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In this work, we have performed a detailed study of the defects created in the bulk of 4H-SiC after thermal oxidation and post oxidation annealing using deep level transient spectroscopy and minority carrier transient spectroscopy (MCTS). The study reveals the formation of several shallow and deep level majority carrier traps in the bandgap. The ON1 (E C − 0.85 eV), ON2a (E C − 1.05 eV), and ON2b (E c − 1.17 eV) levels are the most dominant and are observed across all the samples (E C denotes the conduction band edge). Three shallow levels Ti(k) (E C − 0.17 eV), E 0.23 (E C − 0.23 eV), and C 1 / 2 (E C − 0.36 / 0.39 eV) are observed in the samples. For most of the majority carrier defects, the highest concentration is observed after an NO anneal at 1300 ° C. This behavior is sustained in the depth profile measurements where the defect concentration after the NO anneal at 1300 ° C is significantly higher than for the rest of the samples. The origin of most of the majority carrier defects has been attributed to C interstitial injection from the interface during thermal oxidation and annealing. MCTS measurements reveal two prominent minority carrier traps, labeled O 0.17 (E V + 0.17 eV) and B (E V + 0.28 eV), where the concentration of O 0.17 is independent of annealing parameters while the concentration of the B level increases after the NO anneal (E V denotes the valence band edge). Furthermore, the depth profiles of the defects are used to evaluate their diffusion parameters by solving the diffusion equation to fit the experimental profiles. The defect concentrations decay exponentially with depth, which evidences that the defects were created at or near the SiO 2 –SiC interface and migrate toward the bulk during oxidation and post-oxidation annealing. [ABSTRACT FROM AUTHOR]

With a growing population of about 7.8 billion, humans are generating tons of waste in the form of non-biodegradable plastics on a daily basis that ends up in landfills and oceans. The introduction of packaging has been a blessing to mankind by facilitating the ease of convenience in transportation, delivery and general usage. The downside, however, is that majority of the packaging currently available is harmful to the environment and takes thousands of years to decay. This paper discusses the use of chitosan from shrimp shells and other marine animals' exoskeleton waste in food packaging industries. Chitosan extracted from marine organisms is modulated in the form of packaging material. Compared to conventional packaging, chitosan films are 73.4% biodegradable in soil under laboratory conditions whereas 100% degraded in an open field. Chitosan with metal oxides, essential oils, natural extracts, proteins and other polymers show enhanced tensile strength, water vapor permeability, oxygen and ultraviolet barrier. Chitosan and composite film show antimicrobial activity against gram positive and negative bacteria. Comparative approaches on environmental impacts between conventional plastics and chitosan films are enlightened, thereby highlighting the importance of natural polymers as packaging films that are considered economic based on the addition of additives. [ABSTRACT FROM AUTHOR]

In this research, the Taguchi-based TOPSIS technique was utilized to optimize TIG welding parameters for carbon steel. Five Input factors included welding current CT (80,100 A), gas flow rate GF (10,15 l/min), tip angle TP (30°,60°), Electrode diameter ED (2.4, 3.2 mm) and welding speed WS (100, 150 mm/min) with Taguchi's L12 orthogonal array employed for experimental design. Tests were conducted to evaluate joint hardness and Tensile strength, with specimen assignment based on performance scores via TOPSIS. The ratios of signal to noise for each factor were determined, ranking the welding parameters based on the delta value, in order to evaluate the impact of certain input variables on the output parameters. ANOVA was employed to identify the most significant parameter, and a regression equation was developed to establish a mathematical model correlating the S/N ratio of performance scores with process parameters. Validation was performed through a confirmatory experiment using optimized parameters, yielding results consistent with the proposed approach. TOPSIS identified the combination CT1-GF2-TP1-ED2-WS1 as optimal: 80 A current, 15 l/min gas flow, 60° tip angle, 3.2 mm electrode diameter, and 100 mm/min welding speed. [ABSTRACT FROM AUTHOR]

The study addresses the requirement for better machining of titanium alloy (Ti–6Al–4V) in Electrical Discharge Machining (EDM) with a Cu–TiB 2 ceramic composite powder metallurgy electrode. The electrode underwent analysis for relative density, hardness, and microstructure using field emission scanning electron microscopy and X-ray diffraction. Results indicated a relative density of approximately 90.08%, a microhardness of around 142 H V, and an electrical conductivity of 46% IACS. Response Surface Methodology (RSM) was used as Design of Experiment (DoE) technique for this study to investigate the effects of pulse current (Ip), pulse on time (Ton), and discharge voltage (V) on Material Removal Rate (MRR) and Surface Roughness (SR). Analysis of variance (ANNOVA) at a 5% significance level highlights the significant impact of Ip, Ton & V, and the interaction between selected parameter on MRR and SR. Results show a significant influence of pulse current (Ip) and pulse duration (Ton) on both MRR and SR. Rising pulse current correlates with improved MRR, with optimal parameters identified as a pulse current of 15 A, a pulse on time of 150 μs, and a voltage of 50 V. The study also looks into the usage of reverse polarity to improve surface finish compared to typical EDM methods. The use of Scanning Electron Microscope (SEM) and Energy Dispersive Spectroscopy (EDS) analyses adds depth to the evaluation of surface formations on the workpiece. EDS analysis indicates the presence of 63.13 wt (%) of boron on the machined work surface, affirming the effectiveness of the developed Cu–TiB 2 electrode in machining titanium alloy. [ABSTRACT FROM AUTHOR]

The aim of the study is to measure the Dufour number effects on the flow patterns and heat transfer in an exponentially accelerated infinite vertical plate embedded in a porous medium in the presence of heat source and chemical reaction. Time‐dependent variations in temperature, velocity, and other factors should be taken into consideration due to the flow's unsteadiness. The fluid considered is a gray, absorbing/emitting radiation but nonscattering medium. Using the finite element method, a set of nondimensionless equations is solved analytically. Results are discussed graphically for concentration, temperature, and velocity profiles. Skin friction, Sherwood number, and Nusselt number are also explained for flow parameters through graphs. [ABSTRACT FROM AUTHOR]

Classification of multiple types of spice images is automatically challenging due to conflict between the texture patterns of spice images. This work aims to develop an automatic system for classifying different types of spice images so that the system can choose an appropriate spice to make herbal tea using Caralluma fimbriata. This work considers the following seven spices, namely, cinnamon, citrus peel, clove, ginger, jeera, kokum, mint, and Caralluma fimbriata as one more class for classification. Most of the existing systems need human intervention to choose different spices to make Caralluma fimbriata tea. It is observed that the pattern of different spice images represents different textures. This observation motivated us to extract features based on multi-Sobel kernels. To reduce the number of computations, the proposed work introduces a novel idea of corner detection based on Gaussian distribution. For each corner, the method performed is multi-Sobel kernels for extracting features. The features are fed to convolutional neural network layers for the classification of multiple spice images. The results of our dataset and comparative study with the state-of-the-art methods show that the proposed model is superior to existing methods in terms of classification rate. [ABSTRACT FROM AUTHOR]

This research paper focuses on the removal of the nonsteroidal anti-inflammatory drug, ibuprofen, from an aqueous solution using a dissolved air flotation process. The comparison of different types of cationic surface-active agents such as cetyltrimethyl ammonium bromide (CTAB), tetrabutyl ammonium bromide (TBAB), and octyltrimethyl ammonium bromide (OTAB) have been employed to scrutinize the effective removal of the ecotoxic pharmaceutically active compound. The work included the influencing parameters such as pressure, contact time, surfactant dosage, pH, flow rate, and initial concentration owing to the best-optimized conditions. The maximum removal rate of 96.09% was achieved at 15 min for CTAB, TBAB had 62.36% at 45 min, and 89.6% was obtained for OTAB at 30 min, with 50 mg L−1 as the initial concentration and pH = 4. The removal rate was better with the optimized dosage of CTAB at 0.6 g, TBAB at 1.2 g, and OTAB at 1.0 g. It was observed that the geometric shape of the surface-active agents had greater impacts on the contaminants' efficiency. CTAB and OTAB were combined to find out the best possible removal rate of contaminants. The synergistic effect augments surfactant-based occurrence to be better in forming a good foaming effect and tends to have a lower critical micelle concentration (CMC). From the evaluation of kinetic models, pseudo-second-order flotation kinetics fitted the experimental data best. Furthermore, the formed metabolites that had been identified using gas chromatography-mass spectrometry were found to be less toxic than the parenting compounds. [ABSTRACT FROM AUTHOR]

In this work, the construction of 2D nanostructures of rGO/NiMoO4 and FeOCl/BiOCl-based positive and negative electrode materials for asymmetric capacitor devices. Initially, the positive and negative electrodes were investigated through the three electrode systems. The FeOCl/BiOCl electrode was used in the negative potential region of − 0.6 V to 0 V and the rGO/NiMoO4 electrode was used in the positive potential region (0–0.4 V) also these electrode materials exhibit excellent capacitance values of 228 F/g and 424 F/g calibrated at 1 A/g for negative and positive electrodes, respectively. Moreover, the assembled asymmetric capacitor device showed pseudocapacitance behaviour, high capacitance values of 196 F/g at 1 A/g and good cyclic stability of 92% from its initial capacitance value at 10 A/g. Also, the ASC device showed higher energy density and power density values of 49 Wh/kg and 1.031 kW/kg at 1 A/g. [ABSTRACT FROM AUTHOR]

Over the past decades, malarial infection is considered a dreadful disease which ruins the lives of millions of people all over the globe. Several research works were developed based on machine learning algorithms to categorize the malarial infected person. However effective prediction with precise results is not attained in conventional approaches. For accurate prediction of malarial transmission deep learning technology is designed in this proposed model. This proposed design utilized inception ResNet V2 for the prediction of malarial-infected individuals. Before disease prediction, certain pre-processing techniques such as noise removal, contrast enhancement and segmentation are used to minimize the error rate during the classification process. The function of the proposed model is evaluated using metrics such as accuracy, recall, precision, etc. The simulation analysis shows that the proposed method obtains 0.98% accuracy, 0.02% error, precision is 0.92%, specificity is 0.94% so on. Thus the designed model predicted the malarial disease effectively. [ABSTRACT FROM AUTHOR]

This study identifies the significant risk factors associated with crash frequency on expressways in India. For this crash data was collected from over 391 km of three expressways comprising a total of 13345 crashes. The expressways were divided into 981 segments based on uniform traffic and geometric characteristics, and traffic, geometry, and lighting conditions-related data from these segments was also gathered. The final dataset comprised 6577 data points, and it was analysed using M5P and random effect negative binomial (RENB) models. The results obtained from the modeling approaches were compared based on the correlation coefficient (CC), root mean square error (RMSE), and mean absolute error (MAE). Although both models performed well in identifying the risk factors, the crash prediction of M5P model was better than the RENB model. It provided simple linear equations for sets of conditions defined by the tree. The findings of M5P model also suggest that it better explains the effect of various independent variables on crash probability under different circumstances. Furthermore, sensitivity analysis utilising the M5P model indicates that it can generalise and predict crashes' physical events well. The findings of this study suggest that modification in the design of horizontal and vertical profiles, median openings, interchanges, minor accesses, and bus bays/truck laybys are needed to increase traffic safety on expressways in India. [ABSTRACT FROM AUTHOR]

This study aims at investigating the limitations associated with proximate analysis-based gross calorific value (GCV) modeling for coals. Toward this, a dataset comprising proximate analysis and GCV data of 4792 coal samples collected from various Indian coal basins was generated, and then a GCV prediction model was developed using the popular multivariate linear regression (MLR) technique. Although the developed model appeared to be acceptable in terms of prediction R2 value of 0.934, through a rigorous statistical analysis, it has been shown that grade misclassification and source specific biases are inherent limitations associated with such GCV prediction models. It was found that the grade classification accuracy associated with a GCV prediction model was inversely and linearly proportional to the model’s associated mean absolute error (MAE) value. It has further been demonstrated that even well-validated GCV prediction models available in literature may perform sub-optimally when utilized for grade classification tasks. The analysis presented in this study also confirms that a source-specific bias can be introduced in the GCV prediction models developed using coal samples from varied geographical sources. It has further been shown that the incorporation of a categorical representation of the sample sources in the GCV prediction model could successfully eliminate the source-specific biases. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) have been broadly applied in various fields, such as medicine and agriculture. A network is likely to experience information congestion when many sensors initiate sending data concurrently. This could lead to maximizing in the packet loss ratio, thereby reducing efficiency and affecting the total system performance, so congestion control is an utmost challenge. To solve this problem, a Fuzzy Congestion Control in WSN based on the Spider Monkey Optimization Algorithm (FCC-WSN-SMOA) is proposed. The proposed method combines random early detection with the fuzzy proportional integral derivative controller. The proportional integral derivative (PID) and fuzzy logic conjointly together to help control the target buffer queue. FLC regulates the transmitting rate of every node. Then, FLC input and output parameters are optimized by SMOA. The simulation is activated in MATLAB. The FCC-WSN-SMOA method attains 21.28%, 32.20%, and 17.42% lower packet loss ratio and 16.25%, 26.07%, and 23.38% lower packet loss probability compared with existing methods, such as progressive fuzzy PSO-PID congestion control approach for WSN (FCC-WSN-PSO), optimized fuzzy clustering utilizing moth-flame optimization approach in WSNs (FCC-WSN-MFOA), and time synchronization depending on Improved Wolf Colony Algorithm-Cuckoo Search Optimized Fuzzy PID Controller for Smart Grid (FCC-WSN-IWCA-CSO), respectively. [ABSTRACT FROM AUTHOR]

Leaf spot disease caused by Alternaria perpunctulata (E.G. Simmons) poses a significant threat to Alternanthera philoxeroides (Mart.) Griseb, commonly known as alligator weed and Ceylon Spinach, contributes to persistent weed proliferation in Indian paddy fields. This study focuses on the molecular identification and phylogenetic analysis of the Alternaria perpunctulata strain GVKNSV7, which is responsible for leaf spot disease on A. philoxeroides. Diseased plant leaves were collected from Kakumanu Mandal, Guntur, and local vegetable markets of Visakhapatnam, Andhra Pradesh, India, in December 2021, revealing symptomatic samples with pink to reddish spots on A. philoxeroides leaves. Employing a comprehensive approach that included morphological assessment, pathogenicity testing, and ITS region sequencing, the isolated fungus was conclusively identified as A. perpunctulata with a high sequence similarity of 98.54%. Molecular comparison with sequences in the NCBI database further validated this identification. The resulting genetic sequence, formally catalogued as "Alternaria perpunctulata GVKNSV7," with the GenBank accession number OQ073752, has been successfully submitted to the NCBI database. This study emphasized the utility of ITS sequencing and molecular analyses for accurately identifying and documenting emerging pathogens. Such documentation provides essential insights for developing future bioherbicides and effective weed management strategies. Furthermore, it highlights the potential use of A. philoxeroides as a leafy vegetable, contributing to food security and agricultural sustainability. Overall, this research contributes to understanding Alternaria perpunctulata and its implications for crop health and management practices. [ABSTRACT FROM AUTHOR]

The study focuses on the flow characteristics with geometrical considerations utilizing electrical-conduct Non-Newtonian Fluid. The tubular concentric annulus is the geometry selected for the investigation. The electric-conductive Non-Newtonian Fluid is a character-wise two-constant Bingham model used in this study. The study also utilized geometry, the magnetohydrodynamic (MHD) effect, and the hall current. The employed system had the inner tube spinning and the outer tube stationary. Prandtl's assumptions of boundary layer principles are applied to solve the governing equations using the finite difference method for various fluid flow parameters, the Gauss-Elimination method to compute the results, and the MHD field and geometrical factors with hall current. This paper discusses fluid flow properties such as axial, radial, tangential velocities, and pressure distribution. Further, the mentioned above flow properties impact the heat transfer effect. This research details how flow parameters in the concentric tube flow process affect the development of heat transfer effects in the MHD field and hall current. [Display omitted] • The development of MHD flow and heat transport have been studied computationally. • The impact of Hall current and Magnetic effect on the flow field has been considered. • This work refers to the non-Newtonian fluid as a two-constant yield stress fluid model. • The heat transfer impact has been calculated using the fluid flow properties. • This work has been done using a variety of fluid and geometric factors. [ABSTRACT FROM AUTHOR]

Imaging spectrometry is a powerful tool among remote sensing techniques to analyze the chemical compositions and physical properties of the lunar regolith. Recalibration of the classical method byLucey and co-workers with Moon Mineralogy Mapper (M3) data provides an opportunity to understand the FeO, TiO 2, and optical maturity distributions over the lunar nearside on a global scale. We estimated the chemical compositionsand optical maturity from the M3 spectral reflectance around the 1 µm region. The standard chemical compositions of the lunar landing sites were estimated based on origin optimization and data fitting. We obtained an optimized origin of (0.08, 1.18) for FeO and (0.07, 0.71) for TiO 2. The Spectrophotometric evaluations of Catharina and Cyrillus craters, located in the lunar nearside are categorized under the matured category. The estimation results reveal that the FeO wt % of the Catharina crater varies from 0.0002 to 19.62, and that of the Cyrillus crater varies from 0.0001 to 12.32. TiO 2 wt% of Catharina crater varies from 0.01 to 86.12, while Cyrillus Crater varies from 0.01 to 4.34. The optical maturity (OMAT) of the Catharina crater varies from 0.0001 to 0.19 to that of the Cyrillus Crater Varies from 0.01 to 0.17. The overall summary of the results points out that lunar regolith is a fabulous repository of FeO and TiO 2 and quantitatively this metal abundance may be more than the anticipated values. [ABSTRACT FROM AUTHOR]

Summary: Industrial wireless sensor networks (IWSNs) are very important to improve and simplify the way to manage, monitor, and control industrial factories. IWSNs have many benefits, but because of their vulnerability to extremely complex and variable industrial contexts, they also limit some of the potential that is currently available and present difficulties on numerous fronts. The proposed work develops energy‐efficient routing technique to minimize the overall energy consumption. Heterogeneous mobile nodes are randomly deployed in the experimental region; then, region‐based grid formation is done in the proposed method. In each grid, the base node is selected utilizing the parameters like residual energy and distance of mobile nodes. Hybrid COOT‐HOA optimization is used in the proposed method for selecting the optimal base node in each grid. A deep learning‐based machine learning algorithm called long short‐term memory (LSTM) is utilized to predict the future direction of movement (DOM) of each mobile node in the grid. Estimation of signal parameters via rotational invariance technique (ESPRIT) is utilized to find an active zone from the sender node to direction of base node. Then, the sender node transmits the data to its nearest node in the active region. This proposed energy‐efficient routing algorithm is tested with several metrics which attains better performance like 94% packet delivery ratio, 7% packet loss, average residual energy of 9.5 J, and 3.4 Mbps throughput. Thus, the energy‐efficient routing protocol used in the proposed approach transfers the data in an energy‐efficient manner for IWSN. [ABSTRACT FROM AUTHOR]

A ternary semiconductor zinc titanate (ZT)-metal-organic framework (MOF) composite is synthesized for photovoltaic applications to overcome the shortcomings of both materials when they behave separately including electronic conductivity and specific surface area. As a result, ZTMOF composite is synthesized and employed in making photoanode for dye-sensitized solar cells (DSSCs). The amount of MOF in the composite varies from 0.5, 1 and 2%. The chemisorbed MOF on ZT surface is strongly supported by the PXRD and FT-IR data. It is found that a high surface area of the MOF increases the amount of dye adsorption. The photovoltaic performance and electronic properties of the composite are investigated. Electrochemical impedance analysis of the DSSCs based on MOF reveals that the electronic transport is better than bare titania photoanode, and the photogenerated electrons are less likely to recombine. [ABSTRACT FROM AUTHOR]

This article introduces a new metric called the maximum likelihood estimator (MLE) alpha-hat, which measures the degree of association between species in ecological sites. The authors argue that traditional indices of co-occurrence can be inconsistent and sensitive to fixed margins of contingency tables. They provide analyses of real and artificial datasets to support the reliability and stability of alpha-hat. The article emphasizes the importance of accurately measuring the degree of association in co-occurrence analysis and addresses criticisms of their metric. The authors also discuss the concept of affinity and its relationship to probability and the number of sites in a dataset. They compare the reliability of affinity with standardized co-occurrence count and standardized Jaccard indices, arguing that affinity is a more reliable measure of association. The authors conclude that affinity is a valuable tool for analyzing joint species occurrence. [Extracted from the article]

The majority of research in the carbon-based nanomaterials (CNMs) field has concentrated on the classification and synthesis of CNMs, with relatively few studies focusing on CNMs' roles in photocatalytic hydrogen production. CNMs have proven their potential as an effective addition to the appeal of photocatalytic hydrogen evolution because of their superior chemical and physical properties. This paper explores the recent advancements in photocatalytic hydrogen (H 2) production utilizing CNMs. As evidenced in the literature, carbon quantum dot (CQD)-sensitized titanium dioxide (TiO 2) can demonstrate to have a photocatalytic hydrogen generation activity of 472 mol g−1 h−1 and 1458 mol g−1 h−1 without and with loading metal co-catalyst (Pt). The optimum catalyst, 0.4 CQD/CdS, contributes to the highest H 2 production rate of 309 mmol g−1 h−1 (apparent quantum yield of 32.6%), which is 1.5 times greater than that of bare CdS. This would significantly accelerate the hydrogen production process. There are still challenges to reaching maximum photocatalytic hydrogen production, including low hydrogen storage. The overall price of hydrogen produced via photocatalysis is also higher because of the energy needed to store the hydrogen. Even though the problem is not directly related to the usage of CNMs, this restriction generates uncertainty and limits commercial investment. Given the rising demand for energy and the trend toward green power, it is recommended that extensive industrial uses of photocatalytic hydrogen produced by employing CNMs be investigated for better and more sustainable energy frameworks. • Recent advances in photocatalytic H 2 production are investigated utilizing CNMs. • The optimum catalyst, 0.4 CQD/CdS can produce the highest H 2 at 309 mmol g−1 h−1. • CQD-sensitized TiO 2 can generate 1458 mol g−1 h−1 photocatalytic hydrogen with Pt. • Photocatalytic H 2 production using CNMs should be investigated for industrial use. [ABSTRACT FROM AUTHOR]

Global population growth and the exponential rise in energy consumption have put immense strain on finite fossil fuel supplies and presented a danger to the environment. Waste may be converted into energy using sustainable biological method. Biohydrogen generation from lignocellulosic materials is a clean as well as environmentally friendly path for producing biofuels with a bright future. The advancement of biohydrogen advanced technologies has resulted in the emergence of new power generation, which is important for the long-term utilization of power. Due to the limited energy needs and cost involved, biohydrogen production has many benefits over photo-electrochemical or thermo-chemical methods. Although beneficial and favored, the hydrogen production from biomass and carbohydrate raw material has always been at the lab scale. The main aim of the article was to review the various biomass and biohydrogen from biomass. This paper also goes over the various methods for producing biohydrogen, including dark fermentation, photo-fermentation, combined dark and photo fermentation, bio-photolysis, and microbial electrolysis cells. Each form of production's methods, benefits and drawbacks, and variables affecting microbial electrolysis cell have been investigated. Based on the existing research, nanoparticles can play an essential role in strengthening this process in a sustainable basis, allowing for significant yields of biohydrogen to be achieved on a feasible scale. Due to this topic is still in its initial stages, further research is required to close the current gap in terms of practical applications and commercial viability. • Various sources and types of the plastic waste was discussed. • Occurrence and impact of plastic waste has been reviewed. • Different methods for the thermochemical conversion of plastic waste were elucidated. • Sustainable products from plastic waste were explored. • Circular Economy in handling of plastic waste were addressed. [ABSTRACT FROM AUTHOR]

This qualitative study explored the perception of financial well-being among 30 professional women belonging to the middle to upper class in India through a series of in-depth one-to-one interviews. The major dimensions of financial well-being revealed from the narratives were financial adequacy, financial security, having control over one's finances, being independent, and having a balance in life. The findings were explained based on psychological theories on human motivation. This study improves the present state of knowledge about the financial well-being of women by providing fresh insights into their perspectives, its key driving factors, and the key issues that are critical to their financial futures. The information can be used to develop effective financial intervention strategies and policies to improve financial well-being that target the specific needs of women in an emerging country context. [ABSTRACT FROM AUTHOR]

Background: Allergic rhinitis (AR) is a recurrent, chronic condition with a substantial impact on health and quality of life. The debate persists on whether antihistamines or intranasal corticosteroids constitute the first-line treatment for AR. This study aims to compare the efficacy of fluticasone + azelastine nasal spray and levocetirizine tablets in AR treatment. Objectives: The study clinically evaluates the effectiveness of the two regimens in relieving symptoms and compares their overall efficacy in treating allergic rhinitis. Materials and Methods: Patients at a tertiary care hospital were included, with 100 individuals aged12 to 65 randomized into two groups receiving either fluticasone + azelastine nasal spray or oral levocetirizine for four weeks. Symptom assessment, clinical examination, and laboratory investigations were conducted, with patients maintaining a daily symptom journal. Results: The fluticasone + azelastine group showed a significant reduction in individual symptom severity compared to the levocetirizine group (p<0.01). Symptom scores in the fluticasone + azelastine group decreased by 99-100%, exceeding the 90-91% reduction observed in the levocetirizine group. Except for nasal itching, other symptoms were significantly reduced in the fluticasone + azelastine group. Levocetirizine demonstrated a faster onset of effect compared to fluticasone + azelastine. Conclusion: Fluticasone + azelastine nasal spray is more effective than oral levocetirizine in treating AR. This finding supports the consideration of intranasal corticosteroids as a primary therapeutic approach for allergic rhinitis. [ABSTRACT FROM AUTHOR]

Nowadays, many novel approaches are being tested and practiced in search of the ideal treatment for anal fistula but the best technique is not known. Although comparative clinical trials are being done globally for anal fistula but drawback and limitations are associated with every intervention so we are not able to reach the final conclusion that which approach is best for the management of this perplexing disease and the available evidence suffers from lack of high-quality data with very few large RCTs. Ayurveda science also actively contributes Kshara Sutra application for anal fistula management in Sushruta Samhita and many studies also conducted in various institutions on this methodology but no comparative quality data are available which can help to gain global recognition and acceptance. To find out the efficacy and safety of Kshara Sutra therapy in Anal fistula, a sincere attempt has been made by searching PubMed, Web of Science, and Scopus databases and published information from January 2000 to May 2023 are recorded. A total of 7 articles were included in this meta-analysis with Jadad scores ranging between 4 to 7 for 6 studies and score of 3 for 1 study signifying overall good quality of the articles. The cure rate considering unit cutting time of the fistula tract with Kshara Sutra therapy was found highly significant and negligible rate of recurrence and anal incontinence. Existing literature data reveal Kshara Sutra therapy may be the minimally invasive surgical procedure with promising cure rate and lowest incidence of recurrence and anal incontinence. [ABSTRACT FROM AUTHOR]

Paediatric supracondylar humerus fractures account for 60% of elbow joint fractures in children. This fracture is most common in the first five years of life and peaks at 5-8. Displaced supracondylar fracture humerus causes frequent management problems for the clinician. This fracture can cause Volkmann's ischemic contracture4, myositis ossificans, neurovascular damage, stiff elbow, and malunion if not properly handled. Closed reduction and casting, skin or skeletal traction, closed reduction and percutaneous k wire fixation, open reduction and internal fixation have all been considered for treating displaced supracondylar fracture humerus in children. Traditional treatment for displaced supracondylar fracture humerus is closed manipulation, reduction, and cast immobilization. Malunion may cause elbow joint stiffness and varus or valgus deformity due to loss of reduction and manipulation capability. Open reduction with internal fixation is used for complicated fractures, fractures requiring vascular exploration, and fractures with poor reduction. The most common treatment for displaced supracondylar fracture humerus is closed reduction and percutaneous pinning, according to research. Our observations show that closed reduction with percutaneous pinning and k-wire fixation is safe and effective for displaced supracondylar humerus fractures. Kids aged 5-10 were most involved. Most injuries result from falls while playing. Closed reduction with percutaneous pin fixation improves function and appearance in children with displaced supracondylar humerus fractures. It reduces hospital stays and treatment expenses. It decreases patient morbidity and dependence. Cubitus varus, the most common supracondylar humerus fracture consequence, is rare with this treatment. Pin-securing a swollen elbow beyond 90 degrees can avoid vascular impairment. [ABSTRACT FROM AUTHOR]

In this paper, an endeavour is made to study the feasibility of natural coagulants like Cicer arietinum, Dolichos lablab, Maize seeds, Moringa oleifera seeds, Okra seeds, Orange peel, Peanut seeds, Roselle seeds, Sapodilla seeds and Strychonos potatorum seeds on the reduction of phosphorous content from Industrial wastewater. The batch coagulation test was done to identify the optimum coagulant dosage, pH level at which the removal efficiency is high, mixing time and floc settling time required for the removal of phosphorous in the wastewater. A chart was plotted between the removal efficiency of all the chosen coagulants corresponding to the dosage, pH, mixing time and settling time. The removal percentage of phosphorous is more for Moringa oleifera seeds with 93%, followed by Strychonos potatorum seeds with 89% and Sapodilla seeds with 83% demonstrates the minimum phosphorous removal from the wastewater as 37%. Moringa oleifera seeds can be utilised as an effective coagulant for the removal of phosphorous from the wastewater. The maximum percentage removal efficiency of phosphorous was achieved by Moringa oleifera seeds for the coagulant dosage 5.8 mL, pH of 7, mixing time of 30 minutes, settling time of 30 minute and the final phosphorus concentration of 306.1 mg/L was achieved. Also, the corresponding values of pH, optimum coagulant dosage, mixing time and settling time for the phosphorus removal vary according to each chosen coagulants. Based on this study, a model was developed in accordance with the coagulation dosage and concentration of phosphorus. The overall regression accuracy of the Langmuir hypothesis model was identified as r2 = 0.94. [ABSTRACT FROM AUTHOR]

As the sample size of repeating fast radio bursts (FRBs) has grown, an increasing diversity of phenomenology has emerged. Through long-term multi-epoch studies of repeating FRBs, it is possible to assess which phenomena are common to the population and which are unique to individual sources. We present a multi-epoch monitoring campaign of the repeating FRB source 20180301A using the ultra-wideband low (UWL) receiver observations with Murriyang , the Parkes 64-m radio telescope. The observations covered a wide frequency band spanning approximately 0.7–4 GHz, and yielded the detection of 46 bursts. None of the repeat bursts displayed radio emission in the range of 1.8–4 GHz, while the burst emission peaked at 1.1 GHz. We discover evidence for secular trends in the burst dispersion measure, indicating a decline at a rate of |$-2.7\pm 0.2\, {\rm pc\, cm^{-3}\, yr^{-1}}$|⁠. We also found significant variation in the Faraday rotation measure of the bursts across the follow-up period, including evidence of a sign reversal. While a majority of bursts did not exhibit any polarization, those that did show a decrease in the linear polarization fraction as a function of frequency, consistent with spectral depolarization due to scattering, as observed in other repeating FRB sources. Surprisingly, no significant variation in the polarization position angles was found, which is in contrast with earlier measurements reported for the FRB source. We measure the burst rate and sub-pulse drift rate variation and compare them with the previous results. These novel observations, along with the extreme polarization properties observed in other repeating FRBs, suggest that a sub-sample of FRB progenitors possess highly dynamic magneto-ionic environments. [ABSTRACT FROM AUTHOR]

Agriculture is the backbone of the country’s economy. Sugarcane is a globally important crop and a major source of sugar, ethanol and jaggery. One of the problems faced by the sugar cane industry is the diseases that attack the crops. If these diseases are not identified early, they may result in exterminating the whole crops surrounding them. Manually checking each and every corner of a large farm is physically impossible. Machine learning is the contemporary solution to the problem, which can be resolved using the Convolution Neural Network (CNN) techniques. The drone images of all corners of the farm can be fed into the trained model for distinguishing the health status. For training the model the secondary data is taken from Kaggle, which includes both healthy and unhealthy sugarcane plant images, with various diseases in the unhealthy class. Machine learning models effectively identify early-stage crop diseases. This helps the farmer to treat that part of the affected farm quickly in order to avoid the spread of the disease to the remaining parts of the farm. This study deals with sugarcane disease prediction using the CNN model. Two different layered CNN models (VGG-16 and VGG-19) that were tested. The models were trained based on the images of 2165 containing both healthy and unhealthy leaves. The whole dataset is divided into three parts, validating data, testing data, and training data. The selected model – VGG-19 performed better and was found to be analysing the image up to an accuracy of 92% with 90% precision. [ABSTRACT FROM AUTHOR]

One of the significant features of software quality is software reliability. In the testing phase, faults are identified and corrected by integrating them into software development, thus obtaining better reliability. Here, by utilizing the Elliptical Distributions-centric Emperor Penguins Colony Algorithm (ED-EPCA)-based Test Case Prioritization (TCP), an effectual Fault Detection (FD) technique is proposed using Fishers Yates Shuffled Shepherd Optimization Algorithm (FY-SSOA)-based Test Case Selection (TCS). Initially, for the incoming source code, the Test Case (TC) is created. Then, the significant factors needed for TCS and prioritization are identified. Next, by utilizing the Log Scaling-centered Generalized Discriminant Analysis (LS-GDA) model, the estimated factors are abated further to enhance the TCS along with prioritization for the Fault Detection Process (FDP). Then, using the FY-SSOA, the optimized TCs are selected. Subsequently, with the help of ED-EPCA, the TCs being selected are ranked as well as prioritized. Finally, to validate the proposed system's effectiveness, the model's performance is evaluated in the working platform of Java and analogized with the traditional methodologies. The results indicate that the test case prioritization-based fault detection method is robust with a 99.23% fault detection rate and a small amount of memory usage, which is only 8245475 kb by generating a large number of test cases. [ABSTRACT FROM AUTHOR]

This paper explores the quintessence reconstruction and statefinder perspective of Kaniadakis holographic dark energy model in a flat universe with future event horizon as an IR-cutoff. The cosmological parameters of this model were re-examined from the quintessence behavior aspect. Also we reconstruct, V (ϕ Q) , the potential of quintessence scalar field, which shows the best fit with joint analysis data of JLA+CC+BAO for the priors H 0 = 6 9. 6 ± 0. 7 , H 0 = 7 3. 2 4 ± 1. 7 4. In the w DE – ẁ DE phase space, the freezing-tracker model represents our canonical scalar field. This model exhibits quintessence behavior in the statefinder (r – s) plane for model parameters c and β that are close to 1 and 0. The present values of deceleration q 0 , statefinder (r 0 , s 0) , jerk j 0 and model parameters β , c are consistent with the limits of the observational data. Using the distance modulus μ , we also fit 580 SN Type I Union2.1 data sets to our model. [ABSTRACT FROM AUTHOR]

This study presents a limited buffer secondary service queue with multiple vacations and server breakdowns. The model under consideration includes two types of impatient policies: balking and reneging. After the completion of the essential primary service, only few consumers choose to proceed with secondary service with a certain probability. During the active period of the server, it is subject to breakdown and the broken down server is immediately sent for repair. Further, the server will go on vacation as soon as there are no waiting consumers in the queue. On returning from a vacation, if the system is still empty the server leaves for another vacation and continues to do so until atleast one consumer is found at a vacation termination epoch. The model is analyzed under steady-state conditions and the explicit expressions of various performance indices are evaluated. A few numerical results illustrate how the model parameters have an effect on the performance metrics. [ABSTRACT FROM AUTHOR]

Himalayan musk deer (Moschus leucogaster) is classified as an endangered species by IUCN with a historically misunderstood distribution due to misidentification with other species of musk deer, Moschus spp. Taking advantage of recent genetic analyses confirming the species of various populations in Nepal and China, we produced an accurate estimate of the species' current and future distribution under multiple climate change scenarios. We collected high-quality occurrence data using systematic surveys of various protected areas of Nepal to train species distribution models. The most influential determinants of the distribution of Himalayan musk deer were precipitation of the driest quarter, temperature seasonality, and annual mean temperature. These variables, and precipitation in particular, determine the vegetation type and structure in the Himalaya, which is strongly correlated with the distribution of Himalayan musk deer. We predicted suitable habitats between the Annapurna and Kanchenjunga region of Nepal Himalaya as well as the adjacent Himalaya in China. Under multiple climate change scenarios, the vast majority (85–89%) of current suitable sites are likely to remain suitable and many new areas of suitable habitat may emerge to the west and north of the current species range in Nepal and China. Two-thirds of current and one-third of future suitable habitats are protected by the extensive network of protected areas in Nepal. The projected large gains in suitable sites may lead to population expansion and conservation gains, only when the threat of overexploitation and population decline is under control. [ABSTRACT FROM AUTHOR]

Among the wide variety of dyes present in the environment, cationic dyes are more toxic and have complex structure. The adsorption process of rhodamine B dye was successfully carried out by sulphuric acid-treated inexpensive modified fly ash (MFA) adsorbent via batch experiments. The nature of the adsorbent was characterized by techniques, namely, Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and thermogravimetric analysis (TGA). The maximum removal efficiency of RhB dye was found to be 99.78% by using 0.5 g of adsorbent dosage in 50 mg/L of dye concentration. The SEM images displayed the porous nature of the adsorbent where the EDS analysis displays the elemental compositions present in the adsorbent. XRD pattern shows the crystallinity nature of the adsorbent. Among the batch study parameters, effect of pH plays an important role in the adsorption process. The pH of 4 was found to be an ideal setting for the efficient removal of the dye RhB. The preferable elimination ability was found by keeping the dosage at 5 g/L, contact time 120 min, and dye concentration at 50 mg/L. Adsorption capacity was found to be 36.36 mg/g. This shows the ability of the MFA for the removal of wastewater contaminants. This adsorption process is well suited for the Freundlich isotherm, which displaces the process as a multilayer adsorption. Studies in kinetics and thermodynamics demonstrate that the process was well suited for its exothermic nature and pseudo-second-order. Thermal regeneration studies were carried out, and the adsorbent was effectively recycled and utilized up to four more times with minimal loses in its effectiveness. Therefore, from these obtained results, it is clear that the MFA is an effective adsorbent for the effective removal of dyes from wastewater. [ABSTRACT FROM AUTHOR]

A potential approach to the preparation of affordable activated hydrochar is the hydrothermal carbonization of biomass wastes. In this study, hydrochar was made by hydrothermally carbonizing corncob wastes and then activating them with sodium hydroxide (NaOH). According to the findings, the adsorption capabilities of hydrochar generated at prolonged retention durations, lesser liquid-to-solid ratios, and elevated temperatures were considerably greater. The hydrochars were then analyzed utilizing a variety of characterization methods, and batch tests involving the sorption of rose bengal dye were carried out under a variety of conditions. According to the findings, activated hydrochar has a specific surface area of 12.794 m2/g. In order to better understand and characterize the process of rose bengal dye adsorption, sorption kinetics, and sorption equilibrium onto the produced hydrochars, sorption kinetics and isotherms were further examined through experimental data fitting. The NaOH-activated hydrochar's adsorption capacity was 799.9 mg/g, respectively. The objective of this research was to assess the viability of using NaOH-activated hydrochar derived from corncobs as an economical and efficient sorbent for eliminating anionic dyes like rose bengal from aqueous solutions. Additionally, the study sought to investigate how various factors influence its sorption capabilities and to provide insights into the adsorption process through kinetic and isotherm analyses. [ABSTRACT FROM AUTHOR]

Biogenic amines are responsible for transmitting electrical impulses in biological systems and hold immense physiological significance. To detect these amines under physiological conditions, optical sensing methods are favoured due to their simplicity, affordability, and rapid response. Probes based on macrocycles or molecular capsules have proven superior for chemosensing applications due to their unconventional structures and intriguing properties. Here we report the synthesis of a BF2-containing molecular capsule with high luminescence and long lifetimes in solution and in the solid state. The cylindrical cavity in the capsule encompassing polarised Bδ+–Fδ− bonds facilitates the formation of multiple hydrogen bonds with dopamine, and the complexation is signalled through visual changes in colour and fluorescence. The mechanism of interaction was studied using fluorescence and NMR spectroscopy and further supported by molecular dynamics simulations, which revealed that multiple non-covalent interactions between the molecular capsule and dopamine are responsible for stabilizing the inclusion complex. Finally, we demonstrate the use of the molecular capsule as a simple probe by preparing a test strip using a polymer for the sensing of dopamine under various conditions. [ABSTRACT FROM AUTHOR]