Search Results (118)

Antarctic research stations require reliable low-carbon power under extreme conditions. This study compiles a synchronized PV-meteorological time-series data set on Horseshoe Island (Antarctica) at 30 s, 1 min, and 5 min resolutions and compares four PV module types (monocrystalline, polycrystalline, flexible mono, and semitransparent) under controlled field operation. Model development adopts an interpretable, multi-objective framework: a modified SPEA-2 searches rule sets on the Pareto front that jointly optimize precision and recall, yielding transparent, physically plausible decision rules for operational use. For context, benchmark machine-learning models (e.g., kNN, SVM) are evaluated on the same splits. Performance is reported with precision, recall, and complementary metrics (F1, balanced accuracy, and MCC), emphasizing class-wise behavior and robustness. Results show that the proposed rule-based approach attains competitive predictive performance while retaining interpretability and stability across panel types and sampling intervals. Contributions are threefold: (i) a high-resolution field data set coupling PV output with solar radiation, temperature, wind, and humidity in polar conditions; (ii) a Pareto-front, explainable rule-extraction methodology tailored to small-power PV; and (iii) a comparative assessment against standard ML baselines using multiple, class-aware metrics. The resulting XAI models achieved 92.3% precision and 89.7% recall. The findings inform the design and operation of PV systems for harsh, high-latitude environments. Full article

Agile Earth Observation Satellites (AEOSs), with their maneuverability, can flexibly observe point, line and region targets. However, existing research typically requires distinct algorithms for each target type, lacking a unified modeling and solution framework, which hinders the ability to meet the demands of rapid and coordinated observation of multiple target types in complex scenarios. To address these issues, this paper proposes a unified scheduling model for agile Earth observation satellites based on the Degenerate Quadtree Grid (DQG) and Proximal Policy Optimization (PPO), termed AEOSSP-USM. Firstly, the DQG is first employed to enable unified management and integrated modeling of point, line, and area targets; Secondly, traditional time window calculations based on longitude and latitude are replaced with grid code-based computations using DQG; Finally, the PPO algorithm, a deep reinforcement learning method, is introduced to formulate AEOSSP-USM as a Markov Decision Process (MDP), enabling efficient problem solving. Experimental results demonstrate that the proposed method effectively realizes unified scheduling of heterogeneous targets, improving imaging quality about 3 times, reducing energy consumption by 10%, decreasing memory usage more than 90%, and enhancing computational efficiency by 35 times compared to conventional longitude-latitude strip algorithm. Full article

Background: This paper empirically tests the new multi-level Associationalist Demand Control (ADC) theory by applying the Job Content Questionnaire (JCQ) 2.0 that assesses both a wide range of task characteristics as well as work organizational and external-to-work psychosocial characteristics. Methods: The paper is based on four JCQ 2.0 pilot studies among 16,125 workers in Korea, China, Australia, and Germany. All pilots used the original JCQ task-level scales and then added newly developed proposed items and scales, evolving more comprehensive higher-level scales from pilot to pilot from 2005 to 2011. A brief review of the analytic process is presented, followed by an assessment of the internal consistency and concurrent validity of the final 25 multi-level JCQ 2.0 scales at the task, the organizational, and the external levels. Results: Adequate psychometric properties were established for the JCQ 2.0 pilot scales. The extended set of task-level scales was found to be robust across all samples; the new organizational scales mainly showed adequate internal consistency with α > 0.7 in Australia and Germany (tested only there) and were associated with relevant work- and health-related outcome measures as expected. Similarly, the external-to-work scales (tested only in Germany) had adequate Cronbach’s Alpha values and showed expected associations to relevant outcome scales. Conclusions: Although not all scales were available in all countries, overall, the results support the “functional similarity” of the major scale areas across the four pilot countries and support the underlying extensions of the Demand–Control theoretical constructs to the multi-level psychosocial work assessment for the promotion of workers’ health and wellbeing as suggested by the new ADC model. Full article

Previous studies reported the links between the COVID-19 incidence and weather factors, but few investigated their impact and timing on mortality, at a continental scale. We systematically investigated the temporal relationship of COVID-19 mortality in the European countries in the 1st year of pandemic (March–December 2020) with (i) solar insolation (W/${\mathrm{m}}^2$) at the ground level and (ii) objective sky cloudiness (as decimal cloud fraction), both derived from satellite measurements. We checked the correlations of these factors within a sliding window of two months for the whole period. Linear-mixed effect modeling revealed that overall, for the European countries (adjusted for latitude), COVID-19 mortality was substantially negatively correlated with solar insolation in the previous month (std. beta −0.69). Separately, mortality was significantly correlated with the cloudiness in both the previous month (std. beta +0.14) and the respective month (std. beta +0.32). This time gap of ∼1 month between the COVID-19 mortality and correlated weather factors was previously unreported. The long-term monitoring of these factors might be important for epidemiological policy decisions especially in the initial period of potential future pandemics when effective medical treatment might not yet be available. Full article

Distance learning offers enhanced flexibility and reduced access restrictions, making it increasingly popular among non-traditional students and those juggling academic studies with professional and family obligations. This study explored the associations between study demands and resources (decision latitude and social support from lecturers and peers) and different study outcomes by applying the Job Demands-Resources Model in a distance learning context. Based on the model’s assumptions, we hypothesized that academic demands negatively predict study success in distance learning, while decision latitude and social support from lecturers and peers positively affect it. These associations were expected to be mediated by emotional exhaustion and different dimensions of engagement. The cross-sectional online study involved 286 psychology students from a German distance university. The multivariate path model revealed an association of demands and decision latitude with perceptions of competence and study satisfaction. While demands were significantly correlated with the grade point average, decision latitude was not. Consistent with the model’s assumptions, these effects were partially mediated by exhaustion and engagement. We did not find significant incremental associations of social support with the outcomes. The findings concerning measures to support students in distance education were discussed. Full article

This study presents a novel approach to predicting ship speed based on real-time voyage observation data, aiming to enhance maritime safety and operational efficiency. Observational data from a 20,000-ton bulk carrier, including variables such as latitude, longitude, GPS orientation, wind direction, wind speed, and main engine parameters, were collected and preprocessed to mitigate noise and handle missing values. Six machine learning models—the Backpropagation (BP) Neural Network, Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), XGBoost, and LightGBM—were employed to develop predictive models. Among these, the LightGBM model demonstrated the highest prediction accuracy, achieving a Root Mean Squared Error (RMSE) of 0.188, Mean Absolute Error (MAE) of 0.149, and a coefficient of determination (R²) of 0.978. The results highlight the potential of the LightGBM model in optimizing ship navigation and improving maritime operational efficiency. These findings offer a reliable foundation for further advancements in predictive maritime technologies and route optimization. Full article

This study focuses on Qilu Lake to study how to mitigate the impacts of seasonal droughts and provide technical support for drought resistance decision-making in low-latitude plateau lake basins. Using the Standardized Precipitation Index (SPI), the Vegetation Condition Index (VCI), and the Temperature Condition Index (TCI) as bases, in this study, the applicability of the vegetation health index (VHI) within the basin is investigated, and the optimal weight distribution between the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI) in the VHI is determined. The VHI is then applied to analyze the correlation between drought frequency and severity within the basin. The results indicate that the method is most effective in assessing agricultural drought in the Qilu Lake Basin when the VCI and TCI are weighted at a 4:6 ratio, optimizing the VHI’s evaluative performance. The drought limit water levels of lakes are further divided into short- and long-term drought limit water levels. The short-term drought limit water level is divided into the drought warning water level and the drought emergency water level. The drought warning water level (corresponding to moderate drought conditions, with a frequency of P = 75%) ranges from 1794.53 m to 1795.11 m, while the drought emergency water level (corresponding to extreme drought conditions, with a frequency of P = 95%) ranges from 1793.94 m to 1794.31 m. These levels are set to meet the emergency water demand during droughts in the basin. The long-term drought limit water levels are calculated by accumulating the water deficits of various sectors within the watershed under different agricultural drought conditions, based on the short-term drought limit water levels. By setting the drought limit water level using this method, as well as considering the original water regulation capacity of the lake resources, when the watershed experiences drought, the scheduling method based on this drought limit water level can better alleviate the water supply pressure on various sectors in the local area. Full article

In the 2020 and 2021 vintages, some chemical and phytochemical parameters of the Aglianico and Cabernet Sauvignon cultivars grown in three regions of Southern Italy (Campania, Molise, and Sicily) were determined. In particular, the aim of this study was the investigation of flavanol, monomeric anthocyanin, and pigment contents in grapes and wines. The data collected showed that the main chemical parameters and flavonoids analyzed in the grapes and wines were influenced by the vintage, grape variety, and geographical location. Specifically, in the Aglianico grapes, the latitude and vintage highly influenced the titratable acidity and flavonoids in terms of richness in flavanols, compared to Cabernet Sauvignon. On the other hand, the location of the vineyard influenced monomeric anthocyanins in both varieties, highlighting a relationship of these phytochemicals with soil fertility and availability of certain chemical elements such as nitrogen and iron. All results support the idea that the interaction between grape variety, soil type, and geographical origin plays a decisive role in shaping the characteristics of wine. Full article

In modern fishery management, fishing vessel trajectory data are used to monitor and analyze fishing vessel activities. However, trajectory data are often of low quality, probably due to environmental factors, equipment failures, signal loss and operation errors, leading to numerous outliers in these data. These outliers not only undermine the credibility of the data but also negatively affect the subsequent data mining and decision-making. In this study, a data cleaning method for the identification of outlier points in fishing vessel trajectories based on the Geohash geocoding algorithm is given, which involves several key steps: obtaining and preprocessing the raw trajectory data; generating the corresponding Geohash codes for each ship position based on its latitude and longitude; calculating the reachable distance considering the time interval between the current point and the following points and their speeds; querying the neighborhood of the current point based on the reachable distance; and obtaining all Geohash codes of the reachable areas of the fishing vessels within the time interval as the reachable range grid set of the current position. The reachable range grid set of the current position is compared with the reachable range grid sets of the previous point identified as normal and the next point in the fishing vessel trajectory. If there is no intersection, it is determined that the current fishing vessel position is an outlier, and this point will be excluded. The method proposed in this study is able to effectively identify outliers in trajectory data, achieving efficient and effective trajectory data cleaning and improving the accuracy and reliability of the data. Full article

This study evaluates the ability of three machine learning methods—decision tree classifier (DTC), random forest classifier (RFC), and XGBoost classifier (XGBC)—to classify and predict tropical cyclone (TC) rapid intensification (RI) and non-RI over the Southwest Pacific Ocean basin (SWPO) from 1982 to 2023. Among the 324 TCs within the domain, 81 were identified as RI TCs, exhibiting a 24-h intensity increase of at least 15 ms⁻¹ at least once in their lifetime. Environmental variables used for the input matrix are extracted from the nearest grid cell corresponding to each RI and non-RI event’s geographic location and time of occurrence. Additionally, the geographic location of each event and its initial intensity positions (24-h prior) are also included in the model. The XGBC, with 10-fold cross-validation, became the optimum classifier by achieving the highest classification accuracy, as well as the lowest probability of false detection and the highest AUC score on the unseen data. The model identified the longitude of RI and non-RI events, initial intensity latitude, extent of initial intensity, and relative humidity at 850 hPa as the most important variables in the classification decision. This study will advance storm preparedness strategies for the SWPO nations through correctly predicting RI-TCs and prioritizing early prediction of contributing environmental variables. Full article

Forest mapping using remote sensing has made considerable progress over the past decade, but substantial uncertainties remain in complex regions, particularly where terrain and climate vary dramatically. Yunnan Province, China, represents such a challenging case, with its diverse climatic zones ranging from tropical to temperate and its topography spanning over 6500 m in elevation. These factors contribute to substantial variation in vegetation types, complicating the accurate identification of forest cover through remote sensing. This study aims to enhance forest mapping in Yunnan by leveraging multi-temporal remote sensing data from Sentinel-2 and Landsat 8/9 imagery, incorporating key phenological stages—such as the leaf greening (GRN) period, as well as the senescence, defoliation, and foliation (SDF) stages of deciduous forests—along with kNDVI and terrain factors. A random forest (RF) classifier was applied on the Google Earth Engine (GEE) platform to create a 10 m resolution forest map (LS2-RF). This map achieved an overall accuracy of 96.35% when validated with 1572 ground samples, significantly outperforming existing global datasets, such as Dynamic World (73.88%) and WorldCover (87.66%). These maps agreed well in extensive forested areas; discrepancies were noted in mixed land types, including farmland, urban areas, and regions with fragmented landscapes. In 2022, Yunnan’s forest cover was 60.40%, with higher coverage in the southwestern region and lower in the northeast. The largest forested area was found in Pu’er City, while the smallest was in Yuxi City. Forests were most abundant at elevations between 1500 and 2500 m (occupying 52.29% of the total forest area) and slopes of 15° to 25° (occupying 39.19% of the total forest area). Conversely, forest cover was lowest in areas below 500 m elevation (occupying 0.64% of the total forest area) and on slopes less than 5° (occupying 2.40% of the total forest area). The analysis also revealed a general trend of increasing forest cover with decreasing latitude and longitude, with peak forest coverage at mid-elevations and slopes, followed by a decline at higher elevations. The resultant forest map provides valuable data for ecological assessments, forest conservation initiatives, and informed policy decision-making. Full article

This study explores the use of Temporal Fusion Transformers (TFTs), an AI/ML technique, to enhance the prediction of coastal dynamics along the Western Black Sea coast. We integrate in-situ observations from five meteo-oceanographic stations with modelled geospatial marine data from the Copernicus Marine Service. TFTs are employed to refine predictions of shallow water dynamics by considering atmospheric influences, with a particular focus on wave-wind correlations in coastal regions. Atmospheric pressure and temperature are treated as latitude-dependent constants, with specific investigations into extreme events like freezing and solar radiation-induced turbulence. Explainable AI (XAI) is exploited to ensure transparent model interpretations and identify key influential input variables. Data attribution strategies address missing data concerns, while ensemble modelling enhances overall prediction robustness. The models demonstrate a significant improvement in prediction accuracy compared to traditional methods. This research provides a deeper understanding of atmosphere-marine interactions and demonstrates the efficacy of Artificial intelligence (AI)/Machine Learning (ML) in bridging observational and modelled data gaps for informed coastal zone management decisions, essential for maritime safety and coastal management along the Western Black Sea coast. Full article

Potential evapotranspiration (PET) is a significant factor contributing to water loss in hydrological systems, making it a critical area of research. However, accurately calculating and measuring PET remains challenging due to the limited availability of comprehensive data. This study presents a detailed sustainable model for predicting PET using the Thornthwaite equation, which requires only mean monthly temperature (Tmean) and latitude, with calculations performed using R-Studio. A geographic information system (GIS) was employed to interpolate meteorological data, ensuring coverage of all sub-basins within the Murat River basin, the study area. Additionally, Python libraries were utilized to implement artificial intelligence-driven models, incorporating both machine learning and deep learning techniques. The study harnesses the power of artificial intelligence (AI), applying deep learning through a convolutional neural network (CNN) and machine learning techniques, including support vector machine (SVM) and random forest (RF). The results demonstrate promising performance across the models. For CNN, the coefficient of determination (R²) varied from 96.2 to 98.7%, the mean squared error (MSE) ranged from 0.287 to 0.408, and the root mean squared error (RMSE) was between 0.541 and 0.649. For SVM, the R² varied from 94.5 to 95.6%, MSE ranged between 0.981 and 1.013, and RMSE ranged from 0.990 to 1.014. RF showed the best performance, achieving an R² of 100%, MSE values of 0.326 and 0.640, and corresponding RMSE values of 0.571 and 0.800. The climate and topography data used for all algorithms were consistent, and the results indicate that the RF model outperforms the others. Consequently, The RF model’s superior accuracy highlights its potential as a reliable tool for sustainable PET prediction, supporting informed decision-making in water resource planning. By leveraging GIS, AI, and machine learning, this study enhances PET modeling methodologies, addressing critical water management challenges and promoting sustainable hydrological practices in the face of climate change and resource limitations. Full article

This paper presents a new decision support method aimed at prioritizing processing for an intelligent road problem reporting service. The proposed method uses advanced georeferencing technology to extract the longitude and latitude coordinates in the metadata of photos taken with the smartphone application to capture the complaint scene. This method not only maps out the processing times, but also applies a spatiotemporal clustering technique to link the complaint types and locations with the actual complaint processing times. A validation study of the frequency of reported locations per priority reveals that the complaint-processing prioritization method developed in this study aligns realistically with actual field complaint processing. Furthermore, recognizing the significance of location in processing complaints, the georeferencing technique appears suitable for identifying complaint locations for each report and incorporating this into the decision-making framework. Full article

Air pollution is a growing concern in rural areas where agricultural production can be reduced by it. This article analyses data obtained as part of a research project. The aim of this study is to understand the influence of atmospheric pressure, air temperature, air relative humidity, longitude and latitude of the location, and indoor and outdoor environment on local rural workplace diversity of air pollutants such as carbon monoxide (CO) and suspended particulate matter (SPM), as well as the contribution of these variables to changes in such air pollutants. The focus is on four topics: motivation, innovation and creativity, leadership, and social responsibility. Furthermore, this study developed an artificial neural network (ANN) model to predict CO and SPM concentrations in the air based on data collected from the mentioned inputs. The related sensors were assembled on an Arduino Mega 2560 board to form a field-portable device to detect air pollutants and meteorological parameters. The sensors included an MQ7 sensor for CO concentration measurement, a Sharp GP2Y1010AU0F dust sensor for SPM concentration measurement, a DHT11 sensor for air temperature and air relative humidity measurement, and a BMP180 sensor for air pressure measurements. The longitude and latitude of the location were measured using a smartphone. Measurements were conducted from 20 December 2021 to 16 July 2022. Results showed that the overall average outdoor CO and SPM concentrations were 10.97 ppm and 231.14 μg/m³ air, respectively. The overall average indoor concentrations were 12.21 ppm and 233.91 μg/m³ air for CO and SPM, respectively. Results showed that the ANN model demonstrated acceptable performance in predicting CO and SPM in both the training and testing phases, exhibiting a coefficient of determination (R²) of 0.575, a root mean square error (RMSE) of 1.490 ppm, and a mean absolute error (MAE) of 0.994 ppm for CO concentrations when applying the testing dataset. For SPM concentrations, the R², RMSE, and MAE using the test dataset were 0.497, 30.301 μg/m³ air, and 23.889 μg/m³ air, respectively. The most influential input variable was air pressure, with contribution rates of 22.88% and 22.82% in predicting CO and SPM concentrations, respectively. The acceptable performance of the developed ANN model provides potential advances in air quality management and agricultural planning, enabling a more accurate and informed decision-making process regarding air pollution. The results of short-term estimation of CO and SPM concentrations suggest that the accuracy of the ANN model needs to be improved through more comprehensive data collection or advanced machine learning algorithms to improve the prediction results of these two air pollutants. Moreover, as even lower cost devices can predict CO and SPM concentrations, this study could lead to the development some kind of virtual sensor, as other air pollutants can be estimated from measurements of particulate matters. Full article