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This study aims to provide an overview of rapid prototyping (RP) and shows the potential of this technology in the field of medicine as reported in various journals and proceedings. This review article also reports three case studies from open literature where RP and associated technology have been successfully implemented in the medical field.

Key publications from the past two decades have been reviewed.

This study concludes that use of RP-built medical model facilitates the three-dimensional visualization of anatomical part, improves the quality of preoperative planning and assists in the selection of optimal surgical approach and prosthetic implants. Additionally, this technology makes the previously manual operations much faster, accurate and cheaper. The outcome based on literature review and three case studies strongly suggests that RP technology might become part of a standard protocol in the medical sector in the near future.

The article is beneficial to study the influence of RP and associated technology in the field of medicine.

This research aims to describe the influence of weight per cent of graphite (Gr), applied load and sliding speed on the wear behavior of aluminum (Al) alloy A356 reinforced with silicon carbide (SiC) (10 Wt.%) and Gr (1 Wt.% and 5 Wt.%) particles. The objective is to analyze the effect of the aforementioned parameters on a specific wear rate.

These hybrid composites are obtained by means of the compo-casting process. Tribological analyses were conducted on block-on-disc tribometer at three different loads (10, 20 and 30 N) and three different sliding speeds (0.25, 0.5 and 1 m/s), at the sliding distance of 900 m, in dry sliding wear conditions. Optimization of the tribological behavior was conducted via the Taguchi method, and ANOVA was used for the analysis of the specific wear rate. Confirmation tests are used to foresee and check the experimental results. Examined samples were analyzed via a scanning electron microscope (SEM). Regression models for predicting specific wear rate were developed with Taguchi and ANN (artificial neural network) methods.

The biggest impact on value of specific wear rate has the load (43.006%), while the impact of Wt.% Gr (31.514%) was less. After comparison of the results, i.e. regression models, for predicting the specific wear rate, it was observed that ANN was more efficient than the Taguchi method. The specific wear rate of Al alloy A356 with SiC (10 Wt.%) and Gr (1 Wt.% and 5 Wt.%) decreases with a decrease in the load and weight per cent of Gr-reinforcing material, as well as with a decrease in sliding speed.

The results obtained in this paper using the Taguchi method and the ANN method are useful for improving and further investigating the wear behavior of the SiC- and Gr-reinforced Al alloy A356.

The purpose of this paper is to study Al-based green composite. To make composite samples of aluminium alloy (AA3105) with different weight percentages of rice husk ash (RHA) and eggshell (ES) particles as reinforcement, stir casting method was used.

Several other aspects, including the weight percent of reinforcing agent particles, the applied stress and the sliding speed, were taken into consideration. During the course of the wear test, the sliding distance that was recorded varied from a minimum of 1,000 m all the way up to a maximum of 3,135 m (10, 15, 20, 25 and 30 min). The typical range for normal loads is 8–24 N, and their speed is 1.58 m/s.

With the AA/ES/RHA composite, the wear rates decreases when the grain size of the reinforcing particles enhanced. Scanning electron microscopy images of worn surfaces show that at low speeds, delaminating and ploughing are the main causes of wear. At high speeds, ploughing is major cause of wear. Composites with better wear-resistant properties can be used in wide range of tribological applications, especially in the automotive industry. It was found that hardness increases at the same time as the weight of the reinforcement increases. Tensile and hardness were maximized at 10% reinforcement mix in Al3105.

In this work, ES and RHA has been used to develop green metal matrix composite to support green revolution as promoted/suggested by United Nations thus reducing the environmental pollution.

This paper aims to introduce recently an unregulated unsupervised algorithm focused on voice activity detection by data clustering maximum margin, i.e. support vector machine. The algorithm for clustering K-mean used to solve speech behaviour detection issues was later applied, the application, therefore, did not permit the identification of voice detection. This is critical in demands for speech recognition.

Here, the authors find a voice activity detection detector based on a report provided by a K-mean algorithm that permits sliding window detection of voice and noise. However, first, it needs an initial detection pause. The machine initialized by the algorithm will work on health-care infrastructure and provides a platform for health-care professionals to detect the clear voice of patients.

Timely usage discussion on many histories of NOISEX-92 var reveals the average non-speech and the average signal-to-noise ratios hit concentrations which are higher than modern voice activity detection.

Research work is original.

Sentiment analysis has observed a nascent interest over the past decade in the field of social media analytics. With major advances in the volume, rationality and veracity of social networking data, the misunderstanding, uncertainty and inaccuracy within the data have multiplied. In the textual data, the location of sarcasm is a challenging task. It is a different way of expressing sentiments, in which people write or says something different than what they actually intended to. So, the researchers are showing interest to develop various techniques for the detection of sarcasm in the texts to boost the performance of sentiment analysis. This paper aims to overview the sentiment analysis, sarcasm and related work for sarcasm detection. Further, this paper provides training to health-care professionals to make the decision on the patient’s sentiments.

This paper has compared the performance of five different classifiers – support vector machine, naïve Bayes classifier, decision tree classifier, AdaBoost classifier and K-nearest neighbour on the Twitter data set.

This paper has observed that naïve Bayes has performed the best having the highest accuracy of 61.18%, and decision tree performed the worst with an accuracy of 54.27%. Accuracy of AdaBoost, K-nearest neighbour and support vector machine measured were 56.13%, 54.81% and 59.55%, respectively.

This research work is original.

The purpose of the paper was to assess the functional properties vegetable gourds & the validated health claims so as to help the future researchers to locate the gaps. However, emphasizing on the scientifically available reports was required to make information available in a nutshell to the health-conscious consumers, as well as the researcher from the area of functional foods and nutrition.

The paper is a mini-review of scientific findings in different studies on gourd vegetables. The approach to information collection was finding the research gaps and potential areas for future work with a nutritional perspective.

Ash gourd, bitter gourd and bottle gourd have been extensively studied, and several health benefits and functional components have been reported, while ridge gourd, snake gourd and pointed gourd have been sparsely studied for their therapeutic benefits and the validation thereof; hence, there lies a scope for researchers.

The scarcity of scientific reports compared to the traditional usage and folkloric beliefs was a limitation.

Understanding the nutritional potential of gourd vegetables from scientific reports may influence both the work areas and consumers in the appropriate direction.

This study aims to systematically review various behavioral biases that impact an investor’s decision-making process. The prime objective of this paper is to thematically explore the behavioral bias literature and propose a comprehensive framework that can elucidate a more reasonable explanation of changes in financial markets and investors’ behavior.

Systematic literature review (SLR) methodology is applied to a portfolio of 71 peer-reviewed articles collected from different electronic databases between 2007 and 2021. Content analysis of the extant literature is performed to identify the research themes and existing gaps in the literature.

This research identifies publication trends of the behavioral biases literature and uncovers 24 different biases that impact individual investors’ decision-making. Through thematic analysis, an attribute–consequence–impact framework is proposed that explains different biases leading to individual investors’ irrationality. The study further proposes directions for future research by applying the theory–characteristics–context–methodology framework.

The results of this research will help scholars and practitioners in understanding the existence of various behavioral biases and assist them in identifying potential strategies which can evade the negative effects of these biases. The findings will further help the financial service providers to understand these biases and improve the landscape of financial services.

The essence of the current paper is the application of the SLR method on 24 biases in the area of behavioral finance. To the best of the authors’ knowledge, this study is the first attempt of its kind which provides a methodical and comprehensive compilation of both cognitive and emotional behavioral biases that affect the individual investor’s decision-making.

This study aims to analyse the changes in mechanical and wear performance of aluminium alloy when yttrium oxide particles are incorporated. The microstructures are studied to analyse the change in the grain structures. Worn surfaces are observed via scanning electron microscope to study the wear mechanism in detail.

Stir casting is used to incorporate varying composition of yttrium particles, having an average particle size of 25 micrometer, in aluminium alloy 6063 matrix. Wear testing is carried out by DUCOM manufactured high temperature rotatory tribometer, and an indentation test is used for analysing the microhardness of the fabricated samples.

Microhardness of the material is increased with the increasing content of particulate addition. With the increasing content of reinforcement, more refined grains are produced. The load is transferred from the matrix to more rigid yttrium oxide particles. These factors contributed to escalated microhardness of the reinforced samples. Particulate addition enhanced the wear performance of the material; this might be attributed to increased microhardness and formation of an oxide layer.

Aluminium composites are finding wide applications in various industries, and there is always a requirement of material with enhanced tribological properties. Yttrium oxide particles exhibit improved mechanical properties, and their interaction with the aluminium matrix has not been studied much in the past. So, in this work, yttrium oxide incorporated aluminium matrix is studied.

Energy management is critical to data centres (DCs) majorly because they are high energy-consuming facilities and demand for their services continue to rise due to rapidly increasing global demand for cloud services and other technological services. This projected sectoral growth is expected to translate into increased energy demand from the sector, which is already considered a major energy consumer unless innovative steps are used to drive effective energy management systems. The purpose of this study is to provide insights into the expected energy demand of the DC and the impact each measured parameter has on the building's energy demand profile. This serves as a basis for the design of an effective energy management system.

This study proposes novel tunicate swarm algorithm (TSA) for training an artificial neural network model used for predicting the energy demand of a DC. The objective is to find the optimal weights and biases of the model while avoiding commonly faced challenges when using the backpropagation algorithm. The model implementation is based on historical energy consumption data of an anonymous DC operator in Cape Town, South Africa. The data set provided consists of variables such as ambient temperature, ambient relative humidity, chiller output temperature and computer room air conditioning air supply temperature, which serve as inputs to the neural network that is designed to predict the DC’s hourly energy consumption for July 2020. Upon preprocessing of the data set, total sample number for each represented variable was 464. The 80:20 splitting ratio was used to divide the data set into training and testing set respectively, making 452 samples for the training set and 112 samples for the testing set. A weights-based approach has also been used to analyze the relative impact of the model’s input parameters on the DC’s energy demand pattern.

The performance of the proposed model has been compared with those of neural network models trained using state of the art algorithms such as moth flame optimization, whale optimization algorithm and ant lion optimizer. From analysis, it was found that the proposed TSA outperformed the other methods in training the model based on their mean squared error, root mean squared error, mean absolute error, mean absolute percentage error and prediction accuracy. Analyzing the relative percentage contribution of the model's input parameters based on the weights of the neural network also shows that the ambient temperature of the DC has the highest impact on the building’s energy demand pattern.

The proposed novel model can be applied to solving other complex engineering problems such as regression and classification. The methodology for optimizing the multi-layered perceptron neural network can also be further applied to other forms of neural networks for improved performance.

Based on the forecasted energy demand of the DC and an understanding of how the input parameters impact the building's energy demand pattern, neural networks can be deployed to optimize the cooling systems of the DC for reduced energy cost.

The use of TSA for optimizing the weights and biases of a neural network is a novel study. The application context of this study which is DCs is quite untapped in the literature, leaving many gaps for further research. The proposed prediction model can be further applied to other regression tasks and classification tasks. Another contribution of this study is the analysis of the neural network's input parameters, which provides insight into the level to which each parameter influences the DC’s energy demand profile.

Benchmarking research has explored the role of organizational practices and business processes rooted with human capabilities for achieving growth performance. The role of high performance work system as an organizational practice and business process is yet to be studied. Even if studied, no study has been conducted on the role of training transfer climate on high performance work system and organizational performance. The current research aims at examining high performance work system on organizational performance. Further, the study also examine training transfer climate as a mediating variable between HPWS and organizational performance.

Data collected from 415 executives of a high performance-based power sector company of Gujarat, India. The survey instrument consists of high performance work system, training transfer climate and organizational performance. Confirmatory factor analysis was used for a simultaneous assessment of overall and specific elements of measurement validity and reliability. Structural equation modelling used to test the hypothesized model.

The result proved the capability of high performance work system to predict organizational performance. Further, the result supports the hypothesis that training transfer climate acts as a mediator between high performance work system and organizational performance.

The result has important theoretical and managerial implications. Theoretically, the research extends the scope of benchmarking to high performance work system. The managerial implications have been discussed from the training transfer climate perspectives.

The originality of the study lies with proving the role of high performance work system and training transfer climate as an organizational practice and business process within benchmarking research.