Multiple Linear Regression Models Research Articles

Foot Sole Temperature Correlates with Ankle–Brachial Index, Pulse Wave Velocity, and Arterial Age in Diabetic Patients Without Diagnosis of Peripheral Arterial Disease

Background/Objectives: Some vascular alterations such as peripheral arterial disease (PAD) or arterial stiffness can alter perfusion of the limbs, so we wondered if this is reflected in the temperature of the soles of the feet of diabetic patients who did not have a diagnosis of peripheral arterial disease. Foot sole temperature was correlated with the ankle–brachial index (ABI), carotid—femoral pulse wave velocity (cfPWV), brachial–ankle pulse wave velocity (baPWV), and arterial age. Methods: A total of 175 patients with type 2 diabetes mellitus, without a previous diagnosis of PAD, were recruited. Comorbidities, anthropometry, biochemical analysis results, temperature, ABI, cfPWV, baPWV, and arterial age were recorded. Forty-two temperature records were obtained from the sole of the foot with an FLIR T865 thermal imaging camera. ABI, cfPWV, baPWV, and arterial age were obtained using plethysmographic and oscillometric methods. Statistical analysis was performed with SPSS v.29.0 (correlations and multiple linear regression models). Results: All temperature points analyzed correlated negatively with ABI (p < 0.001) and rho values ranged from −0.168 to −0.210. Likewise, cfPWV, baPWV, and arterial age had similar results, since most temperature records showed low rho values and a negative correlation with these parameters. Four models were developed to explain the variables of interest. Temperature was involved in all of them. The temperature of the first toe was included in the prediction of cfPWV, baPWV, and arterial age. Conclusions: There is an inversely proportional relationship between temperature and ABI, cfPWV, baPWV, and arterial age in diabetic patients without a previous diagnosis of arterial disease. Temperature can be a predictor of these hemodynamic variables.

Age-Friendly Street Construction: The Synergy of the Physical Environment in Old Urban Communities in Suzhou

Community streets play a crucial role in promoting healthy aging and encouraging active behaviors among older adults. This study focuses on two types of activities of older adults: walking and social interaction. We explored the relationship between physical environmental factors and different activity types using a multiple linear regression model. Eye-level green visibility (GSVN) was used to represent the diversity of facilities (DFN), while betweenness (ABN) accounted for the mixed degree of functions (PNi), enhancing model stability. The results indicate that community environmental factors, both individually (R2 = 0.390) and in combination (R2 = 0.409), have significant effects on active behaviors. Notably, the combined variable model demonstrated greater stability. Moreover, the model’s explanatory power for social interaction (R2 = 0.567) was much higher than for walking behavior (R2= 0.137), underscoring the stronger influence of combined variables on social engagement. The results suggest that creating age-friendly urban environments requires a holistic approach that improves infrastructure, green visibility, and functional diversity to promote both mobility and social interaction. These insights provide valuable guidance for designing inclusive urban spaces that support active aging.

The Relationship Between Time-Related Characteristics of Visual Impairment and Psychological Symptoms in Adults Who Are Blind

Introduction The prevalence of depression among blind people is higher than in sighted populations. The purpose of this study was to investigate the associations between time-related characteristics of visual impairment (i.e., onset and duration), as well as depressive symptoms, fatigue, sleepiness, and stress, in the context of self-reported total blindness and the inability to perceive light exposure. Methods This study employed a cross-sectional design utilizing the combination of online survey and telephone survey. The Centre for Epidemiological Studies–Depression Inventory, the Fatigue Syndrome Inventory, the Epworth Sleepiness Scale, and the Perceived Stress Scale were selected for the self-reported questionnaires. For statistical analysis, independent t-tests, analysis of variance tests, Wilcoxon–Mann–Whitney tests, chi-square tests, multiple linear regression models, and multivariable ordinal logistic regressions were conducted using the R software program and its required packages. Results A total of 154 respondents completed the online and telephone surveys. Although the number of years of experiencing blindness showed negative correlations with the scores of fatigue ( r = −.16, p < .05), stress ( r = −.18, p < .05), and depressive feelings ( r = −.21, p < .05) in Model 1, the impacts of the onset of blindness on the depressive symptoms (β = −.02, p = .570) and the presence of severe depressive symptoms, OR = 1.42, 95% CI [0.49, 4.19], p = .518, were insignificant in Model 2. Discussion Blindness seems to be regarded as a more severe chronic condition which affects individuals’ psychological status. Given the effects of the limited range of social activities and daily tasks and the dramatically reduced accessible visual information inputs, the severe depressive symptoms in this group can be understood. Implications for Practitioners Regular counseling and psychological services monitoring depressive symptoms, chronic fatigue, and severe distress should be continued even after the completion of rehabilitation programs.

Relationship Between Mediterranean Diet Adherence and Body Composition Parameters in Older Adults from the Mediterranean Region

In recent decades, the rapid spread of various communication media has led to changes in traditional eating habits. In the Mediterranean region, the classic (Mediterranean) dietary pattern has been lost as a result. This has led to a shift in eating habits towards unhealthy eating patterns, which in turn has resulted in an inadequate distribution of body composition. It is known that, among other things, the number of non-communicable diseases increases with the inadequate distribution of body composition. The aim of our study was to examine the level of adherence to the Mediterranean diet (MD) of older adults in the Mediterranean region in relation to specific body composition parameters. This study included 521 older adults with a mean age of 69.6 ± 6.3 years. Body composition was measured using the BIA 101 Anniversary device (Akern s.r.l., Florence, Italy) and adherence to the MD was assessed using the MEDLIFE index questionnaire. This study found significant differences in body composition between males and females. The mean adherence to the MD was 17.0 ± 3.3 points among the participants and there was higher adherence in females (p = 0.002, ηp2 = 0.019). A multiple linear regression was performed to assess the relationship between the body composition parameters and MD. Multiple linear regression models were significant for reactance, fat mass (%), fat-free mass (%), skeletal muscle index, and total body water (%), with specific individual MEDLIFE items such as the consumption of processed meat, meat, white meat, fruit, vegetables, olive oil and limiting snacks between meals. Moreover, promising correlations were found between certain MD characteristics and BIA parameters, but the overall health effects of the MD remain unclear.

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

BackgroundSerum 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.ObjectivesThis study investigated the relationship between circulating α-Klotho and vitamin B12.MethodsA 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.ResultsA 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.ConclusionThis 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.

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.

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).

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.

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.

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.

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.

Garbage prediction using regression analysis for municipal corporations of Indian cities

AbstractGarbage management is exceptionally critical and poses enormous environmental challenges. It has always been a vital issue in municipal corporations. However, municipal agencies have developed and used garbage management systems. Garbage forecasting still plays a crucial role in the management system and helps improve or create a garbage management system. This research examines the information from 212 cities to suggest a helpful regression model for garbage forecasting and control. To establish a connection between the variables, the descriptive study employs statistical techniques to learn about the composition of data collected from municipal corporations and conduct correlation analysis. Population and garbage depend highly on one another, as evidenced by their correlation coefficient of 0.922,144. The primary research is used to build an alternate hypothesis that shows the chosen variables are highly dependent on one another. The dataset is scaled and divided into a training and testing 80:20 ratio during the pre‐processing data phase. This research aims to do a regression analysis with daily garbage production, urban area, and population as independent variables. This research initiates a variety of regression models, including multiple linear regression (MLR), artificial neural network (ANN), decision tree regression (DTR), and random forest regression (RFR). The MLR model's R2 value of 0.85 indicates that it has the potential to accurately forecast daily garbage production based on just two independent variables and a single dependent variable. Random Forest Regression (RFR) with (MSE: 100,078.749 &amp; MAE: 182.212) shows that it has the lowest MSE among all the models, which provides the most accurate predictions on average and the fit values of 8.85 and 316.23 obtained from the error distribution with a bin value 25. The estimated results from each model are compared to the test data values on line graphs and Taylor plots. The mean square error and the mean absolute error in the analysis and the Taylor plot show that the RFR model is best suited for predicting daily garbage production in a city. This research, therefore, provides a Random Forest model that is optimal for such challenges and is recommended for this class of problem.

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.

Assessment of Fine-Scale Urban Heat Health Risk and Its Potential Driving Factors Based on Local Climate Zones in Shenzhen, China

Cities are facing increased heat-related health risks (HHRs) due to the combined effects of global warming and rapid urbanization. However, few studies have focused on HHR assessment based on fine-scale information. Moreover, most studies only analyze spatial HHR patterns and do not explore the potential driving factors. In this study, we estimated the potential HHRs based on the “hazard–exposure–vulnerability” framework by using multisource data, including the modified thermal–humidity index (MTHI), population density, and land cover. Then, the variations in the HHRs among different local climate zones (LCZs) at the fine spatial scale were analyzed in detail. Finally, we compared the different contributions of the LCZs and types of land cover to the HHRs and their three components by using multiple linear regression models. The results indicate that the spatial pattern of the HHRs was different from those of the individual components, and high-hazard regions do not mean high HHRs. There were huge variations in the HHRs among the different LCZs. The built-up LCZs typically had much higher HHRs than the natural ones, with compact LCZs facing the most severe risk. LCZ 6 (open low-rise buildings) had a relatively low HHR and should be paid more attention in future urban planning. Compared to the LCZs, the land covers better explained the variations in the HHR. In contrast, the LCZs better predicted the land surface temperatures. However, both the LCZs and land covers made only slight contributions to the heat exposure and vulnerability. Furthermore, the manmade buildings and impervious surface areas contributed much more to the HHR than the natural land covers. Therefore, the arrangement of the warming LCZs and land cover types is worthy of further investigation from the perspective of HHR mitigation.

Nurses' perceptions of caring behaviors at referral hospitals in Ethiopia: A mixed-methods approach.

Caring plays a vital role in nursing and serves as an indicator of quality. Nurse caring behaviors are directed towards patients' safety and wellbeing. Nurses' perceptions of caring behaviors are affected by different factors. Understanding the factors linked to how nurses perceive caring behaviors is crucial for enhancing the quality of care. The aim of this study was to examine the factors associated with nurses' perceptions of caring behaviors and gain a more comprehensive understanding. A multicenter sequential explanatory mixed-methods study was conducted among medical-surgical nurses at three referral hospitals in the Amhara region, Ethiopia. Quantitative data were collected using a pretested self-administered survey package including the Amharic version of the Caring Behaviors Inventory-16. Qualitative interviews using semi-structured, open-ended questions were conducted after the survey to complement and clarify the quantitative results, facilitating integrate through a connecting approach. A multiple linear regression model was used to examine the relationship between nurses' perceptions of caring behaviors and associated factors. The qualitative data was examined to find meaningful patterns using codebook thematic analysis. This method was deductive and followed the guidelines by Braun and Clarke, building on the findings from the quantitative results. The overall mean of nurses' perceptions of caring behaviors was 4.75. The multiple linear regression analysis showed that female sex (b = 0.300, P = 0.004), age (b = 0.028, P = 0.001), nurse staffing (b=-0.115, P = 0.001), work-related stress (b = 0.298, P = 0.003), and job satisfaction (b=-0.254, P = 0.016) were associated with nurses' perceptions of caring behaviors. The codebook thematic analysis generated three themes, caring is universal and fundamental in nursing, impediments to provision of comprehensive care, and perception of the work conditions. The findings of the qualitative data complemented the results of the quantitative data. Discrepancies between nurses' expectations and the actual working environment may create uncertainty in care provision and impact quality-of-care. Strategies to harmonize the nurses' working environment should consider work-related factors.

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.

Objective quantification of sound sensory attributes in side-by-side vehicles using multiple linear regression models

The evaluation of sound quality is a pivotal area of research within audio and acoustics. The sound quality evaluation methods commonly used include both objective and subjective, the latter being time-consuming and costly as they rely on listening tests. This research work aims to investigate the use of predictive sound quality models as a way to objectively assess the Desire-to-buy of side-by-side vehicles, in a more efficient, faster, and less costly way than conventional methods. Multiple linear regression algorithms were used to validate the objective models derived from objective physical metrics and perceptual psycho-physical metrics. The sensory profile objective models reported in this paper were constructed using parsimonious linear Lasso and Elastic-net algorithms. Our results show that linear objective models effectively account for each of the perceptual attributes of the sensory profiles and the Desire-to-buy, while only requiring a few physical and psychophysical metrics.

A study on the effectiveness of (+)-usnic acid as oral toxic sugar bait against adult male and female Anopheles gambiae

BackgroundDespite the application of various tools for the control of vectors of Plasmodium falciparum, malaria remains the major killer disease in sub-Saharan Africa accounting for up to 90% of deaths due to the disease. Due to limitations of the useage of chemical insecticides such as resistance, negative impact on the environment and to nontarget organisms, the World Health Organization (WHO) requires that affected countries find alternative vector control tools. This study evaluated the effectiveness of ( +)-usnic acid (UA) as an insecticide through oral administration to male and female Anopheles gambiae as an alternative or additional active ingredient to be used in toxic sugar bait.Methods( +)-usnic acid was diluted using acetone at 5, 10, and 15 mg/ml concentrations in three replicates. A 5 ml mixture of 2% food dye and 10% sugar using chlorine-free water mixed with the dilutions of the ( +)-usnic acid and negative control was made containing 2% food dye and 10% sugar solution. The preparations were soaked on a ball of cotton wool and placed over the net of a cup. 5 male and 5 non-blood-fed female newly hatched starved An. gambiae Kisumu strain were introduced together into a cup and monitored for knockdown and mortalities after 4, 24 48, and 72 h. The data were analysed using a multiple linear regression model using the lm function, a base R function and a posthoc test were conducted on the significant main effects and interaction terms using the emmeans function from the emmeans R package. All analyses were performed in RStudio using base R (version 4.3.3).ResultsThere was high mortality of both male and female An. gambiae after ingestion of the toxic sugar bait. 15 mg/ml usnic acid caused the highest mortality (50%) within the first 4 h compared to 5 and 10 mg/ml ( +)-UA. There was a decline in the mortality rate with increased exposure time from 24 to 72 h, however, there was a significant difference in mortality at 5, 10 and 15 mg/ml. Acute toxicity was associated with ingestion of 15 mg/ml after 24 h. 72 h post-mortality was lower in all concentrations than in the control. High mortality was observed among females over the first 4 h (60%) compared to males (40%) due to higher feeding rate of the toxic agent. The proportion of dead males and females was equal after 24 h while after 48 h, the proportion of dead males was high.There was a significantly lower mortality rate after 72 h for both males and females (0 to 13.3%). Compared to all the treatments, high mortality of males was observed.ConclusionsThe results of this study indicate that ( +)-UA when administered as oral sugar bait to An. gambiae has insecticidal properties and is a suitable ingredient to be used as a toxic agent in the novel attractive toxic sugar bait for the control of malaria vectors. ( +)-UA may be an alternative active ingredient as toxic bait in the effort to reduce and eliminate the transmission of Plasmodium falciparum in Africa.

High resolution (1-km) surface soil moisture generation from SMAP SSM by considering its difference between freezing and thawing periods in the source region of the Yellow River

Soil moisture (SM) is a critical component of the land surface hydrological cycle, significantly impacting various sectors such as hydrology, meteorology, and agriculture. Accurate, high-resolution SM data are essential for effective flood forecasting, water resource management, and understanding the soil freeze-thaw processes in cold regions. This study aims to generate 1 km resolution liquid surface SM (SSM) data with a twice-daily update frequency by downscaling SMAP Level-4 SSM data using random forest (RF) and multiple linear regression (MLR) in the source region of the Yellow River (SRYR), by considering the differences in SM changes between freezing and thawing periods. To obtain the SSM data, 16 downscaling schemes of both RF and MLR were designed for each of the three scenarios. In each downscaling process, both land surface temperature (LST) and normalized difference vegetation index (NDVI) were utilized in MLR and RF models, alongside various combinations of additional variables such as albedo, elevation, leaf area index (LAI), soil texture. Results showed that during the freezing period, RF produced superior SSM estimates when supplemented with NDVI, LST, albedo, elevation, LAI, and soil texture. MLR was more effective during the thawing period when paired with NDVI, LST, elevation, LAI, and soil texture. During the freezing period, the downscaled SMAP SSM exhibited average R, RMSE, ubRMSE of 0.76, 0.029 m3·m-3, and 0.023 m3·m-3, respectively, when compared with in-situ measurements. During the thawing period, the average R, MAE, RMSE, and ubRMSE between the downscaled SMAP SSM and in-situ measurements were 0.52, 0.057 m3·m-3, 0.067 m3·m-3, and 0.054 m3·m-3, respectively, compared to 0.45, 0.070 m3·m-3, 0.083 m3·m-3, and 0.060 m3·m-3 for the original SMAP SSM. Thus, the research significantly enhances both the accuracy and spatial resolution of SMAP SSM estimations, underscoring its vital role in advancing hydrological studies within the SRYR.