M. Bertolini, Davide Mezzogori, Mattia Neroni
et al.
Abstract Machine Learning (ML) is a branch of artificial intelligence that studies algorithms able to learn autonomously, directly from the input data. Over the last decade, ML techniques have made a huge leap forward, as demonstrated by Deep Learning (DL) algorithms implemented by autonomous driving cars, or by electronic strategy games. Hence, researchers have started to consider ML also for applications within the industrial field, and many works indicate ML as one the main enablers to evolve a traditional manufacturing system up to the Industry 4.0 level. Nonetheless, industrial applications are still few and limited to a small cluster of international companies. This paper deals with these topics, intending to clarify the real potentialities, as well as potential flaws, of ML algorithms applied to operation management. A comprehensive review is presented and organized in a way that should facilitate the orientation of practitioners in this field. To this aim, papers from 2000 to date are categorized in terms of the applied algorithm and application domain, and a keyword analysis is also performed, to details the most promising topics in the field. What emerges is a consistent upward trend in the number of publications, with a spike of interest for unsupervised and especially deep learning techniques, which recorded a very high number of publications in the last five years. Concerning trends, along with consolidated research areas, recent topics that are growing in popularity were also discovered. Among these, the main ones are production planning and control and defect analysis, thus suggesting that in the years to come ML will become pervasive in many fields of operation management.
This paper examines the emergence of the ‘gravel bike’, a new and successful category of sports bicycles that gained prominence in the global cycling industry in the late 2010s, to advance the understanding of the role of users in the processes of sociotechnical innovation. The study traces the development of gravel cycling and the gravel bike within the framework of science and technology studies (STS), introducing the concept of ‘user betrayal’ to highlight how innovations initially driven by users can later diverge from their original values and needs. The development of the gravel bike represents a case where users’ input played a crucial role in creating an alternative cycling culture that directly supported the introduction of a new, successful bicycle model. However, the commercialization and institutionalization of gravel cycling, driven by industries, institutions and sporting bodies, has led to a significant shift away from the values that motivated early enthusiasts. This case reveals the tensions between user-driven innovation and the forces of commodification, emphasizing how marketing and institutional pressures can undermine the original needs and ideals of user collectives.
Стаття присвячена формуванню нових наукових рішень щодо дослідження особливостей управління активами на підприємствах України в умовах трансформації ринків товарів та ресурсів (світові, міжнародні, і, особливо, національні, галузеві і регіональні). Розкрита наукова та практична необхідність активізації процесів підвищення адаптивності, гнучкості та ефективності бізнесу в Україні за рахунок управління активами (майном) трансформаційним процесам, які відбуваються на ринках товарів та ресурсів. Метою статті є пошук шляхів підвищення ефективності управління активами на підприємствах України в умовах трансформації ринків товарів та ресурсів (світові, міжнародні, і, особливо, національні, галузеві і регіональні). Методологія: використано комплекс загальних та спеціальних методів на емпіричному і теоретичному рівнях, таких як: метод літературного аналізу для дослідження наукової проблематики у сфері управління активами суб’єктами підприємництва; метод аналізу для визначення тенденцій і особливостей управління активами (майном) загалом, необоротними та оборотними активами на великих, середніх, малих та мікропідприємствах України; метод синтезу для визначення проблематики управління активами (майном) загалом, необоротними та оборотними активами на великих, середніх, малих та мікропідприємствах України; методи індукції та дедукції для формування сукупність рекомендацій щодо підвищення адаптивності, гнучкості та ефективності бізнесу в Україні у контексті управління активами (майном) трансформаційним процесам, які відбуваються на ринках товарів та ресурсів; методи систематизації, групування і логічного узагальнення для систематизації інформації, формування висновків і наукових пропозицій статті. Результати: визначено шляхи підвищення ефективності управління активами на підприємствах України в умовах трансформації ринків товарів та ресурсів (світові, міжнародні, і особливо національні, галузеві і регіональні).
One of the major indicators in evaluating the performance of hospitals and their managers is the average length of stay of patients; given the importance of this indicator, the present study has examined the factors affecting the length of stay of hospitalized patients. This study was conducted with the aim of identifying the key factors affecting the length of stay of patients and providing practical solutions for improving the management of hospital beds. Data from 26,907 patients were analyzed using clustering models, clustering algorithms (K-Means) and association rules extraction (Apriori). The data consists of 10 numerical and discrete columns. The variables include 10 items, which are respectively: gender, marital status, hospitalization department, physician specialty, insurance, blood transfusion, surgery, type of discharge, age, and length of stay. The findings showed that the variables of surgery and blood transfusion have the greatest impact on the average length of stay in the hospital.
The article presents an in-depth analysis of the formation and development of the capital market (stocks and bonds segment) in China. The factors determining the valuation of shares that are in circulation are highlighted. While the first factor identifies an assessment based on the company’s financial statements, the second factor considers current market conditions and investor sentiment. Examining historical data, it is interesting to note that from 2000 to 2006, the total market capitalization of stocks remained stable and amounted to a substantial 4 trillion yuan. However, in 2007 There was a significant shift when the market capitalization grew significantly and reached as much as 30 trillion yuan. This sudden growth can be explained by various reasons, such as increased investor confidence, favorable economic conditions and the introduction of progressive financial regulation. In addition, it is important to note that in the same year, the Shanghai Composite index (a key indicator of the Chinese stock market) reached an unprecedented value of 6,123.04 points. This indicator demonstrated the stability and potential of the Chinese capital market, and attracted both domestic and international investors. Thus, the analysis presented in the article reveals the intricacies of the Chinese capital market and the mechanisms of its assessment. This analysis establishes the importance of both book value and market value in determining the valuation of outstanding shares. In addition, historical trends have highlighted the resilience and vulnerability of the market to external shocks, as evidenced by significant fluctuations in market capitalization. In general, this study helps to understand the Chinese capital market and its evolution over time.
The growing number of vehicles on the roads has resulted in several challenges, including increased accident rates, fuel consumption, pollution, travel time, and driving stress. However, recent advancements in intelligent vehicle technologies, such as sensors and communication networks, have the potential to revolutionize road traffic and address these challenges. In particular, the concept of platooning for autonomous vehicles, where they travel in groups at high speeds with minimal distances between them, has been proposed to enhance the efficiency of road traffic. To achieve this, it is essential to determine the precise position of vehicles relative to each other. Global positioning system (GPS) devices have an intended positioning error that might increase due to various conditions, e.g., the number of available satellites, nearby buildings, trees, driving into tunnels, etc., making it difficult to compute the exact relative position between two vehicles. To address this challenge, this paper proposes a new architectural framework to improve positioning accuracy using images captured by onboard cameras. It presents a novel algorithm and performance results for vehicle positioning based on GPS and video data. This approach is decentralized, meaning that each vehicle has its own camera and computing unit and communicates with nearby vehicles.
Abstract The inflorescences of industrial hemp (Cannabis sativa L.) represent a consistent by-product that is underutilized. Moving from the concept that this plant part has evolved as a natural weapon against phytophagous insects, secreting important secondary metabolites such as cannabinoids and volatile terpenes, herein we assayed the potential of its essential oil as a botanical insecticide. For the purpose, the essential oil was obtained by fresh inflorescences of hemp (monoecious cv. Felina 32) by steam-distillation and analysed by gas chromatography (GC-FID) and gas chromatography-mass spectrometry (GC–MS). The oil was tested against the filariasis vector Culex quinquefasciatus, the peach-potato aphid Myzus persicae, the housefly Musca domestica and the tobacco cutworm Spodoptera littoralis. To prove its harmlessness on non-target invertebrates, it was tested on the multicolored Asian lady beetle, Harmonia axyridis, and Eisenia fetida earthworms, and compared with α-cypermethrin as the positive control. The essential oil composition was dominated by monoterpene and sesquiterpene hydrocarbons, with (E)-caryophyllene (45.4%), myrcene (25.0%) and α-pinene (17.9%) as the most abundant compounds. Results from insecticidal tests showed that the essential oil from inflorescences of industrial hemp cv Felina 32 was highly toxic to M. persicae aphids (LC50 of 3.5 mL L−1) and M. domestica flies (43.3 μg adult−1), while toxicity was moderate towards S. littoralis larvae (152.3 μg larva−1), and scarce against C. quinquefasciatus larvae (LC50 of 252.5 mL L−1) and adults (LC50 > 500 μg cm−2). Contrary to α-cypermethrin, the hemp cv Felina 32 essential oil was not toxic to non-target invertebrate species, including 3rd instar larvae and adults of H. axyridis ladybugs and adults of E. fetida earthworms. Taken together our results shed light on the possible utilization of the crop residue of industrial hemp as a source of environmental-friendly botanical insecticides to be used in Integrated Pest Management and organic agriculture, particularly to manage aphid and housefly populations.
Dzakiya Ishmatul Ulya, Anang Kunaefi, Dwi Rolliawati
et al.
QRIS, a mobile payment transaction system standardized by Bank Indonesia, has become the subject of extensive public discourse on Twitter. Employing VADER for sentiment analysis and LDA for topic modeling, this study aims to capture the nuanced perspectives of the Indonesian public toward QRIS. Our methodology includes real human validation for tweets that have been initially labeled by VADER. Our unique contributions lie in employing a mixed-methods approach for comprehensive sentiment and topic analysis, as well as making our dataset publicly available for future research. We achieve a sentiment labeling accuracy of 81.66%, uncovering that 67% of the sentiment towards QRIS is positive, 28.2% negative, and 4.17% neutral. Positive tweets mostly cover six dominant topics with a value of 0.488037, whereas negative sentiments are concentrated around three dominant topics with a value of 0.383938. These findings not only affirm the generally positive public response towards QRIS but also highlight areas requiring attention for its continued success. Our study translates these insights into actionable recommendations, aiming to provide a multidimensional understanding that stakeholders can leverage for system enhancement. This study serves as a foundation for future works in sentiment analysis and public opinion mining related to financial technologies, particularly in the Indonesian context.
Electrical engineering. Electronics. Nuclear engineering, Information technology
Han Li, Abinet Tesfaye Eseye, Jianhua Zhang
et al.
This paper presents a day-ahead optimal energy management strategy for economic operation of industrial microgrids with high-penetration renewables under both isolated and grid-connected operation modes. The approach is based on a regrouping particle swarm optimization (RegPSO) formulated over a day-ahead scheduling horizon with one hour time step, taking into account forecasted renewable energy generations and electrical load demands. Besides satisfying its local energy demands, the microgrid considered in this paper (a real industrial microgrid, “Goldwind Smart Microgrid System” in Beijing, China), participates in energy trading with the main grid; it can either sell power to the main grid or buy from the main grid. Performance objectives include minimization of fuel cost, operation and maintenance costs and energy purchasing expenses from the main grid, and maximization of financial profit from energy selling revenues to the main grid. Simulation results demonstrate the effectiveness of various aspects of the proposed strategy in different scenarios. To validate the performance of the proposed strategy, obtained results are compared to a genetic algorithm (GA) based reference energy management approach and confirmed that the RegPSO based strategy was able to find a global optimal solution in considerably less computation time than the GA based reference approach.
Abstract The focal research topics in industrial marketing management have evolved significantly since IMM was first published in 1971. Part of this evolution has been the inclusion of research in supply chain management, which is the focus of this paper. The purpose of this paper is to assess themes across the classic articles in supply chain management (SCM) published in IMM, and explore how the field of SCM has evolved since their publication. A systematic literature review was used to assess SCM research in IMM, identify themes and publication patterns, and use these themes to assess gaps in extant research and opportunities for future research. Results of the literature review show that over 58% of the SCM research published in IMM has focused on buyer-supplier relationships and other purchasing and supply management topics. These will continue to be key topics in IMM. In addition, our literature review suggests opportunities for more SCM research in IMM around sustainability, logistics, and manufacturing innovation. These areas within the SCM umbrella are under-researched, yet yield significant opportunities for future research in a time of constant change within companies' distribution models.
Today, with the rapid development of mobile Internet technology, the operation of enterprises is basically based on the mobile network platform. Therefore, the study of the evaluation system of SMEs’ industrial cluster management performance based on wireless network development is proposed. After briefly describing the relevant research of industrial cluster performance evaluation, the knowledge innovation of wireless network era is the core of SME industrial cluster management, and a set of industrial cluster management performance evaluation index system has been constructed. Based on this, a comprehensive evaluation method based on neural network algorithm is designed. In a subsequent experiment, it is demonstrated that this method can evaluate the level of cluster management performance.
The Internet of Things (IoT) is the global network of interrelated physical devices such as sensors, actuators, smart applications, objects, computing devices, mechanical machines, and people that are becoming an essential part of the internet. In an industrial environment, these devices are the source of data which provide abundant information in manufacturing processes. Nevertheless, the massive, heterogeneous, and time-sensitive nature of the data brings substantial challenges to the real-time collection, processing, and decision making. Therefore, this paper presents a framework of an IoT-based Industrial Data Management System (IDMS) which can manage the huge industrial data, support online monitoring, and control smart manufacturing. The framework contains five basic layers such as physical, network, middleware, database, and application layers to provide a service-oriented architecture for the end users. Experimental results from a smart factory case study demonstrate that the framework can manage the regular data and urgent events generated from various factory devices in the distributed industrial environment through state-of-the-art communication protocols. The collected data is converted into useful information which improves productivity and the prognosis of production lines.