Search Results (11,236)

To obtain accurate location information in dynamic environments, we propose a dynamic visual–inertial SLAM algorithm that can operate in real-time. In this paper, we combine the YOLO-V5 algorithm and the depth threshold extraction algorithm to achieve real-time pixel-level segmentation of objects. Meanwhile, to address the situation where dynamic targets are occluded by other objects, we design the object depth extraction method based on K-means clustering. We also design a factor graph optimization with rigid and non-rigid dynamic objects based on object category division, in order to better utilize the motion information of dynamic objects. We use the Kalman filter algorithm to achieve object matching and tracking. At the same time, to obtain as many rigid targets as possible, we design the adaptive rigid point set modeling algorithm to further supplement the rigid objects. Finally, we evaluate the algorithm through public datasets and self-built datasets, verifying its ability to handle dynamic environments. Full article

Air temperature is commonly used to represent plant thermal conditions, although temperatures within woody tissues and the soil–root zone can differ substantially under field conditions. This study characterized the thermal dynamics of xylem tissue and the soil–root interface in almond and olive orchards under Mediterranean field conditions in Southern Spain. Using long-term in-field measurements, temperatures were monitored in branch and trunk xylem tissues and at the soil–root interface, and regression models were developed to provide empirical correction relationships between air and internal temperatures across seasons and sensor position. Branch xylem temperatures closely matched air temperature for both minima and maxima. In contrast, trunk xylem and the soil–root interface showed pronounced thermal buffering. Trunk xylem maximum temperature was significantly (3.4 to 5.4 °C) lower than air temperature during summer. Shaded soil–root interface temperatures were 5.2 to 9.0 °C lower than air temperature in spring and summer but 5.9 to 11.7 °C higher than air temperature in autumn and winter. These patterns indicate a strong capacity of woody tissues and the soil–root system to moderate external thermal conditions. By quantifying air-to-tissue and air-to-soil relationships under field conditions, this study provides microclimatic data that can improve agronomic models and temperature-driven disease risk frameworks for vascular pathogens infecting woody crops. Full article

Crack type classification in concrete structures is essential for assessing structural integrity, yet traditional visual inspections and RA–AF parameter-based Acoustic Emission (AE) methods suffer from subjectivity and limited ability to capture temporal signal dependencies. This study proposes TemporalAE-Net, a self-attention-based machine learning framework designed to classify tensile and shear cracks while explicitly incorporating the temporal evolution of AE signals. AE data were collected from axial tension tests, shear-failure tests, and four-point bending tests on reinforced concrete beams, and a sliding-window reconstruction method was used to transform sequential AE signals into two-dimensional temporal matrices. TemporalAE-Net integrates one-dimensional convolution for local feature extraction and multi-head self-attention for global temporal correlation learning, followed by multilayer perceptron classification. The proposed model achieved an accuracy of 99.72%, outperforming both its ablated variants without convolutional or attention modules and conventional time-series architectures. Generalization tests on 12 unseen specimens yielded 100% correct classifications, and predictions for reinforced concrete beams closely matched established crack-evolution patterns, with shear cracks detected approximately 15 s prior to visual observation. These results demonstrate that TemporalAE-Net effectively captures temporal dependencies in AE signals. Moreover, it provides accurate and efficient tensile–shear crack identification, making it suitable for real-time structural health monitoring applications. Full article

Buildings account for nearly 32% of global energy consumption and serve as key demand-side flexibility resources in power systems with high renewable penetration. However, their utilization is constrained by the lack of an integrated framework that can jointly quantify energy-adjustable margin (BAM) and response duration (RD) under realistic operational and thermal comfort constraints. This study presents a coupled data–physical simulation framework integrating a Particle Swarm Optimization–Long Short-Term Memory–Random Forest (PSO-LSTM-RF) hybrid load forecasting model with EnergyPlus(24.1.0)-based building simulation. The PSO-LSTM-RF model achieves high-accuracy short-term load prediction, with an average R² of 0.985 and mean absolute percentage errors of 1.92–5.75%. Predicted load profiles are mapped to physically consistent baseline and demand-response scenarios using a similar-day matching mechanism, enabling joint quantification of BAM and RD under explicit thermal comfort constraints. Case studies on offices, shopping malls, and hotels reveal significant heterogeneity: hotels exhibit the largest BAM (up to 579.27 kWh) and longest RD (up to 135 min), shopping malls maintain stable high flexibility, and offices show moderate BAM with minimal operational disruption. The framework establishes a closed-loop link between data-driven prediction and physics-based simulation, providing interpretable flexibility indicators to support demand-response planning, virtual power plant aggregation, and coordinated optimization of source–grid–load interactions. Full article

Moving-target detection under strict sensing constraints is a recurring subproblem in surveillance, search-and-rescue, and autonomous robotics. We study a canonical one-dimensional finite grid in which a sensor probes one location per time step with binary observations while the target follows reflecting random-walk dynamics. The objective is to minimize the expected time to detection using transparent, training-free decision rules defined on the belief state of the target location. We compare two belief-driven heuristics with purely online implementation: a greedy rule that always probes the most probable location and a belief-proportional sampling (BPS, probability matching) rule that samples sensing locations according to the belief distribution (i.e., posterior probability of the target location). Repeated Monte Carlo simulations quantify the exploitation–exploration trade-off and provide a self-comparison between the two policies. Across tested grid sizes, the greedy policy consistently yields the shortest expected time to detection, improving by roughly 17–20% over BPS and uniform random probing in representative settings. BPS trades some average efficiency for stochastic exploration, which can be beneficial under model mismatch. This study provides an interpretable baseline and quantitative reference for extensions to noisy sensing and higher-dimensional search. Full article

To address the localization and mapping challenges for quadruped robots autonomously scouting under corn canopies, this paper proposes CK-SLAM, a SLAM algorithm integrating robot motion constraints and crop row features. The algorithm is implemented on the Jueying Mini quadruped robot, fusing data from 3D LiDAR, IMU, and joint sensors. First, an Invariant Extended Kalman Filter (InEKF) fuses multi-source motion information, dynamically adjusting observation noise via a foot contact probability model (derived from joint torque data) to achieve initial motion state estimation and reliable pose references for point cloud deskewing. Second, three feature extraction schemes are designed, inheriting line/plane features from LeGO-LOAM and adding an innovative crop plane feature extraction module, which uses grid filtering, differential evolution for crop row detection, and RANSAC plane fitting to capture corn plant structural features. Finally, a two-step Levenberg–Marquardt iteration realizes feature matching and pose optimization, with factor graph optimization fusing motion constraints and laser odometry for global trajectory and map refinement. CK-SLAM effectively adapts to gait-induced measurement noise and enhances feature matching stability under canopies. Experimental validation across four corn growth stages shows it achieves an average Absolute Pose Error (APE) RMSE of 2.0939 m (15.7%/56.4%/72.2% lower than A-LOAM/LeGO-LOAM/Point-LIO) and an average Relative Pose Error (RPE) RMSE of 0.0946 m, providing high-precision navigation support for automated field monitoring. Full article

Global Navigation Satellite System (GNSS) signal acquisition in high-dynamic environments faces significant challenges due to large Doppler frequency offsets and stringent computational constraints. This paper proposes a frequency-domain compressed acquisition algorithm that reformulates the conventional two-dimensional code-phase/Doppler search as a set of independent one-dimensional sparse recovery problems. Doppler uncertainty is modeled as sparsity in a discretized frequency dictionary, and a low-coherence measurement matrix is designed offline via projected gradient descent with a two-stage annealing strategy. The resulting matrix significantly reduces maximum coherence and supports reliable sparse recovery from a small number of compressed measurements. During online operation, the receiver forms compressed observations for all code phases through efficient matrix operations and recovers sparse Doppler spectra using lightweight orthogonal matching pursuit. Simulation results show that the proposed method achieves a several-fold reduction in computational cost compared with classical parallel code-phase search while maintaining high detection probability at low carrier-to-noise density ratios and under large Doppler offsets, providing an effective solution for resource-constrained GNSS receivers in high-dynamic scenarios. Full article

Background. This study aimed to quantify differences in the internal training load (ITL) of an elite rink hockey (RH) team across days within and between three types of microcycles: pre-season, in-season regular, and in-season congested, to provide insights to optimise microcycle scheduling. Methods. One international-level male RH team comprising seven outfielders (29.6 ± 4.7 years; height, 178.9 ± 2.3 cm; body mass, 77.8 ± 5.7 kg) and one goalkeeper (32 years; height, 180.4 cm; body mass, 83.6 kg) was monitored for 21 microcycles. The ITL was assessed using the session rate of perceived exertion (sRPE) and quantified as time based on a triphasic classification commonly utilised in team sports: low-intensity training (LIT, <80% heart rate maximum (HRmax)), medium-intensity training (MIT, 80–90% HRmax), and high-intensity training (HIT, >90% HRmax). Generalized estimating equations were used to examine differences across within-microcycle training days and between seasonal phases, with linear mixed models applied as sensitivity analyses. Results. Across all phases, significant day-to-day variations in ITL were observed within microcycles (all p < 0.001), with both subjective (sRPE) and objective (LIT–HIT) ITLs progressively decreasing as match days (MDs) approached, showing moderate-to-large population-averaged effects with 95% confidence intervals consistently not crossing zero. The pre-season exhibited the highest overall ITL (p < 0.001), characterised by a substantially greater sRPE and increased time spent across all intensity zones, with the largest magnitudes observed for LIT and MIT compared with the in-season phases. Conclusions. Findings suggest that an international-level RH team progressively reduced the ITL as MDs approached with the highest loads scheduled earlier within microcycles. Moreover, the pre-season had the highest ITLs. This ITL distribution may provide useful guidance for RH coaches and support staff in optimising microcycle planning. Full article

This study presents the development and implementation of a functional system that utilizes large language models (LLMs) to improve the identification, organization, and retrieval of mobility interoperability resources. The established framework assists novice and experienced implementers of mobility services such as planning organizations and multimodal transportation agencies to efficiently access interoperability resources, such as standards and case studies, which are often dispersed and difficult to navigate. The web-based system includes a backend that generates abstracts and tags and a frontend that supports manual or chatbot-based search. A prompt-refinement mechanism suggests improved queries within the context of mobility interoperability when no matches are found. To validate the quality of LLM-generated abstracts and tags, subject matter experts reviewed outputs from multiple prompt iterations to assess accuracy and clarity. Of the 82 resources evaluated, 72% of abstracts met expert expectations for relevance, while 91% of the tags were considered appropriate. A comprehensive case study of 330 representative user queries was also conducted to evaluate the chatbot’s output. Overall, the presented framework aims to reduce cataloging effort, improve classification consistency, and improve accessibility to relevant information. With minimal setup costs, the system offers a scalable and cost-effective solution for managing large, uncatalogued repositories. Full article

We present LazyNet, a compact one-step neural-ODE model for single-cell CRISPR activation/interference (A/I) that operates directly on two-snapshot (“pre → post”) measurements and yields parameters with clear mechanistic meaning. The core log–linear–exp residual block exactly represents multiplicative effects, so synergistic multi-locus responses appear as explicit components rather than opaque composites. On a 53k-cell × 18k-gene neuronal Perturb-seq matrix, a three-replica LazyNet ensemble trained under a matched 1 h budget achieved strong threshold-free ranking and competitive error (genome-wide r ≈ 0.67) while running on CPUs. For comparison, we instantiated transformer (scGPT-style) and state-space (RetNet/CellFM-style) architectures from random initialization and trained them from scratch on the same dataset and within the same 1 h cap on a GPU platform, without any large-scale pretraining or external data. Under these strictly controlled, low-data conditions, LazyNet matched or exceeded their predictive performance while using far fewer parameters and resources. A T-cell screen included only for generalization showed the same ranking advantage under the identical evaluation pipeline. Beyond prediction, LazyNet exposes directed, local elasticities; averaging Jacobians across replicas produces a consensus interaction matrix from which compact subgraphs are extracted and evaluated at the module level. The resulting networks show coherent enrichment against authoritative resources (large-scale co-expression and curated functional associations) and concordance with orthogonal GPX4-knockout proteomes, recovering known ferroptosis regulators and nominating testable links in a lysosomal–mitochondrial–immune module. These results position LazyNet as a practical option for from-scratch, low-data CRISPR A/I studies where large-scale pretraining of foundation models is not feasible. Full article

Objective crime risk and perceived safety constitute distinct yet interrelated dimensions of urban security, whose spatial discrepancies may lead to misaligned policy interventions. This study develops a street-level analytical framework to examine the (mis)match between perceived safety and crime risk in Chaoyang District, Beijing. An enhanced Street-view imagery (SVI) segmentation model with object detection was applied to extract streetscape elements and estimate perceived safety scores, which were then standardized and compared with street-level crime data, yielding two types of matches and two types of mismatches. Three conditions were analyzed using multinomial logit regression: (1) objective unsafety with low perceived safety, (2) objective safety with low perceived safety, and (3) objective unsafety with high perceived safety. Findings demonstrate how visual and social environmental factors jointly shape discrepancies between perceived and actual safety and identify potential determinants to mitigate such (mis)matches. Full article

Background: Rare diseases often lead to delayed diagnosis because clinical knowledge is fragmented across unstructured research, individual case reports, and heterogeneous data formats. This study presents ZebraMap, a multimodal knowledge map created to consolidate rare disease information and transform narrative case evidence into structured, machine-readable data. Methods: Using Orphanet as the disease registry, we identified 1727 rare diseases and linked them to PubMed case reports. We retrieved 36,131 full-text case report articles that met predefined inclusion criteria and extracted publication metadata, patient demographics, clinical narratives (cases), and associated images. A central methodological contribution is an automated large language model (LLM) structuring pipeline, in which free-text case reports are parsed into standardized fields, such as symptoms, diagnostic methods, differential diagnoses, treatments, and outcome that produce structured case representations and image metadata matching the schema demonstrated in our extended dataset. In parallel, a retrieval-augmented generation (RAG) component generates concise summaries of epidemiology, etiology, clinical symptoms, and diagnostic techniques by retrieving peer-reviewed research to enhance missing disease-level descriptions. Results: The final dataset contains 69,146 structured patient-level case texts and 98,038 clinical images, each linked to a particular patient ID, disease entry, and publication. Overall cosine similarity between curated and generated text is 94.5% and performance in information extraction and structured data generation is satisfactory. Conclusions: ZebraMap provides the largest openly accessible multimodal resource for rare diseases and enables data-driven research by converting narrative evidence into computable knowledge. Full article

Blue–green spaces are essential for mitigating urban heat islands. The matching between their supply and demand affects the fairness and effectiveness of urban cooling facilities. This study focuses on the third ring area of Urumqi, Xinjiang, China. Cooling supply indicators and cooling demand indicators for blue–green spaces are established. Using coupling coordination and bivariate spatial autocorrelation models, it evaluates the cooling supply-demand relationship during 2010–2020. Results show that: (1) There is a “suburban cold sources dominated, urban supply turned positive” pattern in the cooling supply of Urumqi’s blue–green spaces. (2) Cooling demand has a significant “dual-core spatial separation”. The physical demands are concentrated in the high-temperature patches around the city, while the social demands are mainly distributed in the core area of the urban district. (3) There is a severe supply–demand spatial mismatch, with extremely low coupling coordination. The core issue is that high-supply cropland cold sources are far from the high-social-demand urban area. This study provides an important scientific basis for formulating effective cooling strategies for oasis cities through the analysis of the supply and demand matching of blue and green space. It uniquely helps safeguard ecological security and residents’ health in arid-zone cities. Full article

Agricultural mechanization and advanced technology have developed significantly in the coffee industry. However, there are still requirements for human laborers to operate, monitor crop health care, and manage production. The integration of advanced technology can significantly enhance the production efficiency and management practices of agricultural enterprises. This paper aims to address these gaps by proposing and implementing a computer vision-based attendance tracking system on mobile platforms that are suitable for the requirements and limitations of agricultural enterprises. First, the face detection process involves interpreting and locating facial structure. Next, the model transforms a photographic image of a human face into digital data based on the unique features and facial structure. We utilize the InsightFace model with the buffalo_l variant, as well as ArcFace with a ResNet backbone, as a facial recognition algorithm. After capturing a facial image, the system conducts a matching process against the existing database to verify identity. Finally, we implement a mobile application prototype on both iOS and Android platforms, ensuring accessibility for farm workers. As a result, our system achieved 95.2% accuracy on the query set, with an average processing time of <200 ms per image (including face detection, embedding extraction, and database matching). The system performs real-time attendance monitoring, automatically recording the entry and exit times of farm workers using facial recognition technology, and enables quick registration of new workers. Our work is expected to enhance transparency and fairness in the human management process, focusing on the coffee farm use case. Full article

Accurate colorimetric quantification presents a significant challenge, as traditional imaging technologies fail to resolve metamerism and even hyperspectral imaging (HSI) is compromised by nonlinearities and specular reflections. This study introduces a high-fidelity colorimetric system using cross-polarized HSI to suppress specular reflections, integrated with a Support Vector Regression (SVR) model to correct the system’s nonlinear response. The system’s performance was rigorously validated, demonstrating exceptional stability and repeatability (average $\Delta E_{00}<0.1$). The SVR calibration significantly enhanced accuracy, reducing the mean color error from $\Delta E_{00}=4.36$ to $0.43$. Furthermore, when coupled with a Random Forest classifier, the system achieved 99.0% accuracy in discriminating visually indistinguishable (metameric) samples. In application-specific validation, it successfully quantified cosmetic color shifts and achieved high-precision skin-tone matching with a fidelity as low as $\Delta E_{00}=0.82$. This study demonstrates that the proposed system, by synergistically combining cross-polarization and machine learning, constitutes a robust tool for high-precision colorimetry, addressing long-standing challenges and showing significant potential in fields like cosmetic science. Full article