Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology

The proliferation of the Internet of Things (IoT) has led to an explosion of data generated by interconnected devices, presenting both opportunities and challenges for intelligent decision-making in complex environments. Traditional Reinforcement Learning (RL) approaches often struggle to fully harness this data due to their limited ability to process and interpret the intricate patterns and dependencies inherent in IoT applications. This paper introduces a novel framework that integrates transformer architectures with Proximal Policy Optimization (PPO) to address these challenges. By leveraging the self-attention mechanism of transformers, our approach enhances RL agents' capacity for understanding and acting within dynamic IoT environments, leading to improved decision-making processes. We demonstrate the effectiveness of our method across various IoT scenarios, from smart home automation to industrial control systems, showing marked improvements in decision-making efficiency and adaptability. Our contributions include a detailed exploration of the transformer's role in processing heterogeneous IoT data, a comprehensive evaluation of the framework's performance in diverse environments, and a benchmark against traditional RL methods. The results indicate significant advancements in enabling RL agents to navigate the complexities of IoT ecosystems, highlighting the potential of our approach to revolutionize intelligent automation and decision-making in the IoT landscape

BenLLMEval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP

Large Language Models (LLMs) have emerged as one of the most important breakthroughs in natural language processing (NLP) for their impressive skills in language generation and other language-specific tasks. Though LLMs have been evaluated in various tasks, mostly in English, they have not yet undergone thorough evaluation in under-resourced languages such as Bengali (Bangla). In this paper, we evaluate the performance of LLMs for the low-resourced Bangla language. We select various important and diverse Bangla NLP tasks, such as abstractive summarization, question answering, paraphrasing, natural language inference, text classification, and sentiment analysis for zero-shot evaluation with ChatGPT, LLaMA-2, and Claude-2 and compare the performance with state-of-the-art fine-tuned models. Our experimental results demonstrate an inferior performance of LLMs for different Bangla NLP tasks, calling for further effort to develop better understanding of LLMs in low-resource languages like Bangla.Comment: First two authors contributed equall

Healthcare Access and Quality Index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990-2015 : a novel analysis from the Global Burden of Disease Study 2015

Background National levels of personal health-care access and quality can be approximated by measuring mortality rates from causes that should not be fatal in the presence of effective medical care (ie, amenable mortality). Previous analyses of mortality amenable to health care only focused on high-income countries and faced several methodological challenges. In the present analysis, we use the highly standardised cause of death and risk factor estimates generated through the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) to improve and expand the quantification of personal health-care access and quality for 195 countries and territories from 1990 to 2015. Methods We mapped the most widely used list of causes amenable to personal health care developed by Nolte and McKee to 32 GBD causes. We accounted for variations in cause of death certification and misclassifications through the extensive data standardisation processes and redistribution algorithms developed for GBD. To isolate the effects of personal health-care access and quality, we risk-standardised cause-specific mortality rates for each geography-year by removing the joint effects of local environmental and behavioural risks, and adding back the global levels of risk exposure as estimated for GBD 2015. We employed principal component analysis to create a single, interpretable summary measure-the Healthcare Quality and Access (HAQ) Index-on a scale of 0 to 100. The HAQ Index showed strong convergence validity as compared with other health-system indicators, including health expenditure per capita (r= 0.88), an index of 11 universal health coverage interventions (r= 0.83), and human resources for health per 1000 (r= 0.77). We used free disposal hull analysis with bootstrapping to produce a frontier based on the relationship between the HAQ Index and the Socio-demographic Index (SDI), a measure of overall development consisting of income per capita, average years of education, and total fertility rates. This frontier allowed us to better quantify the maximum levels of personal health-care access and quality achieved across the development spectrum, and pinpoint geographies where gaps between observed and potential levels have narrowed or widened over time. Findings Between 1990 and 2015, nearly all countries and territories saw their HAQ Index values improve; nonetheless, the difference between the highest and lowest observed HAQ Index was larger in 2015 than in 1990, ranging from 28.6 to 94.6. Of 195 geographies, 167 had statistically significant increases in HAQ Index levels since 1990, with South Korea, Turkey, Peru, China, and the Maldives recording among the largest gains by 2015. Performance on the HAQ Index and individual causes showed distinct patterns by region and level of development, yet substantial heterogeneities emerged for several causes, including cancers in highest-SDI countries; chronic kidney disease, diabetes, diarrhoeal diseases, and lower respiratory infections among middle-SDI countries; and measles and tetanus among lowest-SDI countries. While the global HAQ Index average rose from 40.7 (95% uncertainty interval, 39.0-42.8) in 1990 to 53.7 (52.2-55.4) in 2015, far less progress occurred in narrowing the gap between observed HAQ Index values and maximum levels achieved; at the global level, the difference between the observed and frontier HAQ Index only decreased from 21.2 in 1990 to 20.1 in 2015. If every country and territory had achieved the highest observed HAQ Index by their corresponding level of SDI, the global average would have been 73.8 in 2015. Several countries, particularly in eastern and western sub-Saharan Africa, reached HAQ Index values similar to or beyond their development levels, whereas others, namely in southern sub-Saharan Africa, the Middle East, and south Asia, lagged behind what geographies of similar development attained between 1990 and 2015. Interpretation This novel extension of the GBD Study shows the untapped potential for personal health-care access and quality improvement across the development spectrum. Amid substantive advances in personal health care at the national level, heterogeneous patterns for individual causes in given countries or territories suggest that few places have consistently achieved optimal health-care access and quality across health-system functions and therapeutic areas. This is especially evident in middle-SDI countries, many of which have recently undergone or are currently experiencing epidemiological transitions. The HAQ Index, if paired with other measures of health-systemcharacteristics such as intervention coverage, could provide a robust avenue for tracking progress on universal health coverage and identifying local priorities for strengthening personal health-care quality and access throughout the world. Copyright (C) The Author(s). Published by Elsevier Ltd.Peer reviewe

Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal

State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. However, most AI models are considered “black boxes,” because there is no explanation for the decisions made by these models. Users may find it challenging to comprehend and interpret the results. Explainable AI (XAI) can explain the machine learning (ML) outputs and contribution of features in disease prediction models. Electroencephalography (EEG) is a potential predictive tool for understanding cortical impairment caused by an ischemic stroke and can be utilized for acute stroke prediction, neurologic prognosis, and post-stroke treatment. This study aims to utilize ML models to classify the ischemic stroke group and the healthy control group for acute stroke prediction in active states. Moreover, XAI tools (Eli5 and LIME) were utilized to explain the behavior of the model and determine the significant features that contribute to stroke prediction models. In this work, we studied 48 patients admitted to a hospital with acute ischemic stroke and 75 healthy adults who had no history of identified other neurological illnesses. EEG was obtained within three months following the onset of ischemic stroke symptoms using frontal, central, temporal, and occipital cortical electrodes (Fz, C1, T7, Oz). EEG data were collected in an active state (walking, working, and reading tasks). In the results of the ML approach, the Adaptive Gradient Boosting models showed around 80% accuracy for the classification of the control group and the stroke group. Eli5 and LIME were utilized to explain the behavior of the stroke prediction model and interpret the model locally around the prediction. The Eli5 and LIME interpretable models emphasized the spectral delta and theta features as local contributors to stroke prediction. From the findings of this explainable AI research, it is expected that the stroke-prediction XAI model will help with post-stroke treatment and recovery, as well as help healthcare professionals, make their diagnostic decisions more explainable

Effects of Organic Amendments on Soil Aggregate Stability, Carbon Sequestration, and Energy Use Efficiency in Wetland Paddy Cultivation

A study was conducted to assess the effects of organic amendments on soil aggregates, carbon (C) sequestration, and energy use efficiency (EUE) during five consecutive Boro and Transplanted Aman rice seasons in Bangladesh during 2018–2020. Five treatments (viz., control (only inorganic fertilizers), cow dung (CD), vermicompost (VC), rice straw (RS), and poultry manure (PM)) were used. The organic materials were applied at 2 t C ha−1 season−1 to all the plots, except in the control treatment. Inorganic fertilizers were applied in all treatments in both seasons following integrated nutrient management (INM). The data reveal that PM was found to be more efficient at increasing the water-stable soil aggregates (WSA), followed by the RS, CD, and VC. The WSA in smaller-sized soil aggregates were found to be higher than those in larger-sized soil aggregates. VC was found to be the most effective in terms of C sequestration (29%), followed by PM (26%), CD (22%), and RS (20%). The highest EUE was attributed to the control treatment (9.77), followed by the CD (8.67), VC (8.04), RS (2.10), and PM (1.18), which showed energy wastage in the organic treatments. The system productivity (SP) followed the opposite trend of the EUE. The INM is a better approach to improve the soil health, the C sequestration, and the SP, but it appeared as an energy-inefficient strategy, which suggests that a balanced application of organic and inorganic nutrients is needed in order to achieve yield sustainability and EUE

The burden and trend of diseases and their risk factors in Australia, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: A comprehensive understanding of temporal trends in the disease burden in Australia is lacking, and these trends are required to inform health service planning and improve population health. We explored the burden and trends of diseases and their risk factors in Australia from 1990 to 2019 through a comprehensive analysis of the Global Burden of Disease Study (GBD) 2019. Methods: In this systematic analysis for GBD 2019, we estimated all-cause mortality using the standardised GBD methodology. Data sources included primarily vital registration systems with additional data from sample registrations, censuses, surveys, surveillance, registries, and verbal autopsies. A composite measure of health loss caused by fatal and non-fatal disease burden (disability-adjusted life-years [DALYs]) was calculated as the sum of years of life lost (YLLs) and years of life lived with disability (YLDs). Comparisons between Australia and 14 other high-income countries were made. Findings: Life expectancy at birth in Australia improved from 77·0 years (95% uncertainty interval [UI] 76·9–77·1) in 1990 to 82·9 years (82·7–83·1) in 2019. Between 1990 and 2019, the age-standardised death rate decreased from 637·7 deaths (95% UI 634·1–641·3) to 389·2 deaths (381·4–397·6) per 100 000 population. In 2019, non-communicable diseases remained the major cause of mortality in Australia, accounting for 90·9% (95% UI 90·4–91·9) of total deaths, followed by injuries (5·7%, 5·3–6·1) and communicable, maternal, neonatal, and nutritional diseases (3·3%, 2·9–3·7). Ischaemic heart disease, self-harm, tracheal, bronchus, and lung cancer, stroke, and colorectal cancer were the leading causes of YLLs. The leading causes of YLDs were low back pain, depressive disorders, other musculoskeletal diseases, falls, and anxiety disorders. The leading risk factors for DALYs were high BMI, smoking, high blood pressure, high fasting plasma glucose, and drug use. Between 1990 and 2019, all-cause DALYs decreased by 24·6% (95% UI 21·5–28·1). Relative to similar countries, Australia's ranking improved for age-standardised death rates and life expectancy at birth but not for YLDs and YLLs between 1990 and 2019. Interpretation: An important challenge for Australia is to address the health needs of people with non-communicable diseases. The health systems must be prepared to address the increasing demands of non-communicable diseases and ageing. Funding: Bill &amp; Melinda Gates Foundation.</p