Important Variables Research Articles

The Effect of Bullying Victimization on Adolescent Non-Suicidal Self-Injury: The Mediating Roles of Alexithymia and Self-Esteem.

Non-suicidal self-injury (NSSI) in adolescents is a serious public health issue influenced by the interaction of multiple factors. The purpose of this study was to investigate the multiple mediating roles of alexithymia and self-esteem in the association between bullying victimization and NSSI in a sample of Chinese adolescents. A survey of 1299 adolescents from two public middle schools in Henan Province, China, was undertaken. Data were collected using the Chinese version of the Delaware bullying victimization scale-student (DBVS-S), the Toronto Alexithymia-20 Scale (TAS-20-C), the Rosenberg self-esteem scale (RSES), and the adolescent self-injury questionnaire. Besides, we performed a structural equation modeling (SEM) with latent variables using AMOS 26.0 to examine the relationship between variables and the mediating effects. The SEM analysis found that not only can bullying victimization directly impact NSSI, but that alexithymia and self-esteem have a chain mediating effect in the association between bullying victimization and NSSI. This mediating effect contributed 22.47% to the total effect. These findings validate bullying victimization, alexithymia, and low self-esteem are important variables that affect NSSI among Chinese adolescents. Educators need to implement some prevention and intervention strategies to ameliorate the campus atmosphere and adolescents' mental health aimed at avoiding NSSI behavior in adolescence.

Analysis of Edge Drop on Strip Due to Bending and Elastic Deformation of Back up Rolls in a Four-High Cold Mill

The superficial quality of the strip is a very important issue in steel production. Considering the dimensions, the thickness is one of the most important variables in the production of a strip. In the present study, the elastic curve of Back Up Rolls (BURs) is analyzed, considering them as simply supported beams as well as the effect of rolls on the profile of the strip, specifically in the strip edge producing edge drop. The analysis included theoretical and numerical measurements in the mill. The results showed that there is an instability zone of 76 mm in the strip edge, and this geometry is symmetrical in both ends of the strip. This study not only provides a theoretical basis for the edge drop, but also provides a basis for the understanding of deformation on rolls used in rolling mill processes and their effect on the thickness, profile, shape, and dimensional quality of strips. To reduce the edge drop and significantly improve the surface quality of the strip, it is suggested to complement the simulation by compensating for the elastic curve of BUR, in the process applying bending on Work Roll (WR) combined with the use of positive crowns on it.

Aerodynamic shape optimization using design-variables-screening method

Aerodynamic shape optimization involving a complex geometric model or problem may have tens or hundreds of design variables, necessitating multiple accurate but time-consuming computational fluid dynamics simulations to produce optimal designs, which greatly affects the efficiency of optimization and. To address this challenge, this article proposes an efficient optimization method based on design-variables-screening. Within the framework of the method, a complicated input–output relationship is broken down into quantitative effects. The influence of design variables on the objective function is calculated by the Kriging regression model and functional analysis of variance. In the meantime, a screening strategy is proposed to facilitate the selection of design variables for optimization. The less important design variables in the problems of interest are fixed so that the dimensionality of the problems is reduced to save computational cost. Experimental results on the National Advisory Committee for Aeronautics airfoil (NACA0012) demonstrate that the simplified model with the screening strategy achieves nearly the same reduction in drag coefficient as the conventional method that optimizes all design variables. Moreover, it significantly enhances the efficiency of optimization and contributes to the enhancement of flow stability.

An experimental investigation of F53 super duplex stainless steel using wire cut EDM process

This research focuses on the machinability of F53 super duplex stainless steel using the Wire Electrical Discharge Machining (WEDM) process. F53 Super Duplex Stainless Steel is known for its exceptional corrosion resistance and mechanical properties. It is a popular choice for critical applications in various industries, such as offshore, chemical, and petrochemical. The wire EDM process is chosen for its ability to achieve precise and intricate cuts in hard-to-machine materials like super duplex stainless steel. This study considered important process variables like wire feed rate, servo voltage, pulse-on and pulse-off time, and material removal rate and surface roughness as output responses. Box Behnken method is used to analyse all possible interactions between input and output variables are taken into account, and three different levels of parameter conditions are adopted. It was found that MRR increased by 51.30 % with a gradual increase in pulse on time and applied wire feed and SR decreased by 62.86 % gradually with increasing servo voltage and pulse off time. The optimized values are found to be Ton = 11 µs, WF = 13 m/min, SV = 40 V and Toff = 8 µs. The microhardness study confirms the formation of the melted layer on the machined surface. This research contributes to a deeper understanding of the wire EDM process's applicability to the challenging material properties of F53 super duplex stainless steel, offering insights for the manufacturing and engineering industries aiming to improve the efficiency and precision of machining operations for this advanced material.

Analysis of electrode arrangements for brain stroke diagnosis via electrical impedance tomography through numerical computational models.

Objective.Rapid stroke-type classification is crucial for improved prognosis. However, current methods for classification are time-consuming, require expensive equipment, and can only be used in the hospital. One method that has demonstrated promise in a rapid, low-cost, non-invasive approach to stroke diagnosis is electrical impedance tomography (EIT). While EIT for stroke diagnosis has been the topic of several studies in recent years, to date, the impact of electrode placements and arrangements has rarely been analyzed or tested and only in limited scenarios. Optimizing the location and choice of electrodes can have the potential to improve performance and reduce hardware cost and complexity and, most importantly, diagnosis time.Approach.In this study, we analyzed the impact of electrodes in realistic numerical models by (1) investigating the effect of individual electrodes on the resulting simulated EIT boundary measurements and (2) testing the performance of different electrode arrangements using a machine learning classification model.Main results.We found that, as expected, the electrodes deemed most significant in detecting stroke depend on the location of the electrode relative to the stroke lesion, as well as the role of the electrode. Despite this dependence, there are notable electrodes used in the models that are consistently considered to be the most significant across the various stroke lesion locations and various head models. Moreover, we demonstrate that a reduction in the number of electrodes used for the EIT measurements is possible, given that the electrodes are approximately evenly distributed.Significance.In this way, electrode arrangement and location are important variables to consider when improving stroke diagnosis methods using EIT.

Predictive Model of the Risk of Fall Based on Physical Fitness Assessment in Older Adults

Falls occurring during activities of daily living pose a major threat and are the third most common cause of death in seniors. In clinical evaluations, mostly single tests are used to assess the risk of fall. However, a complex set of tests would lead to a more comprehensive assessment of the risk of falls. The purpose of this study was to develop a predictive model of the risk of falls in older adults aimed to prevent injuries. This study involved 159 older adults (≥65, 77% women) who underwent laboratory testing consisting of questionnaires, physical tests and basic anthropometric data measurement. The data were processed by a statistical method of regression analysis, the Classification and Regression Tree. Based on the analysis a predictive model of the risk of fall for older adults was created. The most important variables for the predictive model were total % of body fat mass, Timed Up and Go Test and 2 minutes walking test. Based on the predictive model, we can design a targeted intervention program for elderly adults to prevent risk of falling, promoting well-being and increase quality of their life.

A novel computational workflow to holistically assess total knee arthroplasty biomechanics identifies subject-specific effects of joint mechanics on implant fixation

Computational studies of total knee arthroplasty (TKA) often focus on either joint mechanics (kinematics and forces) or implant fixation mechanics. However, such disconnect between joint and fixation mechanics hinders our understanding of overall TKA biomechanical function by preventing identification of key relationships between these two levels of TKA mechanics. We developed a computational workflow to holistically assess TKA biomechanics by integrating musculoskeletal and finite element (FE) models. For our initial study using the workflow, we investigated how tibiofemoral contact mechanics affected the risk of failure due to debonding at the implant-cement interface using the four available subjects from the Grand Challenge Competitions to Predict In Vivo Knee Loads. We used a musculoskeletal model with a 12 degrees-of-freedom knee joint to simulate the stance phase of gait for each subject. The computed tibiofemoral joint forces at each node in contact were direct inputs to FE simulations of the same subjects. We found that the peak risk of failure did not coincide with the peak joint forces or the extreme tibiofemoral contact positions. Moreover, despite the consistency of joint forces across subjects, we observed important variability in the profile of the risk of failure during gait. Thus, by a combined evaluation of the joint and implant fixation mechanics of TKA, we could identify subject-specific effects of joint kinematics and forces on implant fixation that would otherwise have gone unnoticed. We intend to apply our workflow to evaluate the impact of implant alignment and design on TKA biomechanics.

Machine learning versus multivariate logistic regression for predicting severe COVID-19 in hospitalized children with Omicron variant infection.

With the emergence of the Omicron variant, the number of pediatric Coronavirus Disease 2019(COVID-19) cases requiring hospitalization and developing severe or critical illness has significantly increased. Machine learning and multivariate logistic regression analysis were used to predict risk factors and develop prognostic models for severe COVID-19 in hospitalized children with the Omicron variant in this study. Of the 544 hospitalized children including 243 and 301 in the mild and severe groups, respectively. Fever (92.3%) was the most common symptom, followed by cough (79.4%), convulsions (36.8%), and vomiting (23.2%). The multivariate logistic regression analysis showed that age (1-3 years old, odds ratio (OR): 3.193, 95% confidence interval (CI): 1.778-5.733], comorbidity (OR: 1.993, 95%CI:1.154-3.443), cough (OR: 0.409, 95%CI:0.236-0.709), and baseline neutrophil-to-lymphocyte ratio (OR: 1.108, 95%CI: 1.023-1.200), lactate dehydrogenase (OR: 1.993, 95%CI: 1.154-3.443), blood urea nitrogen (OR: 1.002, 95%CI: 1.000-1.003) and total bilirubin (OR: 1.178, 95%CI: 1.005-3.381) were independent risk factors for severe COVID-19. The area under the curve (AUC) of the prediction models constructed by multivariate logistic regression analysis and machine learning (RandomForest + TomekLinks) were 0.7770 and 0.8590, respectively. The top 10 most important variables of random forest variables were selected to build a prediction model, with an AUC of 0.8210. Compared with multivariate logistic regression, machine learning models could more accurately predict severe COVID-19 in children with Omicron variant infection.

Abstract WP266: Prediction of Stroke Incidence Using Machine Learning: The Suita Study

Background: This population-based study investigated the potential of machine learning algorithms to predict stroke incidence and identify important risk factors. This study aimed to evaluate the accuracy of these algorithms in constructing a stroke prediction model. Methods: Participants from the Suita study were included, and baseline measurements were used to predict stroke outcomes over a 15-year follow-up period. In total, 7,389 participants and 51 variables were investigated, including demographics, medical history, medical imaging, laboratory data, and lifestyle habits. Initially, unsupervised K-prototype clustering was used to group participants based on their stroke risk. Subsequently, five supervised models (logistic regression, random forest, support vector machine, extreme gradient boosting, and light gradient boosted machine) were applied to predict the stroke outcomes. The Shapley Additive Explanations (SHAP) method determined the most critical variables. Results: Unsupervised clustering revealed significant differences in stroke incidence among the three identified risk clusters (9.1%, 6.6%, and 3.2%). These clusters were categorized into high-, medium-, and low-risk groups. Among the supervised models, the random forest algorithm demonstrated the best performance. The top ten most important variables for predicting stroke incidence were identified using the SHAP, with age being the most influential variable. Other significant risk markers included systolic blood pressure, hypertension, estimated glomerular filtration rate, metabolic syndrome, and blood sugar level. Additionally, elbow joint thickness and fructosamine, hemoglobin, and calcium levels were found to be potential predictors of stroke risk. Notably, the variables identified by the SHAP were consistent with those obtained from the unsupervised clustering approach in the high-risk group. Conclusion: Machine learning algorithms provide accurate predictions of stroke incidence and offer valuable insights into subclinical markers without the need for prior assumptions of causality. This study presents a data-driven machine-learning framework for stroke risk prediction and biomarker identification.

Testing and evaluation on friction surfacing of Alloy 36 over spheroidal graphite iron and its effect on coating thickness and width using artificial neural networks and response surface methodology

AbstractFriction surfacing finds its application in areas which require joining of dissimilar metals with superior wear property. The current work investigates the solid‐state coating of Alloy 36 over ductile cast iron (SG Iron) by friction surfacing process. The effect of process factors on the actual geometry of the coating was investigated through testing and a desirability‐based technique. Spindle speed, axial force, and table traverse speed were the three most important variables for bonding fidelity. Analysis of variance was used to examine how the input parameters affected the output parameters. Experimental findings are validated using artificial neural networks and response surface methodology which are then used to predict how the system will respond to various operating scenarios. The research demonstrates that speed, rather than force or traverse action, has a more substantial effect on both materials. The response surface methodology statistician approach was utilized to identify the most important factors influencing the process parameters. As a result, the load is determined as the most crucial component that impacts bonding to the substrate. According to the data, the output parameters have the most impact on lowering the bonding geometry.

Core requirements of frailty screening in the emergency department: an international Delphi consensus study.

Frailty is associated with adverse outcomes among patients attending emergency departments (EDs). While multiple frailty screens are available, little is known about which variables are important to incorporate and how best to facilitate accurate, yet prompt ED screening. To understand the core requirements of frailty screening in ED, we conducted an international, modified, electronic two-round Delphi consensus study. A two-round electronic Delphi involving 37 participants from 10 countries was undertaken. Statements were generated from a prior systematic review examining frailty screening instruments in ED (logistic, psychometric and clinimetric properties). Reflexive thematic analysis generated a list of 56 statements for Round 1 (August-September 2021). Four main themes identified were: (i) principles of frailty screening, (ii) practicalities and logistics, (iii) frailty domains and (iv) frailty risk factors. In Round 1, 13/56 statements (23%) were accepted. Following feedback, 22 new statements were created and 35 were re-circulated in Round 2 (October 2021). Of these, 19 (54%) were finally accepted. It was agreed that ideal frailty screens should be short (<5 min), multidimensional and well-calibrated across the spectrum of frailty, reflecting baseline status 2-4weeks before presentation. Screening should ideally be routine, prompt (<4h after arrival) and completed at first contact in ED. Functional ability, mobility, cognition, medication use and social factors were identified as the most important variables to include. Although a clear consensus was reached on important requirements of frailty screening in ED, and variables to include in an ideal screen, more research is required to operationalise screening in clinical practice.

Abstract WP70: Novel Weighted FANG-D Scale Scoring to Detect Large Vessel Occlusion

Objective: To determine if a novel weighted FANG-D scoring scale (NWFSS) increases sensitivity and specificity rates of large vessel occlusion (LVO) as compared to those reported with the original FANG-D scale scoring. Methodology: The present study used an observational, descriptive design to identify the important variables (field cut, aphasia, neglect, gaze preference, and dense hemiparesis) associated with the likelihood of detecting LVO. We used the FANG-D scale in patients admitted through the emergency department (ED) of a comprehensive stroke center (CSC), from December 2022 through April 2023. The NIHSS was performed and compared with the NWFSS for analysis. Each component was required to have a score ≥ 2(moderate to severe deficits) except dense hemiparesis which required a score ≥3 (significant weakness) to receive one point on the FANG-D scale. A total score of 2 or higher on FANG-D indicated a positive result suggesting LVO. If aphasia or neglect were present with a NIHSS of≥2, it was considered a positive FANG-D score. Results: A total of 311 patients were included in the study for analysis with a mean age of 67 ±16.4 years of whom 56.6% were female. The sensitivity and specificity rates were 85.7% (Confidence Interval (CI) =72.8%-94.1%) and 90.5% (CI= 86.2%-93.7%) respectively for positive predictive value of NWFSS to identify LVO. The sensitivity and specificity of the NIHSS has been reported as 86% and 60% respectively for LVO detection. The previous FANG-D study group used lower values in calculating the FANG-D scores and reported a sensitivity rate of 98% and specificity of 76.7%. Conclusion: The NWFSS has high specificity and is reliable for LVO prediction. While the NIHSS assesses eleven items sensitive for stroke diagnosis, it may take up to 10 minutes to complete and is not specific for LVO, whereas the FANG-D assesses 5items critical to LVO detection and is simple and fast with completion in &lt;1 minute, minimizing time to intervention. Our study occurred in the ED, but this scale scoring method could be tested in the prehospital setting. The use of a scale with higher specificity for LVO could improve patient outcomes and resource utilization by faster routing of patients to CSCs capable of performing mechanical thrombectomy.

Predicting the Potential Distribution Areas of the Asian Redtail Catfi sh (Hemibagrus wyckioides Fang and Chaux, 1949) in the Chao Phraya River, Thailand, using Maximum Entropy Modelling

Hemibagrus wyckioides is recognized as an economically important freshwater fi sh in Thailand. However, there are no available comprehensive data regarding the geographic distribution and habitat suitability of H. wyckioides in Thailand. In addition, its natural population has tended to continuously decrease. This study aimed to predict the suitable distribution areas of H. wyckioides in the Chao Phraya River, Thailand, using the maximum entropy (MaxEnt) algorithm based on climatic factors, including 11 temperature and 8 precipitation indices from the WorldClim database. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). The jackknife test was used to assess the contribution of each variable to the model. The results revealed that the MaxEnt model performed acceptably well, with an AUC value of 0.939, representing excellent accuracy in predicting the appropriate distribution areas of H. wyckioides in the Chao Phraya River. The potential distribution areas of H. wyckioides were observed mostly along the Chao Phraya River in Sing Buri and Ang Thong Provinces. According to the results of the jackknife test, 11 climatic variables impacted the distribution of H. wyckioides. The annual mean temperature (BIO1), precipitation of driest month (BIO14), and annual temperature range (BIO7) were the most important infl uencing variables, with impact values of 53.5%, 15.9%, and 11.9%, respectively. Therefore, understanding the potential distribution areas can assist researchers in monitoring, management, and conservation activities or with the development of new natural protected areas of H. wyckioides in high-quality areas in the Chao Phraya River.

Bursting oscillation behaviors of a multi-time scales pumped storage power station with governor subsystem nonlinearity

Pumped-storage power station (PSPS) play a crucial role in supporting the grid integration of intermittent energy and require frequent regulation to balance fluctuations. Eliminating potential stability risks under the strong nonlinear and complex multi-scale coupling characteristics to ensure the stability of PSPS is crucial for new power systems. Although governor nonlinear elements have been studied in previous studies, the effects and potential risks of their non-smooth characteristics on the stability of different subsystems have not been reported. In this study, a mathematical model of PSPS considering the non-smooth characteristics of governor is established by introducing a segmentation function. Then the influence laws of their effect on important variables in different subsystems are studied through the time waveforms and phase trajectories. Additionally, sensitivity analysis is employed to identify key parameters affecting stability at various stages. Finally, the risk of bursting oscillation caused by the nonlinear elements is revealed. The results show that the nonlinear elements have less effect on the hydraulic subsystem, but their non-smooth characteristic induces state switching in mechanical subsystems, which poses a risk for frequency oscillations. Adjusting the deadband has an inhibiting effect on both oscillations caused by nonlinear elements and improves stability in hydraulic and mechanical subsystems. This work provides fresh perspectives and theoretical basis for minimizing the risk of bursting oscillations while improving Pumped-storage plant stability.

Ratingcurve: A Python Package for Fitting Streamflow Rating Curves

Streamflow is one of the most important variables in hydrology, but it is difficult to measure continuously. As a result, nearly all streamflow time series are estimated from rating curves that define a mathematical relationship between streamflow and some easy-to-measure proxy like water surface elevation (stage). Despite the existence of automated methods, most rating curves are still fit manually, which can be time-consuming and subjective. Although several automated methods exist, they vary greatly in performance because of the non-convex nature of the problem. In this work, we develop a parameterization of the segmented power law that works reliably with minimal data, which could serve operationally or as a benchmark for evaluating other methods. The model, along with test data and tutorials, is available as an open-source Python package called ratingcurve. The implementation uses a modern probabilistic machine-learning framework, which is relatively easy to modify so that others can improve upon it.

Bovine anaplasmosis in Zimbabwe: spatio-temporal distribution and environmental drivers

Understanding the spatial and temporal distribution of Bovine anaplasmosis is crucial for identifying areas of high prevalence for targeted disease control. This research was aimed at modelling and mapping the B. anaplasmosis potential distribution, and identify hotspots as well as significant variables explaining the occurrence of the disease. The Getis Ord G i * statistic for Hotspot analysis was used as well as MaxEnt ecological niche modelling. The effects of time, land-use, and agro-ecological regions on B. anaplasmosis occurrence were tested using Analysis of Variance (ANOVA). Results showed that several districts in Zimbabwe are suitable for the occurence of the disease for example Binga, Seke, Buhera, Kwekwe, Gweru, Mhondoro, Chegutu, Sanyati, and in the North: Mbire, Muzarabani, Mt Darwin, Shamva, Bindura, Zvimba and Makonde. Morbidity and mortality hotspots were detected in Gokwe-south, Kwekwe, and Chirumhanzu districts. Binga, Gokwe-south, Gutu, Hurungwe, Mazoe, Nkayi, Shamva, and Kwekwe districts also experienced high disease incidences. Temperature seasonality, precipitation seasonality, mean diurnal range, and isothermality were the most important variables in explaining 93% of B. anaplasmosis distribution. Unlike land-use and agro-ecological regions, time (months) had a significant effect on B. anaplasmosis occurrence with July and September having significantly (p < 0.05) higher cases and deaths than the rest of the months. The results of this study provide insights into the management strategies and control of B. anaplasmosis in Zimbabwe. It is thus concluded that geo-spatial techniques, combined with ecological niche modelling can provide useful insights into disease prevalence and distribution and hence can contribute to effective management and control of B. anaplasmosis in Zimbabwe.

Self-Reported Visual Disability and Unemployment: Findings from the National Health Interview Survey

ABSTRACT Purpose To investigate the association between visual impairment and employment status due to disability, utilizing data from the 2022 National Health Interview Survey (NHIS). Methods Adults 18 years of age and older were extracted from the 2022 NHIS dataset. A multivariable logistic regression model was created to evaluate the odds of unemployment (“laid off” and “looking for work”). Persons over the age of 65, as well as persons retired, going to school, self-employed, seasonal, or contract workers were excluded. Independent variables for the model included gender, race, Hispanic ethnicity, urban residency, level of education, citizenship, and self-reported vision. The latter variable was categorized as seeing with “some” difficulty, with “severe” difficulty, “can’t see at all,” and “a lot of difficulty.” Outcomes were reported as odds ratios (OR) with 95% confidence intervals (CI). Results Associations with unemployment included education less than high school (OR 6.05, 95% CI: 3.98-9.18) and high school (OR 3.80, 95% CI 2.78-5.21); severe vision difficulty (OR 3.68 95% CI 1.73-7.86); Asian race (OR 2.53, 95% CI 1.64-3.89); and Black race (OR 1.78, 95% CI 1.31-2.41). The odds of unemployment were marginally elevated for those living in large metropolitan areas, while being born in the United States had a modest protective effect (OR 0.53, 95% CI 0.42-0.66). Conclusion The degree of visual impairment in this post-COVID-19 pandemic survey substantially affects employment, which is consistent with historical studies. Education among those with impaired vision is an important and modifiable variable that can positively influence the chances of employment.

Preoperative counseling for penile implant surgery: standardized approach in a high-volume center of excellence.

Shared-decision making is crucial in today's society. Although penile prosthesis surgery has had significant improvements regarding technique and materials over the last few decades - with the highest satisfaction rate in erectile dysfunction treatment at present time -, dissatisfaction can occur because of bad preoperative counseling with the creation of unrealistic expectations. This paper includes a small narrative review regarding the most important preoperative variables to be discussed in the preoperative setting. Literature search was conducted in December 2023 using PubMed. Additionally, our own standardized flowchart for preoperative counseling was added. The most important aspects of preoperative counseling include a description of the pathophysiology of erectile dysfunction as well as alternative treatments. Information should be given about which implant categories exist. Additionally, some information about the procedure itself as well as the financial aspect should be communicated. The most frequent and most important complications should be illustrated. Last but not least, the patient's expectations should be evaluated. Our added standardized flowchart with figure acts as a practical guideline for professionals as well as for patients. Our standardized approach for preoperative counseling emphasizes reasonable postoperative expectations aiming for a well-informed patient with high postoperative satisfaction. Our general strategy is to underpromise and overdeliver.

Geographic expansion of the introduced Aedes albopictus and other native Aedes species in the Democratic Republic of the Congo

BackgroundAedes albopictus has been reported in several Central African countries, including the Democratic Republic of the Congo (DRC). The establishment of this mosquito species poses a serious threat as a vector of various infectious diseases. Although Ae. albopictus has been reported in the western region of the DRC, information about its distribution is still scarce in the country. The aim of this study was to investigate the current nationwide distribution of the invasive Ae. albopictus, as well as other native Aedes mosquitoes, in the DRC and to identify suitable areas for its future expansion.MethodsTwo entomological surveys were conducted in 2017–2019 and 2022. Based on the occurrence sites of Ae. albopictus, important environmental variables were identified. Then, geographical areas suitable for Ae. albopictus establishment were determined using the maximum entropy model. The distribution and abundance of Ae. albopictus were also compared with those of the major native Aedes species.ResultsAedes albopictus was found in the western, northern, central, and eastern regions of the DRC, but it was not found in the southeastern region. The maximum entropy model predicted that most parts of the DRC are suitable for the establishment of this mosquito. The unsuitable areas encompassed the eastern highlands, known for their low temperatures, and the southeastern highlands, which experience both low temperatures and a long dry season. The native Aedes species found were Aedes aegypti, Aedes simpsoni, Aedes africanus, and Aedes vittatus. Aedes albopictus dominated in the western and northern regions, while Ae. aegypti was more prevalent in other regions.ConclusionsAedes albopictus has been well established in the western and northern regions of the DRC. This mosquito is expanding its distribution while replacing the native Aedes species. Most of the country is suitable for the establishment of this mosquito species, except the highlands of the eastern and the southeastern regions.Graphical

Relationship between Misconduct of Medical Professionalism with Burnout Syndrome and Related Factors

Background: Burnout syndrome (BOS) is a common occupational disease amongst medical physicians; especially anesthesiologists. BOS is known to result in increased expenditure of the healthcare system and decreased patient satisfaction. In order to substitute for good conduct by its medical staff, Tehran University of Medical University established its own guidelines on professionalism as a substitute for professional conduct amongst its personnel. Learning and practicing professionalism is not only a requirement to be competent as a specialist, but also it results in increasing healthcare quality and patient satisfaction. There is a lack of studies on the relationship between BOS and professionalism.&#x0D; Determination of the relationships between professionalism and BOS among TUMS anesthesiology residents.&#x0D; Methods: In this cross-sectional study all anesthesiology residents completed three forms (demographic checklist, Maslach Burnout Inventory (MBI), and professionalism self-conduct). The results were primarily analyzed using STATA 14.0 and relationships were established via linear and binary regression.&#x0D; Results: About 44 percent of residents met criteria for BOS. The risk of developing BOS was significantly higher for residents who adhered to the principles of respect and altruism and for residents training in one of our surveyed hospitals; whereas the risk of developing BOS would be reduced by increasing age and adherence to the principles of justice Burnout subscales scores concerning emotional exhaustion and personal accomplishment were significantly related with higher self-reported scores of altruism and honesty-integrity respectively.&#x0D; Conclusion: The chances of developing BOS could be enhanced by inadequate practice in the field of professional ethics. Therefor the importance of learning and competent practice of professionalism must be acknowledged. Age and professional climate were the most important demographic variables related to BOS in anesthesiology residents