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This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.

We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.

We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

This paper aims to examine how the regional variable in country conflict modeling affects forecast accuracy and identifies a methodology to further improve the predictions.

This paper uses statistical learning methods to both evaluate the quantity of data for clustering countries along with quantifying accuracy according to the number of clusters used.

This study demonstrates that increasing the number of clusters for modeling improves the ability to predict conflict as long as the models are robust.

This study investigates the quantity of clusters used in conflict modeling, while previous research assumes a specific quantity before modeling.

Background: Quality of life is a core concern for cancer patients, which can be negatively affected by illness-related death anxiety; yet understanding of how to appropriately target psycho-oncological interventions remains lacking. We aimed to explore experiential acceptance in cancer patients, and whether acceptance – as an alternative to avoidant coping – was related to and predictive of better quality of life and death anxiety outcomes.

Methods: We used a longitudinal, quantitative design with a follow-up after three months. Seventy-two participants completed a questionnaire-battery measuring illness appraisals, acceptance and non-acceptance coping-styles, quality of life, and death anxiety; 31 participants repeated the battery after three months.

Results: Acceptance was an independent explanatory and predictive variable for quality of life and death anxiety, in the direction of psychological health. Acceptance had greater explanatory power for outcomes than either cancer appraisals or avoidant response styles. Avoidant response styles were associated with greater death anxiety and poorer quality of life.

Conclusions: The findings support the role of an accepting response-style in favourable psychological outcomes, identifying a possible target for future psychological intervention. Response styles that might be encouraged in other therapies, such as active coping, planning, and positive reframing, were not associated with beneficial outcomes.

Using a combination of the geographical information system (GIS) and the Canadian water quality index (WQI), the current study sought to provide a long-term general assessment of the water quality of the Shatt Al-Arab River (SAAR), focusing on its suitability for living organisms. Likewise, SPSS statistics was used to develop a nonlinear WQI regression model for the study area.

The study required four decades of data collection on some environmental characteristics of river water. After that, calculate the WQI and conduct the spatial analysis. Eight variables in total, including water temperature, dissolved oxygen, potential hydrogen ions, electrical conductivity (EC), biological oxygen demand, turbidity, nitrate and phosphate, were chosen to calculate the WQI.

Throughout the study periods, the WQI values varied from 55.2 to 79.83, falling into the categories of four (marginal) and three (fair), with the sixth period (2007–2008) showing the most decline. The present research demonstrated that the high concentration of phosphates, the high EC values, and minor changes in the other environmental factors are the major causes of the decline in water quality. The variations in ecological variables' overlap are a senior contributor to changes in water quality in general. Notably, using GIS in conjunction with the WQI has shown to be very effective in reducing the time and effort spent on investigating water quality while obtaining precise findings and information at the lowest possible expense. Calibration and validation of the developed model showed that this model had a perfect estimate of the WQI value. Due to its flexibility and impartiality, this study recommends using the proposed model to estimate and predict the WQI in the study area.

Even though the water quality of the SAAR has been the subject of numerous studies, this is the only long-term investigation that has been done to evaluate and predict its water quality.

The authors estimate systemic risk in the Korean economy using the econometric measures of commonality and connectedness applied to stock returns. To assess potential systemic risk concerns arising from the high concentration of the economy in large business groups and a few export-oriented sectors, the authors perform three levels of estimation using individual stocks, business groups, and industry returns. The results show that the measures perform well over the study’s sample period by indicating heightened levels of commonality and interconnectedness during crisis periods. In out-of-sample tests, the measures can predict future losses in the stock market during the crises. The authors also provide the recent readings of their measures at the market, chaebol, and industry levels. Although the measures indicate systemic risk is not a major concern in Korea, as they tend to be at the lowest level since 1998, there is an increasing trend in commonality and connectedness since 2017. Samsung and SK exhibit increasing degrees of commonality and connectedness, perhaps because of their heavy dependence on a few major member firms. Commonality in the finance industry has not subsided since the financial crisis, suggesting that systemic risk is still a concern in the banking sector.

The authors aim to study a conceptual model based on behavioural theories (UTAUT-3 model) to evaluate the adoption, usage and recommendation for neobanking services in India.

The authors propose this model based on the UTAUT-3 integrated with perceived risk constructs. Hypotheses were developed to determine the relationships and empirically validated using the PLSs-SEM method. Using the survey method, 680 Delhi NCR respondents participated in the survey.

Empirical results suggested that behavioural intention (BI) to usage, adoption and recommendation affects neobanking adoption positively. The research observed that performance expectancy (PE), effort expectancy (EE), perceived privacy risk (PYR) and perceived performance risk (PPR) are the essential constructs influencing the adoption of neobanking services.

Limited by geographic and Covid-19 constraints, a cross-sectional study was conducted. It highlights the BI of neobanking users tested using the UTAUT-3 model during the Covid-19 period.

The study's outcome offers valuable insights into Indian Neobanking services that researchers have not studied earlier. These insights will help bank managers, risk professionals, IT Developers, regulators, financial intermediaries and Fintech companies planning to invest or develop similar neobanking services. Additionally, this research provides significant insight into how perceived risk determinants may impact adoption independently for the neobanking service.

The present study is intended to assess the risk factors associated with digital buying. Also aims to design and develop an instrument to assess the digital buyers risk factor score (DBRFS) in light of pandemic.

Present investigation uses a quantitative approach to achieve the stated objectives. The survey instrument for the purpose of assessing risk factors associated with digital buying was developed in two phases. The present study adopts theory of planned behaviour (TPB), built based on the theory of reasoned action (TRA). The data were collected and analysed considering 500 valid responses, sampling unit being digital buyers using social media platforms in tyre-II city of India. The data collection was undertaken between June 2021 and August 2021. The instrument is designed and validated using exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA).

The present research identified six perceived risk factors that are associated with digital buying; contractual risk, social risk, psychological risk, perceived quality risk, financial risk and time risk. The DBRFS of male is 3.7585, while female is 3.7137. Thus, risk taking by the male and female is at par. For the age group 15–30, DBRFS is 3.6761, while age group 31–45 noted as 3.7889 and for the 46–50 age groups it is measured as 3.9649.

The marketers are expected to have the knowledge about how people responds to the pandemic. The outcome of the research helps to understand consumer behaviour but disentangling consumer’s “black box” is challenging especially during global distress. The present study outcome helps the digital shopkeepers to respond positively to meet the needs of digital buying.

The scale development and to quantify the DBRFS. A deeper understanding of about digital consumers during pandemics will help digital shopkeepers to connect issues related digital buying.

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?