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Introduction: Microbial-induced calcite precipitation (MICP) involves various microorganisms, such as bacteria, fungi, and algae. This study focuses on producing bio-cement using fungal species and selecting potential candidates isolated from alkaline soil of different regions of Punjab, namely, Majha, Malwa, and Doaba. Methods: The selection of fungi isolates capable of bio-cement production involves several tests, including a urease assay and calcium precipitation. Isolates having high urease enzyme production and the ability to perform calcite precipitation are selected for instrumental analyses such as X-ray diffraction (XRD) and scanning electron microscopy (SEM). The isolates selected for further analysis are S1 (3) with 8.879 ± 2.94 µg/ml, S1 (18) with 8.421 ± 0.13 µg/ml, and S4 (1) with 10.057 ± 0.45 µg/ml urease activity and least free calcium ions that are 2.337 ± 0.5 µg/ml, 3.339 ± 0.5 µg/ml, and 4.074 ± 0.1 µg/ml respectively. Results and discussion: Calcite precipitation is confirmed through XRD and field emission scanning electron microscopy (FESEM). XRD images showing calcite precipitation with sharp crystalline peaks for S1 (3), S1 (18), and S4 (1) are shown. The calcite precipitation is evident in the micrographs of FESEM. These combined results confirm the potential of urease-positive fungi to facilitate calcite production, which could lead to bio-cement development in future research. [ABSTRACT FROM AUTHOR]

Academic data predictions are significantly important for improving the overall education system's effectiveness by providing early identification of weak students and personalized learning strategies. This paper proposes a deep learning model for the identification of weak and strong students using ensemble learning and multiparametric analysis. It combines several machine learning algorithms, including Naive Bayes, Support Vector Machines, Multi-Layer Perceptron, and Logistic Regression using an ensemble learning approach to enhance the model’s performance. Additionally, a custom 1D Convolutional Neural Network (CNN) is designed for classification. It utilizes multiparametric analysis to identify weak and strong students considering various parameters such as age, academic performance, location, and online learning behavior. The evaluation results indicate the performance of the proposed model has been improved in comparison to MLA FIS, SHMM, and DRL by 16.5%, 5.5%, and 2.4%, in terms of precision, 16.4%, 6.5%, and 3.5 % in terms of accuracy and 10.4%, 2.5% and 6.5% in terms of recall. These improvisations described that the model is efficient for multidomain feature extraction, ensemble classification, and high-variance feature selection, which result in a deeper understanding of student performance.Article Highlights: A deep learning model using ensemble learning and multiparametric analysis for identifying weak and strong students. The proposed deep learning model significantly improves identifying weak and strong students, boosting educational effectiveness. By combining various machine learning techniques with a custom CNN, the model enhances precision, accuracy, and recall. It offers a more detailed analysis of student performance, leveraging factors like age and online behavior for better personalized learning. [ABSTRACT FROM AUTHOR]

In this study, entropy production in flow along with heat transport of non-Newtonian fluid via porous medium, is analyzed. For fluid flow via porous medium, a modified Darcy resistance term, is taken in the momentum equation for third-grade fluid. Temperature-dependent viscosity is considered using Vogel's model viscosity. Using adequate transformations, the momentum and heat transport equation are reduced to non-dimensional form and solved analytically invoking homotopy analysis method on MATHEMATICA software. Effects of parameters arising in the study are depicted by graphs on velocity distribution, temperature distribution, and entropy production with Bejan number and discussed. For validity of current findings, the values of velocity and temperature are computed for particular values of the parameters and equated with previously published results, excellent agreement achieved. Furthermore, skin-friction coefficient and Nusselt number values are expressed in tabular form for various values of relevant parameters and discussed. It is noticed that slip parameters (γ) and (β) reduce the entropy generation number (NS). Also noticed that skin friction coefficient upsurges with rising velocity slip parameter (γ) value while, effect of temperature slip parameter (β) is observed to lessen Nusselt number in the absolute sense. [ABSTRACT FROM AUTHOR]

Cotton root rot caused by Macrophomina phaseolina pose a significant threat to cotton production, leading to substantial yield and quality losses. Early and accurate diagnosis of this pathogen in soil is crucial for effective disease management. This study presents a pioneering investigation into the utilization of the nit gene encoding nitrilase for the development of a molecular diagnostic assay aimed at the rapid detection of M. phaseolina in field soils. The methodology involved the design and validation of primers targeting the Nit gene sequence, followed by the optimization of PCR conditions for efficient amplification. Leveraging state‐of‐the‐art molecular techniques, the assay offers a novel protocol to accurately identify the presence of M. phaseolina in soil with high sensitivity and specificity. The specificity of the designed primers was confirmed through PCR amplification using DNA from M. phaseolina and other related fungi. Sensitivity tests demonstrated that the PCR assay reliably detected M. phaseolina DNA at concentrations as low as 1 ng. Furthermore, the performance of the diagnostic assay was rigorously evaluated using field soil samples with a known status of M. phaseolina infection, demonstrating its reliability and efficacy in real‐world scenarios. This study introduces a novel molecular marker for the detection of M. phaseolina and offers a rapid and efficient means for screening M. phaseolina in large soil samples with minimal time and manpower. [ABSTRACT FROM AUTHOR]

The Barrow's Holographic Dark Energy (BHDE) Model is proposed in an axially symmetric anisotropic spacetime which is employing Hubble horizon as an infrared cutoff (IR cutoff). The transition of the universe from a matter dominated era to a dark energy dominated era is investigated in this model. Furthermore, the Equation of State (EoS) parameter explains how the universe evolves with non-extensive term or Barrow parameter Δ for both phase (i) phantom era ( ω B ≤ − 1) and (ii) quintessence era ( ω B ≥ − 1). In order to reconcile the dark energy, the reconstruction of scalar field potential is considered. Some of the physical properties of the model are also described. [ABSTRACT FROM AUTHOR]

Owing to their continuous evolution, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) display disparate pathogenicity in mouse models. Omicron and its sublineages have been dominant worldwide. Compared to pre-Omicron VOCs, early Omicron subvariants reportedly cause attenuated disease in human ACE-2-expressing mice (K18-hACE-2). In late 2022, the frequency of Omicron subvariant XBB.1.5 rapidly increased and it progressively replaced other circulating strains. The emergence of new strains requires current SARS-CoV-2 clinical animal model re-evaluation. In this study, we aim to characterize XBB.1.5 pathogenesis in K18-hACE-2. Herein, we demonstrated that XBB.1.5 infection is associated with significant weight loss, severe lung pathology, and substantial mortality. Intranasal XBB.1.5 infection resulted in 100% mortality in K18-hACE2 mice. High virus titers were detected in the lungs on days 3 and 5 after infection. Moreover, XBB.1.5 productively infected the cells within the nasal turbinate, olfactory bulb, intestines, and kidneys. In addition, in a subset of infected mice, we detected high virus titers in the brain. Consistently, we detected high viral antigen expression in the lungs. Furthermore, we observed severe lung injury hallmarks (e.g., immune cell infiltration, perivascular cuffing, and alveolar consolidation). Using immunofluorescence labeling and cytometric analysis, we revealed that XBB.1.5 infection leads to CD45+ cell influx into the lung parenchyma. We further demonstrated that most immune infiltrates are CD11b+ CD11c+ dendritic cells. Additionally, we detected significant induction of proinflammatory cytokines and chemokines in infected lungs. Taken together, our data show that Omicron subvariant XBB.1.5 is highly pathogenic in K18-hACE2 mice. [ABSTRACT FROM AUTHOR]

The wind farm layout optimization problem (WFLOP) is a significant problem in the field of renewable energy. In this development of wind farm, solving the WFLOP is a crucial task, which entails placing the turbines in the wind farm in the best locations to reduce wake effects and enhance predicted power generation. In recent decades, the WFLOP problem has been solved mathematically. Meanwhile, the growing load demand led to an increase in the complexity of WFLOP. The results obtained by mathematical methods were not accurate enough to suit complexity of WFLOP. Nowadays, researchers from a variety of fields are developing nature‐inspired algorithms (NIAs) to solve difficult real‐world problems. This study is an attempt to review the most important innovations in the field of NIAs to solve the WFLOP problem. The classification of the reviewed literature is based on different applied approaches and models. In addition to specific proposals, the advantages and disadvantages of certain domains are also discussed. This study provides a future direction to the fellow researchers who are working in the field of WFLOP. [ABSTRACT FROM AUTHOR]

Owing to their continuous evolution, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) display disparate pathogenicity in mouse models. Omicron and its sublineages have been dominant worldwide. Compared to pre-Omicron VOCs, early Omicron subvariants reportedly cause attenuated disease in human ACE-2-expressing mice (K18-hACE-2). In late 2022, the frequency of Omicron subvariant XBB.1.5 rapidly increased and it progressively replaced other circulating strains. The emergence of new strains requires current SARS-CoV-2 clinical animal model re-evaluation. In this study, we aim to characterize XBB.1.5 pathogenesis in K18-hACE-2. Herein, we demonstrated that XBB.1.5 infection is associated with significant weight loss, severe lung pathology, and substantial mortality. Intranasal XBB.1.5 infection resulted in 100% mortality in K18-hACE2 mice. High virus titers were detected in the lungs on days 3 and 5 after infection. Moreover, XBB.1.5 productively infected the cells within the nasal turbinate, olfactory bulb, intestines, and kidneys. In addition, in a subset of infected mice, we detected high virus titers in the brain. Consistently, we detected high viral antigen expression in the lungs. Furthermore, we observed severe lung injury hallmarks (e.g., immune cell infiltration, perivascular cuffing, and alveolar consolidation). Using immunofluorescence labeling and cytometric analysis, we revealed that XBB.1.5 infection leads to CD45+ cell influx into the lung parenchyma. We further demonstrated that most immune infiltrates are CD11b+ CD11c+ dendritic cells. Additionally, we detected significant induction of proinflammatory cytokines and chemokines in infected lungs. Taken together, our data show that Omicron subvariant XBB.1.5 is highly pathogenic in K18-hACE2 mice. [ABSTRACT FROM AUTHOR]

The use of synthetic fibers as reinforcement has been restricted as a result of enhanced ecological awareness and stringent laws and regulations by government agencies. Consequently, engineers and researchers are now developing sustainable composites using natural fibers. The goal of this experimental study is to develop sustainable biocomposites with improved structural, static, dynamic, mechanical, and dielectric characteristics using cashew nut shell biomass-based polymer and Bambusa tulda fiber. Fiber from Bambusa tulda has been extracted and subjected to various NaOH solution concentrations (2 to 10% with an interval of 2 (w/v)). The composite has been developed with 30% fiber loaded differently treated bamboo fiber and characterized by performing XRD, FTIR, tensile testing, DMA, and different dielectric tests. The result revealed that chemical treatment increased the crystallinity index of the biocomposites. The maximum crystallinity index has been observed as 51.73% with 6% NaOH-treated bamboo fiber reinforced biocomposites. The maximum tensile strength and storage modulus have been reported as 132.91 MPa and 8378 MPa, respectively for 6% NaOH-treated fiber reinforced biocomposites (BFC_30_T6). BFC_30_T6 exhibits higher properties because of the increased crystallinity index of fiber and better interfacial interaction between fiber and polymer matrix after the chemical treatment. Chemical treatment with more than 6% NaOH concentration reduces the dielectric constant, dielectric loss, and ac conductivity of the composites. Conversely, treatment with 8 and 10% NaOH concentrations results in a quick increase in dielectric and conductivity values. [ABSTRACT FROM AUTHOR]

This retrospective case series evaluated the effectiveness of minimally invasive spine surgery-transforaminal lumbar interbody fusion (MIS-TLIF) using the "trial-in-situ" technique for reducing high-grade spondylolisthesis. The surgical management of grade ≥III spondylolisthesis has been controversial, with various methods documented in the literature, including in-situ fusion, in-situ trans-sacral delta fixation, distraction techniques, and external reduction techniques. Recently, MIS techniques have gained popularity. This study analyzed 18 cases of high-grade spondylolisthesis treated with MIS-TLIF using the "trial-in-situ" technique. The clinical outcomes were assessed using the Visual Analog Scale (VAS) and the modified Oswestry Disability Index (mODI) scores. The spinopelvic parameters and sagittal balance were also analyzed. Preoperatively, the spinopelvic parameters were deranged, with a mean pelvic tilt of 28.31°, which improved to 13.91° postoperatively. Similarly, the sacral slope improved from 45.65° to 38.01°. VAS and mODI scores improved postoperatively, indicating the effectiveness of the "trial-in-situ" technique in reducing high-grade spondylolisthesis and achieving a better sagittal profile and spinopelvic parameters. The findings indicate that MIS-TLIF using the "trial-in-situ" technique is a viable and effective method for treating high-grade spondylolisthesis. [ABSTRACT FROM AUTHOR]

This article provides some new construction methods of partially balanced bipartite block (PBBB) designs for comparing test treatments with more than one control. Partially balanced incomplete block (PBIB) designs based on some association schemes such as triangular association, Latin-square association, group divisible association, and cyclic association are used for developing these methods of construction. A catalog of efficient PBBB designs is included for parameter values v 1 (number of test treatments) ≤ 10 , v 2 (number of control treatments) = 2 , b (number of blocks) ≤ 8 , r 1 (replications of test treatments) ≤ 10 and k (block size) ≤ 6 along with computed variances. [ABSTRACT FROM AUTHOR]

Ensemble characteristics of a 10-member analog ensemble (AE) system for simultaneous prediction of six surface meteorological variables are examined at six station locations in the north-west Himalaya (NWH), India for lead times, 0 h (0 h)[d0], 24 h (d1), 48 h (d2) and 72 h (d3). The maximum (MMX), minimum (MNX) and mean (ME) values of each variable in analog days are found to exhibit statistically significant positive correlations with their corresponding observations at each station location for d0 through d3. The MEs of the variables are found to reproduce statistics (temporal mean, temporal standard deviation), empirical distributions of the observations on the variables reasonably well, and the MEs of the variables exhibit reasonable values of the RMSEs for d0 through d3. The observations on each variable and multiple variables simultaneously fall within their ranges (MMXs, MNXs) in ensemble members for maximum number of days for all lead times. The AE system is found to exhibit high spatial and temporal consistency in its predictive characteristics at six station locations in the NWH. Despite our short length data, these results are very interesting and suggest practical utility of the AE system for simultaneous prediction of variables at local scale utilizing local scale surface meteorological observations. Similar studies on various other types of ensemble systems can help to assess their practical utility for various forecasting applications. [ABSTRACT FROM AUTHOR]

The Au partially embedded nanostructure (PEN) is synthesized by ion irradiation on an Au thin film deposited on a glass substrate using a 50 keV Ar ion. Scanning electron microscopy results show ion beam‐induced restructuring from irregularly shaped nanostructures (NSs) to spherical Au NSs, and further ion irradiation leads to the formation of well‐separated spherical nanoparticles. Higuchi's algorithm of surface analysis is utilized to find the evolution of surface morphology with ion irradiation in terms of the Hurst exponent and fractal dimension. The Au PEN is evidenced by Rutherford backscattering spectrometry and optical studies. Also, the depth of the mechanism behind synthesized PEN is explained on the basis of theoretical simulations, namely, a unified thermal spike and a Monte Carlo simulation consisting of dynamic compositional changes (TRIDYN). Another set of plasmonic NSs was formed on the surface by thermal annealing of the Au film on the substrate. Glucose sensing has been studied on the two types of plasmonic layers: nanoparticles on the surface and PEN. The results reveal the sensing responses of both types of plasmonic layers. However, PEN retains its plasmonic behavior as the NSs are still present after washing with water, which demonstrates the potential for reusability. Research Highlights: Synthesis of PENs by ion irradiationUtilization of Higuchi's algorithm to explore the surface morphology.Unified thermal spike and TRIDYN simulations being used to explain the results.Glucose is only used as a test case for reusability of substrate. [ABSTRACT FROM AUTHOR]

The Thermo physical behaviour of soil is the fundamental behaviour and plays significant position in its "utilization" as "electrical and heat insulation". However, numerous "researchers have studied" thermo-physical properties of soil. Still, the study on the relationship between soil's properties and its cooling effect through evaporation has not been investigated in detail. In this analysis, soil samples and earthen pots were collected from four states of India: Haryana, Delhi, Rajasthan, and Uttar Pradesh. The performance parameters used to evaluate the cooling effect of the soil, and earthen pot samples are "water-holding capacity", porosity, "thermal-conductivity", texture, and particle and bulk density. The application of "high temperature" fluctuate the "internal microstructure" of clay/soil and subsequently disturbs its "physical and thermal properties". The sand/clay is converted into an earthen pot by applying high temperatures in kilns. The pot samples collected are heated in kilns to bring them to the final form for the study. We are studying the properties after that treatment. In this paper, the impact of "firing temperature" (maximum 900°C) on the "thermo-physical properties" of earthen pots made up of soil of a similar kind as the soil samples collected and the impact of thermo-physical properties on the cooling of the earthen pots has been examined via experiments. The results specify that the "thermal conductivity" of soil/clay samples is less compared to earthen pot. The "porosity and water-holding capacity" of the soil are greater than earthen pots because pore space or pore volume has decreased due to the heating effect. From the experimental analysis, it was found that the soil of the Sohna location in Haryana state has a high water cooling capacity due to its high water-holding (49.28%) capacity and porosity (48.75%). The pot made out of clay, having higher porosity, and water-holding capacity, gives the maximum cooling temperature. [ABSTRACT FROM AUTHOR]

Robotic Process Automation (RPA) has emerged as a game-changing technology in data extraction, revolutionizing the way organizations process and analyze large volumes of documents such as invoices, purchase orders, and payment advices. This study investigates the use of RPA for structured data extraction and evaluates its advantages over manual processes. By comparing human-performed tasks with those executed by RPA software bots, we assess efficiency and accuracy in data extraction from invoices, focusing on the effectiveness of the RPA system. Through four distinct scenarios involving varying numbers of invoices, we measure efficiency in terms of time and effort required for task completion, as well as accuracy by comparing error rates between manual and RPA processes. Our findings highlight the significant efficiency gains achieved by RPA, with bots completing tasks in significantly less time compared to manual efforts across all cases. Moreover, the RPA system consistently achieves perfect accuracy, mitigating the risk of errors and enhancing process reliability. These results underscore the transformative potential of RPA in optimizing operational efficiency, reducing human labor costs, and improving overall business performance. [ABSTRACT FROM AUTHOR]

Bacterial keratitis (BK) is an infection that causes inflammation of the cornea and, if severe, can result in blindness. Topical fluoroquinolones combined with corticosteroids have been shown to be useful in the treatment of BK. A rapid, selective, and sensitive bioanalytical method for simultaneous quantification of Gatifloxacin (GAT) and Dexamethasone (DEX) has been developed and validated using tandem mass spectrometry (LC–MS/MS). Optimal separation was accomplished in under 5 min using an Agilent Zorbax C18 column (100 mm × 4.6 mm, 3.5 μm). The mobile phase was composed of a blend of 0.2% formic acid in triple distilled water and methanol with a flow rate of 0.65 mL/min in isocratic mode. GAT and DEX were detected in positive electrospray ionization multiple reaction monitoring mode (MRM), and the retention time was found to be at 1.64 and 2.93 min, respectively. The linearity of GAT and DEX was found to be in the range of 1.56–400 ng mL−1 with good precision and accuracy. The method was validated according to USFDA regulatory guidelines. The validated method was effectively utilized for preclinical pharmacokinetic analysis of GAT and DEX in rabbit tear fluid following the topical application of a commercial formulation. [ABSTRACT FROM AUTHOR]

Composites of carbonaceous materials and the spinel metal oxides have been profoundly analyzed due to their recognized properties, including high capacity, better stability, and good cycle life. The facile synthesis of the binary composite of NiCo2O4-rGO has been done by dextrose-assisted combustion method at temperatures ranging from 300 to 500 °C in a furnace. X-ray diffraction technique has been employed to analyze properties like the crystalline nature and the cubic lattice structure of the samples. The reduction in the restacking of graphene sheets and the agglomeration of NiCo2O4 particles in the composite has been confirmed by Fourier Electron Scanning Electron Microscopy. The Energy-Dispersive X-ray Spectroscopy analysis has been done to confirm the presence of all elements and to find the elemental wt% in the composite. The strengthening of bonds between the elements present in the composite compared to their counterparts is evident from the Fourier Transform Spectroscopy. The Thermogravimetric Analysis confirms the stability of the synthesized composite to withstand higher temperatures. The electrochemical study unveils the specific capacity equal to 400 F g−1 at a 10-mV s−1 scan rate of the synthesized composite. Further, the composite material shows a promising charge–discharge rate and a lower equivalent resistance value affirming that the synthesized composite exhibits good electrochemical activities and can be utilized in energy-storing devices. [ABSTRACT FROM AUTHOR]

When assessing an institution's performance, the level of academic achievement by its students is a crucial factor. Educational Data Mining (EDM) uses ensemble models of Data Mining (DM) to gain a better understanding of student progress and enhance educational quality. The primary challenge in predicting student performance lies in identifying the model with the highest predictive accuracy. This study suggests a multilevel heterogeneous predictive model that uses a group of learning algorithms that were trained with k-fold cross-validation to make predictions more accurate. This work compares the predictive accuracy of different models and proposes Ensemble Learning (EL) modelling for predicting student performance and assessment. We compare the predictive efficacy of individual learning algorithms such as Naïve Bayes (NB), Random Forest (RF), Decision Tree (J48), Multilayer Perceptron (MLP), Decision Table (DT), JRip, and Logistic Regression (LR) with the proposed ensemble model comprising diverse Machine Learning (ML) algorithms. The study's results demonstrate a predictive accuracy rate of 99.50%, providing strong evidence for the effectiveness of the EL model in scientific inquiry. This research contributes to the field by showcasing the value of ensemble approaches in accurately predicting student academic performance and offering insights for educational practitioners and policymakers seeking to enhance educational outcomes. [ABSTRACT FROM AUTHOR]

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Artificial intelligence (AI) has been used in various industries to provide innovative and intelligent features in micro, small and medium enterprises (MSMEs). Big industries started adopting AI for their HR and workforce management (WFM) activities, however, past literature suggests a lack of AI adoption in MSMEs. In the ongoing pandemic, a large number of job loss is reported in the literature. Thus, artificial intelligence-powered intelligent workforce management (WFM) may be critical during or post-pandemic to manage the huge employment in MSMEs. In this study, we develop and test a conceptual framework based on three areas where AI-powered WFM adoption for MSMEs revenue development has been highlighted. These are (a) risk management (workforce), (b) business and marketing, and (c) information exchange. Six hypotheses have been proposed and tested using structural equation modelling (SEM) with responses from 307 employees. According to the research findings, all of the offered hypotheses are significant. The findings suggest to MSME decision-makers that AI-powered WFM may help revenue growth, workforce risk reduction, intelligent business and marketing, and thoughtful, innovative, and safe information exchange. MSMEs are required to use AI in the information sharing that help in workforce risk management, business and marketing, and intelligent workforce management that scale-up the economic growth. [ABSTRACT FROM AUTHOR]

Groundwater arsenic poisoning has posed serious health hazards in the exposed population. The objective of the study is to evaluate the arsenic ingestion from breastmilk among pediatric population in Bihar. In the present study, the total women selected were n = 513. Out of which n = 378 women after consent provided their breastmilk for the study, n = 58 subjects were non-lactating but had some type of disease in them and n = 77 subjects denied for the breastmilk sample. Hence, they were selected for the women health study. In addition, urine samples from n = 184 infants' urine were collected for human arsenic exposure study. The study reveals that the arsenic content in the exposed women (in 55%) was significantly high in the breast milk against the WHO permissible limit 0.64 µg/L followed by their urine and blood samples as biological marker. Moreover, the child's urine also had arsenic content greater than the permissible limit (< 50 µg/L) in 67% of the studied children from the arsenic exposed regions. Concerningly, the rate at which arsenic is eliminated from an infant's body via urine in real time was only 50%. This arsenic exposure to young infants has caused potential risks and future health implications. Moreover, the arsenic content was also very high in the analyzed staple food samples such as rice, wheat and potato which is the major cause for arsenic contamination in breastmilk. The study advocates for prompt action to address the issue and implement stringent legislative measures in order to mitigate and eradicate this pressing problem that has implications for future generations. [ABSTRACT FROM AUTHOR]

Ascorbic acid (AA), known as vitamin C, is a vital bioactive compound that plays a crucial role in several metabolic processes, including the synthesis of collagen and neurotransmitters, the removal of harmful free radicals, and the uptake of iron by cells in the human intestines. As a result, there is an absolute need for a highly selective, sensitive, and economically viable sensing platform for AA detection. Herein, we demonstrate a Pt-decorated MoS2 for efficient detection of an AA biosensor. MoS2 hollow rectangular structures were synthesized using an easy and inexpensive chemical vapor deposition approach to meet the increasing need for a reliable detection platform. The synthesized MoS2 hollow rectangular structures are characterized through field effect scanning electron microscopy (FESEM), energy-dispersive spectroscopy elemental mapping, Raman spectroscopy, and x-ray photoelectron spectroscopy. We fabricate a chemiresistive biosensor based on Pt-decorated MoS2 that measures AA with great precision and high sensitivity. The experiments were designed to evaluate the response of the Pt-decorated MoS2 biosensor in the presence and absence of AA, and selectivity was evaluated for a variety of biomolecules, and it was observed to be very selective towards AA. The Pt-MoS2 device had a higher response of 125% against 1 mM concentration of AA biomolecules, when compared to that of all other devices and 2.2 times higher than that of the pristine MoS2 device. The outcomes of this study demonstrate the efficacy of Pt-decorated MoS2 as a promising material for AA detection. This research contributes to the ongoing efforts to enhance our capabilities in monitoring and detecting AA, fostering advancements in environmental, biomedical, and industrial applications. [ABSTRACT FROM AUTHOR]

Imposing lockdown amid COVID-19 pandemic has severely affected flower cultivation and their trades. Flower plants are very sensitive to the harvesting, and any unexpected delay may cause great loss (~ 50–60%) to the farmers. In 2018–2019, the worth of total production of floriculture products was ~ Rs 571.38 crore. During lockdown, the availability of human laborers and restricted transport has disrupted the supply of flowers to the market. Hence, some alternative options are suggested here for the farmers, for example, conversion of decorative flowers (e.g., anthurium, China aster, globe amaranthus, sweet-william, anemone, sea lavender, etc.) and inflorescence (e.g., Michaelmas daisy, zinnia, statice, ferns, aspidistra, eucalyptus, magnolia, etc.) can also be into value-added products through drying and dehydration technologies. Many dehydration methods such as hot air oven, solar drying, press drying, freeze-drying, embedded drying, glycerine drying, and microwave oven drying polyester drying can be used for flower drying at room temperature (~ 25 °C). These floral and foliage dehydration techniques are quite simple, which can also be operated by unskilled persons. Moreover, it will generate self-employment for the youth and women along with increased revenue than selling fresh flowers. In this review, different techniques of flower drying have been discussed in detail along with the influencing factors, efficiency, economic feasibility, flower waste management and sustainability. Further, it has also been suggested how these techniques could be useful for farmers, researchers, and traders to create value-added products? Hence, the present paper could be very interesting for the flower growers, retailers, students, as well as floricultural scientists who are involved in flower production worldwide. [ABSTRACT FROM AUTHOR]

The food supply chain (FSC) is becoming more sustainable as companies aim to meet demand with lower waste and emissions. Big data analytics (BDA) can help achieve sustainability goals by extracting meaningful information from past data to help create sustainable strategies. However, in the sustainability literature, BDA's role in enabling sustainable FSC innovations is not explored. Thus, this study investigates how data‐driven analytics might improve FSC innovation by adopting creative tactics in every triple bottom line (TBL) component – green, corporate social responsibility (CSR), and financial – to gain a competitive edge. A resource‐based view (RBV) perspective was used to evaluate the links between supply chain (SC) innovation capabilities and competitive advantage (CA) in FSC innovation and sustainability. Indian food processing enterprises were surveyed using a questionnaire to collect data from 200 respondents. Adopting a structural equation modelling (SEM) approach, six hypotheses were evaluated for significance on the surveyed data using AMOS V.20. Since both goodness and badness fit indices were above cut‐off values, the measurement model was robustly evaluated and found to fit the survey data well. Structural model findings supported all study hypotheses. The results indicate that BDA strongly impacts food supply chain TBL and FSC innovation. Data‐driven innovative TBL methods were shown to boost FSC competitiveness. With the growing demand for value‐added innovation in FSC sustainable development, this study uniquely contributes to the current literature by linking BDA and TBL practice innovation to FSC CA. [ABSTRACT FROM AUTHOR]

The present study was conducted on the liver of 6 healthy adult Barbari goats of either sex between the age of 1.5 to 3 years. All the samples were collected from local slaughter units of Ayodhya district, Uttar Pradesh. The liver of Barbari goat was covered with the connective tissue capsule called Glisson's capsule. Capsule was mainly made up of collagen fibers, while reticular fibers were mainly present in stroma of the organ. One cell thick hepatocyte chords framework was found between the peripheral portal veins and central vein which formed the hepatic lobule. Portal triad was formed by hepatic artery, portal vein and bile duct situated at the periphery of lobule. Between the hepatic chords hepatic sinusoids were found. Along with hepatocytes Kupffer cells were also present, which were the non-parenchymal cells having a large nucleus and pink cytoplasm. Few bundles of collagen fibers were found in the hepatic lobules & discontinuous endothelium of sinusoids was found. [ABSTRACT FROM AUTHOR]

Here, we report the synthesis of Sm3+- activated Ca8ZnBi(VO4)7 nanophosphors using the easy, economic, and time-saving urea-aided solution combustion method. The fabricated powder samples that were annealed at 900 °C were examined for their structural, morphological, and optical characteristics. By recording the X-ray diffraction pattern, R3c (161), the space group and the trigonal phase of synthesized phosphor, was verified. Using X-ray energy-dispersive spectroscopy and scanning electron microscopy, the morphology and elemental composition of the phosphors were examined. Using a transmission electron microscope (TEM), the fabricated nanophosphor particle size was examined. A TEM image demonstrates the occurrence of uneven particle agglomeration in the nanoscale range. The synthesized phosphors’ excitation, emission spectra were captured to examine their photoluminescence (PL) behavior. We observed characteristic reddish-orange Sm3+ PL emission when the produced Ca8ZnBi (VO4)7 phosphors material was exposed to UV light at 408 nm, this corresponded to the transition 4G5/2 → 6H7/2, at 605 nm, respectively. Dexter’s theory and the Inokuti–Hirayama (I–H) model were used to confirm that the energy migration among neighbor ions is found to be responsible for concentration quenching and 5 mol % is found to be the optimal concentration of dopant ion. Using the diffuse reflectance (DR) spectrum, the Eg (bandgap) values for Ca8ZnBi(VO4)7 (3.18 eV) and Ca8ZnBi0.95Sm0.05(VO4)7 (3.15 eV) were determined. In addition, a detailed evaluation was conducted on the quantum efficiency (64.74 %), radiative lifetime (0.6329 ms), and color coordinates (0.6522,0.3476). These astounding outcomes demonstrated the synthesized nanomaterials suitability for creating wLEDs, inherent fingerprinting, and other photophysical claims. [ABSTRACT FROM AUTHOR]

Yellow rust, caused by Puccinia striiformis Westend. f. sp. tritici Erikss. (Pst), is one of the most damaging wheat diseases because of their widespread distribution and ability to produce new races that can attack cultivars that were previously resistant. In this study characterization of 48 wheat genotypes was done using 51 primers comprised of stripe rust gene-specific markers and found that WH730 and WH1142 possess the highest number of rust resistant genes while WH1263 bears the lowest Yr genes. These identified resistant Yr genes were exhibited at different frequencies. The resistant genes like, Yr6, Yr18, Yr46, Yr53 and Yr61 were absent in all the 48 studied wheat genotypes. Yr35 and Yr51 had the highest frequency (95.3 and 91.6%, respectively), while Yr16 related to xwmc177 and xwmc18 had lower frequencies (8.0 and 10.4%, respectively). Genetic diversity analysis using 61 molecular markers, and population structure analysis grouped the population into three clusters. Association study of morph-physiological traits showed that the marker xgdm160 was strongly associated with two traits i.e. days to anthesis and days to heading at P value 0.0031 and 0.0048 (P < 0.01), respectively, and showed the highest phenotypic variance (R2). These assessed markers might help develop novel genotypes with numerous genes of resistance against yellow rust races in wheat genotypes by marker-assisted pyramiding. [ABSTRACT FROM AUTHOR]

The aim of this research was to simulate the future water balance of the Silwani watershed, Jharkhand, India, under the combined effect of land use and climate change based on the Soil and Water Assessment Tool (SWAT) and Cellular Automata (CA)-Markov Chain model. The future climate prediction was done based on daily bias-corrected datasets of the INMCM5 climate model with Shared Socioeconomic Pathway 585 (SSP585), which represent the fossil fuel development of the world. After a successful model run, water balance components like surface runoff, groundwater contribution to stream flow, and ET were simulated. The anticipated change in land use/land cover (LULC) between 2020 and 2030 reflects a slight increase (3.9 mm) in groundwater contribution to stream flow while slight decrease in surface runoff (4.8 mm). The result of this research work helps the planners to plan any similar watershed for future conservation. [ABSTRACT FROM AUTHOR]

Groundwater is considered to be the most reliable source of fresh water. Groundwater supplies are under grave danger due to a number of factors, including an increasing population, urbanization, and industry. Finding groundwater with reasonable precision is frequently a difficult task. In this work, the groundwater potential zones (GWPZs) in the Bundelkhand Craton region of India are mapped out with the help of analytical hierarchy processes (AHP) that are based on Geographic Information Systems (GIS) and remote sensing (RS) techniques. Various groundwater affecting elements has been generated with the remote sensing data in GIS environment. The AHP method was used to determine the weights that should be allocated to each affecting elements and their sub-features as well. In order to determine the GWPZs of the research region, each of these thematic layers was combined on top of the previous one after the appropriate weights were assigned. As a result, the GWPZs that were acquired were divided into five distinct classes, which were respectively designated as "very low", "low", "moderate", "high", and "very high" GWPZs. The findings of this study showed that "very high" GWPZ comprises 1.42% (380.55 km2), "high" GWPZ comprises 12.48% (3340.63 km2), "moderate" GWPZ comprises 67.83% (18152.1 km2), "low" GWPZ comprises 17.26 (4619.64 km2), and "very low" GWPZ comprises 1% (267.85 Km2) of the overall studied region. The result that was achieved is verified with the assistance of well discharge rate data. Overall, this research provides a technique to delineate groundwater potentiality, which will be very helpful for managing groundwater resources. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is listed among the top 20 leading causes of death worldwide, highlighting the urgent need for effective methods of early disease prediction for chronic conditions. This paper presents a novel strategy for the early diagnosis of CKD, leveraging machine learning (ML) methods to support researchers in their pursuit of preventative approaches. The study incorporates data from 400 patients, analyzing a comprehensive set of 25 attributes. The missing values in the dataset handled by mean and mode statistics were employed for numerical and nominal dimensions respectively. The recursive feature elimination (RFE) with cross-validation (CV) technique was utilized to identify the most crucial attributes. The resulting predictive model achieved positive outcomes in terms of accuracy, precision, recall, and F -measure across all implemented classification algorithms (random forest (RF), Naive Bayes (NB), decision tree (DT), multi-layer neural network (MLNN), partial decision tree algorithm (PART), repeated incremental pruning to produce error reduction (RIPPER), k  Star and extreme gradient boosting (XGBoost)). Notably, the XGBoost classification algorithm demonstrated an impressive accuracy of up to 99.5%. The utilization of predictive models is indispensable for healthcare professionals, as they play a pivotal role in enabling early detection of CKD and mitigating the risk of kidney failure occurrence. This research offers valuable insights into the development of reliable methods for the early identification of CKD, providing a promising avenue for enhancing patient outcomes and reducing the burden of this prevalent chronic condition. [ABSTRACT FROM AUTHOR]

Accurate estimate of groundwater recharge is crucial for prediction of groundwater table dynamics and dependent eco-hydrological processes. Despite its importance, benchmark data for groundwater recharge at fine (~ daily) temporal resolution is lacking. We present a first-of-a-kind daily groundwater recharge per unit specific yield (RpSy) data over periods of 2–38 years at 485 groundwater monitoring wells in the US. The RpSy data for these locations are calculated from the daily groundwater table time series using the water table fluctuations (WTF) method. Although direct validation of the data is not possible, since it is the first of its kind, we compare the RpSy data with the monthly USGS product to identify similarities and differences. The RpSy dataset may serve as a benchmark for validating the temporal consistency of recharge products and daily simulation results from land surface and integrated hydrologic models. [ABSTRACT FROM AUTHOR]

In the world's flower trade, gladiolus (Gladiolus spp.) is ranked first among bulbous flowers and eighth among cut flowers, with more than 30,000 different cultivars being grown. Mass multiplication and commercialization are restricted by the traditional propagation methods. However, the large-scale proliferation and improvement of the gladiolus have been accomplished with the aid of plant tissue culture and other biotechnological techniques. The current review includes a thorough examination of the growth and development parameters required for successful in vitro gladiolus development as well as cormel formation. Moreover, focus is being given to various techniques and methods such as in vitro cytogenetic stability and modification of chromosome number, in vitro mutagenesis and selection of pest resistance, in vitro identification and selection to develop virus-free germplasm, cryopreservation, synthetic seed technology, identifying virus diseases by RT-PCR, somaclonal variation, and protoplast and somatic hybridization. Molecular markers and their applications for genetic diversity analysis, relationships between different genotypes, and clonal stability analysis in Gladiolus species have been conducted by several research groups worldwide and are also being discussed. The article also covers efforts to enhance the functionality of plant phenotypes through genetic transformation. Future prospects for further improvement of ornamental gladiolus are also explored. Overall, the current review provides insight into the applications of basic and advanced biotechnological tools for gladiolus improvement. [ABSTRACT FROM AUTHOR]

This research paper explores the performance of electrorheological fluids, widely known as smart fluids, which are popular because they experience immediate changes in their characteristics when applied to exterior magnetic or electric field. On application of high voltage, the ER fluid alters its form to semisolid from viscous liquid within a few milliseconds, which is reversible. The reversible nature of ER fluid was employed to absorb vibrations during hard machining. In this research, the consequence of the application of electric field to the ER characteristics of numerous non‐Newtonian fluids with an accumulation of TiO2 and Al2O3 nano elements are investigated. The purpose of this research is to discover the ER sample, which has the highest viscosity, highest breakdown voltage, but less sedimentation. So the selected sample can be used in ER dampers for the purpose of shock absorption. The ER fluid behaves as a mainspring with nonlinear vibration features that are managed by an arrangement of ER fluid, the structure of plunger, and the constraints of applied electric field. In this research, we have employed steel metallic specimens made from EN24T of 302BHN and the machining experiments are conducted to attain the shape parameters that can reduce the tool vibration and help improve the process of cutting at the time of machining with minimum application of fluid utilizing hard metal inserts. In this work, cutting tool parameters were analyzed like cutting force, tool wear, tool vibration amplitude, and surface roughness. It was observed that ER damper has shown its better performance at 5KV. At 5KV, cutting force was the least, tool wear was the least, tool vibration amplitude was the least, and surface roughness was the least. The use of an ER fluid damper decreases tool vibrations and successfully enhances machining performance. The machining industry will be benefited with commercialization of this technique. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) plays a critical role in water and energy budgets at regional to global scales. ET is composed of direct evaporation (E) and plant transpiration (T) where the latter is regulated via stomatal conductance (gsc), which depends on a multitude of plant physiological processes and hydrometeorological forcings. In recent years, significant advances have been made toward estimating gsc using a variety of models, ranging from relatively simple empirical models to more complex and data‐intensive plant hydraulic models. Using machine learning (ML) and eddy covariance flux tower data of 642 site years across 84 sites distributed across 10 land covers globally, here we show that structural constraints inherent in current empirical and plant hydraulic models of gsc limit their effectiveness for predicting ET. These constraints also prevent the models from fully utilizing the available hydrometeorological data at eddy covariance sites. Even if these gsc models are calibrated locally, structural simplifications inherent in them limit their capability to accurately capture gsc dynamics. In contrast, a ML approach, wherein the model structure is learned from the data, outperforms traditional models, thus highlighting that there still is significant room for improvement in the structure of traditional models for predicting ET. These results underscore the need to prioritize improvements in gsc models for more accurate ET estimation. This, in turn, will help reduce uncertainties in the assessments of plants' role in regulating the Earth's climate. Key Points: Current stomatal conductance models underutilize the site‐specific hydrometeorological dataStructural constraints in empirical models are more restrictive compared to plant hydraulic modelsEnhancements are needed for the simplified depiction of the water potential gradient across the root‐xylem‐leaf continuum in plant hydraulic models [ABSTRACT FROM AUTHOR]

Pantothenate synthetase protein plays a pivotal role in the biosynthesis of coenzyme A (CoA), which is a crucial molecule involved in a number of cellular processes including the metabolism of fatty acid, energy production, and the synthesis of various biomolecules, which is necessary for the survival of Mycobacterium tuberculosis (Mtb). Therefore, inhibiting this protein could disrupt CoA synthesis, leading to the impairment of vital metabolic processes within the bacterium, ultimately inhibiting its growth and survival. This study employed molecular docking, structure-based virtual screening, and molecular dynamics (MD) simulation to identify promising phytochemical compounds targeting pantothenate synthetase for tuberculosis (TB) treatment. Among 239 compounds, the top three (rutin, sesamin, and catechin gallate) were selected, with binding energy values ranging from-11 to-10.3 kcal/mol, and the selected complexes showed RMSD (<3 Å) for 100 ns MD simulation time. Furthermore, molecular mechanics generalized Born surface area (MM/GBSA) binding free energy calculations affirmed the stability of these three selected phytochemicals with binding energy ranges from-82.24 ± 9.35 to-66.83 ± 4.5 kcal/mol. Hence, these identified natural plant-derived compounds as potential inhibitors of pantothenate synthetase could be used to inhibit TB infection in humans. [ABSTRACT FROM AUTHOR]

The Oxidation behavior of ternary electronic lead-free solder In–Sn–Zn alloys are determined at 500, 600, and 700 °C by thermo gravimetric assembly system (TGA) which has been hooked up with a CAHN-1000 auto electric balance for alloys xZn = 0.9, xIn/xSn = 1/1, xZn = 0.9, xIn/xSn = ½ and xZn = 0.5, xIn/xSn = ½. The oxidation was carried out in the air for 50 min. The oxidation rate increases with temperature and decreases with time for the given alloy. The rate of oxidation also increases with the increase of zinc content in the alloy. It has also been observed that the alloy's oxidation rate decreases with the increase of In/Sn ratio, and the oxidation processes follow the parabolic rate law. The oxidation rate of alloyxZn = 0.9, xIn/xSn = ½ is higher at 700 °C as compared to the other two alloys. [ABSTRACT FROM AUTHOR]

Salinity gradient energy (SGE), known as blue energy is harvested from mixing seawater with river water in a controlled way using ion exchange membranes (IEMs). Using 2D materials as IEMs improves the output power density from a few Wm−2 to a few thousands of Wm−2 over conventional membranes. In this review, we survey the efforts taken to employ the different 2D materials as nanoporous or lamellar membranes for SGE and provide a comprehensive analysis of the fundamental principles behind the SGE. Overall, this review is anticipated to explain how the 2D materials can make SGE a viable source of energy. [ABSTRACT FROM AUTHOR]

Despite of multifarious dominance of sulfur‐based batteries, polysulfide‐shuttling and use of high‐cost organic electrolytes with flammability risks hinder their applicability as commercial devices. Herein a polysulfide‐free aqueous zinc‐sulfur (Zn─S) rechargeable battery is explored, which offers a low‐cost and environmentally friendly energy storage system being Zn and Sulfur (S) highly abundant with high theoretical capacity. However, the stability of Zn anode is quite challenging due to dendritic growth and corrosion leading to the capacity decay. This work showcases the utilization of ethylene glycol (EG) and iodine (I2) as an electrolyte additive in aqueous zinc acetate [Zn(OAc)2] electrolyte for stabilizing the Zn─S battery performance. EG as an additive is able to mitigate the corrosion rate of the Zn electrode by 15 times which is supported by molecular dynamics simulation. The assembled Zn─S battery delivered an outstanding capacity of 1210 mA h g−1 at 0.1 C with a 91% capacity retention even after 250 cycles, along with remarkable reversible prolonged cycling stability of 3000 cycles at 1 C, with 64.5% capacity retention. More importantly, in situ electrochemical Raman spectroscopy is utilized to monitor the real‐time formation of zinc sulfide (ZnS) as a single discharge product and simultaneously debunking the polysulfide shuttling in the system which is further supported by XPS, UV‐Vis and IR spectroscopy. [ABSTRACT FROM AUTHOR]