Related Environmental Factors Research Articles

2768 Needs of people with dementia in the perioperative environment from the perspective of healthcare professionals

Abstract Introduction The incidence of dementia among patients in perioperative settings is on the rise, presenting significant challenges for healthcare professionals in delivering adequate and appropriate care to this patient population. In order to gain a deeper understanding of the perioperative care needs of patients with dementia, thirty healthcare professionals were interviewed. The focus was on their experiences and perspectives regarding the fulfilment of these needs. Key factors influencing perioperative care were identified and categorised into three main themes: patient-related factors, healthcare professional-related factors, and healthcare environment-related factors. Methods Thirty interviews were conducted with a diverse group of healthcare professionals, including anaesthetists, surgeons, nurses, and other perioperative staff. Thematic analysis was employed to process and interpret the data, identifying recurring themes and sub-themes that reflect the complexities of perioperative care for patients with dementia. Results The analysis revealed three primary themes: 1) Factors related to the patient with dementia: Cognitive impairment and comorbidities uniquely challenge perioperative care. The unfamiliar hospital environment often exacerbates cognitive symptoms, and adherence to postoperative protocols can be problematic. Family involvement is crucial in supporting these patients. 2) Healthcare Professional Factors: Perceptions of dementia, communication issues, pain assessment, and the need for personalised care were highlighted. Training and education deficits among healthcare professionals were evident, impacting the quality of care. 3) Institutional Factors: Organisational policies and resource allocation significantly affect the provision of dementia care. Support for healthcare professionals through ongoing education and the development of dementia-specific guidelines were identified as essential needs. Conclusion Effective perioperative care for patients with dementia requires addressing multifaceted challenges. Improving communication, enhancing education and training for healthcare professionals, involving family members, and ensuring institutional support are critical steps. A comprehensive, empathetic approach can lead to better outcomes and experiences for patients with dementia in the perioperative setting.

Factors influencing compassion satisfaction and compassion fatigue among nurses: a study in a tertiary hospital

BackgroundNursing is a caring profession for which compassion is a core value. Increasing stress and declining job satisfaction are among the major challenges in nursing. Demographic and work-related factors may influence nurses’ compassion satisfaction and compassion fatigue (i.e., burnout and secondary traumatic stress) levels. In this study, the level of compassion fatigue and compassion satisfaction and their associated factors were examined among nurses in a tertiary hospital.MethodsA cross-sectional study was conducted at a tertiary hospital in Malaysia. The data were collected over a period of 6 months via online distribution of the Personal Information Form, Copenhagen Psychosocial Questionnaire (COPSOQ) version III and Professional Quality of Life (ProQOL) version V questionnaires. The Cronbach’s alpha internal consistency of the questionnaire scales was mostly acceptable and above 0.75. Descriptive statistics were used to summarize the sociodemographic and rank domains of work environment-related factors for nurses and their levels of compassion satisfaction and compassion fatigue. Relationships between sociodemographic factors and the levels of compassion fatigue, compassion satisfaction, and burnout were assessed by bivariate analyses. A p value < 0.05 was considered to indicate statistical significance.ResultsA total of 323 registered nurses participated in this study. A majority of the participants were female (91%, n = 294), and the mean age of the participants was 33.01 ± 8.50 years. The majority of the participants had moderate levels of compassion satisfaction (71%, n = 229); 46% (n = 148) had moderate levels of burnout, and 45% (n = 147) had moderate levels of secondary traumatic stress. Hierarchical multiple regression analysis revealed that the health and well-being and demands at work domain were significantly associated with compassion satisfaction, burnout and secondary traumatic stress levels among nurses.ConclusionsIn this study, the majority of the nurses reported decreased compassion satisfaction and increased burnout. These findings provide valuable insights, as there may be detrimental effects on the healthcare industry and retention of nurses if no action is taken to combat compassion fatigue. Recommendations to motivate nurses and reduce demands at work should be explored by healthcare organizations to increase nurses’ performance and job satisfaction.

Short-term impact of low air pressure on plants' functional traits.

Lower atmospheric pressure affects biologically relevant physical parameters such as gas partial pressure and concentration, leading to increased water vapor diffusivity and greater soil water content loss through evapotranspiration. This might impact plant photosynthetic activity, resource allocation, water relations, and growth. However, the direct impact of low air pressure on plant physiology is largely unknown. This study examined the effects of low air pressure, alone and combined with two water inputs, on different functional traits of three plant species transplanted from montane grasslands at 1,500 m a.s.l. during the first four weeks of their early phenological stage: Trifolium pratense, Hieracium pilosella, and Brachypodium rupestre. Using the terraXcube Ecotron facility which can simulate different climatic conditions, we isolated the effect of air pressure from those of other, related environmental factors (temperature, humidity, and solar radiation) by simulating three different elevations with corresponding air pressures: 1,500 m a.s.l. (85 kPa, control scenario), 2,500 m a.s.l. (75 kPa), and 4,000 m a.s.l. (62 kPa) and we used two different water regimes to observe the combined effect of low air pressure and the impact of varying water inputs on plants. In T. pratense and H. pilosella, we observed an increase in stomatal conductance but a reduction in aboveground biomass at the lowest pressure compared to the control scenario after four weeks of incubation. Contrastingly, B. rupestre showed an interactive effect of air pressure and water treatment on chlorophyll and biomass nitrogen content, which were reduced under higher soil water conditions at 85kPa. This study serves as an initial step in isolating the specific impact of air pressure on plant physiology, demonstrating the potential of the facility for future research. The mixed response patterns across species highlight that atmospheric pressure could be a driving factor to consider when assessing plant responses along elevational gradient.

Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel Imagery

The extensive peatlands of the Tibetan Plateau (TP) play a vital role in sustaining the global ecological balance. However, the distribution of peatlands across this region and the related environmental factors remain poorly understood. To address this issue, we created a high-resolution (10 m) map for peatland distribution in the TP region using 6146 Sentinel-1 and 23,730 Sentinel-2 images obtained through the Google Earth Engine platform in 2023. We employed a random forest algorithm that integrated spatiotemporal features with field training samples. The overall accuracy of the peatland distribution map produced is high, at 86.33%. According to the classification results, the total area of peatlands on the TP is 57,671.55 km2, and they are predominantly located in the northeast and southwest, particularly in the Zoige Protected Area. The classification primarily relied on the NDVI, NDWI, and RVI, while the DVI and MNDWI were also used in peatland mapping. B11, B12, NDWI, RVI, NDVI, and slope are the most significant features for peatland mapping, while roughness, correlation, entropy, and ASM have relatively slight significance. The methodology and peatland map developed in this work will enhance the conservation and management of peatlands on the TP while informing policy decisions and supporting sustainable development assessments.

Microbial communities associated with the skin, gill, and gut of large yellow croaker (Larimichthys crocea)

The microbiota inhabiting the surface of fish mucosal tissue play important roles in the nutrition, metabolism and immune system of their host. However, most investigations on microbial symbionts have focused on the fish gut, but the microbiota associated with external mucosal tissues (such as the skin and gill) is poorly understood. This study characterised the traits and dynamic of microbial communities associated with the skin, gill and gut of large yellow croaker (Larimichthys crocea) culturing with net enclosures or pens at different sampling times (with seasonal transition). Results revealed the structure and function of microbial communities differed according to the mucosal tissues of large yellow croaker. The richness and diversity of microbiota in the skin were significantly higher than that in the gill and gut. Discriminative microbial taxa such as Psychrobacter in the skin, Enterobacterales in the gill, and Fusobacterium in the gut, and discriminative predictive functions were identified in the skin, gill and gut. Furthermore, different environmental-related factors (such as sampling time/season and culture method) had impacts on the fish microbiota differently. The diversity and composition of microbiota associated with the skin, gill and gut changed over time, and the difference in skin microbiota across sampling times was most significant among the three tissues. The culture method significantly impacted the diversity and composition of skin microbiota, but no significant difference was found in the gill and gut microbiota between net enclosure and net pen. These results indicated that the skin microbiota of large yellow croaker was more diverse and affected by environmental-related factors than other tissues. This study provides new insights into the structure, environmental response pattern, and relationship with host health of microbiota associated with the mucosal tissues of large yellow croaker.

Meta-analysis of the Occurrence Characteristics and Influencing Factors of Microplastics in Agricultural Soil in China

The potential threat of soil microplastics （MPs, particle sizes smaller than 5 mm） to the agricultural environment and food security production has become a hot issue, but there are few systematic studies on the characteristics and influencing factors of MP pollution in agricultural soil in China. Based on the data of soil MPs and related environmental factors （temperature, precipitation, soil pH, and organic carbon） and social and economic factors （permanent population, gross regional product per capita, gross industrial product per capita, and cultivated land area per capita） extracted from 6 694 samples from 85 published studies from 2020 to 2023, meta-analysis was performed. The characteristics of MPs pollution in agricultural soil and the key factors affecting the accumulation of MPs in soil in six administrative regions of China were analyzed. The results showed that soil MPs had six colors： （semi-） transparent, black, white, yellow, blue, green, and red. There were five kinds of shapes： fragment, fiber, film, particle, and foam. The particle size was between 0.01 and 5.0 mm. There were nine types of MPs： polyethylene （PE）, polypropylene （PP）, polystyrene （PS）, polyester （PES）, polyethylene terephthalate （PET）, polyvinyl chloride （PVC）, rayon （RY）, polyacrylonitrile （PAN）, and polyamide （PA）. The abundance gradient of soil MPs in different regions was as follows： South-central region &gt; Northwest region &gt; Southwest region &gt; East China region &gt; North China region &gt; Northeast region. The （semi-） transparent （27.6%）, fragmented （32.2%）, 0.01~0.5 mm （66.3%）, and PE （37%） types of MPs had the highest detection rates. The characteristics of soil MPs are the result of natural environmental factors （temperature, precipitation, soil pH, organic carbon） and social factors （permanent population, gross regional product per capita, gross industrial product per capita, cultivated land area per capita）.

An interpretation framework of machine learning models: prediction of pavement long-term performance

ABSTRACT Poor interpretation of machine learning models restrains its extensive application to practical engineering. In this regard, this study proposed an interpretation framework of machine learning models used for the prediction of pavement long-term performance, which consists of feature importance based on the SHapley Additive exPlanations (SHAP) method, factor influence analysis based on Accumulated Local Effects (ALE) and Local Conditional Effects, and feature interaction analysis based on H-statistic and second-order ALE. The interpretation framework was validated through its application in the prediction of pavement roughness and rutting. The Gradient Boost Regression Trees model was used as the machine learning model and the generalisation performance of the model exceeded 80%. According to the proposed interpretation framework, it can be concluded that environment-related factors dominated the deterioration of pavement roughness. The influence of precipitation on the pavement roughness followed a logarithmic increase. There was a threshold effect for the influence of traffic load on pavement roughness. In terms of pavement rutting, the traffic load and pavement structure number were the determinants. Among environmental factors, short-wave radiation contributed the most to pavement rutting. Feature interaction was related to the feature level and tended to be more significant at a relatively low feature level.

Critical Human and Organizational Factors for Structural Safety in the Dutch Construction Industry.

Human errors are widely acknowledged as the primary cause of structural failures in the construction industry. Research has found that such errors arise from the situation created by human factors and organizational factors embedded in the task context. However, these contextual factors have not been adequately addressed in the construction industry. Therefore, this study aims to identify the critical Human and Organizational Factors (HOFs) that influence structural safety in frequently performed tasks in structural design and construction. Through a comprehensive literature review, a framework consisting of potential critical factors called the HOPE framework, is presented. To identify the most critical HOFs that contribute to human error occurrences, a questionnaire survey to experts in the Dutch construction industry was conducted. Finally, the resulting framework was compared with three actual structural failures for validation. This study shows that the HOFs should be extended with project-related factors (P) and working environment-related factors (E) due to the fact that these task contextual conditions play a significant role in shaping professionals' on-the-job performance. Furthermore, a survey identified 14 HOFs as critical in contributing to an error-prone situation in the structural design and construction tasks. The presented HOPE framework and the identified critical HOFs for structural safety can assist engineers with better hazard identification and quality assurance in practice.

Tracing the origin of wood at the regional scale with dendrochemical markers: elemental and strontium and neodymium isotopic composition

The dendrochemical approach holds significant promise for determining the geographical origin of wood as a complementary tool to dendroprovenancing based on tree-ring width. Tracing the origin of wood based on its elemental and isotopic composition is in particular interesting for provenance studies at the regional scale, as dendrochemical signatures are indicative of geological and pedological characteristics. Although, absorption of nutrients and trace elements reflects the composition of soil bioavailable pools, it is also modulated by multiple environmental and tree related factors. We assess the dendrochemical approach in oak wood provenance studies regarding the challenges posed by 1) geochemical variation at a local level, and 2) dendrochemical variation related to oak species, age and social status. A combination of 13 elemental concentrations and 2 isotope – Sr and Nd – ratios was analyzed in oak wood from 12 forests situated in the large Seine river catchment in France. The sampling targeted sites with representative types of geological parent material, where adult trees of two common temperate Quercus species (Quercus petraea and Quercus robur) were selected. The impact of tree characteristics was markedly weaker than that of the site factor and the minimal differences in dendrochemical composition between Quercus petraea and Quercus robur were considered without effect on determination of the geographical origin. Linear discriminant analysis based on Ca-normalized elemental concentrations and Sr and Nd isotope ratios correctly classified the origin of 93 % of the sampled trees. Strontium isotope ratio 87Sr/86Sr was the strongest provenance indicator capable of determining the origin of 55 % of wood samples on its own. Our system based on elemental and strontium and neodymium isotopic compositions provides robust indicators for wood tracing and might be applied to provenance studies in environment, physiology, history and archeology.

The Role of Underwater Soundscapes in Shaping the Distribution of Cobitidae Fish: Insights From Multiple Environmental Factors

ABSTRACTSound is critical in fish behaviours, including reproduction, feeding, predator avoidance and habitat selection. To investigate the impact of underwater soundscape on the distribution of Cobitidae fishes, we surveyed the status of Cobitidae fish resources and the underwater soundscape along with related environmental factors at 12 sampling sites downstream of the Songxin Hydropower Station in the Heishui River, a major tributary of the Jinsha River in China. Generalised additive models (GAMs) were employed to identify the primary environmental factors influencing the distribution of Cobitidae fishes and we analysed the correlation between the abundance of Cobitidae fish and primary environmental factors. The study revealed that the water depth, flow velocity, water temperature and sound pressure level (SPL) all had highly significant effects on the distribution of Cobitidae fishes (p &lt; 0.001), with SPL showing the lowest relative importance among these factors. Water depth positively correlated with Cobitidae fish abundance, whereas water temperature, SPL and flow velocity showed negative correlations. This study preliminarily explored the response relationship between the distribution of Cobitidae fish and four main environmental factors, offering a technical foundation for predicting the distribution of Cobitidae fishes in rivers and for implementing conservation measures of fish such as suitable habitat creation.

Spatial-temporal characteristics and the importance of environmental factors in relation to algal blooms in coastal seas

In the prediction of algal blooms with the artificial intelligence (AI) technology, the proper selection of the indices is crucial to the quality of the results. Although eutrophication and climate change have been identified as the main contributors to algal blooms, limited knowledge of the relationships among environmental factors hinders the accurate and correct AI prediction. To explore the importance of environmental factors in the occurrence of algal blooms in coastal seas and facilitate the selection of inputs for the prediction model, this study examines observed water quality data and analyzes their relationship using machine learning methods. It was found that nutrients, water temperature, and salinity play important roles in controlling algal blooms in coastal seas, and their influence varies depending on locations. The high-risk cases of algal bloom mainly occur in environments with medium salinity (22–28 psu), particularly in open estuaries with high nutrient levels. According to the feature importance analysis, water temperature presented the most significant impact on algal bloom risk compared to other environmental factors. The variations of water temperature and Chl-a concentration are synchronous when the water temperature is below 32 °C. Additionally, the relationships between inorganic nutrients (NO3−-NO2− and Ortho-P) and Chl-a exhibit asynchronous across multi-temporal scales. The uptake of inorganic nutrients occurs in large quantities within the 24 h prior to the boom in Chl-a concentration. This demonstrates that variations in concentration of inorganic nutrients can be considered as crucial factors in algal bloom predictions, and their significant decrease can be served as an important signal of an impending algal bloom. This study enhances our understanding of the mechanisms behind algal blooms and facilitates their management and prediction strategies in coastal seas.

A novel hybrid fuzzy analytical hierarchy process–game theory model for prioritizing factors affecting the deterioration of water pipelines

Water pipes face significant aging and degradation problems due to several pipe-related, soil-related, operational, and environmental factors. Hence, the paramount objective of this research paper is to prioritize the criticality of the factors affecting the deterioration of water pipes in Hong Kong. The framework of the developed model is envisioned based on two main modules, namely weight computation and weight aggregation. The first module incorporates identifying and categorizing water deterioration factors. Then, the relative importance priorities of water deterioration factors are scrutinized using seven weight computation methods. These methods encompass analytical hierarchy process (AHP), Monte Carlo AHP, fuzzy AHP, magnitude-based fuzzy AHP, total difference-based fuzzy AHP, spherical fuzzy AHP and Pythagorean fuzzy AHP. In this regard, fuzzy-based and Monte Carlo-based methods are leveraged to circumvent the critical shortcomings of classical AHP. The performances of weight computation methods are analyzed using statistical evaluation indicators of satisfactory index (SAT) and distance between weights (WD). The second module is a hybrid meta-heuristic-based game theory model designated for compiling the importance weights of deterioration factors obtained from the first module. In this context, a set of widely acclaimed meta-heuristics are exploited and examined for optimizing the significance of deterioration factors. Analytical results exemplified that soil-related factors implicate the deterioration process more than pipe-related, operational-related, and environmental-related factors. It was also inferred that water pressure (6.64%) is the most significant factor influencing water pipe deterioration followed by internal corrosion and protection method (6.11%), and then soil corrosivity (6.05%). On the other hand, length (1.93%), rain deficit (1.97%), and street block length (2.33%) constitute the least influencers on water pipe deterioration. Results also demonstrated that spherical FAHP outperformed other variants of AHP accomplishing SAT and WD of 0.065 and 0.057, respectively. Comparative analysis revealed that particle swarm optimization-based game theory is a better mechanism than the remainder of meta-heuristic-based game theory models in obtaining a more accurate compromised-based weighting vector to the experts’ judgments. It is envisaged that this research can assist the water supplies department in identifying, assessing, and prioritizing the impairment causes of water pipelines in Hong Kong. It can also aid in establishing more accurate deterioration models and more cost-effective maintenance intervention programs.

Factors influencing job performance of nurses in COVID-19 care: a study in Egypt.

Coronavirus disease (COVID-19) is an illness that has critical effects on public health and overall circumstances across the world. The performance of nurses represents the vital actions in providing care for COVID-19 patients. The aim of this study was to assess job performance and associated nurses, patients, and environmental-related factors among nurses providing care to COVID-19 patients in Egypt through the following: assessing the nurses' knowledge regarding care of COVID-19 patients; assessing the nurses' practice regarding care of COVID-19 patients; assessing different factors that affect different types of nurses' job performance; and assessing the factors associated with the job performance of nurses providing care to COVID-19 patients.Design A descriptive exploratory study was used to meet the aim of the study.Setting The study was conducted at Ain Shams University COVID-19 Hospital.Study subjects A convenient sample of all 52 available nurses working at Ain Shams University COVID-19 Hospital. Data collection tools: The data was collected using a self-administered questionnaire. It consisted of the following: A. Self-administered nurses' questionnaire: Part I: Demographic characteristics of nurses. Part II: Nurses knowledge assessment. Part III: Factors associated with nurses' job performance. B. Narses's practice observational checklist. Results: The mean age (mean ± SD) of the study population was 40.59 ± 6.90. As regards their years of experience working as nurses, only 32.7% of them have more than ten years of experience. In relation to the total satisfactory level of knowledge about coronavirus, the study reveals that 55.8% of the nurses have unsatisfactory knowledge regarding coronavirus, There is a highly significant relationship between level of practice and years of experience (p-value < 0.001**), while there is a significant relationship between level of practice and educational level and attending the training courses about caring for COVID-19 patients (P-value 0.033*, P-value 0.025*), respectively.Conclusion The study concluded that most of the studied nurses had unsatisfactory knowledge, and more than half had incompetent practice scores about nursing care practices for patients with COVID-19. There was a positive linear correlation between the nurses' knowledge and practice. The factors affecting their performance in caring for patients with COVID-19, lack of cooperation between nurses and patients, lack of supplies and equipment during care were the most frequently reported factors, as were an unequal nurse-patient ratio, many nursing tasks, and a lack of patients' knowledge.Recommendation The study recommends the importance of promoting the nursing care guidelines as a standard procedure in ICUs, and the nursing care guideline educational program for patients with COVID-19 should be scheduled regularly for nurses. Providing the hospitals with supplies to avoid the shortage's effect on nurses' performance.

Diagnostic Validity of Clinical Observations for Detecting Physiologic Swallowing Impairment.

A clinical swallow evaluation (CSE) is a noninvasive and indirect assessment of the anatomical and physiological integrity of the swallowing mechanism in a natural setting. A CSE goes beyond a screening, a dichotomized indicator of dysphagia risk, by comprehensively examining the patient's swallowing through gaining sensory and motor information of oral and pharyngeal function. Information obtained from CSEs in combination with medical, social, and environmental patient related factors allow clinicians to make critical decisions about patients' health and quality of life. It is essential that we have assessment tools with rigorous methodological quality to optimize the accuracy of our clinical judgement. The purpose of this study is to investigate the diagnostic validity of clinical observations obtained through the Mann Assessment of Swallowing Ability (MASA) to better inform clinicians regarding the confidence that the items are testing what they are intended to test and reflect indication of true physiologic swallowing impairment and airway invasion. Area Under the ROC Curve (AUC) analyses revealed that the MASA's diagnostic validity showed acceptable accuracy levels for detecting oral impairment and aspiration, and poor accuracy for detecting pharyngeal impairment and penetration. In the cross-validation analysis, the AUC accuracy level for aspiration changed from acceptable to poor, but remained the same for oral impairment, pharyngeal impairment and penetration. In our sample, acceptable levels for detecting aspiration but poor levels for detecting pharyngeal impairment indicate that the MASA does an adequate job of identifying risk but not explaining the nature of impairment. These results support the need for videofluoroscopic imaging to identify the nature and severity of swallowing impairment, guide intervention and provide recommendations for safe and efficient oral intake.

Prevalence, Predictors and Reasons for Discharge Against Medical Advice Among Patients With Chronic Disease During COVID-19.

Discharge against medical advice (DAMA) is used in healthcare facilities in a situation where patients refuse care or decide to leave the hospital before the treating physician recommends discharge. Previous studies have found DAMA to be prevalent among patients with various chronic conditions. The study had four objectives. The study aimed to investigate: (1) the prevalence of DAMA during COVID-19 (2020-2021) among Jordanian patients with chronic diseases, (2) the association between DAMA and sociodemographic and clinical characteristics of patients with chronic diseases, (3) the predictors of DAMA and (4) the reasons behind DAMA at the patient, hospital and environmental levels. A descriptive cross-sectional correlational design was used in the study. A convenience sampling approach was used to collect data from 1576 patients with chronic diseases from 3 private hospitals. The study found that the prevalence rate of DAMA was 33.3%. There was a significant association between the sociodemographic and clinical characteristics of patients with chronic diseases and DAMA. Health insurance found to be the strongest predictor of DAMA. Finally, the study found that patient, hospital and environmental-related factors had a low impact on DAMA. DAMA is prevalent among patients with chronic diseases in Jordan during COVID-19 pandemic. The current study's findings can serve as an empirical basis for planning and implementing DAMA prevention programs and/or establishing or revising policies for the target population.

Person-, Job-, and Environment-Related Factors Associated with Long-Term Job Retention of People with Physical Disabilities.

Employment provides people with physical disabilities (PWPD) financial independence, enhances their well-being, self-worth, and facilitates a sense of purpose. However, the unemployment rate for PWPD is significantly higher than for individuals without a disability. There is limited knowledge regarding what factors help people with disabilities retain employment beyond the standard 90-day job probationary period. Thus, we investigated person-, job-, and environment-related factors that contribute to long-term job retention for four years or more among PWPD. We recruited a national sample of 1500 PWPD who had work experience after disability onset from panels assembled by a market research organization. We compared a subsample that maintained their longest held job for at least four years with those who had not using multivariate Poisson regression. Three models evaluated the association between job retention and (1) person-, (2) job-, and (3) environment-related factors. Likelihood of job retention was greater for respondents reporting fatigue or emotional problems (vs. pain), advancement opportunities, and receipt of job accommodations. Concerns about limited career advancement opportunities, perception of unimportance about disclosing a disability to coworkers, and difficult work commutes were associated with shorter job retention. Several person-, job-, and environment-related factors play an important role in promoting job retention of people with physical disabilities. Employers, job coaches, and rehabilitation professionals should consider these factors when developing strategies to support the retention of PWPD. Future studies should examine the relationship between these factors and their associations with the employment outcomes of PWPD across work settings and industries.

Several Mechanisms Drive the Heterogeneity in Browning Across a Boreal Stream Network

AbstractOver the past few decades, many catchments in Northern hemisphere have experienced increases in dissolved organic carbon (DOC) concentrations, resulting in a brownish color of the water, known as aquatic browning. Several mechanisms have been proposed to explain browning, but consensus regarding the relative importance of recovery from acid deposition, climate change, and land management remains elusive. To advance our understanding of browning mechanisms, we explored DOC trends across 13 nested boreal catchments, leveraging concurrent hydrological, chemical, and terrestrial ecosystem data to quantify the contributions of different drivers on observed trends. We first identified the related environmental factors, then attributed the individual trends of DOC to potential drivers across space and time. Our results showed that all catchments exhibited increased DOC trends from 2003 to 2021, but the DOC response rates differed by five‐fold. No single mechanism could fully explain the browning; instead, sulfate deposition, climate‐related factors, and site properties jointly controlled the variation in DOC trends. Specifically, the long‐term increases in DOC were primarily driven by recovery from sulfate deposition, followed by increases in terrestrial productivity, temperature, and discharge. However, catchment area and landcover type also regulated the response rate of DOC to these drivers, creating spatial heterogeneity in browning among sub‐catchments despite similar deposition and climate forcing. Interestingly, browning has weakened in the last decade as sulfate deposition has fully recovered and other current drivers are insufficient to sustain the long‐term increases. Our results highlight that multifaceted, spatially structured, and nonstationary drivers must be accounted for to predict future DOC changes.

Development and application of a risk analysis methodology for road traffic accidents

Collision, rollover, swerving, and vehicle fire are road traffic accidents (RTAs) caused by driver, vehicle, and environment-related factors, resulting in diverse severity levels, such as property damages, injuries, and fatality. In addition, vagueness in detectability, e.g., close circuit television and driver admittance, culminates RTAs' risk analysis into a multifaceted problem. The present study performed root cause analysis to investigate RTAs, where the fishbone diagram categorized RTAs (e.g., collision and swerving) and identified primary (e.g., driver and environment) and secondary (e.g., overspeeding and tire burst) root causes. For 1158 km of intercity highways in Qassim (Saudi Arabia), fuzzy-failure mode and effect analysis evaluated 171 failure modes using 3500 RTAs' recorded over three years and knowledge of practice and experts. The study highlights collision, driver-related issues, and property damage as the leading category, root cause, and consequence. Comparing the baseline with the first realistic mitigation (speed camera installation) acknowledged a 53 % reduction in risk probability number. Automated driver assistance systems, rumble strips, variable speed limits, and increasing penalties for traffic violations hypothetically dropped 76 % of cumulative risk. The methodology handles limited data and subjective risk factors to help decision-makers minimize RTAs in Saudi Arabia and other jurisdictions.

Holistic Sensitivity Analysis for Long-Term Energy Demand Prediction of Battery Electric Vehicles

Accurate and robust range estimation algorithms for battery electric vehicles have the potential to reduce range anxiety, increase the acceptance of lower-range vehicles, and improve the overall driving experience. However, developing such algorithms faces challenges due to the complexity of the driver-vehicle-environment system and the multitude of factors influencing a vehicle's energy demand. To address these challenges, this paper introduces a sensitivity analysis focused on driver- and environment-related factors, which are notably difficult to predict. Employing a global sensitivity analysis for factor prioritization, this study delineates and assesses the parameters and their value distributions using a validated vehicle simulation model. The co-simulation of a powertrain and an auxiliaries model enables the parameter-specific investigation of parameters related to the thermal system. The results are scenario-individual parameter rankings that show the importance of the considered factors in prediction algorithms and guide the strategy for the development of these algorithms. The acceleration behavior of the driver, often emphasized in literature, is shown to be of secondary importance to energy consumption. Moreover, factors such as air density and wind speed are identified as crucial in highway driving scenarios, whereas outside temperature and the probability of stopping at traffic lights are critical in urban settings. For validation purposes, the resulting rankings of the sensitivity study are validated by means of a convergence analysis.

Investigating Factors Responsible for More Boredom in Online Live EFL Classes

Although recent years have seen a growing interest in students’ experiences of boredom in foreign language learning, scant attention has been paid to this negative emotion in online live classes. With a mixed-methods approach, this study investigated what factors were responsible for student boredom in online live classes and what made Chinese students majoring in English more bored in online live classes than in traditional in-person classes. Data were collected through a questionnaire and focus group interviews. Quantitative and qualitative data analyses revealed that the main factors responsible for this negative emotion in online live settings were teacher-centered classes, less engagement and interaction, high task challenges, monotonous and long learning activities, textbook-based activities, students’ lack of self-discipline, peer influences, the absence of emotional support, unreliable internet connection, physical fatigue, a locked-down campus, and a dormitory learning environment. Students’ greater boredom in online live settings than in-person settings arose from student-related factors, technological factors, and environment-related factors. This study sheds light on how to alleviate student boredom in foreign language learning in online live settings.