Äta som en häst, en osmaklig kostnad i vår samtid

Det är ett ansträngt ekonomiskt läge i Sverige, både för hushåll och näringsverksamheter som drabbats av höjda kostnader till följd av inflation, energikris och kriget i Europa. Lantbrukarna är några av dem som drabbats hårt då priset på insatsvaror inom jordbruket ökat kraftigt (Jordbruksverket 2022). Följaktligen har Sveriges ridskolor som är beroende av varor från lantbrukarna i sin tur drabbas hårt när priset på foder och strömaterial stigit till följd av det höjda priset på insatsvaror. Ridskolorna uppges vilja hålla nere ridavgifterna nu när köpkraften hos hushållen minskat, för att ge så många som möjligt chansen att rida, men måste samtidigt klara av att bedriva en hållbar verksamhet (SVT 2023a). Således är syftet med uppsatsen att diskutera samt analysera hur ridskolor strategiskt kan bemöta utmaningar och möjligheter vid ökade kostnader. För att besvara studiens syfte har vi i denna undersökning använt oss av beslutsteori och strategic cost management (SCM), då ekonomiska företagsbeslut ofta är förknippade med effektiv hantering och kontroll av kostnader. I uppsatsen har en kvalitativ forskningsmetod använts där semistrukturerade intervjuer genomförts med tre ridskoleverksamheter för att öka förståelsen för vilket sätt ridskoleverksamheterna drabbats av kostnadsökningarna samt vilka strategier de använder sig av. Efter att ha analyserat respondenternas svar genom en tematisk analysmetod vittnar alla tre verksamheterna om tuffa tider med stora kostnadsökningar där det finns både likheter och skillnader i hur de väljer att hantera de ökade kostnaderna. Gemensamt för verksamheterna är att kunder, medlemmar och långsiktiga relationer utgör viktiga komponenter för att klara svåra ekonomiska tider och att det emotionella värdet till verksamheten är större än kostnadsmarginalerna.There is a strained economic situation in Sweden, both for households and businesses that have been affected by increased costs as a result of inflation, the energy crisis and the war in Europe. Farmers have been affected as the price of input goods in agriculture has increased (Jordbruksverket 2022). Consequently, Sweden's riding schools, which are dependent on goods from farmers, have also been affected when the price of feed and bedding material rose because of the increased price of input goods. The riding schools want to keep the fees low now that the purchasing power of households has decreased in order to give as many people as possible the chance to ride horses. At the same time, they must manage to maintain a sustainable business (SVT 2023a). Thus, the purpose of the essay is to discuss and analyze how riding schools can strategically respond to challenges and opportunities in the event of increased costs. In order to answer the purpose of the study, this thesis has used decision theory and strategic cost management (SCM), as financial business decisions are often associated with effective management and control of costs. A qualitative approach was used where semi-structured interviews were conducted with three riding school businesses to increase the understanding of how the riding school businesses were affected by the cost increases and which strategies they use. After analysing the respondents' answers through a thematic analysis, all three businesses agree that times are tough with todays increased costs. The analysis also shows that there are both similarities and differences in how they choose to handle the increased costs. What the businesses have in common is that customers, members, and long-term relationships are important components for managing difficult economic times and that the emotional value to the business is greater than the cost margins

Expecting the Unexpected in Participatory Design

Participatory Design (PD) provides unique benefits in designing technology with and for specific target audiences. However, it can also be an intensive and difficult process, with unexpected situations which can arise at any stage. In this Special Interest Group (SIG), we propose that PD researchers may exchange “war stories” about their unexpected and difficult experiences with PD. This will facilitate reflective discussions and the identification of possible solutions, and enable future PD research to plan for similar situations, thereby making difficulties a little less unexpected.</p

Exploring preconception signatures of metabolites in mothers with gestational diabetes mellitus using a non-targeted approach

BackgroundMetabolomic changes during pregnancy have been suggested to underlie the etiology of gestational diabetes mellitus (GDM). However, research on metabolites during preconception is lacking. Therefore, this study aimed to investigate distinctive metabolites during the preconception phase between GDM and non-GDM controls in a nested case-control study in Singapore.MethodsWithin a Singapore preconception cohort, we included 33 Chinese pregnant women diagnosed with GDM according to the IADPSG criteria between 24 and 28 weeks of gestation. We then matched them with 33 non-GDM Chinese women by age and pre-pregnancy body mass index (ppBMI) within the same cohort. We performed a non-targeted metabolomics approach using fasting serum samples collected within 12 months prior to conception. We used generalized linear mixed model to identify metabolites associated with GDM at preconception after adjusting for maternal age and ppBMI. After annotation and multiple testing, we explored the additional predictive value of novel signatures of preconception metabolites in terms of GDM diagnosis.ResultsA total of 57 metabolites were significantly associated with GDM, and eight phosphatidylethanolamines were annotated using HMDB. After multiple testing corrections and sensitivity analysis, phosphatidylethanolamines 36:4 (mean difference beta: 0.07; 95% CI: 0.02, 0.11) and 38:6 (beta: 0.06; 0.004, 0.11) remained significantly higher in GDM subjects, compared with non-GDM controls. With all preconception signals of phosphatidylethanolamines in addition to traditional risk factors (e.g., maternal age and ppBMI), the predictive value measured by area under the curve (AUC) increased from 0.620 to 0.843.ConclusionsOur data identified distinctive signatures of GDM-associated preconception phosphatidylethanolamines, which is of potential value to understand the etiology of GDM as early as in the preconception phase. Future studies with larger sample sizes among alternative populations are warranted to validate the associations of these signatures of metabolites and their predictive value in GDM.Peer reviewe

OpenMP optimisation of the eXtended Discrete Element Method (XDEM)

The eXtended Discrete Element Method (XDEM) is an extension of the regular Discrete Element Method (DEM) which is a software for simulating the dynamics of granular material. XDEM extends the regular DEM method by adding features where both micro and macroscopic observables can be computed simultaneously by coupling different time and length scales. In this sense XDEM belongs the category of multi-scale/multi-physics applications which can be used in realistic simulations. In this whitepaper, we detail the different optimisations done during the preparatory PRACE project to overcome known bottlenecks in the OpenMP implementation of XDEM. We analysed the Conversion, Dynamic, and the combined Dynamics-Conversion modules with Extrae/Paraver and Intel VTune profiling tools in order to find the most expensive functions. The proposed code modifications improved the performance of XDEM by ~17% for the computational expensive Dynamics-Conversion combined modules (with 48 cores, full node). Our analysis was performed in the Marenostrum 4 (MN4) PRACE infrastructure at Barcelona Supercomputing Center (BSC)

Machine Learning-Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes : Prediction Model Development Study

Publisher Copyright: © Mukkesh Kumar, Li Ting Ang, Cindy Ho, Shu E Soh, Kok Hian Tan, Jerry Kok Yen Chan, Keith M Godfrey, Shiao-Yng Chan, Yap Seng Chong, Johan G Eriksson, Mengling Feng, Neerja KarnaniBackground: The increasing prevalence of gestational diabetes mellitus (GDM) is concerning as women with GDM are at high risk of type 2 diabetes (T2D) later in life. The magnitude of this risk highlights the importance of early intervention to prevent the progression of GDM to T2D. Rates of postpartum screening are suboptimal, often as low as 13% in Asian countries. The lack of preventive care through structured postpartum screening in several health care systems and low public awareness are key barriers to postpartum diabetes screening. Objective: In this study, we developed a machine learning model for early prediction of postpartum T2D following routine antenatal GDM screening. The early prediction of postpartum T2D during prenatal care would enable the implementation of effective strategies for diabetes prevention interventions. To our best knowledge, this is the first study that uses machine learning for postpartum T2D risk assessment in antenatal populations of Asian origin. Methods: Prospective multiethnic data (Chinese, Malay, and Indian ethnicities) from 561 pregnancies in Singapore's most deeply phenotyped mother-offspring cohort study-Growing Up in Singapore Towards healthy Outcomes-were used for predictive modeling. The feature variables included were demographics, medical or obstetric history, physical measures, lifestyle information, and GDM diagnosis. Shapley values were combined with CatBoost tree ensembles to perform feature selection. Our game theoretical approach for predictive analytics enables population subtyping and pattern discovery for data-driven precision care. The predictive models were trained using 4 machine learning algorithms: logistic regression, support vector machine, CatBoost gradient boosting, and artificial neural network. We used 5-fold stratified cross-validation to preserve the same proportion of T2D cases in each fold. Grid search pipelines were built to evaluate the best performing hyperparameters. Results: A high performance prediction model for postpartum T2D comprising of 2 midgestation features-midpregnancy BMI after gestational weight gain and diagnosis of GDM-was developed (BMI_GDM CatBoost model: AUC=0.86, 95% CI 0.72-0.99). Prepregnancy BMI alone was inadequate in predicting postpartum T2D risk (ppBMI CatBoost model: AUC=0.62, 95% CI 0.39-0.86). A 2-hour postprandial glucose test (BMI_2hour CatBoost model: AUC=0.86, 95% CI 0.76-0.96) showed a stronger postpartum T2D risk prediction effect compared to fasting glucose test (BMI_Fasting CatBoost model: AUC=0.76, 95% CI 0.61-0.91). The BMI_GDM model was also robust when using a modified 2-point International Association of the Diabetes and Pregnancy Study Groups (IADPSG) 2018 criteria for GDM diagnosis (BMI_GDM2 CatBoost model: AUC=0.84, 95% CI 0.72-0.97). Total gestational weight gain was inversely associated with postpartum T2D outcome, independent of prepregnancy BMI and diagnosis of GDM (P = .02; OR 0.88, 95% CI 0.79-0.98). Conclusions: Midgestation weight gain effects, combined with the metabolic derangements underlying GDM during pregnancy, signal future T2D risk in Singaporean women. Further studies will be required to examine the influence of metabolic adaptations in pregnancy on postpartum maternal metabolic health outcomes. The state-of-the-art machine learning model can be leveraged as a rapid risk stratification tool during prenatal care.Peer reviewe

Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus

The increasing prevalence of gestational diabetes mellitus (GDM) is contributing to the rising global burden of type 2 diabetes (T2D) and intergenerational cycle of chronic metabolic disorders. Primary lifestyle interventions to manage GDM, including second trimester dietary and exercise guidance, have met with limited success due to late implementation, poor adherence and generic guidelines. In this study, we aimed to build a preconception-based GDM predictor to enable early intervention. We also assessed the associations of top predictors with GDM and adverse birth outcomes. Our evolutionary algorithm-based automated machine learning (AutoML) model was implemented with data from 222 Asian multi-ethnic women in a preconception cohort study, Singapore Preconception Study of Long-Term Maternal and Child Outcomes (S-PRESTO). A stacked ensemble model with a gradient boosting classifier and linear support vector machine classifier (stochastic gradient descent training) was derived using genetic programming, achieving an excellent AUC of 0.93 based on four features (glycated hemoglobin A(1c) (HbA(1c)), mean arterial blood pressure, fasting insulin, triglycerides/HDL ratio). The results of multivariate logistic regression model showed that each 1 mmol/mol increase in preconception HbA(1c) was positively associated with increased risks of GDM (p = 0.001, odds ratio (95% CI) 1.34 (1.13-1.60)) and preterm birth (p = 0.011, odds ratio 1.63 (1.12-2.38)). Optimal control of preconception HbA(1c) may aid in preventing GDM and reducing the incidence of preterm birth. Our trained predictor has been deployed as a web application that can be easily employed in GDM intervention programs, prior to conception.Peer reviewe

Exploring how socioeconomic status affects neighbourhood environments? : effects on obesity risks : a longitudinal study in Singapore

Research on how socioeconomic status interacts with neighbourhood characteristics to influence disparities in obesity outcomes is currently limited by residential segregation-induced structural confounding, a lack of empirical studies outside the U.S. and other 'Western' contexts, and an over-reliance on cross-sectional analyses. This study addresses these challenges by examining how socioeconomic status modifies the effect of accumulated exposures to obesogenic neighbourhood environments on children and mothers' BMI, drawing from a longitudinal mother-child birth cohort study in Singapore, an Asian city-state with relatively little residential segregation. We find that increased access to park connectors was associated with a decrease in BMI outcomes for mothers with higher socioeconomic status, but an increase for those with lower socioeconomic status. We also find that increased access to bus stops was associated with an increase in BMIz of children with lower socioeconomic status, but with a decrease in BMIz of children with higher socioeconomic status, while increased access to rail stations was associated with a decrease in BMIz of children with lower socioeconomic status only. Our results suggest that urban interventions might have heterogeneous effects by socioeconomic status.Peer reviewe

Planning the world's most inclusive PD project

Inclusivity is central to Participatory Design (PD) practice, but despite significant efforts in IDC and beyond, it is still hard to achieve during PD, because of a series of barriers (e.g. access to users, language). Such barriers increase especially when it comes to ensuring and supporting the participation of children with varying or complex needs, or when prospective participants are geographically distributed. This workshop aims to create the basis of a distributed PD (DPD) protocol to provide practical advice in overcoming the challenges of ensuring inclusivity for children with varying or complex needs around the world. The protocol will build on the participants' prior experience and on a live PD design session with children and adults, and be guided by discussions around approaches to address a specific design problem while maximising inclusivity across geographical boundaries and research contexts. It is intended to become a springboard for the world's most inclusive Distributed PD project

Prepregnancy adherence to plant-based diet indices and exploratory dietary patterns in relation to fecundability

Background Modest associations have been reported between specific food groups or nutrients and fecundability [measured by time to pregnancy (TTP)]. Examining overall diets provides a more holistic approach towards understanding their associations with fecundability. It is not known whether plant-based diets indices or exploratory dietary patterns are associated with fecundability. Objectives We examine the associations between adherence to 1) plant-based diet indices; and 2) exploratory dietary patterns and fecundability among women planning pregnancy. Methods Data were analyzed from the Singapore Preconception Study of Long-Term Maternal and Child Outcomes (S-PRESTO) study. Prepregnancy diet was assessed using a semi-quantitative FFQ from which the overall, healthful, and unhealthful plant-based diet indices (oPDI, hPDI, and uPDI, respectively) were calculated. Exploratory dietary patterns were derived using factor analysis based on 44 predefined food groups. Participants were categorized into quintiles based on their dietary pattern scores. TTP (expressed in menstrual cycles) was ascertained within a year from the prepregnancy dietary assessment. Discrete-time proportional hazard models, adjusted for confounders, were used to estimate fecundability ratios (FRs) and 95% CIs, with FR > 1 indicating a shorter TTP. Results Among 805 women, 383 pregnancies were confirmed by ultrasound scans. Compared with women in the lowest quintile, those in the highest quintile of the uPDI had reduced fecundability (FR of Q5 compared with Q1, 0.65; 95% CI, 0.46-0.91; P trend, 0.009). Conversely, greater adherence to the hPDI was associated with increased fecundability (1.46; 95% CI, 1.02-2.07; P trend, 0.036). The oPDI was not associated with fecundability. Among the 3 exploratory dietary patterns, only greater adherence to the Fast Food and Sweetened Beverages (FFSB) pattern was associated with reduced fecundability (0.61; 95% CI, 0.40-0.91; P trend, 0.018). Conclusions Greater adherence to the uPDI or the FFSB dietary pattern was associated with reduced fecundability among Asian women. Greater adherence to the hPDI may be beneficial for fecundability, though this requires confirmation by future studies.Peer reviewe

Participatory Design of the World’s Largest DPD Project with Children

In this workshop, we invite researchers and practitioners as participants in co-designing the protocol for the world’s largest Distributed Participatory Design (DPD) project with children. Participatory Design – whose inclusive benefits are broadly recognised in design – can be very challenging, especially when involving children. The current COVID-19 pandemic has given rise to further barriers to PD with such groups. Recent key barriers include social distancing and government-imposed social restrictions due to the additional health risks to vulnerable children and their families. This disrupts traditional in-person PD (which involves close socio-emotional and often physical collaboration between participants and researchers). However, alongside such barriers, we have identified opportunities for new and augmented approaches to PD across distributed geographies, backgrounds, ages and abilities. We invite the CCI community to examine Distributed Participatory Design (DPD) as a solution for overcoming these new barriers, during and after COVID-19. Together, we offer new ways to think about DPD, and unpick some of its ambiguities. This workshop builds on work conducted in a similar workshop in IDC 2020, and this year will focus on the planning and design of the protocol for the world’s largest DPD project with children