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Geotechnical designs and analyses of earth structures and foundations exclusively involve the assessment and consideration of unsaturated soil shear strength. The laboratory testing equipment and methods for predicting the unsaturated soil shear strength are complicated and more expensive. The experimental program attempted to involve undrained triaxial and filter paper for evaluating the unsaturated soil shear strength of identically compacted clayey soil. This study undertakes a comparison of shear strength in clayey soil under undrained loads, examining both saturated and unsaturated conditions. A 60 kPa air entry suction value is the key point at which linearity between the unsaturated shear strength parameter Øb and effective friction Ø′ with 15° linear slopes turns to non-linearity. Unsaturated shear strength increased by 22.76% in optimally wet conditions, 52.68% in optimum conditions, and 77.81% in optimally dry conditions as compared to saturated shear strength. This study utilizes an artificial neural network (ANN) to predict clayey soil's unsaturated shear strength, finding that the optimal ANN configuration (2-5-1 topology, Levenberg–Marquard optimization, and logsig transfer function) achieved high reliability with a correlation coefficient (R) of 0.9289 and mean square error values of 2.22, 7.12, and 3.012 for training, testing, and validation, respectively. This experimental investigation improves our understanding of clayey soil shear strength and emphasizes the importance of saturation and moisture content in geotechnical assessments under undrained loading conditions. [ABSTRACT FROM AUTHOR]

This research explores into analyzing the mechanical characteristics of banana fiber and ramie fiber composites bolstered with silicon carbide (SiC) nanoparticles. The integration of SiC nanoparticles aims to amplify the mechanical robustness and endurance of these natural fiber composites. Through water absorption assessments, we evaluated the composites' resistance to moisture. In addition, we assessed the mechanical performance via hardness, impact, and three-point bending tests. The results indicate a significant enhancement in the mechanical attributes of both banana and ramie fiber composites due to SiC nanoparticle inclusion. The results reveal a notable enhancement in mechanical characteristics following the incorporation of SiC nanoparticles. Notably, composites containing 8% SiC nanoparticles demonstrated the highest hardness value of 102 HV and the lowest water absorption percentage of 1.5%. In addition, these composites exhibited a superior flexural strength (80 MPa) and modulus of elasticity (4.3 GPa), alongside a maximum impact energy absorption of 18 J. These findings underscore the beneficial influence of SiC nanoparticles on the mechanical properties of the composites, including increased strength, reduced water absorption, enhanced hardness, and improved impact resistance. [ABSTRACT FROM AUTHOR]

To create a hybrid material of ramie and flax fibers with silicon carbide (SiC) nanoparticles, it should have better thermal and mechanical properties. Across industries, sustainable materials are growing in demand for high performance, which is the impetus behind this study. A sustainable future is more about contributing to sustainable alternatives like ramie and flax fibers made from traditional synthetic materials. A key area focused on increasing strength, temperature resistance, and stiffness will be the addition of SiC nanoparticles to hybrid fibers. The research process carefully mixes the ramie and flax fibers with SiC nanoparticles to achieve optimal flexibility, thermal stability, and balance of strength. Hybrid material is examined using characterization techniques such as mechanical testing and scanning electron microscopy to determine the functioning of the microstructure. The possibility of composite material recycling, the impact of hybrid fibers, and accounting for natural fibers for sustainability are considered possible in this research. It should be innovative material development that has significant implications from the findings that meet environmental sustainability goals and satisfy performance requirements. [ABSTRACT FROM AUTHOR]

Electric Discharge Machining (EDM) is essential for shaping and cutting tool steel. EDM's precision in machining difficult materials and tool steel characteristics are well known. EDM efficiency requires reliable performance measurement parameters. The physical shape and mobility of the electrode tool are critical in EDM research. Layer machining is an advanced method that removes material in a sequential manner to produce intricate 3D shapes in tool steel and several other materials. The improvement in layer machining methods with precise toolpath algorithms, adaptive layer thickness management, and real-time monitoring systems is required to maximize precision and efficiency. Response surface methodology, the artificial neural network, and other techniques are necessary to optimize EDM operations and maximize performance. Many researchers experimented with electrode shapes and movement patterns to enhance the removal of material and the quality of surfaces. Investigation of complex electrode structures and innovative tool path strategies has been performed in previous studies. It was very difficult to consider various factors during the EDM operation; hence, the present review summarizes the positive outcomes of previous research. The review emphasizes optimizing pulse duration and discharge current to improve EDM efficiency. The present comprehensive review discusses research on EDM in three main areas: electrode tool geometry and motion, tool steel layer processing, and factors for measuring EDM performance. The objective of the present review is to focus on measuring material removal rates, surface roughness, tool wear, and energy usage. The present review concludes that EDM is crucial to machining tool steel and cutting tool materials. Integrating and hybrid machining technologies can improve performance, and improved optimization techniques are crucial. It also recognizes knowledge gaps and explores new frontiers in this dynamic field. [ABSTRACT FROM AUTHOR]

Medical life-saving techniques include mechanical ventilation. During the COVID-19 epidemic, the lack of inexpensive, precise, and accessible mechanical ventilation equipment was the biggest challenge. The global need exploded, especially in developing nations. Global researchers and engineers are developing inexpensive, portable medical ventilators. A simpler mechanical ventilator system with a realistic lungs model is simulated in this work. A systematic ventilation study is done using the dynamic simulation of the model. Simulation findings of various medical disorders are compared to standard data. The maximum lung pressure (Pmax) was 15.78 cmH2O for healthy lungs, 17.72 for cardiogenic pulmonary edema, 16.05 for pneumonia, 19.74 for acute respiratory distress syndrome (ARDS), 17.1 for AECOPD, 19.64 for asthma, and 15.09 for acute intracranial illnesses and head traumas. All were below 30 cmH2O, the average maximum pressure. The computed maximum tidal volume (TDVmax) is 0.5849 l, substantially lower than that of the healthy lungs (0.700 l). The pneumonia measurement was 0.4256 l, substantially lower than the typical 0.798 l. TDVmax was 0.3333 l for ARDS, lower than the usual 0.497 l. The computed TDVmax for AECOPD was 0.6084 l, lower than the normal 0.700 l. Asthma had a TDVmax of 0.4729 l, lower than the typical 0.798 l. In individuals with acute cerebral diseases and head traumas, TDVmax is 0.3511 l, lower than the typical 0.700 l. The results show the viability of the model as it performs accurately to the presented medical condition parameters. Further clinical trials are needed to assess the safety and reliability of the simulation model. [ABSTRACT FROM AUTHOR]

Natural fiber composites are often sought after in industries, such as automotive and aerospace, due to their low density compared to traditional synthetic composites. Sisal and jute are renewable and biodegradable resources, making them attractive from a sustainability standpoint. The effect of carbon nanotube (CNT) insertion of a composed sisal and jute fiber composite material on the thermal and mechanical characteristics is investigated in this study. The primary objective of this research is to determine the exceptional mechanical strength and heat resistance that allows the composite-filled filling CNT to perform better than others. The methodical experimental approach was used to evaluate the effect of sisal and jute matrix and different amounts of CNTs' mechanical and thermal properties. Thermal behavior is found by thermogravimetric analysis, and mechanical performance is used to qualify tensile strength, flexural strength, and impact resistance. The result suggests that CNTs may have reinforcing properties and significant tensile and flexural strength improvement. Impact resistance improved and increased the toughness of the composite material. The 7% CNT composite exhibited improvements in tensile strength of 63.9% and flexural strength of 46.6%, suggesting the synergistic reinforcing effect of CNTs. The high temperature from the use of need resistance shows promise for the composite material based on the tests of its capability for heat absorption and thermal stability. Various technical contexts are potentially useful in focusing on environmentally friendly material creation that exhibits exceptional thermal and mechanical properties. [ABSTRACT FROM AUTHOR]

This study explores the thermal and mechanical properties of a composite material formed by blending kenaf and basalt fibers with titanium oxide nanoparticles. To assess its performance under various conditions, we conducted thermogravimetric analysis (TGA) and hardness, sorptivity, and water absorption tests. During experimentation, composite samples were created using kenaf and basalt fibers as the reinforcing matrix and TiO2 nanoparticles as the modifier. Initial findings suggest that the addition of TiO2 nanoparticles enhances the mechanical properties and water resistance of the kenaf and basalt fiber composite. Samples reinforced with TiO2 demonstrated superior hardness and lower water absorption, i.e., KBT2 exhibited a hardness of 92 Hv and a water absorption rate of 6.4%. The value of 0.1% for KBT2 from the sorptivity investigation was promising, indicating its potential applicability in moisture-sensitive environments. In addition, TGA results show that KBT2 exhibited 0%, 2.1%, 4.7%, and 8.3% weight loss, showing improved thermal stability and high resilience to higher temperatures. In conclusion, the development of a novel material combining kenaf and basalt fibers reinforced with TiO2 nanoparticles presents a promising eco-friendly, durable, and lightweight option for engineering applications. Realizing the full potential of this composite material necessitates further characterization and optimization research. [ABSTRACT FROM AUTHOR]

This study investigates two configurations of novel solar energy-assisted combined cooling, heating and power (CCHP) systems suitable for the dairy industry. The system uses carbon dioxide (CO2) as the working fluid. The cooling and heating outputs were used for the simultaneous chilling and pasteurization of milk. The electrical power produced in the turbine is used to run the compressor and cater to the miscellaneous electrical demands of the dairy farm, thereby achieving grid independence. A detailed thermodynamic analysis was performed on the two configurations (with and without reheating) to identify the impact of various independent parameters on system performance. In addition, an irreversibility analysis was performed on the components in both configurations to obtain an idea of the losses occurring in the system. It is observed that the addition of the reheater provides a better specific net power output by approximately 4% over a variation of 6000 kPa in the cycle maximum pressure. [ABSTRACT FROM AUTHOR]

This work is an attempt to predict tool wear for turning EN24 material by the hybrid Taguchi-ANN (Taguchi-Artificial Neural Network) method. The objective is to minimize the tool wear. The independent factors are cutting environment, feed rate, depth of cut, nose radius, and tool type. A Spinner numerical control lathe is used to assess performance. As per the Taguchi orthogonal array, 27 experiments are conducted for each value of the uncontrollable factor (spindle vibration). Optimal setting is structured by Taguchi analysis and the response table. The additive model is used to predict the response. Conformity test is carried out to check whether the predicted and experimental values of response are within the range given by the confidence interval. Furthermore, the ANN is used to predict and analyze the tool wear. The result showed that the supremely important parameter is depth of cut and the least important parameter is tool type. The ideal set found is A3, B3, C3, D1, and E3. Through ANN analysis, it is observed that the experimental values are very close to the predicted values of tool wear. The predicted value at optimal setting is 0.0401 mm. The experimental values at optimal setting is 0.0422 mm. In addition, the study showed that when the feed rate and nose radius are both set to high levels and the depth of cut is medium, using an uncoated tungsten carbide tool with minimal lubrication results in the least amount of tool wear. [ABSTRACT FROM AUTHOR]

Background: Two major avoidable reasons for adverse events in hospital are medication errors and intravenous therapy-induced infections or complications. Training for clinical staff and compliance to patient safety principles could address these. Methods: Joint Commission International (JCI) consultants created a standardised, 6-month training programme for clinical staff in hospitals. Twenty-one tertiary care hospitals from across south-east Asia took part. JCI trained the clinical consultants, who trained hospital safety champions, who trained nursing staff. Compliance and knowledge were assessed, and monthly audits were conducted. Results: There was an overall increase of 29% in compliance with parameters around medication preparation and vascular access device management. Conclusion: The programme improved safe practice around preparing medications management and managing vascular access devices. The approach could be employed as a continuous quality improvement initiative for the prevention of medication errors and infusion-associated complications. [ABSTRACT FROM AUTHOR]

Ventilation mode is one of the most crucial ventilator settings, selected and set by knowledgeable critical care therapists in a critical care unit. The application of a particular ventilation mode must be patient-specific and patient-interactive. The main aim of this study is to provide a detailed outline regarding ventilation mode settings and determine the best machine learning method to create a deployable model for the appropriate selection of ventilation mode on a per breath basis. Per-breath patient data is utilized, preprocessed and finally a data frame is created consisting of five feature columns (inspiratory and expiratory tidal volume, minimum pressure, positive end-expiratory pressure, and previous positive end-expiratory pressure) and one output column (output column consisted of modes to be predicted). The data frame has been split into training and testing datasets with a test size of 30%. Six machine learning algorithms were trained and compared for performance, based on the accuracy, F1 score, sensitivity, and precision. The output shows that the Random-Forest Algorithm was the most precise and accurate in predicting all ventilation modes correctly, out of the all the machine learning algorithms trained. Thus, the Random-Forest machine learning technique can be utilized for predicting optimal ventilation mode setting, if it is properly trained with the help of the most relevant data. Aside from ventilation mode, control parameter settings, alarm settings and other settings may also be adjusted for the mechanical ventilation process utilizing appropriate machine learning, particularly deep learning approaches. [ABSTRACT FROM AUTHOR]

New advances in biomedical research have led to the creation of magnesium alloy-based lightweight bone implants that are meant to make implants stronger. Reinforcing particles, such as zirconium (Zr) and calcium (Ca), have been added to make the material stronger and more biocompatible. The main goal of this study is to find the best sources for the friction stir process (FSP), which is used to make a bone fixation plate out of magnesium alloy (ZK60) that is strengthened with zirconium and calcium. The Taguchi L16 Orthogonal Array (OA) was used to find the best settings for the FSP process. These included tool speed (600, 800, 1000, and 1200 rpm), reinforcement content (4%, 8%, 12%, and 16%), welding speed (20, 30, 40, and 50 mm/min), and axial force (5, 10, 15, and 20 kN). After undergoing a thorough examination, the manufactured bone plate was determined to have a microhardness of 106 HV, a tensile strength of 205.38 MPa, and minimal attrition at 0.042 mm3/m. The axial force had significant impacts on tensile and microhardness tests, while reinforcement % had a major influence on wear. This study successfully makes a bone fixation plate that is biocompatible and long-lasting by using the optimized FSP settings. The mechanical properties and resistance to wear are also strong. [ABSTRACT FROM AUTHOR]

Lattice structures are bio-inspired designs made up of repeated unit cells consisting of beams, surfaces, or plates that fit together in an ordered or stochastic fashion. These unit cells are configured according to their size and shape in three dimensions. Simple lattices can be created using standard manufacturing techniques such as computer numerical control machining, welding, and casting. Additive manufacturing enables customers to make complex structures with small features at a reasonable cost. In the present work, using octagon, hexagrid, and rhombic dodecahedron lattice topologies, characterized by unit cell size from 3 × 3 × 3 to 7 × 7 × 7 mm and strut thickness from 0.5 to 1.5 mm, the structural behavior of a solid cylindrical model made of polylactic acid material under compression is analyzed. Finite element analysis (FEA) through Autodesk® Netfabb® solvers was used to assess lattice structure mechanical and structural properties. The results were validated by physical compression testing on additively manufactured samples. Cellular designs with 3 × 3 × 3 mm unit cells and 1.5 mm strut thickness displayed the lowest Von Mises stress and deformation. For different topologies, lowest results were rhombic dodecahedron (stress through FEA as 61.3108 MPa and stress through physical test as 61.05 MPa, deformation through FEA as 0.49921 mm and deformation through physical test as 0.48 mm), octagon (stress through FEA as 77.4147 MPa and stress through physical test as 76.92 MPa, deformation through FEA as 0.53316 mm and deformation through physical test as 0.51 mm), and for hexagrid (stress through FEA as 62.2911 MPa, stress through physical test as 62.15 MPa, deformation through FEA as 0.81997 mm and deformation through physical test as 0.81 mm). The percentage error between the stress through FEA and stress through physical test for octagon, hexagrid, and rhombic dodecahedron lattice topology was found out to be 0.63, 0.22, and 0.43%, respectively. Corresponding percentage deformation error between deformation through FEA and deformation through physical test was also found out to be 4.343, 2.435, and 3.848%, respectively. In addition, the impacts of volume reduction, surface area of the lattice, and relative density were explored pertaining to the structural behavior of the lattice. As a result of the examination into mechanical qualities based on these criteria, rhombic dodecahedron lattice topology with 3 × 3 × 3 mm unit cell size and strut thickness of 1.5 mm was found to be a better choice than octagon and hexagrid lattice topologies for 3D printing components that were loaded under axially compressive loads. [ABSTRACT FROM AUTHOR]

This study examines the effect of different infill patterns and percentages on the compressive strength attributes of carbon fiber-reinforced PETG samples printed using fused deposition modeling, employing response surface methodology. Carbon fiber-enhanced PETG (polyethylene terephthalate glycol) composites represent a cutting-edge advancement in additive manufacturing, drawing significant interest due to their impressive mechanical attributes. The experimentation involves modifying printing parameters such as the infill pattern (tri-hexagon, cubic, or line) and infill density (40%, 60%, and 80%). These parameter values were obtained through a central composite experimental design utilizing response surface methodology. The compressive strength of the 3D-printed carbon fiber-reinforced PETG specimens is assessed following ASTM D695 standards. Research indicates that increasing the density of the infill results in enhanced compressive strength. Specifically, specimens featuring an 80% infill density with a tri-hexagon pattern demonstrate a notable compressive strength of 39.16 MPa. By employing regression analysis and optimization techniques, the study predicts experimental outcomes accurately. These findings offer valuable insights into refining the manufacturing process of carbon fiber-reinforced PETG components. This advancement holds potential benefits across various engineering fields, particularly in automotive and aerospace industries, where strength and durability are essential. [ABSTRACT FROM AUTHOR]

Metal matrix composites have captured considerable interest in tribological applications, largely owing to their remarkable characteristics, which include a high strength-to-weight ratio and a low wear rate. This investigation delves into the exploration of hybrid metal matrix composites, where cobalt and chromium play the role of reinforcing agents within a bronze foundation. These composites were manufactured through a powder metallurgy process, utilizing cobalt and chromium metal powders with a particle size of 40 μm. Various weight percentage ratios (2.5%, 5.0%, and 7.5%) were utilized to create these composite specimens. To assess their tribological performance, the composite samples were subjected to a sliding wear test using a pin on disk machine, following the ASTM G99 standards. The wear characteristics of these composites were analyzed using the Taguchi method, considering parameters such as the applied load, speed, reinforcement percentage, and sliding distance. In addition, we conducted an analysis of variance on the collected data. To analyze the wear behavior of these hybrid metal matrix composites based on bronze, we utilized both multiple linear regression analysis and a signal-to-noise ratio assessment. The results indicate that the inclusion of cobalt and chromium metal powders as reinforcement materials enhances the tribological properties of the bronze matrix material. [ABSTRACT FROM AUTHOR]

Sustainable material development techniques help in finding and employing sustainable materials without compromising quality. Composite materials are crucial in structural design, automotive manufacture, and aeronautical engineering. Advanced materials that use reinforcement and filler ingredients must be developed strategically to improve strength and performance. Hence, this study develops hybrid nanocomposites with hybrid nanofillers (MWCNTs and SiO2) and hybrid fibers (basalt and E-glass) and optimizes the competition to maximize elastic behavior and stiffness. Using a hand layup approach, composite samples were made by altering mass fractions of two filler materials (0%, 1%, 1.5%, and 2% by weight) and epoxy matrices (40%, 38%, 37%, and 36%). Shore D hardness and dynamic mechanical analysis (DMA) were used to evaluate the composites. The storage modulus, loss modulus, and damping coefficients are examined using DMA. Specifically, the largest storage modulus is 4.86 × 1010 Pa at 61 °C, while the peak loss modulus is 1.01 × 1010 Pa at 80 °C. The highest damping coefficient is 0.25. Note that 1.5% MWCNTs and SiO2 filler materials independently contribute to the excellent storage and loss modulus. However, an outstanding damping coefficient was achieved without filler materials. The highest achieved shore D hardness value is 88. Filler materials are used to achieve this high hardness. [ABSTRACT FROM AUTHOR]

Gastrointestinal disorders are a class of prevalent disorders in the world. Capsule endoscopy is considered an effective diagnostic modality for diagnosing such gastrointestinal disorders, especially in small intestinal regions. The aim of this work is to leverage the potential of deep convolutional neural networks for automated classification of gastrointestinal abnormalities from capsule endoscopy images. This method developed a deep learning architecture, GastroNetV1, an automated classifier, to detect abnormalities in capsule endoscopy images. The gastrointestinal abnormalities considered are ulcerative colitis, polyps, and esophagitis. The curated dataset consists of 6000 images with "ground truth" labeling. The input image is automatically classified as ulcerative colitis, a polyp, esophagitis, or a normal condition by a web-based application designed with the trained algorithm. The classifier produced 99.2% validation accuracy, 99.3% specificity, 99.3% sensitivity, and 0.991 AUC. These results exceed that of the state-of-the-art systems. Hence, the GastroNetV1 could be used to identify the different gastrointestinal abnormalities in the capsule endoscopy images, which will, in turn, improve healthcare quality. [ABSTRACT FROM AUTHOR]

Cupola slag is a waste material of the steel and iron industries. Its composition is determined by the cupola furnace and other elements used in steel and iron manufacturing. This paper investigates the characterization behavior of various cupola slag materials. As a result, x-ray fluorescence (XRF), x-ray diffraction (XRD), thermogravimetry differential thermal analysis, and scanning electron microscopy (SEM) methods were used to characterize three cupola slag samples from distinct origins. In addition, various physical properties were used to compare different cupola slags. The specific gravity values of CS-1 (cupola slag-1 sample), CS-2 (cupola slag-2 sample), and CS-3 (cupola slag-3 sample) are 1.36, 2.5, and 2.917, respectively. The density and water absorption for CS-1, CS-2, and CS-3 are 1414.86, 1477.71, and 1796 kg/m3, and 0.37%, 0.32%, and 0.26%, respectively. Cupola slag also includes a larger percentage of lime, according to XRF data, which contributes to its improved binding characteristics. A higher calcium oxide content in CS-3 could facilitate the pozzolanic process. The presence of angular particles that aid in material binding is seen in the SEM image. Compounds with a nanostructure are then flawlessly blended into the mixture and grouped with calcium alumina silicates formed by cement hydration. The XRD pattern of cupola slag exhibits high peaks, indicating that the material is crystalline in character and can be utilized as sand. It also shows the presence of other chemical compounds, such as silica, which ranges from 30% to 45%. CS-1 and CS-2 have comparable XRD patterns. However, CS-3 has a somewhat different pattern because of the greater CaO content. Weight loss begins at higher temperatures, which shows that the material is stable at higher temperatures, according to a thermo-gravimetric study. The differential thermal analysis curve of CS-3 indicates that the material remains stable up to a temperature of 600 °C. The physical characteristics of all cupola slag samples show that cupola slag may be utilized to make sustainable building materials because of its lower specific gravity, density, and water absorption. [ABSTRACT FROM AUTHOR]

Over the past two decades, extensive research has elucidated the significant contributions of various nanomaterial such as metals, metal oxides, carbon nanotubes (single, double, and multi-wall), nanowires, and graphene in improving the tribological and thermal properties of AC & R systems used in both industrial and domestic settings. A recent research paper has specifically focused on the performance enhancement of AAC through the use of Nano-oxides, namely CuO, ZnO, and TiO2, employing mathematical modeling. This study investigates how dispersed Nano-oxides of CuO, ZnO, and TiO2, when added to a base of POE lubricant and HFC-R134a refrigerant, influence the performance of automobile air conditioning systems. The primary focus is on viscosity, heat transfer rate, and thermal conductivity of the working medium. The experimental results are compared with tested data, and further analysis is conducted using TK Solver 6.0 and Origin Lab software. The findings demonstrate that the incorporation of these Nano-oxides has a positive impact on thermal-physical properties (k-Thermal conductivity, ρ-viscosity, ρ-density and Cp-specific-heat) and heat transfer characteristics compared to systems without Nano-materials. Furthermore, there is a notable increase in Coefficient of Performance (COP) ranging from 23–29% with varying volume concentrations of Nano-oxides (0.5% to 2.5%) under atmospheric temperature conditions. Consequently, the combination of copper oxide, Zinc Oxide, and Titania nanoparticles with HFC-R134a as well as R1234ze (E) proves to be an effective approach for optimizing refrigerant properties and improving the performance of automobile air conditioning systems. Thus, Nano-oxides dispersion offer a promising solution for enhancing energy efficiency and reducing the reliance on conventional energy sources in thermal systems. [ABSTRACT FROM AUTHOR]

Breast cancer is a medical condition characterized by the uncontrolled proliferation of cells in the breast tissue. Breast cancer can originate from various parts of the breast, and methods such as Breast ultrasound, Diagnostic mammogram, Breast magnetic resonance imaging, and Biopsy are currently used for its diagnosis. However, these methods have certain limitations, and their size can be a hindrance. To overcome this, low-power, flexible antennas can be designed for bio-communication between medical equipment and external instrumentation. Flexible and wearable antennas have advantages such as affordability, ease of fabrication, and high gain. In this article, a microstrip patch antenna operating at 2.45 GHz and made of polyamide material is designed using High Frequency Structure Simulator software. The simulation results show the patch antenna has a gain of 1, −14.81 dB return loss at 2.45 GHz based on |S11| ≤ −10 dB. The directive radiation pattern with axial ratio of 63.39 dB and voltage standing wave ratio ≤ 3. Furthermore, the hardware development of proposed antenna with polyamide substrate provides the resonance frequency nearing to simulation results as 2.318 GHz with return loss of −28.19 dB. Based on mathematical analysis, simulation and hardware results, the proposed antenna is a superior option for breast cancer detection. [ABSTRACT FROM AUTHOR]

This study primarily focused on assessing the feasibility of utilizing textile effluent sludge (TES) in the creation of sustainable building materials, demonstrating the possibility of using this material and eventually generating new market opportunities. The physical, chemical, and mineralogical characterization of textile sludge was carried out after preliminary crushing and grain-size sorting. Five samples collected from the effluent treatment facility of textile industries situated in the Vidarbha region of Maharashtra, India, underwent characterization to explore their potential innovative applications in the construction sector. Various physical tests, such as specific gravity, density, water absorption, and sieve analysis, were performed on these samples. Chemical characterization was carried out using x-ray diffraction, x-ray fluorescence, a field emission scanning electron microscope coupled with energy-dispersive x-ray spectroscopy, Fourier transform infrared spectroscopy, and thermogravimetry/differential thermal analysis. The results obtained demonstrate the potential of TES in the creation of sustainable building materials. In addition, the mix proportion of TES was further optimized using LINGO software to meet the chemical specifications required for cement. [ABSTRACT FROM AUTHOR]

A robust strategy is imperative to counteract multi-resistant pathogens within the oral cavity and mitigate the occurrence of dental-related diseases. According to the recent Global Oral Health Status Report (2022), over 3.5 billion individuals worldwide have been affected, with more than 2 billion experiencing permanent tooth caries. Despite employing various techniques to combat oral pathogens, these microbes persistently proliferate, culminating in biofilm formation that exhibits resistance to conventional antibiotics. Moreover, there is a notable surge in the rate of implant failure attributable to peri-implantitis. Peptide therapy emerges as a promising and enduring solution, demonstrating adaptability to diverse techniques. The pivotal technique involving Antimicrobial peptides (AMPs) orchestrates damage to the extracellular matrix, encompassing the glucan matrix, disrupting microbial content, and inducing pathogen demise. Notably, AMPs actively participate in the intricate process of oral tissue healing. Nanofibers, characterized by their multifaceted properties and robust mechanical capacity, facilitate protracted drug delivery and manifest optimal biocompatibility. This inherent attribute stimulates the application of AMPs, thereby augmenting healing durations and ensuring sustained therapeutic efficacy against oral diseases. The primary focus of this review centers on Antimicrobial peptides (AMPs) integrated into Nanofibers, offering a comprehensive clinical solution to diverse dental-mediated diseases. The exploration of various AMPs and their modes of action, coupled with the efficacy of peptide durability, sets the stage for futuristic enhancements in oral health therapeutics. [ABSTRACT FROM AUTHOR]

This study presents the design and implementation of a Fractional Order Proportional, Integral, and Derivative (FOPID) controller intended for the regulation of blood glucose levels in individuals with type 1 diabetes mellitus (T1DM). The efficacy of this controller is evaluated through its application to a nonlinear Stolwijk–Hardy model simulating T1DM patients, accounting for a range of physiological conditions. Utilizing a genetic algorithm, the parameters of the FOPID controller are fine-tuned. By conducting a comparative analysis with previously established control algorithms, the performance of the proposed controller is validated, demonstrating its superior performance. The results underscore the significant improvements achieved by the proposed controller in maintaining blood glucose concentrations within safe thresholds, particularly in scenarios involving disruptions due to meals. [ABSTRACT FROM AUTHOR]

Detecting diseases is a vital and crucial step in maintaining healthy, high-yielding plants. The challenge of manually identifying infections is arduous as well. The proposed work is to diagnose plant leaf diseases and discuss their origins and remedies. Image processing is used to discover the infected leaf and provide remedial measures through a mobile robot application. The use of machine learning techniques allows for the detection of leaf diseases using the support vector machine model, the K nearest neighbor model, and the Naïve Bayes classification to categorize the sample leaves. In this paper, the Momordica charantia leaf and the common four diseases dataset are developed, and a classification model is developed to identify and categorize leaf curl, downy mildew, powdery mildew, and angular leaf spot. Based on the disease classification, appropriate chemical pesticides are sprayed by controlling the servo actuated valve in the proposed agriculture robot, which is controlled and validated. The result reveals that the proposed approach has an average accuracy of 82% in identifying the disease type that remains more prevalent in Momordica charantia leaves than other compared classification algorithms. [ABSTRACT FROM AUTHOR]

During the present experimentation, milling machining was performed on two different composites, namely carbon fiber-reinforced polymer composite and jute fiber-reinforced polymer composite, using a computer numerical control vertical machining center. The selected machining parameters were spindle speed (S), feed rate (FR), depth of cut (DOC), and flute number or cutting edge number (FN). The output parameter is the machined surface roughness (Ra). Analysis of variance was used to predict the percentage influence of each parameter on machining quality. The parameter feed rate exhibited a higher influence on the machined surface roughness, followed by spindle speed, flute number, and depth of cut in sequence. Similarly, while milling the carbon fiber composite, the feed rate had the highest influence, followed by the parameter flute number. As for the surface roughness, the feed rate had a greater effect, followed by the spindle speed. Under the same machining conditions, it was observed that the sequence of parameters influencing the jute composite and carbon composite changed in the case of cutting force generation, but the sequence of parameters was the same for both cases in terms of roughness. The outcome of the work confirmed that to achieve a smaller value of roughness in the milling of jute–epoxy composite, the optimum combination should be S = 3000 rpm, FR = 800 mm/min, DOC = 0.25 mm, and FN = 6. Similarly, to achieve the minimum surface roughness value in the milling of carbon–epoxy composite, the optimum combination of parameters should be S = 600 rpm, FR = 100 mm/min, DOC = 0.25, and FN = 6. The average roughness values obtained during the milling of jute–epoxy composite and carbon–epoxy composites are 6.685 and 3.08 μm, respectively. In this present work, it is proved that the optimum combination of parameters to get the minimum surface roughness and the amount of surface roughness produced during milling are highly influenced by the type of reinforced material. The graphs are prepared for the entire range of input parameters to identify the intermediate Ra value at any input value without the use of software. [ABSTRACT FROM AUTHOR]

This study's primary objective is to experimentally investigate the flexural and impact performance of composites composed of hybrid basalt/E-glass fiber-reinforced epoxy infused with multiwalled carbon nano-tubes (MWCNTs) and nano-silica (SiO2) in compliance with ASTM D790 and ASTM D6110 specifications. Recently, manufacturers considered using basalt fiber-based composites for various structural applications due to their excellent mechanical properties, high stiffness, and high strength-to-weight ratio. Each composite laminate was made by hand layup techniques and filled with equal proportions of SiO2 and MWCNT nanoparticles in different weight percentages, such as 0%, 1%, 2%, and 3%. The composites were made by using a symmetric stacking sequence of B/GG/BB/GG/BD/GG/B fibers. MWCNTs and SiO2 were evenly dispersed throughout the epoxy matrix with the assistance of an ultrasonicator and magnetic stirrer. The composite containing 2% fillers has an increased flexural strength by 20% from 307 to 378 MPa and flexural modulus by 30% from 11.181 to 15.901 Gpa, as well as an increased Charpy impact resistance by 45% from 236 to 418 J/m, compared with the composite without fillers. The interfacial interactions between the epoxy matrix, particles, and fibers significantly influenced the composite laminates' flexural and impact characteristics. The accumulation of particles in the epoxy caused by the 3% fillers reduces the flexural strength and flexural modulus and impacts the performance due to the inadequate interfacial contact between the fibers and the epoxy matrix. [ABSTRACT FROM AUTHOR]

Manufacturing has grown challenging because of the increased usage of harder materials, such as titanium alloys, in many industries, such as aerospace, automobiles, and marine. Conventional material removal procedures are not suitable for these tough materials due to their increased hardness and slow machinability. Wire Electrical discharge machining (WEDM) is a modern approach for material removal, particularly for harder materials, such as titanium alloys, nickel alloys, hard particle reinforced metal matrix composites, etc. The research design was performed by deeming the independent factors, such as duration of pulse and applied current. The removal rate of material, surface roughness of the machined region, dimensional deviation, and tolerance errors in form/orientation are considered performance metrics. Taguchi's approach was engaged to assess the process variables, and the importance of the process factors was established using analysis of variance approach. The purpose of this research is to create an AI based decision making tool, which can be utilized to anticipate the various parameters that impact the WEDM material removal process. The discoveries of the present exploration allowing the manufacturers to make better-informed decisions with a developed model's capability by demonstrating that the model's predicted values were in close confirmation to the actual values. [ABSTRACT FROM AUTHOR]

Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing. The complexities of these materials have prompted the creation of cutting-edge machining methods. Wire Electrical Discharge Machining (WEDM) is a technique that has the potential to be useful for the removal of materials that are harder and electrically conductive. In order to create intricate designs, this method is frequently employed. The input factors, including pulse duration (on/off) and peak current, were taken into account during the experimental design process. The rate of material removal, surface roughness, dimensional deviation, and GD&T errors were opted for as performance indicators. The approach proposed by Taguchi was selected for the investigation of the process factors, and an Analysis of Variance was selected to find out the relative momentousness of each factor. From the analysis it is perceived that the applied current is the predominant factor that influences the chosen output characteristics. The aspiration of this article is to evolve a decision-making model based on a hybrid learning method which can be adopted to predict the selected output measures that affect the WEDM process. According to the findings, the value of the ANFIS-GRG, which was predicted to be 0.7777, was in fact closer to that value than any other value. The proposed model has the ability to help make a variety of different production processes more efficient. The analysis showed that the model's functionality was enhanced, which helps producers make well-informed decisions. [ABSTRACT FROM AUTHOR]

Cow dung is an abundant agricultural by‐product that poses disposal challenges. Converting this waste into a valuable energy resource aligns with sustainable waste management practices and contributes to a circular economy. This research aims at Cow dung disposal by carbonization and gasification for hydrogen generation and maximizes it by optimizing the process parameters. In the pyrolysis‐carbonization process, the influence of temperature on cow dung and its biochar characteristics was initially investigated. On the cow dung feedstock, different operating temperatures at about 400, 500, and 600 °C and response times of about 30, 60, and 120 min were tested. With CD450 as the feedstock, the gas concentrations and hydrogen yield were around 62 vol% and 0.69 m3 kg−1, respectively, for longer reaction times. Similar to this, the gas concentration and yield value for hydrogen at 600 °C gasification temperature is around 64 vol% and 0.91 m3 kg−1. Cow dung is evidently acceptable for larger hydrogen production at higher gasification temperatures and reaction times when the carbonization temperature is 450 °C. In addition, when compared to feedstock without adding nanoparticles, including Fe3O4 nanoparticles may increase the hydrogen concentration and yield by around 13.5 % and 4.21 %, respectively. [ABSTRACT FROM AUTHOR]

AbstractThis study utilized response surface methodology (RSM) and artificial neural networks (ANN) to optimize the formulation of polymer composites with three key variables: silane concentration, silane dipping time, and the number of nanoparticles. RSM and analysis of variance were employed to explore the effects of these variables on the composite’s mechanical properties, while ANN was used for analyzing complex interactions between parameters. The results reveal a significant correlation between the predicted and actual response values derived from the models. Notably, silane treatment of fiber was identified as the primary factor influencing the composite’s flexural strength. The study found that all fiber-related properties substantially affected both flexural strength and hardness, with silane dipping time being the most critical factor. Additionally, incorporating nanoparticles improved the fiber matrix’s strength by enhancing agglomeration. ANN achieved a 95% accuracy in predicting flexural strength and hardness. Comparisons among experimental data, the regression model, and ANN confirmed the robustness of the ANN predictions. The optimal composition identified for flexural strength was 5 wt % nanoparticles, 10 wt % silane treatment, and a 20-min dipping time. For hardness, the ideal formulation was 5 wt % nanoparticles, 15% silane treatment, and a 20-min dipping time in silane. This comprehensive approach offers a refined method for developing high-performance polymer composites with tailored properties. [ABSTRACT FROM AUTHOR]

The focus of this scope is on the single phase Cuk and SEPIC power factor correction converter that is attractive to electric bicycle applications. In order to guarantee improved performance, the proposed modified converter is designed to operate in discontinuous conduction mode, which is hidden by changes in supply and load voltage. For rated load, the investigational study achieves reduced input line total harmonic distortion and unity power factor. Line regulation, high efficiency, and load regulation are achieved by means of a closed loop PI control circuit that has a voltage sensor and current loop. The power supply is simulated using MATLAB/Simulink under various operating conditions, and the designed system is validated using a 250 W hardware prototype. The input current is observed to be perfectly sinusoidal, and the IEC 61000-3-2 class A standard is followed in recording the total harmonic distortion of the mains current. [ABSTRACT FROM AUTHOR]

This study explores the transformative impact of three-dimensional printing, or additive manufacturing, in the development of bamboo-based 3D printing parts. Recently, there has been growing interest in incorporating natural fibers, such as bamboo, into polymers to enhance the structural integrity and strength of 3D-printed polymeric materials. This paper thoroughly examines the opportunities and obstacles associated with using additive manufacturing techniques to print bamboo fiber composites. This study includes an analysis of the mechanical properties, thermal properties, biodegradability, and environmental benefits of bamboo fiber composites. It also covers the processing methods and the printing parameters of bamboo fiber composites. This paper review focuses on the future prospects of bamboo fiber composites as a sustainable material in additive manufacturing based on the analysis of the existing literature and the recent research developments. [ABSTRACT FROM AUTHOR]

Breast cancer is a major health concern for women everywhere and a major killer of women. Malignant tumors may be distinguished from benign ones, allowing for early diagnosis of this disease. Therefore, doctors need an accurate method of diagnosing tumors as either malignant or benign. Even if therapy begins immediately after diagnosis, some cancer cells may persist in the body, increasing the risk of a recurrence. Metastasis and recurrence are the leading causes of death from breast cancer. Therefore, detecting a return of breast cancer early has become a pressing medical issue. Evaluating and contrasting various Machine Learning (ML) techniques for breast cancer and recurrence prediction is crucial to choosing the best successful method. Inaccurate forecasts are common when using datasets with a large number of attributes. This study addresses the need for effective feature selection and optimization methods by introducing Recursive Feature Elimination (RFE) and Grey Wolf Optimizer (GWO), in response to the limitations observed in existing approaches. In this research, the performance evaluation of methods is enhanced by employing the RFE and GWO, considering the Wisconsin Diagnostic Breast Cancer (WDBC) and Wisconsin Prognostic Breast Cancer (WPBC) datasets taken from the UCI-ML repository. Various preprocessing techniques are applied to raw data, including imputation, scaling, and others. In the second step, relevant feature correlations are used with RFE to narrow down candidate discriminative features. The GWO chooses the best possible combination of attributes for the most accurate result in the next step. We use seven ML classifiers in both datasets to make a binary decision. On the WDBC and WPBC datasets, several experiments have shown accuracies of 98.25% and 93.27%, precisions of 98.13% and 95.56%, sensitivities of 99.06% and 96.63%, specificities of 96.92% and 73.33%, F1-scores of 98.59% and 96.09% and AUCs of 0.982 and 0.936, respectively. The hybrid approach's superior feature selection improved the accuracy of breast cancer performance indicators and recurrence classification. [ABSTRACT FROM AUTHOR]

Waspaloy, a nickel-based super alloy, has much potential in gas turbine component production. Machining this type of material using the traditional manufacturing method is very hard. Hence, electrical discharge machining (EDM) was selected for the investigation of the machining characteristics of Waspaloy by choosing the control parameters, namely, current, voltage, pulse on time, pulse off time, and spark gap. Gray relational analysis is carried out using 18 experiments according to an orthogonal array (L18) to determine the gray relation grade for this machining process with multiple machining characteristics such as material removal rate (MR), tool wear rate (TW), and surface roughness (SR). The significant objective of the multi objective optimization to attain high MR, low TW, and low SR is found out. From gray relation optimization, the optimum machining conditions for EDM, namely, current: 26 A, voltage: 200 V, pulse on time: 120 μs, pulse on time: 10 μs, and spark gap: 0.15 m, have been identified. The foremost parameter affecting the responses was found to be peak current. [ABSTRACT FROM AUTHOR]

The study aimed to develop and analyze bio-based composites, incorporating moringa bioresin and hemp fibers as reinforcing elements. The composites were fabricated using four weight percentage combinations of epoxy, moringa bioresin, and hemp fiber. The fabricated composites were characterized by their mechanical, thermal, water absorption, biodegradability, and morphological properties. The study revealed that the composite with the highest proportion of moringa bioresin (30 wt. %) exhibited better mechanical properties. Moreover, the flexural strength and Shore D hardness were impacted by both the matrix and reinforcing materials' weight percentages. Thermal analysis showed that the composites had good thermal stability, while water absorption analysis indicated that the composites had good water resistance. Biodegradability analysis showed that the composites had a high rate of biodegradation, making them environmentally friendly. The distribution of reinforcing fibers within the matrix material was found to be uniform through the use of scanning electron microscope based morphological analysis. The results indicate that moringa bioresin and hemp fiber composites have the potential to be used as sustainable alternatives to petroleum-based composites in various applications. [ABSTRACT FROM AUTHOR]

The purpose of this study was to see how silane treatment affected the tensile and impact strength of composites constructed from jute/kenaf/glass fibers with a nano-graphene filler and to investigate the impact of major treatment factors, such as silane concentration, immersion duration, and nano-filler, on composite characteristics. To conduct systematic trials and improve these variables, the Response Surface Methodology (RSM) with central composite designs was used. To precisely forecast the tensile and impact strength of the nano-composite following silane treatment, quadratic models were built. By changing the silane concentration, immersion period, and nano-filler, they discovered ideal conditions for increasing tensile strength. The best ranges for silane concentration and immersion duration were discovered to be 15 wt. % and 30 min, respectively. Given the conditions, the composite impact strength increased by 51% and its tensile strength improved by 22% as compared to the values achieved from RSM optimization. These results highlight the practical importance of silane treatment, especially in improving tensile and impact strength and strengthening the interfacial adhesive characteristics of organic fibers and polymer matrices. [ABSTRACT FROM AUTHOR]

Basalt fibers (BFs) are being explored due to their excellent thermo-mechanical properties and commercial value. However, additional surface modifications are needed to improve the interfacial adhesion of the fibers with the matrix. Therefore, the present work explored the applicability of nitric acid, oxalic acid, and silane reagents as surface modification agents. The fibers were subjected to immersion in the chemicals for a varied length of time. The laminates were fabricated using a wet layup process and compression-cured. The developed composites were subjected to mechanical testing to ascertain the effect of the selected surface treatment acids and the treatment duration on the tensile, impact, and flexural strength. Acid treatment was found to be beneficial for improving the mechanical properties of the laminates due to the generation of several functional groups on the surface of the acid-treated fibers. The mechanical properties of the laminate improved for nitric acid treatment in comparison to oxalic acid treatment. With respect to time, the mechanical properties of the laminate improved when nitric acid treatment time was increased to 30 min from 15 min. However, when oxalic acid treatment was performed for 180 min instead of 90 min, there was an observed reduction in the mechanical properties of the laminates. The highest increase in the tensile strength was noted in composites manufactured using fibers subjected to primary acid treatment and secondary silane treatment. Conclusively, nitric acid treatment was most beneficial as it helped improve the tensile, impact, and flexural strength of the BF/epoxy composites. [ABSTRACT FROM AUTHOR]

In this paper, metal matrix composites of Al6061 strengthened with 6 wt. % SiC were produced by stir casting and further subjected to the Tungsten Inert Gas (TIG) welding fabrication process using ER5356 as the filler material. The effect of TIG welding on hardness and wear resistance was studied by varying current at 150, 170, and 200 A. Samples of cast composite as per ASTM standards were prepared. Hardness tests on TIG-welded composite were carried out on the base, heat-affected, and weld zones. The test results showed higher hardness with an increase in welding current. Sliding wear properties were explored using the pin-on-disc machine. The wear rate and weight loss decreased as the welding current increased. The surface morphological images obtained through SEM indicated that the wear in the non-welded specimen was due to abrasion and delamination, whereas that of the welded specimen showed a reduced delamination and lower wear rate. [ABSTRACT FROM AUTHOR]

Magnesium AZ91/AlN-based surface composites have been synthesized using the friction stir processing (FSP) technique. The influence of particle addition during FSP on microstructural and mechanical properties has been investigated. Composite plates of 5, 10, and 15 vol. % AlN were synthesized using two-pass FSP with an axial force of 10 KN, a tool travel speed of 40 mm/min, and a tool rotation speed of 1200 rpm. The AZ91/AlN surface composites were analyzed microscopically with the use of an optical microscope, an x-ray diffractometer, and a scanning electron microscope. The prepared composites were tested for hardness and tensile strength. Micrographs taken in the stir zone revealed a consistent distribution of AlN particles throughout the AZ91 matrix. The AZ91/AlN surface composites were tested for hardness and tensile strength, and the findings showed that the AlN particles improved the mechanical properties without significantly decreasing ductility. The fracture surfaces of the composites were studied, and the mechanisms of fracture were identified. [ABSTRACT FROM AUTHOR]

The optimization of resources and reduction of costs through efficient inventory management are paramount to organizational success. This study undertakes a comparative analysis of two distinct forecasting methodologies, Exponential Smoothing (ES) and Gradient Boosting (GB), within the framework of materials forecasting aimed at inventory minimization. Our study introduces innovation by methodically scrutinizing these approaches within a unified framework, shedding light on their merits and shortcomings. This comparative analysis gives practitioners a practical roadmap for the optimal forecasting strategy to streamline inventory management operations. Methodologies are evaluated based on their efficiency in predicting material demand, encompassing metrics such as accuracy, computational efficiency, and suitability across various inventory management scenarios. Response surface methodology entails refining processes to modify factorial variables' configurations to attain a desired peak or trough in response. The SPSS results show that the ES method has 43.20%, surpassing the accuracy of the inventory optimization model, which stood at 65.08%. The response surface methodology results show that 45.20% profit was achieved for the variable and operational cost process parameters. This research seeks to unveil the traces of each method, facilitating decision-makers in selecting an optimal forecasting strategy tailored to their specific inventory management requirements. The analysis shows that the ES method surpasses the accuracy of the GB machine learning for material forecasting to minimize inventory. [ABSTRACT FROM AUTHOR]

Offering intended products at an affordable price is a highly challenging task in a business environment. To meet such challenges, industrial experts and researchers have different approaches, such as shifting the purchase of materials from import mode to local vendors and optimizing machining cost by optimizing process parameters, waste reduction, rework reduction, and technological improvements. The novelty of this study lies in reducing material cost by accurate materials forecasting to minimize storage cost, ablation cost, average annual consumption cost, etc. This study aims to compare the efficacy of the Seasonal Decomposition Method (SDM) against Multi-Echelon Inventory Optimization (MEIO) for minimizing inventory through accurate materials forecasting. Through rigorous evaluation and analysis, this research seeks to clarify the strengths and weaknesses of each approach, thereby providing insights into their applicability and effectiveness in addressing inventory management challenges across diverse seasonal demand patterns. The sample size was taken as 50 per group. The G-power applied is about 80%. The significance value obtained is 0.001 (p < 0.05), indicating a statistically significant difference between the two algorithms used for inventory reduction and materials forecasting. SDM (63.43) outperforms MEIO (48.57) in terms of accuracy. SDM yielded a better accuracy when compared to MEIO for inventory minimization and materials forecasting. [ABSTRACT FROM AUTHOR]

Poor planning and scheduling increase buying, storage, and obsolescence expenses. Material shortages increase labor, machine optimum time, etc. Industrial raw materials, semi-finished items, spares, and consumables have distinct consumption patterns, reorder points, purchase lead times, quantity limits, discounts, etc. To save money, machine learning predicts demand and prepares materials. This study employs ARIMA or Support Vector Machine (SVM) machine learning-based forecasting approaches to forecast materials for less inventory. Feature engineering eliminates seasonality, time series, and external demand and ignores data irregularities, missing figures, and disparities. This approach needs to adapt traits to factors, separate test and training data, and consider many future models to represent the best forecasts. Forecast reliability and consistency were examined for each model. Inventory management systems were evaluated for computational complexity and installation ease and found implementation issues. Both models' input data and resilience were examined using sensitivity analysis. Accurate prediction SVM and ARIMA predict material demand differently. Meaningful statistics show the optimal model. Performance differences between SVM and ARIMA enhance model selection. Thinking about the execution of high inventory system integration and computational complexity, response surface methodology chooses factorial variables with the highest or lowest responses. Analysis of variance, factor analysis, and effect modeling expansions demonstrated for the response. [ABSTRACT FROM AUTHOR]

There are a lot of problems with the conventional fusion welding process, so ultrasonic welding has been used for about 20 years and has helped a lot of manufacturing industries, including aviation, medicine, and microelectronics. Ultrasonic welding takes less than one second, making it suitable for mass production. Poor weld quality and joint strength are common issues that industries encounter as a result of this process. Actually, the success and quality of the welding are determined by its control parameters. This research examines the impacts of weld time, vibrational amplitude, and weld pressure on the welding of 0.6 mm thick sheets of two different metals, specifically copper and aluminum (AA2024). Responses, including tensile shear stress, weld area, and T-peel stress, are acquired through experiments that follow a full factorial design including four replicas. The highest recorded tensile shear stress was 4.34 MPa, the maximum weld area measured was 63.6 mm2, and the peak T-peel stress reached 1.22 MPa. A second-order non-linear regression model was constructed using all of these data points, which related the responses to the predictors. Due to the importance of quality in the production sector, the process parameters were determined by the combination of genetic algorithm (GA) and fuzzy logic (FL) approaches. The impact of the weld zone temperature on various quality characteristics has been investigated through experiments. It has been noted from the confirmatory test that FL produces superior output outcomes compared to the genetic algorithm, with FL achieving a fuzzy multi-performance index of 0.94 compared to 0.61 for GA. By conducting microstructural analysis, weld quality levels, including "under-weld," "good weld," and "over-weld," were established. [ABSTRACT FROM AUTHOR]

At present, composites have brought materials to a new era with superior characteristics. The proper selection of materials with optimal processing conditions is one of the prime factors in determining the efficiency of composites. This article introduces a new composite by fabricating a copper matrix with reinforced AlCoCrCuFe High Entropy Alloy (HEA). The ultimate theme of this research is to forecast the wear rate of the Cu–AlCoCrCuFe HEA composite. Using the pin on the disk, the wear test is carried out on varying combinations of parameters. Then Taguchi analysis is carried out to get the precise fit parameters. The wear rate mainly depends on the percentage of HEA, sliding distance, sliding velocity, and the load applied. Correlation analysis is performed to determine the exact parameter determining the optimal wear rate and the coefficient of friction. After feature extraction, the parameters are optimized, and the neural network regression is given the optimized parameters. The network has been trained, and predictions are made using it. The model successfully predicts the wear rate, as evidenced by the declining RMSE of 0.28 and rising R2 values up to 92%. The analysis shows a significant decline in the wear rate with HEA additions. In addition, the wear rate increases with a rise in load, sliding velocity, and sliding distance. [ABSTRACT FROM AUTHOR]

Aluminum alloy and its allied composites are gaining popularity and find their exclusive application in all industries for more than two decades. Due to their increased usage, Al metal matrix composites were experimented by reinforcing them with variety of particles. The current study attempted the use of titanium carbide (TiC) and boron nitride (BN) as reinforcing materials for the Al7050 alloy through the stir casting technique. The percentage of reinforcement of TiC was kept constant as 8% by wt., and the BN was varied as 2% and 3% by wt. Mechanical properties, such as compressive strength, tensile strength, flexural strength, impact energy, microhardness (Vickers), the microstructure were studied using an optical microscope and by corrosion tests using polarization techniques. The results conclude that the Al hybrid composite with 8% TiC and 3% BN provides superior performance. [ABSTRACT FROM AUTHOR]

The essential objective of this work was to investigate the effect of silane treatment on the tensile and impact strength of composites made of jute/kenaf/glass fibers with the TiO2 nano-filler. This study also sought to optimize the factors connected with this treatment method. To develop and evaluate trials, the researchers used Response Surface Methodology (RSM) with central composite designs, emphasizing the optimization of silane concentration, immersion time, and nano-filler content. The researchers were able to effectively create quadratic models to forecast the tensile and impact strengths of the nano-composite throughout the silane treatment procedure. The adjustment of silane concentration, immersion duration, and nano-filler content discovered the best conditions for obtaining maximum tensile strength. The optimal values for silane concentration and immersion time have been identified to be 15 wt. % and 30 min, respectively. Considering these conditions, the composite's tensile strength increased by 32.13% and its impact strength improved by 8.34% above the lowest values achieved from RSM optimization. The results demonstrate the practical value of silane treatment, notably in boosting tensile and impact strengths while enhancing interfacial adhesive among natural fibers and polymer matrices. [ABSTRACT FROM AUTHOR]

In this work, four varieties of hybrid Fiber-Reinforced Polymer (FRP) panels made of kevlar-29 and natural fibers are studied. All panels have kevlar-29 face sheets and natural fiber core, such as jute, flax, sisal, and hemp. This research focuses on the behavior of these hybrid FRP panels under flexural and impact loading so that the panels can be explored for the structural/semi-structural members of army shelters, portable helipad, and roofing panels in high-altitude areas. Natural fibers are chemically treated with NaOH to improve hydrophobicity. The panels are vacuum bagged, the fiber volume fraction is 0.39, and the thickness is close to 4 mm. Three-point flexural loading using the universal testing machine and low-velocity impact loading up to 24 J under drop weight impact test setup is carried out to characterize the panels. Damage area, delamination, permanent deformation, indentation depth, energy absorbed, flexural strength, and modulus are measured. The hybrid flax/kevlar panel and hemp/kevlar panel, each resist impact with permanent deformation less than 0.5 mm up to 24 J. Without significant face sheet or core fiber breakage, the delamination is spread over a small radial distance of 18.5 and 24.5 mm, respectively. Interface matrix breakage causes delamination. The load vs deflection curve is almost linear under flexural loading, and specimens failed under compression at 240 MPa. The numerical simulation is also done using ANSYS and LS-DYNA for detailed study. [ABSTRACT FROM AUTHOR]

The use of aluminum alloys in automobiles is expanding, and the potential for additional increases is significant. Further growth will be determined by improvements in material qualities for existing applications or the discovery of new applications. Alloy A-356 (LM25) is commonly employed for high-quality alloy wheel rims in various motor vehicles, constituting 40% of global car usage. This study introduces 0.2% Cu into the Al–Si–Mg alloy system to enhance the mechanical properties. The alloy blend is cast into a metal mold, subjected to a 4-h cure at 540 °C, quenched with water, and precipitation hardened for 12 h at 1800 °C. Optical and scanning electron microscopes are utilized to analyze the alkali microstructure. The mechanical properties of alloyed and unalloyed castings, including hardness and tensile test results, are examined in untreated and heat-treated states. Fracture surfaces of tensile specimens are scrutinized. Intermetallic compounds formed during solidification are studied using scanning electron microscopy and x-ray diffraction analysis. The tensile strength under unalloyed (LM25) and alloyed (LM25 + 0.2 wt. % Cu) conditions before and after heat treatment [(72, 165.4 and 88.3, 237.1) and (78, 179.6 and 98, 252.9, respectively)] shows a significant increase. [ABSTRACT FROM AUTHOR]

Vehicle emissions are responsible for about 30% of all air pollution in the world. Vehicle emissions can be significantly reduced through the use of selective catalyst reduction (SCR). The present work emphasizes the impact of thermal barrier-coated pistons on diesel engine performance as well as emission qualities. A Ni–Cr–Al–Y bond coat was applied to the tested engine piston that was 50 microns thick and a top coat that was 250 microns thick. These coatings were applied using the plasma spray technique to a combination of 2 mol. % of Gadolinium oxide (Gd2O3), 2 mol. % of Neodymium oxide (Nd2O3), 3 mol. % of Yttria (Y2O3), and continuing 93 mol. % of Zirconia (ZrO2). In a 4-stroke, 1-cylinder diesel engine, the testing was carried out utilizing diesel, Mahua, and Jatropha fuels with and without coating. The selective catalytic reduction technique was employed in the current test to reduce NOx emissions. The findings of this analysis indicate that the brake thermal efficiency of an insulated piston engine improved by 3.9%, and when JB 100 was chosen as the fuel, the insulated piston reduced brake-specific fuel consumption by 3.5% in comparison to the normal piston. In engines coated with SCR, hydrocarbon emissions were lowered by 20.1%, while carbon monoxide emissions were dropped by 13.4%. In comparison to the baseline engine, the oxide of nitrogen emissions were reduced by 39.1%. [ABSTRACT FROM AUTHOR]

The present study is an integrated approach of experimental and simulation processes to investigate the influences of the flushing on the wear, textural feature, heat dissipation, tool-tip temperature, and elemental contents of the electrical discharge machining-tool. The general heat transfer equation in cylindrical coordinates is used to explain the thermal phenomenon through the tool, where the boundary conditions are influenced by convective interactions and dimensionless numbers. The inter-electrode flushing is considered a micro-channel flow, and the used model is conceptualized from "Hazen–Poiseuille observation." It is observed that the adopted model has a prediction error of 8.503% and can accurately explain the thermal consequences of the process. The study reveals that tool wear is influenced by flushing velocity and flushing pressure. The tool-tip temperature reduces with the Reynolds number, and effectual expelling of the debris can be ensued with a flushing of a higher Reynolds number (Re). However, the increment of Re beyond 4500 provides rapid heat dissipation, which produces extensive residual stress and creates cracks on the surface. [ABSTRACT FROM AUTHOR]