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The effective, efficient and optimal design of micromixer is the need in the field of biochemical and biomedical diagnostic systems.

In this paper, multi-objective optimization of split and recombine micromixer (SRM) with different geometrical configurations is carried out. The finite element method-based three-dimensional models are prepared and analyzed using COMSOL Multiphysics 5.0 Software. Taguchi’s design of experiment (DoE), main effect plot analysis, ANOVA and grey relational analysis (GRA) method are used to find out optimum condition. The five geometrical parameters with three levels, namely, angle between inlets, pillar size, pillar shape, aspect ratio and constriction height of SRM are considered as design variables. The mixing index (MXI) and pressure drop (∆P) are considered objective functions.

The MXI is significantly influenced by pillar shape and aspect ratio, whereas the pressure drop (∆P) by constriction height. Maximum MXI (0.97) with minimum pressure drop (64,587 Pa) is the optimal conditions and obtained at 180 deg angle between inlets, 50 µm of pillar size, 1.5 of aspect ratio, 100 µm of constriction height and ellipse shape pillar cross-section, respectively.

This optimized SRM can be combined with lab-on-a-chip for biochemical and biomedical analysis.

This work is useful to obtain optimal geometry of SRM for getting efficient performance of micromixer.

This study aims to improve the performance of hydrodynamic journal bearings through partial grooving on the bearing surface.

Bearing performance analysis is numerically carried out using the thin film flow physics of COMSOL Multiphysics 5.0 software. Initially, the static performance analysis is carried out for hydrodynamic journal bearing system with smooth surface, and the results of the same are validated with results from the literature. In the later part of the paper, the partial rectangular shape micro-textures are modeled on bearing surface. The effects of partial groove pattern on the bearing performance parameters, namely, fluid film pressure, load carrying capacity, frictional power loss and frictional torque, are studied in detail.

The numerical results show that the values of maximum fluid film pressure, load carrying capacity, frictional power loss and frictional torque are considerably improved due to deterministic micro-textures. Bearing surface with partial groove along 90°-180° region results in 81.9 per cent improvement in maximum fluid film pressure and 75.9 per cent improvement in load carrying capacity as compared with smooth surface of journal bearing, with no increase in frictional power loss and frictional torque. Maximum decrease in frictional power loss and frictional torque is observed for partially grooving along 90°-360° region. The simulations are supported by proof-of-concept experimentation.

This study is useful in the appropriate selection of groove parameters on bearing surface to the bearing performance characteristics.

In today’s digital world, real-time health monitoring is becoming a most important challenge in the field of medical research. Body signals such as electrocardiogram (ECG), electromyogram and electroencephalogram (EEG) are produced in human body. This continuous monitoring generates huge count of data and thus an efficient method is required to shrink the size of the obtained large data. Compressed sensing (CS) is one of the techniques used to compress the data size. This technique is most used in certain applications, where the size of data is huge or the data acquisition process is too expensive to gather data from vast count of samples at Nyquist rate. This paper aims to propose Lion Mutated Crow search Algorithm (LM-CSA), to improve the performance of the LMCSA model.

A new CS algorithm is exploited in this paper, where the compression process undergoes three stages: designing of stable measurement matrix, signal compression and signal reconstruction. Here, the compression process falls under certain working principle, and is as follows: signal transformation, computation of Θ and normalization. As the main contribution, the theta value evaluation is proceeded by a new “Enhanced bi-orthogonal wavelet filter.” The enhancement is given under the scaling coefficients, where they are optimally tuned for processing the compression. However, the way of tuning seems to be the great crisis, and hence this work seeks the strategy of meta-heuristic algorithms. Moreover, a new hybrid algorithm is introduced that solves the above mentioned optimization inconsistency. The proposed algorithm is named as “Lion Mutated Crow search Algorithm (LM-CSA),” which is the hybridization of crow search algorithm (CSA) and lion algorithm (LA) to enhance the performance of the LM-CSA model.

Finally, the proposed LM-CSA model is compared over the traditional models in terms of certain error measures such as mean error percentage (MEP), symmetric mean absolute percentage error (SMAPE), mean absolute scaled error, mean absolute error (MAE), root mean square error, L1-norm and L2-normand infinity-norm. For ECG analysis, under bior 3.1, LM-CSA is 56.6, 62.5 and 81.5% better than bi-orthogonal wavelet in terms of MEP, SMAPE and MAE, respectively. Under bior 3.7 for ECG analysis, LM-CSA is 0.15% better than genetic algorithm (GA), 0.10% superior to particle search optimization (PSO), 0.22% superior to firefly (FF), 0.22% superior to CSA and 0.14% superior to LA, respectively, in terms of L1-norm. Further, for EEG analysis, LM-CSA is 86.9 and 91.2% better than the traditional bi-orthogonal wavelet under bior 3.1. Under bior 3.3, LM-CSA is 91.7 and 73.12% better than the bi-orthogonal wavelet in terms of MAE and MEP, respectively. Under bior 3.5 for EEG, L1-norm of LM-CSA is 0.64% superior to GA, 0.43% superior to PSO, 0.62% superior to FF, 0.84% superior to CSA and 0.60% better than LA, respectively.

This paper presents a novel CS framework using LM-CSA algorithm for EEG and ECG signal compression. To the best of the authors’ knowledge, this is the first work to use LM-CSA with enhanced bi-orthogonal wavelet filter for enhancing the CS capability as well reducing the errors.

The paper aims to focus on adjacent buildings response, equipped with damper, to analyze the vibration reduction in the nearby buildings. The nearby buildings were also equipped with dampers. The occurrence of adjacent buildings with adequate or inadequate space in between is a common phenomenon. However, many a times not much attention is paid to provide or check gap adequacy or to connect the two buildings suitably to avoid pounding of two structures on each other. This study emphasizes the utility of providing a damper in between two adjacent buildings for better performance.

The two steel structures taken for study are prototype of two structures normally found in industrial structure such as power plant, where in one of boiler structure is often tall and braced and short structure of turbine building which is moment resistant, modeled in SAP. There could be similar such structures which are often connected to a platform or a walkway with a sliding end, so as not to transfer horizontal force to other structures. If the advantage of stiffness of tall braced structure is taken into account, shorter structure can be suitably connected to braced structure to transfer forces during seismic cases under nonlinear conditions, thereby avoiding pounding (incase gap is too less), reducing response and thus optimizing the section sizes. The structures were subjected to El Centro earthquake, to simulate MCE (which could be the other site TH scaled up as desired for real site PGA), and damper location and parameters were varied to find optimum value which offers reduced base shear, reduced top floor displacement and minimum inter story drift and highest energy absorption by fluid viscous dampers.

The findings show that taller structures, which are braced, have more stiffness; the effect of damper is more pronounced in reducing displacement of shorter moment resistant structure to the tune of 60%, with suitably defined Cd value which is found to be 600 KNs/m for the present study. Thus, advantage of stiffener structure is taken to leverage and reduce the displacement of shorter moment resistant structure in reducing its displacement under nonlinear conditions of seismic case.

This work shows the original findings, of the adjacent buildings response, equipped with damper, to analyze the vibration reduction on other buildings which were planned to be constructed nearby.

The purpose of this paper is to review the advanced technologies and approaches for utilization of waste generated in dairy industry. Whey is highly contaminated, with a high organic load around 100,000 mg O₂/L COD (chemical oxygen demand), and is not used for further processing. The waste generated in different food industries can be utilized in different value addition product with the help of advanced technology.

Major well-known bibliometric information sources are the Web of Science, Scopus, Mendeley and Google Scholar. Several keywords like nutrition value of whey, whey utilization, whey valorization, whey technologies, whey beverages, fruit-based whey beverage, carbonated beverage, probiotic or alcoholic beverages, herbal beverage, fermented beverage and current scenarios were chosen to obtain a large range of papers to be analyzed. A final inventory of 126 scientific sources was made after sorting and classifying them according to different criteria based on topic, academic field country of origin and year of publication.

The comprehensive review of different literature, data sources and research papers seeks to find and discuss various sustainable solutions to this huge waste generated from milk industry. The sustainable use of whey for production and conversion in different types of products can uplift the bio-based economy of industries and thereof national/international economy. The recent upsurge in consumer interest for health-promoting products has opened up new vistas for whey beverages and other whey products research and development.

The paper draws out different sustainable characteristics and technology of whey products available in market, as well as potential products to be launched in the market. Interestingly, over the past few years, dairy industries have applied various technologies to process cheese whey and are in search of new products which can be prepared from the by-product. This review discusses on the recent research development of whey valorization with particular reference to technologies used in the addition to their commercial availability and a way forward.

This research explores the application of machine learning to optimize pricing strategies in the aftermarket sector, particularly focusing on parts with no assigned values and the detection of outliers. The study emphasizes the need to incorporate technical features to improve pricing accuracy and decision-making.

The methodology involves data collection from web scraping and backend sources, followed by data preprocessing, feature engineering and model selection to capture the technical attributes of parts. A Random Forest Regressor model is chosen and trained to predict prices, achieving a 76.14% accuracy rate.

The model demonstrates accurate price prediction for parts with no assigned values while remaining within an acceptable price range. Additionally, outliers representing extreme pricing scenarios are successfully identified and predicted within the acceptable range.

This research bridges the gap between industry practice and academic research by demonstrating the effectiveness of machine learning for aftermarket pricing optimization. It offers an approach to address the challenges of pricing parts without assigned values and identifying outliers, potentially leading to increased revenue, sharper pricing tactics and a competitive advantage for aftermarket companies.

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates.

The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R²) and mean absolute error (MAE).

Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments.

The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study.

This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.