Convolutional neural network architectures have become increasingly complex, which has improved the performance slowly on well-known benchmark datasets in the recent years. In this research, we have analyzed the true need for such complexity. We have introduced G-Net light, a lightweight modified GoogleNet with improved filter count per layer to reduce feature overlaps and complexity. Additionally, by limiting the amount of pooling layers in the proposed architecture, we have exploited the skip connections to minimize the spatial information loss. The investigations on the proposed architecture are evaluated on three retinal vessel segmentation publicly available datasets. The proposed G-Net light outperforms other vessel segmentation architectures by reducing the number of trainable parameters..

Inspired by nature's examples such as lotus leaves and insect wings, superhydrophobic surfaces have been extensively studied and engineered. However, achieving superoleophobic surfaces, which repel oils, has proven to be more challenging due to the oils' lower surface tension than water. This article presents a novel, cost-effective technique for fabricating superoleophobic surfaces with enhanced durability. We demonstrate the effectiveness of this technique by using Perfluorooctyltriethoxysilane (PFOTES) and silicon dioxide nanoparticles on stainless steel mesh substrate. The resulting surfaces exhibit remarkable superoleophobic properties by incorporating re-entrant structures, low surface energy, and high surface roughness, making them ideal for oil-repellency applications.

Ransomware is one of the most dangerous types of malware, which is frequently intended to spread through a network to damage the designated client by encrypting the client’s vulnerable data. Conventional signature-based ransomware detection technique falls behind because it can only detect known anomalies. When it comes to new and non-familiar ransomware traditional system unveils huge shortcomings. For detecting unknown patterns and sorts of new ransomware families,behavior-based anomaly detection approaches are likely to be the most efficient approach. In the wake of this alarming condition, this paper presents an ensemble classification model consisting of three widely used machine learning techniques that include Decision Tree (DT), RandomForest (RF), and K-nearest neighbor (KNN). To achieve the best outcome ensemble soft voting and hard voting techniques are used while classifying ransomware families based on attack attributes. Performance analysis is done by comparing our proposed ensemble models with standalone models on behavioral attributes based ransomware dataset..

In this work, an analog front-end (AFE) circuit for Electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter- all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the $g_m/I_D$ methodology. Both the pre-layout and post-layout simulation were carried out. The layout consumes an area of 0.0058 mm$^2$ without the resistors and capacitors. Analysis of various simulation results were carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz with a variable gain between 44 to 58 dB, a notch depth of -56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of -59.65 dB (less than 1\%), an input referred noise spectral density of $

The research aims to discover the natural occurrence of patulin (PAT) in selected citrus fruits from the central cities of Punjab and Pakistan's Northern cities. Total 2970 samples of twelve citrus fruits; kinnow, orange, grapefruits, bitter orange, mausami, red blood, pineapple, sweet orange, rough lime, sweet lime, kagzi lime and lemon were examined using liquid chromatography fitted with UV detector. The limit of detection (LOD) and quantification limit was 0.04 and 0.12 µg/kg, respectively. The results have shown that 56% of samples of citrus fruits from Punjab's central cities, Pakistan, were discovered to be infected from PAT. The elevated amounts of PAT ranging from 0.04 to 1150 µg/kg were found in citrus fruit samples from Multan cities. Furthermore, 31.7% of samples of citrus fruits from Northern cities of Pakistan were discovered to be infected with PAT, and the elevated amounts were found ranging from 0.04-320 µg/kg from Swat city. About 22.1% of samples of citrus fruits have levels of PAT greater than the suggested limits established by the European Union (EU). PAT's dietary intake levels ranged from 0.10-1.11 µg/kg bw/day from the central cities of Punjab, Pakistan, and 0.13-1.93 µg/kg bw/day were documented from Northern cities of Pakistan

Due to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in today's cyber security industry all over the world. Traditional security solutions are insufficient to address contemporary security issues due to the rapid proliferation of many sorts of cyber-attacks and threats. Utilizing artificial intelligence knowledge, especially machine learning technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of machine learning algorithms, emphasizing how they can be employed for intelligent data analysis and automation in cybersecurity through their potential to extract valuable insights from cyber data. We also explore a number of potential real-world use cases where data-driven intelligence, automation, and decision-making enable next-generation cyber protection that is more proactive than traditional approaches. The future prospects of machine learning in cybersecurity are eventually emphasized based on our study, along with relevant research directions. Overall, our goal is to explore not only the current state of machine learning and relevant methodologies but also their applicability for future cybersecurity breakthroughs.

Artificial Intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence into machines or systems. Thus AI-based modeling is the key to building automated, intelligent, and smart systems according to today's needs. To solve real-world issues various types of AI such as analytical, functional, interactive, textual, and visual AI can be applied to enhance the intelligence and capabilities of an application. However, developing an effective AI model is a challenging task due to the dynamic nature and variation in real-world problems and data. In this paper, we present a comprehensive view on "AI-based Modeling" with the principles and capabilities of potential AI techniques that can play an important role in developing intelligent and smart systems in various real-world application areas including business, finance, healthcare, agriculture, smart cities, cybersecurity and many more. We also emphasize and highlight the research issues within the scope of our study. Overall, the goal of this paper is to provide a broad overview of AI-based modeling that can be used as a reference guide by academics and industry people as well as decision-makers in various real-world scenarios and application domains.

Due to the rising dependency on digital technology, cybersecurity has emerged as a more prominent field of research and application that typically focuses on securing devices, networks, systems, data and other resources from various cyber-attacks, threats, risks, damages, or unauthorized access. Artificial Intelligence (AI), also referred to as a crucial technology of the current Fourth Industrial Revolution (Industry 4.0 or 4IR), could be the key to intelligently dealing with these cyber issues. Various forms of AI methodologies, such as analytical, functional, interactive, textual as well as visual AI can be employed to get the desired cyber solutions according to their computational capabilities. However, the dynamic nature and complexity of real-world situations and data gathered from various cyber sources make it challenging nowadays to build an effective AI-based security model. Moreover, defending robustly against adversarial attacks is still an open question in the area. In this paper, we provide a comprehensive view on "Cybersecurity Intelligence and Robustness", emphasizing multi-aspects AI-based modeling and adversarial learning that could lead to addressing diverse issues in various cyber applications areas such as detecting malware or intrusions, zero-day attacks, phishing, data breach, cyberbullying and other cybercrimes. Thus the eventual security modeling process could be automated, intelligent, and robust compared to traditional security systems. We also emphasize and draw attention to the future aspects of cybersecurity intelligence and robustness along with the research direction within the context of our study. Overall, our goal is not only to explore AI-based modeling and pertinent methodologies but also to focus on the resulting model's applicability for securing our digital systems and society.

The digital world has a wealth of data, such as Internet of Things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science'' including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

In the current age of the Fourth Industrial Revolution ($4IR$ or Industry $4.0$), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding real-world applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world applications areas, such as cybersecurity, smart cities, healthcare, business, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for not only the application developers but also the decision-makers and researchers in various real-world application areas, particularly from the technical point of view.

Artificial Intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (Industry 4.0), which can be used for the protection of Internet-connected systems from cyber-threats, attacks, damage, or unauthorized access. To intelligently solve today's various cybersecurity issues, popular AI techniques involving Machine Learning (ML) and Deep Learning (DL) methods, the concept of Natural Language Processing (NLP), Knowledge Representation and Reasoning (KRR), as well as the concept of knowledge or rule-based Expert Systems (ES) modeling can be used. Based on these AI methods, in this paper, we present a comprehensive view on "AI-driven Cybersecurity" that can play an important role for intelligent cybersecurity services and management. The security intelligence modeling based on such AI methods can make the cybersecurity computing process automated and intelligent than the conventional security systems. We also highlight several research directions within the scope of our study, which can help researchers do future research in the area. Overall, this paper's ultimate objective is to serve as a reference point and guidelines for cybersecurity researchers as well as industry professionals in the area, especially from an AI-based technical point of view.

Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.

Deep learning (DL), which is originated from an artificial neural network (ANN), is one of the major technologies of today's smart cybersecurity systems or policies to function in an intelligent manner. Popular deep learning techniques, such as Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN or ConvNet), Recurrent Neural Network (RNN) or Long Short-Term Memory (LSTM), Self-organizing Map (SOM), Auto-Encoder (AE), Restricted Boltzmann Machine (RBM), Deep Belief Networks (DBN), Generative Adversarial Network (GAN), Deep Transfer Learning (DTL or Deep TL), Deep Reinforcement Learning (DRL or Deep RL), or their ensembles and hybrid approaches can be used to intelligently tackle the diverse cybersecurity issues. In this paper, we aim to present a comprehensive overview from the perspective of these neural networks and deep learning techniques according to today's diverse needs. We also discuss the applicability of these techniques in various cybersecurity tasks such as intrusion detection, identification of malware or botnets, phishing, predicting cyber-attacks, e.g. denial of service (DoS), fraud detection or cyber-anomalies, etc. Finally, we highlight several research issues and future directions within the scope of our study in the field. Overall, the ultimate goal of this paper is to serve as a reference point and guidelines for the academia and professionals in the cyber industries, especially from the deep learning point of view.

A large number of smart devices (things) are being deployed with the swift development of Inter- net of Things (IOT). These devices, owned by different organizations, have a wide variety of services to offer over the web. During a natural disaster or emergency (i.e., a situation), for example, relevant IOT services can be found and put to use. However, appropriate service matching methods are required to find the relevant services. Organizations that manage situation responses and organizations that provide IOT services are likely to be independent of each other, and therefore it is difficult for them to adopt a common ontological model to facilitate the service matching. Moreover, there exists a large conceptual gap between the domain of discourse for situations and the domain of discourse for services, which cannot be adequately bridged by existing techniques. In this paper, we address these issues and propose a new method, WikiServe, to identify IOT services that are functionally relevant to a given situation. Using concepts (terms) from situation and service descriptions, WikiServe employs Wikipedia as a knowledge source to bridge the conceptual gap between situation and service descriptions and match functionally relevant IOT services for a situation. It uses situation terms to retrieve situation related articles from Wikipedia. Then it creates a ranked list of services for the situation using the weighted occurrences of service terms in weighted situation articles. WikiServe performs better than a commonly used baseline method in terms of Precision, Recall and F measure for service matching.

Purpose: The pandemic, named COVID-19, which shook global economy, production and trade has equally affected the social and domestic lives of ours. In this study the COVID-19’s impact has been investigated on lack of social interaction, financial stress and how it influences the domestic violence in a developing country like Pakistan. The study focuses on some of the important factors that are root causes of increased domestic violence in Pakistan. Design/methodology/approach: The study is quantitative and survey questionnaire has been conducted for structural equation modelling (SEM) to evaluate the hypotheses. The modified scale was validated using confirmatory factor analysis, the Purposive and snowball sampling technique is used for collecting data from 200 respondents of Punjab Pakistan for data analysis through SPSS and SmartPLS. Findings: It is revealed that Covid 19 has a significant effect on lack of social interaction and financial stress. Moreover, lack of social interaction and financial stress also enhances domestic violence. Finally, lack of social interaction and financial stress mediates COVID-19 and domestic violence. Originality/value: This study appears to be frontier research which determines the impact of COVID-19 on lack of social interaction, financial stress and domestic violence particularly in a developing country like Pakistan.

In this paper, we present a framework that automatically labels Latent Dirichlet Allocation (LDA) generated topics using sentiment and aspect terms from COVID-19 tweets to help the end-users by minimizing the cognitive overhead of identifying key topics labels. Social media platforms especially Twitter are considered as one of the most influential sources of information for providing public opinion related to a critical situation like the COVID-19 pandemic. LDA is a popular topic modelling algorithm that extracts hidden themes of documents without assigning a specific label. Thus automatic labelling of LDA-generated topics from COVID-19 tweets is a great challenge instead of following the manual labelling approach to get an overview of wider public opinion. To overcome this problem, in this paper, we propose a framework named \texttt{SATLabel} that effectively identifies significant topic labels using \textit{top unigrams features of sentiment terms and aspect terms clusters from LDA generated topics} of COVID-19 related tweets to uncover various issues related to the COVID-19 pandemic. The experimental results show that our methodology is more effective, simpler, and traces better topic labels compare to the manual topic labelling approach.

The makeup of human microbiota has been linked to a number of autoimmune disorders. Recent developments in whole metagenome sequencing and 16S rRNA sequencing technology have considerably aided research into the microbiome and its relationship to disease. Due to the inherent high dimensionality and complexity of data generated by high-throughput platforms, conventional bioinformatics techniques could only provide an inadequate explanation for the most relevant changes and seldom provide correct predictions. Machine learning, on the other hand, is a subset of artificial intelligence applications that enable the untangling of high-dimensional systems and intricate knots in correlation by learning complex patterns and improving automatically from training data without being explicitly programmed. Machine learning is increasingly being utilized to research the influence of microbes on the onset of illness and other clinical features since computer power has increased dramatically in the last few decades. In this review paper, we focused on emerging methodological approaches of supervised machine learning algorithms for identification of autoimmune disorders utilizing metagenomics data, as well as the potential benefits and limitations of machine learning models in clinical applications.

Disease classification based on biological data is an important area in bioinformatics and biomedical research. It helps the doctors and medical practitioners for the early detection of disease and support them as a computer-aided diagnostic tool for accurate diagnosis, prognosis, and treatment of disease. Earlier Microarray gene expression data have wide application for the classification of disease, but now Next-generation sequencing (NGS) has replaced the Microarray technology. From the last few years, RNA sequence (RNA-Seq) data are widely used for the transcriptomic analysis. Hence, RNA-Seq based classification of disease is in its infancy. In this article, we present a general framework for the classification of disease constructed on RNA-Seq data. This framework will guide the researchers to process RNA-Seq, extract relevant features and apply the appropriate classifier to classify any kind of disease.

Almost all single-family detached house in Canada consume huge electricity for space heating and domestic hot water (DHW) purposes. There are many possibilities to design an energy-efficient house. A solar water heating system can be used for domestic water and space heating. Water temperature can be kept constant always by connecting a heat pump or oil burner because solar energy is intermittent. Proper and optimized solar photovoltaic and collector design, tank design, heat pump selection, house insulation, total demand calculation in each section are essential. Energy-Efficient house design has been proposed with water heating and space heating system and compared with the existing system, solar PV based systems, and solar collector based system. The tracking and non-tracking based solar thermal collector based and the solar photovoltaic based system has been compared in this paper and investigate the suitable one for practically applicable and acceptable by the people. Simulation has been done by using the PolySun software. It found that by implementing the proposed PV based system with tracking is highly suitable considering lower cost, high output power, flexibility, easy installation.

Cities are undergoing huge shifts in technology and operations in recent days, and `data science' is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting insights or actionable knowledge from city data and building a corresponding data-driven model is the key to making a city system automated and intelligent. Data science is typically the study and analysis of actual happenings with historical data using a variety of scientific methodology, machine learning techniques, processes, and systems. In this paper, we concentrate on and explore ``Smart City Data Science", where city data collected from various sources like sensors and Internet-connected devices, is being mined for insights and hidden correlations to enhance decision-making processes and deliver better and more intelligent services to citizens. To achieve this goal, various machine learning analytical modeling can be employed to provide deeper knowledge about city data, which makes the computing process more actionable and intelligent in various real-world services of today's cities. Finally, we identify and highlight ten open research issues for future development and research in the context of data-driven smart cities. Overall, we aim to provide an insight into smart city data science conceptualization on a broad scale, which can be used as a reference guide for the researchers, professionals, as well as policy-makers of a country, particularly, from the technological point of view.

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning-based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.

Sentiment analysis is a process of extracting opinions into the positive, negative, or neutral categories from a pool of text using Natural Language Processing (NLP). In the recent era, our society is swiftly moving towards virtual platforms by joining virtual communities. Social media such as Facebook, Twitter, WhatsApp, etc are playing a very vital role in developing virtual communities. A pandemic situation like COVID-19 accelerated people's involvement in social sites to express their concerns or views regarding crucial issues. Mining public sentiment from these social sites especially from Twitter will help various organizations to understand the people's thoughts about the COVID-19 pandemic and to take necessary steps as well. To analyze the public sentiment from COVID-19 tweets is the main objective of our study. We proposed a deep learning architecture based on Bidirectional Gated Recurrent Unit (BiGRU) to accomplish our objective. We developed two different corpora from unlabelled and labeled COVID-19 tweets and use the unlabelled corpus to build an improved labeled corpus. Our proposed architecture draws a better accuracy of 87% on the improved labeled corpus for mining public sentiment from COVID-19 tweets.

Smartphones are prone to SMS phishing due to the rapid growth in the availability of smart mobile technologies driven by Internet connections. Also, detecting phishing SMS is a challenging task due to the unstructured nature of SMS text data with non-linear complex correlations. In this concern, considering the recent advancements in the domain of cybersecurity, we have proposed a hybrid deep learning framework that extracts robust features from SMS texts followed by an automatic detection of Phishing SMS. Due to combining the potential capability of individual models into one hybrid framework, it has outperformed various other individual machine learning and deep learning models. The proposed Phishing Detection framework is an effective hybrid combination of pretrained transformer model, MPNet (Masked and Permuted Language Modeling), with supervised ConvNets (CNN) and Bi-directional Gated Recurrent Units (GRU). It is intended to successfully detect unstructured short phishing text messages that contain complex patterns.

Three-Dimensional mesoporous TiO2 scaffolds of anatase phase possess inherent eximious optical behavior that are beneficial in designing photoelectrodes for solar energy conversion applications. However, substantial efforts have been devoted in maximizing the UV and/or visible light absorption efficiency, and suppressing the annihilation of photogenerated charged species in pristine mesoporous TiO2 structure for improved solar illumination conversion efficiencies. This study provides the fundamental insights into Mxene functionalized mesoporous TiO2 as photoelectrode for the first time. This novel combination of Mxene functionalized TiO2 electrodes with and without TiCl4 treatments have been successfully optimized to intensify the process of photon absorption, charge segregation and photocurrent, resulting in superior photoelectrode performance. The photocurrent measurements of prepared photoelectrodes are significantly enhanced upon increased contents of Mxene due to improved absorption efficiency within the visible region as verified by UV–Vis absorption spectroscopy. Moreover, as evident through structural analysis, the anatase phase of TiO2 is well grown due to increased contents of Mxene and post-deposition heat treatments. Consequently, an appreciable coverage of well-developed grains on FTO surface observed in SEM images. Thereby, the newly fabricated conductive mesoporous TiO2 photoelectrodes are potential candidates for photoinduced energy conversion and storage purposes.

In this study, we construct a new Atangana-Baleanu fractional model for whooping cough disease to predict future dynamics of the disease, as well as to suggest strategies to eliminate the disease in an optimal way. We prove that the proposed model has a unique solution which is positive and bounded. To measure contagiousness of the disease, we determine the reproduction number R0 and use it to examine the local and global stability at equilibrium points that have symmetry. Through sensitivity analysis, we determine parameters of the model that are most sensitive to R0. The ultimate aim of this research is to analyze different disease prevention approaches in order to find the most suitable one. For this, we include the vaccination and quarantine compartments in the proposed model and formulate an optimal control problem to assess the effect of vaccination and quarantine rates on disease control in three distinct scenarios. Firstly, we study the impact of vaccination strategy and conclude findings with presentation of graphical results. Secondly, we examine the impact of quarantine strategy on whooping cough infection with possible elimination from the society. Lastly, we implement vaccination and quarantine strategies together to visualize their combine effect on infection control. In addition to the study of an optimal control problem, we examine the effect of fractional order on disease dynamics as well as the impact of constant vaccination and quarantine rates on disease transmission and control. We determine that the optimal control strategy with the three controls is more effective in reducing the spread of whooping cough infection. Implementation of Toufik-Atangana type numercial scheme both for the state and adjoint equations is another contribution of this article.

Computer network attacks are evolving in parallel with the evolution of hardware and neural network architecture. Despite major advancements in Network Intrusion Detection System (NIDS) technology, most implementations still depend on signature-based intrusion detection systems, which can’t identify unknown attacks. Deep learning can help NIDS to detect novel threats since it has a strong generalization ability. The deep neural network’s architecture has a significant impact on the model’s results. We propose a genetic algorithm based model to find the optimal number of hidden layers and the number of neurons in each layer of the deep neural network (DNN) architecture for the network intrusion detection binary classification problem. Experimental results demonstrate that the proposed DNN architecture shows better performance than classical machine learning algorithms at a lower computational cost.

The present study explained factors influencing citizen satisfaction service in the Investment and One-Stop Service Agency of Tanah Bumbu Regency. In particular, this research analyses the level of citizen satisfaction and the extent to which Awareness, Rules, Organizational, Income, Skill-Ability, and Service Facility Factor influence Citizen Satisfaction. This study uses a mixed methodology with a sequential explanatory strategy. Using the incidental sampling with Slovin's Formula to calculate the number of samples is 93 respondents. The quantitative data were analyzed by the SmartPLS 3.0 program. The findings showed that the level of citizen satisfaction is included in the category "Satisfied". Furthermore, the variable of Citizen Satisfaction is influenced by variables of Awareness, Rules, Organizational, Income, Skill-Ability dan Service Facility Factor for 70,5%. Whereas Awareness, Rules, Organizational, and Skill-Ability Factor has a significant influence on Citizen Satisfaction. Besides, Income and Service Facility Factor do not have a significant influence on Citizen Satisfaction.

Short Message Service (SMS) is becoming the secure medium of communication due to large-scale global coverage, reliability, and power efficiency. As person--to--person (P2P) messaging is less secure than application-to-person (A2P) messaging, anyone can send a message, leading to the attack. Attackers mistreat this opportunity to spread malicious content, perform harmful activities, and abuse other people, commonly known as spam. Moreover, such messages can waste a lot of time, and important messages are sometimes overlooked. As a result, accurate spam detection in SMS and its computational time are burning issues. In this paper, we conduct six different experiments to detect SMS spam from the dataset of 5574 messages using machine learning classifiers such as Multinomial Naïve Bayes (MNB) and Support Vector Machine (SVM), considering variations of \textit{Term Frequency-- Inverse Document Frequency (TF--IDF)} features for exploring the trade-off among accuracy, F1-score and computational time. The experiments achieve the best result of the accuracy of 98.50\%, F1--score of 98\%, and area under roc curve (AUC) of 0.97 for multinomial naïve bayes classifier with TF--IDF after stemming.

Recently, spam emails have become a significant problem with the expanding usage of the Internet. It is to some extend obvious to filter emails. A spam filter is a system that detects undesired and malicious emails and blocks them from getting into the users' inboxes. Spam filters check emails for something "suspicious" in terms of text, email address, header, attachments, and language. However, we have used different features such as word2vec, word n-grams, character n-grams, and a combination of variable length n-grams for comparative analysis in our proposed approach. Different machine learning models such as support vector machine (SVM), decision tree (DT), logistic regression (LR), and multinomial naïve bayes (MNB) are applied to train the extracted features. We use different evaluation metrics such as precision, recall, f1-score, and accuracy to evaluate the experimental results. Among them, SVM provides 97.6 \% of accuracy, 98.8\% of precision, and 94.9\% of f1-score using a combination of n-gram features.

Nigella sativa (NS) is an effective medicinal plant possessing noteworthy antioxidant property. In NS, there are more than hundred phyto-chemicals reported, out of which thymoquinone is the utmost active phyto-constituent having sturdy antioxidative property. Thymoquinone is a cyclicdione, when reacts with sodium azide, converts into α-azido ketones i.e its analogs which are handy with extensive range of reactions. Sodium azide induces stress in plants thereby, modulating the antioxidant system. The present investigation was planned to elucidate the effect of sodium azide at different concentrations (5µM, 10µM, 20µM, 50µM, 100µM and 200µM) on its secondary metabolites (mainly thymoquinone) in NS callus culture extract (NSE). The results showed sodium azide effect on thymoquinone content and a concentration dependent boost in antioxidant property. It was also observed that thymoquinone content and percent yield (analyzed by RP-HPLC; Reverse Phase- High Performance Liquid Chromatography) were minimum (0.033±0.006% and 0.420±0.045%, respectively) at 200 µM sodium azide used. Whereas, antioxidant activity (analyzed by DPPH; 2, 2-diphenyl-1-picrylhydrazyl) was found to be maximum (3.873±0.402%) at same dose. Further, analysis was done for inhibition of oxidative DNA damage at different concentrations of sodium azide on NSE, maximum inhibition of DNA damage (0.243±0.017%) was found at 200 µM concentration of sodium azide. When correlated, strong positive correlation was observed between percent yield and percent thymoquinone, antioxidant property and inhibition of DNA damage. Whereas, strong negative correlation was observed between percent yield and antioxidant property, percent thymoquinone and antioxidant property, percent thymoquinone and inhibition of DNA damage. The findings evidently point out that the content of thymoquinone, antioxidant property and inhibition of DNA damage was affected by sodium azide.

Abstract: In recent years, the construction engineering sector has undergone a transformative shift towards the integration of machine learning (ML) techniques, particularly in predicting the properties of steel fiber-reinforced concrete (SFRC). Despite the theoretical sophistication of existing models, a persistent challenge remains – their opacity, lacking transparency and real-world relevance for practi-tioners. To address this gap and advance our current understanding, this study employs the extra gradient (XG) Boosting algorithm, crafting a comprehensive ap-proach. Grounded in a meticulously curated database drawn from 43 seminal pub-lications, encompassing 420 distinct records, this research focuses predominantly on three primary fiber types: crimped, hooked, and mil-cut. Complemented by hands-on experimentation involving 20 diverse SFRC mixtures, this empirical campaign is further illuminated through the strategic use of Partial Dependence Plots (PDPs), revealing intricate relationships between input parameters and con-sequent compressive strength. A pivotal revelation of this research lies in the identification of optimal SFRC formulations, offering tangible insights for real-world applications. The developed ML model stands out not only for its sophistication but also its tangible accuracy, evidenced by exemplary performance against inde-pendent datasets, boasting a commendable mean target-prediction ratio of 99%. To bridge the theory-practice gap, we introduce a user-friendly digital interface, thri-ughly designed to guide professionals in optimizing and accurately predicting the compressive strength of SFRC. This research thus contributes to the construction and civil engineering sectors by enhancing predictive capabilities and refining mix designs, fostering innovation, and addressing the evolving needs of the industry.

Mucosal vaccines can target specific mucosal surfaces, such as the respiratory, genital, or intestinal mucosa. Conventional systemic immunization, such as intramuscular injections, do not provide mucosal immunity, including IgA, nor do they prevent pathogens from entering the mucous membranes. Nasal immunization protects mucosal membranes, but nasal antigen presentation appears to entail adjuvant to stimulate immunogenicity. Current treatments for mucosal diseases such as inflammatory bowel disease (IBD) and allergic dermatitis only aimed to temporarily relieve symptoms. Moreover, these treatments not only have drug resistance or side effects, but the disease can recur, leading to switching to other treatments. Therefore, there is a need to develop safe and effective treatments that provide better long-term outcomes to reduce the risk of recurrence. Regulatory T (Treg) cells have been used to suppress inflammation. To find advances in mucosal treatment, we investigated the therapeutic effects of intranasal pep27 mutant immunization. Here, a new method of Treg induction through intranasal immunization without adjuvant has been developed to potentially overcome allergies and mucosal inflammation of the lung-gut axis in animal models. The implementation of the pep27 mutant for these therapies should be preceded by studies of Treg resilience through clinical translational studies of dietary changes.

Poliomyelitis, commonly known as “polio” is a paralytic and perilous disease caused by the poliovirus. Due to its highly contagious nature, the virus was a challenge to the world in the late 1980s. . Since 1988 the collective work of the Global Polio Eradication Initiative (GPEI), Centre for Disease Control and Prevention (CDC), and World Health Organization (WHO) through immunizations, communication awareness, and monitoring have helped the world exonerate polio. The mission of polio-free Pakistan was herculean and had confronted enormous challenges in different ways but came out with positive results. In 2019, with only two remaining polio-endemic countries, Pakistan and Afghanistan, GPEI launched an “Endgame strategy 2019-2023” which aims to eradicate polio globally, with a targeted focus, especially on polio-endemic countries, the plan emphasizes the early detection of polio cases for complete eradication and to restrict the spread of polio. Pakistan has achieved a milestone in combating polio despite having a web of factors that have thwarted Pakistan’s polio eradication efforts, but this is not the end, the struggle continues until we really get an internationally verified certification of Polio free nation, for this WHO has designed a multidisciplinary strategy 2022-2026 to really end this polio for once and for all.

In the realm of smart manufacturing, equipment plays a pivotal role, impacting the efficiency and quality of production. Ensuring equipment availability and optimal performance is crucial, necessitating effective maintenance strategies. This paper addresses the complexities of smart device maintenance, which often involves collaboration between multiple entities like device owners, manufacturers, suppliers, and external maintenance services. To enhance this cooperative maintenance, we propose a novel maintenance system based on permissioned blockchain technology, offering superior security and reliability compared to conventional cloud-based platforms. Our contributions include developing an integrated maintenance system framework for smart manufacturing, which facilitates interactions among diverse stakeholders. Additionally, we have developed a prototype of this maintenance system, serving as a practical model for real-world application. Our research primarily demonstrates the practicality and advantages of using permissioned blockchain for maintenance systems in the manufacturing sector, paving the way for more secure, efficient, and collaborative maintenance practices.

Over the years, scarcity of fresh potable water has increased the demand for clean water. Meanwhile, with the advent of nanotechnology, the use of nanomaterials for photocatalytic degradation of pollutants in wastewaters has increased. Herein, a new type of nanohybrids of La and Mn co-doped bismuth ferrite (BiFeO₃) nanoparticles embedded into transition metal carbide sheets (MXene) were prepared by a low-cost double solvent sol-gel method, and investigated for their photocatalytic activity. The photoluminescence results showed that pure BFO has highest electron hole recombination rate as compared to all the co-doped BFO/MXene nanohybrids. The larger surface area and higher electron-hole pair generation rate provides suitable environment for fast photo-degradation of organic molecules. The band gap of the prepared nanohybrids was tuned to 1.96 eV having largest BiFeO₃ surface area (147 m²g⁻¹) reported till date. Moreover, the BLFO/MXene and BLFMO-5/Mxene degraded the 92% organic pollutant from water in dark and remaining in light spectrum as compare to undoped BFO/Mxene due to enhancement of the surface area and electron-hole recombination rate upon doping. Therefore, these synthesized nanohybrids could be a promising candidate for photocatalytic applications in future.

Recently, many authors have been interested to introduce fuzzy implications over t-norms and t-conorms. In this paper, we introduce (S,N) and residuum fuzzy implication for Dubois t-norm and Hamacher's t-norm. Also, new concepts so-called (T,N) and residual fuzzy co-implication in dual Heyting Algebra are investigated. Some examples as well as application are discussed as well.

Due to the substantial growth of internet users and its spontaneous access via electronic devices, the amount of electronic contents is growing enormously in recent years through instant messaging, social networking posts, blogs, online portals, and other digital platforms. Unfortunately, the misapplication of technologies has boosted with this rapid growth of online content which leads to the rise in suspicious activities. People misuse the web media to disseminate malicious activity, perform the illegal movement, abuse other people, and publicize suspicious contents on the web. The suspicious contents usually available in the form of text, audio or video, whereas text contents have been used in most of the cases to perform suspicious activities. Thus, one of the most challenging issues for NLP researchers is to develop a system that can identify suspicious text efficiently from the specific contents. In this paper, a Machine Learning (ML)-based classification model is proposed (hereafter called STD) to classify Bengali text into non-suspicious and suspicious categories based on its original contents. A set of ML classifiers with various features has been used on our developed corpus, consisting of 7000 Bengali text documents where 5600 documents used for training and 1400 documents used for testing. The performance of the proposed system is compared with the human baseline and existing ML techniques. The SGD classifier `tf-idf’ with the combination of unigram and bigram features are used to achieve the highest accuracy of 84.57%.

This study aims to formulate binary cementitious systems containing silica fume (SF) and micro quartz (MQ) to improve cementitious concrete's durability and mechanical properties. In this investigation, we examined the effects of different essential variables, such as the level of SF and MQ replacement and the ratio of the water-binder (w/b). In this study, the w/b ratios were 0.25, 0.3, 0.35, and 0.4. Replacement levels for SF were 8, 10, and 12%, while replacement levels for MQ were 5, 8, 10, 15, 25, and 35%. The porosity and permeability decreased with an increase in replacement levels, regardless of the type of fine material used or the amount of replacement. Meanwhile, the strength increased markedly with SF replacement, reaching its maximum MQ of 25% at the SF level. The strength of the SF mixing was the highest in all mixes, and it remained the highest until 15% of MQ was replaced. The ultrafine size of MQ particles contributes to the improvement of compressive strength, porosity, and permeability in a similar way to SF particles, despite their high crystalline structure. Furthermore, in this study, analysis of variance (ANOVA) was employed to verify the influence of each variable on the studied response.

The fractional Hilbert transform, a generalization of the Hilbert transform, has been extensively studied in the literature because of its extensive use in optics, engineering, and signal processing. In the present work, we aimed to expand the fractional Hilbert transform to a space of generalized functions known as Boehmians. We introduce a new fractional convolution operator for the fractional Hilbert transform to prove a convolution theorem similar to the classical Hilbert transform and also to extend the fractional Hilbert transform to Boehmians. We also construct a suitable Boehmian space on which the fractional Hilbert transform exists. Further, we investigate convergence of the fractional Hilbert transform for the class of Boehmians and discuss the continuity of the extended fractional Hilbert transform.

Presently, nano-crystallization is widely accepted for increasing the solubility and biological barrier permeability of poorly soluble drugs. It improves the bioavailability of therapeutic agents, increasing the effectiveness for treating diseased conditions, and could be safely administered by oral, parenteral, or transdermal routes. Drug nanocrystals are drug particles coated with a thin polymer layer to enhance their stability and could be decorated with ligands for active targeting. In addition, nanocrystals, due to their morphological properties, improve cell internalization. Therefore, passive targeting by high cellular uptake and retention in the mononuclear phagocyte system (MPS) may be expected. Drug nanocrystals are formulated by either top-down or bottom-up methods and could be scaled up for industrial manufacturing. In the past few decades, nanocrystal formulation has been increasingly studied to overcome the limitations of BCS Class II and IV chemotherapeutic agents. The study of cytotoxic effects of drug formulation on cell lines gives an insight for estimating its in-vivo biodistribution. This review highlights the role of morphology, stabilizer, and ligand conjugation on drug targeting and cellular uptake in cancer cells, as well as a brief discussion on nanocrystal production.

Optical switching is an essential part of photonic integrated circuits and the focus of research at the moment. In this research, an optical switch design working on the phenomenon of the guided-mode resonances in 3D photonic crystals-based structure is reported. The optical-switching mechanism is studied in a dielectric slab-waveguide-based structure operating in the near-infrared range in a telecom window of 1.55 µm. The mechanism is investigated by interference of two signals, i.e., the data signal and the control signal. The data signal is coupled into the optical structure and filtered utilizing guided-mode resonance, whereas, the control signal is index guided in the optical structure. The amplification or de-amplification of the data signal is controlled by tuning the spectral properties of the optical sources and structural parameters of the device. The parameters are optimized first using a single-cell model with periodic boundary conditions and later in a finite 3D-FDTD model of the device. The numerical design is computed in an open-source Finite Difference Time Domain simulation platform. Optical amplification in the range of 13.75 % is achieved in the data signal with a decrease in the linewidth up to 0.0079 µm and a quality factor of 114.58. The proposed device presents great potential in the field of photonic integrated circuits, biomedical technology, and programmable photonics.

Chemical fertilizers are commonly used to meet the nutritional demands of the crops and boost their yield. However, their high costs and excessive application in soils increases the cost of production and have negative effects on the soil and environmental health. Vermicompost is an organic amendment that can potentially lessen the dependence on chemical fertilizers with additional advantages of sustainable nutrient supply to crops and maintaining soil health. To evaluate the potential of two diverse vermicomposts, sole and combined application of these vermicomposts with reduced rates of chemical fertilizers were used for tomato cultivation in a field study. The results indicated that vermicompost produced from cow dung combined with chemical fertilizers proved to be more effective in improving tomatoes' growth, physiology, yield and nutritional attributes. Combined application of vermicompost and chemical fertilizer significantly improved root length (21.6%), plant height (167%), SPAD value (13.5%),chlorophyll 'a' (96%), chlorophyll 'b' (161%), relative water content (16%), membrane stability index (18%), carotenoid (87%), yield (82%), photosynthetic rate (148%), and fruit diameter (83%) protein (89%), fat (27.5%), fiber (12%), vitamin C (52%), calcium (54%), magnesium (117%), phosphorus (38%), potassium (128%), as compared to control treatment (NP applied). In addition, significant improvements in different soil physico-chemical properties were also pragmatic. The results suggest that vermicompost application with reduced doses of chemical fertilizers can be used to improve crop yield and soil physico-chemical properties.

Since the first case of 2019 novel coronavirus disease (COVID-19) detected on Jan 30, 2020, in India, the number of cases rapidly increased to 3819 cases including 106 deaths as of 5 April 2020. Taking this into account, in the present work, we are studying a Bats-Hosts-Reservoir-People transmission fractional-order COVID-19 model for simulating the potential transmission with the thought of individual social response and control measures by the government. The real data available about infectious cases from $14^{th}$ March to $26^{th}$ March 2020 is analysed and accordingly various parameters of the model are estimated or fitted. The Picard successive approximation technique and Banach's fixed point theory have been used for verification of the existence and stability criteria of the model. Numerical computations are done utilizing the iterative Laplace transform method. In the end, we illustrate the obtained results graphically. The purpose of this study is to estimate the effectiveness of preventive measures, predicting future outbreaks and potential control strategies using the mathematical model.

There are many pollution and environment problem in the human ecosystem. There are different methods are used to removal of sulfur from sour gases for example Basic Claus process and Modified Claus process . There are different chemical software are used for simulation and optimization of Claus process for example Aspen Plus and Chemcad ECT. The Gibbs free energy method is introduced and model of Claus process. There are new parameter are introduced in reaction furnace to reduce the error from 33% to 7 %. The waste heat boiler is installed at the reaction furnace in which high pressure stream is produced and study the decomposition the hydrogen sulphide. The new rate of reaction is introduced of the enhancement of H2 production in chemical process. The simulation of reaction furnace in Aspen plus software is the maximum utilization of process. Due to suitable operating condition of reaction furnace is caused the maximum destruction of ammonia gas in the reactor. When we are increasing the oxygen concentration and temperature of feed is causing decreasing the ammonia production in reaction furnace. It is below than acceptance value of ammonia is 150 ppm in the reaction furnace. The presence of oxygen components, Sulfur oxide, hydroxide components are effect on decreasing the amount of ammonia in furnace and temperature is about at 1350⁰C. It is noted that when the production of sulfur recovery is decrease in Claus process and the production of carbon monoxide is increase in the thermal section at the existence. Now we are work on parametric studies of furnace that could be causes the production of ammonia destruction and CO emission in the Claus process. Due to optimize the reaction furnace parameter are help to get large of sulfur production, ammonia gas destruction, increased the catalyst life and decreased of dangerous gases.

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on mobile data science and intelligent apps in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-to-day situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

Fruit production is an important part of the gross domestic product for many countries around the world especially to those who have a strong focus on agriculture. However, long-term maintenance and yield stability of fruit production may be threatened by the ongoing climate change and its consequences like extended drought periods, heavy rain events, and floodings. Genome editing, with its progressive technological developments, offers opportunities to adapt relevant fruit plant species to new climatic conditions. Among modern genome editing techniques, CRISPR/Cas, in particular, has the potential to support breeding for those fruit plant species with extended breeding cycles, e.g., perennial fruits. In this review, we discuss CRISPR/Cas and other genome editing techniques in detail and how these techniques can be applied to support the breeding of fruit plant species for adaptation to changing climates. The chronological history of CRISPR/Cas9 systems, their associated computational tools, genomic data sources, transformation methods along with their delivery vehicles, quality improvement, environmental-stress resiliency, limitations, and future perspectives will also be discussed with respect to securing future global fruit production.

Several bio-actors are involved in the occurrence of leishmaniasis, which is made too complex for prevention and control options. Currently, all forms of leishmaniasis are being treated with chemical drugs, which have limitations and adverse effects on patients. Searching the bioactive molecules from plant sources allows the development of a novel drug. The essential oil and solvent extract of the roots of E. kebericho showed antileishmanial activity. But looking for biological activities of the isolated active ingredient from tubers of E. kebericho was scant. Thus, the isolation of the leishmanicidal compound from the roots of E. kebericho through a bioassay-guided technique was an aim of this work. Dehydrocostus lactone showed doubled potent than the standard drug miltefosine on leishmanicidal activities against L. major and L. tropica. Besides, the broad-spectrum biological activities of dehydrocostus lactone could be a potential drug candidate for co-infected patients caused by an association of leishmania and another pathogen.

Human-Robot Interaction (HRI) for collaborative robots has become an active research topic recently. Collaborative robots assist the human workers in their tasks and improve their efficiency. But the worker should also feel safe and comfortable while interacting with the robot. In this paper, we propose a human-following motion planning and control scheme for a collaborative robot which supplies the necessary parts and tools to a worker in an assembly process in a factory. In our proposed scheme, a 3-D sensing system is employed to measure the skeletal data of the worker. At each sampling time of the sensing system, an optimal delivery position is estimated using the real-time worker data. At the same time, the future positions of the worker are predicted as probabilistic distributions. A Model Predictive Control (MPC) based trajectory planner is used to calculate a robot trajectory that supplies the required parts and tools to the worker and follows the predicted future positions of the worker. We have installed our proposed scheme in a collaborative robot system with a 2-DOF planar manipulator. Experimental results show that the proposed scheme enables the robot to provide anytime assistance to a worker who is moving around in the workspace while ensuring the safety and comfort of the worker.

Amid to the COVID-19 pandemic, digital health literacy (DHL) has become a significant public health concern. This research aims to assess information seeking behavior, as well as the ability to find relevant information and deal with DHL among university students in Pakistan. An online-based cross-sectional survey, using a web-based interviewing technique, was conducted to collect data on DHL. Simple bivariate and multivariate linear regression was performed to assess the association of key characteristics with DHL. The results show a high DHL related to COVID-19 in 54.3% of students. Most of the Pakistani students demonstrated ~50% DHL in all dimensions, except of reliability. Multivariate findings showed that gender, sense of coherence and importance of information were found to be significantly associated with DHL. However, a negative association was observed with students’ satisfaction with information. This led to the conclusion that critical operational and navigations skills are essential to achieve COVID-19 DHL and cope with stress, particularly to promote both personal and community health. Focused interventions and strategies should be designed to enhance DHL amongst university students to combat the pandemic.

Aiming at the limitations of single-functionality, limited applicability, and complex designs prevalent in current metasurfaces, we propose a terahertz multifunctional and multiband tunable metasurface utilizing a VO2-metal hybrid structure. This metasurface structure comprises a top VO2-metal resonance layer, a middle polyimide dielectric layer, and a gold film reflective layer at the bottom. This metasurface exhibits multifunctionality, operating independently of polarization and incident angle. The varying conductivity states of the VO2 layers, enable the metasurface to achieve various terahertz functionalities, including single-band absorption, broadband THz absorption, and multiband perfect polarization conversion for linear (LP) and circularly polarized (CP) incident waves. Finally, we believe that the functional adaptability of the proposed metasurface expands the repertoire of options available for future terahertz device designs.

In this work, generalized geometry of second-order tangent groups and affine configuration complexes is proposed. Initially, geometry for higher weights n=4 and weights n=5 is presented through some interesting and suitable homomorphisms, finally, this geometry is extended and generalized for any weight n.

Excessive high fat diet (HFD) consumption can induce food addiction which is believed to involve the communication between the hypothalamus and mesolimbic dopaminergic neurons, originating in the ventral tegmental area (VTA) and projecting to the nucleus accumbens (NAc). These brain regions are densely populated with opioid receptors, raising the possibility that these receptors, and particularly mu opioid receptors (MORs), are involved in mediating reward induced by palatable foods. This study sought to investigate the involvement of MORs in high fat diet (HFD)-induced reward and if there is any difference between male and female subjects in this response. We also assessed if exposure to HFD would potentiate the rewarding action of oxycodone, a relatively selective MOR agonist. The place conditioning paradigm was used to determine if conditioning for a short time (STC, 2 h) or long time (LTC, 16 h) with HFD induces reward or alters the rewarding action of oxycodone. Male and female C57BL/6J mice as well as MOR knockout and wildtype mice of both sexes were tested for basal place preference on day 1 and then conditioned with HFD in one chamber and regular chow diet (RCD) in another chamber and tested for place preference again after three sets of STC and again after a set of LTC. Each set consisted of two conditioning with RCD and two conditioning with HFD. Controls were conditioned with RCD in both conditioning chambers. Following the last place preference test, mice were treated with oxycodone and conditioned in the HFD-paired chamber and saline in the RCD-paired chamber for one hour once a day to explore the possibility if the HFD could alter oxycodone reward. The result showed that HFD induced conditioned place preference (CPP) in both male and female subjects following the LTC. This response was further potentiated after oxycodone conditioning. The latter response was mediated via MORs, as it was blunted in MOR knockout mice. However, HFD-induced CPP was observed following LTC in only female MOR knockout mice, suggesting for sexual dimorphism in this response.

String matching is an important part in many real world applications. It must robust against variations in string field. In record linkage for two different datasets matching should detect two patients in common in spite of small variations. But it becomes difficult in case of confidential data because sometimes data sharing between organizations become restricted for privacy purposes. Several techniques have been proposed on privacy-preserving approximate string matching such as Secure Hash Encoding etc. Relative to other techniques for approximate string matching Homomorphic encryption is very new. In this paper we have proposed a Homomorphic Encryption based approximate string matching technique for matching multiple attributes. There is no solution currently available for multiple attributes matching using Homomorphic encryption. We have proposed two different methods for multiple attributes matching. Compare to other existing approaches our proposed method offers security guarantees and greater matching accuracy.

The Internet of Things (IoT) is one of the most widely used technologies today, and it has a significant effect on our lives in a variety of ways, including social, commercial, and economic aspects. In terms of automation, productivity, and comfort for consumers across a wide range of application areas, from education to smart cities, the present and future IoT technologies hold great promise for improving the overall quality of human life. However, cyber-attacks and threats greatly affect smart applications in the environment of IoT. The traditional IoT security techniques are insufficient with the recent security challenges considering the advanced booming of different kinds of attacks and threats. Utilizing artificial intelligence (AI) expertise, especially machine and deep learning solutions, is the key to delivering a dynamically enhanced and up-to-date security system for the next-generation IoT system. Throughout this article, we present a comprehensive picture on IoT security intelligence, which is built on machine and deep learning technologies that extract insights from raw data to intelligently protect IoT devices against a variety of cyber-attacks. Finally, based on our study, we highlight the associated research issues and future directions within the scope of our study. Overall, this article aspires to serve as a reference point and guide, particularly from a technical standpoint, for cybersecurity experts and researchers working in the context of IoT.

Tea and coffee are the most commonly used beverages throughout the world. Both decoctions are rich in small organic molecules such as phenolics/polyphenolics, purine alkaloids, many methylxanthines, substituted benzoic and cinnamic acids. Many of these molecules are physiologically chemopreventive and chemoprotective agents against many severe conditions such as cancer, Alzheimer, Parkinsonism, inflammation, sleep apnea, cardiovascular disorders, bradycardia, fatigue, muscular relaxation, and oxidative stress. Caffeine, a purine alkaloid, is a common metabolite of both tea and coffee aqueous decoctions and its concentration in tea/coffee depends on the fermentation process, preparation of the water extract and quality of tea leaves/coffee beans. A 250 ml of a coffee cup contains 100-150 mg caffeine while the same volume of strong tea contains 25-40 mg caffeine. The present paper presents the potential of caffeine as a potent chemopreventive agent that can be used for numerous physiological disorders.

There is need to accurately design pipelines to transport the expected increase of CO₂ captured from industrial processes after the signing of the Paris Climate Agreement in 2016. This paper reviews several aspects of CO₂ pipeline design with emphasis on pressure drop and models for the calculation of pipeline diameter. Two categories of pipeline equations were identified. The first category is independent of pipeline length and has two different equations. This category is used to specify adequate pipeline diameter for the volume of fluid transported. The optimum economic pipe diameter equation (Eq. 17) with nearly uniform resultant velocity values at different flow rates performed better than the standard velocity flow equation (Eq. 20). The second category has four different equations and is used to calculate pipeline pressure drop or pipeline distance for the installation of booster stations after specifying minimum and maximum pipeline pressures. The hydraulic equation is preferred because it gave better resultant velocity values and the closest diameter value obtained using Aspen HYSYS (V.10) simulation. The effect of impurities on the pressure behaviour and optimal pipeline diameter and pressure loss due to acceleration were ignored in the development of the models. Further work is ongoing to incorporate these effects into the models.

It is important to add second- or third-phase materials to improve the physical, mechanical, and biodegradable properties of polypropylene (PP) nanocomposites. A unique hybrid bio-nanocomposite was fabricated using microparticle cellulose powder from wastepaper with a polypropylene (PP) polymeric matrix using a low-shear chaotic mixing method. A small amount (2-5%) of layered micro clay was also added to investigate the hybrid effect of nanocomposite fabrication on the mechanical properties. A low-shear chaotic melt mixing method induces the formation of nano cellulose structures and layered clay nanoparticles by via exfoliation. The nanoparticles were characterized using Fourier transform infrared (FTIR) spectroscopy. Mechanical properties such as the flexural strength (FS) and tensile strength (TS) were investigated using an Izod impact tester (IZ), and a dynamic mechanical testing analyzer (DMTA). Further, scanning electron microscopy (SEM) was used to observe the microstructure of the nanocomposite. In addition, differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were conducted to investigate the crystallinity and degradation of the samples, respectively. The addition of wastepaper cellulose powder below 2.5 wt. % did not improve the mechanical properties, however, improvements were observed when more than 5wt.% wastepaper cellulose powder was added. With the addition of a small amount of nano-clay, the mechanical properties were significantly improved, and a synergistic effect could be noticed. The observed results were then compared with PP-clay nanocomposites used in automotive industries to replace cellulose nanocomposites. The application areas can also cover lead production in paper cups and vacuum foaming.

Fossil fuels and carbon origin resources are affecting our environment. Therefore, alternative energy sources have to be established to co-produce energy along with fossil fuels and carbon origin resources until it is the right time to replace them. Microbial Fuel Cell (MFC) is a promising technology in the field of energy production. Compared to the conventional power sources it is more efficient and not controlled by the Carnot cycle. Its high efficiencies, low noise, and less pollutant output could make it revolutionize in the power generation industry with a shift from centrally located generating stations and long-distance transmission lines to dispersed power generation at load sites. In this review, several characteristics of the MFC technology will be highlighted. First, a brief history of abiotic to biological fuel cells and subsequently, microbial fuel cells is presented. Second, the focus is then shifted to elements responsible for the making MFC working with efficiency. Setup of the MFC system for every element and their assembly is then introduced, followed by an explanation of the working machinery principle. Finally, microbial fuel cell designs and types of main configurations used are presented along with scalability of the technology for the proper application.

The mutation databases have vital importance in detecting genetic mutation and their corresponding phenotypes. The emergence of next generation sequencing genetic technologies has accelerated this process, and the growth of genetic disease identification has created a need for quality control and documentation of mutation-related information. Ethnic-specific mutation databases offer valuable information for researchers, and the article evaluates the homogeneity and effectiveness of these databases. The aim to raise awareness of NEMDBs to healthcare professionals, the general public, and researchers studying genetic disorders. The article also recommendations to improve the effectiveness of these databases.

Objective: To explore sex differences in cardiovascular disease (CVD) related risk factors such as diabetes, hypertension, and demographic, anthropometric, biochemical, clinical, and nutritional differences among 40-60 years old Pakistani CVD patients.Design: This was a cross-sectional, descriptive study.Setting: The present study was conducted at the Mayo/King Edward Medical University (K.E.M.U) Hospital Cardiology Department (CCU-I & CCU-II), Lahore, from February 2020 to June 2020. A self-administered questionnaire was used for data collection.Participants: A random sample of 300 cardiac patients was included in the study. Patient records, food frequency questionnaire and 24-hour dietary recall was used to solicit information about demographic, anthropometric, biochemical, and clinical measurements; dietary intake parameters; and CVD risk factors such as hypercholesterolemia, hypertension, diabetes, and smoking. Results: Men CVD patients had higher BMI (33.43 kg/m² vs. 28.69 kg/m²) and higher prevalence of hypertension (81.1% vs. 59%), hypercholesterolemia (82.4% vs. 54.5%) and smoked more cigarettes (72.6% vs. 9%) as compared to women while there was higher prevalence of diabetes among women as compared to men (59% vs. 29.4%). There were 85% women vs. 80% men consuming white bread, while half of the men reported using meat daily, as compared to only 16.6% women. There were 51.5% women and 17% men who reported consuming fruits and vegetables daily. There were 40% men and 16% women who reported consuming soft drinks. Conclusion: The study showed significant sex differences in the nutritional status and risk factors of patients with CVD.

The research aims to discover the natural occurrence of patulin (PAT) in selected citrus fruits from the central cities of Punjab and Pakistan's Northern cities. Total 2970 samples of twelve citrus fruits; kinnow, orange, grapefruits, bitter orange, mausami, red blood, pineapple, sweet orange, rough lime, sweet lime, kagzi lime and lemon were examined using liquid chromatography fitted with UV detector. The limit of detection (LOD) and quantification limit was 0.04 and 0.12 µg/kg, respectively. The results have shown that 56% of samples of citrus fruits from Punjab's central cities, Pakistan, were discovered to be infected from PAT. The elevated amounts of PAT ranging from 0.04 to 1150 µg/kg were found in citrus fruit samples from Multan cities. Furthermore, 31.7% of samples of citrus fruits from Northern cities of Pakistan were discovered to be infected with PAT, and the elevated amounts were found ranging from 0.04-320 µg/kg from Swat city. About 22.1% of samples of citrus fruits have levels of PAT greater than the suggested limits established by the European Union (EU). PAT's dietary intake levels ranged from 0.10-1.11 µg/kg bw/day from the central cities of Punjab, Pakistan, and 0.13-1.93 µg/kg bw/day were documented from Northern cities of Pakistan.

This study aims to assess food security status of rural, peri-urban and urban households and role of socio-economic factors in ensuring food security. A survey was conducted to collect primary (quantitative) data from 630 respondents using proportionate sampling technique from Punjab, Pakistan. Cronbach’s Alpha reveals stability (>0.75) of data scale items. Using Dietary Intake Assessment (DIA), findings reveal urban households as highly food-secure whereas rural household as most vulnerable to food security at 2450kcal/day per person. Study indicates food price increase as the leading factor in preventing food security among households. Contrary, study identifies risk factors associated to food quality under the abundant availability and access as the new dimension to household food insecurity. Further, results of binary logit model show that socio-economic factors such as monthly income, total number of earners, education of household head and access to market are positively related with food security status whereas, social isolation, volatility in food prices and risk factors in food quality are negatively related with household food security. Therefore, study proposes educational orientation and entrepreneurial development as sustainable solution to ensure food security among masses. Besides, further country level researches are advised to deep delve the mounting menace of food security.

The study focused on investigating the natural incidence of deoxynivalenol (DON) in corn and products from corn producing districts of Punjab, Pakistan. The analysis was carried out using HPLC with UV detector and immunoaffinity cleanup columns. The detection limit (LOD) and limit of quantification were 25 and 50 µg/kg, respectively. Total 1220 samples of corn and products were analyzed to detect the DON, and 539 (44.2%) samples were observed to be contaminated with DON (n ≥ LOD). Furthermore, 92 (7.5%) samples of corn & products have DON levels, elevated than the proposed limits of the EU. The data is significantly different from a normal distribution for DON in corn and products samples and from different locations (p < 0.05) for Shapiro-Wilk and Kolmogorov-Smirnov values. However, a significant difference in DON levels was found between corn and corn derived-products types (p ≤ 0.05). The lowest and highest exposure & hazard quotient (HQ) of 0.92 and 9.68 µg/kg bw/d were documented in cornflour samples.

: Background: Pseudomonas aeruginosa is an opportunistic pathogen involved in number of hospital acquired infections such as catheter-associated urinary tract infections, bacteremia, septicemia, skin infections and ventilator-associated pneumoniae. Biofilm formation is an important trait implicated in chronic infections, such as cystic fibrosis and chromic pulmonary obstruction. Method: A total of 266 isolates were collected from the Armed Forces Institute of Pathology (AFIP). Antibiotic susceptibility was assessed by double disk synergy testing. ESBL and Modified Hodge testing was performed for phenotypic confirmation of carbapenemases. Molecular screening of the genes was done by PCR. Micro-dilution broth method was used to determine minimum inhibitory concentrations of antibiotics. Biofilm formation was done by micro-titer plate assay. Results: Overall, 20% of the P. aeruginosa isolates were extensively drug resistant (XDR-PA) and 25% were multi-drug-resistant (MDR-PA). Likewise, 43% of the isolates were ESBL producers and carbapenemase production was detected in 40% of the isolates. Molecular analysis confirmed occurrence of different resistant factors in ESBL positive isolates; 67% carried bla-TEM, 62% bla-CTXM-15, 41% bla-SHV, 34% bla-CTXM-14 and 33% bla-OXA-1. In addition, 68% of carbapenem-resistant isolates were positive for bla-NDM-1, 25% for bla-OXA-48 and 22% for bla-KPC-2. Biofilm formation was tested for 234 isolates, out of which 28% were strong biofilm formers. Moderate and weak biofilm formers were 46% and 23% respectively. Overall, ciprofloxacin, levofloxacin and cefepime showed inhibitory effects on P. aeruginosa biofilms. Antibiotics in combination showed strong synergistic effects (ciprofloxacin &amp; cefepime); while gentamicin and cefepime resulted into complete eradication of P. aeruginosa biofilm. Conclusion: We confirm strong synergistic effects of gentamicin and cefepime that completely eradicated P. aeruginosa biofilm. We further confirm inhibitory effects of ciprofloxacin, levofloxacin and cefepime on P. aeruginosa biofilms.

In this study, our primary objective was to analyze the tradeoff between accuracy and complexity in machine learning models, with a specific focus on the impact of reducing complexity and entropy on the production of landslide susceptibility maps. We aimed to investigate how simplifying the model and reducing entropy can affect the capture of complex patterns in the susceptibility maps. To achieve this, we conducted a comprehensive evaluation of various machine-learning algorithms for classification tasks. We compared the performance of these algorithms in terms of accuracy and complexity, considering both "before" and "after" scenarios of dimensionality reduction using Principal Component Analysis (PCA). Our findings revealed that reducing complexity and lowering entropy can lead to an increase in model accuracy. However, we also observed that this reduction in complexity comes at the cost of losing important complex patterns in the produced landslide susceptibility maps. By simplifying the model and reducing entropy, certain intricate relationships and uncertain patterns may be overlooked, resulting in a loss of information and potentially compromising the accuracy of the susceptibility maps. The analysis encompassed a diverse range of machine learning algorithms, including Random Forest (RF), Extra Trees (EXT), XGboost, LightGBM, Catboost, Naive Bayes (NB), K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), and Decision Trees (DT). Each algorithm was evaluated for its strengths and limitations, considering the tradeoff between accuracy and complexity. Before dimensionality reduction, the algorithms demonstrated promising results, with RF exhibiting excellent AUC/ROC scores and average accuracy. However, computational costs were noted as a potential drawback for RF, especially when dealing with large datasets. EXT showcased robust performance and good accuracy, while XGboost demonstrated its ability to handle complex relationships within large datasets, albeit requiring careful hyperparameter tuning. The efficiency and scalability of LightGBM made it a suitable choice for large datasets, although it displayed sensitivity to class imbalance. Catboost excelled in handling categorical features, but longer training times were observed for larger datasets. NB showcased simplicity and computational efficiency but assumed independence among features. KNN, known for its capability to capture local patterns and spatial relationships, was found to be sensitive to the choice of distance metric. GBM, while capturing complex relationships effectively, was prone to overfitting without proper regularization. DT, with its interpretability and ease of understanding, faced limitations in terms of overfitting and limited generalization. After dimensionality reduction, certain algorithms exhibited improvements in their AUC/ROC scores and average accuracy, including RF, EXT, XGboost, and LightGBM. However, for a few algorithms, such as NB and DT, a decrease in performance was observed. This study provides valuable insights into the performance characteristics, strengths, and limitations of various machine learning algorithms in classification tasks. Researchers and practitioners can utilize these findings to make informed decisions when selecting algorithms for their specific datasets and requirements. We also aim to identify the potential factors contributing to the high accuracy rates obtained from these ensembled algorithms and explore possible shortcomings of non-ensembled algorithms that may result in lower accuracy rates. By conducting a comprehensive analysis of these algorithms, we seek to provide valuable insights into the benefits and limitations of ensembled approaches for landslide susceptibility mapping. Our study sheds light on the challenges faced when balancing accuracy and complexity in machine learning models for landslide susceptibility mapping. It emphasizes the importance of carefully considering the level of complexity and entropy reduction in relation to the specific patterns and uncertainties present in the data. By providing insights into this tradeoff, our research aims to assist researchers and practitioners in making informed decisions regarding model complexity and entropy reduction, ultimately improving the quality and interpretability of landslide susceptibility maps.

Ethylene is an essential plant hormone critical in various physiological processes. These processes include seed germination, leaf senescence, fruit ripening, and the plant's response to environmental stressors. Ethylene biosynthesis is tightly regulated by two key enzymes, namely 1-aminocyclopropane-1-carboxylate synthase (ACS) and 1-aminocyclopropane-1-carboxylate oxidase (ACO). Initially, the prevailing hypothesis suggested that ACS is the limiting factor in the ethylene biosynthesis pathway. Nevertheless, accumulating evidence from various studies has demonstrated that ACO, under specific circumstances, acts as the rate-limiting enzyme in ethylene production. Under normal developmental processes, ACS and ACO collaborate to maintain balanced ethylene production, ensuring proper plant growth and physiology. However, under abiotic stress conditions, such as drought, salinity, extreme temperatures, or pathogen attack, the regulation of ethylene biosynthesis becomes critical for plants’ survival. This review highlights the structural characteristics, examines the transcriptional, post-transcriptional, and post-translational regulation of ACS and ACO, and their role under abiotic stress conditions. Reviews on the role of ethylene signaling in abiotic stress adaptation are available. However, a review that delineates the role of ACS and ACO in abiotic stress acclimation is not available.

H9N2 avian influenza viruses have become globally widespread in poultry over the last two decades and represent a genuine threat both to the global poultry industry but also humans through their high rates of zoonotic infection and pandemic potential. H9N2 viruses are generally hyperendemic in effected countries and have been found in poultry in many new regions in recent years. In this review we examine the current global spread of H9N2 avian influenza viruses as well as their host range, tropism, transmission routes and the risk posed by these viruses to human health.

Salinity is a serious environmental hazard which limits world agricultural production by adversely affects plant physiology and biochemistry. Hence increase tolerance against salt stress is very important. In this study, we explored the function of β-aminobutyric acid (BABA) in enhancing salt stress tolerance in rapeseed (Brassica napus L.). After pretreatment with BABA, seedlings were exposed to NaCl (100 mM and 150 mM) for 2 days. Salt stress increased Na content and decreased K content in shoot and root. It disrupted the antioxidant defense system by producing reactive oxygen species (ROS; H2O2 and O2•−), methylglyoxal (MG) content and causing oxidative stress. It also reduced the growth and photosynthetic pigments of seedlings but increased proline (Pro) content. However, BABA pretreatment in salt-stressed seedlings increased ascorbate (AsA) and glutathione (GSH) contents; GSH/GSSG ratio; and the activities of ascorbate peroxidase (APX), monodehydroascorbate reductase (MDHAR), dehydroascorbate reductase (DHAR), glutathione reductase (GR), glutathione peroxidase (GPX), superoxide dismutase (SOD), catalase (CAT), glyoxalase I (Gly I), and glyoxalase II (Gly II) as well as growth and photosynthetic pigments of plants. In addition, compared to salt stress alone BABA increased Pro content, reduced the H2O2, MDA and MG contents and decreased Na content in root and increased K content in shoot and root of rapeseed seedlings.

In the study of QSAR/QSPR, due to high degree of predictability of pharmaceutical properties, the eccentric-connectivity index has very important place among the other topological descriptors, In this paper, we compute the exact formulas of eccentric-connectivity index and its corresponding polynomial, total eccentric-connectivity index and its corresponding polynomial, first Zagreb eccentricity index, augmented eccentric-connectivity index, modified eccentric-connectivity index and its corresponding polynomial for a class of phosphorus containing dendrimers.

Ring necked-pheasant (Phasianus colchicus) is bird of order Galliformes. Ring necked-pheasant is also known as gallinaceous bird and game bird. It looks like chicken bird. It is national bird of South Dakota. It is mostly found in wild areas but in Pakistan it exists as Domestic bird. It has high proteins and low fat in its meat. The present study was conducted to check the effect of ascorbic acid on sperm Motility, sperm cytoplasmic membrane integrity, sperm livability and acrosome integrity of Ring-necked pheasant at different concentrations of ascorbic acid (0mM, 1mM, 2mM, 3mM and 4mM) were used. Semen was collected by abdominal massage technique. Motility of sperm was greater than 70%.Then it was processed further. The cryopreservation of semen was checked at various stages like Post Dilution, Post Cooling, Post Equilibration and Post Thawing. Semen was cooled from post-dilution stage (37⁰ C) to post-cooling (20⁰C).Then it further cooled gradually to get post-equilibration stage (4⁰ C) within 24 hours. After that 10% glycerol was added to the sample. Then it was transferred to liquid nitrogen (LN₂) cylinder for 24 hours. After thawing stage sample was removed from LN₂ cylinders and placed in water bath for 4 hours to achieve post-thawing. Then performed sperm quality assays at each stage of cryopreservation. Sperm motility was assessed by Neubauer Chamber Hemocytometer. Plasma Membrane Integrity was checked by using Hypo Osmotic Swelling Test with the help to 2% Eosin. Sperm livability was assessed by Lake’s glutamate solution and sperm Acrosome Integrity was checked by dual staining technique by using Formal citrate solution and Giemsa stain. The better results were seen at 3mM treatment of ascorbic acid. Sperm motility percentage was significantly different (P < 0.05) on 3mM treatment on all the stages of cryopreservation rather than other treatments. Plasma membrane integrity assay also showed good results at same concentration (P < 0.05) on all the stages of cryopreservation rather than other treatments. At the sperm livability stage 3mM treatment showed significantly difference (P < 0.05) on all the stages of cryopreservation rather than other treatments. The sperm acrosome integrity showed highest percentage on 3mM treatment (P < 0.05) on all stages of cryopreservation rather than other treatments. Effect of ascorbic acid on all the quality parameters (sperm motility, plasma membrane integrity, livability and acrosome integrity) was showed significant difference (P < 0.05) at 3mM treatment of ascorbic acid as compare to control as well as remaining treatments. It was seen that by increasing the concentration of ascorbic acid from 3mM concentration to onward it shows negative results on its cryopreservation. Now this cryopreserved semen can be transfer from one place to another place to obtain good varieties as well as better genetic characteristics of this species.

Self-compacting concrete is essential for vertical structures due to high reinforcement and usage of pumps for transporting the concrete at the working site. SCC is mainly influenced by self-curing for arid areas. To explore the SCC (Self-Compacting Concrete) mixture and condition influencing the self-curing condition were determined using the experimental procedure. Determination of various parameters was done using T50 slump flow, V-funnel, L-box-box, J ring test, compressive strength, splitting tensile strength, and flexural strength test. After the experimental investigation, it shows that if the dose if the dosage of superplasticizer (SNF%) increases, it will improve the concrete mixture's different properties, such as flowability and ability to pass. Higher water-to-cement ratios (w/c) generally result in enhanced flowability and stability. Mixture 9 (SNF 0.35, w/c 0.5) demonstrates favourable results across all parameters, indicating excellent flowability and stability. Mixture 9 (SNF 0.35, w/c 0.5) also shows favourable flowability and stability characteristics. Lower concentrations, such as 0.5%, may have a positive effect up to a certain point but might not provide sustained improvement over the curing period. On the other hand, concentrations of 1% and 2% demonstrated consistent and significant enhancement in strength throughout the 28 days. It is found that the higher concentrations of PEG 4000 may be more effective in improving the material strength.

Alcoholism is a condition associated with psychiatric and psychiatric problems, where the respiratory system is damaged through the mucociliary ladder mechanism and alveolar macrophage dysfunction. In the time COVID-19 has been observed a dramatic increase in alcohol consumption mediated by levels of anxiety and situations of confinement. In this work we analyze the relationship between alcoholism and SARS, especially with SARS-CoV-2, explained by a degradation of the host defenses of the respiratory epithelium by changing the barrier function, the discharge of cytokines and the functions of the cilia. All of them involved in the defense mechanism. of the lungs. This leads to a worse prognosis for patients precisely because of alcohol consumption. Based on this approach, alcoholism will exacerbate the consequences of COVID-19.

Micro-technology has played a substantial role in bioscience, biomedical and biotechnological research due to its core advantages in modern science and engineering. It has created unique development in various sectors of bio-research and upsurges the efficacy of research at the molecular level in recent years. Microfluidic technology makes it possible to manipulate sample volumes at the micro- and nano-level (called nanofluidics) with terrific control outside in vivo cellular microenvironment, enabling the reduction of discrepancies between in vivo and in vitro environments as well as reducing reaction time and cost. In this review, we discuss various effective integrations of microfluidic technologies into biotechnology and its paradigmatic significance in bio-research, supporting mechanical and chemical in vitro cellular micro-environment. Specific innovations relating to the application of microfluidics to advance microbial life, solitary and co-cultures along with a multiple-type cell culturing, cellular communications, cellular interactions and population dynamics are discussed.

Postpartum depression (PPD) can predispose to physical and mental health problems in women. However, PPD is associated with health and perceived social support but their causal relationship is unclear. Therefore, this study intended to evaluate the association of PPD with insomnia, mental health, and physical health. Convenience sampling technique was used to collect data from 320 (52.8 %) young and middle aged postpartum women, in the outpatient departments of obstetrics and gynecology in Government Maula Bakhsh Hospital, District Head Quarter in Sargodha, Pakistan. The Edinburgh Postnatal depression scale, Pittsburg sleep quality index, Warwick-Edinburgh mental wellbeing scale, Patient health questionnaire, and Multidimensional scale of perceived social support were used to measure study variables. Results revealed a significant positive relationship of PPD with physical health (r= .45, p=.001), while a negative relationship with insomnia (r= -.24, p<.001), and perceived social support (r= -.38, p=.001). Results further confirmed that perceived social support played a moderating role (β = .97, p=.01) in the relationship between PPD and mental health among females. This study concluded that perceived social support has an important role in PPD and women’s health. The study also concluded that poor health is a risk indicator for identifying aid in the early stages of postpartum among women.

Cotton leaf curl Kokhran virus (CLCuKoV) (genus, Begomovirus; family, Geminiviridae) is highly infectious, widespread and the most dangerous pathogen of cotton (Gossypium hirsutum L.) that is responsible to a serious disorder, cotton leaf curl disease (CLCuD). Begomoviruses are spread very efficiently by the whitefly Bemisia tabaci cryptic species, causing economic losses to cotton crop, all over the world. The ‘Lucknow’ strain of CLCuKoV has emerged as a divergent isolate that could cause CLCuD. The monopartite ssDNA genome of CLCuKoV-Lu (2.7 Kb) contains six open reading frames (ORFs) that was shown to encode four major proteins. RNA interference (RNAi)-based antiviral innate immunity is a sequence-specific biological phenomenon and a powerful tool to control plant viruses. The present study aims to determine cotton locus-derived microRNAs (ghr-miRNAs) that are identified for targeting the CLCuKoV-Lu ss-DNA-encoded mRNAs using a predictive approach that involves four computational algorithms, miRanda, RNA22, psRNATarget and RNAhybrid. Mature ghr-miRNA sequences (n=80) from allotetraploid upland cotton (2n = 4x = 52) were selected from the miRBase and were tested for alignment with the CLCuKoV-Lu genome. Among the 80 cotton locus-derived ghr-miRNAs evaluated, only one consensus cotton locus-derived ghr-miRNA (ghr-miR2950) was concluded to have effective ghr-miRNA target site at common nucleotide position 82 in the CLCuKoV-Lu genome respectively, using a stringent criterion, identified by all the algorithms used. The miRNA targeting is reliant on base pairing of miRNA-mRNA target pairings. Conservation of the hybridization binding site of the predicted ghr-miR2950 was validated using multiple sequence alignment within all the strains of CLCuKoV. We constructed a regulatory interaction network of miRNA–mRNA to identify novel targets. The efficacy of the predicted miRNAs against CLCuKoV-Lu was evaluated by RNAi-mediated targeted mRNA cleavage. The current investigated miRNA targets provide evidence for the development of CLCuD-resistant cotton plants.

It is important to add second- or third-phase materials to improve the physical, mechanical, and biodegradable properties of polypropylene (PP) nanocomposites. A unique hybrid bio-nanocomposite was fabricated using microparticle cellulose powder from wastepaper with a polypropylene (PP) polymeric matrix using a low-shear chaotic mixing method. A small amount (2-5%) of layered micro clay was also added to investigate the hybrid effect of nanocomposite fabrication on the mechanical properties. A low-shear chaotic melt mixing method induces the formation of nano cellulose structures and layered clay nanoparticles by via exfoliation. The nanoparticles were characterized using Fourier transform infrared (FTIR) spectroscopy. Mechanical properties such as the flexural strength (FS) and tensile strength (TS) were investigated using an Izod impact tester (IZ), and a dynamic mechanical testing analyzer (DMTA). Further, scanning electron microscopy (SEM) was used to observe the microstructure of the nanocomposite. In addition, differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were conducted to investigate the crystallinity and degradation of the samples, respectively. The addition of wastepaper cellulose powder below 2.5 wt. % did not improve the mechanical properties, however, improvements were observed when more than 5wt.% wastepaper cellulose powder was added. With the addition of a small amount of nano-clay, the mechanical properties were significantly improved, and a synergistic effect could be noticed. The observed results were then compared with PP-clay nanocomposites used in automotive industries to replace cellulose nanocomposites. The application areas can also cover lead production in paper cups and vacuum foaming.

This study was to designed to develop sorafenib-loaded polymeric nanoparticles, surface-modified with pluronic F-127 for the effective treatment of hepatocellular (HCC) and renal cell carcinomas (RCC). Poly lactic coglycolic acid (PLGA) was used to encapsulate different concentrations of sorafenib by a simple modified solvent evaporation method applying pluronic F-127 as a surfactant and coating agent. The preformulation and characterization studies were conducted to evaluate the incompatibilities among sorafenib, PLGA and pluronic F-127, and physicochemical properties of the formulations, respectively. The sizes of the coated and plain PLGA nanoparticles were ~140 ± 14.7 nm and ~ 120 ± 10.1 nm, respectively. It has been confirmed from the in vitro release studies that PLGA sustained the release of sorafenib, avoiding the initial burst release, while the in vivo studies demonstared that surface-modified nanoparticles have greater affinity in the target aorgans than plain PLGA nanoparticles and free drug keeping the dose constant (20 mg/kg body weight). The delivery of sorafenib-loaded polymeric PLGA nanoparticles into the target tissues and their pharmacokinetic parameters have been improved significantly compared to the reported formulations.

In this study, silver-strontium doped hydroxyapatite (AgSr-HA)/chitosan composite coatings were deposited on stainless steel (SS) substrate via electrophoretic deposition (EPD) technique. The EPD parameters such as the concentration of Ag Sr-HA particles in the suspension, applied voltage and deposition time were optimized on by the Taguchi Design of Experiment (DoE) approach. DOE approach elucidated that the “best” coating was obtained at; the deposition voltage of 20V, deposition time of 7 minutes, and at 5 g/L of Ag Sr-HA particles in the suspension. The optimum coatings were characterized by using scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) spectroscopy. SEM images confirmed the deposition of chitosan/Ag Sr-HA on the SS substrate. The wettability studies indicated the hydrophilic nature of the chitosan/Ag Sr-HA coatings, which confirmed the suitability of the developed coatings for orthopedic applications. The average surface roughness of the chitosan/Ag Sr-HA coatings was in a suitable range for the attachment of bone marrow stromal cells. Chitosan/Ag Sr-HA coatings showed a potent antibacterial effect against the Gram-Positive and Gram-negative bacteria.

In the intricate domain of text analysis, syntactic dependency trees play a crucial role in unraveling the web of relations among entities embedded in the text. These trees provide a structural roadmap, guiding us through the complex syntax to pinpoint the interactions and connections between different entities. However, the challenge lies in sifting through this intricate structure to extract relevant information, a task that requires precision and discernment. Traditional approaches often rely on rule-based pruning methods to simplify these dependency structures, focusing on certain parts while discarding others. Yet, this approach has its pitfalls, as it can overlook critical nuances and connections that are vital for a comprehensive understanding of the text. Addressing this gap, our research introduces the Syntactic Dependency-Aware Neural Networks (SDANNs), a groundbreaking model designed to harness the full power of the entire dependency tree. This approach marks a significant departure from the conventional methods. Instead of the rigid rule-based pruning, SDANNs implement a more flexible and dynamic 'soft-pruning' technique. This method allows the model to adaptively focus on the sub-structures within the dependency tree that are most relevant for understanding the relationships between entities. By doing so, it ensures that no vital information is overlooked, and all potential connections are considered. The efficacy of SDANNs is not just theoretical but has been empirically validated through extensive testing and evaluations across a wide range of tasks. These tasks include the extraction of complex relations spanning multiple sentences, as well as detailed analyses at the sentence level. In each of these scenarios, SDANNs have demonstrated a remarkable ability to leverage the full structural complexity of dependency trees. This capability sets them apart from existing models, enabling a more nuanced and comprehensive analysis of textual relations. The results of these evaluations consistently show that SDANNs not only meet but significantly exceed the performance of prior models. This superiority is evident in the way SDANNs handle the multifaceted and often subtle interactions within the text, offering insights that were previously inaccessible with conventional methods. In summary, the Syntactic Dependency-Aware Neural Networks represent a significant advancement in the field of text analysis. By fully embracing the complexity of syntactic dependency trees and employing a sophisticated 'soft-pruning' approach, SDANNs open new avenues for exploring and understanding the intricate relationships that exist within written language. This model stands as a testament to the potential of combining advanced neural network architectures with a deep understanding of linguistic structures, paving the way for more accurate, nuanced, and comprehensive analyses of text.

A simple and rapid method for the extraction of D-series resolvins (RvD1, RvD2, RvD3, RvD4, RvD5) released into Leibovitz's L-15 complete medium by head kidney cells from Atlantic salmon and further liquid chromatography triple quadrupole mass spectrometry determination is proposed. A three-level factorial design was proposed to select the optimal concentrations of internal standards that were used in the evaluation of the performance parameters such as linear range (0.1-50 ng/mL), limits of detection and quantification (0.05 and 0.1 ng/mL, respectively) and recovery values (98.99±0.00). The optimized method was used to determine the stimulated production of resolvins by head kidney cells exposed to docosahexaenoic acid, and the results indicated that it is possible that the production was controlled by circadian responses.

Few switching transitions in high power and medium voltage application of Power converters are desirable. The selective harmonics elimination (SHE) pulse width modulation offers a better quality waveform with lower switching transitions and hence lower switching losses. The SHE is a pre-programmed modulation technique where certain amounts of lower order harmonics are removed and fundamental voltage is controlled. After Fourier analysis of output waveform, a set of nonlinear transcendental equations is obtained which exhibits, multiple, unique or no solution in different range of modulation index (MI). In this paper, an iterative method based on the Jacobian estimate is proposed to solve a highly non-linear set of SHE equations. The proposed technique is easy in implementation and can solve a large number of such equations as computation of the Jacobian matrix in the subsequent iteration is estimated from the previous values. Moreover, the proposed method also removes the singularity problem, especially for large SHE equations. High accuracy in the initial guess is also not essential for this method and can converge to the solution with any random initial guess. The computational and simulation results are given to validate the concept. The hardware result is provided to confirm the computational and simulation results.

The highly crystalline ZnAl Layered Double Hydroxides (ZnAl-NO3- LDHs) are utilized for the potential transformation into Mixed Metal oxides (MMO) through thermal decomposition and used further for the photodegradation of phenol to assess the influence of calcination on ZnAl LDHs with enhanced photoactivity. The structure, composition, and morphological evolution of ZnAl-LDHs to ZnO-based MMO nanocomposites, which are composed of ZnO and ZnAl2O4, after calcination at different temperatures (400-600 ºC), are all thoroughly examined in this work. The final ZnO and ZnAl2O4 base nanocomposites showed enhanced photocatalytic activity. The findings demonstrated that calcining ZnAl-LDHs from 400 to 600 °C increased the specific surface area and also enhanced the interlayer spacing of d003 while the transformation of LDHs into ZnO/ZnAl2O4 nanocomposite through calcining the ZnAl-LDH precursor at 600 °C showed significant photocatalytic properties, leading to complete mineralization of phenol under UV irradiation.

The presence of heavy metals in industrial effluents has recently become a source of concern for human health and the environment. Efficient removal of metal ions and wastewater treatment is essential for protecting public health and the environment. In this study, we present an eco-friendly, green synthesis of reactive and fully dispersed zerovalent iron nanoparticles (nZVI) in carbon nanofibers via an electrospinning technique. In this novel study, green tea (GT) was used as environmentally friendly, polyvinyl alcohol (PVA) was used for the stabilization and reduction of nZVI, and tetraethyl orthosilicate (TEOS) was added to provide flexibility and to increase the specific surface area of the nZVI based nanofibers. GT contains polyphenols, which act as reducing agents, converting iron ions into nZVI and the particles can then serve as the building blocks for the carbon nanofibers. TEOS can also contribute to the stability of the nZVI by coating their surfaces with a silica layer. This silica layer helps prevent the oxidation of the nZVI, thereby reducing their catalytic activity. The nZVI-based composite (∼ 400 nm) / carbonized (∼ 200 nm) nanofibrous mats were fully characterized by scanning electron microscopy. The average size of the nZVI embedded in the composite and carbonized nanofibers can range from 50 nm to 100 nm. The x-ray diffraction (XRD) analysis of nZVI-based carbon nanofibers showed that there is no crystallinity and a single-phase cubic structure. Both energy-dispersive X-ray spectroscopy (EDX) and Fourier transform infrared (FTIR) analysis also confirmed the formation of single-phase nZVI-based composite / carbonized nanofibers. The high intensity of the peaks in the FTIR spectrum indicates the formation of nZVI in the nanofibers structure. The electrospun nZVI-based carbon nanofibers have a higher surface area and better reactivity due to small particle size and large surface area-to-volume ratio. They can provide excellent water stability and dispersibility in water compared to bulk nZVI, which tend to agglomerate and settle quickly. Additionally, nanofiber morphology can enhance the mechanical strength and durability of the composite material. Due to low-cost synthesis and their high efficiency in removing pollutants, these nanofibers could be used as nano-filters for wastewater treatment.

Guava is a vital fruit worldwide, especially in Pakistan, and due to its nutritional value famous in each age group. Due to a very short shelf life, the marketing and export of this fruit faced severe constraints. Therefore, in the current study, edible coating of chitosan (0, 0.5, 1.0, 1.5, and 2.0%) was evaluated on postharvest shelf life when guava fruits were stored (room temperature and 4 °C temperatures) for 12 days. The chitosan treated coating fruits have shown reduced total sugars and malondialdehyde levels compared to untreated control samples. However, a significant difference (p ≤ 0.05) in total sugar and malondialdehyde levels exists between samples stored in m compared to refrigerated temperature (4 °C). The chitosan-coated samples have shown a greater amount of vitamin C, quercetin, rutin, and total phenolic contents than control samples. However, these nutritional parameters' levels were significantly different (p ≤ 0.05) in samples stored at room than samples stored at refrigerated temperature. However, the levels of crude fiber, potassium, and sodium were found statistically nonsignificant (p ≥0.05) in control versus chitosan treated coating treatments. The findings have documented that the coatings of 1.5 and 2.0% were most effective for extension in shelf life and maintaining the nutritional attributes of guava fruit.

In Southeast Asia, the incidence of Leukemia, a malignant blood cancer originating from hema-topoietic progenitor cells, is on the rise, marked by a concerning 54% mortality rate. This study focuses on enhancing early-stage prediction to improve patient recovery prospects significantly. Leveraging Machine Learning and Data Science, we employ protein sequential data from frequently mutated genes such as BCL2, HSP90, PARP, and RB to predict Chronic Myeloid Leukemia (CML). Our approach relies on robust feature extraction techniques, namely Di-peptide Composition (DPC), Amino Acid Composition (AAC), and Pseudo amino acid composition (Pse-AAC), with prior attention to addressing outliers and validating feature selection through the Pearson Corre-lation Coefficient. Data augmentation ensures a well-rounded dataset for analysis. Employing a range of Machine Learning models, including Support Vector Machine (SVM), XGBoost, Random Forest (RF), K Nearest Neighbor (KNN), Decision Tree (DT), and Logistic Regression (LR), we achieve accuracy rates spanning from 66% to 94%. These classifiers undergo comprehensive as-sessment using performance metrics such as accuracy, sensitivity, specificity, F1-score, and the confusion matrix. Our proposed solution, encompassing a user-friendly web application dashboard, presents an invaluable tool for early CML diagnosis with profound implications for practitioners, offering a deploy-able asset within healthcare institutions and hospitals.

Yeast extract is widely used in different food industries as a flavoring agent or vitamin supplement. In this study, a process was optimized for the production of yeast extract from Baker’s yeast (Saccharomyces cerevisiae). A glass vessel stirred fermenter was used for the cultivation of yeast biomass. The effect of various physical and chemical factors was evaluated on the production of yeast cells and optimum conditions for the production of maximum yeast biomass were determined. The optimum growth was obtained at 30ºC with pH 4.5 using molasses as a substrate supplemented with urea at 150rpm. Yeast cells were then separated by centrifugation and ruptured and autolysis was observed to be the most feasible method. Among various method employed to dry the yeast extract, spray dryer appeared as most efficient one. Yeast extract obtained after drying was subjected to different analyses and compared with commercial yeast extract. The produced yeast extract was applied in media preparation to grow different microorganisms including yeast, bacteria and fungi and considerable growth was observed. These results indicated that the developed process is a cost effective alternate approach for the production of yeast extract.

Chronic Myeloid Leukemia, resulting due to chromosomal aberration t(9;22) through formation of oncogenic BCR-ABL fusion oncogene. Modern BCR-ABL inhibitors, called TKIs, have revolutionized CML treatment. CML has three stages: chronic, accelerated, and blast crisis. TKIs work well in CP-CML, where patients survive as long as the normal population, but they don't work in AP- and BC-CML. Even with advances in treatment, BC-CML has an average overall survival of less than a year, giving oncologists little time to clinically intervene. Oncologists can delay or prevent CML advancement by detecting patients at risk of disease progression early and making timely treatment decisions, especially with third and fourth generation TKIs. However, no universal molecular biomarkers exist to diagnose CML patient groups at risk of disease progression. A recent study found that all BC-CML patients have mutant FANCD2. Our study was designed to detect mutant FANCD2 in AP-CML (early progression phase) to investigate its potential as a novel biomarker of early CML progression from chronic phase to accelerated phase due to the urgent need for such a biomarker. Our study comprised of 123 CP-CML (control group) and 60 AP-CML patients (as experimental group) from Hayatabad Medical Complex, Peshawar, Pakistan, from Jan 2020 to July 2023. DNA was extracted from the patients and FANCD2 gene was sequenced using Illumina next generation sequencer (NGS) Illumina MiSeq sequencer. NGS analysis revealed a unique splice site mutation in FANCD2 gene (c. 2022-5C>T). This mutation was detected in all CP-CML patients but in none of CP-CML. The mutation was confirmed by Sanger sequencing. FANCD2 is member of Fanconi anemia (FA-) pathway gene involved in DNA repair and genomic instability. Therefore, our studies show that FANCD2 (c. 2022-5C>T) mutation as a very specific molecular biomarker for early CML progression. We recommend to clinical validate this biomarker is prospective clinical trials.

The wind flow patterns and pressure distribution around a plus-shaped building are significantly influenced by the dimensions of its re-entrant corners and the overall building height. The present study aims to find the wind effects and study of the pressure distribution with different flow patterns. Plan area of the building is around 300 square meter while the height of the building is 50 meters by keeping the equal length of the re-entrant corners. The research is performed using ANSYS CFX academic version and the results are depicted in various forms such as pressure coefficient (Cpe), stream lines in both vertical as well as in specific surface and pressure contours. The analysis is carried out by varying the wind incident angles, from 0° to 90° at the interval of 15°. The building dimension are scale down to perform the test on length scale of 1:100. Since the considered building model exhibits symmetry along both the axes, present study mostly emphasis on examining re-entrant corners located in the building. This method yields valuable information about how air circulates and how pressure is distributed across a broad spectrum of wind angles. This study provides valuable insights into flow circulation and pressure distribution for a wide range of wind angles.

Building situated in the flow path of wind is subjected to differential velocity and pressure distribution around the building envelope. Wind effects are influenced and vary for each individual shape of the tall building. Tall building structures are considered as cantilever structures with fixed ends at the ground. Wind exponential velocity acting along the height of the building makes velocity and pressure distribution more complex as the height of the building structure increases. This study discussed the effect of wind on an irregular cross-section shape. The study was conducted numerically with a building model placed in a virtual wind tunnel using the ANSYS (CFX) software tool. Wind effects are investigated on building model situated in a terrain category - II defined in IS: 875 (Part 3): 2015 wind scale model of 1:100 and turbulence intensity is 5 % and power law index α is considered as 0.143. Validation and verification of the study were made by comparing pressure coefficients on faces of a rectangular model of similar floor area and height as taken for a C-plan diamond-shaped model under similar boundary conditions, wind environment, and solver setting of numerical setup. The values of surface pressures generated on the recessed faces of the model and wind flow patterns within the recessed cavity were studied at wind incident angles 0°, 300, 60°, 1050, 1350 & 1800. The critical suction on all the recessed faces was observed to be at a 105° angle of wind attack.

Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this article focuses on hyperparameter optimization for well-known machine learning models. It explores cutting-edge optimization methods and provides guidance on applying them to different machine learning algorithms. The article also presents real-world applications of hyperparameter optimization by conducting tests on spatial data collections for landslide susceptibility mapping. Based on the experiment's results, both Bayesian optimization and metaheuristic algorithms showed promising performance compared to baseline algorithms. For example, the metaheuristic algorithm improved the overall accuracy of the random forest model. Additionally, Bayesian algorithms, such as Gaussian processes, performed well for models like KNN and SVM. The paper thoroughly discusses the reasons behind the efficiency of these algorithms. By successfully identifying appropriate hyperparameter configurations, this research paper aims to assist researchers, spatial data analysts, and industrial users in developing machine learning models more effectively. The findings and insights provided in this paper can contribute to enhancing the performance and applicability of machine learning algorithms in various domains.

Photosynthesis is a pivotal process that determines the synthesis of carbohydrates required for sustaining growth under normal or stress situation. Stress exposure reduces the photosynthetic potential owing to the excess synthesis of reactive oxygen species that disturb the proper functioning of photosynthetic apparatus. This decreased photosynthesis is associated with disturbances in carbohydrate metabolism resulting in reduced growth under stress. We evaluated the importance of melatonin in reducing heat stress-induced severity in wheat plants (Triticum aestivum L.). The plants were subjected to 25 ˚C (optimum temperature) or 40 ˚C (heat stress) for 15 days at 6 hours time duration and then developed the plants for 30 days. Heat stress led to oxidative stress with increased production of TBARS and H2O2 content and reduced accrual of total soluble sugars, starch and carbohydrate metabolism enzymes which are reflected in reduced photosynthesis. Application of melatonin not only reduced oxidative stress through lowering TBARS and H2O2 content, through augmenting the activity of antioxidative enzymes but also increased the photosynthesis in plant and carbohydrate metabolism that is needed to provide energy and carbon skeleton to the developing plant under stress. However, the increase in these parameters with melatonin was mediated via hydrogen sulfide (H2S), as the inhibition of H2S by hypotaurine (HT; H2S inhibitor) reversed the ameliorative effect of melatonin. This suggests a crosstalk of melatonin and H2S in protecting heat stress-induced photosynthetic inhibition via regulation of carbohydrate metabolism.

Catalase can decompose hydrogen peroxide in plants under controlled and stressed condi-tions. In a stressed environment, the increase in hydrogen peroxide production makes catalase activity a major defense system for plant growth. The current study was conducted to evaluate the catalase activity of pre-isolated and identified bacterial strains Bacillus aryabhattai (AN30), Bacillus megaterium (AN24), Bacillus megaterium (AN31) and Bacillus megaterium (AN35) and their potential for rice seedlings growth promotion. These strains were characterized for quantita-tive catalase, urease, siderophores, and exopolysaccharides production using LB media. Sub-sequently, the effectiveness of these strains was checked by quantifying the catalase activity in the rhizosphere of rice seedlings. Furthermore, the catalase activity was quantified in the roots and shoots of rice seedlings. Plant growth parameters were also studied in a growth room trial. The strain AN24 showed the maximum catalase activity (1.36 mol cm-1), urease ac-tivity (1.35 mol cm 1), exopolysaccharides (4.20 µg mL-1), and siderophores (2.32%) production in LB media. All tested strains showed significantly higher catalase activity in soil compared to control. Among sole application, strain AN24 showed better results; however, consortium application of strains AN24+AN30+AN35+AN31 showed the maximum increase in catalase ac-tivity in the rhizosphere of rice seedlings. Moreover, consortium application of AN24+AN30+AN31+AN35 resulted in the highest increase in catalase activity, dry biomass of shoots and roots, shoot and root length of rice seedlings. The results showed that a consortium of these Bacillus strains with catalase activity has greater potential to enhance an antioxidant defense system and growth promotion of rice seedlings. However, further experimentation under natural conditions is required before using these strains as potential bioinoculants for improving the rice growth and yield.

The rapid adoption of hydrogen as an eco-friendly energy source has necessitated the development of intelligent power management systems capable of efficiently utilizing hydrogen resources. However, guaranteeing the security and integrity of hydrogen-related data has become a significant challenge. This paper proposes a pioneering approach to ensure secure hydrogen data analysis through the integration of blockchain technology, enhancing trust, transparency, and privacy in handling hydrogen-related information. By combining blockchain with intelligent power management systems, the efficient utilization of hydrogen resources becomes feasible. The utilization of smart contracts and distributed ledger technology facilitates secure data analysis, real-time monitoring, prediction, and optimization of hydrogen-based power systems. The effectiveness and performance of the proposed approach are demonstrated through comprehensive case studies and simulations. Notably, our prediction models, including ABiLSTM, ALSTM, and ARNN, consistently delivered high accuracy with MAE values of approximately 0.154, 0.151, and 0.151, respectively, enhancing the security and efficiency of hydrogen consumption forecasts. The blockchain-based solution offers enhanced security, integrity, and privacy for hydrogen data analysis, thus contributing to the advancement of clean and sustainable energy systems. Additionally, the research identifies existing challenges and outlines potential future directions for further enhancing the proposed system. This study adds to the growing body of research on blockchain applications in the energy sector, with a specific focus on secure hydrogen data analysis and intelligent power management systems.

The multidrug efflux transporter ABCB1 is clinically important for drug absorption and distribution and can be a determinant of chemotherapy failure. Recent structure data shows that three glutamines donate hydrogen bonds to co-ordinate taxol in the drug binding pocket. This is consistent with earlier drug structure-activity relationships that implicated the importance of hydrogen bonds in drug recognition by ABCB1. By replacing the glutamines with alanines we have tested whether any or all of Q347, Q725 and Q990 are important for the transport of three different drug classes. Flow cytometric transport assays show that Q347A and Q990A act synergistically to reduce transport of Calcein-AM, BODIPY-verapamil and OREGON GREEN-taxol bisacetate but the magnitude of the effect was dependent on the test drug and no combination of mutations completely abrogated function. Surprisingly, Q725A mutants generally improved transport of Calcein-AM and BODIPY-verapamil, suggesting that engagement of the wild-type Q725 in a hydrogen bond is inhibitory for the transport mechanism. To test transport of unmodified taxol, stable expression of Q347/725A and the triple mutant was engineered and shown to confer equivalent resistance to the drug as the wild-type transporter, further indicating that none of these potential hydrogen bonds between transporter and transport substrate are critical for function of ABCB1. The implications of the data for plasticity of the drug binding pocket are discussed.

The objectives of the current research were to determine the levels of deoxynivalenol (DON) in four different cultivars of corn and subsequently to investigate the fate of DON during pro-cessing steps involved for the production of cornbread. The samples (n = 30) of each cultivar which were found positive were selected for the study. The average level of DON was ranged from LOD to 650 µg/kg. The amount of DON in cornflour samples were ranged from LOD to 630 µg/kg and insignificantly lower than the levels found in corn grain samples (p ≥ 0.05). Further-more, the levels of DON in corn dough samples were insignificantly higher than the levels in cornflour samples (p ≥ 0.05), with levels ranged from LOD to 645 µg/kg. However, the amount of DON in cornbread samples was significantly different from the levels found in corn grains sam-ples (p ≤ 0.05), with levels ranged from LOD to 611.5 µg/kg. The percentage reduction of DON in grains to cornbread samples was 22.4%, 35.6%, 44.5%, and 42.6% in type 1, type 2, type 3, and type 4 cultivars, respectively. The highest dietary exposure and hazard quotient (HQ) of DON was 0.13 and 0.17 µg/kg bw/d, in male and female individuals resulted from the consumption of cornbread samples, respectively.

This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an \textit{optimal} value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

Rare ginsenoside compound K (CK), is an intestinal microbial metabolite with a low natural abundance that is primarily produced by physicochemical processing, side chain modification, or metabolic transformation in the gut. Moreover, CK exhibits potent biological activity compared to primary ginsenosides, which has raised concerns in the field of ginseng research and development as well as ginsenosides-related dietary supplements and natural products. Ginsenosides Rb1, Rb2, and Rc are generally used as a substrate to generate CK via several bio-conversion processes. Current research shows that CK has a wide range of pharmacological actions including boosting osteogenesis, lipid and glucose metabolism, lipid oxidation, insulin resistance, anti-inflammatory, and anti-apoptosis properties. Further research on the bioavailability and toxicology of CK can advance its medicinal application. The purpose of this review is to lay the groundwork for future clinical studies and the development of CK as a therapy for metabolic disorders. Furthermore, the toxicology and pharmacology of CK are investigated as well in this review. The findings indicate that CK primarily modulates signal-ing pathways associated with AMPK, SIRT1, PPARs, WNTs, and NF-kB. It also demonstrates a positive therapeutic effect of CK on nonalcoholic fatty liver disease, obesity, hyperlipidemia, diabetes, and its complications, as well as osteoporosis. Additionally, the analogues of CK showed more bioavailability, less toxicity, and more efficacy against disease states. Enhancing bioavailability and regulating hazardous variables are crucial for its use in clinical trials.

Conductive hearing losses are typically present in disorders of the external/middle ear. However, there is a rare group of inner ear conditions called third windows that can also generate a conductive hearing loss. This is due to an abnormal connection between the middle and the inner ear or between the inner ear and the cranial cavity. X-linked gusher disorder is an extremely rare congenital inner ear dysplastic syndrome with such an abnormal connection due to a characteristic incomplete cochlear partition type 3 and an incomplete internal auditory meatus fundus. The disorder is inherited in an X-linked fashion due to the mutation of the POU3F4 gene. We present 2 siblings diagnosed with the condition and their long term follow ups. They both presented with audiovestibular symptoms and showed progressive mixed losses and bilateral vestibular weakness. They were treated with cochlear implant, digital amplification and with vestibular rehabilitation. Significant others around them were involved in their journey with the medical team and in both, a very favourable outcome was achieved. This is the first time that we are reporting evolving audiovestibular function with vestibular quantification in X-linked gusher disorder and emphasize on the multidisciplinary holistic approach to manage these children effectively.

Influenza is a disease that poses a significant health burden worldwide. Vaccination is the best way to prevent influenza virus infections. However, conventional vaccines are only effective for a short period of time due to the propensity of influenza viruses to undergo antigenic drift and antigenic shift. The efficacy of these vaccines is uncertain from year-to-year due to potential mismatching between the circulating viruses and vaccine strains, mutations arising due to egg adaptation, and potential contamination of the vaccine virus stock. Subsequently, the inability to store these vaccines long-term and vaccine shortages are challenges that need to be overcome. Conventional vaccines are also less effective for certain populations, including the young, old, and immunocompromised. This warrants for diverse efficacious vaccine developmental approaches, involving both active and passive immunization. As opposed to active immunization platforms (requiring the use of whole or portions of pathogens as vaccines), the rapidly developing passive immunization involves administration of either pathogen-specific or broadly acting antibodies against a kind or class of pathogens as a prophylactic treatment to corresponding acute infection. Several antibodies with broadly acting capacities have been discovered that may serve as means to suppress influenza viral infection and allow the process of natural immunity to engage opsonized pathogens whilst boosting immune system by antibody-dependent mechanisms that bridge the innate and adaptive arms. By that, passive immunotherapeutics approach assumes a robust tool that could aid control of influenza viruses. In this review, we comment on some improvements in influenza management and promising vaccine development platforms, with emphasis on the capacity of passive immunotherapeutics especially when coupled with the use of antivirals in the management of influenza infection.