The paper presents an adaptation of numerical solution of first order linear differential equation in fuzzy environment. The numerical method is re-established and studied with fuzzy concept to estimate its uncertain parameters whose values are not precisely known. Demonstrations of fuzzy solutions of the governing methods are carried out by the approaches, namely Modified Runge Kutta method and Runge Kutta Merson method. The results are compared with the exact solution which is found using generalized Hukuhara derivative (gH-derivative) concepts. Additionally, different illustrative examples and an application in industry of the methods are also undertaken with the useful table and graph to show the usefulness for attained to the proposed approaches.

Background: Cancer stem cells (CSC) are small subpopulation of cancer cells that has the potential to self-renew and has the strong proliferative capacity that sustain tumorigenesis capability. This ability of the CSCs to escape immune responses make the CSCs a primary source of functionally altered, immune-resistant, chemo-resistant aggressive tumor cells. Mentioned characteristic determines the potential advantage of targeting CSCs for the treatment of solid tumor. Method: We downloaded different gene expression datasets of CSCs from the NCBI-GEO database and identified common gens by using suitable Venn tool. Then explored the prognostic significance of the particular genes in particular cancer along with analysis of expression of these genes at protein level in human carcinomas by using different webserver. Finally, using toxicogenomic database we selected several important drugs or chemicals. Result: In this study, we identified common targets between three types of solid tumor and the prognostic significance of the identified genes. The identified drugs capable of targeting the expression or signaling network of designated genes of CSC that may contribute in CSC targeted cancer therapy. Conclusion: We identified several CSC associated genes in human breast, lung and ovarian carcinomas and several drugs that can target the function of these genes. Our study also suggests that there is need for more experimental investigations to find out the actual functional activity and the mechanism of action of the genes of CSC.

Wireless energy harvesting (EH) communication has long been considered a sustainable networking solution. However, it has been limited in efficiency, which has been a major obstacle. Recently, strategies such as energy relaying and borrowing have been explored to overcome these difficulties and provide long-range wireless sensor connectivity. In this article, we examine the reliability of the wireless-powered communication network by maximizing the net bit rate. To accomplish our goal, we focus on enhancing the performance of hybrid access points and information sources by optimizing their transmit power. Additionally, we aim to maximize the use of harvested energy by energy-harvesting relays for both information transmission and energy relaying. However, this optimization problem is complex as it involves non-convex variables and requires combinatorial relay selection indicators optimization for decode and forward (DF) relaying. To simplify this problem, we utilize the Markov decision process and deep reinforcement learning framework based on the deep deterministic policy gradient algorithm. This approach enables us to tackle this non-tractable problem, which conventional convex optimisation techniques would be difficult to solve in complex problem environments. The proposed algorithm significantly improves the end-to-end net bit rate of the smart energy borrowing and relaying EH system by 13.22%,27.57%, and 14.12% compared to the benchmark algorithm based on borrowing energy with an adaptive reward for Quadrature Phase Shift Keying, 8-PSK, and 16-Quadrature amplitude modulation schemes, respectively.

Concern for global health security and the environment due to the emergence of antibiotic-resistant bacteria and antibiotic residues in meat and other livestock products has led many countries to restrict the use of antibiotics in animal feed. This experiment was performed to assess the impact of dietary supplementation with a probiotic (Bacillus subtilis) and a postbiotic (Saccharomyces cerevisiae fermentation product) on growth performance, carcass traits, blood haemato-biochemical profile, gut microbiome, gut morphology, and immune response in broilers as an alternative to antimicrobials in poultry production system to minimize the effect on global health security. A total of 324-day-old Ven Cobb 400 broiler chicks were randomly divided into three dietary groups, each containing 12 replicated pens (n = 12), .and each replicate contained nine broiler chickens. The dietary groups consisted of 1) a basal diet without any growth promoting (T1), 2) the basal diet augmented with Bacillus subtilis at 200 g/MT feed (T2), and 3) the basal diet supplemented with Saccharomyces cerevisiae fermentation product at 1.25 kg/MT feed (T3). To calculate body weight gain, all birds and residual feed were weighed on a replicated basis over days 0, 7, 14, 21, 28, 35, and 42; mortality was recorded daily. Each replicate's mortality percentage was calculated and used to adjust BW, ADFI, and FCR calculations at the trial's end. At the end of the trial (42 d), two chickens from each replicate (24 chickens per group) were slaughtered for carcass traits, gut microbiome, and morphology measurements. Blood samples were collected for the haemato-biochemical profile on 35d and antibody titer on 28d and 35d.Feeding with SCFP (T3 group) significantly improved average daily feed intake (ADFI) and average daily gain (ADG) of chickens compared to the T1 (control) and T2 (probiotic) groups from 1 to 14 days of age. FCR was significantly improved (P<0.05) in SCFP-fed birds (T3) relative to the control (T1)over the entire experimental period (1-42 days). Carcass traits and blood haemato-biochemical parameters remained unaffected (P>0.05) by any diets. However, cholesterol levels and concentrations of corticosterone were significantly lower (P<0.05) in SCFP-fed birds (T3) compared to those of the probiotic (T2) and control (T1) groups. Total E. coli, Enterohaemorrhagic E. coli, ESBL-producing Enterobacteriaceae, and Salmonella counts were significantly higher in the T1group compared to T3 and T2 groups (P<0.05) and Salmonella counts were lower in T3 when compared to T2 (P<0.05). However, there was no significant difference in Lactobacillus count among treatment groups. PCR based characterization of ESBL- producing Enterobacteriaceae revealed maximum presence of blaCTX-M-Type (6/15, 40%), followed by blaSHV-Type (5/15, 33.3%) and blaTEM-Type (4/15, 26.6%). No variation in the occurrence pattern of ESBL/beta-lactamase associated genes in the studied ESBL- producing Enterobacteriaceae isolates was observed between the treatment groups. A significant increase in villi height and villi height to crypt depth ratio (VH: CD) (P<0.05) was observed in both T3 and T2 groups. Contrarily, villi widths did not vary across the dietary groups (P>0.05). The control group (T1) displayed a notable deeper in crypts when compared to the T3 and T2 groups (P<0.05). On day 28, the SCFP-fed birds (T3) and those fed the probiotics (T2) exhibited a significant increase (P<0.05) in antibody titers against Newcastle disease virus and Infectious bursal disease virus. Increase in antibody titre against IBDV in birds with the addition of SCFP in the diet was detected for the first time. However, there were no significant differences observed on day 35. The phagocytic activity of neutrophils and lymphocytes in broiler chickens did not show any noticeable variations among the different groups at day 35(P>0.05).It can be concluded that Saccharomyces cerevisiae fermentation product and Bacillus subtilis probiotic could be viable alternatives to antimicrobials in poultry production considering beneficial impacts in broilers fed antibiotic-free diet.

The transition from the Peptide/RNA world to the Protein/RNA world in the hydrothermal vent environment was a major event in the history of life. The advent of proteins utterly changed the conditions of emerging life, representing a watershed in its development. During subsequent translation various protein enzymes emerged driving protocells into a more complex and interconnected system. With their astonishing versatility, the protein enzymes catalyzed crucial biochemical reactions within protocells into more complex biomolecules in diverse metabolic pathways, whereas structural proteins provided strength and permeability in the cell membrane. Four major events followed after availability of various kinds of protein molecules during prebiotic synthesis. These are: (1) the modification of the phospholipid membrane into the plasma membrane; (2) the origin of primitive cytoplasm; (3) the beginnings of the virus world; and (4) the advent of DNA. The first innovation mediated by proteins was the improvement of the cell membrane. The phospholipid membrane was initially evolved in a vent environment from the gradual modification of a fatty acid membrane via an intermediate phosphatidate acid by non-enzymatic reactions. The phospholipid is then synthesized from phosphatidate acid by a series of enzymes. To make the phospholipid membrane more permeable, various protein molecules interacted with the cell membrane. Proteins not only stabilized the wall membrane, but also acted as pumps, preventing some molecules from the protocells from crossing the membrane barriers, while permitting other selected molecules and ions to enter and leave the protocell. The second modification led by proteins is the gradual conversion of the interior of the protocell from a water-like medium into a gel-like cytoplasm, which became the storehouse of a wide range of biomolecules including amino acids, proteins, nucleic acids, ribosomes, as well as salt and water. The third innovation utilizing the newly synthesized proteins was the emergence of the ancient virus world. In the milieu of different kinds of mRNAs in the prebiotic soup, jelly-roll capsid genes originated de novo within genomes of nonviral mRNAs by overprinting. These fragile capsid genes were possibly coated by proteins on the mineral substrate for stability and durability, transforming them into ancient viral particles. These protein coats were random and were not encoded by encased genes. Some protocells might have engulfed these viral particles, when the capsid genes utilized the ribosomes of the host to translate into the appropriate capsid proteins. These capsid proteins then coated the viral genes to make new copies of primordial viruses inside the protocell. Since then, viruses became capsid-encoding organisms. These primordial mRNA viruses parasitized RNA-based protocells, manipulating them to make new copies of themselves. This was the beginning of a relentless war between viruses and their protocellular hosts. The next stage in viral evolution was the emergence of a primitive retrovirus (pre-retrovirus) with a new kind of replicative strategy in a sense that it could turn its RNA into DNA using its own reverse transcriptase enzyme. This is the beginning of the Retro world that facilitated the transition from RNA to DNA genomes. The infection of RNA protocells with pre-retroviruses progressively transferred the RNA genome to a viral DNA genome by retro-transcription. The advent of DNA by the pre-retrovirus marks the fourth innovation, when a number of enzymes had already developed and were utilized by pre-retroviruses. With continued infection, DNA viruses slowly transferred not only their core replication enzymes, such as helicase, primase, and DNA polymerase, to RNA protocells, but also to their DNAs as well. Thus, began the DNA world, when DNA replaced RNA as the major genome of the protocells. With the advent of DNA, replication of information was entirely dissociated from its expression. Because DNA is much more stable than mRNA with more storage capacity, it is a superb archive for information systems in the form of base sequences. DNA progressively took over the replicative storage function of mRNA, leaving the latter for protein synthesis. The new protocell with the DNA genome will diversify into large populations of DNA protocells that will outcompete populations of RNA protocells. Genetic information began to flow from DNA to mRNA to protein in a two-step process involving transcription and translation. In the biological stage, DNA replication was central to the binary fission of the first cell, orchestrated by the duplication of genomes and then the division of the parent cell into two identical daughter cells. It was carried out by a set of enzymes that formed a Z-ring at the site of replication. With the onset of binary fission, the population of primitive cells grew rapidly in the hydrothermal vent environment, undergoing Darwinian evolution and diversification. These primordial hyperthermophiles, presumably the first life, obtained food and energy directly from the vent environment. However, such a situation was self-limiting, so the early cells evolved their own mechanisms for generating metabolic energy and synthesizing the molecules necessary for their reproduction. The earliest fossil record (≥ 3.5 Ga) of biotic activity is preserved in the Archean hydrothermal and sedimentary rocks of the Nuvvuagittuq Craton of Canada, the Isua Craton of Greenland, the Pilbara Craton of Australia, the Kaapvaal Craton of South Africa, and the Singhbhum Craton of India in the form of the carbonaceous remains of microbial cells, cellular microfossils, and stromatolites. These microscopic fossils provide crucial evidence of the origin and early evolution of prokaryotic cells, beginning with hyperthermophiles. Molecular phylogenetic analysis suggests that both domains of life ¬– Bacteria and Archaea probably split from the last universal common ancestor (LUCA), a hyperthermophilic organism. In the younger sequences of these Archean cratons, two kinds of photosynthetic bacteria, anoxygenic green sulfur bacteria, and oxygenic cyanobacteria, appeared in quick succession from the thermophilic ancestor, indicating a shift of niche from a benthic to a planktonic, with reduced thermotolerance. The development of anoxygenic and oxygenic photosynthesis would have allowed life to escape the hydrothermal setting and invade a newly evolved habitat—broad continental shelves to tap solar energy. Cyanobacteria invaded the global ocean, turned it into blue and green, produced oxygen for the first time, and left their signatures in the carbonates and stromatolites.

RNA sequencing techniques, like bulk RNA-seq and Single Cell (sc) RNA-seq, are critical tools for the biologist looking to analyze the genetic activity/transcriptome of a tissue or cell during an experimental procedure. Platforms like Illumina's next-generation sequencing (NGS) are used to produce the raw data for this experimental procedure. This raw FASTQ data must then be prepared via a complex series of data manipulations by bioinformaticians. This process currently takes place on an unwieldy textual user interface like a terminal/command line that requires the user to install and import multiple program packages, preventing the untrained biologist from initiating data analysis. Open-source platforms like Galaxy have produced a more user-friendly pipeline, yet the visual interface remains cluttered and highly technical, remaining uninviting for the natural scientist. To address this, SeqMate is a user-friendly tool that allows for one-click analytics by utilizing the power of a large language model (LLM) to automate both data preparation and analysis (differential expression, trajectory analysis, etc). Furthermore, by utilizing the power of generative AI, SeqMate is also capable of analyzing such findings and producing written reports of upregulated/downregulated/user-prompted genes with sources cited from known repositories like PubMed, PDB, and Uniprot.

Despite the growing capabilities of large language models, concerns exist about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers that can be useful in a variety of industries, especially ones that are “restricted” and have limited data. We consider that bias can occur due to intrinsic model architecture and dataset quality. The two aspects are evaluated using two different metrics we created. We show that our dataset augmentation algorithm reduces bias as measured by our metrics.

A picture is worth a thousand words. Many branches of Science have been historically benefited with plots and visual analyses (lately, image processing and deep learning) alongside with traditional number crunching. In Molecular Biophysics, one such problem is the structure validation problem in proteins which stands with a history of plot-tools being effectively serving the complex problem to its complete resolution. Spanning across six decades, validation of protein structures (from experimental to modeled) dates back to the legendary Ramachandran Plot (with its never ending growth and modern-day applications) to the relatively recent innovation of the Complementarity Plot (CP), establishing the dual nature of complementarity as the physical basis of both binding and folding of proteins. Lately, CP has been extended to serve as a trustworthy free energy predictor utilizing supervised learning in the form of a comprehensive web-server (EnCPdock: https://www.scinetmol.in/EnCPdock/) that can be directly used in the design of protein interfaces. The commentary recapitulate the history of structure validation with a special emphasis on plot tools, highlighting key features and important discoveries worth re-visiting.

The worldwide outbreak of a novel coronavirus, SARS-CoV-2 has caused a pandemic of respiratory disease. Due to this emergency, researchers around the globe have been investigating the evolution of the genome of SARS-CoV-2 in order to design vaccines. Here I examined the evolution of GC content of SARS-CoV-2 by comparing the genomes of the members of the group Betacoronavirus. The results of this investigation revealed a highly significant positive correlation between the GC contents of betacoronaviruses and their divergence from SARS-CoV-2. The betacoronaviruses that are distantly related to SARS-CoV-2 have much higher GC contents than the latter. Conversely, the closely related ones have low GC contents, which are only slightly higher than that of SARS-CoV-2. This suggests a systematic reduction in the GC content in the SARS-CoV-2 lineage over time. The declining trend in this lineage predicts a much-reduced GC content in the coronaviruses that will descend/evolve from SARS-CoV-2 in the future. Due to the three consecutive outbreaks (MERS-CoV, SARS-CoV and SARS-CoV-2) caused by the members of the SARS-CoV-2, the scientific community is emphasizing the need for universal vaccines that are effective across many strains including those, that will inevitably emerge in the near future. The reduction in GC contents implies an increase in the rate of GC→AT mutations than that the mutational changes in the reverse direction. Therefore, understanding the evolution of base composition and mutational patterns of SARS-CoV-2 could be useful in designing broad-spectrum vaccines that could identify and neutralize the present and future strains of this virus.

The emergence of proteins in the prebiotic world was a watershed event at the origin of life. With their astonishing versatility, the protein enzymes catalyzed crucial biochemical reactions within protocells into more complex biomolecules in diverse metabolic pathways, whereas structural proteins provided strength and permeability in the cell membrane. Five major biochemical innovations followed in succession after availability of various kinds of protein molecules during decoding and translation of mRNAs. These are: (1) the modification of the phospholipid membrane into the plasma membrane; (2) the origin of primitive cytoplasm; (3) primitive gene regulation; (4) the beginnings of the virus world; and (5) the advent of DNA. The creative role of viruses during prebiotic synthesis led to the origin of the DNA world, when DNA replaced mRNA as the major genome of the protocells. With the advent of DNA, replication of information was entirely dissociated from its expression. Because DNA is much more stable than mRNA with more storage capacity, it is a superb archive for information systems in the form of base sequences. DNA progressively took over the replicative storage function of mRNA, leaving the latter for protein synthesis. Genetic information began to flow from DNA to mRNA to protein in a two-step process involving transcription and translation. In the biological stage, DNA replication was central to the binary fission of the first cell, orchestrated by the duplication of genomes and then the division of the parent cell into two identical daughter cells. With the onset of binary fission, the population of primitive cells grew rapidly in the hydrothermal vent environment, undergoing Darwinian evolution and diversification by mutation. The habitat of the earliest fossil record (≥ 3.5 Ga) from the Archean sedimentary rocks of Canada, Greenland, Australia, South Africa, and India offers a new window on the early radiation of microbial life. The development of anoxygenic and then oxygenic photosynthesis from early hyperthermophiles would have allowed life to escape the hydrothermal setting to the mesophilic global ocean.

Distinguishable annual growth rings produce in Pinus roxburghii are an asset to find out the age of individual tree. This paper aimed to determine the age of P. roxburghii through coring samples and test the relationship with forest production. The biomass estimated, girths measured at two different sections and heights measured which allowed to determine the rate of tapering of the stand. The regression analysis was performed to test the relationship between various variables. The mean age of the P. roxburghii stand was found to be 23.97 (~24 years). The result showed the significant (p<0.05) positive correlation coefficient has been seen between age with girth at breast height, biomass, volume and carbon stock. However, no significant (p>0.05) correlation (r = 0.08) was found between age and height of the stand. In contrast, a correlation between diameter at breast height (DBH) was significant (p<0.05) and positive with volume, biomass, but no significant (p>0.05) correlation (r = 0.14) found between DBH and height of the stand. However, height has a significant (p<0.05) positive correlation with biomass. The mean biomass was 375 kg and mean annual increment (MAI) was 15 kg per tree. Rate of tapering of the studied stand predicted to be 3 cm diameter decreased with trunk height running at 100 cm from base to upward of P. roxburghii stand and vice versa. Result suggests that height-age relation is very weak whereas age, DBH, biomass and carbon has a significant correlation signifies that time-based forests' production and potential production estimation can be obtained in a relatively accurate way by utilizing the age of stand. The time-based forest production analysis is pioneer work in Nepal. The study affirms the tree ring count in P. roxburghii would be a credible and accurate method to determine the age of standing trees.

Signalling is a strongly influenced area trending to be applied in almost every focus of biological sciences. The part of signalling or communication from cellular level to a whole organism including plant as well as animal drags a vast diversity of wealthy structural compounds. There is immense demand for new bioactive compounds for the pharmaceutical, agro and food industries. Plant-associated microbes present an attractive and promising source. The concept of the microbiome and the significance it has to host health, diseases state, and the role of immune have been the hub of research that has led to advances in our understanding of the massive power of the small unseen majority of the microbes (Peterson Andrew H., 2013). Before we say about microbiome—plant relation, it is important to first understand the working concept of the microbiome. Every organism on earth counts on their neighbours to sustain life. Microbiome can be considered a community of microorgasims who can prove to be loveable and hateful. The analysis of microbiome structure and function was protagonise in studies of human hosts and has been extensively documented as essential to genetic and functional capacity attributed to the host, comprehending aspects of metabolism and physiology. Plants are crowded with microbial organisms, counting those that colonize internal tissues, also those that adhere to external surfaces. The wide diversity of microorganisms in the soil rhizosphere is collectively plant–soil-associated microbes cover the plant microbiome. The intricate involvement of microbiome serves to plant health and as a tank of additional genes that plants can access when needed.

Germplasm is a long-term resource management mission and investment for civilization. For both food and nutritional health, the present changing environmental scenario has become an urgent universal concern. Multiple excellent studies have been previously performed, although the advancement and innovation of practices will require the exploration of the potentiality of crop germplasm. In this study, we emphasized (i) germplasm activates, current challenges and ongoing trends of the crop germplasm, and (ii) how the system biology will be helpful to understand the complex traits such as water use efficiency (WUE), and nitrogen use efficiency (NUE) to mitigate challenges for sustainable development under growing food requirement and climate change conditions. We focused on a vision for transforming PGR into a bio-digital resource system, for the development of climate-smart crops for sustainable food production. Moreover, this review attempted to address current challenges, research gaps and describe the advanced integrated strategies that could provide a platform for future crop improvement research.

Sustainability is a concept shrouded in abstraction. While we have definitions in existence, it is often difficult to explain the concept itself. The current definition of ‘sustainable development’ was given by the Brundtland Commission’s report in 1987. The Earth Summit at Rio in 1992 gave us Agenda 21, an action plan to achieve sustainable development. Now in the 21^st century, philosophers, academicians, and researchers across the globe are paving the way for a new understanding of the term ‘sustainability’, its contextual nature, and its relation to humans, politics, and ecology. This article investigates the origins of the term ‘sustainability’, its derivatives, and the concept of sustainable development. A semantical analysis is carried out to understand the differences between ‘sustainability’ and ‘sustainable development’. Next, the development of the three pillars of sustainability and the application of these concepts in the field of architecture and design is also investigated.

A significantly reduced stress concentration effect and a stable deformation behavior are exhibited by the arc-like S-shaped auxetic structure. Analytical works beyond the bar and hinge method to capture structural effects, such as bending, shearing, etc., in the S-structure are scarce in the literature. The deformation pattern of the S-structure is dominated by bending and shearing within the linear elastic region. In this work, Timoshenko beam theory is used to derive closed-form expressions for overall elastic modulus and Poisson’s ratio. This agrees well with the results of finite element simulations. An adjusted R-square value of over 0.99 and almost 0.82 is obtained for elastic modulus and Poisson’s ratio, respectively. From parametric studies, it’s established that under quasi-static transverse load, strut thickness and the angle `α’ are the most important parameters for controlling elastic modulus, specific energy absorption, negative Poisson’s ratio (NPR) effect, and relative density of the entire auxetic structure. Also, it’s found that energy absorption and elastic modulus increase together. Interestingly, the elastic modulus of the structure under transverse load lies in the range from 3 MPa to 250 MPa, which means this structure is compliant enough and can be used in cushioning, packaging, soft robotics applications, etc.

Information is the currency of life, but the origin of prebiotic information remains a mystery. We propose transitional pathways from the cosmic building blocks of life to the complex prebiotic organic chemistry that led to the origin of information systems. The prebiotic information system, specifically the genetic code, is segregated, linear, and digital and probably appeared during biogenesis four billion years ago. In the peptide/RNA world, lipid membranes randomly encapsulated amino acids, RNA, and protein molecules, drawn from the prebiotic soup, to initiate a molecular symbiosis inside the protocells. This endosymbiosis led to the hierarchical emergence of several requisite components of the translation machine: tRNAs, aaRS, mRNAs, and ribosomes. When assembled in the right order, the translation machine created biosynthetic polypeptides, a process that transferred information from mRNAs to proteins. This was the beginning of the prebiotic information age. The molecular attraction between tRNA and amino acids led to different stages of the translation machines and the genetic code. tRNA is an ancient molecule that designed and built mRNA for storing the information of its cognate amino acid. Each mRNA strand became the storage device for the genetic information that encoded the amino acid sequences in triplet nucleotides. As information appeared in the digital languages of the codon within mRNA, and the genetic code for protein synthesis evolved, the prebiotic chemistry then became more organized and directional. The origin of the genetic code is enigmatic; herein we propose an evolutionary explanation: the demand for a wide range of specific enzymes in the peptide/RNA world was the main selective pressure for the origin of information-directed protein synthesis. We review three main concepts on the origin and evolution of the genetic code: the stereochemical theory, the coevolution theory, and the adaptive theory. These three theories are compatible with our coevolution model of the translation machines and the genetic code. We suggest biosynthetic pathways as the origin of the specific translation machines which provided the framework for the origin of the genetic code. During translation, the genetic code developed in three stages coincident with the refinement of the translation machines: GNC code developed by the pre-tRNA/pre-aaRS /pre-mRNA machine, SNS code by the tRNA/aaRS/mRNA machine, and finally the universal genetic code by the tRNA/aaRS/mRNA/ribosome machine. Our hypothesis provides the logical and incremental steps for the origin of the programmed protein synthesis. In order to understand the prebiotic information system better, we converted letter codons into numerical codons in the Universal Genetic Code Table. We have developed a software called CATI (Codon-Amino Acid-Translator-Imitator) to translate randomly chosen numerical codons into corresponding amino acids and vice versa. This conversion has granted us insight into how the translation might have worked in the peptide/RNA world. There is great potential in the application of numerical codons to bioinformatics such as barcoding, DNA mining, or DNA fingerprinting. We constructed the likely biochemical pathways for the origin of translation and the genetic code using the Model-View-Controller (MVC) software framework, and the translation machinery step-by-step. Using AnyLogic software we were able to simulate and visualize the entire evolution of the translation machines and the genetic code. The results indicate that the emergence of the information age from the peptide/RNA world was a watershed event in the origin of life about four billion years ago.

The Late Heavy Bombardment Period (4.1 to 3.8 billion years ago) of heightened impact cratering activity on young Earth is likely the driving force for the origin of life. During the Eoarchean, asteroids such as carbonaceous chondrites delivered the building blocks of life and water to early Earth. Asteroid collisions created innumerable hydrothermal crater lakes in the Eoarchean crust which inadvertently became the perfect cradle for prebiotic chemistry. These hydrothermal crater lakes were filled with cosmic water and the building blocks of life. forming a thick prebiotic soup. The unique combination of exogenous delivery of extraterrestrial building blocks of life, and the endogenous biosynthesis in hydrothermal impact crater lakes very likely gave rise to life. A new symbiotic model for the origin of life within the hydrothermal crater lakes is here proposed. In this scenario, life arose around four billion years ago through five hierarchical stages of increasing molecular complexity: cosmic, geologic, chemical, information, and biological. During the prebiotic synthesis, membranes first appeared in the hydrothermal crater lakes, followed by the simultaneous origin of RNA and protein molecules, creating the RNA/protein world. These proteins were noncoded protein enzymes that facilitated chemical reactions. RNA molecules formed in the hydrothermal crater basin by polymerization of the nucleotides on the montmorillonite mineral substrate. Similarly, the initial synthesis of abiotic protein enzymes was mediated by the condensation of amino acids on pyrite surfaces. The regular wet-dry cycles within the crater lakes assisted further concentration, condensation, and polymerization of the RNAs and proteins. Lipid membranes randomly encapsulated amino acids, RNA, and protein molecules from the prebiotic soup to initiate a molecular symbiosis inside the protocells, this led to the hierarchical emergence of several cell components. As the role of protein enzymes became essential for catalytic process in the RNA/protein world, Darwinian selection from noncoded to coded protein synthesis led to translation systems and the genetic code, heralding the information stage. In this stage, the biochemical pathways suggest the successive emergence of translation machineries such as tRNAs, aaRS, mRNAs, and of ribosomes for protein synthesis. The molecular attraction between tRNA and amino acid led to the emergence of translation machinery and the genetic code. tRNA is an ancient molecule that created mRNA for the purpose of storing amino acid information like a digital strip. Each mRNA strand became the storage device for genetic information that encoded the amino acid sequences in triplet nucleotides. As information became available in the digital languages of the codon within mRNA, biosynthesis became less random and more organized and directional. The original translation machinery was simpler before the emergence of the ribosome than that of today. We review three main concepts on the origin and evolution of the genetic code: the stereochemical theory, the coevolution theory, and adaptive theory. We believe that these three theories are not mutually exclusive, but are compatible with our coevolution model of translations machines and the genetic code. We suggest biosynthetic pathways as the origin of the translation machine that provided the framework for the origin of the genetic code. During translation, the genetic code developed in three stages coincident with the refinement of the translation machinery: GNC code with four codons and four amino acids during interactions of pre-tRNA/pre-aaRS /pre-mRNA, SNS code consisting of 16 codons and 10 amino acids appeared during the tRNA/aaRS/mRNA interaction, and finally the universal genetic code evolved with the emergence of the tRNA/aaRS/mRNA/ribosome machine. The universal code consists of 64 codons and 20 amino acids, with a redundancy that minimizes errors in translation. To address the question of the origin of the biological information system in the RNA/protein world, we converted letter codons into numerical codons in the Universal Genetic Code Table. We developed a software called CATI (Codon-Amino Acid-Translator-Imitator) to translate randomly chosen numerical codons into corresponding amino acids and vice versa, gaining insight into how translation might have worked in the RNA/protein world. We simulated the likely biochemical pathways for the origin of translation and the genetic code using the Model-View-Controller (MVC) software framework, and the translation machinery step-by-step. We used AnyLogic software to simulate and visualize the evolution of the translation machines and the genetic code. We conclude that the emergence of the information age from the RNA/protein world was a watershed event in the origin of life about four billion years ago.

Lacunary polyoxometalates (POMs) are negatively charged metal−oxo clusters, formally obtained from plenary topologies via fragment removal. Owing to the fragment removal, the lacunary POMs archetypes are rich in nucleophilic terminal oxo ligands, making them suitable for post-synthetic coordination with various heterometals. Trilacunary heteropolytungstates (het-ero-POTs) based on bowl-shaped {W9O30} framework incorporating a central lone-pair containing {XO3} hetergroup (X = AsIII, SbIII, and BiIII) function as all-inorganic scaffolds that in the presence of d-block metal cations typically construct sandwich-like dimers of Hervé and/or Krebs. Herein we review the preparative approaches, compositional and magnetic versatility of the constructing Hervé and Krebs type dimers and discuss prospective use as POMtronics.

Fractal array antennas are multiband arrays having ultra wide band and space filling facility. But Side lobe levels and large number of antenna elements are the major designing challenges of these arrays. In this paper, design and analysis of octagonal fractal array antenna is investigated with Morse-Thue fractal density tapering technique (MTFDT). Due to the proposed technique, a remarkable improvement has observed in Side lobe levels and thinning of the elements can also be attained at the various iterations of octagonal fractal array antenna. These arrays are analyzed and simulated by MATLAB-15 programming.

Globally, we are undergoing compelling changes in virtually every branch of the society and every aspect of our daily lives over the last couple of years due to the COVID-19 pandemic. In the words of Melinda Gates, the famous American Philanthropist, the pandemic has literally “magnified every existing inequality”. There has been much talk and an ongoing debate on the origin of SARS-CoV-2 (or, the novel coronavirus) as to whether have evolved purely naturally or has the contaminating element of human intervention somewhere in its trajectory. Proponents of the pure ‘natural evolution theory’, some of whom are bigshots in the field of virology have seem to take up great effort to stamp their authority right from the very early days of the pandemic on the slow natural evolution of the virus from its ancestrors by several publications in high-profile journals. These well-cited papers however provide rationals that are somewhat contradictory and are backed up with data which maybe argued as far from being complete and/or all-inclusive. As time progressed, alternative theories to that of the pure natural evolution of the virus emerged with strong counter-logics and gradually compiling genomic and epidemiological data along with detailed structural studies of relevant molecular interactions. All said and done, till date, compelling evidences lack on either end of the spectrum making the COVID origin debate still very mucg wide open. Regardless of whether purely natural or human-intervented the damage caused by the virus is unmistakable and has been an alarming concern to medical science lately. To that end, a section of the scientific community is devoting committed efforts using immuno-informatics to design and develop possible multi-epitope combination vaccines to cover a plethora of viral antigens and their plausible future mutations, alongside with the vaccines that are currently available.

We analyze the behavior of relativistic spherical objects within the context of modified $f(R,T)$ gravity considering Tolman VI spacetime, where gravitational lagrangian is a function of Ricci scalar(R) and trace of energy momentum tensor(T) i.e,$ f(R,T)= R+ 2\beta T$, for some arbitrary constant $\beta$. For developing our model, we have chosen $\pounds_{m} = -p$, where $ \pounds_{m}$ represents matter lagrangian. For this investigation, we have chosen three compact stars namely PSR J1614-2230 [Mass=(1.97$\pm$ 0.4)M$_\odot$; Radius= 9.69$_{-0.02} ^{+0.02}$ Km] ,Vela X-1 [Mass=(1.77$\pm$ 0.08)M$_\odot$; Radius= 9.560$_{-0.08} ^{+0.08}$ Km] and 4U 1538-52 [Mass=(9.69)M$_\odot$; Radius= 1.97 Km]. In this theory the equation of pressure isotropy is identical to standard Einstein's theory. So all known metric potential solving Einstein's equations are also valid here. In this paper, we have investigated the effort of coupling parameter ($\beta$) on the local matter distribution. Sound of speed and adiabatic index are higher with grater values of $\beta$ while on contrary mass function and gravitational redshift are lower with higher values of $\beta$ . For supporting the theoretical results, graphical representation are also employed to analyze the physical viability of the compact stars.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of coronavirus disease (COVID-19) that has resulted in a global pandemic. The clinical symptoms of the disease vary from mild illness to acute respiratory issues. Older age, diabetes, cardiac diseases predict poor prognosis in COVID-19 patients. Various reports mention the incidence of liver injury with transient elevations in the levels of aminotransferases (liver function enzymes). The clinical characteristics, etiology and underlying pathophysiological mechanisms associated with liver damage in SARS-CoV2 infected patients need to be explored. This review highlights the severity of the hepatic injury in COVID-19.

Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations—whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context to which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico-chemical properties of the amino acids (native vs. mutant) can still have minimal effects of protein thermodynamics. However, if a mutation causes significant perturbation of either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize on the effects of mutations on two important biophysical characteristics, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical characteristics.

The combined efect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong detterminant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web-platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and non-local complementarity (EC) in PPIs using the Complementarity Plot. It further derives an AI-based ∆G_binding with a prediction accuracy comparable to the state-of-the-art. This book-chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

Magneto-electrochemistry (MEC) is a unique paradigm in science, where electrochemical experiments are carried out as a function of an applied magnetic field, creating a new horizon of potential scientific and technological applications. Over the time, detailed understanding of this research domain was developed to identify and rationalize the possible effects exerted by a magnetic field on the various microscopic processes occurring in an electrochemical system, such as: electrolyte properties governed by charge-transfer process (electric conductivity, viscosity, and diffusivity), mass transfer, electrochemical kinetics and on the structure/quality of products formed either at the working electrode or in the electrochemical cell. Particularly, magnetic field controlled chiral architecture obtained from deposited metal, alloys and catalyst and their excellent enantio-recognition in experimental frame is highly appealing. Interestingly, Hall effect was also demonstrated in electrolytic medium via an impressive experimental technique which is being employed for further theoretical understanding in the field of magneto-electrochemical science. Later, a highly reproducible local temperature variation was observed in electrochemical electrolytes exposed to perpendicular magnetic and electric fields. However, until recent studies, none of the above mentioned reports considered the possibility of a spin-dependent related charge-transfer process. Recent experimental and theoretical studies reveal that electron’s transmission through chiral molecules is spin-selective and this effect has been referred to as chiral-induced spin-selectivity (CISS) effect. The CISS effect pave the way for the building up of a system characterized by a net magnetic moment exploiting the spin-filtering ability of chiral molecules. This interplay between chirality and magnetism may shed light on fundamental scientific aspects underlying the enantio-recognition and highly efficient electron-transfer that occurs in biological process.

Graph coloring is a manifestation of graph partitioning, wherein, a graph is partitioned based on the adjacency of its elements. Partitioning serves potentially as a compartmentalization for any structural problem. Vertex coloring is the heart of the problem which is to find the chromatic number of a graph. The fact that there is no general efficient solution to this problem that may work unequivocally for all graphs opens up the realistic scope for combinatorial optimization algorithms to be invoked. The algorithmic complexity of graph coloring is non-deterministic in polynomial time (NP) and hard. To the best of our knowledge, there is no algorithm as yet that procures an exact solution of the chromatic number comprehensively for any and all graphs within the polynomial (P) time domain. However, several heuristics as well as some approximation algorithms have been attempted to obtain an approximate solution for the same. Here, we present a novel heuristic, namely, the 'trailing path', which returns an approximate solution of the chromatic number within polynomial time, and, with a better accuracy than most existing algorithms. The ‘trailing path’ algorithm is effectively a subtle combination of the search patterns of two existing heuristics (DSATUR and Largest First), and, operates along a trailing path of consecutively connected nodes (and thereby effectively maps to the problem of finding spanning tree(s) of the graph) during the entire course of coloring, where essentially lies both the novelty and the apt of the current approach. The study also suggests that the judicious implementation of randomness is one of the keys towards rendering an improved accuracy in such combinatorial optimization algorithms. Apart from the algorithmic attributes, essential properties of graph partitioning in random and different structured networks have also been surveyed, followed by a comparative study. The study reveals the remarkable stability and absorptive property of chromatic number across a wide array of graphs. Finally, a case study is presented to demonstrate the potential use of graph coloring in protein design – yet another hard problem in structural and evolutionary biology.

Selective hydrogenation of nitrobenzene was carried out under solvent-free conditions using supported AuPd nanoparticles catalyst, prepared by modified impregnation method (MIm), as efficient catalyst. >99% yield of aniline (AN) was obtained after 15 hours at 90 °C, 3 bar H2 that can be used without any further purification or separation, therefore reducing cost and energy input. Supported AuPd nanoparticles catalyst, prepared by MIm, was found to be active and stable even after 4 recycle experiments whereas the same catalyst prepared by SIm deactivated during the recycle experiments. The most effective catalyst was tested for the chemoselective hydrogenation of 4-chloronitrobenzene (CNB) to 4-chloroaniline (CAN). The activation energy of CNB to CAN was found to be 25 kJ mol-1, while that of CNB to AN was found to be 31 kJ mol-1. Based on this, the yield of CAN was maximized (92%) by lowering the reaction temperature to 25 °C.

Low temperature dielectric property of charge/orbital ordered manganite, Pr1-xCaxMnO3 for 0.40≤x≤0.50 is investigated systematically as a function of Ca-content, x. The Ca-content dependence of dielectric permittivity and dissipation factor exhibit distinct maxima around x=0.45. The overall dielectric response of charge ordered Pr1-xCaxMnO3 is dominated by polarization induced by polaron hopping and exhibits thermally activated relaxation behaviour. The dielectric relaxation behaviour over the investigated temperature range is analysed with the help of small polaron hopping model and as well as variable range hopping model. The estimated polaron parameters also display non-monotonic variation with x and exhibit broad minima between x=0.425-0.45. The observed results suggest that a modulation of checker board type charge ordering pattern in Pr1-xCaxMnO3 is possibly taking place in the Ca-content range of investigation 0.40≤x≤0.50.

Introduction: Dengue is a mosquito transmitted arboviral infection. In dengue fever spontaneous bleeding in different parts of body occurs; but spontaneous bleeding into rectus muscle leading to haematoma formation is rare. Case Presentation: A 72-year-old hypertensive female presented with high grade intermittent fever with chills and rigors for last four days. She was diagnosed to have dengue fever (NS1Ag -Reactive) on the day before admission. At admission, on examination she was found to be have dehydration. Immediately she was put on IV fluids, antiemetics and other supportive therapy. At admission she had low platelets, low haemoglobin, total leucocyte counts, with raised liver enzymes. On day 3 of illness, she had significant drop of haematocrit with low blood pressure and subsequently she managed with packed red blood cell (PRBC) transfusion. But on 9th day of illness, she has severe right iliac fossa pain mimicking acute appendicitis. CECT of abdomen showed soft tissue lesion in right lower abdomen (Inflammatory/ hematoma along rectus sheath) which was managed conservatively as per surgical opinion. 10 days past discharge she had no pain in right iliac fossa and size of hematoma was significantly reduced. Discussion: In dengue fever, hematoma can be formed any vulnerable part in the but there are only a few cases reported to be having rectus sheath hematoma. Rectus sheath hematoma (RSH) has been mistaken for many acute abdominal diseases like- acute appendicitis. Our case mimicked acute appendicitis and managed conservatively with IV fluids, analgesics, Blood transfusion as it fits into RSH type II. Conclusion: It is important to be vigilant in the patients who presents with abdominal pain in severe dengue cases. Prompt imaging in relevant areas could make the diagnosis and father treatment possible.

Herbicides are widely used in agriculture to control weeds and enhance crop productivity. However, their application can have significant impacts on soil health and nutrient dynamics. This paper reviews the effects of herbicides on soil properties, microbial communities, and nutrient availability, with a particular focus on calcium deficiency. The review synthesizes findings from peer-reviewed journals and reports, including research conducted in Australia. The correlation between herbicide application and calcium deficiency is explored, highlighting both positive and negative impacts. The paper concludes with recommendations for sustainable herbicide use and alternative weed management strategies.

This academic paper explores the synergistic potential of Agroforestry systems and Climate-Smart Agriculture (CSA) in enhancing climate change adaptation strategies. Focusing on Australia and Pacific Island Countries, the study delves into the pivotal role of these approaches in mitigating the impacts of changing climate variables on agricultural production. By analysing important climate variables, their potential alterations, and their subsequent effects on crop production, the paper advocates for a holistic and forward-thinking approach to sustainable agriculture in the face of climate change.

The global food system has three recognized challenges: (a)increasing the availability of food consumption, (b) reducing food loss, and (c) reducing food waste. The increasing demand for food consumption, increasing quantity of food loss, and corresponding increase in food waste are resulting in serious health, aesthetic, social, economic, and environmental problems due to a lack of appropriate planning and management. Despite its importance, there is no clear, concise, and comprehensive definition of food consumption, loss, and waste. Generally, food consumption, food loss, and food waste are dealt with separately. This article presents a logically constructed ontological framework of food consumption, loss, and waste. It gives equal importance to all three aspects of global food management. The systematic ontological framework is general, and the analysis can be applied to any country. The framework deconstructs the combinatorial complexity of the problem and explicates the pathways to manage the consumption, loss, and waste. The ontological framework encapsulates 19*8*7*4*5*6 = 25,536 possible components of the challenge. A critical analysis based on available data using the framework will help to develop strategies to deal with the problem. It can help to discover the gaps and to find ways to bridge the gaps. It is novel to conceptualize food consumption, loss, and waste together.

Data Extraction is a technique is called as clustering which is used to retrieve data either from the files or data bases or both. This paper focuses on the performance evaluation parameters of the clustering algorithms based on different parameters or conditions or constraints and parameters which are used to perform the clustering process to get the clusters on the data sets. Therefore best clusters are retrieved when best parameters or conditions or constraints or preferences which are applied on the data sources for the clustering process. These parameters or conditions or constraints are opted by the user called as user preferences.

The suitability of an animal model to study human diseases heavily relies on the similarity between the two species at the genetic, epigenetic, gene expression and metabolic levels. However, consistent data from different animal models at each level to evaluate this suitability is lacking. With the availability of genome sequences for many mammalian species it is now possible to compare animal models based on the genomic similarities. Here, we compare the coding sequences (CDS) of five mammalian models that include rhesus macaque, marmoset, pig, mouse and rat with those from human. We identified 10,316 conserved CDS across the five organisms and human based on the sequence similarity. Mapping the human disease-associated single nucleotide polymorphisms (SNPs) from these conserved CDS in each species has identified species-specific association with various human diseases. While associations for a disease such as colon cancer were prevalent in multiple model species, pig showed the highest number (117 diseases) of model-specific human disease associations. Based on the percentage of disease-associated SNP-containing genes, marmoset models are well suited to study many human ailments including behavioral and gastrointestinal diseases. Comparison of gene expression levels of the conserved CDS from colon, heart, kidney, lung, skeletal muscle, and spleen tissues in these models showed correlations with human expression levels in a tissue-specific manner. In the gastrointestinal tissues (colon, spleen), the pig showed the highest correlation while mouse displayed a better correlation in the heart and kidney. This study demonstrated genomic as well as tissue-specific expression-based similarity evaluation of five animal models against human that could help investigators select a suitable animal model to study their targeted disease.

In the present time, the biggest problem of the world is the outbreak of novel coronavirus. Novel coronavirus (COVID-19), this one name has become a part of our daily lives over the past few months. Beyond the boundaries of medical science, coronavirus is now the main subject of research in all fields like Applied Mathematics, Economy, Philosophy, Sociology, Politics upto living room. The epidemic has brought unimaginable changes in our traditional habits and daily routines. Thousands of people in our country are fighting with the rest of the world to survive in various new situations. There are different kinds of coronavirus appeared in different times. In this time, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is responsible for the coronavirus disease of 2019 (COVID-19). This virus was first identified towards the end of 2019 in the city of Wuhan in the province of Hubei in China. Within very short duration of time and very fast, it has spread throughout a large part of the world. In this study, the main aim is to investigate the spreading rate, death rate, recovery rate due to corona virus infection and to study the future of the coronavirus in India by using mathematical modeling based on the previous data. Mathematical models, in this situation, are the important tools in recruiting effective strategies to fight this epidemic. India is at high risk of spreading the disease and is facing many losses in socio-economic aspects. With current infection rates and existing levels of personal alertness, the number of infected people in India will increase at least in the next three months. Proper social awareness, maintain of social distance, large rate of testing and separation may break the chain of the Coronavirus-2.

We investigate entropy generation in unsteady three-dimensional axisymmetric MHD nanofluid flow over a non-linearly stretching sheet. The flow is subject to thermal radiation and a chemical reaction. The conservation equations were solved using the spectral quasi-linearization method. The novelty of the work is in the study of entropy generation in three-dimensional axisymmetric MHD nanofluid and the choice of the spectral quasilinearization method as the solution method. The effects of Brownian motion and thermophoresis are also taken into account when the nanofluid particle volume fraction on the boundary in passively controlled. The results show that as the Hartman number increases, both the Nusselt number and the Sherwood number decrease whereas the skin friction increases. It is further shown that an increase in the thermal radiation parameter corresponds to a decrease in the Nusselt number. Moreover, entropy generation increases with the physical parameters.

Difference in two quantum states defined for a point in space time is represented by space time curvature and the superposition of these quantum states will affect the momentum of mass existed at that point. Analogy of the same with Einstein’s General relativity field equation and Einstein’s gravitational constant , indicated an equation containing ‘Hubble’s constant H , Einstein’s gravitational constant ‘H’ and Siva’s constant ‘K’. Its output is a quadratic equation. Solution to this quadratic equation predicted a fundamental force particle. This solution gives the diameter of that particle. From the fundamental principles, the diameter of this force particle gives the coupling constant of that fundamental force. Prediction and review of this particle may provide us new insights in to quantum physics and standard model of particle physics. From the first principles, it is discussed that this particle can be claimed as a force carrying particle of a fundamental force related to dark matter. This work has provided simple explanation to reconcile quantum nature of space time with smooth space time of General Relativity. This has demanded the necessity to introduce a new postulate in quantum mechanics which is beyond Hilbert space and Dirac notation. A new perspective of uncertainty principle in quantum mechanics has been elaborated.

Specifically this work is based on the concept of ‘dark energy’ as a phenomenal effect of expansion through which a theoretical value of Hubble’s constant has been introduced. It is a constant that is based on a postulate that the square root of ratio of the Siva’s constant ‘K’ and Hubble’s constant ‘H’ is exactly equal to the diameter of the neutral hydrogen atom. That is the, exact theoretical value of Hubble’s constant. This is always constant .However, space-time conversions and the changes in the velocity of light will affect the distance and velocity in Hubble’s equation. Deviation factors in velocity, distance and the Hubble’s constant have been calculated separately which are affected by change of velocity of light with expansion of space time. The observations are related to red shift. So the change factor in terms of Hubble’s constant has been calculated with respect to red shift. Thus a factor which affects the theoretical Hubble’s constant has been calculated to find the exact value of Hubble’s constant that satisfies the experimental results. The present theoretical value of Hubble’s constant has been calculated. The experimental results by plank, ACT (Atacama Cosmology Telescope), WMAP (Wilkinson Microwave Anisotropy Probe) etc. experiments are at par with the theoretical result. Thus the ontology of space time introduced in this theoretical work supposed to be correct and leads to a new theory of origin of universe for which a brief description has been provided.

COVID-19 is characterized by an unprecedented abrupt increase in the viral transmission rate (SARS-CoV-2) relative to its pandemic evolutionary ancestor, SARS-CoV (2003). The complex molecular cascade of events related to the viral pathogenicity is triggered by the Spike protein upon interacting with the ACE2 receptor on human lung cells through its receptor binding domain (RBD_Spike). One potential therapeutic strategy to combat COVID-19 could thus be limiting the infection by blocking this key interaction. In this current study, we adopt a protein design approach to predict and propose non-virulent structural mimics of the RBD_Spike which can potentially serve as its competitive inhibitors in binding to ACE2. The RBD_Spike is an independently foldable protein domain, resilient to conformational changes upon mutations and therefore an attractive target for strategic re-design. Interestingly, in spite of displaying an optimal shape fit between their interacting surfaces (attributed to a consequently high mutual affinity), the RBD_Spike–ACE2 interaction appears to have a quasi-stable character due to a poor electrostatic match at their interface. Structural analyses of homologous protein complexes reveal that the ACE2 binding site of RBD_Spike has an unusually high degree of solvent-exposed hydrophobic residues, attributed to key evolutionary changes, making it inherently ‘reaction-prone’. The designed mimics aimed to block the viral entry by occupying the available binding sites on ACE2, are tested to have signatures of stable high-affinity binding with ACE2 (cross-validated by appropriate free energy estimates), overriding the native quasi-stable feature. The results show the apt of directly adapting natural examples in rational protein design, wherein, homology-based threading coupled with strategic ‘hydrophobic ↔ polar’ mutations serve as a potential breakthrough.

Excessive intracellular glucose in insulin independent tissues including nerve, nephron, lens and retina invites mishandling of metabolism of glucose resulting in a background of increased oxidative stress, advanced glycation end products (AGE) formation, lipid peroxidation and failure of antioxidant defense systems in type 2 diabetes mellitus (T2DM). All these detrimental biochemical anomalies ultimately attack biological membranes and especially capillary beds of retina and glomerulus of kidney, resulting in break-down of inner blood-retinal i.e. initiation of diabetic retinopathy (DR). If these disarrays are corrected to a large extent, development of DR can be avoided or delayed. In this prospective clinical trial, 185 patients with T2DM who received B-vitamins, vitamin-C, and E along with anti-diabetic medication for five years, demonstrated a slower rate of the development of DR and reduced abnormal biochemical mediators like reactive oxygen species (ROS), malondialdehyde (MDA), AGE, and vascular endothelial growth factor (VEGF) compared to 175 T2DM individuals who were treated with only anti-hyperglycemic drugs.

Nickel based superalloys finds extensive usage in manufacturing of intricate part shapes in gas turbine, aircraft, submarine, and chemical industries owing their excellent mechanical property and heat resistant abilities. However, machining of such difficult-to-machine alloys up to the desired accuracy and preciseness is a complex task owing to a rapid tool wear and failure. In view of this, present work proposes an experimental investigation and optimization of process parameters of the cryogenic assisted turning process during machining of Nimonic C-263 super alloy with a multilayer CVD insert. Taguchi’s L-27 orthogonal array is used plan the experiments. Effect of input parameters viz. cutting speed (N), cutting feed (f), depth of cut (d) are studied on responses viz. surface roughness (SR), nose wear (NW) and cutting forces (F) under hybrid cryogenic (direct+indirect) machining environment. A scanning electron microscope (SEM) analysis is carried out to explore the post-machining outcomes on the performance measures. The multiple responses are converted in to single response and ranked according to Taguchi based gray relational grade (TGRG). Feed rate (f) is found to be the most influential parameter from the analysis of variance of GRG. The means of GRG for each level of process parameters are used to improve the optimal process parameters further. Finally, the confirmative experiment is performed with these optimal set of process parameters which showed an improvement of 9.34% in the value of GRG. The proposed work can be beneficial to choose ideal process conditions to enhance the performance of turning operation.

In recent years, hetero atom incorporated specially structured metal-free carbon nanomaterials have drawn huge attention among researchers. In comparison to the un-doped carbon nanomaterials, hetero atoms like nitrogen, sulphur, boron, phosphorous etc. incorporated nanomaterials become well-accepted as potential electrocatalysts in water splitting, supercapacitors and dye-sensitized solar cells. This review emphasizes on the mostly popular synthetic strategies utilized in last two decades and their excellent performance in electrocatalytic studies.

This study investigates the efficiency of BEAM (Biologically Enhanced Agriculture Management) inoculant, derived from compost produced under Dr. David Johnson-Su's composting technique, in promoting plant growth. A winter mix of pasture comprising Oats, Tetila, unhulled Couch, and Haifa was used as the test crop. Seeds were soaked in BEAM inoculant, water (control), and a competitor product 'Companion.' Results revealed significant enhancements in germination rates, foliage growth, and root development in the BEAM-treated group compared to controls. Further research aims to identify the microbial components of BEAM and explore its potential as a suppressant for fusarium species in orchards.

Climate change affects both human and natural systems. Afghanistan ranked globally on the top of highly vulnerable countries to the adverse effects of climate change. The agricultural communities of Afghanistan is highly affected by climate change. Understanding farmers’ real experiences on changing climate become crucial in planning the future adaptation strategies. This study assessed the farmers' perception of climate change and its impacts on farming communities. Primary data were collected through face-to-face interviews conducted with 120 household heads. Additional qualitative data were collected by conducting 4 Focus Group Discussions (FGDs), 4 Historical Timeline Calendars (HTCs), 18 Key Informant Interviews (KII), and sketches of 4 Crop Calendars. The study reveals that climate indicators have varied and changed. The farmers express their experiences of decreasing snowfall in winter and annual rainfall in spring and summer, which led to the intensity and frequency of drought and water shortages for agriculture and rangelands in the upper and lower part of the valleys. The temperatures in winter and summer have increased and led to earlier snow melting, earlier blooming, flowering, and greening of the plants. These changes affected both positively and negatively. There is a half-month new opportunity for cultivation and increased earlier animal ranching in the rangelands. These findings can be valuable inputs for developing effective and efficient adaptation strategies.

Abstract: The COVID-19 pandemic has disrupted global energy markets and caused significant socio-economic impacts worldwide. The pandemic has also impacted the energy sector, with demand for energy reducing due to lockdowns and reduced economic activity. Hence, this paper aims to provide a comprehensive and analytical review of the impact of COVID-19 on the energy sector and discuss the potential role of artificial intelligence (AI) in mitigating some of these effects. This review examines the changes in energy demand patterns resulting from the pandemic and the implications for the energy industry, including the shifts in policymaking, communication, digital technology, energy conversion, environmental, energy market, and power system operation. The analysis of the energy pattern is discussed according to the timeframes which include pre-, mid-, and post-pandemic periods. Furthermore, we explore how AI can be used to improve energy efficiency, optimize energy use, and reduce energy wastages. The potential for AI to contribute to developing more efficient and sustainable energy systems has also been addressed. Lastly, we highlighted AI’s challenges, which play a significant role in the energy sector's response to the COVID-19 pandemic. The recommendations for AI applications in the energy sector for the transition to a more sustainable energy future are outlined. Information corroborated in this review is expected to provide important guidelines for crafting future research areas and directions in preparing the energy sector for any unforeseen circumstances or pandemic-like situations.

The corrosion inhibition behavior of cetyl pyridinium chloride (CPC) on mild steel (MS) in 0.5 M H₂SO₄ solution was examined using the weight loss method and potentiodynamic polarization method. The experimented MS surface was characterized by Fourier Transform Infrared Spectroscopy (FTIR), Field Emission Scanning Electron Microscopy (FESEM), and Atomic Force Microscopy (AFM). Corrosion rate and corrosion efficiency was calsulated via weight loss and potentiodynamic polarization methods where the corrosion rate in 0.5 M H₂SO₄ , 0.000192 M, and 0.0077 M of CPC solution was 12.09×10^-3, 8.23×10^-3, 6.63×10^-5 (mm/Year), respectively and the inhibition efficiency of 94.42% and 98.54%. FESEM studies demonstrated the corrosion attack on MS surface. Also, the EDX showed the presence of nitrogen of CPC. Thus, CPC acts as an important corrosion inhibitor.

The paper attempts to explore the relationship between federalism and rural development. Federalism is a division of power, responsibility and accountability to bring the administrative and political power closer to the ground and essentially to increase the good governance. On the other hand, rural development is a complex and multidimensional issue- especially much demanding for least developed and developing countries. A descriptive and qualitative approach was carried out to study the complex relationship between rural development and federalism. Similarly, a SWOT analysis was carried out to have a better understanding of the relationship. The study found that there is significant potentiality for accelerated development of rural landscape in federalism if carefully executed. However, on the other hand, federalism may pose several risks on rural development and may restrict the development pace if not executed with appropriate care and understanding. Therefore, the study concludes that cooperation and coordination among the federal structures are crucial for better development of the rural economy.

Saponins are a diverse group of naturally occurring plant secondary metabolites present in a wide range of foods ranging from grains, pulses, green leaves to sea creatures. They consist of a hydrophilic sugar moiety linked to a lipophilic aglycone, resulting in an amphiphilic nature and unique functional properties. The amphiphilic structures enable saponins to exhibit surface-active properties, forming stable foams and complexes with various molecules. In the context of food applications, saponins are utilized as natural emulsifiers, foaming agents, and stabilizers. They contribute to texture and stability in food products and have potential health benefits, including cholesterol-lowering and anticancer effects. Saponins possess additional bioactivities that make them valuable in the pharmaceutical industry as anti-inflammatory, antimicrobial, antiviral, and antiparasitic agents to name a few. Saponins can demonstrate cytotoxic activity against cancer cell lines and can also act as adjuvants, enhancing the immune response to vaccines. Their ability to form stable complexes with drugs further expands their potential in drug delivery systems. However, challenges such as bitterness, cytotoxicity, and instability under certain conditions need to be addressed for effective utilization of saponins in foods and related applications. In this paper, we have reviewed chemistry, functionality, and application aspects of saponins from various plant sources and have summarised the regulatory aspects of food application of Quillaja saponins. Further research to explore the full potential of saponins in improving food quality and human health has been suggested. It is expected that this article will be a useful resource for researchers in food, feed, pharmaceutical and material science.

One of the biggest challenges in modern physics is how to unify gravity with quantum theory. There is an absence of a complete quantum theory of gravity, and conventionally it is thought that the effects of quantum gravity occur only at high energies (Planck scale). Here we suggest that certain novel quantum effects of gravity can become significant even at lower energies and could be tested at laboratory scales. We also suggest a few indirect effects of dark energy that can show up at laboratory scales. Using these ideas, we set observational constraints on radio recombination lines of the Rydberg atoms. We further suggest that high-precision measurements of Casimir effects for smaller plate separation could also show some manifestations of the presence of dark energy.

Since December 2019, the world is fighting against coronavirus disease (COVID-19). This disease is caused by a novel coronavirus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This work focuses on the applications of machine learning algorithms in the context of COVID-19. Firstly, regression analysis is performed to model the number of confirmed cases and death cases. Our experiments show that autoregressive integrated moving average (ARIMA) can reliably model the increase in the number of confirmed cases and can predict future cases. Secondly, a number of classifiers are used to predict whether a COVID-19 patient needs to be admitted to an intensive care unit (ICU) or semi-ICU. For this, classification algorithms are applied to a dataset having 5644 samples. Using this dataset, the most significant attributes are selected using features selection by ExtraTrees classifier, and Proteina C reativa (mg/dL) is found to be the highest-ranked feature. In our experiments, random forest, logistic regression, support vector machine, XGBoost, stacking and voting classifiers are applied to the top 10 selected attributes of the dataset. Results show that random forest and hard voting classifiers achieve the highest classification accuracy values near 98%, and the highest recall value of 98% in predicting the need for admission into ICU/semi ICU units.

: This study investigates the use of various thunderstorm indices in predicting severe thunderstorms events during the monsoon season in four different regions in India. The research evaluates the performance of the prediction model using a model skill score and utilizes the Weather Research and Forecasting (WRF) model with the double moment microphysics scheme to simulate model cases. It also compares fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for predicting severe thunderstorms events. The results of this study show that in-corporating thunderstorm indices with model skill scores improves severe thunderstorms fore-casting in the monsoon season in India. The result revealed that determining the optimal threshold for each index is crucial in achieving accurate predictions. The study also highlights the importance of considering multiple indices rather than relying on a single index to predict severe thunderstorms events. The advance indices such as Energy Helicity Index (EHI), Supercell Composite Parameter (SCP), mainly works well with extreme severe thunderstorms. The simplistic indices can predict the weak or severe thunderstorm easily. The use of multiple thunderstorm indices can also help mete-orologists to make more accurate predictions, which can further enhance public safety. In conclu-sion, this study demonstrates the potential of incorporating thunderstorm indices with model skill scores like HSS and TSS and combinations of different skill scores in severe thunderstorms fore-casting during the monsoon season in India. Future research can build upon the findings of this study to develop more accurate and reliable severe weather forecasting models.

Water reuse is critical to national development, sustenance, and survival in this era of climate, demographic, and social changes. There is no national systemic approach to address the challenge systematically. The paper presents a framework and a method to develop a national research strategy for water reuse. It presents an ontology of water reuse strategy that encapsulates the combinatorial complexity of the problem clearly, concisely, and comprehensively. Subsequently, it discusses the method to use the framework to develop a national strategy, adapt it through feedback and learning, and ultimately effect a revolutionary change in the strategy for water reuse.

This article delves into the pivotal role that biodiversity of soil microbes plays in bolstering tree health and overall agroecosystem productivity. With the advent of regenerative agricultural practices, there has been a resurgence of interest in understanding the complex interactions between soil microbiota and plant roots, particularly through processes like rhizophagy. Rhizophagy, the digestion of living root cells by soil microorganisms, stands out as a fundamental mechanism in nutrient cycling and overall tree vitality. By examining the impact of enhanced microbial diversity on tree health, we shed light on the practical applications, such as the utilization of the BEAM Inoculant product, which has demonstrated remarkable efficacy in augmenting soil health and, consequently, tree Vigor

Data is present in the data sources like Files and Data bases, Retrieval of information from that data sources is one of the important issue nowadays. So for retrieval information from the data sources clustering is used. In the present market different types of clustering algorithms were available. But opting of the clustering is based on user requirements. This paper focuses on the study of hierarchical clustering approach on different conditions or measures or with customer choices like clustering process number of clusters generated at each level, number of levels, attributes range for performing the clustering on the given data set. In brief overview we discuss the hierarchical approach for clustering algorithm with the user opting choices.

Background: Bacterial infections constantly have a large impact on public health, because of increased rates of resistance and reduced frequency of development of novel antibiotics. The utility of conventional antibiotics for treating bacterial infections has become increasingly challenging. The aim of the study was to assess the antibacterial effect of β -Lapachone, a novel synthetic compound. Methods: The antibacterial activity of the β -Lapachone compound was examined against laboratory strains by agar well diffusion method, minimal inhibitory concentration (MICs), and minimal bactericidal concentration (MBCs). Growth kinetics inhibition in presence of β -Lapachone on Staphylococcus aureus, Staphylococcus epidermidis and Pseudomonas aeruginosa (ATCC 27853) was assessed by MABA. Crystal violet blue assay was used for biofilm inhibition assays, biofilm eradication assay and for molecular modeling PyMOL was used. Results: β -Lapachone exhibited potent antimicrobial activity against laboratory strains of bacteria with MIC of 0.2 mM for S. saprophyticus and Staphylococcus aureus, and 0.04 mM for Staphylococcus epidermidis and Pseudomonas aeruginosa ATCC 27853. The inhibition of catalase enzyme was found to be the cause for its antibacterial activity. Molecular modeling predicted the binding of β -Lap at active site and heme binding site of catalase, KatA. The activity of some commercial antibiotics was enhanced in association with β -Lap. In addition, β -Lap inhibited the biofilm formation and eradicated the already formed and ultra-mature biofilms of aforesaid bacterial strains.

The concept of neutrosophic number is a significant mathematical tool to deal with real scientific problems because it can tackle indeterminate and incomplete information which exists generally in real problems. In this article, we use neutrosophic numbers (a + bI), where a and bI denote determinate component and indeterminate component respectively. We explore the situations in which the input information is needed to express in terms of neutrosophic numbers. We define score functions and accuracy functions for ranking neutrosophic numbers. We then define a cosine function to determine unknown criteria weights. We define neutrosophic number harmonic mean operators and proved their basic properties. Then, we develop two novel MCGDM strategies using the proposed aggregation operators. We solve a numerical example to demonstrate the feasibility and effectiveness of the proposed two strategies. Sensitivity analysis with variation of “I” on neutrosophic numbers is performed to demonstrate how the preference ranking order of alternatives is sensitive to the change of “I”. The efficiency of the developed strategies is ascertained by comparing the obtained results from the proposed strategies with the existing strategies in the literature.

Municipal solid waste management is a growing worldwide challenge. Landfill mining is a promising technique to reduce MSW landfills. In the present study, arbitrary embankments made of different MSW fractions are analyzed to find the safety factor of their slopes. Properties of Municipal solid waste fines (MSWF), Shredded MSW, MSW Incineration Ash, and MSW having particles less than 35 mm are used in the analysis. For doing the same, finite element-based application 'Plaxis-2D' is used. The safety of slopes is assessed while the water table is inserted, and a uniform load is applied to the embankment to establish how well they will perform in critical situations. It was found that the slope made of shredded MSW is most safe due to its low density and high friction angle, while the least safe slope is made of MSW having less than 35 mm fractions.

The asymptotic dynamics of the classes of rational difference equations (RDEs) of third order defined over the positive real-line as $$\displaystyle{x_{n+1}=\frac{x_{n}}{ax_n+bx_{n-1}+cx_{n-2}}}, \displaystyle{x_{n+1}=\frac{x_{n-1}}{ax_n+bx_{n-1}+cx_{n-2}}}, \displaystyle{x_{n+1}=\frac{x_{n-2}}{ax_n+bx_{n-1}+cx_{n-2}}}$$ and $$\displaystyle{x_{n+1}=\frac{ax_n+bx_{n-1}+cx_{n-2}}{x_{n}}}, \displaystyle{x_{n+1}=\frac{ax_n+bx_{n-1}+cx_{n-2}}{x_{n-1}}}, \displaystyle{x_{n+1}=\frac{ax_n+bx_{n-1}+cx_{n-2}}{x_{n-2}}}$$ is investigated computationally with theoretical discussions and examples. It is noted that all the parameters $a, b, c$ and the initial values $x_{-2}, x_{-1}$ and $x_0$ are all positive real numbers such that the denominator is always positive. Several periodic solutions with high periods of the RDEs as well as their inter-intra dynamical behaviours are studied.

Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Forest officials issue the warnings to the public on the basis of fire danger index classes. There is no fire danger index for the country India due to the sparsely distributed meteorological stations. Previous studies suggest that Static Fire Danger Index (SFDI) as well as Dynamic Fire Danger Index (DFDI) have been derived from the satellite datasets. In this study, we have made an attempt to integrate both the Static and Dynamic fire danger indices and also used the Near Real Time (NRT) data sets that can be available for download through NASA FTP server after one hour of the satellite overpass. In this study, DFDI has been calculated from the MODIS Terra NRT Land Surface Temperature (MOD11_L2) and MODIS TERRA NRT surface reflectance MOD09. Finally, The Forest Fire Danger Index (FFDI) has been developed from the static and dynamic fire danger indices by the additive model and the overall accuracy was around 81.27%. Thus, the FFDI has been useful to predict the fire danger accurately and can be useful anywhere, where the meteorological stations are un-available.

Initial attempts to develop monoclonal antibodies as therapeutics to resolve influenza infections focused mainly on searching for antibodies with the potential to neutralise the virus in vitro with classical haemagglutination inhibition and micro-neutralisation assays. This led to the identification of many antibodies that bind to the head domain of haemagglutinin (HA) which generally have potent neutralisation capabilities that block viral entry or viral membrane fusion. However, this class of antibodies has a narrow breadth of protection in that they are usually strain specific. This led to the emphasis on stalk targeting antibodies which are able to bind a broad range of viral targets that span across different influenza subtypes. Recently, a third class of antibodies targeting the vestigial esterase (VE) domain have been characterised. In this review, we describe the key features of neutralising VE targeting antibodies and compare them with head and stalk class antibodies.

In the present scenario of increased industrialization and transportation in the world leads to increased consumption of fossil fuels which in turns leads to depletion of fossil fuels at a faster rate. Fossil fuels combustion is the dominant source for greenhouse gases and global warming. In view of energy crisis raised in 1970’s and environmental concern, many researches are directed towards search of alternative fuels which can replace consumption of fossil fuels as well as reduce pollution. In developing countries like India which is agriculture land the best alternative fuels are biodiesel and ethanol as they are produced from renewable feedstocks like sugarcane, corn etc. and they are also less hazardous to environment because of lower emission property. Ethanol blends results in significant reduction of emissions of hydrocarbon (HC), carbon monoxide (CO) and particulates matter but increase in nitrogen oxides (NOx). The main purpose of ethanol addition is to reduce the viscosity of biodiesel blends. This paper represents significance of Compression Ratio(CR) on performance, combustion and emission of single cylinder four stroke CI diesel engine by using various compression ratios such as 17.5:1, 18.5:1 and 19.5:1. Experimental research has been conducted with four types of ethanol blends, namely E10, E20, E30 and E40. Ethanol-biodiesel mixture mixed with 2% emulsifier 1% diethyl carbonate and 1% ethyl acetate to maintain similarity and to avoid phase separation. Ethanol subjected to high compression ratio has been used to increase brake thermal efficiency (BTE). The compression ratio has been increased to improve the combustion and performance of the diesel engine.

Natural convection is intensively explored, especially in a valley-shaped trapezoidal enclosure, because of its broad presence in both technical settings and nature. This study deals with a trapezoidal cavity, which is initially filled with linearly stratified air. Though the side walls remain adiabatic, the bottom wall is heated, and the top wall is cooled. For the stratified fluid (air), the temperature of the fluid adjacent to the top and the bottom walls is the same as that of the walls. Natural convection in the trapezoidal cavity is simulated in two dimensions using numerical simulations, by varying Rayleigh numbers (Ra) from 100 to 108 with constant Prandtl number, Pr = 0.71, and aspect ratio, A = 0.5. According to numerical results, the development of transient flow within the enclosure owing to the predefined conditions for boundary may be categorized into three distinct stages: early, transitional, and steady or unsteady. The flow characteristics at each of the three phases and the impact of the Rayleigh number on the flow’s growth are stated in this study. In addition, heat transfer through the bottom and the top surfaces is described in this study.

In the modern days the data is coming from various devices like mobiles, tabs, laptops, computers, IOT devices e.tc and is stored in the electronics form in the systems using the concept of files or data bases. Clustering Technique is used to extract data from these sources. Improving the accuracy of the model is improved using the feature selection or feature engineering or both. The feature engineering is used to add a new feature based on the global mean. Along with that new feature in addition to the feature selection increase the overall accuracy of the model.

Clustering is a data mining task used for the data extraction from the data bases or files. Clustering is used to find unknown groups present in the data sources like files or data bases. This paper focuses on clustering algorithms performance dependency on the parallel clustering platforms and the clustering algorithms along with their clustering criteria. The problems with the present Traditional clustering algorithms were throughput and data source size changes (scalability). So they can’t address the big data. So for handling the huge volumes of data, parallel clustering algorithms along with clustering criteria were used. For processing the big Data Parallel clustering algorithms are of two types based on computing platforms used. They were the horizontal scaling platforms and vertical scaling platforms.

Wood and plants are made of fibers which contain, in addition to cellulose, lignin and hemicelluloses. Xylan and galactoglucomannan are the dominant secondary cell wall hemicelluloses. Besides fibers’ traditional use in textile and paper industries, the fibers are important materials for the biorefinery industry and for developing biocomposites. In the former context, renewable lignocellulosic biomass is used as feedstock to sustainably produce fuels, chemicals, and polymers. On the other hand, in relation to composites, cellulose fibers which are anisotropic play an important reinforcing role in fiber-matrix interactions. However, for these and other applications, structural analysis of the fibers is important and among many analytical techniques used, Raman spectroscopy is one of the methods. However, given the structural similarity between hemicelluloses and cellulose, many of their Raman contributions overlap. While the wavenumber positions corresponding to the Raman contributions of cellulose, hemicelluloses, and lignin are known, extent to which overlapping features of hemicellulose modify the spectrum of cellulose is not yet fully understood. The present investigation focuses on this aspect by using xylan as one of the hemicelluloses. As a model system, samples with various mass ratios of microcrystalline cellulose (cotton) and xylan (birch wood) were prepared and analyzed by FT-Raman spectroscopy. The findings were then applied to interpret Raman spectra of selected xylan containing delignified plant fibers. It is hoped that insights gained would lead to better interpretation of the spectra of natural and treated plant materials.

The study investigates the effect of corporate governance characteristics on the financial performance of 124 Indian-listed companies that have undergone mergers and acquisitions during 2014–2020. It employs several performance measures, such as short-term capital market performance, long-term capital market performance, accounting-based and market-based measures, and firm-level control factors. The study found board size to be a positive and significant factor affecting short-term market performance. Further, it also documents weak linkages with other corporate governance variables like board independence, CEO duality, etc. Regarding control variables, leverage, the company's age, price-to-book ratio, and research and development expenses significantly impact the acquiring companies' financial returns. The study's findings add to our understanding of corporate governance's impact on performance in cases such as mergers and acquisitions.

Lytic bacteriophages have the efficacy to act and eradicate pathogenic bacteria as an attractive tool in the near future. Bacteriophages specifically kill multidrug-resistant bacteria even which have the capacity to form biofilms. The present review mainly focused on the efficacy of bacteriophages and cocktails as therapeutic agents against predominate MDR-bacteria and their biofilms which are isolated from septic wound infections. The body of evidence includes data from studies investigating bacteriophages from sewage samples as novel antibacterial and antibiofilm agents against pathogenic bacteria. The goal of this review is to present an overview on predominant bacteria from septic wound infection, the biofilm-forming capacity of bacteria, lytic effect of bacteriophages and phage cocktails with an emphasis on the application of bacteriophages against septic wound causing bacteria.

In this exploratory work, we have examined the impact of an external static electric field on the H2 storage capacity of clathrate hydrates. To this end, we have chosen the 512 clathrate hydrate and examined the encapsulation of 1 - 4 H2 molecules within it. Results suggest that with the help of moderate electric fields (±0.10 to ±0.31V/Å), the encapsulation of 1 H2 molecule within the 512 clathrate hydrate becomes thermodynamically more favorable. In the cases of 4H2@512 and 2H2@512, however, the concerned processes become thermodynamically more favorable only when the field is applied along a specific direction. No favorable outcome could be observed in the case of 3H2@512, irrespective of the direction of the applied field. It appears from the results obtained herein that it might be possible to design a suitable external electric field to facilitate the hydrogen storage capacity of clathrate hydrates at ambient conditions, at least in certain cases.

Cardiovascular diseases (CVDs) remain the leading cause of mortality. Traditional approaches to diagnosing and treating CVDs often involve interpretation, which can lead to errors. The emergence of Artificial Intelligence (AI) and Machine Learning aids the management of CVDs. AI examines amounts of data and identifies patterns offering solutions for disease detection, risk prediction, and patient care. The American Heart Association's 2023 Scientific Sessions (AHA23) showcased studies that highlight AIs potential to revolutionize cardiac healthcare. This article presents a review of these cutting-edge studies exploring how AI is applied in health, the outcomes of these applications, and their implications for clinical practice. The insights gained from this review will contribute to the discussion on AI's role in healthcare and its capacity to enhance outcomes.

The methamphetamine epidemic continues to worsen each year and has contributed to more overdose deaths than opioids. Methamphetamine was listed in the top ten lethal drugs in 2021 in the United States. The drug has been shown to cause many health problems. One solution to this crisis is through vaccinations. Although still early in the development phase, vaccinations have been found to improve withdrawal symptoms and decrease drug seeking behavior with minimal health side effects compared to medications.

The study investigated the effect of a multi-strain probiotic compound containing Bacillus coagulans, Enterococcus faecalis, Clostridium butyricum and Bacillus mesentericus as in-feed zinc oxide (ZnO) alterna-tive on growth performance, diarrhea incidence, antioxidant status, lipid profile, stress and immunity in weaned piglets. A total of 72 piglets were randomly divided into 3 groups with four replicates of six piglets each. The details of the groups were as follows; (i) weaned control group (WC) received basal diet, (ii) weaned probiotic group (WB) received basal diet and probiotics and (iii) positive control (PC) group received basal diet with 2500 mg/kg ZnO. The experiment lasted for 28 days. Probiotic supple-mentation improved growth performance and reduced diarrhea rate. Probiotics supplementation im-proved lipid profile; significantly lower levels of total cholesterol and low-density lipoprotein cholesterol and higher level of high-density lipoprotein cholesterol in WB group as compared to those of the control group (WC) were recorded. Probiotic supplementation stimulated antioxidant defense system by in-creasing total antioxidant capacity and decreasing lipid peroxidation. Probiotic supplementation down-regulated the stress biomarkers like serum cortisol and serum heat shock proteins. WB group showed higher serum levels of IgG and IgM throughout the study period and higher IgA at day 28 as compared to WC. These data suggest that supplementation of the probiotic minimizes the weaning stress, thereby improves the growth performance, lipid profile, antioxidant status and systemic as well as mucosal immunity. Therefore, the multi-strain probiotic compound may be used to replace ZnO in weaned piglets.

Despite a significant advance in the pathophysiological understanding of peripheral nerve damage, the successful treatment of large nerve defects remains an unmet medical need. In this article, axon growth guidance for peripheral nerve regeneration is systematically reviewed and discussed mainly from the engineering perspective. In addition, the common approach to surgery, bioengineering approaches to emerging technologies (i.e. optogenetic stimulation and magnetic stimulation) for functional recovery are discussed, with pros and cons. Alternatively, clear future perspectives of axon guidance and nerve regeneration are addressed.

This study aims to investigate utilizing Precipitated Calcium Carbonate (PCC), a by-product of sugar beet, as a soil stabilizer for addressing settlement issues beneath pavements. Various tests were conducted to assess the engineering properties of PCC-stabilized subgrades using PCC obtained from Amalgamated Sugar Cooperation in Twin Falls, Idaho. Local loess samples, like those found beneath pavements, were collected for testing purposes. Initial tests involved evaluating the unconfined compressive strength of compacted loess samples, followed by tests on samples mixed with different weight percentages of PCC. The results revealed a significant average increase of 10% to 28% in the strength of loess samples stabilized with 5% PCC compared to the strength of the native soil. The chemical composition and microstructure of PCC were further analyzed through X-ray Diffraction (XRD), Energy Dispersive X-ray Spectrometer (EDX), and Scanning Electron Microscopy (SEM) Tests conducted at the Idaho National Laboratory (INL). XRD analysis indicates the presence of calcium carbonate and silica. EDX analysis unveiled a carbon content of 9% by weight in PCC, which could contribute to the carbon footprint when it breaks down. Additionally, SEM images displayed an irregular microstructure and particle shape of PCC. Furthermore, the inclusion of PCC improved the resistance of loess to saturation collapse.

A global event such as the COVID-19 crisis presents new, often unexpected responses that are fascinating to investigate from both, scientific and social standpoints. Despite several documented similarities, the Coronavirus pandemic is clearly distinct from the 1918 flu pandemic in terms of our exponentially increased, almost instantaneous ability to access/share information, offering an unprecedented opportunity to visualise rippling effects of global events across space and time. Personal devices provide “big data” on people’s movement, the environment and economic trends, while access to the unprecedented flurry in scientific publications and media posts provides a measure of the response of the educated world to the crisis. Most bibliometric (co-authorship, co-citation, or bibliographic coupling) analyses ignore the time dimension, but COVID-19 has made it possible to perform a detailed temporal investigation into the pandemic. Here, we report a comprehensive network analysis based on more than 20000 published documents on viral epidemics, authored by over 75,000 individuals from 140 nations in the past one year of the crisis. In contrast to the 1918 flu pandemic, access to published data over the past two decades enabled a comparison of publishing trends between the ongoing COVID-19 pandemic and those of the 2003 SARS epidemic, to study changes in thematic foci and societal pressures dictating research over the course of a crisis.

Brain, the most complex object known in the universe, uses few watts of power. To mimic it a nuclear power plant will be required and this power house controls the human body, single handedly. Surprisingly, “On the left side, nothing is right and on the right side there is nothing left”. Typically, there are two lateral halves of the brain: Left hemisphere and right hemisphere working distinctly. Left hemisphere is inclined towards logic; Right hemisphere is the root cause of imagination added with critical thinking. In situations like the current pandemic, COVID-19, it is the right half which tends to dominate the processing. This gives birth to mental stress, anxiety thus, aggravating the existing medical condition. Considering this pattern, a survey was conducted in Durg district of Chhattisgarh, which is one of the most hard hit epicentre of the COVID-19 second wave in India. According to this survey, it was revealed that largely women of all age groups (10-25, 26-40, 40 above) were right brained i.e. dominance of right over left hemisphere. Being more imaginative and creative thinkers they are more likely to suffer from mental issues than males. The aim of this research is to improve the mental wellbeing of the citizens in such threatening conditions. To prevent this situation awareness is a must and some stress relieving games have also been created.

Fraudulent transactions pose a significant threat to financial institutions and e-commerce platforms.Machine learning models, trained on historical labeled data (fraudulent vs. legitimate transactions), are often employed to identify and prevent fraud. However, real-world datasets frequently exhibit class imbalance, where fraudulent transactions (minority class) are significantly outnumbered by legitimate transactions (majority class). Machine learning models may perform poorly as a result of this imbalance, underestimating fraud and favouring the majority class. This paper proposes a novel approach to address class imbalance and improve fraud detection accuracy. We explore the implementation of FROST (Feature space RObust Synthetic saTuration) oversampling, a technique specifically designed to generate synthetic samples for the minority class. The FROST function leverages the k-nearest neighbors (KNN) algorithm and a user-defined amplification factor (m) to create synthetic data points that closely resemble existing minority class instances. We integrate the FROST-enhanced oversampling technique into the machine learning pipeline for fraud detection. The paper evaluates the effectiveness of this approach compared to traditional oversampling methods and analyzes its impact on classification accuracy metrics.

The objective of the paper is to introduce new similarity measure for single valued neutrosophic sets based on logarithm function. We define logarithm similarity measure and their weighted similarity measure for single valued neutrosophic sets. Then we define hybrid logarithm similarity measure and weighted hybrid logarithm similarity measure for single valued neutrosophic sets. We prove the basic properties of the proposed measures. We then define an entropy function using logarithm function to determine unknown attribute weights. We develop a novel multi attribute group decision making strategy for single valued neutrosophic sets based on the weighted hybrid logarithm similarity measure. We address an illustrative example to demonstrate the effectiveness and aptness of the proposed strategies. We conduct a sensitivity analysis of the developed strategy. We also make a comparison between the obtained results from proposed strategies and different existing strategies in the literature.

In this study, the partial least squares regression (PLSR) models were developed using no pre-processing, traditional preprocessing, multi-preprocessing 5 range, multi-preprocessing 3 range, genetic algorithm (GA), and successive projection algorithm (SPA) to assess the higher heating value (HHV) and ultimate analysis of grounded biomass for energy usage employing near-infrared (NIR) spectroscopy. A novel approach was utilized based on the assumption that using multiple pretreatment methods across different sections in the entire NIR wavenumber range would enhance the performance of the model. The performance of the model obtained from 200 biomass samples for HHV and 120 samples for ultimate analysis was compared, and the best model was selected based on the coefficient of determination of validation set, root mean square error of prediction, and the ratio of prediction to deviation values. Based on model performance results, the proposed HHV model from GA-PLSR, and the N and O models from the mul-ti-preprocessing PLSR 5 range method could be used for most applications, including research, whereas the C and H models from GA-PLSR performance is fair and applicable only for rough screening. The overall findings highlight that the multi-preprocessing 5-range method, which was attempted as a novel approach in this study to develop the PLSR model, demonstrated better accuracy for HHV, C, N, and O, improving by 4.1839%, 8.1842%, 3.7587%, and 35.9404%, respec-tively. Therefore, it can be considered a reliable and non-destructive alternative method for rap-idly assessing biomass properties for energy usage and can also be used effectively in biomass trading. However, due to the smaller number of samples used in the model development, more samples are needed to update the model for a robust application.

Introduction: To compare the results of patient-specific absolute dosimetry using homogeneous slab phantom and anthropomorphic heterogeneous female pelvic phantom in cervical cancer patients. Materials and method: Thirty RapidArc plans already planned on treatment planning system (TPS) for cervical cancer patients were exported on both the phantoms viz. RW3 slab phantom and AHFP and dose were calculated using an anisotropic analytic algorithm (AAA). All the plans were delivered by linear accelerator (LA) and the dose for each plan was measured by a 0.6cc ion chamber. The percentage (%) variation between planned and measured doses were calculated and analyzed.Results: In the case of slab phantom, the mean percentage variations between planned and measured doses of all rapid arc QA plans were as 1.4299 and standard deviation 0.768 (t=0.00508, ρ= 0.497982) The result is not significant at p < .05. For the AHFP phantom, the mean percentage variations between planned and measured doses of all rapid arc QA plans were as 6.890 and standard deviation 2.565 (t= 3.21604, ρ= 0.001063 <0.05), the outcome is significant. Discussion: In the case of homogenous slab phantom, there is less than a 3% difference in percentage between planned and measured doses with a standard deviation of 0.7682 (t=0.00508, p= .497982. The result is not significant at p< 0.05). The deviations of planned and measured value of dose in the AHFP phantom were found as 10.67% (maximum value), 2.31% (minimum value) and 6.89% (average value) with standard deviation 2.565 (t=3.21604, p=0.001063. The result is significant at p<0.05). Also, the percentage of variation between homogeneous slab phantoms with AHFP phantom, the t-value is -11.17016. The p-value is < .00001. The result is significant at p<0.05. We can see that the outcomes differ significantly due to the influence of heterogeneous media.Conclusion: AHFP phantom results showed more dose variability than slab homogenous phantom outcomes. Therefore, patient-specific absolute dosimetry should be performed using a heterogeneous phantom that closely resembles the actual human body in terms of both density and design.

Plastics are highly beneficial for the day-to-day activities of human beings; however, their decomposition is limited due to their strong covalent bonding. The concept of degradation of these big molecules into smaller ones or monomers was attempted by several researchers in the preceding decades with limited success. Pyrolysis is one of the ideas used to convert plastics into fuel, rather than small molecules, compared to the crowded structure of polymers. Among these plastics, low-density polyethylene (LDPE) is largely used as carry bags throughout the world, and, herein, the results of catalytic pyrolysis of the conversion of LDPE into fuel are reported. Different dosages of zinc oxide (ZnO) were used as a catalyst to do the pyrolysis at a specific temperature in a batch reactor specially designed at our laboratory. 0.6 g of ZnO was found to be the optimal dosage for a 50 g waste LDPE batch to get the maximum oil yield. The yielded oil was analyzed chemically through Fourier transform infrared spectroscopy (FTIR) and Reformulyzer M4 Hydrocarbon Group Type Analyzer. Evaluation of physical and chemical exergy along with exergetic efficiency of the process was done.

Coronaviruses are a large family of RNA viruses which cause respiratory infections ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS) and COVID-19. This article highlights some key findings based on a thorough scanning of genes of 475 SARS-CoV2 genomes, including the co-presence of ORF7a and ORF8 over the 256 SARS-CoV2 genomes and the absence of the gene ORF7b over the 219 SARS-CoV2 genomes collected from various countries including India. The presence of the gene ORF7b is found in the SARS-CoV2 genomes containing the L-type strain which is reported to having much higher virulence as compared to the S-type strain.

Iterative Stochastic decoding is an alternative to standard fixed-point decoding of Low-density parity check codes (LDPC) to reduce inter-node routing. In this paper, we propose a Flexible Field-Programmable Gate Array (FPGA)-Based Stochastic decoding (SD) hardware architecture for LDPC codes in the Fifth-Generation (5G) New Radio (NR) Standard that supports decoding of set of various code rates. This decoder’s runtime flexibility is desirable to switch a better performing code rate automatically based on the channel conditions without the extra time needed for reprogramming of FPGA. An offline design method is implemented to generate the hardware description language (HDL) code description of the decoder for the required code-rate set, which is further synthesized and integrated into the Xilinx Kintex-7 series FPGA board to determine the hardware resource utilisation and processing throughput. The Synopsys design tools were employed during both the simulation and synthesis stages, in combination with TSMC 65-nm CMOS standard cell technology, to facilitate comparative analysis. Compared with state-of-the-art designs, the proposed architecture reduces hardware utilization by up to 26% and achieved energy efficiency by 52%.

Routing a person through a traffic network presents a tension between selecting a fixed route that is easy to navigate and selecting an aggressively adaptive route that minimizes the expected travel time. We propose to create non-aggressive adaptive routes in the middle-ground seeking the best of both these extremes. Specifically, these routes still adapt to changing traffic conditions, however we limit the number of adjustments made in the route. This improves the user experience, by providing a continuum of options between saving travel time and minimizing navigation. We design strategies to model single and multiple route adjustments, and investigate enumerative techniques to solve these models. To alleviate the intractability with handling real-life traffic data, we develop efficient algorithms with easily computable lower and upper bounds. We finally present computational experiments highlighting the benefits of limited adaptability in terms of reducing the expected travel time.

Mitochondria are constantly subjected to stressful conditions due to their unique physiology and organization. The resulting damage leads to mitochondrial dysfunction, which underlies many pathophysiological conditions. Hence, constant surveillance is required to closely monitor mitochondrial health for sound maintenance of cellular metabolism and thus, for viability. In addition to internal mitochondrial chaperones and proteases, mitochondrial health is also governed by host cell protein quality control systems. The Ubiquitin-Proteasome System (UPS) and autophagy constitute the main pathways for removal of damaged or superfluous proteins in the cytosol, nucleus, and from certain organelles such as the ER and mitochondria. Although stress-induced ubiquitin-dependent degradation of mitochondrial outer membrane proteins has been widely studied, mechanisms of intramitochondrial protein ubiquitination have remained largely elusive due to the predominantly cytosolic nature of UPS components, separated from internal mitochondrial proteins by a double membrane. However, recent research has illuminated examples of intramitochondrial protein ubiquitination pathways and highlighted their importance under basal and stressful conditions. Owing to the dependence of mitochondria on the error-prone process of protein import from the cytosol, it is imperative that the cell eliminate any accumulated proteins in the event of mitochondrial import deficiency. Apparently, a significant portion of this activity involves ubiquitination in one way or another. In the present review article, following a brief introduction to mitochondrial protein quality control mechanisms, we discuss our recent understanding of intramitochondrial protein ubiquitination, its importance for the basal function of mitochondria, metabolic implications, and possible therapeutic applications.

The present work encompasses an application-oriented perspective to the possible employment of gold nanoparticles as nanomedicine in cancer therapeutics. The rationale of the work lies in the growing needs for assessment of advanced alternative treatment of cancer employing functionalized nanoparticles as nanomedicine. Gold nanoparticles fabricated via green chemistry methods by leaves of a time-honored medicinal plant, Piper betle were ascertained for their synthesis and properties under the umbrella of characterization of nanoparticles, through various techniques like UV-vis spectroscopy, FTIR spectroscopy, X-ray diffraction, and scanning electron microscopy. The cytotoxicity assay of well-characterized gold nanoparticles was monitored against lung cancer cell line (A549) by metabolic and imaging assays. MTT assay or the metabolic assay was performed for a range of nanoparticles’ concentrations. The results were promising and proved to be a leading-edge venture, envisaging the possibility of gold nanoparticles for cancer therapeutics.

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.

This study investigates how Electronic Livestock Health Recording Systems (ELHRs) facilitates the detection of disease burden and make cluster analysis by applying data analytics tools and techniques. A sample size of 18333 livestock disease cases reported from 2007-2015 by the Ministry of Agriculture of the Federal Democratic of Ethiopia was used for data collection. The results showed that ELHRs are important as livestock disease data preservers, saving costs, and facilitating the extraction of up-to-date and complete information. Euclidean and Manhattan distance performed well at 98%, while cosine distance measurement metrics performed poorly. Finally, with the application of the selected clustering techniques, metrics, tools, and dataset, it has been attempted to successfully detect an optimal number of disease clusters and meet the objectives of the study.

The performance of Yttrium-Barium Copper Oxide (YBCO) high-temperature superconductors in high-frequency applications is significantly affected by the microwave surface resistance (Rs). The paper delves into the basics, measuring methods and factors affecting the resistance (Rs) in YBCO, highlighting its high critical temperature (Tc) and low resistance, positioning it as a promising material. YBCO's compatibility with epitaxial growth and microstructure engineering offers opportunities to reduce grain boundary effects and improve Rs, making it advantageous for high-frequency electronics, communication systems, and MRI coils. This is due to its high critical current density (Jc) and exceptional Rs at practical temperatures. Challenges remain in comprehending and managing Rs in YBCO, despite its favorable characteristics. Utilizing advanced fabrication methods and incorporating nanotechnology allow for customization of YBCO-based devices. Multi-scale modeling and simulation are essential for guiding experimental work and understanding YBCO's performance in high-frequency settings. This study highlights the promise of YBCO for future high-frequency technologies and stresses the importance of more research to overcome hurdles and fully exploit its capabilities, potentially transforming superconducting devices for practical use.

Equality and incomparability multi-label ranking have not been introduced to learning before. This paper proposes new native ranker neural network to address the problem of multi-label ranking including incomparable preference orders using a new activation and error functions and new architecture. Preference Neural Network PNN solves the multi-label ranking problem, where labels may have indifference preference orders or subgroups which are equally ranked. PNN is a nondeep, multiple-value neuron, single middle layer and one or more output layers network. PNN uses a novel positive smooth staircase (PSS) or smooth staircase (SS) activation function and represents preference orders and Spearman ranking correlation as objective functions. It is introduced in two types, Type A is traditional NN architecture and Type B uses expanding architecture by introducing new type of hidden neuron has multiple activation function in middle layer and duplicated output layers to reinforce the ranking by increasing the number of weights. PNN accepts single data instance as inputs and output neurons represent the number of labels and output value represents the preference value. PNN is evaluated using a new preference mining data set that contains repeated label values which have not experimented on before. SS and PS speed-up the learning and PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency.

Accuracy and computational cost are the main challenges of deep neural networks in image recognition. This paper proposes an efficient ranking reduction to binary classification approach using a new feed-forward network and feature selection based on ranking the image pixels. Preference net (PN) is a novel deep ranking learning approach based on Preference Neural Network (PNN), which uses new ranking objective function and positive smooth staircase (PSS) activation function to accelerate the image pixels’ ranking. PN has a new type of weighted kernel based on spearman ranking correlation instead of convolution to build the features matrix. The PN employs multiple kernels that have different sizes to partial rank image pixels’ in order to find the best features sequence. PN consists of multiple PNNs’ have shared output layer. Each ranker kernel has a separate PNN. The output results are converted to classification accuracy using the score function. PN has promising results comparing to the latest deep learning (DL) networks using the weighted average ensemble of each PN models for each kernel on CFAR-10 and Mnist-Fashion datasets in terms of accuracy and less computational cost.

This research suggests a multi-stakeholder framework to increase the use of fintech in Africa, which would help the continent boost its financial inclusion and reach its Sustainable Development Goals (SDGs). The study evaluates previous research and frameworks that have been created to aid in the adoption of fintech by several stakeholders in developing nations, some of which have been tested in African nations with limited success. To begin with, the study suggests the use of the World Bank’s Maximising Finance for Development (MFD) approach through the prioritization of national ownership, creation of an enabling environment for private sector investment, partnering with multilateral development banks and other stakeholders, fostering innovation and digital literacy, and focusing on cost-effective, non-government guaranteed financing. In the same vein, in line with the G20’s High-Level Principles for Digital Financial Inclusion, the adoption of financial technologies and digital financial services in Africa can be promoted through the creation of a country-specific strategy. Each government can create a regulatory environment that encourages innovation and competition, improve its digital infrastructure, increase its digital literacy and awareness, and collaborate with private sector stakeholders to expand financial inclusion. Furthermore, The Better Than Cash Alliance’s strategy to increase fintech adoption can also be implemented through partnership with businesses, international organizations, and other nations. By joining the Alliance, countries can enact rules and regulations that facilitate fintech adoption, promote awareness and education, and design and execute national digital payment infrastructure with the help of fintech companies. Finally, the mSTAR initiative suggests collaborating with USAID, in order to provide support to underrepresented populations, incorporate digital financial services, increase public-private collaboration, and educate and train policymakers, practitioners, and technologists. By implementing these strategies, African nations can accelerate the adoption of fintech solutions and increase financial inclusion.

Initial evaluations of the System of Rice Intensification in India and elsewhere have mainly fo-cused on its impact on yield and income and have usually covered just one or two seasons. Re-searchers at the ICAR-Indian Institute of Rice Research conducted a more comprehensive evalua-tion of SRI methods over six years (six wet and six dry seasons), comparing these methods with three other rice crop management systems: modified, partially mechanized SRI (MSRI); direct-seeded rice (DSR); and normal transplanting with inundated fields (NTP). SRI grain yield was found to be about 50% higher than with NTP (6.35 t ha-1 vs 4.27 t ha-1), while the MSRI yield was essentially the same (6.34 t ha-1) and 16% higher than with DSR (5.45 t ha-1). Compared to NTP, SRI methods significantly enhanced soil microbial populations over time, bacteria by 12%, fungi by 8%, and actinomycetes by 20%. Indicators of biological activity in the rhizosphere were also higher, dehydrogenase by 8.5% and FDA enzymes by 16%. Glucosidase activity, an indicator of soil organic matter, was 78% higher. Relative to normal transplanting methods, SRI reduced GHG emissions by 21%, while DSR reduced them by 23% and MSRI by 13%, which indicated pos-itive effects of the alternate and improved methods over normal transplanting. Economic analy-sis showed that both gross and net economic returns to be higher with SRI than with MSRI and the other management systems evaluated. While the six-year study documented many ad-vantages of SRI crop management, the MSRI version evaluated is a promising adaptation that provide similar benefits but with lower labor requirements.

The purpose of this work is to compare two fracture prediction models with real-world data. The pure Artificial Neural Network (ANN) model emphasizes regression analysis, while the hybrid model (SVM-ANN) focuses on the combination of regression and classification analysis or Support Vector Machine. The results were subsequently tested against logging data by combining the Machine Learning approach with advanced logging tools. In this context, we used electrical image logs and the dipole acoustic tool which together allowed the distinction of 404 open fractures and 231 closed fractures and, consequently the estimation of fracture porosity. The results are then fed into two machine-learning algorithms. Pure Artificial Neural Networks and hybrid models are used to establish comprehensive results, which are subsequently tested to check the accuracy of the models. The outputs obtained from the two methods demonstrate that the hybridized model has a lower Root Mean Square Error (RMSE) than pure ANN. The results of our approach strongly suggest that incorporating hybridized machine learning algorithms in fracture porosity estimations can contribute to the development of more trustworthy static reservoir models in simulation programs. Finally, the combination of Machine Learning (ML) and well-log analysis do permit reliable estimation of fracture porosity in the Ahnet field in Algeria, where, in many places, advanced logging data is absent and costly.

Novel coronavirus disease (COVID-19) has affected nearly 7 million individuals and claimed more than 0.4 million lives to date. There are several reports of gender differences related to infection and death due to COVID-19. This raises important questions such as “Whether there are differences based on gender in risk and severity of infection or mortality rate?” and “What are the biological explanation and mechanisms underlying these differences?” Emerging evidence has proposed sex-based immunological, genetic, and hormonal differences to explain this ambiguity. Besides biological differences, women have also faced social inequities and economic hardships due to this pandemic. Several recent studies have shown that independent of age males are at higher risk for severity and mortality in COVID-19 patients. Although susceptibility to SARS-CoV-2 was found to be similar across both genders in several disease cohorts, a disproportionate death ratio in men can be partly explained by the higher burden of pre-existing diseases and occupational exposures among men. From an immunological point of view, females can engage a more active immune response, which may protect them and counter infectious diseases as compared to men. This attribute of better immune responses towards pathogens is thought to be due to high estrogen levels in females. Here we review the current knowledge about sex differences in susceptibility, the severity of infection and mortality, host immune responses, and the role of sex hormones in COVID-19 disease.

The enterprise software sector is experiencing a significant transformation driven by rapid technological progress and evolving business needs. An effective release management process is crucial for the solution providers and customers, as they incorporate the principles of Industry 4.0 and 5.0. With the growing dependence on software solutions and services, ensuring their quality, reliability, and security is highly necessary. The release management process incorporates strategic planning, regression testing, accurate deployment, and continuous monitoring of software updates. However, the rapid advancements in artificial intelligence (AI) and machine learning (ML) create a unique challenge to the release management process. The existing research needs to address the necessary adaptations for Industry 5.0, mainly in product-based software organizations, to the release management process without a change management system. After working with several software solution stakeholders, based on the responses from SaaS project stakeholders, we have presented a revised software release readiness workflow by incorporating a change management module. The preliminary result based on data over a limited period of time indicates that the revised workflow has the potential to improve the management of slippage, particularly by more effectively addressing NFR-related issues, thereby resulting in more predictable and reliable release cycles.

Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed. A real-time case study of stealthy local covert attacks with false data injection attacks is implemented on the DC-DC converter. A deep learning model is designed to mitigate cyber attacks, and its performance is evaluated.

Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed.

Increasing environmental concern and consumer demand for natural, sustainable and eco-friendly products have prompted the replacement of synthetic surfactants with their natural plant-based alternatives. Saponins are the plant based natural surfactants characterized by their foam forming properties in aqueous solution. Their natural origin makes them eco-friendly, bio-degradable and non-toxic. Further, they possess better physicochemical properties than the syn-thetic ones. They are also reported to exhibit a lot of useful biological activities such as anti-cancer, antifungal, anti-inflammatory, antimicrobial, antioxidant and cholesterol-lowering properties. Because of their excellent surface activity, biological activities and wide distribution in nature, saponin rich plants deserve deeper insight as a sustainable source of natural surfactants as they possess the potential to replace toxic synthetic surfactants abundant today. This review article is intended to provide a brief overview on the saponins with a special notion on their surface-active properties. It encourages further studies on development of commercial formulations based on saponins for the complete replacement of the synthetic counter parts, making better use of plants sources thereby contributing to global agenda of green environment.

The disruption of brain energy metabolism, leading to alterations in synaptic signaling, neural circuitry, and neuroplasticity, has been implicated in severe mental illnesses such as schizophrenia, bipolar disorder, and major depressive disorder. The therapeutic potential of ketogenic interventions in these disorders suggests a link between metabolic disturbances and disease pathology; however, the precise mechanisms underlying these metabolic disturbances and the therapeutic effects of metabolic ketogenic therapy remain poorly understood. In this study, we conducted an in silico analysis of transcriptomic data to investigate perturbations in metabolic pathways in the brain across severe mental illnesses via gene expression profiling. We also examined dysregulation of the same pathways in rodent or cell culture models of ketosis, comparing these expression profiles to those observed in the disease states. Our analysis revealed significant perturbations across all metabolic pathways, with the greatest perturbations in glycolysis, the tricarboxylic acid (TCA) cycle, and the electron transport chain (ETC) across all three disorders. Additionally, we observed some discordant gene expression patterns between disease states and ketogenic intervention studies, suggesting a potential role for ketone bodies in modulating pathogenic metabolic changes. Our findings highlight the importance of understanding metabolic dysregulation in severe mental illnesses and the potential therapeutic benefits of ketogenic interventions in restoring metabolic homeostasis. This study provides insights into the complex relationship between metabolism and neuropsychiatric disorders and lays the foundation for further experimental investigations aimed at appreciating the implications of the present transcriptomic findings as well as developing targeted therapeutic strategies.

The fate of a viral infection in the host begins with various types of cellular responses, such as abortive, productive, latent, and destructive infections. Apoptosis, necroptosis, and pyroptosis are the three major types of regulated cell death mechanisms that play critical roles in viral infection response. Cell shrinkage, nuclear condensation, bleb formation, and retained membrane integrity are all signs of osmotic imbalance-driven cytoplasmic swelling and early membrane damage in necroptosis and pyroptosis. Caspase-driven apoptotic cell demise is considered in many circum-stances as an anti-inflammatory, and some pathogens hijack the cell death signaling routes to initiate a targeted attack against the host. In this review, we selected mechanisms by which viruses interfere with cell death are discussed in-depth and are used to illustrate the general principles and cellular signaling mechanisms of virus-host specific molecule interactions.