bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Efficacy of Disability Employment Service (DES) Providers Working with Autistic Clients
Authors: Hayward Susan M. , Flower Rebecca L. , Denney Kathleen E. , Bury Simon , Richdale Amanda L. , Dissanayake Cheryl , Hedley Darren 
Published: September 2022

AbstractThe efficacy of the Australian Disability Employment Services (DES) for autistic jobseekers has not been examined and is currently undergoing Government reform. To help inform the new DES strategy, we sought the views of: 24 autistic individuals; seven family members of autistic individuals, and; 46 DES employees. Data were collected using surveys and interviews. Data were analysed using Mann Whitney tests plus deductive thematic analysis based on Nicholas and colleagues’ ecosystems model. Participants highlighted a need to adapt existing policies to enhance flexibility of the DES model. There was participant consensus that DES staff require specific education and training to meet the needs of autistic people. Suggestions to inform the new model of DES for autistic people are made.
Work-Related Psychological Wellbeing and Conservative Christian Belief Among Methodist Circuit Ministers in Britain: Distinguishing Between Emotional Exhaustion and Satisfaction in Ministry
Authors: Francis Leslie J. , Haley John M. , McKenna Ursula 
Published: September 2022

AbstractDrawing on data provided by 803 Methodist circuit ministers serving in Great Britain, the present study was designed to test the association between conservative Christian belief and work-related psychological wellbeing as operationalised by the balanced affect model proposed by the Francis Burnout Inventory. After taking into account the effects of personal factors, psychological factors, contextual factors, and experience factors, holding conservative Christian belief was associated with a higher level of positive affect (satisfaction in ministry) but independent of negative affect (emotional exhaustion in ministry).
Household Food Insecurity and Demographic Factors, Low Birth Weight and Stunting in Early Childhood: Findings from a Longitudinal Study in South Africa
Authors: Harper Abigail , Rothberg Alan , Chirwa Esnat , Sambu Winnie , Mall Sumaya 
Published: September 2022

Abstract
Background
Low birthweight (LBW) as well as early childhood stunting are risk factors for increased childhood morbidity in low-and middle-income countries (LMIC). The Covid 19 pandemic has exacerbated food insecurity and unemployment globally, prompting concerns for maternal and child health.

Objectives
We used data from the great recession of 2008 to examine the relationship between household food security and other risk factors with LBW and stunting using a longitudinal sample of South African women and their offspring.

Methods
Food security indicators, alcohol use, blood pressure and other characteristics were examined in relation to LBW (≤ 2500 g), stunting (height for age ≤ 2SD) and severe stunting (height for age ≤ 3SD). Regression modelling with clustering at maternal ID level were employed to adjust for maternal characteristics and women who gave birth more than once during the reference period.

Results
Birthweight data were available for 1173 children and height for age 1216 children. The prevalence of LBW was 14.7% while stunting and severe stunting was 17.8% and 14.5%. Child hunger in the household, maternal hypertension and alcohol use were associated with low birthweight. Food expenditure below the Stats SA poverty line and low dietary diversity was associated with stunting and severe stunting respectively. Maternal height and low birthweight were associated with both stunting and severe stunting.

Conclusions for Practice
Interventions that can improve household food security and nutritional status during the periconceptional and antenatal period may reduce the prevalence of low birthweight and subsequent stunting in low- and middle-income countries.
The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study
Authors: Hassan Esraa , Shams Mahmoud Y. , Hikal Noha A. , Elmougy Samir 
Published: September 2022

AbstractOptimization algorithms are used to improve model accuracy. The optimization process undergoes multiple cycles until convergence. A variety of optimization strategies have been developed to overcome the obstacles involved in the learning process. Some of these strategies have been considered in this study to learn more about their complexities. It is crucial to analyse and summarise optimization techniques methodically from a machine learning standpoint since this can provide direction for future work in both machine learning and optimization. The approaches under consideration include the Stochastic Gradient Descent (SGD), Stochastic Optimization Descent with Momentum, Rung Kutta, Adaptive Learning Rate, Root Mean Square Propagation, Adaptive Moment Estimation, Deep Ensembles, Feedback Alignment, Direct Feedback Alignment, Adfactor, AMSGrad, and Gravity. prove the ability of each optimizer applied to machine learning models. Firstly, tests on a skin cancer using the ISIC standard dataset for skin cancer detection were applied using three common optimizers (Adaptive Moment, SGD, and Root Mean Square Propagation) to explore the effect of the algorithms on the skin images. The optimal training results from the analysis indicate that the performance values are enhanced using the Adam optimizer, which achieved 97.30% accuracy. The second dataset is COVIDx CT images, and the results achieved are 99.07% accuracy based on the Adam optimizer. The result indicated that the utilisation of optimizers such as SGD and Adam improved the accuracy in training, testing, and validation stages.
Semi-intrusive approach for stiffness and strength topology optimization under uncertainty
Authors: Steltner Kai , Pedersen Claus B. W. , Kriegesmann Benedikt 
Published: September 2022

AbstractA semi-intrusive approach for robust design optimization is presented. The stochastic moments of the objective function and constraints are estimated using a Taylor series-based approach, which requires derivatives with respect to design variables, random variables as well as mixed derivatives. The required derivatives with respect to design variables are determined using the intrusive adjoint method available in commercial software. The partial derivatives with respect to random parameters as well as the mixed second derivatives are approximated non-intrusively using finite differences. The presented approach provides a semi-intrusive procedure for robust design optimization at reasonable computational cost while allowing an arbitrary choice of random parameters. The approach is implemented as an add-on for commercial software. The method and its limitations are demonstrated by academic test cases and industrial applications.
A ridesplitting market equilibrium model with utility-based compensation pricing
Authors: Lu Qing-Long , Qurashi Moeid , Antoniou Constantinos 
Published: September 2022

AbstractThe paper develops a theoretic equilibrium model for ridesplitting markets with specific considerations of origin-destination demand patterns, competition with other transport modes, characteristics of en route matching, and spatial allocation of ridesplitting vehicles, to adequately portray the intertwined relationships between the endogenous variables and decisions. The operation property of the market under distance-based unified pricing is analyzed through the response of system performance indicators to the decisions. Moreover, a gradient descent algorithm is derived to find optimal operating strategies in the monopoly scenario and social optimum scenario. Leveraging the tight connection between trip’s utility and level of service (LoS), the paper then proposes a utility-based compensation pricing method to alleviate the inequity issue in ridesplitting, which results from the variance in waiting time and detour time and the implementation of unified pricing. Specifically, the trip fare of those with an initial utility smaller than a threshold will be compensated following a predefined compensation function. We compare its effectiveness and influence in different scenarios through numerical experiments at Munich. The results show that the proposed pricing method can improve the LoS and equity without losing any profit and welfare, and can even achieve increments in maximum profit and social welfare under certain conditions.
Eyewitness evaluation through inference to the best explanation
Authors: Jellema Hylke 
Published: September 2022

AbstractEyewitness testimony is both an important and a notoriously unreliable type of criminal evidence. How should investigators, lawyers and decision-makers evaluate eyewitness reliability? In this article, I argue that Testimonial Inference to the Best Explanation (TIBE) is a promising, but underdeveloped prescriptive account of eyewitness evaluation. On this account, we assess the reliability of eyewitnesses by comparing different explanations of how their testimony came about. This account is compatible with, and complementary to both the Bayesian framework of rational eyewitness evaluation and with prescriptive methods for eyewitness assessment developed by psychologists. Compared to these frameworks, the distinctive value of thinking in terms of competing explanations is that it helps us select, interpret and draw conclusions from the available evidence about the witness’s reliability.
Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project
Authors: Yi Sooyeon , Kondolf G. Mathias , Sandoval-Solis Samuel , Dale Larry 
Published: September 2022

AbstractEnergy use forecasting is crucial in balancing the electricity supply and demand to reduce the uncertainty inherent in the inter-basin water transfer project. Energy use prediction supports the reliable water-energy supply and encourages cost-effective operation by improving generation scheduling. The objectives are to develop subsequent monthly energy use predictive models for the Mokelumne River Aqueduct in California, US. Partial objectives are to (a) compare the model performance of a baseline model (multiple linear regression (MLR)) to three machine learning-based models (random forest (RF), deep neural network (DNN), support vector regression (SVR)), (b) compare the model performance of the whole system to three subsystems (conveyance, treatment, distribution), and (c) conduct sensitivity analysis. We simulate a total of 64 cases (4 algorithms (MLR, RF, DNN, SVR) x 4 systems (whole, conveyance, treatment, distribution) x 4 scenarios (different combinations of independent variables). We concluded that the three machine learning algorithms showed better model performance than the baseline model as they reflected non-linear energy use characteristics for water transfer systems. Among the three machine learning algorithms, DNN models yielded higher model performance than RF and SVR models. Subsystems performed better than the whole system as the models more closely reflected the unique energy use characteristics of the subsystems. The best case was having water supply (t), water supply (t-1), precipitation (t), temperature (t), and population (y) as independent variables. These models can help water and energy utility managers to understand energy performance better and enhance the energy efficiency of their water transfer systems.
A Systematic Review of School Transition Interventions to Improve Mental Health and Wellbeing Outcomes in Children and Young People
Authors: Donaldson Caitlyn , Moore Graham , Hawkins Jemma 
Published: September 2022

AbstractNormative transitions between educational settings can be important life events for young people, having the potential to influence mental health trajectories across the life course. Interventions to target transitions have been used to support children and young people as they transition between school settings, but there is limited synthesis of their effects. Seven databases were searched to identify studies of universal interventions focused on supporting mental health and wellbeing across three main types of educational transition: preschool to elementary school; school to school (including elementary to middle; middle to high and other combinations depending on country); and high school to post-compulsory education. Effect directions for behavioural, psychological/emotional and social measures of mental health were extracted for each study and synthesized using effect direction plot methodology. Searches identified 6494 records for screening. This resulted in 34 papers being included in the review, consisting of 24 different interventions. Social outcomes appeared more amenable to intervention than behavioural outcomes, with mixed findings for psychological measures of mental health. Intervention characteristics shifted based on the age of young person involved in the transition, with greater focus on parenting and school environment during the early transitions, and more focus on social support for the transition to post-compulsory education. A broad range of interventions were identified for supporting mental health and wellbeing across the three types of educational transition with mixed impact and diverse methodologies. More research is needed to identify transferable intervention mechanisms that may hold across different contexts and settings. PROSPERO registration number: CRD42020176336.
Immune and inflammation: related factor alterations as biomarkers for predicting prognosis and responsiveness to PD-1 monoclonal antibodies in cervical cancer
Authors: Liu Xihan , Zhang Xi , Liu Chang , Mu Wendi , Peng Jin , Song Kun 
Published: September 2022

Abstract
Purpose
We aimed to elucidate the potential mechanisms of effective responsiveness to PD-1 monoclonal antibody and evaluate more reliable biomarkers to improve the ability to predict the populations of cervical cancer (CC) suitable for immunotherapy.

Methods
Peripheral blood samples of CC patients undergoing anti-PD-1 therapy were collected before and after treatment. Differentially expressed genes (DEGs) were analyzed between partial response (PR) and progressive disease (PD) patients. A novel prognostic inflammation and immune–related response gene (IRRG) model was constructed and its prognostic role, correlation with tumor immunity and tumor mutation were evaluated.

Results
DEGs in PR patient after treatment could predict the response to PD-1 monoclonal antibodies. Among PR-specific pathways, tumor immunity, leukocyte migration, and cytokine activities were prominently enriched. Additionally, an IRRG signature comprising CTLA4, AZU1, C5, LAT, CXCL2, GDF7, MPL, PPARG and CELA1 was established and validated to predict the prognosis of CC with great accuracy and specificity. This signature could reflect the tumor microenvironment (TME) and tumor mutational burden (TMB). We also found stimulated adaptive immunity and downregulated inflammation at baseline in patients with sensitive responses to PD-1 monoclonal antibody.

Conclusion
We developed an IRRG signature and verified that it was an independent prognostic factor for predicting survival and could reflect a sensitive response to PD-1 monoclonal antibody, which plays a nonnegligible role in the TME of CC. Further investigations are warranted to confirm that patients with stimulated adaptive immunity and downregulated inflammation at baseline could achieve a better survival benefit from PD-1 monoclonal antibody.
Inner design of artificial test bones for biomechanical investigations using topology optimization
Authors: Fritz Christian , Fischer Lukas , Wund Emmy , Zaeh Michael Friedrich 
Published: September 2022

AbstractArtificial or human test bones are used for the biomechanical testing of implants. Human test bones are rare and not always available. These must, therefore, be substituted with artificial test bones. However, current artificial test bones are only available with specific characteristics (e.g., age groups or disease characteristics). Additionally, their mechanical properties are only comparable to a limited extent to those of a human bone. This paper presents a methodology for designing additively manufactured artificial test bones for biomechanical testing that replicate the mechanical behavior of a human bone. Topology optimization methods are used to generate the artificial test bone's internal structure. The geometric model is based on a computed tomography dataset of a human bone. The input data can be manipulated in advance to reproduce defects or disease patterns. The bone was fixed at the distal diaphysis and loaded with different biomechanical forces for topology optimization. Boundary conditions due to possible additive manufacturing processes were incorporated into the optimization to ensure manufacturability. The optimization result is compared with experimental data from a human bone. A bone-like internal structure and increased compliance of the topology-optimized test bone model compared to the commercial model were observed.
A Systematic Review of the Genetic Predisposition to Injury in Football
Authors: McAuley Alexander B. T. , Hughes David C. , Tsaprouni Loukia G. , Varley Ian , Suraci Bruce , Roos Thomas R. , Herbert Adam J. , Jackson Daniel T. , Kelly Adam L. 
Published: September 2022

Abstract
Purpose
Synthesise genetic association studies investigating injury involving football players to identify which genetic variants have the most empirical evidence to date.

Methods
A comprehensive search of the PubMed, SPORTDiscus, and MEDLINE databases until March 11th 2022 identified 34 studies. Inclusion criteria: primary investigations, included football players, examined the association of a genetic variant with injury, and were published in English. Risk of bias was assessed using the Newcastle–Ottawa Scale. A narrative synthesis summarised results.

Results
There were 33 candidate gene studies and one genome-wide study, with 9642 participants across all studies (range = 43–1311; median = 227). Ninety-nine polymorphisms were assessed within 63 genes. Forty-one polymorphisms were associated with injury once. Three polymorphisms had their specific allelic associations with injury replicated twice in independent cohorts: ACTN3 (rs1815739) XX genotype was associated with an increased susceptibility to non-contact muscle injuries, ACAN (rs1516797) G allele was associated with increased susceptibility to anterior cruciate ligament (ACL) injuries, and VEGFA (rs2010963) CC genotype was associated with an increased susceptibility to ACL and ligament or tendon injuries. However, several methodological issues (e.g., small sample sizes, cohort heterogeneity, and population stratification) are prevalent that limit the reliability and external validity of findings.

Conclusion
At present, the evidence base supporting the integration of genetic information as a prognostic or diagnosis tool for injury risk in football is weak. Future participation of organisations in international consortia is suggested to combat the current methodological issues and subsequently improve clarity concerning the underlying genetic contribution to injury susceptibility.
Checking Contact Tracing App Implementations with Bespoke Static Analysis
Authors: Flood Robert , Chan Sheung Chi , Chen Wei , Aspinall David 
Published: September 2022

AbstractIn the wake of the COVID-19 pandemic, contact tracing apps have been developed based on digital contact tracing frameworks. These allow developers to build privacy-conscious apps that detect whether an infected individual is in close proximity with others. Given the urgency of the problem, these apps have been developed at an accelerated rate with a brief testing period. Such quick development may have led to mistakes in the apps’ implementations, resulting in problems with their functionality, privacy and security. To mitigate these concerns, we develop and apply a methodology for evaluating the functionality, privacy and security of Android apps using the Google/Apple Exposure Notification API. This is a three-pronged approach consisting of a manual analysis, general static analysis and a bespoke static analysis, using a tool we have developed, dubbed MonSTER. As a result, we have found that, although most apps met the basic standards outlined by Google/Apple, there are issues with the functionality of some of these apps that could impact user safety.
An Introductory Module in Medical Image Segmentation for BME Students
Authors: Buffinton Christine Miller , Baish James W. , Ebenstein Donna M. 
Published: September 2022

AbstractTo support recent trends toward the use of patient-specific anatomical models from medical imaging data, we present a learning module for use in the undergraduate BME curriculum that introduces image segmentation, the process of partitioning digital images to isolate specific anatomical features. Five commercially available software packages were evaluated based on their perceived learning curve, ease of use, tools for segmentation and rendering, special tools, and cost: ITK-SNAP, 3D Slicer, OsiriX, Mimics, and Amira. After selecting the package best suited for a stand-alone course module on medical image segmentation, instructional materials were developed that included a general introduction to imaging, a tutorial guiding students through a step-by-step process to extract a skull from a provided stack of CT images, and a culminating assignment where students extract a different body part from clinical imaging data. This module was implemented in three different engineering courses, impacting more than 150 students, and student achievement of learning goals was assessed. ITK-SNAP was identified as the best software package for this application because it is free, easiest to learn, and includes a powerful, semi-automated segmentation tool. After completing the developed module based on ITK-SNAP, all students attained sufficient mastery of the image segmentation process to independently apply the technique to extract a new body part from clinical imaging data. This stand-alone module provides a low-cost, flexible way to bring the clinical and industry trends combining medical image segmentation, CAD, and 3D printing into the undergraduate BME curriculum.