Galgotias University

One of the fundamental components of the United Nations’ sustainable development 2030 agenda is quality education. It aims to ensure inclusive and equitable quality education for all. Digital technologies have emerged as an essential tool to achieve this goal. These technologies are simple to detect emissions sources, prevent additional damage through improved energy efficiency and lower-carbon alternatives to fossil fuels, and even remove surplus greenhouse gases from the environment. Digital technologies strive to decrease or eliminate pollution and waste while increasing production and efficiency. These technologies have shown a powerful impact on the education system. The recent COVID-19 Pandemic has further institutionalised the applications of digital technologies in education. These digital technologies have made a paradigm shift in the entire education system. It is not only a knowledge provider but also a co-creator of information, a mentor, and an assessor. Technological improvements in education have made life easier for students. Instead of using pen and paper, students nowadays use various software and tools to create presentations and projects. When compared to a stack of notebooks, an iPad is relatively light. When opposed to a weighty book, surfing an E-book is easier. These methods aid in increasing interest in research. This paper is brief about the need for digital technologies in education and discusses major applications and challenges in education.

An advanced mode of drug delivery system has been developed to overcome the major drawbacks associated with conventional drug delivery systems. This review gives a detailed idea about a nanoemulsion system. Nanoemulsions are nano-sized emulsions, which are manufactured for improving the delivery of active pharmaceutical ingredients. These are the thermodynamically stable isotropic system in which two immiscible liquids are mixed to form a single phase by means of an emulsifying agent, i.e., surfactant and co-surfactant. The droplet size of nanoemulsion falls typically in the range 20-200 nm. The main difference between emulsion and nanoemulsion lies in the size and shape of particles dispersed in the continuous phase. In this review, the attention is focused to give a basic idea about its formulation, method of preparation, characterization techniques, evaluation parameters, and various applications of nanoemulsion.

Artificial Intelligence (AI) has vast potential in marketing. It aids in proliferating information and data sources, improving software's data management capabilities, and designing intricate and advanced algorithms. AI is changing the way brands and users interact with one another. The application of this technology is highly dependent on the nature of the website and the type of business. Marketers can now focus more on the customer and meet their needs in real time. By using AI, they can quickly determine what content to target customers and which channel to employ at what moment, thanks to the data collected and generated by its algorithms. Users feel at ease and are more inclined to buy what is offered when AI is used to personalise their experiences. AI tools can also be used to analyse the performance of a competitor's campaigns and reveal their customers' expectations. Machine Learning (ML) is a subset of AI that allows computers to analyse and interpret data without being explicitly programmed. Furthermore, ML assists humans in solving problems efficiently. The algorithm learns and improves performance and accuracy as more data is fed into the algorithm. For this research, relevant articles on AI in marketing are identified from Scopus, Google scholar, researchGate and other platforms. Then these articles were read, and the theme of the paper was developed. This paper attempts to review the role of AI in marketing. The specific applications of AI in various marketing segments and their transformations for marketing sectors are examined. Finally, critical applications of AI for marketing are recognised and analysed.

In the past few years, therapeutic microRNA (miRNA) and small interfering RNA (siRNA) are some of the most important biopharmaceuticals that are in commercial space as future medicines. This review summarizes the patents of miRNA- and siRNA-based new drugs, and also provides a snapshot about significant biopharmaceutical companies that are investing for the therapeutic development of miRNA and siRNA molecules. An insightful view about individual siRNA and miRNA drugs has been depicted with their present status, which is gaining attention in the therapeutic landscape. The efforts of the biopharmaceuticals are discussed with the status of their preclinical and/or clinical trials. Here, some of the setbacks have been highlighted during the biopharmaceutical development of miRNA and siRNA as individual therapeutics. Finally, a snapshot is illustrated about pharmacokinetics, pharmacodynamics with absorption, distribution, metabolism, and excretion (ADME), which is the fundamental development process of these therapeutics, as well as the delivery system for miRNA- and siRNA-based drugs.

Heavy metals are metals with relatively high density and toxic at very low concentrations. The common heavy metal pollutants can be traced everywhere in minimal quantities. Heavy metals contaminate aquatic environments through various sources like industrial waste, domestic effluents, atmospheric sources, and other metal-based industries, E-Waste. Aquaculture is the rearing of aquatic animals and other organisms. Heavy metal toxicity is responsible for the degradation of the population of aquaculture, causing physical deformities in organisms and polluting the aquatic environment. These toxic heavy metals cause various diseases in fishes. As fishes are part of human consumption, it is indirectly affecting humans also. The food chain is greatly impacted by the introduction of heavy metals in water bodies & aquatic ecosystems. These heavy metals have greater significance on the environment as they persist for longer durations and have bioaccumulative capacities causing degradation of water health.

Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. The Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China, has had disastrous effects on the global community and has overburdened advanced healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020, according to the European Centre for Disease Prevention and Control agency. However, the current rapid and exponential rise in the number of patients has necessitated efficient and quick prediction of the possible outcome of an infected patient for appropriate treatment using AI techniques. This paper proposes a fine-tuned Random Forest model boosted by the AdaBoost algorithm. The model uses the COVID-19 patient's geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the dataset used. The data analysis reveals a positive correlation between patients' gender and deaths, and also indicates that the majority of patients are aged between 20 and 70 years.

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

The levels of heavy metals contaminations in water like, Pb, As, Cd, Hg, Cr, Ni etc. in various water sources as ground, surface, tap water etc. Variety of heavy metals, some of them are potentially toxic and are transferred to the surrounding environment through different pathways. The concentrations determined were more than the maximum admissible and desirable limit when compared with the National and International organizations like WHO (2008), USEPA, EUC, EPA. Water may become contaminated by the accumulation of heavy metals and metalloids through emissions from the rapidly expanding industrial areas, mine tailings, disposal of high metal wastes, leaded gasoline and paints, land application of fertilizers, animal manures, sewage sludge, pesticides, wastewater irrigation, coal, Electronic waste. Heavy metal toxicity has proven to be a major threat and there are several health risks associated with it. The toxic effects of these metals, even though they do not have any biological role, remain present in some or the other form harmful for the human body and its proper functioning.

Internet of Things (IoT) is a worldwide system of “smart devices” that can sense and connect with their surroundings and interact with users and other systems. Global air pollution is one of the major concerns of our era. Existing monitoring systems have inferior precision, low sensitivity, and require laboratory analysis. Therefore, improved monitoring systems are needed. To overcome the problems of existing systems, we propose a three-phase air pollution monitoring system. An IoT kit was prepared using gas sensors, Arduino integrated development environment (IDE), and a Wi-Fi module. This kit can be physically placed in various cities to monitoring air pollution. The gas sensors gather data from air and forward the data to the Arduino IDE. The Arduino IDE transmits the data to the cloud via the Wi-Fi module. We also developed an Android application termed IoT-Mobair, so that users can access relevant air quality data from the cloud. If a user is traveling to a destination, the pollution level of the entire route is predicted, and a warning is displayed if the pollution level is too high. The proposed system is analogous to Google traffic or the navigation application of Google Maps. Furthermore, air quality data can be used to predict future air quality index (AQI) levels.

Presently, nanotechnology is a multi-trillion dollar business sector that covers a wide range of industries, such as medicine, electronics and chemistry. In the current era, the commercial transition of nanotechnology from research level to industrial level is stimulating the world's total economic growth. However, commercialization of nanoparticles might offer possible risks once they are liberated in the environment. In recent years, the use of zebrafish (Danio rerio) as an established animal model system for nanoparticle toxicity assay is growing exponentially. In the current in-depth review, we discuss the recent research approaches employing adult zebrafish and their embryos for nanoparticle toxicity assessment. Different types of parameters are being discussed here which are used to evaluate nanoparticle toxicity such as hatching achievement rate, developmental malformation of organs, damage in gill and skin, abnormal behavior (movement impairment), immunotoxicity, genotoxicity or gene expression, neurotoxicity, endocrine system disruption, reproduction toxicity and finally mortality. Furthermore, we have also highlighted the toxic effect of different nanoparticles such as silver nanoparticle, gold nanoparticle, and metal oxide nanoparticles (TiO2, Al2O3, CuO, NiO and ZnO). At the end, future directions of zebrafish model and relevant assays to study nanoparticle toxicity have also been argued.

During the past few years, severe air-pollution problem has garnered worldwide attention due to its effect on health and wellbeing of individuals. As a result, the analysis and prediction of air pollution has attracted a good deal of interest among researchers. The research areas include traditional machine learning, neural networks and deep learning. How to effectively and accurately predict air pollution becomes an important issue. In this paper, we propose an Aggregated LSTM (Long Short-Term Memory) model (ALSTM) based on the LSTM deep learning method. In this new model, we combine local air quality monitoring station, the station in nearby industrial areas, and the stations for external pollution sources. To improve prediction accuracy, we aggregate three LSTM models into a predictive model for early predictions based on external sources of pollution and information from nearby industrial air quality stations. We exploited the data with 17 attributes collected by Taiwan Environmental Protection Agency from 2012 to 2017 as the training data to build the ALSTM forecasting model, and we tested the model using the data collected in 2018. We conducted some experiments to compare our new ALSTM model with SVR (Support Vector Machine based Regression), GBTR (Gradient Boosted Tree Regression), LSTM, etc., in the prediction of PM2.5 for 1–8 h, and evaluated them using various assessment techniques, such as MAE, RMSE, and MAPE. The results reveal that the proposed aggregated model can effectively improve the accuracy of prediction.

Artificial Intelligence (AI) solutions have been widely used in healthcare, and recent developments in deep neural networks have contributed to significant advances in medical image processing. Much ongoing research is aimed at helping medical practitioners by providing automated systems to analyze images and diagnose acute diseases, such as brain tumors, bone cancer, breast cancer, bone fracture, and many others. This comprehensive review delivers an overview of recent advances in medical imaging using deep neural networks. In addition to the comprehensive literature review, a summary of openly available data sources and future research directions are outlined.

Software-Defined Network (SDN) has become a promising network architecture in current days that provide network operators more control over the network infrastructure. The controller, also called as the operating system of the SDN, is responsible for running various network applications and maintaining several network services and functionalities. Despite all its capabilities, the introduction of various architectural entities of SDN poses many security threats and potential targets. Distributed Denial of Services (DDoS) is a rapidly growing attack that poses a tremendous threat to the Internet. As the control layer is vulnerable to DDoS attacks, the goal of this paper is to detect the attack traffic, by taking the centralized control aspect of SDN. Nowadays, in the field of SDN, various machine learning (ML) techniques are being deployed for detecting malicious traffic. Despite these works, choosing the relevant features and accurate classifiers for attack detection is an open question. For better detection accuracy, in this work, Support Vector Machine (SVM) is assisted by kernel principal component analysis (KPCA) with genetic algorithm (GA). In the proposed SVM model, KPCA is used for reducing the dimension of feature vectors, and GA is used for optimizing different SVM parameters. In order to reduce the noise caused by feature differences, an improved kernel function (N-RBF) is proposed. The experimental results show that compared to single-SVM, the proposed model achieves more accurate classification with better generalization. Moreover, the proposed model can be embedded within the controller to define security rules to prevent possible attacks by the attackers.

BACKGROUND: Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. METHODS: GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. FINDINGS: The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. INTERPRETATION: We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. FUNDING: Gates Foundation.

The prevalence of type-2 diabetes (T2D) is increasing significantly throughout the globe since the last decade. This heterogeneous and multifactorial disease, also known as insulin resistance, is caused by the disruption of the insulin signaling pathway. In this review, we discuss the existence of various miRNAs involved in regulating the main protein cascades in the insulin signaling pathway that affect insulin resistance. The influence of miRNAs (miR-7, miR-124a, miR-9, miR-96, miR-15a/b, miR-34a, miR-195, miR-376, miR-103, miR-107, and miR-146) in insulin secretion and beta (β) cell development has been well discussed. Here, we highlight the role of miRNAs in different significant protein cascades within the insulin signaling pathway such as miR-320, miR-383, miR-181b with IGF-1, and its receptor (IGF1R); miR-128a, miR-96, miR-126 with insulin receptor substrate (IRS) proteins; miR-29, miR-384-5p, miR-1 with phosphatidylinositol 3-kinase (PI3K); miR-143, miR-145, miR-29, miR-383, miR-33a/b miR-21 with AKT/protein kinase B (PKB) and miR-133a/b, miR-223, miR-143 with glucose transporter 4 (GLUT4). Insulin resistance, obesity, and hyperlipidemia (high lipid levels in the blood) have a strong connection with T2D and several miRNAs influence these clinical outcomes such as miR-143, miR-103, and miR-107, miR-29a, and miR-27b. We also corroborate from previous evidence how these interactions are related to insulin resistance and T2D. The insights highlighted in this review will provide a better understanding on the impact of miRNA in the insulin signaling pathway and insulin resistance-associated diagnostics and therapeutics for T2D.

The aim of this article is to analyse the key factors behind the successful implementation of world-class manufacturing practices. Two distinct modelling approaches have been employed to examine the contextual relationship among the critical success factors (CSFs) and to rank them w.r.t. performance areas. CSFs and performance areas were identified through literature review and opinion of experts from industry and academia. Interpretive structural modelling (ISM) is used to develop a hierarchical structure for analysing the interactions among CSFs. Interpretive ranking process (IRP) is then used to examine the dominance relationship. ISM model highlights the importance of excellent top management over other CSFs, whereas IRP model revealed reduction in energy consumption and waste minimisations as the most important CSF when evaluated against various performance areas. This study also gives a comparative account of ISM and IRP and shows that IRP is a more powerful tool, as it goes one step further and considers the relationship of CSFs with measurable performance indicators.

Purpose – The purpose of this paper is to discuss the lean implementation process and its quantified benefits for the production industry with the help of value stream mapping (VSM). Both current and future state maps of the organization's shop floor scenarios are discussed using VSM techniques in order to highlight improvement areas and to bridge the gap between the existing state and the proposed state of shop floor of the selected industry.

Diabetic cases have increased rapidly in recent years throughout the world. Currently, for type-1 diabetes mellitus (T1DM), multiple daily insulin (MDI) injections is the most popular treatment throughout the world. At this juncture, researchers are trying to develop different insulin delivery systems, especially through oral and pulmonary route using nanocarrier based delivery system. This next generation efficient therapy for T1DM may help to improve the quality of life of diabetic patients who routinely employ insulin by the subcutaneous route. In this paper, we have depicted various next generation nanocarrier based insulin delivery systems such as chitosan-insulin nanoparticles, PLGA-insulin nanoparticles, dextran-insulin nanoparticles, polyalkylcyanoacrylated-insulin nanoparticles and solid lipid-insulin nanoparticles. Modulation of these insulin nanocarriers may lead to successful oral or pulmonary insulin nanoformulations in future clinical settings. Therefore, applications and limitations of these nanoparticles in delivering insulin to the targeted site have been thoroughly discussed.

Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance of the dataset and improve the performance of the models, image enhancement techniques were applied to the data for the four categories: pituitary, normal, meningioma, and glioma. MobileNetv3 achieved the highest accuracy of 99.75%, significantly outperforming other existing methods. This demonstrates the potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis.

Decoding human activity accurately from wearable sensors can aid in applications related to healthcare and context awareness. The present approaches in this domain use recurrent and/or convolutional models to capture the spatio-temporal features from time-series data from multiple sensors. We propose a deep neural network architecture that not only captures the spatio-temporal features of multiple sensor time-series data but also selects, learns important time points by utilizing a self-attention mechanism. We show the validity of the proposed approach across different data sampling strategies on six public datasets and demonstrate that the self-attention mechanism gave a significant improvement in performance over deep networks using a combination of recurrent and convolution networks. We also show that the proposed approach gave a statistically significant performance enhancement over previous state-of-the-art methods for the tested datasets. The proposed methods open avenues for better decoding of human activity from multiple body sensors over extended periods of time. The code implementation for the proposed model is available at https://github.com/isukrit/encodingHumanActivity.