Search Results (1,419)

To address the challenges of inefficient path planning, discontinuous trajectories, and inadequate safety margins in autonomous spraying vehicles for greenhouse environments, this paper proposes a hierarchical motion control architecture. At the global path planning level, the heuristic function of the A* algorithm was redesigned to integrate channel width constraints, thereby optimizing node expansion efficiency. A continuous reference path is subsequently generated using a third-order Bézier curve. For local path planning, a state-space sampling method was adopted, incorporating a multi-objective cost function that accounts for collision distance, curvature change rate, and path deviation, enabling the real-time computation of optimal obstacle-avoidance trajectories. At the control level, an adaptive look-ahead distance pure pursuit algorithm was designed for trajectory tracking. The proposed framework was validated through a Simulink-ROS co-simulation environment and deployed on a Huawei MDC300F computing platform for real-world vehicle tests under various operating conditions. Experimental results demonstrated that compared with the baseline methods, the proposed approach improved the planning efficiency by 38.7%, reduced node expansion by 16.93%, shortened the average path length by 6.3%, and decreased the path curvature variation by 65.3%. The algorithm effectively supports dynamic obstacle avoidance, multi-vehicle coordination, and following behaviors in diverse scenarios, offering a robust solution for automation in facility agriculture. Full article

Last-mile logistics is a critical operational and environmental challenge in urban areas. This paper introduces an intelligent path planning system using the Resistive Grid Path Planning Methodology (RGPPM) to optimize distribution based on energy and environmental metrics. The foundational innovation is the integration of electrical-circuit analogies, modeling the distribution network as a resistive grid where optimal routes emerge naturally as current flows, offering a paradigm shift from conventional algorithms. Using a multi-connected grid with georeferenced resistances, RGPPM estimates minimum and maximum paths for various starting points and multi-agent scenarios. We introduce five key performance indicators (KPIs)—Percentage of Distance Savings (PDS), Coefficient of Savings (CS), Coefficient of Global Savings (CGS), Percentage of Load Imbalance (PLI), and Percentage of Deviation with Multi-Agent (PDM)—to evaluate system performance. Simulations for textbook delivery to 129 schools in the Veracruz–Boca del Río area show that RGPPM significantly reduces travel distances. This leads to substantial savings in energy consumption, CO₂ emissions, and operating costs, particularly with electric vehicles. Finally, the results validate RGPPM as a flexible and scalable strategy for sustainable urban logistics. Full article

The rapid adoption of electric vehicles (BEVs) has increased the need to understand how fast-charging strategies influence long-distance travel times under real-world conditions. While most manufacturers specify maximum charging power and standardized driving ranges, these figures often fail to reflect actual highway operation, particularly in adverse weather. This study addresses this gap by analyzing the fast-charging behaviour, net battery capacity and highway energy consumption of 62 EVs from different market segments. Charging power curves were obtained experimentally at high-power DC stations, with data recorded through both the charging infrastructure and the vehicles’ battery management systems. Tests were conducted, under optimal conditions, between 10% and 90% state of charge (SoC), with additional sessions performed under both cold and preconditioned battery conditions to show thermal effects on the batteries’ fast-charging capabilities. Real-world highway consumption values were applied to simulate 1000 km journeys at 120 km/h under cold (−10 °C, cabin heating) and mild (23 °C, no AC) weather scenarios. An optimization model was developed to minimize total trip time by adjusting the number and duration of charging stops, including a 5 min detour for each charging session. Results show that the optimal charging cutoff point consistently emerges around 59% SoC, with a typical deviation of 10, regardless of ambient temperature. Charging beyond 70% SoC is generally inefficient unless dictated by charging station availability. The optimal strategy involves increasing the number of shorter stops—typically every 2–3 h of driving—thereby reducing total trip. Full article

Reintroduction efforts of wildlife species seek to re-establish self-sustaining populations of targeted species within their historical ranges. Our study focuses on the Indian Grey Hornbill, which faced local extinction in the Gir National Park and Sanctuary, Gujarat, India. The last recorded direct sighting of the Indian Grey Hornbill in the study area dates back to the 1930s. Its presence gradually declined, leading to its eventual extinction in the region between 1950 and 1960. Since the declaration of Gir Forest as a sanctuary in 1965 and subsequently as a national park in 1975, habitat conditions have significantly improved. This positive trend created an opportunity for the reintroduction of the hornbills to establish a self-sustaining population. The reintroduction was conducted in two phases. During the first phase, twenty-eight birds were captured from known hornbill ranges within Gujarat, and five of them were equipped with PTT/GSM satellite transmitters. And in the second phase, twelve birds were captured, and six of them were fitted with PTTs to study their ranging patterns, habitat associations, and potential breeding activities. During the establishment or initial phase of reintroduction, the birds exhibited exploratory behaviour, resulting in larger home ranges (mean ± Standard Deviation, SD) (60.87 ± 68.51 km²), which gradually reduced to smaller home ranges (5.73 ± 10.50 km²) during later stages. Similarly, the daily and monthly distances travelled by the birds were significantly greater in the initial phase than in the later one. Nest site selection correlated significantly with girth at breast height (GBH) and tall trees. Our study provides essential information for hornbill reintroduction in the Gir landscape, aiding future conservation efforts for Indian Grey Hornbills. Full article

This study investigates stress detection in dairy cattle by integrating circadian rhythm analysis and deep learning. Behavioral biomarkers, including feeding, resting, and rumination, were continuously monitored using Nedap CowControl sensors over a 12-month period to capture seasonal variability. Circadian features were extracted using the Fast Fourier Transform (FFT), and deviations from expected 24 h patterns were quantified using Euclidean distance. These features were used to train a Long Short-Term Memory (LSTM) neural network to classify stress into three levels: normal, mild, and high. Expert veterinary observations of anomalous behaviors and environmental records were used to validate stress labeling. We continuously monitored 10 lactating Holstein cows for 365 days, yielding 87,600 raw hours and 3650 cow-days (one day per cow as the analytical unit). The Short-Time Fourier Transform (STFT, 36 h window, 1 h step) was used solely to derive daily circadian characteristics (amplitude, phase, coherence); STFT windows are not statistical samples. A 60 min window prior to stress onset was incorporated to anticipate stress conditions triggered by management practices and environmental stressors, such as vaccination, animal handling, and cold stress. The proposed LSTM model achieved an accuracy of 82.3% and an AUC of 0.847, outperforming a benchmark logistic regression model (65% accuracy). This predictive capability, with a one-hour lead time, provides a critical window for preventive interventions and represents a practical tool for precision livestock farming and animal welfare monitoring. Full article

There has been a growing demand for clean energy in recent years, with the advancement of the carbon neutrality vision. Wind power has occupied a significant percentage of clean energy sources. Usually deployed in open fields, on mountaintops, and in offshore areas, wind turbines are particularly vulnerable to lightning strikes due to their unique operational characteristics. Therefore, accurately evaluating the lightning strike risk of wind turbines is an important issue that should be addressed. Current IEC standards lack a physically grounded approach for calculating the equivalent collection area, leading to an overestimation of this value. This paper employs an upward leader initiation model to develop a novel calculation method for the equivalent collection area of wind turbines. By considering the impact of upward leader channel initiation and development, the model demonstrates accuracy through comparison with observational data (0.7761 strikes/year), showing only a −7.1% discrepancy. This study also examines the impact of various blade rotation angles, stepped leader speeds, and peak current of the return stroke on the equivalent collection area. Results indicate that the lightning strike distance specified in IEC standards underestimates the equivalent collection area due to neglecting the upward leader channel, resulting in significant differences compared to our approach, with a maximum deviation of up to 313.12%. Full article

To rationally utilize Morchella germplasm resources, this study investigated 13 phenotypic traits in 231 Chinese Morchella germplasm accessions. Accessions were stratified by cap color and subjected to comparative analyses using four sampling methods, five sampling intensities, two genetic distance metrics, and four hierarchical clustering algorithms to determine the optimal strategy for core collection construction. The optimal sampling strategy for core collection construction was identified using six evaluation. Phenotypic traits of the core collection were evaluated using genetic diversity eigenvalues, t-tests, F-tests, and systematic clustering, with confirmation via principal component analysis. The results indicate that the logarithmic ratio method yielded the smallest differences in group proportions, making it the optimal sampling method. A 15% sample intensity proved optimal, with Euclidean distance outperforming Mahalanobis distance. The longest-distance method was determined to be the optimal clustering approach. Within the optimal sampling strategy combination, the CR value reached its maximum (97.77%). Ultimately, 34 Morchella germplasm resources were extracted, accounting for 14.72% of the total germplasm (original germplasm). The mean values, standard deviations, and genetic diversity of phenotypic traits were similar between the original germplasm and the core collection. However, the coefficient of variation for quantitative traits showed significant differences. In the t-test, only the maturity period showed a significant difference. In the F-test, only the cap length/width and maturity period showed significant differences. Cluster analysis grouped the germplasm resources of the core collection and the original germplasm into relatively consistent clusters. In principal component analysis, the eigenvalues and cumulative contribution rates of the first four principal components were higher for the core collection than for the original germplasm. This indicates that the core collection eliminated most genetic redundancy while preserving the genetic diversity of the original germplasm. The core collection selection is representative and can be effectively utilized as breeding material. This study provides a reference for the effective utilization and germplasm innovation of Morchella germplasm resources. Full article

Rail transportation is one of the most environmentally friendly systems; however, it generates noise and vibrations in the vicinity of railway lines. Therefore, the operation of railways requires appropriate measurements to analyze interactions between rolling stock and railway infrastructure during service. This paper presents a novel railway monitoring system based on the Industrial Internet of Things (IIoT) sensor network concept, enabling the integration of functionalities such as synchronized motion, technical, and environmental measurements. The system features a flexible configuration regarding the number of monitored parameters and scalability in terms of the number of tracks being observed. Selected field studies are presented, leading to the optimal configuration of the measurement system, along with a discussion of key research findings. Signal analysis enables a comprehensive assessment of the impact of rail transport on the environment, particularly by identifying sources of environmental pollution such as vibrations and noise generated by rail vehicles. In this study, 932 units of passing trains (wagons, locomotives, and multiple unit sections) were identified. The average deviation of the distances between recorded axles (relative to the catalog data) was approximately 3.9 cm, with a maximum of 20 cm. Full article

Virtual commands serve as the essential framework for establishing interaction between the virtual pilot and the MCP in autopilot scenarios. Conventional proportional-integral-derivative (PID) controllers are insufficient to ensure accurate flight trajectories due to system hysteresis. To overcome this limitation, a quaternary correlation prediction compensation PID (QCPC-PID) approach is introduced for computing virtual heading commands in autopilot tasks. The method integrates multi-feature statistics, entropy-based predictive compensation, and quaternary correlations. First, flight trajectory error statistics are dynamically calculated using signed error distances to assess deviation levels. Second, a predictive structure based on information entropy is applied to enhance PID compensation. Third, quaternary correlation dependence is established to generate virtual heading commands. The findings confirm the effectiveness of the method in improving flight convergence. The incorporation of predictive structures and quaternary correlations is critical for achieving predictive compensation during PID tuning, thereby reducing flight trajectory deviations. The quaternary correlation prediction compensation method ensures superior performance of PID control in modeling heading adjustment behavior under autopilot conditions. Full article

Background/Objective: Precise preprocedural planning is essential for the safety and efficacy of structural heart interventions. Conventional imaging modalities, while informative, do not allow for direct and accurate visualization, limiting procedural predictability. We aimed to develop and validate a high-resolution micro-computed tomography (microCT)-based reverse modeling workflow that integrates digital reconstructions of metallic cardiac devices into patient imaging datasets, enabling accurate, patient-specific virtual simulation for procedural planning. Methods: Clinical-grade transcatheter heart valves, septal defect occluders, patent ductus arteriosus occluders, left atrial appendage closure devices, and coronary stents were scanned using microCT (36.9 μm resolution). Agreement was assessed by intra-class correlation coefficients (ICC) and Bland–Altman analyses. Device geometries were reconstructed into 3D stereolithography files and virtually implanted within multislice CT datasets using dedicated software. Results: Devices were successfully reverse-modeled with high geometric fidelity, showing negligible dimensional deviations from manufacturer specifications (mean ΔDistance range: −0.20 to +0.20 mm). Simulated measurements demonstrated excellent concordance with postprocedural imaging (ICC 0.90–0.96). The workflow accurately predicted clinically relevant parameters such as valve-to-coronary distances and implantation depths. Notably, preprocedural simulation identified a case at high risk of coronary obstruction, confirmed clinically and managed successfully. Conclusions: The microCT-based reverse modeling workflow offers a rapid, reproducible, and clinically relevant method for patient-specific simulation in structural heart interventions. By preserving anatomical fidelity and providing detailed device–tissue spatial visualization, this approach enhances preprocedural planning accuracy, risk stratification, and procedural safety. Its resource-efficient digital nature facilitates broad adoption and iterative simulation. Full article

The impact of counterion structure, especially variations in constitutional and stereochemical isomers, on the properties and performance of AABSs remains under-explored. This study investigates how structural variations, particularly the stereochemistry of diammonium cyclohexane (DACH) counterions, influence the self-assembly behavior of AABSs. Four AABSs: undecanoyl-glycine, -L-alanine, -L-valine, and -L-leucine, were paired with six DACH counterions representing cis/trans isomers of 1,2-, 1,3-, and 1,4-DACH. Critical micelle concentrations (CMCs) were determined via conductimetry, and micellar sizes were measured using dynamic light scattering. The degree of counterion binding (β) was calculated to probe micelle stability, while geometry-optimized structures of the DACH isomers were obtained using density functional theory. Lastly, pH measurements were taken to probe the protonation of DACH counterions at their natural pH, where both the DACH counterion and AABS headgroups intrinsically behave as buffers. Results indicate that while surfactant hydrophobicity primarily dictates CMC in other AABS/DACH combinations, trans-1,3-DACH leads to consistently higher CMCs. This deviation likely arises from its structural conformation, which positions the amine groups an intermediate distance of ~4.4–4.5 Å apart, allowing a small fraction of divalently charged counterions to form strong electrostatic bridging pockets at the micelle interface. These interactions dominate over headgroup effects, leading to elevated and surfactant-independent CMC values. Regarding size and other unusual trends in the systems, cis- isomers formed slightly larger micelles, and trans-1,4-DACH induces abnormal aggregation in undecanoyl-glycine leading to temperature dependent gel formation. These findings highlight the significant influence of counterion structure on AABS behavior and support counterion design as a strategy for enhancing surfactant performance in sustainable applications. Full article

This paper presents a novel method for locally estimating vehicle density on highways based on vehicle-to-vehicle (V2V) communication, a communication mode within intelligent transport systems (ITSs), enabled via IEEE 802.11p and 3GPP C-V2X technologies. Awareness messages (AMs), such as basic safety messages (BSMs, SAE J2735) and cooperative awareness messages (CAMs, ETSI EN 302 637-2), are periodically broadcast by vehicles and can be leveraged to sense the presence of nearby vehicles. Unlike existing approaches that directly combine the number of sensed vehicles with measured packet reception ratio (PRR) of the AM, our method accounts for the deviations in PRR caused by imperfect channel conditions. To address this, we estimate the actual packet reception probability (PRP)–distance curve by exploiting its inherent downward trend along with multiple measured PRR points. From this curve, two metrics are introduced: node awareness probability (NAP) and average awareness ratio (AAR), the latter representing the ratio of sensed vehicles to the total number of vehicles. The real density is then estimated using the number of sensed vehicles and AAR, mitigating the underestimation issues common in V2V-based methods. Simulation results across densities ranging from 0.02 vehs/m to 0.28 vehs/m demonstrate that our method improves estimation accuracy by up to 37% at an actual density of 0.28 vehs/m, compared with methods relying solely on received AMs, without introducing additional communication overhead. Additionally, we demonstrate a practical application where the basic safety message (BSM) transmission rate is dynamically adjusted based on the estimated density, thereby improving traffic management efficiency. Full article

In horizontal well technology, hydraulic fracturing has been established as an essential technique for enhancing hydrocarbon production. However, the complex architecture of fracture networks challenges conventional monitoring methods. Micro-seismic monitoring, recognized for its superior resolution and sensitivity, enables precise fracture morphology characterization. This study advances diagnostic capabilities through integrated field–laboratory investigations and multi-domain signal processing. Hydraulic fracturing experiments under varied geological conditions generated critical micro-seismic datasets, with quantitative analyses revealing asymmetric propagation patterns (total length 312 ± 15 m, east wing 117 m/west wing 194 m) forming a 13.37 × 10⁴ m³ stimulated reservoir volume. Spatial event distribution exhibited density disparities correlating with geophone offsets (west wing 3.8 events/m vs. east 1.2 events/m at 420–794 m distances). Advanced time–frequency analyses and inversion algorithms differentiated signal characteristics demonstrating logarithmic SNR (Signal-to-Noise Ratio)–magnitude relationships (SNR 0.49–4.82, R² = 0.87), with near-field events (<500 m) showing 68% reduced magnitude variance compared to far-field counterparts. Coupled numerical simulations confirmed stress field interactions where fracture trajectories deviated 5–15° from principal stress directions due to prior-stage stress shadows. Branch fracture networks identified in Stages 4/7/9/10 with orthogonal/oblique intersections (45–65° dip angles) enhanced stimulation reservoir volume (SRV) by 37–42% versus planar fractures. These geometric parameters—including height (20 ± 3 m), width (44 ± 5 m), spacing, and complexity—were quantitatively linked to micro-seismic response patterns. The developed diagnostic framework provides operational guidelines for optimizing fracture geometry control, demonstrating how heterogeneity-driven signal variations inform stimulation strategy adjustments to improve reservoir recovery and economic returns. Full article

This work aims to leverage generative modeling-based anomaly detection to enhance interpretability in AI failure detection systems and to aid data curation for large repositories. For failure detection interpretability, this retrospective study utilized 3339 CT scans (525 patients), divided patient-wise into training, baseline test, and anomaly (having failure-causing attributes—e.g., needles, ascites) test datasets. For data curation, 112,120 ChestX-ray14 radiographs were used for training and 2036 radiographs from the Medical Imaging and Data Resource Center for testing, categorized as baseline or anomalous based on attribute alignment with ChestX-ray14. StyleGAN2 networks modeled the training distributions. Test images were reconstructed with backpropagation and scored using mean squared error (MSE) and Wasserstein distance (WD). Scores should be high for anomalous images, as StyleGAN2 cannot model unseen attributes. Area under the receiver operating characteristic curve (AUROC) evaluated anomaly detection, i.e., baseline and anomaly dataset differentiation. The proportion of highest-scoring patches containing needles or ascites assessed anomaly localization. Permutation tests determined statistical significance. StyleGAN2 did not reconstruct anomalous attributes (e.g., needles, ascites), enabling the unsupervised detection of these attributes: mean (±standard deviation) AUROCs were 0.86 (±0.13) for failure detection and 0.82 (±0.11) for data curation. 81% (±13%) of the needles and ascites were localized. WD outperformed MSE on CT (p < 0.001), while MSE outperformed WD on radiography (p < 0.001). Generative models detected anomalous image attributes, demonstrating promise for model failure detection interpretability and large-scale data curation. Full article

Strabismus, or squint or deviating eyes, is defined as misalignment of the eyes when fixating on an object and is a common problem in ophthalmology. Palsy of the third, fourth or sixth cranial nerve is one of the leading underlying causes for paralytic strabismus, often requiring surgery. However, uncertainty regarding factors influencing surgical success remains. Background/Objectives: The purpose of this study is to review the outcome and influencing factors of strabismus surgery in patients with cranial nerve palsy. Methods: A retrospective study of 57 patients with third cranial nerve (CN3) palsy, fourth cranial nerve (CN4) palsy, sixth cranial nerve (CN6) palsy or combined nerve palsy who underwent strabismus surgery between October 2009 and December 2023 was conducted. Analyzed data included demographic details, type of surgical intervention, etiology of nerve palsy, pre- and postoperative angle of deviation (AOD), vertical deviation (VD), best-corrected visual acuity (BCVA), and refractive error. Results: Mean age was 41.29 ± 23.14 years with a mean follow-up of 10.8 ± 15.38 months. 30 patients (52.63%) had CN6 palsy, 12 patients (21.05%) had CN3 palsy, eight patients (14.04%) had CN4 palsy and seven patients (12.28%) had combined nerve palsy. Brain neoplasm was the most common cause of nerve palsy (33.33%). Mean preoperative AOD improved from 17.54° ± 10.68 to 7.13° ± 8.93 and from 17.21° ± 9.58 to 7.49° ± 9.75 for near and distance, respectively (p < 0.001). Changes in VD, refractive error, and BCVA were not statistically significant. Conclusions: Age, gender, preoperative AOD, subtype and etiology of nerve palsy had no significant influence on surgical outcomes, which are satisfactory in patients with cranial nerve palsy (80.7%). Full article