Number Of Variables Research Articles

Interictal burden in migraine patients at the outset of CGRP monoclonal antibody prevention

BackgroundThe total burden of migraine includes not only the episodes with headache pain but extends throughout the interictal periods. Interictal symptoms and associated psychological responses may profoundly impact well-being and drive treatment-seeking behavior.MethodsA cross-sectional online survey was conducted with participants aged ≥ 18 years, 250 with episodic migraine (EM) and 250 with chronic migraine (CM), having ≥ 4 monthly migraine headache days. All were naïve to galcanezumab or began ≤ 6 months before survey completion. The study evaluated factors associated with the Migraine Interictal Burden Scale (MIBS-4), including social determinants of health and well-being. Multiple linear regression, logistic regression, and random forests (RF) were used to explore predictors of MIBS-4.ResultsThe majority of participants (90%) were female with a mean (standard deviation) age of 40.6 (± 12.0) years and 18.1 (± 12.7) years since the first migraine episode. Sociodemographically, the EM and CM groups were similar. Common comorbidities were anxiety disorder (45%) and depression (44%). Migraine family history was reported in 59% of participants. MIBS-4 was correlated with a number of diverse variables, including well-being, anxiety sensitivity, income, aura symptoms, and the worst migraine pain in the year before starting galcanezumab. Linear and logistic regression identified years since the first symptom, worst migraine attack pain, premonitory symptoms, and income as significant predictors. RF explained more of the variance than multiple linear regression and introduced additional concepts to the prediction of MIBS, identifying well-being (WHO-5 total score), the WHO-5 item “cheerful and in good spirits,” worry about exercise, and fear of missing social obligations as significant predictors. Socioeconomic status and income were also critical explanatory variables for interictal burden (IIB) based on regression modeling and RF. Still, income was the only variable significantly associated with IIB across regression and RF methods.ConclusionsInterictal burden should be considered in the medical care of people with migraine. This additional burden is holistic, with psychosocial and socioeconomic elements in addition to residual symptoms. It is essential to consider this when assessing the impact of IIB.

Development of transient ischemic attack risk prediction model suitable for initializing a learning health system unit using electronic medical records

BackgroundPatients with transient ischemic attack (TIA) face a significantly increased risk of stroke. However, TIA screening and early detection rates are low, especially in developing countries. This study aims to develop an inclusive and practical TIA risk prediction model using machine learning (ML) that performs well in both hospital and resource-limited clinic settings. This model is essential for initiating the first ML-enabled learning health system (LHS) unit designed for routine and equitable TIA screening and early detection across broad populations.MethodsEmploying a novel protocol, this study first standardized data from a hospital’s electronic medical records (EMR) to construct inclusive TIA risk prediction ML models using a data-centric approach. Subsequently, a quantitative distribution of TIA risk factors was applied in feature engineering to reduce the number of variables for a practical ML model. This refined model initiated a TIA ML-LHS unit that is capable of continuously updating with new EMR data from hospitals and clinics. Additionally, the practical model underwent external validation using data from another hospital.ResultsThe inclusive 150-variable ML models, derived from all available EMR variables for TIA, achieved a recall of 0.868 and an accuracy of 0.886 in predicting TIA risk. Further feature engineering produced a practical XGBoost model with 20 variables, maintaining acceptable performance of 0.855 recall and 0.796 accuracy. The initialized TIA ML-LHS unit, based on the practical model, achieved performance metrics of 0.830 recall, 0.726 precision, 0.816 ROC-AUC, and 0.812 accuracy. The model also performed well in external validation, confirming its effectiveness with patient data from different clinical settings.ConclusionsThis study developed the first inclusive and practical TIA XGBoost model from full hospital EHR and initiated the first TIA risk prediction ML-LHS unit. This TIA model, which requires only 20 variables, enables the ML-LHS to serve not only patients in hospitals but also those in resource-limited clinics. These results have significant implications for expanding risk-based TIA screening in community and rural clinics, thereby enhancing early detection of TIA among underserved populations and improving health equity. The novel protocol used in this study is also applicable for initiating ML-LHS units for various preventable diseases, providing a new system-level approach to responsible AI development and applications.

Communication Skills Challenges Experienced by First-year University Students: A Systematic Review

This article isbased on a comprehensive analysis of 20 peer-reviewed research papers published between 2013 and 2023. The study offers a thorough summary of typical communication obstacles faced by first-year students. The purpose of this systematic review paper is to highlight the vital role that communication plays in academic performance and social integration among university students. The review synthesizes findings of research carried out on the communication difficulties particularly experienced by first-year university students. Some scholars contend that effective communication skills are essential in order for university students to successfully complete challenging academic assignments, form bonds with others, and participate actively in their academic and social activities. Among the main obstacles noted are nervousness related to public speaking, problems with academic writing, difficulties taking notes and actively listening, and difficulties participating in group projects. These obstacles have been demonstrated to seriously impair students' social integration, academic performance, and participation in class activities, all of which can have a negative impact on their retention and general well-being. It has further been observed that students who struggle with academic writing and public speaking anxiety are less able to participate fully in class discussions and create high-quality written work, both of which are fundamental for academic exploits. Additionally, difficulties in taking notes and actively listening hinder students' ability to fully comprehend lectures. This systematic review also indicates a number of variables that contribute to these difficulties, including a lack of exposure to academic communication skills at the university level, cultural and linguistic barriers, and poor high school preparation. In addition, the paper emphasizes the need for early intervention and assistance to, first-year students, in order to help them overcome the perceived communication obstacles. The article concludes by providing helpful suggestions to improve communication skills, such as include seminars on communication skills in the curriculum, peer mentorship programs, and extra support services like writing centers and academic advice. The review's conclusions highlight how important it is for universities to emphasize communication skills in their first-year experience programs in order to foster social integration, student engagement, and academic achievement.

GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Imaging Studies

The Granger framework is useful for discovering causal relations in time-varying signals. However, most Granger causality (GC) methods are developed for densely sampled time-series data. A substantially different setting, particularly common in medical imaging, is the longitudinal study design, where multiple subjects are followed and sparsely observed over time. Longitudinal studies commonly track several biomarkers, which are likely governed by nonlinear dynamics that might have subject-specific idiosyncrasies and exhibit both direct and indirect causes. Furthermore, real-world longitudinal data often suffer from widespread missingness. GC methods are not well-suited to handle these issues. In this paper, we propose an approach named GLACIAL (Granger and LeArning-based CausalIty Analysis for Longitudinal studies) to fill this methodological gap by marrying GC with a multi-task neural forecasting model. GLACIAL treats subjects as independent samples and uses the model’s average prediction accuracy on hold-out subjects to probe causal links. Input dropout and model interpolation are used to efficiently learn nonlinear dynamic relationships between a large number of variables and to handle missing values respectively. Extensive simulations and experiments on a real longitudinal medical imaging dataset show GLACIAL beating competitive baselines and confirm its utility. Our code is available at <a href='https://github.com/mnhng/GLACIAL'>https://github.com/mnhng/GLACIAL</a>

Long-term effects of medication-assisted treatment in opioid using offenders in German prisoners—treatment outcomes in prison and after release

Abstract Objectives The aim of this study was to examine the long-term effects of medication-assisted treatment after release from prison. We compared prisoners receiving medications for opioid use disorder (MOUD) to prisoners receiving no MOUD, on a number of outcome variables. Self-reported drug use was confirmed with laboratory tests. Methods A sample of 247 prisoners in German prisons completed the European Addiction Severity Index shortly before release. Subjects were contacted again at 1, 3–6, and 12 months after release for telephone interviews. Results Compared to no-MOUD subjects, MOUD subjects reported fewer illegal opioids and significantly fewer non-prescribed substitution medications. MOUD participants showed high treatment continuity through 12 months. Benefits of MOUD peaked at 3–6 months; effects were reduced at 12 months. Convergent validity of the self-report measure of drug use with laboratory drug screenings was satisfactorily high (~ 71% convergence). Conclusions For long-term sustainability of MOUD effects, additional psychosocial interventions are recommended.

Efficient Neural Network-Based Compact Modeling for Novel Device Structures Using a Multi-Fidelity Model and Active Learning

Neural network (NN)-based compact modeling methodologies are gaining attention due to the challenges of device complexity, narrow model coverage, and SPICE simulation speed in advanced semiconductor technology nodes. As device complexity increases, the number of process and structural variables also increases, which significantly increases the amount of technology computer-aided design (TCAD) simulation data required for NN-based compact modeling. This study proposes a multi-fidelity model and active learning approach to predict global and local variations of nanosheet FETs (NSFETs) with less than 1.5% error, significantly reducing the number of required TCAD simulations by more than half compared with conventional modeling techniques. In addition, the simplified NN model with a smaller training dataset significantly reduces the SPICE simulation time.

Effective Strategies for Enhancing Medication Adherence and Compliance to Improve Patient Outcomes

In order to improve patient outcomes, it examines practical methods for boosting drug adherence and compliance. Medication adherence is critical to attaining intended treatment outcomes and is impacted by a number of variables, such as the nature of the medical illness, patient-related features, therapy-related complications, and the dynamics of the healthcare system. The World Health Organization highlights the significance of adherence programs in the field of public health, positing that enhanced adherence may yield greater benefits than improvements in specific medical treatments. This review addresses the obstacles to adherence. It promotes a patient-centered care strategy that strengthens the bond between doctors and patients and invites them to actively participate in their care decisions. And also emphasizes the potential for improved medication adherence to result in better health outcomes and lower healthcare costs by putting in place customized training tools, enhancing communication tactics, and addressing systemic healthcare challenges. In the end, a multimodal strategy is necessary to get over obstacles and encourage adherence, which will enhance public health in general. Key words Medication Adherence , Patient –Centered Care, Healthcare Communication, Barriers to compliance, Health Outcomes, Health Literacy, Chronic Conditions

Multiple criteria and statistical sentiment analysis on flooding

The World Bank lists flooding as one of the main pressures on a community. Flooding can affect development prospects and potentially reverse decades of progress in the alleviation of poverty and in development. Flooding-related information usually involves many stakeholders, objects, and significant details. We examined linkage between the density of 506 flood management keywords from documents found via Google Search and 32 macroenvironment indicators for 76 countries, developing 506 neural networks models. These models show that flood management keywords are interconnected with the environmental, social, economic, political, and cultural dimensions of the examined countries. The models demonstrate that improvements in a country’s sustainability and performance metrics are followed by an increase in flood keyword use. Microsoft Azure AI and ChatGPT were used to generate abstractive summaries for the 100 most dense and statistically significant flood management keywords and 28 macroenvironment indicators for the individual countries included in our analysis, and for the world. We reduced the number of flood keywords density variables from 506 to 100 by selecting only the most relevant ones in order to generate abstractive summaries. Our Web of Science Sentiment Analysis Articles Model and Map of the World demonstrates that nations with an unfavorable macroenvironment published disproportionately fewer papers on sentiment analysis. This research benefits stakeholders by providing comprehensive and holistic information and data analysis, focusing on evidence-based flooding information.

Predictive Modeling of Personal Medical Insurance Costs: Analyzing Key Factors and Interactions

This study aims to use Kaggle's dataset to predict personal medical insurance costs, including age, sex, BMI, number of children, smoking status and regional variables. Through comprehensive statistical analysis and model improvement, we have determined important forecasting factors and interactions that affect charges. It is mainly found to include non-linear relationships between age and medical expenses, as well as the significant impact of interaction between BMI and smoking. The enhanced regression model combines these interactions and non-linear effects, showing great improvements in terms of interpretation capabilities, the R^2 value is 0.9642. The results provide actionable insights for insurance policy formulation, health management programs, and risk assessment, demonstrating the importance of considering complex variable interactions in predicting medical expenses. Future research should continue exploring these relationships to further refine predictive models.

Price Dimensions and Location in Decision to Transact at A Syari’ah Bank

The development of syari’ah Banks seems to be still experiencing ups and downs. After a boom in its revenue in 1991, Bank Shari'ah grew rapidly, but its contribution did not increase significantly until in 2021 it started a new chapter as Bank Syri'ah Indonesia (BSI). Many studies soon emerged to re-evaluate its services. This study tries to assess services through the service marketing mix. This research was developed using an associative design, trying to connect the elements of the service marketing mix with the customer's decision to transact at BSI. There are five variables used with 34 indicators as the measuring tool. The study population is Bank Syari'ah customers in Palembang City with a sample of 120 people taken by purposive sampling. Furthermore, the primary data obtained using the questionnaire was processed using multiple linear regression. The results of the study prove that of the number of variables used, only price and location are proven to have a significant influence on transaction decisions at BSI.

Binary composite crossover genetic algorithm for locating critical slip surface

Solving slope stability problems requires determining the critical slip surface (CSS) of a slope and its corresponding minimum factor of safety (Min. F), and determining the CSS is a complex optimisation problem. In this paper, we propose a real-coded binary composite crossover genetic algorithm (RGA-BCC) to locate the CSSs of slopes. A reasonable combination of parameters for the binary composite crossover (BCC) operator in the RGA-BCC is determined through six benchmark function experiments. On this basis, the stability of five soil slopes from the literature was analysed using the RGA-BCC in combination with the Morgenstern and Price method, and the results obtained were compared with those in the published literature to demonstrate that the RGA-BCC is able to efficiently determine the CSSs of slopes and their minimum factors of safety. The results of the sensitivity analyses show that having a reasonable setting for the number of control variables is the key to effectively determining the CSS. Moreover, the effects of slope structural characteristics should be considered when setting the number of control variables and population size. Algorithm comparison results show that RGA-BCC has better performance in solving the slope CSS problem compared with differential evolution (DE) and sparrow search algorithm (SSA).

An Efficient Combination of a CPA Loading Protocol, Metal Container, and Storage Method for Successful Articular Cartilage Vitrification

Successful cryopreservation of articular cartilage (AC) depends on a number of variables, including heat transfer, sample packaging for cryogenic storage, cryoprotectant agent (CPA) concentration (toxicity), and CPA permeation into AC. In the first experiment of the present study, we used a combination of our established vitrification protocol (430-min multi-step loading Protocol 8) and the metal Ovarian Tissue Cryosystem (OTC) as a closed vitrification-storage-rewarming container to vitrify 7-mm diameter porcine osteochondral dowels with 2 methods of storage (storage in the OTC with or without a surrounding vitrification solution). In the second experiment, in an attempt to introduce a more reproducible and safe vitrification protocol, we employed our successful Protocol 8 with the OTC as the CPA loading container and a cryobag as the storage container (with or without surrounding vitrification solution). In both experiments, post-warming normalized chondrocyte viabilities were assessed. The results of the first experiment demonstrated that a combination of the established step-wise CPA loading-vitrification-rewarming protocol (Protocol 8) with the metal OTC as the only container through all steps of CPA loading, vitrification, and rewarming produced a positive result. In the second experiment, combination of Protocol 8, the OTC as the CPA loading system and a cryobag as the storage container in both vitrified groups with or without solution resulted in high normalized chondrocyte viabilities (94.63% and 96.17%, respectively) post vitrification in 7-mm diameter porcine osteochondral dowels. Overall, this study demonstrated that by employing our established Protocol 8, using the metal OTC as a CPA loading container and a cryobag as the storage method we could achieve a reproducible method for clinical applications involving vitrified articular cartilage.

Evaluation of boron toxicity in soil and influencing factors: A case study of sodic soils of Panipat, India.

Boron toxicity is an increasingly serious problem leading to soil degradation and vegetation loss in arid and semi-arid environments worldwide. The soils of solonetzic complexes often display this characteristic. This study aimed to investigate the vertical distribution of hot water soluble-boron (hws-B) in the sodic and agricultural soils of Israna block in Haryana, India. The study also examined the influence of various soil variables on hws-B. The samples were collected at six depths to assess the availability of B. Additionally, an assessment was conducted to ascertain the appropriateness of the water sources for irrigation. The findings of our study revealed a notable disparity in the hws-B content between sodic soils and control agricultural soils, with the former exceeding the toxic thresholds. The levels of B available to plants were highest in the upper layers of soil and reduced as the depth increased. A number of variables influenced the water-soluble B content in sodic soils, particularly pH, calcite content, electrical conductivity (EC), organic matter (OM), and calcium-boron ratio (Ca/B). The results indicated that the boron levels in both surface and groundwater were within acceptable limits for irrigation purposes, suggesting that boron toxicity may not be directly attributed to these sources. Our study suggests that sodic soils contain high levels of boron, which can be toxic, and consequently, proper management of these soils is recommended. It is essential to have a comprehensive understanding of boron levels in surface and sub-surface soils, as well as in irrigation water, in order to effectively manage soil and improve crop productivity.

Testing mediating variables and relationships with relative weight analysis

The purpose of this research article is to put forth a new research process for employing relative weight analysis (RWA) to test for mediating variables and relationships. When implementing this new research process, RWA is employed as the primary analysis technique implementing a modified joint significance test with RWA results. An empirical demonstration of this new research process is implemented, and the findings are compared to results from multiple regression analysis implementing the PROCESS macro. In situations where there a relatively large number of independent variables that are highly correlated, the findings suggest that employing RWA is better suited to test for mediating variables and relationships. This is the first article which addresses employing RWA to test for mediating variables and relationships.

Machine learning and regression in the management of runoff in bauxite mines under rehabilitation.

Accurate and reliable forecasting of monthly runoff considering several years of rehabilitation helps in planning and managing the water resources system of bauxite mining areas. A combination of linear regression models and artificial intelligence was proposed and applied to predict runoff from mined areas. The ideal number of input variables was chosen considering a correction threshold (correlation above 95%), stepwise selection, and according to the recursive feature elimination (RFE). The linear regression equations were divided into explanatory and predictive, with the predictive ones being trained and validated with all possible combinations of annual data periods. Five machine learning models (linear model (LM), K-nearest neighbor regression (KNN), support vector machine (SVM), random forest (RF), and Cubist) were trained and used as a prediction instrument on the test set of each subsequent year. Precipitation, accumulated precipitation duration, maximum precipitation intensity, and number of events were the most explanatory variables in most linear regression and machine learning equations. The linear regression model approach presented satisfactory performance indices in predicting surface runoff in a mined bauxite area. The Cubist and RF models were the best predictors for the greatest number of years of rehabilitation, with a coefficient of determination varying with maximum values between 0.52 and 0.88 in validation. There was no best model that consistently presented the best result in all years of rehabilitation, and it is recommended to use the annual model for the respective year of rehabilitation. The linear regression equations and algorithms were useful tools in predicting runoff series and, therefore, promising monthly runoff prediction. The presented approach becomes a starting point in the search to optimize the hydrological planning of surface bauxite mines.

Inference of multiple high-dimensional networks with the Graphical Horseshoe prior

We develop a novel full-Bayesian approach for multiple correlated precision matrices, called multiple Graphical Horseshoe (mGHS). The proposed approach relies on a novel multivariate shrinkage prior based on the Horseshoe prior that borrows strength and shares sparsity patterns across groups, improving posterior edge selection when the precision matrices are similar. On the other hand, there is no loss of performance when the groups are independent. Moreover, mGHS provides a similarity matrix estimate, useful for understanding network similarities across groups. We implement an efficient Metropolis-within-Gibbs for posterior inference; specifically, local variance parameters are updated via a novel and efficient modified rejection sampling algorithm that samples from a three-parameter Gamma distribution. The method scales well with respect to the number of variables and provides one of the fastest full-Bayesian approaches for the estimation of multiple precision matrices. Finally, edge selection is performed with a novel approach based on model cuts. We empirically demonstrate that mGHS outperforms competing approaches through both simulation studies and the application to a bike-sharing and a genomic dataset.

Clinical and Epidemiological Information Required for Lyme Disease Surveillance in a Low-Incidence State, California 2011-2017.

Background: Between January 1, 2011, and December 31, 2017, over 12,000 case reports of Lyme disease (LD) were submitted to the California Reportable Disease Information Exchange for further investigation. The number of case reports has tripled compared to previous years, emphasizing the need for efficient estimation and classification methods. We evaluated whether estimation procedures can be implemented in a low-incidence state such as California to correctly classify a case of LD, similar to those procedures used in high-incidence states. Objective: The purpose of this study was to identify whether a minimum number of variables was sufficient to reliably classify cases in California and potentially reduce workload while maintaining the ability to track LD trends in California. Methods: To determine the relative value of diagnostic information, we compared five candidate logistic regression models that were used to classify cases based on information that varied in its degree of difficulty for collection. Results: Our results using California's surveillance data showed that automatically reported data were not sufficient, additional information such as, a patient's clinical presentation and travel history were necessary in a low-incidence state to improve the overall sensitivity of the models. Conclusion: This study may help inform public health surveillance efforts by demonstrating that both clinical and travel information are required to accurately classify a case of LD in a low-incidence state.

Pengaruh Kualitas Pelayanan, Citra Merek, Harga, dan Kualitas Produk Terhadap Loyalitas Pelanggan

One of the beverage products that has many fans in Indonesia is coffee. Coffee is one of Indonesia's leading plantation commodities that has an important role, especially as a source of foreign exchange, a provider of employment. Jokopi is one of the coffee shop brands that has a contemporary coffee drinking trend. With the tagline "telling and cultivating". The purpose of this study is to determine the effect of Brand Image, brand image, price and Service Quality simultaneously on customer loyalty. In this study, the research method used is a quantitative research method and in this study there are 30 to 500 samples, in addition, if the study will conduct an analysis with multivariate (correlation or multiple regression), then the number of sample members is at least 10 times the number of variables studied. The results of the test together (simultaneously) show that the variables of Service Quality, Brand Image, Price and Product Quality have an effect on Customer Loyalty, this can be proven by the results of F count 15.729 or a significance value of 0.000 <0.05.

Control parameter layout of compressor airfoils and adaptive optimization method for boundary extension

Parameterized optimization methods are effective approaches for achieving high aerodynamic performance in compressors. Traditional parameterized optimization methods rely on a designer's preselected control parameter layout (including control frame orientation, point density distribution, control point displacement direction, number of variables, and variable ranges), which are purely based on empirical knowledge without sufficient theoretical basis. This paper selects the free-form deformation (FFD) method and Bayesian algorithm as the parameterization method and optimization algorithm for compressor airfoil optimization and studies the influence of FFD control parameter layouts on aerodynamic optimization performance. Additionally, an adaptive optimization method for control parameters based on FFD is proposed, where the orientation and density of the control framework can be incorporated as variables into the control parameters. During the optimization process, the range of design variables is adaptively expanded. A comparison between FFD optimization results based on B-spline and Bernstein basis functions shows that the former achieves an average performance improvement of 4% relative to the latter. Furthermore, an optimization method with an infinitely expandable boundary based on Bernstein basis FFD is proposed, which improves the performance by 12% compared to general adaptive boundary expansion methods.

Selecting the Best Power Generation Technologies by Using Multi Criteria Decision Making Analysis

The increasing demand for electric power is prompting engineers to consider sustainable energy production systems in Europe. Challenges include energy supply security, affordability, material efficiency, and environmental protection. Based on a number of variables, this study seeks to determine which sustainable technologies are most effective for producing electricity. Numerous competing criteria are present in the sources of energy production; multi-criteria decision-making methods (MCDM) were applied, and the analysis was conducted using the MULTIMOORA, VIKOR, and TOPSIS methodologies. The results of the MULTI MOORA method revealed the best biomass (CHP) power plants; via VIKOR, the best technology for producing electricity was condensing turbine coal; and for TOPSIS, the best technology was solar photovoltaics. These findings demonstrated that future energy policy should be focused on sustainable energy technologies.