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Communicating risks and mitigation benefits associated with natural hazards such as wind to the general public is challenging given the location-dependency of parameters and the complexity of the problem. Web tools play a crucial role in educating residents, decision-makers, and stakeholders regarding potential wind hazard losses to, for example, residential buildings.However, a notable gap exists on the practical incorporation of mitigation actions within these tools. This gap hampers the collective awareness and understanding among stakeholders, communities, and citizens regarding the tangible advantages of mitigation strategies in reducing wind-related risks. Furthermore, there exists a need to elucidate the functionality and objectives of these tools in a more accessible manner. This study aims to present and outline the wind risk and mitigation calculator tool (WRMCT) within the Hazardaware platform, which is an address-based risk assessment tool. This tool, developed for 196 counties in the Gulf of Mexico coastal area, facilitates users' education of potential risks and benefits associated with mitigation strategies. WRMCT enables users to access location-specific wind risk and interactively suggests potential mitigation actions along with economic savings to support informed decisions and residential risk reduction. WRMCT intends to enhance users’ ability to make informed decisions, take proactive measures in mitigating wind hazards, and contribute to the development of resilient, residential communities.

Abstract Background: Colorectal cancer is the most common gastrointestinal cancer. Investigating the factors that predict survival time for these patients is important.The purpose of this study was comparison of parametric models by estimating the prediction error and also identifying the effective factors on predicted survival time of patients with colorectal cancer. Materials and Methods: This cohort study was conducted with 600 patients who were suffered from colorectal cancer in Taleghani Hospital of Tehran between 2001 to 2005 and they were followed up for at least 5 years. For identifying the effective factors on survival time, of the patients we analyzed the data by some parametric models such as Weibull, Exponential and Log logistic and compared these models with the estimation of prediction error by apparent loss method. Results: Among 600 patients there was 344 men (57.3%) and 256 wemon (47.7%). Of total, 151 patients were died that 62.3% of them were men. Univariate analysis showed that the effect of BMI, sex, staging of tumor, tumor site were significant but in multivariate model staging of tumor and BMI were significant. By the estimation of prediction error, the best model was Log logistic . Conclusion: With respect to the importance of survival time prediction, we found that we can use the prediction error to compare the parametric models. In addition, because of effectiveness of tumor stages and BMI in the patients’ survival time, survival time could be increased by an on-time diagnosis and an appropriate controled diet.

CO2 reformation of methane (CRM) and CO2 methanation are two interconnected processes with significant implications for greenhouse gas reduction and sustainable energy production for industrial purposes. While Ni-based catalysis suffers from poor stability due to coke formation or sintering, we report a super stable remedy. The active sites of mesoporous MgO were loaded using wet impregnation. The incorporation of Ni and promoters altered the physical features of the catalysts. Sm–Ni/MgO showed the smallest crystallite size, specific surface area, and pore volume. The Sm–Ni/MgO catalyst was selected as the most suitable candidate for CRM, with 82 ​% CH4 and H2/CO ratio of approximately 100 ​% and also for CO2 methanation with the conversion of carbon dioxide (82 ​%) and the selectivity toward methane reaches 100 ​% at temperatures above 300 ᵒC. Furthermore, the Sm–Ni/MgO catalyst was stable for 900 ​min of continuous reaction, without significant carbon deposition. This stability was largely due to the high oxygen mobility on the catalyst surface in the presence of Sm. Overall, we demonstrated the efficacy of using promoted Ni catalysts supported by mesoporous magnesia for the improved reformation of greenhouse gases.

This review focuses on thermodynamic and physical parameters, synthesis methods, and reported phases of Magnesium (Mg) containing high-entropy alloys (HEAs). Statistical data of publications concerning Mg-containing HEAs were collected and analyzed. Data on the chemical elements included in Mg-containing HEAs, their theoretical end experimental densities, thermodynamic parameters, physical parameters, fabricated techniques and reported phases were also collected and discussed. On the basis of this information, a new classification for HEAs was proposed. It is also shown that the existing thermodynamic parameters cannot accurately predict the formation of a single phase solid solution for Mg-containing HEAs. The physical parameters of Mg-containing HEAs are within a wide range, and most of the synthesized Mg-containing HEAs have a complex multiphase structure.

Autoimmune disorders (ADs) pose significant health and economic burdens globally, characterized by the body’s immune system mistakenly attacking its own tissues. While the precise mechanisms driving their development remain elusive, a combination of genetic predisposition(s) and environmental triggers is implicated. Interleukin-27 (IL-27), among numerous cytokines involved, has emerged as a key regulator, exhibiting dual roles in immune modulation. This review delves into the molecular structure and signaling mechanisms of IL-27, highlighting its diverse effects on various immune cells. Additionally, it explores the involvement of IL-27 in autoimmune diseases, such as multiple sclerosis (MS) and rheumatoid arthritis (RA), offering insights into its potential therapeutic implications. Moreover, its involvement in autoimmune diseases like type 1 diabetes (T1D), inflammatory bowel disease (IBD), myasthenia gravis (MG), Sjögren’s syndrome (SS), and Guillain-Barré syndrome (GBS) is multifaceted, with potential diagnostic and therapeutic implications across these conditions. Further research is essential to fully understand IL-27’s mechanisms of action and therapeutic potential in autoimmune diseases.

Abstract The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper, and evolutionary were used. Then seven algorithms Bayes net, Naïve Bayes (BN), multivariate linear model (MLM), Support Vector Machine (SVM), logit boost, j48, and Random Forest were applied to identify the best models for heart disease prediction. Precision, F-measure, Specificity, Accuracy, Sensitivity, ROC area, and PRC were measured to compare feature selection methods' effect on prediction algorithms. The results demonstrate that feature selection resulted in significant improvements in model performance in some methods (e.g., j48), whereas it led to a decrease in model performance in other models (e.g. MLP, RF). SVM-based filtering methods have a best-fit accuracy of 85.5. In fact, in a best-case scenario, filtering methods result in + 2.3 model accuracy. SVM-CFS/information gain/Symmetrical uncertainty methods have the highest improvement in this index. The filter feature selection methods with the highest number of features selected outperformed other methods in terms of models' ACC, Precision, and F-measures. However, wrapper-based and evolutionary algorithms improved models' performance from sensitivity and specificity points of view.

Sustainable socio-economic development largely depends on the sustainability of the energy supply from economic, environmental, and public health perspectives. Fossil fuel combustion only meets the first element of this equation and is hence rendered unsustainable. Biofuels are advantageous from a public health perspective, but their environmental and economic sustainability might be questioned considering the conflicts surrounding their feedstocks, including land use change and fuel vs. food conflict. Therefore, it is imperative to put more effort into addressing the downsides of biofuel production using advanced technologies, such as nanotechnology. In light of that, this review strives to scrutinize the latest developments in the application of nanotechnology in producing biodiesel, a promising alternative to fossil diesel with proven environmental and health benefits. The main focus is placed on nanotechnology applications in the feedstock production stage. First, the latest findings concerning the application of nanomaterials as nanofertilizers and nanopesticides to improve the performance of oil crops are presented and critically discussed. Then, the most promising results reported recently on applying nanotechnology to boost biomass and oil production by microalgae and facilitating microalgae harvesting are reviewed and mechanistically explained. Finally, the promises held by nanomaterials to enhance animal fat production in livestock, poultry, and aquaculture systems are elaborated. Despite the favorable features of using nanotechnology in biodiesel feedstock production, the presence of nanoparticles in living systems is also associated with important health and environmental challenges, which are critically covered and discussed in this work.

Abstract The major part of water pollutants includes of organic such as phenolic pollutant, thus there are every hazardous to environment. Present work is a comparative onto surface chemistry and adsorptive characteristics of coordinate organic polymer (Cop-150) and nanoporous carbon (NPC) prepared using solvothermal method. New NPC was successfully synthesized to remove of phenol. FT-IR, XRD, XPS, SEM, TGA, and BET techniques have been used to characterization and confirm physicochemical variation during preparing Cop-150 and NPC. Box–Behnken response surface methodology (BBRSM) was used to optimize four important factors of the pH (2–10), contact time (1–40 min), temperature (25–60 °C), and initial concentration of phenol (5–50 mg L−1). To analyze the data obtained from the adsorption of phenol by synthesized adsorbents, four linear, 2FI, quadratic and cubic models were examined, which the quadratic model was recognized as the best model. To the NPC the equal adsorption capacity 500 mg g−1 is achieved at the initial concentration of phenol = 49.252 mg L−1, contact time = 15.738 min, temperature = 28.3 °C, and pH 7.042. On the other hand, the adsorption capacity for Cop-150 in pH 4.638, the contact time = 19.695 min, the temperature = 56.8 °C, and the initial concentration of phenol = 6.902 mg L−1 was equal to 50 mg g−1. The experimental data at different conditions were investigated by some famous kinetic and isotherm models, which among them, were corresponded to the pseudo-second-order kinetic model and the Langmuir isotherm. Moreover, based to result of thermodynamics to the both Cop-150 and NPC, the adsorption process is exothermic and spontaneous. According to results the Cop-150 and NPC could be used for up to four and five cycles without significantly reducing their performance, respectively.

Abstract Berberine is a benzylisoquinoline alkaloid found in such plants as Berberis vulgaris, Berberis aristata, and others, revealing a variety of pharmacological properties as a result of interacting with different cellular and molecular targets. Recent studies have shown the immunomodulatory effects of Berberine which result from its impacts on immune cells and immune response mediators such as diverse T lymphocyte subsets, dendritic cells (DCs), and different inflammatory cytokines. Multiple sclerosis (MS) is a chronic disabling and neurodegenerative disease of the central nervous system (CNS) characterized by the recruitment of autoreactive T cells into the CNS causing demyelination, axonal damage, and oligodendrocyte loss. There have been considerable changes discovered in MS regards to the function and frequency of T cell subsets such as Th1 cells, Th17 cells, Th2 cells, Treg cells, and DCs. In the current research, we reviewed the outcomes of in vitro, experimental, and clinical investigations concerning the modulatory effects that Berberine provides on the function and numbers of T cell subsets and DCs, as well as important cytokines that are involved in MS.

This study aimed to determine the predictive power of the theory of planned behaviour in predicting the intention and behaviour of positive thinking in school students. A cross-sectional study was performed on 367 high school male students in Iran. Data were gathered using a researcher-made questionnaire whose validity and reliability had been confirmed before. Descriptive and inferential analysis (univariate and multiple logistic regression models, Pearson correlation) was performed using SPSS software V.20. AMOS version 22 was used to perform the path analysis. According to the results, the construct of attitude was the most important construct in predicting positive thinking intention. Finally, the theory of planned behaviour constructs could predict 36% of intention variance and 20% of behaviour variance of positive thinking. Since the construct of attitude is one of the most powerful constructs in predicting student positive thinking intention in male teens, it is recommended that more attention be paid to this construct in educational programs to improve the mental health of male high school students. The results of this study can help psychologists and counsellors, families, and teachers improve students' mental health.