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Recent Progress and Perspective on Batteries made from Nuclear Waste
Get PDFSustainable energy sources are an immediate need to cope with the imminent issue of climate change the world is facing today. In particular, the long-lasting miniatured power sources that can supply energy continually to power handheld gadgets, sensors, electronic devices, unmanned airborne vehicles in space and extreme mining are some of the examples where this is an acute need. It is known from basic physics that radioactive materials decay over few years and some nuclear materials have their half-life until thousands of years. The past five decades of research have been spent harnessing the decay energy of the radioactive materials to develop batteries that can last until the radioactive reaction continues. Thus, an emergent opportunity of industrial symbiosis to make use of nuclear waste by using radioactive waste as raw material to develop batteries with long shelf life presents a great opportunity for sustainable energy resource development. However, the current canon of research on this topic is scarce. This perspective draws fresh discussions on the topic while highlighting future directions in this wealthy arena of research
The role of metacognitions in cyberbullying and cybervictimization among adolescents diagnosed with Major Depressive Disorder and Anxiety Disorders: A case‐control study
Get PDFCyberbullying is becoming increasingly widespread as individuals use technology more widely and frequently. Recent studies have shown a growing vulnerability for cyberbullying and cybervictimization, particularly in the adolescent population. We argue that dysfunctional metacognitions, which have been found to be prominent in various psychiatric disorders, may also play a role in predicting cyberbullying and cybervictimization over and above a variety of established factors including daily Internet use, social media use, depression, and anxiety. For this purpose, we recruited 121 adolescents diagnosed with Major Depressive Disorder (MDD) and 122 adolescents diagnosed with Anxiety Disorders (AD) from the child and adolescent psychiatric department of “Çankırı State Hospital” along with age and gender matched healthy controls (n=120). Participants completed the DSM-5 Depression and Anxiety Severity Scales, the Social Media Disorder Scale (SMDS), the Metacognitions Questionnaire for Children (MCQ-C), and the Revised Cyberbullying Inventory-II (RCBI-II). Cybervictimization scores were found to be higher in the MDD and AD groups when compared to healthy controls. Cyberbullying scores in the MDD group were higher than healthy controls. Additionally, the Superstition, Punishment and Responsibility sub-dimension of the MCQ-C was a significant predictor of cybervictimization in the AD group while controlling for daily Internet use, social media use and anxiety. However, metacognitions were not associated with cyberbullying in the MDD and AD groups, as well as with cybervictimization in the MDD group. We concluded that dysfunctional metacognitions may be a preventive therapeutic target in reducing the impact of cyberbullying in adolescents with AD
North Macedonia
Get PDFThis chapter will reflect of theatre development in North Macedonia since the country’s independence. In the 1990’s the country experienced significant shift in the nation’s theatre context, as well as in the wider historical and cultural context. Since then, theatre in Macedonia has begun to develop its own clear identity. The first part of this chapter will look at emerging theatre-making practices, that move away from more traditional dynamics of author-director duos. The second part of the chapter will look at the relationship between the established institutional structures and at the development of an independent sector in the country
An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features
Get PDFChronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage
MRI-based mechanical analysis of carotid atherosclerotic plaque using a material-property-mapping approach: A material-property-mapping method for plaque stress analysis
Get PDFBackground and objective
Atherosclerosis is a major underlying cause of cardiovascular conditions. In order to understand the biomechanics involved in the generation and rupture of atherosclerotic plaques, numerical analysis methods have been widely used. However, several factors limit the practical use of this information in a clinical setting. One of the key challenges in finite element analysis (FEA) is the reconstruction of the structure and the generation of a mesh. The complexity of the shapes associated with carotid plaques, including multiple components, makes the generation of meshes for biomechanical computation a difficult and in some cases, an impossible task. To address these challenges, in this study, we propose a novel material-property-mapping method for carotid atherosclerotic plaque stress analysis that aims to simplify the process.
Methods
The different carotid plaque components were identified and segmented using magnetic resonance imaging (MRI). For the mapping method, this information was used in conjunction with an in-house code, which provided the coordinates for each pixel/voxel and tissue type within a predetermined region of interest. These coordinates were utilized to assign specific material properties to each element in the volume mesh which provides a region of transition. The proposed method was subsequently compared to the traditional method, which involves creating a composed mesh for the arterial wall and plaque components, based on its location and size.
Results
The comparison between the proposed material-property-mapping method and the traditional method was performed in 2D, 3D structural-only, and fluid-structure interaction (FSI) simulations in terms of stress, wall shear stress (WSS), time-averaged WSS (TAWSS), and oscillatory shear index (OSI). The stress contours from both methods were found to be similar, although the proposed method tended to produce lower local maximum stress values. The WSS contours were also in agreement between the two methods. The velocity contours generated by the proposed method were verified against phase-contrast magnetic resonance imaging (MRI) measurements, for a higher level of confidence.
Conclusion
This study shows that a material-property-mapping method can effectively be used for analyzing the biomechanics of carotid plaques in a patient-specific manner. This approach has the potential to streamline the process of creating volume meshes for complex biological structures, such as carotid plaques, and to provide a more efficient and less labor-intensive method
Safety, Quality Control, And Sustainability In Construction: Exploring The Nexus - A Review
Get PDFThis comprehensive review elucidates the intertwined relationship between safety, quality control, and sustainability within the construction sector, highlighting the critical need for integrating these elements to promote optimal project outcomes and long-term industry advancement. The study commences with an in-depth exploration of existing literature, focusing on diverse methodologies, strategies, and frameworks employed to enhance safety and enforce stringent quality control, thus contributing to the overall
sustainability of construction projects. Safety is identified as a paramount concern in construction, significantly influencing both quality and sustainability. The lack of safety not only jeopardizes human lives but also results in cost overruns and project delays, undermining the overall quality and sustainability.
Quality control, herein, is discussed in relation to its pivotal role in minimizing errors and rework, ensuring
adherence to standards, and facilitating the attainment of sustainability goals through resource efficiency and waste reduction. Sustainability in construction is dissected through its three foundational pillars: economic viability, social equity, and environmental integrity. This review details how the integration of safety and quality control significantly impacts these pillars, highlighting the synergy between construction practices, resource optimization, stakeholder well-being, and ecological preservation. Empirical studies, theoretical frameworks, and case studies form the basis of this review, providing a multifaceted understanding of the interdependence between safety, quality control, and sustainability in construction. The assessment reveals that the construction industry is progressively acknowledging the inherent connection between these components, with contemporary practices and policies increasingly reflecting an integrated approach. The article concludes by underscoring the imperative for continuous research and development, innovations, and policy interventions to strengthen the nexus between safety, quality control, and sustainability in construction. It also advocates for a holistic approach that unifies these elements to drive industry resilience, promote sustainable development, and ensure the well-being and prosperity of communities and the environment
Building a Digital Transformation Maturity Evaluation Model for Construction Enterprises Based on the Analytic Hierarchy Process and Decision-Making Trial and Evaluation Laboratory Method
Get PDFWith digital transformation underway in various Chinese construction enterprises, each enterprise has progressed differently, and a clear direction for future digital transformation and upgrading is lacking. As such, the importance of measuring the level of digitization among Chinese construction enterprises is increasing. This paper presents a model for evaluating digital transformation maturity within construction enterprises. The model considers six aspects: digital strategy, digital business applications, digital technology capabilities, and so on. The digital maturity of enterprises is determined using the Analysis of Hierarchy (AHP)-Decision Making Experiment and Evaluation Laboratory (DEMATEL) method. Technical abbreviations are explained when first used. This study demonstrates that digital business applications are the most significant primary indicator, with a weight of 29.53%. The success of digital transformation in the construction industry is strongly influenced by the interconnection between digital technology and construction sites, as well as other factors such as new technical personnel, digital infrastructure, digital innovation, and innovation iteration ability. It is crucial to understand how digital technology and the construction industry can effectively connect in order to achieve success in this realm. This paper aims to enhance the digital transformation capabilities and efficiency of construction companies and boost their core competitiveness through targeted measures
Mobile phone text messages to support people to stop smoking by switching to vaping: co-development, co-production, and initial testing
No full textBackground: Text messages are affordable, scalable, and effective smoking cessation interventions. However, there is little research on text message interventions specifically designed to support people who smoke to quit by switching to vaping.
Objective: Over three phases, with vapers and smokers, we co-developed and co-produced a mobile phone text message programme. The co-production paradigm allowed us to collaborate with researchers and the community to develop a more relevant, acceptable, and equitable text message programme.
Methods: In Phase 1, we engaged people who vape via Twitter and received 167 responses to our request to write text messages for people who wish to quit smoking by switching to vaping. We screened, adjusted, refined, and themed the messages, resulting in a set of 95 that were mapped against COM-B (Capability, Opportunity, Motivation) behaviour change constructs. In Phase 2, we evaluated the 95 messages from Phase 1 via an online survey, where participants (n= 202, 66 female) rated up to 20 messages on 7-point Likert Scales on 9 constructs: understandability, clarity, believability, helpfulness, interesting; inoffensive; positive; enthusiastic, and how happy they would be to receive the message. In Phase 3, we implemented the final set of text messages as part of a larger randomised optimisation trial where 603 (Mage = 38.33; 369 female) participants received text message support and then rated their usefulness, frequency and provided free-text comments at a 12-week follow up.
Results: For Phase 2, means and SDs were calculated for each message across the 9 constructs. Those with means below the neutral anchor of 4 or with unfavourable comments were discussed with co-vapers and further refined or removed. This resulted in a final set of 78 that were mapped against early, mid, or late stage of quitting to create an order for the messages. For Phase 3, 202 (38%) of participants provided ratings at the 12 week follow up. 70% reported that the text messages had been useful and a significant association between quit rates and usefulness ratings was found (χ2 = 9.64, df = 1, p < 0.01). A content analysis of free-text comments revealed the two most common positive themes were: helpful (28%) and encouraging (13%) and the two most common negative themes were: too frequent (19%) and annoying (9%).
Conclusions: Here we have described the initial co-production and co-development of a set of text messages to help smokers stop smoking by transitioning to vaping. We encourage researchers to use, further develop and evaluate the set of text messages, and adapt it to target populations and relevant contexts
Smart Farming using Artificial Intelligence and edge cloud computing
Get PDFThis thesis investigates the application of artificial intelligence to smart farming. The Amaranthus Viridis crop has been grown within London South Bank University, and
different machine learning models have been used to evaluate the crop dataset.
A comparative analysis of the performance of the machine learning models for the datasets from the Nutrient Film Technique (NFT), Aeroponic (AER), Aggregate (AG), and Floating Hydroponic systems from the Department of Agriculture, University of Peloponnese (UP), Kalamata, Greece to predict the Onion Bulb Diameter (OBD). The dataset has been from four different hydroponics systems, namely Aggregate (AG), Aeroponics (AER), Floating and Nutrient Film Technic (NFT). The onion crop has been grown for 92 days after transplant from the nursery.
Artificial intelligence subsets, such as machine learning and deep neural networks, have been used to evaluate the Amaranthus Viridis crop. The centralised smart farm network models evaluate the provided dataset while sharing the data with the server during training. The decentralised network for a smart farm has been considered, a scenario where the raw dataset has yet to be shared with the server during the dataset evaluation. Federated learning models allowed the researcher to train the models and make predictions of the dependent variables.
Smart farming involves applying information and communication technology to the traditional farm system. It implies the use of the Internet of Things (IoT), edge and cloud computing, and centralised and decentralised machine learning models for predictions of the farm produce. A survey of the existing smart farms identifies the various challenges experienced by farmers, which include low technological know-how of smart farm techniques, inadequate infrastructure provision, computational power issues with technological devices, poor internet facilities, high latency within the existing internet connectivity in the farms, insufficient human, technical skills capacity to manage smart farm operations, security of smart farm data during transmission, data reliability, the communication cost of smart farm network.
The floating hydroponic system involves planting the crop on the water surface, and wool rock holds the crop within the pot, allowing its roots to touch the water beneath as it
grows.
The Onion Bulb Diameter (OBD) dataset for four different hydroponic systems, namely Floating, AER, AG, and NFT hydroponic systems, has been analysed using the XGBoost, Linear regression, Deep Neural Network (DNN), and Federated Split Learning (FSL) models for their predictive and interpretive abilities. To automate the predictions of the OBD, machine learning models have been used to predict the OBD for days not considered manually within the crop life cycle. The model helps the farmers determine the harvest even before the commencement of the new planting season based on the previous planting season dataset. The developed models have been used to analyse the Amaranthus Viridis Leaves image
dataset comprehensively. The Convolutional Neural Network (CNN) model has been used to determine the percentage of the predicted Amaranthus leaves that match the original images from a hydroponic smart farm. The CNN forecasted a higher accuracy than the KNearest Neighbour, Support Vector classifier and Decision Tree model.
The crop growth rate was analysed using machine learning algorithms. The XGBoost models produced higher prediction values for the crop growth rate than the Theil-Sen, Decision
Tree, Support Vector, Quantile and K-Neighbours regressors.
The dataset is shared with the server during training in a centralised network. The federated learning models train the dataset from the smart farm network without having access to the raw dataset with the server. The federated averaging model has been used to investigate the prediction of crop types using climatic parameters as the independent variables. The hyper-tuned federated Learning model predicted the crop chickpea and achieved high accuracy.
The federated Learning smart farm network emulation was used to compare the client, server and centralised node performance. The Server model converged faster than the edge node models, classical centralised models,since it uses all the combined weights from the edge nodes to aggregate the combined models before sending them to the edge node for further training of the raw dataset. The results show that the combined decentralised network model produced a higher accuracy than the classical centralised model