IntroductionEnhancing the total factor productivity (TFP) of forestry ecosystems is central to shifting the forestry industry toward high-quality development. This study investigates the impact of digital government initiatives on forestry ecosystem TFP to understand how digital governance can drive ecological and economic efficiency.MethodsUtilizing panel data from 30 Chinese provinces between 2015 and 2022, this study employs a Dual Machine Learning (DML) model to mitigate endogeneity and estimation bias. This rigorous methodological framework allows for a precise quantitative assessment of the effects and transmission mechanisms of digital government development on forestry ecosystem TFP.ResultsThe empirical results demonstrate three key findings: (1) The expansion of digital government significantly boosts the TFP of forestry ecosystems. (2) Mechanism analysis identifies three primary channels for this improvement: the cultivation of new quality productive forces, the upgrading of forestry industrial structures, and the simplification of operational processes. Furthermore, the broader digital economy acts as a significant positive moderator in this relationship. (3) Heterogeneity analysis reveals that the magnitude of these effects varies across regions, contingent upon local economic development levels and forest resource endowments.DiscussionBased on these findings, the paper proposes policy recommendations to foster institutional innovation, accelerate digital government construction, and implement region-specific strategies. Globally, this study provides empirical evidence for the synergy between digital governance and ecological sustainability. It offers a replicable model for other nations seeking to leverage digital tools to balance economic growth with environmental conservation, thereby contributing to the advancement of global ecological civilization and sustainable development goals.
Autonomous control of multi-stage industrial processes requires both local specialization and global coordination. Reinforcement learning (RL) offers a promising approach, but its industrial adoption remains limited due to challenges such as reward design, modularity, and action space management. Many academic benchmarks differ markedly from industrial control problems, limiting their transferability to real-world applications. This study introduces an enhanced industry-inspired benchmark environment that combines tasks from two existing benchmarks, SortingEnv and ContainerGym, into a sequential recycling scenario with sorting and pressing operations. We evaluate two control strategies: a modular architecture with specialized agents and a monolithic agent governing the full system, while also analyzing the impact of action masking. Our experiments show that without action masking, agents struggle to learn effective policies, with the modular architecture performing better. When action masking is applied, both architectures improve substantially, and the performance gap narrows considerably. These results highlight the decisive role of action space constraints and suggest that the advantages of specialization diminish as action complexity is reduced. The proposed benchmark thus provides a valuable testbed for exploring practical and robust multi-agent RL solutions in industrial automation, while contributing to the ongoing debate on centralization versus specialization.
We present a novel reinforcement learning (RL) environment designed to both optimize industrial sorting systems and study agent behavior in evolving spaces. In simulating material flow within a sorting process our environment follows the idea of a digital twin, with operational parameters like belt speed and occupancy level. To reflect real-world challenges, we integrate common upgrades to industrial setups, like new sensors or advanced machinery. It thus includes two variants: a basic version focusing on discrete belt speed adjustments and an advanced version introducing multiple sorting modes and enhanced material composition observations. We detail the observation spaces, state update mechanisms, and reward functions for both environments. We further evaluate the efficiency of common RL algorithms like Proximal Policy Optimization (PPO), Deep-Q-Networks (DQN), and Advantage Actor Critic (A2C) in comparison to a classical rule-based agent (RBA). This framework not only aids in optimizing industrial processes but also provides a foundation for studying agent behavior and transferability in evolving environments, offering insights into model performance and practical implications for real-world RL applications.
This paper presents HERO (Hierarchical Testing with Rabbit Optimization), a novel black-box adversarial testing framework for evaluating the robustness of deep learning-based Prognostics and Health Management systems in Industrial Cyber-Physical Systems. Leveraging Artificial Rabbit Optimization, HERO generates physically constrained adversarial examples that align with real-world data distributions via global and local perspective. Its generalizability ensures applicability across diverse ICPS scenarios. This study specifically focuses on the Proton Exchange Membrane Fuel Cell system, chosen for its highly dynamic operational conditions, complex degradation mechanisms, and increasing integration into ICPS as a sustainable and efficient energy solution. Experimental results highlight HERO's ability to uncover vulnerabilities in even state-of-the-art PHM models, underscoring the critical need for enhanced robustness in real-world applications. By addressing these challenges, HERO demonstrates its potential to advance more resilient PHM systems across a wide range of ICPS domains.
We quantify the impact of Generative Artificial Intelligence (GenAI) on firm productivity through a series of large-scale randomized field experiments involving millions of users and products at a leading cross-border online retail platform. Over six months in 2023-2024, GenAI-based enhancements were integrated into seven consumer-facing business workflows. We find that GenAI adoption significantly increases sales, with treatment effects ranging from $0\%$ to $16.3\%$, depending on GenAI's marginal contribution relative to existing firm practices. Because inputs and prices were held constant across experimental arms, these gains map directly into total factor productivity improvements. Across the four GenAI applications with positive sales effects, the implied annual incremental value is approximately $\$ 5$ per consumer-an economically meaningful impact given the retailer's scale and the early stage of GenAI adoption. The primary mechanism operates through higher conversion rates, consistent with GenAI reducing frictions and improving consumer experience. Importantly, these effects are not associated with worse post-purchase outcomes, as product return rates and customer ratings do not deteriorate. Finally, we document substantial demand-side heterogeneity, with larger gains for less experienced consumers. Our findings provide novel, large-scale causal evidence on the productivity effects of GenAI in online retail, highlighting both its immediate value and broader potential.
Networked urban systems facilitate the flow of people, resources, and services, and are essential for economic and social interactions. These systems often involve complex processes with unknown governing rules, observed by sensor-based time series. To aid decision-making in industrial and engineering contexts, data-driven predictive models are used to forecast spatiotemporal dynamics of urban systems. Current models such as graph neural networks have shown promise but face a trade-off between efficacy and efficiency due to computational demands. Hence, their applications in large-scale networks still require further efforts. This paper addresses this trade-off challenge by drawing inspiration from physical laws to inform essential model designs that align with fundamental principles and avoid architectural redundancy. By understanding both micro- and macro-processes, we present a principled interpretable neural diffusion scheme based on Transformer-like structures whose attention layers are induced by low-dimensional embeddings. The proposed scalable spatiotemporal Transformer (ScaleSTF), with linear complexity, is validated on large-scale urban systems including traffic flow, solar power, and smart meters, showing state-of-the-art performance and remarkable scalability. Our results constitute a fresh perspective on the dynamics prediction in large-scale urban networks.
This article presents applied research on line balancing within the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process, by Lean Methodology for garment modernization. It explores the application of line balancing in the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process. It aligns with Lean Methodology principles for garment modernization. Without the implementation of line balancing technology, the garment manufacturing process using hanger systems cannot improve output rates. The case study demonstrates that implementing intelligent line balancing in a straightforward practical setup facilitates lean practices combined with a digitalization system and automaton. This approach illustrates how to enhance output and reduce accumulated work in progress.
Fernando Andrés Muñoz-Peña, Jason Steve Pulido-Reina
Soft skills and employee productivity are key factors in sustainability and corporate performance in competitive and dynamic environments. The purpose of this article is to identify current findings related to soft skills, employee productivity and their relationship with organizational performance, and to propose a structural model that allows establishing this relationship. The results of some research conducted individually reveal positive and significant relationships in the proposed fields. Some skills found in the literature are communication, problem solving and decision making. To carry out this research, multiple research papers were collected between 2005 and 2024. A structural equation model was used as a way to propose a relationship between the aforementioned factors. This research theoretically demonstrated that soft skills and employee productivity contribute positively and significantly to organizational performance. Limitations may arise depending on the particularities of the industrial sector and the economic context. Since there is little research in the analyzed fields, this study contributes significantly to the identification of key variables.
Undalov Roman, Ezhkov Vladimir, Ezhkova Asiya
et al.
The effect of nanostructured zeolite on embryonic and early postnatal development of Peking ducks cross ‘STAR-53 medium’ was investigated. The surface of hatching eggs was treated with a suspension of nanostructured zeolite with a particle size of 60-120 nm to evaluate its effect on increasing the productivity of ducks. It was found by electron microscopy that particles of nanostructured zeolite penetrate through the porous structure of the shell (pore size 0.2-2 μm) and reach the embryo, stimulating physiological processes. Radiological studies showed no adverse effects on embryo viability, survival and development. After hatching it was revealed that the absolute live weight gain of ducklings in the experimental group was 7.2% (22.6 g) more than in the control group during the first 10 days of life. The obtained results show the prospectivity of using nanostructured zeolite in industrial poultry farming to increase the productivity and quality of growing birds.
Paula Fraga-Lamas, Tiago M Fernandez-Carames, Oscar Blanco-Novoa
et al.
Shipbuilding companies are upgrading their inner workings in order to create Shipyards 4.0, where the principles of Industry 4.0 are paving the way to further digitalized and optimized processes in an integrated network. Among the different Industry 4.0 technologies, this article focuses on Augmented Reality, whose application in the industrial field has led to the concept of Industrial Augmented Reality (IAR). This article first describes the basics of IAR and then carries out a thorough analysis of the latest IAR systems for industrial and shipbuilding applications. Then, in order to build a practical IAR system for shipyard workers, the main hardware and software solutions are compared. Finally, as a conclusion after reviewing all the aspects related to IAR for shipbuilding, it is proposed an IAR system architecture that combines Cloudlets and Fog Computing, which reduce latency response and accelerate rendering tasks while offloading compute intensive tasks from the Cloud.
Alexander Windmann, Philipp Wittenberg, Marvin Schieseck
et al.
In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges.
Dan Lehman, Tim J. Schoonbeek, Shao-Hsuan Hung
et al.
Recognizing errors in assembly and maintenance procedures is valuable for industrial applications, since it can increase worker efficiency and prevent unplanned down-time. Although assembly state recognition is gaining attention, none of the current works investigate assembly error localization. Therefore, we propose StateDiffNet, which localizes assembly errors based on detecting the differences between a (correct) intended assembly state and a test image from a similar viewpoint. StateDiffNet is trained on synthetically generated image pairs, providing full control over the type of meaningful change that should be detected. The proposed approach is the first to correctly localize assembly errors taken from real ego-centric video data for both states and error types that are never presented during training. Furthermore, the deployment of change detection to this industrial application provides valuable insights and considerations into the mechanisms of state-of-the-art change detection algorithms. The code and data generation pipeline are publicly available at: https://timschoonbeek.github.io/error_seg.
The use of Information Communication Technology (ICT) forms a significant component of the Fourth Industrial Revolution (4IR). This study examined the impact of ICT on agricultural development in South Africa utilizing time series data from 1995 to 2022. Agricultural development was measured through agricultural output and agriculture total factor productivity as dependent variables. Traditional factors of production (land, labor, and capital) together with ICT variables (mobile cellphone subscriptions, Internet usage, and fixed telephone subscriptions) were used. Additional variables such as inflation, human development, access to energy and climate change were used. Data analysis was performed using the ARDL approach. The findings revealed that mobile phone subscriptions and Internet usage positively affect agricultural output and ATFP in the short and long run despite having a negative effect through the second lag in the short run. Fixed telephone subscriptions negatively affect ATFP in the long run while affecting output negatively in the short run through the first lag. Land, human development index, access to energy, and capital generally exhibited an increasing effect on both agricultural output and ATFP both in the short and long run through the various models estimated. Climate change and inflation were generally found to affect both agricultural output and ATFP negatively in the short and long run. The study concluded that ICT plays a significant role in promoting agricultural output and total factor productivity growth. Recommendations included that the South African government should promote the digitalization of the agriculture sector through the provision of ICT infrastructure that can be utilized by both smallholder farmers and large-scale agricultural producers.
Emese Farkas, Péter Sátorhelyi, Zoltán Szakács
et al.
Abstract Cariprazine—the only single antipsychotic drug in the market which can handle all symptoms of bipolar I disorder—involves trans-4-substituted cyclohexane-1-amine as a key structural element. In this work, production of trans-4-substituted cyclohexane-1-amines was investigated applying transaminases either in diastereotope selective amination starting from the corresponding ketone or in diastereomer selective deamination of their diasteromeric mixtures. Transaminases were identified enabling the conversion of the cis-diastereomer of four selected cis/trans-amines with different 4-substituents to the corresponding ketones. In the continuous-flow experiments aiming the cis diastereomer conversion to ketone, highly diastereopure trans-amine could be produced (de > 99%). The yield of pure trans-isomers exceeding their original amount in the starting mixture could be explained by dynamic isomerization through ketone intermediates. The single transaminase-catalyzed process—exploiting the cis-diastereomer selectivity of the deamination and thermodynamic control favoring the trans-amines due to reversibility of the steps—allows enhancement of the productivity of industrial cariprazine synthesis.
Putri Jasmine Solekha, Qurtubi Qurtubi, Haswika Haswika
et al.
The fertilizer industry plays a crucial role in assuring the food security of a nation, but it also faces significant environmental obstacles. These problems often contribute to decreased supply chain efficiency and overall industrial productivity. The industry's focus on profit maximization hinders adopting green supply chain strategies. This paper examines company q's adoption of green supply chain management (GSCM) practices. This study evaluates its performance using the green supply chain operations reference (Green SCOR) model, scoring 73.54 out of 100, classifying it as 'good.' However, there is room for improvement, especially concerning key performance indicators (KPIs). This paper identifies six KPIs that fall below satisfactory levels and offers specific recommendations for improvement. This study significantly contributes to the fertilizer industry by providing actionable insights for practitioners and advancing theoretical understanding by highlighting key overlooked indicators. Furthermore, this research also emphasizes the crucial role of government policies in stimulating the implementation of sustainable supply chain practices.
Industry 4.0 factories are complex and data-driven. Data is yielded from many sources, including sensors, PLCs, and other devices, but also from IT, like ERP or CRM systems. We ask how to collect and process this data in a way, such that it includes metadata and can be used for industrial analytics or to derive intelligent support systems. This paper describes a new, query model based approach, which uses a big data architecture to capture data from various sources using OPC UA as a foundation. It buffers and preprocesses the information for the purpose of harmonizing and providing a holistic state space of a factory, as well as mappings to the current state of a production site. That information can be made available to multiple processing sinks, decoupled from the data sources, which enables them to work with the information without interfering with devices of the production, disturbing the network devices they are working in, or influencing the production process negatively. Metadata and connected semantic information is kept throughout the process, allowing to feed algorithms with meaningful data, so that it can be accessed in its entirety to perform time series analysis, machine learning or similar evaluations as well as replaying the data from the buffer for repeatable simulations.
Christos Anagnostopoulos, Georgios Mylonas, Apostolos P. Fournaris
et al.
Virtual and augmented reality are currently enjoying a great deal of attention from the research community and the industry towards their adoption within industrial spaces and processes. However, the current design and implementation landscape is still very fluid, while the community as a whole has not yet consolidated into concrete design directions, other than basic patterns. Other open issues include the choice over a cloud or edge-based architecture when designing such systems. Within this work, we present our approach for a monitoring intervention inside a factory space utilizing both VR and AR, based primarily on edge computing, while also utilizing the cloud. We discuss its main design directions, as well as a basic ontology to aid in simple description of factory assets. In order to highlight the design aspects of our approach, we present a prototype implementation, based on a use case scenario in a factory site, within the context of the ENERMAN H2020 project.
Aiming at helping users locally discovery retail services (e.g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which greatly challenge current recommender systems. With the real data in Alipay, a feeds-like scenario for O2O services, we find that recurrence based temporal patterns and position biases commonly exist in our scenarios, which seriously threaten the recommendation effectiveness. To this end, we propose COUPA, an industrial system targeting for characterizing user preference with following two considerations: (1) Time aware preference: we employ the continuous time aware point process equipped with an attention mechanism to fully capture temporal patterns for recommendation. (2) Position aware preference: a position selector component equipped with a position personalization module is elaborately designed to mitigate position bias in a personalized manner. Finally, we carefully implement and deploy COUPA on Alipay with a cooperation of edge, streaming and batch computing, as well as a two-stage online serving mode, to support several popular recommendation scenarios. We conduct extensive experiments to demonstrate that COUPA consistently achieves superior performance and has potential to provide intuitive evidences for recommendation
Fikret Basic, Christian Steger, Christian Seifert
et al.
With the advent of clean energy awareness and systems that rely on extensive battery usage, the community has seen an increased interest in the development of more complex and secure Battery Management Systems (BMS). In particular, the inclusion of BMS in modern complex systems like electric vehicles and power grids has presented a new set of security-related challenges. A concern is shown when BMS are intended to extend their communication with external system networks, as their interaction can leave many backdoors open that potential attackers could exploit. Hence, it is highly desirable to find a general design that can be used for BMS and its system inclusion. In this work, a security architecture solution is proposed intended for the communication between BMS and other system devices. The aim of the proposed architecture is to be easily applicable in different industrial settings and systems, while at the same time keeping the design lightweight in nature.