Search Results (3,054)

The accurate and efficient placement of traffic cones is a critical safety and logistical requirement in diverse industrial environments. This study introduces a novel dataset specifically designed for the near-overhead detection of traffic cones, containing both bounding box annotations and apex keypoints. Leveraging this dataset, we systematically evaluated whether classical object detection methods or keypoint-based detection methods are more effective for the task of cone apex localization. Several state-of-the-art YOLO-based architectures (YOLOv8, YOLOv11, YOLOv12) were trained and tested under identical conditions. The comparative experiments showed that both approaches can achieve high accuracy, but they differ in their trade-offs between robustness, computational cost, and suitability for real-time embedded deployment. These findings highlight the importance of dataset design for specialized viewpoints and confirm that lightweight YOLO models are particularly well-suited for resource-constrained robotic platforms. The key contributions of this work are the introduction of a new annotated dataset for overhead cone detection and a systematic comparison of object detection and keypoint detection paradigms for apex localization in real-world robotic applications. Full article

Smart crop technologies offer promising solutions for enhancing agricultural productivity and sustainability, particularly in the face of global challenges such as resource scarcity and climate variability. However, their deployment in infrastructure-limited regions, especially across Africa, faces persistent barriers, including unreliable power supply, intermittent internet connectivity, and limited access to technical expertise. This study presents a PRISMA-guided systematic review of literature published between 2015 and 2025, sourced from the Scopus database including indexed content from ScienceDirect and IEEE Xplore. It focuses on key technological components including multimodal sensing, data fusion, IoT resource management, edge-cloud integration, and adaptive network design. The analysis of these references reveals a clear trend of increasing research volume and a major shift in focus from foundational unimodal sensing and cloud computing to more complex solutions involving machine learning post-2019. This review identifies critical gaps in existing research, particularly the lack of integrated frameworks for effective multimodal sensing, data fusion, and real-time decision support in low-resource agricultural contexts. To address this, we categorize multimodal sensing approaches and then provide a structured taxonomy of multimodal data fusion approaches for real-time monitoring and decision support. The review also evaluates the role of IoT virtualization as a pathway to scalable, adaptive sensing systems, and analyzes strategies for overcoming infrastructure constraints. This study contributes a comprehensive overview of smart crop technologies suited to infrastructure-limited agricultural contexts and offers strategic recommendations for deploying resilient smart agriculture solutions under connectivity and power constraints. These findings provide actionable insights for researchers, technologists, and policymakers aiming to develop sustainable and context-aware agricultural innovations in underserved regions. Full article

During the transition from urban expansion to renewal, optimizing environmental comfort under resource constraints presents critical challenges. While existing research confirms that multisensory interactions critically shape environmental comfort, these insights are rarely operationalized into protocols for resource-constrained contexts. Focusing on historic urban quarters that need to balance modification and preservation, this study quantifies multisensory (acoustic, visual, thermal) interactions and integrations to establish operational resource-optimization strategies. Through laboratory reproduction of 144 field-based experimental conditions (4 sound sources × 3 sound pressure levels × 4 green view indexes × 3 air temperatures), systematic subjective evaluations of acoustic, visual, thermal, and overall comfort were obtained. Key findings demonstrate: (1) Eliminating extreme comfort evaluations (e.g., “very uncomfortable”) within any single sensory domain stabilizes cross-sensory contributions to overall comfort, ensuring predictable cross-domain compensations and safeguarding resource efficacy; (2) Accumulating modest improvements across ≥2 sensory domains reduces per-domain performance threshold for satisfactory overall comfort, enabling constraint resolution (e.g., visual modification limits in historic districts); (3) Cross-domain optimization of environmental factors (e.g., sound source and air temperature) generates mutual enhancement effects, maximizing resource economy, whereas intra-domain optimization (e.g., sound source and sound pressure level) induces competitive inefficiencies. Collectively, these principles establish operational strategies for resource-constrained environmental improvements, advancing sustainable design and governance through evidence-based multisensory approaches. Full article

Rapid and accurate detection of trapped victims is vital in disaster rescue operations, yet most existing object detection methods cannot simultaneously deliver high accuracy and fast inference under resource-constrained conditions. To address this limitation, we propose the LightSeek-YOLO, a lightweight, real-time victim detection framework for disaster scenarios built upon YOLOv11. Our LightSeek-YOLO integrates three core innovations. First, it employs HGNetV2 as the backbone, whose HGStem and HGBlock modules leverage depthwise separable convolutions to markedly reduce computational cost while preserving feature extraction. Secondly, it introduces Seek-DS (Seek-DownSampling), a dual-branch downsampling module that preserves key feature extrema through a MaxPool branch while capturing spatial patterns via a progressive convolution branch, thereby effectively mitigating background interference. Third, it incorporates Seek-DH (Seek Detection Head), a lightweight detection head that processes features through a unified pipeline, enhancing scale adaptability while reducing parameter redundancy. Evaluated on the common C2A disaster dataset, LightSeek-YOLO achieves 0.478 AP@small for small-object detection, demonstrating strong robustness in challenging conditions such as rubble and smoke. Moreover, on the COCO, it reaches 0.473 mAP@[0.5:0.95], matching YOLOv8n while achieving superior computational efficiency through 38.2% parameter reduction and 39.5% FLOP reduction, and achieving 571.72 FPS on desktop hardware, with computational efficiency improvements suggesting potential for edge deployment pending validation. Full article

Larch shoot blight, caused by Neofusicoccum laricinum, threatens global larch resources, while conventional chemical control is constrained by pollution and resistance. To address this gap, we integrated metabolomics, transcriptomics, and antifungal efficacy assays to identify Fraxetin, a disease-induced phytoalexin, and to elucidate its antifungal activity and mechanism. Metabolomics showed infection-triggered accumulation of Fraxetin in resistant Larix olgensis shoots. Antifungal experiments showed that within the range of 68–1088 μg/mL, the optimal antifungal concentration was 1088 μg/mL. When inoculated larches were treated with 1088 μg/mL Fraxetin, the maximum inhibition rate of pathogen growth reached 66.67% within 12 days, and the symptoms of the treated plants were alleviated. Transcriptomics revealed activation of damage responses, disruption of oxidative homeostasis, and compromised membrane integrity in the pathogen under Fraxetin treatment. Physiological measurements confirmed increased lipid peroxidation, redox collapse, membrane leakage, and reduced fungal viability. These findings indicate a lipid peroxidation–mediated oxidative–membrane mode of action and support the potential of plant-derived Fraxetin for more sustainable management of larch shoot blight. Full article

This paper proposes TWISS (TOPSIS + Wrapper Incremental Subset Selection), a novel hybrid feature selection framework designed for electromyographic (EMG) pattern recognition in upper-limb prosthetic control. TWISS integrates the multi-criteria decision-making method TOPSIS with a forward wrapper search strategy, enabling subject-specific feature optimization based on a ranking that combines filter metrics, including Chi-squared, ANOVA, and Mutual Information. Unlike conventional static feature sets, such as the Hudgins configuration (48 features: four per channel, 12 channels) or All Features (192 features: 16 per channel, 12 channels), TWISS dynamically adapts feature subsets to each subject, addressing inter-subject variability and classification robustness challenges in EMG systems. The proposed algorithm was evaluated on the publicly available Ninapro DB7 dataset, comprising both intact and transradial amputee participants, and implemented in an open-source, fully reproducible environment. Two Google Colab tools were developed to support diverse workflows: one for end-to-end feature extraction and selection, and another for selection on precomputed feature sets. Experimental results demonstrated that TWISS achieved a median F1-macro score of 0.6614 with Logistic Regression, outperforming the All Features set (0.6536) and significantly surpassing the Hudgins set (0.5626) while reducing feature dimensionality. TWISS offers a scalable and computationally efficient solution for feature selection in biomedical signal processing and beyond, promoting the development of personalized, low-cost prosthetic control systems and other resource-constrained applications. Full article

Air pollution has become a significant concern for human health, especially in developing countries. Among Primary Pollutants, particulate matter 2.5 (${\mathrm{PM}}_{2.5}$), refers to airborne particles which have a diameter of 2.5 micrometres or less, and has become a widely used measure for monitoring air quality globally. The standard go-to method usually uses Federal Reference Grade sensors to understand air quality. But, they are quite cost-prohibitive, so the popular alternative is low-cost (LC) air quality sensors. Even LC air quality monitors do not cover many areas, especially across the global south. On the other hand, the ubiquitous use of online social media OSM has led to its evolution in participatory sensing. While it does not function as a physical sensor, it can be a proxy indicator of public perception on the topic under study. OSM platforms such as Twitter/X and Reddit have already demonstrated their value in understanding human perception across various domains, including air quality monitoring. This study focuses on understanding air pollution in a resource-constrained setting by examining how the community perception on social media can complement traditional monitoring. We leverage metadata readily available from social media user data to find patterns with air quality fluctuations before and during the pandemic. We use the US Embassy ${\mathrm{PM}}_{2.5}$ data for baseline measurement. In the study, we empirically analyse the variations in quantitative & intent-based community perception in seasonal & pandemic outbreaks with varying air quality. We compare the baseline against temporal & user-specific attributes of Twitter/X relating to tweets like daily frequency of tweets, tweet lags 1–5, user followers, user verified, and user lists memberships across two timelines: pre-COVID-19 (20 March 2019– 29 February 2020) & COVID-19 (1 March 2020–20 September 2020). Our analysis examines both the quantitative and the intent-based community engagement, highlighting the significance of features like user authenticity, tweet recurrence rates, and intensity of participation. Furthermore, we show how behavioural patterns in the online discussions diverged across the two periods, which reflected the broader shifts in the air pollution levels and the public attention. This study empirically demonstrates the significance of X/Twitter metadata, beyond standard tweet content, and provides additional features for modelling and understanding air quality in developing countries. Full article

Larix gmelinii is the fourth most important tree species in China and a typical zonal climax species in the cold temperate region, with high ecological and resource value. However, intensive logging, high-density afforestation, and insufficient scientific management have led to overly dense, homogeneous, and unstable plantations, severely limiting productivity. To clarify the mechanisms by which structural dynamics regulate productivity, we established a space-for-time sequence (T1–T3, T2-D, CK) under a consistent early-tending background. Using the “1 + 4” nearest-neighbor framework and six spatial structural parameters, we developed tree and forest spatial structure indices (TSSI and FSSI) and integrated nine structural–functional indicators for multivariate analysis. The results showed that TSSI and FSSI effectively characterized multi-level stability and supported stability classification. Along the stand-age gradient, structural stability and spatial use efficiency improved significantly, with FSSI and biomass per hectare (BPH) increasing by 91% and 18% from T1 to T3, though a “structural improvement–functional lag” occurred at T2. Moderate thinning markedly optimized stand configuration, reducing low-stability individuals from 86.45% in T1 to 42.65% in T2-D, while DBH, crown width, FSSI, and BPH (229.87 t·hm⁻²) increased to near natural-forest levels. At the tree scale, DBH, tree height, crown width, and TSSI were positive drivers, whereas a high height–diameter ratio (HDR) constrained growth. At the stand scale, canopy density, species richness, and mean DBH promoted FSSI and BPH, while mean HDR and stand density imposed major constraints. A critical management window was identified when DBH < 25 cm, HDR > 10, and TSSI < 0.25 (approximately 10–30 years post-planting). We propose a stepwise, moderate, and targeted thinning strategy with necessary underplanting to reduce density and slenderness, increase diameter and canopy structure, and enhance diversity, thereby accelerating the synergy between stability and productivity. This framework provides a practical pathway for the scientific management and high-quality development of L. gmelinii plantations. Full article

With the proliferation of image-capturing and display-enabled IoT devices, ensuring the authenticity and integrity of visual data has become increasingly critical, especially in light of emerging cybersecurity threats and powerful generative AI tools. One of the major challenges in such sensor-based systems is the ability to protect privacy while maintaining data usability. Reversible data hiding has attracted growing attention due to its reversibility and ease of implementation, making it a viable solution for secure image communication in IoT environments. In this paper, we propose reversible data hiding techniques tailored to the content characteristics of images. Our approach leverages subsampling and quadtree partitioning, combined with multi-stage prediction schemes, to generate a predicted image aligned with the original. Secret information is embedded by analyzing the difference histogram between the original and predicted images, and enhanced through multi-round rotation techniques and a multi-level embedding strategy to boost capacity. By employing both subsampling and quadtree decomposition, the embedding strategy dynamically adapts to the inherent characteristics of the input image. Furthermore, we investigate the trade-off between embedding capacity and marked image quality. Experimental results demonstrate improved embedding performance, high visual fidelity, and low implementation complexity, highlighting the method’s suitability for resource-constrained IoT applications. Full article

Wi-Fi sensing has emerged as a powerful approach to Human Activity Recognition (HAR) by utilizing Channel State Information (CSI). However, current implementations face two significant challenges: reliance on firmware-modified hardware for CSI collection and dependence on GPU/cloud-based deep learning models for inference. To address these limitations, we propose a two-fold embedded solution: a novel CSI collection tool built on low-cost microcontrollers that surpass existing embedded alternatives in packet rate efficiency under standard baud rate conditions and an optimized DenseNet-based HAR model deployable on resource-constrained edge devices without cloud dependency. In addition, a new HAR dataset is presented. To deal with the scarcity of training data, an Empirical Mode Decomposition (EMD)-based data augmentation method is presented. With this strategy, it was possible to enhance model accuracy from 59.91% to 97.55%. Leveraging this enhanced dataset, a compact DenseNet variant is presented. An accuracy of 92.43% at 232 ms inference latency is achieved when implemented on an ESP32-S3 microcontroller. Using as little as 127 kB of memory, the proposed model offers acceptable performance in terms of accuracy and privacy-preserving HAR at the edge; it also represents a scalable and low-cost Wi-Fi sensing solution. Full article

The HIV-1 epidemic continues to challenge global public health, especially in sub-Saharan Africa. The rise in drug-resistant viruses, particularly pan-resistant strains, threatens treatment effectiveness, hindering progress toward UNAIDS viral suppression goals. This is critical in low-to-middle income countries (LMICs) like Zimbabwe, where treatment options and access to drug resistance testing are limited. This cross-sectional study analyzed 102 genotypes from patients with HIV-1 RNA ≥ 1000 copies/mL after at least 6 months on a dolutegravir (DTG)-based ART. HIV-1 genotyping and drug resistance interpretation were performed using the Stanford HIV Drug Resistance Database. Overall, 62% of genotypes harbored at least one drug resistance mutation, with 27% showing integrase strand transfer inhibitor (INSTI)-associated mutations. High-level resistance to DTG and cabotegravir was found in 14% and 23% of integrase sequences, respectively, primarily driven by G118R and E138K/T mutations. Pan-resistance was observed in 18% of complete genotypes, with one case of four class resistance. These results highlight the emergence of INSTI resistance in LMICs. The study underscores the urgent need for enhanced HIV drug resistance testing, continuous surveillance, and strategic optimization of ART regimens in resource-constrained settings to ensure effective HIV management. Full article

Industrial control systems (ICSs) face escalating security challenges due to evolving cyber threats and the inherent limitations of traditional intrusion detection methods, which fail to adequately model spatiotemporal dependencies or interpret complex protocol semantics. To address these gaps, this paper proposes DLG–IDS —a lightweight intrusion detection framework that innovatively integrates dynamic graph construction for capturing real–time device interactions and logical control relationships from traffic, LLM–driven semantic enhancement to extract fine–grained embeddings from graphs, and a spatio–temporal graph neural network (STGNN) optimized via sparse attention and local window Transformers to minimize computational overhead. Evaluations on SWaT and SBFF datasets demonstrate the framework’s superiority, achieving a state–of–the–art accuracy of 0.986 while reducing latency by 53.2% compared to baseline models. Ablation studies further validate the critical contributions of semantic fusion, sparse topology modeling, and localized temporal attention. The proposed solution establishes a robust, real–time detection mechanism tailored for resource–constrained industrial environments, effectively balancing high accuracy with operational efficiency. Full article

While insulator integrity is critical for power grid stability, prevailing detection algorithms often rely on computationally intensive models incompatible with resource-constrained edge devices like unmanned aerial vehicles (UAVs). Key limitations—including redundant feature interference, inadequate sensitivity to small targets, rigid fusion weights, and sample imbalance—further restrict practical deployment. To address those problems, this study presents a lightweight insulator anomaly detection algorithm, LAI-YOLO. First, the SqueezeGate-C3k2 (SG-C3k2) module, equipped with an adaptive gating mechanism, is incorporated into the Backbone network to reduce redundant information during feature extraction. Secondly, we propose a High-level Screening–Feature Weighted Feature Pyramid Network (HS-WFPN) to replace FPN+PAN via selective weighted feature fusion, enabling dynamic cross-scale integration and enhanced small-target detection. Then, a reconstructed lightweight detection head coupled with Slide Weighted Focaler Loss (SWFocalerLoss) mitigates performance degradation from sample imbalance. Ultimately, the layer adaptation for the magnitude-based pruning (LAMP) technique slashes computational demands without sacrificing detection prowess. Experimental results on our insulator anomaly dataset demonstrate that the improved model achieves higher efficacy in identifying insulator anomalies, with mAP@0.5 increasing from 88.2% to 91.1%, while model parameters and FLOPs are diminished to 45.7% and 53.9% of the baseline, respectively. This efficiency facilitates the deployment of edge devices and highlights the method’s considerable application potential. Full article

This paper presents a robust and computationally efficient fault classification framework for wind energy conversion systems (WECS), built upon a Robust Random Vector Functional Link Network (Robust-RVFLN) and validated through real-time simulations on a Real-Time Digital Simulator (RTDS). Unlike existing studies that depend on high-dimensional feature extraction or purely data-driven deep learning models, our approach leverages a compact set of five statistically significant and physically interpretable features derived from rotor torque, phase current, DC-link voltage, and dq-axis current components. This reduced feature set ensures both high discriminative power and low computational overhead, enabling effective deployment in resource-constrained edge devices and large-scale wind farms. A synthesized dataset representing seven representative fault scenarios—including converter, generator, gearbox, and grid faults—was employed to evaluate the model. Comparative analysis shows that the Robust-RVFLN consistently outperforms conventional classifiers (SVM, ELM) and deep models (CNN, LSTM), delivering accuracy rates of up to 99.85% for grid-side line-to-ground faults and 99.81% for generator faults. Beyond accuracy, evaluation metrics such as precision, recall, and F1-score further validate its robustness under transient operating conditions. By uniting interpretability, scalability, and real-time performance, the proposed framework addresses critical challenges in condition monitoring and predictive maintenance, offering a practical and transferable solution for next-generation renewable energy infrastructures. Full article

The Internet of Things (IoT) revolutionizes connectivity, enabling innovative applications across healthcare, industry, and smart cities but also introducing significant cybersecurity challenges due to its expanded attack surface. Intrusion Detection Systems (IDSs) play a pivotal role in addressing these challenges, offering tailored solutions to detect and mitigate threats in dynamic and resource-constrained IoT environments. Through a rigorous analysis, this study classifies IDS research based on methodologies, performance metrics, and application domains, providing a comprehensive synthesis of the field. Key findings reveal a paradigm shift towards integrating artificial intelligence (AI) and hybrid approaches, surpassing the limitations of traditional, static methods. These advancements highlight the potential for IDSs to enhance scalability, adaptability, and detection accuracy. However, unresolved challenges, such as resource efficiency and real-world applicability, underline the need for further research. By contextualizing these findings within the broader landscape of IoT security, this work emphasizes the critical importance of developing IDS solutions that ensure the reliability, privacy, and security of interconnected systems, contributing to the sustainable evolution of IoT ecosystems. Full article