Islamic University

The Internet of Things (IoT) is a methodology or a system that encompasses real-world things to interact and communicate with each other with the assistance of networking technologies. This article describes surveys on advances in IoT-based healthcare methods and reviews the state-of-the-art technologies in detail. Moreover, this review classifies an existing IoT-based healthcare network and represents a summary of all perspective networks. IoT healthcare protocols are analyzed in this context and provide a broad discussion on it. It also initiates a comprehensive survey on IoT healthcare applications and services. Extensive insights into IoT healthcare security, its requirements, challenges, and privacy issues are visualized in IoT surrounding healthcare. In this review, we analyze security and privacy features consisting of data protection, network architecture, Quality of Services (QoS), app development, and continuous monitoring of healthcare that are facing difficulties in many IoT-based healthcare architectures. To mitigate the security problems, an IoT-based security architectural model has been proposed in this review. Furthermore, this review discloses the market opportunity that will enhance the IoT healthcare market development. To conduct the survey, we searched through established journal and conference databases using specific keywords to find scholarly works. We applied a filtering mechanism to collect only papers that were relevant to our research works. The selected papers were then examined carefully to understand their contributions/research focus. Eventually, the paper reviews were analyzed to identify any existing research gaps and untouched areas of research and to discover possible features for sustainable IoT healthcare development.

Effective and low-cost removal of dye and heavy metals from wastewater still is a great challenge for researchers. Adsorption using activated carbon is widely used in removing these toxic pollutants. Physical, chemical, and biological modifications have been studied for improving activated carbon adsorption performance. Literature suggests that chemical modified activated carbon showed maximum adsorption capacity towards dye and heavy from aqueous solution. Chemical modifications, including acid, base, and impregnation, are studied extensively due to reagent availability, easy modification, and tuning facilities of surface functional groups. However, systematic documentation of chemical modifications on activated carbon is required for dye and heavy metals removal efficiency improvement from wastewater. This review focused on the up to date experimental chemically modified activated carbon that showed improved adsorption capacity towards dye and heavy metals from aqueous solution. The available experimental data recommends that an appropriate treatment strategy of a chemical modification process enhanced dye and heavy metals adsorption capacity of the modified activated carbon. Optimum modification process developed textural or surface functional groups properties of modified activated carbon that improved adsorption or binding capacity toward adsorbate or a particular species. In addition, the adsorption capacity of modified and corresponding activated carbon is compared.

Skin cancer is one of the top three perilous types of cancer caused by damaged DNA that can cause death. This damaged DNA begins cells to grow uncontrollably and nowadays it is getting increased speedily. There exist some researches for the computerized analysis of malignancy in skin lesion images. However, analysis of these images is very challenging having some troublesome factors like light reflections from the skin surface, variations in color illumination, different shapes, and sizes of the lesions. As a result, evidential automatic recognition of skin cancer is valuable to build up the accuracy and proficiency of pathologists in the early stages. In this paper, we propose a deep convolutional neural network (DCNN) model based on deep learning approach for the accurate classification between benign and malignant skin lesions. In preprocessing we firstly, apply filter or kernel to remove noise and artifacts; secondly, normalize the input images and extract features that help for accurate classification; and finally, data augmentation increases the number of images that improves the accuracy of classification rate. To evaluate the performance of our proposed, DCNN model is compared with some transfer learning models such as AlexNet, ResNet, VGG-16, DenseNet, MobileNet, etc. The model is evaluated on the HAM10000 dataset and ultimately we obtained the highest 93.16% of training and 91.93% of testing accuracy respectively. The final outcomes of our proposed DCNN model define it as more reliable and robust when compared with existing transfer learning models.

Sirt1 (member of the sirtuin family) is a nicotinamide adenosine dinucleotide (NAD)-dependent deacetylase that removes acetyl groups from various proteins. Sirt1 performs a wide variety of functions in biological systems. The current review focuses on the biological functions of Sirt1 in obesity-associated metabolic diseases, cancer, adipose tissue, aging, cellular senescence, cardiac aging and stress, prion-mediated neurodegeneration, inflammatory signaling in response to environmental stress, development and placental cell survival.

Green synthesis of silver nanoparticles (AgNPs) using biological resources is the most facile, economical, rapid, and environmentally friendly method that mitigates the drawbacks of chemical and physical methods. Various biological resources such as plants and their different parts, bacteria, fungi, algae, etc. could be utilized for the green synthesis of bioactive AgNPs. In recent years, several green approaches for non-toxic, rapid, and facile synthesis of AgNPs using biological resources have been reported. Plant extract contains various biomolecules, including flavonoids, terpenoids, alkaloids, phenolic compounds, and vitamins that act as reducing and capping agents during the biosynthesis process. Similarly, microorganisms produce different primary and secondary metabolites that play a crucial role as reducing and capping agents during synthesis. Biosynthesized AgNPs have gained significant attention from the researchers because of their potential applications in different fields of biomedical science. The widest application of AgNPs is their bactericidal activity. Due to the emergence of multidrug-resistant microorganisms, researchers are exploring the therapeutic abilities of AgNPs as potential antibacterial agents. Already, various reports have suggested that biosynthesized AgNPs have exhibited significant antibacterial action against numerous human pathogens. Because of their small size and large surface area, AgNPs have the ability to easily penetrate bacterial cell walls, damage cell membranes, produce reactive oxygen species, and interfere with DNA replication as well as protein synthesis, and result in cell death. This paper provides an overview of the green, facile, and rapid synthesis of AgNPs using biological resources and antibacterial use of biosynthesized AgNPs, highlighting their antibacterial mechanisms.

Retinal dystrophy (RD) is a heterogeneous group of hereditary diseases caused by loss of photoreceptor function and contributes significantly to the etiology of blindness globally but especially in the industrialized world. The extreme locus and allelic heterogeneity of these disorders poses a major diagnostic challenge and often impedes the ability to provide a molecular diagnosis that can inform counseling and gene-specific treatment strategies. In a large cohort of nearly 150 RD families, we used genomic approaches in the form of autozygome-guided mutation analysis and exome sequencing to identify the likely causative genetic lesion in the majority of cases. Additionally, our study revealed six novel candidate disease genes (C21orf2, EMC1, KIAA1549, GPR125, ACBD5, and DTHD1), two of which (ACBD5 and DTHD1) were observed in the context of syndromic forms of RD that are described for the first time.

Drinking groundwater contaminated with naturally occurring arsenic is a worldwide public health issue. This work describes the research, development and distribution of a filter used by thousands of people in Bangladesh to obtain arsenic-free safe water. The filter removes arsenic species primarily by surface complexation reactions: =FeOH + H(2)AsO(4)(-) --> =FeHAsO(4)(-) + H(2)O (K=10(24)) and =FeOH + HAsO(4)(2-) --> =FeAsO(4)(2-) + H(2)O (K=10(29)) on a specially manufactured composite iron matrix (CIM). The filter water meets WHO and Bangladesh standards, has no breakthrough, works without any chemical treatment (pre- or post-), without regeneration, and without producing toxic wastes. It costs about $40/5 years and produce 20-30 L/hour for daily drinking and cooking need of 1-2 families. The spent material is completely non toxic-solid self contained iron-arsenate cement that does not leach in rainwater. Approved by the Bangladesh Government, about 30,000 SONO filters were deployed all over Bangladesh and continue to provide more than a billion liters of safe drinking water. This innovative filter was also recognized by the National Academy of Engineering-Grainger Challenge Prize for sustainability with the highest award for its affordability, reliability, ease of maintenance, social acceptability, and environmental friendliness, which met or exceeded the local government's guidelines for arsenic removal.

Tailoring optical bandgap of ZnO nanostructured thin films doped with different elements facilitates potential material for photonic applications. Different methods of fabrication process result in different optical and structural properties for the same amount of Mg content. Therefore, details investigation of structural and optical parameters, and their correlation need to be revealed to utilize the fabricated thin films. In this work, Mg-doped ZnO thin film of 200 nm thickness was fabricated by sol-gel spin coating method on a glass substrate for four different Mg content levels. Multiple layers were deposited by a spin coater to increase the film thickness. The prepared thin films were characterized by SEM, XRD, EDS, and UV–Vis spectroscopy. The spectroscopic analysis showed a uniform crystalline nanostructured surface with less structural defects, enhanced transmittance, and higher optical bandgap than that of pure ZnO nanostructured thin film. Change of Mg content from 2% to 8% facilitated tuning of bandgap in the range of 3.30–3.39 eV. Changing trend of structural and optical parameters with Mg content showed non-linear, non-monotonic relation. In-depth analysis of structural and optical properties provides crucial information to form a better view about bandgap dependency on structural parameters.

Adolescents increasingly find it difficult to picture their lives without social media. Practitioners need to be able to assess risk, and social media may be a new component to consider. Although there is limited empirical evidence to support the claim, the perception of the link between social media and mental health is heavily influenced by teenage and professional perspectives. Privacy concerns, cyberbullying, and bad effects on schooling and mental health are all risks associated with this population's usage of social media. However, ethical social media use can expand opportunities for connection and conversation, as well as boost self-esteem, promote health, and gain access to critical medical information. Despite mounting evidence of social media's negative effects on adolescent mental health, there is still a scarcity of empirical research on how teens comprehend social media, particularly as a body of wisdom, or how they might employ wider modern media discourses to express themselves. Youth use cell phones and other forms of media in large numbers, resulting in chronic sleep loss, which has a negative influence on cognitive ability, school performance, and socio-emotional functioning. According to data from several cross-sectional, longitudinal, and empirical research, smartphone and social media use among teenagers relates to an increase in mental distress, self-harming behaviors, and suicidality. Clinicians can work with young people and their families to reduce the hazards of social media and smartphone usage by using open, nonjudgmental, and developmentally appropriate tactics, including education and practical problem-solving.

This article describes and discusses the spread of the coronavirus pandemic in Australia, its impact on people and the economy and policy responses to these impacts. It discusses the implications of these responses for post-pandemic recovery, though noting that the country’s response to the coronavirus disease 2019 (COVID-19) pandemic has, thus far, been among the most successful in the world. Australia’s early physical distancing measures, relatively high per capita testing rates, political stability, national wealth and geographic isolation are among the explanatory factors. This article summarises Australia’s socio-economic responses to the pandemic and shows what this means, especially, for vulnerable groups, and thereby for social inequality, which the pandemic has aggravated and which may become more apparent, still, as debates about paths to economic and social recovery are in some respects already polarising. Although it is relatively early to clearly identify lessons learnt from these responses, it is safe to conclude that further policy development needs to be carefully focused to avoid exacerbating existing inequalities.

Plant proteases used as milk coagulants in cheesemaking are reviewed in this paper. Plant proteases have been used as milk coagulants in cheesemaking for centuries either as crude extracts or in purified form. These coagulants are an alternative to the calf rennet due to the limited availability and high price of rennet, religious factors, diet or ban on recombinant calf rennet in some countries. These enzymes are found in almost all kinds of plant tissues and can be obtained from their natural source or through in vitro culture to ensure a continuous supply of plant proteases. Almost all the enzymes used as milk coagulants belong to aspartic proteases, but enzymes from other groups such as cysteine and serine proteases have also been reported and possess the ability to clot milk under proper conditions. The excessive proteolytic nature of most plant coagulants has limited their use in cheese manufacturing due to lower yields of cheese, bitter flavors and texture defects. The search for new potential milk-clotting enzymes from plants still continues in order to meet the increasing global demand for diversified and good quality cheese production.

Summary Basil seed ( Ocimum basilicum L.) is cultivated in large quantities in different regions of Iran. This seed has reasonable amounts of gum with good functional properties which is comparable with commercial food hydrocolloids. A central composite rotatable design was applied to evaluate the effects of temperature, pH and water/seed ratio on the yield, apparent viscosity and protein content of water‐extracted Basil seed gum. All of the variables significantly ( P < 0.05) affected the extraction yield, whereas the effect of water/seed ratio on apparent viscosity and the effects of pH and water/seed ratio on protein content were not significant ( P > 0.05). Numerical optimisation determined the optimum extraction conditions based on the highest yield and viscosity and the lowest protein content as being temperature 68.71 °C, pH 8.09 and water/seed ratio 65.98:1. Power law model well described non‐Newtonian pseudoplastic behaviour of BSG. Flow behaviour index ( n ) and consistency index ( K ) of 1% crude and pure BSG samples were 0.306, 0.283 and 17.46, 20.22 Pa s n , respectively.

Abstract In the backdrop of today's environmental priorities, where toxicity and stability hinder lead‐based perovskite solar cell (PSC) progress, the emergence of lead‐free alternatives like Cs 2 AgBiBr 6 perovskites has gained significance. This study revolves around the comprehensive evaluation of Cs 2 AgBiBr 6 as a potential photovoltaic (PV) material, using density functional theory (DFT) calculations with CASTEP. Revealing a vital bandgap of 1.654 eV and emphasizing the contributions of Ag‐4 d and Br‐4 p orbitals, this analysis also underscores Ag atoms' dominance in charge distribution. Optically, Cs 2 AgBiBr 6 exhibits UV absorption peaks around 15 eV, intensifying with photon energy up to 3.75 eV, hinting at its promise for solar applications. Guided by DFT, forty configurations involving various electron transport layers (ETLs) and hole transport layers (HTLs) are explored. Among these, CNTS emerges as the prime HTL due to ideal absorber alignment. The spotlight architecture, FTO/AZnO/Cs 2 AgBiBr 6 /CNTS/Au, boasts exceptional efficiency (23.5%), V oc (1.38 V), J sc (21.38 mA cm −2 ), and FF (79.9%). In contrast, FTO/CdZnS/Cs 2 AgBiBr 6 /CNTS/Au achieves a slightly lower 23.15% efficiency. Real‐world intricacies are probed, encompassing resistances, temperature, current–voltage ( J – V ) traits, and quantum efficiency (QE), enhancing practical relevance. These findings are thoughtfully contextualized within prior literature, showcasing the study's contributions to non‐toxic, inorganic perovskite solar technology. This work aspires to positively steer sustainable PV advancement.

Bangladesh is a densely populated developing country. Both industrialization and geological sources have caused widespread heavy metal and metalloid pollution in Bangladesh, which is now posing substantial threats to the local people. In this review, we carried out one of the most exhaustive literature analyses on the current status of Bangladesh heavy metal and metalloid pollution, covering water, soil, and foods. Analysis showed that soils near high traffic and industrial areas contain high concentrations of heavy metals and metalloids. Agricultural land and vegetables in sewage-irrigated areas were also found to be heavy metal- and metalloid-contaminated. River water, sediment, and fish from the Buriganga, Turag, Shitalakhya, and Karnaphuli rivers are highly contaminated with cadmium (Cd), lead (Pb), and chromium (Cr). Particularly, groundwater arsenic (As) pollution associated with high geological background levels in Bangladesh is well reported and is hitherto the largest mass poisoning in the world. Overall, the contamination levels of heavy metals and metalloids vary among the cities, with industrial areas being most polluted. In all, this review provides a quantitative identification of the As, Pb, Cd, and Cr contamination hotspots in Bangladesh based on the literature, which may be useful to environmental restorationists and local policy makers.

Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study's main contribution is the exploration part of the features and its impact on detecting the depression level.

K12 resulted in optimal GC content and higher CAI value followed by incorporating it into the cloning vector pET28+(a). Overall, these results suggest that the designed peptide vaccine can serve as an excellent prophylactic candidate against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Today, primary school teachers face challenges when dealing with digital natives. As a result of the explosion and rapid growth in information technologies that can be used in education, there are increasing demands to adopt technology in education, in order to influence students to learn actively and motivate them to gain an effective learning process. Augmented reality applications show good potential in giving students more active, effective and meaningful learning processes. Moreover, augmented reality attracts research attention for its ability to allow students to be immersed in realistic experiences. Therefore, this study reviews the main benefits of using augmented reality applications in education. It also aims to examine user acceptance of augmented reality applications within an e-learning environment in primary schools, from the teachers’ perspective, as an initial experiment. Moreover, it explores the main barriers and benefits when adopting this technology.

In the present era, human brain tumor is the extremist dangerous and devil to the human being that leads to certain death. Furthermore, the brain tumor arises more complexity of patients life with time. As a result, early detection of tumors is most crucial to save and prolong the patient’s lifetime. Therefore, enhanced brain tumor detection is required in medical fields. Automatic human brain tumor detection in magnetic resonance imaging (MRI) is playing a vital role in several symptomatic and cures applications. However, the existing schemes (e.g., random forest, Fuzzy C-means, artificial neural network (ANN) and wavelet transform) can detect brain tumors with insufficient accuracy and longer execution time (in minutes). In this paper, we propose an enhanced brain tumor detection scheme based on the template-based K-means (TK) algorithm with superpixels and principal component analysis (PCA) which efficiently detects the human brain tumors in lower execution time. At first, we extract essential features using both superpixels and PCA which helps accurately to detect brain tumors. Then, image enhancement is done using a filter that helps to improve accuracy. Finally, the image segmentation is performed through TK-means clustering algorithm to detect the brain tumor. The experimental results show that the proposed detection scheme achieves a better accuracy and a reduced execution time (in seconds) than other existing schemes for the detection of brain tumor in MR image.

Reactive oxygen species (ROS) induce carcinogenesis by causing genetic mutations, activating oncogenes, and increasing oxidative stress, all of which affect cell proliferation, survival, and apoptosis. When compared to normal cells, cancer cells have higher levels of ROS, and they are responsible for the maintenance of the cancer phenotype; this unique feature in cancer cells may, therefore, be exploited for targeted therapy. Quercetin (QC), a plant-derived bioflavonoid, is known for its ROS scavenging properties and was recently discovered to have various antitumor properties in a variety of solid tumors. Adaptive stress responses may be induced by persistent ROS stress, allowing cancer cells to survive with high levels of ROS while maintaining cellular viability. However, large amounts of ROS make cancer cells extremely susceptible to quercetin, one of the most available dietary flavonoids. Because of the molecular and metabolic distinctions between malignant and normal cells, targeting ROS metabolism might help overcome medication resistance and achieve therapeutic selectivity while having little or no effect on normal cells. The powerful bioactivity and modulatory role of quercetin has prompted extensive research into the chemical, which has identified a number of pathways that potentially work together to prevent cancer, alongside, QC has a great number of evidences to use as a therapeutic agent in cancer stem cells. This current study has broadly demonstrated the function-mechanistic relationship of quercetin and how it regulates ROS generation to kill cancer and cancer stem cells. Here, we have revealed the regulation and production of ROS in normal cells and cancer cells with a certain signaling mechanism. We demonstrated the specific molecular mechanisms of quercetin including MAPK/ERK1/2, p53, JAK/STAT and TRAIL, AMPKα1/ASK1/p38, RAGE/PI3K/AKT/mTOR axis, HMGB1 and NF-κB, Nrf2-induced signaling pathways and certain cell cycle arrest in cancer cell death, and how they regulate the specific cancer signaling pathways as long-searched cancer therapeutics.

In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting human brain tumors in a magnetic resonance imaging (MRI) image. In this proposed algorithm, firstly, the template-based K-means algorithm is used to initialize segmentation significantly through the perfect selection of a template, based on gray-level intensity of image; secondly, the updated membership is determined by the distances from cluster centroid to cluster data points using the fuzzy C-means (FCM) algorithm while it contacts its best result, and finally, the improved FCM clustering algorithm is used for detecting tumor position by updating membership function that is obtained based on the different features of tumor image including Contrast, Energy, Dissimilarity, Homogeneity, Entropy, and Correlation. Simulation results show that the proposed algorithm achieves better detection of abnormal and normal tissues in the human brain under small detachment of gray-level intensity. In addition, this algorithm detects human brain tumors within a very short time—in seconds compared to minutes with other algorithms.