Scientific Data Research Articles

A Predictive Model for Academic Performance of Students in MAT101 at Federal University Birnin Kebbi

The research addresses the problem of low academic performance of students taking General Mathematics 1 (MAT101) at the Federal University Birnin Kebbi in Nigeria. MAT101 is a foundational course for several science-related disciplines. However, many students fail to understand its concepts, translating to one of the lowest academic performances, leading to high discontinuation of students’ studies in most science and related fields. To this end, the problem is addressed through a predictive model using data science and machine learning techniques to identify students who are academically at risk. It seeks to answer the appropriateness of the selected statistical and data mining approaches, whether or not they are predictor variables, and validating the model developed. The significance of this study lies in its potential to help instructors identify at-risk students and improve overall performance in MAT101. The research uses data collected from academic records and conducts exploratory data analysis to determine the factors influencing students’ performance. Several classifiers such as Decision Tree and Random Forest were analyzed by evaluating their accuracy. In detail, the difference in the accuracy for the Decision Tree algorithm was 52% and for the Random Forest was 67%. Based on achieved indicators, the Random forest algorithm is more effective in this type of predictive modeling. However, the choice of algorithm depends on specific task requirements, considering factors like speed and accuracy.

The Evolution of Science and Regulation of Dietary Supplements: Past, Present, and Future

Dietary supplement use in the United States is widespread and increasing, especially among certain population groups, such as older Americans. The science surrounding dietary supplements has evolved substantially over the last few decades since their formal regulation in 1994. Much has been learned about the mechanisms of action of many dietary supplement ingredients, but the evidence on their health effects is still building. As is true of much nutrition research, there are many studies that point to health effects, but not all are at the level of scientific evidence (e.g., randomized controlled interventions), rigor, or quality needed for definitive statements of efficacy regarding clinical end points. New technologies and approaches are being applied to the science of dietary supplements, including nutrigenomics and microbiome analysis, data science, artificial intelligence (AI), and machine learning—all of which can elevate the science behind dietary supplements. Products can contain an array of bioactive compounds derived from foods as well as from medicinal plants, which creates enormous challenges in data collection and management. Clinical applications, particularly those aimed at providing personalized nutrition options for patients, have become more sophisticated as dietary supplements are incorporated increasingly into clinical practice and self-care. The goals of this article are to provide historical context for the regulation and science of dietary supplements, identify research resources, and suggest some future directions for science in this field.

Transformer technology in molecular science

AbstractA transformer is the foundational architecture behind large language models designed to handle sequential data by using mechanisms of self‐attention to weigh the importance of different elements, enabling efficient processing and understanding of complex patterns. Recently, transformer‐based models have become some of the most popular and powerful deep learning (DL) algorithms in molecular science, owing to their distinctive architectural characteristics and proficiency in handling intricate data. These models leverage the capacity of transformer architectures to capture complex hierarchical dependencies within sequential data. As the applications of transformers in molecular science are very widespread, in this review, we only focus on the technical aspects of transformer technology in molecule domain. Specifically, we will provide an in‐depth investigation into the algorithms of transformer‐based machine learning techniques in molecular science. The models under consideration include generative pre‐trained transformer (GPT), bidirectional and auto‐regressive transformers (BART), bidirectional encoder representations from transformers (BERT), graph transformer, transformer‐XL, text‐to‐text transfer transformer, vision transformers (ViT), detection transformer (DETR), conformer, contrastive language‐image pre‐training (CLIP), sparse transformers, and mobile and efficient transformers. By examining the inner workings of these models, we aim to elucidate how their architectural innovations contribute to their effectiveness in processing complex molecular data. We will also discuss promising trends in transformer models within the context of molecular science, emphasizing their technical capabilities and potential for interdisciplinary research. This review seeks to provide a comprehensive understanding of the transformer‐based machine learning techniques that are driving advancements in molecular science.This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

Physics event classification using Large Language Models

The 2023 AI4EIC hackathon was the culmination of the third annual AI4EIC workshop at The Catholic University of America. This workshop brought together researchers from physics, data science and computer science to discuss the latest developments in Artificial Intelligence (AI) and Machine Learning (ML) for the Electron Ion Collider (EIC), including applications for detectors, accelerators, and experimental control. The hackathon, held on the final day of the workshop, involved using a chatbot powered by a Large Language Model, ChatGPT-3.5, to train a binary classifier neutrons and photons in simulated data from the GlueX Barrel Calorimeter. In total, six teams of up to four participants from all over the world took part in this intense educational and research event. This article highlights the hackathon challenge, the resources and methodology used, and the results and insights gained from analyzing physics data using the most cutting-edge tools in AI/ML.

HEOM‐QUICK2: A general‐purpose simulator for fermionic many‐body open quantum systems—An update

AbstractMany‐body open quantum systems (OQSs) have a profound impact on various subdisciplines of physics, chemistry, and biology. Thus, the development of a computer program capable of accurately, efficiently, and versatilely simulating many‐body OQSs is highly desirable. In recent years, we have focused on the advancement of numerical algorithms based on the fermionic hierarchical equations of motion (HEOM) theory. Being in‐principle exact, this approach allows for the precise characterization of many‐body correlations, non‐Markovian memory, and non‐equilibrium thermodynamic conditions. These efforts now lead to the establishment of a new computer program, HEOM for QUantum Impurity with a Correlated Kernel, version 2 (HEOM‐QUICK2), which, to the best of our knowledge, is currently the only general‐purpose simulator for fermionic many‐body OQSs. Compared with version 1, the HEOM‐QUICK2 program features more efficient solvers for stationary states, more accurate treatment of non‐Markovian memory, and improved numerical stability for long‐time dissipative dynamics. Integrated with quantum chemistry software, HEOM‐QUICK2 has become a valuable theoretical tool for the precise simulation of realistic many‐body OQSs, particularly the single atomic or molecular junctions. Furthermore, the unprecedented precision achieved by HEOM‐QUICK2 enables accurate simulation of low‐energy spin excitations and coherent spin relaxation. The unique usefulness of HEOM‐QUICK2 is demonstrated through several examples of strongly correlated quantum impurity systems under non‐equilibrium conditions. Thus, the new HEOM‐QUICK2 program offers a powerful and comprehensive tool for studying many‐body OQSs with exotic quantum phenomena and exploring applications in various disciplines.This article is categorized under: Data Science > Computer Algorithms and Programming Software > Simulation Methods Theoretical and Physical Chemistry > Statistical Mechanics

CHEOPS in-flight performance

Context. Since the discovery of the first exoplanet almost three decades ago, the number of known exoplanets has increased dramatically. By beginning of the 2000s it was clear that dedicated facilities to advance our studies in this field were needed. The CHaracterising ExOPlanet Satellite (CHEOPS) is a space telescope specifically designed to monitor transiting exoplanets orbiting bright stars. In September 2023, CHEOPS completed its nominal mission duration of 3.5 yr and remains in excellent operational conditions. As a testament to this, the mission has been extended until the end of 2026. Aims. Scientific and instrumental data have been collected throughout in-orbit commissioning and nominal operations, enabling a comprehensive analysis of the mission’s performance. In this article, we present the results of this analysis with a twofold goal. First, we aim to inform the scientific community about the present status of the mission and what can be expected as the instrument ages. Secondly, we intend for this publication to serve as a legacy document for future missions, providing insights and lessons learned from the successful operation of CHEOPS. Methods. To evaluate the instrument performance in flight, we developed a comprehensive monitoring and characterisation (M&C) programme. It consists of dedicated observations that allow us to characterise the instrument’s response and continuously monitor its behaviour. In addition to the standard collection of nominal science and housekeeping data, these observations provide valuable input for detecting, modelling, and correcting instrument systematics, discovering and addressing anomalies, and comparing the instrument’s actual performance with expectations. Results. The precision of the CHEOPS measurements has enabled the mission objectives to be met and exceeded. The satellite’s performance remains stable and reliable, ensuring accurate data collection throughout its operational life. Careful modelling of the instrumental systematics allows the data quality to be significantly improved during the light curve analysis phase, resulting in more precise scientific measurements. Conclusions. CHEOPS is compliant with the driving scientific requirements of the mission. Although visible, the ageing of the instrument has not affected the mission’s performance. The satellite’s capabilities remain robust, and we are confident that we will continue to acquire high-quality data during the mission extension.

Effectiveness Effectiveness of Wearable Electronic Device for Fall Risk Prevention in Community Elderly: A Literature Review

The increasing incidence of falls in the elderly has become a global health issue. The development of wearable electronic device technology is able to monitor daily activities, measure step counts, physical activity levels, and even sleep patterns of the elderly, while providing early warnings when changes occur that may increase the risk of falls. The aim is to see the effectiveness of using wearable electronic devices in preventing the risk of falls in the elderly in the community. PICO in this study is Population: Community-dwelling elderly, Intervention: wearable electronic device, Comparisons: none, and Outcome: fall prevention. Researchers used 3 search data sources, namely Embase, Pubmed, and Science Direct. The inclusion and exclusion criteria of this study are literature published in electronic data sources from 2018-2023, is a type of quantitative research, the elderly population in the community, English language and available in full text. This literature review includes ten articles that discuss the utilization of wearable electronic device technology, the types of wearable devices used include Inertial Measurement Units (IMU), biosensors, and accelerometers to evaluate the mobility, gait, and balance of the elderly. This literature review shows that wearable electronic device technology can effectively detect falls and motorize the incidence of falls in the elderly

Schisandra chinensis Bee Pollen Extract Inhibits Proliferation and Migration of Hepatocellular Carcinoma HepG2 Cells via Ferroptosis-, Wnt-, and Focal Adhesion-Signaling Pathways.

Bee pollen possesses favorable anticancer activities. As a medicinal plant source, Schisandra chinensis bee pollen (SCBP) possesses potential pharmacological properties, such as reducing cisplatin-induced liver injury, but its anti-liver cancer effect is still rarely reported. This paper aims to investigate the effect and mechanism of SCBP extract (SCBPE) on hepatocellular carcinoma HepG2 cells. The effect of SCBPE on cell proliferation and migration of HepG2 cells was evaluated based on MTT assay, morphology observation, or scratching assay. Furthermore, tandem mass tag-based quantitative proteomics was used to study the effect mechanisms. The mRNA expression levels of identified proteins were verified by RT-qPCR. Tandem mass tag-based quantitative proteomics showed that 61 differentially expressed proteins were obtained in the SCBPE group compared with the negative-control group: 18 significantly downregulated and 43 significantly upregulated proteins. Bioinformatic analysis showed the significantly enriched KEGG pathways were predominantly ferroptosis-, Wnt-, and hepatocellular carcinoma-signaling ones. Protein-protein interaction network analysis and RT-qPCR validation revealed SCBPE also downregulated the focal adhesion-signaling pathway, which is abrogated by PF-562271, a well-known inhibitor of FAK. This study confirmed SCBPE suppressed the cell proliferation and migration of hepatocellular carcinoma HepG2 cells, mainly through modulation of ferroptosis-, Wnt-, hepatocellular carcinoma-, and focal adhesion-signaling pathways, providing scientific data supporting adjuvant treatment of hepatocellular carcinoma using SCBP.

Climate Justice and Climate Adaptation in California: Indigenous Community Climate Adaptation Leadership and Opportunities for Scientific Collaboration

Abstract Climate and weather-related disasters in California illustrate the need for immediate climate change action—both mitigation to reduce impacts and adaptation to protect our communities, relatives, and the ecosystems we depend upon. Indigenous frontline communities face even greater threats from climate impacts due to historical and political legacies of environmental injustice. Climate change adaptation actions have proven challenging to implement as communities struggle to access necessary climate data at appropriate scales, identify effective strategies that address community priorities, and obtain resources to act at a whole-community level. In this paper, we present three examples of Indigenous communities in California that have used a climate justice approach to climate change adaptation. These communities are drawing upon community knowledge and expertise to address the challenges of adaptation planning and taking actions that center community priorities. The three cases address emergency preparation and response, cultural burning and fire management, and community organizing and social cohesion. Across these spheres, they illustrate the ways in which a community-based and climate justice-focused approach to adaptation can be effective in addressing current threats while also addressing the legacy of imposed, socially constructed vulnerability and environmental injustices. Because we recognize the need for multiple knowledges and skills in adaptation actions, we include recommendations that have emerged based on what has been learned through these long-standing and engaged participatory research collaborations for climate scientists who wish to contribute to climate justice-focused adaptation efforts by using scientific data to support—not supplant—community efforts, target funding toward genuine community engagement and adaptation actions, and become aware of the historical and political legacies that created the climate vulnerabilities and injustices evident today.

Reconstructing the trade history: provenance study of Han bronze mirrors in and out of Han China

A rapidly increasing number of bronze mirrors dated to the Chinese Han dynasty (202 BC – AD 220), known for their unique decorative patterns and highly developed alloying techniques, have been widely discovered in both China and beyond, providing fresh materials and scientific data to revisit their geological provenance, production and circulation network along the ancient Silk Road. In this paper, 47 bronze mirrors unearthed in the southeastern provinces of China, including Zhejiang, Anhui and Fujian provinces, have been characterized by typo-chronology, lead isotopic analysis, compositional analysis and metallography. A much wider comparative study is also carried out through a combination of data from China, Japan, Central Asia, and Southeast Asia, leading to a more updated lead isotopic database of the Han mirrors spreading out of China in various directions. Compared with the traditional ‘optimal’ model based on the Han mirrors recovered in Japan, the current study contributes several key changes in the bronze mirror production of the Han dynasty. The systematic analysis of the alloy composition, trace elements and typological studies shows that the bronze mirror industry shifted towards a more standardized production in the middle to late Western Han Dynasty. In contrast to the substantial change of non-mirror bronze productions, the similar distribution of lead isotope data in early and middle to late Western Han mirrors suggests that the ‘official monopoly of salt and iron’ policy was less effective for the management of lead involved in mirror production. Bronze mirrors dated to middle to late Western Han discovered outside Han-China, such as Japan, Thailand, Afghanistan, Xiongnu and the ancient Dian Kingdom, appear to be subjected to a more specific type of lead as a result of the state-centralized policy of the Western Han court.

The Variables Associated with Burnout Among Korean Early Childhood Teachers: A Meta-Analysis

Researchers have done a great deal of research on the variables associated with early childhood teacher burnout, but the findings are numerous and inconsistent. Therefore, this study explored the variables most associated with burnout among early childhood teachers through meta-analysis. The National Assembly Electronic Library in Korea, Riss, the National Electronic Library, and DBpia databases were all thoroughly searched by researchers. Strict inclusion criteria resulted in the inclusion of 201 studies, and 35 variables total—divided into 5 variable groups—were analyzed. The results showed that the most correlated variable group with burnout was the psychological personality protective variable, and the largest effect size variables among the variable groups were age ( r = −.3052), resilience ( r = −.5415), calling ( r = −.526), turnover intention ( r = .5021), and interpersonal relationships ( r = −.4552). However, the variables in the psychological personality risk variable were not statistically significant. Although more thorough validation by follow-up studies is still required, these findings offer a more scientific data reference for the prevention and improvement of burnout among early childhood teachers in Korea and suggest a direction for future research.

Protective Effect and Related Mechanism of Modified Danggui Buxue Decoction on Retinal Oxidative Damage in Mice based on Network Pharmacology.

Age-related macular degeneration (AMD) is one of the common diseases that cause vision loss in the elderly, and oxidative stress has been considered a major pathogenic factor for AMD. Modified Danggui Buxue Decoction (RRP) has a good therapeutic effect on non-proliferatic diabetic retinopathy and can improve the clinical symptoms of patients. This study aimed to predict and verify the protective effect and mechanism of RRP on retinal oxidative damage in mice based on network pharmacology and animal experiments. A total of 15 key active components included in RRP interacted with 57 core targets related to retinal oxidative damage (such as AKT1, NFE2L2, HMOX1), mainly involved in the AGE-RAGE signaling pathway in diabetic complications, PI3K-AKT signaling pathway and so on. Further studies in vivo found that RRP improved the retinal oxidative damage, increased the content of SOD and GSH, decreased the content of MDA in mouse serum, promoted the expression of p-PI3K, p-AKT, Nrf2, HO-1 and NQO1 proteins in the mouse retina, and inhibited the expression of Nrf2 in the cytoplasm. This study revealed that RRP had a protective effect on oxidative damage of the retina in mice, and might exert anti-oxidative effect by activating the PI3K/Akt/Nrf2 signal pathway. This study provided scientific data for the further development of hospital preparations of RRP, and a good theoretical basis for the clinical application of RRP.

Research and publication gaps on kratom and kratom products: a scoping review of current literature.

Kratom plant, products derived from the plant, and plant phytochemicals are of great interest among researchers, clinicians, and consumers. However, there is a paucity of rigorously collected scientific data on their risk/safety profile and public health impact. This scoping review discusses original research articles published between 2022 and 2023. It focuses on identifying publication gaps on topics related to epidemiology, public health, and risk/safety profiles comparing evidence collected by researchers from Southeast Asia and the West. Our review of the Scopus database identified a total of 55 publications, including clinical case reports and case series reports, surveys, studies enrolling human participants, and publications based on large-scale national surveys or large-scale national or international health system database records. Overall, there is dearth of reliable data on key epidemiological factors, including the prevalence rates, and on objective and reliable indices of the risk/safety profiles. Rigorous and systematic studies including improved epidemiological surveillance, human laboratory, and controlled clinical studies are urgently needed to advance our understanding of public health consequences of consuming kratom and kratom-derived products and to improve our understanding of their risk/safety profile and additional analytical studies to better inform development of needed regulatory oversight.

BYORP and Dissipation in Binary Asteroids: Lessons from DART

The near-Earth binary asteroid Didymos was the target of the planetary defense demonstration mission DART in 2022 September. The smaller binary component, Dimorphos, was impacted by the spacecraft in order to measure momentum transfer in kinetic impacts into rubble piles. DART and associated Earth-based observation campaigns have provided a wealth of scientific data on the Didymos–Dimorphos binary. DART revealed the largely oblate and ellipsoidal shape of Dimorphos before the impact, while the postimpact observations suggest that Dimorphos now has a prolate shape. Here we add those data points to the known properties of small binary asteroids and propose new paradigms of the radiative binary Yarkovsky–O’Keefe–Radzievskii–Paddack (BYORP) effect as well as tidal dissipation in small binaries. We find that relatively spheroidal bodies like Dimorphos made of small debris may experience a weaker and more size-dependent BYORP effect than previously thought. This could explain the observed values of period drift in several well-characterized binaries. We also propose that energy dissipation in small binaries is dominated by relatively brief episodes of large-scale movement of (likely surface) materials, rather than long-term steady-state tidal dissipation. We propose that one such episode was triggered on Dimorphos by the DART impact. Depending on the longevity of this high-dissipation regime, it is possible that Dimorphos will be more dynamically relaxed in time for the Hera mission than it was in the weeks following the impact.

Studying K-12 and post-secondary teachers’ reflections on the value of a citizen science project for fostering learning: The case of a winter stream project

Citizen science programs offer the public opportunities to be involved in hands-on scientific data collection and study. Despite a history of incorporating citizen science in the K-12 classroom, little research has focused on projects that are appealing to both K-12 instructors and higher education faculty. Our study investigates initial barriers and motivations for participation in citizen science at multiple education levels using a winter, stream ecology citizen science project. Our findings indicate a growing interest in citizen science programs in winter, as they provide opportunities for collaboration, encourage flexible data collection, and offer tangible results for participants to reflect on their local ecological habitats.

Medical rehabilitation after lower limb injuries in patients with diabetes mellitus: a review

INTRODUCTION. Diabetes mellitus (DM) in patients with lower extremity injuries is a global health problem. The combination of these pathologies in one patient requires rehabilitation measures to restore maximum functional activity and maintain the patients’ initial quality of life. Diabetes as an independent pathology has a negative impact on the patient’s health and also aggravates the recovery period after traumatic bone injury, increasing the risk of adverse outcomes in the early and late postoperative periods. AIM. To analyze the scientific data of the last ten years on the use of medical rehabilitation methods in patients with lower extremity injuries and diabetes mellitus. MAIN CONTENT. The search was conducted in Web of Science, PubMed, and Scopus according to PRISMA guidelines and initially identified 181 records. Of these, 45 articles were included in the study with publication periods 2013–2023. Key words used: lower limb injuries, diabetes mellitus, rehabilitation, mechanotherapy, physical therapy, physiotherapy. The literature review analyzes publications on the basis of which a description is made that in case of injuries of the lower extremities in patients with diabetes, the participation of specialists from a multidisciplinary team is necessary to draw up an optimal rehabilitation program aimed at preventing the occurrence of complications and their progression, achieving a speedy and complete recovery of lost functions. A personalized approach, individually selected therapy methods and the use of modern technologies will help achieve the best results in the recovery of patients with diabetes and lower extremity injuries. CONCLUSION. The review formulated a conclusion about the main objectives of rehabilitation: which include: control of glucose levels and metabolic disorders; examination and care of damaged tissues in order to prevent the development of ulcers and infection of injured skin; gradual increase in physical activity; goals should take into account the individual characteristics of the patient; the use of physiotherapy methods to improve microcirculation and activate tissue regeneration (transcutaneous electrical nerve stimulation (TENS), electrophoresis, magnetic fields, ultratonotherapy, cryotherapy, laser therapy, darsonvalization, pulsed electric fields, ultrasound therapy, ozokerite therapy and peloid therapy); the use of mechanotherapy with biofeedback and robotic systems to increase overall endurance, strength, improve balance function and form the correct walking stereotype (rehabilitation track with visual feedback about walking C-Mill).

Qualitative Methods for Data Scientists

Qualitative Methods for Data Scientists

SoK: Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training

Machine Learning (ML), addresses a multitude of complex issues in multiple disciplines, including social sciences, finance, and medical research. ML models require substantial computing power and are only as powerful as the data utilized. Due to the high computational cost of ML methods, data scientists frequently use Machine Learning-as-a-Service (MLaaS) to outsource computation to external servers. However, when working with private information, like financial data or health records, outsourcing the computation might result in privacy issues. Recent advances in Privacy-Preserving Techniques (PPTs) have enabled ML training and inference over protected data through the use of Privacy-Preserving Machine Learning (PPML). However, these techniques are still at a preliminary stage and their application in real-world situations is demanding. In order to comprehend the discrepancy between theoretical research suggestions and actual applications, this work examines the past and present of PPML, focusing on Homomorphic Encryption (HE) and Secure Multi-party Computation (SMPC) applied to ML. This work primarily focuses on the ML model's training phase, where maintaining user data privacy is of utmost importance. We provide a solid theoretical background that eases the understanding of current approaches and their limitations. We also provide some preliminaries of SMPC, HE, and ML. In addition, we present a systemization of knowledge of the most recent PPML frameworks for model training and provide a comprehensive comparison in terms of the unique properties and performances on standard benchmarks. Also, we reproduce the results for some of the surveyed papers and examine at what level existing works in the field provide support for open science. We believe our work serves as a valuable contribution by raising awareness about the current gap between theoretical advancements and real-world applications in PPML, specifically regarding open-source availability, reproducibility, and usability.

The impact of urban configuration types on urban heat islands, air pollution, CO2 emissions, and mortality in Europe: a data science approach

The world is becoming increasingly urbanised. As cities around the world continue to grow, it is important for urban planners and policy makers to understand how different urban configuration patterns affect the environment and human health. However, previous studies have provided mixed findings. We aimed to identify European urban configuration types, on the basis of the local climate zones categories and street design variables from Open Street Map, and evaluate their association with motorised traffic flows, surface urban heat island (SUHI) intensities, tropospheric NO2, CO2 per person emissions, and age-standardised mortality. We considered 946 European cities from 31 countries for the analysis defined in the 2018 Urban Audit database, of which 919 European cities were analysed. Data were collected at a 250 m × 250 m grid cell resolution. We divided all cities into five concentric rings based on the Burgess concentric urban planning model and calculated the mean values of all variables for each ring. First, to identify distinct urban configuration types, we applied the Uniform Manifold Approximation and Projection for Dimension Reduction method, followed by the k-means clustering algorithm. Next, statistical differences in exposures (including SUHI) and mortality between the resulting urban configuration types were evaluated using a Kruskal-Wallis test followed by a post-hoc Dunn's test. We identified four distinct urban configuration types characterising European cities: compact high density (n=246), open low-rise medium density (n=245), open low-rise low density (n=261), and green low density (n=167). Compact high density cities were a small size, had high population densities, and a low availability of natural areas. In contrast, green low density cities were a large size, had low population densities, and a high availability of natural areas and cycleways. The open low-rise medium and low density cities were a small to medium size with medium to low population densities and low to moderate availability of green areas. Motorised traffic flows and NO2 exposure were significantly higher in compact high density and open low-rise medium density cities when compared with green low density and open low-rise low density cities. Additionally, green low density cities had a significantly lower SUHI effect compared with all other urban configuration types. Per person CO2 emissions were significantly lower in compact high density cities compared with green low density cities. Lastly, green low density cities had significantly lower mortality rates when compared with all other urban configuration types. Our findings indicate that, although the compact city model is more sustainable, European compact cities still face challenges related to poor environmental quality and health. Our results have notable implications for urban and transport planning policies in Europe and contribute to the ongoing discussion on which city models can bring the greatest benefits for the environment, climate, and health. Spanish Ministry of Science and Innovation, State Research Agency, Generalitat de Catalunya, Centro de Investigación Biomédica en red Epidemiología y Salud Pública, and Urban Burden of Disease Estimation for Policy Making as a Horizon Europe project.

The Features of Comorbidity of Stroke in The Indonesian Population: Findings from The Indonesian Family Life Survey (IFLS-5)

Background: Comorbidities are associated with a worse stroke prognosis and more challenging clinical management. However, population-based research related to stroke comorbidities in Indonesia is still limited. Objective: This study aims to evaluate the features of important comorbidities among stroke patients in the Indonesian population. Methods: The data was taken from the 2014–2015 Indonesian Family Life Survey (IFLS-5), which represents 83% of the Indonesian population. Data were analyzed by means of logistic regression models using statistical software for data science (STATA) 13. Results: There was a statistically significant relationship between stroke history and several comorbidities risk factors. The risk of stroke increases in individuals with hypertension (odds ratio (OR): 7.97, 95% confidence interval (CI): 5.19-12.26), diabetes (OR: 1.90, 95% CI: 1.14-3.15), heart disease (OR: 2.67, 95% CI: 1.47-4.85), and hyperlipidemia (OR: 3.68, 95% CI: 2.40-5.64). Of the 309 stroke patients, 41.9% had one comorbid condition; 24.1% had two; and 10.5% had three. Comorbidities increase with age, and 80.50% of stroke patients with comorbidities are aged 50 years or more. Conclusion: This study showed that the most common comorbidities in stroke patients in Indonesia are hypertension, hyperlipidemia, and diabetes.