Search Results (35)

It is important to predict air pollutant emissions from coal-fired power plants using real-time technological parameters to improve environmental efficiency. Since the relationship between emissions and parameters is nonlinear, machine learning models are needed to forecast emissions under various boiler operating modes. This study develops and tests Deep Sparse Transformer Networks for predicting pollutant time series, accounting for long-term dependencies. Data were collected from a 4000 MW coal-fired power plant in Ekibastuz, Kazakhstan, covering 67,527 records for 14 indicators at 10 min intervals. Fractal R/S analysis confirmed long-term memory in SO₂, PM_2.5, and NO_x series, guiding window length selection. The results show that the model achieves slightly better accuracy for SO₂ (R² 0.95–0.38), while NO_x and PM_2.5 have similar dynamics (R² 0.93–0.26). However, accuracy drops notably after 12 points, making the model best suited for short-term forecasts. These findings support environmental monitoring services and help optimize plant parameters, contributing to lower emissions and advancing carbon neutrality goals. Full article

Torque modeling is an important research aspect of multi-degree of freedom (multi-DOF) spherical motors, and it is the key to realizing the accurate control of multi-DOF spherical motors. In this paper, a torque modeling method of the reluctance spherical motor (RSPM) based on the magnetic equivalent circuit (MEC) method is proposed. Firstly, the structure of the RSPM is introduced, and the MEC topology of the RSPM is obtained. The calculation formulas of the reluctances in this topology are given. Then the magnetic flux of the RSPM is solved by the mesh analysis method, and the torque is calculated based on the magnetic field energy storage. Finally, the calculated torque is verified by the finite element method (FEM). The verification results show that the torque modeling of the RSPM based on the MEC method is correct, which consumes less memory and time than the three-dimensional finite element method. Full article

The Canary Islands, a region with high asthma prevalence, are frequently exposed to Saharan Dust Intrusions (SDIs), as are a wide range of countries in Europe. Alpha-1 antitrypsin (SERPINA1 gene) regulates the airway’s inflammatory response. This study analyzed the combined effect of SDI exposure and SERPINA1 variants on bacterial/fungal DNA concentrations in saliva and pharyngeal samples from asthmatic patients. Bacterial and fungal DNAs were quantified by qPCR in 211 asthmatic patients (GEMAS study), grouped based on their exposure to daily PM₁₀ concentrations. Associations between SDI exposure, microbial DNA concentrations, and nine variants in SERPINA1 were tested using linear regression models adjusted for confounders. The ratio between bacterial and fungal DNA was similar in saliva and pharyngeal samples. SDI exposure for 1–3 days was enough to observe significant microbial DNA change. Increased bacterial DNA concentration was detected when SDI exposure occurred 4–10 days prior to sampling, while exposure between days 1 and 3 led to a reduction in the fungal DNA concentration. The T-allele of SERPINA1 SNV rs2854254 prevented the increase in the bacterial/fungal DNA ratio in pharyngeal samples after SDI exposure. The bacterial/fungal DNA ratio represents a potential tool to monitor changes in the microbiome of asthmatic patients. Full article

Forecasting particulate matter with a diameter of 2.5 μm (PM_2.5) is critical due to its significant effects on both human health and the environment. While ground-based pollution measurement stations provide highly accurate PM_2.5 data, their limited number and geographic coverage present significant challenges. Recently, the use of aerosol optical depth (AOD) has emerged as a viable alternative for estimating PM_2.5 levels, offering a broader spatial coverage and higher resolution. Concurrently, long short-term memory (LSTM) models have shown considerable promise in enhancing air quality predictions, often outperforming other prediction techniques. To address these challenges, this study leverages geographic information systems (GIS), remote sensing (RS), and a hybrid LSTM architecture to predict PM_2.5 concentrations. Training LSTM models, however, is an NP-hard problem, with gradient-based methods facing limitations such as getting trapped in local minima, high computational costs, and the need for continuous objective functions. To overcome these issues, we propose integrating the novel orchard algorithm (OA) with LSTM to optimize air pollution forecasting. This paper utilizes meteorological data, topographical features, PM_2.5 pollution levels, and satellite imagery from the city of Tehran. Data preparation processes include noise reduction, spatial interpolation, and addressing missing data. The performance of the proposed OA-LSTM model is compared to five advanced machine learning (ML) algorithms. The proposed OA-LSTM model achieved the lowest root mean square error (RMSE) value of 3.01 µg/m³ and the highest coefficient of determination (R²) value of 0.88, underscoring its effectiveness compared to other models. This paper employs a binary OA method for sensitivity analysis, optimizing feature selection by minimizing prediction error while retaining critical predictors through a penalty-based objective function. The generated maps reveal higher PM_2.5 concentrations in autumn and winter compared to spring and summer, with northern and central areas showing the highest pollution levels. Full article

The incorporation of rice straw (RS) and Chinese milk vetch (CMV) with reduced chemical fertilizers (CFs) is a viable solution to reduce the dependency on CF. However, limited research has been conducted to investigate the impact of CMV and RS with reduced CF on rice production. A field trial was conducted from 2018 to 2021 with six treatments: CK (no fertilizer), F100 (100% NPK fertilizer (CF)), MSF100 (100% CF+CMV and RS incorporation), MSF80 (80% CF+CMV+RS), MSF60 (60% CF+CMV+RS), and MSF40 (40% CF+CMV+RS). The results revealed that compared with the F100, the MSF80 treatment maintained a significantly higher mean grain yield over the four years, with an increase of 5.8~24.5%. MSF80 treatment also improved nitrogen (N), phosphorus (P), and potassium (K) use efficiencies, sustainable yield index, and partial factor productivity. Soil organic matter (SOM), total nitrogen (TN), ammonium N (NH₄⁺-N), nitrate N (NO₃⁻-N), available phosphorus (AP), and available potassium (AK) contents were significantly enhanced under MSF80 across different growth stages in both 2020 and 2021 seasons over F100. Pearson correlation analysis revealed a strong positive correlation among SOM, TN, NH₄⁺-N, AP, AK, and rice yield. Additionally, Partial Least Squares Path Modeling (PLS-PM) demonstrated significant relationships between organic amendments, soil nutrients, nutrient uptake, and yield. The above findings suggest that combining RS returning with CMV incorporation is a long-term sustainable strategy for maintaining soil health, and it could reduce fertilizer addition by 20% without prejudicing rice grain yield under a rice-green manure rotation system. Full article

The accurate estimation of reference crop evapotranspiration (ET₀) is essential for crop water consumption modeling and agricultural water resource management. In the present study, three bionic algorithms (aquila optimizer (AO), tuna swarm optimization (TSO), and sparrow search algorithm (SSA)) were combined with an extreme learning machine (ELM) model to form three mixed models (AO-ELM, TSO-ELM, and SSA-ELM). The accuracy of the ET₀ estimates for five climate regions in China from 1970 to 2019 was evaluated using the FAO-56 Penman–Monteith (P-M) equation. The results showed that the predicted values of the three mixed models and the ELM model fitted the P-M calculated values well. R² and RMSE were 0.7654–0.9864 and 0.1271–0.7842 mm·d⁻¹, respectively, for which the prediction accuracy of the AO-ELM model was the highest. The performance of the AO-ELM combination5 (maximum temperature (T_max), minimum temperature (T_min), total solar radiation (R_s), sunshine duration (n)) was most significantly improved on the basis of the ELM model. The prediction accuracy for the stations in the plateau mountain climate (PMC) region was the best, while the prediction accuracy for the stations in the tropical monsoon climate region (TPMC) was the worst. In addition to the wind speed (U₂) in the temperate continental climate region (TCC)—which was the largest variable affecting ET₀—n, R_a, and total solar radiation (R_s) in the other climate regions were more important than relative humidity (RH) and wind speed (U₂) in predicting ET₀. Therefore, AO-ELM4 was selected for the TCC region (with T_max, T_min, R_s, and U₂ as inputs) and AO-ELM5 (with T_max, T_min, R_s, and n as inputs) was selected for the TMC, PMC, SMC, and TPMC regions when determining the best model for each climate region with limited meteorological data. Full article

The FAO56 Penman–Monteith model (FAO56-PM) is widely used for estimating reference crop evapotranspiration (ET₀). However, key variables such as shortwave radiation (R_s) and net longwave radiation (R_ln) are often unavailable at most weather stations. While previous studies have focused on calibrating R_s, the influence of large R_ln, particularly in high-altitude regions with thin air, remains unexplored. This study investigates this issue by using observed data from Bange in central Tibet to identify the optimal methods for estimating R_s and R_ln to accurately calculate ET₀. The findings reveal that the average daily R_ln was 8.172 MJ m⁻² d⁻¹ at Bange, much larger than that at the same latitude. The original FAO56-PM model may produce seemingly accurate ET₀ estimates due to compensating errors: underestimated R_ln offsetting underestimated net shortwave radiation (R_sn). Merely calibrating R_s does not improve ET₀ accuracy but may exacerbate errors. The Liu-S was the empirical model for R_s estimation calibrated by parameterization over the Tibetan Plateau and the Allen-LC was the empirical model for R_ln estimation calibrated by local measurements in central Tibet. The combination of the Liu-S and Allen-LC methods showed much-improved performance in ET₀ estimation, yielding a high Nash–Sutcliffe Efficiency (NSE) of 0.889 and a low relative error of −5.7%. This strategy is indicated as optimal for ET₀ estimation in central Tibet. Trend analysis based on this optimal strategy indicates significant increases in ET₀ in central Tibet from 2000 to 2020, with projections suggesting a continued rise through 2100 under climate change scenarios, though with increasing uncertainty over time. However, the rapidly increasing trends in precipitation will lead to decreasing trends in agricultural water use for highland parley production in central Tibet under climate change scenarios. The findings in this study provide critical information for irrigation planning to achieve sustainable agricultural production over the Tibetan Plateau. Full article

As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM_2.5. Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km². The increase in PM_2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning. Full article

Farmland soil respiration (Rs) significantly impacts the global carbon (C) cycle. Although nitrogen (N) can promote crop growth and increase yields, its relationship with Rs and its constituents, including autotrophic respiration (Ra) and heterotrophic respiration (Rh), remains unclear. Therefore, a field study was carried out in a cabbage (Brassica pekinensis Rupr) system to probe the impact of N addition on Rs, Ra, and Rh. Five levels of N addition, including 0 kg N hm⁻²·yr⁻¹ (N0), 50 kg N hm⁻²·yr⁻¹ (N50), 100 kg N hm⁻²·yr⁻¹ (N100), 150 kg N hm⁻²·yr⁻¹ (N150), and 200 kg N hm⁻²·yr⁻¹ (N200), started in March 2022. The Rs (Ra and Rh) and soil samples were measured and collected twice a month. The findings revealed the following: (1) N fertilizer enhanced Ra while reducing Rs and Rh; (2) soil temperature (ST), belowground net primary productivity (BNPP), soil inorganic N (SIN), and soil total C/total N (C/N) were the significant elements influencing Ra, and microbial biomass carbon (MBC), SIN, and microbial diversity (MD) were the primary factors influencing Rh; (3) partial least squares-path models (PLS-PM) showed that ST and SIN directly impacted Rh, while ST and BNPP tangentially influenced Ra; (4) 150 kg N hm⁻²·yr⁻¹ was the ideal N addition rate for the cabbage in the region. In summary, the reactions of Ra and Rh to N fertilizer in the Northeast Plains are distinct. To comprehend the underlying processes of Rs, Ra, and Rh, further long-term trials involving various amounts of N addition are required, particularly concerning worsening N deposition. Full article

Northeast China (NEC) is one of the most important national agricultural production bases, and its agricultural water dynamics are essential for food security and sustainable agricultural development. However, the dynamics of long-term annual crop-specific agricultural water and its crop type and climate impacts remain largely unknown, compromising water-saving practices and water-efficiency agricultural management in this vital area. Thus, this study used multi-source data of the crop type, climate factors, and the digital elevation model (DEM), and multiple digital agriculture technologies of remote sensing (RS), the geographic information system (GIS), the Soil Conservation Service of the United States Department of Agriculture (USDA-SCS) model, the Food and Agriculture Organization of the United Nations Penman–Monteith (FAO P-M) model, and the water supply–demand index (M) to map the annual spatiotemporal distribution of effective precipitation (Pe), crop water requirement (ET_c), irrigation water requirement (IWR), and the supply–demand situation in the NEC from 2000 to 2020. The study further analyzed the impacts of the crop type and climate changes on agricultural water dynamics and revealed the reasons and policy implications for their spatiotemporal heterogeneity. The results indicated that the annual average Pe, ET_c, IWR, and M increased by 1.56%/a, 0.74%/a, 0.42%/a, and 0.83%/a in the NEC, respectively. Crop-specifically, the annual average Pe increased by 1.15%/a, 2.04%/a, and 2.09%/a, ET_c decreased by 0.46%/a, 0.79%/a, and 0.89%/a, IWR decreased by 1.03%/a, 1.32%/a, and 3.42%/a, and M increased by 1.48%/a, 2.67%/a, and 2.87%/a for maize, rice, and soybean, respectively. Although the ET_c and IWR for all crops decreased, regional averages still increased due to the expansion of water-intensive maize and rice. The crop type and climate changes jointly influenced agricultural water dynamics. Crop type transfer contributed 39.28% and 41.25% of the total IWR increase, and the remaining 60.72% and 58.75% were caused by cropland expansion in the NEC from 2000 to 2010 and 2010 to 2020, respectively. ET_c and IWR increased with increasing temperature and solar radiation, and increasing precipitation led to decreasing IWR in the NEC. The adjustment of crop planting structure and the implementation of water-saving practices need to comprehensively consider the spatiotemporally heterogeneous impacts of crop and climate changes on agricultural water dynamics. The findings of this study can aid RS-GIS-based agricultural water simulations and applications and support the scientific basis for agricultural water management and sustainable agricultural development. Full article

The accurate mapping of crop types is crucial for ensuring food security. Remote Sensing (RS) satellite data have emerged as a promising tool in this field, offering broad spatial coverage and high temporal frequency. However, there is still a growing need for accurate crop type classification methods using RS data due to the high intra- and inter-class variability of crops. In this vein, the current study proposed a novel Parallel-Cascaded ensemble structure (Pa-PCA-Ca) with seven target classes in Google Earth Engine (GEE). The Pa section consisted of five parallel branches, each generating Probability Maps (PMs) for different target classes using multi-temporal Sentinel-1/2 and Landsat-8/9 satellite images, along with Machine Learning (ML) models. The PMs exhibited high correlation within each target class, necessitating the use of the most relevant information to reduce the input dimensionality in the Ca part. Thereby, Principal Component Analysis (PCA) was employed to extract the top uncorrelated components. These components were then utilized in the Ca structure, and the final classification was performed using another ML model referred to as the Meta-model. The Pa-PCA-Ca model was evaluated using in-situ data collected from extensive field surveys in the northwest part of Iran. The results demonstrated the superior performance of the proposed structure, achieving an Overall Accuracy (OA) of 96.25% and a Kappa coefficient of 0.955. The incorporation of PCA led to an OA improvement of over 6%. Furthermore, the proposed model significantly outperformed conventional classification approaches, which simply stack RS data sources and feed them to a single ML model, resulting in a 10% increase in OA. Full article

Transport models are extremely important to map thousands of proteins and their interactions inside a cell. The transport pathways of luminal and at least initially soluble secretory proteins synthesized in the endoplasmic reticulum can be divided into two groups: the so-called constitutive secretory pathway and regulated secretion (RS) pathway, in which the RS proteins pass through the Golgi complex and are accumulated into storage/secretion granules (SGs). Their contents are released when stimuli trigger the fusion of SGs with the plasma membrane (PM). In specialized exocrine, endocrine, and nerve cells, the RS proteins pass through the baso-lateral plasmalemma. In polarized cells, the RS proteins secrete through the apical PM. This exocytosis of the RS proteins increases in response to external stimuli. Here, we analyze RS in goblet cells to try to understand the transport model that can be used for the explanation of the literature data related to the intracellular transport of their mucins. Full article

Stroke is a leading cause of disability and mortality, resulting in substantial socio-economic burden for healthcare systems. With advances in artificial intelligence, visual image information can be processed into numerous quantitative features in an objective, repeatable and high-throughput fashion, in a process known as radiomics analysis (RA). Recently, investigators have attempted to apply RA to stroke neuroimaging in the hope of promoting personalized precision medicine. This review aimed to evaluate the role of RA as an adjuvant tool in the prognosis of disability after stroke. We conducted a systematic review following the PRISMA guidelines, searching PubMed and Embase using the keywords: ‘magnetic resonance imaging (MRI)’, ‘radiomics’, and ‘stroke’. The PROBAST tool was used to assess the risk of bias. Radiomics quality score (RQS) was also applied to evaluate the methodological quality of radiomics studies. Of the 150 abstracts returned by electronic literature research, 6 studies fulfilled the inclusion criteria. Five studies evaluated predictive value for different predictive models (PMs). In all studies, the combined PMs consisting of clinical and radiomics features have achieved the best predictive performance compared to PMs based only on clinical or radiomics features, the results varying from an area under the ROC curve (AUC) of 0.80 (95% CI, 0.75–0.86) to an AUC of 0.92 (95% CI, 0.87–0.97). The median RQS of the included studies was 15, reflecting a moderate methodological quality. Assessing the risk of bias using PROBAST, potential high risk of bias in participants selection was identified. Our findings suggest that combined models integrating both clinical and advanced imaging variables seem to better predict the patients’ disability outcome group (favorable outcome: modified Rankin scale (mRS) ≤ 2 and unfavorable outcome: mRS > 2) at three and six months after stroke. Although radiomics studies’ findings are significant in research field, these results should be validated in multiple clinical settings in order to help clinicians to provide individual patients with optimal tailor-made treatment. Full article

Accurate estimation of evapotranspiration (ET) is vital for water resource development, planning and management, particularly in the present global warming context. A large number of empirical ET models have been developed for estimating ET. The main limitations of this method are that it requires several meteorological variables and an extensive data span to comprehend the ET pattern accurately, which is not available in most developing countries. The efficiency of 30 empirical ET models has been evaluated in this study to rank them for Pakistan to facilitate the selection of suitable models according to data availability. Princeton Global Meteorological Forcing daily climate data with a 0.25° × 0.25° resolution for 1948–2016 were utilized. The ET estimated using Penman–Monteith (PM) was considered as the reference. Multi-criteria group decision making (MCGDM) was used to rank the models for Pakistan. The results showed the temperature-based Hamon as the best model for most of Pakistan, followed by Hargreaves–Samani and Penman models. Hamon also showed the best performance in terms of different statistical metrics used in the study with a mean bias (PBias) of −50.2%, mean error (ME) of −1.62 mm and correlation coefficient (R2) of 0.65. Ivan showed the best performance among the humidity-based models, Irmak-RS and Ritch among the radiation-based models and Penman among the mass transfer-based models. Northern Pakistan was the most heterogeneous region in the relative performance of different ET models. Full article

The accurate calculation of reference evapotranspiration (ET₀) is the fundamental basis for the sustainable use of water resources and drought assessment. In this study, we evaluate the performance of the second-generation China Meteorological Administration Land Data Assimilation System (CLDAS) and two simplified machine learning models to estimate ET₀ when meteorological data are insufficient in China. The results show that, when a weather station lacks global solar radiation (R_s) data, the machine learning methods obtain better results in their estimation of ET₀. However, when the meteorological station lacks relative humidity (RH) and 2 m wind speed (U₂) data, using RH_CLD and U_2CLD from the CLDAS to estimate ET₀ and to replace the meteorological station data obtains better results. When all the data from the meteorological station are missing, estimating ET₀ using the CLDAS data still produces relevant results. In addition, the PM–CLDAS method (a calculation method based on the Penman–Monteith formula and using the CLDAS data) exhibits a relatively stable performance under different combinations of meteorological inputs, except in the southern humid tropical zone and the Qinghai–Tibet Plateau zone. Full article