bluuebunny/crossref_metadata_2025 huggingface dataset:
Area of interest algorithm for surface deflection areas
Authors: Babel Christoph , Guru Mahish , Weiland Jakob , Bambach Markus
Published: June 2024

In the automotive industry, the process of deep drawing is used for producing most of the outer surface panels. There, surface defects can occur while stamping the part. This paper proposes an area of interest (AOI) algorithm to filter possible surface deflection areas of finite element method (FEM) simulation results. The FEM is well established in the area of sheet metal forming and has shown accurate results in showing surface defects like waviness and sink marks. These two defect types are also the targeted systematic defects. In these deep drawing simulations, every manufacturing step of the sheet metal is calculated and the resulting stresses and strains are analyzed. The paper presents a newly developed post processing method for detecting surface in-corrections on basis of FEM simulation results. The focus of the method is to be independent of an experts knowledge. It should be able to be used by a wide range of non-expert applicants, unlike other post-processing methods know in today’s literature. A comparison between several machine learning (ML) approaches is made. It is shown, that the developed method outperforms current state of the art approaches in terms of the recall rate. In addition, a contour tree dataset of a FEM simulation in combination with an ML approach can be successfully used to learn a multidimensional relationship between the nodes.
A Systematic Review and Meta-Analysis of Social Cognition Among People Living with HIV: Implications for Non-Social Cognition and Social Everyday Functioning
Authors: Vance David E. , Billings Rebecca , Lambert Crystal Chapman , Fazeli Pariya L. , Goodin Burel R. , Kempf Mirjam-Colette , Rubin Leah H. , Turan Bulent , Wise Jenni , Hellemann Gerhard , Lee Junghee
Published: June 2024

Social cognition—the complex mental ability to perceive social stimuli and negotiate the social environment—has emerged as an important cognitive ability needed for social functioning, everyday functioning, and quality of life. Deficits in social cognition have been well documented in those with severe mental illness including schizophrenia and depression, those along the autism spectrum, and those with other brain disorders where such deficits profoundly impact everyday life. Moreover, subtle deficits in social cognition have been observed in other clinical populations, especially those that may have compromised non-social cognition (i.e., fluid intelligence such as memory). Among people living with HIV (PLHIV), 44% experience cognitive impairment; likewise, social cognitive deficits in theory of mind, prosody, empathy, and emotional face recognition/perception are gradually being recognized. This systematic review and meta-analysis aim to summarize the current knowledge of social cognitive ability among PLHIV, identified by 14 studies focused on social cognition among PLHIV, and provides an objective consensus of the findings. In general, the literature suggests that PLHIV may be at-risk of developing subtle social cognitive deficits that may impact their everyday social functioning and quality of life. The causes of such social cognitive deficits remain unclear, but perhaps develop due to (1) HIV-related sequelae that are damaging the same neurological systems in which social cognition and non-social cognition are processed; (2) stress related to coping with HIV disease itself that overwhelms one’s social cognitive resources; or (3) may have been present pre-morbidly, possibly contributing to an HIV infection. From this, a theoretical framework is proposed highlighting the relationships between social cognition, non-social cognition, and social everyday functioning.
Analysis of emergency assembly points for post-earthquake disaster management: a case study of Erzincan, Türkiye
Authors: Akar Alper , Akar Özlem , Konakoğlu Berkant
Published: June 2024

The selection of emergency assembly points within the urban area holds significant importance for ensuring swift and effective intervention in the aftermath of a potential disaster until temporary shelter areas are prepared. In both historical and instrumental periods, Erzincan (Türkiye) has experienced major earthquakes resulting in significant loss of life and property. The likelihood of similar earthquakes occurring in Erzincan in the future remains high. Therefore, it is necessary to identify safe areas where people can assemble after an earthquake, moving away from hazardous zones. The aim of this study is to analyse the capacities and adequacies of emergency assembly points, which constitute a step in disaster management and enhance the earthquake resilience of the city, using Geographic Information Systems (GIS) based on selected criteria (accessibility, spatial distribution, proximity to healthcare facilities, size, capacity adequacy, and proximity to fault avoidance zones), and to propose solutions. The assessment revealed that the available assembly points are not homogeneously distributed throughout the city and are inadequate to meet the available building and population density. Therefore, in addition to the available 38 emergency assembly points, 67 new alternative emergency assembly points have been identified. The proposed emergency assembly points (in the 75–100% range) increased the number of neighborhoods within the 250 m service area by 43.75% and 42.5% for 500 m.The findings underscore the necessity for more comprehensive and effective planning for Erzincan in the event of a potential disaster or emergency.
A spatial evaluation framework of urban physical resilience considering different phases of disaster risk management
Authors: Parizi Sedigheh Meimandi , Taleai Mohammad , Sharifi Ayyoob
Published: June 2024

The physical structure of urban settlements has become increasingly vulnerable to hazards following the growing trends of natural hazards, including earthquakes. The concept of resilience has gained momentum to facilitate better planning and response to such hazards. This research seeks to develop a conceptual spatial framework considering different phases of disaster risk management to evaluate urban physical resilience. Twenty indicators that define urban structure are identified and included in an Interpretive Structural Modeling—Analytic Network Process (ISM-ANP) hybrid model for analysis. The model and the indicator weights are adjusted using statistical and optimization techniques. District 4 of Tehran has been selected as the study area, and the proposed evaluation framework is applied to several zones with different physical urban structures. According to the results, the most important indicators of urban structure are the Robustness of Buildings, Street Width, Building Density, and Aspect Ratio. Sensitivity analysis and scenario-making are performed to explore the desired state of urban physical resilience for each zone. The results of the case study indicate moderate levels of urban physical resilience. The study provides more clear and practical insights into the concept of resilience to help urban planners and decision-makers improve urban physical resilience.
An unmanned tank combat game driven by FPSO-MADDPG algorithm
Authors: Wang Fei , Liu Yi , Zhou Yudong , Zhou Dianle , Yan Dan
Published: June 2024

With the development of artificial intelligence and unmanned technology, unmanned vehicles have been utilized in a variety of situations which may be hazardous to human beings, even in real battle fields. An intelligent unmanned vehicle can be aware of surrounding situations and make appropriate responding decisions. For this purpose, this paper applies Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm for vehicle’s of situation awareness and decision making, inside which a Fast Particle Swarm Optimization (FPSO) algorithm is proposed to calculate the optimal vehicle attitude and position; therefore, an improved deep reinforcement learning algorithm FPSO-MADPPG is formed. A specific advantage function is designed for the FPSO portion, which considers angle, distance, outflanking encirclement. A dedicated reward is designed for the MADPPG portion, which considers key factors like angle, distance, and damage. Finally, FPSO-MADPPG is then used in a combat game to operate unmanned tanks. Simulation results show that our method not only can obtain higher winning rate, but also higher reward and faster convergence than DDPG and MADPPG algorithms.
The power of social norms: Why conceptual engineers should care about implementation
Authors: Nimtz Christian
Published: June 2024

Jennifer Nado has recently argued that conceptual engineers should focus on (re-)designing representations and may safely ignore issues of implementation. I make a general case for the methodological importance of implementation to conceptual engineering. Using the Social Norms Account as a foil, I argue for three claims. (1) Inquiring into methods of implementation is a theoretically challenging and philosophically worthwhile project in and of itself. (2) A sound theoretical understanding of implementation is imperative for theorists of conceptual engineering. It proves vital for their assessment of the prospects for engineering interventions and the political risks inherent in them. (3) A sound theoretical understanding of implementation is imperative for practitioners of conceptual engineering. The usefulness of their representational (re-)designs depends on respecting broadly cognitive and social constraints on the part of the target population. I conclude that conceptual engineers have very good reasons to seriously engage with issues of implementation.
Unveiling therapeutic frontiers: DON/DRP-104 as innovative Plasma kallikrein inhibitors against carcinoma-associated hereditary angioedema shocks - a comprehensive molecular dynamics exploration
Authors: Oduro-Kwateng Ernest , Soliman Mahmoud E. S.
Published: June 2024

Human plasma kallikrein (PKa) is a member of the serine protease family and serves as a key mediator of the kallikrein-kinin system (KKS), which is known for its regulatory roles in inflammation, vasodilation, blood pressure, and coagulation. Genetic dysregulation of KKS leads to Hereditary Angioedema (HAE), which is characterized by spontaneous, painful swelling in various body regions. Importantly, HAE frequently coexists with various cancers. Despite substantial efforts towards the development of PKa inhibitors for HAE, there remains a need for bifunctional agents addressing both anti-cancer and anti-HAE aspects, especially against carcinoma-associated comorbid HAE conditions. Consequently, we investigated the therapeutic potential of the anti-glutamine prodrug, isopropyl(S)-2-((S)-2-acetamido-3-(1H-indol-3-yl)-propanamido)-6-diazo-5-oxo-hexanoate (DRP-104), and its active form, 6-Diazo-5-oxo-l-norleucine (DON), recognized for their anti-cancer properties, as novel PKa inhibitors. Utilizing structure-based in silico methods, we conducted a comparative analysis with berotralstat, a clinically approved HAE prophylactic, and sebetralstat, an investigational HAE therapeutic agent, in Phase 3 clinical trials. Inhibiting PKa with DON resulted in relatively heightened structural stability, rigidity, restricted protein folding, and solvent-accessible loop exposure, contributing to increased intra-atomic hydrogen bond formation. Conversely, PKa inhibition with DRP-104 induced restricted residue flexibility and significantly disrupted the critical SER195-HIS57 arrangement in the catalytic triad. Both DON and DRP-104, along with the reference drugs, induced strong cooperative intra-residue motion and bidirectional displacement in the PKa architecture. The results revealed favorable binding kinetics of DON/DRP-104, showing thermodynamic profiles that were either superior or comparable to those of the reference drugs. These findings support their consideration for clinical investigations into the management of carcinoma-associated HAE.
Exploring prognostic values of DNA ploidy, stroma-tumor fraction and nucleotyping in stage II colon cancer patients
Authors: Lou Yutong , Yang Lujing , Xu Shaojun , Tan Luxin , Bai Yanhua , Wang Lin , Sun Tingting , Zhou Lixin , Feng Li , Lian Shenyi , Wu Aiwen , Li Zhongwu
Published: June 2024

Purpose

To assess the prognostic value of three novel biomarkers, DNA ploidy, stroma-tumor fraction, and nucleotyping, seeking for more accurate stratification in stage II colon cancer.

Methods

A total of 417 patients with complete follow up information were enrolled in this study and divided into three clinical risk groups. IHC was performed to examine MSI status. DNA ploidy, stroma and nucleotyping were estimated using automated digital imaging system. Kaplan–Meier survival curves, Cox proportional hazards regression models, and correlation analyses were carried out to process our data.

Results

In the whole cohort of stage II colon cancer, nucleotyping and DNA ploidy were significant prognostic factors on OS in univariate analyses. The combination of nucleotyping and DNA ploidy signified superior OS and DFS. Difference was not significant between low-stroma and high-stroma patients. In multivariable analyses, nucleotyping and the combination of nucleotyping and DNA ploidy were proven the dominant contributory factors for OS. In the low-risk group, we found the combination of nucleotyping and DNA ploidy as the independent prognostic factor statistically significant in both univariate and multivariable, while in the high-risk group, the nucleotyping.

Conclusions

Our study has proven nucleotyping and the combination of DNA ploidy and nucleotyping as independent prognostic indicators, thus expanding the application of nucleotyping as a predictor from high risk stage II colon cancer to whole risks.
Infection episodes and islet autoantibodies in children at increased risk for type 1 diabetes before and during the COVID-19 pandemic
Authors: Zeller Ivo , Weiss Andreas , Arnolds Stefanie , Schütte-Borkovec Katharina , Arabi Sari , von dem Berge Thekla , Casteels Kristina , Hommel Angela , Kordonouri Olga , Larsson Helena Elding , Lundgren Markus , Rochtus Anne , Snape Matthew D. , Szypowka Agnieszka , Vatish Manu , Winkler Christiane , Bonifacio Ezio , Ziegler Anette-Gabriele ,   for the GPPAD Study Group
Published: June 2024

Objectives

To determine the impact of the COVID-19 pandemic on the incidence rates of infection and islet autoimmunity in children at risk for type 1 diabetes.

Methods

1050 children aged 4 to 7 months with an elevated genetic risk for type 1 diabetes were recruited from Germany, Poland, Sweden, Belgium and the UK. Reported infection episodes and islet autoantibody development were monitored until age 40 months from February 2018 to February 2023.

Results

The overall infection rate was 311 (95% Confidence Interval [CI], 304–318) per 100 person years. Infection rates differed by age, country, family history of type 1 diabetes, and period relative to the pandemic. Total infection rates were 321 per 100 person-years (95% CI 304–338) in the pre-pandemic period (until February 2020), 160 (95% CI 148–173) per 100 person-years in the first pandemic year (March 2020—February 2021; P < 0.001) and 337 (95% CI 315–363) per 100 person-years in subsequent years. Similar trends were observed for respiratory and gastrointestinal infections. Islet autoantibody incidence rates were 1.6 (95% CI 1.0–2.4) per 100 person-years in the pre-pandemic period, 1.2 (95% CI 0.8–1.9) per 100 person-years in the first pandemic year (P = 0.46), and 3.4 (95% CI 2.3–4.8) per 100 person-years in subsequent years (P = 0.005 vs. pre-pandemic year; P < 0.001 vs. first pandemic year).

Conclusions

The COVID-19 pandemic was associated with significantly altered infection patterns. Islet autoantibody incidence rates increased two-fold when infection rates returned to pre-pandemic levels.
Research on charging strategy based on improved particle swarm optimization PID algorithm
Authors: Wang Xiuzhuo , Tang Yanfeng , Li Zeyao , Xu Chunsheng
Published: June 2024

Aiming at the electric vehicle charging pile control system has the characteristics of multi-parameter, strong coupling and non-linearity, and the existing traditional PID control and fuzzy PID control methods have the problems of slow charging speed, poor control performance and anti-interference ability, as well as seriously affecting the service life of the battery, this paper designs a kind of improved particle swarm algorithm to optimize the PID controller of the charging control system for electric vehicle charging piles, and utilizes the improved particle swarm algorithm to Adaptive and precise adjustment of proportional, integral and differential parameters, so that the system quickly reaches stability, so as to improve the accuracy of the system control output current or voltage. Simulation results show that the optimized system response speed of the improved particle swarm algorithm is improved by 3.077 s, the overshooting amount is reduced by 1.01%, and there is no oscillation, which has strong adaptability and anti-interference ability, and can significantly improve the control accuracy and charging efficiency of the charging pile control system.
Forecasting VIX using Bayesian deep learning
Authors: Hortúa Héctor J. , Mora-Valencia Andrés
Published: June 2024

Recently, deep learning techniques are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. In this paper, we leverage probabilistic deep learning for inferring the volatility index VIX. We employ the probabilistic counterpart of WaveNet, Temporal Convolutional Network (TCN), and Transformers. We show that TCN outperforms all models with an RMSE around 0.189. In addition, it has been well known that modern neural networks provide inaccurate uncertainty estimates. For solving this problem, we use the standard deviation scaling to calibrate the networks. Furthermore, we found out that MNF with Gaussian prior outperforms Reparameterization Trick and Flipout models in terms of precision and uncertainty predictions. Finally, we claim that MNF with Cauchy and LogUniform prior distributions yield well-calibrated TCN, and Transformer and WaveNet networks being the former that best infer the VIX values for one and five-step-ahead forecasting, and the probabilistic Transformer model yields an adequate forecasting for the COVID-19 pandemic period.
Network-based time series modeling for COVID-19 incidence in the Republic of Ireland
Authors: Armbruster Stephanie , Reinert Gesine
Published: June 2024

Network-based time series models have experienced a surge in popularity over the past years due to their ability to model temporal and spatial dependencies, arising from the spread of infectious disease. The generalised network autoregressive (GNAR) model conceptualises time series on the vertices of a network; it has an autoregressive component for temporal dependence and a spatial autoregressive component for dependence between neighbouring vertices in the network. Consequently, the choice of underlying network is essential. This paper assesses the performance of GNAR models on different networks in predicting COVID-19 cases for the 26 counties in the Republic of Ireland, over two distinct pandemic phases (restricted and unrestricted), characterised by inter-county movement restrictions. Ten static networks are constructed, in which vertices represent counties, and edges are built upon neighbourhood relations, such as railway lines. We find that a GNAR model based on the fairly sparse Economic hub network explains the data best for the restricted pandemic phase while the fairly dense 21-nearest neighbour network performs best for the unrestricted phase. Across phases, GNAR models have higher predictive accuracy than standard ARIMA models which ignore the network structure. For county-specific predictions, in pandemic phases with more lenient or no COVID-19 regulation, the network effect is not quite as pronounced. The results indicate some robustness to the precise network architecture as long as the densities of the networks are similar. An analysis of the residuals justifies the model assumptions for the restricted phase but raises questions regarding their validity for the unrestricted phase. While generally performing better than ARIMA models which ignore network effects, there is scope for further development of the GNAR model to better model complex infectious diseases, including COVID-19.
Performance assessment of large-scale rooftop solar PV system: a case study in a Malaysian Public University
Authors: Govindarajan Logeswaran , Batcha Mohd Faizal Bin Mohideen , Abdullah Mohammad Kamil Bin
Published: June 2024

Adopting rooftop solar PV systems in various domestic and non-domestic sectors (including commercial, industrial, and agricultural) exhibits their commitment to green energy ventures. This study intends to evaluate the effectiveness of a grid-connected solar system that has been installed so far: a 6.9 MWp photovoltaic (PV) system implemented at University Tun Hussein Onn (UTHM) in Batu Pahat, Johor. This green energy system was installed as a part of the Self-Consumption (SELCO) program utilizing a Supply Agreement for Renewable Energy (SARE) contract. To assess the mounted energy system's efficiency, performance monitoring was carried out between December 2021 and November 2022. By evaluating the enactment of the 6.9 MWp solar system at UTHM, this present work has attempted to compile the achievement of implementation of self-consumption in terms of performance analysis and financial benefits to consumer. Based on International Electrotechnical Commission (IEC) 61724 standard, this study also investigates several performance characteristics of the PV system, such as final yield, clearness index (CI), array yield, PV module efficiency, inverter efficiency, reference yield, and capacity factor (CF) performance ratio (PR). Moreover, an assessment of Carbon Dioxide ($${CO}_{2})$$

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avoidance was also conducted to quantify the amount of $${CO}_{2}$$

CO

2

reduction achieved. In addition, the self-consumption ratio and self-sufficiency ratio pattern for solar power plant in UTHM has been assessed. The monthly clearness index at the project location from December 2021 to November 2022 ranges from 0.63 to 0.66. The PV system was monitored throughout this time and generated 8529 MWh of energy. The installed PV system has shown an efficiency of 11.86%, a final yield of 108.28%, and a PR of 0.78, respectively. Further, the installed 6.9 MWp PV system at UTHM will cut $${CO}_{2}$$

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2

emissions by 5450 tons annually. The 6.9 MWp solar PV system is also estimated to reduce 25.1 RM mil in 25 years. Making the system larger to 6.9 MWp would reduce the consumption from the grid during the week, makes it possible to achieve self-consumption levels of up to 100% and self-sufficiency of this solar plant is relatively low ranges from 22 to 35%. The research finding bridges the gap between policy makers, investors, and the public regarding acceptance of the large-scale Self-Consumption (SELCO) and strengthen the strategic collaborations between solar service provider and educational bodies to support of the government’s agenda to achieve carbon emission intensity reduction in Malaysia.

Graphical Abstract
Usability Testing of a Web Tool for Dissemination and Implementation Science Models
Authors: Gomes Rebekah Natalie , Ford Bryan S. , Tabak Rachel G. , Brownson Ross C. , Malone Sara , Padek Maggie , Glasgow Russell E. , Rabin Borsika
Published: June 2024

Dissemination and Implementation science is dedicated to increasing the speed of evidence-based research translated into practice as guided by one or multiple D&I theories, models, and frameworks. The Dissemination and Implementation Models in Health Research and Practice web tool guides users on how to plan, select, combine, adapt, use, and assess theories, models, and frameworks. This paper describes usability testing to update the web tool. Iterative user testing was conducted with implementation science research and clinical participants to facilitate updates and optimize the functionality of the tool. A multi-step protocol involved quantitative and qualitative data collection including a survey, interviews, and a usability testing session. Data from the pre-testing surveys were summarized as frequencies. Data from the usability testing sessions were analyzed using a hybrid adapted deductive rapid matrix qualitative analysis. Data from the interviews were analyzed by deductive a priori coding. Fifteen interviewees represented different research and clinical groups and levels of expertise utilizing D&I TMFs. Participants were purposively selected to represent a range of disciplines and D&I expertise, all invited via one-time email. The 847 total interview comments were reduced by similarity to 259 comments, and 142 were feasible changes fitting the priorities of the web tool. Changes to content, format, and functionality are described in this paper. The iterative usability testing elicited improvements to the web tool including adding more examples, definitions, visuals, and tutorials and simplifying the written content. The web tool remains flexible for additions concerning health equity, de-implementation, and other issues.
Determinants of adolescents’ pro-sustainable behavior: a systematic literature review using PRISMA
Authors: Bhattarai Prakash C. , Shrestha Richan , Ray Subhasis , Knez Rajna
Published: June 2024

Climate change is a serious contemporary issue around the world. Adolescents are mostly affected due to climate change. Thus, they are expected to behave in an environmentally appropriate manner, which requires the identification of factors influencing pro-sustainable behavior/pro-environmental behavior. The literature offers a wide range of factors influencing adolescents’ pro-sustainable behavior; nonetheless, these factors are not provided with some common themes. Furthermore, no study integrates dispersed research of the area with a comprehensive systematic review. With this consideration, a systematic review was carried out to find comprehensive factors influencing the pro-sustainable behavior of adolescents. For the review, all 21 interdisciplinary studies on factors influencing adolescents' ecological behavior, published between 2010 and 2023 and indexed in Scopus and Web of Science, were analyzed. The analysis revealed that the number of studies in the area increased after 2020, especially in developed countries, and most studies utilized quantitative research methods. The identified key factors influencing adolescents’ pro-environmental behavior are categorized into demographic, internal, and external factors. The findings can be helpful for future researchers to have rigorous discourse on the issue and expand the research in developing countries using mixed methods research. The policy maker can use findings to motivate adolescents to participate in climate change mitigation.
A multiscale analysis of the relationship between urbanization and CO2 emissions using geo-weighted regression model
Authors: Song Shixiong , Tan Haoqi , Zhang Ye , Ma Yongxi
Published: June 2024

It is of great practical significance to explore the relationship between urbanization and CO2 emissions for the low-carbon development of cities. However, the multiscale assessment of spatial relationship between population, land and economic urbanization and CO2 emissions is lacked. In this study, we first adopted the spatial statistical methods to evaluate the spatial pattern of China’s CO2 emissions in 2019. Then, we spatially quantified China’s urbanization of land, population and economy based on statistical data. Finally, we used the geo-weighted regression model to explore the spatial relationship between urbanization and CO2 emissions at the national-economic zone-province scale. The results displayed that there is obvious spatial heterogeneity in the relationship between China’s urbanization and CO2 emissions. The significant positive correlation between urbanization and CO2 emissions were mainly located in the northeastern, eastern and southwestern regions, consistent with the characteristics of the Heihe–Tengchong Line. The uneven development of land, population and economic urbanization would lead to more CO2 emissions. We suggest that China should attend the balanced development of urban land, population and economy, and avoid the additional carbon emissions caused by incongruence, to further the development of low-carbon cities.
A scoping review on occupational therapy services with forcibly displaced individuals in low to middle income countries
Authors: Hassan Humairaa , Paruk Juwairiyya , Gordhan Heema , Maseko Lebogang
Published: June 2024

This study underscores the distinctive role of occupational therapy in advancing the health and wellbeing of forcibly displaced individuals (FDIs), who often encounter contextual and systemic barriers and medical complications that prevent them from engaging in daily tasks. These injustices can be mitigated through health promotion and prevention strategies that aim to enhance participation in daily life activities. There has been limited research in low- to middle-income countries (LMICs) in relation to occupational therapy services for FDIs despite most FDIs originating from LMICs. To address this gap, this study adopted Arksey and O’Malley’s framework for a scoping review to consolidate existing knowledge on the occupational therapy health promotion and prevention of disease and disability services for FDIs in LMICs. Searches were conducted on six online databases. Six studies aligned with the inclusion criteria. Findings revealed that most occupational therapy services are directed towards refugees in Jordan, which contains one of the largest refugee camps. FDIs from Africa and internally displaced people receive the least number of interventions. The occupational therapy services outlined in the six sources primarily consist of health education talks, early childhood interventions, and health promotion activities centred around various occupations such as work and sports groups. Recommendations for policy guidelines include incorporating occupational therapy services within the stipulated FDI services. Our approach aims to situate the contribution of occupational therapy health promotion and prevention of disease and disability services within the broader context of FDIs’ overall health and wellbeing.
Self-compassion, burnout, and biomarkers in a sample of healthcare workers during the COVID-19 pandemic: a cross-sectional correlational study
Authors: Bégin Catherine , Gilbert-Ouimet Mahée , Truchon Manon
Published: June 2024

Background

Burnout among healthcare professionals is a serious problem with multiple consequences for the individuals and organizations affected. Thus, accessible and effective interventions are still needed to prevent and attenuate burnout. Self-compassion has recently been well supported in preventing and reducing burnout in various professions. Current research also demonstrated protective associations between self-compassion and well-being and/or psychological health indicators. Few studies are available on this topic during the COVID-19 pandemic or on healthcare workers from Quebec or Canada. Moreover, only a limited number of studies have looked at the associations of self-compassion with physiological variables.

This cross-sectionnal correlational study attempts to evaluate the association between self-compassion and burnout, among healthcare workers from Quebec (Canada) during the COVID-19 pandemic (n = 416 participants). Associations between their respective components are also tested. A secondary objective is to evaluate if self-compassion is also associated with a set of 38 biomarkers of inflammation (n = 83 participants), potentially associated with the physiological stress response according to the literature.

Participants meeting eligibility criteria (e.g.: residing in the province of Quebec, being 18 years of age or older, speaking French, and having been involved in providing care to COVID-19 patients) were recruited online. Participants completed the Occupational Health and Well-being Questionnaire, and some participated in a blood sample collection protocol.

Results

Results showed significant negative associations between self-compassion, exhaustion, and depersonalization, and a significant positive correlation with professional efficacy. Some self-compassion subscales (mindfulness, self-judgment, isolation, overidentification) were significantly negatively associated with certain biomarkers, even after controlling for confounding variables.

Conclusions

This study adds to the existing literature by supporting the association of self-compassion with burnout, and reveals associations between self-compassion and physiological biomarkers related to the stress response. Future research directions are discussed.
High-resolution spatiotemporal inference of urban road traffic emissions using taxi GPS and multi-source urban features data: a case study in Chengdu, China
Authors: Li Jiaxing , Jiang Chaozhe , Han Ke , Yu Qing , Zhang Haoran
Published: June 2024

The spatial heterogeneity and temporal variability of traffic in urban environments make traffic emissions inference challenging. To address this challenge, this study introduces a novel geographical context-based approach utilizing high-resolution taxi GPS data, incorporating multidimensional contextual factors such as road data, points of interest (POI), weather data, and population density. The proposed method can enhance the precision of traffic emissions inference compared to conventional macroscopic estimation techniques. To overcome the issue of missing data in traffic emissions inference from taxi data, three ensemble machine learning algorithms—Random Forest, Gradient Boosting Decision Trees (GBDT), and eXtreme Gradient Boosting (XGBoost)—are employed. These algorithms efficiently handle a substantial volume of taxi GPS data, achieving reduced computational time and model complexity. The proposed framework establishes localized models for each road segment, taking into consideration both geographical and external features that characterize the urban environment. This localized modeling contributes significantly to a more profound understanding of traffic dynamics. A thorough comparative analysis is conducted to assess the performance of the proposed method. Results indicate that incorporating multidimensional urban features is advantageous for traffic speed inference. Among the ensemble learning models, Random Forest outperforms others when dealing with a small missing rate or limited sample size, while XGBoost exhibits superior performance for larger missing rates or substantial sample sizes. Additionally, an analysis of the feature importance in traffic speed highlights that road network features are the most significant factors, followed by temporal characteristics, spatial attributes, POI data, and weather information. Finally, leveraging inferred traffic speed and volume information, emissions from large-scale urban road traffic are inferred based on the COPERT model. In contrast to methods relying on complex, multi-source data for emission estimation, our approach utilizes simple and easily accessible data, enabling precise estimation of emissions on a large-scale spatiotemporal basis.
Recognizing the role of YouTube in medical education
Authors: Akakpo Martin Gameli , Akakpo Patrick Kafui
Published: June 2024

YouTube is a popular source of educational and recreational videos. Evidence suggests that medical students consult YouTube regularly and use it to supplement information they receive from their medical educators. On the other hand, some medical educators discourage and even avoid YouTube and other internet sources due to ethical concerns. In this paper we assess the importance of YouTube in medical education and relate this to other fields of higher education. Emphasis is placed on how YouTube can be an important tool for self-paced learning and encourages its ethical use. Conclusions from the paper have implications for medical educators, medical curriculum developers and students. For educators, the paper recommends ways to address ethical concerns. Designers of medical education curriculum are advised to include information literacy training at the start of medical school. Students are encouraged to improve information literacy as a control mechanism against inaccurate information.
Ultrasonography (US) examination of acute appendicitis (AA): diagnosis of complicated and uncomplicated forms and when US is not enough
Authors: Comune Rosita , Tamburrini Stefania , Durante Adele , Bonito Giacomo , Ferrari Riccardo , Galluzzo Michele , Tonerini Michele , Pezzullo Filomena , Picchi Stefano Giusto , Masala Salvatore , Liguori Carlo , Scaglione Mariano , Pinto Fabio
Published: June 2024

Ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI) have high diagnostic accuracy in the diagnosis of acute appendicitis. Nowadays imaging is part of the diagnostic algorithm of appendicitis and the advantages are demonstrated by the reduced negative appendectomies rates. US is the preferred first line imaging in acute appendicitis. The US diagnosis of complicated and uncomplicated appendicitis is well characterized by different signs but the paradigm of US to take in mind is that the non-visualization of the appendix cannot exclude acute appendicitis. In US, when the appendix is not visualized, or US is inconclusive, second-line imaging examination should be performed. The aims of this pictorial are 1. to illustrate the US signs of complicated and uncomplicated AA; 2. to describe the conditions in which other imaging modalities should be performed to reach a definitive diagnosis.
Development of WACCM With the Non‐Hydrostatic MPAS‐A Dynamical Core
Authors: Kamali S. , Liu H.‐L. , Skamarock W. , Klemp J. , Vitt F. , Lauritzen P. H.
Published: June 2024

The non‐hydrostatic Model for Prediction Across Scales‐Atmosphere (MPAS‐A) dynamical core has recently been adapted for the Specified Chemistry Whole Atmosphere Community Climate Model (SC‐WACCM). In this study, the mean zonal wind and temperature climatology from SC‐WACCM/MPAS‐A is compared with the results from SC‐WACCM using the finite volume and spectral element dynamical cores, as well as the zonal wind and temperature climatology of Upper Atmosphere Research Satellite mission and SABER. The simulations have been performed at horizontal resolutions of ∼100 km. Generally a good agreement is seen between the results from the three dynamical cores, which verifies that the new dynamical core is working with WACCM.
The 2023 Mw 6.8 Morocco Earthquake: A Lower Crust Event Triggered by Mantle Upwelling?
Authors: Huang Kai , Wei Guoguang , Chen Kejie , Zhang Naiqian , Li Mingjia , Dal Zilio Luca
Published: June 2024

A M6.8 earthquake struck the High Atlas Mountains in Morocco on 8 September 2023, ending a 63‐year seismic silence. We herein attempt to clarify the seismogenic fault and explore the underlying mechanism for this seismic event based on multiple data sets. Utilizing probabilistic Bayesian inversion on interferometric radar data, we determine a seismogenic fault plane centered at a depth of 26 km, striking 251° and dipping 72°, closely aligned with the Tizi n’Test fault system. Given a hypocenter at the Moho depth, the joint inversion of radar and teleseismic data reveals that the rupture concentrates between depths of 12 and 36 km, offsetting the Mohorovičić discontinuity (Moho) at ∼32 km. Considering a strong link between magma activity and failure in lower crust, we propose that the triggering of the earthquake possibly was mantle upwelling that also supports the high topography.
Interpregnancy maternal weight change is not associated with offspring weight and obesity at age 2 years
Authors: Maslin Kate , Ameye Lieveke , Vancoppenolle Diederik , Rochtus Anne , Van Uytsel Hanne , Shawe Jill , Devlieger Roland , Bogaerts Annick
Published: June 2024

Background

Weight retention between pregnancies is associated with increased risk of perinatal complications, but it is unclear whether there is an association with offspring weight status. This study aimed to determine whether maternal interpregnancy weight change is associated with offspring overweight/obesity, controlling for confounding variables.

Subjects/methods

Routinely collected linked data from perinatal and child datasets, in Flanders, Belgium were used. Women having their first and second live births between 2009–2018 were included. The association between maternal interpregnancy weight change and overweight/obesity in the second child at 2 years was examined by logistical regression models.

Results

A total of 33,172 women were included. 52.7% (n = 17478) had a stable interpregnancy BMI, 24.1% (n = 8024) and 8.5% (n = 2821) had moderate and substantial BMI increases respectively. At 2 years, 91.6% (n = 30383) of the second offspring had a healthy weight, 0.6% (n = 210), 7.0% (n = 2312) and 0.8% (n = 267) were in the underweight, overweight and obesity BMI categories respectively. Multivariate analysis showed no statistical evidence that maternal interpregnancy BMI change is independently associated with overweight/obesity in the second child. The strongest independent factors were the first child (sibling) being in the obesity category at 2 years (odds ratio [OR] 7.2, [95% CI, 5.49–9.45] and being born Large for Gestational Age (LGA) (2.13 [1.92–2.37]). The following variables were also independently associated with the outcome measure: maternal African origin (1.90 [1.59–2.26]), maternal obesity at start of first pregnancy (1.33 [1.16–1.53]), excessive gestational weight gain in the second pregnancy (1.15 [1.04–1.28]), being born after a < 1-year interpregnancy time interval (1.17 [1.05–1.30]) and not being exclusively breastfed at 12 weeks old (1.29 [1.10–1.52]).

Conclusion

Sibling obesity and being born LGA were most strongly independently associated with overweight/obesity at 2 years. This supports the need for family interventions and to address risk factors for development of LGA infants. There was no independent association with interpregnancy weight gain, contrary to what was hypothesised.
Transmissible long-term neuroprotective and pro-cognitive effects of 1–42 beta-amyloid with A2T icelandic mutation in an Alzheimer’s disease mouse model
Authors: Célestine Marina , Jacquier-Sarlin Muriel , Borel Eve , Petit Fanny , Lante Fabien , Bousset Luc , Hérard Anne-Sophie , Buisson Alain , Dhenain Marc
Published: June 2024

The amyloid cascade hypothesis assumes that the development of Alzheimer’s disease (AD) is driven by a self-perpetuating cycle, in which β-amyloid (Aβ) accumulation leads to Tau pathology and neuronal damages. A particular mutation (A673T) of the amyloid precursor protein (APP) was identified among Icelandic population. It provides a protective effect against Alzheimer- and age-related cognitive decline. This APP mutation leads to the reduced production of Aβ with A2T (position in peptide sequence) change (Aβice). In addition, Aβice has the capacity to form protective heterodimers in association with wild-type Aβ. Despite the emerging interest in Aβice during the last decade, the impact of Aβice on events associated with the amyloid cascade has never been reported. First, the effects of Aβice were evaluated in vitro by electrophysiology on hippocampal slices and by studying synapse morphology in cortical neurons. We showed that Aβice protects against endogenous Aβ-mediated synaptotoxicity. Second, as several studies have outlined that a single intracerebral administration of Aβ can worsen Aβ deposition and cognitive functions several months after the inoculation, we evaluated in vivo the long-term effects of a single inoculation of Aβice or Aβ-wild-type (Aβwt) in the hippocampus of transgenic mice (APPswe/PS1dE9) over-expressing Aβ1-42 peptide. Interestingly, we found that the single intra-hippocampal inoculation of Aβice to mice rescued synaptic density and spatial memory losses four months post-inoculation, compared with Aβwt inoculation. Although Aβ load was not modulated by Aβice infusion, the amount of Tau-positive neuritic plaques was significantly reduced. Finally, a lower phagocytosis by microglia of post-synaptic compounds was detected in Aβice-inoculated animals, which can partly explain the increased density of synapses in the Aβice animals. Thus, a single event as Aβice inoculation can improve the fate of AD-associated pathology and phenotype in mice several months after the event. These results open unexpected fields to develop innovative therapeutic strategies against AD.
Utility of carboplatin therapeutic drug monitoring for the treatment of neonate and infant retinoblastoma patients in the United Kingdom
Authors: Millen Gerard C. , Lawford Alice , Duncan Catriona , Jenkinson Helen , Veal Gareth J. , Barnett Shelby
Published: June 2024

Background

Retinoblastoma is the most common intra-ocular malignancy in children and frequently presents in very young patients who commonly require intravenous carboplatin. Delivering this is challenging due to a lack of uniform dosing recommendations, rapid changes in physiological function and the risk of side-effects.

Methods

We conducted a retrospective review of neonates and infants in the UK with retinoblastoma, who have undergone carboplatin therapeutic drug monitoring (TDM). We report on the pharmacokinetic, treatment efficacy and toxicity data.

Results

In total, 29 patients (median age 5 weeks at treatment onset) underwent a total of 74 TDM guided cycles of chemotherapy, involving real time sampling and dose adjustment. An additional 13 patients underwent TDM sampling to modify doses between cycles. Without the adoption of TDM guided dosing, carboplatin exposures would have been ≥20% outside the target AUC in 38/78 (49%) of treatment cycles. Excellent responses and a reassuringly low incidence of toxicities were observed following dose adjustment, despite the young patient age and the implementation of dose increases in the majority of cases.

Conclusions

Real time TDM is safe, effective and deliverable for neonates and infants receiving carboplatin for retinoblastoma and should be considered standard of care up to the age of 6 months.
Sustained human outbreak of a new MPXV clade I lineage in eastern Democratic Republic of the Congo
Authors: Vakaniaki Emmanuel Hasivirwe , Kacita Cris , Kinganda-Lusamaki Eddy , O’Toole Áine , Wawina-Bokalanga Tony , Mukadi-Bamuleka Daniel , Amuri-Aziza Adrienne , Malyamungu-Bubala Nadine , Mweshi-Kumbana Franklin , Mutimbwa-Mambo Léandre , Belesi-Siangoli Freddy , Mujula Yves , Parker Edyth , Muswamba-Kayembe Pauline-Chloé , Nundu Sabin S. , Lushima Robert S. , Makangara-Cigolo Jean-Claude , Mulopo-Mukanya Noella , Pukuta-Simbu Elisabeth , Akil-Bandali Prince , Kavunga Hugo , Abdramane Ombotimbe , Brosius Isabel , Bangwen Eugene , Vercauteren Koen , Sam-Agudu Nadia A. , Mills Edward J. , Tshiani-Mbaya Olivier , Hoff Nicole A. , Rimoin Anne W. , Hensley Lisa E. , Kindrachuk Jason , Baxter Cheryl , de Oliveira Tulio , Ayouba Ahidjo , Peeters Martine , Delaporte Eric , Ahuka-Mundeke Steve , Mohr Emma L. , Sullivan Nancy J. , Muyembe-Tamfum Jean-Jacques , Nachega Jean B. , Rambaut Andrew , Liesenborghs Laurens , Mbala-Kingebeni Placide
Published: June 2024

Outbreaks of monkeypox (mpox) have historically resulted from zoonotic spillover of clade I monkeypox virus (MPXV) in Central Africa and clade II MPXV in West Africa. In 2022, subclade IIb caused a global epidemic linked to transmission through sexual contact. Here we describe the epidemiological and genomic features of an mpox outbreak in a mining region in eastern Democratic Republic of the Congo, caused by clade I MPXV. Surveillance data collected between September 2023 and January 2024 identified 241 suspected cases. Genomic analysis demonstrates a distinct clade I lineage divergent from previously circulating strains in the Democratic Republic of the Congo. Of the 108 polymerase chain reaction-confirmed mpox cases, the median age of individuals was 22 years, 51.9% were female and 29% were sex workers, suggesting a potential role for sexual transmission. The predominance of APOBEC3-type mutations and the estimated emergence time around mid-September 2023 imply recent sustained human-to-human transmission.