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The study aimed to investigate the relationship between the intention to avoid food waste (IAFW) and the use of food-sharing technologies, such as internet platforms and mobile applications. The study utilized a model based on the theory of planned behavior (TPB) as an extension of the unified theory of acceptance and use of technology (UTAUT2).

An online platform tool (Prolific), and online self-report questionnaires were used to gather empirical data on 309 individuals. These data were then analyzed using two-step structural equation modeling.

The model explained 76% of the variance in user adoption of food-sharing mobile applications and internet platforms, supporting seven out of the nine tested hypotheses. Effort expectancy, social influence and IAFW were found to be the significant determinants of the behavioural intention to use food-sharing mobile applications and internet platforms (BITA). IAFW partially mediated the relationship between perceived behavior control and BITA. Age played a moderator role between the adoption of food-sharing mobile applications and internet platforms. However, IAFW did not play a mediating role between environmental concerns and BITA. The facilitating condition construct had an insignificant impact on BITA.

The current study was affected by some limitations. First, the data may not be considered as statistically representative because they were gathered online. However, the varied sociodemographic backgrounds of the respondents would boost the reliability of the findings. However, it would be prudent to use caution when extrapolating these findings to other contexts and cultures. Second, environmental concerns and perceived behavior control related to the avoidance of food waste behavior, as well as other factors that affect technology acceptance, may alter with time. Data from cross-sections may cause difficulties in following such changes. Thus, we recommend that longitudinal research studies aimed at building on our findings should be conducted. A qualitative study may help gain deeper insights into the relationship between IAFW related behavior and the adoption of various technologies to share leftover food, thereby revealing further details regarding different perspectives held by various respondents.

The positive relationship between environmental concerns and IAFW underlines the significance of investing in this area to raise social awareness and public concern for environmental safety. Additional initiatives aimed at increasing public concern regarding environmental issues may increase the overall IAFW. Instead of concentrating on a single source pertaining to the avoidance of food waste, the government and policy regulations should focus on regulating and eliminating waste from all sources that generate waste. The adoption of technology to share leftover meals may be influenced by social factors. Increased advertising for food-sharing mobile apps and online platforms may persuade more users to join. Additionally, building additional platforms and mobile apps in these fields with friendlier interactions may improve the cyber environment, making it easier for people to use them. By providing information, tools and assistance to promote the reduction of food wastage, policymakers may create interventions that enhance public perception and behavior toward the reduction of food waste. In conclusion, the findings of our study indicated that the social impact and ease of use are important factors in determining the adoption of food-sharing technology. Cooperation with social influencers, policymakers and developers may lead to the development of user-friendly technology that may improve accessibility to food-sharing technology.

In order to encourage the adoption of food-sharing technology among various age groups, policymakers may create initiatives that take the specific requirements and traits of each group into consideration. Policymakers and governments may also create legislation and regulations that are tailored to guarantee food safety and health security for users of food-sharing technology, such as instructions for handling and storing food as well as safeguards against food fraud and contamination.

This study addressed practical issues related to managing and reducing household food wastage through social sharing via mobile applications and internet platforms. The proposed model, which integrated TPB with UTAUT2 in the context of food wastage and technology acceptance, contributes to the current body of knowledge.

The study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.

Categorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.

Meta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R², were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.

The study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.

The study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.

The authors aimed to examine how the level of digitalization in Poland and Ukraine affects the contribution of small and medium-sized enterprises (SMEs) to the countries’ gross domestic product (GDP).

The study involved a comparative analysis and statistical modeling of the impact of key economic factors on the contribution of SMEs to Poland’s and Ukraine’s GDP in the 2010–2020 period. The authors used principles of the theory of economic growth and calculated the coefficient of digital competitiveness as a composite indicator consisting of a number of global indices.

The study revealed significant differences between both countries, which can be attributed to a higher level of digitalization in Polish SMEs. The authors used the Polish experience to recommend how to reform Ukraine’s digital economy in postwar recovery.

The contribution of SMEs to Poland’s GDP is higher than that of Ukraine’s because of the higher entrepreneurship rate in the Polish micro and small enterprises (MSEs) sector. The authors found that a unit change in the integrated coefficient of digital competitiveness is related to the greatest change in the contribution of SMEs to the country’s GDP when the other factors in the model equation remain fixed.

This study aims to examine the impact and contribution of priority and non-priority sectors, as well as their sub-sectors, on the gross non-performing assets of public, private and foreign sector banks.

The Reserve Bank of India's database on the Indian economy is used to retrieve data over 13 years (2008–2021). Public sector (12), private sector (22) and foreign sector (44) banks are represented in the sample. Two-way ANOVA, multiple regression and panel regression statistical techniques are used in SPSS and EViews to examine the data. Further, the results are also validated by using robustness testing by applying the fully modified ordinary least square (FMOLS) and dynamic least square (DOLS) regression.

The results showed that, for private and foreign banks, the non-priority sector makes up the majority of the total gross non-performing assets, although both the priority and non-priority sectors are substantial for public sector banks. The largest contributors to the total gross non-performing assets in public, private and foreign banks are industries, agriculture and micro and small businesses. The FMOLS displays robustness results that are qualitatively similar to the baseline result.

Based on the study's findings about the patterns of non-performing assets originating from these specific industries, banks might improve the way in which these advanced loans are managed.

There has not been much research done on the subject of sub-sector-specific non-performing assets and how they affect total gross non-performing assets across the three sector banks. The study's primary focus will be on the issue of non-performing assets in the priority’s and non-priority’s sub-sectors, namely, agricultural, micro and small businesses, food credit, industries, services, retail loans and other priority and non-priority sectors.

The purpose of this study is to better understand technology adoption and working from home (WFH) behaviour of micro and small enterprises (MSE) with 4 to 49 employees during the first (2020) and second (2021) COVID-19 lockdowns in Switzerland.

This study uses two data sets gathered using computer-assisted telephone interviewing surveys conducted with 503 managing directors of Swiss MSEs after the first and 506 MDs after the second COVID-19 lockdown period.

The study revealed that during the COVID-19 pandemic, WFH arrangements are related to the adoption of technology by Swiss industry groups. Furthermore, industry characteristics and technology adoption strategies are also associated with the long-term prospect of WFH. The overall result confirms the predominant role of technology pioneers.

The study focuses on MSEs in Switzerland during a specific period. The data set includes mainly quantitative data. Future studies could investigate larger enterprises in international contexts, integrating employees’ viewpoints founded on long-term gathered qualitative data. The implications of this study include predictions about future WFH behaviour in Swiss MSEs.

To the best of the authors’ knowledge, this is the first study collecting data in Swiss MSEs after the two COVID-19 lockdowns in 2020 and 2021. As a result, this study offers a unique perspective on a specific business segment, which accounts for around 70% of global employment.

The Government’s investment in infrastructure projects is considerably high, especially in bridge construction projects. Government authorities must establish an initial forecasted budget to have transparency in transactions. Early cost estimating is challenging for Quantity Surveyors due to incomplete project details at the initial stage and the unavailability of standard cost estimating techniques for bridge projects. To mitigate the difficulties in the traditional preliminary cost estimating methods, there is a requirement to develop a new initial cost estimating model which is accurate, user friendly and straightforward. The research was carried out in Sri Lanka, and this paper aims to develop the artificial neural network (ANN) model for an early cost estimate of concrete bridge systems.

The construction cost data of 30 concrete bridge projects which are in Sri Lanka constructed within the past ten years were trained and tested to develop an ANN cost model. Backpropagation technique was used to identify the number of hidden layers, iteration and momentum for optimum neural network architectures.

An ANN cost model was developed, furnishing the best result since it succeeded with around 90% validation accuracy. It created a cost estimation model for the public sector as an accurate, heuristic, flexible and efficient technique.

The research contributes to the current body of knowledge by providing the most accurate early-stage cost estimate for the concrete bridge systems in Sri Lanka. In addition, the research findings would be helpful for stakeholders and policymakers to propose policy recommendations that positively influence the prediction of the most accurate cost estimate for concrete bridge construction projects in Sri Lanka and other developing countries.

The purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).

We estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.

We state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.

This study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models.

In the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg–Marquardt backpropagation algorithm was used to train the neural networks.

The optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4–10-1). In predicting MRR, the 60–20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70–15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively.

This is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).

This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.

In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.

This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.

According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.