Search Results (25,596)

The loss of sand foundation often induces differential settlement in immersed tunnel segments, potentially causing structural damage and reducing structural resilience. Accurately assessing the damage characteristics and their effects on resilience during sand foundation loss is essential for ensuring tunnel safety. This study adopts a typical immersed tunnel project as a case study. Long-term structural deformation data acquired by distributed optical fiber sensing technology and sand foundation detection data are adopted to analyze the response characteristics and damage state of the tunnel. A refined three-dimensional tunnel–stratum interaction model is established and validated against monitoring data to investigate mechanical response characteristics, including deformation and bending moment distributions. A redundancy factor is proposed as a quantitative index for tunnel resilience under foundation loss, and a multi-level resilience grading framework is established accordingly. Furthermore, the evolution of tunnel resilience under various displacement recovery ratios, which represent the extent of differential settlement remediation, is investigated using the refined numerical model. Field detection results show that over 50% of the foundation area is affected by loosening or voids. These defects are highly consistent with regions of abnormal structural deformation, leading to a bending–torsional deformation mode, with a maximum joint differential settlement of 106.7 mm. Stress concentration occurs in the tunnel floor above denser sand zones, with a maximum crack width of 0.43 mm. The tunnel is classified as severely damaged (low resilience) based on the proposed standard, with a redundancy factor of 1.59. Bending-torsional deformation and stress concentration are gradually mitigated as the displacement recovery ratio increases. The redundancy factor exhibits a parabolic relationship with the recovery ratio, indicating that tunnel resilience can be restored to a relatively high level when the displacement recovery ratio exceeds 70%. The proposed redundancy factor and grading framework provide quantitative guidance for designing and optimizing resilience improvement strategies following sand foundation loss. Full article

Safe rail transit operation is essential for socioeconomic development and the protection of public life and property. Traditional railway foreign object intrusion detection methods suffer from insufficient detection accuracy and poor real-time performance under complex scene conditions. While deep learning has achieved remarkable performance in general object detection tasks, existing lightweight detectors still face prominent challenges in railway scenarios, including severe background clutter, drastic variations in target scales, and constrained edge computing resources. To tackle the above issues, this paper proposes YOLOv11n-LSL, an improved lightweight and high-precision detector based on YOLOv11n. Specifically, a C2PSA-SWSA shifted-window self-attention module is designed to suppress background interference and improve the feature representation of small targets; an SPPF-LSKA large-kernel attention module is introduced to construct a large and adaptive receptive field, thereby improving the detection capability for foreign objects of different scales; in addition, the lightweight adaptive decoupled head (LADH) is introduced to alleviate feature conflicts between the classification and regression branches and reduce network parameter redundancy. Comparative experiments on a self-built railway foreign object intrusion dataset show that the proposed YOLOv11n-LSL outperforms the original YOLOv11n, achieving 84.6% mAP@0.5, 88.1% precision, and 79.5% recall, which are 3.0, 6.0, and 7.8 percentage points higher than the baseline, respectively. The model only contains 2.522 M parameters and achieves a single-frame latency of 12.3 ms. Compared with the original YOLOv11n with 2.583 M parameters, the parameter count is reduced by 2.4%, and the inference latency is decreased by 41.1%. The experimental results show that the proposed method effectively improves detection accuracy in complex railway scenes while maintaining lightweight inference performance. Full article

Gas flowmeters are a key reference for natural gas trade settlement. To advance on-site verification technology, this paper presents air and natural gas flow test systems employing a clamp-on gas ultrasonic flowmeter, and systematically investigates the effects of pipeline parameters, pressure conditions, transducer usage, and noise reflection on measurement accuracy. Quantitative results show that measurement errors can be controlled within ±2% by selecting appropriate transducer types and installing them beyond 20D downstream of disturbances. Moreover, the proposed dual-transducer synchronous measurement—using two sets of transducers at the same location in different acoustic directions and averaging the results—effectively suppresses radial velocity effects, reducing installation and flow-related errors to within ±1.5%. Installing a single layer of acoustic damping material (≥30 mm beyond the transducer) further improves the coherent signal-to-noise ratio above 30 dB, ensuring reliable performance under noisy conditions. These quantitative findings offer practical guidelines for on-site calibration of natural gas flowmeters, contributing to improved fairness in gas trade measurement. Full article

Cnidium monnieri (L.) Cusson (Apiaceae) is a well-known insectary plant in farmlands. Its vegetative and flowering stages can promote the migration of natural enemies. However, the arrival of natural enemies in crop fields often lags behind the establishment of pest populations. As gibberellin can accelerate plant growth and flowering, we investigated whether gibberellin treatment could advance the recruitment of natural enemies by altering the phenology of C. monnieri. Field experiments were conducted in 2021 and 2022 to evaluate the effects of different gibberellin concentrations on plant phenology, growth traits, and natural-enemy abundance. For field validation, C. monnieri strips established in wheat fields were treated with water or 50 mg/L gibberellin. The 50 mg/L treatment advanced the onset of flowering by 21 days and the first detection of natural enemies on C. monnieri by 14 days in 2021 and 20 days in 2022. It also significantly increased natural-enemy abundance on C. monnieri in 2022. In wheat fields, the same treatment resulted in earlier detection and significantly greater abundance of natural enemies in 2022. The earlier detection of natural enemies was temporally consistent with the advancement of flowering. These findings indicate that manipulating the flowering phenology of insectary plants may improve the timing of natural-enemy establishment and strengthen conservation biological control. Full article

Plant diseases destroy 20–40% of global food production annually, posing a critical threat to food security for a projected population of 9.7 billion by 2050. Conventional diagnostic approaches relying on expert visual assessment are slow, costly, and unsuitable for modern agricultural scales. While deep convolutional neural networks demonstrated early promise, single-modality, image-centric systems consistently fail under real-world field conditions characterized by variable lighting, co-occurring infections, and cultivar diversity. This review synthesizes a decade of progress across four interconnected frontiers: the evolution of deep learning architectures for plant disease detection; the adaptation of foundation models including CLIP, SAM, and DINOv2 to agricultural contexts; the development of multimodal fusion frameworks integrating imagery, environmental, genomic, and hyperspectral data; and the transition from static disease diagnosis to descriptive comparison of reported metrics, which suggested that multimodal approaches frequently reported improved diagnostic performance relative to corresponding single-modality baselines, although direct cross-study comparison was limited by methodological heterogeneity. A systematic review following PRISMA guidelines identifies eligible comparative studies. Descriptive comparison of reported performance metrics across these studies indicated that multimodal approaches generally achieved higher accuracy and sensitivity than single-modality models, particularly for pre-symptomatic disease detection. Eight critical research gaps are identified, including the absence of a unified agricultural foundation model and limited climate-aware forecasting under non-stationary climate projections. A structured research agenda is proposed to accelerate translation from laboratory performance to globally equitable, field-deployable crop protection systems. Full article

Lameness remains one of the most significant welfare and economic challenges in modern dairy production. Traditional detection methods, particularly visual locomotion scoring, are limited by subjectivity, inconsistent application, and infrequent monitoring, which often delays the recognition of painful lesions. This review synthesizes recent validation studies of automated lameness detection (ALD) technologies and evaluates their diagnostic performance, validation design, and practical implementation across dairy systems. Studies were screened for relevance by a single reviewer based on title, abstract, and full-text content. Included studies represented sensor-based, pressure-based, vision-based, and multimodal detection platforms, with reported accuracies ranging from approximately 70% to 98% depending on modality and environmental setting. Vision-based systems demonstrated strong performance in controlled conditions, whereas field-validated systems showed more moderate but potentially more generalizable accuracy. Pressure-based platforms reported high diagnostic discrimination via area under the curve (AUC) analysis but face infrastructural limitations in commercial settings. Risk-of-bias assessment indicated that controlled experimental studies without external validation may overestimate deployment performance. Despite technological advances, variability in validation protocols, lesion thresholds, and environmental robustness limits direct comparison across systems. Future research should prioritize multi-farm external validation, standardized benchmarking frameworks, and multimodal integration within precision livestock farming ecosystems to improve reliability and adoption. Full article

As urban underground spaces and infrastructure development accelerate, subsurface elements such as buried pipelines, integrated utility tunnels, subway tunnels, cavity defects, and deep-seated hidden hazards become increasingly intertwined. Consequently, urban target detection is characterized by pronounced scale discrepancies, intense environmental interference, and severely confined operational spaces. The transient electromagnetic method (TEM) is highly valuable for rapid surveys and hazard identification in urban underground spaces owing to its inherent advantages, including non-contact operation, adaptability to hardened pavements, high sensitivity to low-resistivity anomalies, and the ability to probe a broad range of depths. In recent years, research has shifted from improving isolated instrumentation to synergistically optimizing sensing platforms, transmitter–receiver systems, anti-interference methodologies, and imaging interpretation workflows. Specifically, small-loop configurations and high-frequency excitation technologies have improved shallow-sounding capabilities in confined urban spaces; anti-interference techniques have increased data reliability in complex noise environments; and apparent resistivity mapping, virtual wave-field migration, and rapid inversion methodologies have enabled profiling results to transition from qualitative identification to fine-scale interpretation. Concurrently, the evolution of ground-towed, UAV-borne, helicopter-borne, and semi-airborne platforms has progressively endowed urban TEM profiling with continuous, mobile, and scenario-specific operational capabilities. Looking to the future, further technical breakthroughs in urban TEM technology are required to improve shallow-resolution, deep-seated penetration, multi-source interference decoupling, and real-time concurrent imaging. Full article

Image-level monitoring and variety-level detection of three specialty tomato cultivars, Kiss, Millennium, and White Jade, remain challenging in unstructured orchards because of foliage occlusion, overlapping fruit clusters, and variable illumination. Conventional downsampling may weaken fine spatial details of small targets, whereas larger detectors can impose computational demands that are unsuitable for mobile or edge-based agricultural platforms. To address these limitations, we propose WCAF-YOLO, a lightweight two-dimensional tomato detector based on a modified YOLOv26n architecture. The model replaces the P3 backbone downsampling operation with space-to-depth convolution (SPD-Conv) to retain fine-grained spatial information. Its weighted channel-aware fusion (WCAF) neck combines learnable branch weighting with parameter-free three-dimensional attention to refine fused features. Bounding-box regression uses focaler-minimum point distance intersection over union (Focaler-MPDIoU). Across five random seed runs on the internal held-out test subset of a custom single-site orchard dataset, WCAF-YOLO obtained a mean ${\mathrm{mAP}}_{50–95}$ of $0.9048\pm 0.0013$ and a mean recall of $0.9280\pm 0.0019$. The corresponding mean improvements over the YOLOv26n baseline were 2.14 and 3.42 percentage points, respectively. The model contained 2.36 M parameters and required 6.36 GFLOPs. Under the evaluated protocol, the model combined a compact parameter count with higher mean detection metrics than the YOLOv26n baseline. The detector outputs two-dimensional bounding boxes and variety labels for image-level orchard monitoring and variety-level assessment. Integration into agricultural field platforms remains to be validated. Full article

Botulinum neurotoxin type A (BoNT-A) is used across coexisting craniocervical conditions, and therapeutic response is often overinterpreted as evidence of a shared generator. The bruxism literature shows why. Across six controlled studies with event-level outcome measurement, reported effects on event frequency track not dose or injection field but whether the rule used to detect an event could follow the amplitude the toxin had just reduced; none yet combines a placebo arm with an amplitude-independent event definition. This targeted critical narrative review interprets representative evidence mechanistically rather than assessing efficacy. It examines chronic migraine, tension-type headache, myogenous temporomandibular disorders, and bruxism, with somatosensory tinnitus as a cross-modal boundary case. Efficacy is protocol-specific in chronic migraine and uncertain elsewhere. Tracing studies locate somatosensory routes to the cochlear nucleus in the spinal trigeminal and dorsal column nuclei; a direct mesencephalic-trigeminal-to-cochlear projection has not been demonstrated in the tracing literature reviewed, so somatic–auditory plausibility does not establish the masticatory proprioceptive route invoked by muscle-targeted rationales. Because BoNT-A affects motor output, peripheral nociceptive signaling, and muscle spindle input—the last of these probably differing in availability across injection fields—we frame it as a site-dependent, multi-output perturbation. That pharmacology is established; what is offered here is the inferential framing and the anatomical constraint following from it. Full article

Non-mechanical beam steering technology based on liquid crystal optical switches and liquid crystal polarization gratings holds significant application prospects in fields such as laser communication, radar detection, and optical information processing. Traditional nematic liquid crystal optical switches exhibit slow response speeds, whereas novel ferroelectric liquid crystal optical switches, despite their fast response, are hampered in engineering applications by complex fabrication processes, the large number of devices required for cascading, and substantial module thickness. To address these issues, this paper proposes and demonstrates a fast non-mechanical beam steering scheme by cascading a stressed polymer network liquid crystal (SPNLC) optical switch with a liquid crystal polarization grating. The SPNLC is fabricated by mechanically shearing a polymerized liquid crystal–polymer composite, enabling sub-millisecond response and continuous linear phase modulation without the need for an alignment layer. A 30-μm-thick SPNLC half-wave plate was prepared, which introduces a phase retardation of 3.6 μm under a driving voltage of 300 V, and the rise time and fall time are measured to be approximately 526 μs and 560 μs at a driving voltage of 20 V with a 1 kHz square wave, and 470 μs and 538 μs at 27 V under the same waveform conditions. Cascaded with a passive polarization grating, the waveplate enables fast electrical switching of the beam between the ±1st diffraction orders. Furthermore, a two-dimensional multi-angle beam deflector was constructed based on a supra-binary cascade scheme. Experimental results confirm that the system possesses sub-millisecond response, large phase retardation, continuous tunability, and an alignment-layer-free fabrication process, demonstrating its feasibility for large-range fast beam scanning. Full article

Malaria remains one of the most significant infectious diseases worldwide, requiring rapid, sensitive, and accessible diagnostic tools to improve disease management and control. Conventional diagnostic methods, including microscopy, rapid diagnostic tests, and nucleic acid amplification techniques, present limitations in sensitivity, specificity, cost, or field applicability. This review examines the emerging role of aptamer-based biosensors (aptasensors) as innovative alternatives for malaria detection. Aptamers are synthetic nucleic acid ligands that offer high affinity and specificity toward malaria biomarkers while providing advantages over antibodies, including improved stability, lower production costs, and ease of chemical modification. The review discusses aptamer selection methodologies, major Plasmodium biomarkers targeted for detection, and the integration of aptamers into electrochemical, optical, magnetic, and microfluidic biosensing platforms. Current advances demonstrate the potential of aptasensors to enable highly sensitive, selective, and portable point-of-care diagnostics for malaria surveillance and management. Full article

Repeated non-equidistant unmanned aerial vehicle (UAV) inspections of slope-road cracks require measurements from different distances, poses, and image scales to remain comparable and sufficiently precise for engineering-state decisions. Existing studies rarely integrate cross-view physical conversion, measurement uncertainty, and a project-defined minimum detectable change. We develop an engineering-detectability framework that defines cross-period criteria for crack width and displacement and derives equivalent widths for ideal, representative non-standard, and arbitrary viewpoints. First-order error propagation and reliability allocation convert the minimum detectable change into accuracy requirements for range, field of view, and normalized image coordinates. Crack-boundary coordinates and localization uncertainties provide a common interface for interchangeable detection and photogrammetric modules. Validation combines a controlled fixed-camera sequence with a close-range field-camera multiview test of seven physical openings under local coplanarity. All six determinate stages in the controlled sequence agreed with the digital image correlation (DIC) comparison, while one borderline stage required review. Across the seven openings, the four-view means gave a mean absolute error (MAE) of 0.196 mm and a root mean square error (RMSE) of 0.270 mm, with cross-view coefficients of variation (CVs) of 0.33–4.93%. An illustrative error budget demonstrates reverse screening of system configurations from project thresholds. The framework therefore connects viewpoint-equivalent measurements, uncertainty constraints, and engineering-state decisions in an auditable chain. Full article

LiDAR-based 3D object detection for autonomous driving must balance localization accuracy with real-time inference, while sparse point measurements make small-scale objects such as pedestrians and cyclists particularly challenging to represent at long range. This paper presents PES-PointPillars, an enhanced PointPillars detector with three coordinated design changes. First, pinwheel-shaped convolution (PConv) replaces selected backbone convolutions to expand horizontal and vertical receptive fields for sparse structural patterns. Second, an Improved Inter-Layer Feature Correlation (I-EFC) module uses soft gating and adaptive thresholding to fuse multi-level features through continuous, input-dependent weights. Third, a Smooth L1-NWD (SNWD) loss applies normalized Wasserstein distance to planar position and scale while retaining Smooth L1 regression for vertical position, height, and orientation. Using the parameter settings and configuration of the original PointPillars implementation, the locally executed PES-PointPillars experiment achieves Moderate 3D average precision values of 77.1% for cars, 46.7% for pedestrians, and 62.9% for cyclists at 68.3 FPS on the KITTI validation split. Relative to the source-reported PointPillars reference, the corresponding numerical differences are 2.1, 3.2, and 3.8 percentage points. The reported component-wise and staged ablations show category-dependent gains, with the complete model providing the strongest aggregate performance among the evaluated configurations. Full article

Real-time detection of chemical warfare agents (CWAs) and toxic industrial chemicals underpins military protection, counter-terrorism, and emergency response. Yet field instruments usually fail for a reason unrelated to sensitivity: they cannot identify agents that are not already in their reference libraries, such as novel analogs, mixtures, and degradation products. We argue that this unknown-agent problem is a structural limitation of single-modality sensing, because any one class of information (molecular bonds, ion mobility, elemental composition, or chemical reactivity) is rarely sufficient to resolve an unfamiliar threat. We review the dominant field modalities, including FTIR, Raman/SERS, ion mobility and field-asymmetric ion mobility spectrometry, laser- and spark-induced plasma spectroscopy, metal-oxide sensor arrays, and portable mass spectrometry, and show that their weaknesses are largely complementary. We then set out the principle of orthogonal multimodal sensing, in which complementary information axes are combined by machine learning with anomaly and open-set detection so that unfamiliar agents are recognized as such rather than misidentified. Four hybrid architectures are critically compared, and we examine spark-induced decomposition diagnostics, consumable-free self-decontaminating field systems with edge AI, and the open challenges of standardized datasets, calibration transfer, and validation, before outlining a roadmap toward field-relevant recognition of unidentified chemical threats. Full article

Smart-environment cybersecurity increasingly depends on experimental platforms that can reproduce attacks against buildings, homes, and cities under realistic conditions. However, the literature remains fragmented across testbed design, attack demonstration, and defensive validation. This makes it particularly difficult to judge what kind of security evidence each study actually provides. This review systematically analyses 28 experimentally grounded studies published from 2020 onwards, focusing on how testbed realism, cyber–physical coupling, and evaluation mode shape the strength of the resulting claims. The corpus spans physical, hybrid, emulated, and dataset-driven environments across smart buildings, smart homes, and smart cities. Through our investigation, we discern a clear asymmetry in the field. Detection-oriented studies dominate, especially those based on emulation or public datasets, while live evidence for prevention, response, containment, and recovery is comparatively scarce. Availability and integrity/control attacks are the most frequently exercised, whereas authentication compromise and software exploitation remain rare because they are harder to stage on real hardware. Moreover, an important observation we arrive at is that physical and hardware-in-the-loop platforms support the strongest cyber–physical evidence, but emulated and replayed environments remain valuable for scale and reproducibility. At the same time, public datasets and offline classification results do not by themselves establish operational resilience in a live smart environment. To make these distinctions explicit, we introduce a cross-domain taxonomy of testbed architectures, attack families, and defensive control coverage, and map the evidence strength of reported mitigations using NIST cybersecurity framework-derived operational functions. Last, we identify open challenges, including weak recovery evaluation, limited reuse of reference testbeds, and the need for live, context-aware datasets, outlining promising future directions. Full article