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.

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.

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.

An extensive thermodynamic study of N-methylformamide (CAS RN: 123-39-7) and N,N-dimethylformamide (CAS RN: 68-12-2), is presented in this work. The liquid heat capacities of N-methylformamide were measured by Tian–Calvet calorimetry in the temperature interval (250 – 300) K. The vapor pressures for N-methylformamide and N,N-dimethylformamide were measured using static method in the temperature range 238 K to 308 K. The ideal-gas thermo-dynamic properties were calculated using a combination of the density functional theory (DFT) and statistical thermodynamics. A consistent thermodynamic description was developed using the method of simultaneous correlation, where the experimental and selected titerature data for vapor pressures, vaporization enthalpies, and liquid phase heat capacities and calculated ide-al-gas heat capacities are treated together to ensure overall thermodynamic consistency of the results. Resulting vapor pressure equation is valid from the triple point to the normal boiling point temperature.

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.

Climate change, one of the severest environmental threats to humankind, disproportionately affects low-income developing countries in the global South. Having no feasible mitigation alternatives, these countries resort to adaptation efforts in addressing the climate perturbations. Climate change adaptation (or resilience) is primarily a localized course of action that depends on individuals, social networks, economies, ecologies, political structures, and the capabilities of all those to work collectively to absorb, learn from and transform in the face of new realities. With a view to controlling the floods that shattered the life and economy of the then East Pakistan, which is Bangladesh now, during the mid-twentieth century, the coastal embankment project (CEP) was instituted as an adaptation strategy to natural disasters in southwestern Bangladesh. Based on the qualitative analysis of primary and secondary data, this paper seeks to critically evaluate the efficacy of the CEP in terms of the space for feasible action and ecological modernization. The findings of this research indicate that the CEP has become an unrealistic venture that hinders the growing economic activity of shrimp aquaculture in the area. This paper is expected to contribute to generate further theoretical and empirical discourse on evaluating similar development projects around the globe.

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 $

Classification of human actions from uni-modal and multi-modal datasets is an ongoing research problem in computer vision. This review is aimed to scope current literature on data-fusion and action-recognition techniques and to identify gaps and future research direction. Success in producing cost-effective and portable vision-based sensors has dramatically increased the number and size of datasets. The rise in number of action recognition datasets intersects with advances in deep-learning architectures and computational support, both of which offer significant research opportunities. Naturally, each action-data modality - such as RGB, depth, skeleton, and infrared - has distinct characteristics; therefore, it is important to exploit the value of each modality for better action recognition. In this article we will focus solely on areas such as data fusion and recognition techniques in the context of vision with a uni-modal and multi-modal perspective. We conclude by discussing research challenges, emerging trends, and possible future research directions.

Materials based on platinum metals with very good catalytic activity were tested in the reduction of p–nitrophenol (pNP) to p–aminophenol (pAP). The majority of presented catalytic materials are based on the adsorption of p–nitrophenol on the surface of the catalyst simultaneously with molecular hydrogen, coming from sodium borohydride in an aqueous medium, which reduces it. In the present paper, a catalytic material based on osmium dispersed in n–decanol or n–dodecanol is presented, which also works as an emulsion membrane, for the absorption of p–nitrophenol and molecular hydrogen at the n–alcohol aqueous solution interface. The hydrogenation of p–nitrophenol is carried out in a reaction and separation column in which the acid receiving phase emulsion is dispersed in the osmium nanodispersion, in n–alcohols. This emulsion circulates from the base of the column to its upper part in co-current or counter-current with the source phase consisting of p–nitrophenol and sodium borohydride. Experiments show that the circulation of counter-current phases is superior to that in co-current, and the emulsion based on n–decanol is more efficient than the one based on n–dodecanol. The increase in the pH difference between the source and receiving phases leads to the increase in the conversion of p–nitrophenol to p–aminophenol. The efficiency of separation of the reaction product in the receiving phase is lower than the conversion at the same operating time. The apparent catalytic rate constant (kapp) of the new catalytic material based on the emulsion membrane with nanodispersion of osmium nanoparticles (0.1×10−3 for n–dodecanol and 0.9×10−3 for n-decanol) is lower by an order of magnitude compared to those based on adsorption on catalysts from platinum metal group. The advantage of the tested membrane catalytic material is that it extracts p–aminophenol in the acid receiving phase. The paper proposes a mechanism of the studied reduction system.

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..

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.

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.

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 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.

Dystonias, characterized by excessive muscle contractions resulting in involuntary postures and movements, impact 3 million people globally, making them the third most common movement disorder. Often accompanied by tremors, dystonias have epidemiological links and non-motor features shared with isolated tremor, such as essential tremor. Both dystonia and tremor present with balance dysfunction and abnormal involuntary movements, potentially linked to abnormal cerebellar function. This study explores the perception of one’s own linear movement, heading, particularly discrimination of heading direction, in isolated cervical dystonia, isolated tremor, and their combination. The focus is on the perception of heading direction during isolated visual star-cloud movement (visual heading perception) and en bloc body movement (vestibular heading perception). Nineteen participants underwent a two-alternative forced-choice task in the vestibular and visual domains. Results reveal elevated vestibular heading perception thresholds in cervical dystonia with or without tremor, and isolated tremor compared to healthy controls. Vestibular heading perception threshold was comparable in cervical dystonia with tremor and isolated tremor, but it was even worse in isolated cervical dystonia. Visual heading perception, however, remained less affected all three conditions – isolated cervical dystonia, isolated tremor, and their combination. These findings indicate shared deficits and distinctions in the perception of linear translational heading across movement disorders, such as isolated cervical dystonia, tremor, or their combination, offering insights into their pathophysiology, particularly the involvement of cerebellum regions responsible for vestibular processing.

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.

Sustainable Development Goal 12 requires countries to achieve responsible consumption and production patterns without exceeding safe environmental limits for natural resource use. However, little is known about how cropland impacts from the agri-food sector contribute to the exceedance of national environmental limits for consumption and production. Using a multi-regional input-output model, we linked the cropland impacts of agri-food production to countries of consumption while considering the exceedance of production-based and consumption-based environmental limits. We defined national consumption-based environmental limits via the fair-share approach and quantified national production-based environmental limits according to the biophysical limit of available arable land. We then classified countries into quadrants according to their exceedance of consumption and/or production environmental limits. We found that the USA, Australia and other high-income countries were exceeding both consumption-based and production-based environmental limits. High-population but low-income countries such as India and China were within safe consumption-based environmental limits but exceeded production-based environmental limits. Brazil and other countries of the Americas incurred substantial environmental costs due to the conversion of forests into cropland to produce food for export. We identified patterns in international trade relationships that could inform national-level responsible agri-food consumption and production patterns across the global supply chain, thereby contributing to Sustainable Development Goal 12. More stringent regulations and commitments in national and international policies are required to reduce the exceedance of consumption-based and production-based environmental limits and avoid exceeding the global land-system change planetary boundary.

The COVID-19 has caused gigantic negative effects on populace wellbeing, society, education, and the economy in Bangladesh. The aim is to deliver a comprehensive overview of the observed and the possible impacts that could appear in the coming days. The study is based on secondary information. During the early period, due to a lack of accurate facts about the case affected and death tension up-and-down among the nations. The total number of confirmed cases is increasing following geometric patterns in Bangladesh. Dairy farmers, vegetable producers, pharmaceuticals, poultry farmers are in deep crisis due to lower prices. Also, the pandemic has seriously affected educational systems, banking, FDI, ready-made garments, remittances, etc Finally, it is not possible to mitigate the effects of pandemic individually but the integrated effort from the state authority as well as concern people of all sectors need to come forward.

The main inhibitory neurotransmitter in the mammalian brain is 4-aminobutyric acid (GABA). Phthalimide acts as a sodium channel antagonist, one of the finest targets for treating epilepsy is the sodium channel and JNK kinase activity is inhibited to treat epilepsy. a novel compound with multiple targets of action for the treatment of epilepsy also anti-oxidant activity has been designed. The compounds are designed in two series aldehydes and aniline. Docking of all the designed compounds has been done with the suitable protein GABA-AT, sodium channel, JNK protein, and anti-oxidant from protein bank. QSAR was done for constructing many linear regression models. In combination with the developed Docking and QSAR outcomes, ADMET has been performed for the active compounds. The result shows that 11 of the compounds have excellent binding energy in docking with all the receptors and also have good QSAR results and follow the ADMET. The structural understanding of human GABA-AT inhibition was obtained from molecular dynamics simulation studies for compound no. 11 at 5 nanoseconds. Synthesis and pharmacological activity also suggest that the designed compound no.11 be considered the best Novel Compound.

Mankind faces a coronavirus pandemic originating from a seafood market in Wuhan, China since December 2019. The pathogen was named novel coronavirus (n-CoV) and bats are the identified key reservoir. The coronavirus disease 2019 (COVID-19) quickly spread over China across the globe, turned into a pandemic with exponentially increasing numbers of cases and significant mortality rate. China reacted with lockdowns and strict control measurements to prevent spreading the virus. The treatment of severe cases was hampered by lack of specific vaccines. Vaccine-development and production is a painstaking process and can only be enforced by international cooperation. Different supportive treatment options surfaced due to combinations of antiviral agents with antibiotic drugs. Elderly, male, immune-suppressed patients with co-morbidities showed a high mortality rate. Health literacy, strong immune system, adequate serum 25-hydroxyvitamin D (25(OH)D) concentrations and healthy life style choices can support fast recovery. Antibiotic resistance needs to be addressed by development of new generation antimicrobials against nosocomial infections in preparation for future outbreaks. Plant-biosynthesis of nanomaterials and antiseptics may help in prevention and recovery rate. Prevalence of COVID-19 maybe inversely related to BCG vaccination, endemicity of malaria, humidy and temperature but directly with latitude. Recommendations to prevent the spread of COVID-19 should be followed strictly.

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.

Phase transitions- both in the classical and in the quantum version- are the perfect playground for appreciating universality at work. Indeed, the fine details become unimportant and a classification in very few universality classes is possible. Very recently, a striking deviation from this picture has been discovered: some antiferromagnetic spin chains with competing interactions show a different set of phase transitions depending on the parity of number of spins in the chain. The aim of this article is to demonstrate that the same behavior also characterizes the most simple quantum spin chain: the Ising model in a transverse field. By means of an exact solution based on a Wigner-Jordan transformation, we show that a first order quantum phase transition appears at zero applied field in the odd spin case, while it is not present in the even case. A hint of a possible physical interpretation is given by the combination of two fact: at the point of the phase transition, the degeneracy of the ground state in the even and the odd case substantially differ, being respectively 2 and 2N, with N the number of spins; the spin of the most favorable kink states changes at that point.

The study shows that the volumes and surfaces of the $n$-simplices, $n$-orthoplices, $n$-cubes, and $n$-balls are holomorphic functions of $n$, which makes those objects omnidimensional. Furthermore, the volume of an $n$-simplex is shown to be a bivalued function of $n$, and thus the surfaces of $n$-simplices and $n$-orthoplices are also bivalued functions of $n$. Applications of these formulas to the omnidimensional polytopes inscribed in and circumscribed about $n$-balls reveal previously unknown properties of these geometric objects in negative dimensions. In particular, for $0 < n < 1$, the volumes of the omnidimensional polytopes are larger than those of circumscribing $n$-balls, while their volumes and surfaces are smaller than the volumes of inscribed $n$-balls. Reflection relations around $n = 0$ for volumes and surfaces of these polytopes inscribed in and circumscribed about $n$-balls are disclosed. Specific products and quotients of volumes and surfaces of the omnidimensional polytopes and $n$-balls are shown to be independent of the gamma function.

Amphiphilic block copolymers of N-vinyl pyrrolidone (NVP) and various vinyl esters (VEs), PNVP-b-PVEs, namely vinyl butyrate (VBu), vinyl decanoate (VDc) and vinyl stearate (VSt) were synthesized by RAFT polymerization techniques. The sequential addition of monomers methodology was employed starting from the polymerization of NVP followed by the polymerization of the VEs monomer. The reaction was conducted at 80oC in dioxane solutions using AIBN as initiator and O-ethyl S-(phthalimidylmethyl) xanthate as the CTA. The block copolymers were characterized by Size Exclusion Chromatography (SEC) and NMR spectroscopy. The thermal transitions of the copolymers were studied by Differential Scanning Calorimetry (DSC), whereas their thermal stability via Thermogravimetric Analysis (TGA) and Differential Thermogravimetry (DTG).

The main goal of this study was to introduce glycerol as a plasticizer to Polyvinyl Alcohol (PVA) membranes in order to increase their flexibility. The qualities of PVA membranes produced with various glycerol concentrations were evaluated. With the modified PVA membranes containing 5% glycerol, encouraging results were obtained. These membranes had superb optical characteristics and a 90% transmittance rate. This shows that the membranes are suited for applications needing excellent optical clarity since they permit light to travel through with little distortion. Importantly, FTIR analysis demonstrated that the addition of glycerol had no effect on the PVA membranes' chemical composition or structural integrity. In comparison to pure PVA Membrane, the modified membranes' water barrier qualities showed a considerable drop of up to 50%. The membranes are useful for applications requiring moisture management or breathability because of the decline, which points to an increase in permeability to water vapour. The large increase in flexibility, which was achieved by the PVA membranes containing 5% glycerol and beyond the brittleness typically associated with pure PVA, was a noteworthy discovery. The possible applications are expanded due to the increased flexibility, especially in industries where adaptability and flexibility are essential. Additionally, the addition of glycerol improved the thermal stability of the membranes, as they showed increased resistance to breakdown or decomposition at high temperatures. Additionally, the modified membranes' mechanical qualities were better than those of pure PVA, indicating that the glycerol addition improved their overall mechanical performance and tensile strength. PVA membranes were effectively made more flexible by adding 5% glycerol as a plasticizer. This led to membranes with superior optical qualities, reduced water barrier properties, greater flexibility, improved thermal stability, and maybe improved mechanical capabilities. These discoveries open up new opportunities for the use of these flexible PVA membranes in a range of settings, such as flexible packaging, biomedical materials, and other industries that need adaptive and flexible materials.

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.

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.

To achieve n-type doping in diamond, extensive investigations employing first principles have been conducted on various models of phosphorus doping and boron-phosphorus co-doping. The primary focus of this study is to comprehensively analyze the formation energy, band structure, density of states, and ionization energy of these structures. It is observed that within a diamond structure solely composed of phosphorus atoms, the formation energy of an individual carbon atom is excessively high. However, the substitution of two carbon atoms leads to the conversion of diamond into a p-type semiconductor. Upon examining the P-B co-doping structure, it is revealed that the doped impurities exhibit a tendency to form more stable cluster configurations. As the separation between the freely doped atoms and the cluster impurity structure increases, the overall stability of the structure diminishes, consequently resulting in an elevation of the ionization energy. Analysis of the electronic density of states demonstrates that the electron orbit of the B atom makes negligible contributions to the impurity levels during P-B doping.

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.

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.

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.

Antioxidant, anti-inflammatory and chemo-preventive features have been reported for the carotenoid diatoxanthin. Diatoxanthin is only produced by a few groups of microalgae where it functions in photoprotection. Its large-scale production in microalgae is currently not feasible. In fact, rapid conversion into the inactive pigment diadinoxanthin is triggered when the cells are removed from the high-intensity light source, which will be the case during large-scale harvesting of microalgae biomass. Zeaxanthin epoxidase (ZEP) 2 and/or ZEP3 have been suggested to be responsible for the back-conversion of high-light accumulated diatoxanthin to diadinoxanthin in low light in diatoms. Using the CRISPR/Cas9 gene editing technology we knocked out the ZEP2 and ZEP3 genes in the already commercially used marine diatom Phaeodactylum tricornutum to investigate their role in the diadinoxanthin-diatoxanthin cycle, and to determine if one of the mutant strains could function as a diatoxanthin production line. Light shift experiments proved that ZEP3 encodes the enzyme converting diatoxanthin to diadinoxanthin in low light. Loss of ZEP3 caused the high-light accumulated diatoxanthin to be stable for several hours after the cultures had been returned to low light, suggesting that zep3 mutant strains could be suitable as commercial production lines of diatoxanthin.

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.

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.

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.

Objective We aim to assess the quality of life for women with genitourinary fistulas before and after surgery, utilizing the WHOQOL-BREF questionnaire to contrast the preoperative and postoperative results. Materials and Methods: This quasi-experimental study was conducted at the Department of Urology, SIUT, Karachi, Pakistan, from January to December 2023. We used the WHOQOL-BREF questionnaire to assess the quality of life. Participants completed the questionnaire both before the surgical intervention and three months after successful fistula repair. The collected data were entered and analysed using IBM SPSS v26, with a significance level set at p≤ 0.05. Results The participants had a mean age of 35.50 years, with a standard deviation of 8.87 years. Before undergoing genitourinary fistula repair, the average WHOQOL-BREF score was 12.17 with a standard deviation of 5.77. Following the repair, the mean score showed a significant improvement, rising to 96.67 with a standard deviation of 11.86. This marked improvement was statistically significant, with a p-value of 0.0001. Conclusion This study concludes that genitourinary fistula repair at our tertiary care hospital significantly and positively impacts patients' quality of life. The findings emphasize the critical role of timely and appropriate surgical intervention in improving overall well-being. Continued comprehensive care and support are vital for these patients' holistic recovery and long-term health.

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.

Machinery parts gradually wear out over time due to regular usage. To improve machinery health and prevent critical issues, a reliable prognosis framework can be implemented by monitoring the behaviour of machinery parts and issuing warnings before they reach a critical state. To achieve this, vibration data from roller bearings experiencing various fault conditions have been collected. Different techniques from the literature were combined to analyze the distinct configurations in the vibration data sets and identify the main defects in roller bearings. The significant features extracted from this analysis were then used to create optimized stochastic model equations, separately regressing inner and outer race fault features to healthy bearing features under random conditions. These models can help engineers design more dependable systems, optimize their performance, and minimize the risk of failures and downtime.

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.

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.

This paper presents a theory on graph topological structure and graph coloring, proving that for any N-order graph structure (with a topological structure similar to ), the maximum number of colors required for coloring is less than or equal to . The Four Color Theorem is just one special case of this theory, with the maximum structure size for a four-color graph being a 4-order structure graph, hence requiring only a maximum of 4 colors for coloring.

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.

This study investigates the performance and environmental impact of n-butanol blended with Jet-A fuel in turbo-engines, aiming to assess its viability as a sustainable aviation fuel (SAF). The research involves experimental testing of various blends, ranging from low to high concentrations of methanol, to determine their effects on engine performance and emissions. The experimental setup includes comprehensive measurements of engine parameters such as thrust, fuel consumption rates, and exhaust gas temperatures. Emissions of nitrogen oxides (NOx), particulate matter (PM), carbon monoxide (CO), and unburned hydrocarbons (HC) are also analysed to evaluate environmental impacts. Key findings indicate that n-butanol/Jet-A blends can significantly enhance combustion efficiency and reduce emissions compared to conventional Jet-A fuel. Higher n-butanol concentrations lead to improved thermal efficiency and lower NOx and CO emissions. However, PM emissions slightly increase with higher methanol content. The study underscores the potential of n-butanol as a SAF for turbo-engines, highlighting its ability to mitigate environmental impacts while maintaining or improving engine performance. This research supports the feasibility of integrating n-butanol into Jet-A blends for turbo-engine applications, emphasizing its role in achieving more environmentally friendly aviation operations.

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.

Fractional differential mathematical model unfolding the dynamics of the COVID-19 pandemic in India is presented and explored in this paper. The purpose of this study is to estimate the future outbreak of disease and potential control strategies using mathematical models in India as a whole country as well as in some of the states of the country. This model is calibrated based on reported cases of infections over the month of April 2020 in India. We have used iterative fractional complex transform method to find approximate solutions of the model having modified Riemann Liouville fractional differential operator. We have also carried out a comparative analysis between actual and estimated cumulative cases graphically, moreover, most sensitive parameters for basic reproduction number$(R_0)$ are computed and their effect on transmission dynamics of COVID-19 pandemic is investigated in detail.

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.

Public restrooms are often a hub of microbial contamination, and the examination of bacterial contamination in these facilities can serve as an important indicator of the transmission of infectious diseases. This study was conducted to determine the prevalence of bacterial contamination in public restrooms based on the economic class of the building. Samples were collected from various spots in 32 restrooms found in 10 shopping malls, classifying them into two categories: upper-end restrooms and lower-end restrooms. The findings showed that the level of contamination was higher in the lower-end restrooms, with the seat being the most contaminated area. The most dominant Gram-positive bacteria were coagulase-negative staphylococci species, making up 86% of the identified Gram-positive isolates. The most dominant Gram-negative bacteria identified were Klebsiella pneumoniae and Pseudomonas aeruginosa. The antibiotic sensitivity test results revealed the presence of multi-drug resistant bacteria among the Gram-positive and negative isolates, including Staphylococcus haemolyticus, Staphylococcus kloosi, Acinetobacter baumanii, and Pseudomonas aeruginosa. In conclusion, the study underscores the significance of monitoring bacterial contamination in public restrooms and the need for measures to reduce the spread of infectious diseases. Further research is crucial to gain a complete understanding of the bacterial contamination in public restrooms and their resistance patterns, to ensure the safety and health of the public. The implementation of improved cleaning practices and hands-free designs in restrooms can help reduce the risk of cross-contamination and prevent the spread of diseases.

This paper presents a new proof of the infinite nature of prime numbers. Grounded in fundamental Set theory and Analysis, the proof uses the method of contradiction to demonstrate the absurdity of assuming the set of all prime numbers to be finite.

The generation of arithmetic sequences involves arithmetic operations. However, is it possible to generate such sequences without being subject to any arithmetic or related operations, all by just hand? The answer may be negative, as the very rule defining these sequences itself relies on arithmetic operations. Nonetheless, this paper presents a few results demonstrating that the exact sequences of $an$, where, $a \in \{ 2,3,\cdots,9,10\}$ and $n \in \mathbb N \cup \{0\}$, can indeed be generated up to any desired number of terms without relying on any arithmetic operations and others derived from such. This is done by the formation of a specialized table that leverages numerical patterns and the notion of concatenation.

Reducing fertilizer-N rate, applying nitrification inhibitor (NI), and incorporating straw are widely recommended to improve N use efficiency of crops and decrease N losses. However, how these practices affect crop N uptake by regulating the fertilizer-N fate during the growing season is not well understood. We conducted a field 15N tracer study to explore the effectiveness of reduced N application, NI, and straw incorporation on fertilizer-N fates for maize cultivation in Northeast China. The following six treatments were used: 1) no N fertilization (control); 2) 200 kg urea-N ha−1 (100%N); 3) 200 kg urea-N ha−1 and 2400 kg dry-straw ha−1 (100%N+S); 4) 160 kg urea-N ha−1 (80%N, i.e., 20% reduction of N fertilization); 5) 160 kg urea-N ha−1 and nitrification inhibitor (Nitrapyrin) (80%N+NI); 6) 160 kg urea-N ha−1, nitrification inhibitor and 2400 kg dry-straw ha−1 (80%N+NI+S). Our results showed that the five N fertilization treatments yielded 16%–25% more grain and 39%–60% more crop N uptake than the control receiving no N fertilizer, but the differences among the five treatments were not statistically significant. In contrast to plant growth, changes in fertilizer-N fates in soil and plant pools were more pronounced. Compared with the 100%N treatment, 20% fertilizer-N reduction (80%N) decreased 15N concentration in topsoil and plant pools but increased the proportion of plant 15N recovery at harvesting (N use efficiency, NUE15N, 60% vs. 50%) (P &lt; 0.05). Nitrapyrin co-application (80%N+NI) delayed soil nitrification and increased soil 15N retention at harvesting (52% vs. 36%), thereby decreasing NUE15N significantly (P &lt; 0.05). Straw incorporation (100%N+S) decreased fertilizer-N retention compared with NI co-application because it promoted NUE15N significantly (P &lt; 0.05). The combined application of NI and straw (80%N+NI+S) was more conducive to fertilizer-N retention in soil. Our findings demonstrate that NI and straw additions are efficient strategies for stabilizing fertilizer-N in soils and potentially minimizing N loss; however, their effects on NUE15N vary and the related mechanism must be further clarified in long-term trials.

Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impacts from over-height vehicles. Impacts can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts to railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data is transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events like impact detection that require rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications.

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.

Transportation contributes significantly to greenhouse gas emissions, with electrification offering a promising solution. This study evaluates the degradation of State of Health (SOH) over 2.5 years for electrified buses under different charging protocols: DC Fast charging and Flash charging. The vehicles’ models, charging protocols, and financial implications are discussed. High-level simulations reveal that after 2.5 years, the Flash charging vehicle’s SOH reaches 88.93%, while the DC fast charging vehicle reaches 89.46%, showing a 0.47% difference. This slight variance is attributed to Flash charging vehicles avoiding full charge/discharge cycles, reducing battery aging impact. Considering this minimal SOH degradation difference, the choice between DC Fast charging and Flash charging primarily hinges on capital expenditure disparities. Flash charging, with its lower initial battery capacity and reduced capital costs, presents a compelling alternative for city busoperators seeking to electrify their fleets.

DFT calculations on the photoisomerization reactions of pyridazine N-oxide derivatives are presented. The irradiation of 3,6-diphenylpyridazine N-oxide allowed to obtain the first excited singlet state that underwent to a ring opening reaction induced to the oxygen migration to the adjacent carbon atom, with the formation of a diazo derivative. This intermediate can give a ring closure reaction to give a pyrazole derivative, or, with a higher transition energy, it can lose nitrogen allowing the formation of 2,5-diphenylfuran. The irradiation of 3-phenylpyridazine N-oxide allowed the formation only of 2-phenylfuran. In this case, calculations allowed to explain this behavior. The first excited singlet state gave a photoisomerization reaction with the formation of the corresponding diazo intermediate. This intermediate could not be converted into the corresponding pyrazole derivative because it was converted into a N-formyl pyrazole never reported in literature. The only possible reaction is the loss of nitrogen and the formation of 2-phenylfuran.

The compound, C27H23N3 (1), crystallizes in the triclinic system of the P-1 space group. The unit cell comprises a dimer of 1, in which the monomers are linked by two complementary hydrogen bonds between N1 and H1-C1 of another molecule. The dimers form chains along the a-axis through intermolecular interactions between the N2 acceptor atoms and C17 donors from molecules in the nearest neighbouring dimer. These interactions form extended sheets of the dimers of 1, along the ab plane. The quinolinylmeth-2-yl groups of 1 lie in almost orthogonal planes and their N1/2(q) donor atoms being away from the apical amino N3 atom.

The study examines n-balls, regular n-simplices, and n-orthoplices in real dimensions using novel recurrence relations that removed indefiniteness present in known formulas. They show that in the negative, integer dimensions volumes of n-balls are zero if n is even, positive if n = -4k - 1, and negative if n = -4k - 3, for natural k. Volumes and surfaces of n-cubes inscribed in n-balls in negative dimensions are complex, wherein for negative, integer dimensions they are associated with integral powers of the imaginary unit. The relations are continuous for n Î ℝ and show that the constant of π is absent for 0 ≤ n < 2. For n < -1 self-dual n-simplices are undefined in the negative, integer dimensions, and their volumes and surfaces are imaginary in the negative, fractional ones, and divergent with decreasing n. In negative, integer dimensions n-orthoplices reduce to the empty set, and their real volumes and imaginary surfaces are divergent in negative, fractional ones with decreasing n. Out of three regular, convex polytopes present in all natural dimensions, only n-orthoplices, n-cubes (and n-balls) are defined in the negative, integer dimensions.

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%.

Photocatalysis is a promising method for methylene blue (MB) degradation due to its effectiveness and environmental compatibility. Among photocatalysts, titanium dioxide (TiO2) has been widely used for MB degradation due to its exceptional photocatalytic activity. However, the wide bandgap limits the degradation efficiency of TiO2 under visible light. Herein, an interstitial nitrogen-doped TiO2 (5%NT/TiO2) used thiourea as the N source is fabricated for the visible light-derived MB degradation. 5%NT/TiO2 exhibits an extended visible light absorption range. Besides, photoelectrochemical measurement shows an improvement in photocurrent response and charge transfer behavior on N/TiO2. Thus, 5%NT/TiO2 has enhanced photocatalytic activity compared to pristine TiO2 and substitutive N-doped TiO2 (5%NAB/TiO2). The accelerated photocatalytic MB degradation process on N/TiO2 should be mainly attributed to the interstitial N doping, which causes the appearance of new energy states and extended optical properties.

Nanocrystalline silicon oxide (nc-SiOx:H) is a multipurpose material with its varied application in solar cells as a transparent front contact, intermediate reflector, back reflector layer and even tunnel layer for passivating contacts, owing to easy tailoring of its optical properties. In this work, we systematically investigate the influence of gas mixture (SiH4, CO2, PH3 and H2), RF power and process pressure on the optical, structural and passivation properties of thin n-type nc-SiOx:H films prepared in an industrial, high throughput, plasma-enhanced chemical vapor deposition (PECVD) reactor. We provide a detailed description of the n-type nc-SiOx:H material develop-ment using various characterization techniques (SEM, EDX, Raman spectroscopy, and spectro-scopic ellipsometry) with a focus on the relationship between the material properties and the pas-sivation they provide to n-type c-Si wafers characterized by their effective carrier lifetime (τeff). Further, we also outline the parameters to be kept in mind while developing different n-type nc-SiOx:H layers for different solar cell applications. We report a tunable optical gap (1.8 – 2.3 eV) for our n-type nc-SiOx:H films as well as excellent passivation properties with τeff up to 3.5 ms (iVoc ~ 715 mV) before annealing. Oxygen content plays an important role in determining the crystallinity and hence passivation quality of the deposited nanocrystalline silicon oxide films.

The study of angiosperm flora, endemic plants in the literature, and investigations of ‘POWO Build a checklist’ were used to compile an updated list of Bangladesh's endemic plants. According to the study, at least 49 (1.24%) of Bangladesh's 3,956 plant species are endemic to the country including 5 varieties, belonging to 41 genera in 26 families. Magnoliopsida account for thirty of them, Liliopsida for the remaining sixteen, and Pteridohpytes for the final three. The four families Zingiberaceae (9 taxa), Araceae (6 taxa), Rubiaceae (4 taxa), and Euphorbiaceae (3 taxa), include the most endemic taxa. The five most numerous endemic taxa belong to the Curcuma genus. The endemic flora, which is mostly found in the districts of Chattogram, Moulvibazar, and Rangamati, consists of 13 tree species, 4 shrubs, 5 climbers, and 27 herbaceous plant species.

Using coherent optical detection and digital signal processing, laser phase noise and equalization enhanced phase noise can be effectively mitigated using the feed-forward and feed-back carrier phase recovery approaches. In this paper, theoretical analyses of feed-back and feed-forward carrier phase recovery methods have been carried out in the long-haul high-speed n-level phase shift keying (n-PSK) optical fiber communication systems, involving a one-tap normalized least-mean-square (LMS) algorithm, a block-wise average algorithm, and a Viterbi-Viterbi algorithm. The analytical expressions for evaluating the estimated carrier phase and for predicting the bit-error-rate (BER) performance (such as the BER floors) have been presented and discussed in the n-PSK coherent optical transmission systems by considering both the laser phase noise and the equalization enhanced phase noise. The results indicate that the Viterbi-Viterbi carrier phase recovery algorithm outperforms the one-tap normalized LMS and the block-wise average algorithms for small phase noise variance (or effective phase noise variance), while the one-tap normalized LMS algorithm shows a better performance than the other two algorithms for large phase noise variance (or effective phase noise variance). In addition, the one-tap normalized LMS algorithm is more sensitive to the level of modulation formats.

Air pollution is a critical issue impacting urban environments, leading to severe health problems and environmental degradation. This comprehensive review examines the potential of green systems—specifically green walls, active green walls, and urban greenery systems—to mitigate atmospheric pollutants such as particulate matter (PM), volatile organic compounds (VOCs), and carbon dioxide (CO2). By systematically analyzing 44 recent studies, the review highlights the pollutant capture efficiency of various green technologies and plant species in both indoor and outdoor settings. Active green walls, particularly those utilizing plant species such as Chlorophytum comosum and Sansevieria trifasciata, were found to be highly effective, with VOC reduction efficiencies up to 96.34%, PM reductions by 65.42%, and CO2 reduction rates reaching 4.8% under optimal conditions. The review identifies key strengths in current research, including diverse experimental setups and the use of sophisticated measurement techniques, but also notes significant limitations such as variability in experimental conditions and a lack of long-term performance data. The study underscores the importance of proper maintenance to sustain green systems' efficacy and highlights the potential issue of pollutant resuspension, which remains under-researched. Practical implications for urban planning are discussed, advocating for the integration of effective green systems into urban infrastructure to enhance air quality and public health. Recommendations for future research include the need for standardized metrics, long-term studies, economic feasibility analyses, and real-world validation of simulation models to better understand and optimize green systems for urban air pollution mitigation.

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.

In this study, Fe, N co-doped biochar (Fe@N co-doped BC) was synthesized by carbonization-pyrolysis method and used as carbocatalysts to activate peroxymonosulfate (PMS) for sulfamethoxazole (SMX) removal. In the Fe@N co-doped BC/PMS system, the degradation rate of SMX (10.0 mg⋅L−1) was 90.2% within 40 min under optimal conditions. The radical quenching experiments, and electron spin resonance (ESR) analysis suggested that sulfate radicals (SO4•−), hydroxyl radicals (•OH) and singlet oxygen (1O2) participated in the degradation process. After the reaction, the proportion of pyrrolic N apparently decreased from 57.9% to 27.1%. Pyrrolic N served as active sites to break the inert carbon network structure and promote the generation of reactive oxygen species (ROSs). Besides, pyrrolic N showed stronger interaction with PMS and significantly reduced the activation energy required for the reaction (∆G = 23.54 kcal/mol). The utilization potentiality of Fe@N co-doped BC was systematically evaluated in terms of its reusability, and selectivity to organics. Finally, the intermediates of SMX were also detected.

Neuropathic pain (NeP) is a complex and debilitating condition that impacts millions of people globally. Although various treatment options exist, their effectiveness is often limited, and they can be accompanied by significant side effects. In recent years, there has been increasing interest in targeting the N-methyl-D-aspartate receptor (NMDAR) as a potential therapeutic approach to alleviate different types of neuropathic pain. This narrative review aims to provide a comprehensive examination of NMDAR antagonists, specifically ketamine, memantine, methadone, amantadine, carbamazepine, valproic acid, phenytoin, dextromethorphan, riluzole, and levorphanol, in the management of NeP. By analyzing and summarizing current preclinical and clinical studies, this review seeks to evaluate the efficacy of these pharmacologic agents in providing adequate relief for NeP.

Nowadays, the identification and characterization of grapevine cultivars resilient to climate and water stress while preserving quality traits is crucial for the wine industry. Therefore, the objective of this work was to characterize according to their aromatic potential 9 white and 6 red minority cultivars recently recovered from Castilla-La Mancha region (Spain), subjected at two different water deficit regimes: rainfed, with subsistence irrigation, and irrigated. For this, the varietal aromatic potential index (IPAv) and the detailed aromatic composition were analyzed by HS-SBSE-GC/MS, in extracts of two different ph. For IPAv values, red varieties did not show a clear trend with respect to irrigation. However, in white minority varieties, higher values were obtained under irrigation conditions. Thus, a clear differentiation of the minority varieties in comparison to the references was observed, primarily attributed to the content of esters and acids, in both white and red varieties. A notable contrast was observed at different pH, indicating a greater extractability of certain compounds like linalool, under more acidic conditions. This suggests that some recovered minority cultivars could be promising for cultivation in semi-arid regions with limited water, contributing to the wine sector sustainability in the future.

The reaction of berberine derivatives containing at O-9 position N,N-disubstituted acetamide fragments with sodium borohydride in methanol at 0 °C leads to a mild reduction of the "C" cycle with the formation of corresponding tetrahydroberberine derivatives with moderate to good yields.

The acylation of unsymmetrical N-benzylbispidinols in aromatic solvents without external base led to formation of supramolecular gels, which possess different thickness and stability depending on the substituents in para-positions of benzylic group and nature of acylating agent as well as on the nature of the solvent used. Structural features of the native gels as well as of their dried forms were studied by complementary techniques including FT IR- and ATR-spectroscopy, AFM, TEM, SEM, SAXS. Structures of the key crystalline compounds were established by X-ray diffraction. Analysis of obtained data allowed speculating on the crucial structural and condition factors that governed the gel formation. The most important factors were: (i) absence of base, either external or internal; (ii) presence of HCl; (iii) presence of carbonyl and hydroxyl groups to allow hydrogen bonding; (iv) presence of two (hetero)aromatic rings at both sides of the molecule. The hydrogen bonding involving amide carbonyl, hydroxyl at 9th position and, very probably, ammonium N-H+ and Cl- anion appear to be responsible for the formation of infinite molecular chains required for the first step of gel formation. Subsequent lateral cooperation of molecular chains into fibers occured, presumably, due to the aromatic pi-pi-stacking interactions. sc-CO2 drying of the gels gave rise to aerogels morphology different from that of air dried samples.

Herein, two title compounds, N-Benzoyl-N’-4-cyanophenyl thiourea (I) and N-(4-nitrobenzoyl)-N’-4-cyanophenyl thiourea (II) were synthesized in high yield, via aroyl isocyanate and 4-aminobenzonitrile. The structure of the prepared compounds was characterized by elementary analysis and FT-IR, ¹H, ¹³C NMR spectroscopic methods. Cyclic voltammetric (CV) experiments were performed with glassy carbon electrode. The reduction potential values of different functional group such as nitro, cyano in title compounds were investigated using CV curves.

A gold(I) complex with a triphenylphosphine and a monodentate N,N-dimethyldithiocarbamate ligand was synthesized and characterized by Raman spectroscopy and single crystal X-ray diffraction. DFT calculations (Gaussian 09, PBE1PBE/Lanl2dz) were undertaken for a single complex in the gas-phase. The DFT-optimized structure is in good agreement with the crystal structure and the DFT-calculated Raman spectrum also is in excellent agreement with the experimental spectrum. Assignments of the Raman peaks are based on these DFT calculations. Frontier molecular orbitals were calculated and their nature is discussed.

Ulcerative colitis (UC) is a chronic inflammatory disease, the incidence of which is increasing worldwide. However, the etiology and pathogenesis of UC remains unclear. The n-butanol extract of pulsatilla decoction (BEPD) of the traditional Chinese medicine herbal formula has been shown to be effective in treating UC. We aimed to explore the molecular mechanism underlying the effects of BEPD on UC, in particular its effects on neutrophil extracellular trap (NET) formation by neutrophils. High-performance liquid chromatography was used to determine the principal compounds of BEPD. UC was induced in mice using dextran sodium sulfate, and mice were treated with 20, 40, or 80 mg/kg BEPD daily for seven days. Colonic inflammation was determined by assessing the disease activity index, histopathology, colonic mucosal damage index, colonic mucosal permeability, and pro- and anti-inflammatory cytokine levels. The infiltration and activation status of neutrophils in the colon were determined by analyzing the levels of chemokine (C-X-C motif) ligand (CXCL)1 and CXCL2, reactive oxygen species, Ly6G, and numerous NET proteins. Our results showed that BEPD improved the disease activity index, histopathology, and colonic mucosal damage index scores of mice with UC, and restored colonic mucosal permeability compared with untreated mice. The expression levels of the pro-inflammatory cytokines interleukin-1β, interleukin-6, and tumor necrosis factor-α in colon tissues were significantly decreased, while the expression levels of anti-inflammatory cytokines in colon tissues were significantly increased, exceeding those of control mice. In addition, BEPD reduce the expression of the neutrophil chemokines CXCL1 and CXCL2 in the colon tissue of mice with UC, reduced neutrophil infiltration, reduced reactive oxygen species levels, and significantly reduced the expression of NET proteins. BEPD also significantly reduced NET formation. The results of this study suggest that BEPD exerts therapeutic effects in a murine model of UC by inhibiting neutrophil infiltration and activation in the colon, as well as by inhibiting the expression of key proteins involved in NET formation and reducing NET formation, thereby alleviating local tissue damage and disease manifestations.

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.

The process of developing therapies to treat rare diseases is fraught with financial, regulatory, and logistical challenges that have limited our ability to build effective treatments. Recently, a novel type of therapy called antisense therapy has shown immense potential for the treatment of rare diseases, particularly through single-patient N-of-1 trials. Several N-of-1 antisense therapies have been developed recently for rare diseases, including the landmark study of milasen. In response to the success of N-of-1 antisense therapy, the Food and Drug Administration (FDA) has developed unique guidelines specifically for the development of antisense therapy to treat N-of-1 rare diseases. This policy change establishes a strong foundation for future therapy development, and addresses some of the major limitations that previously hindered the development of therapies for rare diseases.

Bats are natural hosts for a wide variety of viruses and could therefore be the reservoir, source and spillover for several mammalian viruses, including adenoviruses. We examined faecal samples from 12 bats (five species of the family Vespertilionidae) from central Russia (Moscow Region) and found that two of them (16%) were positive for mastadenoviruses. One of the identified viruses was isolated from faeces of Nyctalis noctule and named BatAdV/MOW15-Nn19. The genome of the BatAdV/MOW15-Nn19 was 37,915 bp in length. Comparative studies have shown significant divergence of the nucleotide sequence of the BatAdV/MOW15-Nn19 genome from European mastadenoviruses, the highest genome sequence similarity was to isolate WA3301 from an Australian bat. Phylogenetic analysis, as well as a comparative analysis of the protein coding genes to homologous available from public database, showed evidence of novel species within Mastadenovirus genus (preliminary named «K»). Analysis of the short part of the DNA polymerase gene of BatAdV/MOW15-Nn19 reveals few NCBI records from Europe with high identity to the new virus, i.e. mastadenoviruses related to the new species are circulating in European bats. The partial genome of another mastadenovirus from Pipistrellus nathusii was identified, it represented bat adenovirus species B, which is widely distributed in European Vespertilionidae bats. New data on the genetic diversity of bat adenoviruses may help to better understand how they evolved in mammals and spread across Europe.

We have carried out a systematic review of the scientific literature published on PM and, more specifically, on its presence in fish and seafood of interest to the population from Mediterranean area. Given its preference and wide acceptability in the scientific community, we followed the PRISMA. The potential functional and clinical toxicity in humans due to MP in fish must be considered by the chemical composition of the microplastics the presence of PM and its additives, associated contaminants and pathogens in fish and shellfish, as well as the possible effects on human health, requires improving legislation on evaluation and providing a basis for consumer protection.

The existences of the N→C dative bonds in the complexes between N-containing molecules and fullerenes have been verified both theoretically and experimentally. However, finding stable N→C dative bonds is still a highly challenging task. In this work, we investigated computationally the N→C dative bonds in the complexes formed by fullerene C60 with 1,2,5-chalcogenadiazoles, 2,1,3-benzochalcogenadiazoles and 1,2,4,5-chalcogenatriazoles, respectively. It was found that the N→C dative bonds are formed along with the formation of the N–Ch···C (Ch = S, Se, Te) chalcogen bonds. In the gas phase, from S-containing complexes through Se-containing complexes to Te-containing complexes, the intrinsic interaction energies become more and more negative, which indicates that the N–Ch···C chalcogen bonds can facilitate the formation of the N→C dative bonds. The intrinsic interaction energies are compensated by the large deformation energy of fullerene C60. The total interaction energies of Te-containing complexes are negative, while both of the total interaction energies of S-containing complexes and Se-containing complexes are positive. This means that the N→C dative bonds in the Te-containing complexes are more easily observed in experiments in comparison with those in the S-containing complexes and Se-containing complexes. This study provides a new theoretical perspective on the experimental observation of the N→C dative bonds in the complexes involving fullerenes. Further, the formation of stable N→C dative bonds in the complexes involving fullerenes can significantly change the properties of fullerenes, which will greatly simulate and expand the application range of fullerenes.

Introduction: Oral Nicotine Pouches (ONPs) are the new form of nicotine pouches that have become a type of emerging smokeless tobacco product sold by various tobacco companies. These smokeless tobacco products are being marketed for usage all over as Snus containing tobacco-derived nicotine (Natural) or as tobacco-free nicotine (Synthetic) as substitutes for other tobacco products that are limited in public places. ONPs have become popular tobacco products among adolescents/young adults and over 50% of young adult users of ONP use flavored ONP, such as menthol/mint, tobacco, dessert/candy, and fruity, being the most popular flavors. Various new ONP flavors are currently popular locally as well in the online market. Tobacco, Menthol, and fruit-flavored ONPs could motivate cigarette smokers to alteration to ONPs. Methods: We expanded our knowledge on natural/synthetic ONP flavors wheels to available data on ONPs, describing in detail their flavors and brands (US and Europe) in both natural/Synthetic ONP categories. We classified over 152 snus and 228 synthetic ONPs into the following flavor categories: “Tobacco”, “Menthol/Mint”, “Fruity”, “Candy/Deserts”, “Drink”, “Aroma”, “Spices” and “Mixed Flavors”. Results: Based on total numbers, wefound that the most popular ONPs flavor sold as tobacco and menthol to be among natural ONPs, among synthetic ONPs fruity and menthol as the most prominent flavors with varying concentrations of nicotine and other flavoring chemicals including coolant WS-23. We also illustrated possible molecular targets and toxicities due to exposure to ONPs activating several signaling cascades like AKT and NF-B might possibly lead to apoptosis and epithelial mesenchymal transition (EMT). Conclusions: Considering the marketing of ONP products with various flavor profiles and most of these products containing tobacco/menthol/fruit flavor, it is likely to have regulation and marketing disclaimer on some of these products. Further, it would be logical to determine how the market reacts in terms of compliance and non-compliance with flavor restrictions by the regulatory agencies.

Pigs are a social species and they stablish hierarchies for a better use of resources and to reduce conflicts. However, in pig production, the opportunities for growing can differ in dominant and subordinate animals. In the present study a system was tested to perform a Dominance versus Subordinate test in growing pigs to investigate how the hierarchy affects feeding behaviour, growth and gut microbiota assessed in faeces. 64 animals housed in eight different pens were used, with four castrated males and four females in each one, weighing 18kg at arrival and maintained during the whole growing period, until 140 kg. Three stool samplings were obtained from the animals directly from the anus to avoid contamination of the faeces on 58, 100 and 133 days after the start of the study to investigate the microbiota composition. The dominant animals had higher gains during the growing period that the subordinate. In addition, they were performing more visits to the feeder throughout the day. Differential abundance patterns were observed in five bacterial genera, Oliverpabstia, Peptococcus, and Faecalbacterium being more abundant in dominant animals and Holdemanella and Acetitomaculum being overrepresented in subordinate ones. This microbial biomarker accurately classified dominant versus subordinate groups of samples with an AUC of 0.92.

Nanoporous membranes promise energy-efficient water desalination. Hexagonal boron nitride (h-BN), like graphene, exhibits outstanding physical and chemical properties, making it a promising candidate for water treatment. We employed Car–Parrinello molecular dynamics simulations to establish accurate modeling of Na+ and Cl- permeation through hydrogen passivated nanopores in graphene and h-BN membranes. In these complex systems the classical potentials cannot properly account for the physics at play. We demonstrate that ion separation works well for the h-BN by imposing a barrier of 0.13 eV and 0.24 eV for Na+ and Cl- permeation respectively, compared to the graphene where the Cl- ion faces a negative barrier of 0.68 eV and is prone toward blockade and Na+ permeation is associated with a slightly negative barrier of 0.03 eV. From the change in the solvation shell, we get the following trend: Na/h-BN > Na/G > Cl/G > Cl/h-BN. We argue that the trend in the permeation barrier changes to Cl-h-BN > Na-h-BN > Na-G > Cl-G due to a combination of several interactions, including the distortion of the water network induced by the ions, the ion-water interaction, and the ion-nanopore interaction. Overall, the desalination performance of h-BN surpasses that of their graphene counterparts.

5-Pentadecylresorcinol (C15) is a natural alkylresorcinol that has been shown to protect against complications caused by imbalanced nutrition. Although the exact mechanisms of beneficial activity of C15 are not known, we assume that the protective effects of C15 on metabolic health are mediated by their modulatory influence on the composition of the intestinal microbiota and functional activity. Cobamides and vitamin B12 are believed to be crucial modulators of mammalian gut ecosystems. We proposed that C15 may influence the representation of enzymes and pathways for vitamin B12 synthesis in the gut microbiome, providing compositional and functional changes in the microbial community. High-throughput metagenome sequencing of the contents of the small and large intestines of C57Bl6 mice fed a regular or high-fat diet with or without C15 supplementation was performed followed by reconstruction of the metabolic activity of the microbiota to clarify the role of C15 in vitamin B12 synthesis by the gut microbiota. It has been established that C15 significantly increases the representation of the cobalamin salvage pathway and enzymes in the microbiome of the large intestine of mice fed a high-fat diet. The genera Clostridium, AF12, and [Ruminococcus] had shown the highest number of correlations with enzymes for B12 synthesis and were negatively associated with the representation of probiotic bacteria. Therefore, the beneficial effect of C15 on the gut microbiota community can be achieved by modulating B12 synthesis that, in turn, serves as one of the key regulators of gut microbiota ecology.

The terpolymers of N-vinylpyrrolidone (VP) with acrylic acid and triethylene glycol methacrylate were synthesized with more than 90% yield by radical copolymerization in ethanol from monomeric mixtures of different composition (98 : 2 : 2, 95 : 5: 2 and 98 : 2: 5). Their physical and chemical characteristics such as monomer composition, absolute molecular masses and hy-drodynamic radii in aqueous media at different temperature were determined. The temperature effect on the behavior of terpolymers in water and aqueous saline buffer solution with pH 7.4 has been studied and the ability of a copolymer enriched with dimethacrylates units was discovered to respond to a temperature change in these media. On the basis of terpolymers and linear polyvi-nylpyrrolidone (PVP), polymer compositions of hydrophobic dye methyl pheophorbide a, MPP, that is fluorescent in the red region of the spectrum, were obtained; the sizes of the polymer nanostructures of MPP in water were determined. The structures based on terpolymers were demonstrated to be more stable than MPP-PVP ones. It was carried out the quantum-chemical modelling of the possible structures of MPP-copolymer that formed as a result of intermolecular interaction between MPP and terpolymer’s chains; the possibility of the formation of hydrogen bonds between the donors of the electrons of terpolymer units and the proton donors of the MPP molecule was shown. Using the MTT test, it was established that copolymers of various compositions are low toxic for non-tumor Vero cells and HeLa tumor cells. It was shown that the cytotoxicity of polymer particles loaded with MPP for FetMSC and HeLa cells increased. Using fluorescence microscopy, the accumulation and distribution of polymer particles in non-tumor (FetMSC) and tumor (HeLa) cells was studied and an increase in the accumulation of MPP with both types of particles was shown.

A convenient and efficient strategy for the preparation of 2-sulfonylindoles has been achieved through iodophor/H2O2-mediated 2-sulfonylation of indoles with readily available sulfonyl hydrazides in aqueous phase. Iodophor is commercially available and serves as the green catalyst and aqueous phase. A series of 2-sulfonylated products from indoles and N-methylpyrrole were synthesized in moderate yields in only 10 min. Control experiments were also conducted to reveal the mechanism. This method is environment friendly, easy to operate and suitable for a wide range of substrates.

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.

A second order rational difference equation $$x_{n+1}=px_{n}+\frac{q}{x_{n-1}^2}$$ with the parameters $p$ and $q$ which lies in $(0,1)$, is studied. The dynamics of the equilibrium is characterized through the trichotomy of the parameter $p<\frac{1}{2}$, $p=\frac{1}{2}$ and $p>\frac{1}{2}$. It is found that there is no periodic solution of period $2$ and $3$ but there exists periodic solutions with only periodic solution $5$ and $10$ are achieved computationally.

This literature review provides an in-depth exploration of the research conducted on the investigation and measurement of residual stress (RS) in Wire Arc Additive Manufacturing (WAAM) products, particularly focusing on how process parameters influence the phenomenon. RS plays a crucial role in determining the mechanical behavior and structural integrity of WAAM components. This review article gives an understanding of the relationship between process parameters and RS to optimize the WAAM processes and ensure the durability of the final products. It also summarizes key findings, measurement techniques, challenges, and future directions in this evolving field. The review also analyzes a measurement of RS in the products of WAAM as a function of process parameters depending on examining various research studies, techniques, and methodologies employed in their studies. Experimental measuring techniques and numerical analysis of RS to determine the impacts of RS in mechanical response in products of WAAM were discussed. By offering a comprehensive overview of current research trends and insights, this review serves as a valuable resource to guide future investigations, fostering the advancement of WAAM as a robust and efficient manufacturing technology.

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.

(1) Background: The diagnosis of lumbosacral radiculopathy involves anamnesis, assessment of sensitivity and strength, diagnostic imaging—usually magnetic resonance imaging (MRI)—and electrodiagnostic testing (EDX), typically electromyography (EMG) and electroneurography (ENG). MRI evaluates the structures supporting the spinal cord, while EDX evaluates root functionality. The present study aimed to analyze the concordance of MRI and EDX findings in patients with clinically suspected radiculopathy. Additionally, we investigated the comparison between these two reference tests and various clinical variables and questionnaires. (2) Methods: We designed a prospective epidemiological study of consecutive cases with an observational, descriptive, cross-sectional, and double-blind nature following the STROBE guidelines, encompassing 142 patients with clinical suspicion of lumbosacral radiculopathy. (3) Results: Of the sample, 58.5% tested positive for radiculopathy using EDX as the reference test, while 45.8% tested positive using MRI. The comparison between MRI and EDX in the diagnosis of radiculopathy in patients with clinical suspicion was not significant; the overall agreement was 40.8%. Only the years with symptoms were comparatively significant between the positive and negative radiculopathy groups as determined by EDX. (4) Conclusion: The comparison between lumbar radiculopathy diagnoses in patients with clinically suspected pathology using MRI and EDX as diagnostic modalities did not yield statistically significant findings. MRI and EDX are complementary tests assessing different aspects in patients with suspected radiculopathy; degeneration of the structures supporting the spinal cord does not necessarily imply root dysfunction.

The Coronavirus disease 2019 (COVID-19) pandemic began in Jan. 2020 in Wuhan, China with a new coronavirus designated SARS-CoV-2. The principle cause of death from COVID-19 disease quickly emerged as Acute Respiratory Distress Syndrome (ARDS). A key ARDS pathogenic mechanism is the “Cytokine Storm”. This is a dramatic increase in the blood of inflammatory cytokines. In the last 2 years of the pandemic new pathology has emerged in COVID-19 survivors in which a variety of long-term symptoms emerge. This condition is called “Long COVID”. The spike protein on the surface of the virus (target for the new mRNA/DNA vaccines) is composed of joined S1-S2 subunits. Upon S1 bind-ing to the human ACE2 receptor on cells, the S1 subunit is cleaved and the S2 subunit me-diates entry of the virus. The S1 protein is then released into the blood, which might be one of the pivotal triggers for initiation and/or perpetuation of the cytokine storm. In this study, we tested the hypothesis that the spike S1 protein may activate inflammatory sig-naling and cytokine production independent of the virus. Our data support a potential role for spike S1 activation of inflammatory signaling and cytokine production in human lung and intestinal epithelial cells in culture. These data support a potential role for the SARS-CoV-2 spike S1 protein in COVID-19 pathogenesis.

In polymer-based dental composites, wear is a three-body wear system mainly abrasive, because of the food particles and wear products suspended in the oral cavity, which are transferred to the microcavities of the surface of the replacements. Due to this fact, the incorporation of nanodiamond as reinforcement in these polymer matrix composites, which promotes the creation of a solid lubricant tribofilm surface could be advantageous. When nanodiamond is added in contents of 0.8, 1.6 and 3.2 wt %, an increase in microhardness from 95 up to 420 %, compared to the one of neat BisGMA/TEGDMA, is observed. Additionally, the incorporation of a content of 1.6 wt % is enough to cause a diminution of ~78 % in the friction coefficient and a reduction of the specific wear rate and Archard’s coefficient of ~50 %. Nevertheless, the addition of relatively high contents reduces the effectiveness of photoinitiation and UV-curing, which is related to the scattering and absorption of UV radiation by ND.

Background: Maternal influenza vaccination provides effective protection against influenza in-fections in pregnant women and new-borns. In India, the influenza vaccine has not yet been of-fered through immunization programs, owing to the lack of sufficient safety data for Indian pregnant women. Methods: This cross-sectional observational study enrolled 558 women ad-mitted to the obstetrics ward of a civic hospital in Pune. Study-related information was obtained from the participants through interviews using structured questionnaires and hospital records. Univariate and multivariate analysis were used and chi-square with unadjusted and adjusted odds ratio were estimated to account for vaccine exposure and the temporal nature of each out-come, respectively. Results: The protective effect of maternal influenza vaccination against delivering very low birth weight infants was observed. (aOR 2.29, 95% CI 1.03 to 5.58, p=0.03). No association was observed between maternal influenza vaccination for spontaneous abortion (OR 1.42, 95%CI 0.75, 2.68), chorioamnionitis (OR 0.60, 95% CI, 0.1, 3.63), gestational hypertension (OR 1.73, 95% CI 0.72, 4.16) and preterm birth (OR 1.64, 95% CI 0.91, 2.97). Interpretation: These results show that the in-fluenza vaccine administered during pregnancy is safe and has a lower risk of negative birth outcomes.