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1 – 10 of 120Mukesh Soni, Nihar Ranjan Nayak, Ashima Kalra, Sheshang Degadwala, Nikhil Kumar Singh and Shweta Singh
The purpose of this paper is to improve the existing paradigm of edge computing to maintain a balanced energy usage.
Abstract
Purpose
The purpose of this paper is to improve the existing paradigm of edge computing to maintain a balanced energy usage.
Design/methodology/approach
The new greedy algorithm is proposed to balance the energy consumption in edge computing.
Findings
The new greedy algorithm can balance energy more efficiently than the random approach by an average of 66.59 percent.
Originality/value
The results are shown in this paper which are better as compared to existing algorithms.
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Keywords
Yangze Liang and Zhao Xu
Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components…
Abstract
Purpose
Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.
Design/methodology/approach
The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.
Findings
The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.
Originality/value
The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.
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Nhu Ngoc Nguyen, Phong Tuan Nham and Yoshi Takahashi
This study aims to examine the relationship between a team’s value diversity (VD) and creativity and investigate the moderating effect of emotional intelligence (EI) to explain…
Abstract
Purpose
This study aims to examine the relationship between a team’s value diversity (VD) and creativity and investigate the moderating effect of emotional intelligence (EI) to explain inconsistent results regarding this relationship.
Design/methodology/approach
We conducted a cross-sectional sequential study with 340 employees and tested the hypothesis in a laboratory setting with 180 undergraduate students.
Findings
EI had a moderating effect on the relationship between a team’s VD and creativity in that the relationship was positive among teams with high EI. However, the relationship tended to be negative in the long term among teams with low EI.
Practical implications
Managers should pay attention to how teams benefit from VD because it can help or harm team performance. By assigning people with different EI levels into suitable teams and providing EI interventions, organizations may manage affective consequences and enjoy more benefits of cognitive consequences resulting from VD.
Originality/value
No previous study has investigated the effect of a team’s EI in the relationship between VD and team creativity. Drawing on the categorization-elaboration model of diversity and affective events theory, through the present two-study design, we obtained data from multiple sources and improved limitations in measurements of previous studies, thereby broadening the literature by highlighting the dynamic relationship between a team’s EI, VD and creativity in the Vietnamese context.
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Ding Liu and Chenglin Li
Safety training can effectively facilitate workers’ safety awareness and prevent injuries and fatalities on construction sites. Traditional training methods are time-consuming…
Abstract
Purpose
Safety training can effectively facilitate workers’ safety awareness and prevent injuries and fatalities on construction sites. Traditional training methods are time-consuming, low participation, and less interaction, which is not suitable for students who are born in Generation Z (Gen Z) and expect to be positively engaged in the learning process. With the characteristic of immersive, interaction, and imagination, virtual reality (VR) has become a promising training method. The purpose of this study is to explore Gen Z students’ learning differences under VR and traditional conditions and determine whether VR technology is more suitable for Gen Z students.
Design/methodology/approach
This paper designed a comparison experiment that includes three training conditions: VR-based, classroom lecturing, and on-site practice. 32 sophomore students were divided into four groups and received different training methods. The eye movement data and hazard-identification index (HII) scores from four groups were collected to measure their hazard-identification ability. The differences between the participants before and after the test were tested by paired sample t-test, and the differences between the groups after the test were analyzed by one-way Welch’s analysis of variance (ANOVA) test.
Findings
The statistical findings showed that participants under VR technology condition spent less time finding and arriving at the Areas of Interest (AOIs). Both the eye movement data and HII scores indicated that VR-based safety training is an alternative approach for Gen Z students to traditional safety training methods.
Originality/value
These findings contribute to the theoretical implications by proving the applicability of VR technology to Gen Z students and empirical implications by guiding colleges and universities to design attractive safety training lessons.
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Ruan Wang, Jun Deng, Xinhui Guan and Yuming He
With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data…
Abstract
Purpose
With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.
Design/methodology/approach
Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.
Findings
The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.
Originality/value
This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.
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Keywords
Volodymyr Novykov, Christopher Bilson, Adrian Gepp, Geoff Harris and Bruce James Vanstone
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Ruizhen Song, Xin Gao, Haonan Nan, Saixing Zeng and Vivian W.Y. Tam
This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based…
Abstract
Purpose
This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.
Design/methodology/approach
Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.
Findings
This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.
Practical implications
Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.
Originality/value
Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.
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Roopa Modem, Sethumadhavan Lakshmi Narayanan, Murugan Pattusamy and Nandan Prabhu
This study addresses a central research question: Does employees' personal initiative, with a benevolent political will, lead to career growth prospects in a work environment…
Abstract
Purpose
This study addresses a central research question: Does employees' personal initiative, with a benevolent political will, lead to career growth prospects in a work environment replete with perceived organizational politics? Drawing upon self-determination, signalling, and social cognitive theories, the authors examine how perceptions of organizational politics operate to limit the influence of benevolent political will – induced personal initiative on career growth prospects.
Design/methodology/approach
This research adopts a quantitative research design. This multi-wave, multi-sample and multi-source investigation includes 730 subordinate-supervisor dyads from India's information technology, education and manufacturing companies. The sample comprises 236 full-time faculty members from higher educational institutions and 496 mid-level managers from technical and service departments of information technology and manufacturing companies.
Findings
The results indicate that benevolent political will is significantly related to career growth prospects. In addition, perceptions of organizational politics shows a crossover interaction effect. The findings reveal that the indirect relationship between benevolent political will and career growth prospects changed significantly from those with a low perception of organizational politics to significantly negative among those perceiving organizational politics as high.
Practical implications
This study provides several implications for practice regarding personal initiative, benevolent political will and perceptions of organizational politics.
Originality/value
The significant contributions of this study are to provide new insights into the relationship between benevolent political will and career growth prospects and to unravel the paradoxical nature of the personal initiative phenomenon.
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This study aims to explore the effects of expertise diversity on project efficiency and creativity in health-care project teams.
Abstract
Purpose
This study aims to explore the effects of expertise diversity on project efficiency and creativity in health-care project teams.
Design/methodology/approach
This study analyzes hierarchical linear models using multi-source data from 50 project teams in a large health-care organization in the USA. This data set includes self-reported survey responses from 274 team members and human resource information for all 515 members across the 50 teams. Expertise diversity is operationalized by professional diversity and positional diversity reflecting two dimensions, domain and level, of the concept of expertise.
Findings
This study reveals that professional diversity is negatively related to project efficiency and project creativity, whereas positional diversity is positively related to project efficiency.
Originality/value
Successfully managing a project team of experts within a limited time frame is a challenge for organizations. This study advances the understanding of the double-edged sword effect of expertise diversity on project teams, focusing on professional and positional diversity. It provides important insights for human resource development in terms of the composition of project teams regarding members’ expertise.
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Yubing Sui, Adeel Luqman, Manish Unhale, Francesco Schiavone and Maria Teresa Cuomo
This study develops and validates a theoretical model of real-time mobile connectivity, examining how employees' perceptions of their relationship with supervisors influence their…
Abstract
Purpose
This study develops and validates a theoretical model of real-time mobile connectivity, examining how employees' perceptions of their relationship with supervisors influence their emotional experiences. Through quasi-experiments, the authors investigate the behavioral patterns and emotional responses associated with real-time mobile connectivity in organizations, with a focus on messaging apps that indicate message read status. Specifically, they explore how supervisors' attentiveness or inattentiveness in mobile connectivity impacts emotional ambivalence (anxiety and pride) among subordinates. Additionally, they examine the downstream effects of this emotional ambivalence on employees' workplace thriving and job performance across various dimensions.
Design/methodology/approach
To address the paradox of real-time mobile connectivity, a quasi-experimental design involving 320 team members from 46 teams was implemented. Multi-level structural equation modeling was employed to analyze within-person variance and evaluate the proposed hypotheses.
Findings
The findings indicate that employees who do not receive timely indications from their supervisors are more likely to experience elevated levels of anxiety, while those who receive prompt indications experience a sense of pride. Moreover, the indirect effects of the real-time mobile connectivity paradox on employee performance, mediated by anxiety (negatively) and pride (positively), are fully explained through workplace thriving.
Research limitations/implications
This study provides insights into the emotional ambivalence experienced in the workplace due to real-time mobile connectivity, highlighting its implications for organizational competitiveness. Integrating resource conservation theory and cognitive appraisal theory of emotion, the study explores the mediating role of workplace thriving and the impact on employee performance through pride and anxiety. Generalizability requires considering organizational settings and cultural contexts while acknowledging limitations such as a focus on messaging apps and specific samples. Future research should explore these dynamics in diverse contexts and identify additional factors influencing the relationship between real-time mobile connectivity and employee outcomes.
Practical implications
This study provides valuable insights for managers regarding the significance of message indications, as their attentiveness can elicit emotional reactions from employees that subsequently impact workplace thriving and job performance.
Originality/value
This study pioneers the exploration of the paradox of real-time mobile connectivity in the workplace, uncovering the discrete emotions experienced by employees. Furthermore, it elucidates the subsequent opposing effects on workplace thriving and job performance, contributing to the existing literature and knowledge in this area.
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