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People with long-term conditions are more adherent to protective behaviours against infectious disease
Get PDFObjectivesTo investigate the relationship between long-term condition (LTC) status and adherence to protective behaviours against infectious disease (face covering, physical distancing, hand hygiene).Study designRepresentative cross-sectional observational survey in summer 2020 in Scotland.MethodsIndependent variable is LTC status (LTC, disability, no LTC); dependent variable is adherence to protective behaviours (face covering, hand hygiene, social distancing); moderator variables are age, gender and area deprivation; mediator variables are perceived threat and psychological distress. P values of p < 0.05 were taken as statistically significant.Results3972 participants of whom 2696 (67.9 %) indicated not having a LTC. People with no LTC had lowest adherence to protective behaviours, perceived threat and psychological distress. Age did not moderate the relationship between LTC status and adherence; females were more adherent than males and this gender difference was greater in people with disability compared to people with no LTC; adherence was greater for people with a LTC in the more deprived areas compared to the least deprived areas whereas adherence in those with no LTC was not related to area deprivation; threat appraisal partially mediated the relationship between having a LTC or disability and adherence; psychological distress did not mediate the relationship between LTC status and adherence.ConclusionsThis study addresses a gap in evidence about protective behaviours of people with LTCs. Perceptions of threat may be useful intervention targets against winter flu and during future pandemics in order to protect people with LTCs who are one of the most vulnerable groups of the population
A novel intelligent optimal control methodology for energy balancing of microgrids with renewable energy and storage batteries
No full textA price-based demand response (DR) program is essential for maintaining energy balance in a smart power grid (SPG). Given the uncertainty and stochastic nature of renewable energy sources (RESs) and loads, dynamic pricing strategies are required to minimize instant energy shortage risks and ensure energy balancing. This study introduces an optimal adaptive control methodology based on an elastic demand control mechanism using dynamic pricing to address energy balancing in renewable smart microgrids. The proposed optimal adaptive controller, referred to as the ant colony optimization algorithm tuned super-twisting sliding mode controller (ACO-STSMC), effectively handles system nonlinearities and enhances the response of the system to uncertainties and variability of RESs and loads. The ACO-STSMC regulates energy price signals, manages the net load demand, and responds to RESs generation fluctuations, ultimately achieving and maintaining an energy balance in renewable energy smart microgrids. The system exhibits a minimal mismatch between generation and demand, avoids instant demand overshots, and maintains low-energy pricing signal volatility. The findings demonstrate that the developed ACO-STSMC outperforms benchmark controllers such as PSO-PI, PSO-FOPI, PSO-STSMC, ACO-PI, and ACO-FOPI in terms of energy balancing in renewable-energy smart microgrids. The results also confirm that the elastic DR based on dynamic energy pricing with the ACO-STSMC can effectively track the generation of renewable energy smart microgrids
The effect of electro blow spinning parameters on the characteristics of polylactic acid nanofibers: Towards green development of high-performance biodegradable membrane
Get PDFElectro-blow spinning represents a novel and emerging hybridised technology for producing high-quality, large-scale nanofibers. The applied pressure, accompanied by an electric field, functions as a drafting force to generate ultrafine, homogeneous nanofibers. Herein, we utilised a sustainable solvent to produce polylactic acid nanofibers via the electro-blow spinning technique. A parametric study investigating the effect of polymer concentration, pressure, and voltage on the characteristics of the produced nanofibers was thoroughly conducted. The produced nanofibers were tested using scanning electron microscopy, Fourier-transform infrared spectroscopy, differential scanning calorimetry, and tensile test. The findings revealed that air pressure plays a crucial role in the electro-blow spinning process, while introducing electric field enhances the spinnability and stretchability of nanofibers. Increasing the applied pressure and voltage led to finer fibres with improved crystallinity and mechanical strength. However, excessive pressure or voltage can cause jet instability, resulting in defects such as fused or broken fibres. Our study suggests that the most recommended parameters for uniform and high-quality nanofibers are 10 wt%, 5 bar, and 20 kV. Moreover, the polylactic acid nanofibers were further evaluated for air filtration performance using a customised filtration setup to determine their suitability as a facemask material. The results indicated a notably high filtration efficiency, reaching up to 98 %, with a corresponding pressure drop between 137 and 163 Pa. These preliminary findings strongly suggest that produced nanofibers are highly promising candidates for medical textile applications, particularly in the development of facemasks
Sex-and length-dependent variation in migratory propensity in brown trout
Get PDFIn partially migratory species, individuals either migrate at some point(s) in life or reside within their natal habitat throughout life. For salmonid fish, migration creates opportunities for feeding and growth, but it is also associated with increased mortality risk. Such trade-offs likely differ between the sexes, since reproductive output is more closely tied to body size in females than males. However, testing hypotheses on sex-specific migratory behaviour in would-be first-time migratory salmonids is difficult, since sexes are generally morphologically indistinguishable prior to maturation. Previous studies have evaluated the influence of sex on migration based on dissection of migratory juveniles or the sex ratio of returning adults. However, both approaches are potentially biased by differential survival during migration. Here, we utilise advances in minimally invasive genetic sex-determination methods for salmonids to investigate sex-specific, spring out-migration propensity in potamodromous brown trout (Salmo trutta) in a pre-Alpine, central European lake. We show that there are marked differences in migratory behaviour between males and females, with small (~10 cm) females being approximately twice as likely to migrate out of their natal river in spring compared to similarly sized males, which generally migrate for the first time at larger sizes (in similar proportions to larger females). This study highlights how novel genetic sex-determination techniques can provide insight into the sex- and size-specific life-history trade-offs that shape migration propensity. Moving forward, these techniques should become useful tools for ecologists and fisheries managers
Studies in Scottish Literature - Special Issue: Hugh MacDiarmid at 100: Essays on the anniversary of his first publication
No full textSpecial Issue edito
DMF-Net: A Dual-Encoding Multi-Scale Fusion Network for Pavement Crack Detection
No full textCurrently, cracks are the most common defect in pavement diseases. Long-term non-maintenance can lead to crack lengthening and expansion, causing serious traffic accidents, as well as shortening the service life of pavement cracks. Therefore, it is of utmost importance to maintain cracks at an early stage. Due to the effect of some challenging factors, such as various shape information of the cracks, complex textured backgrounds, light shadows, similar texture objects, micro cracks and other factors, accurate crack detection still faces a certain challenges. To solve the above problems, a dual-encoding multi-scale fusion network based on the combination of convolutional neural network (CNN) and transformer network is proposed, named DMF-Net. To obtain stronger feature representations, a dual-encoding path is built to acquire global context features and local detail information simultaneously, where global context features are extracted based on the transformer branch, and the local detail features are extracted based on the CNN branch to detect tiny details of the cracks. Meanwhile, an interactive attention learning (IAL) module is introduced to effectively fuse the global features from the transformer branch and the local detail information from the CNN branch, achieving mutual communication and learning of different feature information. In addition, to enrich the feature representation ability, an attention-based feature enhancement (AFE) module is introduced to acquire more global contexts. Furthermore, faced with the crack detection task with class imbalance issue, a triple attention module (TAM) is built to emphasize the micro cracks. Finally, in the segmentation prediction stage, the deep supervision mechanism is also introduced to accelerate the convergence speed of the model, and serve effective multi-scale feature fusion. Compared with the current mainstream segmentation models, excellent performance has been obtained, which could provide a feasible scheme ..
Bare‐Bones particle Swarm optimization‐based quantization for fast and energy efficient convolutional neural networks
Get PDFNeural network quantization is a critical method for reducing memory usage and computational complexity in deep learning models, making them more suitable for deployment on resource-constrained devices. In this article, we propose a method called BBPSO-Quantizer, which utilizes an enhanced Bare-Bones Particle Swarm Optimization algorithm, to address the challenging problem of mixed precision quantization of convolutional neural networks (CNNs). Our proposed algorithm leverages a new population initialization, a robust screening process, and a local search strategy to improve the search performance and guide the population towards a feasible region. Additionally, Deb's constraint handling method is incorporated to ensure that the optimized solutions satisfy the functional constraints. The effectiveness of our BBPSO-Quantizer is evaluated on various state-of-the-art CNN architectures, including VGG, DenseNet, ResNet, and MobileNetV2, using CIFAR-10, CIFAR-100, and Tiny ImageNet datasets. Comparative results demonstrate that our method delivers an excellent tradeoff between accuracy and computational efficiency
Revolutionizing family businesses with artificial intelligence: a perspective article
No full textPurposeThis perspective article addresses the essential need to comprehend what artificial intelligence (AI) entails and how it can revolutionize the family business sector.Design/methodology/approachThis article presents a literature review and the authors' perspectives to unravel the potential benefits of AI in family businesses.FindingsIt is crucial to understand what AI is and its various types before the authors can discern which AI tools can be beneficial or tailored to family businesses. A promising area for future research is the use of Theory of Mind AI, which can help minimize conflicts of interest among family business members and assist in clear decision-making and succession planning. However, integrating AI brings about ethical implications, such as data privacy concerns, the need for transparency in AI decision-making and the necessity to ensure fair AI practices. These are all critical issues that the future research will need to tackle.Originality/valueThis article emphasizes the potential of AI to be customized and incorporated to tackle the distinct dynamics, obstacles and opportunities prevalent in family-owned businesses. It also proposes future research areas that could enhance the application of AI in such enterprises
DTL-IDS: An optimized Intrusion Detection Framework using Deep Transfer Learning and Genetic Algorithm
Get PDFIn the dynamic field of the Industrial Internet of Things (IIoT), the networks are increasingly vulnerable to a diverse range of cyberattacks. This vulnerability necessitates the development of advanced intrusion detection systems (IDSs). Addressing this need, our research contributes to the existing cybersecurity literature by introducing an optimized Intrusion Detection System based on Deep Transfer Learning (DTL), specifically tailored for heterogeneous IIoT networks. Our framework employs a tri-layer architectural approach that synergistically integrates Convolutional Neural Networks (CNNs), Genetic Algorithms (GA), and bootstrap aggregation ensemble techniques. The methodology is executed in three critical stages: First, we convert a state-of-the-art cybersecurity dataset, Edge_IIoTset, into image data, thereby facilitating CNN-based analytics. Second, GA is utilized to fine-tune the hyperparameters of each base learning model, enhancing the model’s adaptability and performance. Finally, the outputs of the top-performing models are amalgamated using ensemble techniques, bolstering the robustness of the IDS. Through rigorous evaluation protocols, our framework demonstrated exceptional performance, reliably achieving a 100% attack detection accuracy rate. This result establishes our framework as highly effective against 14 distinct types of cyberattacks. The findings bear significant implications for the ongoing development of secure, efficient, and adaptive IDS solutions in the complex landscape of IIoT networks
Multifunctional flexible and stretchable electrochromic energy storage devices
Get PDFElectrochromic energy storage devices (EESDs) including electrochromic supercapacitors (ESC) and electrochromic batteries (ECB) have received significant recent attention in wearables, smart windows, and colour-changing sunglasses due to their multi-functionality, including colour variation under various charge densities. The performance of EESDs is mainly dependent on the properties of three major components (i) the current collector/substrate (cc/substrate) (ii) the electrolyte and (iii) electrochromic materials (ECM). Among various EESDs, advanced flexible or stretchable devices offer better functionality than conventional rigid glass-based devices and are easily integrated with any curved surface. However, in flexible or stretchable EESDs, delamination, dissociation and degradation critically affect the lifecycle and stable performance and are key issues to solve for widespread deployment of the technology. A detailed review of the materials and their performance as flexible EESDs is therefore propitious for the design and engineering of next-generation ECBs and ESCs. In this review, we considered the importance of various materials and their implementation in flexible and stretchable EESD fabrication along with their potential application in sustainable energy systems
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