Enhancing computational scalability in Blockchain by leveraging improvement in consensus algorithm

Accommodating an increasing number of users in the Blockchain network has moved to the forefront of discussion. It is also evident that without jeopardizing the data security in Blockchain, it is of indispensable need to devise an appropriate method for improving the scalability trait of Blockchain. In this article, we have proposed a consensus method that is having the potential to improve the scalability of the Private Blockchain. The system, at first, mitigates latency arising from kernel schedulers, ensuring that the application consistently has access to an available core for transaction processing. Secondly, the committee system alleviates the network's workload, preventing spurious transactions from monopolizing network resources and impeding its efficiency. Extensive experimentation is made by considering various scenarios of transaction with CPU isolation and application sticking to core 2 with varied priority. Based on the number of transactions performed per second, the proposed system is compared with different existing consensus mechanisms working in various types of Blockchains. Also, a detailed discussion is presented on the critical analysis of the adopted research mechanism. Overall, the proposed systems outperforms to other systems in various parameters of blockchain network scalability

A Silent Fatal Presentation of Pulmonary Embolism: Reflection and Discussion.

Acute pulmonary embolism is a common medical condition that clinicians face in practice. It is important to have a prompt diagnosis with proper management as it is associated with high morbidity and mortality. However, a timely diagnosis is often difficult to obtain especially when the presenting symptoms are atypical, but the consequence could be fatal. We present an 80-year-old gentleman who presented with a near-syncope episode who subsequently was found to have acute extensive bilateral pulmonary embolisms after a code blue event

A Comparative Study of Single and Multi-Stage Forecasting Algorithms for the Prediction of Electricity Consumption Using a UK-National Health Service (NHS) Hospital Dataset

Data Availability Statement: Restrictions apply to the availability of the electricity consumption data. The data belong to Medway NHS Foundation Trust but were collected using systems provided by EnergyLogix. Data, however, can be made available with the approval of the corresponding author (A.T.), Medway NHS Foundation Trust, and EnergyLogix. As for the weather data, they were obtained from [24].Copyright © 2023 by the authors. Accurately looking into the future was a significantly major challenge prior to the era of big data, but with rapid advancements in the Internet of Things (IoT), Artificial Intelligence (AI), and the data availability around us, this has become relatively easier. Nevertheless, in order to ensure high-accuracy forecasting, it is crucial to consider suitable algorithms and the impact of the extracted features. This paper presents a framework to evaluate a total of nine forecasting algorithms categorised into single and multistage models, constructed from the Prophet, Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and the Least Absolute Shrinkage and Selection Operator (LASSO) approaches, applied to an electricity demand dataset from an NHS hospital. The aim is to see such techniques widely used in accurately predicting energy consumption, limiting the negative impacts of future waste on energy, and making a contribution towards the 2050 net zero carbon target. The proposed method accounts for patterns in demand and temperature to accurately forecast consumption. The Coefficient of Determination (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were used to evaluate the algorithms’ performance. The results show the superiority of the Long Short-Term Memory (LSTM) model and the multistage Facebook Prophet model, with R2 values of 87.20% and 68.06%, respectively.Engineering and Physical Sciences Research Council (EPSRC) grants, EP/T517896/1

'I once wore an angry bird t-shirt and went to read Qur’an': asymmetrical institutional complexity and emerging consumption practices in Pakistan

This article brings together theories of institutional logics and the exploration of the lives of tweens in Pakistan to understand how emerging consumption practices fit within Pakistani children’s daily lives, and how institutional complexity that includes the dominance of religion under Pakistani Islamization is negotiated to separate and maintain the differences between them. We identify resolutions to asymmetrical institutional complexity in the consumption of character T-shirts: spatial–temporal practices, visual practices, symbolic substitution practices and single logic practices. We contribute to an understanding of how consumption happens in an Eastern Muslim culture, and how multiple institutional logics shape the consumption practices of children, by articulating how halal consumption practices, far from being essentialist, or presented as market segmentation, form from negotiations and reflections at the boundaries where Islam and Market logics meet

Roadmap on printable electronic materials for next-generation sensors

The dissemination of sensors is key to realizing a sustainable, ‘intelligent’ world, where everyday objects and environments are equipped with sensing capabilities to advance the sustainability and quality of our lives—e.g., via smart homes, smart cities, smart healthcare, smart logistics, Industry 4.0, and precision agriculture. The realization of the full potential of these applications critically depends on the availability of easy-to-make, low-cost sensor technologies. Sensors based on printable electronic materials offer the ideal platform: they can be fabricated through simple methods (e.g., printing and coating) and are compatible with high-throughput roll-to-roll processing. Moreover, printable electronic materials often allow the fabrication of sensors on flexible/stretchable/biodegradable substrates, thereby enabling the deployment of sensors in unconventional settings. Fulfilling the promise of printable electronic materials for sensing will require materials and device innovations to enhance their ability to transduce external stimuli—light, ionizing radiation, pressure, strain, force, temperature, gas, vapours, humidity, and other chemical and biological analytes. This Roadmap brings together the viewpoints of experts in various printable sensing materials—and devices thereof—to provide insights into the status and outlook of the field. Alongside recent materials and device innovations, the roadmap discusses the key outstanding challenges pertaining to each printable sensing technology. Finally, the Roadmap points to promising directions to overcome these challenges and thus enable ubiquitous sensing for a sustainable, ‘intelligent’ world