Hasil untuk "Industrial directories"

Nigar Hashimzade, Haoran Sun

Industrial policy has returned to the centre of economic governance, particularly in the high-tech sectors where positive network externalities in demand make market dominance self-reinforcing. This paper studies the welfare effects of an industrial policy targeting a sector with network externalities in a two-country model with strategic trade and R&D investment. We show how the welfare consequences of this policy are determined by the interaction between the strength of the externality, the type of R&D, and the degree of product differentiation between the home and the imported goods. When externalities are weak or the goods are close substitutes, the business-stealing effect produces a race to the bottom that dissipates more surplus than it creates. Under sufficiently strong externalities and weak substitutability or complementarity of the goods, industrial policy competition can make both countries simultaneously better off compared to the laissez-faire outcome because of the mutual business-enhancement effect. The case is stronger for the product innovation than for the process innovation, as the former directly affects the demand and triggers a stronger network effects than the latter which operates indirectly through the supply. Thus, the network externalities create an opportunity for a win-win industrial policies, but its realisation depends on the market structure and the nature of innovation.

Mingchun Sun, Rongqiang Zhao, Zhennan Huang et al.

In industrial scenarios, data augmentation is an effective approach to improve model performance. However, its benefits are not unidirectionally beneficial. There is no theoretical research or established estimation for the optimal sample size (OSS) in augmentation, nor is there an established metric to evaluate the accuracy of OSS or its deviation from the ground truth. To address these issues, we propose an information-theoretic optimal sample size estimation (IT-OSE) to provide reliable OSS estimation for industrial data augmentation. An interval coverage and deviation (ICD) score is proposed to evaluate the estimated OSS intuitively. The relationship between OSS and dominant factors is theoretically analyzed and formulated, thereby enhancing the interpretability. Experiments show that, compared to empirical estimation, the IT-OSE increases accuracy in classification tasks across baseline models by an average of 4.38%, and reduces MAPE in regression tasks across baseline models by an average of 18.80%. The improvements in downstream model performance are more stable. ICDdev in the ICD score is also reduced by an average of 49.30%. The determinism of OSS is enhanced. Compared to exhaustive search, the IT-OSE achieves the same OSS while reducing computational and data costs by an average of 83.97% and 93.46%. Furthermore, practicality experiments demonstrate that the IT-OSE exhibits generality across representative sensor-based industrial scenarios.

Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas et al.

The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems.

Juha-Matti Runtti, Usman Virk, Pekka Kyosti et al.

6G radio access architecture is envisioned to contain a network of short-range in-X subnetworks with enhanced capabilities to provide efficient and reliable wireless connectivity. Short-range communications in industrial environments are actively researched at the so-called mid-bands or FR3, e.g., in the EU SNS JU 6G-SHINE project. In this paper, we analyze omni-directional radio channel measurements at 10--12 GHz frequency band to estimate large-scale channel characteristics including power-delay profile, delay spread, K-factor, and pathloss for 254 radio links measured in the Industrial Production Lab at Aalborg University, Denmark. Moreover, we perform a comparison of estimated parameters with those of the 3GPP Indoor Factory channel model.

Aaditya Baranwal, Abdul Mueez, Jason Voelker et al.

Large-scale Vision-Language Models (VLMs) have transformed general-purpose visual recognition through strong zero-shot capabilities. However, their performance degrades significantly in niche, safety-critical domains such as industrial spill detection, where hazardous events are rare, sensitive, and difficult to annotate. This scarcity -- driven by privacy concerns, data sensitivity, and the infrequency of real incidents -- renders conventional fine-tuning of detectors infeasible for most industrial settings. We address this challenge by introducing a scalable framework centered on a high-quality synthetic data generation pipeline. We demonstrate that this synthetic corpus enables effective Parameter-Efficient Fine-Tuning (PEFT) of VLMs and substantially boosts the performance of state-of-the-art object detectors such as YOLO and DETR. Notably, in the absence of synthetic data (SynSpill dataset), VLMs still generalize better to unseen spill scenarios than these detectors. When SynSpill is used, both VLMs and detectors achieve marked improvements, with their performance becoming comparable. Our results underscore that high-fidelity synthetic data is a powerful means to bridge the domain gap in safety-critical applications. The combination of synthetic generation and lightweight adaptation offers a cost-effective, scalable pathway for deploying vision systems in industrial environments where real data is scarce/impractical to obtain. Project Page: https://synspill.vercel.app

Kessia Nepomuceno, Fabio Petrillo

Context: Fairness in systems has emerged as a critical concern in software engineering, garnering increasing attention as the field has advanced in recent years. While several guidelines have been proposed to address fairness, achieving a comprehensive understanding of research solutions for ensuring fairness in software systems remains challenging. Objectives: This paper presents a systematic literature mapping to explore and categorize current advancements in fairness solutions within software engineering, focusing on three key dimensions: research trends, research focus, and viability in industrial contexts. Methods: We develop a classification framework to organize research on software fairness from a fresh perspective, applying it to 95 selected studies and analyzing their potential for industrial adoption. Results: Our findings reveal that software fairness research is expanding, yet it remains heavily focused on methods and algorithms. It primarily focuses on post-processing and group fairness, with less emphasis on early-stage interventions, individual fairness metrics, and understanding bias root causes. Additionally fairness research remains largely academic, with limited industry collaboration and low to medium Technology Readiness Level (TRL), indicating that industrial transferability remains distant. Conclusion: Our results underscore the need to incorporate fairness considerations across all stages of the software development life-cycle and to foster greater collaboration between academia and industry. This analysis provides a comprehensive overview of the field, offering a foundation to guide future research and practical applications of fairness in software systems.

Ruike Lyu, Anna Li, Jianxiao Wang et al.

In many countries, declining demand in energy-intensive industries such as cement, steel, and aluminum is leading to industrial overcapacity. Although industrial overcapacity is traditionally envisioned as problematic and resource-wasteful, it could unlock energy-intensive industries' flexibility in electricity use. Here, using China's aluminum smelting industry as a case study, we evaluate the system-level cost-benefit of retaining energy-intensive industries overcapacity for flexible electricity use in decarbonized energy systems. We find that overcapacity can enable aluminum smelters to adopt a seasonal operation paradigm, ceasing production during winter load peaks that are exacerbated by heating electrification and renewable seasonality. This seasonal operation paradigm could reduce the investment and operational costs of China's decarbonized electricity system by 23-32 billion CNY/year (11-15% of the aluminum smelting industry's product value), sufficient to offset the increased smelter maintenance and product storage costs associated with overcapacity. It may also provide an opportunity for seasonally complementary labor deployment across the aluminum smelting and thermal power generation sectors, offering a potential pathway for mitigating socio-economic disruptions caused by industrial restructuring and energy decarbonization.

Afrasyab kheirdast, Seyed Ali Jozi, Sahar Rezaian et al.

Background and objective In providing fire management strategies using international and integrated methods, no study has been conducted in district 19 of Tehran, Iran, so far. Therefore, this study aims to propose strategies for fire management and reducing the vulnerability of worn-out buildings in this district using the A’WOT analysis and Freeman’s stakeholder matrix. Method This is a strategic and applied study. Participants included 20 crisis management experts and managers of the fire departments. The information was collected using interviews and brainstorming. The AʼWOT analysis, a hybrid analytic hierarchy process (AHP)-SWOT method, was used. After determining the internal factors (strength and weakness) and external factors (threat and opportunity), strategies were identified and ranked in a hierarchical manner. Freeman’s stakeholder matrix was used to present the strategic fire management plans. The sensitivity analysis was done using Expert choice software, version 11.  Results The final score obtained from the internal factor evaluation (IFE) and external factor evaluation (EFE) matrices showed that the strategies were offensive and competitive, respectively. Based on the evaluation of quantitative strategic planning, IFE, and EFE matrices, the strategic plans were finally ranked as follows: “the use of local expert forces familiar with the region” with a score of 5.22, “using movable conex boxes to command operations” with a score of 5.08 and “building new stations with advanced firefighting equipment” with a score of 4.96. Based on Freeman’s stakeholder matrix, the offensive strategies “building new fire stations with advanced firefighting equipment,” with a score of 107 and “visiting worn-out buildings and holding a maneuver to increase the personnel operational capacity” with a score of 98 were placed in the first and second ranks, respectively. Conclusion By examining the weaknesses, strengths, opportunities, and threats in district 19 of Tehran, we presented strategies to reduce the fire vulnerability of worn-out buildings. The AʼWOT analysis is a proper scientific and operational method for fire management in worn-out buildings in this district. Freeman’s stakeholder matrix can be a suitable model for ranking fire management strategies. Based on this matrix, offensive strategies to manage the fires in the study area can be predicted and implemented.

Alireza Barekatain, Hamed Rahimi Nohooji, Holger Voos

The aim of this study is to investigate an automated industrial manipulation pipeline, where assembly tasks can be flexibly adapted to production without the need for a robotic expert, both for the vision system and the robot program. The objective of this study is first, to develop a synthetic-dataset-generation pipeline with a special focus on industrial parts, and second, to use Learning-from-Demonstration (LfD) methods to replace manual robot programming, so that a non-robotic expert/process engineer can introduce a new manipulation task by teaching it to the robot.

Damian Hostettler

Purpose: Industrial robots allow manufacturing companies to increase productivity and remain competitive. For robots to be used, they must be accepted by operators on the one hand and bought by decision-makers on the other. The roles involved in such organizational processes have very different perspectives. It is therefore essential for suppliers and robot customers to understand these motives so that robots can successfully be integrated on manufacturing shopfloors. Methodology: We present findings of a qualitative study with operators and decision-makers from two Swiss manufacturing SMEs. Using laddering interviews and means-end analysis, we compare operators' and deciders' relevant elements and how these elements are linked to each other on different abstraction levels. These findings represent drivers and barriers to the acquisition, integration and acceptance of robots in the industry. Findings: We present the differing foci of operators and deciders, and how they can be used by demanders as well as suppliers of robots to achieve robot acceptance and deployment. First, we present a list of relevant attributes, consequences and values that constitute robot acceptance and/or rejection. Second, we provide quantified relevancies for these elements, and how they differ between operators and deciders. And third, we demonstrate how the elements are linked with each other on different abstraction levels, and how these links differ between the two groups.

Maksim Papenkov

Accurate industry classification is critical for many areas of portfolio management, yet the traditional single-industry framework of the Global Industry Classification Standard (GICS) struggles to comprehensively represent risk for highly diversified multi-sector conglomerates like Amazon. Previously, we introduced the Multi-Industry Simplex (MIS), a probabilistic extension of GICS that utilizes topic modeling, a natural language processing approach. Although our initial version, MIS-1, was able to improve upon GICS by providing multi-industry representations, it relied on an overly simple architecture that required prior knowledge about the number of industries and relied on the unrealistic assumption that industries are uncorrelated and independent over time. We improve upon this model with MIS-2, which addresses three key limitations of MIS-1 : we utilize Bayesian Non-Parametrics to automatically infer the number of industries from data, we employ Markov Updating to account for industries that change over time, and we adjust for correlated and hierarchical industries allowing for both broad and niche industries (similar to GICS). Further, we provide an out-of-sample test directly comparing MIS-2 and GICS on the basis of future correlation prediction, where we find evidence that MIS-2 provides a measurable improvement over GICS. MIS-2 provides portfolio managers with a more robust tool for industry classification, empowering them to more effectively identify and manage risk, particularly around multi-sector conglomerates in a rapidly evolving market in which new industries periodically emerge.

Reza Khanmohammadi, Simerjot Kaur, Charese H. Smiley et al.

This paper investigates the relationship between scientific innovation in biomedical sciences and its impact on industrial activities, focusing on how the historical impact and content of scientific papers influenced future funding and innovation grant application content for small businesses. The research incorporates bibliometric analyses along with SBIR (Small Business Innovation Research) data to yield a holistic view of the science-industry interface. By evaluating the influence of scientific innovation on industry across 10,873 biomedical topics and taking into account their taxonomic relationships, we present an in-depth exploration of science-industry interactions where we quantify the temporal effects and impact latency of scientific advancements on industrial activities, spanning from 2010 to 2021. Our findings indicate that scientific progress substantially influenced industrial innovation funding and the direction of industrial innovation activities. Approximately 76% and 73% of topics showed a correlation and Granger-causality between scientific interest in papers and future funding allocations to relevant small businesses. Moreover, around 74% of topics demonstrated an association between the semantic content of scientific abstracts and future grant applications. Overall, the work contributes to a more nuanced and comprehensive understanding of the science-industry interface, opening avenues for more strategic resource allocation and policy developments aimed at fostering innovation.

Leonid Savitskyi , Serhii Beznosenko , Roman Gorbach

Зважаючи на значущу роль інформаційно-комунікаційних систем на сучасному театрі бойових дій і враховуючи отриманий досвід ведення бойових операцій на сході України та після повномасштабного вторгнення російської федерації 24 лютого 2022 року, кібербезпека набуває надзвичайно важливого значення. Метою статті є огляд існуючих алгоритмів захисту та висловлення концептуальних поглядів на побудову систем захисту від кібератак із використанням методів штучного інтелекту. У статті застосовано теоретичні методи, а саме аналіз публікацій і досліджень за тематикою протидії кібератакам та захист систем передачі інформації. Також були використані загальнонаукові методи досліджень, серед яких використано аналітичні методи в оцінюванні ефективності системи, що розглядається у статті. Під час побудови графіків вжито елементи статистики та графо-аналітичні методи. Застосований методичний підхід дав змогу проаналізувати матеріали за темою дослідження, піддати аналізу отримані дані та удосконалити існуючі концептуальні погляди. У статті викладено сутність таких підходів до вирішення проблеми забезпечення безпеки інформаційно-комунікаційних систем як фрагментарний і комплексний. Також ретельно проаналізовано основні методи виявлення кібератак, а саме сигнатурний аналіз (метод виявлення зловживань) та метод виявлення аномалій. За результатами ретельного дослідження цих методів можна зазначити, що для досягнення високого рівня захищеності інформаційних ресурсів в інформаційно-комунікаційних системах обов’язково слід застосовувати методи, що базуються на виявленні аномалій. Ці методи проявляють неперевершену здатність виявляти найновіші кібератаки 0-day. Крім того, у статті проведено всебічний огляд основних засобів для виявлення та протидії кібератакам. Серед них такі технології: «Система виявлення вторгнень/Система запобігання вторгненням» (Intrusion Detection System/Intrusion Prevention System), «Мережевий екран» (Firewall), антивірусні програми та технології зі штучним інтелектом «Управління подіями та інформацією про безпеку» (Security information and event management). Пропонується для підвищення ефективності систем захисту в галузі кібербезпеки застосовувати елементи штучного інтелекту. Крім того, проведено огляд вже відомих даних і рішень у сфері кібербезпеки та виклад концептуальних поглядів авторів на застосування штучного інтелекту у цій сфері. На основі цих даних запропоновані нові та більш досконалі рішення. Основна мета інтегрування штучного інтелекту до системи захисту від кібератак полягає у його спроможності виявляти невідомі раніше кібератаки на основі сигнатур уже відомих атак. У роботі авторами також запропоновано визначення терміну «шаблон атаки». Спираючись на розглянуті в статті методи та запропоновані рішення можна покращити кіберзахист воєнно-оборонної сфери. Робота сприяє вдосконаленню процесів захисту від кібератак, що є критично важливим як для військових, так і для цивільних структур. Отже, ця стаття не лише спрямована на розвиток теоретичних основ захисту від кібернетичних загроз, але й має безпосередню практичну значущість у підвищенні рівня безпеки та ефективності захисних механізмів в інформаційно-комунікаційних системах. Впровадження такого підходу не лише дасть змогу істотно підвищити рівень кібернетичної захищеності в інформаційно-комунікаційних системах, але й може стати платформою для автоматичного створення експлойтів і сигнатур кібератак на підставі виявлених аномалій.

Ye Mingzhi

In recent years, human motion recognition in computer vision has become a hot research direction in this field. Based on 2D human motion recognition technology, real-time extraction of motion features from 2D planes is used to recognize human movements. This method can only learn the position contour and color information of the image. It cannot directly reflect the motion situation, which results in low recognition accuracy and efficiency. In response to this issue, this study proposes a combination method of motion recognition and 3D pose estimation to recognize and classify basketball movements. First, the 2D skeleton model is obtained by extracting the feature information in the video action, which is converted into a 3D model, and the model is replaced by the time-space convolutional network to establish a human action recognition model. The experiment showed that when the number of iterations reached 6, the accuracy of the spatio-temporal graph convolutional network algorithm model reached 92%. Comparing the accuracy rates of different algorithm models, the average accuracy rates of convolutional neural network, long short memory network, graph convolution, long short model of action recognition and graph convolution model of action recognition were 61.6%, 65.4%, 72.5%, 76.8% and 90.3% respectively. The results show that the proposed 3D recognition algorithm can accurately recognize different basketball movements. This study can provide reference for basketball coaches and athletes in basketball training.

T. Meynard, H. Foch, P. Thomas et al.

E. Agbaglo, J. Afful

In recent times, the language in public spaces (as seen in street names, school names, names of buildings, names of metro stations, names of tourist attractions, and commercial signs) has attracted scholarly attention in onomastics, with the focus on how it reflects the linguistic situation of urban landscapes and how it can be used to construct several identities. The present study aimed to investigate names of hotels in Accra – the capital city of Ghana, with considerable financial, cultural, and industrial significance – using Landry & Bouris’s (1997) Linguistic Landscape as a theory. The data comprises 160 hotel names accessed from the website of Yello Ghana, a well-known business directory. The analysis revealed, first, that most of the hotels deployed English monolingual names, with a few utilising bilingual names. Closely allied to this finding is the trend towards globalisation, as captured in some names of hotels. These key findings have implications for the scholarship in onomastics, urban landscape, language policy and planning, and further research.

Winne Ayunda Gaiska, Naufal Gama Affandyar, Muhammad yasin

This study aims to find out how the distribution of spatial patterns of the development of small and medium industries and household industries in Mojokerto Regency. The research method used is the Data Collection Method with the Annual Large and Medium Industry Survey (IBS) carried out in full for all industrial companies classified as large and medium which are recorded in the BPS Industry Directory (complete enumeration) and several industry surveys conducted by researchers by conducting a sample and obtained the results that making small and medium industries and home industries a strategy for industrial development in triggering economic growth in Mojokerto Regency.

Reynaldi Dwi Junianta, S. Iriani, D. Nugrohoseno et al.

This research analyzed the influence of disability-friendly corporate branding on corporate brand equity. Corporate branding is built through corporate values, corporate image, and corporate culture. This research uses a quantitative approach, with a population of corporate employees and customers in East Java. A purposive sampling technique was used to take samples from the population with the following criteria: (1) located in the East Java region; (2) an area with Special Needs High School with more than 50 students; (3) data recorded in the industrial directory in East Java Province region. A total of 180 respondents were obtained through distributing questionnaires online. The research data was then processed using SEM-AMOS to carry out analysis. The research results show that there is a significant influence of disability-friendly corporate branding on corporate brand equity. This research conducted research on company efforts to create disability-friendly branding for their companies using a corporate branding approach. Companies need to carry out corporate branding by through implementing the values of inclusivity, a corporate image that cares for people with disabilities, and a culture of equality. This will help companies to increase corporate brand equity which can provide company benefits that contribute to the implementation of SDGs 4, 8, and 10.

Ibnu Abdul Ghoni, Josafat Gracia Ginting, Sopiah

Rapid industrialization has caused environmental degradation in recent years. Industrial activities produce global warming, climate change, drought, forest fires, ecological degradation, and environmental damage. Many companies think their business has no environmental impact, yet it does. The organization finally resolved to limit environmental pollution and save resources after seeing the phenomenon. Modern organizations no longer prioritize profitability and business benefits. Companies today prioritize ecologically friendly practices. This Systematic Literature Review collects some of the literature that discusses the influence of GHRM on EP. The research objective is to explore the literature related to the effect of GHRM on EP. This research was compiled using Preferred Reporting Items for Systematic Review and Meta-Analysis. The data collection method uses data inclusion. Some of the literature obtained was 75 literature taken from three sources, namely The Directory of Open Access Journals (DOAJ), Google Scholar, and Scopus with the help of Publish or Perish software. Then a screening was carried out and as many as 30 pieces of literature were obtained. The results show that of the 30 pieces of literature, 27 kinds of literature provide GHRM results with a positive and significant effect on EP. 1 literature shows that GHRM has no significant effect on EP, 1 literature makes GHRM a moderating variable and 1 literature makes GHRM a mediating variable.

Alexis Newton, Kaustubh Dhole

A major shift from skilled to unskilled workers was one of the many changes caused by the Industrial Revolution, when the switch to machines contributed to decline in the social and economic status of artisans, whose skills were dismembered into discrete actions by factory-line workers. We consider what may be an analogous computing technology: the recent introduction of AI-generated art software. AI art generators such as Dall-E and Midjourney can create fully rendered images based solely on a user's prompt, just at the click of a button. Some artists fear if the cheaper price and conveyor-belt speed that comes with AI-produced images is seen as an improvement to the current system, it may permanently change the way society values/views art and artists. In this article, we consider the implications that AI art generation introduces through a post-industrial revolution historical lens. We then reflect on the analogous issues that appear to arise as a result of the AI art revolution, and we conclude that the problems raised mirror those of industrialization, giving a vital glimpse into what may lie ahead.