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Characterization of gut microbiota on gender and age groups bias in Thai patients with autism spectrum disorder
Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates
Abstract Improving the accuracy of reference evapotranspiration (RET) estimation is essential for effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The FAO-56 Penman-Monteith (PM-FAO56) model, a widely endorsed approach for RET estimation, often encounters limitations due to the lack of complete meteorological data. This study evaluates the performance of eight empirical models and four machine learning (ML) models, along with their hybrid counterparts, in estimating daily RET within the Gharb and Loukkos irrigated perimeters in Morocco. The ML models examined include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), with hybrid combinations of RF-M5P, RF-XGBoost, RF-LightGBM, and XGBoost-LightGBM. Six input combinations were created, utilizing Tmax, Tmin, RHmean, Rs, and U2, with the PM-FAO56 model serving as the benchmark. Model performance was assessed using four statistical indicators: Kling-Gupta efficiency index (KGE), coefficient of determination (R2), mean squared error (RMSE), and relative root squared error (RRSE). Results indicate that the Valiantzas 2013 (VAL2013b) model outperformed other empirical models across all stations, achieving high KGE and R2 values (0.95–0.97) and low RMSE (0.32–0.35 mm/day) and RRSE (8.14–10.30%). The XGBoost-LightGBM and RF-LightGBM hybrid models exhibited the highest accuracy (average RMSE of 0.015–0.097 mm/day), underscoring the potential of hybrid ML models for RET estimation in subhumid and semi-arid regions, thereby enhancing water resource management and irrigation scheduling.
Generation and propagation of high fecundity gene edited fine wool sheep by CRISPR/Cas9
Comparative response of fennel, ajwain, and anise in terms of osmolytes accumulation, ion imbalance, photosynthetic and growth functions under salinity
Electro-tactile modulation of muscle activation and intermuscular coordination in the human upper extremity
Exploring the utility of different bulking agents for speeding up the composting process of household kitchen waste
Abstract Household kitchen waste (HKW) is produced in large quantity and its management is difficult due to high moisture content and complex organic matter. Aerobic composting of HKW is an easy, efficient, cost-effective and eco-friendly method. This study is designed to achieve a zero-waste concept and to convert HKW. We optimized the type and size of three different bulking agents to speed up the composting process. The tested bulking agents were fallen leaves, sawdust and fly ash. The results showed a higher and longer thermophilic phase (55oC) for 11 days in C2. Higher moisture content (69%) and higher organic matter degradation (38.4%) were also observed in C2. The pH range in all compost treatments was 7-8.5, Electrical conductivity range was 1.8–3.55 mS/cm, C/N ratio range was 15.4–18.1, water holding capacity range was 3.25–4.3 g water/g dry sample, total potassium range was 1.52–1.61%, total phosphorous range was 0.83–1.14%. The highest germination index (119.1%) was also obtained in C2. The highest chili height (16.7 cm), greater number of leaves (20), greater shoot fresh weight (4.75 g) and root fresh weight (1.2 g) was obtained in the presence of C2. Similarly, greater water WHC (2.8 g water/g DW), higher porosity (55.49%) and higher aggregate stability (54.14%) of soil was also obtained by C2. This research effectively reduced the maturation time to 32 days and converted kitchen waste into compost (resource). This is a very practical idea for home composting and kitchen gardening to combat food security issues in developing countries.
Landslide hazard assessment of an urban agglomeration in central Guizhou Province based on an information value method and SVM, bagging, DNN algorithm
Machine learning validation of the AVAS classification compared to ultrasound mapping in a multicentre study
Plasma S100A8/A9 level predicts response to immune checkpoint inhibitors in patients with advanced non-small cell lung cancer
Trip route optimization based on bus transit using genetic algorithm with different crossover techniques: a case study in Konya/Türkiye
Sex-modulated association between thyroid stimulating hormone and informant-perceived anxiety in non-depressed older adults: Prediction models and relevant cutoff value
Abstract The aim of this study was to assess the association between thyroid function and perceived anxiety in non-depressed older adults. Non-depressed Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants with complete Thyroid Stimulating Hormone (TSH) and neuropsychiatric inventory (NPI/NPI-Q) were included. The association between anxiety and thyroid function was assessed by logistic regression and sex stratification. Restricted cubic splines were applied to evaluate non-linearity in the association. The median age of 2,114 eligible participants was 73 years (68–78), 1,117 (52.84%) were males, and the median TSH was 1.69 µIU/mL. There was a significant association between TSH and informant-perceived anxiety in the total study population (OR Model1 = 0.86, 95%CI 0.76–0.97, p = 0.011), even after adjusting for bio-demographical (adj.OR Model2 = 0.85, 95%CI 0.75–0.96, p = 0.007), and socio-cognitive confounders (adj.OR Model3 = 0.84, 95%CI 0.73–0.96, p = 0.009). Sex-stratification showed similar significant results in all male-specific models (OR Model1-male = 0.71, 95%CI: 0.58–0.85, p Model1-male < 0.001). In the general population and males, a TSH value of 2.4 µIU/dL was a significant cutoff under which anxiety odds were significantly high, even after adjusting for confounders. The sex-dependent association between TSH levels and perceived anxiety in non-depressed older adults is a novel finding that has to be further explored for a better understanding of the underlying neurobehavioral biology.