Model Assessment Research Articles

Insulin Resistance Among Children and Adolescents with Subclinical Hypothyroidism: A Case-Control Study

Background: Subclinical hypothyroidism (SCH) is marked by elevated thyroid-stimulating hormone (TSH) levels while maintaining normal thyroxine levels. Although the link between insulin resistance (IR) and overt hypothyroidism is well-established, less is known about this relationship in children and adolescents with SCH. Objectives: This study aimed to explore the association between insulin resistance and SCH in individuals aged 4 to 18 years. Methods: In this case-control study, 30 patients with SCH aged 4 - 18 from the Endocrinology Clinic at Imam Reza Hospital, Shiraz, Iran, were chosen as the case group through convenient sampling. Additionally, 30 healthy children and adolescents from the same clinic were randomly selected as controls. Exclusion criteria included positive Anti-TPO, goiter, previous thyroid disease, systemic diseases, chronic or acute diseases, diabetes, and a BMI ≥ 85%. Levels of TSH, insulin, fasting blood sugar (FBS), triglycerides, cholesterol, low-density lipoprotein, and high-density lipoprotein were compared between the groups. Data were analyzed using SPSS 25. Results: The study found that levels of FBS, insulin, and Homeostatic Model Assessment for IR (HOMA-IR) were significantly higher in the SCH group than in the control group. There was a significant correlation between TSH levels and both insulin levels and IR in the SCH group. Subclinical hypothyroidism was found to significantly increase the risk of developing IR among pediatric patients compared to healthy controls. Conclusions: Overall, our findings indicate a positive correlation between TSH levels and IR in children with SCH. Additionally, HOMA-IR levels were significantly higher with elevated TSH in the SCH group compared to healthy children. It is recommended that IR be evaluated and treated timely in SCH patients to prevent future complications.

A new hybrid control strategy for improving ride comfort on lateral suspension system of railway vehicle

The ride comfort is a key research area for the quality improvement of railway vehicles. Several control methods have been developed to improve the ride comfort, in which the semi-active control approach inherits the advantages of passive and active control approaches. However, the semi-active control may perform ineffectively in vibration attenuation when the carbody load changes. To address this issue, we proposed a novel hybrid control method that combines the skyhook (SH) and displacement velocity (DV) control methods to reduce vibrations of the carbody in the lateral direction under carbody load changes. First, a hybrid control model built in Matlab software was applied to a quarter railway vehicle model for a preliminary assessment of the ride comfort. Second, to provide a more comprehensive evaluation, the proposed model was applied to a whole railway vehicle model using co-simulation (Matlab-Simpack). In the quarter model, under the sinusoidal excitation, the hybrid control showed a lower acceleration by 48.5% and 42.8% when the carbody load reduced to a half and a quarter, respectively, compared to the passive control. This finding was confirmed with the whole model. Moreover, in the whole model, under the track irregular excitation, the hybrid control showed a lower acceleration by 26.6% and 17.4% when the carbody load reduced to a half and a quarter, respectively. Further, the proposed method showed an acceptable safety level, measured by the derailment coefficient (0.0743 at a half load and 0.089 at a quarter load). In conclusions, the proposed hybrid SH-DV control exhibits high robustness, adaptability, effectiveness, and acceptable safety level in response to variations in body load.

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.

Human Health Risk Assessment of Radionuclide Contamination in Drinking Water

This study investigates the human health risks of uranium, radium, radon, and other drinking water radionuclides and their mitigation strategies. It was implemented through literature review, field sampling, and analytical methods. Samples were taken from various sources, including groundwater, surface water, municipal supplies, and private wells. ICPI-MS and liquid scintillation counters were used for radiation measurements. Statistical analysis and risk assessment models were used to measure health risks and treatment effectiveness. Groundwater sources were the main sources of radionuclides, with private wells being the main sources. The elimination efficiencies of reverse osmosis were exceptional, reaching up to 99%. The elderly population (60+ years) were the most likely to have cancer, with the highest risks for bladder cancer, lung cancer, kidney cancer, and leukemia. The frequency of radionuclide contamination in drinking water sources varied, with the U.S. Environmental Protection Agency, Nigerian EPA, and Canada having the strictest schedules. The results emphasize the urgent need for monitoring programs, effective treatment technologies, and targeted risk management strategies to cope with radionuclide contamination. Government advice includes improving the regulatory system, developing advanced treatment methods, long-term epidemiological studies, public awareness, interdisciplinary collaboration, scientific exploration of alternative water sources, and prioritizing interventions for vulnerable populations.

Quantifying risks of lane-changing behavior in highways with vehicle trajectory data under different driving environments

Improper lane-changing behavior may have a great impact on the safety of the surrounding vehicles, and accurate risk assessment of lane-changing behavior can detect improper lane-changing behavior. Many existing risk assessment methods are based on the traditional minimum safe distance model as the theoretical foundation, not taking into account the effects of different weather and road conditions. Moreover, the initially calculated safe lane-changing distance through the model is generally the limit safety distance, which deviates to some extent from the initial distance maintained when the vehicle begins to change lanes during the actual driving process. The ratio of these two distances can reflect the degree of risk between the changing vehicle and its neighboring vehicles during lane-changing. For this reason, a lane-changing safety coefficient is defined and lane-changing risk assessment model based on the hyperbolic tangent function is constructed by combining the change characteristics of this coefficient. An environmental adjustment factor was introduced into the model to consider the influence of different driving weather and road conditions on the lane-changing risk assessment. Then, based on survey data from the survey questionnaire, the weights of the influencing factors were determined by using the hierarchical analysis method, and the environmental adjustment factor in the model was calibrated by using the fuzzy comprehensive assessment method. Finally, to verify the effectiveness of the model, the lane-changing trajectories of 373 vehicles were extracted from the HighD dataset. The risk assessment value of lane-changing was calculated by using the constructed model and was classified into high-risk, medium-risk and low-risk by using the K-means++ clustering algorithm. Various evaluation results were compared with the evaluation results from the expert evaluation method and the classical risk evaluation indexes, and the evaluation results are basically the same, which verifies the effectiveness of the lane-changing risk model established.

Positive association of the anti-aging protein α-Klotho with insulin resistance and its inverse L-shaped relationship with glycaemic control in the middle-aged and elderly population.

α-Klotho has been linked to insulin resistance (IR) in basic research. However, experimental evidence is inconsistent, and there is a lack of data from human research. This study seeks to elucidate the association of α-Klotho with IR in a nationwide, multiracial population. A total of 5289 participants aged 40-79 years were included in the National Health and Nutrition Examination Survey (NHANES) spanning 2007-2016. Serum α-Klotho was measured using enzyme-linked immunosorbent assays (ELISA), and IR was evaluated by the homeostatic model assessment of insulin resistance (HOMA-IR). Weighted multivariate logistic and linear regression analysis, subgroup analysis stratified by demographic characteristics, medical condition or obesity status, and sensitivity analysis using propensity score matching (PSM) were performed. Restricted cubic splines (RCS) were performed to explore the nonlinear relationship. In the fully adjusted logistic regression model, a significant positive association was observed between log-transformed α-Klotho and IR (OR = 3.63, 95% CI: 1.56, 8.45), particularly in males or nonobese individuals (Pinteraction < 0.05). In the linear regression model, log10(α-Klotho) was associated with fasting blood glucose (FBG, β = 1.25, 95% CI: 0.74, 1.76) and glycosylated hemoglobin (HbA1c, β = 0.49, 95% CI: 0.20, 0.77). RCS revealed an inverse L-shaped dose-response relationship of α-Klotho with FBG and HbA1c (Pnonlinear <0.05). Beyond the inflection point of log10(α-Klotho) at 2.79, β coefficients sharply rose for these glycaemic control indicators. The study provides clinical evidence supporting a positive association between α-Klotho and IR. Moreover, the inverse L-shaped relationship suggests that α-Klotho should reach a certain level to predict glycaemic changes effectively.

Metformin-induced changes of the gut microbiota in patients with type 2 diabetes mellitus: results from a prospective cohort study.

The influence of the microbiota on hypoglycemic agents is becoming more apparent. The effects of metformin, a primary anti-diabetes drug, on gut microbiota are still not fully understood. This prospective cohort study aims to investigate the longitudinal effects of metformin on the gut microbiota of 25 treatment-naïve diabetes patients, each receiving a daily dose of 1500 mg. Microbiota compositions were analyzed at baseline, and at 1, 3, and 6 months of medication using 16S rRNA gene sequencing. Prior to the 3-month period of metformin treatment, significant improvements were noted in body mass index (BMI) and glycemic-related parameters, such as fasting blood glucose (FPG) and hemoglobin A1c (HbA1c), alongside homeostasis model assessment indices of insulin resistance (HOMA-IR). At the 3-month mark of medication, a significant reduction in the α-diversity of the gut microbiota was noted, while β-diversity exhibited no marked variances throughout the treatment duration. The Firmicutes to Bacteroidetes ratio. markedly decreased. Metformin treatment consistently increased Escherichia-Shigella and decreased Romboutsia, while Pseudomonas decreased at 3 months. Fuzzy c-means clustering identified three longitudinal trajectory clusters for microbial fluctuations: (i) genera temporarily changing, (ii) genera continuing to decrease (Bacteroides), and (iii) genera continuing to increase(Lachnospiraceae ND3007 group, [Eubacterium] xylanophilum group, Romboutsia, Faecalibacterium and Ruminococcaceae UCG-014). The correlation matrix revealed associations between specific fecal taxa and metformin-related clinical parameters HbA1c, FPG, Uric Acid (UA), high-density lipoproteincholesterol (HDL-C), alanine aminotransferase (ALT), hypersensitive C-reactive protein (hs-CRP), triglyceride (TG) (P < 0.05). Metacyc database showed that metformin significantly altered 17 functional pathways. Amino acid metabolism pathways such as isoleucine biosynthesis predominated in the post-treatment group. Metformin's role in glucose metabolism regulation may primarily involve specific alterations in certain gut microbial species rather than an overall increase in microbial species diversity. This may suggest gut microbiota targets in future studies on metabolic abnormalities caused by metformin.

Research impact assessment: Developing and applying a viable model for the social sciences

Abstract Prompted by research funding agencies and regulatory bodies, universities need to demonstrate and manage the impact of their research. Therefore, models for evaluating research impacts that can be applied quickly and at a reasonable cost are needed. However, the development of these models presents considerable challenges: research impact is multidimensional and can occur over time because of multiple interactions between different agents. Furthermore, impact assessment can demand substantial time and resources. Considering this challenging context, this study proposes a research impact assessment model capable of optimizing benefits and costs. The model was based on a multidimensional impact perspective and an application-oriented way of generating and transmitting knowledge. It was applied to a large teaching and research institution with a predominant role in the social sciences. The assessment involved a survey of the leaders of 133 research projects. The main findings of the application of the assessment model were as follows: first, knowledge production is transitioning from a traditional mode (aimed to impact science) to a new mode (aimed to impact multiple stakeholders); second, project leaders still perceive the prevalence of scientific impact over other types of impact; and third, the survey revealed how certain characteristics of the knowledge production mode relate to (perceived) impact. The viable research impact assessment model was demonstrated to be useful and cost-effective; thus, with adaptations and improvements, it may be used by other institutions, particularly those with internal research funding programs. This article also presents the model’s limitations, suggesting directions for future research.

Deciphering smooth muscle cell heterogeneity in atherosclerotic plaques and constructing model: a multi-omics approach with focus on KLF15/IGFBP4 axis

BackgroundRuptured atherosclerotic plaques often precipitate severe ischemic events, such as stroke and myocardial infarction. Unraveling the intricate molecular mechanisms governing vascular smooth muscle cell (VSMC) behavior in plaque stabilization remains a formidable challenge.MethodsIn this study, we leveraged single-cell and transcriptomic datasets from atherosclerotic plaques retrieved from the gene expression omnibus (GEO) database. Employing a combination of single-cell population differential analysis, weighted gene co-expression network analysis (WGCNA), and transcriptome differential analysis techniques, we identified specific genes steering the transformation of VSMCs in atherosclerotic plaques. Diagnostic models were developed and validated through gene intersection, utilizing the least absolute shrinkage and selection operator (LASSO) and random forest (RF) methods. Nomograms for plaque assessment were constructed. Tissue localization and expression validation were performed on specimens from animal models, utilizing immunofluorescence co-localization, western blot, and reverse-transcription quantitative-polymerase chain reaction (RT-qPCR). Various online databases were harnessed to predict transcription factors (TFs) and their interacting compounds, with determination of the cell-specific localization of TF expression using single-cell data.ResultsFollowing rigorous quality control procedures, we obtained a total of 40,953 cells, with 6,261 representing VSMCs. The VSMC population was subsequently clustered into 5 distinct subpopulations. Analyzing inter-subpopulation cellular communication, we focused on the SMC2 and SMC5 subpopulations. Single-cell subpopulation and WGCNA analyses revealed significant module enrichments, notably in collagen-containing extracellular matrix and cell-substrate junctions. Insulin-like growth factor binding protein 4 (IGFBP4), apolipoprotein E (APOE), and cathepsin C (CTSC) were identified as potential diagnostic markers for early and advanced plaques. Notably, gene expression pattern analysis suggested that IGFBP4 might serve as a protective gene, a hypothesis validated through tissue localization and expression analysis. Finally, we predicted TFs capable of binding to IGFBP4, with Krüppel-like family 15 (KLF15) emerging as a prominent candidate showing relative specificity within smooth muscle cells. Predictions about compounds associated with affecting KLF15 expression were also made.ConclusionOur study established a plaque diagnostic and assessment model and analyzed the molecular interaction mechanisms of smooth muscle cells within plaques. Further analysis revealed that the transcription factor KLF15 may regulate the biological behaviors of smooth muscle cells through the KLF15/IGFBP4 axis, thereby influencing the stability of advanced plaques via modulation of the PI3K-AKT signaling pathway. This could potentially serve as a target for plaque stability assessment and therapy, thus driving advancements in the management and treatment of atherosclerotic plaques.

A Review of quantum algorithms for prediction of hazardous asteroids

Quantum computing (QC) and quantum machine learning (QML), two emerging technologies, have the potential to completely change how we approach solving difficult problems in physics and astronomy, among other fields. Potentially Hazardous Asteroids (PHAs) can produce a variety of damaging phenomena that put biodiversity and human life at serious risk. Although PHAs have been identified through the use of machine learning (ML) techniques, the current approaches have reached a point of saturation, signaling the need for additional innovation. This paper provides an in-depth examination of various machine learning (ML) and QML techniques for precisely identifying potentially hazardous asteroids. The study attempts to provide information to improve the efficiency and accuracy of asteroid categorization by combining QML techniques like deep learning with a variety of machine learning (ML) algorithms, such as Random Forest and support vector machines. The study highlights weaknesses in existing approaches, including feature selection and model assessment, and suggests directions for further investigation. The results highlight the significance of developing QML techniques to increase the prediction of asteroid hazards, consequently supporting enhanced risk assessment and space exploration efforts. Paper reviews might not be related because the study only looks at generic paper reviews.

Organizational ethical climate: influence on employee meaning and well-being

PurposeThe organizational environment can influence how employees experience meaningfulness. This study examines the mediating role of meaningful work between organizational ethical climates and the affective well-being of employees. We also test for the moderating role of self-regulatory traits in this relationship.Design/methodology/approachPartial least squares – structural equation modelling (PLS-SEM) was employed to test the hypothesized model using responses from 430 working professionals. Recommended robustness checks were conducted before model assessment and hypotheses testing.FindingsThe findings suggest that a caring ethical climate is positively related to affective well-being. Meaningful work dimensions, i.e. unity with others, inspiration and balancing tensions partially mediate the relationship between the caring climate and affective well-being. Integrity with self and balancing tensions fully mediate the negative effect of an instrumental climate on affective well-being. Positive mediation of unity with others and negative mediation of reality were observed between a law and code climate and affective well-being. Moderating effects of self- and other-orientation and self-monitoring were also observed.Research limitations/implicationsThe study presents significant insights, however, a few limitations must be discussed. The study has relied on cross-sectional data which may be addressed in future studies.Practical implicationsIn times when organizations are spending in large amounts in ensuring meaningful work and employee well-being, this study suggests internal mechanisms that can bring positive impact in employees' work life. Leaders should assess how employees perceive the ethical climate of the organization in order to provide better meaningful work opportunities to the workforce.Social implicationsHaving meaningful work and experiencing affective well-being are significant for a collective betterment of society. Meaningful work encourages individuals in identifying how their work if affecting the society. A affectively happy workforce is essential in building a mentally healthy society.Originality/valueThis study contributes to the investigation of organizational factors that help employees find meaning in their work. Based on ethical climate theory, this study highlights how organizations can redesign and modify their ethical climates to provide opportunities for employees to experience meaningful work and improve their affective well-being.

Comparison of numerical calculations and ALOHA modeling in consequence assessment of chlorine gas emissions from ethylene dichloride reactors.

Incidents involving chemical storage tanks in the petrochemical industry are significant events with severe consequences. Within the petrochemical industry, EDC is a sector that produces ethylene dichloride through the reaction of chlorine and ethylene. The present research was conducted to evaluate the consequences of chlorine gas released from the EDC reactor in a petrochemical industry in southern Iran. Data regarding reactor specifications were obtained from the factory's technical office, while climatic data was acquired from the Meteorological Organization. The consequences of chlorine gas release from the reactor were assessed in four predefined scenarios using numerical calculation methods and modeling with the ALOHA software. The numerical calculation method involved thermodynamic fluid path analysis, discharge coefficient calculations, and wind speed impact analysis. The hazard radius was determined based on the ERPG1-2-3 index. Results showed that in the scenario of chlorine gas release from EDC reactors, according to the ALOHA model, an increase in wind speed from 3 to 7m/h led to an expanded dispersion radius. At a radius of 700m from the reactor, the maximum outdoor concentration reached 3.12ppm, decreasing to 2.27ppm at 800m and further to 1.53ppm at 1000m. The comparison of numerical calculations and modeling using the ALOHA software indicates the desirable conformity of the results with each other. The R2 coefficient for evaluating the conformity of the results was 0.9964, indicating the desired efficiency of the model in evaluating the consequences of the release of toxic gasses from the EDC tank. The results of this research can be useful in designing the site and emergency response plan.

Assessment of the WRF model in reproducing a flash-flood heavy rainfall event over Kosovo

Assessment of the WRF model in reproducing a flash-flood heavy rainfall event over Kosovo

Two-Dimensional Customer Segmentation for Digital Custom Coupon Issuance Approach

With the improvement of large information and profound learning innovation, enormous information and profound learning innovation have likewise been applied to the showcasing field, which was a piece of business organization. Client agitate the board is one of the main areas of promoting. In this paper, we proposed a technique to forestall client beat and increment buy transformation rate by giving tweaked rebate coupons to clients with high stir rate in light of huge information continuously. In the wake of sectioning client portions with two-layered fragment examination, a continuous beat rate assessment model in light of clickstream information was created for each portion. From that point onward, we gave tweaked coupons to our clients. At last, we tried the transformation rate and deals development. A two-layered group investigation based stir rate assessment joined with a proposal framework was viewed as essentially more helpful than the particular straightforward models. Utilizing this proposed model, it is feasible to increment deals via consequently assessing the client's stir likelihood and shopping penchant without the weight of showcasing costs in the web based shopping center

Development of a maturity assessment model for sustainable tourism

ABSTRACT Sustainability has become one of the most pressing worldwide concerns for the tourism sector. Destination managers face the challenge of developing strategies and assessing their success in pursuing sustainable development. However, the concept of sustainability is still vague, and existing measurement frameworks are complex and fail to sufficiently include relevant factors such as digitalisation. This article adopts a design science research (DSR) approach to address these issues, creating a conceptual solution for assessing sustainability in tourism destinations and providing directions for destinations’ future development based on a maturity model concept. Objectives for this conceptual solution are developed by critically reviewing relevant literature and existing assessment models for sustainable tourism and operationalised by designing a maturity assessment model for sustainable tourism. To validate this model, interviews with tourism experts and stakeholders are conducted in the Algarve region, Portugal. The study concludes by acknowledging limitations and offering directions for further research.

Capability Assessment of Cultivating Innovative Talents for Higher Schools Based on Machine Learning

The innovation capability largely determines the initiative for future development of a region. Higher school is the main position for training innovative talents. Accurate and comprehensive assessment of innovation cultivation capability is an important basis of higher schools for continuous improvement. Thus, this paper focuses on assessing innovative talent cultivation capability. First, by CIPP model (Context, Input, Process and Product Evaluation), an assessment indicator system is built, consisting of 89 indicators in 21 categories. Then, based on indicator characteristics, this paper uses public data statistics, database retrieving, student survey, teacher survey, support personnel and expert investigation, to collect indicator values. After this, by a powerful machine learning algorithm, gradient Boosting regression tree, a capability assessment model is established. And based on collected data, established model is compared with several regression models in innovative talent cultivation capability assessing. Results confirm the performance superiority of our solution.

Building confidence: Developing image-based methods to incorporate fishery-collected data in the American lobster stock assessment

The American lobster, Homarus americanus, supports the most valuable single-species fishery in North America; however to-date, a reliable and robust method to determine age does not exist, and thus some of the more traditional catch-at-age stock assessment methods cannot be used to determine status. In lieu of this, the Atlantic States Marine Fisheries Commission’s American Lobster Stock Assessment Model uses a probabilistic growth transition matrix to determine how lobsters of different sizes will grow and recruit into the fishery. Developing and updating the growth transition matrix to reflect current growth dynamics requires estimates of molt increment and interval. Tagging studies are an inexpensive method to collect this type of information, but relying on fishing industry reported recapture data and buy-in can be challenging, resulting in varied return rates and uncertainty in measurement accuracy. Here, we report on a subset of recaptured lobsters released in the Gulf of Maine and Georges Bank regions between 2015 and 2020, where in addition to recapture locations, harvesters were encouraged to submit images of recaptured lobsters alongside a standard lobster gauge as a scale to estimate carapace length. Some fishermen were able to provide both direct measurements and images. Images were analyzed in ImageJ to estimate individual lobster carapace length (CL); and each image was assigned a quality score. For all groups, image-derived lengths were correlated with measured observations, regardless of the overall image quality, with only a slight underestimation at the maximum end of CL. This image-based method for length estimation provides high-quality length predictions regardless of image quality and can significantly increase the likelihood of harvesters contributing data with broad potential to engage commercial fisherman in collaborative science. Using this method we were able to expand the dataset of length records from this project by 251 or 33 %, providing additional data at a rate similar to tagging an additional 6000 lobsters.

Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions

The inherent uncertainty in hydro-geotechnical parameters presents a significant challenge for accurately predicting rainfall-triggered shallow landslides in mountainous regions. In this study, a novel probabilistic framework was developed and implemented in the “Py.GIS-FSLAM-FORM” software, designed to address the complexities associated with parameter uncertainty, correlation, and distribution. By combining the Fast Shallow Landslide Assessment Model (FSLAM) with the First-Order Reliability Method (FORM), we have enhanced the traditional probabilistic approach to create more accurate landslide susceptibility maps. This study emphasizes the uncertainly of geotechnical parameters and the critical influence of hydrological conditions on landslide susceptibility, especially focusing on the interaction between antecedent recharge (qa) and event rainfall (Pe). In our study area (Val d’Aran, Spain), the probabilistically based results revealed that areas of very high susceptibility are significantly affected by event rainfall, particularly on slopes of 30–40 degrees and aspects between 100 and 250 degrees. The variability in geotechnical parameters, especially the coefficient of variation (COV) in cohesion and friction angle, plays a crucial role in landslide susceptibility assessment, with increased COVs leading to greater landslide uncertainty. Additionally, cross-negative correlations and non-normal distributions of geotechnical parameters substantially influence the spatial distribution of landslides, notably when combining antecedent recharge with event rainfall. These results highlight the importance of incorporating parameter variability and hydrological conditions in susceptibility models to improve the precision of regional landslide forecasts. While the study was performed in Val d'Aran, its methodologies and conclusions are relevant to mountainous areas worldwide, offering insights for refining landslide prediction models and susceptibility assessments, contributing to global efforts in landslide disaster prevention.

Ultraprocessed Food Consumption and Cardiometabolic Risk Factors in Children

High intake of ultraprocessed foods (UPFs) has been associated with higher cardiometabolic risk in adults; however, the evidence in children is limited. To investigate the association between UPF consumption and cardiometabolic risk factors in the Childhood Obesity Risk Assessment Longitudinal Study (CORALS). This baseline cross-sectional analysis was conducted using the data of CORALS participants recruited between March 22, 2019, and June 30, 2022. Preschool children (aged 3-6 years) were recruited from schools and centers in 7 cities in Spain. Inclusion criteria included informed consent signed by parents or caregivers and having a completed a set of questionnaires about the child's prenatal history at home. Exclusion criteria included low command of Spanish or unstable residence. Energy-adjusted UPF consumption (in grams per day) from food frequency questionnaires and based on the NOVA food classification system. Age- and sex-specific z scores of adiposity parameters (body mass index [BMI], fat mass index, waist-to-height ratio, and waist circumference) and cardiometabolic parameters (diastolic and systolic blood pressure, fasting plasma glucose, homeostasis model assessment for insulin resistance, high-density and low-density lipoprotein cholesterol, and triglycerides) were estimated using linear regression models. Of 1509 enrolled CORALS participants, 1426 (mean [SD] age, 5.8 [1.1] years; 698 boys [49.0%]) were included in this study. Mothers of children with high UPF consumption were younger, had a higher BMI, were more likely to have overweight or obesity, and had lower education levels and employment rates. Compared with participants in the lowest tertile of energy-adjusted UPF consumption, those in the highest tertile showed higher z scores of BMI (β coefficient, 0.20; 95% CI, 0.05-0.35), waist circumference (β coefficient, 0.20; 95% CI, 0.05-0.35), fat mass index (β coefficient, 0.17; 95% CI, 0.00-0.32), and fasting plasma glucose (β coefficient, 0.22; 95% CI, 0.06-0.37) and lower z scores for HDL cholesterol (β coefficient, -0.19; 95% CI, -0.36 to -0.02). One-SD increments in energy-adjusted UPF consumption were associated with higher z scores for BMI (β coefficient, 0.11; 95% CI, 0.05-0.17), waist circumference (β coefficient, 0.09; 95% CI, 0.02-0.15), fat mass index (β coefficient, 0.11; 95% CI, 0.04-1.18), and fasting plasma glucose (β coefficient, 0.10; 95% CI, 0.03-0.17) and lower HDL cholesterol (β coefficient, -0.07; 95% CI, -0.15 to -0.00). Substituting 100 g of UPFs with 100 g of unprocessed or minimally processed foods was associated with lower z scores of BMI (β coefficient, -0.03; 95% CI, -0.06 to -0.01), fat mass index (β coefficient, -0.03; 95% CI, -0.06 to 0.00), and fasting plasma glucose (β coefficient, -0.04; 95% CI, -0.07 to -0.01). These findings suggest that high UPF consumption in young children is associated with adiposity and other cardiometabolic risk factors, highlighting the need for public health initiatives to promote the replacement of UPFs with unprocessed or minimally processed foods.

Handwritten Digit Recognition of MNIST dataset using Deep Learning Convolutional Neural Network (CNN)

With applications ranging from postal services to digitized document processing, handwritten digit identification is a key problem in the fields of machine learning and computer vision. By utilizing their capacity to automatically extract hierarchical characteristics from unprocessed pixel data, Convolutional Neural Networks (CNNs) have become effective instruments for addressing this job. This study presents a thorough investigation of a CNN-based method that uses the MNIST dataset to recognize handwritten numbers. We explore the design, implementation, and performance assessment of the CNN model, demonstrating its ability to achieve high accuracy on tasks involving the recognition of numbers. We also go over the significance of our results for the larger picture of image categorization and suggest directions for further investigation and advancement. Key Words: CNN, MNIST, Convolutional Layer, Pooling layer, Max Pooling, Neural Networks, Preprocessing, Dropout Layer, Activation layer, Rectified Linear Unit (ReLU), Epochs, MNIST dataset