Search Results (1,388)

Crop aeroponic cultivation still faces issues such as insufficient precision in water supply control and scientifically-based irrigation scheduling. To address this challenge, the present study aims to establish a precision irrigation protocol adapted to the characteristics of crop aeroponic cultivation. Using coriander (Coriandrum sativum L.) as the experimental subject, crop water requirements were estimated utilizing both the FAO56 P-M equation and its revised form. The RMSE between the water requirement measured values and the calculated values using the P-M formula is 2.12 mm, the MAE is 2.0 mm, and the MAPE is 14.29%. The RMSE between the water requirement measured values and the calculated values using the revised P-M formula is 0.88 mm, the MAE is 0.82 mm, and the MAPE is 5.78%. The results indicate that the water requirement values calculated using the revised P-M formula are closer to the measured values. For model development, this study used coriander evapotranspiration as a basis. Major environmental variables influencing water requirement were selected as input features, and the daily reference water requirement served as the output. Three modeling approaches were implemented: Random Forest (RF), Bagging, and M5P Model Tree algorithms. The results indicate that, in comparing various input combinations (C1: air temperature, relative humidity, atmospheric pressure, wind speed, radiation, photoperiod; C2: air temperature, relative humidity, wind speed, radiation; C3: air temperature, relative humidity, radiation), the RF model based on C1 input demonstrated superior performance with RMSE = 0.121 mm/d, MAE = 0.134 mm/d, MAPE = 2.123%, and R² = 0.971. It significantly outperforms the RF models with other input combinations, as well as the Bagging and M5P models across all input scenarios, in terms of convergence rate, determination coefficient, and comprehensive performance. Its predictions aligned more closely with observed data, showing enhanced accuracy and adaptability. This optimized prediction model demonstrates particular suitability for forecasting water requirements in aeroponic coriander production and provides theoretical support for efficient, intelligent water-saving management in crop aeroponic cultivation. Full article

Municipal wastewater treatment relies primarily on biological methods, yet effective disposal of residual sludge remains a major challenge. Converting sludge into biochar via oxygen-limited pyrolysis presents a novel approach for waste resource recovery. This study prepared sludge-based biochar (SBC) through one-step pyrolysis of sewage sludge and applied it to activate peroxymonosulfate (PMS) for degrading diverse contaminants. Characterization (SEM, XPS, FTIR) revealed abundant pore structures and diverse surface functional groups on SBC. Using Acid Orange 7 (AO7) as the target pollutant, SBC effectively degraded AO7 across pH 3.0–9.0 and catalyst dosages (0.2–2.0 g·L⁻¹), achieving a maximum observed rate constant (k_obs) of 0.3108 min^–1. Salinity and common anions showed negligible inhibition on AO7 degradation. SBC maintained 95% degradation efficiency after four reuse cycles and effectively degraded sulfamethoxazole, sulfamethazine, and rhodamine B besides AO7. Mechanistic studies (chemical quenching and ESR) identified singlet oxygen (¹O₂) and superoxide radicals (O₂^•- ) as the dominant reactive oxygen species for AO7 degradation. XPS indicated a 39% reduction in surface carbonyl group content after cycling, contributing to activity decline. LC-MS identified five intermediates, suggesting a potential degradation pathway driven by SBC/PMS system. ECOSAR model predictions indicated significantly reduced biotoxicity of the degradation products compared to AO7. This work provides a strategy for preparing sludge-derived catalysts for PMS activation and pollutant degradation, enabling effective solid waste resource utilization. Full article

Accurate spatial interpolation of environmental data requires utilizing flexible models that can capture complex spatial patterns. In this paper, we present two improved dual kriging (DK) models comprising a nonlinear trend function that combines Gaussian radial basis functions with a first-order polynomial. The proposed model, DK–RBFP, and its extension, DK–RBFPGA, which includes k-means clustering and a genetic algorithm for parameter optimization, respectively, exhibit enhanced performance in capturing spatial variation. The complete monotonicity of the covariance function and the strict positive definiteness of the coefficient matrix provide theoretical support for the uniqueness of the DK solution. When applied to datasets of PM_2.5 concentrations for northern Thailand, both models perform better than the conventional DK model using a second-order polynomial trend (DK–POLY), as evidenced by accuracy metrics including the mean absolute percentage error (MAPE), the mean squared error (MSE), and the root mean square error (RMSE). The outcomes indicate that integrating nonlinear trend components with data-driven optimization significantly enhances accuracy and flexibility in environmental spatial predictions. Full article

Background: Breast cancer (BC) is the most common cancer and the leading cause of cancer death in women. Hereditary BC risk accounts for 25% of all cases. Pathological variants in known BC precursor genes explain only about 30% of hereditary BC cases, while the underlying genetic factors in most families remain unknown. Identifying hereditary cancer risk factors will help improve genetic counseling, cancer prevention, and cancer care. Methods: Here, we used whole-exome sequencing (WES) to identify genetic variants in 105 Vietnamese patients with BC and 50 healthy women. BC-associated variants were screened by the Franklin software and the criteria of the American College of Medical Genetics and Genomics (ACMG) and evaluated based on in silico analysis. Results: In total, 56 variants were identified in 37 genes associated with BC, including ACVR1B, APC, AR, ARFGEF1, ATM, ATR, BARD1, BLM, BRCA1, BRCA2, CASP8, CASR, CHD8, CTNNB1, ESR1, FAN1, FGFR2, HMMR, KLLN, LZTR1, MCPH1, MLH1, MSH2, MSH3, MSH6, NF1, PMS2, PRKN, RAD54L, RB1CC1, RECQL, SLC22A18, SLX4, SPTBN1, TP53, WRN, and XRCC3 in 41 patients. Among them, 12 variants were novel, and 10 variants were assessed as pathogenic/likely pathogenic by ACMG and ClinVar. Variants of uncertain significance (VUS) were evaluated using in silico prediction software to predict whether they are likely to cause the disease in patients. Conclusions: This is the first WES study to identify BC-associated genetic variants in Vietnamese patients, providing a comprehensive database of BC susceptibility gene variants. We suggest using WES as a tool to identify genetic variants in BC patients for risk prediction and treatment guidance. Full article

Accurate and high-resolution spatio-temporal prediction of PM_2.5 concentrations remains a significant challenge for air pollution early warning and prevention. Advanced artificial intelligence (AI) technologies, however, offer promising solutions to this problem. A spatio-temporal prediction model is designed in this study, which is built upon a seq2seq architecture. This model employs an improved graph convolutional neural network to capture spatially dependent features, integrates time-series information through a gated recurrent unit, and incorporates an attention mechanism to achieve PM_2.5 concentration prediction. Benefiting from high-resolution satellite remote sensing data, the regional, multi-step and high-resolution prediction of PM_2.5 concentration in Beijing has been performed. To validate the model’s performance, ablation experiments are conducted, and the model is compared with other advanced prediction models. The experimental results show our proposed Spatio-Temporal Graph Convolutional Network with Attention Mechanism (STGCA) outperforms comparison models in multi-step forecasting, achieving root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of 4.21, 3.11 and 11.41% for the first step, respectively. For subsequent steps, the model also shows significant improvements. For subsequent steps, the model also shows significant improvements, with RMSE, MAE and MAPE values of 5.08, 3.69 and 13.34% for the second step and 6.54, 4.61 and 16.62% for the third step, respectively. Additionally, STGCA achieves the index of agreement (IA) values of 0.98, 0.97 and 0.95, as well as Theil’s inequality coefficient (TIC) values of 0.06, 0.08 and 0.10 proving its superiority. These results demonstrate that the proposed model offers an efficient technical approach for smart air pollution forecasting and warning in the future. Full article

Particulate matter, particularly PM_2.5, poses a significant threat to public health due to its ability to disperse widely and its detrimental impact on the respiratory and circulatory systems upon inhalation. Consequently, it is imperative to maintain regular monitoring and assessment of particulate matter levels to anticipate air pollution events and promptly mitigate their adverse effects. However, predicting air quality is inherently complex, given the multitude of variables that influence it. Deep learning models, renowned for their ability to capture nonlinear relationships, offer a promising approach to address this challenge, with hybrid architectures demonstrating enhanced performance. This study aims to develop and evaluate a hybrid model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for forecasting PM_2.5 levels in India, Milan, and Frankfurt. A comparative analysis with established deep learning and machine learning techniques substantiates the superior predictive capabilities of the proposed CNN-RNN model. The findings underscore its potential as an effective tool for air quality prediction, with implications for informed decision-making and proactive intervention strategies to safeguard public health. Full article

This review aims to provide a comprehensive overview of how oxidative stress drives inflammation, structural remodeling, and clinical expression of childhood asthma, while critically appraising emerging redox-sensitive biomarkers and antioxidant-focused preventive and therapeutic strategies. Oxidative stress arises when reactive oxygen species (ROS) and reactive nitrogen species (RNS) outpace airway defenses. This surplus provokes airway inflammation: ROS/RNS activate nuclear factor kappa-B (NF-κB) and activator protein-1 (AP-1), recruit eosinophils and neutrophils, and amplify type-2 cytokines. Normally, an antioxidant network—glutathione (GSH), enzymes such as catalase (CAT) and superoxide dismutase (SOD), and nuclear factor erythroid 2-related factor 2 (Nrf2)—maintains redox balance. Prenatal and early exposure to fine particulate matter <2.5 micrometers (µm) (PM_2.5), aeroallergens, and tobacco smoke, together with polymorphisms in glutathione S-transferase P1 (GSTP1) and CAT, overwhelm these defenses, driving epithelial damage, airway remodeling, and corticosteroid resistance—the core of childhood asthma pathogenesis. Clinically, biomarkers such as exhaled 8-isoprostane, hydrogen peroxide (H₂O₂), and fractional exhaled nitric oxide (FeNO) surge during exacerbations and predict relapses. Therapeutic avenues include Mediterranean-style diet, regular aerobic exercise, pharmacological Nrf2 activators, GSH precursors, and mitochondria-targeted antioxidants; early trials report improved lung function and fewer attacks. Ongoing translational research remains imperative to substantiate these approaches and to enable the personalization of therapy through individual redox status and genetic susceptibility, ultimately transforming the care and prognosis of pediatric asthma. Full article

Capsule-based self-healing technologies offer a promising solution to extend pavement service life without requiring external activation. The effect of the capsule content on the mechanical behaviour of self-healing asphalt mixtures still needs to be understood. This study presents a numerical evaluation of the isolated effect of incorporating capsules containing encapsulated rejuvenators, at different volume contents, on the stiffness and strength of asphalt mixtures through a three-dimensional discrete-based programme (VirtualPM3DLab), which has been shown to predict well the experimental behaviour of asphalt mixtures. Uniaxial tension–compression cyclic and monotonic tensile tests on notched specimens are carried out for three capsule contents commonly adopted in experimental investigations (0.30, 0.75, and 1.25 wt.%). The results show that the effect on the stiffness modulus progressively increases as the capsule content grows in the asphalt mixture, with a reduction ranging from 4.3% to 12.3%. At the same time, the phase angle is marginally affected. The capsule continuum equivalent Young’s modulus has minimum influence on the overall rheological response, suggesting that the most critical parameter affecting asphalt mixture stiffness is the capsule content. Finally, while the peak tensile strength shows a maximum reduction of 12.4% at the highest capsule content, the stress–strain behaviour and damage evolution of the specimens remain largely unaffected. Most damaged contacts, which mainly include aggregate–mastic and mastic–mastic contacts, are highly localised around the notch tips. Contacts involving capsules remained intact during early and intermediate loading stages and only fractured during the final damage stage, suggesting a delayed activation consistent with the design of healing systems. The findings suggest that capsules within the studied contents may have a moderate impact on the mechanical properties of asphalt mixtures, especially for high-volume contents. For this reason, contents higher than 0.75 wt.% should be applied with caution. Full article

Air pollution is steadily increasing due to industrialization, economic activities, and transportation. High levels pose a significant threat to human health and well-being worldwide. Saudi Arabia is a growing country with air quality indices ranging from moderate to unhealthy. Although there are many monitoring stations distributed throughout the country, mathematical modeling of air pollution is still crucial for health and environmental decision-making. From this perspective, in this study, a data-driven approach based on pollutant records and a Deep Learning (DL) Long Short-Term Memory (LSTM) algorithm is carried out to perform temporal modeling of selected pollutants (PM₁₀, PM_2.5, CO and O₃) based on time series combined with a spatial modeling focused on selected cities (Riyadh, Jeddah, Mecca, Rabigh, Abha, Dammam and Taif), covering ~48% of the total population of the country. The best forecasts were provided by LSTM in cases where the datasets used were of relatively large size. Numerically, the obtained performance metrics such as the coefficient of determination (R²) ranged from 0.2425 to 0.8073. The best LSTM results were compared to those provided by two ensemble methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), where the merits of LSTM were confirmed mainly in terms of its ability to capture hidden relationships. We also found that overall, meteorological factors showed a weak association with pollutant concentrations, with ambient temperature exerting a moderate influence. However, incorporating ambient temperature into LSTM models did not lead to a significant improvement in predictive accuracy. The developed approach can be used to support decision-making in environmental and health domains, as well as to monitor pollutant concentrations based on historical time series records. Full article

Skid resistance, or friction, on a road surface is a critical parameter in functional highway assessments, given its direct relationships with safety and accident frequency. Therefore, road administrations must collect friction data across their road networks to ensure safe roads for users. In addition, having a predictive model of skid resistance for each road section is essential for an efficient pavement management system (PMS). Traditionally, road authorities disregard rural roads, since they are more focused on freeways and traffic-intense roads. This study develops a model for predicting minimum-available skid resistance, which occurs in summer, measured using the Sideway-force Coefficient Routine Investigation Machine (SCRIM), on bituminous pavements in the single-carriageway road network of the Province of Gipuzkoa, Spain. To this end, traffic volume data available in the PMS of the Provincial Council of Gipuzkoa, such as the annual average daily traffic (AADT) and the AADT of heavy vehicles (AADT.HV), were uniquely used to forecast skid-resistance values collected in summer. Additionally, a methodology for eliminating outliers is proposed. Despite the simplicity of the model, which does not include information about the materials at the surface layer, a coefficient of determination (R²) of 0.439 was achieved. This model can help road authorities identify the roads for which lower skid-resistance values are most likely to occur, allowing them to focus their attention and efforts on these roads, which are key infrastructure in rural areas. Full article

Particulate matter (PM), a complex mixture of solid particles and liquid droplets, originates from both natural sources, such as sand, pollen, and marine salts, and anthropogenic activities, including vehicle emissions and industrial processes. While PM itself is not inherently toxic in all its forms, it often acts as a carrier of xenobiotic toxicants, such as heavy metals and organic pollutants, which adhere to its surface. This combination can result in synergistic toxic effects, significantly enhancing the potential harm to biological systems. Due to its small size and composition, PM can penetrate deep into the respiratory tract, acting as a physical “shuttle” that facilitates the distribution and bioavailability of toxic substances to distant organs. The omnipresence of PM in the environment leads to unavoidable and constant exposure, contributing to increased morbidity and mortality rates, particularly among vulnerable populations like the elderly, children, and individuals with pre-existing health conditions. This exposure also imposes a substantial financial burden on healthcare systems, as treating PM-related illnesses requires significant medical resources and leads to higher healthcare costs. Addressing these challenges necessitates effective mitigation strategies, including reducing PM exposure, improving air quality, and exploring novel approaches such as AI-based exposure prediction and nutritional interventions to protect public health and minimize the adverse effects of PM pollution. Full article

In regions characterized by complex terrain and diverse pollution sources, high-precision air pollution prediction remains challenging due to nonlinear spatiotemporal coupling and the difficulty of modeling local pollutant agglomeration. To address these issues, this study proposes a CNN–LSTM–Transformer multimodal prediction framework integrated with Bayesian Optimization. First, the Local Moran’s Index (LMI) is introduced as a spatial perception feature and concatenated with pollutant concentration sequences before being input into the CNN module. This design enhances the model’s ability to identify local pollutant clustering and spatial heterogeneity. Second, the LSTM architecture adopts a dual-channel structure: the main channel employs bidirectional LSTM to extract temporal dependencies, while the auxiliary channel uses unidirectional LSTM to capture evolutionary trends. A Transformer with a multi-head attention mechanism is then introduced to perform global modeling. Bayesian Optimization is employed to automatically adjust key hyperparameters, thereby improving the model’s stability and convergence efficiency. Empirical results based on atmospheric pollution monitoring data from Sichuan Province during 2021–2024 demonstrate that the proposed model outperforms various mainstream methods in predicting six pollutants in Chengdu. For instance, the MAE for PM_2.5 decreased by 14.9–22.1%, while the coefficient of determination (R²) remained stable between 87% and 89%. The accuracy decay rate across four-day forecasts was controlled within 12.4%. Furthermore, in PM_2.5 generalization prediction tasks across four other cities—Yibin, Zigong, Nanchong, and Mianyang—the model exhibited superior stability and robustness, achieving an average R² of 87.4%. These findings highlight the model’s long-term stability and regional generalization capability, offering reliable technical support for air pollution prediction and control strategies in Sichuan Province and potentially beyond. Full article

In the modern era of HIV treatment, people co-infected with HIV and HBV still face poor liver outcomes, including liver fibrosis, liver cirrhosis, and hepatocellular carcinoma. We investigated baseline characteristics and long-term liver function outcomes in 435 people living with HIV and HBV co-infection, focusing on HCC-associated point mutations (PMs) and PreS region deletion mutations. PMs were present in 72.9% of participants and were associated with male predominance, lower HBV genotype C prevalence, reduced HBV DNA and HBeAg levels, and higher HBsAg and HBeAb positivity. However, PMs did not significantly impact liver function or fibrosis progression over six years of ART follow-up. In contrast, PreS deletions were found in 21.8% of cases and stratified into PreS1, PreS2, and PreS1+2 deletions. PreS2 and PreS1+2 deletions were linked to older age, higher HBsAg and AFP levels, elevated liver enzymes, and lower platelet counts. These groups also exhibited significantly worse liver fibrosis markers (APRI and FIB-4), with PreS2 deletions consistently showing the highest values throughout the follow-up. Despite the initial improvement with ART, patients with PreS2 and PreS1+2 deletions maintained higher fibrosis and cirrhosis risks over six years. In summary, while PMs were not predictive of liver disease progression, PreS deletion mutations (especially in the PreS2 region) were associated with poorer liver outcomes, indicating their potential as biomarkers for fibrosis risk in co-infected individuals with long-term ART. Full article

This study aimed to evaluate the protective effects of Inula japonica leaf extract against PM₁₀-induced oxidative stress in normal human keratinocytes. Keratinocytes were pretreated with various concentrations of Inula japonica leaf extract and subsequently exposed to PM10. Cell viability, ROS production, gene and protein expression (qRT-PCR and Western blot), and UHPLC-MS profiling were assessed. Network pharmacology analysis was conducted using database-predicted compounds of Inulae Flos. The extract significantly reduced PM₁₀-induced ROS generation and restored the expression of epidermal barrier-related genes such as loricrin. It also inhibited phosphorylation of MAPKs (ERK, p38) and modulated apoptotic and inflammatory markers including Bax, p53, MMP-9, and COX-2. UHPLC-MS analysis identified eight compounds not previously reported in our earlier study, which may contribute to the extract’s protective effects. Inula japonica leaf extract exerts protective effects against PM₁₀-induced skin damage by reducing oxidative stress and inflammation in keratinocytes. These findings support its potential as a therapeutic candidate for pollution-related skin disorders. Full article

The aim of this study was to investigate the method of estimating actual crop evapotranspiration (ET_cact) in a greenhouse using other measured meteorological parameters when solar radiation (R_s) data are missing. The study estimated ET_cact of greenhouse green peppers by combining solar radiation estimation models with the Penman–Monteith (PM) model and evaluated model performance. The results showed that the prediction accuracy of the temperature-based solar radiation model was higher than the model based on sunshine hours in the Hexi Corridor region. The effect of the insulation cover on the incident solar radiation in the greenhouse is modeled by introducing a ramp function. In terms of crop coefficients (K_cb), the initial K_cb value of green peppers in the 2023 growing season was generally consistent with the updated FAO-56 standard values, whereas the initial K_cb values (0.17) were higher than the standard values in the 2023–2024 growing season. During the two growing seasons, the mid-stage K_cb values were 1.01 in the 2023 growing season and 0.82 in the 2023–2024 growing season. The study also found that PM–RT4, PM–RT5, and PM–RT6 models were all able to accurately predict the ET_cact of greenhouse green peppers during the 2023 growing season. The PM–RT4 model performed well in both growing seasons, with R² = 0.8101 in the 2023 growing season and R² = 0.7561 in the 2023–2024 growing season. Our research supports the PM–RT4 model as appropriate to estimate green pepper actual evapotranspiration in Gobi solar greenhouses (GSGs) and may be further used to improve irrigation scheduling for green peppers grown in GSGs. Full article