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Background: The CAMS Regional System provides crucial, reliable pollen forecasts for allergenic pollen types. These robust predictions support the scientific and medical communities, aiding in the diagnosis, evaluation, and protection of allergic populations. So, the main goal of this study was to evaluate which model, or models best represent and simulate the olive and grass pollen data of the Évora region in the years 2021 to 2024. Results: The results showed that there are statistically significant differences between the data of the models and between the years for each of the pollen types considered. These differences were not just in pollen concentrations; they also appeared in characteristics of the pollen season, like its duration, maximum peak concentration, start date and exposure level. According to Taylor diagrams, applying moving average for normalized data, it was shown that MOCAGE best represents and simulates olive concentration data. For grass pollen SILAM, EURAD-IM and MOCAGE were the best performers. Conclusions: CAMS data can enhance the quality of life of the allergic population, as well as support the scientific and medical community to improve, assist and create mitigation measures that reduce exposure and consequently significantly reduce the occurrence of allergic disease. Full article

Accurate prediction of reinforced concrete shear strength is essential for structural safety, yet datasets often contain a mix of raw geometric and material properties alongside physics-informed engineered features, making optimal feature selection challenging. This study introduces a statistically principled framework that advances feature reduction for neural networks in three novel ways. First, it extends the artificial neural network-based signal-to-noise ratio (ANN-SNR) method, previously limited to classification, into regression tasks for the first time. Second, it couples ANN-SNR with a confidence-interval (CI)-based stopping rule, using the lower bound of the baseline ANN’s R² confidence interval as a rigorous statistical threshold for determining when feature elimination should cease. Third, it systematically evaluates both raw experimental variables and physics-informed engineered features, showing how their combination enhances both robustness and interpretability. Applied to experimental concrete shear strength data, the framework revealed that many low-SNR features in conventional formulations contribute little to predictive performance and can be safely removed. In contrast, hybrid models that combined key raw and engineered features consistently yielded the strongest performance. Overall, the proposed method reduced the input feature set by approximately 45% while maintaining results statistically indistinguishable from baseline and fully optimized models (R² ≈ 0.85). These findings demonstrate that ANN-SNR with CI-based stopping provides a defensible and interpretable pathway for reducing model complexity in reinforced concrete shear strength prediction, offering practical benefits for design efficiency without compromising reliability. Full article

As an important ecosystem service, water conservation is influenced by land use related to human activities. In this study, we first evaluated spatial and temporal changes in water conservation in Baicheng City, western Jilin Province, from 2000 to 2020. Then, we identified three different scenarios: the natural development scenario (NDS), cropland protection scenario (CPS), and ecological protection scenario (EPS). We coupled the Patch-generating Land Use Simulation (PLUS) and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models to predict the distribution of land use types and water conservation in Baicheng City under these scenarios for 2030. The results showed the following: (1) The average water conservation in Baicheng City from 2000 to 2020 was 7.08 mm. (2) Areas with higher water conservation were distributed in the northwest and northeast, while lower water conservation areas were distributed in the central and southwest of Baicheng City. (3) The simulation results of the future pattern of land use show an increasing water conservation trend in all three scenarios. Compared with the other two scenarios, the ecological protection scenario is the most suitable option for the current development planning of Baicheng City. Under the ecological protection scenario (EPS), ecological land is strictly protected, the area of agricultural land increases to some extent, and the overall structure of changes in land use becomes more rational. This study provides a reference for land resource allocation and ecosystem conservation. Full article

Background: Head and neck squamous cell carcinoma (HNSCC) is a prevalent malignancy with poor clinical outcomes. microRNA-7-5p (miR-7-5p) has been described as both a tumour suppressor and an oncomiR depending on the tissue context, but its role in HNSCC remains unclear. This study aimed to clarify the clinical significance and biological function of miR-7-5p in HNSCC by integrating data from multiple sources. Methods: A systematic review of the literature was conducted to identify studies analysing miRNA expression in human head and neck tissues. A meta-analysis of individual patient data from Gene Expression Omnibus (GEO), ArrayExpress, and The Cancer Genome Atlas (TCGA) was performed to assess miR-7-5p expression in tumours and normal tissues, and its associations with clinical parameters and prognostic outcomes. Bioinformatics analyses were used to predict miR-7-5p target genes, classify hub genes, and perform gene ontology enrichment analysis. MicroRNA in situ hybridisation (miRNA ISH) and real-time quantitative PCR (RT-qPCR) were conducted on tissue samples, HNSCC cell lines, and an in vitro model of oral oncogenesis to validate miR-7-5p expression patterns. Results: miR-7-5p was significantly upregulated in tumours compared to normal tissues and associated with larger tumour size, HPV-negative status, poor disease-specific survival, and shorter progression-free intervals. Bioinformatics analysis highlighted miR-7-5p target genes enriched in pathways related to cell growth, survival, and tumourigenesis. Despite evidence supporting the anti-cancer role of exogenous miR-7-5p in preclinical models, the observed endogenous upregulation in tumours suggests that miR-7-5p expression may represent a compensatory or stress-responsive mechanism during tumourigenesis, rather than acting as a primary oncogenic driver. Conclusions: This study provides new insights into the complex role of miR-7-5p in HNSCC, supporting its potential as both a biomarker and a therapeutic target. Understanding the context-specific functions of miR-7-5p is essential for its development as an RNA-based therapeutic in HNSCC. Full article

In existing non-satellite navigation systems, visual localization is widely adopted for its high precision. However, in scenarios with highly similar building structures, traditional visual localization methods that rely on direct coordinate prediction often suffer from decreased accuracy or even failure. Moreover, as scene complexity increases, their robustness tends to decline. To address these challenges, this paper proposes a Sketch Line Information Consistency Generation (SLIC) model for indirect building localization. Instead of regressing geographic coordinates, the model retrieves candidate building images that correspond to hand-drawn sketches, and these retrieved results serve as proxies for localization in satellite-denied environments. Within the model, the Line-Attention Block and Relation Block are designed to extract fine-grained line features and structural correlations, thereby improving retrieval accuracy. Experiments on multiple architectural datasets demonstrate that the proposed approach achieves high precision and robustness, with mAP@2 values ranging from 0.87 to 1.00, providing a practical alternative to conventional coordinate-based localization methods. Full article

Visual–language tracking often faces challenges like target deformation and confusion caused by similar objects. These issues can disrupt the alignment between visual inputs and their textual descriptions, leading to cross-modal semantic drift and feature-matching errors. To address these issues, we propose SATrack, a Semantic-Aware Alignment framework for visual–language tracking. Specifically, we first propose the Semantically Aware Contrastive Alignment module, which leverages attention-guided semantic distance modeling to identify hard negative samples that are semantically similar but carry different labels. This helps the model better distinguish confusing instances and capture fine-grained cross-modal differences. Secondly, we design the Cross-Modal Token Filtering strategy, which leverages attention responses guided by both the visual template and the textual description to filter out irrelevant or weakly related tokens in the search region. This helps the model focus more precisely on the target. Finally, we propose a Confidence-Guided Template Memory mechanism, which evaluates the prediction quality of each frame using convolutional operations and confidence thresholding. High-confidence frames are stored to selectively update the template memory, enabling the model to adapt to appearance changes over time. Extensive experiments show that SATrack achieves a 65.8% success rate on the TNL2K benchmark, surpassing the previous state-of-the-art UVLTrack by 3.1% and demonstrating superior robustness and accuracy. Full article

Bitcoin’s extreme price volatility presents significant challenges for investors and traders, necessitating accurate predictive models to guide decision-making in cryptocurrency markets. This study compares the performance of machine learning approaches for Bitcoin price prediction, specifically examining XGBoost gradient boosting, Long Short-Term Memory (LSTM), and GARCH-DL neural networks using comprehensive market data spanning December 2013 to May 2025. We employed extensive feature engineering incorporating technical indicators, applied multiple machine and deep learning models configurations including standalone and ensemble approaches, and utilized cross-validation techniques to assess model robustness. Based on the empirical results, the most significant practical implication is that traders and financial institutions should adopt a dual-model approach, deploying XGBoost for directional trading strategies and utilizing LSTM models for applications requiring precise magnitude predictions, due to their superior continuous forecasting performance. This research demonstrates that traditional technical indicators, particularly market capitalization and price extremes, remain highly predictive in algorithmic trading contexts, validating their continued integration into modern cryptocurrency prediction systems. For risk management applications, the attention-based LSTM’s superior risk-adjusted returns, combined with enhanced interpretability, make it particularly valuable for institutional portfolio optimization and regulatory compliance requirements. The findings suggest that ensemble methods offer balanced performance across multiple evaluation criteria, providing a robust foundation for production trading systems where consistent performance is more valuable than optimization for single metrics. These results enable practitioners to make evidence-based decisions about model selection based on their specific trading objectives, whether focused on directional accuracy for signal generation or precision of magnitude for risk assessment and portfolio management. Full article

Accurate prediction of earthquake—induced building damage is essential for timely disaster response and effective risk mitigation. This study explores a machine learning (ML)-based classification approach using data from the 2015 Gorkha, Nepal earthquake, with a specific focus on reinforced concrete (RC) structures. The original dataset from the 2015 Nepal earthquake contained 762,094 building entries across 127 variables describing structural, functional, and contextual characteristics. Three ensemble ML modelsGradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) were trained and tested on both the full dataset and a filtered RC-only subset. Two target variables were considered: a three-class variable (damage_class) and the original five-level damage grade (damage_grade). To address class imbalance, oversampling and undersampling techniques were applied, and model performance was evaluated using accuracy and F1 scores. The results showed that LightGBM consistently outperformed the other models, especially when oversampling was applied. For the RC dataset, LightGBM achieved up to 98% accuracy for damage_class and 93% accuracy for damage_grade, along with high F1 scores ranging between 0.84 and 1.00 across all classes. Feature importance analysis revealed that structural characteristics such as building area, age, and height were the most influential predictors of damage. These findings highlight the value of building-type-specific modeling combined with class balancing techniques to improve the reliability and generalizability of ML-based earthquake damage prediction. Full article

In Ecuador, the transmission of dengue has steadily increased in recent decades, particularly in coastal cities like Manta, where the conditions are favorable for the proliferation of the Aedes aegypti mosquito. The objective of this study was to model the spatial distribution of dengue transmission risk in Manta, a coastal city in Ecuador with consistently high incidence rates. A total of 148 georeferenced dengue cases from 2018 to 2021 were collected, and environmental and socioeconomic variables were incorporated into a maximum entropy model (MaxEnt). Additionally, climate and social zoning were performed using a multi-criteria model in TerrSet. The MaxEnt model demonstrated excellent predictive ability (training AUC = 0.916; test AUC = 0.876) and identified population density, sewer system access, and distance to rivers as the primary predictors. Three high-risk clusters were identified in the southern, northwestern, and northeastern parts of the city, while the coastal strip showed lower suitability due to low rainfall and vegetation. These findings reveal the strong spatial heterogeneity of dengue risk at the neighborhood level and provide operational information for targeted interventions. This approach can support more efficient surveillance, resource allocation, and community action in coastal urban areas affected by vector-borne diseases. Full article

This research explores the phenomenon of plate-end (PE) debonding in reinforced concrete (RC) beams strengthened with fiber-reinforced polymer (FRP) composites. This type of failure represents a key mechanism that undermines the structural performance and efficiency of FRP reinforcement systems. Despite the widespread use of FRP in structural repair due to its high strength and corrosion resistance, PE debonding—often triggered by shear or inclined cracks—remains a major challenge. Traditional computational models for predicting PE debonding suffer from low accuracy due to the nonlinear relationship between influencing parameters. To address this, the research employs machine learning techniques and SHapley Additive exPlanations (SHAP), to develop more accurate and explainable predictive models. A comprehensive database is constructed using key parameters affecting PE debonding. Machine learning algorithms are trained and evaluated, and their performance is compared with existing normative models. The study also includes parameter importance and sensitivity analyses to enhance model interpretability and guide future design practices in FRP-based structural reinforcement. Full article

This study explores the application of generative artificial intelligence (AI) in financial risk forecasting, aiming to assess its potential in enhancing both the accuracy and interpretability of predictive models. Traditional methods often struggle with the complexity and nonlinearity of financial data, whereas generative AI—such as large language models and generative adversarial networks (GANs)—offers novel solutions to these challenges. The study begins with a comprehensive review of current research on generative AI in financial risk prediction, with a focus on its roles in data augmentation and feature extraction. It then investigates techniques such as Generative Adversarial Explanation (GAX) to evaluate their effectiveness in improving model interpretability. Case studies demonstrate the practical value of generative AI in real-world financial forecasting and quantify its contribution to predictive accuracy. Furthermore, the study identifies key challenges—including data quality, model training costs, and regulatory compliance—and proposes corresponding mitigation strategies. The findings suggest that generative AI can significantly improve the accuracy and interpretability of financial risk models, though its adoption must be carefully managed to address associated risks. This study offers insights and guidance for future research in applying generative AI to financial risk forecasting. Full article

Cryptocurrencies such as Bitcoin can be classified as commodities under the Commodity Exchange Act (CEA), giving the Commodity Futures Trading Commission (CFTC) jurisdiction over those cryptocurrencies deemed commodities, particularly in the context of futures trading. This paper presents a method for predicting both long- and short-term trends in selected cryptocurrencies based on the Fractal Market Hypothesis (FMH). The FMH applies the self-affine properties of fractal stochastic fields to model financial time series. After introducing the underlying theory and mathematical framework, a fundamental analysis of Bitcoin and Ethereum exchange rates against the U.S. dollar is conducted. The analysis focuses on changes in the polarity of the ‘Beta-to-Volatility’ and ‘Lyapunov-to-Volatility’ ratios as indicators of impending shifts in Bitcoin/Ethereum price trends. These signals are used to recommend long, short, or hold trading positions, with corresponding algorithms (implemented in Matlab R2023b) developed and back-tested. An optimisation of these algorithms identifies ideal parameter ranges that maximise both accuracy and profitability, thereby ensuring high confidence in the predictions. The resulting trading strategy provides actionable guidance for cryptocurrency investment and quantifies the likelihood of bull or bear market dominance. Under stable market conditions, machine learning (using the ‘TuringBot’ platform) is shown to produce reliable short-horizon estimates of future price movements and fluctuations. This reduces trading delays caused by data filtering and increases returns by identifying optimal positions within rapid ‘micro-trends’ that would otherwise remain undetected—yielding gains of up to approximately 10%. Empirical results confirm that Bitcoin and Ethereum exchanges behave as self-affine (fractal) stochastic fields with Lévy distributions, exhibiting a Hurst exponent of roughly 0.32, a fractal dimension of about 1.68, and a Lévy index near 1.22. These findings demonstrate that the Fractal Market Hypothesis and its associated indices provide a robust market model capable of generating investment returns that consistently outperform standard Buy-and-Hold strategies. Full article

More energy efficient heat pumps can be designed if the industry is able to identify reliable optimization schemes able to predict how a fixed amount of money is best spent on the different individual components. For example, how to optimally design and size the different heat exchangers (HEs) in a heat pump with respect to cost and performance. In this work, different optimization algorithms and HE area integral approximations are compared for heat pumps with two and three HEs, with or without ejectors. Since the main goal is to identify optimal numerical schemes, not optimal designs, heat transfer is simplified, assuming a constant U-value for all HEs, which reduces the computational work significantly. Results show that high-order HE area approximations are $10-400$ times faster than conventional trapezoidal and adaptive integral methods. High-order schemes with $45$ grid points ($N$) obtained $80-100$% optimization success rates. For subcritical processes, the LMTD method produced accurate results with $N\le 5$, but such schemes are unreliable and difficult to extend to real HE models with non-constant U. Results also show that constrained gradient-based optimizations are $10$ times faster than particle swarm, and that conventional GA optimizations are extremely inefficient. This study therefore recommends applying high-order HE area approximations and gradient-based optimizations methods developing accurate optimization schemes for the industry, which include realistic heat transfer coefficients. Full article

Management of smart greenhouses represents a transformative advancement in precision agriculture, enabling sustainable intensification of food production through the integration of multi-sensor networks, intelligent control, and sophisticated data filtering techniques. Unlike conventional greenhouses that rely on manual monitoring, smart greenhouses combine environmental sensors, Internet of Things (IoT) platforms, and artificial intelligence (AI)-driven decision making to optimize microclimates, improve yields, and enhance resource efficiency. This review systematically investigates three key technological pillars, multi-sensor monitoring, intelligent control, and data filtering techniques, for smart greenhouse environment management. A structured literature screening of 114 peer-reviewed studies was conducted across major databases to ensure methodological rigor. The analysis compared sensor technologies such as temperature, humidity, carbon dioxide (CO₂), light, and energy to evaluate the control strategies such as IoT-based automation, fuzzy logic, model predictive control, and reinforcement learning, along with filtering methods like time- and frequency-domain, Kalman, AI-based, and hybrid models. Major findings revealed that multi-sensor integration enhanced precision and resilience but faced changes in calibration and interoperability. Intelligent control improved energy and water efficiency yet required robust datasets and computational resources. Advanced filtering strengthens data integrity but raises concerns of scalability and computational cost. The distinct contribution of this review was an integrated synthesis by linking technical performance to implementation feasibility, highlighting pathways towards affordable, scalable, and resilient smart greenhouse systems. Full article

The optimal utilization of UAV-integrated photovoltaic (PV) systems demands accurate modeling that accounts for dynamic flight conditions. This paper introduces a novel computational intelligence-based framework that models the behavior of a moving PV system mounted on a UAV. A unique mathematical approach is developed to translate UAV flight dynamics, specifically roll, pitch, and yaw, into the tilt and azimuth angles of the PV module. To adaptively estimate the diode ideality factor under varying conditions, the Grey Wolf Optimization (GWO) algorithm is employed, outperforming traditional methods like Particle Swarm Optimization (PSO). Using a one-year environmental dataset, multiple machine learning (ML) models are trained to predict maximum power point (MPP) parameters for a commercial PV panel. The best-performing model, Rational Quadratic Gaussian Process Regression (RQGPR), demonstrates high accuracy and low computational cost. Furthermore, the proposed ML-based model is experimentally integrated into an incremental conductance (IC) MPPT technique, forming a hybrid MPPT controller. Hardware and experimental validations confirm the model’s effectiveness in real-time MPP prediction and tracking, highlighting its potential for enhancing UAV endurance and energy efficiency. Full article