Transmission of values and religious concepts to children is one of the most important issues in the third millennium and it has drawn varied and different views among experts and scholars in the world. Research specialized in religious literature for children and adolescents create new capacities in the presentation of religious concepts to the group. Plans have been considered to transfer values and religious concepts in the curricula of primary school children in the group in Iran. It is one of the topics that the authors note to the introduction of the minutiae of religion in the first three elementary grades. In this study, the collection and analysis methods providing content related to the minutiae of religion in reading books the first till third sections of the years 2013-2015. In addition, the plan includes aspects of other branches of religion in these books on information collected from text books that collected and classified. The result is that "definitely good and forbidding the evil" and "prayer" have the highest frequency of applications in the selected books. Further branches made of branches of religion in these books, represents the values of religious, moral and social as well.

Introduction: Various studies have widely documented how weight (being overweight, underweight or obese) plays a significant role in increasing health problems and adversely affects the quality of life. Out of all the weight-related issues, obesity is the number one contributing factor for causing health problems. To overcome this factor, a trial was conducted where a drug 'Lite plus’ (Garcinia Cambogia and Green Coffee bean) was given to a select number of people for a period of three months. Materials and Methods: 'Lite Plus' is a drug which contains Garcinia cambogia 500 mg + Green Coffee Bean Extract 400 mg, with a recommended dosage of 2 tablets/day with or before meals for 3 months. Most of the patients included in our study were females suffering from obesity-related problems such as PCOs and Infertility. During the trial, all patients were given the recommended dosage of 'Lite plus', while those with diabetes had the dose amended accordingly. Results: As a result of this trial, it was found that the use of 'Lite Plus' was very effective in decreasing weight/BMI with continuous use. After 6 weeks, out of the 250 respondents participating in the trial, 232 got a reduction in their BMI, and after 12 weeks (end of the trial), 29 respondents (11.6%) reached the desired level of BMI. Conclusion: Garcinia cambogia and Green Coffee bean are effective and safe in weight reduction.

In this paper, a new full-Newton step feasible interior point method for convex quadratic programming is presented and analyzed. The idea behind this method is to replace the complementarity condition with a non-negative variable weight vector and use the algebraic equivalent transformation for the obtained equation. Under the selection of appropriate parameters, the quadratic rate of convergence of the new algorithm is established. In addition, the iteration complexity of the algorithm is obtained. Finally, some numerical results are presented to demonstrate the practical performance of the proposed algorithm.

The aim of this study was to develop a valid and reliable questionnaire to assess vitamin D-related knowledge, attitude and practice (KAP) in Iranian adults who may be at increased risk of vitamin D deficiency. This study was conducted on 527 subjects, aged ≥20 years from Public health care centers in Tehran, Iran. Based on results of literature review and in-depth interviews, the 38-item vitamin D-related KAP questionnaire with four subscale was developed: 1) general knowledge; 2) nutritional knowledge; 3) attitudes; 4) behaviors. Validity of the developed vitamin D-KAP questionnaire was assessed, utilizing face, content, and construct validity methods. Internal consistency was calculated to assess reliability of the current developed questionnaire. A total of 572 (54.1% female) adults, aged 30.2±7.9 years, participated in the study. All items were perceived as relevant and comprehendible by participants. Content validity was confirmed by the panel of experts. The internal consistency, as measured by Cronbach’s alpha coefficients, exceeded the minimum reliability standard of 0.60 for four subscales. The EFA suggested a four-factor construct and the results of the CFA indicated acceptable fit indices for the proposed model. No ceiling effects were observed except for general knowledge (1.2%). Floor effects detected were 0, 1.1, 2.4, and 8.7% for practice, attitude, general knowledge, and nutrition knowledge, respectively. General knowledge had the highest score (79.59±14.52) and nutrition knowledge, the lowest score (42.58±20.40) among the four sub-scales. Results confirm the initial validity and reliability of the vitamin D-related knowledge, attitude and practice questionnaire. Further investigations in different urban and rural population are recommended.

Digital machine intelligence has evolved from its inception in the form of computation of numbers to AI, which is centered around performing cognitive tasks that humans can perform, such as predictive reasoning or complex calculations. The state of the art includes tasks that are easily described by a list of formal, mathematical rules or a sequence of event-driven actions such as modeling, simulation, business workflows, interaction with devices, etc., and also tasks that are easy to do “intuitively”, but are hard to describe formally or as a sequence of event-driven actions such as recognizing spoken words or faces. While these tasks are impressive, they fall short in applying common sense reasoning to new situations, filling in information gaps, or understanding and applying unspoken rules or norms. Human intelligence uses both associative memory and event-driven transaction history to make sense of what they are observing fast enough to do something about it while they are still observing it. In addition to this cognitive ability, all bio-logical systems exhibit autopoiesis and self-regulation. In this paper, we demonstrate how machine intelligence can be enhanced to include both associative memory and event-driven transaction history to create a new class of knowledge-based assistants to augment human intelligence. The digital assistants use global knowledge derived from the Large Language Models to bridge the knowledge gap between various participants interacting with each other. We use the general theory of information and schema-based knowledge representation to create the memory and history of various transactions involved in the interactions.

The holy grail of Artificial Intelligence (AI) has been to mimic human intelligence using computing machines. Autopoiesis which refers to a system with well-defined identity and is capable of re-producing and maintaining itself and cognition which is the ability to process information, apply knowledge, and change the circumstance are associated with resilience and intelligence. While classical computer science (CCS) with symbolic and sub-symbolic computing has given us tools to decipher the mysteries of physical, chemical and biological systems in nature and allowed us to model, analyze various observations and use information to optimize our interactions with each other and with our environment, it falls short in reproducing even the basic behaviors of living organisms. We present the foundational shortcomings of CCS and discuss the science of infor-mation processing structures (SIPS) that allows us to fill the gaps. SIPS allows us to model su-per-symbolic computations and infuse autopoietic and cognitive behaviors into digital machines. They use common knowledge representation from the information gained using both symbolic and sub-symbolic computations in the form of system-wide knowledge networks consisting of knowledge nodes and information sharing channels with other knowledge nodes. The knowledge nodes wired together fire together to exhibit autopoietic and cognitive behaviors.

Low-lying coastal communities are often threatened by compound flooding (CF), which can be determined through the joint occurrence of storm surges, rainfall and river discharge either successively or in close succession. The trivariate distribution can demonstrate the risk of the compound phenomenon more realistically rather than considering each contributing factor independently or in a pairwise dependency. Recently vine copula has been recognized as the highly flexible approach to constructing a higher dimensional joint density framework. In such construction, parametric class copula with parametric univariate marginal distributions is often involved. Such incorporation can lack flexibility due to parametric functions with prior distribution assumptions about their univariate marginal and/or copula joint density. This study introduces the vine copula approach in a nonparametric setting by introducing Bernstein and Beta kernel copula density in establishing trivariate flood dependence. The proposed model is applied to 46 years of flood characteristics collected on the west coast of Canada. The univariate flood marginal distribution is modelled using nonparametric kernel density estimation (KDE). The 2-D Bernstein estimator and Beta kernel copulas estimator are tested independently in capturing pairwise dependencies to establish D-vine structure in a stage-wise nesting approach in three alternative ways, each by permutating the location of the conditioning variable. The best-fitted vine structure is selected using goodness-of-fit (GOF) test statistics. The performance of the nonparametric vine approach is also compared with the vine constructed in the parametric and semiparametric fitting procedure. Investigation reveals that the D-vine constructed using Bernstein copula with normal KDE marginals nonparametrically performed well in capturing dependence of the compound events. Finally, the derived nonparametric model is used in the estimation of trivariate OR- and AND-joint return periods, further employed in estimating failure probability (FP) statistics. The trivariate return periods for the AND-joint case are higher than for the OR-joint case for the same flood combination. Also, the trivariate flood hazard results in a high-value FP than bivariate and univariate events. Ignoring the trivariate dependence could result in the underestimation of FP

Firstly, the author do dynamic analysis for reaction-diffusion Gilpin-Ayala competition model with Dirichlet boundary value, involved in harmful species. Existence of multiple stationary solutions is verified by way of Mountain Pass lemma, and the local stability result of the null solution is obtained by employing linear approximation principle. Secondly, the author utilize variational methods and LMI technique to deduce the LMI-based global exponential stability criterion on the null solution which becomes the unique stationary solution of the ecosystem with delayed feedback under a reasonable boundedness assumption on population densities. Particularly, LMI criterion is involved in free weight coefficient matrix, which reduces the conservatism of the algorithm. In addition, a new impulse control stabilization criterion is also derived. Finally, two numerical examples show the effectiveness of the proposed methods. It is worth mentioning that the obtained stability criteria of null solution presented some useful hints on how to eliminate pests and bacteria.

Making computing machines mimic living organisms has captured the imagination of many since the dawn of digital computers. However, today’s artificial intelligence technologies fall short in replicating even the basic autopoietic and cognitive behaviors found in primitive biological systems. According Charles Darwin, the difference in mind between humans and the higher animals, great as it is, certainly is one of degree and not of kind. Autopoiesis refers to the behavior of a system that replicates itself and maintains its own identity and stability while facing fluctuations caused by external influences. Cognitive behaviors model the system’s state, sense internal and external changes, analyze, predict and take action to mitigate any risk to its functional fulfilment. How did intelligence evolve? what is the relationship between the mind and body? Answers to these questions should guide us to infuse autopoietic and cognitive behaviors into digital machines. In this paper we use recent advances in our understanding of general theory of information, and the role of structures in managing the transformations between information and knowledge to pave the path to infuse autopoietic and cognitive functions into digital computing and build a new class of intelligent machines going beyond the current state of the art.

In this paper, by using the variational method, a sufficient condition for the unique existence of the stationary solution of the reaction-diffusion ecosystem is obtained, which directly leads to the global asymptotic stability of the unique equilibrium point. Moreover, delayed feedback ecosystem with reaction-diffusion item is considered, and utilizing impulse control results in the globally exponential stability criterion of the delayed ecosystem. It is worth mentioning that the Neumann zero-boundary value that the infected and the susceptible people or animals should be controlled in the epidemic prevention area and not allowed to cross the border, which is a good simulation of the actual situation of epidemic prevention. And numerical examples illuminate the effectiveness of impulse control, which has a certain enlightening effect on the actual epidemic prevention work . That is, in the face of the epidemic situation, taking a certain frequency of positive and effective epidemic prevention measures is conducive to the stability and control of the epidemic situation. Particularly, the newly-obtained theorems quantifies this feasible step. Besides, utilizing Laplacian semigroup derives the $p$th moment stability criterion for the impulsive ecosystem.

With 500+ papers and 20+ books spanning many scientific disciplines, Mark Burgin has left an indelible mark and legacy for future explorers of human thought and information technology professionals. In this paper, I discuss his contribution to the evolution of machine intelligence using his general theory of information (GTI) based on my discussions with him and various papers I co-authored during the past eight years. His construction of a new class of digital automata to overcome the barrier posed by the Church-Turing Thesis, and his contribution to Super-Symbolic Computing with Knowledge Structures, Cognizing Oracles, and Structural Machines are leading to practical applications changing the future landscape of information systems. GTI provides a model for the operational knowledge of biological systems to build, operate, and manage life processes using 30+ trillion cells capable of replication and metabolism. The schema, and associated operations derived from GTI are also used to model a digital genome specifying the operational knowledge of algorithms executing the software life processes with specific purposes using replication and metabolism. The result is a digital software system with a super-symbolic computing structure exhibiting autopoietic and cognitive behaviors that biological systems also exhibit. We discuss here one of these applications.

This paper reports applying Minimax principle and impulsive differential inequality to derive the existence of multiple stationary solutions and the global stability of a positive stationary solution for a delayed feedback Gilpin-Ayala competition model with impulsive disturbance. The conclusion obtained in this paper reduces the conservatism of the algorithm compared with the known literature, for the impulsive disturbance is not limited to impulsive control.

In real financial market, the delayed market feedback and the delayed effect of government macro-control are inevitable. And both the delay of market feedback and the delay of macro-control effect bring about the mathematical difficulties in studying stabilization and synchronization of the hyper-chaotic financial system. However, employing Lyapunov function method, differential mean value theorem, suitable bounded hypotheses and pulse control technology results in the globally asymptotical stabilization and synchronization criteria. It is the first paper to drive the stabilization and synchronization criteria under the assumptions of the double delays. Finally, numerical examples illuminate the effectiveness of the proposed methods.

In this paper, the existence of two nontrivial stationary solutions for the nonlinear Gilpin Ayala two species competition model is given by using the mountain pass lemma, and the local stability criterion of the trivial solution is given by using Lyapunov function method. Based on the local stability criterion, we give some suggestions on how to avoid the population extinction. This is, when the population is on the verge of extinction, we should try our best to avoid the diffusion behavior and reduce the diffusion coefficient, otherwise the species are easy to go extinct. Numerical example shows the effectiveness of the proposed method.

In this paper, the author utilizes Saddle Theorem and variational methods to deduce existence of at least six stationary solutions for reaction-diffusion Gilpin-Ayala competition model (RDGACM). To obtain the global stabilization of the positive stationary solution of the RDGACM, the author designs a suitable impulsive event triggered mechanism (IETM) to derive the global exponential stability of the the positive stationary solution. It is worth mentioning that the new mechanism can exclude Zeno behavior and effectively reduce the cost of impulse control through event triggering mechanism. Besides, compared with existing literature, the restrictions on the parameters of the RDGACM are relaxed so that the methods used in existing literature can not be applied to the relaxed case of this paper, and so the author makes comprehensive use of Saddle Theorem, orthogonal decomposition of Sobolev space $H_0^1(\Omega)$ and variational methods to overcome the mathematical difficulty. Numerical examples show the effectiveness of the methods proposed in this paper.

In this paper, Lyapunov-Razumikhin technique, design of state-dependent switching laws, a fixed point theorem and variational methods are employed to derive the existence and the unique existence results of (globally) exponentially stable positive stationary solution of delayed reaction-diffusion cell neural networks under Dirichlet zero boundary value, including the global stability criteria \textbf{in the classical meaning}. Next, sufficient conditions are proposed to guarantee the global stability invariance of ordinary differential systems under the influence of diffusions. New theorems show that the diffusion is a double-edged sword in judging the stability of diffusion systems. Besides, an example is constructed to illuminate that any non-zero constant equilibrium point must be not in the phase plane of dynamic system under Dirichlet zero boundary value, or it must lead to a contradiction. Next, under Lipschitz assumptions on active function, another example is designed to prove that the small diffusion effect will cause the essential change of the phase plane structure of the dynamic behavior of the delayed neural networks via a Saddle point theorem. Finally, a numerical example illustrates the feasibility of the proposed methods.

While investigating the outcomes of balloon dilatation procedures in patients with congenital obstructive lesions of the heart, several parallel observations have been made. The purpose of this review is to present these observations/phenomena/innovations related to balloon dilatation of pulmonary stenosis (PS), aortic stenosis (AS), and aortic coarctation (AC). In subjects who had balloon pulmonary valvuloplasty (BPV), development of infundibular obstruction, electrocardiographic (ECG) changes, changes in right ventricular filling, role of balloon/annulus ratios on the results of BPV, and double balloon vs. single balloon BPV will be reviewed. In patients who had balloon aortic valvuloplasty (BAV), causes of aortic insufficiency and trans-umbilical venous approach for BAV will be tackled. In children who had balloon angioplasty (BA) of AC, aortic remodeling and biophysical response after BA of AC will be discussed.

In this paper, the authors employ Mountain Pass Lemma, the method of weak solution regularization and Lyapunov function method to derive the unique existence of globally exponential stable positive stationary solution of a single-species model with diffusion and delayed feedback. The obtained stability criterion illuminates that under some suitable conditions, a certain internal competition is conducive to the overall stability of the population, and a certain amount of family planning is conducive to the overall stability of the population. A numerical example and three tables show the effectiveness of the proposed methods.

In this paper, we define a (p,v)-extension of Hurwitz-Lerch Zeta function by considering an extension of beta function defined by Parmar et al. [J. Classical Anal. 11 (2017) 81–106]. We obtain its basic properties which include integral representations, Mellin transformation, derivative formulas and certain generating relations. Also, we establish the special cases of the main results.

A new generalization of extended beta function and its various properties, integral representations and distribution are given in this paper. In addition, we establish the generalization of extended hypergeometric and con uent hypergeometric functions using the newly extended beta function. The properties these extended and con uent hypergeometric functions such as integral representations, Mellin transformations, dif- ferentiation formulas, transformation and summation formulas are also investigated.

Human papillomavirus (HPV) is one the most common sexually transmitted infections (STI), spreading through direct sexual contact to oropharynx and anogenital regions. Although most infected individuals may be asymptomatic and naturally undergo viral clearance, in some individuals’ HPV infection causes carcinogenesis of the infected epithelia. HPV now accounts for over 70% of oropharyngeal carcinomas (OPC). Over 90% of these OPC are associated with HPV 16, one of the 14 high-risk subtypes. In recent years, HPV positive (HPV+) OPC has been one of the most rapidly rising cancers, by new cancer incidence rates, especially among men. They present some unique infection driven cellular and molecular features, in comparison to other head and neck carcinomas (HNC), constituting distinct preventive, therapeutic and prognostic strategies - with significant strides made in this area. However, HPV+OPC are routinely detected at a later stage, adding to the disease burden and overall survival. In this review, we provide a summary of HPV from a STI to malignant transformation, while also parsing through the immune response environment. We further collate key literature around infection sites, diagnostics and clinical management, while throwing light on the current state of epidemiology, transmission, screening and prevention strategies. Indeed, our improving understanding of the disease’s molecular biology has added some success to the prognostic outcomes of certain HPV+OPC across clinical trials examining targeted therapeutics and immunotherapy. This work is directed towards improving our current understanding of HPV and associated oncogenic landscape, to guide screening, early detection, triaging and further prevention and management strategies.

By partly generalizing the Lipschitz condition of existing results to the generalized Lipschitz one, the author utilizes a fixed point theorem, variational method and Lyapunov function method to derive the unique existence of globally asymptotical input-to-state stability of positive stationary solution for Gilpin-Ayala competition model with diffusion and delayed feedback under Dirichlet zero boundary value. Remarkably, it is the first paper to derive the unique existence of the stationary solution of reaction-diffusion Gilpin-Ayala competition model, which is globally asymptotical input-to-state stability. And numerical examples illuminate the effectiveness and feasibility of the proposed methods.

Network centrality analyses have proven successful in identifying important nodes in diverse host-pathogen interactomes. The current study presents a comprehensive investigation of the human interactome and SARS-CoV-2 host targets. We first constructed a comprehensive human interactome by compiling experimentally validated protein-protein interactions (PPIs) from eight distinct sources. Additionally, we compiled a comprehensive list of 1,449 SARS-CoV-2 host proteins and analyzed their interactions within the human interactome, which identified enriched biological processes and pathways. Seven diverse topological features were employed to reveal the enrichment of SARS-CoV-2 targets in the human interactome, with Load centrality emerging as the most effective metric. Furthermore, a novel approach called CentralityCosDist was employed to predict SARS-CoV-2 targets, which proved effective in expanding the pool of predicted targets. Pathway enrichment analyses further elucidated the functional roles and potential mechanisms associated with predicted targets. Overall, this study provides valuable insights into the complex interplay between SARS-CoV-2 and the host cellular machinery, contributing to a deeper understanding of viral infection and immune response modulation.

The interaction between oceanographic, meteorological, and hydrological factors can result in an extreme flooding scenario in the low-lying coastal area, called compound flooding (CF) events. For instance, rainfall and storm surge (or high river discharge) can be driven by the same meteorological, tropical or extra-tropical cyclones, resulting in a CF phenomenon. The trivariate distributional framework can significantly explain compound events' statistical behaviour reducing the associated high-impact flood risk. Resolving heterogenous dependency of the multidimensional CF events by incorporating traditional 3-D symmetric or fully nested Archimedean copula is quite complex. The main challenge is to preserve all lower-level dependencies. An approach based on decomposing the full multivariate density into simple local building blocks via conditional independence called vine or pair-copulas is a much more comprehensive way of approximating the trivariate flood dependence structure. In this study, a parametric vine copula of a drawable (D-vine) structure is introduced in the trivariate modelling of flooding events with 46 years of observations of the west Coast of Canada. This trivariate framework searches dependency by combining the joint impact of annual maximum 24-hr rainfall and the highest storm surge and river discharge observed within the time ±1 day of the highest rainfall event. The D-vine structures are constructed in three alternative ways by permutation of the conditioning variables. The most appropriate D-vine structure is selected using the fitness test statistics and estimating trivariate joint and conditional joint return periods. The investigation confirms that the D-vine copula can effectively define the compound phenomenon's dependency. The failure probability (FP) method is also adopted in assessing the trivariate hydrologic risk. It is observed that hydrologic events defined in the trivariate case produce higher FP than in the bivariate (or univariate) case. It is also concluded that hydrologic risk increases (i) with an increase in the service design life of the hydraulic facilities and (ii) with a decrease in return periods.

This study will investigate different signals and events/news that determined the stock market's movements. As we know, many factors affect the stock market on a daily, weekly, and monthly basis, e.g., rate of interest, exchange rate, and oil prices, etc. Our research will investigate the impact of daily events/news in the KSE-100 index due to several policies announced and events/news in the country because the daily movements in the stock market can be determined only by different signals and events/news. Time series data is collected daily for particular reasons from "The News" (Daily Newspaper, Sunday edition) from 2010 to 2019. The results of this study show that political and global news affects the stock market index ferociously. For investors, the investment in blue chips is not less than a safe haven. When day-to-day transactions are concerned, there is always a higher panic attack than the herd behaviour in the stock exchange. Investors tend to make prompt responses to negative rather than positive news, which makes them risk averters. Our finding also confirmed that the ARCH/GARCH model is better than the simple OLS method concerning stock market upheaval.

Background: People with Down syndrome (DS) have a trisomy of chromosome 21 and marked deficits in verbal memory but relatively preserved visuospatial perception. Verbal memories are related to semantic knowledge. Little is known about the organization of semantic knowledge in people with DS, which was the aim of this study. Aims: The extent of false memory in people with DS was examined. Possible differences in the number of false memories induced by nominal and verbal themes were examined. Methods and Procedures: In the study phase, ten word lists with semantically related associates were presented. In the recognition phase, participants made judgments on whether each word had been represented previously as semantically related lures, old words, and semantically unrelated new words. Outcomes and Results: Differences in the processing of nominal and verbal theme-related lures were observed among people with DS. When processing nominal theme-related lures, people with DS showed delayed false positives. When processing verbal theme-related lures, people with DS showed no differences compared to controls. Similar patterns have been observed in people with DS when processing nominal theme-related associates and verbal theme-related associates. People with DS have delayed recognition of associates. When processing unrelated words, they showed significantly higher false positives than controls for both nominal and verbal eliciting themes. Further analyses of the themes revealed differences in false positives across syntactic categories among groups. Conclusions and Implications: People with DS showed a delayed recognition of associates and deviance in recognizing unrelated words in the nominal and verbal theme categories. People with DS were delayed in terms of nouns, but deviated with verbs in associated and unrelated words. Additionally, people with DS showed distinct patterns in processing five nominal lures and three verbal lures, suggesting that they formed false memories differently based on distinct syntactic categories. Thus, it was concluded that people with DS develop a deviant semantic structure. Category-based interventions for people with DS should be implemented in order to improve their semantic knowledge through lexical connections.

Poxviridae have been successful pathogens throughout recorded history, infecting humans among a variety of other hosts. Although eradication of the notorious smallpox has been a globally successful healthcare phenomenon, the recent emergence of Monkeypox virus, also belonging to the Orthopoxvirus genus and causing human disease, albeit milder than smallpox, is a cause of significant public health concern. The ongoing outbreak of monkeypox, demonstrating human-human transmission, in previously non-endemic countries, calls for critical need into further research in the areas of viral biology, ecology and epidemiology to better understand, prevent and treat human infections. In the wake of these recent events, it becomes important to revisit poxviral infections, their pathogenesis and ability to cause infection across multiple non-human hosts and leap to a human host. The poxviruses that cause human diseases include Monkeypox virus, Molluscum contagiosum virus and Orf virus. In this review we summarize the current understanding of various poxviruses causing human diseases, provide insights into their replication and pathogenicity, disease progression and symptoms, preventive and treatment options and their importance in shaping modern medicine through application in gene therapy, oncolytic viral therapies for human cancers or as poxvirus vectors for vaccines.

Novel pH responsive semi-interpenetrating polymer hydrogels based on tamarind gum-co-poly(acrylamidoglycolic acid) (TMGA) polymers have been synthesized using simple free radical polymerization in the presence of bis[2-(methacryloyloxy)ethyl] phosphate as a crosslinker and potassium persulfate as a initiator. In addition, these hydrogels have been used as templates for green synthesis of silver nanoparticles (13.4±3.6 nm in diameter, TMGA-Ag) by using leaf extract of Teminalia bellirica as reducing agent. Swelling kinetics and equilibrium swelling behavior of the TMGA hydrogels have been investigated in various pH environment the maxium % equilibrium swelling behavior observed i.e., 2882±1.2. The synthesized hydrogels and silver nanocomposites have been characterized by the UV, FTIR, XRD, SEM and TEM. TMGA and TMGA-Ag hydrogels have been investigated to study the characteristics of drug delivery and antimicrobial study. Doxorubicin hydrochloride, a chemotherapeutic agent successfully encapsulated with maximum encapulstaion efficiency i.e., 69.20±1.2 and performed in vitro release studies in pH physiological and gastric environment at 37 ℃. The drug release behavior is examined with kinetic models such as zero order, first order, Higuchi, Hixson Crowell, Korsmeyer-Peppas. These release data was the best fitted with the Korsemeyer-Peppas transport mechanism with n=0.91. Treatment effect on HCT116 Cell, human colon cancer cells were assessed with cell viability and cell cycle analysis. Antimicrobial activity of TMGA-Ag hydrogels is studied against to Staphylococcus aureus and Klebsiella pneumonia. Finally, the results demonstrate that TMGA and TMGA-Ag are promising candidates for anti-cancer drug delivery and inactivation of pathogenic bacteria, respectively.

The newly emerged variants of SARS-CoV-2 created a potential threat among societies and highlighted one of the significant concerns in facing the pandemic. SARS-CoV-2 variants harboring mutations in the structural protein, especially in the RBD domain of spike protein, have raised concern about potential immune escape. The spike protein of SARS-CoV-2 play a vital role in infection and is an important target for neutralizing antibodies. The mutations that occur in the structural proteins, especially in the spike protein, lead to changes in the virus attributes of transmissibility, an increase in disease severity, a notable reduction in neutralizing antibodies generated, and thus a decreased response to vaccines and therapy. The immune response against SARS-CoV-2 has been reported mainly through innate immune responses rather than adaptive immune responses. SARS-CoV-2 invades the host's innate immunity, possibly through inducing cytokine storm, impairing type I IFN responses, and suppressing antigens presentation to T cells. Therefore, the adaptive immune response is required to combat SARS-CoV-2 infection. The SARS-CoV-2 infections activated both arms of adaptive immunity; humoral and cell-mediated immunity. The observed multiple mutations in the RBD domain of the spike protein compromised the adaptive immune response and showed immune escape because it increases the affinity of spike protein binding with the ACE-2 receptor of host cells and increases resistance to neutralizing antibodies. Cytotoxic T-cell responses are crucial in controlling SARS-CoV-2 infections from the infected tissues and clearing them from circulation. CD8+cytotoxic T cells identify and directly kill the infected cells by releasing soluble mediators perforin and granzymes. The functional exhaustion of cytotoxic T cells may increase the severity of the disease. The expression of activation markers of T cells could indicate hyperactivation or functional exhaustion; the exact implication is not yet understood in SARS-CoV-2 infections. These mutations at critical residues influence the antigenic profile of SARS-CoV-2, which may evade the immune responses and thus reduce the immunogenicity and efficacy of vaccines. Therefore, the spike protein may be recognized as a primary target for vaccines and drugs. This review article summarizes the impact of mutations in the spike protein of SARS-CoV-2, especially mutations of RBD, on immunogenicity, immune escape, and vaccine-induced immunity, which could potentially contribute to future studies focusing on vaccine design and immunotherapy.

Cancer being a multi-factorial disease, a multi-target approach is needed to treat it with minimal side-effects. Therefore, combined use of a proven herbal formula with chemo-therapy could be the better therapeutic. It may maintain homeostasis by synergistically targeting multiple signaling pathways involved in tumorigenesis, thus circumventing recurrence, resistance, and immunosuppression. Here we aim to determine the effectiveness and safety of an FDA-approved herbal formula, SR033 in combination with 5-fluorouracil (5FU) and paclitaxel (PTX). We report chemo-sensitization and chemo-synergy in HeLa and MCF-7 cell lines, immunomodulation of COX-2, PD-L1, VEGF and TGFβ and overall survival in-vitro and in-vivo via oral and topical delivery of SR033. In-vitro, SR033/5FU significantly reverses 5FU and PTX-resistance, lower COX-2, and PD-L1, resulting in TGFβ/ VEGF axis modulation. Furthermore, the study suggests that due to anti-oxidative potential of SR033, oral administration exerts chemo-preventive activity and restores cellular compartment in solid tumor and ascites-bearing mice models. Furthermore, the topical application, SR033L with low-dose 5FU increases survival rate, reduces tumor growth, and completely restores local and peripheral neutrophil-lymphocyte ratio in solid tumor mice model. The study indicates that SR033 could be a strong candidate in combination with low-dose 5FU and PTX for a holistic approach to cancer management.

Amidst the rapid advancements in artificial intelligence (AI) applications, determining the alignment between AI evaluations and human judgment remains crucial, especially in sectors like finance, healthcare, and education. This study delves into a unique scenario—a teenage entrepreneur pitch competition, mirroring real-world challenges faced by these young innovators. The objective was to assess the congruence between evaluations by ChatGPT, an AI model, and human judges. The results indicate a remarkable alignment between AI and human evaluations across several criteria. The implications of this study are far-reaching, highlighting AI's potential in ensuring unbiased and reliable evaluations in pitch competitions and beyond. However, navigating the juncture of AI and human judgment mandates continued research and ethical considerations.

In this paper, impulsive control on a single-species Markovian jumping ecosystem leads to a stability criterion, and the newly-obtained theorems improve the related existing results. Numerical examples illuminate the effectiveness.

Financial system is essentially chaotic and unstable if there is not any external inputs. By means of Lyapunov function method, design of switching law, novel fuzzy assumption, $L^p$ estimation technique and Laplace semigroup theory, the author presents the boundedness and LMI-based (globally) asymptotical input-to-state stability criteria of financial systems. Particularly, the globally asymptotical stability in the meaning of switching implies that when the time $t$ is big enough, the dynamic of any subsystem must approach its unique equilibrium point. Besides, the global financial crisis often erupts periodically, which illuminates that the global stability in the classical sense is actually meaningless. So the stability in the meaning of switching proposed in this paper is suitable and appropriate. Numerical examples illuminate the effectiveness of the obtained results.

Cellular membranes are essential for compartmentalization, maintenance of membrane permeability and fluidity in all three domains of life. Archaea belong to the third domain of life and have a distinct phospholipid composition. Membrane lipids of archaea are ether-linked molecules, specifically dialkyl glycerol diethers (DGDs) and glycerol dialkyl glycerol tetraethers (GDGTs). Terbinafine has been proposed as an inhibitor of GDGT biosynthesis in Archaea based on radiolabel incorporation studies. Here, we have studied the early cellular responses of the archaeon Sulfolobus acidocaldarius to terbinafine whose membrane is mostly composed of the membrane-spanning GDGTs. Depletion of GDGTs and an accompanying accumulation of DGDs upon treatment with terbinafine was growth phase and dose dependent. Additionally, a major shift in the saturation of caldariellaquinones was observed, resulting in the accumulation of unsaturated molecules. Transcriptomic data indicated that terbinafine primarily targets the respiratory complex along with genes involved in motility, fatty acid metabolism and GDGT cyclization. Combined, these findings suggest that respiratory stress is the preliminary response of S.acidocaldarius to terbinafine while targeting multiple proteins involved in isoprenoid biosynthesis and saturation.

Objective: To compare the incidence of skin abrasions caused by traditional straight edge flip flop straps versus rolled inner seam (RIS) designs in a controlled laboratory setting using preserved fetal pig skin as a proxy for human skin.Methods: This study utilized a custom-built apparatus to apply friction from two flip flop strap designs to preserved fetal pig skin samples. Three trials were conducted for each strap design to determine the time to initial abrasion onset. A paired t-test was employed for statistical analysis.Results: The RIS design demonstrated a statistically significant delay in abrasion onset compared to the standard design across all trials (p < 0.05), indicating a gentler interaction with the skin.Conclusion: The RIS strap design may offer a beneficial alternative to traditional straps by reducing the friction that can lead to skin abrasions, suggesting a potential to decrease the risk of dorsal foot ulcers. Further studies involving human subjects are warranted to validate these findings in a real-world context.Keywords: Flip flop strap design, skin abrasions, dorsal foot ulcers, footwear design, developing countries, diabetic foot care.

Syntrophic acetate oxidation operates close to the thermodynamic equilibrium and very little is known about the participating organisms and their metabolism. Clostridium ultunense is one of the most abundant syntrophic acetate-oxidising bacteria (SAOB) found in engineered biogas processes operating with high ammonia concentrations. It has been proven to oxidise acetate in cooperation with hydrogenotrophic methanogens. There is evidence that the Wood-Ljungdahl (WL) pathway plays an important role in acetate oxidation. In this study we analysed the physiological and metabolic capacities of C. ultunense on genome scale and conducted a comparative study of all known characterised SAOB, namely Syntrophaceticus schinkii, Thermacetogenium phaeum, Tepidanaerobacter acetatoxydans and Pseudothermotoga lettingae. The results clearly indicated physiological robustness beneficial for anaerobic digestion environments and revealed unexpected metabolic diversity with respect to acetate oxidation and energy conservation systems., Unlike S. schinkii and Th. phaeum, C. ultunense clearly does not employ the oxidative WL pathway for acetate oxidation, as its genome (and that of P. lettingae) lack important key genes. In both those species, a proton motive force is likely formed by chemical protons involving putative electron-bifurcating [Fe-Fe] hydrogenases rather than proton pumps. No genes encoding a respiratory Ech hydrogenase, as involved in energy conservation in Th. phaeum and S. schinkii, were identified in C. ultunense and P. lettingae. Moreover, two respiratory complexes sharing similarities to the proton-translocating ferredoxin:NAD⁺ oxidoreductase (Rnf) and the Na+ pumping NADH:quinone hydrogenase (NQR) were predicted. These might form a respiratory chain involved in reduction of electron acceptors other than protons. However, involvement of these complexes in acetate oxidation in C. ultunense and P. lettingae needs further study. This genome-based comparison provides a solid platform for future meta-proteomics and meta-transcriptomics studies and for metabolic engineering, control and monitoring of SAOB.

Abstract Purpose – The purpose of this study is to investigate the impact of spiritual leadership on knowledge hiding behavior of employees in agriculture research institutes of Khyber Pakhtunkhwa, Pakistan. The study also aims to analyze theoretically and then investigate empirical evidence concerning the mediation pathway (namely, professional commitment) to clarify the significant association amid spiritual leadership and subordinate's knowledge hiding behavior. Design/methodology- The cross-sectional data using five point lickert scale was used in this survey based study to investigate the given hypotheses. In order to account for the prim effect and any potential for common method bias, data was gathered at two different time points with a gap of four weeks. Smart PLS4 was used to assess a sample of 298 complete and valid responses for hypotheses testing. Findings – The results show that spiritual leadership impacts negatively the employees’ knowledge hiding behavior. Moreover, this relationship is mediated by professional commitment. Originality/value – First, in contrast to the majority of past studies that concentrated on antecedents of knowledge sharing, the present study examined the impact of spiritual leadership on employee’s knowledge hiding behaviors, which are two opposing concepts. Secondly, the study examined empirically, the mediation effect of professional commitment. These three variables have not been studied together previously.

Risk assessment management have been a hot topic for the researchers since a very long time. Software risk management is an important part of project management as it contains the identification, analysis, estimation and monitoring of different risks present in the system. This helps developers in decision making while assessing the problems that could arise in the software systems. Risk management is very complex in large scale system as these systems have very complex development. The paper describes risk management techniques for large scale system. Furthermore we have provided a detailed comparative analysis of these techniques with commonly identified risks in software systems and have provided a systematic order for risk management process to ensure risk mitigation.

The benefits of event-driven architecture (EDA) derive from how systems and components are loosely coupled, which can facilitate independent development and deployment of systems, improved scaling and fault tolerance, and integration with external systems, especially in comparison to monolithic architectures. With the advent of new technologies such as containers, and microservices, a new generation of distributed event streaming platforms are commonly used in event-driven architecture for efficient event-driven communication. However, the asynchronous and distributed nature of EDA poses several problems that include handling failures, the dependence of an end-to-end transaction on individual component stability, etc. In this paper, we describe a new approach to designing self-regulating distributed applications with autopoietic and cognitive workflow management. This approach is based on the new science of information processing structures derived from the General Theory of Information. Just as a genome enables self-organizing and self-regulating biological structures, a digital genome enables a specific software application with several components, the ability to use distributed resources and self-regulate the evolution of the system based on functional and non-functional requirements, and best-practice policies that maintain the stability, safety, and survival under non-deterministic fluctuations in the demand for resources. In addition, cognitive workflow management assures end-to-end transaction delivery.

The escalating global population and subsequent demand for agricultural products have led to a surge in agricultural waste generation, posing significant disposal challenges. Conventional disposal methods such as burning and dumping not only harm the environment but also jeopardize human health and safety. Recognizing the urgent need for sustainable waste management, researchers have increasingly focused on repurposing agricultural plant waste as a valuable resource. This paper presents a comprehensive review of the potential of agricultural plant waste in wealth creation and sustainable development. It highlights the detrimental impacts of current disposal methods and emphasizes the necessity for alternative approaches. By analyzing the physical, mechanical, and chemical properties of plant fibers, particularly cellulose, hemicellulose, and lignin, this review underscores their suitability for diverse applications. Moreover, it explores the emerging trend of utilizing pineapple leaf fiber, a sustainable and lightweight material, in structural applications such as UAV construction. With its exceptional mechanical properties and biodegradability, pineapple leaf fiber holds promise as a viable alternative to traditional materials, contributing to a more sustainable future. In conclusion, this review advocates for a paradigm shift towards embracing agricultural plant waste as a valuable asset for economic prosperity and environmental sustainability. It underscores the importance of continued research and technological advancements to unlock the full potential of agricultural waste in fostering a circular economy and driving sustainable development globally.

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

Recently, different extensions of the fractional derivative operator are found in many research papers. The main aim of this paper is to establish an extension of the extended Caputo fractional derivative operator. The extension of an extended fractional derivative of some elementary functions derives by considering an extension of beta function which includes the Mittag-Leffler function in the kernel. Further, an extended fractional derivative of some familiar special functions, the Mellin transforms of newly defined Caputo fractional derivative operator and the generating relations for extension of extended hypergeometric functions also presented in this study.

The main objective of this paper is to introduce k-fractional derivative operator by using the definition of k-beta function. We establish some results related to the newly defined fractional operator such as Mellin transform and relations to k-hypergeometric and k-Appell's functions. Also, we investigate the k-fractional derivative of k-Mittag-Leffler and Wright hypergeometric functions.

The main objective of this present paper is to establish the extension of an extended fractional derivative operator by using an extended beta function recently defined by Parmar et al. by considering the Bessel functions in its kernel. Also, we give some results related to the newly defined fractional operator such as Mellin transform and relations to extended hypergeometric and Appell's function via generating functions.

Most of the research in data mining concentrated on finding the positive associations that exist in frequent patterns. Frequent patterns have no bearing on the time duration and, therefore, lose interestingness. The Regularity of an itemset is related to the time at which the transaction occurred and the time distance between the transactions. Frequent Regular item sets can be generated considering the Frequency and the Regularity of the occurrence of the Item Sets. The frequent and regular Item sets will be so huge that they cannot be handled in real time. The count of Frequent and regular item sets gets reduced drastically when the properties “closed” and “Maximality” are applied. Finding negative associations among such patterns are important in the field of medicine. Many drugs administered to cure some diseases have counter reactions leading to many other diseases. Negative associations among the mined patterns are important as they reveal significant contradictions frequently among medical drugs administered by medical practitioners to cure some diseases. This paper proposes a method that mines medical databases to find regular, frequent patterns applied with closed and maximal properties and find negative associations among those patterns. The proposed algorithm is accurate to 98% and performs 9% more efficiently than the other algorithms presented in the literature.

Periodontal diseases and Thalassemia Major (TM), which are two chronic inflammatory diseases, are associated with increased levels of proinflammatory cytokines in bio-fluids. Interleukin (IL)-6 and IL-8 have been identified as potent proinflammatory biomarkers in periodontal diseases as well as in patients with β-thalassemia (TM-β). This suggests that patients with TM-β and gingival inflammation have two chronic inflammatory conditions, each of which may affect the other. The objective of this pioneer research was to evaluate the effect of oral hygiene maintenance and care on serum levels of IL-6 and IL-8. 31 participants selected after screening and were given Non-surgical periodontal therapy(NSPT). IL-6 and IL-8 were evaluated by enzyme linked immunohistochemistry assay (ELISA) at baseline and compared at 6 weeks after NSPT. Statistical analysis was done with SPSS v23. The comparison from the baseline to 6-weeks follow-up for both IL-6 and IL-8 was done using Wilcoxon Signed rank test. The descriptive statistics are represented as median and inter quartile range (IQR). Highly significant improvement (p-&lt;0.000) was found for IL-6 and IL-8 when compared at 6 weeks from baseline. Results provide a gateway to new modality in lowering systemic burden of the disease, thereby reducing morbidity, via local measures or oral care.

Selenium (Se) is an essential trace element for the health and immunity of cattle. Double-muscled Belgian Blue cows are well known to be prone to nutritional deficiencies. Colostrum Se level is also a key factor to promote immunoglobulin intake in young calves. The main objectives of this study were to assess (1) the plasma and colostrum Se statuses of properly-supplemented Belgian-Blue cows on commercial farms and (2) the relationship between Se concentrations in plasma and colostrum. Secondary objectives were to assess relationships between plasma or colostrum Se concentrations and dietary Se supplementation as well as blood biomarkers. Blood and colostrum samples were collected from 49 Belgian Blue cows on 5 commercial farms in Belgium. They received 5 different rations with Se supplementation ranging from 0.5 to 2 ppm including 20% to 83% of organic form. Results showed that average Se concentration was 90 +- 15 µg/L in plasma and 79 +- 26 µg/L in colostrum, consistent with previous studies on well-supplemented cows. No relationship was observed between Se concentrations in plasma and colostrum, suggesting that colostrum Se testing would be a complementary indicator for improving calf Se supplementation. Relationships between plasma or colostrum Se and dietary Se or blood biomarkers emphasized the complexity of Se metabolism in observational studies under field conditions.

The purpose of this study is to explore conceptual approaches in co-production studies and to examine current research trends of the study. The conceptual paper includes research articles related to co-production in public administration field. By thoroughly scrutinizing 32 research works of co-production, this study highlights major loopholes in the field of the study. The contributions of the study are: (1) identifying two common characteristics of co-production, (2) categorising three types of co-producing by end-users, and (3) finding that goals and success of co-production are more beneficial for service providers though its initial approach is citizen-centric approach. We suggest that future studies should be (1) to focus on reasons for co-production failures or success, (2) to discover further hindrances for co-production in service production, (3) to examine influencing factors on service providers as well as institutional impacts on co-production process, and (4) to include practical assessment in co-production study.

Background: Trichomoniasis is the most common non-viral STI globally and yet is not a reportable disease. Trichomonas Vaginalis is an important source of reproductive morbidity and may increase risk of acquisition and transmission of HIV. WHO and CDC recommend various regimens of Nitro-Imidazoles for treatment. The common Nitro-Imidazoles used for Trichomoniasis are Metronidazole and Tinidazole, which vary in their cost, efficacy and side effect profile and it is relevant to study these factors, for better management of the patients. Objectives: This study aims to compare and study the efficacy, compliance of various treatment regimens, their outcomes and side-effects for Trichomoniasis, among STI clinic attendees in Trinidad. Methods: A clinical trial study was designed and after obtaining the informed consent a routine clinical examination was conducted and the swabs for Trichomoniasis tests were collected for diagnosis from the 692 participants. Out of 692 participants, Eighty two (82) patients with established diagnosis of Trichomonas infection were quasi-randomly treated using different regimens. Compliance to treatment, side effects and outcome were evaluated. Results: The prevalence of the Trichomoniasis in population attending our STI clinic is 11.9% and prevalence of HIV is 9%. Of the total 82 participants for the treatment, 80% were females; nearly 90% of the patients belonged to age group 15-45 years and over 60% were below 30 yrs. Among those diagnosed for Trichomonas vaginalis, 13.3% had associated HIV infection. The compliance with respect to single dose treatment was significantly better than the long duration oral regimen and has significant relation with side effects of the treatment. The outcome is generally better and comparable and shows no significant difference between different treatment regimens used in the study. Conclusions: Metronidazole and Tinidazole are commonly used drugs in various regimens. compliance is better with those treated with Tinidazole and Metronidazole stat, than with other groups. Outcome is comparable between these regimens, especially when combined with other important factors like abstinence and treatment of the partners. The treatment regimens mainly differ in the compliance and side effects profile, which suggests that to improve the compliance the drugs with less side effects, short course regimen would be a preferred choice.

Duckweed (Lemnaceae) rises as a crucial model system due to its unique characteristics and wide-ranging utility. The significance in physiological research and phytoremediation highlights the intricate potential of Duckweed in the current era of plant biology. Special attention to Duckweed has been brought due to its distinctive features of nutrient uptake, ion transport dynamics, detoxification, intricate signaling, and stress tolerance. In addition, Duckweed can alleviate environmental pollutants and enhance sustainability by participating in bioremediation processes and wastewater treatment. Furthermore, insights into the genomic complexity of Lemnaceae species and the flourishing field of transgenic development highlight the opportunities for genetic manipulation and biotechnological innovations. The novel methods for germplasm conservation of Duckweed can be adopted to preserve genetic diversity for future research endeavors and breeding programs. This review centers on prospects in Duckweed research promoting interdisciplinary collaborations and technological advancements to drive its full potential as a model organism.

The Necessity of Human resources beyond perception of human understanding towards the Evaluation of video quality or Data screening methodology is conducted based on human perception level since it is concerned with how visual content is perceived by a observer based on observations with his/ her perception on a particular video sequence. Therefore, we considered that the subject has to grade the encoded video sequences under certain test environment conditions based on ITU-Recommendations. Since Human perception is considered as the true judgment and precise measurement of visual content, data screening has became quite essential and quite comfortable to general public due to introduction of User Experience(UX) concept by User Experience community. The translations of A recurrent neural network is based on certain principles, for instance we considered natural language processing which is certainly adaptable towards understanding sequential data and use patterns to predict the consistency within observers. In our research, we adapted principles based on Recurrent Neural Networks while assuming consistency within observers for predicting video quality within data screening environment towards subjective experiments. Moreover,this research work explores the trade offs between Human perception on visual content and consistency of observations within individual observer.

This investigation aimed to knowledge of the presence of Brucella spp .in the samples from Babylon province and the relationship with environmental factors. The seasonal collection of animal soil samples for various villages in the middle, south, and north of Babylon province, Hilla City, Iraq, with physicochemical parameters (air and soil temperature, pH, EC, TDS, salinity, TOC), as well as heavy metals (Fe, Cu, Cd, and Pb). The results showed the presence of Brucella species in some of soil samples and its percentage varies according to the four seasons, where the highest percentage of Brucella spp. was in summer season and the lowest in winter season and these percentages were related to seasonal environmental indicators. After sending the positive bacterial samples isolated from the soil for sequencing analysis, it was found that the bacterial samples isolated from the soil have new strains recorded in Genbank as following: five strains for Brucella intermedia and one strain for Brucella rhizosphaerae, Also, phylogenetic analysis was for the drawing of phylogenetic tree and the determination of phylogenetic relationship for Knowledge the degree of convergence between species and their source. Through our research, we concluded that proportion of Brucella spp. presence in summer was high and also environmental factors which included (Air and Soil temperature, pH, EC, TDS and Salinity) were high, except TOC was lower in summer season whereas heavy metals for Cu, Fe, Cd were lower in summer except Pb was high. This indicated that differences of seasons affect to Brucella ratio in soil. The findings of sequencing for the bacterial samples from soil demonstrated that B. intermedia was found in both the spring and fall seasons while B.rhizosphaerae found in the spring season , These types of Brucella had genetic variations. Also concluded that during phylogenetic analysis of the 16S rRNA sequences indicated that B.intermedia was nearest to Pakistan and other strain of B.intermedia was nearest to Russia while B.rhizosphaerae was nearest to Ukraine and Pakistan.

Concrete being one of the most frequently used construction materials in the field of construction. The use of concrete has increased with the passage of time as the more and more construction activities are taking place around the globe. Different materials have been added in to the concrete mix to provide the concrete more strength and make it more durable and long lasting. This paper looks into a detailed comparison of concrete strength when casted with in the presence of carbon fibers, steel fibers and glass fibers. The compressive strength for cubes and cylinder with carbon fibers is the highest as compare to steel fibers as well as glass fibers. The compressive strength of concrete is measured on 3 day, 7 day, 14 day and 28 day strength. The compressive strength of cube on 28th day strength is 37.52 MPa for carbon fiber steel, for steel fiber it is 34.22 MPa and with glass fiber the strength is 29.335 MPa whereas the compressive strength of cylinder with carbon fibers the 34.88 MPa, with steel fiber it is 34.22 MPa and with glass fiber the strength is 27.447 MPa. For the cubes the maximum difference between different fibers inducted concrete sample is 28% approximately and for cylinder it is 27%, respectively. On the other hand, the tensile strength of concrete was also gained with the carbon fiber. It was determined that carbon fiber provided about 16% and 12.8% tensile strength higher than glass and steel fibers respectively.

Gene editing is now easy to make new disease models through in-vivo and in vitro tests. It can potentially be used to make animals with single-gene or multiple-gene changes. The mutant strains with changed germlines are no longer needed with in vivo gene editing, which uses the CRISPR-Cas9 system to target cells of interest in their normal tissues. Whereas, the AAVs and other viral vectors have made it possible to change cells selectively. Gene editing hade made it possible to use human induced pluripotent stem cells (iPSC) to model diseases that run in families. Researchers can compare and contrast the human genomes of many different ethnic and racial groups using this method. Scientists may be able to make a disease in a lab dish using iPSCs from a patient. Using CRISPR, iPSCs made from patient cells can be fixed if they have certain problems. This shows that gene therapy is possible and shows what happens when cells aren't working right. The fact that CRISPR-Cas9 can change the DNA by just one nucleotide has had a huge effect on biological studies. CRISPR is becoming more and more popular, which shows how useful, easy, and effective it is. With the broad use of CRISPR-based apps, the tool is now used for much more than just changing genes. This method can be used to screen the whole genome, control the translation of genes based on their sequence, and edit several genes at the same time. Scientists can now model diseases in different species and learn more about how genes work because of these advances. Genome-wide association studies and genome-editing tools like CRISPR are giving us a good look at the future of personalized medicine.

With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

Abstract:Background: Foot ulcers, especially dorsal foot ulcers caused by straps in footwear, can lead to discomfort, pain, and severe complications, particularly in individuals with diabetes and leprosy. While plantar foot pressure measurements have been extensively studied, research on the impact of strap-induced pressure on the dorsal foot aspect remains limited.Research question: The main research question of this prospective study is: Does the implementation of a rolled inner seam strap design (RIS) in flip-flops result in reduced pressure at the toehold area compared to traditional strap designs (Control) in healthy volunteers?Methods: We studied healthy female volunteers, with 10 feet (5 left and 5 right) per arm of the study. Two types of flip-flops, RIS and Control, were compared in a standardized 5-step walk trial on a hardwood floor. Pressure at the toehold was measured using Pressure Sensitive Switches with portable, accurate, ultra-thin pressure sensors. Paired t-tests compared the maximum pressure at the toehold between RIS and Control flip-flops (p < 0.05).Results: The study revealed a statistically significant difference in pressure at the toehold between the RIS and Control flip-flops (p = 0.0014). The mean difference of -40.90 grams (95% CI: -61.22 to -20.58) indicated that the RIS exerted significantly lower pressure than the Control flip flops. The RIS group had a mean pressure of 29.30 grams. The Control group had a mean pressure of 70.20 grams at the toehold area.Significance: The findings of this study have significant clinical implications for individuals at risk of foot ulcers, particularly those with neuropathic conditions such as diabetes and leprosy. The implementation of the RIS design in flip-flops may offer a practical and cost-effective solution to reduce strap-induced pressure and mitigate the risk of strap-related foot ulcers, especially in resource-limited settings where comprehensive foot care is lacking.

The General Theory of Information (GTI) tells us that information is represented, processed and communicated using physical structures. The physical universe is made up of structures combining matter and energy. According to GTI, “Information is related to knowledge as energy is related to matter.” GTI also provides tools to deal with transformation of information and knowledge. We present here, the application of these tools for the design of digital autopoietic machines with higher efficiency, resiliency and scalability than the information processing systems based on the Turing machines. We discuss the utilization of these machines for building autopoietic and cognitive applications in a multi-cloud infrastructure.

Knowledge processing is an important feature of intelligence in general and artificial intelligence in particular. To develop computing systems working with knowledge, it is necessary to elaborate means of working with knowledge representations (as opposed to data) because knowledge is an abstract structure. There are different forms of knowledge representations derived from data. One of the basic forms is called a schema. The goal of this paper is the development of theoretical and practical tools for processing schemas. To achieve this goal, we use schema representations elaborated in the mathematical theory of schemas and use structural machine as a powerful theoretical tool for modeling parallel and concurrent computational processes. We describe the schema of autopoietic machines as physical realizations of structural machines. An autopoietic Machine is a technical system capable of regenerating, reproducing and maintaining itself by production, transformation and destruction of its components and the networks of processes downstream contained in them. We present the theory and practice of designing and implementing autopoietic machines as information processing structures integrating both symbolic computing and neural networks. Autopoietic machines use knowledge structures containing the behavioral evolution of the system and its interactions with the environment to maintain stability by counteracting fluctuations.

A new generation of universal tools and languages for modeling and simulation multi-physical domain applications emerged and became widely accepted, which generate large-scale systems of differential algebraic equations (DAEs) automatically. Motivated by the characteristics of DAEs systems with large dimension, high index or block structures, we first propose a modified Pantelides’ algorithm (MPA) for any high order DAEs based on its Σ matrix, which is similar to Pryce’s Σ method. By introducing a vital parameter vector, a modified Pantelides’ algorithm with parameter has been presented.It leads to a block Pantelides’ algorithm (BPA) naturally which can immediately compute the crucial canonical offsets for whole (coupled) systems with block-triangular form. We illustrate these algorithms by some examples. And numerical experiments show that the time complexity of BPA can be reduced by at least O(ℓ) compared to the MPA, which is mainly consistent with the results of our analysis.

As reported by us and others previously (1, 2), the D614G mutation appeared in the spike glycoprotein (SPG) of the SARS-CoV-2 (the pathogen behind COVID-19) at the early stages of the pandemic and then G614 containing variant of SARS-CoV-2 became the predominant strain in most human populations across the world. However, one of the most recent reports from India (3) stated the incidence of G614 to be only 26% in the Indian population. This report is contradictory to the information available through the GenBank (4) SARS-CoV-2 sequence deposits made by various laboratories from India. The above stated report currently circulating in the Indian media is likely to create a public perception that the Indian strain is less contagious and such a notion could be harmful to people’s welfare. In view of this concern we have re-evaluated, updated and recalculated the incidence of the G614 variant in the Indian population by analyzing 395 Indian SARS-CoV-2 genomic sequences available in the GenBank as of June 26, 2020. In our analysis we have categorized the samples by the month in which the samples were collected. We have used an alignment-free software tool named Compare (5, 6), and the Basic Local Alignment Search Tool (BLAST) (7) in the present analysis. We finally inspected each of the 395 sequences physically for the presence of aspartic acid (D) or glycine (G) at the 614^th position of the spike glycoprotein. We analyzed an Australian cohort in parallel for comparison. We have found that the prevalence of G614 variant in the Indian samples for the month of June 2020 is 90.6%. The trends are similar with the Australian samples.

ChatGPT, a groundbreaking natural language processing technology released just three months ago, has attracted significant attention due to its remarkable capabilities. This AI milestone has urged researchers, industry, decision-makers, and governments to examine this technology, including its implications, threats, and benefits. Despite the short period since its release, several researchers have examined ChatGPT from different perspectives. This paper presents a comprehensive review of ChatGPT, highlighting its technical novelties compared to previous models and analyzing existing research from various perspectives. We followed a rigorous methodology to conduct a critical review of existing research on ChatGPT and developed a taxonomy for the different areas of study. Additionally, we identify future challenges and research trends associated with ChatGPT. Our paper is the first critical review of ChatGPT literature, providing valuable insights for practitioners and policymakers. This paper serves as a reference for researchers seeking to advance research on ChatGPT, including its applications and development.

Against the backdrop of the current carbon peaking and carbon neutrality policies, higher requirements have been put forward for the upgrading and construction of smart grids. Non-intrusive load monitoring (NILM) technology is a key technology for advanced measurement systems at the end of the power grid. It obtains detailed power information of the load, without the need for traditional hardware deployment. The key step to solve this problem is load decomposition and identification. This paper first utilized Long Short Term Memory-Denoising Autoencoder(LSTM-DAE) to decompose the mixed current signal on the household busbar and obtain the current signals of multiple independent loads that constituted the mixed current. Then, the obtained independent current signals were combined with voltage signals to generate multi-cycle colored Voltage-Current(VI) trajectories, which were color-coded according to the background. These colored VI trajectories with background colors formed a feature library. When the CNN network was used for load recognition, considering the influence of hyperparameters on recognition results, the BOA algorithm was used for optimization, and the optimized CNN network was employed for VI trajectory recognition. Finally, the proposed method was validated using the PLAID dataset. Experimental results showed that the proposed method exhibited better performance in load decomposition and identification.

The enzymology of the key steps in the archaeal phospholipid biosynthetic pathway has been elucidated in recent years. In contrast, the complete biosynthetic pathways for proposed membrane regulators consisting of polyterpenes such as carotenoids, respiratory quinones and polyprenols remain unknown. Notably, the multiplicity of geranylgeranyl reductases (GGRs) in archaeal genomes has been correlated with the saturation of polyterpenes. Although GGRs, which are responsible for the saturation of the isoprene chains of phospholipids have been identified and studied in detail, there is little information regarding the structure and function of the paralogs. Here, we discuss the diversity of archaeal membrane-associated polyterpenes which is correlated with the genomic loci, structural and sequence-based analyses of GGR paralogs.

Monkeypox, once a rare zoonotic disease, was endemic to some African countries since its original identification among humans in 1970. Since then, cases in non endemic regions were linked to returning travelers or those who had contact with transported animals. The causative agent, Monkeypox virus, belongs to Orthopoxviruses, the same family as Variola; the causative organism for smallpox. Although most Monkeypox outbreaks until recently were linked to zoonotic transmission, secondary human-human transmission in smallpox unvaccinated individuals was observed in a small proportion of overall cases. Smallpox was declared eradicated in 1980 and since its eradication, monkeypox virus is the most significant poxvirus to cause human disease. The 2022 monkeypox outbreak marks a significant paradigm shift in the human and poxvirus association, with new modes of transmission, concerns of viral evolution and entrenchment as a sexually transmitted disease. Monkeypox clinically resembles smallpox but is far milder. At this time there are no approved therapies for monkeypox and antiviral agents effective against smallpox are being utilized. Additionally, preventive strategies being utilized include smallpox vaccination like JYNNEOS and ACAM2000. In this narrative review, we discuss the virology, epidemiology, transmission, clinical manifestations, diagnosis, management and prevention strategies associated with monkeypox.

Increased smart devices in various industries is creating numerous sensors in each of the equipment prompting the need for methods and models for sensor data. Current research proposes a systematic approach to analyze the data generated from sensors attached to industrial equipment. The methodology involves data cleaning, preprocessing, basics statistics, outlier, and anomaly detection. Present study presents the prediction of RUL by using various Machine Learning models like Regression, Polynomial Regression, Random Forest, Decision Tree, XG Boost. Hyper Parameter Optimization is performed to find the optimal parameters for each variable. In each of the model for RUL prediction RMSE, MAE are compared. Outcome of the RUL prediction should be useful for decision maker to drive the business decision; hence Binary classification is performed, and business case analysis is performed. Business case analysis includes the cost of maintenance and cost of non-maintaining a particular asset. Current research is aimed at integrating the machine intelligence and business intelligence so that the industrial operations optimized both in resource and profit.

Globally 30% of landslides occur in the northeastern part of India [1]. One of the major earthquake events in Sikkim, India occurred on 18th September 2011 (Mw 6.9) led to over 300 landslides and 122 human deaths [2]. These landslides not only controlled by natural disasters but initiated due to human activities. The present study considered Lungchok landslide occurred in south district of Sikkim due to 2011 seismic event. The study focused on the failure mechanism of the landslide based on finite element analysis by adopting eight different cases. The deformation characteristic was investigated for dry and saturated slope conditions under static and dynamic behavior considering vehicle loads using GeoStudio software. The FEM analysis has been carried out using load deformation and linear elastic. The analysis shows that the failure of the slope was not sudden due to the 2011 earthquake event, but progressive failure was observed with time and construction activity. The paper demonstrates that, an increase in infrastructure development including construction by hill cutting increased the initiation of landslide with soil erosion. The cracks developed after 2011 earthquake event led to further deformations during future disasters required effective stabilization measures.

The SARS coronavirus 2 (SARS-CoV-2) is the causative agent of the 2019 coronavirus disease (COVID-19) pandemic that has executed 6.9 million people and infected over 765 million. It’s become a major worldwide health alarm and is also known to cause abnormalities in various systems, including the hematologic system. COVID-19 infection primarily affects the lower res-piratory tract and can lead to a cascade of events, including a cytokine storm, intravascular thrombosis, and subsequent complications such as arterial and venous thromboses. COVID-19 can cause thrombocytopenia, lymphopenia, and neutrophilia, which are associated with worse out-comes. Prophylactic anticoagulation is essential to prevent complication and death rate associated with the virus's effect on the coagulation system. It is crucial to recognize these complications early and promptly start therapeutic anticoagulation to improve patient outcomes. While rare, COVID-19-induced disseminated intravascular coagulation exhibits some similarities to DIC induced by sepsis. LDH, D-dimer, ferritin, and CRP biomarker are often increase in serious COVID-19 cases and poor prognosis. Understanding the pathophysiology of the disease and identifying risk factors for adverse outcomes is critical for effective management of COVID-19.

Globally, seagrass meadows provide critical ecosystem services. However, seagrasses are globally degraded at an accelerated rate. The lack of information on seagrass spatial distribution and seagrass health status seriously hinders seagrass conservation and management. Therefore, this study proposes to combine remote sensing big data with a new hybrid machine learning model (RF-SWOA) to predict potential seagrass habitats. The multivariate remote sensing data is used to train the machine learning model, which can improve the prediction accuracy of the model. This study shows that a hybrid machine learning model (RF-SWOA) can predict potential seagrass habitats more accurately and effectively than traditional models. At the same time, it has been shown that the most important factors influencing the potential habitat of seagrass in the Hainan region were the distance from land (38.2%) and the depth of the ocean (25.9%). This paper provides a more accurate machine learning model approach for predicting the distribution of marine species, which can help develop seagrass conservation strategies to restore healthy seagrass ecosystems.

Using the OCT platform, we can generate high-resolution data on retinal nerve fiber layer (RNFL) thickness in a given eye at a large number of angular-points defined on a circular coordinate system. However, insightful characteristics of OCT RNFL data such as its circularity and granularity generally remain unutilized in its common downstream statistical analyses. We present a new circular statistical framework that defines an Angular Decay function and thereby provides a directional representation of an eye in terms of its focused RNFL loss. By applying to a clinical cohort of Asian Indian eyes, the generated circular data was modeled with a finite mixture of von Mises distributions, which led to unsupervised identification in different age-groups of 2 recurrent clusters of glaucomatous eyes with distinct directional signatures of RNFL decay. New indices of global and local RNFL loss were computed for comparing the structural differences between these glaucoma clusters across the age-groups.

Sterol homeostasis in mammalian cells and tissues involves balancing three fundamental processes: de novo sterol biosynthesis; sterol import (e.g., from blood-borne lipoproteins); and sterol export. In complex tissues, composed of multiple different cell types (such as the retina), import and export also may involve intra-tissue, inter-cellular sterol exchange. Disruption of any of these processes can result in pathologies that impact the normal structure and function of the retina. Here, we provide a brief overview of what is known currently about sterol homeostasis in the vertebrate retina and offer a proposed path for future experimental work to further our understanding of these processes, with relevance to the development of novel therapeutic interventions for human diseases involving defective sterol homeostasis.

Background: People with Williams syndrome (WS) are characterized with hypersociability, fluency in languages, and advantageous face-processing skills, leading to the proposal of a social module. Previous studies on the mentalizing abilities of people with WS using two-dimensional pictures and mindreading from eyes, including normal-like, delayed, and deviant behaviors, have yielded mixed results. This study thus examined the mentalizing ability of people with WS through structured computerized animations of false belief tasks to investigate whether inferences about other people’s minds can be improved in this population. Method: Participants were shown animations with unexpected location and content changes. After viewing each animation, participants had to answer four types of questions: character identification, reality, memory, and false belief. Their responses were recorded and analyzed. Results: Comprehension of false belief was observed in 4-year-old healthy children, whereas children with WS showed unsuccessful comprehension of false belief (until they attained a mental age of 5.3 years), suggesting an improvement in theory of mind resulting from viewing structured computerized animations. This age is earlier than that reported by previous studies for using theory of mind to pass false belief tests (8.5 years old), even challenging the age at which individuals failed to pass the tests (12.10 years old). Conclusions: Structured computerized animations enhanced the mentalizing ability of people with WS to a certain extent. Compared to the typically developing controls, people with WS presented with a lower developmental level in processing false belief tasks. The educational implication of this study is to develop computerized social skills interventions for people with WS.

The developed microstructure features a long with mechanical properties in vacuum brazing of commercially pure Ti-alloy using Ti₂₀Zr₂₀Cu_60-x-Ni_x (x=10, 20, 30, 40 and 50) metallic filler. Brazing temperatures and holding times employed in this study were 1240-1279 K (967-1006^oC) for a period of 10 min, respectively. The mechanical properties of brazed joints were evaluated by nanoindention at a constant peak load of 5000 μN and tensile tests. The number of intermetallic phase, such as NiTi₂, Ti₂Cu, (Ti, Zr)₂Cu, (Ti, Zr)₂Ni, β(Ti, Zr), α-Ti and NiTi. The solid solution matrix have been identified at 1279 K out of these different regions the NiTi₂ rich region had the highest nanohardness of 17 GPa, It is interesting to note that among five different glasses, the Ti₂₀Zr₂₀Cu₁₀Ni₅₀ has the highest yield strength of 17 GPa, which is mainly due to NiTi₂ phase. Based on the tensile test results all cracks propagate along the brittle intermetallic compounds like NiTi₂ in the reaction layer the reduction of the strength of the joints and fracture behaviour upon propagation of the crack, which shows the morphological cleavage including facets characteristics.

Neurological disorders, such as amyotrophic lateral sclerosis, Parkinson’s disease, and Alzheimer’s disease, are commonly associated with persistent neuro-inflammation, and there is an urgent need to discover new therapeutic agents that may target the various pathways involved in neurodegeneration. In this study, we investigated the therapeutic potential of folecitin, a flavonoid isolated from Hypericum oblongifolium, against lipopolysaccharide (LPS)-induced oxidative stress associated with neurodegeneration, amyloidogenic Aβ production pathway, and memory dysfunction in mice. LPS was administered i.p. at 250 µg/kg/day for 3 weeks, followed by the administration of folecitin at a dose of 30 mg/kg/day for the last two weeks. A Western blot technique was used to assess the expression of different proteins involved in oxidative stress, neurodegeneration, and neuronal synapse. Results indicated that folecitin significantly reduced LPS-induced apoptotic neurodegeneration, including the expression of BAX, Caspase-3, and PARP-1 proteins, inhibited BACE1, and the amyloidogenic Aβ production pathway. Folecitin improved both pre- and post-neuronal synapse, as well as memory dysfunction. Furthermore, folecitin significantly activated endogenous antioxidant proteins such as Nrf-2 and HO-1 via stimulating the phosphorylation of Akt proteins. These findings suggest that folecitin may be a suitable lead to design new drugs for neurotoxin-triggered neurodegenerative disorders.

This review paper aims to understand the current processing plant maintenance systems and further identify on-stream phased array corrosion mapping (PACM) to reduce turnaround maintenance (TAM) activity during plant operations. Reducing the TAM duration and extending the TAM interval are common goals of most researchers. Thus, a detailed review was performed to understand the maintenance systems and the problems faced. Furthermore, a review of the current PACM application and the possibility of applying it during on-stream inspection was also per-formed. PACM has better detectability for localized corrosion, and the results can be obtained for a range of thicknesses, which is the main advantage of this method. The challenge is to apply it to a surface with high temperatures, owing to the greater probe contact and the limitation of ultrasonic properties. This literature review provides a more precise direction for future research to evaluate PACM on piping that can be utilized in inspection during plant operation at elevated temperatures. Detecting and monitoring corrosion growth without shutdown.

Abstract. Graphene family (GF)-Calcium phosphate (CaP) composites, holds great potential as components of bone regeneration materials. GF nanomaterials can be modified physically as well as chemically, which are to be biomimetic, mechanically to be hard due to their capability to support exceptional mechanical, thermal and electronic properties. These biocompatible GF composites coated on the calcium phosphate can enhance and tolerate stem cell growth and differentiation, proliferation into various lineages or interact with bioorganisms. The development in CF-CaP materials and their physicochemical/biointeractions and strategies towards osteoblasts is a great concern to promote faster healing and reconstructions of large bone imperfections or defects. In this review, we explain the influence of the CF-CaP and summarize recent developments on designing CF-CaP architectures as multifunctional bone regeneration platforms. We have also explored the typical biological applications concerning these CF-CaP based bioactive nanocomposites. Furthermore, the future viewpoints and evolving challenges will also be emphasized. Due to the shortage of full-length reviews in this emerging research field, this review would be caught great attention and encourage various new chances across a wider range of disciplines.

The main aim of this paper is to establish two generalized integral formulas involving the extended Mittag–Leffler function based on the well known Lavoie and Trottier integral formula and the obtain results are express in term of extended Wright-type function. Also, we establish certain special cases of our main result.

: Mistrust, amplified by numerous artificial intelligence (AI) related incidents, has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle this challenge. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced to XAI. However, many deficiencies, particularly lack of explanation assessment methods and uncertainty quantification, plague this young field. In this paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as marker for anomalies expanding the prognostic explanation scope to include model’s confidence. Also, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of PHM-XAI publications. The quality of the explanation is finally examined employing local accuracy and consistency properties. The method is tested on real-world gas turbine anomalies and synthetic turbofan data failure prediction. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.

This study explores the correlation between payment statistics and sector-wise Gross Value Added (GVA) in the Indian economy from 2011 to 2022, assessing the importance of payment and settlement systems. Through regression analysis, the impact of payment statistics on GVA across diverse sectors is assessed. The results indicate that, during the specified period, payment statistics exhibit a significant influence on sector-wise GVA. These findings underscore the pivotal role of payment and settlement systems in the Indian financial landscape.

In this study, we investigate the existence of positive solutions within a system of Riemann-Liouville fractional differential equations that incorporate the (r1,r2,r3)-Laplacian operator while being subject to three-point boundary conditions. These equations incorporate various fractional derivatives and are influenced by parameters represented as (ψ1,ψ2,ψ3). Our approach involves employing techniques such as cone expansion and compression of the functional type, in conjunction with the Leggett-Williams fixed point theorem, to establish the existence of positive solutions. To emphasize the practical significance of our findings in the realm of fractional differential equations, we provide two illustrative examples.

Pakistan being an agricultural country is raising 146.5 million commercial and domestic poultry birds which generate around 544,831 tons of waste. This waste finds its final disposal in agricultural land as soil fertilizer or disposal site amendment. The uncontrolled use of poultry litter for this purpose results in environmental impacts such as the emission of methane, a greenhouse gas. However, other options like thermochemical conversion of this waste can offer a better solution wherein poultry litter can be used as low-cost carbon sources for the synthesis of Carbon Nanotubes (CNTs). In this study, efforts have been made to utilize this cheap and plenty of available carbon source for synthesis of CNTs in the presence of Ni/Mo/MgO as a catalyst, through pyrolysis. The optimum mole ratio of catalyst (4:0.2:1) was found to yield more carbon product. Furthermore, process parameters such as temperature, time, polymer & catalyst weight were also optimized. The best possible process parameters that resulted (pyrolysis time (12 min), temperature (825◦C), and catalyst weight (100 mg) good yield of CNTs . The structure and morphology of produced nanotubes were confirmed through X-ray Diffractometer (X-RD) & Scanning Electron Microscopy (SEM). The environmental application of the nanotubes was tested in synthetic chromium solution in the lab using a batch experiment. Different experimental conditions (pH, adsorbent dosage and contact time) were optimized to enhance the adsorption of Cr (VI) by carbon nanotubes and UV-Visible spectrophotometer was used at 540nm to measure the absorbance of Cr (VI). Results show that up to 81.83% of Cr (VI) removal was achieved by using 8 mg of CNTs at pH 3 with 400 rpm at 180 min of contact time. Thus, it was concluded that poultry litter can be a useful source for the synthesis of CNTs and thereby removal of Cr (VI) from industrial tanneries wastewater.

Neuron degeneration is the most common old age complications worldwide. Alzheimer's disease (AD) is one of the most common neurodegenerative condition with limited drug options and significant adverse effects. Hence, there is a growing interest in exploring natural products for their potential pharmacological activities against AD. This study investigates the efficacy of Verbesina encelioides flower extract (VFE) in alleviating neurobehavioral damage induced by Scopolamine in rat mimicking AD model. GC-MS analysis showed that V. encelioides flower extract is rich in carotenoids like spirilloxanthin. Various behavioural models were used to assess the rats' learning and memory capabilities. Additionally, biochemical tests were conducted on the AD rat models to evaluate oxidative stress in the brain, including superoxide dismutase (SOD), nitric oxide (NO), reduced glutathione (GSH), and anti-acetylcholinesterase (AChE) enzyme assays. Findings from this study suggest that the VFE significantly improved the rats' cognitive aptitude and alleviated them from oxidative stress by reducing free radical generation. Based on the results obtained from GC-MS analysis, in silico interaction analysis of gingkolide A and spirilloxanthin with acetylcholinesterase was carried out. In summary, this study suggests that V. enceloides flower extract improves learning abilities and mitigates cognitive deficits caused by scopolamine. Thus V. enceloides could be potentially seen as natural therapeutic intervention against neurodegenerative disorders likes Alzheimer’s disease and dementia.

The goal of this study was to determine the relative susceptibility of four common equine wound pathogens to ten different types of antimicrobial agents based on minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). Additionally, to describe the comparison of bactericidal activity of medical grade Manuka honey, local honey, and commercial, food-grade honey to other commonly used wound dressings. The objective is to provide veterinary practitioners with comparative data on the use of a variety of antimicrobial dressings for inhibiting growth of common wound bacteria. MIC and MBC for Manuka, store, and local honeys were comparable to those of sterile gauze, sugar, and hypertonic saline. Across bacterial species, local honey proved to have more bactericidal activity when compared to Manuka and commercial, food-grade honey. The MIC and MBC for PHMB gauze and foam was consistently at a higher dilution compared to the other antimicrobials. The majority of antimicrobials exhibited stronger inhibitory and bactericidal activity against a Streptococcus zooepidemicus isolate obtained from a wound compared to other bacteria that were ATCC lab acclimated. Additional research for in-vivo applications needs to be done to see whether differences exist in effective wound management.

Babchi (Psoralea corylifolia) oil is an important essential oil used in several traditional medicines to cure various disorders. This phytotherapeutic agent possesses number of pharmacological activities including antibacterial, antifungal, antioxidant, anti-inflammatory, immunomodulatory and antitumor. However, volatile nature, poor stability and solubility of babchi oil (BO) restrict its pharmaceutical applications. Hence, the aim of the present work was to encapsulate this oil in β-cyclodextrin nanosponges (NS) in order to overcome above limitations. To fabricate nanosponges, β-cyclodextrin was crosslinked with diphenyl carbonate in different molar ratios viz.1:2, 1:4, 1:6, 1:8 and 1:10. The blank nanosponges were loaded with babchi oil using freeze-drying method. Particle size of the babchi oil loaded nanosponges was found to lie between 200-500 nm, with low polydispersity index. Further, zeta potential, Fourier transform infrared spectroscopy, X-ray diffraction, thermal analysis and electron microscopy were carried out for characterization of babchi oil nanosponges. Results obtained from spectral analysis ascertained the formation of inclusion complexes. Additionally, solubilisation efficiency of the babchi oil was checked in distilled water and found enhanced by 4.95 times with optimized β-cyclodextrin nanosponges. The cytotoxicity study was carried out by MTT assay using HaCaT cell lines. A significant improvement in photostability of essential oil was also observed by inclusion in nanosponges. Lastly, the optimized formulation was tested for antibacterial activity using Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli. Hence, encapsulation of BO in nanosponges resulted in efficacious carrier system in terms of solubility, photostability as well as safety of this oil along with handling benefits.

Sentinel-1 SAR data preprocessing is essential for several earth observation applications, including land cover classification, change detection, vegetation monitoring, urban growth, natural hazards, etc. The information can be extracted from the 2x2 covariance matrix [C2] of Sentinel-1 dual-pol (VV-VH) acquisitions. To generate the covariance matrix from Sentinel-1 single look complex (SLC) data, several preprocessing steps are required. The ESA SNAP S-1 toolbox can be used to preprocess the data to generate a [C2] matrix. The polarimetric analysis in respective application fields often starts with the covariance matrix. However, due to limited availability of Sentinel-1 SLC data preprocessing workflow standards for polarimetric applications in contemporary research methods, downstream applications unable to comply with these workflows directly. In this paper, we propose a couple of generic practices to preprocess Sentinel-1 SLC data in SNAP S-1 toolbox, which would be beneficial for the radar remote sensing user community.

Allama Iqbal Open University (AIOU) is the largest distance learning institute of Pakistan and providing education to 1.4 million students. This is fairly a large setup across the country where students are geographically distributed. Currently the system works on a manual approach which is not efficient. Allocation of tutors and study centers to students plays a key role in distance learning for a better learning environment. Assigning tutors and study centers to distance learning students is a challenging task when there is huge geographical spread. The utilization of geospatial technologies in open and distance learning can fix allocation problems. This research analyzes the real data of twin cities Islamabad and Rawalpindi. The results show that the geospatial technologies can be used for efficient and proper resource utilization and allocation, which in turn can save the time and money. The overall idea fits into improved distance learning framework and related analytics.

The fluid flow field at the upper airways is highly complex due to the complex structure of the airway. The inhaled particle flow, the air streamline and the interaction of the continuum and discrete phase could significantly affect the transport behaviour of the inhaled particles. A range of analytical, mathematical and computational fluid dynamics (CFD) models analyzed the airflow and particle transport in different idealized and asymmetric airway models. A precise understanding of the continuum and discrete phase interaction in realistic human airways is missing, and this study aims to develop a CFD-DEM model for particle transport in realistic airways. This study uses the CFD model for the continuum phase and the discrete element method (DEM) for the discrete phase. A soft sphere approach is used for the interaction of the discrete phase. Proper validation is performed for particle transport efficiency. The CFD-DEM model analyzed the particle transport in an idealized and realistic airway model, and different methods are used to analyze the transport behaviour. During the particle-particle interaction, a stagnation point and a high-pressure zone are observed at the airway model's carinal angle. The numerical results report higher deposition efficiency (DE) for particle-particle interaction than without interaction. The flow field becomes highly complex with the spring constant values, and higher DE is found for high spring constant values. The spring dashpot friction-dshf method shows higher deposition at the upper part of the airways than other interaction methods. The findings of this study and more case-specific analysis would improve the knowledge of aerosol transport in airways and the health risk assessment of the patient.

We construct a novel family of summation-integral type hybrid operators in terms of shape parameter $\alpha\in \lbrack 0,1]$ in this paper. Basic estimates, rate of convergence, and order of approximation are also studied using the Korovkin theorem and the modulus of smoothness. We investigate the local approximation findings for these sequences of positive linear operators utilising Peetre's K-functional, Lipschitz class, and second-order modulus of smoothness.

Black hole's quasinormal frequencies are basically the complex numbers which provide information about the relaxation of perturbations and depend on the characteristics of the spacetime and types of perturbations. In this paper, we evaluate the quasinormal modes of Hayward black hole in Einstein Gauss-Bonnet gravity, Hayward black hole in anti-de Sitter space (AdS) spacetime, and 4-dimensional black hole in Einstein-Lovelock gravity. By utilizing the WKB resonance technique, we examine the quasinormal modes frequencies $\omega$ by shifting the charge parameter $Q$ (it is also identified with the cosmological constant), circular harmonic index $l$, and mass of scalar field $m$. We also study the relaxation rate for those black holes and find out that the relaxation rate increases with increasing values of $Q$. We observe that real and imaginary components of the quasinormal modes are not linear functions as similar to Reisnner Nordstr\"{o}m-AdS. For large values of charge, quasinormal ringing becomes slower to settle down to thermal equilibrium and hence the frequency of the oscillation becomes smaller.

The world is looking towards organizations for social responsibility to contribute to a sustainable environment. Employees’ organizational citizenship behavior for the environment (OCBE) is a voluntary environmental-oriented behavior that is important for organizations’ environmental performance. Based on social learning theory, the study examined the effects of responsible leadership in connection with OCBE by using a sample of 520 employees of manufacturing and service sector including engine manufacturing, petroleum plants banking and insurance sector organizations of China. Further, the role of psychological ownership and employee environmental commitment were used as mediators and moderators simultaneously. The direct, mediation, and moderation model results exposed a positive relationship between responsible leadership and OCBE via employee psychological ownership and employee environmental commitment. The study also revealed that the indirect effect is stronger when employees hold higher employee environmental commitment. The theoretical and practical implications for environmental sustainability in respect of organizations as well as future research directions are discussed.

Australia has the highest small-mammal extinction rate in the world, and there are ample of reasons that have led to the higher extinction rates such as introduction of feral species, wildfires, climate change and uneven population distribution. All these factors instil a dire need to protect the species in captive environment. Stress has a major role to play in maintaining an adequate welfare conditions for wildlife, hence quantification of the stress levels in captive animals help us understand the effectiveness of the methods used to house the animals. In this study, we assessed the stress levels of captive marsupials namely Rufous bettongs (Aepyprymnus rufescens) (n=9), Long-nosed potoroo (Potorous tridactylus) (n=4) and Greater bilby (Macrotis lagotis) (n=5) by measuring the faecal cortisol metabolites using the Enzyme-Immuno assay (EIA) (R4866). We carried out a laboratory validation of the EIA for the Rufous bettongs and long-nosed potoroo by demonstrating parallelism between the pooled faecal samples and the standard curve. We also used the iterative baseline approach to calculate the baseline levels and detect peak concentrations of FCM in all the studied species. No significant differences were found in the FCM and baseline levels in the potoroos. Results highlighted a significant difference (p &lt; 0.05) in the individual FCM and baseline levels of the bettongs. Additionally, significant differences (p &lt; 0.05) were found in the FCM and baseline levels between male and female bettongs. In case of bilbies, the FCM and baseline levels showed no significant differences, however the male and female baseline levels were significantly different (p &lt; 0.05). Our study provides the baseline and peak FCM concentrations in Long-nosed potoroos and Rufous bettongs under captive management. Future studies can use the iterative baseline approach and validated EIA to detect any possible variations in the FCM concentration and baseline values in captive marsupials created due to factors like sex, grouping, and housing conditions.

Video lectures are becoming more popular and in demand as online classroom teaching is becoming more prevalent. Massive Open Online Courses (MOOCs), such as NPTEL, have been creating high-quality educational content that is freely accessible to students online. A large number of colleges across the country are now using NPTEL videos in their classrooms. So more video lectures are being recorded, maintained, and uploaded. These videos generally contain information about that video before the lecture begins. We generally observe that these educational videos have metadata containing five to six attributes: Institute Name, Publisher Name, Department Name, Professor Name, Subject Name, and Topic Name. It would be easy to maintain these videos if we could organize them according to their categories. The indexing of these videos based on this information is beneficial for students all around the world to efficiently utilise these videos. In this project, we are trying to get the metadata information mentioned above from the video lectures.

The severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) Omicron variant of concern has been the dominant cause of worldwide COVID-19 cases since 2022. All the Omicron sublineage viruses have demonstrated high transmissibility and an ability to escape vaccine-induced immunity. While first-generation vaccines, including monovalent vaccines, continue to provide protection against severe disease, hospitalization and mortality, their efficacy against Omicron sub variants remains sparse. These vaccines have also been associated with rapidly waning protection against primary COVID-19 and COVID-19 reinfections conferred by evolving Omicron sublineages.. This led to the development and deployment of updated vaccines and the introduction of the bivalent booster. Through this review, we highlight the brief journey of the variants of concern leading to the dominance of Omicron and the effectiveness of the key vaccines against these variants, including the updated (bivalent) boosters.

The present study investigation, our results on characterization of commercially pure-Ti alloy brazed with metallic glass ribbons of Ti20Zr20Cu60-xNix (x = 30, 40, and 50) metallic glass ribbons were produced using a vacuum melt spinner. These ribbons were then used as filler materials for vacuum brazing of two Ti alloy plates at 1268, 1277 and 1279 K for a period of 10 min. Field-Emission Scanning Electron Microscopy (FESEM), Transmission Electron Microscopy (TEM) and the energy dispersive X-ray spectroscopy (EDX). The as-spun ribbons showed fully amorphous structure when examined on both surfaces by XRD and also verified by TEM investigation. The brazing joint of two Ti-plates using the metallic glass ribbon when brazed with Ni50 was found to be of very high strength. FESEM characterization of the cross-section of the brazed joints shows sub-micron size grains uniformly distributed in the matrix with brighter appearance. FESEM and EDX analysis revealed that the sub-micron grains are rich in Ti & Ni while the matrix phase mainly consisted of Ti. BSE image along with EDS Analysis indicated that the brazed joint has a presence of NiTi2 and Cu2 (Ni Zr) phases which could be responsible for increase in the strength of the brazed joint.

Taste signaling is a complex process that is linked to obesity and its associated metabolic syndromes. The sweet taste is mediated through a heterodimeric G protein coupled receptor (GPRC) in a species-specific manner and at multi-tissue specific levels. The sweet receptor recognizes a large number of ligands with structural and functional diversities to modulate different amplitudes of downstream signaling pathway(s). The human sweet-taste receptor has been extremely difficult to study by biophysical methods due to inadequate methods for producing large homogeneous quantities of the taste-receptor protein and a lack of reliable in vitro assays to precisely measure productive ligand binding modes leading to activity upon their interactions with the receptor protein. We report a multimodal high throughput assays to monitor ligand binding, receptor stability and conformational changes to model the molecular interactions between ligand-receptor. We applied saturation transfer difference nuclear magnetic resonance spectroscopy (STD-NMR) complemented by differential scanning calorimetry (DSC), circular dichroism (CD) spectroscopy, and intrinsic fluorescence spectroscopy (IF) to characterize binding interactions. Our method using complementary NMR and biophysical analysis is advantageous to study the mechanism of ligand binding and signaling processes in other GPCRs.

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

Coaxial rotors can be found in multirotor vehicles for the added thrust compared to independent rotors while keeping similar area footprints but, performance losses should be considered. This experimental study analyzes the effects of varying motor throttle and propeller pitch values in motor-propeller systems with two to four coaxial rotors. The results show that in a two-rotor coaxial system, to lessen the adverse effects of a front rotor’s backwash and to operate at the maximum performance, only the back motor should be operated initially up to 75% duty cycle before using the front motor up to its 75% duty cycle. Additional thrust requirements should be generated from the back rotor and then from the front rotor up to their maximum duty cycles. In two, three, and four-rotor coaxial setups, total thrust output generated is 1.6, 2.1, and 2.5 times the thrust output at system thrust performance of 86%, 76%, and 66%, respectively of that of an isolated rotor. In a four-rotor coaxial setup, maximum system performance is achieved when the propeller pitch values are gradually increased from the first to the last rotor. The gradual increments in propeller pitch values also result in more uniform thrust sharing among rotors.

Drought is one of the most serious abiotic stressors in the environment, restricting agricultural production by reducing plant growth, development, and productivity. To investigate such a complex and multifaceted stressor and its effects on the plants, a systems biology-based approach is necessitated, entailing the generation of co-expression networks, identification of highly-priority transcription factors (TFs) dynamic mathematical modeling, and computational simulations. Here, we studied a high-resolution drought transcriptome of Arabidopsis. We revealed distinct temporal transcriptional signatures and demonstrated the involvement of specific biological pathways. Generation of a large-scale co-expression network followed by network centrality analyses identified 117 TFs that possess critical properties of hubs, bottlenecks, and high clustering coefficients nodes. Dynamic transcriptional regulatory modeling on integrated TF-target and transcriptome datasets uncovered major transcriptional events during the course of drought stress. Mathematical transcriptional simulations allowed us to ascertain the activation status of major TFs as well as the transcriptional intensity and amplitude of their target genes. Finally, we validated our predictions by providing experimental evidence of gene expression under drought stress for a set of four TFs and their major target genes using qRT-PCR. Taken together, we provided a systems-level perspective on the dynamic transcriptional regulation during drought stress in Arabidopsis and uncovered numerous novel TFs that can potentially be used in future genetic crop engineering programs.

Autism Spectrum Disorder (ASD) is a neuro-developmental disorder that limits social interactions, cognitive skills, and abilities. Since ASD can last during an affected person's entire life cycle, the diagnosis at the early onset can yield a significant positive impact. The current medical diagnostic systems (e.g., DSM-5/ICD-10) are somewhat subjective; rely purely on the behavioral observation of symptoms, and hence, some individuals often go misdiagnosed or late-diagnosed. Therefore, researchers have focused on developing data-driven automated diagnosis systems with less screening time, low cost, and improved accuracy while significantly reducing professional intervention. Human Activity Analysis (HAA) is considered one of the most promising niches in computer vision research. This paper aims to analyze its potentialities in the automated detection of autism by tracking the exclusive characteristics of autistic individuals such as repetitive behavior, atypical walking style, and unusual visual saliency. This review provides a detailed inspection of HAA-based autism detection literature published in 2011 on-wards depicting core approaches, challenges, probable solutions, available resources, and scopes of future exploration in this arena. According to our study, deep learning outperforms machine learning in ASD detection with a classification accuracy of 76\% to 95\% on different datasets comprise of video, image, or skeleton data that recorded participants performing a large number of actions. However, machine learning provides satisfactory results on datasets with a small number of action classes and has a range of 60\% to 93\% accuracy among numerous studies. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

Multiple sclerosis (MS) is an immune-mediated, chronic inflammatory disease of the central nervous system (CNS), characterized by demyelination, axonal degeneration, and cognitive impairment. It also has an important impact on the quality of life of patients and their family members. An estimated 2,500,000 people in the world have multiple sclerosis. Neurophysiological parameters, like sensitivity to demyelination and the strength of excitatory and inhibitory synaptic interactions in the cerebral cortex, can be identified through transcranial magnetic stimulation (TMS) in patients affected by multiple sclerosis (MS). These parameters can be valid and objective parameters that can be correlated with the progression of MS, and can provide reliable indices for the severity of illness and the efficacy of drugs used to treat it. The discovery of specific and detailed neurophysiological parameters as surrogate end points for disease activity could represent an important step in clinical trials. Changes in cortical connectivity have already been demonstrated in MS, but in clinical practice, other measures are usually used to evaluate disease activity. We speculate that TMS may be more effective in identifying disease progression that leads to long-term disability, compared to standard surrogate markers, due to the fact that it represents a direct measure of synaptic transmission(s) in MS.

In this study, we evaluated the potential of fungal endophytes to control yellow rust in wheat (Triticum aestivum L.) as endophytes are beneficial microbes and alternate to pesticides for confronting pathogens. The in-vitro efficacy of the fungal endophytes isolated from different desert plants was evaluated and the best four namely Colletotrichum lindemuthianum, Piriformospora indica, Acremonium lolii and Trichoderma viride were selected. Seeds of two susceptible wheat genotypes namely Fareed-06 and Shafaq-06 obtained from screening experiment were inoculated by dipping in four endophytic spore suspensions and were sown using randomized complete block design under factorial arrangement. Data concerning about area under disease progress curve, final disease severity percentage, coefficient of infection,1000- grains weight and grain yield were recorded. Results showed that endophyte P. indica showed significant decrease in final disease severity (FDS) and area under disease progress curve (AUDPC), resultantly 12.2% grain yield gain in rust susceptible wheat genotypes of Fareed-06 and Shafaq-06 followed by the endophytes T. viride, C. lindemuthianum and A. lolii with the grain yield gain of 10.6%, 06.2% and 04.2% respectively. In crux, fungal endophytes are valuable microbes which can be employed to induce tolerance against P. striiformis in yellow rust vulnerable areas for better and sustainable wheat production.