Search Results (589)

As the global energy transition accelerates, the integration of solar power and artificial intelligence (AI) technologies offers new pathways for sustainable local development. This study examines four Serbian municipalities—Šabac, Sombor, Pirot, and Čačak—to assess how AI-enabled solar power systems can enhance energy resilience, reduce emissions, and support community-level sustainability goals. Using a mixed-method approach combining spatial analysis, predictive modeling, and stakeholder interviews, this research study evaluates the performance and institutional readiness of local governments in terms of implementing intelligent solar infrastructure. Key AI applications included solar potential mapping, demand-side management, and predictive maintenance of photovoltaic (PV) systems. Quantitative results show an improvement >60% in forecasting accuracy, a 64% reduction in system downtime, and a 9.7% increase in energy cost savings. These technical gains were accompanied by positive trends in SDG-aligned indicators, such as improved electricity access and local job creation in the green economy. Despite challenges related to data infrastructure, regulatory gaps, and limited AI literacy, this study finds that institutional coordination and leadership commitment are decisive for successful implementation. The proposed AI–Solar Integration for Local Sustainability (AISILS) framework offers a replicable model for emerging economies. Policy recommendations include investing in foundational digital infrastructure, promoting low-code AI platforms, and aligning AI–solar projects with SDG targets to attract EU and national funding. This study contributes new empirical evidence on the digital–renewable energy nexus in Southeast Europe and underscores the strategic role of AI in accelerating inclusive, data-driven energy transitions at the municipal level. Full article

Accurate short-term weather forecasting plays a vital role in disaster response, agriculture, and energy management, where timely and reliable predictions are essential for decision-making. Graph neural networks (GNNs), known for their ability to model complex spatial structures and relational data, have achieved remarkable success in meteorological forecasting by effectively capturing spatial dependencies among distributed weather stations. However, most existing GNN-based approaches rely on pairwise station connections, limiting their capacity to represent higher-order spatial interactions. Moreover, their dependence on supervised learning makes them vulnerable to spatial heterogeneity and temporal non-stationarity. This paper introduces a novel spatial–temporal pretraining framework, Hypergraph-enhanced Meteorological Pretraining (HyMePre), which combines hypergraph neural networks with self-supervised learning to model high-order spatial dependencies and improve generalization across diverse climate regimes. HyMePre employs a two-stage masking strategy, applying spatial and temporal masking separately, to learn disentangled representations from unlabeled meteorological time series. During forecasting, dynamic hypergraphs group stations based on meteorological similarity, explicitly capturing high-order dependencies. Extensive experiments on large-scale reanalysis datasets show that HyMePre outperforms conventional GNN models in predicting temperature, humidity, and wind speed. The integration of pretraining and hypergraph modeling enhances robustness to noisy data and improves generalization to unseen climate patterns, offering a scalable and effective solution for operational weather forecasting. Full article

This study examines the geostrategic role of Hungarian-language higher education institutions beyond Hungary’s border. These institutions not only fulfil an educational function but also play a role in preserving identity and geopolitics in the national policy of the Hungarian state. This research is based on a narrative review of the literature, which analyses the demographic situation of Hungarians living beyond the borders and the tools used to support higher education by synthesising domestic and international literature, statistical data, and forecasts. The results highlight that Hungarian-language higher education plays a key role in preserving ethnocultural identity and increasing the chances of success in the homeland, but also faces constraints such as labour market disadvantages resulting from a lack of state language skills. This study concludes that, in order to ensure the sustainability of Hungarian higher education beyond the border, it is necessary to strike a balance between identity preservation and integration, thereby promoting geopolitical stability and cultural cohesion with the majority society. Full article

Accurate short-term precipitation forecasting (nowcasting) based on weather radar data is essential for managing weather-related risks, particularly in applications such as airport operations, urban flood prevention, and public safety during outdoor events. This study proposes a computationally efficient nowcasting method based on a Lagrangian advection scheme, estimating both the translation and rotation of radar-observed precipitation fields without relying on machine learning or resource-intensive computation. The method was tested on a two-year dataset (2022–2023) over Tuscany, using data collected from the Italian Civil Protection Department’s radar network. Forecast performance was evaluated using the Critical Success Index (CSI) and Mean Absolute Error (MAE) across varying spatial domains (1° × 1° to 2° × 2°) and precipitation regimes. The results show that, for high-intensity events (average rate > 1 mm/h), the method achieved CSI scores exceeding 0.5 for lead times up to 2 h. In the case of low-intensity rainfall (average rate < 0.3 mm/h), its forecasting skill dropped after 20–30 min. Forecast accuracy was shown to be highly sensitive to the temporal stability of precipitation intensity. The method performed well under quasi-stationary stratiform conditions, whereas its skill declined during rapidly evolving convective events. The method has low computational requirements, with forecasts generated in under one minute on standard hardware, and it is well suited for real-time application in regional meteorological centres. Overall, the findings highlight the method’s effective balance between simplicity and performance, making it a practical and scalable option for operational nowcasting in settings with limited computational capacity. Its deployment is currently being planned at the LaMMA Consortium, the official meteorological service of Tuscany. Full article

Radar echo extrapolation is a critical forecasting tool in the field of meteorology, playing an especially vital role in nowcasting and weather modification operations. In recent years, spatiotemporal sequence prediction models based on deep learning have garnered significant attention and achieved notable progress in radar echo extrapolation. However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. Typically, radar echo intensity values ranging from −5 to 70 dBZ with a resolution of 5 dBZ are converted into 0–255 grayscale images from pseudo-color representations, which inevitably results in the loss of important echo details. Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. These variables are encoded jointly with high-resolution (0.5 dB) radar mosaic data to form multiple radar cells as input. A multi-channel radar echo extrapolation network architecture (MR-DCGAN) is then designed based on the DCGAN framework; (3) Since radar echo decay becomes more prominent over longer extrapolation horizons, this study departs from previous approaches that use a single model to extrapolate 120 min. Instead, it customizes time-specific loss functions for spatiotemporal attenuation correction and independently trains 20 separate models to achieve the full 120 min extrapolation. The dataset consists of radar composite reflectivity mosaics over North China within the range of 116.10–117.50°E and 37.77–38.77°N, collected from June to September during 2018–2022. A total of 39,000 data samples were matched with the initial zero-hour fields from RMAPS-NOW, with 80% (31,200 samples) used for training and 20% (7800 samples) for testing. Based on the ConvLSTM and the proposed MR-DCGAN architecture, 20 extrapolation models were trained using four different input encoding strategies. The models were evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR). Compared to the baseline ConvLSTM-based extrapolation model without physical variables, the models trained with the MR-DCGAN architecture achieved, on average, 18.59%, 8.76%, and 11.28% higher CSI values, 19.46%, 19.21%, and 19.18% higher POD values, and 19.85%, 11.48%, and 9.88% lower FAR values under the 20 dBZ, 30 dBZ, and 35 dBZ reflectivity thresholds, respectively. Among all tested configurations, the model that incorporated three physical variables—relative humidity (rh), u-wind, and v-wind—demonstrated the best overall performance across various thresholds, with CSI and POD values improving by an average of 16.75% and 24.75%, respectively, and FAR reduced by 15.36%. Moreover, the SSIM of the MR-DCGAN models demonstrates a more gradual decline and maintains higher overall values, indicating superior capability in preserving echo structural features. Meanwhile, the comparative experiments demonstrate that the MR-DCGAN (u, v + rh) model outperforms the MR-ConvLSTM (u, v + rh) model in terms of evaluation metrics. In summary, the model trained with the MR-DCGAN architecture effectively enhances the accuracy of radar echo extrapolation. Full article

Understanding how hospital length of stay (LoS) evolves with successive SARS-CoV-2 variants is central to the multiscale modeling and forecasting of COVID-19 and other respiratory virus dynamics. Using records from 1249 COVID-19 patients admitted to Chungbuk National University Hospital (2021–2023), we quantified LoS across three distinct variant phases (Pre-Delta, Delta, and Omicron) and three age groups (0–39, 40–64, and 65+ years). A gamma-distributed multi-state model—capturing transitions between semi-critical and critical wards—incorporated variant phase and age as log-linear covariates. Parameters were estimated via maximum likelihood with 95% confidence intervals derived from bootstrap resampling, and Monte Carlo iterations yielded detailed LoS distributions. Omicron-phase stays were 5–8 days, shorter than the 10–14 days observed in earlier phases, reflecting improved treatment protocols and reduced virulence. Younger adults typically stayed 3–5 days, whereas older cohorts required 8–12 days, with prolonged admissions (over 30 days) clustering in the oldest group. These time-dependent transition probabilities can be integrated with real-time bed-availability alert systems, highlighting the need for variant-specific ward/ICU resource planning and underscoring the importance of targeted management for elderly patients during current and future pandemics. Full article

To address the challenge of insufficient spatial resolution in remote sensing precipitation data, this paper proposes a novel Multi-Scale Residual Generative Adversarial Network (MSRGAN) for reconstructing high-resolution precipitation images. The model integrates multi-source meteorological information and topographic priors, and it employs a Deep Multi-Scale Perception Module (DeepInception), a Multi-Scale Feature Modulation Module (MSFM), and a Spatial-Channel Attention Network (SCAN) to achieve high-fidelity restoration of complex precipitation structures. Experiments conducted using Weather Research and Forecasting (WRF) simulation data over the continental United States demonstrate that MSRGAN outperforms traditional interpolation methods and state-of-the-art deep learning models across various metrics, including Critical Success Index (CSI), Heidke Skill Score (HSS), False Alarm Rate (FAR), and Jensen–Shannon divergence. Notably, it exhibits significant advantages in detecting heavy precipitation events. Ablation studies further validate the effectiveness of each module. The results indicate that MSRGAN not only improves the accuracy of precipitation downscaling but also preserves spatial structural consistency and physical plausibility, offering a novel technological approach for urban flood warning, weather forecasting, and regional hydrological modeling. Full article

With the availability of more ground-based remote-sensing meteorological equipment at Hong Kong International Airport, many more interesting features of terrain-disrupted airflow have been observed, such as the applications of short-range Doppler LIDAR. This paper documents a number of new features observed at the airport area, such as the hydraulic jump-like feature, vortex, and extensive mountain wake/reverse flow. The technical feasibility of using a numerical resolution weather prediction model to simulate such features is also explored. It is found that the presently available input data and numerical model may not be able to capture the fine features of the atmospheric boundary layer, and thus they are not very successful in reproducing many small-scale terrain-disrupted airflow features downstream of an isolated hill. On the other hand, more larger-scale terrain-disrupted flow features may be better captured, but there are still limitations with the available turbulence parameterization schemes. This paper aims at documenting the newly observed flow features at the Hong Kong International Airport, enhancing the understanding of low-level windshear, and evaluating the outputs of numerical resolution simulations for reproducing such observed features and its technical feasibility on short-term forecasting. Full article

Understanding the relationships between diversity and functional traits in plant communities is essential for elucidating ecosystem functions, forecasting community succession, and informing ecological restoration efforts in arid regions. Although the current research on plant functional traits and diversity has improved our ability to predict ecological functions, there are still many problems, such as how environmental changes affect the relationship between species diversity and plant functional traits, and how these interactions affect plant community functions. We examined the relationships among leaf and fine root functional traits, species diversity, and functional diversity at the community level, along with their environmental interpretations, in a plant community within the desert–oasis transition zone of the Hexi Corridor, where habitats are undergoing significant small-scale changes. During dune succession, plant community composition and diversity exhibited significant variation. Plants are adapted to environmental changes through synergistic combinations of above-ground and below-ground traits. Specifically, plants in fixed dunes adopted a “slow investment” strategy, while those in semi-fixed and mobile dunes employed a “fast investment” approach to resource acquisition. A strong coupling was observed between plant community functional traits and species diversity. Soil phosphorus content and compactness emerged as primary factors influencing differences in plant community functional traits and composition. These soil factors indirectly regulated fine root functional traits and diversity by affecting species diversity, thereby driving community succession. Our study elucidates the “soil—diversity—community functional trait” linkage mechanisms in the successional process of desert plants. This research provides scientific support for the restoring and reconstruction of degraded ecosystems in arid zones. Full article

This paper aims to outline which predictors are able to forecast being (not) successful in internationalisation after receiving export support and how accurately they can perform this task. Using data on export grant recipients from an Estonian export support programme, 15 theoretically motivated predictors grouped into four domains are used to forecast 24 different proxies of (non-)success with logistic regression and neural networks. The domains focus on firms’ general characteristics, earlier financial and export performance, and export-grant-specific characteristics. The highest areas under the curve exceed the 0.9 threshold, therefore indicating excellent predictive abilities, while more specific (non-)success proxies can be predicted less accurately than general ones. Predictors portraying firm size and export support size emerge as the best in the case of both methods, while in different neural networks, at least one predictor from each of the four domains is among the most important ones. These results lead to multiple practical implications concerning how to select firms into export grant programmes. Full article

Objectives: This study aims to assess the clinical efficacy of micro-fragmented adipose tissue (MFAT) therapy over three years in patients with KOA and to determine whether short-term improvements at three months can forecast long-term outcomes. Methods: A retrospective, observational study was conducted on 335 patients diagnosed with KOA who received a single MFAT injection. The patients were followed up at 3 months, 6 months, 1 year, 2 years, and 3 years, with assessments using the Visual Analog Scale (VAS), Oxford Knee Score (OKS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Knee Injury and Osteoarthritis Outcome Score (KOOS). Statistical analysis was performed to assess the differences in preoperative and postoperative scores (VAS, OKS, WOMAC, KOOS) to evaluate the predictive role of 3-month score changes on long-term clinical outcomes. Results: All measured scores (VAS, OKS, WOMAC, KOOS) showed significant improvement at 3 months, with sustained improvements through 3 years (p < 0.001). Early score changes at 3 months were significantly associated with improved clinical outcomes at 1, 2, and 3 years (p < 0.05). Logistic regression confirmed early post-treatment improvements as independent predictors of long-term benefit, except for the VAS score at 3 years (p = 0.098). A comparative analysis between completers and dropouts showed no baseline differences; however, significant outcome differences emerged at later follow-up points. Due to insufficient data at the 3-year mark among dropouts, statistical comparisons were not possible for that time point. Conclusions: MFAT treatment was associated with consistent symptomatic improvement in patients with KOA, and early clinical response at 3 months served as a reliable predictor of long-term pain and function outcomes. While this study focused on patient-reported symptom relief and not structural regeneration, the results support MFAT as a minimally invasive option for symptom management. Early post-treatment response may serve as a useful tool for clinicians to predict long-term therapeutic success and personalize treatment strategies for KOA patients. Full article

Background/Objectives: The goal of educators is to leave no learner behind. Ultrasounds require dexterity and 3D image interpretation. They are technologically complex, and current medical residency programs lack a reliable means of assessing this ability among their trainees. This prompts consideration as to whether background characteristics or certain pre-existing skills can serve as indicators of learning aptitude for ultrasounds. The objective of this study was to determine whether these characteristics and skills are indicative of learning aptitude for ultrasounds. Methods: This prospective study was conducted with third-year medical students rotating in emergency medicine at the New York Presbyterian Brooklyn Methodist Hospital, Brooklyn, NY, USA. First, students were given a pre-test survey to assess their background characteristics. Subsequently, a psychomotor task (Purdue Pegboard) and visual–spatial task (Revised Purdue Spatial Visualization Tests) were administered to the students. Lastly, an ultrasound task was given to identify the subxiphoid cardiac view. A rubric assessed ability, and proficiency was determined as a 75% or higher score in the ultrasound task. Results: In total, 97 students were tested. An analysis of variance (ANOVA) was used to ascertain if any background characteristics from the pre-test survey was associated with the ultrasound task score. The student’s use of cadavers to learn anatomy had the most correlation (p-value of 0.02). Assessing the psychomotor and visual–spatial tasks, linear regressions were used against the ultrasound task scores. Correspondingly, the p-values were 0.007 and 0.008. Conclusions: Ultrasound ability is based on hand–eye coordination and spatial relationships. Increased aptitude in these abilities may forecast future success in this skill. Those who may need more assistance can have their training tailored to them and further support offered. Full article

Requirements for Unmanned Aerial Vehicle (UAV) applications in low-altitude operations are escalating, which demands resilient Position, Navigation and Timing (PNT) solutions incorporating global navigation satellite system (GNSS) services. However, UAVs often operate in stringent environments with degraded GNSS performance. Practical challenges often arise from dense, dynamic, complex, and uncertain obstacles. When flying in complex environments, it is important to consider signal degradation caused by reflections (multipath) and obscuration (Non-Line of Sight (NLOS)), which can lead to positioning errors that must be minimized to ensure mission reliability. Recent works integrate GNSS reliability maps derived from pseudorange error estimations into path planning to reduce loss-of-GNSS risks with PNT degradations. To accommodate multiple constraint conditions attempting to improve flight resilience against GNSS-degraded environments, this paper proposes a reinforcement learning (RL) approach to feature GNSS signal quality awareness during path planning. The non-linear relations between GNSS signal quality in the form of dilution of precision (DoP), geographic locations, and the policy of searching sub-minima points are learned by the clipped Proximal Policy Optimization (PPO) method. Other constraints considered include static obstacle occurrence, altitude boundary, forbidden flying regions, and operational volumes. The reward and punishment functions and the training method are designed to maximize the success criteria of approaching destinations. The proposed RL approach is demonstrated using a real 3D map of Indianapolis, USA, in the Godot engine, incorporating forecasted DoP data generated by a Geospatial Augmentation system named GNSS Foresight from Spirent. Results indicate a 36% enhancement in mission success rates when GNSS performance is included in the path planning training. Additionally, the varying tensor size, representing the UAV’s DoP perception range, exhibits a positive proportion relation to a higher mission rate, despite an increment in computational complexity. Full article

This article examines the growing role of machine learning (ML) in promoting next-generation climate change adaptation through the improved integration and performance of renewable energy systems. As climate change accelerates, innovative solutions are urgently needed to enhance the resilience and sustainability of energy infrastructure.ML offers powerful capabilities to handle complex data sets, forecast energy supply and demand, and optimize grid operations. This review highlights key applications of ML, such as predictive maintenance, intelligent grid management, and the real-time optimization of renewable energy resources. It also examines current challenges, including data availability, model transparency, and the need for interdisciplinary collaboration, both in technology development and policy and regulation. By synthesizing recent research and case studies, thisarticle shows how ML can significantly improve the performance, reliability, and scalability of renewable energy systems. This review emphasizes the importance of aligning technological advances with policy and infrastructure development. Successful implementation requires not only ensuring technological capabilities (robust infrastructure, structured data sets, and interdisciplinary collaboration) but also the careful consideration and alignment of ethical and regulatory factors from strategic to regional and local levels. Machine learning is becoming a key enabler for the transition to more adaptive, efficient, and low-carbon energy systems in response to climate change. Full article

Modelling events that change over time is one of the most difficult problems in data analysis. Forecasting of time-varying electric power values is also an important problem in data analysis. Regression methods, machine learning, and deep learning methods are used to learn different patterns from data and develop a consumption prediction model. The aim of this study is to determine the most successful models for short-term power consumption prediction with deep learning and to achieve the highest prediction accuracy. In this study, firstly, the data was evaluated and organized with exploratory data analysis (EDA) on a ready dataset and the features of the data were extracted. Studies were carried out on long short-term memory (LSTM), gated recurrent unit (GRU), simple recurrent neural networks (SimpleRNN) and bidirectional long short-term memory (BiLSTM) architectures. First, four architectures were used with 11 different optimization methods. In this study, it was seen that a high success rate of 0.9972 was achieved according to the R² score index. In the following, the first study was tried with different epoch numbers. Afterwards, this study was carried out with 264 separate models produced using four architectures, 11 optimization methods, and six activation functions in order. The results of all these studies were obtained according to the root mean square error (RMSE), mean absolute error (MAE), and R²_score indexes. The R²_score indexes graphs are presented. Finally, the 10 most successful applications are listed. Full article