Hasil untuk "Management. Industrial management"

Ezz El-Din Hemdan, Amged Sayed

In recent years, cutting-edge technologies, such as artificial intelligence (AI), blockchain, and digital twin (DT), have revolutionized the healthcare sector by enhancing public health and treatment quality through precise diagnosis, preventive measures, and real-time care capabilities. Despite these advancements, the massive amount of generated biomedical data puts substantial challenges associated with information security, privacy, and scalability. Applying blockchain in healthcare-based digital twins ensures data integrity, immutability, consistency, and security, making it a critical component in addressing these challenges. Federated learning (FL) has also emerged as a promising AI technique to enhance privacy and enable decentralized data processing. This paper investigates the integration of digital twin concepts with blockchain and FL in the healthcare domain, focusing on their architecture and applications. It also explores platforms and solutions that leverage these technologies for secure and scalable medical implementations. A case study on federated learning for electroencephalogram (EEG) signal classification is presented, demonstrating its potential as a diagnostic tool for brain activity analysis and neurological disorder detection. Finally, we highlight the key challenges, emerging opportunities, and future directions in advancing healthcare digital twins with blockchain and federated learning, paving the way for a more intelligent, secure, and privacy-preserving medical ecosystem.

Musharafa Saleem, A. Al-Zubaidi, Neyara Radwan et al.

The present research analyzes the properties of a Casson ternary nanofluid over a stretching sheet with Thomson and Troian slip conditions, taking into consideration the influences of electromagnetohydrodynamic (EMHD). The ternary nanofluid comprises three different nanoparticles, which include titanium dioxide (TiO2), copper (Cu), and silver (Ag), all being suspended in oil, which is the base fluid. They are involved because of their good thermal conductivity and chemical stability, and AgCuTiO2 /Oil nanofluid is a composite of copper, titanium oxide, and oil. Hence, carrying out the said procedure, the ternary nanofluid becomes AgCuTiO2/Oil. The sheet is, however, thought to be stretching vertically while the flow is determined by the effect of the gravity force through the free convention. Moreover, the phenomena of EMHD, porous medium, thermal slip, thermal radiation, Joule heating, and heat source/sink are included to make the energy equation more real-life. This leads to a set of partial differential equations (PDEs) based mathematical models transformed into ordinary differential equations (ODEs)-appropriate similarity transformation. The Runge–Kutta–Fehlberg (RKF-45) method solves the given ordinary differential system. According to the research’s findings, the temperature of the ternary Casson nanofluid rises when the suspension of silver, copper, and titanium dioxide nanoparticles increases, and the velocity of flow for merely silver and copper decreases when the density decreases. This causes the flow rate to be constricted through the velocity slip condition, at the same time as the nanofluid’s temperature increases.

Zhenning Yang, Archit Bhatnagar, Yiming Qiu et al.

Cloud infrastructure is the cornerstone of the modern IT industry. However, managing this infrastructure effectively requires considerable manual effort from the DevOps engineering team. We make a case for developing AI agents powered by large language models (LLMs) to automate cloud infrastructure management tasks. In a preliminary study, we investigate the potential for AI agents to use different cloud/user interfaces such as software development kits (SDK), command line interfaces (CLI), Infrastructure-as-Code (IaC) platforms, and web portals. We report takeaways on their effectiveness on different management tasks, and identify research challenges and potential solutions.

Avi Shaked, Nan Messe

For securing systems, it is essential to manage their vulnerability posture and design appropriate security controls. Vulnerability management allows to proactively address vulnerabilities by incorporating pertinent security controls into systems designs. Current vulnerability management approaches do not support systematic reasoning about the vulnerability postures of systems designs. To effectively manage vulnerabilities and design security controls, we propose a formally grounded automated reasoning mechanism. We integrate the mechanism into an open-source security design tool and demonstrate its application through an illustrative example driven by real-world challenges. The automated reasoning mechanism allows system designers to identify vulnerabilities that are applicable to a specific system design, explicitly specify vulnerability mitigation options, declare selected controls, and thus systematically manage vulnerability postures.

M. Mehdi Kholoosi, Triet Huynh Minh Le, M. Ali Babar

Artificial Intelligence (AI) has revolutionized software development, particularly by automating repetitive tasks and improving developer productivity. While these advancements are well-documented, the use of AI-powered tools for Software Vulnerability Management (SVM), such as vulnerability detection and repair, remains underexplored in industry settings. To bridge this gap, our study aims to determine the extent of the adoption of AI-powered tools for SVM, identify barriers and facilitators to the use, and gather insights to help improve the tools to meet industry needs better. We conducted a survey study involving 60 practitioners from diverse industry sectors across 27 countries. The survey incorporates both quantitative and qualitative questions to analyze the adoption trends, assess tool strengths, identify practical challenges, and uncover opportunities for improvement. Our findings indicate that AI-powered tools are used throughout the SVM life cycle, with 69% of users reporting satisfaction with their current use. Practitioners value these tools for their speed, coverage, and accessibility. However, concerns about false positives, missing context, and trust issues remain prevalent. We observe a socio-technical adoption pattern in which AI outputs are filtered through human oversight and organizational governance. To support safe and effective use of AI for SVM, we recommend improvements in explainability, contextual awareness, integration workflows, and validation practices. We assert that these findings can offer practical guidance for practitioners, tool developers, and researchers seeking to enhance secure software development through the use of AI.

Jaxson Brown, Duc-Son Pham, Sie-Teng Soh et al.

Industrial Control Systems (ICSs) are complex interconnected systems used to manage process control within industrial environments, such as chemical processing plants and water treatment facilities. As the modern industrial environment moves towards Internet-facing services, ICSs face an increased risk of attacks that necessitates ICS-specific Intrusion Detection Systems (IDS). The development of such IDS relies significantly on a simulated testbed as it is unrealistic and sometimes hazardous to utilize an operational control system. Whilst some testbeds have been proposed, they often use a limited selection of virtual ICS simulations to test and verify cyber security solutions. There is a lack of investigation done on developing systems that can efficiently simulate multiple ICS architectures. Currently, the trend within research involves developing security solutions on just one ICS simulation, which can result in bias to its specific architecture. We present ICS-SimLab, an end-to-end software suite that utilizes Docker containerization technology to create a highly configurable ICS simulation environment. This software framework enables researchers to rapidly build and customize different ICS environments, facilitating the development of security solutions across different systems that adhere to the Purdue Enterprise Reference Architecture. To demonstrate its capability, we present three virtual ICS simulations: a solar panel smart grid, a water bottle filling facility, and a system of intelligent electronic devices. Furthermore, we run cyber-attacks on these simulations and construct a dataset of recorded malicious and benign network traffic to be used for IDS development.

Marek Tiits, Tarmo Kalvet, Chahinez Ounoughi et al.

Researchers have long used gravity models to analyze international trade patterns, identify export opportunities, and negotiate trade agreements. Recent research has emphasized the significance of relatedness and product complexity research in developing robust economic development strategies. This paper presents a novel approach, incorporating relatedness and product complexity as integral elements for interpreting export potential within gravity models powered by machine learning. Our approach stands out for its proficiency in accurately predicting bilateral trade values at a detailed product group level, providing valuable insights for policymakers and other stakeholders. The research leverages random forest machine learning models for predictions and incorporates relatedness and complexity to reveal new dimensions in international trade analysis.

Mykhailo Koshil, Tilman Wegener, Detlef Mentrup et al.

Visual inspection, or industrial anomaly detection, is one of the most common quality control types in manufacturing. The task is to identify the presence of an anomaly given an image, e.g., a missing component on an image of a circuit board, for subsequent manual inspection. While industrial anomaly detection has seen a surge in recent years, most anomaly detection methods still utilize knowledge only from normal samples, failing to leverage the information from the frequently available anomalous samples. Additionally, they heavily rely on very general feature extractors pre-trained on common image classification datasets. In this paper, we address these shortcomings and propose the new anomaly detection system AnomalousPatchCore~(APC) based on a feature extractor fine-tuned with normal and anomalous in-domain samples and a subsequent memory bank for identifying unusual features. To fine-tune the feature extractor in APC, we propose three auxiliary tasks that address the different aspects of anomaly detection~(classification vs. localization) and mitigate the effect of the imbalance between normal and anomalous samples. Our extensive evaluation on the MVTec dataset shows that APC outperforms state-of-the-art systems in detecting anomalies, which is especially important in industrial anomaly detection given the subsequent manual inspection. In detailed ablation studies, we further investigate the properties of our APC.

Francisco Salas-Molina

Organizations use cash management models to control balances to both avoid overdrafts and obtain a profit from short-term investments. Most management models are based on control bounds which are derived from the assumption of a particular cash flow probability distribution. In this paper, we relax this strong assumption to fit cash management models to data by means of stochastic and linear programming. We also introduce ensembles of random cash management models which are built by randomly selecting a subsequence of the original cash flow data set. We illustrate our approach by means of a real case study showing that a small random sample of data is enough to fit sufficiently good bound-based models.

Oussama Zoubia, Nagaraj Bahubali Asundi, Adamantios Koumpis et al.

In the digital age, data has emerged as one of the most valuable assets across various sectors, including academia, industry, and healthcare. Effective data preservation involves the management of data to ensure its long-term accessibility and usability. Given the importance and sensitivity of data, the need for effective management is a crucial necessity. One of the big recent proposed approaches for data management is the FAIR Digital Objects (FDOs) which has emerged to revolutionize the field of data management and preservation. Central to this revolution is the alignment of FDOs with the FAIR principles (Findable, Accessible, Interoperable, Reusable), particularly emphasizing machine-actionability and interoperability across diverse data ecosystems. This paper presents "FDO Manager" a Minimum Viable Implementation of FDOs, tailored specifically for the use case and field of research artefacts such as datasets, publications, and code. The paper discusses the core ideas behind the FDO Manager, its architecture, usage and implementation details, as well as its potential impact, demonstrating a simple and abstract implementation of FDOs in the research realm.

N. N. Shelomentseva, O. V. Grushina, T. A. Krasnoshtanova

In the present paper, the consequences of the introduction of project financing against the backdrop of crises in 2020 and 2022 are analyzed. The subject interactions in the course of housing construction under the conditions of project financing are considered. A multi-criteria economic-mathematical model for the interest coordination of economic subjects in housing construction has been proposed. The model permits to understand and evaluate the economic consequences of choosing the possible options from the standpoint of each of the economic subjectss. The numerical calculations of choosing two (in pairs), and all three (developer, bank and consumer) economic subjects were performed using the proposed multi-criteria model with the stated limitations. The MATLAB software was employed to solve optimization problems and plotting. The solutions acceptable to the subjects were chosen from a set of Pareto-optimal alternatives. Despite the fact that all subjects of housing construction are involved in the interaction, this interaction does not occur simultaneously, but in a complex subordinate manner: the bank took the dictating position in project financing, and the consumer pays for everything. The state should play a role of the subject, which should coordinate the interests of the developer, the bank and the population. The task of the state is to create such conditions in the housing construction market so that economic subjects are interested in coordination of their interests to find a compromise. This opens routes for further research.

Olena Barabash, Vadim Ziuziun, Lyubov Kubiavka

The objects of the critical transport infrastructure are located in all regions of Ukraine, and the question of the safety of these objects is extremely relevant. A functional approach should be used to form an effective safety management unit for critical transport objects. Therefore, in order to achieve an acceptable level of safety of the critical transport infrastructure, it is necessary to have an effective mechanism for achieving this result, which can be achieved through the formation and efficient management of regional programs for the safe operation of critical transport infrastructure objects. Management models for regional safety programs at critical transport infrastructure facilities based on the existing approaches to construction of models of program and project management are proposed in the article. Critical transport infrastructure includes highways, state-owned transport enterprises, subway facilities, gas stations, bridges, sea and river ports, airports, and pipelines. These facilities are strategic for the state and, consequently, vulnerable, so they require special protection. In order to form an effective apparatus for environmental safety management in critical transport infrastructure facilities, the application of a program approach is proposed in the article. And to assess the life cycle of regional environmental safety programs of critical transport infrastructure facilities based on the Deming cycle, a spiral model was developed, which is the environment for the operation of schematic, system, and service models of the environmental safety management program. Development of approaches to the management of regional programs for the environmentally safe operation of critical transport infrastructure facilities, based on the formation of strategic objectives and their decomposition, will be aimed not only at solving existing problems of critical transport infrastructure in the region but factors related to the occurrence of dangerous events for them and the elimination of the causes leading to these problems. A system model for managing regional safety programs for objects of critical transport infrastructure is proposed.

Mahmut Nedim Alpdemir

Robust Autoencoders separate the input image into a Signal(L) and a Noise(S) part which, intuitively speaking, roughly corresponds to a more stable background scene (L) and an undesired anomaly (or defect) (S). This property of the method provides a convenient theoretical basis for divorcing intermittent anomalies that happen to clutter a relatively consistent background image. In this paper, we illustrate the use of Robust Deep Convolutional Autoencoders (RDCAE) for defect detection, via a pseudo-supervised training process. Our method introduces synthetic simulated defects (or structured noise) to the training process, that alleviates the scarcity of true (real-life) anomalous samples. As such, we offer a pseudo-supervised training process to devise a well-defined mechanism for deciding that the defect-normal discrimination capability of the autoencoders has reached to an acceptable point at training time. The experiment results illustrate that pseudo supervised Robust Deep Convolutional Autoencoders are very effective in identifying surface defects in an efficient way, compared to state of the art anomaly detection methods.

О.О. Мартинчук, В.І. Василишин, У Лісян et al.

The article considers possible variants of application of various MIMO schemes for the communication with the drone or unmanned aerial vehicle. The model of multipath propagation of radio waves taking into account polarization parameters of antennas is given. The main focus is on the use of MIMO technology with polarization-orthogonal channels and channels with double polarization. The evaluation of the efficiency of using full polarization reception in comparison with MIMO channels of one polarization is given. Attention is paid to the presence of cross-polarization solution between the channels.

Irfanullah Khan, Biswajit Sarkar

This study is the first to consider a distribution-free approach in a newsvendor model with a transfer of risk and back-ordering. Previously, in many articles, discrete demand is considered. In this model, we consider a newsvendor selling a single seasonal item with price-dependent stochastic demand. Competition in markets has forced the retailer and manufacturer to coordinate in decentralized supply chain management. A coordination contract is made between a retailer and manufacturer to overcome the randomness of demand for a short-life-cycle product. The retailer pays an additional amount per product to transfer the risk of unsold items. The manufacturer bears the cost of unsold products from the retailer. Shortages are allowed with back-ordering costs during the season. The distribution-free model is developed and solved with only available demand data of mean and standard deviation. Stackelberg’s game approach is used to calculate the optimal ordering quality and price. This model aims to maximize expected profit by optimizing unit selling price and ordered quantity through coordination. To illustrate that the model is robust, numerical experiment and sensitivity analyses are conducted for both decentralized and centralized supply chain management. For applicability of the model in the real-world business scenario, managerial insights are provided with sensitivity analysis.

Nicola Comand, Alberto Doria

Vibratory conveyors are widely used to feed raw materials and small parts to processing equipment. Up to now, most of the research has focused on materials and parts that can be modeled as point masses or small blocks. This paper focuses on the conveying of cylindrical parts. In this case, the rolling motion is an essential feature of conveyor dynamics. First, the dynamic equations governing the rolling motion are stated, and the effects of friction and rolling resistance coefficients on the behavior of the system are analyzed. Then, a non-linear numerical model is developed in MATLAB. It takes into account the transition between pure rolling and rolling with sliding and the impacts of the cylindrical part on the edges of the conveyor. Numerical results showing the effect of the operative parameters of the conveyor and of friction properties on the traveled distance are presented and discussed. Finally, a comparison between numerical and experimental results is presented.

Mangirdas Morkunas, Elzė Rudienė

The present paper studies the importance of social servicescape factors to customer satisfaction in middle-priced restaurant services. This paper fills the existing literature gap on the importance of social servicescape factors onto customers’ satisfaction in middle-priced services. A survey of 514 respondents from three capitals of the Baltic States was conducted for the purpose of the present study. Descriptive statistics together with an independent samples <i>t</i>-test and partial least squares path analysis were employed for data processing. The results obtained confirmed the hypothesis about the importance of social servicescape attributes to customer satisfaction. The study also highlighted the difference in gender attitudes towards intangible aspects of service delivery. The research confirmed the existence of a relationship between customer satisfaction and repurchase intentions, although to a lesser extent than could have been anticipated from the literature review. The findings of the study covered by the present paper allow us to position middle-priced restaurants closer to luxury ones compared to casual restaurants

Gelmar García-Vidal, Laritza Guzmán-Vilar, Alexander Sánchez-Rodríguez et al.

Small and medium-sized enterprises (SMEs) have to face the time post COVID-19. The pandemic impacted the SMEs with great force worsening the well-known situation of lack of resources and its tendency to disappear in the very early years of existence. This systemic crisis jeopardizes SMEs in many ways and it is necessary to find ways to emerge and survive from this crisis. This manuscript conducts a literature study on more than 100 manuscripts that present recommendations from McKinsey & Company for SMEs to face post-pandemic time. Through the application of the entropy-weight coefficient method this paper finds priorities from Ecuadorian SMEs out of essential elements proposed at the literature review, to introducing at the managing process to face post COVID-19 era.

Salim A. Mohammed Ali, Emad H. Al-Hemairy

V2X (Vehicle-to-Everything) evolution over cellular networks has been an excelling topic with the advances of high throughput and low latency LTE networks, and the introduction of 5G networks. According to recent researches, obtaining acceptable End-to-End (E2E) delay has been a challenging design process since data travels through several steps from the originating source to the data center and vice versa. V2X latency comprises mainly of three levels: source processing, cellular network, and data center processing. Delay reduction can be achieved on the three levels. However, many conventional solutions have not reached the required and acceptable range of latency to enable V2X communication over cellular networks. In this paper, a general review of challenges to make V2X feasible on cellular network has been discussed, and the proposed solutions in the literature has been introduced. As a conclusion, a various types of aiding tools to design and test V2X tools are given, so that a right path should be taken to consider challenges and improving design metrics.

Alireza Moradi, Shahamat Hosseinian, Seyed Kamal Vaezi

Background & Purpose: A requirement for the success of knowledge workers’ competence development plans is to take their thinking preferences into account. This research proposed a nature-based model of strategies of developing skills building and competencies of knowledge workers according to their preferred thinking style. Methodology: This was an applied, inductive, interpretative, and qualitative research conducted through the grounded theory. The participants of the research included 21 experts of Human Resource Management, selected by theoretical sampling. Data were analyzed using Corbin and Strauss’ Grounded Theory, through three stages of open, axial, and selective coding. Findings: Aprocess modelhas introduced in which Skill building and competence development of knowledge workers is the central phenomena of this model. The causal factors includ the three categories of individual, social-environmental, and organizational-legal. The strategies consist of three categories of competence development, skill building, and personal development based on A, B, C, and D’s preferences. The intervening factors are three categories of cultural-organizational, personal attitude, and senior managers’ attitude. Moreover, considerations of development facilitating factors comprise the contextual factors of the model. Finally, the outcomes of the model include the three categories of personal, organizational, and social development. Conclusion: Preparing and implementing plans for the development of competencies and skills of Knowledge workers based on their fourfold thinking preferences enhance the success and efficiency of these plans. For this purpose, competencies and skills related to each of these thinking preferences are identified and the actions necessary for their development are determined in this research.