In this paper, we introduce Wide-TSNet, a novel hybrid approach for predicting Bitcoin prices using time-series data transformed into images. The method involves converting time-series data into Markov transition fields (MTFs), enhancing them using histogram equalization, and classifying them using Wide ResNets, a type of convolutional neural network (CNN). We propose a tripartite classification system to accurately represent Bitcoin price trends. In addition, we demonstrate the effectiveness of Wide-TSNet through various experiments, in which it achieves an Accuracy of approximately 94% and an F1 score of 90%. It is also shown that lightweight CNN models, such as SqueezeNet and EfficientNet, can be as effective as complex models under certain conditions. Furthermore, we investigate the efficacy of other image transformation methods, such as Gramian angular fields, in capturing the trends and volatility of Bitcoin prices and revealing patterns that are not visible in the raw data. Moreover, we assess the effect of image resolution on model performance, emphasizing the importance of this factor in image-based time-series classification. Our findings explore the intersection between finance, image processing, and deep learning, providing a robust methodology for financial time-series classification.

Abstract Transformation of economic systems is widely regarded as an essential strategy to tackle interacting global crises. In response, there are diverse transformative approaches seeking holistic human and planetary wellbeing. However, mainstreaming these ‘new’ economic approaches is hampered by vested interests and intellectual lock-in. They are also diffuse and struggling to develop sufficient discursive power to gain more widespread traction in policy. To bring coherence, we undertake a qualitative content analysis of diverse transformative new economic document sources from science and practice, synthesizing ten ecological, social, political economy and holistic principles cutting across 38 approaches. They include: (1) social-ecological embeddedness and holistic wellbeing; (2) interdisciplinarity and complexity thinking; (3) limits to growth; (4) limited substitutability of natural capital; (5) regenerative design; (6) holistic perspectives of people and values; (7) equity, equality, and justice; (8) relationality and social enfranchisement; (9) participation, deliberation, and cooperation; and (10) post-capitalism and decolonization. We also consider opportunities and barriers for applying these principles in the context of global crises. Future efforts can further consolidate transformative new economics through building discourse coalitions between approaches, synthesizing methodologies and conceptual models, and validating principles more explicitly within Global South contexts.

These days, people are paying greater attention to the stock market, precising stock prediction can considerably boost the rate of return. This article will utilize Uber Inc.'s stock forecast as an exploratory objective to conduct a study on its stock price, specially, this article mainly compares three models: Linear Regression, Random Forest Regressor and Long Short-Term Memory (LSTM) and uses four parameters (MSE, RMSE, MAE and R2) to evaluate and determine which model has the most accuracy in predicting a stock's closing price. In light of the experimental findings, the results of linear regression yield a R^2 of 0.98, RMSE of 1.27, MSE of 1.62, and MAE of 0.94; the results for random forest regression are R^2 equal to 1.00, RMSE is 0.52, MSE amount as 0.27, and MAE up to 0.38; and the LSTM results are R^2 come up to 0.97, RMSE is 1.68, MSE equal to 2.82, and MAE is 1.25. Based on the result, this article concludes that Random Forest is the most accurate in predicting Uber's stock price.

BackgroundFindings from cardiovascular outcome trials suggest that treatment with fenofibrate may reduce the progression of diabetic retinopathy. However, no dedicated large-scale randomised trials have yet investigated this hypothesis.MethodsLENS is a streamlined randomised double-masked placebo-controlled trial, based in Scotland, assessing whether treatment with fenofibrate (145mg tablet daily or, in the context of impaired renal function, on alternate days) in people with early retinopathy reduces progression to referable diabetic retinopathy (defined in NHS Scotland’s Diabetic Eye Screening grading scheme as referable background or proliferative retinopathy, or referable maculopathy in either eye) or treatment with retinal laser, intra-vitreal injections or vitrectomy. Adults with diabetes mellitus and non-referable retinopathy (mild background retinopathy in both eyes or observable background retinopathy in one/both eyes at the most recent NHS retinal screening assessment; or observable maculopathy in one/both eyes in the previous 3 years) were eligible. Potential participants were identified from routinely collected healthcare data, and followed up using regular contact from the research team and linkage to national electronic morbidity, mortality, biochemistry and retinal screening records. Study treatment was mailed to participants.ResultsBetween 18 September 2018 and 27 July 2021, 1151 participants were randomised. Their mean age was 61 (SD 12) years, 312 (27%) were female, and 305 (26%) had type 1 diabetes. 96% had bilateral mild background retinopathy and 10% had observable maculopathy.ConclusionsLENS will provide a robust evaluation of the efficacy of treating people at risk of progression of diabetic retinopathy with fenofibrate. Results are anticipated in mid-2024.Trial registrationsNCT03439345; ISRCTN15073006; EuDRACT 2016-002656-24

Recent evidence indicates that microbial biofilm aggregates inhabit the lungs of COPD patients and actively contribute towards chronic colonization and repeat infections. However, there are no contextually relevant complex biofilm models for COPD research. In this study, a meta-analysis of the lung microbiome in COPD was used to inform development of an optimized biofilm model composed of genera highly associated with COPD. Bioinformatic analysis showed that although diversity matrices of COPD microbiomes were similar to healthy controls, and internal compositions made it possible to accurately differentiate between these cohorts (AUC = 0.939). Genera that best defined these patients included Haemophilus, Moraxella and Streptococcus. Many studies fail to account for fungi; therefore, Candida albicans was included in the creation of an interkingdom biofilm model. These organisms formed a biofilm capable of tolerating high concentrations of antimicrobial therapies with no significant reductions in viability. However, combined therapies of antibiotics and an antifungal resulted in significant reductions in viable cells throughout the biofilm (p < 0.05). This biofilm model is representative of the COPD lung microbiome and results from in vitro antimicrobial challenge experiments indicate that targeting both bacteria and fungi in these interkingdom communities will be required for more positive clinical outcomes.

Context: Social care need in prisons is increasing in many countries. However, the delivery of social care in prisons has been (at best) inconsistent and there has been no previous review to inform provision for people on release. Objective: To identify and synthesise what is known about the social care needs of people on release from prison and how best to meet these. Method: A scoping review encompassing systematic searches of 26 electronic databases (January 2010–July 2021) included a wide range of literature. No exclusions were made on the basis of study design, method or quality. Findings were organised according to their contribution to the research questions. Findings: Forty-six documents met the review criteria of which 27 were from the UK. Just two focused specifically on the topic of interest and most of the extracted material was descriptive in nature. Almost no information was found on the number of people released from prison in need of social care. However, the challenges of providing care for this group appeared well understood. Although there were many examples of good practice and widespread consensus about its enablers, outcome information was lacking. Limitations: In keeping with the nature of the review, the quality of the literature was not formally assessed. Implications: The review identified several promising initiatives ranging from prison buddy schemes to pre-release training in everyday living skills and personalised pathway documents. Conclusions: Policy makers and researchers must now shift their attention to the effectiveness of particular interventions in improving social care outcomes.

People with severe to profound intellectual disabilities experience similar or higher levels of depression than those with more mild intellectual disabilities. Yet, there is an absence of evidence about how to adapt existing psychological therapies for this population. A behavioural activation intervention (BeatIt) for people with mild to moderate intellectual disabilities was adapted for people with severe to profound intellectual disabilities and depression. Key considerations include: (i) beginning with a more in-depth assessment process; (ii) including the person in session activities and developing a relationship with them; (iii) formulation and the use of film to document the link between activity and mood; and (iv) addressing barriers to change at an individual and inter-personal level and considering how the carer could support the person's engagement in activity. Successfully adapting BeatIt represents a first step towards gathering evidence about the effectiveness of behavioural activation for people with severe to profound intellectual disabilities.

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