Search Results (1,564)

Accurately predicting the ionosphere’s Total Electron Content (TEC) is significant for ensuring the regular operation of satellite navigation and communication systems and space weather prediction. To further improve the accuracy of TEC prediction, this paper proposes a TEC prediction model based on the grid-optimized Gate Recurrent Unit (GRU). This model has the following main features: (1) it uses statistical learning methods to interpolate the missing data of TEC observations; (2) it constructs a sliding time window by using the multi-dimensional time series features of two types of solar activity indices to support modeling; (3) It adopts grid search combined with optimization of network depth, time step length, and other hyperparameters to significantly enhance the model’s ability to extract the characteristics of the ionospheric 11-year cycle and seasonal variations. Taking the EGLIN station as an example, the proposed model is verified. The experimental results show that the root mean square error of the GRU model during the period from 2019 to 2020 was 0.78 TECU, which was significantly lower than those of the CCIR, URSI, and statistical machine learning models. Compared with the other three models, the RMSE error of the GRU model was reduced by 72.73%, 72.64%, and 57.38%, respectively. The above research verifies the advantages of the proposed model in predicting TEC and provides a new idea for ionospheric modeling. Full article

Chimonanthus praecox, a traditional ornamental plant in China, is admired for its ability to bloom during the cold winter season and is recognized as an outstanding woody cut flower. MADS-box genes encode transcription factors essential for plant growth and development, with key functions in regulating flowering time and the formation of floral organs. In this study, 74 MADS-box genes (CpMADS1–CpMADS74) were identified and mapped across 11 chromosomes, with chromosome 1 harboring the highest number (13 genes) and chromosome 3 the fewest (3 genes). Physicochemical property analysis revealed that all CpMADS proteins are hydrophilic and predominantly nuclear-localized. Phylogenetic analysis classified these genes into Type I and Type II subfamilies, highlighting a clear divergence in domain structure. Eighty simple sequence repeat (SSR) loci were detected, with dinucleotide repeats being the most abundant, and the majority located in Type II MADS genes. From 23 C. praecox samples, 10 polymorphic SSR markers were successfully developed and PCR-validated, enabling a cluster analysis that grouped these cultivars into three distinct clusters. This study offers significant insights into the regulation of flowering, floral organ development, genetic linkage map construction, and the application of marker-assisted selection in C. praecox. Full article

With rapid global urbanization development, impermeable surface increase, urban population growth, building area expansion, and rising energy consumption, the urban heat island (UHI) effect is becoming increasingly serious. However, the spatial distribution of the UHI cannot be accurately extracted. Therefore, we focused on Luoyang City as the research area and combined the Getis-Ord-Gi* statistic and the greenest image to extract the UHI based on the Google Earth Engine using land surface temperature–spatial autocorrelation characteristics and seasonal changes in vegetation. As bare land considerably influenced the UHI extraction results, we combined the greenest image with the initial extraction results and applied the maximum normalized difference vegetation index threshold method to remove this effect on UHI distribution extraction, thereby achieving improved UHI extraction accuracy. Our results showed that the UHI of Luoyang continuously expanded outward, increasing from 361.69 km² in 2000 to 912.58 km² in 2023, with a continuous expansion rate of 22.95 km²/year. Furthermore, the urban area had a higher UHI area growth rate than the county area. Analysis indicates that the UHI effect in Luoyang has increased in parallel with the expansion of the building area. Intensive urban construction is a primary driver of this growth, directly exacerbating the UHI effect. Additionally, rising temperatures, population growth, and gross domestic product accumulation have collectively contributed to the ongoing expansion of this phenomenon. This study provides scientific guidance for future urban planning through the accurate extraction of the UHI effect, which promotes the development of sustainable human settlements. Full article

Despite the widespread accessibility of vaccines and antivirals, seasonal influenza virus epidemics continue to pose a threat to public health. In this study, we constructed a recombinant replication-deficient simian adenovirus type 25 vector carrying the full-length hemagglutinin (HA) of the H1N1 influenza virus, named rSAd25-H1. Both systemic and mucosal humoral immune responses, as well as the protective efficacy, were assessed in mice immunized via the intramuscular (IM) or intranasal (IN) route. A single-dose IM or IN administration of rSAd25-H1 elicited a robust systemic IgG antibody response, including hemagglutination inhibition antibodies. As expected, only IN immunization was able to induce IgA production in serum and respiratory mucosa. Notably, a single dose of rSAd25-H1 at the highest dose (10¹⁰ viral particles) conferred complete protection against lethal homologous H1N1 challenge in mice despite the route of administration. These findings demonstrate the potential of simian adenovirus type 25-based vectors as a promising candidate for intranasal vaccine development targeting respiratory pathogens. Full article

Governments worldwide are mandating e-invoicing and real-time VAT reporting, yet many cash-intensive service SMEs continue to under-report VAT, eroding fiscal revenues. This study investigates whether financial technology (FinTech) adoption can reduce this under-reporting among tourism SMEs in Greece—an economy with high seasonal spending and a persistent shadow economy. This is the first micro-level empirical study to examine how FinTech tools affect VAT compliance in this sector, offering novel insights into how technology interacts with behavioral factors to influence fiscal behavior. Drawing on the Technology Acceptance Model, deterrence theory, and behavioral tax compliance frameworks, we surveyed 214 hotels, guesthouses, and tour operators across Greece’s main tourism regions. A structured questionnaire measured five constructs: FinTech adoption, VAT compliance behavior, tax morale, perceived audit probability, and financial performance. Using Partial Least Squares Structural Equation Modeling and bootstrapped moderation–mediation analysis, we find that FinTech adoption significantly improves declared VAT, with compliance fully mediating its impact on financial outcomes. The effect is especially strong among businesses led by owners with high tax morale or strong perceptions of audit risk. These findings suggest that FinTech tools function both as efficiency enablers and behavioral nudges. The results support targeted policy actions such as subsidies for e-invoicing, tax compliance training, and transparent audit communication. By integrating technological and psychological dimensions, the study contributes new evidence to the digital fiscal governance literature and offers a practical framework for narrowing the VAT gap in tourism-driven economies. Full article

In this study, we consider monthly load forecasting, which is an essential decision for energy infrastructure planning and investment. This study focuses on the Texas power grid, where electricity consumption has surged due to rising industrial activity and the increased construction of data centers driven by growing demand for AI. Based on an extensive exploratory data analysis, we identify key characteristics of monthly electricity demand in Texas, including an accelerating upward trend, strong seasonality, and temperature sensitivity. In response, we propose a regression-based forecasting model that incorporates a carefully designed set of input features, including a nonlinear trend, lagged demand variables, a seasonality-adjusted month variable, average temperature of a representative area, and calendar-based proxies for industrial activity. We adopt a rolling forecasting approach, generating 12-month-ahead forecasts for both 2023 and 2024 using monthly data from 2013 onward. Comparative experiments against benchmarks including Holt–Winters, SARIMA, Prophet, RNN, LSTM, Transformer, Random Forest, LightGBM, and XGBoost show that the proposed model achieves superior performance with a mean absolute percentage error of approximately 2%. The results indicate that a well-designed regression approach can effectively outperform even the latest machine learning methods in monthly load forecasting. Full article

After the 2011 earthquake, lake water depletion has become a widespread issue in Sikkim, especially in regions classified as high to very high seismic zones, where many lakes have turned into seasonal water bodies. This study investigates Chochen Lake in the Barapathing area of Sikkim’s Pakyong district, which is facing severe water seepage and instability. The problem, intensified by the 2011 seismic event and ongoing local construction, is examined through subsurface fracture mapping using Vertical Electrical Sounding (VES) and profiling techniques. A statistical factor method, applied to interpret VES data, helped identify fracture patterns beneath the lake. Results from two sites (VES-1 and VES-2) reveal significant variations in weathered and semi-weathered soil layers, indicating fractures at depths of 17–50 m (VES-1) and 20–55 m (VES-2). Higher fracture density near VES-1 suggests increased settlement risk and ground displacement compared to VES-2. Contrasting resistivity values emphasize the greater instability in this zone and the need for cautious construction practices. The findings highlight the role of seismic-induced fractures in ongoing water depletion and underscore the importance of continuous dewatering to stabilize the swampy terrain. Full article

Corydalis yanhusuo W. T. Wang, commonly known as Yanhusuo, is an important and rare medicinal plant resource in China. Its habitat integrity is facing severe challenges due to climate change and human activities. Establishing an integrative quality zoning system for this species is of significant practical importance for resource conservation and adaptive management. This study integrates multiple data sources, including 121 valid distribution points, 37 environmental factors, future climate scenarios (SSP126 and SSP585 pathways for the 2050s and 2090s), and measured content of tetrahydropalmatine (THP) from 22 sampling sites. A predictive framework for habitat suitability and spatial distribution of effective components was constructed using a multi-model coupling approach (MaxEnt, ArcGIS spatial analysis, and co-kriging method). The results indicate that the MaxEnt model exhibits high prediction accuracy (AUC > 0.9), with the dominant environmental factors being the precipitation of the wettest quarter (404.8~654.5 mm) and the annual average temperature (11.8~17.4 °C). Under current climatic conditions, areas of high suitability are concentrated in parts of Central and Eastern China, including the Sichuan Basin, the middle–lower Yangtze plains, and coastal areas of Shandong and Liaoning. In future climate scenarios, the center of suitable areas is predicted to shift northwestward. The content of THP is significantly correlated with the mean diurnal temperature range, temperature seasonality, and the mean temperature of the wettest quarter (p < 0.01). A comprehensive assessment identifies the Yangtze River Delta region, Central China, and parts of the Loess Plateau as the optimal integrative quality zones. This research provides a scientific basis and decision-making support for the sustainable utilization of C. yanhusuo and other rare medicinal plants in China. Full article

Improving the energy efficiency of residential buildings is essential for achieving global climate goals and reducing environmental impact. This study analyzes the Total Performance approach using the example of a modern semi-detached house built by a Polish developer, as an example. The building is designed with integrated systems that minimize energy consumption while maintaining resident comfort. The building is equipped with an air-to-water heat pump, underfloor heating, mechanical ventilation with heat recovery, and automatic temperature control systems. Energy efficiency was assessed using ArCADia–TERMOCAD 8.0 software in accordance with Polish Technical Specifications (TS) and verified by monitoring real-time electricity consumption during the heating season. The results show a PED from non-renewable sources of 54.05 kWh/(m²·year), representing a 23% reduction compared to the Polish regulatory limit of 70 kWh/(m²·year). Real-time monitoring conducted from December 2024 to April 2025 confirmed these results, indicating an actual energy demand of approximately 1771 kWh/year. Domestic hot water (DHW) preparation accounted for the largest share of energy consumption. Despite its dependence on grid electricity, the building has the infrastructure to enable future photovoltaic (PV) installation, offering further potential for emissions reduction. The results confirm that Total Performance strategies are not only compliant with applicable standards, but also economically and environmentally viable. They represent a scalable model for sustainable residential construction, in line with the European Union’s (EU’s) decarbonization policy and the goals of the European Green Deal. Full article

Simulation is an important technical tool in corn threshing operations, and the establishment of the corn kernel model is the core part of the simulation process. The existing modeling method is to treat the whole kernel as a rigid body, which cannot be crushed during the simulation process, and the calculation of the crushing rate needs to be considered through multiple criteria such as the contact force, the number of collisions, and so on. Aiming at the issue that kernel crushing during maize threshing cannot be accurately modeled in discrete element simulations, in this study, a sub-area crushing model was constructed; representative samples with 26%, 30% and 34% moisture content were selected from a double-season maturing region in China; based on the physical dimensions and biological structure of the maize kernel, three stress regions were defined; and mechanical property tests were conducted on each of the three stress regions using a texturometer as a way to determine the different crushing forces due to the heterogeneity of the maize structure. The correctness of the model was verified by stacking angle and mechanical property experiments. A discrete element model of corn kernels was established using the Bonding V2 method and sub-area modeling. Bonding parameters were calculated by combining stacking angle tests and mechanical property tests. The flattened corn kernel was used as a prototype, and the bonding parameters were determined through size and mechanical property tests. A 22-ball bonding model was developed using dimensional parameters, and the kernel density was recalculated. Results showed that the relative error between the stacking angle test and the measured mean value was 0.31%. The maximum deviation of axial compression simulation results from the measured mean value was 22.8 N, and the minimum deviation was 3.67 N. The errors between simulated and actual rupture forces at the three force areas were 5%, 10%, and 0.6%, respectively. The decreasing trend of the maximum rupture force for the three moisture levels in the simulation matched that of the actual rupture force. The discrete element model can accurately reflect the rupture force, energy relationship, and rupture process on both sides, top, and bottom of the grain, and it can solve the error problem caused by the contact between the threshing element and the grain line in the actual threshing process to achieve the design optimization of the threshing drum. The modeling method provided in this study can also be applied to breakable discrete element models for wheat and soybean, and it provides a reference for optimizing the design of subsequent threshing devices. Full article

In order to achieve the efficient utilization of agricultural by-products and overcome the bottleneck of animal feed shortages in dry seasons, this study utilized corn stover (CS; Zea mays L.) as a material to systematically investigate the dynamic changes in the fermentation quality, bacterial community structure, and pathogenic risk of silage under different fermentation times (0, 3, 7, 15, and 30 days). CS has high nutritive value, including crude protein and sugar, and can serve as a carbon source and a nitrogen source for silage fermentation. After ensiling, CS silage (CSTS) exhibited excellent fermentation quality, characterized by relatively high lactic acid content, low pH, and ammonia nitrogen content within an acceptable range. In addition, neither propionic acid nor butyric acid was detected in any of the silages. CS exhibited high α-diversity, with Serratia marcescens being the dominant bacterial species. After ensiling, the α-diversity significantly (p < 0.05) decreased, and Lactiplantibacillus plantarum was the dominant species during the fermentation process. With the extension of fermentation days, the relative abundance of Lactiplantibacillus plantarum significantly (p < 0.05) increased, reaching a peak and stabilizing between 15 and 30 days. Ultimately, lactic acid bacteria dominated and constructed a microbial symbiotic network system. In the bacterial community of CSTS, the abundance of “potential pathogens” was significantly (p < 0.01) lower than that of CS. These results provide data support for establishing a microbial regulation theory for silage fermentation, thereby improving the basic research system for the biological conversion of agricultural by-products and alleviating feed shortages in dry seasons. Full article

Soccer’s global popularity as the world’s favorite sport is driven by many factors, with high player salaries being one of the key reasons behind its appeal. These salaries not only reflect on-field performance, but also capture a broader evaluation of player value. Despite the increasing use of performance data in sports analytics, a critical gap remains in establishing fair compensation models that comprehensively account for both quantifiable and intangible contributions. To address these challenges, this study adopts machine learning (ML) techniques that model player salaries based on a combination of performance metrics and contextual features. This research focuses on reducing bias and improving transparency in salary decisions through a systematic, data-driven approach. Utilizing a dataset spanning the 2016–2022 seasons, we apply both traditional and automated ML frameworks to uncover the most influential factors in salary determination. The results indicate a nearly 17% improvement in R² and about a 30% reduction in MAE after incorporating the newly constructed features and methods, demonstrating a significant enhancement in model performance. Gradient Boosting demonstrates superior effectiveness, revealing a group of significantly underestimated and overestimated players, and showcasing the model’s proficiency in detecting valuation discrepancies. Full article

Universities in severely cold regions face the dual challenge of adapting to seasonal climate variations while enhancing outdoor thermal comfort in outdoor leisure plazas. This study takes a university in Hohhot as a case study. Through field investigations conducted in summer and winter, thermal benchmarks were established. Based on this, an orthogonal experimental design was developed considering greenery layout, plant types, and surface albedo. ENVI-met was used to simulate and analyze the seasonal regulatory effects of landscape elements on the microclimate. The results show that: (1) the lower limit of the neutral PET range in Hohhot in winter is −11.3 °C, and the upper limit in summer is 31.3 °C; (2) the seasonal contribution of landscape elements to PET ranks as follows: plant types > greenery layout > surface albedo; and (3) the proposed optimization plan achieved a weighted increase of 6.0% in the proportion of activity area within the neutral PET range in both summer and winter. This study is the first to construct outdoor thermal sensation categories for both summer and winter in Hohhot and to establish a thermal comfort optimization evaluation mechanism that considers both diurnal and seasonal weightings. It systematically reveals the comprehensive regulatory effects of landscape elements on the thermal environment in severely cold regions and provides a nature-based solution for the climate-responsive design of campus plazas in such areas. Full article

Measuring the small tree trunk variations during the day–night cycle, seasonal cycles, as well as those caused by the plant’s growth and health regime is a very important action in horticulture or forestry because by analyzing the collected data, assessments can be made on the health of the trees, but also on the climatic conditions and changes in a certain region. This can be performed with devices called dendrometers. This paper presents a new type of approach to these measurement types in which the trunk volume changes are highly sensitively converted into the axial stress on sensitive elements made of magnetic materials in wire form in which the giant stress impedance effect occurs. Finally, by electronic processing of the signals provided by the sensitive elements, digital words with a decimal value proportional to the diameter variations are obtained. This paper presents the operating principle, the constructive details and the experimental results obtained by testing the device in the laboratory and in-field. The proposed dendrometer, compared to those available commercially, has the advantage of good resolution and sensitivity, good immunity to temperature variations, the possibility of transmitting the result remotely, robustness and low price. Some metrological parameters obtained from the experimental testing are the following: resolution 1.6 µm, linearity 1.4%, measurement range 0 to 5 mm, temperature coefficient 0.012%/°C. Full article

The construction sector presently consumes about 40% of global energy and generates 36% of CO₂ emissions, making facade retrofits a priority for decarbonising buildings. This review clarifies how ventilated facades (VFs), wall assemblies that interpose a ventilated air cavity between outer cladding and the insulated structure, address that challenge. First, the paper categorises VFs by structural configuration, ventilation strategy and functional control into four principal families: double-skin, rainscreen, hybrid/adaptive and active–passive systems, with further extensions such as BIPV, PCM and green-wall integrations that couple energy generation or storage with envelope performance. Heat-transfer analysis shows that the cavity interrupts conductive paths, promotes buoyancy- or wind-driven convection, and curtails radiative exchange. Key design parameters, including cavity depth, vent-area ratio, airflow velocity and surface emissivity, govern this balance, while hybrid ventilation offers the most excellent peak-load mitigation with modest energy input. A synthesis of simulation and field studies indicates that properly detailed VFs reduce envelope cooling loads by 20–55% across diverse climates and cut winter heating demand by 10–20% when vents are seasonally managed or coupled with heat-recovery devices. These thermal benefits translate into steadier interior surface temperatures, lower radiant asymmetry and fewer drafts, thereby expanding the hours occupants remain within comfort bands without mechanical conditioning. Climate-responsive guidance emerges in tropical and arid regions, favouring highly ventilated, low-absorptance cladding; temperate and continental zones gain from adaptive vents, movable insulation or PCM layers; multi-skin adaptive facades promise balanced year-round savings by re-configuring in real time. Overall, the review demonstrates that VFs constitute a versatile, passive-plus platform for low-carbon buildings, simultaneously enhancing energy efficiency, durability and indoor comfort. Future advances in smart controls, bio-based materials and integrated energy-recovery systems are poised to unlock further performance gains and accelerate the sector’s transition to net-zero. Emerging multifunctional materials such as phase-change composites, nanostructured coatings, and perovskite-integrated systems also show promise in enhancing facade adaptability and energy responsiveness. Full article