Search Results (902)

This paper investigates solar energy production forecasting at a monthly temporal resolution using a pooled neural network framework applied to the Chikalov photovoltaic systems in southwestern Bulgaria. The study considers several related PV installations with unequal time-series lengths and formulates the forecasting task as one-step-ahead prediction of the next monthly total energy yield, measured in kWh, in a global pooled setting. Two complementary neural architectures are compared: a multilayer perceptron (MLP), which serves as a nonlinear feed-forward benchmark based on lagged observations and seasonal descriptors, and a gated recurrent unit (GRU), which explicitly models sequential temporal dependence. In both cases, seasonality is represented through cyclical calendar encodings, while model selection is performed by chronological hyperparameter search using a separate validation block. Forecast accuracy is assessed by RMSE, MAE, coefficient of determination (R²), MAPE, and sMAPE, and uncertainty is quantified through validation residual prediction intervals. The results show that the MLP achieves stronger validation performance, whereas the GRU provides better final out-of-sample generalization after refitting on the combined training and validation data. For both architectures, the best configurations are obtained with a 12-month input horizon, indicating that one full annual cycle contains the most informative memory for forecasting monthly aggregated photovoltaic energy yield in the considered dataset. After refitting on the combined training and validation data, the GRU achieved the best final out-of-sample performance, with RMSE = 296.38 kWh, MAE = 213.16 kWh, R² = 0.9231, MAPE = 7.52%, and sMAPE = 7.49%. Overall, the findings demonstrate that pooled neural modeling is an effective framework for monthly PV production forecasting and can provide practically useful support for sustainable energy planning, monitoring, and optimization. Full article

Radiotherapy simulation is essential for accurately targeting tumors while preserving healthy tissue, ensuring treatment precision and safety. This study aimed to develop an integrated radiotherapy simulation system capable of automated segmentation, dose estimation, and collision detection within a virtual planning environment to enhance efficiency and reduce costs in radiotherapy treatment planning. The Point Transformer model was applied to organ point cloud data derived from CT medical imaging for automated segmentation. Farthest point sampling (FPS) was employed to downsample the data before training. To enhance the accuracy and anatomical fidelity of the AI-generated segmentation results, reconstruction and refinement algorithms, including k-d tree, outlier removal, marching cubes, and surface smoothing, were implemented. Beam penetration simulation with the ray casting algorithm was employed for correction-based dose estimation. A collision detection module was incorporated to identify potential machine–machine or machine–patient interactions. The entire workflow was executed within a Unity 3D-based virtual simulation environment. As a result, the Point Transformer model demonstrated high segmentation accuracy, achieving Dice scores of 93.86 $\pm$ 1.50% for single-organ and 91.86 $\pm$ 3.25% for multi-organ cases, surpassing the performance of PointNet++. Applying ray casting for the refined surface meshes generated through post-processing enabled accurate dose estimation with discrepancies of 3.5% (brain), 5.9% (liver), and 13.8% (lung) compared to a Pinnacle TPS. The proposed method provides a low-cost and adaptable solution that enables easy modification and further development, making it particularly suitable for widespread applications in radiotherapy research, education, and clinical workflow optimization. Full article

In complex obstacle environments, the RRT* algorithm, an asymptotically optimal variant of the Rapidly exploring Random Tree (RRT), and its related variants often suffer from slow generation of the initial feasible solution, unstable sampling efficiency, and high computational costs associated with nearest-neighbor search and collision checking. To address these issues, this paper proposes a coordinated lightweight improved RRT* algorithm. First, a bidirectional growth mechanism combined with goal-biased sampling is introduced to enhance search directionality and improve the efficiency of initial feasible path generation. After an initial path is obtained, informed elliptical sampling is adopted, and the sampling weights are adaptively allocated among the elliptical region, the global space, and goal-biased sampling, thereby balancing local convergence and global exploration. Furthermore, a spatial-hash structure with a dynamic neighborhood radius is employed to accelerate nearest-neighbor search, while lazy collision checking and a two-stage collision-detection mechanism are incorporated into parent selection to reduce redundant expansions and unnecessary exact collision checks. Simulation results in mixed-type and single-type obstacle environments show that the proposed algorithm improves planning efficiency while maintaining competitive path quality. These results demonstrate that the proposed method has good engineering applicability for global path planning in complex environments. Full article

UAV aerial cinematography has become increasingly important in film production, surveying, and smart-city applications due to its efficiency and creative potential. However, existing UAV filming workflows still rely heavily on manual operation and professional piloting skills, resulting in complex mission design, limited planning autonomy, and inconsistent visual quality. To address these challenges, this paper proposes a unified aesthetics-aware trajectory planning framework for multi-region-of-interest (multi-ROI) UAV aerial cinematography that automatically generates safe, efficient, and visually coherent flight paths from user-specified ROIs. The proposed framework consists of three main components. First, for each ROI, candidate viewpoints are sampled using a spiral trajectory, and a learning-based aesthetic evaluation network is applied to select visually optimal viewpoints for local trajectory generation. Second, transition trajectories between ROIs are generated using a Goal-biased Bidirectional Rapidly exploring Random Tree Star (Goal-biased BiRRT*) planner and evaluated through a multi-objective cost function to determine the most suitable transition paths. Third, the global connection of multiple ROIs is formulated as a Set Traveling Salesman Problem (STSP) to obtain an efficient visiting sequence. By integrating learning-based aesthetic evaluation with hierarchical trajectory planning and coordinated multi-ROI route organization, the proposed framework jointly considers flight feasibility, planning efficiency, visual composition quality, and trajectory continuity within a unified planning pipeline. Experimental results demonstrate that the proposed method generates more visually appealing and coherent aerial trajectories than traditional manual or rule-based approaches, while significantly reducing operational complexity. The proposed system provides an effective solution for autonomous UAV aerial cinematography with improved global consistency, aesthetic performance, and practical planning capability in complex environments. Full article

Biological cellular structures exhibit a high degree of systematic organization in both morphological configuration and functional coordination, providing important biomimetic insights for urban spatial organization. To address issues in traditional high-density residential areas, such as homogeneous spatial structures and insufficient accessibility of public spaces, this study proposes a planning method for five-minute living circle residential areas based on a biomimetic cellular structure within the framework of space syntax theory. Taking a residential area in Wuhan, China, as a case study, a cell-like spatial structure model was constructed. Convex space analysis, axial analysis, and visibility analysis were conducted using Depthmap software to quantitatively evaluate key syntactic indicators, including integration, connectivity, mean depth, and choice. The results show that, compared with the original planning scheme, the biomimetic cellular planning model significantly optimized the spatial structure of the residential area by relying on the functionally synergistic mechanisms of selective permeability of the cell membrane, whole-area permeation of the cytoplasm, central regulation of the nucleus, distributed coordination of organelles, and efficient transport through cellular microfilaments. In the sample living circle, the overall integration increased from 1.27 to 1.64, the mean depth decreased from 3.79 to 3.18, and spatial connectivity increased from 3.74 to 5.44. Meanwhile, the synergy of the road network increased from 0.44 to 0.86, indicating marked improvements in spatial accessibility, connectivity, and the degree of coordination within the spatial structure. In addition, the visibility analysis showed that the pedestrian aggregation capacity of the public core space was enhanced, and the spatial vitality of public activity spaces in the residential area was improved. The findings demonstrate that the spatial organization model based on biomimetic cellular principles can effectively enhance spatial efficiency and social vitality in five-minute living circle residential areas, providing a quantifiable design method and theoretical framework for bio-inspired urban planning. Full article

We present a hybrid algorithm for 3D obstacle-avoidance path planning of a six-axis robotic arm in cable inspection environments. It improves on traditional RRT, which suffers from blind sampling and low efficiency, and APF, which tends to become stuck in local optima and has unstable potential fields. For the bidirectional RRT, we introduce target-biased sampling and a dynamic step-size expansion strategy driven by target attraction to enhance sampling directionality. For the APF, we optimize the potential field function by incorporating shape and size factors, use simulated annealing to overcome local optima, and apply Gaussian filtering to smooth the potential field. A triangular inequality pruning strategy with a target chain is then used to optimize the initial path, combined with cubic B-spline curves for path smoothing, and we design a simplified collision detection method to reduce computational cost. Simulation experiments are carried out in 2D and 3D spaces, as well as in a robotic arm setup that mimics cable inspection. Compared with basic RRT, bidirectional RRT, and the RRT-APF fusion algorithm, our method achieves significant improvements in average iteration count, planning time, path length, and number of generated nodes. The resulting trajectories are shorter and smoother, effectively boosting the efficiency and quality of 3D obstacle-avoidance path planning for six-axis robotic arms, and offering a practical solution for engineering scenarios such as power line inspection. Full article

In vegetated coastal deltas, direct optical retrieval of surface soil salt content (SSC, 0–10 cm) is often hindered by canopy masking, mixed pixels, and seasonal variability in surface conditions. To improve SSC mapping under vegetation cover, this study developed a thresholded normalized difference vegetation index area-under-the-curve (NDVI-AUC) metric that integrates only the portion of the seasonal NDVI trajectory exceeding an ecologically defined threshold. Taking Dongying in the Yellow River Delta (YRD), China, as the study area, daily NDVI time series were reconstructed in Google Earth Engine (GEE) from Sentinel-2, Landsat-8/9, MODIS, and a Sentinel–Landsat fusion stream. An empirical electrical conductivity (EC)–SSC calibration was used to harmonize multi-year observations and construct a unified dataset of 177 topsoil samples collected in 2022, 2024, and 2025, which was divided into calibration (n = 118) and validation (n = 59) sets. Threshold traversal and Savitzky–Golay (SG) sensitivity tests were performed, and the negative exponential model was retained as the primary model after comparison with alternative monotonic decreasing functions. Across sensors, SSC showed a consistent inverse nonlinear relationship with NDVI-AUC. Threshold selection influenced model performance more strongly than SG smoothing. The Sentinel–Landsat fusion stream performed best, with R² values of 0.731 and 0.725 for calibration and validation, respectively, followed closely by Sentinel-2 (R² = 0.718 and 0.713). Landsat-8/9 showed moderate performance, whereas MODIS mainly represented background-scale patterns. The optimal 10 m implementation was further used to reconstruct annual SSC maps for 2021–2025, revealing stable coastal hotspots, localized bidirectional changes, and a modest model-derived decline in regional SSC. Overall, thresholded NDVI-AUC provides a simple, interpretable, and process-based metric for SSC mapping in vegetated coastal soils and can support agricultural decision makers in annual salinity hotspot screening and land management planning. Full article

Path planning in cluttered environments constitutes a critical challenge for mobile robotics. Although optimal solutions can be obtained by classical methods such as A*, they have the disadvantage of being computationally expensive in complex environments. In this paper, we propose a novel deep learning-based framework for 2D trajectory prediction in grid environments. The framework employs attention mechanisms specifically designed for path planning tasks. In particular, we design an Attention U-Net architecture that employs attention gates for effective path area focusing and residual connections for efficient feature selection. To validate our method, the Attention U-Net architecture is trained on 5000 randomly sampled 40 × 40 environments and tested on a separate test set of 200 environments. The experimental results show that the Attention U-Net architecture significantly outperforms the A* algorithm. It expands 62% fewer nodes (207.6 vs. 543.11) and achieves near-optimal path lengths (99.8% of optimal) and planning speed (0.78 ms vs. 1.19 ms). Furthermore, the Attention U-Net architecture achieves a 100% success rate for A* path planning with the attention heuristic, demonstrating the effectiveness of the attention heuristic for path planning. Full article

In electronic assembly, inspection is worthwhile only when the cost of testing is justified by the losses avoided by preventing defective products from reaching customers. This study examines that balance by developing a mathematical model that integrates one-sided acceptance sampling with an expected-cost framework covering component inspection, finished-product inspection, exchange loss, and the disassembly of defective products. The analysis is first developed for a two-component assembly case and then extended to a multi-stage, multi-component process. Because defect rates are often estimated from limited samples rather than known in advance, interval-based parameter correction is introduced and compared with an electrical-test dataset of 80,000 cleaned records from 866 lots. The data give a final-product defective rate of 1.335%, with a 95% confidence interval of 1.255–1.415%, which is well below the nominal 10% rate used in the baseline scenarios. Nevertheless, the distribution across stable lots shows a pronounced right tail, indicating that some lots remain riskier than the average level suggests. Routine full inspection of finished products is therefore difficult to justify at low average defect rates, whereas higher exchange losses or upper-tail lots can make tighter inspection economically reasonable. The model provides a practical route from sampling evidence to inspection and cost-control decisions in electronic assembly. Full article

Effective mission planning, path search, and path following are critical for unmanned aerial vehicles (UAVs) operating in complex, dynamic, and resource-constrained environments. Classical path planning approaches, including graph-based search, sampling-based methods, and trajectory optimization, provide structured solutions with performance guarantees but often exhibit limited adaptability to uncertainty, environmental disturbances, and evolving mission constraints. Reinforcement learning (RL) offers a complementary capability by enabling adaptive decision-making and online response to dynamic obstacles and partial observability. This paper examines UAV path planning and navigation within a Risk-Calibrated, Certifiably Safe, and Resource-Aware (RCSR) framework, with emphasis on its implications for mission planning, path search, and path following. Classical planning techniques are reviewed alongside recent advances in RL-based navigation for single-UAV and multi-UAV systems. Particular attention is given to safe reinforcement learning, constrained optimization, and runtime assurance mechanisms that address safety, regulatory compliance, and resource limitations in real-world deployments. Through a comparative analysis of classical, learning-based, and hybrid planning architectures, this work highlights key trade-offs among adaptability, safety, computational cost, and energy efficiency. The paper concludes by identifying hybrid learning–planning approaches as a practical direction for scalable, reliable, and deployable UAV mission planning systems. Full article

The generation of reliable and up-to-date national land cover information is essential for environmental management, climate action, and territorial planning. In Colombia, the CORINE Land Cover Colombia (CLCC) framework has been the official reference for land cover monitoring since 2000, traditionally updated through expert-based Computer-Assisted PhotoInterpretation (CAPI) at a 1:100,000 scale. However, increasing demands for higher spatial resolution and more frequent temporal updates have made process optimization necessary, driving the incorporation of cloud-based processing and artificial intelligence (AI), including machine learning and deep learning algorithms. This study presents a semi-automated methodology for generating a new generation of harmonized CLCC-compatible raster land cover maps at a 1:50,000 scale—offering four times greater spatial detail than the official vector product—with the capacity for semi-automated annual updates. The approach combines legend harmonization from 55 to 23 classes, historical CORINE Land Cover (CLC) polygon-guided sample generation, spectral stability analysis, and regionalized classification across 190 homogeneous subregions, supported by a reproducible cloud-based architecture. National land cover maps were produced for 2020, 2022, and 2024 with thematic accuracies above 80% and Kappa coefficients up to 0.87, alongside change maps for the 2022–2024 period capturing key dynamics in agricultural frontier expansion, wetland variability, and urban expansion. The resulting products also provide structured inputs for expert-based CAPI workflows, supporting the continuous updating of the official 1:100,000 CLCC map. The results demonstrate the operational capacity of integrating AI, cloud computing, and expert knowledge to strengthen Colombia’s national land cover monitoring system. Full article

As universities strive for socially sustainable environments, Informal Learning Spaces (ILS) serve as vital social infrastructure. However, previous studies often isolate physical environmental stimuli from internal psychological decision-making and treat harsh climates as absolute barriers. To address this gap, this study integrates Environment–Behavior Studies (EBS) and the Theory of Planned Behavior (TPB) to construct a comprehensive behavioral model for ILS in hot–humid climates. Using Structural Equation Modeling on 377 samples from Guangzhou, China, the study quantifies the interaction between physical spatial affordances and internal psychological mechanisms. The results reveal a critical shift in behavioral drivers: when psychological agency is introduced, the driving force of high-quality Space Design (path coefficient = 0.269) surpasses the restrictive impact of the severe Climate Environment (coefficient = 0.218). This demonstrates that architectural affordances can actively buffer physiological discomfort. Internally, Perceived Behavioral Control (PBC)—acting as an empirical proxy for user agency—emerges as the sole psychological dimension directly driving actual spatial usage (coefficient = 0.131), whereas personal attitudes and peer pressure show no significant direct behavioral impact. Furthermore, the direct behavioral influence of operations management becomes non-significant when mediated by psychological expectations. Ultimately, this study reframes ILS optimization, demonstrating that socially sustainable campus revitalization in hot–humid regions must prioritize empowering user autonomy and enhancing robust morphological design over administrative upgrades or mere passive climate endurance. Full article

Water utilities are increasingly expected to combine technical reliability with social inclusion, risk communication, and service continuity. This empirical paper reports a cross-sectional mixed-mode household survey conducted in Rzeszów, Poland, based on 384 complete questionnaire records. For a city of approximately 200,000 inhabitants, this sample size matched the conventional planning benchmark associated with a 95% confidence level and a 5% maximum error under simple-random-sampling assumptions; however, because recruitment was mixed-mode and non-probabilistic, the results are interpreted as evidence from the realized sample rather than as formally weighted population estimates. The questionnaire covered routine service evaluation, interruption experience, preparedness, communication preferences, vulnerability-related burden, and willingness to support reliability enhancement. The analytical workflow combined descriptive statistics, reliability analysis, Bartlett’s test of sphericity, the Kaiser–Meyer–Olkin measure, principal component analysis, Mann–Whitney U tests, Kruskal–Wallis tests, chi-square tests, Spearman correlation, binary logistic regression, correspondence analysis, and CHAID-type segmentation. The highest ratings were recorded for continuity of supply (mean = 4.18) and pressure stability (mean = 4.15), whereas price fairness received the lowest mean score (3.17). Interruptions were reported by 40.1% of respondents and were associated with lower overall satisfaction. Logistic regression showed that continuity rating (OR = 4.029) and water quality rating (OR = 2.305) increased the odds of high satisfaction, whereas longer interruptions reduced them (OR = 0.354). Additional analyses showed that interruptions lasting 12 h or more markedly increased the odds of high nuisance among affected households (OR = 5.914), while respondents aged 51 years or more had lower odds of declaring emergency-information awareness (OR = 0.468). Internal bootstrap validation indicated only mild optimism (optimism-corrected AUC = 0.825). The findings indicate that customer satisfaction in urban water services is shaped primarily by continuity, perceived water quality, and disruption burden, while communication and preparedness needs remain socially differentiated. Full article

In accordance with the national strategy of “carbon peaking by 2030 and carbon neutrality by 2060” and Hunan Province’s target of achieving carbon peaking in the construction sector by 2030, this study uses carbon emission data from Hunan’s construction sector for the period 2005–2022 as a research sample to conduct research on carbon emission accounting, analysis of influencing factors, and peak prediction. The carbon emission coefficient method was employed to calculate industry-wide carbon emissions. Using the STIRPAT model combined with ridge regression, we identified and quantified the driving factors of carbon emissions. A CNN-LSTM-Attention hybrid deep learning model was constructed, and three development scenarios—high-carbon, baseline, and low-carbon—were established to simulate the evolution of carbon emissions in Hunan’s construction industry from 2023 to 2040. The results indicate that carbon emissions from Hunan’s construction industry showed an overall upward trend during the study period, with indirect emissions constituting the primary component. Through variable optimization, the core positive drivers and negative restraints of carbon emissions in the construction industry were identified. The constructed hybrid model demonstrated excellent fitting performance, with prediction accuracy significantly higher than that of traditional machine learning and single deep learning models. Carbon emission trends varied significantly across different development scenarios, with the low-carbon development scenario identified as the optimal path for achieving the industry’s carbon peak target. These findings provide a theoretical basis and data support for the low-carbon transition of Hunan Province’s construction sector, as well as for the formulation and optimization of carbon peaking implementation plans. Full article

The continuous advancement of autonomy and intelligence in marine shipping has made the safe and efficient navigation of unmanned surface vehicles in complex waters a major research focus. As a key link of the autonomous decision-making system for unmanned surface vehicles (USVs), local path planning needs to achieve real-time collision avoidance and motion optimization under dynamic obstacles, multiple rule constraints, and strong environmental uncertainty. In recent years, reinforcement learning has gradually become an important technical route for local path planning of USVs by virtue of its autonomous decision-making ability in high-dimensional continuous state space and adaptability to complex nonlinear problems. Combined with the evolution of the algorithm paradigm and its functional positioning in different water scenarios, this paper systematically reviews the relevant literature by examining the evolution of algorithmic paradigms; focuses on summarizing deep Q-network (DQN), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Twin Delayed Deep Deterministic Policy Gradient (TD3), along with the collaborative architectures integrated with traditional planning methods such as A* and Rapidly-exploring Random Tree (RRT); and summarizes the performance characteristics, advantages, and limitations of various methods in typical scenarios. The review shows that the main bottlenecks of current research include insufficient reward mechanism design, low sample utilization efficiency, difficulty in transferring from simulation to real ships, and insufficient safety and trustworthiness verification. This paper looks forward to the future development trends from the two directions of data fusion and security enhancement in order to provide reference for related research. Full article