Search Results (14,285)

In the last two decades, South Korea has seen an increase in extreme rainfall coinciding with the proliferation of impermeable surfaces due to urban development. When underground drainage systems are overwhelmed, pluvial flooding can occur. Therefore, recognizing drainage systems as key flood-conditioning factors is vital for identifying flood-prone areas and developing predictive models in highly urbanized regions. This study evaluates and maps urban pluvial flood susceptibility in Seoul, South Korea using the machine learning techniques such as logistic regression (LR), random forest (RF), and support vector machines (SVM), and integrating traditional flood conditioning factors and drainage-related data. Together with known flooding points from 2010 to 2022, sixteen flood conditioning factors were selected, including the drainage-related parameters sewer pipe density (SPD) and distance to a storm drain (DSD). The RF model performed best (accuracy: 0.837, an area under the receiver operating characteristic curve (AUC): 0.902), and indicated that 32.65% of the study area has a high susceptibility to flooding. The accuracy and AUC were improved by 7.58% and 3.80%, respectively, after including the two drainage-related variables in the model. This research provides valuable insights for urban flood management, highlighting the primary causes of flooding in Seoul and identifying areas with heightened flood susceptibility, particularly relating to drainage infrastructure. Full article

This paper develops a penalized exponentially tilted (ET) likelihood to simultaneously estimate unknown parameters and select variables for growing dimensional models with missing response at random. The inverse probability weighted approach is employed to compensate for missing information and to ensure the consistency of parameter estimators. Based on the penalized ET likelihood, we construct an ET likelihood ratio statistic to test the contrast hypothesis of parameters. Under some wild conditions, we obtain the consistency, asymptotic properties, and oracle properties of parameter estimators and show that the constrained penalized ET likelihood ratio statistic for testing the contrast hypothesis possesses the Wilks’ property. Simulation studies are conducted to validate the finite sample performance of the proposed methodologies. Thyroid data taken from the First People’s Hospital of Yunnan Province is employed to illustrate the proposed methodologies. Full article

The automated and precise classification of lung and colon cancer from histopathological photos continues to pose a significant challenge in medical diagnosis, as current computer-aided diagnosis (CAD) systems are frequently constrained by their dependence on singular deep learning architectures, elevated computational complexity, and their ineffectiveness in utilising multiscale features. To this end, the present research introduces a CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction and feature selection to overcome the aforementioned constraints. Initially, it extracts deep attributes from two separate layers (pooling and fully connected) of three pre-trained CNNs (MobileNet, ResNet-18, and EfficientNetB0). Second, the system uses the benefits of canonical correlation analysis for dimensionality reduction in pooling layer attributes to reduce complexity. In addition, it integrates the dual-layer features to encapsulate both high- and low-level representations. Finally, to benefit from multiple deep network architectures while reducing classification complexity, the proposed CAD merges dual deep layer variables of the three CNNs and then applies the analysis of variance (ANOVA) and Chi-Squared for the selection of the most discriminative features from the integrated CNN architectures. The CAD is assessed on the LC25000 dataset leveraging eight distinct classifiers, encompassing various Support Vector Machine (SVM) variants, Decision Trees, Linear Discriminant Analysis, and k-nearest neighbours. The experimental results exhibited outstanding performance, attaining 99.8% classification accuracy with cubic SVM classifiers employing merely 50 ANOVA-selected features, exceeding the performance of individual CNNs while markedly diminishing computational complexity. The framework’s capacity to sustain exceptional accuracy with a limited feature set renders it especially advantageous for clinical applications where diagnostic precision and efficiency are critical. These findings confirm the efficacy of the multi-CNN, multi-layer methodology in enhancing cancer classification precision while mitigating the computational constraints of current systems. Full article

In the context of sustainable development, breaking free from resource endowment constraints and promoting energy transformation are long-term goals of concern. The digital economy empowers the development of the energy industry and provides a feasible path for improving energy efficiency. This article selects interprovincial panel data from China to analyze the direct and indirect impacts of China’s digital economy on green total factor energy efficiency (GTFEE), as well as spatial spillover effects. Based on the calculation of green total factor energy efficiency, static and dynamic panel models are used to analyze the direct impact of the digital economy on green total factor energy efficiency through index decomposition and threshold models, as well as the indirect impact of digital economy technology effects on it. The research results indicate that the direct impact of the digital economy on GTFEE exhibits a positive U-shaped effect. Indirect impact analysis shows that technological innovation has a significant dual threshold effect on the variables of green total factor energy technology efficiency index and green total factor energy technology progress index. Further analysis using the spatial Durbin model shows that the digital economy has nonlinear spatial spillover effects on GTFEE, with regional heterogeneity and resource endowment differences. Studying the impact of digital economy development on green all-factor energy efficiency is of great practical significance in order to propose suggestions for promoting green and sustainable development. Full article

Early sowing to avoid stress later in the season is limited by low early spring temperatures and unpredictable cold spells within recommended sowing dates. To achieve successful crop production, it is essential to understand plant stress responses, enabling breeders and producers to better address climate change challenges. Researching genetic variability for cold stress is key to developing cold-tolerant crops. In response, a study investigating the effects of low-temperature treatment and cold priming in the early vegetative development on soybean biomass, chlorophyll a fluorescence (ChlF) and pigment-related spectral reflectance indices (PR_SRIs) was conducted in a controlled environment with 12 soybean genotypes. Priming began 16 days after sowing (DAS), followed by a 48-h recovery and a subsequent 48-h low-temperature treatment. During priming and stress treatments, temperatures and relative air humidity were set to 10/5 °C and 70/90% (day/night), with a light intensity of 300 μmol/m²/s. The results showed that low temperatures negatively impacted biomass and physiological parameters, with priming having neutral or negative effects. The parameters ET₀/TR₀, RE₀/RC, TR₀/DI₀, F_m, F_v, ARI1, and ARI2 were identified as relatively appropriate non-destructive alternatives for biomass analysis, aiding in genotype screening and stress detection. Genotypic variation in response to cold stress suggests potential for selecting cold-tolerant varieties. Full article

Photosynthetic organisms are primary sources of marine-derived molecules, particularly ω3 fatty acids (FAs), which influence the quality of marine foods. It is reported that tropical organisms possess lower FA nutritional quality than those from colder oceans. However, the high biodiversity known for tropical areas may help compensate for this deficiency by producing a high diversity of molecules with nutritional benefits for the ecosystem. Here we addressed this aspect by analyzing the FA profiles of 20 photosynthetic organisms from the salty and warm Red Sea, a biodiversity hot spot, including cyanobacteria, eukaryotic microalgae, macroalgae, mangrove leaves, as well as three selected reef’s photosymbiotic zooxanthellate corals and jellyfish. Using direct transesterification, gas chromatography-mass spectrometry, FA absolute quantification, and nutritional indexes, we evaluated their lipid nutritional qualities. We observed interspecific and strain-specific variabilities in qualities, which the unique environmental conditions of the Red Sea may help to explain. Generally, eukaryotic microalgae exhibited the highest nutritional quality. The previously unanalyzed diatoms Leyanella sp. and Minutocellus sp. had the highest eicosapentaenoic acid (EPA) contents. The bioprospected Red Sea photobiota exhibited pharmaceutical and nutraceutical potential. By sourcing and quantifying these bioactive compounds, we highlight the untapped rich biodiversity of the Red Sea and showcase opportunities to harness these potentials. Full article

Complex diseases such as type 2 diabetes are influenced by both environmental and genetic risk factors, leading to a growing interest in identifying gene–environment (G × E) interactions. A three-step variable selection method for single-index varying-coefficients models was proposed in recent research. This method selects varying and constant-effect genetic predictors, as well as non-zero loading parameters, to identify genetic factors that interact linearly or nonlinearly with a mixture of environmental factors to influence disease risk. In this paper, we extend this approach to a binary response setting given that many complex human diseases are binary traits. We also establish the oracle property for our variable selection method, demonstrating that it performs as well as if the correct sub-model were known in advance. Additionally, we assess the performance of our method through finite-sample simulations with both continuous and discrete gene variables. Finally, we apply our approach to a type 2 diabetes dataset, identifying potential genetic factors that interact with a combination of environmental variables, both linearly and nonlinearly, to influence the risk of developing type 2 diabetes. Full article

Smear-ripened cheeses are fermented dairy products characterised by an increased content of biogenic amines (BAs). The high contents of these bioactive compounds can negatively affect consumers. The study aimed to observe the contents of BAs and po-lyamines (PAs) in Olomoucké tvarůžky cheeses depending on selected factors (year, batch, ripening/storage time, shape, weight, specific surface area, acidity, and salt content). The results showed that the variability was explained primarily by the batch (83% for the sum of BAs) and by the year (63% for the sum of PAs). The storage time significantly influenced the contents of putrescine, cadaverine, spermidine, and spermine (the explained variability was only 1–3%). The total BA contents negatively correlated with weight (r = −0.6374; p < 0.001) and positively with specific surface area (r = +0.4349; p < 0.001). A negligible positive correlation coefficient was found between the total BAs and pH (r = +0.1303). A low negative correlation was also found between the total BAs and salt content (r = −0.1328). Compared to previous studies, the total average BA contents were considerably low. In conclusion, this type of cheese does not represent a serious problem for most consumers. Full article

Vegetable production is vital to smallholder farmers, who often struggle to overcome pests, diseases, and extreme weather. Agroecological cropping systems offer sustainable solutions to these issues but their adoption rates in Tanzania remain low. This study examines the factors influencing smallholder farmers’ adoption of selected agroecological cropping systems for vegetable production in Tanzania, which remains underexplored. Using a multistage sampling technique, cross-sectional data were gathered from 525 crucifer and traditional African vegetable farming households within the Arusha and Kilimanjaro regions. Multivariate probit regression analysis, which accounts for the simultaneous adoption of multiple systems, revealed several significant variables influencing adoption. The number of training sessions attended and access to market information positively influenced adoption (p < 0.01), while gross income from vegetable production also had a positive influence (p < 0.05). Conversely, the age of the household head and the region where the farm was located showed negative effects on adoption (p < 0.05). These findings highlight the need for targeted extension services and training sessions focusing on the benefits, methods, and management techniques of agroecological cropping systems. Gender-sensitive policies and interventions should also be developed to address the factors influencing the adoption of agroecological cropping systems. Full article

Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features. Full article

Royal jelly and medical grade honey are traditionally used in treating wounds and infections, although their effectiveness is often variable and insufficient. To overcome their limitations, we created novel amphiphiles by modifying the main reparative and antimicrobial components, queen bee acid (hda) and 10-hydroxyl-decanoic acid (hdaa), through peptide bonding with specific tripeptides. Our molecular design incorporated amphiphile targets as being biocompatible in wound healing, biodegradable, non-toxic, hydrogelable, prohealing, and antimicrobial. The amphiphilic molecules were designed in a hda(hdaa)-aa1-aa2-aa3 structural model with rational selection criteria for each moiety, prepared via Rink/Fmoc-tBu-based solid-phase peptide synthesis, and structurally verified by NMR and LC–MS/MS. We tested several amphiphiles among those containing moieties of hda or hdaa and isoleucine–leucine–aspartate (ILD-amidated) or IL-lysine (ILK-NH₂). These tests were conducted to evaluate their prohealing and antimicrobial hydrogel properties. Our observation of their hydrogelation and hydrogel-rheology showed that they can form hydrogels with stable elastic moduli and injectable shear-thinning properties, which are suitable for cell and tissue repair and regeneration. Our disc-diffusion assay demonstrated that hdaa-ILK-NH₂ markedly inhibited Staphylococcus aureus. Future research is needed to comprehensively evaluate the prohealing and antimicrobial properties of these novel molecules modified from hda and hdaa with tripeptides. Full article

Background: Breast cancer (BC) is a major health concern globally and the second leading cause of cancer-related mortality in women in Saudi Arabia. Although peoples’ awareness of BC risk factors has been previously examined, studies on obesity-related BC awareness in the Qassim region are inconclusive. We aimed to evaluate knowledge and awareness of obesity-related BC risk among Saudi women in the Qassim region. Methods: This is a cross-sectional study with a stratified random sampling technique of 400 Saudi women randomly selected from the Qassim region through an online platform and community health centers. An online closed-ended pretested validated structured questionnaire was completed by the participants using a Google Forms link. The categorical variables were frequency and percentage. The chi-square test was used to study the relationship between the dependent and independent variables. Results: There is moderate to poor knowledge regarding breast cancer risk factors. The results showed poor knowledge about obesity after menopause as a risk factor for BC (49%). Over half of the participants (51.0%) did not consider obesity a BC risk factor. The need for self-examinations and mammogram screenings showed moderate (59.6%) and poor awareness levels (4.75%). Conclusions: The findings highlight a noticeable gap in knowledge and awareness about obesity-related BC risks, as well as a limited awareness of the need for breast self-examinations and mammogram screenings. These results underscore the urgent need for targeted awareness campaigns and educational programs in the Qassim region to address this critical health issue. Promoting breast self-examination practices, weight management, and regular mammogram screenings could significantly enhance early detection, improve prognosis, and reduce BC-related mortality among Saudi women in the Qassim region. Full article

Nitrogen serves as a critical nutrient influencing the yield and quality of processed tomatoes; however, traditional methods for assessing its levels are both labor-intensive and costly. This study aimed to explore an efficient monitoring approach by analyzing the relationship between leaf nitrogen content (LNC) and canopy spectral reflectance characteristics throughout the growth stages of processed tomatoes at the Laolong River Tomato Base in Changji City, Xinjiang. The experimental design incorporated nine treatments, each with three replicates. LNC data were obtained using a dedicated leaf nitrogen content analyzer, while drones were utilized to capture multispectral images for the extraction of vegetation indices. Through Pearson correlation analysis, the optimal spectral variables were identified, and the relationships between LNC and spectral variables were established using models based on backpropagation (BP), multiple linear regression (MLR), and random forests (RFs). The findings revealed that the manually measured LNC data exhibited two peak values, which occurred during the onset of flowering and fruit setting stages, displaying a bimodal pattern. Among the twelve selected vegetation indices, ten demonstrated spectral sensitivity, passing the highly significant 0.01 threshold, with the Normalized Difference Chlorophyll Index (NDCI) showing the highest correlation during the full bloom stage. The combination of the NDCI and RF model achieved a prediction accuracy exceeding 0.8 during the full bloom stage; similarly, models incorporating multiple vegetation indices, such as RF, MLR, and BP, also reached prediction accuracies exceeding 0.8. Consequently, during the seedling establishment and initial flowering stages (vegetation coverage of <60%), the RF model with multiple vegetation indices was suitable for monitoring LNC; during the full bloom stage (vegetation coverage of 60–80%), both the RF model with the NDCI and the MLR model with multiple indices proved effective; and during the fruit setting and maturation stages (vegetation coverage of >80%), the BP model was more appropriate. This research provides a scientific basis for the cultivation management of processed tomatoes and the optimization of nitrogen fertilization within precision agriculture. It advances the application of precision agriculture technologies, contributing to improved agricultural efficiency and resource utilization. Full article

Agricultural cooperatives are the main vehicle for farmers to engage in green agriculture. With the digital transformation in rural areas, it is crucial to explore how cooperative members can effectively access online information and integrate it into green production decision-making processes. Based on the survey data of 530 members of rice planting cooperatives in Heilongjiang Province in China, this paper selected eight green production behaviors commonly used by rice farmers as explained variables, and constructed an ordered probit model. Using the social capital theory, the impact and mechanism of internet use on cooperative members’ green production behavior were examined. The results showed the following: (1) Internet use facilitates the cooperative members’ green production behavior. This conclusion remains valid even after addressing the endogeneity test and robustness test. (2) The heterogeneity analysis revealed that the internet is particularly effective in enhancing the green production behaviors of farmers who are less educated, middle-aged, and those with strong connections to cooperatives. (3) A further mechanism test indicates that internet use not only significantly influences farmers’ trust in cooperatives but also aids them in comprehending the cooperative’s production specifications, thereby further advancing the improvement in green production behaviors. (4) Members’ satisfaction with cooperative sales can serve as a substitute for the internet in influencing their green production behavior. Full article

Coconut oil (CNO) is often characterized as an “artery-clogging fat” because it is a predominantly saturated fat that ostensibly raises total cholesterol (TChol) and LDL cholesterol (LDL-C). Whereas previous analyses assessed CNO based on the relative effects on lipid parameters against other fats and oils, this analysis focuses on the effects of CNO itself. Here, we review the literature on CNO and analyze 984 lipid profile data sets from 26 CNO studies conducted over the past 40 years. This analysis shows considerable heterogeneity among CNO studies regarding participant selection, the amount consumed, and the study duration. The analysis reveals that, overall, CNO consumption gives variable TChol and LDL-C values, but that the HDL-cholesterol (HDL-C) values increase and triglycerides (TG) decrease. This holistic lipid assessment, together with the consideration of lipid ratios, shows that CNO does not pose a health risk for heart disease. Because the predominantly medium-chain fatty acid profile of CNO is significantly different from that of lard and palm oil, studies using these as reference materials do not apply to CNO. This paper concludes that the recommendation to avoid consuming coconut oil due to the risk of heart disease is not justified. Full article