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Standardized assessment of evidence supporting the adoption of mobile health solutions: A Clinical Consensus Statement of the ESC Regulatory Affairs Committee
Get PDFMobile health (mHealth) solutions have the potential to improve self-management and clinical care. For successful integration into routine clinical practice, healthcare professionals (HCPs) need accepted criteria helping the mHealth solutions’ selection, while patients require transparency to trust their use. Information about their evidence, safety and security may be hard to obtain and consensus is lacking on the level of required evidence. The new Medical Device Regulation is more stringent than its predecessor, yet its scope does not span all intended uses and several difficulties remain. The European Society of Cardiology Regulatory Affairs Committee set up a Task Force to explore existing assessment frameworks and clinical and cost-effectiveness evidence. This knowledge was used to propose criteria with which HCPs could evaluate mHealth solutions spanning diagnostic support, therapeutics, remote follow-up and education, specifically for cardiac rhythm management, heart failure and preventive cardiology. While curated national libraries of health apps may be helpful, their requirements and rigour in initial and follow-up assessments may vary significantly. The recently developed CEN-ISO/TS 82304-2 health app quality assessment framework has the potential to address this issue and to become a widely used and efficient tool to help drive decision-making internationally. The Task Force would like to stress the importance of co-development of solutions with relevant stakeholders, and maintenance of health information in apps to ensure these remain evidence-based and consistent with best practice. Several general and domain-specific criteria are advised to assist HCPs in their assessment of clinical evidence to provide informed advice to patients about mHealth utilization
Data as a potential path for the automotive aftersales business to remain active through and after the decarbonisation
Get PDFDecarbonisation of transport implies transition and shift for many traditional businesses, one of which is the automotive aftersales. One of the strategies identified to address decarbonisation and meet GHG emissions targets is the adoption of Electric Vehicles (EVs) and other green power train technologies. Nonetheless, decarbonisation and EVs are not the only drivers that menace the automotive aftersales. Data also appears as an emerging actor in the industry that could prompt influential transformations in conjunction with EVs and decarbonisation strategies.In this paper we aim to identify and understand the aftersales stakeholders’ perspectives related to the current challenges posed by decarbonisation strategies and examine potential responses that the automotive aftersales business could undertake. Semi-structured interviews with a variety of automotive industry experts were used. The interview topics were based on results from an online survey and extensive document analysis. The interview sample involved expert stakeholders from original equipment manufacturers (OEMS) and the independent aftermarket (IAM) with experience in Latin American and European markets. The interviews addressed three main topics: (1) Trends and future expectations; (2) Impacts on aftersales; and (3) Public policy. This paper focuses primarily on impacts of decarbonisation on automotive aftersales and the potential role of data in that business.Results show that investment in technology will be a condition for businesses that want to remain active in the industry which could affect small players. Furthermore, experts agree that incumbent manufacturers are not filling the technology gap that the energy transition is creating in the automotive sector, a consequence of which will be the entrance of new players from other sectors. The aftersales business will be transformed to B2B which potentially means loss of bargaining control of current players. Moreover, policy makers are seen as unreliable to lead the transition agenda.These results will be used to inform further scenario-based research to develop future scenarios for aftersales business under different strategies and suggest recommendations for new business models considering decarbonisation strategies
A Divine Fury
No full textA religious serial killer is haunting Florence and only Cesare Aldo can stop them. A Divine Fury is an atmospheric historical thriller by D. V. Bishop, set in Renaissance Italy.'Full of dash and atmosphere' - The Times'Fast becoming a serious rival to C. J. Sansom and S. J. Parris' – Historical Novel SocietyFlorence. Autumn, 1539.Cesare Aldo was once an officer for the city’s most feared criminal court. Following a period of exile, he is back – but demoted to night patrol, when only the drunk and the dangerous roam the streets.Chasing a suspect in the rain, Aldo discovers a horrifying scene beneath Michelangelo’s statue of David. Lifeless eyes gaze from the face of a man whose body has been posed as if crucified. It’s clear the killer had religious motives.When more bodies appear, Aldo believes an unholy murderer is stalking the citizens of Florence. Watching. Hunting. Waiting for the perfect moment to strike again . .
Chaotic Quantum Encryption to Secure Image Data in Post Quantum Consumer Technology
Get PDFThe rapid advancement in consumer technology has led to an exponential increase in the connected devices, resulting in an enormous and continuous flow of data, particularly the image data. This data needs to be processed, managed, and secured efficiently, especially in the quantum-enabled consumer technology era. This paper, in this regards, presents a quantum image encryption scheme featuring a novel two-phase chaotic confusion-diffusion architecture. The proposed architecture consists of four distinct confusion-diffusion modules that perform a simultaneous qubit and pixel-level encryption on both the position and intensity of quantum encoded pixels. Moreover, quantum circuits for ’qubit-level chaotic transformation’ and ’chaos-based selective perfect shuffle operation’ have been implemented, which collectively enhance the encryption strength of the proposed scheme. Extensive evaluation has been performed based on various statistical security parameters, such as entropy and correlation. When subjected to differential attacks, the proposed scheme proved its resilience exhibiting ideal results of average 99.6% NPCR (Number of Pixels Change Rate) and 33.5% UACI (Unified Average Changing Intensity). Besides, the proposed scheme also demonstrated resilience against occlusion attacks. Tests involving 50% data occlusion from encrypted images validated the proposed scheme’s capability to successfully decrypt the tampered images, recovering maximum information
Arabic text classification based on analogical proportions
Get PDFText classification is the process of labelling a given set of text documents with predefined classes or categories. Existing Arabic text classifiers are either applying classic Machine Learning algorithms such as k-NN and SVM or using modern deep learning techniques. The former are assessed using small text collections and their accuracy is still subject to improvement while the latter are efficient in classifying big data collections and show limited effectiveness in classifying small corpora with a large number of categories. This paper proposes a new approach to Arabic text classification to treat small and large data collections while improving the classification rates of existing classifiers. We first demonstrate the ability of analogical proportions (AP) (statements of the form ‘x is to as is to ’), which have recently been shown to be effective in classifying ‘structured’ data, to classify ‘unstructured’ text documents requiring preprocessing. We design an analogical model to express the relationship between text documents and their real categories. Next, based on this principle, we develop two new analogical Arabic text classifiers. These rely on the idea that the category of a new document can be predicted from the categories of three others, in the training set, in case the four documents build together a ‘valid’ analogical proportion on all or on a large number of components extracted from each of them. The two proposed classifiers (denoted AATC1 and AATC2) differ mainly in terms of the keywords extracted for classification. To evaluate the proposed classifiers, we perform an extensive experimental study using five benchmark Arabic text collections with small or large sizes, namely ANT (Arabic News Texts) v2.1 and v1.1, BBC-Arabic, CNN-Arabic and AlKhaleej-2004. We also compare analogical classifiers with both classical ML-based and Deep Learning-based classifiers. Results show that AATC2 has the best average accuracy (78.78%) over all other classifiers and the best average precision (0.77) ranked first followed by AATC1 (0.73), NB (0.73) and SVM (0.72) for the ANT corpus v2.1. Besides, AATC1 shows the best average precisions (0.88) and (0.92), respectively for the BBC-Arabic corpus and AlKhaleej-2004, and the best average accuracy (85.64%) for CNN-Arabic over all other classifiers. Results demonstrate the utility of analogical proportions for text classification. In particular, the proposed analogical classifiers are shown to significantly outperform a number of existing Arabic classifiers, and in many cases, compare favourably to the robust SVM classifier
Rehabilitation of brachial plexus injury in contact sport: Where are the data that underpin clinical management? A scoping review
Get PDFBackground Although a common injury there is a lack of published primary data to inform clinical management of sports related brachial plexus injuries.Methods A systematic search was completed in Medline, CINAHL, PubMed, SPORTDiscus and Web of Science databases and Google Scholar from inception to August 2023 according to the PRISMA-ScR guidelines. Methodological quality assessment of included articles was with the Joanna Briggs Institute tool. Studies providing primary data as to the rehabilitative management of diagnosed or suspected brachial plexus injuries sustained when playing contact sports were included.Results Sixty-five studies were identified and screened, of which, 8 case reports were included, incorporating 10 participants with a mean age of 19.8 (±4.09) years. There was wide heterogeneity in injury severity, injury reporting, physical examination and imaging approaches documented. 9 of 10 participants returned to competitive sports, though follow-up periods also varied widely. Whilst return to play criteria varied between studies, the most consistent indicator was pain-free shoulder range of motion and strength.Conclusions There is a distinct lack of data available to inform evidence-based rehabilitation management of sports related brachial plexus injury. Only 8 individual case reports contain published data reporting on 10 athletes. Further reporting is critical to inform clinical management
Using artificial intelligence tools to predict and alleviate poverty
Get PDFThis paper presents a thorough time series forecasting model intended to project future performance with respect to Sustainable Development Goal 1 (SDG 1) and the corresponding poverty index scores for the years 2024–2030, with a particular emphasis on the United States, Saudi Arabia, China, Egypt, and Sweden. A one-dimensional Convolutional Neural Network (1D-CNN) is used in the model to examine and extract patterns from historical socio-economic data from 2000 to 2022. The algorithm uses deep learning techniques to efficiently extract temporal correlations from the data, allowing for accurate predictions of each country's progress towards ending poverty and raising living standards. Because it can effectively handle time series data and find connections and patterns in earlier observations to guide future advancements in poverty alleviation tactics, the 1D-CNN architecture was chosen. The model was trained and validated using historical data to ensure predictions for the following years were based on real dynamics. To reflect the viability of reaching SDG 1 targets, forecasts were also limited to a realistic range of 0 to 100. The findings show that the model can correctly forecast shifts in poverty levels, which is consistent with expected worldwide patterns. These projections offer insightful information for international organisations, stakeholders, and policymakers involved in sustainability programs. By giving decision-makers the insight they need to deploy resources and carry out successful interventions effectively, the strategy speeds up the process of reaching SDG 1
The mechanism of supply chain efficiency in enterprise digital transformation and total factor productivity
No full textDigital transformation (DT) represents a crucial measure for enterprises to enhance production efficiency. Supply chain construction has become a key strategic focus, and its efficient operation serves as a vital safeguard for elevating overall production efficiency within the enterprise. Based on the data of China's A-share listed enterprises from 2001 to 2022, this paper employed econometric models to investigate the impact of enterprise DT on total factor productivity (TFP), with a particular emphasis on examining the mechanistic role played by supply chain efficiency (SCE). The research findings reveal that: (1) Enterprise DT can significantly enhance TFP. The robustness of these results persists even after undergoing a series of endogeneity tests. (2) SCE plays a crucial mechanistic role in the impact of enterprise DT on TFP. The research results have been fully empirically tested, enterprises can significantly promote the inventory turnover with the help of digital technology, and then improve the TFP. The research findings and corresponding policy recommendations can provide a theoretical basis for enterprises in formulating DT strategies and delineating the supply chain development direction. This holds significant practical implications for enhancing enterprise production efficiency and achieving carbon neutral goal
Development and evaluation of an ovarian hormone profile classification tool for female athletes: step one of a two-step process to determine ovarian hormone profiles
Get PDFObjective: This study aimed to develop a reliable, comprehensive and fit-for-purpose tool for classifying ovarian hormone profiles (OHPs) (step one of a two-step process) in postmenarcheal to perimenopausal female athletes. Methods: The OHP classification tool was designed by a team of sport scientists, practitioners and medics and is intended for use by sport practitioners. It incorporates self-reported data and guides subsequent verification methods. Written feedback was received from practitioners currently working with elite female athletes (n=5), ensuring its applicability in an applied sport setting. In addition, inter-user (n=2) and intra-user (n=30) repeatability was assessed. Results: All practitioners agreed that the online tool was user-friendly. Four (out of five) practitioners stated they would include the tool in their practice, with the fifth stating that they did not have the capacity to incorporate it in their practice at present. The OHP classification tool showed excellent test–retest reliability with Cronbach’s alpha values exceeding 0.9. Conclusion: This tool facilitates the classification of OHPs and promotes discussions between athletes and practitioners, enhancing understanding and management of ovarian hormone health in sportswomen
Thin-Film Technologies for Sustainable Building-Integrated Photovoltaics
Get PDFThis study investigates the incorporation of thin-film photovoltaic (TFPV) technologies in building-integrated photovoltaics (BIPV) and their contribution to sustainable architecture. The research focuses on three key TFPV materials: amorphous silicon (a-Si), cadmium telluride (CdTe), and copper indium gallium selenide (CIGS), examining their composition, efficiency, and BIPV applications. Recent advancements have yielded impressive results, with CdTe and CIGS achieving laboratory efficiencies of 22.10% and 23.35%, respectively. The study also explores the implementation of building energy management systems (BEMS) for optimizing energy use in BIPV-equipped buildings. Financial analysis indicates that despite 10.00–30.00% higher initial costs compared to conventional materials, BIPV systems can generate 50–150 kWh/m2 annually, with simple payback periods of 5–15 years. The research emphasizes the role of government incentives and innovative financing in promoting BIPV adoption. As BIPV technology progresses, it offers a promising solution for transforming buildings from energy consumers to producers, significantly contributing to sustainable urban development and climate change mitigation
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