Search Results (1,223)

Varroa destructor (Bee mite) and Deformed Wing Virus are primary causes of honeybee colony collapse. This study developed an automated AI-based vision inspection system for detecting bee mites and deformed bees using the YOLO algorithm. The system integrates an RGB camera, a beecomb rotation motor, and an image transmission module to enable automated dual-sided image acquisition of the beecomb. The image characteristics of normal bees, bee mites, and deformed bees were analyzed, and YOLO-based object detection models were developed to classify them. Six YOLO models—based on YOLOv8 and YOLOv11 architectures across three model sizes (nano, small, and large)—were evaluated on 405 test images (6441 objects). The proposed system reduced the inspection time from 240 s required for manual method to 20 s per beecomb, achieving 12-fold efficiency improvement. Comparative analysis showed model-task specialization: YOLOv8l excelled in detecting small bee mites (F1: 92.5%, mAP[0.5]: 92.1%), while YOLOv11s achieved the highest performance for morphologically diverse deformed bees (F1: 95.1%). Error analysis indicated that detection performance was influenced by morphological characteristics. Deformed bee detection errors correlated with overlap in wing-to-body ratio: DB Type II exhibited 18.6% miss rate, while DB Type III achieved perfect detection. In bee mite detection, a sensitivity–specificity trade-off was observed: YOLOv11l had the lowest false negatives (2.5%) but highest false positives, while YOLOv8l demonstrated superior discrimination. These results demonstrate the practical potential of the proposed system for field deployment in apiaries, supporting early pest diagnosis and improved colony health management. The model-task specialization framework provides guidance for architecture selection based on object characteristics. Future work will focus on multi-location validation and real-time monitoring integration. Full article

The management of end-of-life photovoltaic panels has become a focal point for circular economy initiatives, given the significant waste volumes generated by the global energy transition. This study addressed the challenge of identifying optimal recycling solutions characterized by conflicting objectives, such as material recovery efficiency, economic feasibility, and environmental impact. Given that photovoltaic waste contains valuable materials alongside elements requiring specialized handling, the selection of appropriate processing technologies has been prioritized by research and industrial sectors. To resolve these trade-offs, a hybrid Multi-Criteria Decision-Making (MCDM) framework was implemented, combining the Best–Worst Method (BWM) with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE II). The BWM was employed to determine criteria weights based on expert evaluations, focusing on the relationships between the most and least significant factors to ensure mathematical consistency. Subsequently, the PROMETHEE II facilitated a complete ranking of technological alternatives by calculating net preference flows, allowing for a nuanced comparative analysis of diverse recovery processes. Through this approach, the research established a clear performance hierarchy among established and emerging recycling pathways. These findings provided a structured quantitative basis for decision-makers to identify balanced solutions for industrial implementation, supporting long-term sustainability goals and the preservation of secondary raw materials. Full article

This paper formulates the “Minimum Vertex Cut with Reachable Set” (MVCRS) problem as an optimization framework to suppress botnet propagation in networked systems, and clarifies its computational complexity and algorithmic solutions. Building a firewall to minimize damage is essential for addressing botnet propagation in Internet of Things (IoT) networks. We define the basic MVCRS problem as minimizing the sum of the weight of the deployed resources and the resulting propagation scope. While we demonstrate that the constrained version of the problem is NP-complete, we show that the fundamental trade-off optimization model can be solved in polynomial time by reducing it to the maximum flow–minimum cut problem. This provides a theoretical baseline for optimal resource allocation in cybersecurity. Experimental evaluations reveal the limitations of conventional heuristics. In community-structured networks, the degree-based greedy algorithm overlooks critical bridge nodes, yielding an optimality gap of up to 72.6% above the theoretical minimum cost. Conversely, our exact algorithm consistently guarantees the optimal minimum cost (a 0% gap) with high statistical stability across diverse topologies. Furthermore, it scales efficiently to solve 100,000-node IoT networks within practical time limits, proving to be a reliable and efficient foundation for botnet suppression in complex real-world systems. Full article

Traditional homogeneous materials often face an inherent trade-off between strength and toughness, restricting their application in high-performance impact protection. Mechanical metamaterials overcome this fundamental limitation by integrating structure and material. The 3D-printed Bouligand laminates (3DPBLs), a type of mechanical metamaterial, are renowned for their exceptional impact resistance. While the 3DPBLs have been proven to provide superior resistance under normal impact, actual service conditions inevitably involve complex, multi-directional loading. We aimed to investigate the 3DPBLs’ oblique impact resistance here. To this purpose, samples of 3DPBLs with varying helical angles (0°, 7°, 15°, 60°, 90°) were fabricated and subjected to low-velocity drop-weight impact tests at impact angles of 0°, 30°, 45°, and 60° to evaluate their damage evolution and energy dissipation. The experimental investigation exhibited distinct temporal evolutions of contact forces, with the 15° helical configuration identified as the optimal design. Further numerical analysis using a finite element model (validated with a deviation < 10%) is conducted to simulate performance under diverse impact angles in order to validate the reasonability of the experimental investigation. Mechanistically, 3DPBLs enhance impact resistance by increasing fracture tortuosity through their periodically rotated layered structure. These findings establish a theoretical foundation for developing high-performance, lightweight, and toughened protective materials. Full article

The global exotic pet trade has expanded in recent decades, raising concerns related to animal welfare, biodiversity conservation, and compliance with international regulations. Reptiles and amphibians constitute a major component of this trade, yet information on species availability and trade practices at exotic pet fairs remains limited. The primary aim of this study was to identify the reptile and amphibian species offered for sale at an exotic pet fair in Warsaw, Poland. Secondary objectives were to assess the declared origin of the animals and the availability of information regarding their legal and conservation status. Photographic documentation of all exhibition tables was used to record species identity, number of individuals, and labeling practices. In total, 818 individuals representing 74 species from 31 families were recorded. Reptiles, particularly squamates, dominated the assemblage, while amphibians accounted for a smaller proportion of the animals offered. More than half of the individuals were labeled as captive-bred, whereas only a small fraction were identified as imported or wild-caught; however, information on origin was absent for nearly half of the animals. Over 50% of the recorded species were listed in Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora, yet no visible information on legal or conservation status was provided at the point of sale. These findings indicate that inconsistent labeling limits transparency and informed decision-making by buyers. Full article

High-quality training data are essential for the performance and generalization of artificial intelligence systems, particularly in dynamic environments such as adaptive stream processing for disaster response. However, constructing large and representative datasets remains costly and time-consuming, especially in domains where real data are scarce or difficult to obtain. Large Language Models (LLMs) provide powerful capabilities for synthetic text generation, yet the quality of generated data strongly depends on the design of input prompts. Prompt engineering is therefore critical, but it remains largely manual and difficult to scale, particularly in black-box settings where model internals are inaccessible. This work introduces EVOLMD-MO, a multi-objective evolutionary framework for automated prompt learning aimed at generating high-quality synthetic text datasets using black-box LLMs. The proposed approach formulates prompt optimization as a multi-objective search problem in which candidate prompts evolve through genetic operators guided by two complementary objectives: semantic fidelity to reference data and generative diversity of the produced samples. To support scalable optimization, the framework integrates a modular multi-agent architecture that decouples prompt evolution, LLM interaction, and evaluation mechanisms. The evolutionary process is implemented using the NSGA-II algorithm, enabling the discovery of diverse Pareto-optimal prompts that balance semantic preservation and diversity. Experimental evaluation using large-scale disaster-related social media data demonstrates that the proposed approach consistently improves prompt quality across generations while maintaining a stable trade-off between fidelity and diversity. Compared with a single-objective baseline, EVOLMD-MO explores a significantly broader semantic search space and produces more diverse yet semantically coherent synthetic datasets. These results indicate that multi-objective evolutionary prompt learning constitutes a promising strategy for black-box LLM-driven data generation, with potential applicability to adaptive data analytics and real-time decision-support systems in highly dynamic environments, pending broader validation across domains and models. Full article

In the late 1990s, there was much speculation on what music and music education would look like at the beginning of the 21st century. Few predicted the level of change that we have witnessed since then. In fact, developments in technologies, demographics, societies and global relations that have taken place in the world over the past 100 years would have been neigh unimaginable decade by decade, and keep coming with ever-increasing intensity. Travel, trade and technology have connected people and cultures in myriad and often wonderful ways. But inequities, divisions, and conflicts also reached new heights, with the first half of the 2020s subject to a seemingly endless stream of natural and manmade disasters and conflicts. Inevitably, all of these developments impacted on the world of music in general, and also on music education. In this essay, I try to summarise some key experiences and observations of my own first fifty years of living musical diversity (a world that started to open before me when I began learning Indian sitar in Amsterdam in 1975), and efforts across five continents that I have been involved in or researched. Juxtaposing this with key literature on the topic provides a broad basis for presenting ideas and views on progress towards giving musical practices from across the globe an appropriate place in music education at all levels: in community settings, schools, and institutions for professional training of performers and educators. In that process, I identify three critical junctures which can simultaneously present obstacles and opportunities for positive change: (1) terminologies, social inclusion, and the politics of diversity; (2) musical dynamics, technology, and institutional change; and (3) evolutions and revolutions in music learning and teaching. These inform a challenging but clear agenda for scholars, policy makers, institutional leaders, practising musicians and music educators worldwide who strive for more inclusive, diverse, equitable and relevant practices. Full article

Cooperative multi-UAV pursuit–evasion under occlusions and sensor noise is challenged by intermittent observability of the evader, varying observation-window lengths, and non-stationary evader tactics, all of which destabilize prediction and undermine safety-constrained cooperation. To address these challenges, we propose a safe decision-making framework that uses behavior mode and subgoal inference as intermediate representations for interpretable, uncertainty-aware cooperation. Specifically, an observation-driven generative intent–subgoal model infers the evader’s behavior mode and subgoal from short observation windows. Building on this model, a length-agnostic trajectory predictor is trained via multi-window knowledge distillation and consistency regularization to produce future trajectory predictions with calibrated uncertainty for arbitrary observation-window lengths, thereby reducing cross-window inference inconsistency and lowering online computational cost. Based on these predictions, we derive belief and risk features and develop a belief–risk-gated hierarchical multi-agent policy based on soft actor-critic with a safety projection layer, enabling adaptive strategy switching and a controllable trade-off between efficiency and safety. Experiments in obstacle-rich pursuit–evasion environments with randomized layouts and diverse obstacle configurations demonstrate more stable cooperative capture, safer maneuvering, and lower decision variance than representative baselines, indicating strong robustness and real-time feasibility. Specifically, across different observation-window settings, the proposed method improves the normalized expected return by approximately 5–7% over the strongest baseline and reduces pursuer losses by roughly 22–25%. Moreover, its end-to-end decision latency consistently remains within the 50 ms control cycle. Full article

Madeira’s agricultural sector is characterised by pronounced structural heterogeneity, land fragmentation, and increasing socio-economic and environmental pressures. However, comprehensive typological and spatial analyses remain limited, particularly in small island contexts. This study addresses this gap by providing a typological and spatial analysis of the agrarian structure in the Autonomous Region of Madeira, Portugal, using 2019 Agricultural Census data. An integrated framework combining Principal Component Analysis (PCA), Partitioning Around Medoids (PAM) clustering, and Random Forest validation—representing a novel approach in agrarian typology studies—is employed to identify three agricultural models: Intensive Subtropical Agriculture (24.1% of parishes), characterised by small holdings and high labour intensity; Extensive Traditional Agriculture (64.8%), featuring moderate farm size and diversified cropping; and Pasture-based Agriculture (11.1%), dominated by larger farms and low labour input. The results confirm significant structural trade-offs, including a strong inverse relationship between farm size and labour intensity (r = −0.653) and a negative correlation between specialisation and crop diversity (r = −0.673). Spatially, the models exhibit clear territorial differentiation, with subtropical systems concentrated in southern coastal areas and traditional systems prevailing in northern and interior regions. These findings support the hypothesis of a hybrid agrarian transition. Despite relying on cross-sectional data, the results provide a robust basis for targeted and place-based policy design within the Common Agricultural Policy (CAP) framework. Full article

Although soil nematodes are central to belowground energy flow, how vegetation and soil aggregate characteristics interactively regulate the nematode community structure and energy dynamics remains poorly understood. We investigated 80 soil samples from five vegetation types—Prunus armeniaca L. (AV), Pinus tabuliformis Carrière (PT), Caragana korshinskii (CK), Medicago sativa L. (MS), and native grass Stipa bungeana (SB)—and four aggregate sizes (LMA > 2 mm, MMA 0.25–2 mm, SMA 0.053–0.25 mm, and MA < 0.053 mm) on the Loess Plateau. Vegetation types showed clear functional differentiation, in which AV dominated bacterivore diversity and energy flux in LMA, CK enhanced fungivore and herbivore energy flow, SB supported omnivore–carnivore energy flux, and PT exhibited suppressed communities. Fauna analysis of the EI (enrichment index)–SI (structural index) plot revealed aggregate-dependent food web structuring, where all vegetation types clustered in quadrant C (structured, low enrichment) in small aggregates, while PT and MS shifted to quadrant D (structured, enriched) in larger aggregates. SEM showed that energy flux and energy uniformity are driven by nematode abundance (p < 0.01) and diversity (p < 0.01), respectively, with soil aggregates promoting uniformity (p < 0.05) but suppressing total flux (p < 0.05), thus revealing a trade-off between energy throughput and distribution equity. CK maximizes total energy flux, while AV maintains high energy uniformity; as such, they could be keystone restoration species in the study area. This study provides mechanistic insights into soil food web energetics and offers an empirical foundation for optimizing vegetation restoration strategies on the Loess Plateau. Full article

This paper proposes a collaborative energy management strategy based on power prediction and deep reinforcement learning (DRL) to address the trade-offs among economic efficiency, durability, and dynamic performance in fuel cell hybrid power systems (FCHPS) under dynamic driving conditions. First, a hybrid prediction model termed LSTM-LSSVM with Cascade Correction (LSTM-LSSVM-CC) is developed. The cascade correction (CC) mechanism adopts a hierarchical structure to capture both low-frequency steady-state trends and high-frequency dynamic fluctuations, which are typically challenging for single models to represent. By integrating an online residual correction mechanism, this model generates accurate future power demand sequences. Second, a Dynamic Spatio-Temporal Fusion (DSTF) method is introduced to construct a high-dimensional DRL state space. This approach integrates predicted data, historical residuals, and real-time system states, enabling the agent to perform anticipatory decision-making. Third, a Dynamic Hierarchical Adaptive Multi-Objective Optimization Framework (DHAMOF) is designed. This framework dynamically adjusts objective weights and constraint boundaries based on real-time operating characteristics, enabling adaptive switching of optimization priorities across diverse scenarios. Furthermore, a closed-loop control architecture comprising “prediction–decision–execution–feedback” is established. By incorporating rolling horizon optimization and a proportional-integral (PI) residual compensation mechanism, the proposed architecture effectively suppresses prediction error accumulation and mitigates communication delays. Simulation results under combined CLTC-P and WLTP driving cycles demonstrate that, compared to conventional fixed-weight strategies, the proposed method achieves an 11.3% reduction in hydrogen consumption, a 30.9% decrease in $SOC$ fluctuation range, and a 55.3% reduction in power tracking error. Moreover, under disturbance scenarios involving prediction errors, sensor noise, and a 200 ms communication delay, the system exhibits superior robustness: the increase in hydrogen consumption is limited to within 8.3 g/100 km, and the power tracking error is reduced by 65.6% relative to uncorrected baselines. This collaborative optimization approach overcomes the limitations of traditional open-loop prediction and fixed-weight control, offering a novel technical pathway for the high-efficiency and stable operation of fuel cell hybrid power systems. Full article

The rapid growth of 5G necessitates wireless receivers capable of high-speed, low-latency communication under complex channel conditions. Traditional receivers struggle with the performance–complexity trade-off in massive MIMO systems, where linear detectors underperform and maximum likelihood (ML) detection becomes computationally prohibitive. Deep-learning-based model-driven approaches have demonstrated a favorable balance between detection performance and computational cost. However, despite their algorithmic promise, the transition of these learned detectors into practical, real-time systems is critically hampered by inefficient hardware mapping, resulting in suboptimal throughput, high resource overhead, and limited scalability. To bridge this gap, this paper presents LwAMP-Net, a dedicated FPGA accelerator for a lightweight learned AMP detector. We propose a modular and multi-mode hardware architecture for LwAMP-Net, featuring an outer-product-based dataflow that mitigates pipeline stalls and multi-mode processing elements that adapt to diverse computation patterns. These innovations jointly enhance computational parallelism and resource utilization on the FPGA. Implemented on a Xilinx XC7VX690T FPGA for a 128 × 8 MIMO system with 16QAM, the accelerator achieves a 49.2% higher normalized throughput per iteration, an 85.4% improvement in throughput per LUT slice, and a 12.7% improvement in throughput per DSP compared to the state-of-the-art methods. This work provides a complete architectural solution for deploying high-performance, hardware-efficient learned MIMO detectors in real-world systems. Full article

Amid the wave of digital transformation and customer service automation, traditional chatbots are increasingly challenged by their inability to handle unstructured data and complex queries. This issue is particularly critical in the import trade industry, where customer service representatives must respond promptly to diverse inquiries involving quality anomalies, order tracking, and product substitution. Existing rule-based or keyword-driven chatbots often fail to provide accurate responses, resulting in reduced customer satisfaction and increased operational burdens. This study proposes and implements a “Retrieval-Augmented Generation (RAG)-based Customer Service Chatbot,” integrating the RAG framework with a Neo4j-based knowledge graph, specifically tailored for the import trade domain. The system constructs a dedicated QA dataset, knowledge graph, and dynamic learning mechanism. It semantically vectorizes internal documents, meeting records, quality assurance procedures, and historical dialogues, establishing interrelated knowledge nodes to enhance the chatbot’s comprehension and response accuracy. The study also incorporates GPT-based response evaluation and a high-score caching strategy, enabling dynamic learning and knowledge enhancement. Experiments were conducted using 101 representative enterprise-level queries across six categories, reflecting real-world operational scenarios and inquiry needs. The results demonstrate that the combination of knowledge graphs and RAG technology effectively reduces AI hallucinations and improves response coverage and accuracy, thereby addressing complex problems in customer service applications. This paper not only presents a feasible AI implementation model for the import trading industry but also offers a practical architectural reference for domain-specific knowledge management in the import trade and allied sectors. Full article

This study examines the heterogeneous effects of key macroeconomic determinants on economic growth in selected MENA economies using a pooled quantile regression framework. Unlike conventional mean-based approaches, this method captures variation across different segments of the conditional growth distribution. Using annual data for eight MENA economies over the period 2000–2023, the analysis evaluates how foreign aid, budget deficits, foreign direct investment, and trade openness are associated with growth under different economic conditions. The results reveal that foreign aid and fiscal deficits are more strongly associated with growth at lower quantiles, indicating greater relevance during low-growth episodes, while their effects weaken or become insignificant at higher quantiles. In contrast, foreign direct investment shows a stronger and more consistent positive association at higher quantiles, whereas trade openness is mainly significant at the lower end of the growth distribution. These findings provide distribution-sensitive evidence on growth determinants in structurally diverse MENA economies. From a policy perspective, the results suggest that macroeconomic strategies should be tailored to country-specific growth conditions rather than relying on uniform policy frameworks across the region. Full article

Nanocellulose materials (CNMs), encompassing cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs), and bacterial nanocellulose (BNC), have emerged as a versatile and sustainable class of bio-based nanomaterials with significant promise for applications in mechanical engineering. This review systematically examines the processing of nanocellulose via mechanical, chemical, and enzymatic routes, alongside surface modification strategies that enhance performance and address scalability challenges. A principal advantage of CNMs lies in their exceptional mechanical properties, including superior strength, stiffness, and toughness, which position them as high-performance, sustainable reinforcement agents for advanced composites. Beyond mechanical reinforcement, CNMs exhibit a suite of functional properties critical for engineering design, such as thermal stability, tunable conductivity, effective gas/moisture barrier performance, and improved tribological behavior. These characteristics enable their use in diverse high-value applications, including lightweight composites, protective coatings, energy storage devices, sensors, actuators, and intelligent material systems. Furthermore, the inherent renewability, biodegradability, and recyclability of nanocellulose align closely with the principles of a circular economy and green engineering. However, the successful integration of CNMs into mainstream manufacturing requires overcoming key challenges. These include the energy intensity of certain production processes, inherent moisture sensitivity, long-term stability under operational conditions, and compatibility with established industrial techniques. Life-cycle analyses reveal important environmental trade-offs that must be navigated. Overall, nanocellulose represents a renewable, multi-functional material platform whose unique combination of mechanical performance, functional versatility, and environmental benefits is poised to drive innovation in next-generation engineering materials. Full article