Impact Of Multiple Factors Research Articles

Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine Learning

Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine Learning

Factors influencing travel time and getaway time of helitack crews on wildfires in the province of Quebec

Background Reducing the delay between the detection of a fire and the arrival of the initial attack (IA) crew can have a significant impact on the likelihood of the IA’s success. Aims The objective of this study was to identify factors influencing same-day getaway time, next-day getaway time and travel time of helitack IA crews in the province of Quebec, Canada. Methods Using generalised linear modelling and model selection, we analysed the impact of multiple factors on these three distinct times. Key results Our results show that factors such as the distance between the departure base and the fire, the number of flight legs to reach a fire, dispatch hour, departure base location, the fire’s rate of spread, Julian date, the number of active fires, fuel type and the fire’s size at detection all influenced getaway time and travel time with varying degrees of influence. Conclusions The factors with the highest influence were distance for travel time and dispatch hour for both same-day and next-day getaway times. Implications Addressing these high-impact factors through the modification of deployment policies and the positioning of helitack crews could help reduce response times.

The Research on Factors Influencing Diabetes

Diabetes is a serious disease for people, causing massive damage to body. However, people learn a little about the diabetes, showing that it is very important to analyze the relationships between diabetes and several factors. We incorporate multiple factors such as age and gender into a unified framework to jointly test the impact of multiple factors on the dependent variable. Whether a person owning the diabetes is been seen as the result. This paper established logistic regression model and using a comprehensive dataset sourced from Kaggle containing these factors to estimate paraments and using F1-score and Receiver Operating Characteristic (ROC) curve to evaluate the whole model. The results show that the estimation of parameter HbA1c is 2.53, playing the most important role in the diabetes. Blood glucose, Age, and BMI account, either. Apart from that, it could be learned from the Stand Error (SE) and P-value about the accuracy. The characters of F1-score and ROC curve show that this model performs exceptionally well in identifying diabetes cases. A conclusion could be drawn that a person could try to exercise or improve diet to release the symptom of diabetes.

The effect of failure on energy efficiency in maritime vessels autopilot systems

The increasing demands for energy efficiency and environmental sustainability in the maritime industry have underscored the critical importance of optimizing autopilot systems, which, despite their significance, are often overlooked in ship energy efficiency management. The objective of this study is to enhance the energy efficiency of ships by focusing on the efficiency of autopilot systems, which play a significant role in the management of energy efficiency. The research emphasizes the need for effective decision support systems for ship operators, not only for optimizing ship speeds but also for making informed operational decisions. By utilizing Fuzzy Fault Tree Analysis (FFTA), the study identifies and prioritizes the causes of efficiency losses in autopilot systems and examines their frequency. Based on expert opinions, the research delves into the complexity of autopilot systems and the interactions among various components. Notably, the study highlights the impact of multiple factors on the efficiency of complex autopilot systems, elucidating their relationships through Minimum Cut Sets (MCS) analysis. Furthermore, attention was drawn to the “Improper Alarm Input” event caused by insufficient knowledge and awareness among ship operators, which hinders the effective use of autopilot systems. The findings demonstrate that decision support systems can increase energy efficiency and contribute to the reduction of operational errors by reducing the human factor, which is 99% effective on the “Inefficient Heading Control System”. Additionally, proper utilization of autopilot systems can lead to a decrease in a ship's carbon footprint and operating costs. Overall, the results can affect strategic decisions in ship energy efficiency management and encourage significant steps toward achieving International Maritime Organization's (IMO) sustainability objectives.

Sustainability Assessment of Brown Trout Populations in Serbia (Central Balkans) Using the Modified ESHIPPO Model

In the inland waters of the Balkans, many brown trout populations have been severely depleted. Therefore, identifying potential threats to their continued survival and ranking populations based on their biological and evolutionary importance enables setting conservation priorities. To assess the sustainability of the brown trout populations in the territory of Serbia (central Balkans), a modification of the ESHIPPO model was performed. The main modification involves incorporating the investigated populations’ genetic structure into the model. Therefore, the new ESHIPPOsalmo model includes an analysis of biological parameters and the impact of multiple factors, including habitat alterations, invasive species, pollution, human population growth, and over-exploitation. In order to investigate individual levels of influence of the model’s analyzed parameters, a combination of supervised and unsupervised machine learning methods was used. The structure of the model is based on general and easily measurable indicators, which enables its application in any salmonid river in the world. By evaluating the parameters of the ESHIPPOsalmo model, we were able to establish that, of the analyzed populations from 46 localities, 37% have a moderate level of sustainability, 43% low, and 20% critically low.

Mapping the journey of patients and care partners living with adult-onset leukoencephalopathy with axonal spheroids and pigmented glia: developing a framework for improvements in care.

Aim: To identify and raise awareness of healthcare service gaps for individuals with adult-onset leukoencephalopathy with axonal spheroids and pigmented glia (ALSP).Materials & methods: An ALSP patient journey map from symptom onset throughout disease course was developed using existing literature, patient and clinician feedback from a structured workshop and community survey data regarding attitudes toward genetic testing.Results: ALSP diagnosis is frequently delayed due to low awareness of this rare condition and symptom overlap with more common neurological conditions. Multiple factors impact patients' decision-making regarding genetic testing for ALSP, symptom management and participation in research studies.Conclusion: These results highlight the challenges faced by individuals with ALSP and should support program development to improve patient care.

A study on load sharing characteristics of the planetary gear train system with cracked gears

Load sharing characteristic is very important for the reliability of the planetary gear train. Cracked gears can make the load sharing performance become worse. Nevertheless, previous studies have not paid enough attention to the influence of cracked gears on the load sharing behavior, when compared with the impact of multiple factors under healthy condition. An 18 degrees of freedom dynamic model for the spur planetary gear train is established based on the centralized parameters theory in this paper. The influences of cracked gears on the load sharing behavior are investigated, which include the cracked sun gear, the planet gear containing cracked side meshing with the sun gear, the planet gear having cracked side meshing with the ring gear and the cracked ring gear. Additionally, the sensitivity analysis of the external and internal load sharing behavior to different cracked components is conducted. The results indicate that the load distribution becomes more uneven with the propagation of crack in the sun gear and ring gear, while the change regulation is different for each cracked planet gear. Moreover, the load sharing characteristic of the external meshing is more sensitive to the cracked sun gear and the planet gear 1, which has the crack side meshing with the ring gear. While the load sharing characteristic of the internal meshing is more sensitive to the cracked ring gear and the planet gear 3, which owns the crack side meshing with the ring gear than others. The output of this paper will be useful for the crack fault diagnose and fault source identification of the planetary gear train system.

Evaluating the impact of multiple uncertainty shocks on China's airline stocks volatility: A novel joint quantile perspective

This study proposes a new joint quantile impulse response function (jQIRF) and applies that function to investigate the impact of multiple uncertainty shocks on the volatility of China's airline industry. The jQIRF not only allows one to examine the joint impact of multiple factors on the target variable; the impact of these factors on specific quantiles or states of the target variable can also be examined. The empirical results show that, compared to traditional IRF, the proposed jQIRF successfully reveals the positive impact of multiple uncertainties on the volatility of the airline industry and obtains a narrower confidence interval for IRF. The jQIRF also successfully corrects the overestimation bias caused by simple aggregation in generalized IRF. In addition, empirical results at different quantiles show the existence of a “leverage effect” in the impact of uncertainty on airline volatility. This means that, in more volatile market environments, the positive joint impact of uncertainties is stronger. However, research that has focused on individual airline stocks indicates that the airlines appear to be capable of implementing measures to stabilize stock volatility, thereby mitigating the negative impact of uncertainties on the airline industry. Overall, the proposed jQIRF and empirical conclusions in this paper help to more accurately assess the impact of multiple factors on the airline industry from a joint perspective. This ability is beneficial for both policymakers and investors.

Key factors influencing students’ academic performance

Academic achievement is a multifaceted outcome influenced by a multitude of factors spanning across educational, socioeconomic, and individual characteristics. Understanding the key determinants of students’ academic performance is paramount for educators, policymakers, and institutions to enhance learning outcomes and facilitate targeted interventions. The absence of a cohesive framework for understanding academic achievement poses a barrier to effective interventions and policies, hindering educators and policymakers from implementing targeted strategies for student improvement. To address this challenge, it is crucial to conduct a thorough examination of the factors influencing academic performance, prioritizing their identification, exploring interactions, and examining their relative impact. Through this research, educational stakeholders can gain insights to develop evidence-based strategies that foster equitable learning outcomes and pave the way for academic success for all students. In this research, linear regression is chosen as the primary model due to its ability to model relationships between variables, making it suitable for assessing the impact of multiple factors on academic performance. Simulation results show that the proposed model achieved 98% accuracy in predicting the factors that are affecting students’ academic performance. The findings derived from the linear regression analysis offer statistical insights into the key factors influencing students’ academic performance. It also incorporates visual aids to enhance the clarity and interpretability of the results. This research interprets the implications of these key factors and discusses their broader significance in the context of students’ academic performance.

Study on the Selection of Recycling Strategies for the Echelon Utilization of Electric Vehicle Batteries under the Carbon Trading Policy

Global climate change has prompted all sectors of society to take urgent action to reduce carbon emissions. Electric vehicles are the key to low-carbon transportation transformation, but their popularity has led to difficulties in disposing of used batteries. Improper handling will pollute the environment and violate the original goal of promoting low-carbon practices. Therefore, it is crucial to establish a sustainable battery-recycling and disposal system. This study uniquely incorporates the concept of battery echelon utilization into its analytical framework using a Stackelberg game model, exploring the equilibrium strategies for stakeholders in a closed-loop supply chain under carbon emission constraints. We analyzed the impact of multiple factors in the recycling process, as well as the influence of digital technology, on enterprise pricing, recycling efficiency, and the choice of recycling channels. The study found that the market pricing of batteries and electric vehicles is not influenced by recycling participants, but is instead related to the application of digital technology. Numerical simulations further reveal that the battery’s echelon utilization rate and carbon emission limit policies jointly motivate enterprises to be more proactive in recycling. In the joint recycling model, battery suppliers can achieve more substantial profit growth compared to electric vehicle manufacturers, providing new insights and directions for innovation and the development of collaborative models within the supply chain.

Impact of multiple factors on the use of an UAV-mounted infrared thermography method for detection of debonding in facade tiles

Anomalies detection of adhesive tiling systems on the building facade plays a crucial role in structural healthy of buildings. Among defects, the debonding is a major potential threat due to its invisibility and universality. In recent years, there has been a growing trend in utilizing Unmanned Aerial Vehicles (UAVs) equipped with infrared lenses to acquire image data of facade tiles. Numerous previous studies mainly concentrate on the improvement of detection accuracy, and fewer studies investigated the physical range and lighting conditions when capturing infrared photographs using UAVs. This study based on experiments analyses the effects of three operating conditions factors, namely angle, distance, and solar illumination, on the detection effectiveness of tiles debonding by means of calculating the F2 score. There are some reasonable proposals for improving data quality and planning the flight route when using visual methods to detect the debonding defect of ceramic tiles on the building facade.

Exploring factors affecting the injury severity of highway-rail grade crossing crashes during the COVID-19 pandemic

ABSTRACT The outbreak of COVID-19 pandemic could alter the injury severity pattern of highway-rail grade crossing (HRGC) crashes. To compare factors affecting injury severities before and during the pandemic, HRGC crashes occurring between 2018 and 2021 in the United States were collected. A random parameters logit model with heterogeneity in means was developed to investigate the impact of multiple factors during the pandemic (2020-2021). Besides, a multinomial logit model was developed for the pre-pandemic (2018-2019) crashes. The present study indicates that the existence of old drivers and illuminated crossings generated random parameters with heterogeneity in means during the pandemic, which were affected by the driver in vehicle, estimated train speed, and crossing AADT indicators. As for the pre-pandemic period, no significant random parameters and unobserved heterogeneity were captured. Significant variables also differed markedly between these two periods, with no significant driver-related or vehicle-related indicators identified from 2018 to 2019.

Examining the relationship between proposed anti-LGBTQ+ legislation and LGBTQ+ college student mental health: findings from the Healthy Minds Study, 2021–2022

Objective: This study aimed to investigate the association between proposed anti-LGBTQ+ legislation and depressive symptoms among LGBTQ+ college students. Participants: Participants (N = 72,135) included LGBTQ+ college students (N = 21,466) from over 530 colleges and universities, including technical and community institutions, across the United States who took part in the Healthy Minds Study (HMS). Methods: Data on proposed anti-LGBTQ+ legislation introduced in 2021–2022 were collected from all 50 US states, categorized, and analyzed. Individual-level data for 2021–2022 were obtained from the HMS, and a multilevel analysis was conducted to assess the association between proposed anti-LGBTQ+ legislation and depressive symptoms while considering individual factors. Results: A significant positive association between proposed anti-LGBTQ+ legislation and increased depressive symptoms among LGBTQ+ college students was found. This association remained significant after controlling for individual-level stressors and identities, including a sense of belonging, campus environment perceptions, first-generation student status, and transgender or gender non-conforming identity (TGNC). Conclusions: The findings underscore the need for mental health support and inclusive policies for LGBTQ+ college students, particularly in regions where anti-LGBTQ+ legislation is proposed or enacted, to mitigate the potential negative impact of multiple factors on their mental well-being.

A new multivariate index for ecological security assessment in the China-Myanmar border region

The coordination of development efforts and ecological conservation in China's border regions is a significant challenge due to the overlap of biodiversity hotspots, ecologically fragile zones, and impoverished areas. Achieving the harmonious integration of ecological preservation and economic development relies on the fundamental assessment of ecological security (ES). However, comprehensive assessments of ES in border regions remain limited. This study introduces a new index, the multivariate ecological security index (MESI), which integrates ecosystem vigor, organization, elasticity, services and risk. Here, the MESI was utilized to assess the temporal and spatial changes in ES and its associated impact factors in the China-Myanmar border region (CMBR) from 2000 to 2020. The MESI provides a clear representation of the actual ES status in the CMBR, exhibiting a significant correlation with the eco-environmental quality index (EEQI; p < 0.01). The ES status exhibited notable spatial heterogeneity in the CMBR, consisting primarily of both relatively safe and safe levels, which accounted for approximately 85% of the total area. From 2000 to 2020, the CMBR experienced a gradual improvement in ES status, with the area experiencing an increase in the ES level accounting for 23.41% of the total area, which exceeded the proportion of the area experiencing a decrease in the ES level (4.71%). The combined impact of multiple factors exerted a greater influence on ES than did individual factors alone. Notably, human factors increasingly influenced the ES status during the study period. The results of this study provide valuable insights for ecological preservation and sustainable management in the CMBR, and the MESI can be extended to assess the ES of other regions.

Polypyrrole-coated zinc/nickel oxide nanocomposites as adsorbents for enhanced removal of Pb(II) in aqueous solution and wastewater: An isothermal, kinetic, and thermodynamic study

One common heavy metal that is extremely harmful to the kidneys, reproductive system, and nervous system of humans is lead (Pb). This study aimed to investigate the effectiveness of a novel nanocomposite adsorbent, synthesised from polypyrrole, zinc oxide, and nickel (PPy/ZnO-NiO), in eliminating Pb(II) ions from a water-based solution. This adsorbent offers excellent metal selectivity, environmental stability, ease of manufacture, and reusability. Several techniques, namely XRD, FTIR, TGA, SEM, TEM, EDX, and BET, were employed to characterize the synthesised nanocomposite adsorbent. The study examined the impact of multiple factors influencing the adsorption process, including pH, temperature, contact period, metal ion concentration, and adsorbent dose, along with isotherms, kinetics, and thermodynamics processes. The adsorption of Pb ions was examined via various isotherm models, including the Temkin, Freundlich, and Langmuir, and thus various factors inherent to pseudo-first-order, pseudo-second-order, and Elovich kinetics were determined. The data revealed that both the Langmuir sorption model and pseudo-second-order kinetics match the adsorption data well. The highest adsorption capacities of the PPy/ZnO-NiO nanocomposite adsorbent for Pb(II) metal ions were 436.48, 450.48, and 469.56 mg/g at 298, 305, and 310 K, respectively, and the optimal pH was 5. An actual water sample that had been tainted with Pb(II) ions was used to test the applicability of the PPy/ZnO-NiO nanocomposite for the removal of Pb(II) ions. Based on the available data, the PPy/ZnO-NiO nanocomposite appears to be effective in terms of elimination of Pb(II) from five different raw water samples, such as tap water, surface water, groundwater, sewage plant wastewater, and domestic wastewater.

A Study on Forecasting Sales of New Energy Vehicles in China Based on Time Series Analysis

Combining the methods of multiple linear regression and time series analysis, this paper forecasts the sales trend of China's new energy electric vehicles in the next 10 years by using the data of new energy electric vehicle sales from 2013-2022, and comprehensively analyzes the impact of multiple factors, such as policy, technology, economy, market, and environment, on China's new energy electric vehicle sales. The study selected 16 secondary indicators including government subsidies, vehicle sales, number of charging piles, GDP per capita, consumer satisfaction, carbon emissions, etc. for data analysis, highlighting the key role of technological factors in the development of the industry. The study concludes that China's new energy electric vehicle industry is expected to usher in a period of sustained growth as technology continues to advance and the market matures.

Frequency Regulation in Nanocomposite Sandwich Joined Conical-Conical Shells with Open Cell Foam Partially Supported by Pasternak Elastic Foundation Using GDQ

This study investigated the natural vibration of a sandwich conical-conical shell that is partially supported by Winkler–Pasternak foundations. The sandwich system comprises an open-cell foam (OCF) core and laminated composite face sheets reinforced with graphene platelets using functionally graded models (FG-GPLRC). To account for through-the-thickness shear deformations and rotary inertias, the first-order theory of shells is combined with Donnell-type kinematic assumptions. Hamilton’s principle is utilized to establish the general motion equations and related boundary and continuity conditions. The resulting system of equations is discretized using the semi-analytical generalized differential quadrature (GDQ) approach. An eigenvalue problem is formulated to analyze the corresponding mode shapes and vibration frequencies for the shell ends and continuity criteria under various boundary conditions. Parametric experiments are conducted for sandwich conical-conical shells partially supported by Winkler–Pasternak foundations. This study examined the impact of multiple factors on the frequency of a joined shell structure, including the total thickness, FG-GPL face sheet thickness, various boundary conditions, the length of each cone segment, and the Winkler and shear elastic foundation. Notably, this research also analyzed the effects of a partial elastic foundation on the structure’s frequency for the first time.

Health Insurance Annual Premium Forecast Analysis

This study focuses on predicting an important socioeconomic indicator: health insurance costs and provides an in-depth exploration of the impact of multiple factors on insurance costs. The data used in the study comes from a representative health insurance company, and its data individuals include indicators of multiple dimensions, such as the age, gender, body fat percentage, family size of the insured, as well as whether they have smoking habits and the specific region where they are located. and other information. In order to accurately reveal the impact of these factors on insurance premiums, we adopted a machine learning model, the Lasso regression model, for modeling and prediction, supplemented by the calculation of correlation coefficients to quantify the strength of the relationship between these factors and insurance premiums. After in-depth exploration and analysis, the research results show that among all factors considered, age, body fat percentage and whether you smoke have a significant impact. It is particularly noteworthy that the factor of smoking has the most significant impact on insurance costs. In addition, the study also revealed that women and insured persons living in the southeast region tend to choose higher premiums. These research results not only have a certain enlightenment effect on theoretical research, but also have significant reference value for the practice of the insurance industry. It can help insurance companies more accurately identify and evaluate potential risks, and set more scientific and reasonable insurance rates accordingly.

Blood pressure difference between pre and post-menopausal women and age-matched men: A cross-sectional study at a tertiary center.

Hypertension is a prominent risk factor for cardiovascular and cerebrovascular diseases. Gender differences and menopausal status contribute to blood pressure changes across the lifespan which have not been completely characterized. Our study aims to explore the impact of multiple factors on blood pressure levels in previously healthy women and men. Factors of interest included gender, menopausal status, age, and body mass index. Healthy women and men were recruited through healthcare facility announcements. Detailed menopausal history was obtained from females. The authors measured each participant's systolic and diastolic blood pressure at our outpatient clinics twice on the same day, and two different days, one week apart, and the authors included the mean of the averaged two readings for each participant. The study sample consisted of 313 participants. Female gender was a significant predictor of lower systolic and diastolic blood pressure (p<.001), while age significantly correlated with higher systolic blood pressure readings (p=.004). Although systolic blood pressure levels were significantly higher in postmenopausal females (124mmHg) compared to premenopausal females (116mmHg), our multiple linear regression analysis revealed that postmenopausal status did not significantly predict changes in either systolic or diastolic blood pressure. Our study demonstrates significant associations between blood pressure levels and various factors such as gender and age. This could emphasize the intricate interplay of demographic and clinical factors in blood pressure variations among individuals, highlighting the importance of a holistic approach to diagnosing hypertension, which considers various individual factors, including gender and age.

Multi‐factor Analysis of Capacity Allocation Optimization for Novel Wind‐Hydrogen Coupled Power Generation Systems

Wind power generation has the problem of wind resource waste. Wind‐hydrogen coupled can enhance wind power's utilization and revenue. Currently, there is no discussion of the impact of multiple factors on the system configuration. In this study, take the annual profit of the wind‐hydrogen coupled power generation systems (WHCPGS) as the objective function, and construct the multi‐factor capacity configuration model. Using particle swarm optimization (PSO) to solve the model, and analyzes the impact of multi‐factors on the system's annual profit and revenue mode. The results show that with 30 wind turbines, the coupled system annual profit increases by 43% compared to the alone system. The higher investment does not make the system more rewarding. The configuration results are the same for investments of 0.8 billion ¥ and 1 billion ¥. The price at 0.20 ¥ kW−1 h−1, the coupled system has the optimal annual profit. Fuel cells’ (FCs) electricity price and hydrogen transportation distance are crucial factors influencing the mode of the system profit. This study has specific reference significance for the practical application of capacity configuration of the system.