Search Results (228)

High-resolution, long-term gridded meteorological datasets from in situ observations are crucial for ecosystem monitoring, soil diagnostics, hydrological modelling, and Earth system model evaluation. This study presents two enhanced real-time adaptations of Thornton’s Daymet V4 interpolation method. Daymet4-r1 uses a traditional calibration strategy with exhaustive parameter search, while Daymet4-r2 applies a global optimization algorithm (find_min_global from the dlib library) to adjust parameters automatically at each time step. Both methods were tested over Tuscany using high-resolution terrain and a dense observation network. Validation with leave-one-out method was carried out for the period 1995–2011 for both versions, while Daymet4-r2 underwent extended evaluation from 1991 to 2024 to assess seasonal dynamics and long-term variability. Results show that Daymet4-r2 outperforms Daymet4-r1 and the original Daymet V4 for all variables (mean absolute error of 1.24 mm, 1.06 °C, 1.29 °C, 6.26%, 0.78 m/s, and 2.04 hPa for precipitation, maximum and minimum temperature, relative humidity, wind speed, and sea level pressure, respectively). The largest improvement was observed in minimum temperature due to an enhanced approach for detecting and modelling thermal inversions. The high performance, flexibility, and ability of Daymet4-r2 to operate without prior calibration highlight its potential for model verification, real-time environmental monitoring, and integration into climate services. Full article

The rapid proliferation of low-altitude unmanned aerial vehicle (UAV) applications has made autonomous identification technology critical for flight safety and collaborative operations. In this paper, we propose and systematically analyze an autonomous identification scheme based on Bluetooth Low Energy (BLE) technology. We formulate a comprehensive system model that integrates link budget, packet collision, identification success probability, and power consumption. By incorporating safety interval constraints and a three-channel integrated reception probability, we employ an exhaustive search algorithm to optimize monitoring strategy parameters, thereby achieving an optimal trade-off between the Recognition Success Rate (RSR) and power consumption. Simulation results indicate that, at a PHY 1 Mbps rate, the optimal monitoring strategy theoretically approaches the Target Level of Safety (TLS) requirements for civil UAVs under the defined model assumptions, with a power consumption of 19.24 mW and an Average First Identification Delay (AFID) of 105 ms. Furthermore, simulation analysis verifies the scheme’s feasibility under dynamic topology, interference, and multi-UAV scenarios, providing a solid theoretical and technical reference for the practical implementation of autonomous UAV identification. Full article

The greater cane rat algorithm (GCRA) draws inspiration from the seasonal behavioral patterns of the greater cane rats: extensive roaming during the non-breeding period for global exploration, and aggregative foraging during the reproductive period for local exploitation. The GCRA leverages independent movement and population aggregation to iteratively update positions in pursuit of the optimal solution, which exhibits inherent structural deficiencies: precipitous population diversity collapse, lethargic convergence dynamics, suboptimal computational precision, high susceptibility to local optima, and severe dimensional scalability. This paper proposes a modified complex-valued encoding GCRA (CGCRA) that exploits the mathematical structure of complex numbers to construct a two-dimensional search domain on the complex plane and facilitate collaborative optimization. The CGCRA maps the decision variables onto the complex domain, the real part executes the native foraging mechanism for local fine-grained exploitation, and the imaginary part exploits phase rotation to generate global exploratory perturbations. The CGCRA leverages a dual-encoding redundancy mechanism with inherent error tolerance to attenuate result volatility, augment information capacity and population heterogeneity, elevate search adaptability and disturbance rejection, accelerate parallel computation and exploration efficiency, and facilitate spatial transformation and multi-dimensional data manipulation. Twenty-three benchmark functions and twelve real-world engineering designs are employed to assess the CGCRA’s stability and practical feasibility rigorously. The CGCRA delivers comprehensive spatial mapping and adaptive coordination to facilitate population collaboration and bolster resilience, expedite exhaustive research, and advance optimization efficiency. The experimental results demonstrate that the CGCRA emphasizes instructive superiority and practical utility to regulate exploration and exploitation, reduce result dispersion, mitigate search stagnation, accelerate convergence efficiency, elevate solution precision, and fortify stability and robustness. Full article

Approximate nearest neighbor search (ANNS) serves as the fundamental querying method in large-scale and high-dimensional vector datasets, for which proximity graph (PG)-based algorithms are the preferred solution, offering the best balance between query efficiency and accuracy. However, PG-based algorithms’ optimal performance across multiple indicators depends on extensive manual parameter configuration. To this end, we propose PGformer, an end-to-end framework that predicts performance to identify optimal graph configurations. PGformer integrates attention-driven global representation learning and neighbor-aware embedding extraction to capture comprehensive structural features. For large-scale scenarios, we implement a linear-complexity attention mechanism that maintains global accuracy while reducing computational overhead. These learned representations are then used to jointly estimate multiple performance metrics, facilitating precise candidate selection. The experimental results show that PGformer achieves competitive and often improved recommendation quality compared with traditional baselines, particularly in large-scale settings. In the evaluated scenarios, PGformer reduces recommendation time by about 40% and selects configurations that are close to the exhaustive-search reference optimum under recall-constrained objectives. Nevertheless, PGformer still relies on offline profiling data and a discretized candidate configuration space, which motivates further investigation under broader deployment environments and stronger distribution shifts. Full article

Non-contact physiological monitoring based on remote PPG (rPPG) offers a viable alternative for the care of pediatric populations, particularly for children with cerebral palsy (CP) who present unique communication and mobility challenges. This paper presents a review of the literature on the use of rPPG for the estimation of vital signs and its application in emotional monitoring. Following the PRISMA 2020 guidelines as a methodological framework for searching and filtering, an exhaustive search was conducted in the IEEE Xplore and Scopus databases covering the period from 2017 to 2024. A total of 35 studies were selected for analysis. The review examines the evolution of rPPG algorithms—from classical mathematical approaches to recent deep-learning-based architectures—identifying critical technical challenges such as motion artifacts caused by spasticity and variations in lighting conditions. The results reveal that while rPPG has reached technical maturity for monitoring core physiological parameters such as heart rate, its application to robust emotion detection in children with CP remains limited. The main limitation identified across the surveyed literature is the critical scarcity of public or clinical datasets featuring pediatric CP cohorts. Finally, the potential of multimodal integration—combining rPPG with eye-tracking and wearable sensors—is discussed as a promising pathway toward objective emotional monitoring. Such an approach could enhance communication, support rehabilitation processes, and ultimately improve the quality of life of children with cerebral palsy and their caregivers. Full article

This paper studies subset construction in NP-complete problems from the perspective of structured exploration of combinatorial search spaces. Classical approaches rely on exhaustive enumeration of subsets, which leads to exponential growth in time and memory requirements. To address this limitation, we introduce an indexed framework based on the correspondence between a finite set and its associated index set. Within this framework, subsets are represented as ordered index sequences, allowing subset construction to be reformulated as a constraint-guided search process over index space. Candidate subsets are characterized by numerical descriptors derived from their indices (referred to as index certificates), which guide and filter the construction process. Subset generation is further organized through admissible index intervals that restrict feasible transitions and reduce the effective search space. The framework is based on an index-based representation and structured traversal of pairwise index combinations. Computational experiments on representative instances illustrate the behavior of the indexed construction procedure and indicate its efficiency relative to classical enumeration-based methods for small and medium-sized instances. The proposed approach provides a structured perspective on combinatorial search and offers a basis for further development of algorithms based on constrained exploration of subset structures. Full article

Image segmentation is the first step of image processing. It allows us to comprehend and extract information from the digital image. Multilevel thresholding is one of the most commonly used image segmentation techniques because of its simplicity and effectiveness. However, with the higher threshold level required, the more complex the process of finding the optimal threshold values becomes more complex. In this research, an effective optimization-based image segmentation technique using Otsu’s multilevel thresholding technique as the objective function to overcome the difficulties of finding the best threshold values is proposed. Instead of using the exhaustive search process, which requires more time, the best threshold values are obtained using different optimization techniques based on the nature of the image. In this study, the optimized threshold values are computed based on Otsu’s scheme, Sobel filter with Brown Bear Optimization Algorithm (BBOA), which is compared with thresholds computed based on the Artificial Bee Colony (ABC) algorithm, Jaya Algorithm (JA), Moth Flame Optimization (MFO) algorithm, Whale Optimization Algorithm (WOA) algorithm, and Particle Swarm Optimization (PSO) algorithm for segmentation. The objective function includes the sum of the variances of all four channels, namely, Red, Green, Blue, and Gray (Sobel). The superiority of the proposed method (BBOA_S) is tested on ten natural color benchmark images to verify results, and the quality of the suggested method is evaluated quantitatively by applying popular image quality assessment parameters, including PSNR, SSIM, and FSIM. The experimental results clearly show the efficiency of the suggested method of segmentation. In fact, the suggested method of segmentation has a higher PSNR value, up to 26.11, compared to other optimization methods, which have lower values. Similarly, the suggested method has a high value of structural similarity, up to 0.988, indicating that it performs a great job in terms of structural similarity. The proposed method also highly minimizes the reconstruction error, as indicated by the minimum values of the MSE, which are close to 194. This is better compared to the results of most of the other methods, which were carried out on the same images. Although the FSIM values of the proposed method are comparable to the values of the other methods, it is evident that the proposed method is good and reliable, as indicated by the overall quantitative and visual assessment. Therefore, it is clear that the use of Otsu’s multi-layer thresholding and the Sobel filter effect in combination with BBOA is effective and good. Full article

Blade Tip Timing (BTT) is an essential non-contact technique for monitoring vibrations in rotating machinery, but its practical accuracy is often degraded by noise, undersampling, and spectral leakage. This paper proposes a multi-stage robust Bayesian high-resolution identification framework that systematically addresses these challenges. A recursive digital algorithm based on Kalman filtering estimates the rotational speed without requiring once-per-revolution probes, effectively suppressing sensor noise. An attention-enhanced dynamic convolutional autoencoder then generates channel-specific window functions to minimize spectral leakage. The core identification algorithm extracts phases via all-phase FFT and employs sub-bin interpolation to overcome the resolution limitation of conventional FFT. A Tukey-biweight-based robust aggregation strategy is used to suppress the influence of abnormal or unequal-quality sensor channels during multi-channel phase fusion. A Bayesian prior distribution over the vibration order guides the estimation toward physically plausible values under noisy conditions. Finally, a coarse-to-fine multi-stage search strategy drastically reduces computational burden while preserving accuracy. Experiments on a rotor-blade test bench at constant and variable speeds show that the method reduces the noise floor by about 60 dB, achieves a maximum frequency identification error of 7.84%, and accelerates the search by approximately 48.6% compared to exhaustive search. The proposed method provides a reliable and efficient solution for blade health monitoring. Full article

Wireless sensor networks increasingly support time-critical monitoring applications, where coverage optimization must often be performed under limited computational resources. This work addresses a previously underexplored WSN coverage problem involving range-free, angular-limited sensors with transmitter-induced sensing degradation and discrete sector orientation. We formulate a mixed combinatorial problem that jointly optimizes $K$-out-of-$N$ sensor activation and sector assignment under strict feasibility constraints. A constraint-aware genetic algorithm with repair-based feasibility enforcement is proposed and validated against the global optimum obtained via exhaustive enumeration, enabling direct quantification of optimality. The repair mechanism corrects infeasible offspring after each genetic operation to guarantee that exactly K sensors remain active, eliminating the need for penalty-based constraint handling. A brute-force search is used to establish the global optimum of our small-scale scenario, serving as a ground-truth optimality benchmark for evaluating the proposed method. The purpose of this comparison is not to assess competitiveness against other metaheuristic algorithms, but to quantify how closely the proposed approach approximates the true optimal solution under strict problem constraints. The constraint-aware genetic algorithm is developed using an integer chromosome encoding, two initialization strategies, two crossover pairing schemes, elitism, and per-gene mutation, combined with alternative constraint-handling strategies. Two experimental series evaluate the impact of population size, crossover method, mutation probability, and constraint handling using problem-specific metrics, alongside convergence and fitness statistics. The proposed algorithm reliably reaches near-optimal solutions with significantly reduced computational cost when compared to exhaustive search. By integrating problem-specific constraints directly into the process, the proposed evolutionary optimization method effectively balances solution quality and execution time, making it well suited for scenarios requiring rapid sensor reconfiguration. Full article

Multi-threshold image segmentation is an important research topic in the fields of computer vision and image processing. Its core objective is to efficiently determine the optimal threshold combination within a high-dimensional and complex search space. However, as the number of thresholds and image complexity increase, the computational cost of traditional exhaustive search methods grows exponentially. Meanwhile, conventional swarm intelligence algorithms often suffer from unstable convergence, premature stagnation, and parameter sensitivity when dealing with high-dimensional composite functions. To address these issues, this paper proposes an enhanced optimization algorithm termed the Parrot Optimizer with Artistic Design Strategy (PO-ADS). The proposed method constructs a multi-strategy cooperative optimization framework that integrates an Evolution Feedback–Based Adaptive Control Strategy (EFACS), a Multi-Operator Cooperative Evolution Strategy (MOCES), and an Artistic Design Strategy (ADS). These strategies enable dynamic parameter adjustment, adaptive balance between global exploration and local exploitation, and structured perturbation enhancement mechanisms. Experimental results on the CEC2020 and CEC2022 benchmark suites demonstrate that PO-ADS significantly outperforms seven state-of-the-art optimization algorithms across different dimensional settings in terms of optimization accuracy, convergence speed, and stability. The Friedman test results show that, on the CEC2020 benchmark suite, PO-ADS achieves average ranks of 1.72 (30-dimensional) and 1.85 (50-dimensional), both statistically superior to the comparative algorithms. Furthermore, PO-ADS is applied to multi-threshold image segmentation based on the Otsu criterion. The results indicate that the proposed method achieves optimal or near-optimal performance in terms of SSIM, PSNR, FSIM, and objective function values. Overall, the experimental findings confirm that PO-ADS not only possesses strong numerical optimization capability but also demonstrates robust and practical applicability in real-world image segmentation tasks. Full article

Rapid and accurate calculation of lightning-induced electric fields in complex terrain is essential for lightning protection and electromagnetic compatibility analysis. Although conventional full-wave numerical methods such as the finite element method can achieve high-fidelity results, they are computationally expensive and inefficient for large-scale or repetitive engineering analysis. To enable efficient and reliable computation of lightning-induced electrostatic fields over complex terrain, this paper proposes a fast computational framework that integrates multi-level conformal mapping with a multi-objective optimization strategy based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). In the proposed method, irregular terrain boundaries are transformed into analytically tractable domains using multi-level conformal mapping, while the critical mapping parameter is reformulated as a dual-objective optimization problem that simultaneously minimizes the maximum local error and the mean global error. Unlike traditional approaches that rely on empirical tuning or exhaustive traversal of mapping parameters, the proposed framework establishes a closed-loop adaptive optimization process that generates a Pareto-optimal solution set, enabling flexible trade-off selection according to practical accuracy requirements. The method is validated against high-fidelity finite element simulations for representative terrain profiles. The results demonstrate that the proposed approach achieves comparable maximum-error performance while reducing mean error and significantly improving parameter-optimization efficiency relative to exhaustive search methods. The proposed framework provides an adaptive and efficient computational solution for preliminary assessment of lightning-induced electric fields in complex terrain environments, and lays a foundation for future extensions toward more realistic multi-dimensional and transient analyses. The improvements in computational accuracy and efficiency offer significant practical value for rapid lightning protection assessment in large-scale complex terrain engineering, enabling parametric analysis and scheme comparison during the preliminary engineering design stage with sufficient reliability. Full article

Artificial Intelligence (AI) is increasingly used in recruitment, performance management, and algorithmic work management, with potentially divergent implications for worker inclusion, exclusion, and discrimination. This systematic review synthesizes peer-reviewed evidence on (i) which AI applications in labor-market settings are linked to inclusion/exclusion outcomes, (ii) the mechanisms and contextual moderators shaping these effects, and (iii) governance and human-resource management responses proposed in the literature. Guided by PRISMA 2020, we searched Scopus and Web of Science (Title/Abstract/Keywords) for English-language journal articles published between 2015 and 2025. Nineteen studies met the eligibility criteria and were analyzed using qualitative thematic synthesis. The evidence indicates an ambivalent pattern: AI can support inclusion through assistive technologies and improved matching, but it can also exacerbate occupational polarization, digital exclusion, and discriminatory outcomes when models are trained on biased data or deployed without transparency and accountability. Outcomes depend on complementary organizational practices, workers’ access to skills, and the regulatory environment. Based on an evidence map of the included studies, we propose a hybrid governance model combining technical and organizational audits, inclusive upskilling/reskilling, participatory regulation, and responsible HR policies to align AI innovation with decent and inclusive work. Given the focused Title/Abstract/Keywords query and the small, heterogeneous corpus, the findings are interpreted as a scoped evidence map rather than an exhaustive census of all AI-and-work research. The model’s contribution lies in integrating four interdependent governance layers—technical, organizational, workforce, and regulatory—within a single labor-market framework. Accordingly, the review should be read as a focused qualitative evidence synthesis, and the proposed model as an evidence-informed conceptual framework that warrants future empirical validation. Full article

This study presents a machine learning framework for the reconstruction of fatigue life and fracture toughness in natural fiber-reinforced composites, evaluating the predictive accuracy of six regression algorithms—Random Forest, Gradient Boosting, Support Vector Machine, Neural Network, Ridge Regression, and Lasso Regression—using a controlled synthetic dataset of 600 samples generated from established Basquin fatigue and Rule of Mixtures fracture equations, incorporating stochastic noise calibrated to experimental scatter (CV = 15–50%), with log-normal noise standard deviation of 0.20 for fatigue life and Gaussian noise standard deviation of 0.15 for fracture toughness. The dataset encompasses eight natural fiber types (flax, jute, sisal, hemp, bamboo, coconut, banana, and pineapple) and five matrix systems (epoxy, polyester, PLA, vinyl ester, and polyurethane). Models were evaluated using a 70-15-15 train–validation–test split with 5-fold cross-validation and exhaustive grid search hyperparameter optimisation. Gradient Boosting achieved R² = 0.93 for fatigue life and Stacking Ensemble achieved R² = 0.87 for fracture toughness, representing 97% and 89% of their respective noise-ceiling values (theoretical maximum R² of 0.96 and 0.98 given the programmed noise levels). The ML models perform supervised function approximation—learning to reconstruct the programmed generation equations rather than discovering novel physical composite behaviour—and function as automated surrogates for the governing equations. Feature importance analysis identified engineered composite indicators, stress amplitude, and fiber length as the most influential parameters. The framework provides a reproducible ML evaluation pipeline as a methodological template for future experimental composite studies. Full article

Old urban areas are often prone to waterlogging and sewage contamination owing to their haphazard spatial arrangements, extensive impervious surfaces, and insufficient drainage infrastructure, thereby posing significant risks to both public safety and aquatic ecosystems. Sponge City retrofitting offers a viable solution. Currently, the study area is facing issues of waterlogging and pollution caused by rainfall. Conventional modeling approaches for optimizing the spatial allocation of Low-Impact Development (LID) practices typically quantify only the overall retrofit proportion. However, these methods fail to specify the optimal placement of individual facilities to balance hydrological benefits against construction costs. To bridge this gap between theoretical optimization and practical implementation, this study proposes an iterative approximation framework. First, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was coupled with the Storm Water Management Model (SWMM) to generate a Pareto front, from which optimal solutions were selected using the Analytic Hierarchy Process (AHP). The configuration was further refined through multiple iterations of “exhaustive search combined with Euclidean distance” analysis to determine the optimal types and locations of LID facilities. The results show that: In Scenario 3, the Euclidean distance after LID retrofitting achieved a narrowing gap from 5 to 3 to 1. This indicates that the proposed progressive approximation solving process can be directly applied to specific retrofit targets, providing concrete construction guidance for LID retrofitting in older communities’ areas. Conclusions showed that (1) the specific locations for implementing LID facilities within sub-catchments become progressively clearer, ultimately defining precise retrofitting sites. (2) The proposed progressive approximation approach effectively and systematically reduces this disparity. (3) Retrofitted LID measures effectively managed stormwater and controlled pollution. Full article

Visual exploration of time series data is essential for uncovering meaningful insights in domains such as healthcare monitoring and financial analysis, yet it remains computationally challenging due to the combinatorial explosion of potential subsequence comparisons. For long time series, an exhaustive comparison of all possible subsequence pairs becomes prohibitively expensive, limiting interactive exploration. This paper presents the TiVEx (Time Series Visual Exploration) family of algorithms for efficiently discovering the top-k most dissimilar subsequence pairs in comparative time series analysis. TiVEx achieves scalability through three complementary strategies: TiVEx-sharing exploits computational reuse across overlapping subsequence windows, eliminating redundant distance calculations; TiVEx-pruning employs distance-based upper bounds to eliminate unpromising candidates without exhaustive evaluation; and TiVEx-hybrid integrates both mechanisms to maximize efficiency gains. The key observation is that overlapping subsequences share a substantial computational structure, which can be systematically exploited while maintaining result optimality through provably correct pruning bounds. Extensive experiments on six diverse datasets demonstrate that TiVEx-hybrid achieves up to 84% reduction in distance calculations compared to exhaustive search while producing identical top-k results. Compared to state-of-the-art subsequence comparison methods, TiVEx-hybrid achieves 2.3× improvement in computational efficiency. Our effectiveness analysis confirms that TiVEx achieves result quality within 5% of exhaustive search even when exploring only a subset of candidate positions, enabling scalable visual exploration without compromising insight quality. Full article