4208 research outputs found
Sort by
Studies on the Reactions of Lactone Intermediates Derived from Levulinic Acid: Telescoped Routes to Higher Levulinate Ester Biofuels
Full text linkThe development of efficient strategies for the synthesis of levulinate esters is of significant current interest due to their potential as biofuels and fuel additives. Herein, we report a novel strategy to access levulinate esters derived from higher alcohols directly from levulinic acid through the in situ generation of lactone intermediates employing commercial heterogeneous catalysts, such as Amberlyst-15. This strategy employs a telescoped approach in which the lactonization/ring-opening reactions are combined into an operationally simple one-pot procedure. This strategy is advantageous as it employs a readily available and inexpensive catalyst and proceeds in short reaction times to produce excellent yields of higher levulinate esters with high selectivity. Furthermore, the Amberlyst-15 catalyst is fully recyclable and can be re-used without loss of activity or selectivity
Uncomplicated Type B Aortic Dissection: A European Multicentre Cross-Sectional Evaluation
Full text linkBACKGROUND: A multicentre European randomized control trial - European Uncomplicated Type B Aortic Repair (EU-TBAR) is being developed to compare pre-emptive thoracic endovascular aortic repair (TEVAR) with custom-made devices versus conventional optimal medical therapy. The pretrial set-up is confluent on different pillars, including evaluation of 1) European activity, trends, and governance; 2) outcome reporting; and 3) cost evaluation. This article aimed to demonstrate the observational cross-sectional survey results from participating centers and highlight the risk assessment, activity, practices, and governance of uncomplicated type B aortic dissection (uTBAD).METHODS: This observational cross-sectional European survey used a questionnaire that examined the understanding, risk assessment, local governance oversight, and clinical activity of uTBAD. The data were collected and managed using Research Electronic Data Capture (REDCap).RESULTS: Out of 43 surveyed surgeons, 37 (86%) responded within a month from 14 European countries. Most reported low annual uTBAD encounters, with autumn being the most common season for cases. Pre-emptive TEVAR was recommended by 43.2% of participants, who favored subacute intervention timing. The Gore TAG was the most used TEVAR device, and custom devices were available for 73% of respondents. Risk factors for uTBAD were ranked, with 'Rapid Aortic Enlargement' deemed most critical. A majority of centers had protocols and multidisciplinary teams, with most having readily available radiology services. Only 45.9% had transfer services to specialized centers.CONCLUSIONS: uTBAD remains a misnomer of a dynamic, ongoing disease process requiring early diagnosis and intervention. Pre-emptive TEVAR in high-risk uTBAD is becoming more common, with encouraging results prompting an expansion of indication criteria to a broader uTBAD population managed conservatively. Nevertheless, further evidence is needed through large randomized controlled trials, mainly European collaboratives, to reach a definitive conclusion on the optimum surgical management of uTBAD.</p
Integrating Cognitive Intelligence and VANET for Effective Traffic Congestion Detection in Smart Urban Mobility
Full text linkThe delay in transporting essential goods is primarily attributed to widespread traffic congestionglobally. This issue not only results in significant time and fuel wastage but also poses a considerablechallenge in efficiently disseminating traffic information and managing road conditions for authorities.Addressing these challenges, vehicular ad hoc networks (VANETs) have emerged as a crucial component of the cognitive intelligent transportation system (C-ITS). To tackle this issue effectively, vehicle-to-vehicle (V2V) communication plays a crucial role in fostering cooperation and optimizing route management within transportation networks. This paper proposes an innovative congestion detection system that integrates the fuzzy k-means (FKM) clustering technique with the fuzzy analytical hierarchy process (FAHP). Utilizing the simulation of urban mobility (SUMO) simulator, a detailed transport network is modeled where vehicle parameters indicative of congestion are collected, integrated using sensor fusion, and analyzed. These parameters are processed using FKM clustering and a mathematical mean algorithm to identify key congestion indicators. Subsequently, FAHP prioritizes these collected parameters, pinpointing congestion hotspots within specific routes. By incorporating cognitive intelligence, the system continuously refines congestion detection and response strategies, enhancing traffic flow efficiency and enabling proactive congestion avoidance. This approach promises a more effective congestion detection methodology with minimal installation costs. Moreover, it can be effortlessly integrated into vehicles to facilitate congestion avoidance strategies, thereby enhancing overall traffic flow efficiency and mitigating the negative impacts of traffic congestion on transportation networks globally
Enhancing Plant Disease Detection Using Attention-Augmented Residual Networks and Faster Region–Convolutional Networks
Full text linkRapid and accurate detection of plant diseases is crucial for agricultural productivity and food security. Traditional methods are labor-intensive and often unreliable. To overcome these limitations, this research introduces an innovative approach that integrates attention mechanisms into residual networks (ResNets) and utilizes Generative Adversarial Networks (GANs) for data augmentation. The method incorporates Attention-Augmented Residual Networks (AARN), which enhance feature extraction and classification by focusing on critical image regions. A Conditional GAN (cGAN) generates synthetic images of diseased and healthy plants, increasing dataset diversity. By combining AARN with Faster Region-Convolutional Neural Network (Faster-RCNN), detection capabilities are further enhanced. Training the AARN model on this augmented dataset improves generalization, achieving an impressive 98.78%accuracy in plant disease classification. The attention-augmented residuals boost the Faster-RCNN’s effectiveness by 23.84%, improving feature relevance and reducing overfitting. Comparative analysis shows that this method outperforms existing techniques in accuracy, precision, recall, and F1-score, offering a robust solution for plant disease detection. This integration of advanced deep learning techniques significantly improves automated plant disease identification, benefiting agricultural management practices
A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning
Full text linkThe growing use of IoT has brought enormous safety issues that constantly demand stronger hide from increasing risks of intrusions. This paper proposes an Advanced LSTM-CNN Secure Framework to optimize real-time intrusion detection in the IoT context. It adds LSTM layers, which allow for temporal dependencies to be learned, and CNN layers to decompose spatial features which makes this model efficient in identifying threats. It is important to note that the used BoT-IoT dataset involves various cyber attack typologies like DDoS, botnet, reconnaissance, and data exfiltration. These outcomes present that the proposed LSTM-CNN model has 99.87% accuracy, 99.89% precision, and 99.85% recall with a low false positive rate of 0.13% and exceeds CNN, RNN, Standard LSTM, BiLSTM, GRU deep learning models. In addition, the model has 90.2% accuracy in conditions of adversarial attack proving that the model is robust and can be used for practical purposes. Based on feature importance analysis using SHAP, the work finds that packet size, connection duration, and protocol type should be the possible indicators for threat detection. These outcomes suggest that the Hybrid LSTM-CNN model could be useful in improving the security of IoT devices to provide increased reliability with low false alarm rates
An Efficient Data Driven Model for WSN using Artificial Neural Network
Full text linkModern communication systems rely on WSNs as the most effective medium for remote monitoring and data acquisition across different environments. However, their inherent power constraints make providing security while maintaining efficiency in WSN nodes challenging. The existing solutions in the literature often fail to strike the best possible balance between security effectiveness and resource utilization. This paper proposes a data-driven intrusion detection system using a neural-network architecture optimized for a resource-constrained WSN environment. Based on the benchmark NSL-KDD dataset, our model uses medium-sized neural networks with activation functions and hidden layers to process patterns in the network traffic equally well. The study finds that our ANN-based approach outperforms the current techniques with 98.62% accuracy and a minimal computational cost of 2,053 units. This outcome surpassed all the comparable methods, including Gaussian SVM, which attained 98.52% accuracy with a cost of 2,194 units, and Decision Tree at a cost of 5,731 units, with an accuracy of 97.83% respectively
Twelve Recommendations for Supporting Activity Engagement in Extra Care Housing
Full text linkExtra Care Housing (ECH) provides supported accommodation for older adults needing care by Housing Associations. It focuses on social engagement and activities that improve health outcomes, though promoting tenant participation poses challenges. This study aimed to explore barriers and facilitators to social engagement and activity participation in ECH from tenant and staff perspectives, examine the impact of the COVID-1 pandemic as a lens for understanding the effects of crises situations on care more broadly, and develop recommendations to enhance social engagement in the ECH setting. A qualitative design was utilized employing a multi-stakeholder approach. Participants (n = 16) were recruited across three ECH sites in Wales. Semi-structured interviews were conducted individually with six staff and four tenants, and a focus group conducted with six tenants. Two themes were identified for both staff and tenants which then informed the development of 12 actionable recommendations which are aimed at supporting tenants to meaningfully engage. These relate to staff considerations, forward planning, activity types and accessibility and inclusivity. Adopting these recommendations may help housing associations enhance the provision and support of social engagement and activities within the extra care housing setting, potentially benefiting the mental/physical health of tenants.</p
The Mental Health and Well‐Being of Adults With Intellectual Disabilities During the COVID‐19 Pandemic Across the UK: A Four‐Wave Longitudinal Analysis
Full text linkBackground: Research concerning the impact of the COVID‐19 pandemic on the mental health and well‐being of adults with intellectual disabilities has been cross‐sectional and small scale. We examined the trajectory of mental health and well‐being across the pandemic period across the UK and the factors which predicted different mental health trajectories. Method: Adults with intellectual disabilities participated in co‐designed structured interviews. Four waves of data were collected between December 2020 and late 2022. At Wave 1, 621 adults with intellectual disabilities participated, with 355 at Wave 4. Well‐being, pandemic anxiety, depression, anxiety, anger and loneliness outcomes were measured. Latent class mixed modelling was used to identify subgroups and within‐group trajectories. Results: Well‐being and pandemic anxiety remained relatively stable across time, but levels of anger, depression, anxiety and loneliness reduced gradually over time. Overall patterns masked trajectory subgroups, with differences in intercept and steepness of decline or increase in mental health problems. Different factors were generally influential for trajectory class membership and overall change across time for outcomes. Leaving the house for exercise or green spaces reported increasing well‐being and reduced loneliness. Similarly, those working, volunteering or in education at Wave 1 were found to have increasing well‐being and reduced loneliness, sadness and worry, and increasing wellbeing and reducing anger if they were working pre‐pandemic. Conclusions: Social connection and engagement in purposeful activity were vital to maintaining the mental health and well‐being of people with intellectual disabilities. Factors that were found to reduce mental well‐being during the pandemic should be considered in planning for future major public health challenges and in promoting better mental well‐being for people with intellectual disabilities in everyday life
Applying Web-Based Technologies to Better Understand Access to Services using Public Transport
Full text linkDivergences in the framing of inclusive education across the UK: a four nations critical policy analysis
Full text linkCommitments to inclusive education have been articulated in policy across the UK, in the context of increasingly inclusive rhetoric in education policy globally over recent years. This paper uses a critical policy analysis approach to understand the framing of inclusion within national legislation, policy documents and associated key resources from across the four UK nations of England, Scotland, Wales and Northern Ireland. The paper seeks to understand how the four UK nations articulate and portray their inclusive education policies and the political and ideological motivations and priorities that are apparent within these policies. Through this analysis, we find not only divergence between the four nations, but also within the policy documents of each nation. While documentation from Scotland shows a clearer voice and fewer examples of problematising the learner, across all UK nations we see complicated messaging and a lack of coherence in inclusive education policy