Search Results (522)

Saxitoxin (STX) is a highly potent marine biotoxin, and trace contamination in aquatic environments can pose serious risks to human health. Therefore, reliable detection of low-concentration STX is crucial for water safety monitoring. Here, we developed a robust electrochemical aptasensor based on a gold nanoparticle/chitosan (AuNP/CS) conductive network for STX detection in freshwater samples. The chitosan matrix provides a three-dimensional scaffold for aptamer immobilization, while interconnected AuNPs create efficient electron-transfer pathways across the sensing interface. This integrated architecture improves interfacial conductivity and supports stable target-induced aptamer recognition. The aptasensor exhibits a linear response from 1 to 1000 nM and a limit of detection of 0.74 nM, with an apparent dissociation constant K_d = 70.29 ± 29.2 nM, together with high batch-to-batch consistency and long-term stability, retaining 93% of its initial response after 25 days. In spiked freshwater samples, the aptasensor achieved recoveries ranging from 99.10% to 111.33%, demonstrating the practical potential of this platform for monitoring STX contamination in aquatic environments. Full article

This decade-long surveillance study (2016–2025) investigates the acquisition of multidrug-resistant (MDR) bacteria in South Atlantic cetaceans to evaluate how ecological niches modulate exposure to biological pollution. Analyzing clinical isolates from stranded cetacean carcasses (n = 346), we used Generalized Linear Mixed-Effects Models (GLMMs) to mitigate multi-center analytical biases and compare resistance profiles across coastal and oceanic species. We observed a significant, progressive upward trend in the overall MDR probability over the decade. Although raw MDR was higher in the demersal-feeding Pontoporia blainvillei (58.2%) than in the sympatric, water-column-foraging Sotalia guianensis (22.0%), multivariate modeling revealed that this difference was primarily driven by the geographic stranding location rather than by intrinsic foraging ecology. High gastrointestinal MDR (73.9%) suggests dietary intake as a primary biological gateway. Demographic modeling revealed a significant sex-based association in P. blainvillei, with females facing a higher risk (p = 0.009), although the specific ecological or physiological mechanisms underlying this difference remain unknown. High MDR rates in deep-diving Lagenodelphis hosei (70.8%) and Kogia breviceps (67.9%) suggest that resistant pathogens may reach bathypelagic food webs, potentially via vertical trophic pathways. These findings suggest that spatial environmental contamination, alongside foraging and reproductive ecologies, is a key driver of exposure to the anthropogenic resistome. Because carcass-based sampling inherently targets a diseased or senescent fraction, these high prevalences may overestimate the resistome burden of healthy free-ranging populations. The detection of human pathogens across coastal and offshore habitats indicates persistent deficiencies in terrestrial effluent management, reinforcing cetaceans as One Health sentinels. Full article

The microbiological quality of silage, a fundamental component of cattle feed, is critical to both animal health and human safety. This long-term study evaluated the microbiological status of grass silages collected from Polish cattle farms, aiming to assess the prevalence of zoonotic agents and hygiene indicators in relation to pre-ensiling and ensiling agricultural practices. A total of 160 grass silage samples were randomly collected nationwide by veterinary officers across various municipalities. The researchers measured pH values and conducted analyses to determine the presence and loads of pathogens, including Listeria spp., Clostridium spp., Salmonella spp., Escherichia coli, and Bacillus cereus. Only 17.5% of grass silages fell within the optimal target pH range. Conversely, 80.6% exceeded the upper reference threshold, indicating widespread poor fermentation and low silage quality. Listeria species were detected in 5.6% of the forages, occurring exclusively in silages with an incorrect pH. Clostridium spp. were highly prevalent, testing positive in 82.5% of all samples, while C. perfringens (predominantly toxotype A) was identified in 26.2%. Both Salmonella spp. and botulinum neurotoxin-producing Clostridia were detected in separate, single samples, both characterized by high pH levels. Additionally, fungal counts reached high, non-feedable levels exceeding 6 log₁₀ cfu/g in 10.6% of the tested silages. Silage inoculation, practiced by 26.2% of farmers, significantly lowered pH values and reduced the counts of spoilage microorganisms. Additionally, inoculated silages had a more than three-fold greater likelihood of achieving the correct pH. In contrast, organic fertilization was associated with an increase in the mean counts of Clostridium spp. and B. cereus. In conclusion, grass silage is highly vulnerable to poor fermentation and microbial contamination. Full article

Detecting far-range cyclists from LiDAR point clouds is challenging because cyclists are small road users and are often represented by only a limited number of points at long distances. This study proposes a training-time object-level temporal merge teacher that uses frame-level object IDs in the Waymo Open Dataset to improve single-frame 3D detection of sparse far-range cyclists while preserving the inference-time efficiency of PointPillars. Current-frame cyclist objects are defined as sparse/far targets when their ground-truth boxes are at least 40 m from the ego vehicle and contain no more than 20 LiDAR points. During teacher construction, previous-frame cyclist points with the same object ID are aligned to the current object coordinate system and used to construct the teacher input. The student detector receives only the original single-frame point cloud during inference; therefore, previous frames and ground-truth object IDs are not required at deployment. In validation experiments, the F2 temporal-merge teacher improved cyclist L2 APH from 0.1500 to 0.1903 over the baseline, and the F2 KD student achieved cyclist L2 AP/APH scores of 0.3119/0.1906. The results indicate that the temporal-merge teacher, pair-based fine-tuning, and KD signals can jointly improve cyclist AP for a single-frame detector. However, because the no-KD control also outperformed the baseline, the observed improvement should be interpreted as the combined effect of the temporal teacher construction and the training pipeline rather than the isolated effect of the KD loss alone. Full article

Underwater object detection is a core technology for environmental perception and autonomous operation of unmanned underwater vehicles (UUVs). However, optical and acoustic sensing alone suffer from physical limitations, leading to missed and false detections in turbid, low-light, or long-range conditions. To overcome these limitations, this paper develops an acoustic–optical multimodal fusion detection module (AOMFDM) tailored for UUV deployment. The module employs dual YOLOv5 models for separate processing of sonar and optical images. An interference source quantification estimation network is introduced to extract environmental degradation features, including noise, blur, illumination, contrast, and color cast. A heterogeneous feature map matching network and a deep sparse autoencoder are further designed to achieve cross-modal alignment and fusion of acoustic and optical features. Additionally, attention mechanisms, anchor-based box annotation, and weighted boxes fusion (WBF) are incorporated to enhance detection robustness. For model training and evaluation, we construct the Underwater Sonar Detection (USD) and Underwater Optical Detection (UOD) datasets, covering diverse water qualities, illumination levels, target materials, and interference scenarios. Experimental results demonstrate that, by exploiting the complementarity of acoustic and optical modalities together with adaptive alignment strategies, the proposed module significantly boosts both detection reliability and generalization capability for UUVs in challenging underwater environments. Full article

Background: Implant-surface decontamination is required before reconstructive treatment of peri-implantitis, but no protocol has demonstrated consistent clinical superiority. Objective: To compare the immediate reduction in recoverable bacterial target DNA after electrolytic cleaning and 0.2% chlorhexidine (CHX). Materials and Methods: Forty nonsmoking patients with peri-implantitis (one implant per patient) were randomized to GalvoSurge (n = 20) or CHX (n = 20). Microbiological samples were collected from the exposed implant surface before and immediately after the assigned decontamination protocol and standardized terminal sterile-saline irrigation. Five periodontal pathogens were assessed using semiquantitative DNA-based real-time PCR (qPCR). Laboratory categories were converted to an exploratory summed ordinal score (range 0–15). A post hoc cumulative logit model compared post-treatment scores between groups while adjusting for the pretreatment score. Results: Pretreatment scores were higher in the GalvoSurge group (median 4.5, IQR 3.0–6.0) than in the CHX group (median 3.0, IQR 2.75–4.0; p = 0.043). After baseline adjustment, GalvoSurge was associated with lower post-treatment scores (adjusted common odds ratio for a higher score, 0.047; 95% profile-likelihood CI, 0.009–0.202; p < 0.001). Unadjusted analyses also favored GalvoSurge for absolute reduction (median 4.0 vs. 1.0), relative reduction (83.3% vs. 26.7%), and post-treatment score (median 1.0 vs. 2.0; all p < 0.001). Complete target non-detection occurred in 7/20 (35.0%) and 2/20 (10.0%) patients, respectively (Fisher’s exact p = 0.127). Conclusions: In this exploratory randomized pilot sample, GalvoSurge was associated with a greater immediate reduction in recoverable bacterial target DNA than CHX after adjustment for pretreatment score. DNA-based qPCR does not distinguish viable from non-viable bacteria; therefore, the findings do not establish bacterial killing, sterility, or long-term clinical superiority. Full article

Manual fruit thinning is labor-intensive and inefficient, making the development of intelligent visual detection systems a crucial approach for improving the quality and production efficiency of the pear industry. However, in natural orchard environments, young pear fruits are small in size with slender fruit stalks, and their texture and color characteristics are highly similar to those of tender branches. Furthermore, variations in illumination and occlusions caused by branches and leaves make it difficult for existing detection models to simultaneously and accurately identify fruits and fruit stalks, limiting their application in automated thinning equipment. In this study, Yuluxiang pear was selected as the research object, and image data were collected under diverse field conditions, including forward-lighting, backlighting, close-range shooting, long-range shooting and fruit overlapping. A dedicated dataset containing 3057 images was established. Based on the RT-DETR-R18 network, a lightweight and high-precision fruit–stalk synchronous detection model was proposed. Specifically, the backbone network was reconstructed by integrating GCConv with C2f modules to enhance global feature extraction for slender fruit stalks. The bottleneck structure was optimized using GCConvC3 to reduce feature degradation under occlusion conditions, and an additional 4× down-sampling P2 detection head was introduced to improve the detection capability for small targets. To fully validate the model performance and stability, three types of experiments were conducted in this study: ablation experiments, repeated experiments with different random seeds, and comparative experiments. Ablation experiments verified the cumulative performance improvements brought by the introduced modules. Repeated experiments with different random seeds were performed to explore training randomness-induced performance fluctuations, and the results demonstrated that the proposed model maintains stable overall detection accuracy with minor metric fluctuations. Comparative experiments demonstrated that the proposed model achieved a compact parameter size of only 15.97 M, with a precision of 95.0% for young pear fruit detection and an mAP50 of 83.0% for fruit stalk detection, outperforming all comparative models in overall mAP50. The training convergence curves and Grad-CAM++ visualization results further confirmed the stable optimization process and enhanced feature attention capability of the proposed model. By achieving a favorable balance between detection accuracy and model lightweightness, this approach provides effective technical support for the development of intelligent fruit thinning equipment and vision-based systems for smart pear orchards. Full article

Object detection in remote sensing imagery remains challenging due to vast scale variations, complex backgrounds, and the prevalence of small, densely packed targets. Existing CNN-based detectors are limited by restricted receptive fields, while Transformer-based methods incur prohibitive computational overhead for high-resolution inputs. In this paper, we propose SFSMamba-DETR, a detection framework that integrates state space models with Dual-Scale Window Attention for efficient and accurate remote sensing object detection. Specifically, we design a Selective Feature Scanning (SFS) module that uses the Mamba-based 2D Selective Scan mechanism to model long-range spatial dependencies with linear computational complexity. To capture both fine-grained local patterns and broader contextual cues simultaneously, we introduce a Dual-Scale Window Attention (DSWA) mechanism that operates at two complementary window scales with multi-kernel convolution bridging. These modules are orchestrated within a Cross-scale Feature Aggregation Module (CFAM) that performs hierarchical multi-scale fusion in a hybrid encoder. Extensive experiments on three primary benchmarks (MAR20, UCAS-AOD, and the Jilin-1 Satellite Aircraft Detection Dataset), together with supplementary results on DOTA and DIOR, demonstrate that SFSMamba-DETR achieves strong detection accuracy while maintaining competitive inference speed. Full article

Slow-time coding techniques, including Code Division Multiple Access (CDMA), Doppler Division Multiple Access (DDMA), and joint CDMA–DDMA coding, are widely used in Multiple-Input Multiple-Output (MIMO) millimeter-wave radar systems to improve transmit-channel isolation, angular resolution, and field of view (FOV). However, traffic radar applications also require a high range resolution and long unambiguous detection range, which cannot be fully achieved by MIMO coding alone. This paper proposes a stepped-frequency Doppler-coded integrated joint division multiple access (SF-DC-JDMA) waveform that embeds stepped-frequency (SF) modulation into a jointly encoded CDMA–DDMA MIMO framework. In the proposed design, inter-group CDMA coding provides group-level transmit separation, intra-group DDMA modulation supports Doppler-domain Tx identification, and stepped-frequency synthesis improves range resolution. The resulting waveform combines multi-Tx orthogonality with synthesized wide-band ranging, enabling simultaneous channel separation, high-resolution range estimation, and long-range detection. The simulations and real-scene measurements demonstrate that, under the same range coverage, the proposed SF-DC-JDMA waveform achieves a significantly improved range resolution relative to the conventional FMCW waveform integrated by CDMA and DDMA modulation, and yields denser point-cloud representations of traffic targets. Full article

Precise and real-time posture recognition of scallions during harvesting and post-harvest processing is critical for automated conveying, orientation adjustment, bundling, and packaging. Nevertheless, their slender and flexible form, varied spatial orientations, target overlap, lighting fluctuations, background interference, and constrained computational resources of edge devices present significant obstacles to reliable visual perception. This research introduces GOPD-YOLO, a lightweight oriented object-detection network built on the YOLOv8-OBB framework. The network integrates partial-convolution-based lightweight feature extraction to minimize redundant computation, large separable-kernel attention to boost long-range structural representation, and a shared detail-enhanced detection head to improve boundary- and orientation-sensitive prediction. A custom dataset comprising 1500 conveyor-belt images under diverse scallion posture scenarios was developed for model training and assessment. GOPD-YOLO attained a precision of 90.6%, a recall of 94.5%, an mAP@0.5 of 93.2%, and an mAP@0.5:0.95 of 71.5%, with 2.38 million parameters, 6.6 GFLOPs, and a model size of 4.9 MB. Relative to YOLOv8n-OBB, GOPD-YOLO enhanced recall by 3.8 percentage points while decreasing parameter count and model size by 22.7% and 22.2%, respectively. Deployment tests were performed on the Jetson Orin NX Super platform across varying conveyor speeds, lighting conditions, and scallion stacking levels to evaluate the model’s practical utility. These results indicate GOPD-YOLO’s potential as a lightweight vision-based solution for scallion posture recognition in automated harvesting and post-harvest processing. Full article

Background: Spinal muscular atrophy (SMA) is mainly caused by biallelic SMN1 inactivation. While most patients carry homozygous deletions, 3–5% are compound heterozygotes, making molecular diagnosis challenging. Methods: A tiered diagnostic strategy was applied to 17 pediatric patients, combining copy number analyses (MLPA and targeted long-read sequencing, tLRS), sequence variant detection (RT-PCR cloning and sequencing, allele-specific long-range PCR with nested PCR, and tLRS), and structural variant analysis (ultra-long-read sequencing, Ultra-LRS). Results: Copy numbers were concordant between MLPA and tLRS. MLPA-suggested gene conversions were confirmed by tLRS, while discordant total copy numbers were resolved as large deletions by Ultra-LRS. RT-PCR cloning, and sequencing identified SMN1 variants in 11/12 cases and confirmed aberrant splicing in three cases, but failed for large deletions. AS-LR-PCR with nested PCR characterized the variants in 13/15 but failed in gene conversion cases. tLRS achieved definitive diagnosis in all cases, and Ultra-LRS precisely delineated breakpoint junctions of two large deletions. Conclusions: A hierarchical complementary strategy integrating copy number, sequence, and structural analyses is essential for the accurate diagnosis of compound heterozygous SMA. Full article

Feature engineering has become a common step in AI-based HVAC forecasting, often involving variables calculated from raw building management system (BMS) measurements, such as temperature differences, setpoint tracking deviations, airflow balance indicators, rolling statistics, and temporal or seasonal descriptors. Accurate short-term forecasting can provide a baseline of expected operation for anomaly and fault detection and can support control optimization and operator decision making. However, real-world deployment is complicated due to differences in BMS sensor availability and data quality, as well as the preprocessing and maintenance burden associated with complex feature sets. The actual contribution of these features to the performance of AI forecasting remains underexplored, particularly for short-term prediction of air handling unit (AHU) operation. This study evaluates the impact of features on short-term AHU forecasting using three deep learning (DL) architectures: Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks, and a hybrid CNN–LSTM model. An actual operational AHU dataset from a BMS was used to predict key operational variables, including supply and extract air temperatures, supply and extract fan operating signals, and supply air temperature setpoint-tracking error. Fan signal balance was additionally evaluated as a derived indicator calculated from the two predicted fan signals. Four input configurations were evaluated: (i) full (74 inputs), containing raw BMS measurements, short-cycle temporal variables, engineered and dynamic features, and annual-calendar information; (ii) no annual calendar (68 inputs), identical to full but excluding annual-calendar variables; (iii) raw + short-cycle temporal (20 inputs); and (iv) raw-only (12 inputs). The models used a 60-min input history to forecast the following 30-min at one-minute resolution. Persistence and Ridge models were included as reference baselines. All models were trained and tested on identical data splits and forecasting horizons to ensure a fair comparison. Each DL experiment was repeated across five independent runs, and performance was evaluated using MAE, RMSE, and R². The TCN showed the strongest overall DL performance. Raw-only achieved the highest mean R² in 11 of 15 architecture–target comparisons using just 12 inputs. The best mean DL R² ranged from 0.916 for the fan signals to 0.993 for extract air temperature. Annual-calendar features improved the TCN results but provided no consistent benefit for the LSTM or CNN–LSTM. Ridge slightly outperformed the best DL configurations for temperature-related targets, reflecting the strong short-term continuity of these signals. These findings show that recent raw BMS measurements contain most of the information needed for accurate 30-min AHU forecasting, while explicit seasonal and engineered features provide limited additional value. The resulting simpler models may in the future be used as forecasting components in predictive control and fault detection systems. However, their control and energy-saving benefits must be tested separately. Full article

Although the bounding boxes produced by an object detector provide real-time target localization cues for anti-UAV visual perception, they remain susceptible to geometric deviations under long-range small-target conditions, complex backgrounds, motion blur, and cross-domain environmental variations. Consequently, these detector outputs cannot always serve directly as stable observations for state estimation, trajectory prediction, and interception control. To address this issue, this paper proposes CDBR-Net, a causally conditioned domain-robust observation correction network for post-detection UAV bounding-box refinement. CDBR-Net employs a shared encoder, disentangled multi-branch representations, and a quality-aware gating mechanism to jointly produce a corrected observation box, a robust representation, and an observation uncertainty estimate. To preserve this conditional environment-transformation symmetry without suppressing geometry-induced symmetry breaking, CDBR-Net constructs geometry-conditioned cross-domain sample pairs and imposes cross-domain consistency and geometry-sensitivity preservation constraints. After training on 8749 post-detection observations, CDBR-Net is evaluated on 997 aligned validation observations from three simulated scene domains. It reduces the YOLO bounding-box mean absolute error (MAE) by 6.8%, from 0.002924 to 0.002725, and increases the intersection over union (IoU) by 0.007979, from 0.852167 to 0.860146. It further reduces MAE by 1.7% and increases IoU by 0.002031 relative to the YOLO + MLP Residual baseline. Ablation studies demonstrate the complementary roles of geometry-conditioned cross-domain consistency and geometry-sensitivity preservation. These results indicate that CDBR-Net provides a more stable and geometrically consistent post-detection observation interface for anti-UAV visual perception. Full article

With the accelerated progress of computational aesthetics and digital interior design, the demand for real-time and precise detection of aesthetic objects on edge devices has become increasingly pressing in applications such as intelligent design assistance, domestic aesthetic assessment, and augmented reality-based interior staging. As a core task in digital home aesthetics governance, virtual interior furnishing, household cultural archive development, and automated aesthetic evaluation, multi-view aesthetic object detection plays an essential role. However, this task still faces substantial difficulties arising from pronounced viewpoint variation, scale inconsistency, reflective materials, intricate decorative patterns, and cluttered indoor scenes. To address these issues, this study presents GhostNetV2-YOLO, a lightweight yet robust detection framework designed for accurate localization of aesthetic objects under unconstrained multi-view acquisition settings. The task is formally defined as closed-set detection of 10 pre-selected home aesthetic decorative items, including both planar decorative pieces and three-dimensional ornamental objects, and all performance claims are bounded within the horizontal bounding box detection paradigm. The framework incorporates three complementary components tailored to the target task. First, a task-adapted GhostNetV2 backbone is employed to enable efficient multi-scale feature extraction and long-range dependency modeling, with optimization specifically oriented toward structured aesthetic objects with stable global contours under viewpoint variation. Second, an improved Attention-based Intra-scale Feature Interaction (AIFI) module is introduced, integrating compressed QKV projection, linear attention, depthwise spatial refinement, and channel gating so that reflection-induced noise and background disturbance can be effectively reduced. Third, an enhanced Distance-IoU regression loss is adopted, in which explicit edge alignment and dynamic sample weighting are incorporated to improve boundary regression accuracy for rectangular and regularly contoured aesthetic objects. These designs jointly enhance contextual representation, boundary localization, and computational efficiency. Extensive experiments on two newly constructed multi-view aesthetic object datasets (AestheticHome-12K and AestheticHome-2K) demonstrate that the proposed detector achieves 94.80 ± 0.32%/94.20 ± 0.37% mAP@0.5, 96.30 ± 0.28%/95.60 ± 0.31% precision, and 94.70 ± 0.35%/93.80 ± 0.39% recall across two datasets (reported as mean ± standard deviation of 5 independent training runs with distinct random seeds), with only 2.89 M parameters and 6.0 GFLOPs. Statistical significance is verified via paired two-tailed t-tests with Bonferroni correction (adjusted p < 0.05) for all performance comparisons against baseline models. Compared with the YOLOv11n baseline, the method improves mAP@0.5 by 1.87–2.09 percentage points and recall by 3.27–3.48 percentage points while reducing computational cost. Notably, it also achieves 79.2–80.5% mAP@0.5:0.95, outperforming the baseline by 4.7–4.9 percentage points, indicating significantly superior localization accuracy under stricter criteria. The proposed model achieves a remarkable balance between accuracy and efficiency, making it highly suitable for deployment on resource-constrained edge devices commonly used in digital design and home aesthetic monitoring systems. The results indicate that combining lightweight long-range feature extraction optimized for rigid aesthetic objects, compact attention-based feature interaction for interference suppression, and geometry-aware regression tailored for aesthetic targets provides an effective and efficient solution for robust aesthetic object detection in real-world computational aesthetics and digital interior design applications. Full article

Accurate detection of transmission line components is crucial for the stability and security of power grid operations. However, accurate power line inspection is always affected by complex factors, such as multi-scale objects, complex background interference, and object occlusion, etc. To tackle these complexities, this paper leverages the long-range dependency modeling advantages of the Transformer architecture, and an improved real-time end-to-end Detection Transformer (RT-DETR) with multi-scale feature reconstruction, referred to as MFRRT-DETR, for Unmanned Aerial Vehicle (UAV) inspection systems is presented. Specifically, an enhanced attention-based backbone network integrated via an aggregated pixel-focus attention (APFA) module is built which uses a dual-path design with fine-grained and coarse-grained branches to combine pixel-level focus with global perception to enhance the interaction between local and global features, alleviating the limitations of the local receptive field in Convolutional Neural Networks (CNNs). To further overcome the issues of target overlap, occlusion, and foreground–background confusion, a context-guided spatial feature reconstruction feature pyramid network (CGR-FPN) module is proposed which strengthens foreground representation and effectively fuses multi-scale features, improving performance in crowded scenes. Additionally, a Focaler–Shape IoU loss function is introduced to mitigate class imbalance issues and localization errors by focusing on hard samples and optimizing bounding box regression, particularly for long and wide irregular rectangular targets. Experiments show that the proposed MFRRT-DETR significantly outperforms advanced detection models, which effectively validates the detection efficiency and accuracy of the proposed model in complex inspection scenarios, making it a promising solution for UAV-based power line inspection. Full article