Industrial Networks Research Articles

A Comprehensive Comparison of Simplified Volterra Equalization and Kramers–Kronig Schemes in 200 Gb/s/λ PON Downlink Transmission

The emerging high-bandwidth services of 6G, such as high-definition video transmission and real-time interaction, have promoted the progress of the fiber optic access network industry, driving its development towards the next-generation PON with higher speed and larger system capacity. In response to the future requirements of 200 Gb/s/λ PON for both 20 km and 30 km downlink transmission scenarios, this paper proposes a Simplified Volterra Equalization (SVLE) scheme based on Nyquist PAM4 single-sideband modulation direct detection (SSBM-DD) scheme. In order to verify its advantages, the IQ-modulated Kramers–Kronig reception (KK) scheme is introduced for comparison. Simulation validation platforms for two schemes are conducted, and the performance comparison of the SVLE and KK schemes is carried out. In both, the impact of the carrier signal power ratio (CSPR) on receiver sensitivity, the influence of input optical power on power budget and receiver sensitivity, and the tolerance of receiver sensitivity to the linewidth of the DFB laser are investigated in the simulation. Finally, a comprehensive comparison of the two schemes is presented in terms of system performance, cost, and DSP complexity. In the 20 km downlink transmission scenario, the SVLE scheme outperforms the KK scheme by 4.9 dB in terms of power budget. The total number of multiplications of the SVLE scheme is 37, while that of the KK scheme is 4358. Therefore, the DSP complexity of the SVLE scheme is much lower than that of the KK scheme. The results of the comparison demonstrate that, in downlink transmission scenarios, the SVLE scheme is more suitable than the KK scheme as it exhibits a higher power budget, lower DSP complexity, and lower cost. Consequently, the proposed SVLE scheme could be a highly promising solution for future ultra-high-speed PON downlink transmission.

Proposal for a protection system of an industrial electrical network

An electrical protection system in an industry Works by detecting and acting against abnormal conditions in the electrical system with the objective of guaranteeing the safety of people, protecting equipment and ensuring the continuity of industrial process. Taking into account the importance of guaranteeing adequate electrical protection system in an industrial activity in this research a proposal for the protection system for an industrial electrical network is presented. As a previous step proposal, the methodology desingned for the coordination of protections for an industrial electrical network was proposed. The proposal was designed taking into account four operating scenarios required to calibrate the industry’s protection devices. The short circuit analysis maximum in each of the system bars for each of the scenarios allowed determining the maximum phase failure currents it is 44.44% for emergency 1 and 66.66% for emergency 2, while the maximum ground fault current was founding emergency scenario2. At the news to the four scenarios of the industrial network in the actuation times of the devices of protection, there is not considerable variation; this is justified by the current time graph because when there is a serious short circuit current, the action time should be shorter. On the contrary, when there is a small current time will be greater

Enhanced Smart Industry Wireless Sensor Networks for Smart Factory

Industrial Wireless Sensor Networks are increased in recent years due to provide real-time monitoring, decision-making and controlling. Industrial Wireless Sensor Networks used in many factories such as: food production, aircraft factory and pharmaceutical factory. The aim of this research is to focus on the Industrial Wireless Sensor Networks in smart factory and the challenges that need to be considered in use such as: its location, energy consumptions and security. The research discuss different issues are highlighted the importance of using real-time decision-making to improve the performance of the process and provide service monitoring for industrial process improvement.

Blockchain-based concept for total site heat integration: A pinch-based smart contract energy management in industrial symbiosis

Industrial symbiosis has gained prominence in pursuing sustainable industrial practices, aiming to optimise resource utilisation and reduce environmental impacts. A critical aspect of this endeavour is the efficient management of energy resources within an industrial ecosystem. This paper presents a novel approach to enhance Total Site Heat Integration (TSHI) implementations by employing Blockchain as a facilitator for decentralised energy management. TSHI is an efficient and widely applied method for industrial symbiosis concerning energy flows, which employs the steam mains in site utility systems as the platform for exchanging heat between industrial processes at various temperature levels. It is shown that the synergy of Pinch Analysis and Smart Contract technologies is capable of facilitating energy integration of processes belonging to independent market actors, compared with the currently dominant integration inside a single company. The proposed framework leverages Blockchain as a distributed ledger to enable secure, transparent, and automated energy management across multiple industrial entities in an industrial symbiotic network. The integration of Pinch Analysis principles ensures that the Heat Integration process is optimised to improve the overall energy efficiency. Smart contracts enable automatic negotiation and execution of energy transactions based on predefined rules, minimising the time lag for concluding deals on energy resource exchange and conservation. This paper examines several scenarios to illustrate the implementation of the proposed Blockchain-based TSHI concept within an industrial symbiosis network. It is demonstrated that up to 16 % cost savings are possible by simply enabling transparency via Blockchain. The results could drive innovative development to revolutionise decentralised energy management in a complex industrial ecosystem, especially by synchronising energy exchanges in time.

C-SLA-MLO: Enhancing SLA Compliance in Industrial Wi-Fi through Cooperative Multilink Operation

In the dynamic landscape of Industry 4.0, robust wireless connectivity emerges as a critical enabler for Industrial Internet of Things (IIoT) applications. With the advent of Wi-Fi 7’s multilink operation (MLO), the demand for schedulers capable of meeting diverse Service Level Agreement (SLA) requirements within industrial environments becomes paramount. Addressing this need, this paper introduces Cooperative SLA-MLO (C-SLA-MLO), a novel distributed multilink scheduler that balances data across MLO channels to fulfill the SLA of STAs operating within the same BSS. We provide a simulation-based evaluation where we compare C-SLA-MLO with other benchmark approaches, in terms of SLA compliance when varying the SLA characteristics, the level of QoS, or background traffic, showcasing an average reduction of 90% in SLA deviation in the industrial use case. This study contributes practical insights for enhancing wireless connectivity in IIoT, offering potential benefits for industrial network optimization by exploiting the MLO feature brought by the new IEEE 802.11be.

Research on the Development and Policy Evolution of CCUS Industry at Home and Abroad

CCUS is internationally recognized as one of the three major pathways to achieve carbon neutrality goals. It is an important choice for realizing zero emissions from large-scale fossil energy utilization and a feasible technological solution to offset carbon emissions in industries such as power, steel, and cement where emissions reductions are challenging. Systematic analysis of the development and policy evolution of the CCUS industry at home and abroad can provide theoretical basis and practical guidance for China's energy transition and development under the background of carbon emission peaking and achieving carbon neutrality. Guided by the strategic goals of national energy security, carbon emission peaking, and achieving carbon neutrality, this study aims to analyze the global development process and stage characteristics of the CCUS industry, investigate the supporting policies in the CCUS field and their evolution patterns, summarize the current status and trends of the CCUS industry at home and abroad, and provide reference for the implementation of national energy green and low-carbon transformation and the construction of a new energy system. In terms of industry, European and American countries emphasize national-level technological guidance and macro-control. The United States has introduced the progressive 45Q tax credit policy, and the European Union has included CCUS in its carbon trading system. The US National Carbon Capture Center provides a testing environment and facilities for CCUS technology research and development, and has established a carbon dioxide industry cluster and transportation hub. In terms of policies, tax credits and carbon trading policies in Europe and America have attracted various types of capital investment, establishing a relatively complete legal framework system. These regions have been leading in CCUS technology research and deployment, holding dominant positions and decision-making power in mainstream international CCUS organizations such as the CSLF, IEA, GCCSI, and OGCI. This study benchmarks the forward-looking and strategic development status of the CCUS industry at home and abroad, elucidates the challenges facing CCUS industry development, and proposes future trends and policy support needs for the CCUS industry. The research reveals that Europe and America emphasize national-level technological guidance and macro-control, focusing on the construction of regional industrial networks and having established a relatively complete legal and regulatory framework system. Compared to other countries, China needs to establish national-level guidance on CCUS development, regional carbon dioxide capture and transportation networks, and enact specific laws, regulations, and technical standards for CCUS.

Development Artificial Neural Network (ANN) computing model to analyses men's 100¬meter sprint performance trends

Coaches and analysts face a significant challenge of inaccurate estimation when analyzing Men's 100 Meter Sprint Performance, particularly when there is limited data available. This necessitates the use of modern technologies to address the problem of inaccurate estimation. Unfortunately, current methods used to estimate Men's 100 Meter Sprint Performance indexes in Iraq are ineffective, highlighting the need to adopt new and advanced technologies that are fast, accurate, and flexible. Therefore, the objective of this study was to utilize an advanced method known as artificial neural networks to estimate four key indexes: Accelerate First of 10 meters, Speed Rate, Time First of 10 meters, and Reaction Time. The application of artificial neural networks in the sports industry in the Republic of Iraq is crucial to ensure successful players. In this study, an artificial neural network model was built to predict Men's 100 meter indexes. Several factors related to the construction of artificial neural networks were studied, including network architecture and internal factors and their impact on the performance of the models. As a result, four easy equations were developed to calculate the four key indexes. The findings of the study indicate that these networks can predict Men's 100 Meter indexes with a high degree of reliability 98.034% and accounting coefficients R = 0.9143.

A Computational Framework for Enhancing Industrial Operations and Electric Network Management: A Case Study

A Computational Framework for Enhancing Industrial Operations and Electric Network Management: A Case Study

The assembly of locally rooted industrial networks in the Pearl River Delta region: insights for the regeneration of industrial land

ABSTRACT Locally rooted industrial networks pose significant challenges to the sustainable regeneration of industrial land in China. This paper examines the process of rural industrialization in the Shunde District of the Pearl River Delta (PRD) region and its relationships to current regeneration practices from a historical perspective. Drawing on assemblage thinking, the paper traces the four stages of Shunde’s industrial networks assembly process and depicts the ongoing regeneration activities, delving into how heterogeneous entities come together and (re)assemble unique networks attached to the locality over time. The findings show that the assembly and regeneration of industrial networks are subject to diverse interactions between multiple translocal assemblages and Shunde as a place-assemblage. These interactions are conditioned by the distributed agency that emerges from the components of the place-assemblage. Local agency’s enactment of sustainable futures requires a comprehensive understanding of the historical development of local industrial networks and the socio-spatial relationships between heterogeneous entities within or across assemblages to inform and precede interventions such as sustainable regeneration policies and practices.

Global solar photovoltaic industry network dynamics 2007–2023. Inter-organizational relationships as a source of competitive advantage?

Aiming a cleaner production in course of fighting the ongoing global warming, solar photovoltaic (PV) together with wind and hydro energy, indicate the most important industry segments in the transformation from fossils to renewable energy sources. During the last two decades, the solar PV industry experienced decisive changes of its global business network configurations where Chinese firms comparatively have gained competitive advantages. According to the business network theory, inter-organizational relationships indicate important vehicles for exchanging valuable resources such as advanced technology and privileged market knowledge aiming to gain leading industry positions.Based on a sample of globally leading solar PV manufacturers originated in Canada, China, Germany, South Korea, and the United States of America we conduct a detailed analysis and provide insights into solar PV industry upstream and downstream network dynamics examined for the period 2007–2023. We found that Chinese firms have focused more on upstream industry network relationships compared to their Western counterparts.Thus, we emphasize on the importance of building competitive regional solar PV industry networks instead of relying only on government subsidies which often come along with rather short-term effects.

Complex Network-Based Resilience Assessment of the Integrated Circuit Industry Chain

With the improvement of social production efficiency and the enhancement of the supply chain system, the traditional linear supply chain model is gradually evolving into a more complex and dynamic industrial chain network. This article uses complex network theory combined with the basic attributes of the industrial chain and supply chain to conduct a comprehensive and in-depth analysis of the integrated circuit industry chain. Firstly, a cooperative network model of the integrated circuit industry chain in Shaanxi Province is established based on the supply chain relationships of enterprises. Secondly, the study analyzes the basic characteristics of the collaborative network model. Thirdly, this study explores the efficiency, resilience, and innovation capacity of industrial chains using a novel set of indexes: the industry chain efficiency index (ICEI), the industry chain resilience index (ICRI), and the industry chain innovation capability index (ICICI). By employing principal component analysis (PCA), the study provides a comprehensive evaluation of industrial chain performance. The findings reveal that the ICEI highlights the critical importance of average path length and network density, showing that shorter paths and higher density are associated with greater efficiency. The ICRI emphasizes the roles of average degree and standard deviation, indicating that higher connectivity and lower variability contribute to resilience. The ICICI identifies the clustering coefficient and network density as key factors, suggesting that tight-knit networks foster innovation. These results offer significant insights into the dynamics of industrial chain collaboration and provide practical recommendations for enhancing supply chain management. Finally, the effectiveness of the proposed method is demonstrated through a case study. The results of the case study indicate the following: (1) Key Enterprises’ Identification: The analysis identified key enterprises like Samsung Semiconductor and HT-tech with the highest betweenness centrality, highlighting their crucial intermediary roles within the network; (2) Efficiency and Innovation Assessment: Compared with foreign-owned and other immigrant businesses, local businesses generally perform below average in terms of efficiency and resilience, indicating that there is room for improvement in technology adoption and innovation capabilities.

USING NEURAL NETWORK TECHNOLOGY IN CONSTRUCTION ACTIVITIES

The article discusses the basics of using neural networks in the activities of construction organizations. The main goal of the study was to substantiate the advantages and prospects of using neural networks in the construction industry. As part of the study, the essence of this variant of machine learning was initially determined, their classical architecture was justified, key elements were identified, and the principle of operation was described. Subsequently, options for using neural networks within the construction industry were systematized (design of construction projects, analysis of costs for the implementation of construction projects, management of construction projects, monitoring of labor and equipment productivity, assessment of the quality of materials used and inventory management, ensuring energy efficiency, risk analysis and ensuring the safety of work performed, etc.) and a distinction was made between the areas of application of neural networks within the stages of the life cycle of construction projects, which formed the basis of scientific novelty. The results of the study can be applied in the activities of construction organizations to increase the efficiency of work carried out at various stages of construction.

Towards a Software-Defined Industrial IoT-Edge Network for Next-Generation Offshore Wind Farms: State of the Art, Resilience, and Self-X Network and Service Management

Offshore wind farms are growing in complexity and size, expanding deeper into maritime environments to capture stronger and steadier wind energy. Like other domains in the energy sector, the wind energy domain is continuing to digitalize its systems by embracing Industry 4.0 technologies such as the Industrial Internet of Things (IIoT), virtualization, and edge computing to monitor and manage its critical infrastructure remotely. Adopting these technologies creates dynamic, scalable, and cost-effective data-acquisition systems. At the heart of these data-acquisition systems is a communication network that facilitates data transfer between communicating nodes. Given the challenges of configuring, managing, and troubleshooting large-scale communication networks, this review paper explores the adoption of the state-of-the-art software-defined networking (SDN) and network function virtualization (NFV) technologies in the design of next-generation offshore wind farm IIoT–Edge communication networks. While SDN and NFV technologies present a promising solution to address the challenges of these large-scale communication networks, this paper discusses the SDN/NFV-related performance, security, reliability, and scalability concerns, highlighting current mitigation strategies. Building on these mitigation strategies, the concept of resilience (that is, the ability to recover from component failures, attacks, and service interruptions) is given special attention. The paper highlights the self-X (self-configuring, self-healing, and self-optimizing) approaches that build resilience in the software-defined IIoT–Edge communication network architectures. These resilience approaches enable the network to autonomously adjust its configuration, self-repair during stochastic failures, and optimize performance in response to changing conditions. The paper concludes that resilient software-defined IIoT–Edge communication networks will play a big role in guaranteeing seamless next-generation offshore wind farm operations by facilitating critical, latency-sensitive data transfers.

SCADA World: An Exploration of the Diversity in Power Grid Networks

Despite a growing interest in understanding the industrial control networks that monitor and control our critical infrastructures (such as the power grid), to date, SCADA networks have been analyzed in isolation from each other. They have been treated as monolithic networks without taking into consideration their differences. In this paper, we analyze real-world data from different parts of a power grid (generation, transmission, distribution, and end-consumer) and show that these industrial networks exhibit a variety of unique behaviors and configurations that have not been documented before. To the best of our knowledge, our study is the first to tackle the analysis of power grid networks at this level. Our results help us dispel several misconceptions proposed by previous work, and we also provide new insights into the differences and types of SCADA networks.

Funding and conclusions of network meta-analyses on targeted therapies in inflammatory diseases: an overview

ObjectivesTo explore the association between industry funding and network meta-analyses’ (NMAs) conclusion, and the use in Clinical Practice Guidelines (CPGs) of NMAs. Study Design and SettingThis was an overview of NMAs and CPGs. We searched PubMed/MEDLINE, Epistemonikos, and several guideline databases up to February 18th 2023. We included CPGs from the last 5 years and NMAs of randomized controlled trials that evaluated targeted therapies in immune-mediated inflammatory diseases. Data extraction and outcome assessments were done in duplicate by independent authors. ResultsWe included 216 NMAs and 99 CPGs. 31% (67/216) were industry-funded. The proportion of industry-funded NMAs that cited one treatment as being best was 44% (25/57) compared to 26% (30/116) for nonindustry-funded (OR = 2.24 [1.15-4.39]; aOR = 1.76 [0.81-3.81]). The abstract's conclusion of 39/67 (58%) industry-funded and 69/149 (46%) nonindustry-funded NMAs were considered unsupported by the results (OR = 1.61 [0.90-2.89]; aOR = 1.40 [0.71-2.78]). All industry-funded NMAs that cited one treatment as best cited their own sponsored drug. 59/99 (60%) CPGs included at least one NMA, with 23/59 (39%) of them citing industry-funded NMAs. ConclusionsWe did not find evidence that industry-funded NMAs were more likely to have unsupported conclusions or to cite only one treatment as being best in their conclusions compared to non-industry-funded NMAs. However, almost all industry-funded NMAs favored their own treatments. Even though 40% of the CPGs did not rely on NMA, over a third of those who did used industry-funded NMAs. Limitations include the possible misclassification due to undisclosed funding and potential confounders that have not been accounted for.

WSS-YOLO: An improved industrial defect detection network for steel surface defects

Surface defects in steel manufacturing can compromise product quality and safety. Detecting these diverse and complex defects under industrial conditions is challenging. This paper proposes WSS-YOLO, a YOLOv8-based model, for accurate defect detection on industrial steel. Firstly, the dynamic non-monotonic focusing mechanism based on WIoU loss was employed to focus on anchor boxes with ordinary quality, thereby improving the overall performance of the detector. Secondly, the C2f-DSC module based on dynamic snake convolution is designed to enable the model to adaptively adjust the receptive field. Finally, GSConv and VOV-GSCSP modules are introduced into the neck network to reduce the computational complexity and parameter quantity while ensuring the accuracy of the model. This paper conducts extensive experiments on the public datasets NEU-DET and GC10-DET, achieving mAP of 82.3% and 72.0%, respectively, outperforming other excellent models. Moreover, the effectiveness of the proposed method in industrial defect detection is also validated.

The Influence of Brand Image Brand Awareness and Promotion on Purchase Decisions for Ubiquiti Brand IT Network Devices in Indonesia

The background of this research is the increasing demand and competition in the wireless network device industry. Ubiquiti, as one of the major players in the industry, needs to understand how brand image, brand awareness, and promotional activities influence consumers' purchasing decisions. This study aims to analyze the influence of brand image, brand awareness, and promotion variables on purchasing decisions on Ubiquiti brand IT network products. Data were collected from 97 respondents, and analysis was conducted on the demographics of respondents, product use, and the influence of independent variables on purchasing decisions. Research analysis method using SPSS. The results showed that the majority of product users were men aged 25-29 years who worked as employees and used the product for less than 1 year. Statistical analysis revealed that the three variables of brand image, brand awareness, and promotion significantly influence purchasing decisions. Brand awareness has been shown to have the most influence, followed by brand image and promotion. This finding is reinforced by brand awareness's largest adjusted R square value (96.8%). The main conclusion is that brand awareness has the most significant influence on consumer purchasing decisions Ubiquiti brand IT network products, followed by brand image and promotion. The implication of this study is the importance of companies in designing effective marketing and branding strategies to improve consumer purchasing decisions. The research provides valuable insights for Ubiquiti and other companies in the wireless networking industry to better understand the factors driving consumer buying behavior, so they can develop targeted marketing strategies and increase their market share.

THE IMPORTANCE OF INVESTMENTS IN THE FORMATION OF THE "GREEN ECONOMY" IN INDUSTRY NETWORKS

The transition to a "green economy" necessitates significant investments across all sectors, particularly within industrial networks. This abstract examines the crucial role of these investments in fostering sustainable practices, promoting innovation, and driving economic growth. By exploring the multifaceted impact of green investments on industry networks, the abstract highlights the importance of public-private partnerships, policy incentives, and technological advancements in enabling a successful and equitable transition. Furthermore, the abstract emphasizes the need for a comprehensive framework that considers both economic and environmental objectives, ensuring that green investments contribute to a prosperous and sustainable future.

A Review of the Information Technology and Operational Technology Convergence Using Internet of Things within the Circular Economy

Abstract The historical separation of Information Technology and Operational Technology in organizations, particularly in the industrial sector, is being challenged by the digital transformation. This shift towards a converged platform is driven by technological advancements, market pressures, and environmental and regulatory considerations. This paper explores the convergence of Information Technology and Operational Technology through the Internet of Things within the Circular Economy. A comprehensive overview of the current scientific literature is provided using a systematic review method. Initially, the evolving landscape of the Information Technology and Operational Technology integration is reviewed, then Internet of Things and Circular Economy are presented. The findings of this study include the challenges of the converged solution implementation, such as the technical integration complexities, cybersecurity risks, organizational and cultural barriers, and workforce skills gap. Furthermore, the role of the Internet of Things is demonstrated using novel smart circular loops, products, and applications showcasing innovative ideas such as product-as-a-service models, digital circular practices, products with embedded sensors, energy harvesting, and modular and upgrade capabilities as well as novel digital factory use cases such as smart manufacturing and industrial symbiosis networks. The key findings also outline the best practices and recommendations for a successful implementation of this converged infrastructure, highlighting the importance of a strategic implementation and cross-domain collaboration to achieve operational efficiency and environmental sustainability. The insights provided in this paper are valuable for academics, practitioners, and policymakers aimed to understand and harness the digital technologies for advancing sustainable practices towards a smart circular economy.

QoS Provision for Industrial IoT Networking: Multiantenna NOMA Based on Partial CSIT

QoS Provision for Industrial IoT Networking: Multiantenna NOMA Based on Partial CSIT