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The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

The quantum of metal particle waste generation in manufacturing industries is posing a great concern for the environment. The iron forging industries generate a huge amount of grinding sludge (GS) waste, which is disposed into the earth. The accumulation of this waste in dump yards causes an increase in soil and air pollution levels.

In the current investigation, an effort was made to use this waste GS for the progress of aluminum-based composite. To maintain uniform distribution of reinforcing material, the friction stir processing technique was used.

The characterization based on the SEM image of the Al/GS composite revealed that uniform dispersal of reinforcement content can be attained in a single tool pass. Number of grains/inch was approximately 2,402. XRD of GS powder confirmed the presence of SiO₂, Fe₂O₃, Al₂O₃ and CaO phases. These phases proved GS to be a better reinforcement with aluminum alloy. Tensile strength and hardness were significantly improved in comparison to the aluminum alloy. Thermal expansion and corrosion weight loss were evaluated to observe the influence of GS addition.

The studies proved that the use of GS as reinforcement material can help in curbing the menace of soil pollution to a large extent.

The aim of this study was to determine the effect of anxiety levels of adults on their nutritional status during the COVID-19 pandemic in Kayseri province, Turkey.

A total of 898 adults consisting of 479 individuals with and 419 individuals without a positive diagnosis of COVID-19 were included in the study. The individuals’ socio-demographic characteristics, health status, nutritional habits, anthropometric measurement and Fear of COVID-19 Scale (FCV-19S) information were obtained online.

The mean FCV-19S score of the participants was 17.49 ± 6.02. FCV-19S score was higher in those who reduced their consumption of protein sources compared to those who did not change and those who increased (p < 0.001). It was determined that FCV-19S scores of participants who increased their consumption of fruit/vegetables, sweets and sugar were higher than those who did not change their consumption of such items (p = 0.007). The FCV-19S scores of individuals who did not change their onion/garlic and snack consumption were lower than those who decreased or increased the consumption of these nutrients (p = 0.001, p = 0.002).

Education programs can be organized especially targeting vulnerable populations, such as women, individuals with chronic diseases and those experiencing COVID-19 symptoms. These programs can be conducted by dietitians and psychologists in collaboration, focusing on promoting healthy eating habits and coping strategies during stressful times.

It was determined that those who changed their nutrition habits during the COVID-19 pandemic had higher fear levels than those who did not. Individuals with high fear paid more attention to healthy nutrition than individuals without fear.

The purpose of this paper is to evaluate the effect of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃) nanoparticles-reinforced pure aluminium (Al) on the mechanical properties of hybrid aluminium matrix nanocomposites (HAMNCs).

The HAMNCs were fabricated via a vacuum die-assisted stir casting route by a two-step feeding method. The varying weight percentages of TiO₂ and Y₂O₃ nanoparticles were added as 2.5, 5, 7.5 and 10 Wt.%.

Scanning electron microscope images showed the homogenous dispersion of nanoparticles in Al matrix. The tensile strength by 28.97%, yield strength by 50.60%, compression strength by 104.6% and micro-hardness by 50.90% were improved in HAMNC1 when compared to the base matrix. The highest values impact strength of 36.3 J was observed for HAMNC1. The elongation % was decreased by increasing the weight percentage of the nanoparticles. HAMNC1 improved the wear resistance by 23.68%, while increasing the coefficient of friction by 14.18%. Field emission scanning electron microscope analysis of the fractured surfaces of tensile samples revealed microcracks and the debonding of nanoparticles.

The combined effect of TiO₂ and Y₂O₃ nanoparticles with pure Al on mechanical properties has been studied. The composites were fabricated with two-step feeding vacuum-assisted stir casting.

This paper aims to analyze deeply the effect of surface roughness conditions of the common interface of the two-layered riveted cantilever beams on their frictional damping during free lateral vibration at first mode. Here, the product, (µ × α), and damping ratio, ξ, are the parameters whose variations are analyzed in this investigation. For this, the influencing parameters considered are the natural frequency of vibration, f; the amplitude of initial excitation, y; and surface roughness value, Ra.

For experimentally evaluating logarithmic damping decrement, d, the frequency response function analyzer for the case of free lateral vibrations was used. Later, for evaluating the product, µ × α (where µ is the kinematic coefficient of friction and α is the dynamic slip ratio), and then, the damping ratio, ξ, the empirical relation suggested for logarithmic damping decrement, d, of riveted cantilever beams was used. After this, the full and reduced quadratic models of the product, µ × α, ξ, response surface methodology (RSM) with the help of Design Expert 11 software was used. Corresponding main effects plots, surface plots and prediction comparison plots were obtained to observe the variations of the product, µ × α, ξ for the variations of influencing parameters: f, y and Ra. Finally, a machine learning technique such as artificial neural networks (ANNs) using “nntool” present in MATLAB R13a software was used to predict the ξ for the different combinations of f, y and Ra.

The full and reduced quadratic regression models for the product, (µ × α) and the damping ratio, ξ of riveted cantilever beams for free lateral vibrations of the first mode in terms of the parameters: f, y and Ra were obtained. In addition, the main effects plots, surface plots and prediction comparison plots for the product, µ × α, ξ, with the corresponding experimental values of the product, µ × α, ξ, were obtained. Also, the execution of ANNs using MATLAB R13a software is proved to be the more accurate tool for the prediction of damping ratios in comparison to quadratic regression equations obtained from Design Expert 11 software. In the end, the assumption that the effect of surface roughness value on the product, (µ × α), and the damping ratio, ξ, is negligible is proved to be true using the main effects plots for the product, (µ × α) and ξ obtained from the Design Expert 11 software.

Obtaining the full and reduced quadratic regression equations for the product, (µ × α), and ξ of the two-layered riveted cantilever beams in terms of parameters: f, y and Ra was done. In addition, the conditions for the corresponding minimum and maximum values of the product, (µ × α), and ξ were obtained. Later, the main effects plots, surface plots and comparison plots of the predicted product, (µ × α), and ξ versus experimental product, (µ × α), and ξ were also obtained. Finally, the predicted values of the product, (µ × α), and ξ using the ANNs tool are observed to be the more accurate values in comparison to that obtained from RSM using the Design Expert 11 software.

The utilization of mobile fitness applications (apps) is on the rise, making user retention and engagement critical factors in the commercial success of these apps. However, research in this area is limited and fragmented. The objective of this study is to conduct a thorough review of the available literature on the effects of digital innovations, gamification, artificial intelligence (AI) and machine learning (ML) on user engagement with fitness mobile apps. The findings reveal the relationships between gamification, the use of AI/ML and technology adoption on user engagement, interaction and intent to use. Additionally, the study highlights the importance of understanding how user experience, customer experience and brand experience impact customer retention and contribute to the overall success of mobile fitness apps. Furthermore, the study also identifies the gaps in the current research and recommends further studies to be conducted in these areas. Future research is encouraged to incorporate elements from the experience domains to provide consumers with engaging interactions and improve retention and commercial success for mobile fitness apps.

The aim was not to perform a systematic review but firstly to search in PubMed, Science Direct, Scopus and Web of Science databases on the papers published in the last five years using tools for reviewing the statement of preferred information item for systematic reviews without focusing on a randomized analysis and secondly to perform a bibliometric analysis on the properties of films and coatings added of tocopherol for food packaging.

On January 24, 2022, information was sought on the properties of films and coatings added of tocopherol for use as food packaging published in PubMed, Science Direct, Scopus and Web of Science databases. Further analysis was performed using bibliometric indicators with the VOSviewer tool.

The searches returned 33 studies concerning the properties of films and coatings added of tocopherol for food packaging, which were analyzed together for a better understanding of the results. Data analysis using the VOSviewer tool allowed a better visualization and exploration of these words and the development of maps that showed the main links between the publications.

In the area of food science and technology, the development of polymers capable of promoting the extension of the shelf life of food products is sought, so the knowledge of the properties is vital for this research area since combining a biodegradable polymeric material with a natural antioxidant active is of great interest for modern society since they associate environmental preservation with food preservation.

The aim of this study is to utilise the Theoretical Domains Framework (TDF) to identify the main barriers and facilitators of positive attitude towards trying Virtual Active Sports (VAS).

200 individuals took part in an online cross-sectional survey based on 11 domains within the TDF. Linear logistic regression analysis was performed on the participant’s self-reported attitudes and believes. Based on the results from the regression analyses, a list of suggested behaviour change techniques was designed using the Behaviour Change Wheel (BCW) framework and the BCT taxonomy (BCTTv1).

This research suggested that Beliefs about Consequences, Beliefs about Capabilities, Goal Conflict, Coping Planning, and Environmental Context and Resources are the main factors, influencing the positive attitude towards VAS and the self-reported desire to try it in the future. Future interventions were recommended and supported by 22 possible BCTs identified using the BCW approach. The TDF and BCW proved to be useful models for identifying both internal and external factors influencing fitness fans during the adoption of the new sportstech.

The main contribution of the present work was the implementation of a structured and effective approach derived from the healthcare domain to design solutions for behaviour change in the emerging and expanding virtual sports context.

This paper aims to report the effect of titanium oxide (TiO₂) particles on the specific wear rate (SWR) of alkaline treated bamboo and flax fiber-reinforced composites (FRCs) under dry sliding condition by using a robust statistical method.

In this research, the epoxy/bamboo and epoxy/flax composites filled with 0–8 Wt.% TiO₂ particles have been fabricated using simple hand layup techniques, and wear testing of the composite was done in accordance with the ASTM G99-05 standard. The Taguchi design of experiments (DOE) was used to conduct a statistical analysis of experimental wear results. An analysis of variance (ANOVA) was conducted to identify significant control factors affecting SWR under dry sliding conditions. Taguchi prediction model is also developed to verify the correlation between the test parameters and performance output.

The research study reveals that TiO₂ filler particles in the epoxy/bamboo and epoxy/flax composite will improve the tribological properties of the developed composites. Statistical analysis of SWR concludes that normal load is the most influencing factor, followed by sliding distance, Wt.% TiO2 filler and sliding velocity. ANOVA concludes that normal load has the maximum effect of 31.92% and 35.77% and Wt.% of TiO2 filler has the effect of 17.33% and 16.98%, respectively, on the SWR of bamboo and flax FRCs. A fairly good agreement between the Taguchi predictive model and experimental results is obtained.

This research paper attempts to include both TiO₂ filler and bamboo/flax fibers to develop a novel hybrid composite material. TiO₂ micro and nanoparticles are promising filler materials, it helps to enhance the mechanical and tribological properties of the epoxy composites. Taguchi DOE and ANOVA used for statistical analysis serve as guidelines for academicians and practitioners on how to best optimize the control variable with particular reference to natural FRCs.