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The purpose of this study is to increasing the gauge factor, reducing the hysteresis error and improving the stability over cyclic deformations of a conductive polylactic acid (CPLA)-based 3D-printed strain sensor by modifying the sensing element geometry.

Five different configurations, namely, linear, serpentine, square, triangular and trapezoidal, of CPLA sensing elements are printed on the thermoplastic polyurethane substrate material individually. The resistance change ratio of the printed sensors, when loaded to a predefined percentage of the maximum strain values over multiple cycles, is recorded. Finally, the thickness of substrate and CPLA and the included angle of the triangular strain sensor are evaluated for their influences on the sensitivity.

The triangular configuration yields the least hysteresis error with high accuracy over repeated loading conditions, because of its uniform stress distribution, whereas the conventional linear configuration produces the maximum sensitivity with low accuracy. The thickness of the substrate and sensing element has more influence over the included angle, in enhancing the sensitivity of the triangular configuration. The sensitivity of the triangular configuration exceeds the linear configuration when printed at ideal sensor dimensional values.

The 3D printing parameters are kept constant for all the configurations; rather it can be varied for improving the performance of the sensor. Furthermore, the influences of stretching rate and nozzle temperature of the sensing material are not considered in this work.

The sensitivity and accuracy of CPLA-based strain sensor are evaluated for modification in its geometry, and the performance metrics are enhanced using the regression modelling.

Lithium-polymer batteries have common usage in aviation industry especially unmanned aerial vehicles (UAV). Overheating is a serious problem in lithium-polymer batteries. Various cooling methods are performed to keep lithium-polymer batteries in the desired temperature range. The purpose of this paper is to examine pouch type lithium-polymer battery with plate fins by using particle image velocimetry (PIV) and computational fluid dynamics (CFD) for UAV.

Battery models were produced with a 3D printer. The upper surfaces of fabricated battery models were covered with plate fins with different fin heights and fin thicknesses. Velocities were obtained with PIV and CFD. Temperature dissipations were acquired with numerical simulations.

At the end of the study, the second battery model gave the lowest temperature values among the battery models. Temperature values of the seventh battery model were the highest temperatures. Fin cooling reduced the maximum cell temperatures noticeably. Numerical simulations agreed with PIV measurements well.

This paper takes into account two essential tools such as PIV and CFD, for fluid mechanics, which are significant in the aviation industry and engineering life.

The originality of this paper depends on investigation of both PIV and CFD for UAV and developing a cooling method that can be feasible for landing and take-off phases for UAV.

The present study integrates inclusive leadership and protection motivation theory to propose a new model predicting employees' intention to work from home during an emergency situation such as the COVID-19 pandemic.

A questionnaire was developed to collect data from 939 Taiwanese and Vietnamese office employees using a non-probability convenience sampling method. A total of 887 valid questionnaires were used for further analysis. The data were analysed following a two-stage structural equation modelling using SPSS 22 and AMOS 20 software. The validity and reliability of the instrument were tested and ensured.

The results revealed that inclusive leadership and factors related to protection motivation theory– including perceived severity and perceived vulnerability – have positive direct and indirect effects on employees' work-from-home intentions through the mediating role of employees' work-from-home-related attitudes. Protection motivation theory factors were found to have a stronger effect on employees' work-from-home intention than inclusive leadership. Differences in the relationship between perceived vulnerability, perceived severity and employees' intentions towards working from home were also discovered among participants from the two studied countries.

The integration of inclusive leadership and protection motivation theory brings into light what will drive employees' intention to work from home during an emergency situation. The present study has several theoretical and practical implications for scholars, governments, managers and policymakers that can help them improve management policies for working from home in the future.

Based on integrating inclusive leadership and protection motivation theory to explore employees' intention to work from home during an emergency situation, the present study demonstrated that inclusive leadership and protection motivation theory should be considered for studies on working from home in a pandemic setting.

The use of recycled coarse aggregate in concrete structures promotes environmental sustainability; however, performance of these structures might be negatively impacted when it is used as a replacement to traditional aggregate. This paper aims to simulate recycled concrete beams strengthened with carbon fiber-reinforced polymer (CFRP), to advance the modeling and use of recycled concrete structures.

To investigate the performance of beams with recycled coarse aggregate concrete (RCAC), finite element models (FEMs) were developed to simulate 12 preloaded RCAC beams, strengthened with two CFRP strengthening schemes. Details of the modeling are provided including the material models, boundary conditions, applied loads, analysis solver, mesh analysis and computational efficiency.

Using FEM, a parametric study was carried out to assess the influence of CFRP thickness on the strengthening efficiency. The FEM provided results in good agreement with those from the experiments with differences and standard deviation not exceeding 11.1% and 3.1%, respectively. It was found that increasing the CFRP laminate thickness improved the load-carrying capacity of the strengthened beams.

The developed models simulate the preloading and loading up to failure with/without CFRP strengthening for the investigated beams. Moreover, the models were validated against the experimental results of 12 beams in terms of crack pattern as well as load, deflection and strain.

In this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of data (LAD) and ant colony optimization (ACO) algorithms in extracting patterns of high impact loads and normal loads from historical railway records. In addition, the patterns are employed in establishing a classification model used for classifying unseen observations. A case study representing real-world impact load data is presented to illustrate the impact of the proposed approach in improving railway services.

Application of artificial intelligence and machine learning approaches becomes an essential tool in improving the performance of railway transportation systems. By using these approaches, the knowledge extracted from historical data can be employed in railway assets monitoring to maintain the assets in a reliable state and to improve the service provided by the railway network.

Results achieved by the proposed approach provide a prognostic system used for monitoring the conditions surrounding rail wheels. Incorporating this prognostic system in surveilling the rail wheels indeed results in better railway services as trips with no-delay or no-failure can be realized. A comparative study is conducted to evaluate the performance of the proposed approach versus other classification algorithms. In addition to the highly interpretable results obtained by the generated patterns, the comparative study demonstrates that the proposed approach provides classification accuracy higher than other common machine learning classification algorithms.

The methodology followed in this research employs ACO algorithm as an artificial intelligent technique and LDA as a machine learning algorithm in analyzing wheel impact load alarm-collected datasets. This new methodology provided a promising classification model to predict future alarm and a prognostic system to guide the system while avoiding this alarm.

This study aims to examine the effect of self-construal on solo dining intentions and its underlying mechanism through consumer emotions. Furthermore, the study also investigates the moderating effect of the composition of other diners on the relationship between self-construal and solo dining intentions.

A 2 (self-construal: independent vs interdependent) × 2 (other diners: solo diners vs social diners) between-subjects experimental design was adopted to test the hypotheses. The data were collected from 317 Chinese consumers, followed by an analysis using IBM SPSS 23.0 and AMOS 23.0.

The findings indicate that consumers with an independent self-construal are more likely to have the intention to dine alone at a restaurant. Nevertheless, this effect is contingent upon the composition of other diners. The effect is significant only when nearby diners are social diners, and perceived enjoyment partly mediates the relationship. Conversely, when nearby diners are also solo diners, consumers' self-construals do not significantly affect their solo dining intentions. Moreover, the results indicate that consumers generally experience low levels of perceived stress when dining alone.

This study incorporates individual personality traits into research on solo diners and highlights the crucial role of positive emotions in solo dining, which provides insights for relevant enterprises to develop effective marketing strategies.

The purpose of this study is to evaluate food supply chain stakeholders’ intention to use Industry 5.0 (I5.0) drones for cleaner production in food supply chains.

The authors used a quantitative research design and collected data using an online survey administered to a sample of 264 food supply chain stakeholders in Nigeria. The partial least square structural equation model was conducted to assess the research’s hypothesised relationships.

The authors provide empirical evidence to support the contributions of I5.0 drones for cleaner production. The findings showed that food supply chain stakeholders are more concerned with the use of I5.0 drones in specific operations, such as reducing plant diseases, which invariably enhances cleaner production. However, there is less inclination to drone adoption if the aim was pollution reduction, predicting seasonal output and addressing workers’ health and safety challenges. The findings outline the need for awareness to promote the use of drones for addressing workers’ hazard challenges and knowledge transfer on the potentials of I5.0 in emerging economies.

To the best of the authors’ knowledge, this study is the first to address I5.0 drones’ adoption using a sustainability model. The authors contribute to existing literature by extending the sustainability model to identify the contributions of drone use in promoting cleaner production through addressing specific system operations. This study addresses the gap by augmenting a sustainability model, suggesting that technology adoption for sustainability is motivated by curbing challenges categorised as drivers and mediators.

The goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.

The path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.

The algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.

An improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.

This study aims to examine the determinants of adoption intention toward the rooftop solar photovoltaic (RSPV) systems among residents of peri-urban villages of Gurugram, Haryana, India. This study also analyzes the impact of the adoption of RSPV systems on carbon neutrality from a behavioral perspective.

Data was collected using a self-administrated structured questionnaire from 208 male villagers (195 usable) of 22 villages using the purposive sampling technique.

Results revealed that relative advantage, followed by simplicity, trialability, observability and compatibility, positively and significantly impact villagers’ attitude toward adopting RSPV systems in their homes. Perceived severity and perceived vulnerability significantly influence the perceived behavioral control of villagers toward adopting the RSPV systems. The results show villagers’ attitudes, subjective norms and perceived behavioral control are the essential predictors of their adoption intention of the RSPV systems. Most notably, carbon neutrality was significantly affected by villagers’ adoption intention of RSPV systems as the renewable energy source in their homes.

The findings of this study provide that innovation attributes are important factors in shaping the adoption intentions of customers toward RSPV systems. This study is also the extent of previous studies measuring customers’ perception of adopting renewable energy in developed and emerging countries worldwide.