Oudemansiella apalosarca, a newly identified edible fungus, exhibits industrial cultivation potential because of its short production cycle and lack of a casing layer. However, the absence of genomic data has hampered its development and varietal enhancement. This study reports the first genome sequence of O. apalosarca, comprising 53.13 Mb across 27 scaffolds and 14,650 protein-coding genes, with superior scaffold N50 and BUSCO values compared to those of other Oudemansiella genomes. Phylogenetic analysis of single-copy orthologous proteins from 25 fungal genomes revealed close relations to O. raphanipes and Mucidula mucidula, with significant protein collinearity within the Physalacriaceae family. Cultivation in complete darkness yielded pure white, small-cap, and long-stipe fruiting bodies, indicating industrial advantages. Differential transcriptome analysis of the cap and stipe under varying light conditions identified key genes and pathways regulating phenotypic changes. Kyoto Encyclopedia of Genes and Genomes (KEGG) assessment and gene set enrichment analysis (GSEA) revealed significant negative regulation of DNA replication pathway genes in the cap, with the downregulation of 10 cell cycle and mismatch repair genes. Genes related to cell wall formation and carbon metabolism were upregulated, thus promoting stipe elongation. The tyrosine metabolism pathway influenced cap coloration, with tyrosinase identified as a multicopy gene. Phylogenetic analysis revealed diverse evolutionary origins. Key tyrosinase-related genes A6_A10477 and A6_A09603 were overexpressed in the light, revealing their role in melanin formation. In summary, this study provides genomic resources for O. apalosarca breeding improvement and elucidates light-induced regulatory mechanisms in its development, thereby providing theoretical and technical support for industrial applications.
Shu-Chuan Hsu, Ying-Kai Liao, Kuo-Chung Huang
et al.
This study employs a meta-analytic approach to synthesize empirical evidence on the psychological and behavioral determinants of fast fashion consumption. Integrating the Theory of Planned Behavior (TPB) with brand-related constructs—perceived scarcity, perceived quality, and self-congruity—this research examines how these factors shape consumer attitudes, subjective norms, perceived behavioral control, consumption intentions, brand loyalty, and word-of-mouth. Using studies published between 2004 and 2024, a random-effects meta-analysis reveals that brand attitude is the strongest predictor of purchase intention, while self-congruity with fashion brands significantly enhances all TPB components. Perceived quality exerts a cross-cutting influence on both cognitive and social evaluations, reinforcing the multidimensional nature of consumer judgments. The findings extend the TPB framework by embedding symbolic and perceptual brand dimensions, offering a more comprehensive explanatory model of fashion consumption. From a managerial perspective, the results suggest that marketing strategies emphasizing authentic scarcity cues and alignment with consumers’ self-identity can strengthen emotional attachment, perceived control, and loyalty. The study concludes with theoretical and practical implications for designing culturally sensitive and identity-driven branding strategies in the fast fashion sector.
ABSTRACT This article presents an overview of how industrial relations have been shaped by constitutional devolution across the United Kingdom. It shows that the devolved national governments in Scotland, Wales and Northern Ireland and English regional authorities led by Metro Mayors, have developed distinctive industrial relations policies that have led to variation in labour market regulation. These policies have typically been developed under the rubric of ‘fair work’ and have been pursued through a variety of methods. The latter include acting as a ‘good employer’, legislation, public procurement, and soft forms of regulation such as good employment charters which have been widely adopted by devolved authorities. The article concludes by reviewing the debate over devolution within industrial relations and notes how political change might foster further experimentation in the future.
As an emerging technology that enables seamless integration between the physical and virtual worlds, the Metaverse has great potential to be deployed in the industrial production field with the development of extended reality (XR) and next-generation communication networks. This deployment, called the Industrial Metaverse, is used for product design, production operations, industrial quality inspection, and product testing. However, there lacks of in-depth understanding of the enabling technologies associated with the Industrial Metaverse. This encompasses both the precise industrial scenarios targeted by each technology and the potential migration of technologies developed in other domains to the industrial sector. Driven by this issue, in this article, we conduct a comprehensive survey of the state-of-the-art literature on the Industrial Metaverse. Specifically, we first analyze the advantages of the Metaverse for industrial production. Then, we review a collection of key enabling technologies of the Industrial Metaverse, including blockchain (BC), digital twin (DT), 6G, XR, and artificial intelligence (AI), and analyze how these technologies can support different aspects of industrial production. Subsequently, we present numerous formidable challenges encountered within the Industrial Metaverse, including confidentiality and security concerns, resource limitations, and interoperability constraints. Furthermore, we investigate the extant solutions devised to address them. Finally, we briefly outline several open issues and future research directions of the Industrial Metaverse.
Md Bokhtiar Al Zami, Shaba Shaon, Vu Khanh Quy
et al.
Industrial networks are undergoing rapid transformation driven by the convergence of emerging technologies that are revolutionizing conventional workflows, enhancing operational efficiency, and fundamentally redefining the industrial landscape across diverse sectors. Amidst this revolution, Digital Twin (DT) emerges as a transformative innovation that seamlessly integrates real-world systems with their virtual counterparts, bridging the physical and digital realms. In this article, we present a comprehensive survey of the emerging DT-enabled services and applications across industries, beginning with an overview of DT fundamentals and its components to a discussion of key enabling technologies for DT. Different from literature works, we investigate and analyze the capabilities of DT across a wide range of industrial services, including data sharing, data offloading, integrated sensing and communication, content caching, resource allocation, wireless networking, and metaverse. In particular, we present an in-depth technical discussion of the roles of DT in industrial applications across various domains, including manufacturing, healthcare, transportation, energy, agriculture, space, oil and gas, as well as robotics. Throughout the technical analysis, we delve into real-time data communications between physical and virtual platforms to enable industrial DT networking. Subsequently, we extensively explore and analyze a wide range of major privacy and security issues in DT-based industry. Taxonomy tables and the key research findings from the survey are also given, emphasizing important insights into the significance of DT in industries. Finally, we point out future research directions to spur further research in this promising area.
In the field of industrial inspection, Multimodal Large Language Models (MLLMs) have a high potential to renew the paradigms in practical applications due to their robust language capabilities and generalization abilities. However, despite their impressive problem-solving skills in many domains, MLLMs' ability in industrial anomaly detection has not been systematically studied. To bridge this gap, we present MMAD, the first-ever full-spectrum MLLMs benchmark in industrial Anomaly Detection. We defined seven key subtasks of MLLMs in industrial inspection and designed a novel pipeline to generate the MMAD dataset with 39,672 questions for 8,366 industrial images. With MMAD, we have conducted a comprehensive, quantitative evaluation of various state-of-the-art MLLMs. The commercial models performed the best, with the average accuracy of GPT-4o models reaching 74.9%. However, this result falls far short of industrial requirements. Our analysis reveals that current MLLMs still have significant room for improvement in answering questions related to industrial anomalies and defects. We further explore two training-free performance enhancement strategies to help models improve in industrial scenarios, highlighting their promising potential for future research.
Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames, and existing large-scale VAD researches primarily focus on road traffic and human activity scenes. In industrial scenes, there are often a variety of unpredictable anomalies, and the VAD method can play a significant role in these scenarios. However, there is a lack of applicable datasets and methods specifically tailored for industrial production scenarios due to concerns regarding privacy and security. To bridge this gap, we propose a new dataset, IPAD, specifically designed for VAD in industrial scenarios. The industrial processes in our dataset are chosen through on-site factory research and discussions with engineers. This dataset covers 16 different industrial devices and contains over 6 hours of both synthetic and real-world video footage. Moreover, we annotate the key feature of the industrial process, ie, periodicity. Based on the proposed dataset, we introduce a period memory module and a sliding window inspection mechanism to effectively investigate the periodic information in a basic reconstruction model. Our framework leverages LoRA adapter to explore the effective migration of pretrained models, which are initially trained using synthetic data, into real-world scenarios. Our proposed dataset and method will fill the gap in the field of industrial video anomaly detection and drive the process of video understanding tasks as well as smart factory deployment.
Egocentric vision aims to capture and analyse the world from the first-person perspective. We explore the possibilities for egocentric wearable devices to improve and enhance industrial use cases w.r.t. data collection, annotation, labelling and downstream applications. This would contribute to easier data collection and allow users to provide additional context. We envision that this approach could serve as a supplement to the traditional industrial Machine Vision workflow. Code, Dataset and related resources will be available at: https://github.com/Vivek9Chavan/EgoVis24
Purpose. The goal of the study is to develop scientific and methodological approaches and practical recommendations for improving the economic mechanism of reproduction of the natural resource potential of agro-industrial production (AIP).
Results. The article substantiates the priorities for improving the reproduction of the natural resource potential of agro-industrial production on the example of Ukraine. The conceptual foundations of forming an information system for ensuring the rational use and reproduction of natural resources in the agrarian sector are highlighted in detail. The conceptual foundations of forming a system of principles, goals and criteria for information support of the processes of reproduction of the natural resource potential of AIP, the environmental and economic mechanism for managing the features of environmental and innovative development of the state are considered in more detail. For optimal redistribution of the resource component of agro-industrial production, methodological recommendations for the economic assessment of natural resources in the agrarian sector are presented. In order to ensure the rational reproduction of natural resources of the agricultural sector, the features of the economic assessment of natural resources of agro-industrial production are analyzed. The essence and content of the concept of the natural resource potential of the agricultural sector is revealed, under which it is proposed to understand a complex system of natural objects, the users of which are enterprises of agro-industrial production with existing technology and socio-economic relations, a component of the environmental and economic potential of the state. This made it possible to develop a system of information support for the reproduction of the natural resource potential of AIP based on a systematic analysis of environmental and economic relations of nature management in the agrarian sector.
Scientific novelty. The economic category of the natural resource potential of the AIP is developed in the direction of reflecting the structure and relationships between its elements; the definition of the economic assessment of natural resource elements is deepened, which is considered in the study as a criterion indicator of the comparative economic efficiency of the use and reproduction of the natural resource potential of the AIP; scientific and practical foundations of information support for economic processes of reproduction of natural resources in the agricultural sector are improved on the basis of development of an information system for expert assessment of their quality, monitoring, audit, and passport of the environmental management object.
Practical value. The results of the study are used to improve the economic mechanism of reproduction of natural resources of the agrarian sector in the region. The scientific and practical recommendations are used to determine the indicators of economic assessment of the elements of natural resources of the agrarian sector; to calculate the economic efficiency of conservation and reproduction of the natural resource potential of the agro-industrial complex; to provide financial support for regional reproduction programmes and processes; to develop tools and methods of agro-ecological regulation.
The development of today's modernization era is indirectly caused by the encouragement of the dynamics of life. One manifestation of the dynamics of life is in the form of highly sophisticated digitalization that can help meet people's needs. Today's digitalization has an important role in all aspects and brings the world closer to reach without limits. The use of technology is encouraged for the benefit of society with fast and rapid business practices. Legal relations do not have to use face-to-face, transactions and legal relations are sufficient to be carried out using internet facilities through computer technology and communication technology, so that the limitations on distance reach can be removed and facilitate innovation in human life. The problem that will arise in the future is whether the notary is then ready to anticipate these changes, can this change make the notary profession become a notary that is flexible in accordance with the demands of the dynamics of life, the notary profession in this case needs to examine the possibility of acknowledging the existence of making and authenticating electronic deeds through an cyber notary mechanism. However, the concept of cyber notary in Indonesia is a matter of debate, because it is contrary to the general concept held by notaries so far. Apart from that, the thing that is being debated next is the responsibility for keeping the State archives, namely the minutes of digital deeds. This research is a legal research using a normative juridical approach, the data used are primary data and secondary data which are analyzed using quantitative analysis. The results of this study, starting from the concept of Cyber notary in making authentic deeds carried out by using electronic means, the notary as a profession in the field of law must be accommodating to the development of globalization and the industrial revolution which requires the use of a digital system which is intended to make it easier for people to do something legal action in particular. Then, regarding minutes of notary deeds in digital form, there should be a special institution to store minutes of digital notary deeds with the aim of providing a form of legal certainty.
Ankush Meshram, Markus Karch, Christian Haas
et al.
Since 2010, multiple cyber incidents on industrial infrastructure, such as Stuxnet and CrashOverride, have exposed the vulnerability of Industrial Control Systems (ICS) to cyber threats. The industrial systems are commissioned for longer duration amounting to decades, often resulting in non-compliance to technological advancements in industrial cybersecurity mechanisms. The unavailability of network infrastructure information makes designing the security policies or configuring the cybersecurity countermeasures such as Network Intrusion Detection Systems (NIDS) challenging. An empirical solution is to self-learn the network infrastructure information of an industrial system from its monitored network traffic to make the network transparent for downstream analyses tasks such as anomaly detection. In this work, a Python-based industrial communication paradigm-aware framework, named PROFINET Operations Enumeration and Tracking (POET), that enumerates different industrial operations executed in a deterministic order of a PROFINET-based industrial system is reported. The operation-driving industrial network protocol frames are dissected for enumeration of the operations. For the requirements of capturing the transitions between industrial operations triggered by the communication events, the Finite State Machines (FSM) are modelled to enumerate the PROFINET operations of the device, connection and system. POET extracts the network information from network traffic to instantiate appropriate FSM models (Device, Connection or System) and track the industrial operations. It successfully detects and reports the anomalies triggered by a network attack in a miniaturized PROFINET-based industrial system, executed through valid network protocol exchanges and resulting in invalid PROFINET operation transition for the device.
Machine learning is expected to enable the next Industrial Revolution. However, lacking standardized and automated assembly networks, ML faces significant challenges to meet ever-growing enterprise demands and empower broad industries. In the Perspective, we argue that ML needs to first complete its own Industrial Revolution, elaborate on how to best achieve its goals, and discuss new opportunities to enable rapid translation from ML's innovation frontier to mass production and utilization.
This study aims to evaluate the industrial work practice program at the Integrated Islamic Vocational School through the Context, Input, Process, Product and Outcome approaches. This research is a quantitative description. The method in this study using a questionnaire. The research sample was one principal, two deputy head of industrial relations, one deputy head of curriculum, four teachers who supervised the industrial practice program, and 133 students. The results of the study show the suitability or relevance of the dimensions: 1) Context consists of the goals, competencies and work ethic of students as well as links and matches; 2) Input consists of planning, provisioning, curriculum, students, human resources, infrastructure and financing; 3) Process, consisting of mapping, implementation, monitoring, effectiveness; 4) Product consists of a competency test and certification; 5) Outcome consisted of student absorption and change in attitude skills. The results of the research can contribute to evaluating the overall industrial work practice program from various dimensions, so that it can increase the competency absorption of Integrated Islamic Vocational Schools in the business and industrial world.
The authors of the article emphasize the increase of commercial interest in labor management in the field of housing and communal services as an element of stability of socio-technical and economic development of any organizations (enterprises).
The article discloses the main problems of labor management in housing and communal services, due to the specifics of the industry, the current worn-out state of the fixed assets of the industry (housing, engineering networks and structures) and its impact on increasing the material and labor costs of operating enterprises (organizations). The problems of increasing labor intensity in the content of housing and communal services fixed assets are aggravated by the imperfect organization of labor at enterprises (organizations) of the industry, the shortage of professional managerial personnel capable of effectively managing the housing stock of microdistricts, settlements, condominimums.
The management solution of the identified problems is aimed at improving the labor management system, starting with the optimization of the labor rationing system.
Purpose: finding effective ways to solve problems of labor management at enterprises/organizations of housing and communal services of Tomsk and Tomsk region in conditions of market conditions of economy, innovative technical and technological changes.
Methodology: within the framework of this study, methods of analogy, comparison, observation, description, comparative analysis, expert, analytical and statistical were applied.
Results: the author’s approach of labor audit in housing and communal services organizations is proposed, which makes it possible to make an adequate assessment of some elements of the labor management system. A list of necessary documents for conducting an audit of the labor management system in housing and communal services organizations is proposed. The results of the audit showed that at the moment in the analyzed enterprises housing and communal services are completely absent and require the development of regulatory, methodological and organizational local documentation necessary for effective labor management.
Scope of results: the results of these studies can be applied at the industry level to ensure a unified approach and effective regulation of social and industrial relations between the employer and the employee. The author’s results are aimed at improving the labor potential and the quality of the labor force for the implementation of national projects and programs in the conditions of market economic conditions, innovative technical and technological changes.
Through fieldwork on an assembly plant in the automotive industry, this article seeks to show that working time has adopted a flexible and fragmented nature following the concomitant transformations of work organization and industrial relations. This time is linked to the production flow and volume, which makes it less regular and predictable from the point of view of employees. Their working hours can vary upward or downward depending on market or industrial constraints. Added to this flexibility is the desynchronization of the time of the various workshops and shifts at the plant. As a result, employees and their representatives have less and less control over working time that seems to be imposed on them from the outside. Faced with this situation, the demands of the employees’ representatives are shrinking. They try to restore a certain regularity and synchronicity to the working time when they do not seek to negotiate salary compensation. The current changes in the organization of work therefore call into question – but in a discreet way – one of the dominant measures of work under the Fordian regime by profoundly reforming the methods of negotiating working time.
Industrial robots are important machines applied in numerous modern industries that execute repetitive tasks with high accuracy, replacing or supporting dangerous jobs. In this kind of system, with increased complexity in which cost is related to the time the system keeps working, the system must operate with a minimum number of failures. In other words, a quality aspect important in industry is reliability. We hypothesize that Automated Acceptance Testing improves reliability for industrial robot program. We present the research question, the motivation for this study, our hypothesis and future research efforts.
Ilaria Malanchini, Patrick Agostini, Khurshid Alam
et al.
Two main trends characterize today's communication landscape and are finding their way into industrial facilities: the rollout of 5G with its distinct support for vertical industries and the increasing success of machine learning (ML). The combination of those two technologies open the doors to many exciting industrial applications and its impact is expected to rapidly increase in the coming years, given the abundant data growth and the availability of powerful edge computers in production facilities. Unlike most previous work that has considered the application of 5G and ML in industrial environment separately, this paper highlights the potential and synergies that result from combining them. The overall vision presented here generates from the KICK project, a collaboration of several partners from the manufacturing and communication industry as well as research institutes. This unprecedented blend of 5G and ML expertise creates a unique perspective on ML-supported industrial communications and their role in facilitating industrial automation. The paper identifies key open industrial challenges that are grouped into four use cases: wireless connectivity and edge-cloud integration, flexibility in network reconfiguration, dynamicity of heterogeneous network services, and mobility of robots and vehicles. Moreover, the paper provides insights into the advantages of ML-based industrial communications and discusses current challenges of data acquisition in real systems.
The renegotiated North American Free Trade Agreement (NAFTA), now called the United States–
Mexico–Canada Agreement (USMCA), contains two interesting innovations: the requirement of a
minimum average wage in the manufacturing of motor vehicles (the Labour Value Content clause)
and a detailed prescription for the reform of Mexican labour law. Both could serve as models for
future labour chapters in trade agreements. The assessment contained in this article is based on the
views of those who demanded renegotiation of the labour-related provisions of NAFTA, experts
on labour rights in free trade agreements (FTAs) and ethics criteria. The assessment results in a
split picture. The labour-related provisions came about under ethically problematic circumstances
and their complexity leaves much room for criticism. Yet, the idea of inserting a wage floor in an
FTA, as well as monitoring and sanctioning mechanisms for ensuring internationally recognised
labour rights, merits further consideration for future trade agreements.
KEYWORDS: globalisation; industrial relations; competitiveness; trade agreements; outsourcing
Martin Schiersch, Veselina Mironova, Maximilian Schmitt
et al.
Monitoring mobility- and industry-relevant events is important in areas such as personal travel planning and supply chain management, but extracting events pertaining to specific companies, transit routes and locations from heterogeneous, high-volume text streams remains a significant challenge. This work describes a corpus of German-language documents which has been annotated with fine-grained geo-entities, such as streets, stops and routes, as well as standard named entity types. It has also been annotated with a set of 15 traffic- and industry-related n-ary relations and events, such as accidents, traffic jams, acquisitions, and strikes. The corpus consists of newswire texts, Twitter messages, and traffic reports from radio stations, police and railway companies. It allows for training and evaluating both named entity recognition algorithms that aim for fine-grained typing of geo-entities, as well as n-ary relation extraction systems.
Industrial Artificial Intelligence (Industrial AI) is an emerging concept which refers to the application of artificial intelligence to industry. Industrial AI promises more efficient future industrial control systems. However, manufacturers and solution partners need to understand how to implement and integrate an AI model into the existing industrial control system. A well-trained machine learning (ML) model provides many benefits and opportunities for industrial control optimization; however, an inferior Industrial AI design and integration limits the capability of ML models. To better understand how to develop and integrate trained ML models into the traditional industrial control system, test the deployed AI control system, and ultimately outperform traditional systems, manufacturers and their AI solution partners need to address a number of challenges. Six top challenges, which were real problems we ran into when deploying Industrial AI, are explored in the paper. The Petuum Optimum system is used as an example to showcase the challenges in making and testing AI models, and more importantly, how to address such challenges in an Industrial AI system.