Multiple Regression Models Research Articles

Inverted U-shaped relationship between serum vitamin B12 and α-Klotho levels in US adults: a cross-sectional study.

Serum vitamin B12 and α-Klotho are important markers associated with aging. Limited studies have been conducted on the relationship between vitamin B12 and α-Klotho. This study investigated the relationship between circulating α-Klotho and vitamin B12. A total of 4,502 American adults with circulating vitamin B12 levels and α-Klotho levels from the National Health and Nutrition Examination Survey (2011-2014) were included. A weighted multiple linear regression model was used to evaluate the correlation between vitamin B12 and α-Klotho levels. To clarify potential non-linearities, smoothed curve fitting and threshold effects analysis were employed. A statistically significant non-linear relationship was found between vitamin B12 levels and circulating α-Klotho levels after adjusting for potential confounders. We observed an inverted U-shaped relationship between serum vitamin B12 levels and circulating α-Klotho levels. Notably, serum vitamin B12 levels below the threshold (1,020 pg/mL) exhibited a positive correlation with circulating α-Klotho levels (β = 0.14, 95% confidence interval (CI): 0.09-0.18, p < 0.0001). Conversely, serum vitamin B12 levels above the threshold (1,020 pg/mL) exhibited a negative correlation with circulating α-Klotho levels (β = -0.12,95% CI: -0.17--0.06, p < 0.0001). Sensitivity analyses were performed and consistent results were obtained. This study demonstrated an inverted U-shaped relationship between circulating vitamin B12 and α-Klotho in American adults. The optimal concentration of serum vitamin B12 in American adults was found.

20-HETE, Blood Pressure, and Vascular Stiffness in Young Adults.

Cytochrome-P450 eicosanoids from arachidonic acid, including vasodilator epoxyeicosatrienoic acids (EETs) and the vasoconstrictor 20-HETE, are important in regulating blood pressure, vascular tone, and cardiac and renal function. This study examined plasma 20-HETE and EETs in relation to blood pressure and vascular stiffness in a cohort of 1054 community-dwelling young adults from the Raine Study at 27 years. Plasma 20-HETE, EETs, and their hydroxylated products were measured by liquid chromatography-tandem mass spectrometry. Pulse wave velocity and aortic distensibility were measured to assess vascular stiffness. Sex differences were assessed using univariate analysis. Multiple regression models assessed the relationship between 20-HETE and systolic blood pressure (SBP) and measures of vascular stiffness. The regression model for SBP showed a positive relationship with plasma 20-HETE (β, 0.092; P<0.0001), after adjusting for sex (P<0.0001), body mass index (P<0.0001), (ln)triglycerides (P<0.0001), and (ln)HOMA-IR (P=0.015), and accounted for 29% of the variance in SBP. 20-HETE was positively related to pulse wave velocity (β, 0.059; P=0.021); after adjusting for sex (P<0.0001), SBP (P<0.0001), (ln)triglycerides (P=0.014), (ln)HOMA-IR (P=0.0001), (ln)high-sensitivity C-reactive protein (P=0.005), and age (P=0.002), the model accounted for 34% of the variance in pulse wave velocity. 20-HETE was inversely associated with aortic distensibility (β, -0.051; P=0.029), independent of sex (P<0.0001), SBP (P<0.0001), and (ln)HOMA-IR (P<0.0001); the model accounted for 25% of the variance in aortic distensibility. Plasma EETs were not significant predictors of SBP or vascular stiffness. In young adults, 20-HETE may play a fundamental role in regulating blood pressure and vascular stiffness independent of numerous cardiometabolic risk factors and cytochrome 450-derived EETs. URL: https://www.anzctr.org.au; Unique identifier: CTRN12617001599369.

The Economic Benefits of a Major Canadian Forestry Contribution Program for Indigenous Peoples

This paper evaluates whether the First Nations Forestry Program (FNFP), a contribution program for Indigenous Peoples in Canada, had a statistically measurable effect on the well-being of participating reserves’ inhabitants. Funding data from 1,078 projects was paired with reserve inhabitant profiles from two Statistics Canada censuses. Multiple regression models were then used to test for the statistical significance of project-funding treatments. The results suggest that from 2006 to 2010, the FNFP significantly affected the after-tax incomes of reserve inhabitants who had worked in forestry, as well as a lesser effect on the after-tax incomes of reserve families.

Multimodal Temperature Readout Boosts the Performance of CuInS2/ZnS Quantum Dot Nanothermometers.

Fluorescent nanothermometers are positioned to revolutionize research into cell functions and provide strategies for early diagnostics. Fluorescent nanostructures hold particular promise to fulfill this potential if nontoxic, stable varieties allowing for precise temperature measurement with high thermal sensitivities can be fabricated. In this work, we investigate the performance of micelle-encapsulated CuInS2/ZnS core/shell colloidal quantum dots (QDs) as fluorescent nanothermometers. We demonstrate four temperature readout modes, which are based on variations in the photoluminescence intensity, energy, and lifetime and on a specific ratio of excitation efficiencies. We further leverage this multimodal readout to construct a fifth, multiparametric thermometer calibration based on the multiple linear regression (MLR) model. We show that the MLR approach boosts the thermometer sensitivity by up to 7-fold while reducing the readout error by about a factor of 3. As a result, our QDs offer the highest sensitivities among semiconducting QDs emitting in the first biological window. The obtained results indicate that CuInS2/ZnS QDs are excellent candidates for intracellular in vivo thermometry and provide guidelines for further optimization of their performance.

Aboveground Carbon Stock Estimation Based on Backpack LiDAR and UAV Multispectral Imagery at the Forest Sample Plot Scale

Aboveground carbon stocks (AGCs) in forests play an important role in understanding carbon cycle processes. The global forestry sector has been working to find fast and accurate methods to estimate forest AGCs and implement dynamic monitoring. The aim of this study was to explore the effects of backpack LiDAR and UAV multispectral imagery on AGC estimation for two tree species (Larix gmelinii and Betula platyphylla) and to emphasize the accuracy of the models used. We estimated the AGC of Larix gmelinii and B. platyphylla forests using multivariate stepwise linear regression and random forest regression models using backpack LiDAR data and multi-source remote sensing data, respectively, and compared them with measured data. This study revealed that (1) the diameter at breast height (DBH) extracted from backpack LiDAR and vegetation indices (RVI and GNDVI) extracted from UAV multispectral imagery proved to be extremely effective in modeling for estimating AGCs, significantly improving the accuracy of the model. (2) Random forest regression models estimated AGCs with higher precision (Xing’an larch R2 = 0.95, RMSE = 3.99; white birch R2 = 0.96, RMSE = 3.45) than multiple linear regression models (Xing’an larch R2 = 0.92, RMSE = 6.15; white birch R2 = 0.96, RMSE = 3.57). (3) After combining backpack LiDAR and UAV multispectral data, the estimation accuracy of AGCs for both tree species (Xing’an larch R2 = 0.95, white birch R2 = 0.96) improved by 2% compared to using backpack LiDAR alone (Xing’an larch R2 = 0.93, white birch R2 = 0.94).

Optimization of big data analysis resources supported by XGBoost algorithm: Comprehensive analysis of industry 5.0 and ESG performance

To enable state-owned enterprises in Industry 5.0 to better carry out M&A activities, it is important and necessary to provide early warning of M&A risks, which directly affects the interests of both parties and even affects the effectiveness of state-owned enterprise reform. The author proposes the optimization of big data analysis resources supported by the XGBoost algorithm: a comprehensive analysis of Industry 5.0 and ESG performance. Design a comprehensive evaluation system to measure the M&A risk of state-owned listed companies. Using Python programming language to achieve data crawling and processing. Build an early warning model using the XGBoost algorithm. To further evaluate the effectiveness of the early warning model, comparative experiments were conducted. Using multiple linear regression models to study the significant factors of merger and acquisition risk. The experimental results show that the prediction accuracy based on the XGBoost algorithm is 80 %, which performs the best among all models and has stronger reliability and applicability. ConclusionThe return on investment capital, operating profit margin, and net profit from paid consideration are more important and effective in predicting merger and acquisition risks; The total asset turnover rate, return on investment capital, equity balance, and audit quality are more conducive to suppressing merger and acquisition risks.

Early Childhood Education and Care Attendance and Its Association With Outdoor Play, Screen Time and Sleep Duration of Young Children: Findings From the CHAT Trial in Australia.

Effects of attending early childhood education and care (ECEC) on health behaviours of young children remain unclear. This study aimed to investigate whether ECEC attendance was associated with outdoor play, screen time, sleep duration and family demographics of children aged 2 and 3 years. Secondary analysis was conducted using data extracted from two linked trials conducted in Australia, 2017-2020. Telephone surveys were conducted with participating mothers for data collection. Multiple logistic and linear regression models were built to investigate the associations of ECEC attendance with outdoor play, screen time, sleep duration and family demographics among young children. At ages 2 and 3 years, 797 and 537 mothers completed surveys, respectively. Of respondents, 65% and 72% of children attended ECEC, respectively. Children who attended ECEC had 17 min (95% CI 3.8-30.5) and 28 min (95% CI 14.1-41.9) more daily outdoor playtime and had 13 min (95% CI 4.0-21.5) and 19 min (95% CI 6.4-30.7) less daily screen time at home at 2 and 3 years, respectively. Although ECEC attendance was not associated with sleep duration, children who attended ECEC were more likely to meet all three recommendations (outdoor play, screen time and sleep) with adjusted odds ratio (AOR) 1.84 (95% CI 1.24-2.72) at age 2 and AOR 2.34 (95% CI 1.28-4.28) at age 3. Mothers who were employed, first-time mothers, spoke English at home or had a high household income were more likely to use ECEC services. ECEC services may hold promise to influence outdoor play and screen time among young children. Children from lower socio-demographic background had a lower rate of ECEC attendance. Future health promotion programmes for young children need to also consider children who do not attend ECEC.

Research on the green development path of prefabricated building industry based on intelligent technology

PurposeIn order to make the construction industry develop in the direction of greening, this paper analyzes whether the application of intelligent technology in prefabricated buildings can achieve carbon emission reduction, starting from the problems of weak technology and insufficient encouragement policies in the prefabricated building industry. It also designs dynamic and adjustable incentives for the smart transformation of prefabricated buildings and makes recommendations to facilitate the transformation of assembly manufacturers into “smart factories”.Design/methodology/approachThis paper takes the intelligent technology for carbon reduction, energy efficiency and policy design in the prefabricated buildings industry as the starting point. Based on in-depth expert interviews and questionnaire survey data, a linear multiple regression model is used to establish an association network of intelligent technology in the production and transportation, construction, operation and maintenance, demolition and scrapping stages. On this basis, an evolutionary game theory is used to construct a smart transformation and carbon reduction utility game model between the government and manufacturers, and relevant suggestions for smart empowerment of green construction development technology combinations and policy settings are proposed.FindingsAn assembly manufacturing plant with smart empowerment is an important way to achieve green and sustainable development in the construction industry. Among them, BIM and IoT have made a greater impact on carbon emission reduction of prefabricated buildings in all stages of the whole life cycle. The government’s proposed energy efficiency incentives and environmental tax amount will effectively increase companies' motivation for smart transformation of prefabricated buildings. However, when the environmental tax amount is low, the government should strengthen the regulation of the industry in order to increase the speed of smart transformation of assembly manufacturers. Therefore, a reasonable setting of the environmental tax rate and energy-saving incentives and flexible adjustment of the regulatory efforts can maximize the functional utility of the government in the process of smart transformation.Research limitations/implicationsThis paper focuses on the impact of intelligent technologies on the overall carbon emissions of the industry and provides an evolutionary analysis of the strategic game between the government and assembly manufacturers, the main players in the smart transformation process of prefabricated buildings. However, smart technologies for different categories of assembly manufacturing plants and strategic options for a wider range of stakeholders have not been examined in depth.Originality/valueDifferent from existing research, this study focuses on exploring the strategic game between the government and assembly manufacturers in the smart transformation of prefabricated buildings. It provides an innovative explanation of the connection between intelligent technology and carbon emissions. The study develops an evolutionary game model for both parties, addressing the research gap on the combined effects of policy incentives and intelligent technology on carbon reduction and efficiency improvement in the prefabricated buildings industry. This research not only offers practical reference for the government in designing incentive mechanisms and establishing regulatory systems but also provides feasible practical guidance for the smart transformation and carbon reduction efforts of assembly manufacturing plants.

Joint association of serum sodium and frailty with mild cognitive impairment among hospitalized older adults with chronic diseases: a cross-sectional study.

To examine the associations of serum sodium and frailty with the risk of mild cognitive impairment (MCI) among hospitalized older adults with chronic diseases. A cross-sectional study was conducted in 403 hospitalized older adults with chronic diseases. Serum sodium concentration was assessed by the ion-selective electrode method, frailty status was evaluated by the FRAIL scale, and MCI was determined by the Montreal Cognitive Assessment (MoCA). Multiple logistic regression models were used to estimate the associations of serum sodium and frailty with MCI. Participants with the lowest tertile of serum sodium had a higher risk of MCI than those in the middle tertile group (OR = 1.75, 95% CI: 1.01-3.04). Below 143 mmol/L, the risk of MCI was 1.38 (95% CI: 1.03-1.84) for per 1 SD decrease in serum sodium. Compared with the robust group, frailty was significantly associated with an increased risk of MCI (OR = 3.94, 95% CI: 1.92-8.10). Moreover, in comparison with participants with the middle tertile of serum sodium and who were robust/prefrail, those with frailty and either the lowest (OR = 5.53, 95% CI: 2.08-14.67) or the highest tertile of serum sodium (OR = 3.48, 95% CI: 1.20-10.05) had higher risks of MCI. Both lower and higher serum sodium impose a significantly higher risk for MCI in older adults with frailty. This could inform the design of clinical trials and the development of guidelines and recommendations for correcting serum sodium and frailty in hospitalized older adults with chronic diseases.

Racism, Attachment Styles, and Mental Health among Asian American Adolescents

Theory suggests that a secure base can moderate the effects of racism on mental health outcomes among people of Color (Mikulincer &amp; Shaver, 2022). Thus, the current study tested this hypothesis with a sample of 301 Asian American adolescents who completed a 25-minute online survey. Two hierarchical multiple regression models examined whether secure, anxious, and avoidant attachment with mothers and fathers moderated the link between racism and mental health. We found that when Asian American adolescents reported frequent experiences of racism, secure attachment with fathers was not enough to mitigate the effects of racism. Specifically, simple slopes indicated that at high and moderate levels of secure attachment with fathers, adolescents reported slightly better mental health with few racism experiences but indicated a slight decrease in mental health with frequent racism experiences. Findings highlight the importance of dismantling racial discrimination to support Asian American adolescents’ mental health.

The relationships between resilience and child health behaviors in a national dataset.

Resilience mechanisms at the individual, family, and environmental levels may improve health outcomes despite potentially harmful stress exposure partly through the practice of positive health behaviors. We performed a secondary analysis of 2016-2021 National Survey of Children's Health data to assess the relationships between three resilience domains - child, family, neighborhood - and six health behaviors using multiple regression models adjusted for the other resilience domain(s) and potential confounders. Analysis revealed significant associations between each resilience domain and multiple health behaviors in a total weighted analytic sample of 70,156,540 children. For each outcome, the odds of better health behaviors were highest with high resilience in all possible domains. For example, among children ages 0-5 years, the adjusted odds of having "good quality" vs. "poor quality" sleep for those with "high" resilience in all domains were 2.21 times higher (95% CI 1.78, 2.63) than for those with "low" resilience in all domains. This line of research may help to inform the design of resilience and health behavior promotion interventions by targeting multiple socio-ecological domains of influence to improve health and development outcomes in children exposed to experiences or sources of potential stress. This study assessed the associations between three socio-ecological resilience domains (child, family, and neighborhood) and six child and family health behaviors in a national dataset. Resilience exists within multiple socio-ecological levels and supports healthy functioning despite experiencing stress. Studies in adults and limited pediatric sub-populations show associations between resilience and health behaviors, which in turn influence numerous health outcomes. Resilience at three levels of socio-ecological levels was found to be associated with the performance of multiple child and family health behaviors in a nationally representative general pediatric population. These findings have important implications for child and family health promotion efforts.

Character strength patterns, social support and psychological distress in adolescents and young adults with cancer: A multicenter cross-sectional study

ObjectiveAdolescents and young adults with cancer (AYAC) experience severe psychological distress worldwide. Social support was associated with reduced distress among cancer patients, but the role of character strength patterns in this association is unexplored. This study explored whether character strength patterns influence the association between social support and psychological distress in AYAC. MethodsA multicenter cross-sectional study was conducted at four hospitals in Changsha, China. Participants completed questionnaires assessing sociodemographic, distress, social support, and character strengths. Latent profile analysis determined character strength patterns, and multiple linear regression models examined the association. Interaction terms tested moderation effects, and adjusted models examined confounding, all stratified by gender. ResultsAmong 728 participants, three character strength patterns emerged: low (LCS), moderate (MCS), and high (HCS). Across all patterns, higher social support was significantly associated with lower distress in AYAC (female: β ​= ​−0.124, 95% CI ​= ​−0.051 to −0.015; male: β ​= ​−0.180, 95% CI ​= ​−0.080 to −0.030; P ​< ​0.001). AYAC with MCS and HCS experienced significantly less distress than those with LCS, across both genders. However, the association between character strength patterns and lower distress was stronger in males (MCS: β ​= ​−0.384, 95% CI ​= ​−1.898 to −1.033; HCS: β ​= ​−0.777, 95% CI ​= ​−3.420 to −2.495; P ​< ​0.001) compared to females (MCS: β ​= ​−0.284, 95% CI ​= ​−1.215 to −0.700; HCS: β ​= ​−0.593, 95% CI ​= ​−2.776 to −2.102; P ​< ​0.001). ConclusionsCharacter strength patterns didn't moderate or confound the association between social support and psychological distress, but had stronger protective effects against psychological distress than social support. Higher character strength patterns showed a stronger association with lower psychological distress, especially for males.

Machine Learning-Based Predictions of Flow and Heat Transfer Characteristics in a Lid-Driven Cavity with a Rotating Cylinder

Machine learning-based predictions of heat transfer characteristics in lid-driven cavities are transforming the field of computational fluid dynamics (CFD). Lid-driven cavities are a fundamental problem in fluid mechanics, characterized by the motion of a fluid inside a square cavity driven by the motion of one of its walls. The goal of this study was to develop multiple machine-learning regression models and highlight the discrepancies between the predicted and actual average Nusselt numbers. Additionally, the study utilized physics-informed neural networks (PINNs) to model the flow and thermal behavior at both low and high Reynolds numbers. The results were compared among actual data from computational fluid dynamics (CFD) simulations, PINN models trained with CFD data, and purely PINN models created without any prior data input. The findings of this study showed that the random forest model exhibited an exceptional stability in its predictions, consistently maintaining low errors even as the Reynolds number increased compared with other machine-learning regression models. Further, the results of this study in terms of flow and thermal behavior within the cavity were found to depend significantly on the PINN method. The data-driven PINN exhibited a much lower mean average errors at both Reynolds numbers, while the physics-based PINN showed lower physics loss.

UTILIZATION OF CLASSROOM ENVIRONMENT AND ITS EFFECTS ON ACADEMIC PERFORMANCE IN SUB COUNTY LEVEL SECONDARY SCHOOLS IN UASIN GISHU COUNTY, KENYA

School infrastructure is a key pillar for effective learning process in any educational institution. The purpose of this study was to utilization of classroom environment affect academic performance in Sub County level secondary schools in Uasin Gishu County. This study was guided by Production theory. The study was guided by pragmatism philosophy and adopts descriptive survey research design. The study area was carried out in Uasin Gishu County. The target population of the study was 18960 respondents. Stratified simple random and purposive sampling was employed to select the respondents. The sample size was 530 respondents comprising of 48 principals, 97 teachers, 384 students and a Quality Assurance and Standards Officer The data collection instruments were questionnaire, interview schedules and observation check list. A pilot study was carried out in five public schools in the neighboring Trans Nzoia County to check on the consistence of the instruments. The researcher yielded both quantitative and qualitative data. Quantitative data was analyzed using descriptive statistics that resulted in percentages and frequencies that were represented in tables, pie charts and bar graphs and inferential statistics. Qualitative data was analyzed thematically. Therefore, from the research findings based on the multiple regression models, the coefficient of determination (R squared) of 0.441 showed that 44.1% of the variation in student performance was explained by the utilization of selected school infrastructure. The study found out that the classroom environment influenced academic performance. The study concluded that the more conducive the classroom environment the higher the academic performance. The research recommended that sub county level secondary schools management in Uasin Gishu County should develop infrastructural development initiatives to solve the inadequacy of these facilities and enhance their utilization in the schools. There should be consistent routine supervision of quality of facilities in schools done by the government to ensure that schools are operating with good facilities that would help the learners. KEY WORDS: School infrastructure utilization of classroom environment, academic performance

Multi-modal characterisation of early-stage, subclinical cardiac deterioration in patients with type 2 diabetes.

Type 2 diabetes mellitus (T2DM) is a major risk factor for heart failure with preserved ejection fraction and cardiac arrhythmias. Precursors of these complications, such as diabetic cardiomyopathy, remain incompletely understood and underdiagnosed. Detection of early signs of cardiac deterioration in T2DM patients is critical for prevention. Our goal is to quantify T2DM-driven abnormalities in ECG and cardiac imaging biomarkers leading to cardiovascular disease. We quantified ECG and cardiac magnetic resonance imaging biomarkers in two matched cohorts of 1781 UK Biobank participants, with and without T2DM, and no diagnosed cardiovascular disease at the time of assessment. We performed a pair-matched cross-sectional study to compare cardiac biomarkers in both cohorts, and examined the association between T2DM and these biomarkers. We built multivariate multiple linear regression models sequentially adjusted for socio-demographic, lifestyle, and clinical covariates. Participants with T2DM had a higher resting heart rate (66 vs. 61 beats per minute, p < 0.001), longer QTc interval (424 vs. 420ms, p < 0.001), reduced T wave amplitude (0.33 vs. 0.37mV, p < 0.001), lower stroke volume (72 vs. 78ml, p < 0.001) and thicker left ventricular wall (6.1 vs. 5.9mm, p < 0.001) despite a decreased Sokolow-Lyon index (19.1 vs. 20.2mm, p < 0.001). T2DM was independently associated with higher heart rate (beta = 3.11, 95% CI = [2.11,4.10], p < 0.001), lower stroke volume (beta = -4.11, 95% CI = [-6.03, -2.19], p < 0.001) and higher left ventricular wall thickness (beta = 0.133, 95% CI = [0.081,0.186], p < 0.001). Trends were consistent in subgroups of different sex, age and body mass index. Fewer significant differences were observed in participants of non-white ethnic background. QRS duration and Sokolow-Lyon index showed a positive association with the development of cardiovascular disease in cohorts with and without T2DM, respectively. A higher left ventricular mass and wall thickness were associated with cardiovascular outcomes in both groups. T2DM prior to cardiovascular disease was linked with a higher heart rate, QTc prolongation, T wave amplitude reduction, as well as lower stroke volume and increased left ventricular wall thickness. Increased QRS duration and left ventricular wall thickness and mass were most strongly associated with future cardiovascular disease. Although subclinical, these changes may indicate the presence of autonomic dysfunction and diabetic cardiomyopathy.

Understanding Crop Diversification Among Smallholder Farmers: Socioeconomic Insights from Central Malawi

In Eastern and Southern Africa (ESA), smallholder rain-fed systems are vital, yet they are challenged by land degradation, soil fertility decline, and climate risks. To address these challenges, crop diversification has been promoted as a potential pathway to enhance productivity, improve nutritional security, and offer a viable pathway out of poverty and hunger. This study explores crop diversification among 150 smallholder households in the Kasungu, Mchinji, and Lilongwe districts of Malawi, where the project Sustainable Intensification of Maize Legume Systems in East and Southern Africa (SIMLESA) has engaged the smallholder farmers in conservation agriculture (CA)-based sustainable intensification participatory research and development for seven years since 2010. This study used Simpson’s diversity index (SDI) to estimate crop diversification, and a multiple linear regression model (MLRM) to analyze how smallholder farmers’ socio-economic characteristics influence adoption. The findings show a prevalence of small farms of less than 1.5 hectares, with most farmers perceiving crop diversification as beneficial for soil fertility. Key adoption constraints include labor shortages and a lack of legume seeds. SIMLESA participants lead in crop rotations, with a 63% higher adoption rate, and show the highest crop diversity, with a 99% increase in farmers growing three crops and a 74% increase in those growing four crops compared to non-SIMLESA farmers. The SDI values were 0.39 for non-SIMLESA, 0.48 for SIMLESA neighbors, and 0.57 for SIMLESA participants. Access to NGO inputs, larger farm sizes, and participation in research programs were positively associated with diversification, while food insufficiency was negatively associated with its adoption. The study highlights the importance of integrating participatory research methods to promote development initiatives effectively.

Warm Arctic-Cold Eurasia pattern helps predict spring wildfire burned area in West Siberia.

Extreme wildfires have devastating impacts on multiple fronts, and associated carbon greatly heats the earth's climate. Whether and how to predict wildfires becomes a critical question. In this study, we find that the preceding-winter "warm Arctic-cold Eurasia" (WACE) pattern significantly enlarges the spring burned area in West Siberia. The winter WACE and accompanying snow reduction result in dryness and vegetationexposure in West Siberia in spring, increasing fire risks. A multiple linear regression model is constructed that successfully predicts the spring burned area in West Siberia one season in advance (R-squared coefficient=0.64). The same predictors also well predict the corresponding fire carbon emissions. Independent predictions for spring burned area in 2019 and 2020 are very close to observations, with a mean absolute percentage error of only 3.0%. The findings of this study provide a possibility for guarding humans against extreme wildfires and predicting sharp rises in carbon emissions.

Assessing the burden of submicroscopic Plasmodium infections in a pre-elimination malaria setting in sub-Saharan Africa, Guinea-Bissau.

Submicroscopic Plasmodium infections can be a source of persistent malaria transmission. The aim of this study was to assess their frequency, distribution, morbidity and associated factors in a pre-elimination malaria setting in sub-Saharan Africa, Guinea-Bissau, where the Plasmodium falciparum is the predominant Plasmodium species. Dried fingerprick whole blood samples from 601 participants in the 2017 national, household-based, cross-sectional survey to estimate malaria prevalence were subjected to DNA extraction. The DNA was used in nested end-point PCR assays targeting genus- and species-specific regions of the Plasmodium 18S rRNA genes. Statistical analysis of socio-demographic, clinical and molecular data was carried out using the Statistical Package for the Social Sciences, version 29. Factors associated with submicroscopic P. falciparum infections and their magnitude were sought using Chi-square test and multiple logistic regression models, respectively. Statistically significant level was considered at P-value < 0.05. Nested PCR assays detected submicroscopic P. falciparum infections in 20.3% (95% CI = 16.8-23.8) of individuals microscopically negative for Plasmodium species in the general population and in 21.4% (95% CI = 9.9-36.5) of microscopically negative pregnant women. Submicroscopic Plasmodium malariae infections were also detected as co-infections in 3.0% individuals who were microscopically positive only for P. falciparum. Infections with other Plasmodium species were not detected. Submicroscopic P. falciparum infections were not associated with age, sex, or the presence of fever. A logistic regression model adjusted for ethnicity and health region showed that individuals from the Balanta and Bijagos ethnic groups, most of whom live in the low malaria-transmission areas of Quinara and Bissau, and the Bijagos archipelago, respectively, were less likely to have submicroscopic P. falciparum infections than individuals from the large Fula ethnic group, most of whom live in the high malaria-transmission area of Gabu. Submicroscopic P. falciparum infections were not associated with anaemia in children under 5years of age. The results obtained highlight the contribution of asymptomatic and submicroscopic P. falciparum infections to malaria transmission in high malaria-transmission areas and the need for molecular-based tools to detect submicroscopic Plasmodium species.

Association of depression and anxiety with health-related quality of life in beginning Medical Sciences Student.

The present study sought to examine the association between depression and anxiety with health-related quality of life (HRQoL) among clinical and non-clinical medical students. This study was conducted on newly entrance students (2019 and 2020) in Neyshabur University of Medical Science (NUMS). 471 students participated in this cross-sectional study. The Beck Depression (BDI-II), Anxiety Inventory (BAI), and SF-12 questionnaires were used. To assess the correlation between depression, anxiety, and HRQoL, the Pearson correlation was used. The independent t-test was performed to evaluate the association among different variables with HRQoL. Further, a multiple linear regression model was used to control the confounding effects of the covariates. The mean age of the study population was 21.9 (SD = 6.1), majority of them were female (62.4%). More than 21% and about 32% of students had mild to severe levels of depression and anxiety, respectively. According to correlation analysis (Pearson's r), the correlations between depression, anxiety, and total HRQoL were relatively strong and negative. The multiple linear regression model revealed a statistically significant association between depression and anxiety with different subscales, components, and total HRQoL (p < 0.05). It has been confirmed that the different levels of depression and anxiety in medical students are negatively associated with HRQoL.

Causes and Effects of Academic Staff Turnover Intentions on Organizational Performance: Comparative Studies on Ambo and Haramaya Universities

The purpose of this study was to conduct a comparative analysis of the causes of academic staff turnover intentions and their impact on the performance of Ambo and Haramaya Universities. The study primarily focused on factors such as socio-demographic characteristics, job satisfaction, compensation, work environment, and leadership style as determinants of academic staff turnover. A stratified sampling method was used to determine the sample population, while a convenience sampling technique was employed to collect data from the sample. Out of a total population of 1,672 academic staff members, a sample size of 143 was determined using Yamane&amp;apos;s sampling formula. Of the 143 distributed questionnaires, 133 were returned, with 10 remaining uncollected. Both primary and secondary data sources were utilized, with primary data gathered through questionnaires. The research was designed as a descriptive and explanatory study to both describe relevant statistical data and explain the causes and effects observed in the study. Data were analyzed using descriptive statistics, including frequencies, percentages, and numerical values, as well as inferential statistics, such as correlation analyses and multiple regression models. University performance was measured through various indicators, including stakeholder engagement and satisfaction, revenue generation from business entities, quality of education, research and community services, the expansion of universities with modern facilities, and the number of academic programs offered. The findings revealed that the causes of academic staff turnover have a positive relationship with university performance, while turnover itself has a negative relationship with university performance. This implies that if universities effectively manage the causes of academic staff turnover, their overall performance is likely to improve.