The “industry 4.0” phenomenon is expected to influence almost every aspect of business value chains, and hence it has been increasingly analyzed by management scholars. However, the overarching intellectual structure emerging from this new stream of literature has not yet been synthesized in a framework nor critically discussed. Furthermore, despite being part of the rhetoric in several recent industrial governmental plans, industry 4.0 in service sectors has not been systematically reviewed to date. By leveraging a systematic quantitative literature review, a data-driven approach and a quantitative methodology—embedding both bibliographic coupling and network analysis techniques—this study provides a clear visualization of the emerging intellectual structure of industry 4.0 in management studies. We also develop a framework based on the most recurrent themes emerging from the results of bibliometric and network analyses—the latter could be used by management scholars to understand studies surrounding industry 4.0. As service businesses can create and capture value generated through the 4th Industrial Revolution as well as manufacturing firms, we suggest that scholarly attention should also be directed toward the service industries and provide a research agenda.
Giampaolo Bovenzi, Francesco Cerasuolo, Domenico Ciuonzo
et al.
Generative Artificial Intelligence (GenAI) models such as LLMs, GPTs, and Diffusion Models have recently gained widespread attention from both the research and the industrial communities. This survey explores their application in network monitoring and management, focusing on prominent use cases, as well as challenges and opportunities. We discuss how network traffic generation and classification, network intrusion detection, networked system log analysis, and network digital assistance can benefit from the use of GenAI models. Additionally, we provide an overview of the available GenAI models, datasets for large-scale training phases, and platforms for the development of such models. Finally, we discuss research directions that potentially mitigate the roadblocks to the adoption of GenAI for network monitoring and management. Our investigation aims to map the current landscape and pave the way for future research in leveraging GenAI for network monitoring and management.
<p style="text-align: left;"><strong>Abstract:</strong></p>
<p style="text-align: left;">Increasing organizational productivity by focusing on effectiveness along with the satisfaction of service recipients of service organizations is an inevitable necessity. The present study is an attempt to investigate the impact of knowledge management dimensions on organizational effectiveness in the field of police crime prevention, considering the mediating role of employees' spiritual intelligence. This study is quantitative in terms of data, applied in terms of objective, and correlational in nature. The statistical population of this research consisted of level one and two managers of Strategic Studies Center, Police Science Research Institute, Amin University and Prevention Police in 2022. Based on stratified and simple random sampling, the sample size included 103 participants. The collected data were analyzed by structural equation method using SPSS and Lisrel software. The findings of the research show that knowledge management dimensions have a direct effect of 67% and indirect effect of 48% through the spiritual intelligence of employees on organizational effectiveness. The direct effect of employees' spiritual intelligence on organizational effectiveness is 56%. Also, the dimensions of knowledge management predict 73% of changes in employees' spiritual intelligence. As a result, strengthening the variables of creation, distribution and application of knowledge in the direction of organizational effectiveness should be given serious attention. In addition, the spiritual intelligence of employees as a mediating variable should be strengthened since by strengthening the indicators of spiritual intelligence, the indirect effect of knowledge management dimensions on the organizational effectiveness of the police in the field of crime prevention can be increased.</p>
<p style="text-align: left;"><strong>Key Words:</strong> organizational effectiveness, organizational knowledge, police organization, spiritual intelligence</p>
<p style="text-align: left;"> </p>
<p style="text-align: left;"><strong>1.Introduction</strong></p>
<p style="text-align: left;">Understanding the implications of the dimensions and indicators of knowledge management and spiritual intelligence on organizational effectiveness can be valuable for officials and managers who seek to improve and strengthen performance. However, the necessity of investigating the knowledge management, spiritual intelligence and organizational effectiveness of the police, especially in the field of crime prevention, can be seen as a response to the current environmental conditions and the needs of managers and commanders. On the other hand, increasing the effectiveness of the organization in order to improve the performance of the employees requires nobility and understanding of the direct and indirect effect of the knowledge management and spiritual intelligence components and indicators on the effectiveness of the organization. In fact, by improving the knowledge management and spiritual intelligence indicators, the organizational effectiveness of the police can be improved. In order to achieve organizational goals, including crime prevention, the present research tries to determine the dimensions of the direct and indirect effect of knowledge management through spiritual intelligence as a mediator on the organizational effectiveness of the police in crime prevention.</p>
<ol style="text-align: left;" start="2">
<li><strong>Literature Review</strong></li>
</ol>
<p style="text-align: left;">The present research, which is conducted with the aim of knowing the impact of knowledge management dimensions on organizational effectiveness in the police crime prevention with the mediating role of employees' spiritual intelligence, is based on the dimensions of knowledge management, defined by Bhatt (2001) who considers knowledge management as the process of creating, presenting, distributing and applying knowledge, and spiritual intelligence of Wellman who emphasizes the seven dimensions of spiritual intelligence, including mastery, mindfulness, extrasensory perception, unity, intelligence, trauma, and childhood spirituality, as well as the effectiveness of Robbins (2008) including quality, education development, motivation and flexibility.</p>
<p style="text-align: left;"><strong>3.Methodology</strong></p>
<p style="text-align: left;">This study is quantitative in terms of data, applied in terms of objective, and correlational in nature. The statistical population of this research consisted of level one and two managers of the Strategic Studies Center, Research Institute of Police Sciences and Social Order, Amin University of Police Sciences and Prevention Police in 2022, including 140 participants. The sampling method was based on stratified and simple random sampling method. According to the formula for determining the sample size, 103 participants constituted the sample of the study. Hypotheses testing was conducted using mean tests to analyze the data and calculate the population mean and standard deviation. Additionally, a structural equation model was employed in order to perform multivariate regression, factor analysis, path analysis, and to assess the causal relationship among variables. Also, to measure hidden variables measurable and obvious indicators were used. The data was analyzed using SPSS and Lisrel software.</p>
<p style="text-align: left;"><strong>4.Result</strong></p>
<p style="text-align: left;">The research data and the results obtained through path analysis show that the dimensions of knowledge management not only have a significant direct effect on organizational effectiveness, but also have a greater and stronger effect on spiritual intelligence and that investing through spiritual intelligence has a double effect of 88% directly and indirectly on organizational effectiveness. For this reason, the third and fourth hypotheses of the research were also confirmed.</p>
<p style="text-align: left;"><strong>5.Conclusion</strong></p>
<p style="text-align: left;">The significant effect of knowledge management dimensions on organizational effectiveness has been confirmed in the conducted research. Also, in this research, the effect of knowledge management on organizational effectiveness in crime prevention, and more importantly, the significant large effect of organizational structure on spiritual intelligence have been confirmed. Furthermore, the indirect effect of knowledge management through spiritual intelligence on organizational effectiveness shows that the mediating variable, in addition to the direct effect on organizational effectiveness in the field of crime prevention, also indirectly affects the dimensions which in turn facilitates the management of organizational effectiveness knowledge. Therefore, it is possible to restore and develop the indicators of creation, presentation, distribution and use throughout the organization, especially within the executive layers of the police, and at the same time, consider the indicators of spiritual intelligence including mastery, concern, extrasensory perception and unity which is derived from the nature of humans, in order to increase the intensity of the direct and indirect effect of knowledge management on organizational effectiveness. It should be mentioned that, based on the the findings of the present research, organizational knowledge helps to strengthen spiritual intelligence.</p>
<p style="text-align: left;"> </p>
<p style="text-align: left;"> </p>
<p style="text-align: left;"> </p>
<p style="text-align: left;"> </p>
Abstract Assembly system design defines proper configurations and efficient management strategies to maximize the assembly system performances. Beyond assembly line balancing and scheduling, several other dimensions of this problem have to be considered. Furthermore, the assembly system design has to consider the industrial environment in which the system operates. The latest industrial revolution, namely Industry 4.0, leverages Internet connected and sensorized machines to manufacture customer-designed products. This paper proposes an original framework which investigates the impact of Industry 4.0 principles on assembly system design. The traditional dimensions of this problem are described along with the industrial environment evolution over the last three centuries. Concerning the latest industrial revolution, the technology innovations which enabled the manufacturing process digitalization are presented. The application of these enabling technologies to the assembly domain results in a new generation of assembly systems, the here defined assembly system 4.0. Finally, the distinctive characteristics of these novel systems are proposed and described in detail.
Youcef Remil, Anes Bendimerad, Romain Mathonat
et al.
The management of modern IT systems poses unique challenges, necessitating scalability, reliability, and efficiency in handling extensive data streams. Traditional methods, reliant on manual tasks and rule-based approaches, prove inefficient for the substantial data volumes and alerts generated by IT systems. Artificial Intelligence for Operating Systems (AIOps) has emerged as a solution, leveraging advanced analytics like machine learning and big data to enhance incident management. AIOps detects and predicts incidents, identifies root causes, and automates healing actions, improving quality and reducing operational costs. However, despite its potential, the AIOps domain is still in its early stages, decentralized across multiple sectors, and lacking standardized conventions. Research and industrial contributions are distributed without consistent frameworks for data management, target problems, implementation details, requirements, and capabilities. This study proposes an AIOps terminology and taxonomy, establishing a structured incident management procedure and providing guidelines for constructing an AIOps framework. The research also categorizes contributions based on criteria such as incident management tasks, application areas, data sources, and technical approaches. The goal is to provide a comprehensive review of technical and research aspects in AIOps for incident management, aiming to structure knowledge, identify gaps, and establish a foundation for future developments in the field.
The increasing industrial development and energy demand have necessitated the maximization of available energy use and the deployment of renewable resources. Effective energy management, optimal modeling, and efficient planning are essential to transform the power system into a high-efficiency, optimal model. This study focuses on the initial step of modeling smart buildings (SBs) equipped with non-responsive devices and renewable photovoltaic sources. A comprehensive energy management (EM) plan is formulated for these buildings, incorporating the KNX protocol for solar energy system management. Batteries are integrated into the building model to store energy during periods of low consumption and serve as generators during peak load conditions, with the primary goal of minimizing power system losses and related costs. To address the complexity of this model, whale optimization algorithm (WOA) is employed for optimization. Optimal candidate buses are selected for interconnected building management based on a suggested sensitivity analysis to minimize losses. Cost performance is then assessed, considering energy production and sales. The findings indicate that substantial control of operating costs can be achieved through strategic management of battery charging and discharging, as well as the utilization of photovoltaic units. The proposed model is evaluated across various scenarios using a test system comprising 30 modified system, demonstrating its effectiveness in enhancing energy efficiency and management.
Raenald Syaputra, Taghfirul Azhima Yoga Siswa, Wawan Joko Pranoto
Banjir merupakan salah satu bencana alam yang sering terjadi di Indonesia, termasuk di Kota Samarinda dengan 18-33 titik desa terdampak dari tahun 2018-2021. Penggunaan machine learning dalam mengklasifikasi bencana banjir sangat penting untuk memprediksi kejadian di masa mendatang. Beberapa penelitian sebelumnya terkait klasifikasi data banjir dalam 3 tahun terakhir telah dilakukan. Namun, dari beberapa penelitian tersebut memunculkan masalah terkait dengan dataset high dimensional yang dapat menurunkan performa model klasifikasi dan menyebabkan overfitting. Selain itu, masalah lain juga muncul dalam hal imbalance data yang menyebabkan bias terhadap kelas mayoritas dan representasi yang tidak akurat. Oleh karena itu, permasalahan dataset high dimensional dan imbalance data merupakan tantangan spesifik yang harus diatas dalam klasifkasi data banjir Kota Samarinda. Penelitian ini bertujuan mengidentifkasi fitur-fitur yang diperoleh dari seleksi fitur Genetic Algorithm (GA) yang memiliki pengaruh terhadap akurasi klasifikasi data banjir Kota Samarinda menggunakan algoritma Support Vector Machine (SVM), serta meningkatkan akurasi klasifikasi data banjir di Kota Samarinda dengan mengimplementasikan algoritma SVM yang dikombinasikan dengan metode Synthetic Minority Oversampling Technique (SMOTE) untuk oversampling, seleksi fitur dengan GA dan optimasi menggunakan Particle Swarm Optimization (PSO). Teknik validasi yang digunakan adalah 10-fold cross validation dan evaluasi performa menggunakan confusion matrix. Data yang digunakan berasal dari BPBD (Badan Penanggulangan Bencana Daerah) dan BMKG (Badan Meteorologi, Klimatologi, dan Geofisika) Kota Samarinda pada tahun 2021-2023 terdiri dari 11 fitur dan 1.095 record. Hasil penelitian menunjukkan bahwa fitur-fitur penting yang terpilih melalui GA adalah temperatur maksimum, kecepatan angin maksimum, arah angin maksimum, arah angin terbanyak, lamanya penyinaran matahari dan kecepatan angin rata-rata. Dengan kombinasi metode SVM, SMOTE, GA dan PSO, akurasi klasifikasi data banjir mencapai 82,28%. Namun, penelitian ini juga menghadapi tantangan seperti kontradiksi hasil dengan penelitian lain terkait penggunaan SMOTE dan variasi hasil akibat karakteristik dataset serta metode pembagian data yang berbeda. Hasil penelitian ini dapat digunakan oleh pemerintah daerah dan badan penanggulangan bencana daerah Kota Samarinda untuk memprediksi kejadian banjir dengan lebih akurat, serta memungkinkan tindakan pencegahan yang lebih efektif. Penerapan hasil penelitian ini dapat meningkatkan efektivitas dalam mitigasi bencana banjir Kota Samarinda.
The electricity consumption of the textile industry accounts for 2.12% of the total electricity consumption in society, making it one of the high-energy-consuming industries in China. The textile industry requires the use of a large amount of industrial steam at various temperatures during production processes, making its dispatch and operation more complex compared to conventional electricity–heat integrated energy systems. As an important demand-side management platform connecting the grid with distributed resources, a virtual power plant can aggregate textile industry users through an operator, regulating their energy consumption behavior and enhancing demand-side management efficiency. To effectively address the challenges in load regulation for textile industry users, this paper proposes a coordinated optimization dispatching method for electricity–steam virtual-based power plants focused on textile industrial parks. On one hand, targeting the impact of different energy prices on the energy usage behavior of textile industry users, an optimization dispatching model is established where the upper level consists of virtual power plant operators setting energy prices, and the lower level involves multiple textile industry users adjusting their purchase and sale strategies and changing their own energy usage behaviors accordingly. On the other hand, taking into account the energy consumption characteristics of steam, it is possible to optimize the production and storage behaviors of textile industry users during off-peak electricity periods in the power market. Through this electricity–steam optimization dispatching model, the virtual power plant operator’s revenue is maximized while the operating costs for textile industry users are minimized. Case study analyses demonstrate that this strategy can effectively enhance the overall economic benefits of the virtual power plant.
Data-centric AI has shed light on the significance of data within the machine learning (ML) pipeline. Recognizing its significance, academia, industry, and government departments have suggested various NLP data research initiatives. While the ability to utilize existing data is essential, the ability to build a dataset has become more critical than ever, especially in the industry. In consideration of this trend, we propose a "Data Management Operations and Recipes" to guide the industry in optimizing the building of datasets for NLP products. This paper presents the concept of DMOps which is derived from real-world experiences with NLP data management and aims to streamline data operations by offering a baseline.
Communication in millimeter wave (mmWave) and even terahertz (THz) frequency bands is ushering in a new era of wireless communications. Beam management, namely initial access and beam tracking, has been recognized as an essential technique to ensure robust mmWave/THz communications, especially for mobile scenarios. However, narrow beams at higher carrier frequency lead to huge beam measurement overhead, which has a negative impact on beam acquisition and tracking. In addition, the beam management process is further complicated by the fluctuation of mmWave/THz channels, the random movement patterns of users, and the dynamic changes in the environment. For mmWave and THz communications toward 6G, we have witnessed a substantial increase in research and industrial attention on artificial intelligence (AI), reconfigurable intelligent surface (RIS), and integrated sensing and communications (ISAC). The introduction of these enabling technologies presents both open opportunities and unique challenges for beam management. In this paper, we present a comprehensive survey on mmWave and THz beam management. Further, we give some insights on technical challenges and future research directions in this promising area.
Dor Freidin, MD, Roei Singolda, MD, Shai Tejman-Yarden, MD, MSc, MBA
et al.
Introduction:. This study was designed to compare VR stereoscopical three-dimensional (3D) imaging with two-dimensional computed tomography angiography (CTA) images for evaluating the abdominal vascular anatomy before autologous breast reconstruction.
Methods:. This prospective case series feasibility study was conducted in two tertiary medical centers. Participants were women slated to undergo free transverse rectus abdominis muscle, unilateral or bilateral deep inferior epigastric perforator flap immediate breast reconstruction. Based on a routine CTA, a 3D VR model was generated. Before each procedure, the surgeons examined the CTA and then the VR model. Any new information provided by the VR imaging was submitted to a radiologist for confirmation before surgery. Following each procedure, the surgeons completed a questionnaire comparing the two methods.
Results:. Thirty women between 34 and 68 years of age were included in the study; except for one, all breast reconstructions were successful. The surgeons ranked VR higher than CTA in terms of better anatomical understanding and operative anatomical findings. In 72.4% of cases, VR models were rated having maximum similarity to reality, with no significant difference between the type of perforator anatomical course or complexity. In more than 70% of the cases, VR was considered to have contributed to determining the surgical approach. In four cases, VR imaging modified the surgical strategy, without any complications.
Conclusions:. VR imaging was well-accepted by the surgeons who commented on its importance and ease compared with the standard CTA presentation. Further studies are needed to determine whether VR should become an integral part of preoperative deep inferior epigastric perforator surgery planning.