Search Results (2,307)

Hands-free command interfaces are essential for users who cannot reliably operate joysticks or upper-limb myoelectric control. Neck–shoulder surface electromyography (sEMG) is a promising alternative; however, performance is often reported using window-level validation which can overestimate accuracy due to overlap and trial leakage, and false-trigger behavior is not always quantified when an idle REST state is included. This pilot study presents a motion-capture (MoCap)-referenced decoding framework that uses four bilateral upper trapezius (UT) and sternocleidomastoid (SCM) sEMG channels with integrated inertial measurement units (IMUs). Optical MoCap was used as an external kinematic reference to support baseline-posture assessment and movement-execution quality control. Seven commands were decoded (shrug L/R, double shrug, rotation L/R, rotation + shrug L/R). To enable an eight-class formulation, a REST class was defined using low-activity segments extracted from baseline recordings and included in the evaluation. Computationally efficient time-domain sEMG features, pattern/symmetry descriptors, and baseline-referenced IMU kinematics (including an SCM yaw-range indicator) were classified using linear discriminant analysis (LDA), k-nearest neighbors (kNN), and linear support vector machine (SVM), evaluated using within-subject testing, trial-wise grouped cross-validation, and leave-one-subject-out (LOSO) testing. Across six participants, within-subject mean best-per-subject accuracy was 96.02% (seven-class) and 96.35% (eight-class); and pooled trial-wise accuracy reached 92.1% and 90.5%, respectively. Under LOSO, best-configuration accuracy decreased to 60.4% and 63.8% for the seven-class and eight-class formulations, respectively. Across the top LOSO configurations, REST FAR ranged from approximately 9.8% to 25.6%. These findings demonstrate controlled offline pilot feasibility and quantify key generalization and REST false-activation trade-offs, providing a foundation for future validation in larger, more diverse, and clinically relevant populations. Full article

Introduction: As apex and mesopredators, elasmobranchs help maintain marine ecosystem balance by shaping food-web structure and habitat connectivity, yet more than one-third of species are threatened with extinction. Identifying where and when they aggregate within atoll systems is therefore a prerequisite for spatially explicit conservation planning. Lhaviyani Atoll, in the central Maldives, lies within a recognised Indian Ocean elasmobranch hotspot and hosts two Important Shark and Ray Areas (ISRAs), yet fine-scale information on aggregation sites, habitat partitioning and seasonal use remains limited. Objective: To map persistent activity hotspots, characterise habitat partitioning between sharks and batoids, quantify seasonal and inter-annual dynamics, and provide an ecological basis for habitat-focused conservation in Lhaviyani Atoll. Methodology: Using a seven-year (2017–2024) opportunistic dive-log dataset of 12,732 SCUBA surveys and 142,994 elasmobranch records across 94 dive sites, spatial kernel-density estimation was applied separately to sharks and batoids to identify activity hotspots and visualise spatial overlap. Habitat associations were examined across substrate types and reef geomorphic zones. Seasonal and inter-annual dynamics in relative abundance and diversity (Shannon, Pielou’s evenness) were quantified across monsoon phases and the 2017–2024 period. Results: Twenty-eight species (14 sharks, 14 batoids) were recorded, including 23 listed as threatened on the IUCN Red List (4 Critically Endangered, 12 Endangered, 7 Vulnerable). Four persistent activity hotspots were identified along the northern atoll rim, two overlapping with the Fushifaru Kandu and Kuredhu–Huravalhi–Komandoo ISRAs. Sharks were concentrated along more complex exposed and semi-sheltered slopes and high-flow channels, with significantly higher occurrence on reef and sheltered reef slopes and lower occurrence on rubble and sand substrates; batoids were distributed broadly within lagoonal habitats with no strong substrate or geomorphic preferences. Relative abundance and diversity peaked during the late southwest monsoon (August–September) and declined into the northeast monsoon (December–March); after 2021, diversity and evenness increased while overall abundance declined. Conclusions: Persistent hotspots, contrasting habitat use by sharks and batoids, and consistent monsoonal seasonality support the ecological relevance of existing ISRAs in Lhaviyani Atoll, while providing finer-scale information on habitat partitioning and additional priority areas for threatened elasmobranchs, including four Critically Endangered species. Full article

The interaction of electromagnetic waves with atmospheric aerosols plays a significant role in communication systems and multimedia information transmission. Understanding the interaction of vortex light beams with an aerosol-laden atmosphere is indispensable for establishing a framework of the environmental channel. During the interaction, different optical effects such as absorption and scattering will result in energy attenuation, and this yields the deterioration of the transmission feature of the vortex beam signal. In this study, we present a theoretical analysis of Gaussian vortex beams (GVBs) scattering by diverse aerosol (unformed carbon, dust, sulphate, silicate, soot, and nitrate) particles in the atmosphere on the basis of the well-established generalized Lorenz–Mie theory (GLMT). Combined with the lognormal distribution model for aerosol particles, the attenuation and transmission characteristics of GVBs for different aerosol particles are analyzed. The extinction efficiency $\left(Q_{ext}\right)$ factor of GVB, caused by the absorption and scattering of various aerosols, becomes smaller compared to that of a basic Gaussian beam (GB). Increasing the OAM mode index, the energy attenuation and transmission caused by aerosol absorption and scattering further decrease. Moreover, this research provides a basis to analyze the optical characteristics of the twisted beams in different atmospheric channels, such as wireless communication networks over aerosol-laden systems and material interactions. Full article

To satisfy the demands of 5G communication and reliable data connectivity for unmanned aerial vehicles (UAVs), a novel two-element dual-band MIMO antenna with an inherent self-decoupling property based on orthogonal linear polarization diversity is proposed. Distinct from conventional designs relying on extra decoupling components, the antenna realizes isolation enhancement via coupled currents between annular strips and S-shaped strips without additional decoupling structures, representing the core design novelty. Fabricated on a low-cost 1.6 mm thick FR4 substrate, the antenna features compact overall dimensions of 60 mm × 30 mm × 1.6 mm, covering the 2.40–2.73 GHz ISM band and 3.38–3.63 GHz 5G Sub-6 GHz band. Measured results demonstrate that the reflection coefficient remains below −10 dB across the entire operating bands, with port isolation exceeding 27 dB for the 2.4 GHz band and 20 dB for the 3.5 GHz 5G band. The measured realized gain is 0.7–1.5 dB in the lower band and 2.3–2.9 dB in the upper band. The radiation efficiency, which is obtained exclusively from ANSYS HFSS 2025 R1 simulation, is higher than 90% for the lower band and over 80% for the upper band. The calculated envelope correlation coefficient (ECC) is less than 0.15 throughout the working bandwidth, which effectively suppresses inter-channel electromagnetic interference and mitigates channel fading caused by varying UAV attitudes to improve system channel capacity. Further verifications via epoxy encapsulation and co-simulation on an eight-rotor UAV platform prove slight frequency drift after packaging and installation, whereas its bandwidth and isolation still meet practical engineering requirements. Benefiting from a compact layout and omnidirectional radiation performance, the proposed low-cost MIMO antenna is convenient for conformal integration into a UAV fuselage, improving the practicability of UAV-aided emergency communication, equipment inspection and 5G network coverage. Full article

Brachymystax lenok tsinlingensis is an endangered salmonid endemic to China. Traditional trapping methods frequently fail to detect this rare fish in low-density mountain streams, hampering evidence-based conservation. Here, we employed environmental DNA quantitative PCR (eDNA-qPCR) with species-specific primers to assess the spatial biomass distribution of this species in the Zhouzhi Heihe River. Concurrently, we surveyed plankton, benthic macroinvertebrates, and physicochemical water parameters. eDNA detected the target species at 12 of 14 sites, with reliable quantification achieved at 9 sites, suggesting that the method may be more effective than conventional trapping for detecting this species under the studied low-density conditions. eDNA-derived relative biomass exhibited pronounced spatial heterogeneity, ranging from 6.0 × 10⁻⁴ to 1.5 × 10⁻² g/cm³. Water depth showed a significant positive association with biomass (r = 0.5347), whereas phytoplankton Shannon diversity (a measure of species richness and evenness) was significantly negatively correlated (r = −0.5447). Flow velocity displayed a negative trend that did not reach statistical significance (r = −0.5009). Plankton and benthic communities indicated overall ecological conditions but did not directly explain the observed spatial variation in fish biomass. These findings indicate that the spatial pattern of B. lenok tsinlingensis is primarily shaped by local physical habitat structure, with deeper, hydraulically more complex channel units serving as key microhabitats. eDNA-qPCR thus represents an effective, low-disturbance monitoring tool for this endangered cold-water fish and provides a scientific basis for targeted habitat protection and restoration. Full article

Sediment deposition in water diversion channels threatens the operational safety and water supply reliability of large-scale inter-basin water transfer projects. This study investigates the deposition patterns and sediment reduction strategies for the diversion channel of the Shigu Water Source Project, a key intake hub of the Central Yunnan Water Diversion Project on the Jinsha River. A one-dimensional total-load sediment mathematical model (HELIU-2) was used to simulate deposition volume, particle size distribution, and sediment concentration at the pumping station intake under eight design scenarios spanning high-, medium-, and low-sediment years. Results show that over 95% of the deposited sediment in front of the pumping station is finer than 0.05 mm. Dredging reduces the deposition thickness at the pump intake by 13–25% in high-sediment years, significantly enhancing sediment trapping efficiency and reducing both average and maximum sediment concentrations. Longer diversion channels increase total deposition by 9–13% but reduce intake sediment concentration by 2–5% and decrease local deposition thickness by 27–42%, especially in high-sediment years. These findings provide quantitative support for optimizing desilting basin layout, channel length design, and dredging schedules. The proposed modeling framework and mitigation strategies may provide a reference for other large-scale water diversion systems facing similar sedimentation challenges. Full article

This article investigates the intricate interplay between ressentiment—as social emotion—social love, and solidarity in democratic societies, focusing on the urban environment as the primary stage where these processes materialize. Far from being a marginal emotion, ressentiment is deeply intertwined with democratic life, arising from the gap between proclaimed values and lived conditions. It represents an affective reaction to the perceived betrayal of the promise of equality inscribed in democratic ideals. The discussion explores how perceptions of injustice can fracture trust and intensify divisions, but also how they, under certain conditions, can be redirected toward political engagement and common action. The city, characterized by density, diversity, and the continuous negotiation of difference, can serve as a privileged arena for this transformation. Urban space does not merely reflect inequalities; it actively shapes social processes and provides the infrastructure through which collective sentiments are articulated. In this context, “social love” is conceptualized not as a sentimental aspiration, but as a relational force capable of redirecting the moral indignation of ressentiment, far from strategies of grievance politics toward constructive forms of social and political belonging. Cities can function as laboratories of solidarity where grievances are reframed into collective projects that strengthen social cohesion. Mitigating the destructive potential of ressentiment requires addressing its structural roots through inclusive urban policies and dialogical spaces. An approach grounded in social love can counter fragmentation, mobilizing emotions in the service of substantive equality. In this perspective, the city can become a space and a laboratory for change, where resentment can be channeled as a generative force capable of sustaining widespread forms of social love and a sense of the common good. Full article

Secure vehicle-to-vehicle communication is essential for electric-vehicle platoons because broadcast wireless links may expose safety and control messages to passive eavesdropping. This paper investigates a physical-layer security (PLS) framework for electric-vehicle (EV) platoons under Rician fading, representing the line-of-sight conditions common in highway platooning. The proposed Jamming-Aided Cooperative Relay Selection (JACRS) framework uses an amplify-and-forward relay, destination-assisted full-duplex friendly jamming, residual self-interference modelling, and a strict total transmit power budget. Relay selection is evaluated using a full-channel state information (CSI) secrecy-selection benchmark, a practical legitimate-link CSI rule, and a deterministic platoon-geometry-aware rule based on Cooperative Adaptive Cruise Control (CACC) position information without instantaneous eavesdropper CSI. Monte Carlo simulations, supported by semi-analytical secrecy-outage and deterministic-slot benchmarks, compare the proposed scheme with Rayleigh and no-jamming amplify-and-forward (AF) baselines. Under the simulated geometry, the scheme achieves a peak ergodic secrecy rate close to 5.0 bps/Hz at 40 dBm and reduces interception risk by 78.07% relative to the Rayleigh baseline. Relay diversity reduces secrecy outage from 14.14% to 0.04% under full CSI and to 0.22% using legitimate-link CSI. The geometry-aware rule reduces the gap between practical legitimate-link selection and the full-CSI benchmark, indicating that platoon position information can improve relay selection under the tested conditions. Full article

Introduction: Elasmobranchs play a vital role in marine food webs through top-down control and the structuring of ecosystem stability, yet more than one-third of species face extinction. The Maldives, a recognised Indian Ocean hotspot for shark and batoid diversity, designated its EEZ as a shark sanctuary in 2010, but multispecies elasmobranch occurrence patterns and environmental drivers remain poorly characterised in Lhaviyani Atoll in the central Maldives, which hosts two Important Shark and Ray Areas (ISRAs). Recreational SCUBA networks can turn routine dive activity into long-term conservation evidence, already informing nearly 10% of the western Indian Ocean ISRAs. Objective: To characterise spatiotemporal patterns of elasmobranch assemblages in Lhaviyani Atoll (2017–2024), quantify how environmental and geomorphic drivers shape relative abundance, diversity, and hotspots, and provide evidence for targeted elasmobranch conservation. Methodology: A seven-year opportunistic dive-log dataset of 12,732 SCUBA surveys and 142,994 elasmobranch records across 94 dive sites was analysed. Effort-standardised relative abundance and community metrics (Shannon diversity, Pielou’s evenness) were modelled against sea surface temperature (SST), salinity, dissolved oxygen, chlorophyll-a, zonal current velocity, substrate type, and reef geomorphology using generalised additive models (GAMs). Spatial analyses identified persistent northern-rim aggregation areas aligned with ISRAs. Results: Twenty-eight species (14 sharks, 14 batoids) were recorded, including 23 threatened on the IUCN Red List (4 Critically Endangered, 12 Endangered, 7 Vulnerable). Relative abundance and diversity peaked during the late southwest monsoon (August–September) and declined during the northeast monsoon (December–March). After 2021, diversity and evenness increased while overall abundance declined. Relative abundance was primarily driven by SST, salinity, and current velocity; for sharks, dissolved oxygen and chlorophyll-a were additionally significant, whereas batoid abundance was driven mainly by temperature, oxygen, and current velocity. Four persistent hotspots along the northern atoll rim were identified, with sharks concentrated along exposed slopes and channels, and batoids distributed broadly within lagoonal habitats. Conclusions: Long-term citizen science dive-log monitoring is cost-effective for elasmobranch conservation in remote tropical seascapes. These results show how dive-industry partnerships can inform conservation governance over a decade after sanctuary designation, supporting targeted, habitat-focused management as shark and batoid conservation frameworks continue to evolve. Full article

Object detection in complex industrial environments is prone to being affected by insufficient dynamic weighting of local and global features, as well as illumination variations and impurities. Moreover, existing models suffer from excessive model complexity, which directly impairs computational efficiency. To more accurately distinguish Chinese herbal materials with diverse morphologies, this paper proposes the MobileAttn module. Drawing on the idea of token representation in the Transformer architecture, this module extracts contextual information through global feature compression, fuses it with tokens to generate a spatial attention map, and realizes dynamic recalibration of convolutional features. This process enhances the feature weights of key semantic regions, suppresses redundant background information, and improves feature discriminability. To address illumination interference, brightness-aware weights are combined with dual-path (channel and spatial) attention for global control, dynamically reducing the impact of illumination; this component is named LightAttn. When Chinese herbal materials contain common industrial unknown impurities (e.g., small stones and weeds), an impurity detection auxiliary module, a post-processing step independent of the main detection network, is proposed. This module refines Non-Maximum Suppression (NMS) logic to distinguish target Chinese herbal materials from interfering impurities. Subsequently, it accurately locates and marks impurities on the conveyor belt, thereby achieving effective unknown impurity detection. Experimental results demonstrate that, compared with the original YOLOv11 on the Chinese herbal materials detection task, the optimized model achieves a 1.7% improvement in the overall mean Average Precision (mAP@0.5:0.95). On a per-class basis, gains are particularly pronounced for certain challenging high-aspect-ratio Chinese herbal materials. Prunella vulgaris and orange peel achieve respective AP improvements of 5.8% and 4.1%. Meanwhile, the model parameter count is reduced by 23.1% and the computational complexity by 20.3%. The F1-Score of the impurity detection results is 86.38%, verifying the effectiveness of the impurity detection auxiliary module. Full article

Accurate and robust detection of leaf diseases is a key enabler for precision agriculture and large-scale crop health monitoring. Despite the strong generalization of modern one-stage detectors (e.g., YOLOv8), two domain-specific challenges remain: (i) weak or blurry lesion boundaries hinder precise localization, and (ii) low color contrast between diseased and healthy tissues forces models to rely on subtle texture patterns rather than salient shapes. To tackle these challenges, we reframe the core agricultural disease detection task as the identification of “asymmetric morphological anomalies” and propose a domain-tailored enhancement framework. First, we introduce an Edge Enhancement Module (EEM) that explicitly strengthens boundary-aware representations. Inspired by the natural symmetry of healthy leaves, our EEM is specifically designed to capture symmetry-breaking boundary discontinuities and localized asymmetric edges caused by disease lesions. Our method enhances edge and texture cues that are indicative of disease lesions, which often exhibit local asymmetries and boundary discontinuities. The EEM includes a Differential Normalized Pooling Block (DNPB) that highlights edge responses through discrepancies between max pooling and average pooling, which also models cross-group edge correlations. Second, the Lightweight Texture-Sensitive Feature Enhancement (LTSFE) mechanism amplifies texture-discriminative channels under low-contrast conditions by leveraging complementary global statistics and efficient channel mixing, all with negligible computational overhead. We evaluated our method on a self-constructed dataset of 106,434 images with 225,640 annotations covering diverse crops. Experiments show that the proposed method achieves state-of-the-art accuracy (81.54% mAP@0.5:0.95) while maintaining real-time inference (142 FPS), consistently outperforming strong baselines. Ablations confirm the effectiveness and complementarity of EEM and LTSFE, demonstrating that domain-specific architectural design, inspired by biological symmetry, can substantially improve agricultural vision systems. Full article

Recent advances in satellite technology have significantly progressed, yet acquiring high-quality images with meaningful labels for Earth observation missions remains a costly and time-intensive process. Furthermore, captured scenes frequently exhibit defects such as misaligned color channels, extensive cloud cover, or repetitive patterns in similar environments. Fortunately, the evolution of generative artificial intelligence offers a solution by enabling the creation of realistic synthetic scenes, simulating the characteristics of any targeted imager, and thereby mitigating the scarcity of authentic data. This paper demonstrates the feasibility of transferring knowledge from specialized AI-generated datasets to Earth observation missions. Leveraging a novel dataset of Spanish map tiles, Pix2Pix, CUT, and ControlNet models were implemented to synthesize satellite imagery. To analyze structural and topological generalizability, identical U-Net instances were trained on the resulting collections for building, road, and water segmentation tasks, and subsequently tested on independent authentic imagery. The results reveal a clear decoupling between visual realism and functional utility. Incorporating synthetic samples into hybridized training datasets successfully surpassed the limitations of using real data alone, increasing maximum Dice scores by 0.9% (to 54.1% for buildings), 2.3% (to 38.6% for roads), and 4.1% (to 46.5% for waterbodies). This systematic validation establishes structural-guided synthetic data augmentation as a robust, adaptable strategy for Earth observation applications across diverse sensors and geometric objectives. Full article

With the emergence of mobile sixth-generation (6G) integrated sensing and communication (ISAC) scenarios, conventional multicarrier waveforms face challenges in maintaining reliable communication and robust physical-layer security. In this paper, we propose a multi-layer encryption multiple-input multiple-output (MIMO) affine frequency division multiplexing (AFDM) with index modulation (IM) scheme, which exploits the inherent flexibility of the AFDM modulation parameter $c_2$ and subcarrier IM to construct a multi-dimensional physical-layer security mechanism. To enable sensing and exploit MIMO spatial diversity, a unified downlink MIMO configuration is adopted, where sensing and communication share the same transmit waveform, receive array, and physical propagation environment. The proposed configuration enables multi-dimensional parameter estimation, including delay, Doppler, and angle. The obtained sensing information further assists beamforming design, channel reconstruction, and signal equalization. Furthermore, the base station and user equipment share synchronized secret keys, and a unified detection framework is developed to balance computational complexity and detection accuracy while remaining compatible with the multi-dimensional encryption structure of the MIMO-AFDM-IM system. Simulation results verify the effectiveness of the proposed scheme in mobile scenarios, demonstrating enhanced multi-dimensional sensing accuracy, improved resistance to eavesdropping, and superior communication reliability and energy efficiency (EE). Full article

Industrial robot after-sales question answering often depends on multimodal evidence, such as error screenshots, interface displays, and wiring diagrams, which are difficult for conventional text-based retrieval-augmented generation (RAG) systems to exploit effectively. To address this issue, we design a dual-channel multimodal RAG system that converts image content into retrievable textual knowledge through the collaboration of optical character recognition (OCR) and structured semantic description. In the proposed system, OCR is used to extract explicit textual cues, such as error codes, parameter fields, and interface prompts, while expert-authored semantic descriptions complement implicit visual evidence, including device parts, fault phenomena, and contextual scene information. The transformed knowledge is further integrated into a hybrid retrieval pipeline that combines dense retrieval and BM25, followed by Reciprocal Rank Fusion (RRF) and Maximal Marginal Relevance (MMR) reordering to improve both relevance and contextual diversity. Experiments on a real-world industrial robot after-sales dataset show that the proposed method achieves an overall question-answering accuracy of 87.9%, outperforming the LLM-only baseline by 35.6 percentage points. For image-related questions, accuracy improves from 46.7% to 83.3%. These results indicate that the proposed framework provides a deployment-friendly and interpretable system-level alternative to end-to-end multimodal model fine-tuning for industrial after-sales question answering. Full article

River restoration is increasingly promoted as a nature-based solution, but evidence of its ecological effectiveness in mountain gravel-bed rivers remains limited. Macroinvertebrate responses to hydromorphological restoration are variable and are still rarely evaluated using an integrated approach combining taxonomic, biotic index, and trait-based components. This study examined whether the hydromorphological restoration of the upper Raba River was associated with measurable environmental and ecological differences between the restored and unrestored sections. Six river sections were analyzed, including three restored and three unrestored sections. The environmental characterisation included hydromorphological and physicochemical variables. Benthic macroinvertebrates were sampled in shallow marginal and main-current habitats, and the analyses included assemblage metrics, biotic indices, taxonomic composition, indicator taxa, and functional traits. The restored sections showed greater channel complexity, including a larger active channel zone, a larger number of active channels, and a coarser substrate. These differences were accompanied by higher Shannon diversity, higher values of the Polish Biological Monitoring Working Party index (BMWP-PL), a higher percentage of individuals of Ephemeroptera, Plecoptera and Trichoptera (%EPT), distinct assemblage composition, and shifts in indicator taxa and selected functional traits. The results highlight the value of multidimensional assessment frameworks to evaluate the effects of restoration on mountain rivers. Full article