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Is smokeless tobacco use associated with lower health-related quality of life? A cross-sectional survey among women in Bangladesh
Get PDFData Availaibility: The data supporting this research are available from the authors on reasonable request.Introduction:
Bangladesh has 22 million adult users of smokeless tobacco (ST). The prevalence among women is higher (24.8%). Health-related quality of life outcome (HRQoL) for ST use is little known. We investigated the association between HRQoL and daily ST use among adult women in Bangladesh.
Methods:
Using multi-stage design, a cross-sectional survey was conducted. Adult women (randomly selected) were surveyed from 4 purposively selected divisions (Dhaka, Chittagong, Khulna and Rangpur). Female ST users and non-users were compared using HRQoL scores. Self-perceived Visual Analogue Scale (EQ-VAS) values and HRQoL scores were modelled to examine their association with ST use.
Results:
A total of 2610 women (1149 users and 1461 non-users) were surveyed. The proportion reported any type of problem in all health dimensions was significantly higher among female ST users than non-users (mobility: 43.3% vs 19.5%, self-care: 29.6% vs 11.9%, usual activities: 48.7% vs 21.8%, pain or discomfort: 69.8% vs 40.6%, and anxiety or depression: 61.3% vs 37.5%). The average HRQoL scores were 0.79 (95% CI: 0.78–0.81) and 0.90 (95% CI: 0.89– 0.90) for users and non-users, respectively. Moreover, EQ-VAS average values were significantly higher for non-users [80.7 (95% CI: 79.9–81.6) vs 70.27 (95% CI: 69.2–71.2)]. Controlling the sociodemographics, ST use significantly reduced the HRQoL score by an average of 0.15 points. The EQ-VAS values on average decreased by 0.04 points for ST use.
Conclusions:
ST use is significantly associated with the HRQoL of females in Bangladesh. Considering the higher prevalence of ST, especially among women, HRQoL hazards need to be communicated for awareness building.This work was supported by the Health Economics Unit (HEU), Health Services Division, Ministry of Health and Family Welfare, Government of the People’s Republic of Bangladesh (grant number: EOI Ref. No. 45.05.0000.007.31.013.20.911) under the Health Nutrition and Population Sector Program (HPNSP)
Optimal execution for a risk-averse trader
Get PDFThis thesis was submitted for the award of Master of Philosophy and was awarded by Brunel University LondonWe solve optimal stochastic control problems for a risk-averse trader who uses
market orders and/or limit orders to liquidate a large position in a risky asset.
In each case we aim to maximise terminal wealth, while managing the loss due
to the price impact of our own trader’s trading activity. We solve the problems
using various utility functions for the trader’s risk-aversion and penalty functions
for the trader’s urgency to liquidate the position and reduce market risk. We
compare and contrast the performance of the strategies, and compare them to
industry benchmarks such as TWAP and VWAP.EPSR
Federated learning framework for prediction based load distribution in 5G network slicing
Get PDFThe 5G technology brings transformative changes across sectors like healthcare, automotive, and entertainment by integrating massive IoT networks and supporting dense device connectivity. Network slicing in 5G further ignites the capability by allowing tailored virtual networks for specific applications, enhancing operational efficiency and user experience across diverse scenarios. In this paper we propose a framework to use Federated Learning (FL) in 5G network slicing to support service assignment. The aim is to optimize the network traffic allocation among various slices. It first predicts the load on each network slice and then the incoming traffic is allocated to a slice which is most suitable and not heavily loaded. The DeepSlice dataset on 5G slicing is horizontally splited into multiple segments to train a federated CNN model which are deployed across multiple clients. The model is analyzed with varying number of clients and parameters such as accuracy and loss are observed. The performance of federated approach is compared with centralized approach of prediction keeping essential hyper parameters unchanged. Outcomes in terms of training and testing is presented for better interpretation of the proposed framework. Observation shows that the federated learning outperform the centralized technique in accuracy as well as loss
How did the COVID-19 pandemic affect cancer patients in England who had hospital appointments cancelled?
Get PDFData availability:
The data that has been used is confidential.Highlights:
• The paper examines appointment cancellations for English cancer patients during COVID-19.
• Pandemic patients waited 19 more days for rescheduled appointments than pre-pandemic.
• Pandemic cohort had 14% fewer outpatient, 32% fewer inpatient visits, 50% less hospitalized.
• No mortality difference suggests hospitals prioritized acute cases despite fewer resources.
• Later cancellations less disruptive; provider-initiated linked to higher survival rates.Nicodemo receives funding from Horizon Europe [grant number ES/T008415/1] and from the National Institute for Health Research Applied Research Collaboration Oxford and Thames Valley at Oxford Health NHS Foundation Trust (NIHR200172), and Consortium iNEST (Interconnected North-Est Innovation Ecosystem) funded by the European Union Next-Generation EU (Piano Nazionale di Ripresa e Resilienza (PNRR) – Missione 4 Componente 2, Investimento 1.5 – D.D. 1058 23-06-2022, ECS-00000043). This study was supported by the NPO “Systemic Risk Institute” number LX22NPO5101, funded by European Union - Next Generation EU (Ministry of Education, Youth and Sports, NPO: EXCELES)
Ostrom’s Institutional Grammar and Categorising Legal Rules about Outer Space
No full textOuter space paradoxically might be thought of as both belonging to no one and only accessible to a few powerful global actors, pointing to a scenario of potential conflict between proposed ideal rules of conduct relating to the exploration of outer space and the reality of largely unconstrained agency by small number of space players. Ostrom’s Institutional Grammar seeks to provide a typology of forms of regulation that can allow definition and comparison of different regulatory regimes, especially regarding common or shared resources. Several global attempts have been date to date to regulate the exploration of space, in particular, the relatively early Outer Space Treaty of 1967, while the Artemis Accords proposed by the United States are a more recent development. This paper seeks to relate the existing framework of space regulation to Ostrom’s account of an institutional grammar of rules to consider the diversity of potential regulatory approaches to space
Enriching the concept of employer branding: investigating its impact in the service sector
Get PDFPurpose:
The purpose of this paper is to extend the research on employer branding (EB) by identifying elements of EB according to the perceptions of employees working in the service sector and investigating the impact of EB on employer of choice and organizational performance.
Design/methodology/approach:
Around544 respondents helped to test the model. The research considers development, growth opportunities, equality and justice as new elements of EB, along with organizational culture, salary, incentives and work–life balance.
Findings:
EB significantly influences employer of choice through organizational commitment and employer brand advocacy. Organizational performance is influenced by EB through job satisfaction and employee performance. Nevertheless, no significant relation was observed between EB and employer of choice through person–organization fit. The EB’s impact on employee performance through employee retention was not significant.
Originality/value:
The study suggests reflecting on the importance of the role played by new elements of EB and on the existence of a direct relationship between employee performance and EB. Despite the widespread belief that EB primarily serves as a recruitment tactic to attract candidates, this paper shows that the positive impacts on company performance stem more from outcomes related to current employees than from prospective applicants
An intelligent cloud monitoring system for multiple 3D printers based on IoT
Get PDFIn the Industry 4.0 era, 3D printing has become a cornerstone of intelligent manufacturing, necessitating precise real-time monitoring and control to ensure efficiency and quality. This research developed an IoT-based cloud monitoring system for 3D printers to enhance remote monitoring and control capabilities. The system collects real-time sensor data on parameters such as temperature and humidity, along with live video feeds. These multimodal data are pre-processed at the edge and further analyzed in the cloud. Users can monitor the 3D printing status via a custom visual interface and adjust parameters based on data analysis to optimize printing quality. To evaluate the system's effectiveness, experiments were conducted to explore the relationship between process parameters and surface roughness and to optimize the printing process and quality using the Taguchi design of experiments. Five factors were considered: nozzle temperature, bed temperature, printing speed, layer height, and ambient temperature. The significant printing process factors were identified, and optimal factor levels were determined to enhance print quality. The experimental results demonstrated that the system can significantly improve the quality and response speed of 3D printing process monitoring. Additionally, it enhances the reliability and user experience of 3D printing
The impact of AI on education and careers: What do students think?
Get PDFData availability statement:
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://pure.bangor.ac.uk/admin/workspace.xhtml.Introduction: Providing one-on-one support to large cohorts is challenging, yet emerging AI technologies show promise in bridging the gap between the support students want and what educators can provide. They offer students a way to engage with their course material in a way that feels fluent and instinctive. Whilst educators may have views on the appropriates for AI, the tools themselves, as well as the novel ways in which they can be used, are continually changing. Methods: The aim of this study was to probe students' familiarity with AI tools, their views on its current uses, their understanding of universities' AI policies, and finally their impressions of its importance, both to their degree and their future careers. We surveyed 453 psychology and sport science students across two institutions in the UK, predominantly those in the first and second year of undergraduate study, and conducted a series of five focus groups to explore the emerging themes of the survey in more detail. Results: Our results showed a wide range of responses in terms of students' familiarity with the tools and what they believe AI tools could and should not be used for. Most students emphasized the importance of understanding how AI tools function and their potential applications in both their academic studies and future careers. The results indicated a strong desire among students to learn more about AI technologies. Furthermore, there was a significant interest in receiving dedicated support for integrating these tools into their coursework, driven by the belief that such skills will be sought after by future employers. However, most students were not familiar with their university's published AI policies. Discussion: This research on pedagogical methods supports a broader long-term ambition to better understand and improve our teaching, learning, and student engagement through the adoption of AI and the effective use of technology and suggests a need for a more comprehensive approach to communicating these important guidelines on an on-going basis, especially as the tools and guidelines evolve.The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article
Editorial: Emerging trends in large-scale data analysis for neuroscience research
Get PDFGenerative AI statement:
The author(s) declare that Gen AI was used in the creation of this manuscript. To generate some text and suggest revisions to existing text.Neuroscience has witnessed a surge in data generation due to advancements in experimental techniques like electrophysiology, imaging, and genomics. To gain deeper insights into the brain's structure and function in health and disease, it has become essential to conduct large-scale data analyses.
Analyzing large datasets in neuroscience offers various applications, such as uncovering patterns in neuronal activity, building theoretical models, and predicting behavior. This has created an increasing demand for scalable, efficient, and robust data analysis and machine-learning methods that can handle the vast volume of data generated. By collaborating with domain experts, this research initiative seeks to push the frontiers of large-scale data analysis in neuroscience and foster innovative discussions to meet the field's emerging needs.
The primary aim of this Research Topic is to showcase recent progress in data-driven approaches for studying the brain. It focuses on tackling challenges in managing, processing, and interpreting large-scale neuroscience data while identifying future research opportunities. This Research Topic will delve into state-of-the-art tools and methods for analyzing, integrating, and interpreting extensive neuroscience datasets
Time-domain spectra of ultrasonic wave transmitted through granite and gypsum samples containing artificial defects
Get PDFData Availability Statement: The dataset is open-source via FigShare: https://doi.org/10.6084/m9.figshare.24954945. We welcome researchers to use this dataset for their research.The internal defects in rock masses can significantly impact the quality and safety of geotechnical projects. Mechanical waves, as a common nondestructive testing (NDT) method, can reflect the external and internal structures of rock or rock masses. Analyses on the reflected and transmitted waves enable nondestructive identification and assessment of potential defects within rocks. Previous studies mainly focused on the variation of single or limited wave features like main frequency, amplitude and energy between the intact and non-intact samples. In fact, most information contained in the waveforms is neglected. Techniques of data mining can provide a powerful tool to reveal this information and therefore a more accurate determination of the internal structures. In this study, 995,412 NDT data from 14 types of granite and gypsum samples with different cross-section shapes and different types of defects are recorded by an ultrasonic wave generation and collection system. This dataset can be used not only as the training data for defect classification in NDT but also as a good reference for conventional NDT analyses. Besides, time-series data analysis is an opportunity and challenging issue, this dataset holds great potential for broader application in general time-series classification analysis.Open Fund of State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining & Technology. Grant Number: SKLGDUEK2115
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