proposed scheme reduces the MAE by approximately 92.3%. Ablation studies confirm that multi-scale actions reduce the average search steps by approximately 69.5% compared with a single-scale baseline. The proposed scheme also retains stable performance across BLE, Wi-Fi, and Zigbee infrastructures when trained under a representative path-loss setting without retraining and maintains sub-meter accuracy under mild shadow fading. These results confirm that the proposed scheme can improve positioning accuracy and search efficiency for indoor wireless localization. Full article

The article is devoted to developing methodological approaches to multi-criteria resource optimization of technological solutions in Nature Use Projects, considering the growing shortage of water and energy resources, climate change, and post-war transformation of Ukraine’s agricultural sector. The need to transition from traditional technical and economic optimization models to integrated assessment approaches, which consider ecological, resource, and economic aspects of the project implementation effectiveness, is substantiated. The methodological basis of the study is a combination of Multi-Criteria Decision-Making and the Water-Energy-Food Nexus concept, enabling the necessary adaptive management and formalizing the process of project decision-making under multifactor uncertainty. A set of indicators of resource-ecological and economic efficiency is proposed, including indicators of productivity, weather and climate risk, resource use, environmental reliability, investment attractiveness, etc. A key feature of this approach is the transformation of resource-ecological indicators into a value form, ensuring their integration with economic indicators within a single optimization model. Based on a machine experiment for the conditions of the Kherson region, an assessment of the effectiveness of various irrigation regimes, which differ from the project irrigation regime in terms of watering and irrigation norms, in terms of their level of provision with water and energy resources, was carried out. It was determined that, under the studied conditions, in dry years (p = 70%), the permissible deficit threshold is approximately 30%, achieving a compromise between economic efficiency and environmental acceptability. Adaptive management of irrigation regimes has been shown to reduce the resource intensity of production without a significant loss of productivity. This creates a basis for revising outdated design standards, which focused on 100% satisfaction of water needs, in favor of adaptive models that account for the real resource potential of the territory. This approach transforms irrigation from a resource-intensive industry into a tool for sustainable territorial development, where the priority is the efficiency of each cubic meter of water and kilowatt-hour of energy used, rather than gross collection. It has been proven that the implementation of resource optimization as a basic principle of natural resource project management contributes to increasing the efficiency of natural capital use, minimizing ecological risks, and ensuring the sustainable development of the agricultural sector. The obtained results can be used to substantiate engineering solutions in projects for the restoration and modernization of water management and land reclamation systems in Ukraine. Full article

Buildings account for a substantial proportion of global energy consumption and greenhouse-gas emissions, largely due to the widespread use of conventional heating, ventilation, and air-conditioning (HVAC) systems. Hybrid systems that integrate passive and active technologies have emerged as a promising strategy for reducing energy demand while maintaining adequate indoor environmental conditions. This study evaluates the thermal and airflow performance of a hybrid air-conditioning system (HACS) that combines transparent thermal insulation (TTI) filled with R-410A refrigerant and a pig-fat-based organic phase-change material (PCM). Experimental measurements of heat flux, temperature, airflow velocity, and CO₂ concentration were conducted in a controlled prototype system. In parallel, computational simulations were performed using computational fluid dynamics (CFD) and multizone airflow modeling. The hybrid system incorporates a TTI container acting as a solar absorber and a galvanized-steel PCM container filled with 10 kg of pig fat used as latent heat storage. Heat-flux measurements were obtained using an HFS-5 sensor connected to a data acquisition system, while airflow velocity and temperature were monitored with analog data loggers. Indoor CO₂ concentrations were recorded using a dedicated CO₂ meter and simulated using CONTAMW software version 3.4.0.8. The experimental results show that the TTI and PCM containers reached average heat-flux values of 77.04 W/m² and 55.31 W/m², respectively. Airflow within the system is induced by buoyancy forces arising from temperature gradients generated by heat transfer processes at the surfaces of the TTI and PCM, resulting in a mixed air stream with an average temperature of 37.54 °C during winter operation. Recorded CO₂ concentrations remained between 290 and 413 ppm, indicating high indoor air quality levels. The overall experimental campaign spanned 6 years and 3 months. CFD simulations confirmed the airflow patterns and heat-transfer behavior observed experimentally. The findings demonstrate that hybrid air-conditioning systems combining refrigerant-filled transparent insulation with bio-based phase-change materials can effectively enhance passive thermal performance while maintaining acceptable indoor air quality. The integration of photovoltaic-powered ventilation systems could further the operational autonomy and overall energy efficiency of such hybrid systems. Full article

The shift toward more electric aircraft has intensified thermal management challenges due to increased heat load from electrical actuators, power electronics and energy storage systems concentrated within confined fuselage bays. A Conventional Environmental Control System (ECS) alone is not sufficient to dissipate such high localized heat loads. This creates the need for innovative heat dissipation and heat reuse strategies. This paper presents two thermal management concepts evaluated at the Fraunhofer Flight Test Facility. The first, developed in the ORCHESTRA project, integrates a bilge skin heat exchanger with modified ventilation to dissipate elevated heat loads. The second, under investigation in the TheMa4HERA project, focuses on reusing avionics heat to warm the FWD cargo hold, thereby reducing ECS power demand. Both concepts depend on convective heat exchange, characterized using Fraunhofer’s Convective Heat Transfer Meter (CHM) to determine key heat transfer coefficients. In parallel, an aircraft-level thermal model was developed, validated against experimental data and subsequently used for virtual demonstration of a ground test scenario. Full article

Because smart microgrids can flexibly integrate distributed energy resources and support grid-connected and islanded operation modes, they enhance power supply reliability and promote the efficient utilization of renewable energy. However, the open communication environment and physically exposed infrastructure introduce critical security challenges, including risks of physical hijacking and data leakage. Many existing authentication protocols either fail to address these threats or rely on heavyweight cryptographic operations such as bilinear pairings and modular exponentiation, resulting in high computational and communicational overhead. To address these issues, a lightweight authentication and key agreement (AKA) protocol for smart microgrids is proposed. The protocol symmetrically integrates Physical Unclonable Functions (PUFs) into the smart meter (SM) and smart microgrid control center (SMC) to protect stored secret information against capture attacks. Meanwhile, the SM and SMC register with the data center (DC) in a symmetric manner. During the AKA phase, the DC only assists in authenticating the identities of the SM and SMC online in a symmetric way, without participating in session key computation, thereby reducing the trust burden and computational load on the smart meters and control center. Formal security proof and informal security analysis demonstrate that the proposed protocol can resist known attacks such as physical hijacking and data leakage. Compared with existing smart microgrid authentication protocols, the proposed protocol has performance advantages and the lowest computational cost, confirming its suitability for resource-constrained microgrid environments. Full article