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Feedback is beneficial to students' learning process and overall achievement, but one of the common complaints from students about college education is regarding feedback. So, this study aimed to examine whether digital written feedback positively influences students' learning experience and attitude toward learning. Digital written feedback refers to feedback provided through a learning management system as handwritten feedback added to a scanned copy of the student's paper-based assessment using a smart pad and smart pencil. The study surveyed a diverse set of college students' (n = 175) perceptions regarding feedback satisfaction, learning satisfaction, the usefulness of digital written feedback, and perceived achievement via an online survey. A one-way analysis of variance found a significant difference between feedback satisfaction and learning satisfaction according to the feedback method. A multiple regression analysis indicated the satisfaction and perceived usefulness of digital written feedback predicted perceived achievement. From the students' open responses regarding the benefits of digital written feedback, five themes emerged, namely usability, psychological relief, accessibility, self-regulation, and acceptance. The implications for digital written feedback are discussed. [ABSTRACT FROM AUTHOR]

Purpose: Studies comparing the outcome of spine surgery with that of large-joint replacement report equivocal findings. The patient-reported outcome measures (PROMs) used in such studies are typically generic and may not be sufficiently sensitive to the successes/failures of treatment. This study compared different indices of "success" in patients undergoing surgery for degenerative disorders of the lumbar spine, hip, or knee, using a validated, multidimensional, and joint-specific PROM.Methods: Preoperatively and 12 months postoperatively, 4594 patients (3937 lumbar spine, 368 hip, 269 knee) undergoing first-time surgery completed a PROM that included the Core Outcome Measures Index (COMI) for the affected joint. The latter comprises a set of single items on pain, function, symptom-specific well-being, quality of life, and disability-all in relation to the specified joint problem. Other single-item ratings of treatment success were made 12 months postoperatively.Results: In multiple regression analyses, controlling for confounders, the mean improvement in COMI at 12 months was greatest for the hip patients and lowest for those with degenerative spinal deformity (= the statistical reference group) (p < 0.05). Compared with spinal deformity, the odds of achieving "success" were: higher for hip (OR 4.6; 95% CI 2.5-8.5) and knee (OR 4.0; 95% CI 2.1-7.7) (no difference between spine subgroups) for "satisfaction with care"; higher for hip (OR 16.9; 95% CI 7.3-39.6), knee (OR 6.3; 95% CI 3.4-11.6), degenerative spondylolisthesis (OR 1.6; 95% CI 1.2-2.2), and herniated disc (OR 1.7; 95% CI 1.2-2.4) for "global treatment outcome"; and higher for hip (OR 13.8; 95% CI 8.8-21.6), knee (OR 5.3; 95% CI 3.6-7.8), degenerative spondylolisthesis (OR 1.6; 95% CI 1.3-2.1), and herniated disc (1.5; 95% CI 1.1-2.0) for "patient-acceptable symptom state". Patient-rated complications were the greatest in degenerative spinal deformity (29%) and the lowest in hip (18%).Conclusions: The current study is the largest of its kind and the first to use a common, but joint-specific instrument to report patient-reported outcomes after surgery for degenerative disorders of the spine, hip, or knee. The findings provide a sobering account of the significantly poorer outcomes after spine surgery compared with large-joint replacement. Further work is required to hone the indications and patient selection criteria for spine surgery. The data should be used to lobby research funding-bodies, governmental agencies, industry, and charitable foundations to invest more in spine research/registries, in the hope of ultimately improving spine outcomes. These slides can be retrieved under Electronic Supplementary Material. [ABSTRACT FROM AUTHOR]

This research analyses the influence of data analytics in enhancing the supply chain management process. From the global perspective, companies are focusing to remain competitive and foster growth by controlling the cost. In a typical supply chain management (SCM), the factors like capacity management, demand and expenses are regarded as recognized constraints. However, in the reality, there are uncertainties revolving around the overall consumer demand, risk involved in transportation, lead time differences and other aspects. The demand uncertainties tend to impact the SC performance in a wider span; hence companies tend to apply data analytics as a unique tool to forecast the demand, analyse the risk aspects and frame strategies to reduce the lead time. Hence, this study will enable in analysing the nature of impact which data analytics influences in supporting the SC process in the organisation. Major theme of the paper is intended to apprehend the critical influence of the big data analytics towards the supply chain management in selected companies in Europe, the researchers intends to measure the critical drivers of BDA in enhancing the SCM process and thereby support in realising the goals of the organisation. The researchers has collated data from 135 managers from the supply chain process in 15 different companies from Europe, the study tries to apply Multiple regression analysis through SPSS and Structural equation modelling through partial least squares modelling was used to test the hypothesis. The final results obtained states that the data analytics tend to possess positive influence on the supply chain management process, supports the management in reducing the enhancing supplier relationship and enable in creating better supplier network design. This paper intends to provide clear and concise aspect on the current overview of literature related to data analytics and its effect on supply chain management process. It also reveals the theoretical aspects of the research and provides outlines on future research directions. The study will be unique in stating the role of data analytics on SCM process by integrating the procedural and management perspectives. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to assess the level of knowledge, attitude and practice of university teachers regarding plagiarism in Bangladesh. An online questionnaire consisted of 20 knowledge questions, 23 attitude items, and 18 practice questions was created using Google Forms. The link to the questionnaire was sent via email to university teachers. The total correct answers for knowledge and practice questions, and the total attitude score were converted to percentile scores and categorized accordingly as poor (< mean – 1 SD), average (mean ± 1 SD), and good (> mean + 1 SD). Bivariate analyses were conducted to compare the total knowledge, attitude and practice scores based on demographic and academic variables. Multiple linear regressions were used to identify the association between knowledge, attitude and practice scores, and other covariates. The findings revealed an average level of knowledge, attitude and practice regarding plagiarism among the majority of university teachers. The knowledge, attitude and practice scores were significantly higher for teachers who attended academic writing workshops compared to those who did not attend such events. Demographic and academic variables did not impact knowledge and attitude scores. However, the number of papers published in the last two years and their indexing in Web of Science (WoS) or Scopus significantly impacted attitude and knowledge scores. The multiple regression analyses showed that the practice score was significantly associated with age, highest education, and knowledge. [ABSTRACT FROM AUTHOR]

The phenomenon of rural in-migration is emerging as a significant trend in various global hotspots, playing a crucial role in reshaping these rural areas from social and spatial perspectives. The decision-making process for rural in-migrants regarding settlement is complex. Although previous studies have acknowledged that settlement intentions are influenced by social and environmental factors, such as the economy, natural environment, and public service facilities, many have relied on a stated-preference approach to measure migrants' settlement intentions. This method may overlook the inherent complexity and fluidity of the decision-making process. Drawing upon the Theory of Planned Behavior (TPB), this paper formulates questions aligned with the three stages of decision-making to examine the settlement intentions of rural in-migrants. Employing multiple linear regression analysis, the study assesses the impact of social and environmental factors on the settlement decisions of rural in-migrants in four villages surrounding Dali, a renowned rural destination in southwest China that attracts a diverse range of migrants. The findings highlight the pivotal role of public service facilities' quality in rural villages in the decision-making process, particularly among those who have already formulated settlement plans. Additionally, the research reveals that female in-migrants place a heightened emphasis on the quality of these facilities. Furthermore, a higher level of education correlates with a broader diversity of influencing factors. As migrants age, a wider range of considerations comes into play, with the natural environment emerging as a key factor for older in-migrants. This article emphasizes the equal importance of continuously improving public service facilities, strengthening economic vitality, and protecting rural natural landscapes to support the development of rural communities. [ABSTRACT FROM AUTHOR]

An SHS-CFSHS X-joint is fabricated by welding two square hollow section (SHS) braces to a concrete-filled square hollow section (CFSHS) chord. In this paper, the stress concentration factors (SCFs) of SHS-CFSHS X-joints are investigated through experimental tests and finite element analysis (FEA), with the hot spot stress method serving as the analytical approach. Eight specimens are designed and manufactured, with FE models built in software ANSYS. These FE models are validated against the test results. The specimens are tested under brace axial tension to determine the SCFs of the X-joints. It shows that the concrete filled in the chord effectively reduces the SCFs of the X-joints. To further explore various load conditions and the influence of the parameters, FEA is carried out and a total of 64 FE models are built. Based on the FEA results, multiple regression analysis is used to obtain the SCF formulae of SHS-CFSHS X-joints under axial tension load and in-plane bending load in the brace, respectively. Comparison and analysis of the SCF results obtained from experimental tests, the proposed formulae, and FE simulations reveal that the formulae presented in this study are both conservative and suitable for predicting SCFs. [ABSTRACT FROM AUTHOR]

Background: The concept of Positive Health (PH) has gained increasing attention as a way of measuring individuals' ability to adapt in the face of contextual challenges. However, a suitable measurement instrument for PH that encompasses contextual factors has not yet been developed. This paper responds to this need by developing a Context-specific Positive Health (CPH) measurement instrument that aligns with the Capability Approach (CA). Methods: The measurement instrument was developed and tested among a representative sample of 1002 Dutch internet survey panel members with diverse sociodemographic backgrounds. The instrument was developed in two stages: a preparation phase consisting of focus groups and expert consultations, and a validation among a representative panel of Dutch citizens. The goal of the preparation phase, was to pilot test and refine previously proposed Positive Health questionnaires into an initial version of the CPHQ. The validation phase aimed to examine the initial CPHQ's factorial validity using Factor Analysis, and its concurrent validity using Multivariate Regression Analysis. Results: The developed questionnaire demonstrated adequate factorial and concurrent validity. Furthermore, it explicitly includes an assessment of resilience, this being a key component of PH. Conclusions: The introduced measurement tool, the CPHQ, comprises 11 dimensions that we have labeled as follows: relaxation, autonomy, fitness, perceived environmental safety, exclusion, social support, financial resources, political representation, health literacy, resilience, and enjoyment. In this article, we present four major contributions. Firstly, we embedded the measurement in a theoretical framework. Secondly, we focused the questionnaire on a key concept of Positive Health - the "ability to adapt." Thirdly, we addressed issues of health inequality by considering contextual factors. Finally, we facilitated the development of more understandable measurement items. [ABSTRACT FROM AUTHOR]

National and international organizations have introduced policies aimed at sustainable development. These policies are designed to encourage sustainable forms of business to meet the Sustainable Development Goals (SDGs) of the 2030 Agenda. Regional inequalities in sustainable development may be exacerbated by disparate levels of innovation. This paper analyzes the variations between clusters of countries according to the degree to which they have achieved the SDGs and their levels of innovation facilitators. Two types of analyses were employed. First, cluster analysis was used to examine changes in groups of regions with similar innovation characteristics between 2015 and 2020. Data for 122 countries were gathered from the World Bank, the SDG Index, and the Global Innovation Index. Second, multiple linear regression analysis was used to assess the power of the variables in the model to explain the level of sustainable development. The results reveal four clusters (low, medium, high, and very high innovative facilitators and sustainable development), as well as movements between those clusters from 2015 to 2020. The multiple linear regression analysis shows that the variables have explanatory power with respect to the dependent variable of sustainable development. This analysis also reveals different degrees of importance of the variables for each cluster. The findings highlight the need to consider the limitations of economic growth in terms of innovation facilitators to promote sustainable development. If policymakers recognize the limitations of economic growth and the physical ecosystem, degradation of the environment can be avoided, even when there is innovation. Global and individual social welfare can thus be ensured. This study offers valuable insights into how to achieve sustainable development through innovation facilitators by providing in-depth knowledge of the individual characteristics of innovation systems and considering the limitations of economic growth. [ABSTRACT FROM AUTHOR]

The dysregulation in heat balance, the main cause of exertional heat stroke, occurs not only in midsummer but also in the cold season. Possible causes of this are a reduction in convection and evaporation due to tailwinds and an acceleration of radiant heat inflow. Although the amount of radiant heat that reaches the surface can be estimated, the actual amount of heat that flows into the body cannot be specified yet. This paper made an experimental attempt at this. A device is made up of a temperature controllable heat sink and heat flow detector, which keeps the surface temperature constant and has a heat exchange coefficient comparable to that of the human body surface. The output of this device (total heat exchange) was divided into radiant heat exchange and other heat exchange using a standard radiant heat calibrator, Leslie cube. A phenomenon, in which a wet surface while the surface temperature was low absorbed larger heat than that of the dry surface, was found. And authors named this "hidden heat inflow". As a result of multiple regression analyses, both radiant heat exchange and other heat exchanges are closely related to the surface temperature, and the maximum difference in total heat exchange during the experiment reached 200 kcal/m2/h. It has been suggested that this phenomenon may also occur on the surface of human skin. One of the causes of this "hidden heat inflow" is considered to be the decrease in evaporative cooling due to the decrease in surface temperature. However, this alone cannot explain all of the phenomena, so water vapor aggregation may also be involved. A "hidden heat inflow" as a sufficient heat source for exertional heat stroke or collapse during a marathon race on a cold day was evidenced experimentally. [ABSTRACT FROM AUTHOR]

This paper focusses on real-time detection and abnormal state prediction through a set of wireless equipment of fire water system to provide a better solution for fire inspection. The main goal of this research is to improve the fire extinguishing efficiency. Firefighting and rescue operations are urgent, and concealed or dry fire hydrants may cause failure cases of firefighting. To prevent this situation, this paper proposed the wireless equipment of fire water system to monitor the multiple indicators of outdoor hydrant, indoor hydrant, and sprinkler network. The objective of the experiment is to realize the real-time monitoring and obtain the data information of the state of the fire water system. Also, the multiple linear regression analysis method is adopted to analyze the monitored water pressure data to achieve rapid identification of abnormal water pressure value. The results indicate that the data transmission of the wireless equipment of fire water system is reliable, the pressure is accurately monitored below 0.40 MPa, and the average relative error of abnormal water pressure monitoring results is less than 6%. The new equipment and algorithm are used not only to monitor the state of water supply system, but also to depict the change of water consumption in the monitoring environment and predict the unconventional state, which provides directional help for fire patrol and inspection. [ABSTRACT FROM AUTHOR]

Recurrence quantification analysis (RQA) is a time-series analysis method that uses autocorrelation properties of typing data to detect regularities within the writing process. The following paper first gives a detailed introduction to RQA and its application to time series data. We then apply RQA to keystroke logging data of first and foreign language writing to illustrate how outcome measures of RQA can be understood as skill-driven constraints on keyboard typing performance. Forty native German students performed two prompted writing assignments, one in German and one in English, a standardized copy task, and a standardized English placement test. We assumed more fluent and skilled writing to reveal more structured typing time series patterns. Accordingly, we expected writing in a well-mastered first language to coincide with higher values in relevant RQA measures as compared to writing in a foreign language. Results of mixed model ANOVAs confirmed our hypothesis. We further observed that RQA measures tend to be higher, thus indicating more structured data, whenever parameters of pause, burst, and revision analyses indicate more fluent writing. Multiple regression analyses revealed that, in addition to typing skills, language proficiency significantly predicts outcomes of RQA. Thus, the present data emphasize RQA being a valuable resource for studying time series data that yields meaningful information about the effort a writer must exert during text production. [ABSTRACT FROM AUTHOR]

Purpose: Research shows that women who experience intimate partner violence (IPV) are at risk for a range of adverse mental health outcomes, including depression, post-traumatic stress disorder (PTSD), and suicidality. However, few studies use measures that reflect the broad range of IPV exposures that women experience. This paper examines the associations between multiple IPV types and adverse mental health among a sample of survivors engaged in care. Methods: This article draws on data from a web-based survey administered to 377 women seeking IPV services in a Southwestern U.S. state. Multivariate regression analyses with multiple imputation were conducted to investigate the net association between multiple types of IPV and three mental health outcomes: depression, PTSD, and a prior suicidal attempt or threat, controlling for socio-demographics and self-rated health. Results: Multivariate analyses indicated significant associations between reproductive coercion (β = 6.89, p <.001), technology-facilitated violent threats (β = 5.27, p <.05), and psychological vulnerability/abuse (β = 4.45, p <.05) and symptoms of depression. Findings also indicated significant net associations between reproductive coercion (β = 7.05, p <.001), technology-facilitated humiliation (β = 4.87, p <.05), and psychological vulnerability/abuse (β = 7.76, p <.001) and increased PTSD symptomology. Finally, psychological vulnerability/abuse (aOR = 3.36, p <.01) increased the likelihood of reporting a prior suicidal attempt or threat. Conclusion: Women reporting reproductive coercion, technology-facilitated abuse, and psychological vulnerability/abuse had a higher risk of adverse mental health. More research is needed to understand the mechanisms that underlie these varied associations and to develop a cohesive practice model attuned to integrated needs. [ABSTRACT FROM AUTHOR]

It is still controversial whether environmental supervision, as a compulsory means of environmental protection, can improve the environmental accounting information disclosure quality (EAIDQ). There is a growing debate on if different ownership structures will lead to differences in the environmental accounting information disclosed by enterprises? Thus, through constructing multiple linear regression analysis, we empirically studied the impact of ownership structure and environment supervision on the EAIDQ from both internal and external aspects. The findings presented that (1) the overall EAIDQ is not significantly affected by ownership concentration, check-and-balance of ownership, and environmental supervision. (2) The monetary environmental accounting information disclosure quality (M-EAIDQ) is lower than the non-monetary environmental accounting information disclosure quality (NM-EAIDQ). The M-EAIDQ of state-owned enterprises is higher than that of non-state-owned enterprises. The correlation between environmental supervision and the M-EAIDQ is significantly negative, and a significant positive correlation between environmental supervision and the NM-EAIDQ. The research results of this paper can supplement the existing literature and provide a basis for the government to establish environmental accounting information disclosure standards. [ABSTRACT FROM AUTHOR]

For young children, positive parenting is predictive of their prosocial development and positive emotional well-being. Understanding the factors that promote or undermine positive parenting is of particular importance for families at risk of child welfare involvement. For Latinx families, conceptualizations of risk are better viewed through a cultural lens. This paper explores predictors of positive parenting among Latinx families (Mexican and Puerto Rican) who are vulnerable to child welfare involvement. Weighted data were drawn from Wave 1 of the National Survey on Child and Adolescent Well-being II—Restricted Release (NSCAW-II), a national sample that approximated a probability sample of child welfare involved families. After controlling for all other variables in the model, being married and using only non-violent parenting were related to higher positive parenting scores. Experiencing IPV within the last 12 months was related to significantly lower positive parenting scores. Results from the study highlight the need for a trauma-informed approach to culturally specific services for Latinx families who are vulnerable to the child welfare system. The connection between IPV experiences and the context of positive parenting is discussed. [ABSTRACT FROM AUTHOR]

Research to date has shown that HIV infection is a highly stressful experience for individuals, and one of the key adaptive resources after such painful experiences may be forgiveness. The aim of the present study was to examine the associations between dispositional forgiveness (assessed using Mullet's Forgivingness Questionnaire and Toussaint's Forgiveness Scale), perceived stress (single-item measure of stress symptoms), health perception (EuroQol visual analogue version of the scale) and life satisfaction (Satisfaction With Life Scale) in people living with HIV (PLWH) in France. Paper surveys were completed by 222 PLWH aged 18–78 (57% male). Multiple regression analysis revealed that sensitivity to circumstances, unconditional forgiveness, self-forgiveness, and forgiveness of others were significant predictors of health and happiness. Mediation analysis showed that these relationships are completely mediated by perceived stress. The present findings suggest that forgiveness and perceived stress may be important variables for healing in PLWH. Interventions designed to improve forgiveness and self-forgiveness may result in improved health and life satisfaction in PLWH. [ABSTRACT FROM AUTHOR]

The paper reports a study conducted with the goal of expanding the possibilities of using the previously obtained multiple nonlinear regression equation describing the dependence of protein content in wheat grain (Y, %) on the amount of crude gluten (X1, %) and 1000-grain weight (X2, g). It has been demonstrated that the original and three improved equations can be used in breeding practice to predict wheat grain protein content. The algorithm and the results of testing the predictive ability and prediction accuracy of the equations using independent data were presented. Analysis of the experimental data from 288 Russian and foreign publications, with a total number of observations n = 4604, obtained for more than 300 winter and spring soft and durum wheats in Russia and abroad (Albania, Belarus, Bulgaria, Egypt, Kazakhstan, Lithuania, Poland, Slovakia, and Ukraine) within the period from 1959 to 2020 showed that 755 observations, or 16.4%, fell beyond the variation limits accepted by GOST 10846-91: Method of Protein Determination. At the same time, the accuracy of protein content prediction reached 83.6%. The developed equations can be used for predicting protein content in almost all cases when the results of protein and crude gluten content analysis in wheat grain, as well as 1000-grain weight, are provided for actual or fixed moisture content or are recalculated for absolute dry matter. [ABSTRACT FROM AUTHOR]

Load profile coefficients (LPCs) represent the pattern of electricity usage daily and yearly for electrical energy consumers. It is important to determine the LPCs accurately and reliably, in order to minimize the imbalance costs in the Electricity Energy Market. Reliable methods and sufficient measurement data are required to make accurate forecasts. The local distribution company (TLDC) already calculates the profile coefficients by taking the average of the consumptions without meteorological measurements in Turkey. TLDC determines the LPC by receiving hourly consumption data directly from the consumers. In this paper, the mathematical forecasting models (MFMs) have been produced for determining LPC Duzce in Turkey using the multiple regression analysis method for the first time. Firstly, hourly electrical energy consumption and meteorological temperatures were measured in some predetermined residential subscribers. The MFMs have been produced by using the measured data, and then, LPCs have been determined by using the MFMs. The electrical energy consumptions have been estimated using the determined LPCs, and the estimation results have been compared with the measurement data. The MFMs have been subjected to suitability tests accepted in the literature, and the performances of the models have been verified. According to the results obtained, it has been seen that the MFMs can estimate loads with an accuracy of up to 96% depending on the future changing meteorological conditions, and it has been proposed as a quick and practical method for LPCs calculation. The paper shows that the produced MFMs provide obtaining satisfactory results for energy consumption forecasting for Duzce in Turkey. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a significant impact on religion and its practice. This paper aims to examine how the pandemic affects religious activities, donations, and finances over time and across regions within the United Methodist Church (UMC) in the USA. To address this question, we analyze survey data collected during the pandemic from 2963 churches in the USA by United Methodist Communications. Our analysis utilizes several quantitative techniques, including Z-tests, one-way analysis of variance (ANOVA), and multinomial logistic regressions. The results indicate a decrease in church attendance over time, with a more pronounced effect observed in non-urban areas (suburban, small town, and rural). Similarly, while church donations and finances mitigate over time across churches, churches in urban areas experience a quicker rebound compared to those in non-urban areas. Lastly, we find that church attendance and donations positively affect finances. These findings hold important implications for churches in various regions, offering insights to develop strategies for navigating the challenges posed by the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate disparities in urine drug testing (UDT) during perinatal care at a single academic medical center. This retrospective cohort study included patients who had a live birth and received prenatal care at our institution between 10/1/2015 and 9/30/2020. The primary outcomes were maternal UDT during pregnancy (UDTPN) and UDT only at delivery (UDTDEL). Secondary outcomes included the number of UDTs (UDTNUM) and the association between a positive UDT test result and race/ethnicity. Mixed model logistic regression and negative binomial regression with clustering based on prenatal care locations were used to control for confounders. Of 6,240 live births, 2,265 (36.3%) and 167 (2.7%) received UDTPN and UDTDEL, respectively. Black (OR 2.09, 95% CI 1.54–2.84) and individuals of Other races (OR 1.64, 95% CI 1.03–2.64) had greater odds of UDTPN compared to non-Hispanic White individuals. Black (beta = 1.12, p < 0.001) and Hispanic individuals (beta = 0.78, p < 0.001) also had a positive relationship with UDTNUM. Compared to individuals with non-Medicaid insurance, those insured by Medicaid had greater odds of UDTPN (OR 1.66, 95% CI 1.11–2.49) and had a positive relationship with UDTNUM (beta = 0.89, p < 0.001). No significant associations were found for UDTDEL and race/ethnicity. Despite receiving more UDT, Black individuals were not more likely to have a positive test result compared to non-Hispanic White individuals (OR 0.95, 95% CI 0.72–1.25). Our findings demonstrate persistent disparities in substance use testing during the perinatal period. Significance: What is already known on this subject?: Prior literature has shown evidence of racial disparities in urine drug testing during pregnancy-related hospital admissions and at delivery. What this study adds?: This paper found that disparities in urine drug testing also exist during the prenatal care period at an urban academic institution. We also found a positive relationship between the number of tests received and being of Black or Hispanic race/ethnicity. Future research should investigate health care system-related interventions to mitigate disparities in urine drug testing. [ABSTRACT FROM AUTHOR]

Background: Frailty has been associated with increased postoperative mortality and morbidity; however, the use of the modified frailty index (mFI-11) to assess patients undergoing surgery for diverticular disease has not been widely assessed. This paper aims to examine frailty, evaluated by mFI-11, to assess postoperative morbidity and mortality among patients undergoing operative intervention for colonic diverticular disease. Methods: We used data from the Healthcare Cost and Utilization Project National Inpatient Sample (October 1, 2015–December 31, 2019). ICD-10-CM codes were utilized to identify a cohort of adult patients with a primary admission diagnosis of diverticulitis. mFI-11 items were adapted to correspond with ICD-10-CM codes. Patients were stratified into robust (mFI < 0.27) and frail (mFI ≥ 0.27) groups. Primary outcomes were in-hospital postoperative morbidity and mortality. Secondary outcomes included system-specific postoperative complications, length of stay (LOS), total admission cost, and discharge disposition. Multivariable regression models were fit. Results: Of the 26,826 patients, there were 24,194 patients with mFI-11 < 0.27 (i.e., robust) and 2,632 patients with mFI-11 ≥ 0.27 (i.e., frail). Adjusted analysis showed significant increases in postoperative mortality (aOR 2.16, 95% CI 1.38–3.38, p = 0.001) and overall postoperative morbidity (aOR 1.84, 95% CI 1.65–2.06, p < 0.001). LOS was higher in the frail group (MD 1.78 days, 95% CI 1.46–2.11, p < 0.001) as well as total cost (MD $25,495.19, 95% CI $19,851.63-$31,138.75, p < 0.001). Conclusion: In the elective setting, a high mFI-11 (i.e., presence of the variables comprising the index) could alert clinicians to the possibility of implementing preoperative optimization strategies. In the emergent setting, a high mFI-11 may help guide prognostication for these vulnerable patients. [ABSTRACT FROM AUTHOR]

A meta-analysis was performed to determine pooled prevalence of non-suicidal self-injury (NSSI), suicide behaviors (including ideation, attempts), and associated risk factors among runaway and homeless youth (RHY). The databases PubMed, Scopus, Web of Science, and Cochrane Library were searched for relevant studies published from January 1995 to May 2023. Initially, 8465 papers were screened, resulting in 69 included studies. The results showed that among RHY, lifetime prevalence rates were 42% for NSSI, 38% for suicidal ideation, and 27% for suicide attempts. Lifetime prevalence of NSSI and suicide behaviors was higher among adolescent minors (aged 12–17 years) compared to young adults (aged 18–24 years). Also, NSSI and suicide behaviors were associated with having a childhood history of physical and sexual abuse. Developing an impactful community-based suicide prevention campaign tailored for RHY appears warranted. Peer groups and mentorship programs would provide invaluable support for young individuals, as supportive friendships protect against NSSI. [ABSTRACT FROM AUTHOR]

Machine learning algorithms have proven useful in the estimation, classification, and prediction of water quality parameters. Similarly, indexical modeling has enhanced the evaluation and summarization of water quality. In Nigeria, works that have incorporated machine learning modeling in water quality analysis are scarce. Although studies across the globe have utilized overall index of pollution (OIP) and water quality index (WQI), works that have simulated and predicted them using machine learning algorithms seem to be scarce. Studies have not simulated nor predicted OIP. In this paper, several physicochemical parameters were analyzed and used for groundwater quality modeling in southeastern Nigeria based on integrated data-intelligent algorithms. Standard methods were followed in all the analysis and modeling performed in this work. OIP and WQI were computed, and their results revealed that 80% of the groundwater resources are suitable for drinking whereas 20% are highly polluted and unsuitable. Pearson's correlation analysis and R-mode hierarchical clustering revealed the possible sources of contamination. Meanwhile, agglomerative Q-mode hierarchical clustering and K-means (partitional) clustering were used to show the spatial demarcations of water quality in the area. Both clustering algorithms identified two main water quality classes—the suitable and unsuitable classes. Furthermore, multiple linear regression (MLR) model and multilayer perceptron neural networks (MLP-NN) were used for the estimation and prediction of the water quality indices. With low modeling errors, both MLR and MLP-NN showed very strong predictions, as their determination coefficient ranged between 0.999 and 1.000. However, MLR slightly outperformed the MLP-NN in the prediction of OIP. The findings of this paper would enhance sustainable water management in the study region and also contribute great insights to the national and global water quality prediction literatures. [ABSTRACT FROM AUTHOR]

This paper examines how family atmosphere, parental self-efficacy, parenting methods and children's patterns of mobile device use result in negative outcomes for children's use of mobile devices. The hierarchical multiple regression analysis found that a stressful and conflictive family atmosphere and higher dependence on mobile devices promote negative outcomes. The self-efficacy of parents in helping their children to avoid the negative outcomes was also found to be positive associated the actual negative outcomes that their children had experienced in using mobile devices. Data were collected from a sample of 1561 students from 39 secondary schools in Hong Kong, including students studying at the Form one to Form five levels (equivalent to the seventh to eighth grade) and 1233 of their parents. Hierarchical linear regression analysis was applied to examine the magnitude of the associations between these variables. The paper highlights the importance of avoiding a tense atmosphere and conflicts between parents and children. For service providers, this paper provides a comprehensive reference to understand how parents and children think about family dynamics and relationships under the impact of technological development on individual families. Also, parents would be more advantageous if they were more efficacious about themselves in helping children to avoid the negative outcomes. This study can help service providers determine which intervention approaches can effectively support families in managing parent–child conflicts, developing cohesiveness, and facilitating children's proper use of new technologies. [ABSTRACT FROM AUTHOR]

Cappadocia in Turkey is an important region with emphasis on ethnic and historic structures and geological features. Because of volcanic eruptions, ash flow materials deposited at different levels. These different colored ignimbrite levels have a wide range of geomechanical properties. In the region, the Kızılkaya ignimbrite covers a very large area and is frequently used as building dimension stone in for the restoration of old historical buildings such as churches, caravansaries and mosques as well as new buildings. It contains tuffaceous matrix with some minerals and rock fragments. The rock has variable character. In this study, the relationship between petrographic and physico-mechanical properties of the Kızılkaya ignimbrite was investigated. Rate of change between uniaxial compressive strength and the other index properties of the rock was statistically examined utilizing multiple regression analysis (MRA) and Artificial Neural Network (ANN) methods. The study has shown that uniaxial compressive strength (UCS) of the ignimbrite can be successfully estimated by the use of dry unit weight, effective porosity, sonic velocity, and grain matrix ratio (GMR) parameters. A new UCS prediction equation is proposed in this paper for the ignimbrite with variable character. Since properties of the ignimbrite at different distances from the eruption area changes significantly, the approach presented in this study can be considered for the ignimbrites especially covering a large area with different welding degrees and grains. [ABSTRACT FROM AUTHOR]

This paper presents the development of a novel model for predicting setup of closed-ended steel pipe (CEP) piles driven in predominantly fine-grained soils using gene expression programming (GEP). The proposed GEP model incorporates both pile and soil properties and is based on dynamic pile load test data obtained from 109 piles installed at 59 different sites. Multiple variable regression analyses conducted on the compiled dataset showed that the most influential parameters in predicting the time-dependent resistance of driven piles were the resistance mobilized at the end of pile installation, time elapsed after installation, pile shaft surface area, and average silt content along the pile length. The data were divided into a training set for model calibration and a validation set for model verification. Sensitivity analysis was performed to assess the model's robustness. A comparison of the new GEP model with existing pile setup models in the literature was carried out using the collected data, which demonstrated that the proposed GEP model significantly outperformed the tested models. Additionally, data from 22 additional CEP piles were compiled from the literature for model verification purposes. The results showed that the proposed GEP model can predict the total pile resistance with good accuracy. [ABSTRACT FROM AUTHOR]

Jilin Province is one of the important grain-producing regions in China, the frequent drought and waterlogging events in this region have seriously impacted local agricultural production, therefore, it is particularly necessary to explore the spatiotemporal variations in drought and waterlogging and how they effect on maize yields. In this paper, we use the daily meteorological data recorded at 27 meteorological stations in Jilin Province from 1961 to 2020 to calculate the modified crop water deficit index (mCWDI), which were based on the daily crop coefficient (Kc) corresponding to different growth stages for each station. The spatiotemporal evolution processes of drought and waterlogging during the growing season in Jilin Province were analysed by the linear regression model, and the impacts of drought and waterlogging conditions on maize yields in Jilin Province under different growing seasons were quantified by means of the correlation analysis and multiple regression analysis methods. The results showed that the effective precipitation during the whole reproductive period showed a spatial distribution pattern of decreasing from southeast to northwest, with precipitation totals ranging from 335.02 to 677.38 mm, while the spatial distribution of the water demand showed the opposite trend. The south-eastern region of Jilin Province was in a state of water surplus, while the precipitation in other areas could not meet the water requirements of maize, resulting in decreasing drought frequency trends from northwest to southeast and from the early stage to the developmental stage of maize; in addition, increasing trends were observed in the middle and late reproductive stages of maize. The waterlogging frequency in the south-eastern region showed the spatial distribution characteristics of being higher in the early and late reproductive periods and lower in the development period of maize, and the growth rate of the maize-waterlogging frequency was higher in central Jilin Province than in other areas. Moreover, drought showed a more significant negative correlation with the maize yield in Jilin Province, while waterlogging showed a positive correlation. The relative importance results show that drought has a greater impact on maize yields than waterlogging, and the impacts of drought and waterlogging events on maize yields are mainly concentrated in the middle and late growth periods. The findings could inform the development of contingency plans for farmers to minimize crop losses and ensure food security in the region. [ABSTRACT FROM AUTHOR]

This paper studies the spatiotemporal variability of the IRt index (defined as the difference between the percentage of precipitation corresponding to warm days and cold days in year t) calculated at 35 stations in the Iberian Peninsula during 1951–2019. Spatial variability is estimated using cluster analysis (Euclidean distance, Ward's method), which allows regionalization of the data set for each year's season. For each cluster, the < IR > index is defined as the average of the indexes corresponding to each cluster element. The temporal evolution of the < IR > indices is analyzed by studying the appearance of trends using the Mann–Kendall test and the possible existence of various subperiods using the cumulative sum of deviations and the t-test for the difference between the means. The role of EA, EA/WR, NAO, SCAN and WeMO teleconnection patterns in modulating the relationship between temperature and precipitation is analyzed by applying multiple regression analysis. The results show a trend towards positive (or less negative) values in the relationship between temperatures and precipitation (of varying magnitude depending on the region and season) and the contribution of atmospheric dynamics to these changes. [ABSTRACT FROM AUTHOR]

As an important representation of sustainable land development, land function has become a research hot spot in the land research field. To explore the spatiotemporal evolution pattern and its driving mechanism of land function in China, an evaluation index system of land function was constructed. We adopted the weighted sum method to evaluate the land function and used the dynamic change degree of the land function and the coefficient of variation to analyze the spatiotemporal evolution characteristics of the land function. The driving mechanism of land function evolution was studied using grey relational analysis and multiple stepwise regression. The results showed that (1) from 200 to 2019, the total land function (TLF) increased by 10.41%, initially increasing and then decreasing. The land social function (LSF) and land bearing function (LBF) increased by 42.05% and 17.35%, respectively, while the land productive function (LPF), land cultural leisure function (LCF), and land ecological function (LEF) decreased by 3.64%, 4.25%, and 2.51%, respectively. (2) The spatial distribution of the LPF and LCF decreased from east to west, the distribution law of the LSF spread from the edge to the inside, the LBF showed a distribution state of high in the southeast and low in the northwest, and the spatial change law of the LEF decreased from west to east. (3) The driving factors of land function were the built-up area, urbanization rate, population density, and proportion of education expenditure. This paper provides a reference for improving land functions and for the sustainable utilization of land resources. [ABSTRACT FROM AUTHOR]

Mineralogy-based brittleness index is the simplest and most widely used index in characterizing the brittle property of shale gas/oil reservoirs. Traditionally, mineral compositions are usually determined by laboratory X-ray diffraction (XRD) analysis or interpreted from element logging, among which the former is time-consuming and non-continuous while the latter is costly and not timely. In this paper, a new rapid and economical method is proposed to obtain the mineral composition of shale from a statistical perspective, in the case of the Wufeng-Longmaxi shale, from the southeastern Sichuan Basin, China. The targeted shale cores from one scientific exploration well were scanned in situ to obtain the elemental data by using a non-destructive portable X-ray fluorescence (XRF) and sampled at each scanned site for laboratory XRD analysis to acquire the mineral composition data. Then we established the conversion models for shale with various sub-lithologies (including calcareous shale, sandy shale, and carbonaceous shale) based on a series of multiple regression analyses. These models were verified with the good consistency between predicted and tested mineral compositions of shale cores from another scientific exploration well. The mineralogy-based brittleness characterization results also indicated small differences between the predicted and tested brittleness index values, with the brittleness index curves matching well with the variations of experimental total organic carbon (TOC) with depth and conventional geophysical logging curves. The findings shed light on the application of portable XRF in determining mineral compositions and further the brittleness index of shale, which is essential for fracability evaluation and hydraulic fracturing design of shale gas/oil reservoirs. Highlights. Highlights: Conversion models from elements to minerals for various sub-lithologic shales were established. Brittleness indexes based on converted mineral compositions were closely approximate to the experimentally tested values. A rapid method to determine the brittleness of shale by using a non-destructive portable X-ray fluorescence was proposed. The method from elements to minerals to brittleness was successfully applied in the Wufeng-Longmaxi shale from the southeastern Sichuan Basin. [ABSTRACT FROM AUTHOR]

Under the background of the Belt and Road Initiative, this paper constructs a measurement equation for China's exports to the five Central Asian countries based on the gravity model of international trade for the purpose of forecasting China's future export growth potential. This paper selects China's GDP, trading partners' GDP, geographical distance, borders, railway freight volume, population growth rate and WTO as explanatory variables, based on what we construct multiple measurement equations. We use panel data from 2010 to 2017 to perform multiple regression analysis under the random effects model. We find that the model using China's GDP, trading partners' GDP, geographical distance, and borders as explanatory variables has a higher degree of fitness and each key explanatory variable is significant. Basing on the obtained measurement equation and the World Bank's forecast of the economic growth of various countries, we forecast that China's exports to the five Central Asian countries can maintain stable growth in the short term. By 2022, China's total exports to the five Central Asian countries will reach 2,353,510.82 million USD which will be 1.09 times that of 2017. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to examine the long-term influence of having a child at risk of different developmental delays (communication, mobility, self-care, relating, learning, coping, or behaving) on parental labor force participation as the child grows. Method: A retrospective cohort was conducted using data from the Longitudinal Study of Australian Children survey, Waves 1–8 covering birth to 15 years of age of children. Multivariable logistic regressions were used to explore the odds ratio of mothers being out of the labor force at different children's ages. Cox proportional hazards models were utilized to identify the 'risk' of mothers returning to the workforce after leaving. All models were adjusted for the mother's age, education attainment, and employment status at time of birth, as well as marital status at the current wave. Results: There were 5,107 records of children, and 266 of them were at risk of any developmental delays at age 4–5 years. This sample represents 243, 026 children born in Australia in 2003/04. After adjusting for potential confounders, mothers of children at risk of each type of developmental delay (except mobility and self-care) had greater odds of being out of, and not returning to the labor force from children aged 2–3 to 14–15 years, when compared to mothers of children who are not at risk of developmental delays. Similar differences were found for fathers but were distinctly small and with narrower fluctuations, compared to mothers. Conclusion: Policies and programs funded by the government are greatly needed to support the mothers of children at risk of developmental delays. Significance: What is already known: Maternal labour force participation has been negatively affected by having a child with chronic health conditions, whereas paternal labour force participation is not significantly influenced. What this paper adds: Having a child at risk of developmental delays negatively influenced maternal and paternal labour force participation, where the influence on fathers was distinctly small and with narrower fluctuations during the 14 to 15 years of follow-up. [ABSTRACT FROM AUTHOR]

This discussion article presents a critical appraisal of three empirical correlations developed via multiple regression analysis and presented in the Kayabali et al. [Geotech Geol Eng 41:4473–4485, 2023. https://doi.org/10.1007/s10706-023-02527-0] (the Authors') investigation for the determination of the soil consistency limits. Specifically, based solely on British Standard (BS) fall-cone (FC) test data, the Authors purport that the correlations given by Equations 2 and 3 of their paper can be used to predict the ASTM rolling-plate plastic limit (i.e., PLRP), while their Equation 4 can be used to predict the BS FC liquid limit (i.e., LLFC). The Authors demonstrated that these correlations gave good predictions of the measured LLFC and PLRP water contents (i.e., wL(FC) and wP(RP), respectively) for 87 fine-grained soils they sourced from different parts of Central Turkey. Employing newly compiled large and diverse consistency limits databases assembled from the published research literature, this discussion article confirms that the Authors' Equations 2 and 3 generally produce poor wP(RP) predictions for the fine-grained soils comprising these databases, invariably overestimating (often seriously) their measured plastic limit values. Hence, the Discussers recommend that the Authors' Equations 2 and 3 (being generally unreliable beyond the investigated Turkish soils) should not be used in geotechnical engineering practice. While the Authors' single-point LLFC method given by their Equation 4 broadly appears as a good wL(FC) predictor for the newly compiled database soils, it is noted that there already exist well-established and standardised single-point LLFC methods. [ABSTRACT FROM AUTHOR]

The present paper focuses on predicting radon concentrations in thermal waters from three thermal water physicochemical properties: pH, temperature, and electrical conductivity. To achieve this, an artificial neural network model and a multiple regression analysis model were created. While developing both models, the data of 109 radon measurements in thermal waters acquired from the literature were employed. When the experimental radon concentrations were compared to those predicted by both models, the artificial neural network model predicted radon concentrations that were substantially closer to the experimental values. A variety of performance measures were also computed for evaluating both models' prediction ability. The artificial neural network model outperformed based on the measures computed, demonstrating the applicability and accuracy of the model in radon concentration prediction in thermal waters. The study demonstrates that the developed artificial neural network model for this research may be used to predict the radon concentration in thermal waters using three thermal water physicochemical parameters. [ABSTRACT FROM AUTHOR]

Sexual distress, as well as sexual pleasure, are essential components of sexual health. Recently, associations between transdiagnostic factors such as worry and rumination and sexual distress and pleasure have been found. However, considering the adequacy of the transdiagnostic approach to explain and intervene in psychopathology and well-being, there is a need to address further sexual distress and pleasure and their relation to specific transdiagnostic factors. This paper aimed to explore a theory-driven model of sexual distress and sexual pleasure using multiple regression analysis with specific dimensions of repetitive negative thinking such as worry, rumination (brooding and reflection), and emotional regulation (emotion suppression and cognitive reappraisal) as predictors while controlling for emotional factors (anxiety and depression). The sample comprised 412 individuals in a monogamous relationship for at least a year. Results showed significant, positive, and moderate correlations between sexual distress and all emotional and transdiagnostic factors. For sexual pleasure, negative associations were found with depression and anxiety, worry, and brooding. Reflection was a significant predictor of sexual distress when emotional factors were controlled in the regression model. Depression was a significant predictor for both sexual distress and sexual pleasure. This study significantly contributes to our understanding of the complex relationship between transdiagnostic factors and sexual distress and pleasure, offering valuable insights that expand upon previous studies in this field and further advance our knowledge about sexual well-being. [ABSTRACT FROM AUTHOR]

The Electronic Personal Health Record (EPHR) provides an innovative service for citizens and professionals to manage health data, promoting patient-centred care. It enhances communication between patients and physicians and improves accessibility to documents for remote medical information management. The study aims to assess the prevalence of awareness and acceptance of the EPHR in northern Italy and define determinants and barriers to its implementation. In 2022, a region-wide cross-sectional study was carried out through a paper-based and online survey shared among adult citizens. Univariable and multivariable regression models analysed the association between the outcome variables (knowledge and attitudes toward the EPHR) and selected independent variables. Overall, 1634 people were surveyed, and two-thirds were aware of the EPHR. Among those unaware of the EPHR, a high prevalence of specific socio-demographic groups, such as foreign-born individuals and those with lower educational levels, was highlighted. Multivariable regression models showed a positive association between being aware of the EPHR and educational level, health literacy, and perceived poor health status, whereas age was negatively associated. A higher knowledge of the EPHR was associated with a higher attitude towards the EPHR. The current analysis confirms a lack of awareness regarding the existence of the EPHR, especially among certain disadvantaged demographic groups. This should serve as a driving force for a powerful campaign tailored to specific categories of citizens for enhancing knowledge and usage of the EPHR. Involving professionals in promoting this tool is crucial for helping patients and managing health data. [ABSTRACT FROM AUTHOR]

The present paper considers the results of long-term (up to 17 years) in situ and laboratory research carried out on oiled French, Spanish, and Russian seacoasts. The objective of this research is to quantify the influence of geographical factors on the rates of natural transformation of the heavy fuel oil stranded ashore and to develop an empirical statistical model in order to evaluate the self-cleansing capacity of the coastal environment. In a number of field campaigns, 363 samples of weathered oil slicks and tar balls have been collected and analysed with the use of thin-layer chromatography combined with optical and gravimetric methods. The results obtained have been subjected to multiple nonlinear regression analyses. It has been shown that heavy fuel oil natural attenuation is more active in continental or estuarine environments influenced by nutrient-rich freshwater runoff and characterised by a higher number of sunny days, solar irradiation, and large temperature fluctuations. On the oceanic coasts, especially in sectors with low hydrodynamic energy, these processes take more time. The resulting model allows for the identification and mapping of the most vulnerable seacoasts, characterised by a low potential to degrade oil pollution. This information may be used in the contingency plans in order to optimise clean-up techniques and associated costs. [ABSTRACT FROM AUTHOR]

The temperature rise of key internal components and ambient temperature changes during machining processes are the main causes of thermal displacement in machine tool spindle. This wastes materials and reduces working efficiency. A highly accurate and robust thermal spindle displacement estimation scheme is proposed in this paper which is based on the convolutional neural network (CNN) technique. Several key signals of specific dimensions related to spindle thermal displacement were first collected as two-dimensional (2D) signal maps. A multi-level feature expression for these 2D signal maps was extracted using convolution and pooling. A relationship between the extracted features and spindle thermal displacement was then learned using the neural architecture of the full connection layer. Optimized hyperparameter settings were determined by design of experiments (DoE) applied to the proposed CNN model. Experimental results showed that the proposed method had better performance than the multiple regression analysis (MLR) or back propagation neural network (BPNN) methods in terms of estimation accuracy and robustness at different spindle speeds. [ABSTRACT FROM AUTHOR]

Most head and cross-regulators on the main or branch canals of the Egyptian irrigation network are of few vent number types which are considered as multi-vent regulators. Some gates from the multi-vent regulator could be closed during the maintenance period or low water supplies. This paper investigates downstream flow characteristics due to the submerged hydraulic jump (H.J) created by downstream (DS) multi-gate regulators for the various gate operating scenarios. The present study was carried out experimentally and theoretically by applying 1-D momentum and continuity equations in the longitudinal direction at control volume to estimate the relative depth of the submerged (H.J) that occurred DS of multi-gate regulators under probable multi-gate operation cases. Accurate equations are developed for forecasting the relative depth of submerged jumps. These equations could be applied in cases of symmetric and un-symmetric operations of regulators of multi-vents. [ABSTRACT FROM AUTHOR]

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Due to the outstanding advantages in light alloy material processing, friction stir welding technology is of great significance for obtaining high-quality welding products and accelerating aerospace lightweight. At present, most related studies predict joint quality before welding through computer technology. However, due to the complex thermo-mechanical coupling in the welding process, there may be a large deviation between the predicted results and the actual results, resulting in energy waste. In addition, these prediction models have poor real-time and versatility. Therefore, a new joint quality prediction method based on entropy production analysis is proposed in this paper. Firstly, based on non-equilibrium thermodynamics and extrusion theory, the entropy generation analysis model of the friction stir welding system is deduced. Using Liouvile's Formula, the analytic solution of the entropy generation analysis model with unknown parameters is obtained. Secondly, combined with numerical simulation and multiple regression, the unknown parameters of the entropy generation analysis model are determined. Finally, multiple sets of welding experiments are designed to verify the effectiveness of the entropy generation analysis model. The welding process is analyzed by the proposed entropy generation analysis model to achieve the quality prediction of friction stir welded joint. [ABSTRACT FROM AUTHOR]

In recent years, haze has occurred in many parts of China. This phenomenon has a very negative impact on people's health and production and life. For a long time, the main research direction of environmental science research experts in China is to study and analyze the generation, causes, and changes of large particles in haze. This paper points out the variance parameter model of PM2.5 change and the improvement and optimization of College Students' health indicators. One of the assumptions of multiple regression analysis is the false homogeneity of variance. Often, the differences found in psychological or behavioral data can lead to network cations in the absence of missing nonlinear predictive conditions, including predictions not observed in the causal model. Although homogeneities exist in test methods, they require parametric models to identify inhomogeneous structures. The purpose of this article is to propose a simple measurement that can detect leaks described in nonlinear terms without the need for parametric model differences. This measure uses the variance of the squared residuals. Simulation studies have shown that the sample size and magnitude of effect are large enough to satisfy the type I error rate and power supply countermeasures. This is superior to the nonlinear heteroscedastic parameter model by omitting the nonlinear term in the analytical model. We also demonstrated the performance of using the measures from an industrial psychology dataset. [ABSTRACT FROM AUTHOR]

Individual perceptions of pay satisfaction and pay justice are closely related social indicators. Notwithstanding their apparent resemblance, there are essential theoretical differences between those two concepts. Yet, we know little if people merely consider pay satisfaction and pay justice as two sides of the same coin. This paper theorizes two situations in which people's perceptions of pay satisfaction and pay justice should differ in meaningful ways. First, their pay level should affect people's self-interest and thus have a stronger effect on their pay satisfaction than on pay justice. Second, pay inequality in the workplace should affect people morally and thus should have a stronger effect on their pay justice perception than on pay satisfaction. These hypotheses were tested with linked employer-employee data collected in Germany (N = 2.695). Results of regression analyses with multiple dependent variables show that people's pay satisfaction and pay justice perceptions increased with the level of their individual pay and decreased with the degree of workplace inequality. The pay effect was significantly stronger on pay satisfaction than on pay justice while the workplace inequality effects did not differ significantly. These results suggest that people under specific circumstances differentiate between pay satisfaction and pay justice. Consequently, researchers should consider that their results and conclusions might differ if they analyze pay satisfaction instead of pay justice and vice versa. [ABSTRACT FROM AUTHOR]

Background: In an earlier paper, we examined the relationship between headache-attributed disability, measured as proportion of time in ictal state, and lost productivity. In a linear model, we found positive and significant associations with lost paid worktime, lost household worktime and total lost productivity (paid + household), but with high variance, which was increased when headache intensity was introduced as a factor. We speculated that analyses based on headache frequency alone as the independent variable, eliminating both the subjectivity of intensity estimates and the uncertainties of duration, might show stronger associations. Methods: Focusing on migraine, we used individual participant data from 16 countries surveyed either in population-based studies or in the Eurolight project. These data included frequency (headache days/month), usual attack duration (hours), usual headache intensity ("not bad", "quite bad", "very bad") and lost productivity from paid and household work according to enquiries using the Headache-Attributed Lost Time (HALT) questionnaire. We used multiple linear regressions, calculating regression equations along with unstandardized and standardized regression coefficients. We made line and bar charts to visualize relationships. Results: Both frequency and intensity were significant predictors of lost productivity in all multiple linear regressions, but duration was a non-significant predictor in several of the regressions. Predicted productivity in paid work decreased among males by 0.75–0.85 days/3 months for each increase of 1 headache day/month, and among females by 0.34–0.53 days/3 months. In household chores, decreases in productivity for each added day/month of headache were more similar (0.67–0.87 days/3 months among males, 0.83–0.89 days/3 months among females). Visualizations showed that the impact of duration varied little across the range of 2–24 h. The standardized regression coefficients demonstrated that frequency was a much better predictor of lost productivity than intensity or duration. Conclusion: In the relationship between migraine-attributed impairment (symptom burden) and lost productivity, frequency (migraine days/month) is the dominating variable – more important than headache intensity and far more important than episode duration. This has major implications for current practice in headache care and for health policy and health-resource investment. Preventative drugs, grossly underutilized in current practice, offer a high prospect of economic benefit (cost-saving), but new preventative drugs are needed with better efficacy and/or tolerability. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the relationship between emotions and colors by showing robot animated emotion faces and colors to the participants through a series of surveys. We focused on representing a visualized emotion through a robot's facial expression and background colors. To complete the emotion design with animated faces and color background, we gave an experimental design for surveying the users' thoughts. We took an example of a robot animated face by using the ASUS Zenbo. We selected 11 colors as our color background and 24 facial expressions from Zenbo. To analyze our results from questionnaires, we used histograms to show the basic data situation and the multiple logistic regression analysis (MLRA) to see the marginal relationships. We separated our questionnaires into positive and negative questionnaires and divided the dataset into three cases to discuss the different relationships between color and emotion. Results showed that people preferred the blue color no matter whether the face was showing positive or negative emotion. The MLRA also showed the correct percentage is outstanding in case 2, either positive emotion or negative emotion. Participants thought Zenbo's robotic animated face was the same as they thought. Through our experimental design, we hope that people can consider more colors with emotion to design the human–robot interface that will be closer to the users' thoughts and make life more colorful with comfortable reactions with robots. [ABSTRACT FROM AUTHOR]

Flyrock is one of the major safety hazards induced by blasting operations. However, few studies were for predicting blasting-induced flyrock distance from the perspective of engineers. The present paper attempts to provide an engineer-friendly equation predicting blasting-induced flyrock distance. Data used in the present study contains s seven blasting parameters including borehole diameter, blasthole length, powder factor, stemming length, maximum charge per delay, burden, and flyrock distance is obtained. Data is inputted into Random Forest for feature selection. The selected features are formulated as two candidate equations, including Multiple Linear Regression (MLR) equation and Multiple Nonlinear Regression (MNR) equation. Those two candidates are respectively referred by Particle Swarm Optimization for searching optimum values for the coefficients of selected features. It is proved that MLR equation has better accuracy. MLR equation is compared with two empirical equations and the MLR equation based on least squares method. It is found that the coefficient of correlation of the proposed MLR equation reaches 0.918, which is the highest compared with the scores of other three equations. The present study utilizes feature selection process to screen inputs, which effectively excludes irrelevant parameters from being considered. Plus the contribution of Particle Swarm Optimization, the accuracy of the obtained equation can be guaranteed. [ABSTRACT FROM AUTHOR]

Conducting a precise landslide susceptibility assessment at the slope scale is challenging due to complex parameters and limited historical hazards data. This paper proposes a susceptibility assessment model based on the frequency ratio (FR) coupled with multiple regression analysis to solve these problems. Assessment factors were identified through field surveys and remote sensing interpretation. We utilized multiple methods, such as logistic regression (LR), partial least squares regression (PLSR), ridge regression (RR), stepwise regression (SR), and discriminant analysis (DA), and established five coupled models (FR-LR, FR-PLSR, FR-RR, FR-SR, and FR-DA). The reliability of the susceptibility assessment results was systematically verified. The results showed the following: (1) for the distribution of the susceptibility index, FR-PLSR, FR-RR, and FR-DA are close to the standard normal distribution; (2) for the prediction accuracy, the AUC indexes of the five models are relatively large, ranging from 0.86 to 0.87; and (3) for the susceptibility zoning reliability, FR-PLSR and FR-RR showed better performance, with a relatively smaller area proportion and larger susceptibility intensity for high susceptibility regions. Finally, the coupled models FR-PLSR and FR-RR were recommended. The areas of slope units categorized as high-, medium-, low-, and extra low-susceptibility are 16.14 km2, 73.85 km2, 98.96 km2, and 39.49 km2, respectively, accounting for 7.06%, 32.33%, 43.32%, and 17.29% of the study area. This work enriches the theoretical understanding of the susceptibility assessment models with limited historical hazards data and provides an effective coupled model for high-accuracy landslide assessment and prevention at the slope scale. [ABSTRACT FROM AUTHOR]

Background: Undernutrition among children is a primary concern in sub-Saharan African countries such as Ethiopia. The objective of this paper is to measure the magnitude of undernutrition and associated factors among children under 2 years of age in the Jimma Zone of Southwest Ethiopia. Methods: A community-based cross-sectional study with stratified multistage sampling was conducted. Data were collected using a semi-structured questionnaire and anthropometric measurements. Bivariate and multivariable logistic regression models were fitted using SPSS 20.0 for Windows. Adjusted odds ratios (AOR) with 95% confidence intervals (CI) were used to measure the strength of the association between dependent and associated variables at a p value <0.05. Results: Among the study participants, 142 (25.4%) were stunted, 73 (13.1%) were underweight, and 54 (9.7%) were wasted. Older child age [AOR = 1.1, 95% CI (1.1, 1.2)], male sex [AOR = 2.601, 95% CI (1.681, 4.025)], and residing in an urban area [AOR = 0.526, 95% CI (0.323, 0.857)] were significantly associated with stunting. Independent predictors of wasting were younger child age [AOR = 0.894, 95% CI (0.833, 0.96)], living in a lowland area [AOR = 3.3, 95% CI (1.4, 8.1)], and living a in poor household [AOR = 6.2, 95% CI (2.2, 17.7)]. Underweight was significantly associated with male sex [AOR = 2.09, 95% CI (1.179, 3.703)], delayed initiation of breastfeeding [AOR = 2.1, 95% CI (1.0, 4.2)], and living in a poor household [AOR = 2.2, 95% CI (1.0, 4.6)]. Conclusions: Child malnutrition was found to be a moderate public health problem in the study area. Urban dwellers had higher levels of undernutrition than rural dwellers. Children living in lowlands had higher undernutrition than those living in highland areas. [ABSTRACT FROM AUTHOR]

This paper examines the mental health of service providers working with Syrian refugees. Using the Professional Quality of Life framework, we hypothesize greater stress/less support from the work, person, client environment is associated with symptoms of STS, depression, and anxiety. We surveyed a sample of 104 service providers throughout Istanbul late 2018. Multivariable logistic regression examined associations between work (organizational support, caseload, supervision), person (perceived social support), client environment (trauma disclosure, percent Syrian refugees) on STS, depression, and anxiety. We found rates of moderate-to-severe STS to be 27.88%; depression 40.38%; and anxiety 29.81%. Our hypothesis was partially supported. Lower organizational support was associated with moderate-to-severe STS (aOR 0.91, 95% CI 0.84, 0.99) while lower social support with anxiety (aOR 0.89, 95% CI 0.81, 0.99). Caseload, supervision, trauma disclosure, percent refugees did not show significant associations. Organizations working with Syrian refugees may benefit from enhancing organizational support and promoting social support for staff. [ABSTRACT FROM AUTHOR]

This paper develops an artificial intelligence based automated valuation model (AI-AVM) using the boosting tree ensemble technique to predict housing prices in Singapore. We use more than 300,000 private and public housing transactions in Singapore for the period from 1995 to 2017 in the training of the AI-AVM models. The boosting model is the best predictive model that produce the most robust and accurate predictions for housing prices compared to the decision tree and multiple regression analysis (MRA) models. The boosting AI-AVM models explain 91.33% and 94.28% of the price variances, and keep the mean absolute percentage errors at 8.55% and 5.34% for the public housing market and the private housing market, respectively. When subject the AI-AVM to the out-of-sample forecasting using the 2018 housing sale samples, the prediction errors remain within a narrow range of between 5% and 9%. [ABSTRACT FROM AUTHOR]

This paper mainly analyzes the changing trend of corn price and the factors that affect the price of corn. Using the data and regression analysis, the univariate nonlinear and multivariate linear regression models are established to predict the corn price, respectively. First, this paper establishes a univariate nonlinear regression model with time as the independent variable, and corn price is used as the dependent variable through the analysis of the trend of big data related to Chinese corn price from 2005 to 2016 by MATLAB, which is the computer-based analysis and processing method. The variation of the maize price with time was fitted. To a certain extent, the price trend of corn is predicted. However, the estimated price of corn in 2017 with this model will deviate from the actual value. According to the changes of related policies in our country, we analyzed the deviation of the original model, and the relationship between supply and demand is the main underlying factor that affects the price of corn. This paper selects maize-related big data from 2005 to 2016, we set its production consumption, import and export volume as independent variables, and we still use maize price as the dependent variable to establish a multiple linear regression model. At this stage, the time series analysis of the independent variable has obtained the forecast value of each independent variable in 2017, and then the model is used to predict the corn in 2017 more accurately. [ABSTRACT FROM AUTHOR]