This paper addresses the issues of target unreachability and local optima in traditional artificial potential field (APF) methods for UAV swarm path planning by proposing an improved collaborative obstacle avoidance algorithm. By introducing a virtual target position function to reconstruct the repulsive field model, the repulsive force exponentially decays as the UAV approaches the target, effectively resolving the problem where excessive obstacle repulsion prevents UAVs from reaching the goal. Additionally, we design a dynamic virtual target point generation mechanism based on mechanical state detection to automatically create temporary target points when UAVs are trapped in local optima, thereby breaking force equilibrium. For multi-UAV collaboration, intra-formation UAVs are treated as dynamic obstacles, and a 3D repulsive field model is established to avoid local optima in planar scenarios. Combined with a leader–follower control strategy, a hybrid potential field position controller is designed to enable rapid formation reconfiguration post-obstacle avoidance. Simulation results demonstrate that the proposed improved APF method ensures safe obstacle avoidance and formation maintenance for UAV swarms in complex environments, significantly enhancing path planning reliability and effectiveness.

This study presents an optimization-driven design of a wireless communications network to continuously transmit environmental variables—temperature, humidity, weight, and water usage—in poultry farms. The reference site is a four-shed facility in Quito, Ecuador (each shed 120m×12m) with a data center located 200m from the sheds. Starting from a calibrated log-distance path-loss model, coverage is declared when the received power exceeds the receiver sensitivity of the selected technology. Gateway placement is cast as a mixed-integer optimization that minimizes deployment cost while meeting target coverage and per-gateway capacity; a capacity-aware greedy heuristic provides a robust fallback when exact solvers stall or instances become too large for interactive use. Sensing instruments are Tekon devices using the Tinymesh protocol (IEEE 802.15.4g), selected for low-power operation and suitability for elongated farm layouts. Model parameters and technology presets inform a pre-optimization sizing step—based on range and coverage probability—that seeds candidate gateway locations. The pipeline integrates MATLAB R2024b and LpSolve 5.5.2.0 for the optimization core, Radio Mobile for network-coverage simulations, and Wireshark for on-air packet analysis and verification. On the four-shed case, the algorithm identifies the number and positions of gateways that maximize coverage probability within capacity limits, reducing infrastructure while enabling continuous monitoring. The final layout derived from simulation was implemented onsite, and end-to-end tests confirmed correct operation and data delivery to the farm’s data center. By combining technology-aware modeling, optimization, and field validation, the work provides a practical blueprint to right-size wireless infrastructure for agricultural monitoring. Quantitatively, the optimization couples coverage with capacity and scales with the number of endpoints M and candidate sites N (binaries M+N+MN). On the four-shed case, the planner serves 72 environmental endpoints and 41 physical-variable endpoints while keeping the gateway count fixed and reducing the required link ports from 16 to 4 and from 16 to 6, respectively, corresponding to optimization gains of up to 82% and 70% versus dense baseline plans. Definitions and a measurement plan for packet delivery ratio (PDR), one-way latency, throughput, and energy per delivered sample are included; detailed long-term numerical results for these metrics are left for future work, since the present implementation was validated through short-term acceptance tests.

Auditory cueing has become an increasingly practical tool in gait rehabilitation; however, the specific sound features that modulate gait performance remain unclear. This study investigated how tempo and auditory continuity, two fundamental acoustic features, influence spatiotemporal gait parameters in healthy adults. Thirty-five participants walked under six auditory conditions combining discrete, continuous, and hybrid feedback at slow (60 BPM) and fast (120 BPM) tempi, with gait metrics captured via a pressure-sensor walkway and subjective responses gathered through questionnaires. Compared with the silent baseline, auditory cueing significantly affected cadence [F(1.88, 63.75) = 8.95, p < 0.001, ηp2 = 0.21]; velocity [F(1.69, 57.49) = 10.15, p < 0.001, ηp2 = 0.23]; and stride length [F(1.74, 59.26) = 6.87, p = 0.003, ηp2 = 0.17]. Slower tempi reduced gait parameters, while the combined continuous and discrete conditions produced the greatest modulation. Participants reported that they had attempted to synchronize their steps with the auditory cues, which may have led to small adjustments in their natural walking speed and stride patterns, especially during the slower tempo. This suggests that rhythmic structure and sound continuity affect both perceptual and motor processes. Overall, sound continuity exerted a stronger influence on gait than tempo alone. These findings advance understanding of sensorimotor synchronization and highlight the potential of designing tailored auditory feedback systems to enhance movement awareness and inform clinical gait-rehabilitation strategies.

Low-permeability glutenite reservoirs in the Qaidam Basin, NW China, exhibit intricate pore networks and strong heterogeneity that hinder effective hydrocarbon development. Here, we integrate thin-section petrography, scanning electron microscopy (SEM), mercury injection capillary pressure (MICP), and nuclear magnetic resonance (NMR) to characterize pore types and establish quantitative links between fractal dimension and petrophysical properties. The reservoirs are mainly pebbly sandstones and sandy conglomerates with 15–23% quartz, 27–37% feldspar, and 2–20% carbonate/muddy matrix. Helium porosity ranges from 5.12% to 18.11% (mean 9.39%) and air permeability from 60 to 3270 mD (mean 880 mD). Fine pores (1–10 μm) dominate, throats are short and poorly connected, and illite (up to 16.76%) lines pore walls, further reducing permeability. Fractal analysis yields weighted-average dimensions of 2.55, 2.50, and 2.15 for macro-, meso-, and micropores, respectively, giving an overall dimension of 2.52. Higher dimensions correlate negatively with porosity and permeability. Empirical models (quadratic for porosity and exponential for permeability) predict core data within 0.86% and 5.4% error, validated by six blind wells. Reservoirs are classified as Class I (>12%, >1.0 mD), Class II (8–12%, 0.5–1.0 mD), and Class III (<8%, <0.5 mD), providing a robust tool for stimulation design and numerical simulation.

Kinetic façades represent a climate-responsive design solution that improves building adaptability by responding to seasonal needs such as daylighting and shading. They offer an attractive retrofit strategy that improves both the esthetics and environmental performance of buildings. This study investigated the integration of an origami-inspired kinetic façade into a student dormitory building located in Dhahran, Saudi Arabia. Using numerical simulations, 35 façade configurations were analyzed under varying conditions of façade orientations, closure ratios (from 5% to 95%), and cavity depths (from 20 cm to 100 cm). The findings highlight the critical impact of kinetic façade design characteristics on daylight availability and solar exposure and the required trade-off between these two variables. In this context, this study observed that at higher façade closure ratios, increasing cavity depth could effectively mitigate daylight reduction by promoting reflected daylight penetration inside the cavity. As for heat gains and cooling load reduction, mid-range façade closure, 50 cm in this study, achieved balanced performance across the three examined orientations. However, the southern façade showed slightly higher efficiency compared to the eastern and western façades, which achieved lower cooling reductions and showed a similar UDI compromise. Thus, a dynamic façade operation is recommended, where higher closure ratios could be applied during peak solar hours on the east in the morning and the west in the afternoon to maximize cooling savings, while moderate closure ratios can be maintained on the south to preserve daylight. Future work should incorporate real-time climatic data and smart control technologies to further optimize kinetic façade performance.

The design philosophy of a new smart gas meter is presented, based on an ultrasonic sensor employing LoRa and/or NB-IoT protocols and blockchain technologies to overcome the data integrity and security issues with a completely modular design. The architecture is organized into two separate blocks, the former for measurement and the latter for communication, and it presents original characteristics with respect to the state of the art. The accuracy of measured data is studied, paying attention to the fluid dynamic effects of the geometrical layout on the flow rate ultrasonic sensor and the environmental temperature and pressure for variable gas flow rate values. As for data security issues, the proposed solution is critically analyzed with reference to the data string organization and the procedure by which the data are stored and prepared for transmission into the blockchain. Finally, a local network of counters is designed and simulated in order to check the compliance of the provided hardware and software solutions with the predicted computational load.

The advancing speed of the coal mining face has a significant impact on the mining-induced stress and energy accumulation of the surrounding rock. To explain the influence mechanism from a mesoscopic perspective, this study conducted a uniaxial compression test on the coal–rock combination body under different quasi-static loading rates, and analyzed their mechanical properties, failure characteristics, acoustic emission characteristics and energy evolution characteristics. The main findings are as follows: The uniaxial compressive strength and elastic modulus of the coal–rock combination body show a variation law of first increasing and then decreasing with the increase in loading rate, while the degree of impact failure significantly increases gradually as the loading rate rises. With the increase in loading rate, there is a tendency that the AE parameters concentrate from the first two stages to the latter two stages. The post-peak residual elastic energy density of the coal–rock combination body increases gradually with the increase in loading rate. The formation of the advancing speed effect of mining-induced stress concentration and elastic energy accumulation in coal–rock masses is caused by the “competitive” interaction between fracture propagation and coal matrix damage when the coal component in the coal–rock combination is deformed under stress.

To address the engineering challenges of powertrain excitation noise and aggravated low-frequency interior noise caused by armored structures in special-purpose vehicles, this study proposes an in-vehicle acoustic response analysis method based on vibro-acoustic coupling theory. This study presents a method for analyzing in-vehicle acoustic response under engine excitation, integrating Panel Acoustic Contribution Analysis (PACA) with a vibro-acoustic coupling model tailored for armored vehicles. The framework experimentally reveals a condition-independent resonance at 26.5 Hz and reproduces engine-order peaks at 40 Hz, 93.3 Hz, and 140 Hz. Quantitative comparison shows ΔSPL ≤ 2.5 dB and RMSE ≤ 2.2 dB between simulation and experiment, confirming model robustness. Based on these results, conceptual Dynamic Vibration Absorber (DVA) placement guidelines are proposed for dominant panels, providing practical engineering insights for NVH mitigation in armored vehicles.

Accurate prediction of concrete compressive strength is essential for ensuring structural safety in civil engineering, particularly in road and bridge construction, where inadequate strength can lead to deformation, cracking, or collapse. Traditional non-destructive testing (NDT) methods, such as the Rebound Hammer Test, estimate strength using regression-based formulas fitted with measurement data; however, these formulas, typically optimized via the least squares method, are highly sensitive to initial parameter settings and exhibit low robustness, especially for nonlinear relationships. Meanwhile, AI-based models, such as neural networks, require extensive datasets for training, which poses a significant challenge in real-world engineering scenarios with limited or unevenly distributed data. To address these issues, this study proposes a gradient-boosting artificial bee colony (GB-ABC) algorithm for robust regression curve fitting. The method integrates two novel mechanisms: gradient descent to accelerate convergence and prevent entrapment in local optima, and a point density-weighted strategy using Gaussian Kernel Density Estimation (GKDE) to assign higher weights to sparse data regions, enhancing adaptability to field data irregularities without necessitating large datasets. Following data preprocessing with Local Outlier Factor (LOF) to remove outliers, validation on 600 real-world samples demonstrates that GB-ABC outperforms conventional methods by minimizing mean relative error rate (RER) and achieving precise rebound-strength correlations. These advancements establish GB-ABC as a practical, data-efficient solution for on-site concrete strength estimation.

The aim of this study is to determine the reliability of regular and spatial cross-validation methods in predicting subfield-scale maize yields using phenological measures derived by Sentinel-2. Three maize fields from eastern Croatia were monitored during the 2023 growing season, with high-resolution ground truth yield data collected using combine harvester sensors. Sentinel-2 time series were used to compute two vegetation indices, Enhanced Vegetation Index (EVI) and Wide Dynamic Range Vegetation Index (WDRVI). These features served as inputs for three machine learning models, including Random Forest (RF) and Bayesian Generalized Linear Model (BGLM), which were trained and evaluated using both regular and spatial 10-fold cross-validation. Results showed that spatial cross-validation produced a more realistic and conservative estimate of the performance of the model, while regular cross-validation overestimated predictive accuracy systematically because of spatial dependence among the samples. EVI-based models were more reliable than WDRVI, generating more accurate phenomenological fits and yield predictions across parcels. These results emphasize the importance of spatially explicit validation for subfield yield modeling and suggest that overlooking spatial structure can lead to misleading conclusions about model accuracy and generalizability.