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Effect of Mediterranean diet on body mass index and fatigue severity in patients with multiple sclerosis: A systematic review and meta-analysis of clinical trials
No full textBackground
Recent studies imply that Mediterranean Diet (MeD) may play an important role in the management of Multiple Sclerosis (MS). This meta-analysis aimed to evaluate the effectiveness of MeD in addressing MS-related complications.
Methods
A thorough search was performed in MEDLINE (PubMed), Scopus, EMBASE, ScienceDirect, Google Scholar, Web of Science, and the Central Cochrane Library, covering trials published until September 2023. The quantitative data were synthesized using random effect models through STATA14.
Results
After analyzing 228 entries, we found five Randomized Controlled Trials (RCTs) with a total of 540 participants, who had an average disease duration of 8.5 years. The combined effect size revealed a decrease in Body Mass Index (BMI) (Weighted Mean Difference [WMD] = −0.88 kg/m2; 95 % Confidence Interval [CI] = −1.68, −0.08; P = 0.030). There was also a non-significant marginal improvement in fatigue severity (WMD = −8.29; 95 % CI = −16.74, 0.16; P = 0.054).
Conclusion
Adherence to MeD may improve BMI and fatigue severity in MS patients. Further RCTs are needed to confirm the current results
Hidden in Plain Sight: A Data-Driven Approach to Safety Risk Management for Highway Traffic Officers
No full textHighway traffic officers (HTOs) are often exposed to life-threatening workplace incidents while performing their duties. However, scant research has been undertaken to address these safety concerns. This research explores case study data from highway incident reports (held by National Highways, a UK government company) and employs deep neural network (DNN) in unearthing patterns which inform safety decision makers on pertinent safety challenges confronting HTOs. A mixed philosophical stance of positivism and interpretivism was adopted to synthesise the findings made. A four-phase sequential method was implemented to evaluate the validity of the research viz.: (i) architectural design; (ii) data exploration; (iii) predictive modelling; and (iv) performance evaluation. The DNN model’s predictive performance is benchmarked against three other machine learning models, namely Support Vector Machines (SVM), Random Forest (RF), and Naïve Bayes (NB). The DNN model outperformed the other three models. Findings from the data exploration also show that most work operations undertaken by HTOs have a medium risk level with night shifts posing the greatest risk challenges. Carriageways and traffic management enclosures had the highest incident occurrence. This is the first study to uncover such hidden patterns and predict risk levels using a database specifically for HTOs. This study presents evidence-based information for proactive risk management for HTOs
Enhancing Performance of Continuous-Variable Quantum Key Distribution (CV-QKD) and Gaussian Modulation of Coherent States (GMCS) in Free-Space Channels under Individual Attacks with Phase-Sensitive Amplifier (PSA) and Homodyne Detection (HD)
No full textIn recent research, there has been a significant focus on establishing robust quantum cryptography using the continuous-variable quantum key distribution (CV-QKD) protocol based on Gaussian modulation of coherent states (GMCS). Unlike more stable fiber channels, one challenge faced in free-space quantum channels is the complex transmittance characterized by varying atmospheric turbulence. This complexity poses difficulties in achieving high transmission rates and long-distance communication. In this article, we thoroughly evaluate the performance of the CV-QKD/GMCS system under the effect of individual attacks, considering homodyne detection with both direct and reverse reconciliation techniques. To address the issue of limited detector efficiency, we incorporate the phase-sensitive amplifier (PSA) as a compensating measure. The results show that the CV-QKD/GMCS system with PSA achieves a longer secure distance and a higher key rate compared to the system without PSA, considering both direct and reverse reconciliation algorithms. With an amplifier gain of 10, the reverse reconciliation algorithm achieves a secure distance of 5 km with a secret key rate of 10−1 bits/pulse. On the other hand, direct reconciliation reaches a secure distance of 2.82 km
A Scientometric Review and Analysis of Studies on the Barriers and Challenges of Sustainable Construction
No full textDespite numerous concerns about climate change and the deterioration of nature, the construction industry is still one of the largest consumers of minerals and natural resources. In recent decades, sustainable construction using renewable and recyclable materials, reducing energy, and the adoption of more green technologies with the aim of reducing harmful impacts on the environment have received profound worldwide attention. The more key stakeholders involved strive to achieve sustainability, the more barriers they may face, which requires investigating them to have an effective plan to recognize, prevent, and control them. This paper reviews, classifies, and analyzes the major barriers of sustainable construction between January 2000 and April 2023. In this scientometric study, 153 articles were selected from the Web of Science database. Then, bibliometrics, the creation of maps from network data, as well as the illustration and exploration of those maps were conducted with the HistCite 12.03.1 and VOSviewer 1.6.20 software programs. The analytical results showed that the most profound barriers of sustainable construction are classified into 12 groups: price, economic parameters, awareness, technical, policy and regulations, design, management and government, environmental, social, materials, planning, and market
BIM and Lean in Construction
No full textThe recognition of the connection between Building Information Modelling (BIM) and Lean Construction is relatively recent. Although these fields share a common objective, namely to improve the efficiency of construction, they had been advanced by their own communities or researchers and practitioners, with practically no interaction
Review of A Cultural Legal Studies Reflection on Tom Tyler’s Game: Animals, Video Games, and Humanity. Minneapolis: University of Minnesota Press, 2022, 248 pp., ISBN 9781760641849 (paperback)
No full textCounter-Radicalisation in UK Higher Education: A Vernacular Analysis of ‘Vulnerability' and the Prevent Duty
No full textThe UK Government defines vulnerability to radicalisation as, ‘the process by which a person comes to support terrorism and extremist ideologies associated with terrorist groups’. Given this relationship between radicalisation and terrorism, in 2015 the UK Government passed legislation to enhance the national capacity to pre-emptively identify vulnerable people by co opting public sector workers. This responsibility (‘the Prevent duty’) has mandated the monitoring of citizen’s behaviours based on a relationship between vulnerability, radicalisation, and terrorism that is far from concrete. Despite this, the duty is presented as a clear and actionable framework designed to support frontline workers identify vulnerability and report cases of concern. It is within this context that our paper adopts a vernacular approach to present findings from focus groups and interviews with university students and staff about their comprehension, experiences, and evaluations of vulnerability and the duty. We approach these insights as valuable (but oft neglected) instances of ‘everyday’ security knowledge and argue that they are particularly valuable in the context of a duty that co opts those within Higher Education as counter-radicalisation practitioners and subjects. Our paper argues that conceptual, operational, and normative disconnects between Government policy and vernacular insights ‘on the ground’ mean that the duty assumes an uncertain position within UKHE to the detriment to of its stated objectives
Memory consolidation in honey bees is enhanced by down-regulation of Down syndrome cell adhesion molecule and changes its alternative splicing
No full textDown syndrome cell adhesion molecule (Dscam) gene encodes a cell adhesion molecule required for neuronal wiring. A remarkable feature of arthropod Dscam is massive alternative splicing generating thousands of different isoforms from three variable clusters of alternative exons. Dscam expression and diversity arising from alternative splicing have been studied during development, but whether they exert functions in adult brains has not been determined. Here, using honey bees, we find that Dscam expression is critically linked to memory retention as reducing expression by RNAi enhances memory after reward learning in adult worker honey bees. Moreover, alternative splicing of Dscam is altered in all three variable clusters after learning. Since identical Dscam isoforms engage in homophilic interactions, these results suggest a mechanism to alter inclusion of variable exons during memory consolidation to modify neuronal connections for memory retention
Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases
No full textCardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study’s primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study’s outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model’s diagnostic accuracy for heart disease
Exploring the Effect of Display Type on Co-Located Multiple Player Gameplay Performance, Immersion, Social Presence, and Behavior Patterns
No full textWith advances in virtual reality (VR) technology, immersive head-mounted displays (HMDs) have become widely accessible. These devices have made social games and platforms like VRChat popular. Although the literature points to several factors that affect immersion and social presence, there has been no study that has explored the effect of social display setup on immersion and gameplay in multi-player social games. This work aims to shed light on this issue and investigates the effect of social display setup on gameplay performance and experience (i.e., immersion and social presence) in a multi-player competitive social game (i.e., Jenga). We conducted a one-way between-subjects experiment with 24 participants equally distributed in three groups (4 pairs of 2 participants in each group, who were all strangers to each other) according to three social display setups (2 small-screen tablets, 1 shared 40-inch large TV, and 2 VR HMDs). Our results indicate that (1) players gave a lower rating to challenge in the VR-based social setting than in the small-screen tablet display setting, and (2) gameplay behavior patterns are different among these social display setups