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Many existing trajectory optimization algorithms use parameters like maximum velocity or acceleration to formulate constraints. Due to the ignoring of the quadrotor actual tracking capability, the generated trajectories may not be suitable for tracking control. The purpose of this paper is to design an online adjustment algorithm to improve the overall quadrotor trajectory tracking performance.

The authors propose a reference trajectory resampling layer (RTRL) to dynamically adjust the reference signals according to the current tracking status and future tracking risks. First, the authors design a risk-aware tracking monitor that uses the Frenét tracking errors and the curvature and torsion of the reference trajectory to evaluate tracking risks. Then, the authors propose an online adjusting algorithm by using the time scaling method.

The proposed RTRL is shown to be effective in improving the quadrotor trajectory tracking accuracy by both simulation and experiment results.

Infeasible reference trajectories may cause serious accidents for autonomous quadrotors. The results of this paper can improve the safety of autonomous quadrotor in application.

This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.

A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.

The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.

This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.

The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.

While prior research has established that traumatic brain injury (TBI) is a risk factor for violent offending, there is little understanding of mechanisms that may underpin this relationship. This is problematic, as a better understanding of these mechanisms could facilitate more effective targeting of treatment. This study aims to address these gaps in the extant literature by examining TBI as a predictor of violent offending and test for mediation effects through cognitive constructs of dual systems imbalance and hostility among a sample of justice-involved youth (JIY).

The Pathways to Desistance data were analyzed. The first three waves of this data set comprising the responses of 1,354 JIY were analyzed. Generalized structural equation modeling was used to test for direct and indirect effects of interest. A bootstrap resampling process was used to compute unbiased standard errors for determining the statistical significance of mediation effects.

Lifetime experience of TBI was associated with increased violent offending frequency at follow-up. Hostility significantly mediated this relationship, but dual systems imbalance did not. This indicated that programming focused on reducing hostility among JIY who have experienced TBI could aid in reducing violent recidivism rates.

To the best of the author’s knowledge, this study was the first to identify significant mediation of the relationship between TBI and violent offending through hostility.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

This study aims to conceptualise a customer-centric model based on an online customer experience (OCE) construct, mediated by e-loyalty (EL) and e-trust (ET), to improve the continuous usage intention (CUI) of Indian digital banks from Generation Y and Z perspectives.

This study used an online survey method to gather data from a sample of 466 digital banking users, from which usable questionnaires were obtained. The obtained data were subjected to thorough analysis using PLS-SEM to further study the research hypotheses.

The main factors that determine digital banks’ OCE are perceived enjoyment, e-service quality, information quality and e-convenience. Additionally, relevant constructs were evaluated using an importance-performance map analysis.

This study used convenience sampling for the urban population using digital banking; therefore, the outcome may be generalised to a limited extent. It would be valuable to imitate studies in other countries to strengthen digital banking further.

There is a lack of research on digital banking and OCE in India; thus, this study helps rectify this issue while providing valuable insights. This study differs from others in that it examines the connections between OCE, EL, ET and the bottom line of financial institutions, using these factors as dependent variables instead of traditional measures.

In the present era, the achievement of employee Islamic performance has become a significant challenge for organizations. The purpose of the study is to examine the effect of Islamic leadership on employee Islamic performance directly and indirectly by bridging the connections between employees’ Islamic organizational values, Islamic organizational culture, and Islamic work motivation among the employees of Egyptian banks.

The authors used quantitative methods in this study and based its findings on the data received from 312 respondents in response to a questionnaire.

By using SmartPLS 4, this study’s findings demonstrate that Islamic leadership has a positive and significant effect on Islamic organizational values, culture, employee Islamic performance and work motivation. While Islamic organizational values and Islamic organizational culture do not significantly impact employee Islamic performance, Islamic work motivation is a significant predictor of employee Islamic performance. On the one hand, Islamic organizational values and Islamic organizational culture do not mediate the relationship between Islamic leadership and employee Islamic performance. On the other hand, Islamic work motivation is a mediating variable that significantly develops the relationship between Islamic leadership and employee Islamic performance.

The study’s findings support policymakers and human resource management practitioners to develop plans and strategies which enhance the Islamic performance of organizations’ employees. In addition, this study’s findings provide insights for researchers and academicians in developing Islamic leadership within their organizations so that they operate by Islamic values and codes.

Finally, by offering an integrated model of Islamic leadership, Islamic organizational values, Islamic organizational culture and employee Islamic performance, this study’s findings fill the gaps in the context of bank employees in a developing country, namely, Egypt.

The study aims to investigate how enterprise information systems strategies-enabled strategy-making (ISS-SM) influences organizational agility (OA) via the mediated role of IT-enabled dynamic capabilities (ITDC) under environmental dynamism (ED). The study also investigates natural country moderation associated with the business context of the countries where the respondents are located might influence these relationships.

The study aims to investigate how enterprise ISS-SM influences OA via the mediated role of ITDC under ED. The study also investigates natural country moderation associated with the business context of the countries where the respondents are located that might influence these relationships.

The results demonstrate that ISS-SM influences ITDC to gain OA independent of the ED level. Indian and Brazilian firms show no different effects in the relationship of the research model. However, post hoc analysis revealed that strong ISS-SM on OA is fully mediated by ITDC under higher ED with a substantial coefficient of determination, more prominent for Indian firms characterized by young-age and middle-size firms, agribusiness and government sectors.

The fundamental to enabling practice and praxis of the strategy-as-practice approach to OA gains mediated through ITDC in different business context conditions.

The research contributes to extending the literature on the enterprise information systems strategy and information technologies capabilities.

This paper aims to develop a dynamic civil facility degradation prediction model to forecast the reliability performance tendency and remaining useful life under imperfect maintenance based on the inspection records and the maintenance actions.

A real-time hidden Markov chain (HMM) model is proposed in this paper to predict the reliability performance tendency and remaining useful life under imperfect maintenance based on rare failure events. The model assumes a Poisson arrival pattern for facility failure events occurrence. HMM is further adopted to establish the transmission probabilities among stages. Finally, the simulation inference is conducted using Particle filter (PF) to estimate the most probable model parameters. Water seals at the spillway hydraulic gate in a Taiwan's reservoir are used to examine the appropriateness of the approach.

The results of defect probabilities tendency from the real-time HMM model are highly consistent with the real defect trend pattern of civil facilities. The proposed facility degradation prediction model can provide the maintenance division with early warning of potential failure to establish a proper proactive maintenance plan, even under the condition of rare defects.

This model is a new method of civil facility degradation prediction under imperfect maintenance, even with rare failure events. It overcomes several limitations of classical failure pattern prediction approaches and can reliably simulate the occurrence of rare defects under imperfect maintenance and the effect of inspection reliability caused by human error. Based on the degradation trend pattern prediction, effective maintenance management plans can be practically implemented to minimize the frequency of the occurrence and the consequence of civil facility failures.

High performance of new hires is of imminent interest to organizations in the hospitality sector. Yet, limited studies have focused on the relevant traits of new hires that improve on-job performance. This study aims to identify and understand a few critical traits that predict high performance across multilevel roles within the hospitality sector.

Drawing on the human capital theory, through a time-lagged field study spanning 16 months, this study used multisource data for 540 active job opportunities and 205 new hires within the hospitality industry. This study used partial least squares-based structural equation modeling and analyzed the various traits that predict high on-job performance.

This study finds that humility is a significant predictor of job performance and wholly mediates the effect of interpersonal understanding, self-confidence and flexibility on new hires’ performance.

This study enhances talent management research for the hospitality sector by determining the critical traits of new hires that can predict superior on-job performance.

COVID-19 pandemic has overturned the work and family life challenging the world in unpredictable ways that were previously unimaginable. With universities shutting down and emergence of online classes, this phenomenon is prevalent among academicians as well. With this background, the current study aims to investigate the direct relationships between workplace isolation (WPI) during COVID-19 and work–family conflict (WFC) with psychological stress (PS) mediating and organizational identification (OI) moderating the relationship between the two.

The authors employed time lagged survey and collected data at three different time intervals (T1, T2, T3) from 203 academicians working across various universities and colleges in India. The data were analyzed quantitatively using SPSS PROCESS Macro and AMOS.

The results indicated that WPI during COVID-19 has a significant positive relationship with PS and WFC . It was also found that PS partially mediated the relationship between WPI during COVID-19 and WFC. Further, OI emerged as a potential moderator.

Based on the current empirical studies, it remains unclear if WPI during COVID-19 is associated with WFC. Therefore, drawing upon stress–strain–outcome (SSO) model and the conservation of resource theory, this study makes a significant contribution to the existing body of literature by exploring the unexplored associations. To the best of the authors’ knowledge, such an association has not received much scholarly attention before.