bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of team-based learning to ophthalmology in China
Authors: Wu Wenyi , Pu Li , Zhang Endong , Xiong Siqi , Zhou Xiaolai , Xia Xiaobo , Wen Dan 
Published: October 2022

ObjectivesThe purpose of this study was to explore whether team-based learning (TBL) was more effective than traditional didactic lectures (TDLs) in improving medical students' problem-solving and study skills in the clinical course of ophthalmology. In addition, we were also concerned about Chinese students' satisfaction with TBL.MethodsOur study program involved 275 students of the 5-year clinical medicine program from Central South China University, of which 140 were enrolled in a modified TBL course. A questionnaire that included closed-ended and open-ended items was distributed to students immediately following the completion of the TBL session, and 108 valid questionnaires were collected. Descriptive statistics were used to analyze quantitative data. The effects of the TBL module on students' performance were measured between the groups using a one-way between-group analysis of variance (ANOVA) test by the individual readiness assurance test (IRAT), the group readiness assurance test (GRAT), and final examination scores (FESs), compared with a class without the TBL session.ResultsWith our modified TBL strategy, 140 students achieved a mean test score of 72.65 on test questions that assessed their knowledge of ophthalmology compared to 135 students who achieved a mean score of 70.8 using the TDL method (p = 0.3434). The performance in a pre-class quiz was significantly better in the GRAT compared to the IRAT. In comparison to the TDL session, the modified TBL was preferred and acceptable by most medical students.ConclusionsBy applying the modified TBL to ophthalmology, students improved their performance, self-study, and teamwork, and their class engagement and satisfaction were enhanced. However, TBL should be further optimized and developed to enhance educational outcomes.
Lay health worker intervention in pre-diabetes management: Study protocol of a pragmatic randomized controlled trial for Chinese families living in inadequate houses
Authors: Chan Crystal Ying , Hoi Becky Pek-kei , Wong Eliza Lai-yi 
Published: October 2022

IntroductionLay health workers, despite their lack of formal trainings, are important partners in providing accessible care to people with risk to develop diabetes in the community. While pre-diabetes and diabetes are more prevalent among people with low socio-economic status, including those living in inadequate houses. However, this population often have accessibility problems to formal care services due to their financial and social disadvantages. In a high-income, developed Chinese society, this pragmatic randomized controlled trial seeks to investigate the effect of a 6-months lay health worker intervention in diabetes management among people living in sub-divided flats units in Hong Kong.Methods and analysisIn this trial, 222 Chinese primary caregivers living in inadequate houses and with diabetes risk will be recruited via non-profit organizations serving in districts with low average household incomes and prevalent subdivided flats in Hong Kong. Adopting a 6 months wait-list control, participants will be randomized to receive a 6-months lay health worker intervention of 5 components, including (1) lay health worker training and support; (2) health professional training; (3) formulation of a targeted care plan for the health and nutritional needs of the families; (4) case management approach; and (5) financial subsidy for lay health workers to sustain the practice. The control group will receive usual care and health information on diabetes risk management. Glycated hemoglobin (HbA1c) and fasting blood glucose will be taken at the entry and exit assessment of this trial as primary outcomes.DiscussionOur randomized controlled trial is one of the first to investigate the effect of lay health worker intervention on pre-diabetes management in a low-income Chinese population residing in inadequate houses. This study could provide insights to consider alternative service provision models to people living with diabetes risk in the community, by providing a care option to be supported by community health workers and enhanced community participation of care providers. This study attempts to evaluate the impact of a lay health worker intervention using a mixed-method study design. Despite its contribution, this study might be subjected to sampling bias since all the participants will be recruited from non-profit organizations serving deprived populations.Trial registration numberChiCTR2100052080 in Chinese Clinical Trial Registry. URL: https://www.chictr.org.cn/edit.aspx?pid=134928&amp;htm=4.
Metabolite-protein interactions: Native mass spectrometry and collision induced affinity selection mass spectrometry in natural product screening
Authors: Gu Yushu , Liu Miaomiao , Quinn Ronald J. 
Published: October 2022

Understanding molecular level interactions between the metabolome and proteome, two of the most important classes of molecules in biology, will generate deeper insight into the function of metabolites (natural products) which have a central role in interactions with therapeutic targets. Drug discovery in today’s pharmaceutical environment is driven by high-throughput screening of large chemical libraries. It is now 10 years since we published a paper on the development of natural product fraction libraries with control of LogP properties. We have now turned our attention to using pure natural product libraries to address the timeframe issues associated with isolation and characterization of the active constituent(s). Native mass spectrometry can be used as a robust platform for identifying the interactions between natural products and their protein targets. The recent development of Collision-Induced Affinity Selection mass spectrometry, a technique using capture of ligand-protein complexes followed by collision induced dissociation to identify library hits followed by direct ligand-protein confirmation in native mass spectrometry also enables screening of a greater proportion of human proteins. We will review native mass spectrometry-based approaches to use natural product extracts, pre-fractionated natural product libraries and pure natural product libraries for screening against molecular targets. We will also discuss some of the other mass-spectrometry based applications that have been implicated in natural product drug discovery.
Video Gaming Faith: Playing Out Theologies of Religions
Authors: Jones Gregory D. 
Published: October 2022

Modern religious plurality invites religious and non-religious people to navigate four interreligious dialogical problems: (1) the inability to fully articulate faith, (2) the lack of persuasive religious language, (3) the reality of violence among the religions, and (4) the liquescent “truth” of modern times. How can plurality be framed for people whose sense of relationality is shaped by their participation in virtual worlds? One answer emerges in this autoethnographic consideration of how video gaming “plays out” fresh understandings of the interreligious encounter and relationality. Adopting a Christian perspective, the first section summarizes the major theologies of religions. These theologies correspond with video-game experiences of interreligious cooperation and contest found in playing out the enrichment and diminishment of (1) Christian spirit in Spiritual Warfare (NES), (2) human connection in Final Fantasy VI (Super NES), (3) sense of salvation in Final Fantasy X (PS2), and (4) symbiotic sacredness in Journey (iOS). These play experiences clarify a concept of expansive relationality among religions that is termed shared contest. The conclusion advances a contestant theology of religions; God removes every obstacle to including all in the company of God’s people, and God provides a playground of cooperation and contest for each religious tradition.
Comparing antibiotic prescriptions in primary care between SARS-CoV-2 and influenza: a retrospective observational study
Authors: Sijbom Martijn , Büchner Frederike L , Saadah Nicholas H , de Boer Mark GJ , Numans Mattijs E 
Published: October 2022

BackgroundAntibiotics are frequently prescribed during viral respiratory infection episodes in primary care. There is limited information about antibiotic prescription during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in primary care and its association with risk factors for an adverse course.AimTo compare the proportion of antibiotic prescriptions between patients with COVID-19 and influenza or influenza-like symptoms, and to assess the association between antibiotic prescriptions and risk factors for an adverse course of COVID-19.Design & settingAn observational cohort study using pseudonymised and coded routine healthcare data extracted from 85 primary care practices in the Netherlands.MethodAdult patients with influenza and influenza-like symptoms were included from the 2017 influenza season to the 2020 season. Adult patients with suspected or confirmed COVID-19 were included from the first (15 February 2020–1 August 2020) and second (1 August 2020–1 January 2021) SARS-CoV-2 waves. Proportions of antibiotic prescriptions were calculated for influenza and COVID-19 patients. Odds ratios (ORs) were used to compare the associations of antibiotic prescriptions in COVID-19 patients with risk factors, hospital admission, intensive care unit (ICU) admission, and mortality.ResultsThe proportion of antibiotic prescriptions during the first SARS-CoV-2 wave was lower than during the 2020 influenza season (9.6% versus 20.7%), difference 11.1% (95% confidence interval [CI] = 8.7 to 13.5). During the second SARS-CoV-2 wave, antibiotic prescriptions were associated with being aged ≥70 years (OR 2.05; 95% CI = 1.43 to 2.93), the number of comorbidities (OR 1.46; 95% CI = 1.18 to 1.82), and admission to hospital (OR 3.19; 95% CI = 2.02 to 5.03) or ICU (OR 4.64; 95% CI = 2.02 to 10.62).ConclusionAntibiotic prescription was less common during the SARS-CoV-2 pandemic than during influenza seasons, and was associated with an adverse course and its risk factors. The findings suggest a relatively targeted prescription policy of antibiotics in primary care during COVID-19.
Hormone replacement therapy prescribing in menopausal women in the UK: a descriptive study
Authors: Alsugeir Dana , Wei Li , Adesuyan Matthew , Cook Sarah , Panay Nicholas , Brauer Ruth 
Published: October 2022

BackgroundRecent studies on the prescribing of hormone replacement therapy (HRT) medicines to treat symptoms of menopause are lacking.AimTo describe the prescribing of HRT in a cohort of UK menopausal women.Design & settingPopulation-based drug utilisation study using IQVIA Medical Research Database (IMRD-UK).MethodPrimary care data of women with recorded menopause and/or aged ≥50 years between January 2010 and November 2021 were extracted from the database. The incidence rate of women who received their first prescription for HRT was calculated annually using person-years-at-risk (PYAR) as the denominator. Incidence rates of HRT were estimated by type and route of administration. Relative changes in annual incidence rates were expressed as percentages and the average percentage change was assessed using linear regression. Annual prescribing prevalence per 100 women was calculated using mid-year menopausal population estimates.ResultsThe incidence rate of prescribing of HRT increased from 5.01 in 2010 to 18.16 per 1000 PYAR in 2021, a relative increase of 13.64% (95% confidence interval [CI] = 6.97 to 20.30) per year. The incidence rate of fixed combinations of HRT increased from 3.33 to 12.23 per 1000 PYAR in 2010 and 2021, respectively. Transdermal formulations of HRT increased from 1.48 to 14.55 per 1000 PYAR in 2010 and 2021, respectively. The overall proportion of women in receipt of a prescription for HRT changed from 7.89% in 2010 to 6.86% in 2020.ConclusionThis study shows a steady increase in the number of women receiving their first prescription for HRT during the study period, which suggests regained acceptance of HRT medicines.
ANALISIS BANTUAN SOSIAL TERHADAP KESEJAHTERAAN MASYARAKAT TANAH LAUT PADA MASA PANDEMI COVID-19
Authors: Nasrullah Muhammad Abdianor , Annisa Rahma 
Published: October 2022

Penelitian ini bertujuan untuk menganalisis bagaimana pengaruh bantuan sosial yaitu BLT dan BPNT terhadap tingkat kesejahteraan Masyarakat kabupaten Tanah Laut pada masa pandemi Covid-19. Data diperoleh dari kuesioner yang disebarkan kepada responden di kabupaten Tanah Laut. Hasil penelitian menunjukkan bahwa bantuan sosial yang disalurkan oleh pemerintah memiliki hubungan atau pengaruh negatif terhadap kesejahteraan masyarakat kabupaten Tanah Laut pada masa pandemi Covid-19 ini. Berdasarkan nilai koefisien korelasi dan koefisien determinasi, dapat diketahui bahwa pemberian bantuan sosial tidak memiliki sumbangsih yang cukup besar terhadap kesejahteraan masyarakat pada masa pandemi. Dimana bantuan sosial berpengaruh hanya sekitar 0,04% terhadap kesejahteraan masyarakat. Sedangkan sisanya 99,96% disebabkan oleh faktor-faktor lainnya. Salah satu hal yang menjadi faktor tidak cukup berpengaruhnya bantuan sosial terhadap kesejahteraan masyarakat berdasarkan pernyataan responden adalah masih banyaknya kasus penyelewangan bantuan dan sistem pembagian bantuan yang masih butuh banyak perbaikan.
Gestión de proceso de ventas para el mejoramiento de la rentabilidad
Authors: Villacres-Guato Galo Arturo , Romero-Fernández Ariel José , Gallegos-Riofrío Rodrigo Xavier 
Published: October 2022

La investigación se realizó con el objetivo de determinar, la influencia de la gestión del proceso de ventas en el mejoramiento y rentabilidad de la Empresa TvCable de la ciudad de Ambato. Para la solución de la problemática se propone mejorar la publicidad digital, para que sea enviada a través de redes sociales, y abarcar una mayor base de datos de clientes, además se propone mantener continuamente el proceso de capacitación, dando una retroalimentación quincenal en temas orientados al desarrollo del vendedor como: cierre de ventas efectivo, conocimiento del producto, manejo de herramientas publicitarias y uso de redes sociales, entre otras con el objetivo primordial de elevar el volumen de sus ventas mensuales.
Calidad de atención al usuario. Caso: Hospital nacional docente Madre Niño San Bartolomé, Lima, Perú
Authors: Cuentas-Robles Adelmo Rodomiro 
Published: October 2022

La investigación tiene como objetivo determinar cómo se relaciona la Calidad de atención al usuario, consulta externa del área de Odontoestomatológia en un contexto de emergencia sanitaria Covid - 19, en el Hospital Nacional Docente Madre-Niño San Bartolomé Lima 2022.El estudio fue de tipo correlacional, y diseño transversal directo, considerando como muestra a 82 usuarios del servicio. Los resultados fueron analizados a través de la correlación de R de Pearson, que alcanzado un índice de significancia del 0.008 ptos., a su vez el índice de correlación entre ambas variables llega a un 0.954 ptos., que es equivalente a un 95.4% de correlación, determinando la validez de la hipótesis general alterna estableciendo que la calidad de atención al usuario se relaciona de manera directa y significativa, consulta externa del área de Odontoestomatología, tal cual se establece dentro del estudio dentro del contexto de emergencia sanitaria Covid - 19 en el Hospital.
Beyond-Cell Communications via HAPS-RIS
Authors: Alfattani Safwan , Yadav Animesh , Yanikomeroglu Halim , Yongacoglu Abbas 
Published: October 2022

<p>The ever-increasing number of users and new services in urban regions can lead terrestrial base stations (BSs) to become overloaded and, consequently, some users to go unserved. Compounding this, users in urban areas can face severe shadowing and blockages, which means that some users do not receive a desired quality of service (QoS). Motivated by the energy and cost benefits of reconfigurable intelligent surfaces (RIS) and the advantages of high altitude platform stations (HAPS), including their wide footprint and strong line-of-sight (LoS) links, we propose a solution to service the stranded users using the RISaided HAPS. More specifically, we propose to service the stranded users by a dedicated control station (CS) via a HAPS equipped with RIS (HAPS-RIS). Through this approach, users are not restricted from being serviced by the cell they belong to; hence, we refer to this approach as beyond-cell communication. As we demonstrate in this paper, beyond-cell communication works in tandem with legacy terrestrial networks to support uncovered or unserved users. Optimal transmit power and RIS unit assignment strategies for the users based on different network objectives are introduced. Numerical results demonstrate the benefits of the proposed beyond-cell communication approach. Moreover, the results provide insights into the different optimization objectives and their interplay with minimum quality-of-service (QoS) and network resources, such as transmit power and the number of reflectors. </p>
Distributed Precoding For Decentralized Estimation In Coexisting IoT Networks
Authors: Garg Navneet , Ratnarajah Tharmalingam , Vakamulla Venkata Mani , Sellathurai Mathini 
Published: October 2022

<p>With the increasing number of applications of internet-of-things (IoT) devices, co-channel interference is unavoidable among coexisting wireless networks, where multiple IoT devices transmit their observations to their respective destinations (access points (APs)). In this scenario, we present a joint precoding and power allocation solution to minimize the mean squared error (MSE), while satisfying power constraints at individual IoT devices. In this regard, first, the necessary feasibility condition for the joint convexity of the optimization problem is derived, ensuring the global optimum solution. Subsequently, based on the solution, an iterative MSE algorithm is formulated and analyzed for convergence. Further analysis shows that the total resulting MSE at APs is limited by the observation signal-to-noise-ratio (SNR). It leads to the inference that in order to avoid the MSE saturation at APs at higher SNRs, the transmit power at IoT devices should be scaled proportional to and less than the observation SNR. Next, we compare the performance of our solution with two classical methods, namely, the minimum variance distortionless precoding (MVDP) and interference alignment (IA) methods, which are modified and enhanced for the given system. Simulations verify the above inference, and the global convergence of the MSE algorithm, with robustness to initializations yielding the better precoders and power allocation as compared to MVDP's and IA's in terms of the averaged total MSE performance.</p>
DCGCN: Dynamic community graph convolutional network for traffic forecasting
Authors: Xu Yi , Zhang Dalin , Peng Yunjuan , Wang Nan , Lu Lingyun , Liu Jiqiang 
Published: October 2022

<p>Traffic forecasting is one of the core issues in transportation systems. Graph convolution based spatiotemporal model can process the complex and highly nonlinear traffic data, but rely heavily on the graph construction, some creative works make innovations in dynamic graph modeling and expansion of the graph. However, these works ignored the dynamic community structure of traffic graph and the inter-community traffic flow interaction, and not fully exploited the spatiotemporal characteristics. In this paper, we propose a dynamic community graph convolution model (DCGCN), which can more precisely characterize the dynamic traffic network and capture the dynamic spatiotemporal dependencies by capturing the community properties and community interactions of traffic flow. First, we propose a noval method to capture inter-community traffic flow interactions. Second, we take advantage of the spatiotemporal properties of traffic data and introduce a community-based dynamic traffic graph learning mechanism to construct traffic networks adaptively. Finally, we fuse inter-community traffic flow interactions and dynamic traffic graph to update the traffic graph, introduce a multi-attention spatiotemporal convolution for traffic forecasting. We conduct experiments on three large-scale real datasets containing multiple traffic scenarios, prove the superiority of our model compared with SOTA models such as ASTGCN, MATGCN, and DCRNN. Meanwhile, the visualization in these datasets shows the method can effectively identify the stations that have a large impact in the forecasting process and dynamic community changes, and the extracted inter-station dependencies and traffic flow region interactions are interpretable. The source code is available athttps://github.com/1998XuYi/DCGCN.</p>
Measuring Spatio-Temporal Civil War Dimensions Using Community-Based Dynamic Network Representation (CoDNet)
Authors: Vega Oliveros Didier Augusto , Koren Ore 
Published: October 2022

<p>Civil war exhibits complex geospatial trends over time, which may be missed by models that rely on count-based operationalizations. Here, the spatial and temporal correlation values of monthly civil war events are transformed into their influence degree symbol, which measures geospatial concentration, spread, and intensity of civil war. We then measure variation in these degrees over time to identify relevant Spatio-temporal civil war aspects. The network model is constructed using 0.5 degree grid locations as nodes, counting nearby and over-time connections. We then extract the temporal community structure behind the data. We use ground truth data to visualize how our measures correlate with observed patterns, thereby illustrating our method provides accurate depictions of geospatial civil war dynamics. We also evaluate the impact of several indicators highlighted by past research and our community-based spatio-temporal measures and comparing it to the preprocessed count indicator. Our findings indicate that the relationship between state capacity and climate stress show opposite correlations with civil war as those identified by studies that use count based indicators. Counterintuitively, our results show that conflict intensifies and spreads in locations where the state is stronger and where climate conditions are improved. </p>
Reducing the Latency using Fog Computing with IoT in Real Time
Authors: Joseph Maria 
Published: October 2022

<p>In today's modern world, the Internet is highly crucial. A larger number of wired and wireless sensors characterize the Internet of Things (IoT)<strong>. </strong>These sensors are networked devices that create massive amounts of data, reaction time, latency, and security vulnerabilities. These problems create delay in decision making in some situation which is very extreme in case of Healthcare, Smart vehicles, Industry 4.0 etc., the data moving from the sensor devices have to reach the cloud and from there it has to reach back to receivers. Therefore, in order deal with such huge data we use fog computing, a well-known distributed architecture. Fog computing works with the aim to enhance the processing, intelligence, and accumulation of data closer to the Edge devices. The proposed framework helps in reduce latency as we place a Fog node device between the cloud and the edge device where data is generated and sent to the cloud and retrieved from the cloud. The framework is designed in such way that the different sensor devices can be placed on it and collects the sensor data from them. To test the framework functioning, without the fognode and with the fognode, comparing the latency with packet transfer rate from sensor devices to the cloud and vice versa. In this paper, we are considering two different case studies to test the proposed functioning of the framework with the following case studies i) Related to medical are like diabetes and cardiovascular illnesses are considered for prediction based on patient health records and ii) Vehicle theft where the owner get an alert of the theft of the vehicle within few seconds of the theft. In Medical case study initially, patient health data is acquired and stored using Sensor devices. </p>