Search Results (3,256)

The study investigates the projected impact of climate change on water runoff in the upper Parsęta catchment, a postglacial lowland basin located in northwestern Poland. In the first step of the analysis, hydrological simulations for the period 2005–2022 were conducted using the Soil and Water Assessment Tool (SWAT). Model calibration and validation, performed in SWAT-CUP with the SUFI2 algorithm, yielded satisfactory performance (R² = 0.66–0.80; PBIAS = 0.43–13.87). Based on the calibrated model, projected simulations were performed for three future decades (2021–2030, 2031–2040, and 2041–2050) under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Climate input data were derived from the KLIMADA 2.0 national database, which was developed using down-scaled regional climate model output from the EURO-CORDEX ensemble and statistical bias-correction methods to generate high-resolution projections. Under RCP4.5, mean annual runoff increased by approximately 13–26%, while under RCP8.5, the changes were more variable, ranging from 2% to 28% relative to the 2011–2020 baseline. Seasonal analyses revealed enhanced autumn–winter runoff and lower spring–summer flows. The findings highlight that moderate climate forcing can lead to substantial alterations in hydrological regimes in postglacial lowland catchments, in certain decades comparable in magnitude to those projected under extreme forcing, underscoring the need for adaptive water management in northern Poland. Full article

Non-line-of-sight (NLOS) propagation remains a major obstacle to high-accuracy ultra-wideband (UWB) indoor positioning. To address this issue, this study investigates solutions from two complementary perspectives: NLOS identification and error mitigation. First, an NLOS signal classification model is proposed based on multidimensional statistics of the channel impulse response (CIR). The model incorporates an attention mechanism and an improved snake optimization (ISO) algorithm, achieving significantly enhanced classification accuracy and robustness. For error mitigation, a UKF–BiLSTM dual-directional mutual calibration framework is proposed to dynamically compensate for NLOS errors. The framework embeds the constant turn rate and velocity (CTRV) motion model within an unscented Kalman filter (UKF) to enhance trajectory modeling. It establishes a bidirectional correction loop with a bidirectional long short-term memory (BiLSTM) network. Through the synergy of physical constraints and data-driven learning, the framework adaptively suppresses NLOS errors. Experimental results show that the proposed framework achieves state-of-the-art–comparable performance with improved model efficiency in complex indoor UWB positioning scenarios. Full article

An advanced artificial intelligence (AI)-powered mobile automated impurity removal system was developed and integrated into potato harvesting machinery for decentralized agricultural environments in Japan. As opposed existing stationary AI systems in centralized processing facilities, this mobile prototype enables on-field impurity removal in real time through a systematic dual-evaluation methodology. The system integrates the YOLOX-small architecture with precision pneumatic actuators and achieves 40–50 FPS processing under dynamic field conditions. Algorithm validation across 10 morphologically diverse potato varieties (Danshaku, Harrow Moon, Hokkaikogane, Kitaakari, Kitahime, May Queen, Sayaka, Snowden, Snow March, and Toyoshiro) using count-based analysis showed exceptional recognition, with potato misclassification rates of 0.08 ± 0.03% (range: 0.01–0.32%) and impurity detection rates of 89.99 ± 1.25% (range: 80.00–93.30%). Cross-farm validation across seven commercial farms in Hokkaido confirmed robust algorithm consistency (PMR: 0.08 ± 0.03%, IDR: 90.56 ± 0.82%) without farm-specific calibration, establishing variety-independent and environment-independent operation. Field validation using weight-based analysis during actual harvesting at 1–4 km/h confirmed successful AI-to-field translation, with 0.22–0.42% potato misclassification and adaptive impurity removal of 71.43–85.29%. The system adapted intelligently, employing conservative sorting under high-impurity loads (71.43% removal, 0.33% misclassification) to prioritize potato preservation while maximizing efficiency under standard conditions (85.29% removal, 0.30% misclassification). The dual-evaluation framework successfully bridged the gap between AI accuracy in laboratory settings and effectiveness in agricultural operations. The proposed AI algorithm surpassed project targets for all tested conditions (>60% impurity removal, <1% potato misclassification). This successful integration demonstrates technical feasibility and commercial viability for widespread agricultural automation, with a validated 50% reduction in labor (four workers to two workers). This implementation provides a comprehensive validation methodology for next-generation autonomous harvesting systems. Full article

Leaf area index (LAI) is a key indicator of crop growth and development and is widely used in both agricultural research and precision farming applications. PlanetScope imagery is generally used for monitoring crop growth due to its high revisit frequency, broad spatial coverage, and cost-effective access to consistent high-resolution multispectral data. Therefore, we developed regression models to estimate peanut LAI, combining PlanetScope spectral bands and vegetation indices (VIs). Specifically, we compared the performance of random forest (RF), eXtreme Gradient Boosting (XGBoost), and Partial Least Squares Regression (PLSR) regression algorithms for peanut LAI estimation. Our results showed that most of the VIs exhibited strong relationships with LAI. Thirteen VIs were individually evaluated for estimating LAI using the aforementioned algorithms, and our results showed that the best single predictors of LAI are: TSAVI (RF: R² = 0.87, RMSE = 0.83 m²/m², RRMSE = 24.20%; XGBoost: R² = 0.77, RMSE = 0.95 m²/m², RRMSE = 27.96%); and RTVIcore (PLSR: R² = 0.68, RMSE = 1.12 m²/m², RRMSE = 32.88%). The top six ranked VIs were used to calibrate the RF, XGBoost, and PLSR algorithms. Model validation indicated that RF achieved the highest accuracy (R² = 0.844, RMSE = 0.858 m²/m², RRMSE = 25.17%), followed by XGBoost (R² = 0.808, RMSE = 0.92 m²/m², RRMSE = 26.99%), whereas PLSR showed comparatively lower performance (R² = 0.76, RMSE = 0.983 m²/m², RRMSE = 28.85%). Further results showed that PlanetScope VIs provided superior model accuracy in estimating peanut LAI compared to the use of spectral bands alone. Additionally, integrating spectral bands with VIs reduced LAI estimation accuracy, underscoring the importance of selecting predictor variables in ensuring optimal model performance. Overall, the presented results are significant for future crop monitoring using RF to reduce overreliance on multiple models for peanut LAI estimation. Full article

Microwave imaging (MWI) is a non-invasive technique for visualizing the anomalies of biological tissues. The imaging process is accomplished by comparing the electrical parameters of healthy tissues and malignant tissues. This work introduces a microwave imaging system for tumor detection in breast tissue. The experiment is performed in a homogeneous background medium, where a high dielectric contrast material is used to mimic the tumor. The proposed imaging system is experimentally evaluated for multiple tumor locations and sizes using a horn antenna. Reflection coefficients obtained from the monostatic configuration of the horn antenna are used for image reconstruction. The evaluation metrics, such as localization error, absolute area error, DICE score, Intersection over Union (IoU), precision, accuracy, sensitivity and specificity, are computed from the reconstructed image. A modified version of the beamforming algorithm improves the quality of reconstructed images by providing a minimum accuracy of 96% for all test cases, with an evaluation time of less than 48 s. The proposed methodology shows promising results under a controlled environment and can be implemented for clinical applications after adequate biological studies. This methodology can be used to calibrate any antenna system or phantom, as it has high contrast in conductivity, leading to better imaging. The present study contributes to Sustainable Development Goal (SDG) 3 by ensuring healthy lives and promoting wellbeing for all ages. Full article

Paper-based analytical devices have emerged as a versatile and cost-effective platform for on-site chemical and biological analysis. The integration of fluorescence detection with smartphone imaging has significantly enhanced the analytical performance and portability of these systems, enabling sensitive, rapid, and user-friendly detection of diverse analytes. This review highlights recent advancements in paper-based fluorescence sensing technologies, focusing on their design principles, materials, and detection strategies. Emphasis is placed on the use of nanomaterials, quantum dots, and carbon-based fluorophores that improve sensitivity and selectivity in food, bioanalytical, and environmental applications. The role of smartphones as optical detectors and data processing tools is explored, underscoring innovations in image analysis, calibration algorithms, and app-based quantification methods. Full article

Breast cancer remains the most frequently diagnosed cancer in women worldwide, with outcomes strongly dependent on stage at detection. Conventional imaging modalities such as mammography, ultrasound and MRI are limited by reduced sensitivity in dense breasts, radiation exposure, high cost and restricted availability in low-resource settings. This review critically examines microwave imaging (MWI) as a non-invasive, radiation-free and an emerging resource-efficient breast imaging modality that exploits dielectric contrast between healthy and malignant breast tissues. We first summarise experimental and clinical evidence on breast dielectric properties and their implications for numerical phantoms and device design. We then review passive, active (tomographic and radar-based) and hybrid MWI systems, including key clinical prototypes such as SAFE, MammoWave, MARIA and Wavelia, and analyse associated image-reconstruction algorithms from classical inverse scattering to advanced beamforming, Huygens-based methods and AI based reconstruction. Finally, we discuss outstanding challenges—tissue heterogeneity, calibration, hardware constraints and computational complexity—and identify future directions including AI-assisted reconstruction, multimodal hybrid imaging and large-scale clinical validation needed to translate MWI into routine breast cancer screening and diagnosis. Full article

The unbridled pursuit of statistical rationality has precipitated a crisis of value rationality in e-commerce ecosystems, leading to algorithmic involution—a dilemma characterized by destructive hyper-competition. To reconcile this theoretical paradox and explore effective governance pathways, this paper constructs a tripartite evolutionary game model involving e-commerce platforms, government regulators, and consumers. Simulation results indicate that high-intensity government deterrence constitutes the necessary stability foundation of hard constraints, while consumer activism acts as the decisive accelerator of the soft environment contingent on high synergistic gains and low information screening costs. Furthermore, a platform’s pivot toward “algorithm for good” is not driven by altruism, but by the rational calibration between short-term extractive gains and long-term benevolent returns. Sensitivity analysis confirms that reducing the ratio of these two factors is the effective lever to speed up system convergence. Finally, effective governance requires restructuring this payoff matrix by establishing dynamic penalty mechanisms and transparent low-cost feedback channels to render ethical algorithmic behavior a dominant strategy in terms of economic rationality. This research aims to guide the e-commerce ecosystem from a zero-sum game of involution toward a sustainable equilibrium of multi-party value co-creation. Full article

Accurate medium-term evapotranspiration (ET) forecasting is critical for irrigation scheduling and hydrological assessments. To address biases in numerical weather prediction (NWP) systems, we developed a hybrid GWO_XGB model integrating Extreme Gradient Boosting (XGBoost) with Gray Wolf Optimizer (GWO) for bias correction. Using the corrected data, we evaluate four hybrid models—Support Vector Machine (SVM) and XGBoost, each optimized with either GWO or Grasshopper Optimization Algorithm (GOA)—for 1- to 10-day ET forecasts across 11 farmland stations in Europe and North America (2003–2014). The results showed that the GWO_XGB model demonstrated the best comprehensive performance (average RMSE = 0.476 mm d⁻¹, R² = 0.829), while the GWO_SVM model performed the weakest (average RMSE = 0.572 mm d⁻¹, R² = 0.761). Forecast accuracy of Rs and VPD declined with lead time, with the 1-day forecasts being most accurate (RMSE range: 2.005–3.061 MJ mm d⁻¹). Using calibrated NWP data, the highest 1-day forecast accuracy was achieved (average RMSE = 0.715 mm d⁻¹), with GWO_XGB remaining the best (1–3 days average RMSE = 0.667 mm d⁻¹; 10-day cumulative forecast RMSE = 0.698 mm d⁻¹). Overall, the GWO_XGB model combined with NWP calibration provides reliable short- to medium-term ET forecasts for agricultural water management. Full article

Precise sensor integration is crucial for autonomous vehicle (AV) navigation, yet traditional extrinsic calibration remains costly and labor-intensive. This study proposes an automated calibration approach that uses metaheuristic algorithms (Simulated Annealing (SA), Genetic Algorithms (GA), and Particle Swarm Optimization (PSO)) to independently optimize rotational and translational parameters, reducing cross-compensation errors. Bayesian optimization is used offline to define the search bounds (and tune hyperparameters), accelerating convergence, while computer vision techniques enhance automation by detecting geometric features using a checkerboard reference and a Huber estimator for noise handling. Experimental results demonstrate high accuracy with a single-pose acquisition, supporting multi-sensor configurations and reducing manual intervention, making the method practical for real-world AV applications. Full article

This article examines differences between conventional manual measurements of tram operations and data extracted automatically using the REWIZOR program, based on the Yolo10s algorithm. The study addresses the broader question of how artificial intelligence can support analyses of passenger exchange processes in public transport and improve the efficiency of data collection. Measurements conducted in four Polish cities included tram types, stop times, and detailed boarding and alighting durations, while the REWIZOR software enabled automatic detection of stop times and passenger flows based on video recordings. The results show that, although both approaches yield consistent qualitative information regarding doors and passenger counts, significant quantitative discrepancies arise. These differences stem mainly from methodological inconsistencies and varying definitions of boarding, alighting, and stop times, as well as from software-related detection errors. The findings indicate that AI-based measurements require calibration against reference methods to allow reliable comparison with conventional datasets. As currently implemented, REWIZOR can be used effectively for internal analyses of passenger flows, if all compared data come from the same system. Further development—such as implementing simultaneous tracking of people and heads—may considerably improve accuracy and facilitate wider applicability in public transport studies. Full article

This article develops a method for the early detection of low-intensity DDoS attacks based on a three-factor vector metric and implements an applied hybrid neural network traffic analysis system that combines preprocessing stages, competitive pretraining (SOM), a radial basis layer, and an associative Grossberg output, followed by gradient optimisation. The initial tools used are statistical online estimates (moving or EWMA estimates), CUSUM-like statistics for identifying small stable shifts, and deterministic signature filters. An algorithm has been developed that aggregates the components of fragmentation, reception intensity, and service availability into a single index. Key features include the physically interpretable features, a hybrid neural network architecture with associative stability and low computational complexity, and built-in mechanisms for adaptive threshold calibration and online training. An experimental evaluation of the developed method using real telemetry data demonstrated high recognition performance of the proposed approach (accuracy is 0.945, AUC is 0.965, F1 is 0.945, localisation accuracy is 0.895, with an average detection latency of 55 ms), with these results outperforming the compared CNN-LSTM and Transformer solutions. The scientific contribution of this study lies in the development of a robust, computationally efficient, and application-oriented solution for detecting low-intensity attacks with the ability to integrate into edge and SOC systems. Practical recommendations for reducing false positives and further improvements through low-training methods and hardware acceleration are also proposed. Full article

Duplex stainless steel (S22053) is increasingly favoured in construction and marine engineering due to its superior corrosion resistance, toughness, and high strength-to-weight ratio. This study presents a comprehensive investigation into the mechanical behaviour of duplex stainless steel under both monotonic and cyclic loading. First, monotonic behaviour is characterized, and the applicability of existing constitutive models is verified. Addressing the complexity of parameter identification for the cyclic constitutive model, a genetic algorithm (GA)-based calibration framework for the Chaboche model is proposed. This approach overcomes the subjectivity and inefficiency of traditional manual fitting. The proposed method is validated against experimental hysteresis curves, demonstrating high accuracy and providing a reliable basis for the seismic design of duplex stainless steel structures. Full article

Additive manufacturing of hydrogel-based scaffolds requires concurrent control of material rheology and extrusion dynamics, especially in multi-material architectures. In this work, we develop a modular multi-material extrusion-based 3D-printing platform that combines a filament-fed extruder for thermoplastic polymers with a piston-driven extruder for viscous gel inks, together with an empirical calibration procedure for gel dosing. The calibration algorithm optimizes the pre-extrusion and retraction displacement (E_Pr/R) based on stepwise extrusion experiments and reduces the discrepancy between theoretical and measured deposited mass for shear-thinning alginate gels to below the prescribed tolerance. The calibrated system is then used to fabricate two representative hybrid constructs: partially crosslinked sodium alginate scaffolds with an internal hollow channel supported by a removable polycaprolactone framework, and self-supporting structures based on a sodium alginate–chitosan polyelectrolyte complex obtained by sequential co-extrusion. The resulting constructs remain mechanically stable after ionic crosslinking and solvent treatment and can subsequently be converted into highly porous scaffolds by freeze- or supercritical drying. The proposed combination of hardware architecture and extrusion calibration enables reproducible multi-material 3D printing of hydrogel–thermoplastic hybrid scaffolds and can be readily adapted to other gel-based inks for tissue engineering applications. Full article

Hyperspectral reconstruction (SR) refers to the computational process of generating high-dimensional hyperspectral images (HSI) from low-dimensional observations. However, the superior performance of most supervised learning-based reconstruction algorithms is predicated on the availability of fully labeled three-dimensional data. In practice, this requirement demands complex optical paths with dual high-precision registrations and stringent calibration. To address this gap, we extend the fully supervised paradigm to a semi-supervised setting and propose SSHSR, a semi-supervised SR method for scenarios with limited spectral annotations. The core idea is to leverage spectrally aware mini-patches (SA-MP) as guidance and form region-level supervision from averaged spectra, so it can learn high-quality reconstruction without dense pixel-wise labels over the entire image. To improve reconstruction accuracy, we replace the conventional fixed-form Tikhonov physical layer with an optimizable version, which is then jointly trained with the deep network in an end-to-end manner. This enables the collaborative optimization of physical constraints and data-driven learning, thereby explicitly introducing learnable physical priors into the network. We also adopt a reconstruction network that combines spectral attention with spatial attention to strengthen spectral–spatial feature fusion and recover fine spectral details. Experimental results demonstrate that SSHSR outperforms existing state-of-the-art (SOTA) methods on several publicly available benchmark datasets, as well as on remote sensing and real-world scene data. On the GDFC remote sensing dataset, our method yields a 6.8% gain in PSNR and a 22.1% reduction in SAM. Furthermore, on our self-collected real-world scene dataset, our SSHSR achieves a 6.0% improvement in PSNR and a 11.9% decrease in SAM, confirming its effectiveness under practical conditions. Additionally, the model has only 1.59 M parameters, which makes it more lightweight than MST++ (1.62 M). This reduction in parameters lowers the deployment threshold while maintaining performance advantages, demonstrating its feasibility and practical value for real-world applications. Full article