Search Results (2,071)

Intelligent sensing systems are increasingly used in wearable devices, enabling advanced tasks across various application domains including robotics and human–machine interaction. Ensuring these systems are energy autonomous is highly demanded, despite strict constraints on power, memory and processing resources. To meet these requirements, embedded neural networks must be optimized to achieve a balance between accuracy and efficiency. This paper presents an integrated approach that combines Hardware-Aware Neural Architecture Search (HW-NAS) with optimization techniques—weight reshaping, quantization, and their combination—to develop efficient classifiers for a multisensory glove. HW-NAS automatically derives 1D-CNN models tailored to the NUCLEO-F401RE board, while the additional optimization further reduces model size, memory usage, and latency. Across three datasets, the optimized models not only improve classification accuracy but also deliver an average reduction of 75% in inference time, 69% in flash memory, and more than 45% in RAM compared to NAS-only baselines. These results highlight the effectiveness of integrating NAS with optimization techniques, paving the way towards energy-autonomous wearable systems. Full article

Relation extraction in the equipment domain often exhibits asymmetric patterns, where entities participate in multiple overlapping relations that break the expected structural symmetry of semantic associations. Such asymmetry increases task complexity and reduces extraction accuracy in conventional approaches. To address this issue, we propose a symmetry- and asymmetry-aware dual-path retrieval and in-context learning-based large language model. Specifically, the BGE-M3 embedding model is fine-tuned for domain-specific adaptation, and a multi-level retrieval database is constructed to capture both global semantic symmetry at the sentence level and local asymmetric interactions at the relation level. A dual-path retrieval strategy, combined with Reciprocal Rank Fusion, integrates these complementary perspectives, while task-specific prompt templates further enhance extraction accuracy. Experimental results demonstrate that our method not only mitigates the challenges posed by overlapping and asymmetric relations but also leverages the latent symmetry of semantic structures to improve performance. Experimental results show that our approach effectively mitigates challenges from overlapping and asymmetric relations while exploiting latent semantic symmetry, achieving an F1-score of 88.53%, a 1.86% improvement over the strongest baseline (GPT-RE). Full article

Household food waste remains a persistent challenge despite widespread pro-environmental intentions. Drawing on Brehm’s Motivational Intensity Theory, this study examined how perceived difficulty and perceived ability interact with motivation to predict self-reported food waste. We surveyed 939 participants in Flanders and Spain, measuring motivation to avoid waste, self-rated perceived ability to manage food, meal planning perceived difficulty, and food waste. Moderated moderation analyses revealed that motivation and perceived ability each independently predicted lower waste. Crucially, a significant three-way interaction showed that motivation most effectively reduced waste when perceived difficulty was low and perceived ability was high; when perceived difficulty exceeded perceived ability, motivation had no mitigating effect. These findings underscore that effort mobilization influenced by both individual capacity and situational demands is key to closing the intention–behavior gap in food waste. Practically, interventions should go beyond raising awareness to simplify tasks and bolster consumers’ skills, aligning action demands with realistic effort levels. Full article

As urbanization intensifies, the challenge of preserving industrial heritage while fostering authentic intergenerational connections has become increasingly salient. This study investigates how artificial intelligence (AI) and augmented reality (AR) technologies can be applied to enhance authenticity and promote both hedonic and eudaimonic well-being within the context of heritage tourism. Using a facility in Shanghai as a case study, we propose a cultural co-creation mechanism that transforms implicit intergenerational memories into shared cultural resources through digital interaction. The study first evaluates public awareness and participation needs in the context of industrial heritage revitalization. In response, we design an immersive platform that enables visitors of different generations to co-create meaning through historical scene reconstruction, multisensory engagement, and collaborative storytelling. A novel five-sense encoding strategy is introduced to reinterpret the enclosed spatial characteristics of industrial architecture as an experiential form of storytelling. This process fosters a deeper connection to place, contributing to authenticity and well-being. Prototype testing results suggest that this AI-AR-enabled co-creation system supports meaningful cultural attachment, improves authenticity, and facilitates the sustainable transmission of heritage. This research provides a replicable model for integrating digital technology, community participation, and authenticity in the well-being-oriented revitalization of industrial heritage sites. Full article

Cyberattacks on pipeline operational technology systems pose growing risks to energy infrastructure. This study develops a physics-informed simulation and optimization framework for analyzing cyber–physical threats in petroleum pipeline networks. The model integrates networked hydraulic dynamics, SCADA-based state estimation, model predictive control (MPC), and a bilevel formulation for stealthy false-data injection (FDI) attacks. Pipeline flow and pressure dynamics are modeled on a directed graph using nodal pressure evolution and edge-based Weymouth-type relations, including control-aware equipment such as valves and compressors. An extended Kalman filter estimates the full network state from partial SCADA telemetry. The controller computes pressure-safe control inputs via MPC under actuator constraints and forecasted demands. Adversarial manipulation is formalized as a bilevel optimization problem where an attacker perturbs sensor data to degrade throughput while remaining undetected by bad-data detectors. This attack–control interaction is solved via Karush–Kuhn–Tucker (KKT) reformulation, which results in a tractable mixed-integer quadratic program. Test gas pipeline case studies demonstrate the covert reduction in service delivery under attack. Results show that undetectable attacks can cause sustained throughput loss with minimal instantaneous deviation. This reveals the need for integrated detection and control strategies in cyber–physical infrastructure. Full article

Sustainability concerns and rising consumer environmental awareness (CEA) have fundamentally reshaped competitive dynamics in modern supply chains. This study examines the influence of CEA on pricing and environmental effort competition between store brand (SB) and national brand (NB) products in a two-stage supply chain with one manufacturer and one retailer. We develop a mathematical model to evaluate strategic interactions under three power structures: Manufacturer Stackelberg (MS), Retailer Stackelberg (RS), and Vertical Nash (VN), considering two environmental investment scenarios: NB-only investment and bilateral SB-NB investment. Our findings indicate that (i) when only NB products invest environmentally, CEA increases environmental effort levels, wholesale prices, and retail prices for both brands, expanding total channel value rather than merely redistributing profits; (ii) CEA and channel competition on jointly determine optimal channel power structure, with MS dominating in differentiated markets with low CEA while RS yields superior outcomes under high competition and high CEA; (iii) retailers consistently achieve maximum profits under VN structure through balanced negotiation positions; and (iv) bilateral environmental investment causes price convergence across structures, shifting competitive focus from governance to operational excellence. By integrating environmental investment, channel power structure, and channel competition into a unified framework, this study offers managers practical decision tools for selecting optimal channel structures based on observable market conditions. Furthermore, it demonstrates how grocery retail chains and consumer goods manufacturers can transform environmental initiatives from compliance costs into value creation mechanisms that enhance both profitability and sustainability. Full article

In recent years, convolutional neural network-based object detectors have achieved extensive applications in remote sensing (RS) image interpretation. While multi-scale feature modeling optimization remains a persistent research focus, existing methods frequently overlook the symmetrical balance between feature granularity and morphological diversity, particularly when handling high-aspect-ratio RS targets with anisotropic geometries. This oversight leads to suboptimal feature representations characterized by spatial sparsity and directional bias. To address this challenge, we propose the Parallel Interleaved Convolutional Kernel Network (PICK-Net), a rotation-aware detection framework that embodies symmetry principles through dual-path feature modulation and geometrically balanced operator design. The core innovation lies in the synergistic integration of cascaded dynamic sparse sampling and symmetrically decoupled feature modulation, enabling adaptive morphological modeling of RS targets. Specifically, the Parallel Interleaved Convolution (PIC) module establishes symmetric computation patterns through mirrored kernel arrangements, effectively reducing computational redundancy while preserving directional completeness through rotational symmetry-enhanced receptive field optimization. Complementing this, the Global Complementary Attention Mechanism (GCAM) introduces bidirectional symmetry in feature recalibration, decoupling channel-wise and spatial-wise adaptations through orthogonal attention pathways that maintain equilibrium in gradient propagation. Extensive experiments on RSOD and NWPU-VHR-10 datasets demonstrate our superior performance, achieving 92.2% and 84.90% mAP, respectively, outperforming state-of-the-art methods including EfficientNet and YOLOv8. With only 12.5 M parameters, the framework achieves symmetrical optimization of accuracy-efficiency trade-offs. Ablation studies confirm that the symmetric interaction between PIC and GCAM enhances detection performance by 2.75%, particularly excelling in scenarios requiring geometric symmetry preservation, such as dense target clusters and extreme scale variations. Cross-domain validation on agricultural pest datasets further verifies its rotational symmetry generalization capability, demonstrating 84.90% accuracy in fine-grained orientation-sensitive detection tasks. Full article

Microplastic (MPL) and nanoplastic (NPL) contamination in soils is widespread, impacting soil invertebrates, microbial communities, and soil–plant systems. Here, we compiled the information from 100 research articles from 2018 onwards to enhance and synthesize the status quo of MPLs’ and NPLs’ impacts on such groups. The effects of these pollutants depend on multiple factors, including polymer composition, size, shape, concentration, and aging processes. Research on soil invertebrates has focused on earthworms and some studies on nematodes and collembolans, but studies are still limited to other groups, such as mites, millipedes, and insect larvae. Beyond soil invertebrates, plastics are also altering microbial communities at the soil–plastic interface, fostering the development of specialized microbial assemblages and shifting microbial functions in ways that remain poorly understood. Research has largely centered on bacterial interactions with MPLs, leaving understudied fungi, protists, and other soil microorganisms. Furthermore, MPLs and NPLs also interact with terrestrial plants, and their harmful effects, such as adsorption, uptake, translocation, and pathogen vectors, raise public awareness. Given the complexity of these interactions, well-replicated experiments and community- and ecosystem-level studies employing objective-driven technologies can provide insights into how MPLs and NPLs influence microbial and faunal diversity, functional traits, and soil ecosystem stability. Full article

With the in-depth integration of electric vehicles (EVs) and smart grids, the Cyber–Physical System for Vehicle–Grid (CPSVG) has become a crucial component of power systems. However, its inherent characteristic of deep cyber–physical coupling also renders it vulnerable to cyberattacks, particularly False Data Injection Attacks (FDIAs), which pose a severe threat to the safe and stable operation of the system. To address this challenge, this paper proposes an FDIA defense method based on K-Nearest Neighbor (KNN) and Graph Autoencoder (GAE). The method first employs the KNN algorithm to locate abnormal data in the system and identify the attacked nodes. Subsequently, Graph Autoencoder is utilized to reconstruct the tampered and contaminated data with high fidelity, restoring the accuracy and integrity of the data. Simulation verification was conducted in a typical vehicle–grid interaction system scenario. The results demonstrate that, compared with various scenarios such as no defense, traditional detection mechanisms, and only location-based data elimination, the proposed KNN-GAE method can more accurately identify and repair all attacked data. It provides reliable data input that is closest to the true values for subsequent state estimation, thereby significantly enhancing the system’s state awareness capability and operational stability after an attack. This study offers new insights and effective technical means for ensuring the security defense of the Vehicle–Grid Interaction Cyber–Physical System. Full article

Decision Support Systems (DSSs) are increasingly shaping high-performance sport by translating complex time series data into actionable insights for coaches and practitioners. This paper outlines a structured, five-stage DSS development pipeline, grounded in the Schelling and Robertson framework, and demonstrates its application in professional basketball. Using changepoint analysis, we present a novel approach to dynamically quantify Most Demanding Scenarios (MDSs) using high-resolution optical tracking data in this context. Unlike fixed-window methods, this approach adapts scenario duration to real performance, improving the ecological validity and practical interpretation of MDS metrics for athlete profiling, benchmarking, and training prescription. The system is realized as an interactive web dashboard, providing intuitive visualizations and individualized feedback by integrating validated workload metrics with contextual game information. Practitioners can rapidly distinguish normative from outlier performance periods, guiding recovery and conditioning strategies, and more accurately replicating game demands in training. While illustrated in basketball, the pipeline and principles are broadly transferable, offering a replicable blueprint for integrating context-aware analytics and enhancing data-driven decision-making in elite sport. Full article

In recent years, the convenience and potential for information extraction offered by Remote Sensing Image–Text Retrieval (RSITR) have made it a significant focus of research in remote sensing (RS) knowledge services. Current mainstream methods for RSITR generally align fused image features at multiple scales with textual features, primarily focusing on the local information of RS images while neglecting potential semantic information. This results in insufficient alignment in the cross-modal semantic space. To overcome this limitation, we propose a Multi-Scale Semantic-Aware Remote Sensing Image–Text Retrieval method (MSSA). This method introduces Progressive Spatial Channel Joint Attention (PSCJA), which enhances the expressive capability of multi-scale image features through Window-Region-Global Progressive Attention (WRGPA) and Segmented Channel Attention (SCA). Additionally, the Image-Guided Text Attention (IGTA) mechanism dynamically adjust textual attention weights based on visual context. Furthermore, the Cross-Modal Semantic Extraction Module (CMSE) incorporated learnable semantic tokens at each scale, enabling attention interaction between multi-scale features of different modalities and the capturing of hierarchical semantic associations. This multi-scale semantic-guided retrieval method ensures cross-modal semantic consistency, significantly improving the accuracy of cross-modal retrieval in RS. MSSA demonstrates superior retrieval accuracy in experiments across three baseline datasets, achieving a new state-of-the-art performance. Full article

Background and Clinical Significance: Granulomatous reactions are rare but clinically significant complications of aesthetic procedures involving dermal fillers, particularly in individuals with underlying immune dysregulation. These reactions present diagnostic and therapeutic challenges, especially when associated with undiagnosed or latent autoimmune diseases. This case illustrates the interaction between filler composition, immune status, and the risk of delayed inflammatory responses, underscoring the need for thorough patient evaluation and individualized management strategies. Case Presentation: A 49-year-old woman developed delayed-onset subcutaneous nodules following midface augmentation with two filler types: a monophasic, cross-linked hyaluronic acid gel (concentration 20 mg/mL, 1.0 mL per side) injected into the deep malar fat pads, and a calcium hydroxyapatite suspension (30% CaHA microspheres in a carboxymethylcellulose carrier, 0.5 mL per side) placed in the subdermal plane along the zygomatic arch. The procedure was performed in a single session using a 22 G blunt cannula, with no immediate adverse events. High-resolution ultrasound demonstrated hypoechoic inflammatory nodules without systemic symptoms. A retrospective review of her medical history revealed a latent, previously undisclosed diagnosis of granulomatosis with polyangiitis (GPA). The immune-adjuvant properties of calcium hydroxyapatite likely triggered a localized pro-inflammatory response in this predisposed patient. A conservative, staged, non-invasive therapeutic protocol—saline infiltration, intradermal polynucleotide injections, and manual lymphatic drainage—achieved complete clinical and radiological resolution without systemic immunosuppression or surgical intervention. Conclusions: This case highlights the critical importance of pre-procedural immunological assessment in aesthetic medicine. Subclinical autoimmune conditions may predispose patients to delayed granulomatous reactions after filler injections. An individualized, conservative management strategy can effectively resolve such complications while minimizing the risks associated with aggressive treatment. Greater awareness of immune-mediated responses to dermal fillers is essential to ensure patient safety and optimize clinical outcomes. Full article

Recipes available on the web often lack nutritional transparency and clear indicators of dietary suitability. While searching by title is straightforward, exploring recipes that meet combined dietary needs, nutritional goals, and ingredient-level preferences remains challenging. Most existing recipe search systems do not effectively support flexible multi-dietary reasoning in combination with user preferences and restrictions. For example, users may seek gluten-free and dairy-free dinners with suitable substitutions, or compound goals such as vegan and low-fat desserts. Recent systematic reviews report that most food recommender systems are content-based and often non-personalized, with limited support for dietary restrictions, ingredient-level exclusions, and multi-criteria nutrition goals. This paper introduces DietQA, an end-to-end, language-adaptable chatbot system that integrates a Knowledge Graph (KG), Retrieval-Augmented Generation (RAG), and a Large Language Model (LLM) to support personalized, dietary-aware recipe search and question answering. DietQA crawls Greek-language recipe websites to extract structured information such as titles, ingredients, and quantities. Nutritional values are calculated using validated food composition databases, and dietary tags are inferred automatically based on ingredient composition. All information is stored in a Neo4j-based knowledge graph, enabling flexible querying via Cypher. Users interact with the system through a natural language chatbot friendly interface, where they can express preferences for ingredients, nutrients, dishes, and diets, and filter recipes based on multiple factors such as ingredient availability, exclusions, and nutritional goals. DietQA supports multi-diet recipe search by retrieving both compliant recipes and those adaptable via ingredient substitutions, explaining how each result aligns with user preferences and constraints. An LLM extracts intents and entities from user queries to support rule-based Cypher retrieval, while the RAG pipeline generates contextualized responses using the user query and preferences, retrieved recipes, statistical summaries, and substitution logic. The system integrates real-time updates of recipe and nutritional data, supporting up-to-date, relevant, and personalized recommendations. It is designed for language-adaptable deployment and has been developed and evaluated using Greek-language content. DietQA provides a scalable framework for transparent and adaptive dietary recommendation systems powered by conversational AI. Full article

Visual impairment affects millions worldwide, creating significant barriers to environmental interaction and independence. Existing assistive technologies often rely on cloud-based processing, raising privacy concerns and limiting accessibility in resource-constrained environments. This paper explores the integration and potential of open-source AI models in developing a fully offline assistive system that can be locally set up and operated to support visually impaired individuals. Built on a Raspberry Pi 5, the system combines real-time object detection (YOLOv8), optical character recognition (Tesseract), face recognition with voice-guided registration, and offline voice command control (VOSK), delivering hands-free multimodal interaction without dependence on cloud infrastructure. Audio feedback is generated using Piper for real-time environmental awareness. Designed to prioritize user privacy, low latency, and affordability, the platform demonstrates that effective assistive functionality can be achieved using only open-source tools on low-power edge hardware. Evaluation results in controlled conditions show 75–90% detection and recognition accuracies, with sub-second response times, confirming the feasibility of deploying such systems in privacy-sensitive or resource-constrained environments. Full article

Background: Antimicrobial resistance (AMR) is a critical global health issue. Like other low- and middle-income countries, the misuse of antimicrobial medicine, including widespread self-medication, exacerbates AMR in Bangladesh. Making future generations aware of AMR through educational interventions is an effective tool in combating AMR. This research focuses on understanding the effects of AMR awareness interventions on the knowledge, attitudes, and behaviors of the schoolchildren in the selected district of Bangladesh. Methods: In this study, 241 students of the 12- to 16-year-old age group participated in a two-day program. The programs include four hours of activities, including reading comics and coloring books, presentations, quizzes, and watching an animation about AMR on the first day, followed by an art competition on the second day. To assess changes in knowledge earlier and after the intervention, pre- and post-tests were conducted. Results: This pilot study demonstrates that using age-appropriate interactive educational tools can significantly improve students’ knowledge about AMR, showing a mean difference of 1.28 (p < 0.001). The regulatory step of the Directorate General of Drug Administration, incorporating red identification marks on antibiotic packaging, makes it easier and shows that 93.36% of students could identify antibiotics, which helps them to be aware of these types of medicines. Interventions were equally effective for boys and girls and science and commerce students, and these helped participants recognize the inappropriate practices of antibiotic use in their daily lives. Conclusions: This study identified the importance of incorporating AMR issues into the educational curriculum to address AMR for future generations. Full article