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In the aerial transportation area, fuel costs are critical to the economic viability of companies, and so urgent measures should be adopted to avoid any unnecessary increase in operational costs. In particular, this paper addresses the case of missed approach manouevres, showing that it is still possible to optimize the usual procedure.

The costs involved in a standard procedure following a missed approach are analysed through a simulation model, and they are compared with the improvements achieved with a fast reinjection scheme proposed in a prior work.

Experimental results show that, for a standard A320 aircraft, fuel savings ranging from 55% to 90% can be achieved through the reinjection method.

To the best of the authors’ knowledge, this work is the first study in the literature addressing the fuel savings benefits obtained by applying a reinjection technique for missed approach manoeuvres.

To improve the position tracking efficiency of the upper-limb rehabilitation robot for stroke hemiplegia patients, the optimization Learning rate of the membership function based on the fuzzy impedance controller of the rehabilitation robot is propose.

First, the impaired limb’s damping and stiffness parameters for evaluating its physical recovery condition are online estimated by using weighted least squares method based on recursive algorithm. Second, the fuzzy impedance control with the rule has been designed with the optimal impedance parameters. Finally, the membership function learning rate online optimization strategy based on Takagi-Sugeno (TS) fuzzy impedance model was proposed to improve the position tracking speed of fuzzy impedance control.

This method provides a solution for improving the membership function learning rate of the fuzzy impedance controller of the upper limb rehabilitation robot. Compared with traditional TS fuzzy impedance controller in position control, the improved TS fuzzy impedance controller has reduced the overshoot stability time by 0.025 s, and the position error caused by simulating the thrust interference of the impaired limb has been reduced by 8.4%. This fact is verified by simulation and test.

The TS fuzzy impedance controller based on membership function online optimization learning strategy can effectively optimize control parameters and improve the position tracking speed of upper limb rehabilitation robots. This controller improves the auxiliary rehabilitation efficiency of the upper limb rehabilitation robot and ensures the stability of auxiliary rehabilitation training.

In the COVID-19 era, sign language (SL) translation has gained attention in online learning, which evaluates the physical gestures of each student and bridges the communication gap between dysphonia and hearing people. The purpose of this paper is to devote the alignment between SL sequence and nature language sequence with high translation performance.

SL can be characterized as joint/bone location information in two-dimensional space over time, forming skeleton sequences. To encode joint, bone and their motion information, we propose a multistream hierarchy network (MHN) along with a vocab prediction network (VPN) and a joint network (JN) with the recurrent neural network transducer. The JN is used to concatenate the sequences encoded by the MHN and VPN and learn their sequence alignments.

We verify the effectiveness of the proposed approach and provide experimental results on three large-scale datasets, which show that translation accuracy is 94.96, 54.52, and 92.88 per cent, and the inference time is 18 and 1.7 times faster than listen-attend-spell network (LAS) and visual hierarchy to lexical sequence network (H2SNet) , respectively.

In this paper, we propose a novel framework that can fuse multimodal input (i.e. joint, bone and their motion stream) and align input streams with nature language. Moreover, the provided framework is improved by the different properties of MHN, VPN and JN. Experimental results on the three datasets demonstrate that our approaches outperform the state-of-the-art methods in terms of translation accuracy and speed.

The effectiveness of Six Sigma programs has varied across different industries and organizations, and leadership styles have been identified as a critical success factor for the installation of Six Sigma initiatives. Therefore, this study aims to investigate the specific elements of leadership styles that are linked with the successful deployment of Six Sigma programs in the automobile industry.

To conduct the study, the researchers utilized a Likert scale questionnaire with a rating system of 1–7 and a simple random sampling method. The survey was distributed to 2,325 potential participants, with 573 responses received, mostly from Germany, the United Kingdom and Sweden. Out of those responses, 260 completed questionnaires were received. The study utilized a mixed-methods research design and exploratory research approaches to investigate the implication of leadership style on the success of Six Sigma implementation. The research employed several analysis techniques, including Structural Equation Modeling (SEM), exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and Survey methods.

Through various SEM methods, such as EFA and CFA, the study revealed two vital leadership elements: (1) the long-term success of Six Sigma depends on leadership’s support and recognition of it as an improvement strategy and (2) leadership must commit to the organization’s suppliers to ensure quality and the provision of defect-free products.

By incorporating the identified key elements of leadership into their strategies, organizations and researchers can ensure the sustainable implementation of Six Sigma.

This research presents a distinct contribution to the evaluation of leadership style components within the European automotive sector, utilizing a mixed-methods research design and incorporating a variety of descriptive statistics.

The study aims to define the term “generation” by proposing an integrated design based on age-period-cohort effects and by proposing an Indian generational cohort framework categorizing Indian generational cohorts into four categories, namely, Baby Boomers, GenX, GenY, and GenZ. The study further aimed to capture the existing generational differences between GenY and GenZ cohorts in the Indian teams on team climate, transactive memory system, and team leader humility.

For the first two objectives a literature review methodology along with the author's proposition was adopted. An integrated design was proposed by reviewing the relevant sociological literature to define generations. Thereafter, an Indian cohort framework was proposed categorizing them into four groups Baby Boomers, GenX, GenY, and GenZ. Following that, for the last objective, i.e. to identify the differences between cohorts, empirical data were collected by a structured questionnaire that was disseminated to GenY and GenZ Indian working professionals. A total of 229 responses were used for observing the differences or similarities between GenY and GenZ cohorts on the study variables by employing an independent samples t-test.

The study proposed an integrated design (age, period, and cohort effect). Following that, an Indian generational cohort's framework has been outlined categorizing Indian cohorts based on their birth years, age groups, developmental stages, formative years, major Indian historical events, and various characteristics possessed by them. Moreover, the empirical findings support the existing generational disparities and depict that GenZ holds a higher inclination towards transactive memory systems and team climate whereas GenY holds more inclination toward leader humility.

The study put forth its contribution to research scholars by categorizing Indian generational cohorts in a rationalized manner based on an integrated design (age-period-cohort) effect. The study would further assist concerned authorities and managers in formulating HR policies to deal with the underlying generational differences highlighted by the study.

As there lies a paucity of generational frameworks in the Indian context, this study is the first attempt in this direction which categorizes Indian generational cohorts based on a unique integrated design including age-period-cohort effects. In addition, the study also investigated these cohorts in Indian organizations to observe the existing variations.

Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings and item contents in making preference predictions. This study aims to improve news recommendation by proposing a GAN-based news recommendation model considering both ratings (implicit feedback) and the latent features of news content.

The collaborative topic modeling (CTM) can improve user preference prediction by combining matrix factorization (MF) with latent topics of item content derived from latent topic modeling. This study proposes a novel hybrid news recommendation model, Hybrid-CFGAN, which modifies the architecture of the CFGAN model with enhanced preference learning from the CTM. The proposed Hybrid-CFGAN model contains parallel neural networks – original rating-based preference learning and CTM-based preference learning, which consider both ratings and news content with user preferences derived from the CTM model. A tunable parameter is used to adjust the weights of the two preference learnings, while concatenating the preference outputs of the two parallel neural networks.

This study uses the dataset collected from an online news website, NiusNews, to conduct an experimental evaluation. The results show that the proposed Hybrid-CFGAN model can achieve better performance than the state-of-the-art GAN-based recommendation methods. The proposed novel Hybrid-CFGAN model can enhance existing GAN-based recommendation and increase the performance of preference predictions on textual content such as news articles.

As the existing CFGAN model does not consider content information and solely relies on history logs, it may not be effective in recommending news articles. Our proposed Hybrid-CFGAN model modified the architecture of the CFGAN generator by adding a parallel neural network to gain the relevant information from news content and user preferences derived from the CTM model. The novel idea of adjusting the preference learning from two parallel neural networks – original rating-based preference learning and CTM-based preference learning – contributes to improve the recommendation quality of the proposed model by considering both ratings and latent preferences derived from item contents. The proposed novel recommendation model can improve news recommendation, thereby increasing the commercial value of news media platforms.

It is well known in the mining industry that the increase in failures and breakdowns is due mainly to a poor maintenance policy for the equipment, in addition to the difficult access that specialized personnel have to combat the breakdown, which translates into more machine downtime. For this reason, this study aims to propose a remote assistance model for diagnosing and repairing critical breakdowns in mining industry trucks using augmented reality techniques and data analytics with a quality approach that considerably reduces response times, thus optimizing human resources.

In this work, the six-phase CRIPS-DM methodology is used. Initially, the problem of fault diagnosis in trucks used in the extraction of material in the mining industry is addressed. The authors then propose a model under study that seeks a real-time connection between a service technician attending the truck at the mine site and a specialist located at a remote location, considering the data transmission requirements and the machine's characterization.

It is considered that the theoretical results obtained in the development of this study are satisfactory from the business point of view since, in the first instance, it fulfills specific objectives related to the telecare process. On the other hand, from the data mining point of view, the results manage to comply with the theoretical aspects of the establishment of failure prediction models through the application of the CRISP-DM methodology. All of the above opens the possibility of developing prediction models through machine learning and establishing the best model for the objective of failure prediction.

The original contribution of this work is the proposal of the design of a remote assistance model for diagnosing and repairing critical failures in the mining industry, considering augmented reality and data analytics. Furthermore, the integration of remote assistance, the characterization of the CAEX, their maintenance information and the failure prediction models allow the establishment of a quality-based model since the database with which the learning machine will work is constantly updated.

The purpose of this study is to determine the influence of project critical success factors (CSFs) on change management in the context of business process management (BPM). Despite widespread interest in BPM, the existing literature is insufficient in addressing the antecedents that contribute to change management in business process projects.

Key factors of change management success in BPM projects were initially identified in a systematic literature review (SLR) and were used as antecedents of change management through a structural equation modeling (SEM) with 464 business project stakeholders. Next, a neural network analysis allowed the key factors to be ranked non-linearly. Finally, a latent class analysis (LCA) was performed to determine the sample's heterogeneous groups based on their project management characteristics.

Project management, top management support and technological competencies were the main CSFs identified as having positive effects on change management. The most important factor is project management, followed by top management support, which plays a crucial mediating role in enabling change management. Although relevant, technological competencies were secondary in the study. Regarding project management CSF, four heterogeneous classes of individuals were determined.

Although this study provides an opportunity to observe CSFs, it does not address the need to analyze the phenomenon in different classifications of projects, regarding maturity, complexity, project management approach and other aspects that differentiate projects in a meaningful way.

The study allows practitioners to understand the critical factors underlying change management and take necessary actions to manage it, recognizing that individuals have heterogeneous profiles regarding project management.

This study pioneeringly discusses the CSFs of change management BPM projects to enable successful change management, ranking the main factors and mapping heterogeneous profiles.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

Limited research exists on the perceptions of police within specific ethnic minority groups. The primary purpose of this study is to investigate the experiences of Korean and Korean American residents in the Metro Atlanta area regarding their perceptions of cooperation with the police, particularly in relation to hate crimes, along with their perceptions of police legitimacy and other relevant factors. By focusing on this specific population, the study aims to shed light on their unique perspectives and contribute to a deeper understanding of the complex dynamics between ethnic minorities and law enforcement.

The authors’ sample comprised 128 Korean residents who were asked about their demographics, victimization experiences, self-rated English proficiency and police legitimacy. Multiple linear regression analyses were employed to investigate the impact of police legitimacy, victimization experiences and English-speaking skills on the participants' level of cooperation with the police.

Police legitimacy and self-rated levels of English proficiency emerged as the most significant factors in predicting the level of cooperation among residents with the police. Furthermore, individuals who have experienced crime victimization in the past were more willing to cooperate with the police compared to those who have not. Additionally, men showed a higher tendency to cooperate with the police compared to women participants.

The findings of this study suggest important implications for the policies and strategies aimed at enhancing the relationship between the Korean American community and the police. These implications include the need for improved language support for non-English speaking community members and the importance of building trust and fostering mutual understanding to cultivate positive police-community relations. By implementing measures based on these findings, it is recommended to promote a more inclusive and effective approach to policing within the Korean American population.