Hasil untuk "Industrial relations"

Daniel Ruan, Salma Mozaffari, Sigrid Adriaenssens et al.

Industrial robots are increasingly deployed in contact-rich construction and manufacturing tasks that involve uncertainty and long-horizon execution. While learning-based visuomotor policies offer a promising alternative to open-loop control, their deployment on industrial platforms is challenged by a large observation-execution gap caused by sensing, inference, and control latency. This gap is significantly greater than on low-latency research robots due to high-level interfaces and slower closed-loop dynamics, making execution timing a critical system-level issue. This paper presents a latency-aware framework for deploying and evaluating visuomotor policies on industrial robotic arms under realistic timing constraints. The framework integrates calibrated multimodal sensing, temporally consistent synchronization, a unified communication pipeline, and a teleoperation interface for demonstration collection. Within this framework, we introduce a latency-aware execution strategy that schedules finite-horizon, policy-predicted action sequences based on temporal feasibility, enabling asynchronous inference and execution without modifying policy architectures or training. We evaluate the framework on a contact-rich industrial assembly task while systematically varying inference latency. Using identical policies and sensing pipelines, we compare latency-aware execution with blocking and naive asynchronous baselines. Results show that latency-aware execution maintains smooth motion, compliant contact behavior, and consistent task progression across a wide range of latencies while reducing idle time and avoiding instability observed in baseline methods. These findings highlight the importance of explicitly handling latency for reliable closed-loop deployment of visuomotor policies on industrial robots.

Yen-Chia Hsu, Despoina Touska

This report describes a dataset for industrial smoke segmentation, published on a figshare repository (https://doi.org/10.21942/uva.31847188). The dataset is licensed under CC BY 4.0.

Zeyi Liu, Shuang Liu, Jihai Min et al.

With the rapid development of industrial intelligence and unmanned inspection, reliable perception and safety assessment for AI systems in complex and dynamic industrial sites has become a key bottleneck for deploying predictive maintenance and autonomous inspection. Most public datasets remain limited by simulated data sources, single-modality sensing, or the absence of fine-grained object-level annotations, which prevents robust scene understanding and multimodal safety reasoning for industrial foundation models. To address these limitations, InspecSafe-V1 is released as the first multimodal benchmark dataset for industrial inspection safety assessment that is collected from routine operations of real inspection robots in real-world environments. InspecSafe-V1 covers five representative industrial scenarios, including tunnels, power facilities, sintering equipment, oil and gas petrochemical plants, and coal conveyor trestles. The dataset is constructed from 41 wheeled and rail-mounted inspection robots operating at 2,239 valid inspection sites, yielding 5,013 inspection instances. For each instance, pixel-level segmentation annotations are provided for key objects in visible-spectrum images. In addition, a semantic scene description and a corresponding safety level label are provided according to practical inspection tasks. Seven synchronized sensing modalities are further included, including infrared video, audio, depth point clouds, radar point clouds, gas measurements, temperature, and humidity, to support multimodal anomaly recognition, cross-modal fusion, and comprehensive safety assessment in industrial environments.

Haijun Liu, Chenyang Du, Beizi Chen et al.

A three-dimensional composite indicator system encompassing 'green growth, green welfare, and green wealth' was developed for 14 prefectures and municipalities in Xinjiang. This framework aims to illuminate the spatial and temporal evolution patterns and influence mechanisms of green development in the region, thereby providing empirical insights for sustainable practices in arid areas. The findings indicate that: (1) the level of green development in Xinjiang exhibited a fluctuating upward trend from 2001 to 2021; (2) the green development index revealed significant spatial differentiation, characterized by a concentration of development in the north and dispersion in the south; and (3) panel regression model analysis identified nine core factors, including SO2 emissions per 10,000 yuan of GDP and per capita water consumption, as critical drivers of green development. The driving mechanisms are analyzed through the lenses of inter-regional geography, developmental philosophies, demand structures, economic conditions, spatial configurations, and inter-regional relations, culminating in the development of a sustainable model emphasizing 'ecological protection, economic growth, and regional synergy.' Furthermore, proposing strategic options for regional green development—such as enhancing awareness of green practices, improving environmental management systems, promoting industrial transformation towards greener practices, and establishing effective spatial governance—is crucial for addressing the ecological and environmental challenges faced by arid and semi-arid zones. These strategies are vital for advancing the achievement of global sustainable development goals.

Yujing MENG, Jiaxiu CAI, Lu YIN

ObjectiveIn the context of profound demographic change and rapid urban restructuring, the spatial role of university campuses in Japan has undergone a fundamental transformation. Once conceived as inward-looking and self-sufficient “ivory tower” enclaves located on the urban periphery, campuses are increasingly being reconfigured as open and integrated nodes embedded within the metropolitan fabric. This paradigm shift is closely tied to Japan’s declining youth population, intensifying competition among universities, and evolving policy frameworks that regulate land use and higher education. Campus landscapes, in this process, are not merely ornamental green spaces but active agents of transformation that mediate the campus-city relations. The objective of this research is therefore to investigate how campus landscapes, as a spatial and social interface, respond to demographic pressures, policy incentives, and urban redevelopment agendas. By examining the synergistic evolution of universities and their host cities, the research aims to provide insights into the mechanisms that underpin this transformation and to extract lessons relevant to the forthcoming landscape transitions in Chinese higher education institutions.MethodsThe research adopts a multi-scalar approach that combines historical trajectory analysis, case-based comparative study, and theoretical synthesis. First, the historical evolution of Japanese university campuses from 1945 to the present is traced and periodized into three major phases: the expansion phase (1945–1980s), when demographic booms and policy restrictions encouraged suburban relocation and the creation of enclosed, inward-looking campuses; the peak phase (1980s–2000s), marked by intensifying competition, partial return to urban centers, and the emergence of vertical and compact campus typologies; and the contraction phase (2000s to present), characterized by severe demographic decline, urban concentration, and increasing demands for publicness and integration. Second, representative case studies are selected from metropolitan Tokyo, regional cities, and newly developed urban districts. These are analyzed through spatial observation, planning documents, and secondary literature to identify common strategies and contextual variations. Third, the research synthesizes empirical findings into a typological framework of three strategic modes — “catalyst”, “regenerator”, and “stabilizer” — and further generalizes these into a theoretical three-pillar model composed of demographic dynamics, policy instruments, and spatial strategies. This model is used to explain the synergistic evolution mechanism of campus landscapes and urban environments.ResultsThe analysis shows that campus landscape transformation in Japan is not an isolated architectural endeavor but a systemic process shaped by demographic, institutional, and spatial forces. In newly developed urban areas and large-scale redevelopment zones, universities frequently operate as catalysts, strategically positioned to anchor emerging districts. Here, landscape strategies emphasize publicness, multi-functionality, and accessibility. For instance, the Toyosu Campus of Shibaura Institute of Technology integrates open terraces, green staircases, and community-oriented plazas that attract both students and local residents, thereby stimulating district-level vitality. In historic city centers and post-industrial neighborhoods, universities act as regenerators, using landscape interventions to repair urban fabric and reinvigorate cultural identity. Examples include the Kitasenju Campus of Tokyo Denki University, which deploys pedestrian linkages and unified pavement to soften campus — city boundaries, and Kyoto City University of Arts, which integrates riverside ecological restoration with cultural events to generate a “memory landscape”. In smaller regional cities, universities often serve as stabilizers, embedding themselves in local social and demographic structures through service-oriented landscapes and shared facilities. Fukuchiyama Public University, for example, co-locates community dining halls and elderly care facilities within its campus landscape, while university consortia in Kyoto pool resources to create a multi-institutional network of open sports fields, libraries, and cultural spaces accessible to local communities. The proposed three-pillar model explains the underlying mechanism of these transformations. Demographic decline provides the fundamental pressure, reducing the student-age population from over two million in the early 1990s to just above one million in the 2020s, with further decline projected. Policy instruments translate these demographic pressures into spatial outcomes, with such instruments ranging from restrictive measures such as the 1959 Factory Location Law to liberalizing interventions like the 1991 revision of university establishment standards, and most recently, the 2017 enrollment cap in central Tokyo. Spatial strategies, materialized through landscape design, serve as the ultimate vehicles through which demographic and policy drivers are enacted: Open courtyards, pedestrian corridors, cultural event spaces, and service-based green infrastructures become concrete manifestations of institutional adaptation. The interplay of these three pillars — demographics, policies, and spatial strategies — constitutes the synergistic evolution dynamic of campus landscapes and cities.ConclusionJapanese experience shows campus landscapes have moved beyond their traditional role as green buffers to become strategic nodes of governance, cultural renewal, and social inclusion. By adopting roles of catalyst, regenerator, and stabilizer, campuses now shape urban growth, support community services, and sustain regional resilience. The proposed three-pillar model provides a structural lens for interpreting such changes. For China, where higher education faces slowed growth and demographic transition, these findings are highly relevant. Suburban university towns face the risk of under-use, while urban campuses must balance scarcity with public engagement. Japanese precedents suggest strategies of vertical compaction, boundary softening, and service-oriented integration can enhance publicness and urban alignment. Policymakers, meanwhile, should design flexible regulations balancing equity and autonomy. Future research should incorporate quantitative tools such as GIS metrics, user surveys, and cross-national comparison to further validate the three-pillar model and refine its applicability. Ultimately, campus landscapes must be understood not as passive backdrops but as active instruments in reshaping campus – city relations in an era of demographic and urban transformation.

Hai Dinh-Tuan

The convergence of Information Technology (IT) and Operational Technology (OT) in Industry 4.0 exposes the limitations of traditional, hierarchical architectures like ISA-95 and RAMI 4.0. Their inherent rigidity, data silos, and lack of support for cloud-native technologies impair the development of scalable and interoperable industrial systems. This paper addresses this issue by introducing CRACI, a Cloud-native Reference Architecture for the Industrial Compute Continuum. Among other features, CRACI promotes a decoupled and event-driven model to enable flexible, non-hierarchical data flows across the continuum. It embeds cross-cutting concerns as foundational pillars: Trust, Governance & Policy, Observability, and Lifecycle Management, ensuring quality attributes are core to the design. The proposed architecture is validated through a two-fold approach: (1) a comparative theoretical analysis against established standards, operational models, and academic proposals; and (2) a quantitative evaluation based on performance data from previously published real-world smart manufacturing implementations. The results demonstrate that CRACI provides a viable, state-of-the-art architecture that utilizes the compute continuum to overcome the structural limitations of legacy models and enable scalable, modern industrial systems.

Alpana Dubey, Suma Mani Kuriakose, Nitish Bhardwaj

We propose an approach to generate synthetic data to train computer vision (CV) models for industrial wear and tear detection. Wear and tear detection is an important CV problem for predictive maintenance tasks in any industry. However, data curation for training such models is expensive and time-consuming due to the unavailability of datasets for different wear and tear scenarios. Our approach employs a vision language model along with a 3D simulation and rendering engine to generate synthetic data for varying rust conditions. We evaluate our approach by training a CV model for rust detection using the generated dataset and tested the trained model on real images of rusted industrial objects. The model trained with the synthetic data generated by our approach, outperforms the other approaches with a mAP50 score of 0.87. The approach is customizable and can be easily extended to other industrial wear and tear detection scenarios

N.V. Shmeleva, D.Yu. Clegg, D.Kh. Mikhailidi

Background. The crisis has highlighted the need to revise the industrial policy. In ecosystem integration of industry, science, education and business it is necessary to have a balance of vertical and horizontal links, as the sustainability of the territory's development is determined by such factors as: the level of links between functional ecosystems and actors within the ecosystem. In ecosystem integration of industry, science, education and business, it is necessary to ensure a balance of vertical and horizontal links for the effective distribution of economic resources, accessibility of innovation infrastructure, knowledge and technology. The aim of the study is to develop the theory and methodology of regional integration management in the context of sustainable development of industrial territories. Materials and methods. The methodological approach proposed in the article can be used as a comprehensive toolkit for improving the sustainability of industrial territories and ecosystems. The author's approach is based on the theory of complex synergetic systems and entropy as a measure of the system state level (stationary equilibrium, nonstationary non-equilibrium and their combinations). Results. It is proved that only with the balance of all types of capitals the sustainable development of territories is possible. Sustainability assessment of industrial-territorial ecosystem of Vladimir region based on entropic approach is carried out. The primary attention is paid to the enterprises uniting around glass production. The implementation of the obtained results in the practice of territorial management will ensure the intensification of economic activity of the subjects of the Russian Federation, which will eventually affect the growth of macroeconomic indicators and the improvement of social indicators of regional development. Conclusions. Sustainable development of the territory should be considered as a process of development of the territory's potential through the formation of aggregate capital. The development of strategies of industrial territories should be carried out since collaborative relations with suppliers, customers, distributors and dealers, research centers, universities, public and governmental organizations.

Zehra Zeynep Sadıkoğlu

With the incentives for volunteering in civil society in Türkiye since the 2000s, the number of active civil society organizations (CSOs) and the rate of formal volunteering, which refers to volunteering under the umbrella of a CSO, have increased. Thus, CSOvolunteer relations and the management of these relations have become important. In regard to this importance, the study explores the problem areas in CSO-volunteer relations through the concept of the psychological contract and investigates the connection between volunteer expectations and volunteering experiences in Türkiye. The study aims to understand and describe the factors and processes that create obstacles to volunteer work or that make its sustainability difficult in Türkiye. The qualitative findings are based on 22 focus group discussions with 10 groups (i.e., CSO managers, CSO professionals, CSO volunteer coordinators, academicians, public bureaucracy, local governments, international organizations, volunteer initiatives, those with formal volunteer experience, and those without formal volunteer experience), have a high potential to provide information about volunteering, and indicate that problems exist related to developing a sense of belonging to CSOs in the relational dimension of a volunteer’s psychological contract in Türkiye and problems to exist related to the volunteer management processes of CSOs in the transactional dimension. The facts that volunteers are not recognized as stakeholders at either the managerial or organizational level in CSOs and that their contributions are not sufficiently valued result in a violation of the psychological contract in the relational dimension. The lack of a professional approach starting from the first contact with a volunteer and the lack of a framework regarding volunteers’ rights and responsibilities result in a violation in the transactional dimension.

Jomy Joseph

Despite the struggles for design disciplines to confront their colonial legacies and practices, the question remains: who can truly afford a decolonizing practice worthy of the name? This paper will investigate why Industrial Design, as a discipline, has been glaringly absent from the decolonial conversation, and the critical institutional gaps between decolonial thought and action. I will investigate the pragmatic relations between labor, value, care work, and social reproduction within the political economy of design that dissuade and constrain the discipline from articulating its responsibility to transform its social and material realities. In setting this provocation, I argue that if decolonizing design is to be anything more than an epistemological curiosity, moving beyond the niche corners of design academia, it will need a diverse ecology of accomplices—to imagine other lives for itself and become other beings.

Е. V. Potaptseva, O. S. Bryantseva, E. V. Presniakova

Objective: to study the evolution of the industrial policy of the Union State of Russia and Belarus from 1999 to 2024.   Methods: analysis of strategic documents, statistical analysis of macroeconomic indicators and study of regional aspects of economic integration. We studied the statistics of joint ventures with Belarusian capital in Russia and with Russian capital in Belarus, which indicates the changing economic conditions in the Union State.   Results: the industrial policy of the Union State is systematically developing and is aimed at deepening the economic integration of the two countries. It relies on the implementation of strategic agreements, planning and financing of joint scientific and technical programs. The study shows that despite the existence of strategic agreements, such as the 1999 Agreement on a Common Structural Industrial Policy and the 2023 Agreement on a Common Industrial Policy, a significantpart of the planned activities and projects has remained only partially implemented. The article elaborates on the key changes in the content of industrial policy, including increased emphasis on integration projects, import substitution and development of new types of competitive products. The study of the statistics of joint ventures with Belarusian capital in Russia and with Russian capital in Belarus points to changing economic conditions in the Union State. Since 2017, the number of joint ventures has been decreasing, which can be a consequence of both the reduction of barriers in international trade and economy and the decreased interest of enterprises in bilateral relations. The industrial policy of the Union State is currently aimed at overcoming dependence on the supply of foreign equipment and components, creating a common economic and scientific-technical space, financing and developing scientific-industrial cooperation based on union programs and projects.   Scientific novelty: it consists in analyzing the evolution of the common industrial policy of the Union State for more than two decades. For the first time such a detailed analysis covered the whole period from the signing of the initial agreement in 1999 to the last intergovernmental agreement in 2023. This allows getting a holistic view of the development of approachesand transformation of the Union State industrial policy.   Practical significance: for the first time, the study provides an insight into the dynamics of economic integration of the Russian and Belarus regions within the Union State through the statistics of joint Russian-Belarusian enterprises in the regions. This can be useful for researchers, business community and authorities to understand the processes of industrial policy impact on the real sector of the economy.

Ashok Urlana, Charaka Vinayak Kumar, Ajeet Kumar Singh et al.

Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks. From natural language processing and sentiment analysis to content generation and personalized recommendations, their unparalleled adaptability has facilitated widespread adoption across industries. This transformative shift driven by LLMs underscores the need to explore the underlying associated challenges and avenues for enhancement in their utilization. In this paper, our objective is to unravel and evaluate the obstacles and opportunities inherent in leveraging LLMs within an industrial context. To this end, we conduct a survey involving a group of industry practitioners, develop four research questions derived from the insights gathered, and examine 68 industry papers to address these questions and derive meaningful conclusions. We maintain the Github repository with the most recent papers in the field.

Xuejiao Li, Cheng Yang, Charles Møller et al.

In the era of Industry 4.0, artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as perceived. A significant factor contributing to this lag is the data issues in AI implementation. How to address these data issues stands as a significant concern confronting both industry and academia. To address data issues, the first step involves mapping out these issues. Therefore, this study conducts a meta-review to explore data issues and methods within the implementation of industrial AI. Seventy-two data issues are identified and categorized into various stages of the data lifecycle, including data source and collection, data access and storage, data integration and interoperation, data pre-processing, data processing, data security and privacy, and AI technology adoption. Subsequently, the study analyzes the data requirements of various AI algorithms. Building on the aforementioned analyses, it proposes a data management framework, addressing how data issues can be systematically resolved at every stage of the data lifecycle. Finally, the study highlights future research directions. In doing so, this study enriches the existing body of knowledge and provides guidelines for professionals navigating the complex landscape of achieving data usability and usefulness in industrial AI.

Maryam Ahang, Todd Charter, Mostafa Abbasi et al.

Condition monitoring is essential for ensuring the safety, reliability, and efficiency of modern industrial systems. With the increasing complexity of industrial processes, artificial intelligence (AI) has emerged as a powerful tool for fault detection and diagnosis, attracting growing interest from both academia and industry. This paper provides a comprehensive overview of intelligent condition monitoring methods, with a particular emphasis on chemical plants and the widely used Tennessee Eastman Process (TEP) benchmark. State-of-the-art machine learning (ML) and deep learning (DL) algorithms are reviewed, highlighting their strengths, limitations, and applicability to industrial fault detection and diagnosis. Special attention is given to key challenges, including imbalanced and unlabeled data, and to strategies by which models can address these issues. Furthermore, comparative analyses of algorithm performance are presented to guide method selection in practical scenarios. This survey is intended to benefit both newcomers and experienced researchers by consolidating fundamental concepts, summarizing recent advances, and outlining open challenges and promising directions for intelligent condition monitoring in industrial plants.

Xiaomeng Zhu, Talha Bilal, Pär Mårtensson et al.

This paper is about effectively utilizing synthetic data for training deep neural networks for industrial parts classification, in particular, by taking into account the domain gap against real-world images. To this end, we introduce a synthetic dataset that may serve as a preliminary testbed for the Sim-to-Real challenge; it contains 17 objects of six industrial use cases, including isolated and assembled parts. A few subsets of objects exhibit large similarities in shape and albedo for reflecting challenging cases of industrial parts. All the sample images come with and without random backgrounds and post-processing for evaluating the importance of domain randomization. We call it Synthetic Industrial Parts dataset (SIP-17). We study the usefulness of SIP-17 through benchmarking the performance of five state-of-the-art deep network models, supervised and self-supervised, trained only on the synthetic data while testing them on real data. By analyzing the results, we deduce some insights on the feasibility and challenges of using synthetic data for industrial parts classification and for further developing larger-scale synthetic datasets. Our dataset and code are publicly available.

Tatiana M. Romanenko, Elena N. Bogdanova

The era of accelerating globalization and urbanization has necessitated adaptations in Arctic economic systems. Among the key factors are the active industrial development of Arctic regions, social and cultural shifts among the indigenous peoples of the North, and changes in the modern market. The reindeer husbandry sector developed against the backdrop of collectivization in the 1930s and market reforms in the 1990s. This study aims to analyze shifts in the economic model of contemporary reindeer husbandry in the Nenets Autonomous Okrug (NAO) within the context of global transformations. The research novelty lies in examining the evolutionary transition of reindeer herding farms to a new management system during the Soviet period and investigating the factors influencing its effectiveness. While management improvements resulted in a profound restructuring of the mindset of reindeer herders, the onset of political reforms resulted in the shift to market relations and the upheaval of the industry during the crisis-ridden years of perestroika. The ensuing decline stemmed from the disintegration of the economic system and the government’s not being ready to search for the right economic reforms. By employing the principal component method, the study identified two primary factors impacting the unstable nature of meat production: the number of reindeer per herder and the proportion of adult female reindeer in the herd. In farms facing challenges such as understaffing, low morale, or a high percentage of inexperienced workers, caution should be exercised in adopting advanced techniques. This involves adjusting the reindeer load per herder, managing larger herds, and increasing the number of adult female reindeer in the herd. An individual approach and an analysis of previous years’ work are crucial to prevent negative trends in production indicators such as the number of calves, adult livestock preservation, and unforeseen loss prevention. Promising directions for future research include an analysis of the effectiveness of support programs for reindeer herding farms, encompassing both subsidies and trainings for young professionals in the industry.

B. Prabadevi, N. Deepa, Nancy Victor et al.

With the advent of new technologies and endeavors for automation in almost all day-to-day activities, the recent discussions on the metaverse life have a greater expectation. Furthermore, we are in the era of the fifth industrial revolution, where machines and humans collaborate to maximize productivity with the effective utilization of human intelligence and other resources. Hence, Industry 5.0 in the metaverse may have tremendous technological integration for a more immersive experience and enhanced communication.These technological amalgamations are suitable for the present environment and entirely different from the previous perception of virtual technologies. This work presents a comprehensive review of the applications of the metaverse in Industry 5.0 (so-called industrial metaverse). In particular, we first provide a preliminary to the metaverse and industry 5.0 and discuss key enabling technologies of the industrial metaverse, including virtual and augmented reality, 3D modeling, artificial intelligence, edge computing, digital twin, blockchain, and 6G communication networks. This work then explores diverse metaverse applications in Industry 5.0 vertical domains like Society 5.0, agriculture, supply chain management, healthcare, education, and transportation. A number of research projects are presented to showcase the conceptualization and implementation of the industrial metaverse. Furthermore, various challenges in realizing the industrial metaverse, feasible solutions, and future directions for further research have been presented.

Ben-Hur Francisco Cardoso, Eva Yamila da Silva Catela, Guilherme Viegas et al.

Research on productive structures has shown that economic complexity conditions economic growth. However, little is known about which type of complexity, e.g., export or industrial complexity, matters more for regional economic growth in a large emerging country like Brazil. Brazil exports natural resources and agricultural goods, but a large share of the employment derives from services, non-tradables, and within-country manufacturing trade. Here, we use a large dataset on Brazil's formal labor market, including approximately 100 million workers and 581 industries, to reveal the patterns of export complexity, industrial complexity, and economic growth of 558 micro-regions between 2003 and 2019. Our results show that export complexity is more evenly spread than industrial complexity. Only a few -- mainly developed urban places -- have comparative advantages in sophisticated services. Regressions show that a region's industrial complexity is a significant predictor for 3-year growth prospects, but export complexity is not. Moreover, economic complexity in neighboring regions is significantly associated with economic growth. The results show export complexity does not appropriately depict Brazil's knowledge base and growth opportunities. Instead, promoting the sophistication of the heterogeneous regional industrial structures and development spillovers is a key to growth.

Yusuke Kato, Ryo Okumura, Tadahiro Taniguchi

The process of industrial box-packing, which involves the accurate placement of multiple objects, requires high-accuracy positioning and sequential actions. When a robot is tasked with placing an object at a specific location with high accuracy, it is important not only to have information about the location of the object to be placed, but also the posture of the object grasped by the robotic hand. Often, industrial box-packing requires the sequential placement of identically shaped objects into a single box. The robot's action should be determined by the same learned model. In factories, new kinds of products often appear and there is a need for a model that can easily adapt to them. Therefore, it should be easy to collect data to train the model. In this study, we designed a robotic system to automate real-world industrial tasks, employing a vision-based learning control model. We propose in-hand-view-sensitive Newtonian variational autoencoder (ihVS-NVAE), which employs an RGB camera to obtain in-hand postures of objects. We demonstrate that our model, trained for a single object-placement task, can handle sequential tasks without additional training. To evaluate efficacy of the proposed model, we employed a real robot to perform sequential industrial box-packing of multiple objects. Results showed that the proposed model achieved a 100% success rate in industrial box-packing tasks, thereby outperforming the state-of-the-art and conventional approaches, underscoring its superior effectiveness and potential in industrial tasks.

Marian Ehret, Mohd Azizuddin Mohd Sani

This paper investigates how Sino-German relations would be impacted by the Russo-Ukrainian War of 2022. The relations between both Eurasian partners were found to be significantly influenced by US-German relations. The theory of Historical Cycles by cultural philosopher Oswald Spengler was used as an analytical tool. As a consequence of the war, findings suggested a far-reaching, permanent decline of Germany’s industrial-financial base until the 2030s. A potential Trump government could facilitate such a result. Also triggered by the war, Berlin would predominantly act in alignment with an American lead – Beijing and Berlin’s strategic partnership could fail. While China and Russia would most likely stay free of America’s influence, Washington would probably be able to dominate Germany, Europe, the West, and perhaps most of the world.