Multinomial Regression Models Research Articles

Reasons for alcohol use from 1976 to 2020 in the United States among individuals aged 18 to 30: Historical changes and mediation of cohort effects in binge drinking.

Alcohol use is declining among US adolescents/early young adults and increasing among other adults, with increases in adult binge drinking more concentrated in females than males. Reasons for drinking are historically patterned by age and sex, and if historically variant, could suggest that changes over time could in part explain age- and sex-differential cohort effects. We analyzed longitudinal Monitoring the Future data for individuals born from 1958 to 1990. These individuals were aged 29/30 from 1987 to 2020, and first surveyed at age 18 from 1976 to 2008 (N = 14,190). Five reasons for drinking were analyzed (social, enhancement, avoid problems, relax, boredom). Drinking for social reasons and to relax were most prevalent. Total effects of birth cohort predicting past-2-week binge drinking were estimated with polynomial regression models by age;indirect effects through mediators were estimated. Drinking reasons exhibited dynamic time trends across birth cohort and sex. Notable increases were observed in social reasons: among women aged 29/30, social reasons increased from 53% to 87% from 1987 to 2020. Social reasons to drink had prominent positive indirect effects at adult ages (age 23/24 and above among men; age 19 and above among women), indicating that binge drinking would have increased less were it not for the increase in social reasons for drinking. Social reasons also mediated adult male/female differences, indicating that part of the reason sex differences are diminishing is the more rapid increases in social reasons for drinking among women. Indirect effects were also observed for drinking to relax and for boredom, and limited indirect effects were observed for enhancement and to avoid problems. Changing endorsement of drinking reasons, especially social reasons, among US adult drinkers mediate cohort effects in binge drinking in the US adult population and explain in part why binge drinking is converging by sex.

Brain health index as a predictor of possible vascular dementia in the Mexican health and aging study 2012-2015.

To determine the burden of disease among subjects at risk of developing stroke or dementia, brain health indexes (BHI) tend to rely on anatomical features. Recent definitions emphasize the need of a broader perspective that encompasses cardiovascular risk factors (CVRFS) and lifestyle components which can be considered partial contributors to optimal brain health. In this study, we aimed to establish the association and risk detected by a Brain Health Index and the risk of possible vascular dementia (PVD) using data from the Mexican Health and Aging Study (MHAS) 2012-2015. The MHAS is a longitudinal study of adults aged ≥ 50 years. We analyzed the data obtained between 2012 and 2015. CVRFS included in the index were diabetes mellitus, hypertension, myocardial infarction, depression, obesity, physical inactivity, and smoking history. A PVD diagnosis was established when scores in the Cross-Cultural Cognitive Examination were below reference norms and limitations in ≥1 instrumental activities of daily living and a history of stroke were present. A multinomial regression model was developed to determine the association between BHI scores and PVD. In 2015, 75 PVD cases were identified. Mean age was 67.1 ±13.2 years, 35.8% were female, and the mean educational level was 5.8 ±5.5 years. In cases with a higher score in the BHI, the model revealed a hazards ratio of 1.63 (95% CI: 1.63-1.64, p< 0.001) for PVD. In this longitudinal study, with the use of a feasible multifactorial BHI in the Mexican population, a greater score was associated with a 1.63-fold risk of developing PVD during the 3-year follow-up, while the risk for stroke was 1.75. This index could potentially be used to predict the risk of PVD in adults with modifiable CVRFS.

#1377 Evaluation of state-wide health system implementation of renal supportive care shows substantial net benefits

Abstract Background and Aims In 2016, the New South Wales (NSW) Health system implemented a service to make the principles and practice of palliative medicine comprehensively available to patients receiving chronic kidney disease (CKD) care across NSW. Summative evaluation of the economic value that this multi-disciplinary State-wide RSC service provided following implementation was prospectively planned in the domains of patient experience and economic value. This abstract reports the first cost-benefit analysis of a RSC service. Method De-identified patient-level data was extracted from the NSW Renal Supportive Care Register (a linked dataset comprising admitted patient, non-admitted patient, emergency department episodes and death registrations). Data was compared between 2015/16, prior to implementation of the State-wide RSC service; and 2018/19, following settled operation of the service. A cost-benefit analysis model was constructed to estimate the economic value of the RSC service between 2015/16 and 2018/19; and the projected value to 2029/30 under different scenarios. Multivariate Cox regressions were estimated to measure survival benefits, and a polynomial regression model was employed to estimate the impact of RSC on the share of patients receiving dialysis. Results Considering the actual impact of the NSW RSC service; in 2015/16, 5% (n = 443) of all ESKD patients, (defined as receiving dialysis &amp;gt;3 months, non-dialysis conservative care or in receipt of a functioning transplant) received any RSC service. By 2018/19, 16% (n = 1460) of all ESKD patients had received RSC within NSW. The proportion of patients receiving RSC from a non-dialysis conservative care or dialysis context remained stable between 2015/16 and 2018/19 at 41% and 29% respectively. Patients receiving RSC were estimated to have increased survival by 3-21 weeks, depending on patient characteristics, compared to the scenario of no RSC service. Considering the projected value of ongoing RSC services; scenarios with different assumptions were modelled. Assuming that the proportion of patients receiving different ESKD modalities and the costs of care remained stable, the RSC service would deliver a net financial benefit to the commissioning health system (avoided costs of service minus RSC costs) of ${\$}$109 million Australian Dollars (AUD), with a return on investment of 212%, compared to the scenario of no RSC service. When the economic value for the whole NSW community from increased survival benefit was included, the projected value was ${\$}$275 million AUD, with return on investment of 535%. The projected economic value of RSC was modelled under scenarios of increasing access to the service by ESKD patients (Figure). Increasing RSC access from the achieved 16% to 20% of ESKD patients was projected to deliver maximal economic value, with a 3% fall in ESKD patients receiving dialysis, increasing survival benefit (${\$}$221 million AUD) and economic value ${\$}$94 million AUD. As access to RSC expands further, modelling indicates that additional dialysis substitution becomes less likely but benefits still accrue from survival up to a maximum viable rollout of 50% of the ESKD population receiving RSC, when survival benefits plateau. Conclusion Economic evaluation of this implementation of the Statewide RSC service across NSW Health shows it has been associated with both a net economic benefit and survival benefits four years after implementation. It is projected to provide an economic value of ${\$}$275 million AUD over ten years, which would increase with greater participation of ESKD patients suitable for RSC.

Complementary technology adoption and smallholder commercialization: Panel data evidence from Ethiopia

AbstractAgricultural transformation involves the transition from subsistence agriculture marked by cultivating crops for auto‐consumption to cultivating crops for output markets. This transition from subsistence agriculture to market‐oriented agriculture can be a key policy boost to economic development, but evidence on the key entry points to increasing smallholder commercialization remains scarce. We examine the relationship between the adoption of improved maize varieties (IMVs), inorganic fertilizers, and smallholder commercialization. We model commercialization as a two‐step decision process involving market participation and the extent of participation (sales quantity) conditional on participation. Given these two related steps, we estimate a double‐hurdle model in both linear and non‐linear forms. Employing a three‐wave panel dataset from Ethiopia, we use the household fixed effects and correlated random effects model with the control function approach. We find the adoption of IMVs to be significantly associated with both market participation and the extent of participation. This relationship is also true for fertilizers, where we show a positive association between fertilizer use and commercialization. Given the seeming complementarity in the use of both IMVs and fertilizers, we further estimate their joint adoption. We use the multinomial endogenous switching regression model where we show greater commercialization gains under joint adoption. These findings are in line with a growing literature supporting the bundling of agricultural technologies. Given these insights, we provide empirical and policy support to the scaling of agricultural technologies as they have the potential to induce agricultural transformation by unlocking market opportunities.

Versatile Simplistic Correction of T-Higrow Sensors for Improved Soil Moisture Measurement Accuracy

Abstract The use of soil moisture sensors for irrigation can help reduce water and energy consumption and risks of groundwater contamination, which are essential aspects for pursuing sustainable development goals. However, increased adoption of this technology is limited by calibration requirements, technical complexities, and sensor costs. In this work, a simplified method for reducing the measurement error of a recently released low-cost soil sensor (T-Higrow) is presented. The method only requires measurements of a dry sample from the target soil, which are inputted into a simple correction formula to reduce the measurement error at higher moisture levels. The requirements of the proposed method are simple enough for most labs or extension services. This method was compared to the commonly used linear, polynomial, and logarithmic regression models based on repeated bench-scale experiments within 0-35% moisture range in silt and sandy loam soils and in silica sand. Uncorrected sensor readings correlated well with soil moisture (r: 0.94-0.98), but with significant overestimation (25-60% error). The simplified correction method showed comparable error reduction to regression models across all soil types. All methods reduced error down to 2-10% (0.02-0.1 cm3/cm3) and maintained high correlations (r &gt;0.94), except for logarithmic regression which reduced correlation by around 3%. Variability amongst sensor measurements was generally low (Standard Deviation: 0.01-0.03) particularly at moisture ranges below 20%, this was also the case for sensor-to-sensor variability (Standard Deviation: 0.01-0.03). Sensor evaluation and calibration works are needed to increase the accessibility to this technology for improved water and energy conservation.

Client preferences, therapy activities and preference-activity match as predictors of therapy outcome

ABSTRACT Objective: This study investigated whether distinct types of psychotherapy activities, the client’s preference towards these activities prior to therapy, and the degree of match between client preferences and therapy activities, served as predictors of treatment outcomes. Methods: A total of 621 clients (M age = 42 years, 71.7% female) received individual psychotherapy by 54 psychologists. Associations between activity preferences, therapy activities, and preference-activity match as predictors, and symptom change and treatment dropout as outcomes were analyzed using multilevel longitudinal and logistic modelling and polynomial regression models with response surface analysis. Results: No type of therapy activity or activity preference significantly predicted symptom change in therapy, while higher levels of inward orientation therapy activities predicted an increased risk of dropout. Moreover, matching and higher levels of inward orientation and affect expression activities predicted an increased risk of dropout, and matching and higher levels of outward orientation activities predicted a decreased risk of dropout. Finally, a preference-activity mismatch in affect suppression predicted an increased risk of dropout from therapy, both at higher and lower levels of affect suppression. Conclusion: Distinct types of therapy activity preferences may, especially when (mis)matched with similar levels of the same therapy activities, differentially predict particular dropout from therapy. Trial registration: ClinicalTrials.gov identifier: NCT05630560.

KNN Local Linear Regression for Demarcating River Cross-Sections with Point Cloud Data from UAV Photogrammetry URiver-X

Aerial surveying with unmanned aerial vehicles (UAVs) has been popularly employed in river management and flood monitoring. One of the major processes in UAV aerial surveying for river applications is to demarcate the cross-section of a river. From the photo images of aerial surveying, a point cloud dataset can be abstracted with the structure from the motion technique. To accurately demarcate the cross-section from the cloud points, an appropriate delineation technique is required to reproduce the characteristics of natural and manmade channels, including abrupt changes, bumps and lined shapes. Therefore, a nonparametric estimation technique, called the K-nearest neighbor local linear regression (KLR) model, was tested in the current study to demarcate the cross-section of a river with a point cloud dataset from aerial surveying. The proposed technique was tested with synthetically simulated trapezoidal, U-shape and V-shape channels. In addition, the proposed KLR model was compared with the traditional polynomial regression model and another nonparametric technique, locally weighted scatterplot smoothing (LOWESS). The experimental study was performed with the river experiment center in Andong, South Korea. Furthermore, the KLR model was applied to two real case studies in the Migok-cheon stream on Hapcheon-gun and Pori-cheon stream on Yecheon-gun and compared to the other models. With the extensive applications to the feasible river channels, the results indicated that the proposed KLR model can be a suitable alternative for demarcating the cross-section of a river with point cloud data from UAV aerial surveying by reproducing the critical characteristics of natural and manmade channels, including abrupt changes and small bumps as well as different shapes. Finally, the limitation of the UAV-driven demarcation approach was also discussed due to the penetrability of RGB sensors to water.

Comparing frequencies of adolescent suicide attempters pre- and during COVID-19 pandemic school terms

BackgroundThe COVID-19 pandemic had and still have a major impact on adolescent mental health and consequently on suicidal behavior. However, few studies have investigated whether the pandemic has changed the patterns and the triggers of suicidality peaks in adolescents, e.g., seasonal patterns or family conflicts. We hypothesized that the pandemic modified suicidality rates: an increment of suicide attempts would be observed in the first semester of the academic year during COVID-19 pandemic compared to the same period of previous academic year; and the precipitating factors would be more related to social stressors during the pandemic school year.MethodsA retrospective cross-sectional study was conducted to assess the precipitating factors, also including school-related factors and cognitive skills, of adolescent suicide attempters occurred in the first and second semesters of the year before the pandemic lockdown (study periods 1 and 2) and the year after (study periods 3 and 4).ResultsThe sample consisted of 85 adolescents aged between 12 and 17 recruited consecutively from March 2019 to March 2021 at emergency ward because of suicide attempt. Forty-eight adolescents (55.3% of the sample) were attended before the lockdown (pre-pandemic group) and 38 (44.7%) the year after. The results showed a higher proportion of female suicide attempters in period 4 (Sept 2020-Feb 2021) respect to period 3 (Mar 2020-Aug 2020), i.e., pandemic semesters compared with the increment observed between period 2 and 1 (prepandemic semesters; Fisher’s exact test = 4.73; p = 0.026). The multinomial regression models showed a significant effect in the frequency of adolescents who attempted suicide (ratio χ2 = 15.19, p = 0.019), accounted by the differences between period 4 (Sept 2020-Feb 2021) and period 1 (Mar 2019-Aug 2019), with depressive symptoms being a significant contributing factor (Exp(b) = 0.93; p = 0.04). Additionally, social triggers and age were found to be significant risk factors for suicide attempts in the first semester of the pandemic academic year (period 4) compared to the same semester of the pre-pandemic year (period 2; Exp(b) = 0.16, p = 0.01, and Exp(b) = 1.88, p = 0.006, respectively).ConclusionsDuring the pandemic, the decrement between first and second semester of the number of females attempting suicide was more pronounced than in the prepandemic school year -though this findings lacked statistical power due to very limited sample size-. Change in the frequency of adolescent attempting suicide in the different school periods was associated with greater severity of depressive symptoms. Social relations in back-to-school after the lockdown were also associated with the number of adolescents attempting suicide.

Modelling the architecture of hazelnut (Corylus avellana) Tonda di Giffoni over two successive years

Abstract Hazelnut (Corylus avellana) cultivation is increasing worldwide. A 3D model of its structure could improve the managerial techniques such as pruning. This study aims to analyse, over two successive years, hazelnut architectural development to implement a functional structural plant model. 104 one-year-old shoots of own-rooted hazelnut trees were selected and analyzed in winter 2020 and 2021. Exploratory analyses, generalized linear models, and multinomial regression models were used to describe the architectural processes. The existence of sylleptic shoots on hazelnut one-year-old shoots, characterized by the presence of the male inflorescence on apical position, was detected. Along proleptic shoots the branching pattern was described by (1) blind nodes located in the proximal part (2) sylleptic shoots and mixed buds in the median part (3) vegetative buds in the distal part. Apical bud died during the growing season, suggesting that Tonda di Giffoni has a sympodial branching. The models revealed dependencies among buds located at the same node, in the case of proleptic shoots. Especially, the probability of a bud to burst depended on both its type (i.e., mixed or vegetative) and the presence of other buds, either mixed or vegetative. Based on these local models and on a flow diagram, which defines the steps that lead to the construction of hazelnut tree architecture, a first functional-structural plant model of hazelnut tree architecture was built. Further experiments will be needed and should be repeated over following years to extend this study toward the juvenility phase and tree architecture over time.

Secular trend and projection of overweight and obesity among Chinese children and adolescents aged 7-18 years from 1985 to 2019: Rural areas are becoming the focus of investment.

The urban-rural disparities in overweight and obesity among children and adolescents are narrowing, and there is a need for long-term and updated data to explain this inequality, understand the underlying mechanisms, and identify priority groups for interventions. We analyzed data from seven rounds of the Chinese National Survey on Students Constitution and Health (CNSSCH) conducted from 1985 to 2019, focusing on school-age children and adolescents aged 7-18 years. Joinpoint regression was used to identify inflection points (indicating a change in the trend) in the prevalence of overweight and obesity during the study period, stratified by urban/rural areas and sex. Annual percent change (APC), average annual percent change (AAPC), and 95% confidence interval (CI) were used to describe changes in the prevalence of overweight and obesity. Polynomial regression models were used to predict the prevalence of overweight and obesity among children and adolescents in 2025 and 2030, considering urban/rural areas, sex, and age groups. The prevalence of overweight and obesity in urban boys and girls showed an inflection point of 2000, with AAPC values of 10.09% (95% CI: 7.33-12.92%, t =7.414, P <0.001) and 8.67% (95% CI: 6.10-11.30%, t =6.809, P <0.001), respectively. The APC for urban boys decreased from 18.31% (95% CI: 4.72-33.67%, t =5.926, P =0.027) to 4.01% (95% CI: 1.33-6.75%, t =6.486, P =0.023), while the APC for urban girls decreased from 13.88% (95% CI: 1.82-27.38%, t =4.994, P =0.038) to 4.72% (95% CI: 1.43-8.12%, t =6.215, P =0.025). However, no inflection points were observed in the best-fit models for rural boys and girls during the period 1985-2019. The prevalence of overweight and obesity for both urban and rural boys is expected to converge at 35.76% by approximately 2027. A similar pattern is observed for urban and rural girls, with a prevalence of overweight and obesity reaching 20.86% in 2025. The prevalence of overweight and obesity among Chinese children and adolescents has been steadily increasing from 1985 to 2019. A complete reversal in urban-rural prevalence is expected by 2027, with a higher prevalence of overweight and obesity in rural areas. Urgent action is needed to address health inequities and increase investments, particularly policies targeting rural children and adolescents.

Predicting Indian GDP with Machine Learning: A Comparison of Regression Models

In recent years, the application of machine learning algorithms in economic forecasting has gained significant traction. This study focuses on predicting the Gross Domestic Product (GDP) of India by harnessing advanced machine learning techniques. We assembled a comprehensive dataset incorporating time series analysis and inflation rates from diverse sources. Our investigation involved a comparative analysis employing both linear and polynomial regression methods to discern the most accurate predictive model. Our findings underscore the superiority of the polynomial regression model, which excelled in capturing non-linear relationships between independent variables and GDP. Specifically, the polynomial regression model achieved an impressive prediction accuracy rate of surpassing the accuracy achieved by the linear regression model. This study underscores the critical role of advanced machine learning algorithms in economic forecasting. By emphasizing the significance of high-quality datasets and the application of techniques like polynomial regression, we illustrate how these factors can substantially enhance the precision of economic forecasts. These insights hold substantial implications for policy makers and businesses alike, aiding informed decision-making and effective economic policy development. This study serves as a valuable reference for future research, highlighting the importance of advanced machine learning methods and robust data sources in economic forecasting.

Geospatial and Socioeconomic Disparities Influencing the Management of Craniosynostosis.

Various social determinants of health have been described as predictors of clinical outcomes for the craniosynostosis population. However, literature lacks a granular depiction of socioeconomic factors that impact these outcomes, and little is known about the relationship between patients' proximity to the care center and management of the condition. This study retrospectively evaluated patients with craniosynostosis who presented to a tertiary children's hospital between 2000 and 2019. Outcomes of interest included age at presentation for surgery, incidence of reoperation, and length of follow-up. Patient addresses were geocoded and plotted on two separate shapefiles containing block group information within San Diego County. The shapefiles included percent parental educational attainment (bachelor's degree or higher) and median household income from 2010. The year 2010 was chosen for the shapefiles because it is the median year of data collection for this study. Multivariate linear, logistic, and polynomial regression models were used to analyze the relationship between geospatial and socioeconomic predictors and clinical outcomes. There were 574 patients with craniosynostosis included in this study. The mean ± SD Haversine distance from the patient's home coordinates to the hospital coordinates was 107.2 ± 321.2 miles. After adjusting for the suture fused and insurance coverage, there was a significant positive correlation between distance to the hospital and age at index surgery (P = 0.018). There was no correlation between distance and incidence of reoperation (P = 0.266) or distance and duration of follow-up (P = 0.369). Using the same statistical adjustments, lower parental percent educational attainment and lower median household income correlated with older age at index surgery (P = 0.008 and P = 0.0066, respectively) but were not correlated with reoperation (P = 0.986 and P = 0.813, respectively) or duration of follow-up (P = 0.107 and P = 0.984, respectively). The results offer evidence that living a greater distance from the hospital and socioeconomic disparities including parental education and median household income may serve as barriers to prompt recognition of diagnosis and timely care in this population. However, the geospatial and socioeconomic factors studied do not seem to hinder incidence of reoperation or length of follow-up, suggesting that, once care has been initiated, longitudinal outcomes may be less impacted.

'I secretly wish...' Caregivers' expression of wish for death of persons with severe dementia.

Qualitative evidence suggests that caregivers may express a wish for death of persons with severe dementia (PwSD). No study has assessed the extent to which it happens, stability of this wish over time and the factors associated with it. We examined caregivers' wish for death of PwSDs overtime and the factors associated with this wish. 215 caregivers of community dwelling PwSDs were surveyed every 4months for 2years. Using the mixed-effects multinomial regression model, we assessed the PwSD and caregiver factors associated with caregivers' wish for PwSDs' death. At baseline, 27% caregivers expressed a wish for PwSDs' life to end sooner. Overall, 43% of the caregivers expressed a wish for PwSDs' death at least once during the study period and 11% expressed it consistently. Caregivers' perception of PwSDs' lower quality of life (RRR: 1.05, 95% CI: 1.00, 1.10), higher functional dependency (RRR: 1.1, 95% CI: 1.01, 1.21), eating difficulty (RRR: 2.25, 95% CI: 1.26, 4.04) and suffering (RRR: 1.92, 95% CI: 1.05, 3.52) were associated with this wish. Caregivers who were emotionally close to PwSDs were less likely (RRR: 0.25, 95% CI: 0.11, 0.55) while those who understood that dementia is a terminal illness were more likely (RRR: 2.01, 95% CI: 1.03, 3.92) to express this wish. Caregivers' wish for PwSDs' death changed over time and was primarily driven by their perception of PwSDs' poor well-being and awareness of their illness being terminal, indicating a need for increased support in this challenging caregiving context.

Food insecurity and its contributing determinants among rural households in the south-western region of Bangladesh, 2021: A cross-sectional study

IntroductionThe onset of the COVID-19 pandemic has disrupted food access, resulting in substantial consequences for food insecurity and contributing to adverse individual and public health outcomes. To comprehensively evaluate these challenges and grasp their implications for food security, this study aimed to evaluate the contributing determinants of food insecurity among rural households in the southwestern region of Bangladesh. Study designA cross-sectional study was conducted using a validated questionnaire in selected 310 rural household respondents from the southwestern region, of Bangladesh. MethodsHousehold food insecurity status was the outcome variable for the analysis. Multinomial logistic regression analysis was used to explore and predict risk factors correlated with food insecurity among southwestern Bangladeshi households. ResultsWe found that 59% and 27.5% of households were suffering from moderate food insecurity and severe food insecurity. The multinomial regression model revealed that respondents residing in Kusthia (RRR=5.56 CI:2.67–8.4 and RRR=6.65, CI:3.37–9.22) aged between 30–40 years (RRR=2.32, 95% CI:1.84–3.77 and RRR=1.87, 95% CI:1.48–3.97) and 40–50 years (RRR=1.86 95% CI:1.46–3.82 and RRR=1.95, 95% CI:1.75–3.26) were significantly associated with mild-to-moderate and severe food insecurity. Respondents with a monthly family income of <58.96 USD (3.38 times and 2.18 times), had ≥5 family members (2.68 times and 1.89 times), and with poor income during the pandemic (4.25 times and 2.75 times) were more likely to be moderate and severe food insecure. ConclusionThe results emphasized that during the COVID-19 lockdown in Bangladesh, rural households faced diverse levels of food insecurity, ranging from moderate to severe. It suggests that efforts to raise awareness and implement support strategies for those at higher risk should not only focus on income but also consider additional factors such as family size, adults aged 30-40 years, and occupation.

Dynamic round robin with adaptive time quanta (DRRATQ): A proposed algorithm

The CPU scheduling technique influences the performance and efficiency of operating systems. Round-robin scheduling algorithm is ideal for time-shared systems, but it is not optimal for real-time operating systems since it yields more context switching, longer waiting time, and high turnaround time. The performance of the algorithm is predominantly influenced by the designated time quantum; however, determining a suitable time quantum is extremely challenging. This paper presents a CPU scheduling algorithm that provides a better tradeoff between waiting time, turnaround time, response time, and number of context switch by using hypothesis-based quanta generation approach. It combines the CPU burst requirements of actual processes with some noisy data and plots them against the presumed CPU quanta to get quanta densities so that a polynomial regression model can fit the data points with the highest adjusted R-squared. Then applying some complex inferential statistic, the required quanta is obtained. The scheduling is dynamic in nature because it generates the next CPU quanta in reference to the quanta that have been used in the previous cycle with remaining CPU burst requirements of the process, and it is also adaptive in nature because, at each cycle, it uses ‘d’ (5, 5, 4, 3, 2) degree of freedom to calculate the Jarque-Bera Statistics to accept/reject the hypothesis. The algorithm is implemented in ‘R’ and the performance has been evaluated on a sample size of five processes with some noisy data which outperforms the conventional RR and significantly reduces the performance parameters mentioned above. Implementing this algorithm to a time-sharing or distributed environment will undoubtedly improve system performance and will help to avoid issues like thrashing, incorporate aging, CPU affinity, and starvation. Since the proposed algorithm is work-conservative, therefore can be implemented in network packet switching, statistical multiplexing, and real-time systems.

Effect of social capital on enrolment of informal sector occupational groups in the national health insurance scheme in Ghana: a cross-sectional survey

BackgroundEnrolment of informal sector workers in Ghana’s National Health Insurance Scheme (NHIS) is critical to achieving increased risk-pooling and attainment of Universal Health Coverage. However, the NHIS has struggled over the years to improve enrolment of this subpopulation. This study analysed effect of social capital on enrolment of informal sector workers in the NHIS.MethodsA cross-sectional survey was conducted among 528 members of hairdressers and beauticians, farmers, and commercial road transport drivers’ groups. Descriptive statistics, principal component analysis, and multinomial logit regression model were used to analyse the data.ResultsSocial capital including membership in occupational group, trust, and collective action were significantly associated with enrolment in the NHIS, overall. Other factors such as household size, education, ethnicity, and usual source of health care were, however, correlated with both enrolment and dropout. Notwithstanding these factors, the chance of enrolling in the NHIS and staying active was 44.6% higher for the hairdressers and beauticians; the probability of dropping out of the scheme was 62.9% higher for the farmers; and the chance of never enrolling in the scheme was 22.3% higher for the commercial road transport drivers.ConclusionsSocial capital particularly collective action and predominantly female occupational groups are key determinants of informal sector workers’ participation in the NHIS. Policy interventions to improve enrolment of this subpopulation should consider group enrolment, targeting female dominated informal sector occupational groups. Further studies should consider inclusion of mediating and moderating variables to provide a clearer picture of the relationship between occupational group social capital and enrolment in health insurance schemes.

Big data resolving using Apache Spark for load forecasting and demand response in smart grid: a case study of Low Carbon London Project

Using recent information and communication technologies for monitoring and management initiates a revolution in the smart grid. These technologies generate massive data that can only be processed using big data tools. This paper emphasizes the role of big data in resolving load forecasting, renewable energy sources integration, and demand response as significant aspects of smart grids. Meters data from the Low Carbon London Project is investigated as a case study. Because of the immense stream of meters' readings and exogenous data added to load forecasting models, addressing the problem is in the context of big data. Descriptive analytics are developed using Spark SQL to get insights regarding household energy consumption. Spark MLlib is utilized for predictive analytics by building scalable machine learning models accommodating meters' data streams. Multivariate polynomial regression and decision tree models are preferred here based on the big data point of view and the literature that ensures they are accurate and interpretable. The results confirmed the descriptive analytics and data visualization capabilities to provide valuable insights, guide the feature selection process, and enhance load forecasting models' accuracy. Accordingly, proper evaluation of demand response programs and integration of renewable energy resources is accomplished using achieved load forecasting results.

Sustainable practices in cocoa production. The role of certification schemes and farmer cooperatives

In Côte d'Ivoire and Ghana, many small-scale cocoa producers cultivate cocoa in unshaded or low-shaded plots, leading to challenges such as reduced biodiversity, soil fertility depletion, and increased soil erosion. To assess the adoption of sustainable agricultural practices in the cocoa sector, we develop a scale that incorporates dimensions of agroforestry, soil conservation, pest and disease management and farm sanitation. Using data from >1700 cocoa producers, we examine farmer participation in cooperatives and three main certification schemes (incl. Fairtrade, Rainforest Alliance and Organic) to understand their roles in promoting sustainable practices. We apply a multinomial endogenous switching regression model to control for potential selection bias and estimate the impact of participating in certification schemes, farmer cooperatives or both. In Côte d'Ivoire, econometric results show that joint participation in both a certification scheme and a farmer cooperative is associated with a significantly higher sustainability score. In Ghana, certification scheme membership shows the highest effect.

Digital Image Analysis to Evaluate Sensory Attributes of Protein-Enriched Whole-Wheat Bread

New food products or reformulated food products require intensive sensory assessment using a group of panelists before launching in the market. Sometimes, the sensory results obtained by the panelists are inconclusive due to their subjective scores. An indirect and accurate method to evaluate the sensory attributes using images is highly beneficial to conduct preliminary screening during product development stages. Therefore, the objective of this study was to determine the potential of red-green-blue (RGB) color images to evaluate the sensory qualities of whole wheat bread reformulated with pea and soy protein isolates as model food. In this study, reformulated whole wheat (WW) bread was used as model food to determine the potential of digital color images in assessing the selected sensory attributes. Seven types of WW bread was evaluated by ten untrained panelists. Four features (edge detection, pore numbers, pore area and Hu-moment similarity) were extracted from the images of the bread slices and compared with measured sensory scores. In general, the polynomial regression models yielded higher R2 values than linear regression models. The R2 values in polynomial regression models ranged 0.82-0.97, 0.60-0.92, 0.55-0.96, 0.77-0.99, 0.67-0.97, and 0.50-0.87 for chewiness, graininess, moistness, taste, desired aroma and overall acceptability, respectively. Hu-moment similarity provided the highest R2 values for the sensory attributes in polynomial regression models. In conclusion, although image-based sensory assessment may not substitute the current human sensory, it can provide valuable information to supplement the decision making process.

Distribution of Normative Percentiles of Liver Stiffness Measurement Using Ultrasound Shear Wave Elastography in an Adult Asian Indian Population

Abstract Objective The aim of this study was to determine the normative percentiles for liver stiffness measurement (LSM) using shear wave elastography in an adult Asian Indian population as part of the preventive radiology initiative of the Indian Radiological and Imaging Association (IRIA). Methods LSMs were ascertained by two-dimensional (2D) shear wave elastography using the Mindray Resona series of ultrasound machines. The image quality was assessed using the motion stability index (M-STB) and reliability (RLB) map. Ten acquisitions were documented, and an interquartile range-to-median (IQR/M) ratio ≤30% kilopascal (kPa) units was considered a good-quality measurement. A subgroup of the study population without comorbidities was chosen to derive the normative percentile distribution of LSM using a generalized least squares multivariable fractional polynomial regression model that adjusted for sex and body mass index (BMI). The effectiveness of the estimated percentiles was assessed on the entire study population using the greater than 90th percentile value of the LSM as the cutoff for abnormality. Results The study included 852 people who underwent shear wave elastography from June 2022 to July 2023. The magnitude of compensated advanced chronic liver disease (cACLD) and clinically significant portal hypertension (CSPH) was 6.81% (95% confidence interval [CI]: 5.30–8.7) and 4.91% (95% CI: 3.67–6.60), respectively. The normative percentiles were estimated from 282 persons without associated comorbidity and risk factors. The mean age (standard deviation [SD]) of the normal individuals was 40.90 ± 12.92 years, and 210 (71.47%) were males. The mean age (SD) of the 570 persons excluded from the normative percentiles analysis was 47.94 (12.49) years and 72.11% were males. The sex- and BMI-adjusted age-specific 90th percentiles of LSM were 8.76, 8.78, 8.96, 8.97, 9.25, and 9.45 kPa for 18 to 20, 21 to 30, 31 to 40, 41 to 50, 51 to 60, and 61 to 70 years, respectively. Conclusion The sex- and BMI-adjusted age-specific 90th percentiles for LSM using shear wave elastography in Asian Indian adults are almost similar to the greater than 9 kPa cutoff proposed by the Society of Radiologists in Ultrasound Liver Elastography Consensus Statement guidelines to discriminate cACLD and CSPH from normal individuals.