Search Results (1,075)

Predictive maintenance (PdM) is a cornerstone of smart manufacturing, enabling the early detection of equipment degradation and reducing unplanned downtimes. This study proposes an advanced machine learning framework that integrates the Extreme Learning Machine (ELM) with a novel hybrid metaheuristic optimization algorithm, the Polar Lights Salp Cooperative Optimizer (PLSCO), to enhance predictive modeling in manufacturing processes. PLSCO combines the strengths of the Polar Light Optimizer (PLO), Competitive Swarm Optimization (CSO), and Salp Swarm Algorithm (SSA), utilizing a cooperative strategy that adaptively balances exploration and exploitation. In this mechanism, particles engage in a competitive division process, where winners intensify search via PLO and losers diversify using SSA, effectively avoiding local optima and premature convergence. The performance of PLSCO was validated on CEC2015 and CEC2020 benchmark functions, demonstrating superior convergence behavior and global search capabilities. When applied to a real-world predictive maintenance dataset, the ELM-PLSCO model achieved a high prediction accuracy of 95.4%, outperforming baseline and other optimization-assisted models. Feature importance analysis revealed that torque and tool wear are dominant indicators of machine failure, offering interpretable insights for condition monitoring. The proposed approach presents a robust, interpretable, and computationally efficient solution for predictive maintenance in intelligent manufacturing environments. Full article

Non-cooperative spatial target detection plays a vital role in enabling autonomous on-orbit servicing and maintaining space situational awareness (SSA). However, due to the limited computational resources of onboard embedded systems and the complexity of spaceborne imaging environments, where spacecraft images often contain small targets that are easily obscured by background noise and characterized by low local information entropy, many existing object detection frameworks struggle to achieve high accuracy with low computational cost. To address this challenge, we propose YOLO-GRBI, an enhanced detection network designed to balance accuracy and efficiency. A reparameterized ELAN backbone is adopted to improve feature reuse and facilitate gradient propagation. The BiFormer and C2f-iAFF modules are introduced to enhance attention to salient targets, reducing false positives and false negatives. GSConv and VoV-GSCSP modules are integrated into the neck to reduce convolution operations and computational redundancy while preserving information entropy. YOLO-GRBI employs the focal loss for classification and confidence prediction to address class imbalance. Experiments on a self-constructed spacecraft dataset show that YOLO-GRBI outperforms the baseline YOLOv8n, achieving a 4.9% increase in mAP@0.5 and a 6.0% boost in mAP@0.5:0.95, while further reducing model complexity and inference latency. Full article

This paper addresses the core issues of slow coverage rate growth and high repeated detection rates in multi-UAV cooperative search operations within unknown areas. A distributed cooperative search algorithm based on the maximum entropy mechanism is proposed to resolve these challenges. It innovatively integrates the entropy gradient decision framework with DMPC-OODA (Distributed Model Predictive Control-Observe, Orient, Decide, Act) rolling optimization: environmental uncertainty is quantified through an exponential decay entropy model to drive UAVs to migrate toward high-entropy regions; element-wise product operations are employed to efficiently update environmental maps; and a dynamic weight function is designed to adaptively adjust the weights of coverage gain and entropy gain, thereby balancing “rapid coverage” and “accurate exploration”. Through multiple independent repeated experiments, the algorithm demonstrates significant improvements in coverage efficiency—by 6.95%, 12.22%, and 59.49%, respectively—compared with the Search Intent Interaction (SII) mode, non-entropy mode, and random mode, which effectively enhances resource utilization. Full article

Background. Peripartum cardiomyopathy (PPCM) is a rare but potentially life-threatening condition typically presenting as heart failure with reduced ejection fraction in the last month of pregnancy or in the first five months following delivery in women without other known causes of heart failure. PPCM incidence and prevalence are highly variable in different populations and geographical areas. The etiology of PPCM is likely multifactorial, with genetic predisposition, autoimmune conditions, nutritional deficiencies, hormonal and metabolic changes, myocardial inflammation, enhanced oxidative stress, vascular dysfunction, and angiogenic imbalance all listed as possible contributing factors. Objectives. The complexity and multifactorial nature of PPCM can be explored by large-scale “omics” investigations, and their integration has the potential to identify key drivers and pathways that have the largest contribution to the disease. The scarcity of relevant knowledge and experience with most rare diseases raises the unique need for cooperation and networking. Methods and results. In the context of PPCM, we hypothesize that the creation of prospective patient registries could represent an answer to this criticality. Therefore, we created a multicenter national registry of PPCM in different geographical areas in Italy. Conclusions. We expect that the integration of clinical, imaging and omics-based data might provide novel insights into PPCM pathophysiology and allow in the future early detection, risk assessment, and patient-specific therapeutic interventions, thereby offering new perspectives in precision medicine. Full article

Respirable crystalline silica is a well-established occupational hazard in construction work. Despite increased awareness, consistent exposure control remains a challenge, particularly in dynamic and resource-constrained environments. Respirable crystalline silica exposure in construction environments challenges the achievement of the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-Being) and SDG 8 (Decent Work and Economic Growth). Respirable crystalline silica particles cause severe health complications, including silicosis, lung cancer, cardiovascular diseases, and autoimmune disorders, representing a significant barrier to achieving SDG 3.9’s target of reducing deaths and illnesses from hazardous chemical exposures by 2030. This systematic review evaluates two decades of advancements (2004–2024) in respirable crystalline silica identification, characterisation, and mitigation within construction, synthesising evidence from 143 studies to assess progress toward sustainable occupational health management. This review documents a paradigmatic shift from traditional exposure assessment toward sophisticated monitoring approaches incorporating real-time detection systems, virtual reality–Computational Fluid Dynamics simulations, and wearable sensor technologies. Engineering controls, including local exhaust ventilation, wet suppression methods, and modified tool designs, have achieved exposure reductions exceeding 90%, directly supporting SDG 8.8’s commitment to safe working environments for all workers, including migrants and those in precarious employment. However, substantial barriers persist, including prohibitive costs, inadequate infrastructure, and regional regulatory disparities that particularly disadvantage lower-resourced countries, contradicting the Sustainable Development Goals’ principles of leaving no one behind. The findings advocate holistic approaches integrating technological innovation with context-specific regulations, enhanced international cooperation, and culturally adapted worker education to achieve equitable occupational health protection supporting multiple Sustainable Development Goals’ objectives by 2030 and also highlighting potential areas for future research. Full article

The escalating crisis of multidrug resistance, together with the persistence of antibiotic residues in clinical and environmental matrices, demands integrated strategies that couple sensitive detection, efficient decontamination, and controlled delivery. However, current techniques for quantifying avibactam (AVI)—a broad-spectrum β-lactamase inhibitor—such as HPLC-UV lack the sensitivity and specificity required for both therapeutic drug monitoring and environmental surveillance. Encapsulation of AVI within cyclodextrins (CDs) may simultaneously enhance its stability, bioavailability, and detectability, while the high binding affinities of CDs position them as molecular traps capable of scavenging residual AVI. In this study, the inclusion complexation of AVI with various CDs was examined through molecular dynamics (MD) simulations, experimental isothermal titration calorimetry (ITC), and non-covalent interaction (NCI) analysis. Stable 1:1 inclusion complexes were observed between AVI and β-cyclodextrin (β-CD), 2,6-dimethyl-β-cyclodextrin (DM-β-CD), and 2-hydroxypropyl-β-cyclodextrin (HP-β-CD), with standard Gibbs free energies of binding (ΔG°) of –3.64, –3.24, and –3.11 kcal/mol, respectively. In contrast, γ-cyclodextrin (γ-CD) exhibited significantly weaker binding (ΔG° = –2.25 kcal/mol). DFT-based NCI analysis revealed that cooperative interaction topology and cavity complementarity, rather than the sheer number of localized contacts, govern complex stability. Combined computational and experimental data establish β-CD derivatives as effective supramolecular hosts for AVI, despite an entropic penalty in the DM-β-CD/AVI complex. These CD–AVI affinities support the development of improved analytical methodologies and pharmaceutical formulations, and they also open avenues for decontamination strategies based on molecular trapping of AVI. Full article

The growing sophistication of cyber threats has posed significant challenges for organizations in terms of accurately detecting and responding to incidents in a coordinated manner. Despite advances in the application of machine learning and automation, many solutions still face limitations such as high false positive rates, low scalability, and difficulties in interorganizational cooperation. This study presents MICRA (Modular Intelligent Cybersecurity Response Architecture), a modular conceptual proposal that integrates dynamic data acquisition, cognitive threat analysis, multi-layer validation, adaptive response orchestration, and collaborative intelligence sharing. The architecture consists of six interoperable modules and incorporates techniques such as supervised learning, heuristic analysis, and behavioral modeling. The modules are designed for operation in diverse environments, including corporate networks, educational networks, and critical infrastructures. MICRA seeks to establish a flexible and scalable foundation for proactive cyber defense, reconciling automation, collaborative intelligence, and adaptability. This proposal aims to support future implementations and research on incident response and cyber resilience in complex operational contexts. Full article

Geospatial sensing and data-driven technologies (GSDDTs) are playing an increasingly important role in transforming (agro)forestry practices across the Western Balkans 6 region (WB6). This review critically examines the current state of GSDDT application in six WB countries (also known as the WB6 group)—Albania, Bosnia and Herzegovina, Kosovo*, Montenegro, North Macedonia, and Serbia—with a focus on their contributions to sustainable (agro)forest management. The analysis explores the use of unmanned aerial vehicles (UAVs), light detection and ranging (LiDAR), geographic information systems (GIS), and satellite imagery in (agro)forest monitoring, biodiversity assessment, landscape restoration, and the promotion of circular economy models. Drawing on 25 identified case studies across WB6—for example, ALFIS, Forest Beyond Borders, ForestConnect, Kuklica Geosite Survey, CREDIT Vibes, and Project O₂ (including drone-assisted reforestation in Kosovo*)—this review highlights both technological advancements and systemic limitations. Key barriers to effective GSDDT deployment across WB6 in the (agro)forestry sector and its cross-border cooperation initiatives include fragmented legal frameworks, limited technical expertise, weak institutional coordination, and reliance on short-term donor funding. In addition to mapping current practices, this paper offers a comparative overview of UAV regulations across the WB6 region and identifies six major challenges influencing the adoption and scaling of GSDDTs. To address these, it proposes targeted policy interventions, such as establishing national LiDAR inventories, harmonizing UAV legislation, developing national GSDDT strategies, and creating dedicated GSDDT units within forestry agencies. This review also underscores how GSDDTs contribute to compliance with seven European Union (EU) acquis chapters, how they support eight Sustainable Development Goals (SDGs) and their sixteen targets, and how they advance several EU Green Agenda objectives. Strengthening institutional capacities, promoting legal alignment, and enabling cross-border data interoperability are essential for integrating GSDDTs into national (agro)forest policies and research agendas. This review underscores GSDDTs’ untapped potential in forest genetic monitoring and landscape restoration, advocating for their institutional integration as catalysts for evidence-based policy and ecological resilience in WB6 (agro)forestry systems. Full article

In recent years, devastating wildfires have occurred in less fire-prone areas, and an increase in boreal region wildfires is expected in the future. Using a qualitative comparative approach based on a literature review and policy document analysis, this study aims to examine the wildfire management systems and practices in Sweden and Finland, focusing on the remarkably different outcomes of the 2018 wildfire season. Despite experiencing similar climatic conditions, in Sweden a total of approximately 25,000 hectares of forest burned, compared to the 1200 hectares in Finland. The analysis examines thematic areas from general disaster management and wildfire-specific elements. The main differences in the organizational structures between the two countries are identified. Ecological aspects of boreal forests, fire suppression effectiveness, and response times are compared, and current and emerging technologies for fire detection and suppression, such as unmanned aerial vehicles, are presented. The role of volunteer fire brigades and their sustainability in rural areas, together with the effectiveness of host nation support arrangements and international cooperation mechanisms, are discussed. Based on this comparison of identified best practices and lessons learned, the authors provide recommendations for improving wildfire resilience both in Finland and Sweden, as well as in other boreal region countries. Full article

Background/Objectives: Loss and fragmentation of habitat from agricultural conversion led to the near extirpation of the pygmy rabbit (Brachylagus idahoensis Merriam, 1891) population in the Columbia Basin (CB) of Washington, USA. Recovery efforts began in 2002 and included captive breeding, translocations from other regions for genetic rescue, and reintroduction into native habitat in three sites: Sagebrush Flat (SBF), Beezley Hills (BH), and Chester Butte (CHB). Methods: We used noninvasive and invasive genetic sampling to evaluate demographic and population genetic parameters on three translocated populations of pygmy rabbits over eight years (2011–2020). For each population, our goal was to use fecal DNA sampling and 19 microsatellite loci to monitor spatial distribution, apparent survival rates, genetic diversity, reproduction, effective population size, and the persistence of CB ancestry. Over the course of this study, 1978 rabbits were reintroduced as part of a cooperative conservation effort between state and federal agencies. Results: Through winter and summer monitoring surveys, we detected 168 released rabbits and 420 wild-born rabbits in SBF, 13 released rabbits and 2 wild-born in BH, and 16 released rabbits in CHB. Observed heterozygosity (H_o) values ranged from 0.62–0.84 (SBF), 0.59–0.80 (BH), and 0.73–0.77 (CHB). Allelic richness (AR) ranged from 4.67–5.35 (SBF), 3.71–5.41 (BH), and 3.69–4.65 (CHB). Effective population (N_e) within SBF varied from 12.3 (2012) to 44.3 (2017). CB ancestry persisted in all three wild populations, ranging from 15 to 27%. CB ancestry persisted in 99% of wild-born juveniles identified in SBF. Apparent survival of juvenile rabbits differed across years (1–39%) and was positively associated with release date, release weight, and genetic diversity. Survival of adults (0–43%) was positively influenced by release day, with some evidence that genetic diversity also positively influenced adult apparent survival. Conclusions: Noninvasive genetic sampling has proven to be an effective and efficient tool in monitoring this reintroduced population, assessing both demographic and genetic factors. This data has helped managers address the goals of the Columbia Basin recovery program of establishing multiple sustainable wild populations within the sagebrush steppe habitat of Washington. Full article

The proliferation of small, agile unmanned aerial vehicles (UAVs) has exposed the limits of single-sensor surveillance in cluttered airspace. We propose an Internet of Things-enabled integrated sensing and communication (IoT-ISAC) framework that converts cellular base stations into cooperative, edge-intelligent sensing nodes. Within a four-layer design—terminal, edge, IoT platform, and cloud—stations exchange raw echoes and low-level features in real time, while adaptive beam registration and cross-correlation timing mitigate spatial and temporal misalignments. A hybrid processing pipeline first produces coarse data-level estimates and then applies symbol-level refinements, sustaining rapid response without sacrificing precision. Simulation evaluations using multi-band ISAC waveforms confirm high detection reliability, sub-frame latency, and energy-aware operation in dense urban clutter, adverse weather, and multi-target scenarios. Preliminary hardware tests validate the feasibility of the proposed signal processing approach. Simulation analysis demonstrates detection accuracy of 85–90% under optimal conditions with processing latency of 15–25 ms and potential energy efficiency improvement of 10–20% through cooperative operation, pending real-world validation. By extending coverage, suppressing blind zones, and supporting dynamic surveillance of fast-moving UAVs, the proposed system provides a scalable path toward smart city air safety networks, cooperative autonomous navigation aids, and other remote-sensing applications that require agile, coordinated situational awareness. Full article

The goals of the European Union towards the energy transition imply profound changes in the energy field, so as to promote sustainable energy development while fostering economic growth. To achieve these changes, the incorporation of sustainable technologies supporting decentralisation, energy efficiency, renewable energy production, and demand flexibility is of vital importance. Blockchain has the potential to change energy services towards this direction. To optimally exploit blockchain, innovative business models need to be designed, identifying the opportunities emerging from unmet needs, while also considering potential risks so as to take action to overcome them. In this context, the scope of this paper is to examine the opportunities and the risks that emerge from the adoption of blockchain in four innovative business models, while also identifying mitigation strategies to support and accelerate the energy transition, thus proposing optimal approaches of exploitation of blockchain in energy services. The business models concern Energy Performance Contracting with P4P guarantees, improved self-consumption in energy cooperatives, energy efficiency and flexibility services for natural gas boilers, and smart energy management for EV chargers and HVAC appliances. Firstly, the value proposition of the business models is analysed and results in a comprehensive SWOT analysis. Based on the findings of the analysis and consultations with relevant market actors, in combination with the examination of the relevant literature, risks are identified and evaluated through a qualitative assessment approach. Subsequently, specific mitigation strategies are proposed to address the detected risks. This research demonstrates that blockchain integration into these business models can significantly improve energy efficiency, reduce operational costs, enhance security, and support a more decentralised energy system, providing actionable insights for stakeholders to implement blockchain solutions effectively. Furthermore, according to the results, technological and legal risks are the most significant, followed by political, economic, and social risks, while environmental risks of blockchain integration are not as important. Strategies to address risks relevant to blockchain exploitation include ensuring policy alignment, emphasising economic feasibility, facilitating social inclusion, prioritising security and interoperability, consulting with legal experts, and using consensus algorithms with low energy consumption. The findings offer clear guidance for energy service providers, policymakers, and technology developers, assisting in the design, deployment, and risk mitigation of blockchain-enabled business models to accelerate sustainable energy development. Full article

To address challenges in automated charging operations of bulk explosive trucks in open-pit mines—specifically difficulties in borehole identification, positioning inaccuracies, and low operational efficiency—this study proposes an intelligent collaborative charging system integrating three modular components: (1) an explosive transport vehicle (with onboard terminal, explosive compartment, and mobility system enabling optimal routing and quantitative dispensing), (2) a charging robot (equipped with borehole detection, loading mechanisms, and mobility system for optimized search path planning and precision positioning), and (3) interconnection systems (coupling devices and interfaces facilitating auxiliary explosive transfer). This approach resolves three critical limitations of conventional systems: (i) mechanical arm-based borehole detection difficulties, (ii) blast hole positioning inaccuracies, and (iii) complex transport routing. The experimental results demonstrate that the intelligent cooperative charging method for open-pit mines achieves an 18% improvement in operational efficiency through intelligent collaboration among its modular components, while simultaneously realizing automated and intelligent charging operations. This advancement has significant implications for promoting intelligent development in open-pit mining operations. Full article

The growing use of remote-sensing technologies has placed greater demands on object-detection algorithms, which still face challenges. This study proposes a hierarchical adaptive feature aggregation network (HAF-YOLO) to improve detection precision in remote-sensing images. It addresses issues such as small object size, complex backgrounds, scale variation, and dense object distributions by incorporating three core modules: dynamic-cooperative multimodal fusion architecture (DyCoMF-Arch), multiscale wavelet-enhanced aggregation network (MWA-Net), and spatial-deformable dynamic enhancement module (SDDE-Module). DyCoMF-Arch builds a hierarchical feature pyramid using multistage spatial compression and expansion, with dynamic weight allocation to extract salient features. MWA-Net applies wavelet-transform-based convolution to decompose features, preserving high-frequency detail and enhancing representation of small-scale objects. SDDE-Module integrates spatial coordinate encoding and multidirectional convolution to reduce localization interference and overcome fixed sampling limitations for geometric deformations. Experiments on the NWPU VHR-10 and DIOR datasets show that HAF-YOLO achieved mAP50 scores of 85.0% and 78.1%, improving on YOLOv8 by 4.8% and 3.1%, respectively. HAF-YOLO also maintained a low computational cost of 11.8 GFLOPs, outperforming other YOLO models. Ablation studies validated the effectiveness of each module and their combined optimization. This study presents a novel approach for remote-sensing object detection, with theoretical and practical value. Full article

Hyperspectral image (HSI) denoising is a crucial step in image preprocessing as its effectiveness has a direct impact on the accuracy of subsequent tasks such as land cover classification, target recognition, and change detection. However, existing methods suffer from limitations in effectively integrating multi-scale features and adaptively modeling complex noise distributions, making it difficult to construct effective spatial–spectral joint representations. This often leads to issues like detail loss and spectral distortion, especially when dealing with complex mixed noise. To address these challenges, this paper proposes a multi-scale differentiated denoising network based on spatial–spectral cooperative attention (MDSSANet). The network first constructs a multi-scale image pyramid using three downsampling operations and independently models the features at each scale to better capture noise characteristics at different levels. Additionally, a spatial–spectral cooperative attention module (SSCA) and a differentiated multi-scale feature fusion module (DMF) are introduced. The SSCA module effectively captures cross-spectral dependencies and spatial feature interactions through parallel spectral channel and spatial attention mechanisms. The DMF module adopts a multi-branch parallel structure with differentiated processing to dynamically fuse multi-scale spatial–spectral features and incorporates a cross-scale feature compensation strategy to improve feature representation and mitigate information loss. The experimental results show that the proposed method outperforms state-of-the-art methods across several public datasets, exhibiting greater robustness and superior visual performance in tasks such as handling complex noise and recovering small targets. Full article