Impact Of Multiple Factors Research Articles

Predicting Student Performance in Online Learning: A Multidimensional Time-Series Data Analysis Approach

As an emerging teaching method, online learning is becoming increasingly popular among learners. However, one of the major drawbacks of this learning style is the lack of effective communication and feedback, which can lead to a higher risk of students failing or dropping out. In response to this challenge, this paper proposes a student performance prediction model based on multidimensional time-series data analysis by considering multidimensional data such as students’ learning behaviors, assessment scores, and demographic information, which is able to extract the characteristics of students’ learning behaviors and capture the connection between multiple characteristics to better explore the impact of multiple factors on students’ performance. The model proposed in this paper helps teachers to individualize education for students at different levels of proficiency and identifies at-risk students as early as possible to help teachers intervene in a timely manner. In experiments on the Open University Learning Analytics Dataset (OULAD), the model achieved 74% accuracy and 73% F1 scores in a four-category prediction task and was able to achieve 99.08% accuracy and 99.08% F1 scores in an early risk prediction task. Compared with the benchmark model, both the multi-classification prediction ability and the early prediction ability, the model in this paper has a better performance.

Investigating the Impact of Multiple Factors on CO2 Emissions: Insights from Quantile Analysis

This study investigates the impacts of alternative energy use, urbanization, GDP, agriculture, ICT development, and FDI on carbon dioxide (CO2) emissions in the 14 leading CO2-emitting countries in Asia. This research comprises various econometric techniques, including MMQR, FMOLS, DOLS, and Driscoll–Kraay, to extend the data analysis from 1996 to 2020. The findings provide significant support for an inverted U-shaped link between economic expansion and environmental deterioration, known as the environmental Kuznets curve. Moreover, this paper verifies that the GDP square, renewable energy use, and agriculture are shown to help to decrease pollution, as indicated by the research findings. On the contrary, urbanization and the GDP are demonstrated to be variables that contribute to carbon emissions. Furthermore, the panel quantile regression models validate that the impacts of each explanatory variable on CO2 emissions vary across various quantiles. Finally, this analysis provides valuable suggestions to scholars, environmentalists, politicians, and authorities for identifying and mitigating the main cause of emissions.

A hospital-based study of survival in oral cancer patients of Tata Memorial Hospital, Mumbai.

Oral cancer represents a significant global public health concern, with the death rate for lip and oral cavity malignancies experiencing a 1.40-fold increase worldwide in the past three decades. This retrospective study aimed to comprehensively understand overall survival (OS) and the influence of sociodemographic and clinical factors on patients diagnosed with oral cavity cancer. The study focused on oral cancer patients enrolled in 2016 and treated at Tata Memorial Hospital, Mumbai, with a follow-up period extending to 5 years until 2021. Utilising the Kaplan-Meier technique and log-rank test, we examined OS and variations based on sociodemographic factors, while the Cox proportional hazard model allowed us to investigate the simultaneous impact of multiple factors on OS. A total of 1,895 eligible participants were included. The overall 5-year survival rate was 65%. After adjusting for age, gender, education, primary site, tumour grade, TNM staging, treatment intention, status and modality, we found in our study oral cancer patients aged more than 60 years (HR = 1.37, 95% CI: 1.01-1.85, p-value 0.03), patients who had poorly differentiated carcinoma (HR = 2.44, 95% CI: 1.56-3.81, p-value < 0.001), belonged to stage IV as per TNM staging (HR = 2.44, 95% CI: 1.65-3.61, p-value < 0.001), patient who have received partial treatment (HR = 2.44, 95% CI: 1.65-3.61, p-value < 0.001) and only chemotherapy (HR = 3.56, 95% CI: 2.43-5.23, p-value < 0.001) found to have a higher hazard of dying while literate (HR = 0.73, 95% CI: 0.56-0.95, p-value 0.02) are protective. The retrospective nature of the study posed constraints in exploring additional variable associations. Overall early detection, appropriate treatment, and regular follow-up are critical for improving the survival rate of patients with oral cavity cancer. This research proposes that improving the socioeconomic status and promoting proactive treatment-seeking behaviour is crucial for enhancing the survival of oral cancer patients. Cancer hospitals, in collaboration with the wider public healthcare system in India, which includes clinicians and policymakers, should consider these suggestions to enhance cancer treatment and control in low-middle-income countries.

A vignette study analysing factors influencing attributions of responsibility to victims and perpetrators of intimate partner violence against women

Evidence shows that blaming attitudes towards victims of intimate partner violence against women (IPVAW) are widespread, creating a social climate that condones this form of violence. The aim of the current study is to analyse the potential impact of multiple factors surrounding the scenario and some personal and attitudinal characteristics of the respondents on the responsibility attributed to victims and perpetrators of IPVAW. To achieve this, a factorial survey design in which each respondent (N = 1,007; 51.1% women) received a unique vignette describing a hypothetical case of IPVAW was implemented in an online survey conducted in Spain. We found that most respondents (78.9%) indicated that the victim was not at all responsible for her own victimisation, whereas 73.7 per cent indicated that the perpetrator was very responsible for his own behaviour. Our results also show the prominent role that attitudes, as opposed to many characteristics of the abuse, play in evaluations of victim blame (that is, sexist beliefs and acceptability of IPVAW). Our findings reveal some persistence of victim-blaming attitudes despite years of public awareness and education efforts in Spain.

How does autonomous vehicles affect taxi industry? A two-stage Van Damme based tripartite evolutionary game perspective

As urban passenger transportation focuses more on economic efficiency and road safety, autonomous driving technology is being introduced in the taxi industry. Interests differ among automobile manufacturers, taxi companies, and the government in promoting the adoption of autonomous taxis (ATs). This paper investigates the impact of multiple factors in the mixed traffic operation mode on the tripartite game using the two-stage Van Damme model. It also analyzes the game-breaking key to automobile manufactures' decision in the dual stable situation. The findings are as follows: (1) Under government non-promotion, independent R&D guides taxi companies to adopt ATs more effectively. Under government promotion, the social acceptance and loss of technology dominance have varying impacts on the game results. (2) Adjusting infrastructure cost and subsidy intensity can lead to a single stable situation when taxi companies and the government adopt a wait-and-see approach. (3) The loss of technology dominance shifts game leader from automobile manufacturers to the government. (4) Taxi companies would only adopt ATs when the social acceptance is moderate, as it exhibits an inverted U-shaped relationship. These findings provide valuable insights for how to better promote the adoption of ATs under both promotion and non-promotion by the government.

IMPACTS OF GEOSPATIAL FACTORS ON VILLAGE SITE SELECTION ALONG THE GREAT WALL WATCHTOWER BASED ON LOGISTIC REGRESSION: A CASE STUDY IN GUBEIKOU SECTION, BEIJING, CHINA

Abstract. The Great Wall was listed as a World Heritage Site in 1987. In recent years, the Chinese government placed significant emphasis on the preservation of the Great Wall and its invaluable resources. However, it remains challenging to quantitatively analyze the impact of multiple factors and further identify the Great Wall resource sites. In this paper, a spatial quantitative analysis framework with binary logistic regression is implemented for the impact factors of village site selection along the Great Wall watchtowers. Elevation, slope, orientation, water distance, road distance, distance-to-watchtowers were selected as candidates. 177 villages were analyzed, and the model calculation was assessed with the confusion matrix and ROC curve. Youden index is used to determine the optimal threshold for the uncertain villages, subsequently. And we classified villages into three categories: villages with strong, medium and weak relevance with Great Wall resource sites. Experimental results show that the strong and medium relevance villages accounted for 68.36%. These villages are primarily located in the central part of the study area, characterized by flat terrain and closely aligned with the road network. Village density decreases with distance from the watchtowers. Additionally, out of the 40 uncertain villages, 5 were confirmed as villages while the remaining were classified as non-villages. The conclusions of this study can serve as a reference for evaluating and predicting suitable conservation and planning initiatives for the Great Wall and its surrounding villages in the future.

A multi‐DOE approach to investigate the effect of different factors on the properties of ternary binders

AbstractThe Design of Experiment (DOE) is a widely recognized approach to designing and analyzing experiments. It is a statistical method utilized to simultaneously examine the impact of multiple factors on the results. In this study, two different DOE methods, including mixture and factorial design were employed to evaluate the influence of various factors on the performance of ternary binders. To assess the effect of each component such as Portland cement (PC), calcium aluminate cement (CAC) and sulphate (CŜHx) on the workability and strength of ternary binders, mixture design was utilized. A two‐stage factorial design was implemented to investigate the impact of various factors on the fresh and hard properties of ternary binders. In the first stage the influence of composition, supplementary cementitious material (SCM) and cement replacement ratio were examined. In the second stage the impact of composition, CAC and CŜHx type were analyzed. Principal Component Analysis was also employed to analyse the continuous data obtained from the XRD patterns. The study results indicated that increased CAC and CŜHx content in the mixture enhances the workability of the pastes. High compressive strengths were found to be a result of increased cement content in the mixture and low SCM substitution.

Defaunated and invaded insular tropical rainforests will not recover alone: Recruitment limitation factors disentangled by hierarchical models of spontaneous and assisted regeneration

Abstract Most tropical forests are now severely degraded and their ability to recover is highly dependent on frugivores which ensure seed dispersal for most woody plants. The global collapse of large vertebrates therefore raises major concerns about tropical forest succession, but few field studies have been conducted to disentangle recruitment limitations during disrupted succession. This study took place on Réunion (Mascarenes) where all large native frugivores have been extinct since human colonisation in 1665 and where multiple invasions threaten native ecosystems. We set up 20 experimental blocks on a lava flow dated back to 1800, in plant‐impoverished post‐defaunation vegetation bordered by old‐growth forests. We assessed fecundity, seed dispersal and seedling recruitment of the complete fleshy‐fruited plant community and used Bayesian analyses to disentangle the impact of multiple factors on these key processes. In the same blocks, we sowed four native trees assumed to be disperserless to test their capacity to establish, controlling for two additional post‐dispersal limitations (seed predation and competition with invasive plants). On the flow, small‐seeded native plants were fairly dispersed but did not recruit, probably due to strong competition with invasive plants; the few native species that recruited somehow were mostly medium‐seeded plants that were still dispersed; large‐seeded plants were absent from seed rain (which shows that invasive frugivores did not replace extinct ones) and subsequently from spontaneous recruitment. Instead, some alien plants, notably the tiny‐seeded highly dispersed Clidemia hirta and the medium‐seeded Psidium cattleianum largely dominated seedling recruitment. Native plants recruited better at the forest margin, including some large‐seeded species nearby mother trees. Sown large‐seeded species were able to emerge and survive in all plots whatever the treatment, which demonstrates that dispersal loss was the primary cause of regeneration failure on the flow. Synthesis. The strong modulation of the establishment capacity of native plants by seed mass shows that invasive plants win by forfeit of large‐seeded plants after native frugivores loss. Our study emphasises the fundamental role of dispersal loss and competition with invasive plants in the disruption of ecological succession, as well as the urgency of restoring seed dispersal and strengthening biosecurity regulations.

Judging the Severity of Intimate Partner Violence Against Women: The Role of Situational Factors and Respondents’ Characteristics in Public Opinion

Public perceptions of the severity of intimate partner violence against women (IPVAW) play a crucial role in its prevention. This study examines the impact of multiple factors related to the scenario and respondent characteristics on the perceived severity of IPVAW. To achieve this, a factorial survey design was implemented using a non-probability-based sample in Spain ( N = 1007). The results indicate that 90.3% of respondents rated the scenario as severe. The perceived severity was influenced by both the details of the scenario and the characteristics of the respondents. These findings can inform the design of targeted awareness campaigns.

Multi-objective optimization design of solar air collector with a frustum-shaped protrusion

The artificial convex is arranged on the absorber plate of a solar air collector can significantly improve the solar utilization efficiency. The heat transfer efficiency is determined by the geometric types of protrusion and its arrangement in the heating channel. In this study, four parameters, the height and gap of the protrusion structure, the arrangement spacing of the V-shaped structure, and the airflow attack angle are considered to reflect the effect of protrusion on fluid flow and heat transfer characteristics in the solar air collector. Considering the interactive coupling relationship among various structure and location parameters, taking the maximum average Nusselt number, minimum friction coefficient, and maximum comprehensive heat transfer coefficient (THPP) as the objective functions, the Response Surface Method (RSM) is adopted to comprehensively analyze the impact of multiple factors on the thermal efficiency of solar air collector, and thus obtains the optimal design parameters of solar air collector with the optimal comprehensive performance. Comparing with the univariate method, the THPP value of the optimized structure by RSM is 3.6% higher than that by the univariate method. The value of thermal efficient of the optimized structure by RSM is 26% higher than that of the smooth plate solar air collector. When the Reynolds number is higher than 10000, the value of thermal efficient of the optimized structure by RSM is much higher than those of the published references. In addition, the contribution of different influencing factors to improving the comprehensive performance of solar air collector was obtained, which is meaningful for providing theoretical basis for engineering optimization design.

Injury severity analysis of highway-rail grade crossing crashes in non-divided two-way traffic scenarios: A random parameters logit model

Highway-rail grade crossing (HRGC) crashes in non-divided two-way traffic scenarios have caused numerous fatalities and injuries over the years. Although crucial to the safety of multimodal transportation systems, these crossings have received little attention and previous studies did not fully account for the unobserved heterogeneity and its potential interactive effects. To bridge these gaps, the HRGC crashes occurring between 2019 and 2020 in the United States were collected from the Federal Railroad Administration's Office of Safety Analysis System. A random parameters logit model with heterogeneity in means was developed to investigate the impact of multiple factors associated with crossings, crashes, drivers, vehicles, and the environment. The present study indicates that did not stop behavior generates the random parameter with heterogeneity in means that is influenced by the dark and land with commercial power indicators. Furthermore, the findings show that factors such as estimated vehicle speed > 25 MPH, train speed > 45 MPH, going around the gate, old driver, female driver, motorcycle, and the driver was in vehicle indicators would increase the likelihood of more severe injury outcomes in HRGC crashes. Notably, the adverse crossing surface and truck indicators demonstrate unexpected marginal effects by reducing the likelihood of severe injury outcomes at non-divided two-way traffic HRGCs. This study emphasizes the importance of considering unobserved heterogeneity in the context of HRGC crashes. The findings can serve as a foundation for developing targeted interventions aimed at enhancing road and railway safety.

Setup accuracy and margins for surface-guided radiotherapy (SGRT) of head, thorax, abdomen, and pelvic target volumes

The goal of the study was to evaluate the inter- and intrafractional patient setup accuracy of target volumes located in the head, thoracic, abdominal, and pelvic regions when using SGRT, by comparing it with that of laser alignment using patient skin marks, and to calculate the corresponding setup margins. A total of 2303 radiotherapy fractions of 183 patients were analyzed. All patients received daily kilovoltage cone-beam computed tomography scans (kV-CBCT) for online verification. From November 2019 until September 2020, patient setup was performed using laser alignment with patient skin marks, and since October 2020, using SGRT. The setup accuracy was measured by the six degrees of freedom (6DOF) corrections based on the kV-CBCT. The corresponding setup margins were calculated using the van Herk formula. Analysis of variance (ANOVA) was used to evaluate the impact of multiple factors on the setup accuracy. The inter-fractional patient setup accuracy was significantly better using SGRT compared to laser alignment with skin marks. The mean three-dimensional vector of the translational setup deviation of tumors located in the thorax, abdomen, and pelvis using SGRT was 3.6 mm (95% confidence interval (CI) 3.3 mm to 3.9 mm) and 4.5 mm using laser alignment with skin marks (95% CI 3.9 mm to 5.2 mm; p = 0.001). Calculation of setup margins for the combined inter- and intra-fractional setup error revealed similar setup margins using SGRT and kV-CBCT once a week compared to laser alignment with skin marks and kV-CBCT every other day. Furthermore, comparable setup margins were found for open-face thermoplastic masks with AlignRT compared to closed-face thermoplastic masks with laser alignment and mask marks. SGRT opens the possibility to reduce the number of CBCTs while maintaining sufficient setup accuracy. The advantage is a reduction of imaging dose and overall treatment time. Open-face thermoplastic masks may be used instead of closed-face thermoplastic masks to increase the patient's comfort.

Biomechanical investigation of tasks concerning manual materials handling using response surface methodology

In typical manual material handling, the variations in walking pattern are decided by various factors, such as load being handled, frequency of handling, walking surface, etc. Traditional gait analysis protocols commonly evaluate individual factor within specified ranges associated with particular activities or pathologies. However, existing literature underscores the concurrent impact of multiple factors on gait. This study identifies five pivotal factors—walking speed, surface slope, load carried, carrying method, and footwear—as contributors to gait alterations. To address risk factors in manual material handling activities, we propose a unique design-of-experiment-based approach for multi-task gait analysis. Unraveling the relationship between manual handling attributes and human gait holds paramount importance in formulating effective intervention strategies. We optimized the five input factors across a cohort of 15 healthy male participants by employing a face-centered central composite design experimentation. A total of 29 input factor combinations were tested, yielding a comprehensive dataset encompassing 18 kinematic gait parameters (such as cadence, step length etc., measured using inertial measurement system), the isolated impacts of factors, and the interplay of two-factor interactions with corresponding responses. The results illuminate the optimal scenarios of input factors that enhance individual gait performance—these include wearing appropriate footwear, employing a backpack for load carriage, and maintaining a moderate walking pace on a medium slope with minimal load. The study identifies walking speed and load magnitude as primary influencers of gait mechanics, followed by the chosen carrying method. In consequence, the insights gained advocate for the refinement of manual material handling tasks based on the outcomes, effectively mitigating the risk of musculoskeletal disorders by suggesting the interventions for posture correction.

Determinants influencing burnout levels among Ukrainian EFL educators: theoretical constructs and personal experience

The purpose of this article is to explore and analyse the theoretical aspects of factors affecting burnout levels among Ukrainian EFL (English as a Foreign Language) teachers. This article aims to provide a comprehensive understanding of the main contributors to teacher burnout and stress, as well as examine the potential impact of the socio-political and educational context in Ukraine. By delving into these factors, the article seeks to offer valuable insights for improving teacher well-being and retention, and ultimately, enhance the quality of English language education in Ukraine. This investigation synthesized and analysed existing literature on burnout among Ukrainian EFL teachers, revealing a complex interplay of work-related factors, such as workload, classroom management, and student behaviour, and the unique socio-political context in Ukraine. The ongoing conflict and political turmoil in Ukraine, coupled with language policy reforms and national testing systems, have emerged as critical factors affecting EFL teachers’ burnout levels. Additionally, the full-scale armed aggression of Russia against Ukraine has further heightened stress and burnout among teachers due to the disruption of normal school functioning and increased emotional and psychological demands. Addressing the specific challenges faced by EFL teachers in Ukraine, including the impact of the armed aggression, is crucial for promoting well-being and resilience. Previously unsolved aspects of the problem, such as the cumulative impact of multiple factors and coping strategies, warrant further investigation. The insights gained from this analysis can inform the development of targeted interventions and strategies to support teacher well-being and retention, ultimately enhancing the quality of English language education in Ukraine. A continued focus on understanding and addressing burnout among Ukrainian EFL teachers is essential for fostering a sustainable and resilient teaching workforce. The scientific novelty of this investigation lies in its comprehensive analysis of the factors contributing to burnout among Ukrainian EFL teachers, with a particular emphasis on the unique socio-political context and ongoing armed aggression. By synthesizing and analysing existing literature, this investigation identifies previously unsolved aspects of the general problem and highlights potential avenues for future research. The insights gained from this study contribute to the existing body of knowledge on teacher burnout and inform the development of targeted interventions and strategies to support teacher well-being and retention in the specific context of Ukraine. The practical value of this research is its potential to inform the development of targeted interventions and strategies that address the specific challenges faced by Ukrainian EFL teachers, ultimately supporting their well-being and retention. By identifying key factors contributing to burnout, this investigation provides valuable insights for policymakers, educational institutions, and teacher support services in designing and implementing effective measures to mitigate teacher burnout. The findings also highlight the importance of offering tailored professional development opportunities, promoting positive teacher-student relationships, and addressing workload and classroom management challenges. This research serves as a foundation for fostering a more sustainable and resilient teaching workforce, which is essential for enhancing the quality of English language education in Ukraine.

Detection and attribution of the start of the growing season changes in the Northern Hemisphere

Global climate change has led to significant changes in land surface phenology. At present, research on the factors influencing the start of the growing season (SOS) mainly focuses on single factor effects, such as temperature and precipitation, ignoring the combined action of multiple factors. The impact of multiple factors on the spatial and temporal patterns of the SOS in the Northern Hemisphere is not clear, and it is necessary to combine multiple factors to quantify the degrees of influence of different factors on the SOS. Based on the GIMMS3g NDVI dataset, CRU climate data and other factor data, we used geographic detector model, random forest regression model, multiple linear regression, partial correlation analysis and Sen + Mann-Kendall trend analysis to explore the variation of the SOS in the Northern Hemisphere to reveal the main driving factors and impact threshold of 17 influencing factors on the SOS. The results showed that (1) during the past 34 years (1982–2015), the SOS in Europe and Asia mainly showed an advancing trend, whereas the SOS in North America mainly showed a delaying trend. (2) The SOS was mainly controlled by frost frequency, temperature and humidity. Increasing frost frequency inhibited the advancement of the SOS, and increasing temperature and humidity promoted the advancement of the SOS. (3) There were thresholds for the influences of the driving factors on the SOS. Outside the threshold ranges, the response mechanism of the SOS to driving factors changed. The results are important for understanding the response of the SOS to global climate change.

Investigating factors associated with medicinal cannabis authorization dosage among military Veterans in Canada

LAY SUMMARY This work investigated factors associated with medicinal cannabis authorization dosage among 9,104 Canadian Armed Forces Regular Force Veterans in Canada with a valid Cannabis for Medicinal Purposes reimbursement on Dec. 31, 2020, and identified various socio-demographic, Veterans Affairs Canada (VAC) pensionable conditions, and military service characteristics associated with higher-dose medicinal cannabis authorizations. Among those with higher dose reimbursements were Veterans under the age of 30 years, males, those receiving benefits for health conditions (e.g., hearing loss, musculoskeletal, or mental health conditions), those participating in VAC rehabilitation services, those with an earlier year of reimbursement, those who were released involuntarily from service, and those indicating land military environment service at date of release. In statistical models investigating the impact of multiple factors, some of the strongest associations with higher dosages were observed for Veterans with mental health conditions, those with earlier reimbursements, and province of residence. Introduction: Since 2008, Veterans Affairs Canada (VAC) has provided Canadian Armed Forces Regular Force Veterans with reimbursement of Cannabis for Medical Purposes (CMP) authorizations. The authorized dosage and authorization criteria have changed with time. This study investigated factors associated with CMP authorizations and dosage among CMP-authorized Veterans. Methods: CMP authorizations among 9,104 Veterans residing in Canada on Dec. 31, 2020, were linked with VAC reimbursement, VAC client, and military personnel records. Multivariable logistic regression models were used to examine relationships between CMP dosage and socio-demographic, health, and military characteristics. Results: Among Veterans with CMP authorizations, the strongest associations with a larger authorization dosage (4–10 grams vs. 1–3 grams) were observed for Veterans receiving benefits for mental health conditions in combination with other health conditions (OR = 3.47 compared with those with no mental health conditions). A larger authorization dosage was associated with province of residence (OR = 3.36 for New Brunswick compared with Ontario), earlier year of authorization (OR = 2.19 for 2014) compared with 2016, being male (OR = 1.68), active participation in a rehabilitation program (OR = 1.45), land environment at the time of release from military service (OR = 1.24) compared with air environments, and involuntary release from service (OR = 1.65) and medical release (OR = 1.11) compared with voluntary release. Discussion: Factors associated with larger CMP authorization dosage among military Veterans in Canada appeared multifactorial, spanning socio-demographic, health, and military characteristics. This complexity should be considered by treatment providers and clinicians working with military Veterans.

Spatial-temporal differentiation and driving factors of ecological resilience in the Yellow River Basin, China

Ecological problems are considered to be the key constraint to high-quality development in the Yellow River Basin (YRB), so accurately grasping and understanding the spatial–temporal differentiation characteristics and driving factors of ecological resilience (ER) in the YRB is crucial for the sustainable development of the YRB. This study develops a three-dimensional ER evaluation index system comprising resistance (RE), adaptability (AD), and recovery (RC), measures the ER of 76 cities in the YRB using the entropy method, and investigates the spatial–temporal evolutionary characteristics and drivers of ER in the YRB using the Dagum Gini coefficient (DGC), Kernel density estimation (KDE) and geographic detector methods (GDM). The results show that the spatial differences in ER of the YRB showed a narrowing trend. The primary cause of the overall differences was the interregional differences. There is no significant polarization of ER in the basin as a whole, and the differentiation between the three regions is gradually weakening. Economic development (ED) is a key factor influencing the spatial differences in ER in the YRB and downstream regions. Average annual precipitation (AP) and adaptability (AD) are the most important drivers affecting ER in the upper and middle reaches, respectively. The combined impact of multiple factors on the spatial differences of ER in the YRB is significantly more pronounced than that of individual factors. This study provides empirical evidence and decision-making reference to comprehensively grasp the current ER development characteristics in the YRB, and to develop and implement differentiated regional ecological conservation strategies.

A quantitative study of the factors influencing human evacuation from ships

Due to the constraints of various factors influencing human evacuation on board, it remains a challenging problem to accurately quantify the impact of these factors on the evacuation process. To analyse the multiple influential factors of human evacuation from ships, a specific framework based on orthogonal experiments is proposed in this paper to comprehensively investigate the impact of multiple factors on the evacuation time and the efficiency of the evacuation process. Heeling angles, unavailable stairs, and priorities of evacuees are identified as influential factors according to the characteristics of human evacuation from ships. The analysis results show that the heeling angle has a very significant effect on both evacuation time and efficiencies, and the efficiencies decrease as the heeling angle increases. Unavailable stairs also have a significant effect on evacuation results, the magnitude of which depends on the number of stairs nearby. While the effect of priorities of evacuees on evacuation results is relatively less important, it can be found that priority evacuation of pedestrians with impaired mobility will aid to achieve optimal evacuation results. In conclusion, the findings of this study can help managers quickly develop effective evacuation strategies in emergencies to further improve the safe operation of passenger ships.

Stochastic capacity analysis for a distributed connected automated vehicle virtual car-following control strategy

Capacity analysis of the pure connected automated vehicle (CAV) traffic remains a challenging problem due to the high-dimensional factors involved in the control design. Especially, the communication loss and communication topology greatly impact the headway variation of CAVs and hence capacity with stochastic properties. This study provides a stochastic framework to mathematically derive multiple factors’ impact including free-flow speed, control gains, communication loss, and traffic arrival pattern on the pure CAV traffic capacity based on a virtual car-following control strategy targeting a single-lane highway and merging section. To begin with, we first mathematically derive the stochastic capacity for a single-lane highway based on a multi-predecessor-based linear feedback and feedforward car-following model for generic stochastic communication loss models. For a further illustration, a detailed analysis is conducted based on a well-known Signal-to-Interference-plus-Noise Ratio (SINR) communication loss model. We then extend the derivation to a merging section by a virtual car-following concept considering traffic arrival pattern’s stochasticity. Numerical sensitivity analyses have been conducted to systematically evaluate the impact of multiple factors mentioned. As the result indicated, the stochastic communication loss and traffic arrival pattern do have a significant impact on the pure CAV traffic capacity of the above scenarios.

Natural Gas Demand Forecasting Model Based on LASSO and Polynomial Models and Its Application: A Case Study of China

China aims to reduce carbon dioxide emissions and achieve peak carbon and carbon neutrality goals. Natural gas, as a high-quality fossil fuel energy, is an important transition resource for China in the process of carbon reduction, so it is necessary to predict China’s natural gas demand. In this paper, a novel natural gas demand combination forecasting model is constructed to accurately predict the future natural gas demand. The Lasso model and the polynomial model are used to build a combinatorial model, which overcomes the shortcomings of traditional models, which have low data dimensions and poor prediction abilities. In the modeling process, the cross-validation method is used to adjust the modeling parameters. By comparing the performance of the combinatorial forecasting model, the single forecasting model and other commonly used forecasting models, the results show that the error (2.99%) of the combinatorial forecasting model is the smallest, which verifies the high accuracy and good stability advantages of the combinatorial forecasting model. Finally, the paper analyzes the relevant data from 1999 to 2022 and predicts China’s natural gas demand in the next 10 years. The results show that the annual growth rate of China’s natural gas demand in the next 10 years will reach 13.33%, at 8.3 × 1011 m3 in 2033, which proves that China urgently needs to rapidly develop the gas supply capacity of gas supply enterprises. This study integrates the impact of multiple factors on the natural gas demand, predicts China’s natural gas demand from 2023 to 2033, and provides decision-making support for China’s energy structure adjustment and natural gas import trade.