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Abstract This paper, offers a new method for simulating variable-order fractional differential operators with numerous types of fractional derivatives, such as the Caputo derivative, the Caputo–Fabrizio derivative, the Atangana–Baleanu fractal and fractional derivative, and the Atangana–Baleanu Caputo derivative via power-law kernels. Modeling chaotical systems and nonlinear fractional differential equations can be accomplished with the utilization of variable-order differential operators. The computational structures are based on the fractional calculus and Newton’s polynomial interpolation. These methods are applied to different variable-order fractional derivatives for Wang–Sun, Rucklidge, and Rikitake systems. We illustrate this novel approach’s significance and effectiveness through numerical examples.

Air compressors are critical components in many industries whose catastrophic failure results in huge financial losses and downtime leading to accidents. Hence, real time fault diagnosis of air compressor is essential to predict the health condition of air compressor and plan scheduled maintenance thereby reducing financial losses and accidents. Fault diagnosis using transfer learning aids in real time fault detection. In the present study, five air compressor conditions were considered namely, check valve fault, inlet and outlet reed valve fluttering fault, inlet reed valve fluttering fault, outlet reed valve fluttering fault, and good condition. The raw vibration data was converted to radar plot images that were pre-processed and classified using four pre-trained networks (ResNet-50, GoogLeNet, AlexNet, and VGG-16). The hyperparameters like epochs, batch size, optimizer, train-test split ratio, and learning rate were varied to find out the best network for air compressor fault diagnosis. ResNet-50 among all other pre-trained networks produced the maximum classification accuracy (average of five trials) of 98.72%.

Concrete structures exposed to high temperatures often experience thermal damage, which can significantly reduce their structural integrity. Fiber-Reinforced Polymer (FRP) wrapping is a common retrofitting technique used to restore the strength and durability of thermally affected concrete. This study investigates the effectiveness of Basalt Fiber-reinforced Polymer wrapping on different concrete specimens with varying orientations of wrapping. This study mainly focuses on various parameters as fiber orientation and wrapping methodology. A series of 15 columns of dimension 150mm x 150mm with a length of 700mm are cast and subjected to axial compression loading in a pre-heated condition until the initial cracking occurs. These cracked columns are made to strengthen using Basalt Fiber with different patterns of orientation of laminates. The retrofitted columns are subjected to axial compression loading to determine their maximum load-bearing capacities. The test results of this experiment is assessed and compared with conventional RC columns of same grade. The strength of retrofitted columns versus conventional ones are compared and analyzed. The maximum load bearing capacity for a particular pattern of wrapping is determined.

In the present investigation, process parameters were optimized in order to enhance the reduction of calcium oxide (CaO) from sugarcane molasses using acid centrifugation treatment. To predict the effects of process factors on CaO reduction efficiency, a response surface approach with a central composite design was selected. The polynomial quadratic equation was used to predict CaO removal efficiency, and the analysis of variance (ANOVA) test was utilized to assess the relevance of process factors. The appropriateness of the developed model was determined by regression analysis, which yielded a higher R‐squared value of 0.99334 ± 0.01. At the optimum process parameters of 100°C temperature, 50°Bx, and 3.50 pH, the CaO clarification efficacy of 66.17 wt.% was achieved. The experimental results indicated that for acidic centrifugation treatment, the experimentally observed CaO reduction of 65.94 wt% is in close agreement with the model equation's predicted maximum CaO reduction of 66.17 wt% with a t‐test value of 0.497726. Under such conditions, 0.982 wt.% CaO sugarcane molasses was obtained, which is low when compared to the world average of 1.5% CaO content of sugarcane molasses. Furthermore, the implementation of an artificial neural network (ANN) provided a better prediction model for CaO reduction, with a substantial R‐squared value of 0.99866. However, the genetic algorithm (GA) optimization resulted in an actual CaO reduction of 66.21 wt.% with a t‐test value of 0.497726. [ABSTRACT FROM AUTHOR]

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The suspension system in an automobile is essential for comfort and control. Implementing a monitoring system is crucial to ensure proper function, prevent accidents, maintain performance, and reduce both downtime and costs. Traditionally, diagnosing faults in suspension systems has relied on specialized setups and vibration analysis. The conventional approach typically involves either wavelet analysis or a machine learning approach. While these methods are effective, they often demand specialized expertise and time consumable. Alternatively, using deep learning for suspension system fault diagnosis enables faster and more precise real-time fault detection. This study explores the use of vision transformers as an innovative approach to fault diagnosis in suspension systems, utilizing spectrogram images. The process involves extracting spectrogram images from vibration signals, which serve as inputs for the vision transformer model. The test results demonstrate that the proposed fault diagnosis system achieves an impressive accuracy rate of 98.12% in identifying faults. [ABSTRACT FROM AUTHOR]

Bush frogs from Garo and Khasi Hills were collected, and a thorough examination based on morphology and geographic distribution revealed that two of the populations sampled from Garo Hills during this study agree with the holotypes and descriptions of the taxa Ixalus garo and I. kempiae, described more than a century ago. The original description lacked information on several morphological characters, genetic material, and photographs of the species in life. The absence of such information posed challenges in field identification of these species. Our phylogeny shows them to be nested within the Raorchestes clade, and therefore, we allocate these two species to this genus and formally announce their rediscovery. We also redescribe these two species here based on additional adult vouchers and report geographic range extensions from new localities. Based on molecular and morphological analysis, we reassess the status of the recently described Raorchestes cangyuanensis, resulting in its placement under the subjective junior synonymy of R. kempiae. We also describe a morphologically distinct new species from this region. This paper bridges an important gap in the knowledge of the genus Raorchestes in this part of India and highlights the importance of systematic surveys in documenting and understanding amphibian diversity in the region.

Abstract Background A tongue abscess is a rare but serious condition marked by a localized accumulation of pus within the tongue’s tissues. Despite the tongue’s frequent exposure to trauma, bites, and foreign objects, it typically resists infections. However, an acute lingual abscess can be life-threatening due to the risk of airway obstruction from tongue swelling. Common symptoms include increasing pain, fever, swelling, immobility, edema, and redness of the tongue. The case report aimed to highlight the unusual clinical presentation and location of a deep-seated abscess in the anterior third of the tongue, as well as the radiographic approach to diagnosing the lesion. Case presentation A 30-year-old female patient presented with sudden onset tongue pain and swelling persisting for 4–5 days. Clinical examination revealed a solitary diffuse swelling in the anterior two-thirds of the tongue, approximately 3 × 2 cm in size. Diagnostic imaging, including a plain radiograph, ultrasound (USG), and MRI, was conducted. The patient was hospitalized for incision and drainage of the abscess, followed by a 5-day course of intravenous antibiotics. Conclusions A tongue abscess, though rare, is a serious condition indicated by tongue swelling, pain, fever, dysphagia, and dyspnea. Diagnosis involves physical examinations and imaging studies like ultrasound or MRI. Immediate treatment includes maintaining airway patency, draining the abscess, and administering broad-spectrum antibiotics. Prompt recognition and treatment are essential to prevent severe complications such as airway obstruction and sepsis. Regular follow-up ensures complete resolution and prevents recurrence.

This research aims to develop nutrient-rich savory crackers using legume-based composite flour containing moth bean (VAF) and red lentil flour (LCF). The study employed Optimal (Custom) Design in Response Surface Methodology (RSM) to determine the best formulations. The independent variables were refined wheat flour (RWF) (63.75%, 72.25%), LCF (5.31%, 15.93%), and VAF (5.31%, 15.93%), while the dependent variables included spread ratio, bulk density, water absorption index (WAI), antioxidant activity, and color difference. The optimized formulation (RWF: 63.75 g, VAF: 15.43 g, LCF: 5.82 g) resulted in crackers with 14.43% protein, a spread ratio of 10.82, bulk density of 783.4 kg/m3, WAI of 1.84, antioxidant activity of 76.6%, and a color difference of 18.51. VAF and LCF are effective for developing nutrient-rich crackers that maintain desirable sensory attributes, potentially addressing nutritional deficiencies by providing a convenient source of protein and antioxidants. [ABSTRACT FROM AUTHOR]

The Gaoligong Mountains are characterized by large variations in elevation and topography, which support high levels of biodiversity and endemism that remain largely understudied. Herein, based on the integration of morphological comparisons and phylogenetic reconstruction, we describe a new species of Rhacophorus from the northern Gaoligong Mountains, Yunnan Province, China. The new species, Rhacophorus dulongensis sp. nov., is morphologically distinguishable from its congeners based on the differences in body size, head length, tibia length, snout and tongue shape, toe webbing formula and coloration, ventral skin texture and coloration, dorsal pattern and coloration, body macroglands, iris coloration, and pattern of markings on flanks. Phylogenetically, it differs from its congeners by uncorrected p-distances of >4.8% for the 16S rRNA gene fragment. Rhacophorus dulongensis sp. nov. is likely to be found in Myanmar, considering its type locality lies close to the China-Myanmar border. The phylogenetic analysis revealed that the “widespread” species, R. rhodopus, is a species complex and a composite of five distinct lineages. The results revealed that R. napoensis is also found in Vietnam, making it a new country record for Vietnam. Interestingly, R. dulongensis sp. nov. likely breeds in bamboo, a hidden behavioral characteristic that makes them easy to overlook. Given the ongoing habitat loss and degradation in the region, further biological exploration is urgently needed in the Gaoligong Mountains as a biodiversity reservoir.

Industrially important metal working fluids are reported to be vulnerable to microbial growth, leading to loss of its working efficiency. The present study is dedicated towards the green synthesis of Zinc Oxide (ZnO) nanoparticles using Eclipta prostrata leaf extract and investigated for antimicrobial activity. Characterization studies using Scanning Electron Microscopy (SEM) and Fourier Transform Infrared spectroscopy (FT-IR) displayed the presence of agglomerated rod shaped ZnO nanoparticles with characteristic peak between 700 and 500 cm−1, respectively. Antimicrobial testing against gram-positive and gram-negative bacteria, as well as fungi, showed the ZnO nanoparticles' effectiveness. Against gram-positive bacteria, inhibition ranged from 0.48 to 1.51 at concentrations of 25 μl to 100 μl. Against gram-negative bacteria, inhibition ranged from 0.82 to 2.02, and against fungi, inhibition ranged from 1.20 to 2.34, all at the highest concentration tested. The results of the study indicate that the ZnO nanoparticles were effective against both fungi and bacteria, more effectively against gram negative bacteria. The results suggest that the green synthesized ZnO nanoparticles from Eclipta prostrata can be exploited to reduce the load of microbial contamination in metal working fluids.

The 4′-(4-chlorobenzoyl)-3,-(2,6-dichlorophenyl)-1′-methylspiro(indole-3,2′-pyrrolidin)-2(1H)-one (SOD1) compound has been synthesized and the single crystals are grown by the slow evaporation method using ethanol. Initially, FT-IR and NMR (1H and 13C NMR) spectroscopic studies were used to confirm the structure of the compound. Single crystal X-ray diffraction data were used to determine the solid state structure of the grown single crystals. The title compound SOD1 crystallizes in the orthorhombic space group Pbca. The crystal packing analysis of the compound shows that distinct hydrogen bonding intermolecular interactions construct the R22(12), R22(8), and R12(8), supramolecular synthons to stabilize the packing modes. The propensity of intermolecular interactions is quantified by the Hirshfeld surface analysis, 2-D fingerprint plots and enrichment ratio analysis. Further, the 3-D packing topology of the molecular fragments is visualized through energy frameworks, which reveal the predominance of dispersion energy over other interaction energies. The optimized structure's geometric parameters were computed using DFT calculations using B3LYP/6–311+G(d,p) basis set and compared with the crystal structure parameters obtained by the X-ray diffraction studies. To explore the anticancer activity of the spiro compound, molecular docking with the 6FS1 protein was performed and the binding score of -7.7 kcal/mol was observed. Further, the binding interaction was explored by the dynamics simulation studies.

In the present investigation, process parameters were optimized in order to enhance the reduction of calcium oxide (CaO) from sugarcane molasses using acid centrifugation treatment. To predict the effects of process factors on CaO reduction efficiency, a response surface approach with a central composite design was selected. The polynomial quadratic equation was used to predict CaO removal efficiency, and the analysis of variance (ANOVA) test was utilized to assess the relevance of process factors. The appropriateness of the developed model was determined by regression analysis, which yielded a higher R-squared value of 0.99334 ± 0.01. At the optimum process parameters of 100°C temperature, 50°Bx, and 3.50 pH, the CaO clarification efficacy of 66.17 wt.% was achieved. The experimental results indicated that for acidic centrifugation treatment, the experimentally observed CaO reduction of 65.94 wt% is in close agreement with the model equation’s predicted maximum CaO reduction of 66.17 wt% with a t-test value of 0.497726. Under such conditions, 0.982 wt.% CaO sugarcane molasses was obtained, which is low when compared to the world average of 1.5% CaO content of sugarcane molasses. Furthermore, the implementation of an artificial neural network (ANN) provided a better prediction model for CaO reduction, with a substantial R-squared value of 0.99866. However, the genetic algorithm (GA) optimization resulted in an actual CaO reduction of 66.21 wt.% with a t-test value of 0.497726.

BACKGROUND AND OBJECTIVE: Iron, an essential micronutrient, significantly contributes to growth, immune health, and cognitive development in human health. Inadequate dietary iron intake leads to iron deficiency anemia, affecting nearly 1.6 billion people, especially pregnant women and preschool children. Biofortification and fortification of iron in wheat is an acceptable and cost-effective strategy to alleviate iron deficiencies. This study aims to address iron deficiencies through the strategy of fortification and biofortification of wheat varieties. The study places specific emphasis on the proximate composition and iron/mineral content of different wheat varieties. To achieve these objectives, different spectrometric methods were employed to analyze the wheat samples.METHODS: Proximate and mineral quantification were carried out following standard Association of Official Analytical Chemists methods using ultraviolet-visible spectrophotometry, atomic absorption spectrometry, inductive coupled plasma-mass spectroscopy, and prediction was carried out using near-infrared spectra combined with chemometrics.FINDINGS: The samples had moisture content (1.1 - 4.5 percent), protein (18.0 - 22.6 percent), fat (0.3 - 0.6 percent), gluten (6.3 - 10.3 percent), fiber (0.3 - 1.4 percent), alcoholic acidity (0.04 - 0.08 percent), ash (0.9 - 1.7 percent), and carbohydrate (71.1 - 75.2 percent). Iron was determined and compared by spectrophotometric methods. Iron concentration ranged from (0.7 to 6.3 milligrams/100 grams) in ultraviolet-visible analysis, (0.7 to 6.74 milligrams/100 grams) in atomic absorption spectrometry, and (0.81 to 6.8 milligrams/100 grams) in inductive coupled plasma-mass spectroscopy. The obtained results were compared with the standard "Food Composition and Food Safety Standard Authority of India" and predicted using near-infrared spectra combined with chemometrics.CONCLUSION: The work aims to investigate the nutritional content of various wheat varieties, particularly focusing on iron content, which could potentially have implications for improving dietary strategies and addressing nutritional deficiencies. The biofortified varieties (HI-8663 and HI-1605) were found to have high iron content when compared to normal wheat. The acquired results bridge the intricate relationship between plant-based diets, micronutrient deficiencies, providing valuable insights into combating iron deficiencies in public health with the potential achievement of improved nutritional understanding, optimized wheat selection, advanced analytical techniques, education, awareness, and iron deficiency mitigation.

The enhanced catalytic properties of bimetallic nanoparticles have been extensively investigated. In this study, bimetallic Ag-M (M = Au, Pt, or Pd) cotton fabrics were fabricated using a combination of electroless deposition and galvanic replacement reactions, and improvement in their peroxidase-mimicking catalytic activity compared to that of the parent Ag fabric was studied. The Ag-Pt bimetallic nanozyme fabric, which showed the highest catalytic activity and ability to simultaneously generate hydroxyl (•OH) and superoxide (O 2 •- ) radicals, was assessed as a urine glucose sensor. This nanozyme fabric sensor could directly detect urinary glucose in the pathophysiologically relevant high millimolar range without requiring sample predilution. The sensor could achieve performance on par with that of the current clinical gold standard assay. These features of the Ag-Pt nanozyme sensor, particularly its ability to avoid interference effects from complex urinary matrices, position it as a viable candidate for point-of-care urinary glucose monitoring., (© 2024. The Author(s).)

This paper, offers a new method for simulating variable-order fractional differential operators with numerous types of fractional derivatives, such as the Caputo derivative, the Caputo-Fabrizio derivative, the Atangana-Baleanu fractal and fractional derivative, and the Atangana-Baleanu Caputo derivative via power-law kernels. Modeling chaotical systems and nonlinear fractional differential equations can be accomplished with the utilization of variable-order differential operators. The computational structures are based on the fractional calculus and Newton's polynomial interpolation. These methods are applied to different variable-order fractional derivatives for Wang-Sun, Rucklidge, and Rikitake systems. We illustrate this novel approach's significance and effectiveness through numerical examples., (© 2024. The Author(s).)

As the amount and complexity of biomedical data continue to increase, machine learning methods are becoming a popular tool in creating prediction models for the underlying biomedical processes. Although all machine learning methods aim to fit models to data, the methodologies used can vary greatly and may seem daunting at first. A comprehensive review of various machine learning algorithms per biomedical applications is presented. The key concepts of machine learning are supervised and unsupervised learning, feature selection, and evaluation metrics. Technical insights on the major machine learning methods such as decision trees, random forests, support vector machines, and k-nearest neighbors are analyzed. Next, the dimensionality reduction methods like principal component analysis and t-distributed stochastic neighbor embedding methods, and their applications in biomedical data analysis were reviewed. Moreover, in biomedical applications predominantly feedforward neural networks, convolutional neural networks, and recurrent neural networks are utilized. In addition, the identification of emerging directions in machine learning methodology will serve as a useful reference for individuals involved in biomedical research, clinical practice, and related professions who are interested in understanding and applying machine learning algorithms in their research or practice., (© 2024. The Author(s) under exclusive licence to Biomedical Engineering Society.)