bluuebunny/crossref_metadata_2025_split huggingface dataset:
“We talk about the dangers”: exploring teachers’ and learners’ perspectives on alcohol education in Ugandan primary schools
Authors: Valeckaite Neda , Kühl Melf-Jakob , Babirye Juliet N. , Kiirya Aisha K. , Engebretsen Ingunn M. S. 
Published: August 2024

Abstract

adolescents are affected through own or other’s drinking. Schools have an opportunity to address this issue. While curriculum-based alcohol education is a widely used educational approach, there is limited research on its delivery and effectiveness in Ugandan primary schools. This study aimed to explore the teachers’ and learners’ perspectives and teaching methods on alcohol education used in Ugandan primary schools.
Methods: This was a qualitative study which enrolled six schools from Mbale, Eastern Uganda. We conducted twelve key informant interviews with teachers and twelve focus group discussions with the learners aged 11-13 years. The data was coded in NVivo and analysed using reflexive thematic analysis.
Results: Alcohol as a topic was taught during science classes in Ugandan primary schools. Four main topics were covered during the lessons: what alcohol is and how it is made; the effects of alcohol use; why people drink; and, how to avoid it. The dangers of alcohol use were strongly emphasized by the teachers and the children were advised to avoid places and people including children that use alcohol. Teachers often used moralising and didactic methods of teaching to underline their message against alcohol use.
Conclusion: To our knowledge this is the first study to describe alcohol education in primary schools in Mbale district, Uganda. This study shows that teachers considered alcohol education as an important topic aiming to teach children about the dangers and negative effects of alcohol use.
Genome-wide markers reveal geographical isolation by distance and barriers as well as local heterogeneity in the genetic structure of a seagrass
Authors: Hosokawa Shinya , Momota Kyosuke , Sato Masaaki , Watanabe Kenta , Watanabe Yuki , Homma Shota , Okura Shota , Uwai Shinya , Kosako Taichi , Uchiyama Yusuke 
Published: August 2024

Abstract

Gene flow is a crucial concept in the delineation of conservation units for natural populations of a species. Seagrasses are marine species targeted for conservation because their abundance has declined worldwide during the last century. However, we cannot determine how to delineate conservation units with inadequate knowledge of the genetic structure of seagrasses. This study explored the genetic structure of Zostera marina L. (eelgrass) populations in three semi-enclosed areas using single nucleotide polymorphisms within abundant, genome-wide loci. Genome-wide markers revealed that the genetic structure was isolated by geographical distance and barriers through a narrow strait in an area with linear dimensions less than 200 km. The genetic distance created by the barrier was 6.7 times the genetic distance due to 100 km of geographic distance. The markers revealed the intra-site variability in genetic structure and the heterogeneity among sites on scales less than ~10 km that had not been recognized previously. Our results imply that the use of genomic tools will focus seagrass conservation efforts more locally than before and that assessing relative genetic differences can make delineating conservation units a reality. Identifying the evolutionary and quantitative meaning of genetic differences will be a next challenge for delineating seagrass conservation units.
Cloud Security Modeling: Using TCP Deltas with Data Analytics and Machine Learning Techniques
Authors: Hussain Mohamed Fazil , Hasan Salwa Sayeedul , Rauf Hasan 
Published: August 2024

Abstract

The 21st-century surge in computing infrastructure across mobile, centralized, and decentralized realms in the cloud has ushered in a new era of diverse applications, along with its Nemesis the Cybercrime. However, this progress comes hand in hand with amplified security risks, such as Ransomware Attacks. Security measures such as Multi-Factor Authentication, Strong Passwords, Network Segmentation, Endpoint Security, Robust Firewall and Intrusion Detection systems, and user awareness are on the Rise. Still, these efforts fall short when endpoint security fails, or a user is masqueraded to hack the system for ransomware. Essential security measures, including resource-intensive static and dynamic scans, strain these infrastructures, leading to a noticeable lag in user experience. This study delves into how to use Data Analytics/ Machine Learning (ML) techniques with TCP/IP 3-way handshake data to continuously monitor the TCP.Delta times at the server side of the load balancer to flag suspicious activities, along with measuring user lag in accessing these resources which are under security scans. The CI/CD pipeline will be used to provide Q's about abnormal load balancer traffic along with measuring lag in the response time using a generic methodology that revolves around TCP.Delta time and advanced Data Analytics/ML techniques.
Thrombomodulin Resistance: a Novel Prothrombotic Pathway in Covid-19
Authors: Marco-Rico Ana , Montaño Adrián , López-Jaime Francisco , Fernández-Bello Ihosvany , Marco-Vera Pascual 
Published: August 2024

Abstract

Hypercoagulability and endothelial dysfunction are strongly involved in the worsening of the clinical condition in COVID-19 patients. In severe cases, the inflammatory process triggers the release of angiopoietin 2, which could decrease circulating thrombomodulin (TM), a major regulatory mechanism in thrombin generation. Although some studies have described an increased TM resistance, further data are needed to obtain robust results. The objective of our study was to evaluate TM resistance in hospitalized COVID-19 patients using the thrombin generation test and its correlation with clinical events. Forty-seven hospitalized COVID-19 patients were included (mean age was 59 years (15–90) and 42.6% were women). Measurement of endogenous thrombin potential (ETP) revealed that 39.4% of patients had a % of ETP inhibition < 40%, suggesting TM resistance. Twenty-three% of patients (n = 11/47) presented at least one severe clinical event (SCE). Significant resistance to TM was observed in patients with SCE: % of ETP inhibition was 20.8% vs 48.3% in the non-SCE group. Higher resistance to TM and higher ETP values significantly correlated with increased clot stiffness (r = 0.339/r = 0.322). The ETP (in presence of TM) and the resistance to TM proved to be good predictors of SCE with an AUC of 0.756 and 0.803, To conclude, thrombin generation can be a powerful tool for risk stratification in hospitalized COVID-19 patients. In addition, increased resistance to TM is associated with the development of SCE and can be considered as a new independent marker of poor prognosis.
Base Station Switch off Methods for Mobile Communication without Effecting QoS
Authors: Sudhakar Duda , Sunehra Dhiraj , prashanthi Sudhakar 
Published: August 2024

Abstract

During low traffic hours switching off the base stations is an effective way of saving energy in mobile communication network. To serve increased traffic and to fulfill large and high speed data demand, operators are deploying more number off base stations. This is leading to more energy consumption in the mobile communication network. So minimizing the energy consumption in the mobile communication has become a primary concern. In communication system maximum energy is consumed at the base stations. Hence by switching off some of the base stations at low traffic hours, energy can be saved. In this work we developed static and dynamic base station switch off methods to minimize the energy consumption during low traffic condition. Using this base station switch off methods we are able to save 12.63%, 10.81% and 12.66% of power for three different traffic scenarios.
Analysis of the digital accessibility of selected Polish government portals for people with disabilities
Authors: Zając Piotr , Królak Aleksandra 
Published: August 2024

Abstract

The aim of this paper is to analyse digital accessibility of selected Polish governmental portals and their respective mobile applications, i.e., “Internetowe Konto Pacjenta” (Internet Patient Account) and “mObywatel” (mCitizen), for users with disabilities. The analysis was performed from the perspective of the applications compliance with the web content accessibility guidelines by World Wide Web Consortium, selected rules of Universal Design and elements of User Experience. The study included the use of automatic tools and user tests. The testing group consisted of 18 persons: 6 persons with motor disability, 6 with visual impairment including 1 blind person, 2 persons with other disabilities and 4 persons with multiple disabilities. The participants completed given scenarios and the supervisor was noting down the users’ observations and remarks. Significant incompliances with the aforementioned guidelines were found by the ways a result of simplified auditing of the platform. Usability tests for the study group were an additional element of the project.
FEdroid: Lightweight and Interpretable Detection of Android Malware Using Local Key Information and Feature Selection
Authors: Huang Hong , Huang Weitao , Zhou Yinghang , Luo Wengang , Wang Yunfei 
Published: August 2024

Abstract

The Android operating system, as the most widely adopted mobile platform globally due to its open-source nature and flexibility, faces significant security challenges, particularly from malicious software threats. Existing research on malicious software detection often involves complex feature engineering, which can be cumbersome and prone to noise, lacking effective feature selection mechanisms. Moreover, some studies employing deep learning methods exhibit lower efficiency. This paper proposes a lightweight and interpretable Android malicious software detection system, named "FEdroid," based on machine learning. The system simplifies the analysis process and extracts crucial information by focusing on code sections that utilize sensitive APIs. It utilizes XGBoost for cross-feature selection, concentrating on a minimal yet essential feature set to enhance detection accuracy while reducing device resource consumption. Experimental results demonstrate that the system achieves 98.26\% accuracy and only 1.86\% false negative rate on 18,653 APK samples, significantly improving detection efficiency and accuracy while effectively lowering resource consumption. Furthermore, by applying Shapley values for interpretability analysis, the transparency and comprehensibility of the classifier model are substantially enhanced, thereby significantly boosting the overall interpretability of the system.
Independent or interdependent? Migration timing dynamics and their association with external stressors
Authors: Oh Woi Sok , Barfuss Wolfram , Donges Jonathan , Levin Simon , Rubenstein Daniel 
Published: August 2024

Abstract

Human migration is one of the defining topics of our time, but the dynamics of when and how often migration events occur are poorly understood. Often, a single, context-unaware model is assumed without empirical evidence. Spatiotemporal dependencies and heterogeneities cannot be explained by such a viewpoint. These gaps produce misunderstanding of migration, leading to imprecise predictions and ineffective responses. To address the gaps, we make use of recent advances in data collection and quantitative techniques, investigating Somali internal migration as a case study. We find that whether migration timings are independent (Poisson) or interdependent (bursty) depends on their coincidence with conflicts and extreme weather events. Interdependent migration timings are often observed in peaceful districts, while independent dynamics are found in those with higher coincidence of migration and conflict-involved stressors. These findings help us identify whether migration is self-induced or externally driven, develop more predictive models, and build context-specific migration policies.
Investigation of fibre-like shaped particles in a turbulent jet-in-crossflow channel flow configuration
Authors: Taborda Manuel A. , Sommerfeld Martin 
Published: August 2024

Abstract

Detailed experimental studies on the dispersion of non-spherical particles in different flow configurations, conducted in a test facility built around a horizontal closed water channel are presented. Specifically, a jet in cross-flow configuration was selected for measuring the dispersion of fibre-like particles injected through the small jet perpendicular into the developed horizontal channel flow. The glass fibres had a mean diameter of approximately 40 µm and aspect ratio with a modal value around 4. Experimental data were collected for different channel flow Reynolds numbers between 15,000 and 35,000 and a jet Reynolds number of 11,000. The motion and dispersion of non-spherical particles within the channel was analysed by a planar shadow imaging technique combined with Particle Tracking Velocimetry (PTV) for both, fibres and tracer particles. The discrimination between both phases was conducted on the basis of particle size and shape. The three selected channel flow velocities provided a jet being entrained more in the channel core as well as near the upper and lower channel walls. Therefore, also the interaction of the fibres with solid walls was of great interest. The collected experimental data were used for the validation of point-particle Euler/Lagrange numerical predictions including the required non-spherical particle transport and wall collision models. Special Attention was given to the treatment of the fibre-wall collisions using a hard-sphere model, modifying the post-collisional linear and rotational momentum. A reasonable good agreement with the experimental results was observed when the proper fluid modelling and fibre dynamics was included in the simulations.
Herbal Remedies for Type 2 Diabetes Mellitus- An explicit Review
Authors: Grover Rohit , Mittal Vishnu , Sharma Anjali 
Published: August 2024

Background:
Type 2 Diabetes Mellitus (T2DM) is a prevalent metabolic disorder characterized
by insulin resistance and hyperglycemia. Herbal remedies have been explored as alternative
or adjunctive therapies for managing T2DM due to their perceived safety and potential antidiabetic
properties.

Aim:
This study aimed to provide a comprehensive examination of the current state of knowledge
regarding herbal remedies for managing Type 2 Diabetes Mellitus (T2DM). It explored bioactive
compounds in herbs implicated in glycemic control and evaluated their potential therapeutic effects
on T2DM from both traditional and contemporary perspectives.

Objective:
The primary objectives of this in-depth study included scrutinizing the literature on herbal
interventions for T2DM, identifying and evaluating the bioactive compounds in herbs implicated in
glycemic control, assessing the mechanisms of action responsible for their antidiabetic properties,
and investigating the safety profile and potential side effects associated with their use.

Methods:
A systematic literature search was conducted across multiple databases, including PubMed,
Scopus, and Web of Science. Articles published up to the present date, including peer-reviewed research
articles, clinical trials, and systematic reviews, were considered for inclusion in this review.

Results and Discussion:
The review presented a thorough analysis of herbal remedies commonly
associated with T2DM management. Reductions in HbA1c levels and fasting blood glucose concentrations
indicated that the investigated herbal medicines, including fenugreek, cinnamon, and bitter
melon, have shown potential results in improving glycemic control in people with type 2 diabetes but
more study is needed to verify their safety & effectiveness. The safety profile and possible adverse
effects of herbal interventions are also addressed. While some herbs show promise in glycemic control,
caution is advised in their use due to the limited number of high-quality clinical trials. The findings
highlight the need for further research to validate the efficacy and safety of herbal remedies for
T2DM.

Conclusion:
The study provided valuable insights into the current understanding of herbal interventions
for T2DM and underscored the importance of continued research in this field for the development
of evidence-based therapeutic approaches.
Multimodal Medical Image Fusion Method based on the Swin Transformer
and Self-supervised Contrast Learning
Authors: Wang Yuwei , Wang Lei , Huang Zizhen , Zhang Yukun , Han Yaolong 
Published: August 2024

Background:
Though great progress has been made in deep learning-based fusion
methods, there still are some troubling challenges, such as low contrast, weak feature preservation,
the loss of global information, and poor color fidelity.

Methods:
A multimodal medical image fusion method based on the Swin Transformer and selfsupervised
contrast learning is proposed. The Swin Transformer can well utilize the hierarchical
attention mechanisms to model the feature dependencies at different scales, and effectively capture
both the global and local information. Due to the four defined loss functions, self-supervised contrastive
learning can maximize the similarity between positive samples and minimize the similarity
between positive and negative samples to make the fused images closer to the source images.

Results:
Compared with the seven state-of-the-art methods, the proposed fusion method can effectively
deal with darkness, brightness imbalance, edge artifacts, and pseudo-color distortion.
Furthermore, for MRI-CT fusion, the mean SSIM, CC, STD and QCB are increased by 11.29%,
3.09%, 20.4% and 17.3%, respectively; for MRI-PET fusion, it can achieve the highest value of
all the six objective indicators, with average increases of EN 10.96%, QAB/F 19.30%, SSIM
10.07%, CC 4.40%, STD 15.52% and QCB 15.84% respectively and for MRI-CT fusion, the
mean SSIM, CC, STD and QCB are increased by 11.29%, 3.09%, 20.4% and 17.3%, respectively.

Conclusion:
All the experimental results show significant advantages in both subjective and objective
evaluation. The proposed method can maintain the image brightness, detail sharpness and
edge information and effectively integrate the structural and functional information between different
modalities. The objective indicators, such as the SSIM, CC, STD, QAB/F and QCB, can be
significantly improved, especially in MRI-PET fusion, where all indicators reached the highest
value. As a whole, it significantly enhances the image detail feature and texture while the contrast
and brightness are well preserved.
Voltage Regulation for PV-integrated Power Grid using Improved Multi-Objective Particle Swarm Optimization
Authors: Kewen Li , Lei Yu , Qianyi Chen , Xinhao Lin , Shifeng Ou , Lyuzerui Yuan 
Published: August 2024

Introduction:
With the widespread integration of distributed photovoltaics into the
power grid, challenges such as voltage fluctuations and increased deviations have arisen, alongside
heightened demands for power quality and economic dispatch.

Method:
This study tackles the complexities of integrating distributed photovoltaic systems into
the power grid, encompassing network flow variations, node voltage fluctuations, and risks of exceeding
limits while also considering scheduling costs. A comprehensive control strategy is proposed,
integrating the coordinated output of reactive power adjustable devices, energy storage systems
(ESS), and transformers. Subsequently, a multi-objective optimization mathematical model
is formulated with the aims of minimizing network loss, operation cost, and voltage deviation. An
enhanced Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is introduced, leveraging
a ring neighborhood topology and specialized crowding distance measurement to establish a
stable network in both decision and target spaces, devoid of any niche parameters.

Results:
This approach eliminates subjective influences while ensuring rationality. Tests on the IEEE
30-node network example demonstrate that the algorithm can generate a large number of Pareto optimal
solutions, highlighting its advantages in the application of reactive power optimization.

Conclusion:
The method proposed in this paper can fully utilize the dispatchable equipment in the
power grid, effectively reducing network losses and voltage deviations while maintaining low operating
costs.
Comparing Federal Communications Commission and Microsoft Estimates of Broadband Access for Mental Health Video Telemedicine Among Veterans: Retrospective Cohort Study
Authors: O'Shea Amy MJ , Mulligan Kailey , Carter Knute D , Haraldsson Bjarni , Wray Charlie M , Shahnazi Ariana , Kaboli Peter J 
Published: August 2024

Background
The COVID-19 pandemic highlighted the importance of telemedicine in health care. However, video telemedicine requires adequate broadband internet speeds. As video-based telemedicine grows, variations in broadband access must be accurately measured and characterized.

Objective
This study aims to compare the Federal Communications Commission (FCC) and Microsoft US broadband use data sources to measure county-level broadband access among veterans receiving mental health care from the Veterans Health Administration (VHA).

Methods
Retrospective observational cohort study using administrative data to identify mental health visits from January 1, 2019, to December 31, 2020, among 1161 VHA mental health clinics. The exposure is county-level broadband percentages calculated as the percentage of the county population with access to adequate broadband speeds (ie, download >25 megabits per second) as measured by the FCC and Microsoft. All veterans receiving VHA mental health services during the study period were included and categorized based on their use of video mental health visits. Broadband access was compared between and within data sources, stratified by video versus no video telemedicine use.

Results
Over the 2-year study period, 1,474,024 veterans with VHA mental health visits were identified. Average broadband percentages varied by source (FCC mean 91.3%, SD 12.5% vs Microsoft mean 48.2%, SD 18.1%; P<.001). Within each data source, broadband percentages generally increased from 2019 to 2020. Adjusted regression analyses estimated the change after pandemic onset versus before the pandemic in quarterly county-based mental health visit counts at prespecified broadband percentages. Using FCC model estimates, given all other covariates are constant and assuming an FCC percentage set at 70%, the incidence rate ratio (IRR) of county-level quarterly mental video visits during the COVID-19 pandemic was 6.81 times (95% CI 6.49-7.13) the rate before the pandemic. In comparison, the model using Microsoft data exhibited a stronger association (IRR 7.28; 95% CI 6.78-7.81). This relationship held across all broadband access levels assessed.

Conclusions
This study found FCC broadband data estimated higher and less variable county-level broadband percentages compared to those estimated using Microsoft data. Regardless of the data source, veterans without mental health video visits lived in counties with lower broadband access, highlighting the need for accurate broadband speeds to prioritize infrastructure and intervention development based on the greatest community-level impacts. Future work should link broadband access to differences in clinical outcomes.