Leading countries and research networks advancing clean production and environmental sustainability in Southeast Asia

Purpose This study aims to assess the response of the Association of Southeast Asian Nations (ASEAN) to cleaner production and environmental sustainability, with a specific focus on identifying the leading countries and research networks driving these efforts. Design/methodology/approach A benchmarking academic journal was chosen, and the journal’s archive was comprehensively examined. To construct the data set, a conventional keyword search technique was applied in February 2023 to filter for ASEAN affiliations. The study used hybrid bibliometric analyses and multi-criteria decision analysis (MCDA) to analyze the collected data and address the research purpose. Findings The data analysis revealed a rising research trend, particularly after 2014. Malaysia had the most publications, followed by Thailand and Singapore, and their publications had the most cumulative citations among ASEAN countries. Research collaborations between Malaysia, Thailand and Singapore were frequent, but participation from other countries was low. The research topics on which ASEAN members focused were also identified, but it became apparent that there was little coordination. A scant few collaborations involving more than two countries were observed; thus, the MCDA analysis concluded that research leadership was absent in ASEAN countries. Originality/value This study contributes insights to the existing literature and offers a valuable overview of the research direction and collaboration status of cleaner production and environmental sustainability in the ASEAN region, thus benefiting policymakers. Additionally, this study introduces a novel approach combining bibliometrics analysis with MCDA to assess research collaboration, thus providing a novel methodology for future research policy evaluations

'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

Elimination of Aflatoxins from Two Selected Nigerian Vegetable Oils using Magnetic Chitosan Nanoparticles

Abstract Activated charcoal and imarsil (local adsorbent) had shown significant Aflatoxin (AF) decontamination potentials in vegetable oil at a low AF contamination level of ≤ 9 ng/L. AF contamination in vegetable oils can be more than a hundred-fold of this. Therefore, it is needed to investigate the potential of other adsorbents at higher AF contamination levels. Magnetic Chitosan Nanoparticle (MCNP) was synthesized, and its aflatoxins extraction efficiency from two edible vegetable oils was investigated. MCNP exhibited extraction efficiencies of 82.80 – 100% and 94.87 – 100% in palm kernel oil and palm oil at the contamination levels of 579.6 and 964.6 ng/L respectively. Total aflatoxins cleanup of the palm oil and palm kernel oil was possible at 30 °C within 30 and 60 minutes, respectively, at the optimized condition of 4.4 mg/L MCNP. MCNP concentration, temperature of extraction, and contact time were significant (p < 0.05) in palm kernel oil, while these conditions were not significant (p > 0.05) in palm oil. The results of the present investigation depict that the AF extraction efficiency of MCNP depends on the type of vegetable oil and that MCNP could be a credible alternative for AF decontamination of the investigated vegetable oil.Keywords: aflatoxins, chitosan, contamination, nanoparticles, vegetable oils AbstrakArang aktif dan imarsil (adsorben lokal) mempunyai potensi dekontaminasi aflatoksin (AF) yang signifikan dalam minyak nabati dengan tingkat kontaminasi AF rendah, yaitu ≤ 9 ng/L. AF dalam minyak nabati dapat lebih dari seratus kali lipat tingkat kontaminasi tersebut. Oleh karena itu, penelitian potensi adsorben lain perlu dilakukan pada tingkat kontaminasi AF yang lebih tinggi. Penelitian ini menganalisis sintesis Magnetic Chitosan Nanoparticle (MCNP) dan efisiensi ekstraksi aflatoksin dari dua minyak nabati konsumsi (minyak inti sawit dan minyak sawit). Efisiensi ekstraksi minyak inti sawit dan minyak sawit pada tingkat pencemaran 579,6 dan 964,6 ng/L, MCNP masing-masing sebesar 82,80 - 100% dan 94,87 - 100%. Pembersihan aflatoksin total pada minyak sawit dan minyak inti sawit dapat terjadi pada suhu 30 °C dalam waktu masing-masing 30 dan 60 menit, pada kondisi optimal MCNP, yaitu 4,4 mg/L. Konsentrasi MCNP, suhu ekstraksi, dan waktu kontak signifikan (p < 0,05) pada minyak inti sawit, tetapi kondisi ini tidak signifikan (p > 0,05) pada minyak kelapa sawit. Hasil penelitian ini menunjukkan bahwa efisiensi ekstraksi AF MCNP tergantung pada jenis minyak nabati dan MCNP dapat menjadi alternatif untuk dekontaminasi AF dari minyak nabati yang diteliti.Kata kunci: aflatoksin, kitosan, kontaminasi, minyak nabati, nano partike

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

Secure Satellite Downlink with Hybrid RIS and AI-Based Optimization

In this paper, we explore a secure multiuser multiple-input single-output (MISO) satellite downlink communication system, enhanced by the integration of a hybrid reconfigurable intelligent surface (RIS). The study formulates a robust joint design for satellite and RIS beamforming, aimed at maximizing the secrecy rate of the overall system. Both the active and passive elements of the RIS are optimized, taking into account practical models that reflect real-world constraints, such as outdated channel state information (CSI) and the power consumption of the system. To address the highly complex, dynamic, and multidimensional nature of the beamforming design problem, deep reinforcement learning (DRL) techniques are employed. Simulation results demonstrate the effectiveness of the proposed beamforming strategy, highlighting significant performance improvements when utilizing hybrid-RIS compared to traditional passive RIS solutions in wireless communication systems

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

Emerging technologies and innovative approaches to combat antimicrobial resistance: A narrative review of next-generation therapeutic strategies

Antimicrobial resistance (AMR) is one of the most pressing global health challenges, with approximately 700,000 deaths annually directly attributable to resistant bacterial infections. This alarming trend threatens to undermine decades of medical progress. The widespread misuse and overuse of antibiotics have accelerated the emergence of multidrug-resistant (MDR) pathogens, leading to increased morbidity, mortality, and healthcare costs. This review examines the intricate mechanisms underlying the development of AMR and discusses innovative next-generation therapeutic strategies and emerging approaches for combating resistant pathogens. CRISPR-based antimicrobials demonstrated over 90 % in vitro efficacy in selectively eliminating MDR pathogens. Nanotechnology-based solutions, such as those utilizing silver and gold nanoparticles, have demonstrated potent bactericidal activity in preclinical settings; however, toxicity and regulatory concerns persist. Bacteriophage therapy and antimicrobial peptides (AMPs) are advancing through early clinical trials, offering targeted activity and immune-modulating effects. Artificial intelligence (AI)-driven drug discovery has already been clinically integrated, accelerating the design of antibiotics and predicting resistance with high efficiency. Comparative analysis reveals that AI tools possess the highest readiness level, while CRISPR and AMPs are promising but remain in early development stages. These emerging strategies collectively present significant potential to complement or replace conventional antibiotics in addressing AMR. Despite their potential, these technologies face significant implementation challenges, including technical limitations, economic barriers, ethical considerations, and regulatory complexities. This review emphasizes the critical need for multidisciplinary collaboration, sustainable funding models, and global policy frameworks to effectively translate these innovations into clinical practice. The AMR crisis can only be addressed through international collaboration, combining scientific innovation and supportive policy environments