Search Results (2,536)

Real-time object detection in Unmanned Aerial Vehicle (UAV) imagery is critical yet challenging, requiring high accuracy amidst complex scenes with multi-scale and small objects, under stringent onboard computational constraints. While existing methods struggle to balance accuracy and efficiency, we propose RTUAV-YOLO, a family of lightweight models based on YOLOv11 tailored for UAV real-time object detection. First, to mitigate the feature imbalance and progressive information degradation of small objects in current architectures multi-scale processing, we developed a Multi-Scale Feature Adaptive Modulation module (MSFAM) that enhances small-target feature extraction capabilities through adaptive weight generation mechanisms and dual-pathway heterogeneous feature aggregation. Second, to overcome the limitations in contextual information acquisition exhibited by current architectures in complex scene analysis, we propose a Progressive Dilated Separable Convolution Module (PDSCM) that achieves effective aggregation of multi-scale target contextual information through continuous receptive field expansion. Third, to preserve fine-grained spatial information of small objects during feature map downsampling operations, we engineered a Lightweight DownSampling Module (LDSM) to replace the traditional convolutional module. Finally, to rectify the insensitivity of current Intersection over Union (IoU) metrics toward small objects, we introduce the Minimum Point Distance Wise IoU (MPDWIoU) loss function, which enhances small-target localization precision through the integration of distance-aware penalty terms and adaptive weighting mechanisms. Comprehensive experiments on the VisDrone2019 dataset show that RTUAV-YOLO achieves an average improvement of 3.4% and 2.4% in mAP50 and mAP50-95, respectively, compared to the baseline model, while reducing the number of parameters by 65.3%. Its generalization capability for UAV object detection is further validated on the UAVDT and UAVVaste datasets. The proposed model is deployed on a typical airborne platform, Jetson Orin Nano, providing an effective solution for real-time object detection scenarios in actual UAVs. Full article

Soil erosion in vineyards is a major environmental problem, particularly in hilly Mediterranean environments. Our study evaluated the effectiveness of permanent grass cover (PG), continuous tillage (CT), and green manure (GM) in reducing soil erosion. Furthermore, a new software tool (ISUMmate_1.1.xlsm), based on the improved stock unearthing method (ISUM), was developed and tested to quantify soil mobilization between successive transects along vineyard inter-row. The field trial was carried out over a three-year period in a Tuscany (Italy) vineyard. The results showed that PG significantly improved aggregate stability and soil organic carbon (SOC) content, while exhibiting the lowest erosion rates. In contrast, GM showed the highest erosion rates as a result of soil disturbance associated with cultivation operations and the occurrence of unexpected intense rainfalls. ISUMmate_1.1 has proven to be a reliable tool for monitoring both water- and tillage-induced erosion, providing valuable information for sustainable vineyard management. Full article

Recent research has increasingly emphasized the synergies between Supply Chain Management Practices (SCMPs) and Quality Management Principles (QMPs), particularly through the emerging concept of Supply Chain Quality Management (SCQM). Despite this recognition, empirical evidence on how these practices interact to influence performance remains very limited, especially in the context of developing countries. This study addresses the gap by interviewing 70 Tunisian agri-food companies to investigate the relationships between five dimensions of SCMP, strategic supplier partnerships, customer relationship, information sharing, information quality and postponement, and the seven principles of ISO9001 QMP: leadership, engagement of people, improvement, customer focus, process approach, evidence-based decision making, and relationship management. Using factor analysis and structural equation modelling, the study explores the mediating role of competitive advantage (CA): price/cost, product quality, product innovation, delivery dependability and time-to-market—on operational performance. The findings indicate that analyzing SCMP, QMP, and CA as aggregated blocks does not produce significant explanatory correlations. Instead, judiciously reorganizing their sub-constructs into five integrated groups provides a more effective model: (1) information and decision capacity, (2) customer-centric innovation, (3) process management and agility, (4) supplier and network management, and (5) leadership and workforce engagement. This integrated classification offers managers a coherent framework for implementing SCMP and QMP to enhance competitiveness results. Full article

The study investigates the electrical energy parameters in the distribution system of a mining facility located in Murmansk Oblast, Russia, focusing on power quality (PQ) issues arising substantially from mine hoist operation conditions. Despite compliance with Russian standards related to PQ, discrepancies were observed between PQ measurement results and problems inherent in the system, such as transformer failures. The research employed two instruments, Resurs-UF2M and Metrel MI2892, to conduct a PQ survey, comparing their data aggregation methods and measurement accuracy. Various data aggregation intervals were also used to evaluate the impact of resolution on PQ assessment. Results revealed significant discrepancies between the instruments, with Metrel MI2892 providing a more reliable and detailed dataset, while Resurs-UF2M failed to capture rapid transients and enable profound PQ analysis to be performed. The research identified eight PQ indices exceeding permissible levels, attributed to the electromagnetic influence of high-power mining equipment. The findings underscore the limitations of current regulatory frameworks and measurement methods, emphasizing the need for revised standards to improve diagnostic accuracy. The research highlights the importance of proper instrument selection and configuration to mitigate PQ disturbances, prevent equipment failures, and enhance power system reliability in mining facilities. Full article

In response to China’s “Dual Carbon” goals, this paper proposes a Transmission Network Expansion Planning (TNEP) model that explicitly incorporates the operational flexibility of Virtual Power Plants (VPPs). Unlike conventional approaches that focus mainly on transmission investment, the proposed method accounts for the aggregated dispatchable capability of VPPs, providing a more accurate representation of distributed resources. The VPP aggregation model is characterized by the inclusion of electric vehicles, which act not only as load-side demand but also as flexible energy storage units through vehicle-to-grid interaction. By coordinating EV charging/discharging with photovoltaics, wind generation, and other distributed resources, the VPP significantly enhances system flexibility and provides essential support for grid operation. The vertex search method is employed to delineate the boundary of the VPP’s dispatchable feasible region, from which an equivalent model is established to capture its charging, discharging, and energy storage characteristics. This model is then integrated into the TNEP framework, which minimizes the comprehensive cost, including annualized line investment and the operational costs of both the VPP and the power grid. The resulting non-convex optimization problem is solved using the Quantum Particle Swarm Optimization (QPSO) algorithm. A case study based on the Garver-6 bus and Garver-18 bus systems demonstrates the effectiveness of the approach. The results show that, compared with traditional planning methods, strategically located VPPs can save up to 6.65% in investment costs. This VPP-integrated TNEP scheme enhances system flexibility, improves economic efficiency, and strengthens operational security by smoothing load profiles and optimizing power flows, thereby offering a more reliable and sustainable planning solution. Full article

Detecting underwater structural defects is vital for hydraulic engineering safety. Diverse patterns of underwater structural defects, i.e., the morphology and scale characteristics, pose difficulties on feature representability during detection. Any single feature morphology is insufficient to fully characterize diverse types of underwater defect patterns. This paper proposes a novel hybrid neural network to enhance feature representation of underwater structural multi-defects, which in turn improves the accuracy and adaptability of underwater detection. Three types of convolution operations are combined to build Hybrid Aggregation Network (HanNet), enhancing the morphological representation for diverse defects. Considering the scale difference of diverse defects, the Multi-Scale Shared Feature Pyramid (MSFP) is proposed, facilitating adaptive representation for diverse sizes of structural defects. The defect detection module leverages an Adaptive Spatial-Aware Attention (ASAA) at the backend, enabling selective enhancement of salient defect features. For model training and evaluation, we, for the first time, build an underwater structural multi-defects sonar image dataset containing a wide range of typical defect types. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods, significantly improving defect detection accuracy, and provides an effective solution for detecting diverse structural defects in complex underwater environments. Full article

This paper outlines the inception, conceptualization and primary morphological selection of a robotic system that employs raw lunar regolith for constructing protective berms and shelters on the Moon’s surface. The lunar regolith is considered the most readily available material for in situ resource utilization on the Moon. The lunar environment is characterized, and the operational task is defined, informing the development of high-level system requirements and a functional analysis through the glass-box method. The key morphological areas are identified, and candidate concepts are evaluated using the Analytic Hierarchy Process (AHP). The evaluation process employs a new approach to aggregating expert data through the ZM${}_{II}$ method to establish priorities of the design criteria, which eliminates the need for pairwise comparisons in data collection. Each criterion is associated with a specific and quantifiable metric, which is then used to evaluate the morphologies during the AHP. The selected morphologies are determined as: a vibrating hopper for intake (normalized decision value of 27.5% out of 5 candidate solutions), a roller system for container deployment and filling (26.2% out of 7), a magnetic RCU interface (22.6% out of 7), and a 4-DoF manipulator to place the RCUs in the environment (23.6% out of 5). The final morphology is selected by combining the decision values across the primary morphological areas into a unified decision metric. This is followed by the preliminary selection of the system’s surrounding architecture. The design conceptualization is performed within a real-life operational scenario, namely, to create a blast berm for the landing pad using the lunar regolith provided by an existing excavator. The next phase of the work will include the system’s detailed design, as well as investigations on the requirements for a variety of construction tasks on the lunar surface. Full article

The role of distributed energy resources in distribution networks is evolving to support system operation, facilitated by their participation in local flexibility markets. Future scenarios envision a significant share of low-power resources providing ancillary services to efficiently manage network congestions, offering a competitive alternative to conventional grid reinforcement. Additionally, the interaction between distribution and transmission systems enables the provision of flexibility services at higher voltage levels for various applications. In such cases, the aggregated flexibility of low-power resources is typically represented as a capability envelope at the interface between the distribution and transmission network, constructed by accounting for distribution grid constraints and subsequently communicated to the transmission system operator. This paper revisits this concept and introduces a novel approach for envelope construction. The proposed method is based on a surrogate model composed of a limited set of standard power flow components—loads, generators, and storage units—enhancing the integration of distribution network flexibility into transmission-level optimization frameworks. Notably, this advantage can potentially be achieved without significant modifications to the optimization tools currently available to grid operators. The effectiveness of the approach is demonstrated through a case study in which the adoption of distribution network surrogate models within a coordinated framework between transmission and distribution operators enables the provision of ancillary services for transmission resilience support. This results in improved resilience indicators and lower control action costs compared to conventional shedding schemes. Full article

Reverse Logistics (RL) plays a fundamental role in supply by addressing returns, undelivered or damaged products, exchanges, and environmental concerns, directly contributing to more sustainable supply chain practices. Although firms recognize the importance and benefits of this concept, their supply chain remains focused on direct logistics, often overlooking RL’s potential to enhance sustainability performance. The aim of this article is to analyse the interaction between the barriers that challenge or prevent the implementation of RL in Small and Medium-sized Enterprises (SMEs). First, a literature review identified 22 barriers to developing RL in SMEs. Then, through experts’ opinions gathered in a Focus Group (FG), an Interpretive Structural Modeling (ISM) model was used to understand the hierarchy relations between barriers, and a Matrix Cross Impact Matrix Multiplication (MICMAC) analysis was carried out to aggregate the barriers in four categories according to their influencing power and dependence. Applying the methodology to the Portuguese case resulted in an ISM model with seven hierarchical levels and a MICMAC diagram without dependent barriers. Moreover, six key barriers emerged, namely, Lack of adequate organizational structure and support for RL practices, Lack of corporate social responsibility, Complexity of the operation, Lack of shared understanding of best practices, Difficulty with members of the supply chain, and Lack of support from supply chain players, which proved to be the most critical as they are positioned at the highest hierarchical levels of the ISM model and fall within the independent variable quadrant of the MICMAC analysis, thus revealing a strong driving power over the other barriers. The findings highlight that overcoming these barriers is crucial for SMEs to unlock the full sustainability potential of RL and transition towards supply chain models that are greener through a reduced carbon footprint, improved resource efficiency, and the adoption of circular economy practices. Academically, this research advances the literature by applying the ISM–MICMAC approach to SMEs, offering novel insights into the structural role of barriers in reverse logistics implementation. Full article

The demand for reliable Environmental Product Declarations (EPDs) of asphalt mixtures is growing, particularly as they are increasingly used in public road construction tenders across Europe. However, the reliability and comparability of EPDs remain limited due to two main challenges: (i) significant variability in dataset selection for key materials such as bitumen and aggregates, and (ii) uncertainty regarding the influence of operational factors, including aggregate moisture, mixing temperature, and transportation. The objective of this research is to assess the influence of dataset selection and operational parameters on the environmental performance of an asphalt mixture, focusing on improving the reliability of EPDs. Within this research, a Life Cycle Assessment (LCA) was conducted using a cradle-to-gate approach (A1–A3), including modules C1–C4 and D, in compliance with EN 15804:2019+A2:2020. Primary data were collected from an asphalt plant in Lithuania, while secondary data were obtained from the Ecoinvent database. The sensitivity analyses were performed to investigate the variation of data set choices and key operational factors that influence the environmental impact. The assessment was carried out using the Simapro 9.6 software and the EF 3.1 impact assessment method. The results indicate significant sensitivity to dataset selection, particularly for bitumen and dolomite production, leading to environmental impact variations of up to 41.8% and 35.3%, respectively. Among operational factors, reducing aggregate moisture from 5% to 3% by sheltering stockpiles helps achieve the highest environmental impact reduction (3.2% under the Aggregate Single Score), while lowering mixing temperatures to 130 °C resulted in a 1.6% decrease. Transportation mode selection contributed to emission variations between 1.8% and 6.7%, with long-distance aggregate transport increasing emissions by up to 14.6%. The research findings underscore the critical need for harmonizing dataset selection and optimizing operational processes to improve asphalt sustainability. Standardizing datasets is essential for ensuring fair and transparent EPD generation for asphalt mixtures, particularly when used in road construction tenders, as seen in several European countries. Future research should explore the integration of reclaimed asphalt (RA) and assess its potential environmental benefits. Full article

In line with EU decarbonization targets for the heavy-duty transport sector, this study proposes an analytical methodology to assess the impact of diesel performance additives on fuel consumption in Euro 6 heavy-duty vehicles, the prevailing standard in the circulating European road tractor fleet. A fleet of five N3-category road tractors equipped with tanker semi-trailers was monitored over two phases. During the first 10-month baseline phase, the vehicles operated with standard EN 590 diesel (containing 6–7% FAME); in the second phase, they used a commercially available premium diesel containing performance-enhancing additives. Fuel consumption and route data were collected using a GPS-based system interfaced with the engine control unit via the OBD port and integrated with the fleet tracking platform. After applying data filtering to exclude low-quality or non-representative trips, a 1% reduction in fuel consumption was observed with the use of fuel with additives. Route-level analysis revealed higher savings (up to 5.1%) in high-load operating conditions, while most trips showed improvements between −1.6% and −3.4%. Temporal analysis confirmed the general trend across varying vehicle usage patterns. Aggregated fleet-level data proved to be the most robust approach to mitigate statistical variability. To evaluate the potential impact at scale, a European scenario was developed: a 1% reduction in fuel consumption across the 6.75 million heavy-duty vehicles in the EU could yield annual savings of 2 billion liters of diesel and avoid approximately 6 million tons of CO₂ emissions. Even partial adoption could lead to meaningful environmental benefits. Alongside emissions reductions, fuel additives also offer economic value by lowering operating costs, improving engine efficiency, and reducing maintenance needs. Full article

Aflatoxin B1 (AFB1) and its metabolite aflatoxin M1 (AFM1) are stable and carcinogenic mycotoxins that are commonly found in dairy products, posing serious food safety concerns. However, conventional degradation methods face limited degradation efficiency and high energy demand. Here, we develop an innovative polyvinylidene fluoride (PVDF) composite membrane incorporating Fe/Co-based metal-organic frameworks (MOF) (Named Fe/Co-MIL-88B(NH₂)) and CaO₂ for targeted aflatoxin removal from milk. This system integrates two synergistic mechanisms: (1) hierarchical porous MOF structures enabling superior aflatoxin adsorption capacity and peroxidase-like catalytic activity, and (2) CaO₂ acts as a controllable-release H₂O₂ donor, supplying a steady flux of reactive oxygen species without the addition of exogenous H₂O₂. Moreover, the PVDF membrane with mechanical stability offers uniform immobilization of active components, which prevents the aggregation of nanozymes. As a result, the integrated membrane achieves high degradation efficiency for AFB1 and AFM1, exceeding 95% within 60 min. By eliminating external oxidant addition and minimizing collateral nutrient damage, the technology demonstrates remarkable operational stability (>10 cycles) and milk quality preservation capability. This breakthrough establishes an efficient and reusable detoxification method, providing new opportunities for mycotoxin mitigation in dairy products through spatiotemporal control of reactive oxygen species. Full article

This study aimed to evaluate the incorporation of graphene-based additives, graphene nanoplatelets (GNPs) and graphene oxide quantum dots (GOQDs), into polymeric fiber matrices used as biofilm supports in anaerobic digestion systems, determining additive specific effects by benchmarking the impregnated matrices against the same nylon carrier without additives under identical operational conditions. Modified matrices were assessed through BMP assays using the liquid fraction of fruit and vegetable waste (LF-FVW) as substrate. Intermediate GNP and GOQD loadings (FM50 and FMDOT50) achieved the highest methane yields (317.9 ± 20.2 and 348.4 ± 20.0 mL CH₄/g COD_(rem)) compared with the control fiber matrix (301.0 ± 20.1 mL CH₄/g COD_(rem)). Scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) analyses confirmed nanomaterial retention on the matrix surface and interaction with microbial aggregates. Embedding the nanostructures within the fiber enhanced biofilm formation and methane yield while minimizing nanomaterial washout. Future work will focus on advanced physicochemical characterization (XRD, XPS, BET, and EDX mapping), leaching tests to assess long term stability, and scale up evaluation for full scale anaerobic digestion applications. Full article

Under the dual pressures of global climate change and energy structure transition, the offshore wind–solar–current energy coupling hydrogen production (OCWPHP) system has emerged as a promising integrated energy solution. However, its complex multi-energy structure and harsh marine environment introduce systemic risks that are challenging to assess comprehensively using traditional methods. To address this, we develop a novel risk assessment framework based on hesitant fuzzy sets (HFS), establishing a multidimensional risk criteria system covering economic, technical, social, political, and environmental aspects. A hybrid weighting method integrating AHP, entropy weighting, and consensus adjustment is proposed to determine expert weights while minimizing risk information loss. Two aggregation operators—AHFOWA and AHFOWG—are applied to enhance uncertainty modeling. A case study of an OCWPHP project in the East China Sea is conducted, with the overall risk level assessed as “Medium.” Comparative analysis with the classical Cumulative Prospect Theory (CPT) method shows that our approach yields a risk value of 0.4764, closely aligning with the CPT result of 0.4745, thereby confirming the feasibility and credibility of the proposed framework. This study provides both theoretical support and practical guidance for early-stage risk assessment of OCWPHP projects. Full article

As the scale of distributed resources continues to expand, decentralization and multi-agent characteristics bring significant challenges to low-carbon dispatching and market participation of power grids. To this end, this paper proposes a collaborative optimization scheduling framework with distributed resource aggregators (DRAs) as the main body, innovatively coupling carbon Emission trading (CET) with electric vehicle carbon quota participation, and the renewable energy quota (RPS) with tradable green certificate (TGC) transaction as the carrier, as well as constructing the connection path between the two to realize the integrated utilization of environmental rights and interests. Based on the ε-constraint method, a bi-objective optimization model of economic cost minimization and carbon emission minimization is established, and a multi-dimensional evaluation system, covering the internal and overall operation performance of the aggregator, is designed. The example shows that, under the proposed CET-RPS coupling mechanism, the total cost of DRA is about 23.4% lower than that of the existing mechanism. When the carbon emission constraint is relaxed from 2700 t to 3000 t, the total cost decreases from CNY 2537.32 to CNY 2487.74, indicating that the carbon constraint has a significant impact on the marginal cost. This study provides a feasible path for the large-scale participation of distributed resources in low-carbon power systems. Full article