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In September 2022, former students and colleagues of Thomas A. Kochan organized a Festschrift, a "festival of writing," to honor his impact on Industrial Relations and related fields. The Festschrift generated more than two dozen articles and commentaries in a forthcoming book, as well as the four articles featured here in the ILR Review. These articles include "A Tale of Two Locals," focused on contrasting responses to the 2019 GM-UAW strike; "The Need for Speed," focused on the role of employers in immigrant work visa prioritization; "Incentives to Comply with the Minimum Wage," comparing enforcement in the United States and the United Kingdom; and "Gendered Job Search" focused on gender differences in reservation wages and job applications. The articles highlight the roles of labor, management, and government as actors in an industrial relations system. Together these articles and the additional collection in the forthcoming book exemplify Tom Kochan's leadership in advancing industrial relations theory, policy, and practice. [ABSTRACT FROM AUTHOR]

Hydrofluorocarbons are a class of fluorinated molecules used extensively in residential and industrial refrigeration systems. This study examines the potential of using adsorption processes with the silicalite-1 zeolite to separate a mixture of difluoromethane (CH2F2, HFC-32) and pentafluoroethane (CF3CF2H, HFC-125) at various concentrations. Pure adsorption data were measured using a XEMIS gravimetric microbalance, whereas binary data were determined using the Integral Mass Balance method. Grand canonical Monte Carlo molecular simulations were performed with the Cassandra package. We found that the results from molecular simulations are in satisfactory agreement with experimental loading measurements. Moreover, we show that ideal adsorbed solution theory could not quantitatively match the experimental or computational measurements of binary adsorption or selectivity. Molecular simulations show that refrigerant molecules do not have a uniform distribution in the zeolite framework. [ABSTRACT FROM AUTHOR]

The gradual shift in power relations between organized employers and employees since the 1970s has increasingly affected the functioning of national industrial relations systems. According to a broad literature, the most important of these consequences is an increase in employer discretion. This article tests this claim by performing a longitudinal content analysis on three Dutch collective contracts. It develops an analytical framework based on four dimensions of employer discretion. Results show that although employer discretion did increase on all four dimensions between the mid-1970s and the 2020s, significant temporal and sectoral variation has occurred. In addition, the article argues that a loss of democratic influence by employees intensified the increase of the one-sided decision-making powers of employers, and that the collective contract is being transformed from a joint labor–capital effort to solve workplace problems to a management instrument. [ABSTRACT FROM AUTHOR]

The dynamic characteristics (i.e. autocorrelation and cross-correlation) in modern industrial systems have raised great challenges to process fault detection. To cope with the dynamics and uncertainty of dynamic industrial processes, this paper proposes a new process control method, dynamic convolutional gated recurrent unit attention auto-encoder (DCGRUA-AE) for fault detection in dynamic processes. Firstly, DCGRUA-AE integrates a convolutional gated recurrent unit (CGRU) with a local convolution layer to learn both global and local features of dynamic process data in an unsupervised fashion. Secondly, a dual attention module is embedded in the deep network to preserve effective features. Finally, DeconvGRU combined with a dense layer is used as the encoder to reconstruct the original process data. Two statistics (i.e. T-square (T2) and squared prediction error (SPE)) based on DCGRUA-AE are used to set the control limits for fault detection. The feasibility and superiority of DCGRUA-AE-based fault detection method have been verified on four industrial processes. The experimental results indicate that the CGRU and dual attention mechanism can significantly improve the fault detection performance of DCGRUA-AE in dynamic processes. The hybrid of CGRU, attention mechanism and auto-encoder provides a new method for fault detection in dynamic industrial processes. [ABSTRACT FROM AUTHOR]

The industrial Internet of Things (IIoT) is prominently emerging in applications of large-scale and wide-area applications, such as oilfield management, smart grid management, real-time equipment monitoring, and integration of traffic management systems for smart cities. Relying on short-range wireless technologies (e.g., WirelessHART and ISA100.11a), traditional wireless solutions for industrial automation find it challenging to support the expansive scale of today's IIoT. To address this limitation, we propose to adopt LoRaWAN, a prominent low-power wide-area network technology, for industrial automation. LoRaWAN for industrial automation poses some unique challenges. The fundamental building blocks of any industrial automation system are feedback control loops that largely rely on real-time communication. LoRaWAN traditionally adopts a simple protocol based on ALOHA with no collision avoidance or Listen Before Talk with Clear Channel Assessment and Random Backoff mechanisms to minimize energy consumption, which are less suitable for real-time communication. Existing real-time protocols for short-range technologies cannot be applied to a LoRaWAN network due to its unique characteristics such as asymmetry between downlink and the uplink spectrum, predefined modes (or classes) of operation, and concurrent reception through orthogonal spreading factors. In this paper, we address these challenges and propose RTPL- a Real-Time communication Protocol for LoRaWAN networks. RTPL is a low-overhead and conflict-free communication protocol allowing autonomous real-time communication of low-energy devices and exploits LoRa's capability of parallel communication. We implement our approach on LoRa devices and evaluate through both physical experiments and extensive simulations. All results show that RTPL achieves on average 75% improvement in real-time performance without sacrificing throughput or energy compared to traditional LoRaWAN. [ABSTRACT FROM AUTHOR]

Modern industrial systems routinely generate data in high volume at high velocity. These high-dimensional data streams (HDDS) provide valuable information at granular levels to quality personnel during root cause investigation in cases of a system fault. The goal of fault analysis using HDDS is twofold: (1) identify abnormal data streams and (2) locate the change point when the processes become out of control. Existing research has largely focused on addressing the two issues separately. In this article, we propose a unified framework by formulating the problem as optimal control of hierarchical missed discovery rates in multiple classifications. Theoretically, we establish that our approach minimizes the number of false discoveries while controlling the missed discovery rates at desired levels. Numerically, we develop a computationally efficient algorithm for solving the optimization and demonstrate its superior performance over the existing methods. A data-driven version of the proposed approach is suggested as well. An application to a real data set in semiconductor manufacturing shows that our approach works well in practice. [ABSTRACT FROM AUTHOR]

This study introduces a sophisticated intelligent predictive maintenance system for industrial conveyor belts powered by a random forest machine learning model. The random forest model was evaluated against established models such as logistic regression, neural networks, decision trees, and gradient boosting, demonstrating superior performance. The model achieved 100% accuracy in classifying gearbox lubricant levels and sprocket conditions, highlighting its potential for addressing critical challenges in predictive maintenance, such as avoiding unexpected downtime. However, further validation with larger datasets and varied operational environments is recommended to confirm robustness. This performance highlights its effectiveness in multiclass fault detection and overfitting mitigation, establishing a new standard in predictive maintenance technology. The system, enhanced by a comprehensive sensor array, not only adeptly captures but also intelligently analyzes critical operational data, providing proactive and data-driven insights for maintenance decision-making. This study not only affirms the dominance of the random forest model in predictive analytics but also underscores its pivotal role in optimizing maintenance strategies, enhancing operational efficiency, and ensuring the reliability of conveyor systems in industrial settings. [ABSTRACT FROM AUTHOR]

AbstractElectricity price forecasting is one of the cornerstones of modern-day power system operation. Over the last two decades, the field has seen many methodological advancements. On one end, traditional statistical methods remain widely prevalent in today’s energy industry, as they have proven consistently to “stand the test of time.” On the other end, emerging predictive techniques (e.g., machine and deep learning) have demonstrated immense potential. Yet, due to limited benchmarking studies, the magnitude of improvement realized by such emerging methods relative to their statistical counterparts is not entirely clear. In response, two technical divisions of the Institute of Industrial & Systems Engineers (IISE) partnered with the Pacific Gas and Electric Company (PG&E)—one of the largest utility companies in the United States—in order to organize an electricity price forecasting challenge in 2024. Using three years of pricing signals and exogenous information from California’s electricity market, the competition challenged teams of researchers and practitioners to design their own models and submit forecasts for day-ahead electricity prices, which were independently evaluated against a test set that has been reserved from all teams. This paper introduces the challenge, as well as an overview of the methods used by the top-performing contestants. A distilled summary of the key insights and lessons learned by the challenge organizers is then presented, together with their relevance to the topic of electricity price forecasting. This is then followed by recommendations for future similarly focused competitions. To accelerate the research and development on this important topic, all data used in the challenge have been made publicly available by the challenge organizers. [ABSTRACT FROM AUTHOR]

Bacterial biofilms pose significant challenges, from healthcare-associated infections to biofouling in industrial systems, resulting in significant health impacts and financial losses globally. Classic antimicrobial methods often fail to eradicate sessile microbial communities within biofilms, requiring innovative approaches. This review explores the structure, formation, and role of biofilms, highlighting the critical importance of exopolysaccharides in biofilm stability and resistance mechanisms. We emphasize the potential of microbial enzymatic approaches, particularly focusing on glycosidases, proteases, and deoxyribonucleases, which can disrupt biofilm matrices effectively. We also delve into the importance of enzymes such as cellobiose dehydrogenase, which disrupts biofilms by degrading polysaccharides. This enzyme is mainly sourced from Aspergillus niger and Sclerotium rolfsii, with optimized production strategies enhancing its efficacy. Additionally, we explore levan hydrolase, alginate lyase, α-amylase, protease, and lysostaphin as potent antibiofilm agents, discussing their microbial origins and production optimization strategies. These enzymes offer promising avenues for combating biofilm-related challenges in healthcare, environmental, and industrial settings. Ultimately, enzymatic strategies present environmentally friendly solutions with high potential for biofilm management and infection control. [ABSTRACT FROM AUTHOR]

ABSTRACT Measuring element is always accompanied by uncertainty disturbances and multiple faults during the long time operation of industrial system in complex environment such as blade and pitch system of floating wind turbines. Timely detection of the fault and fault‐tolerant control (FTC) perform a significant part in ensuring the stable operation of the system and saving maintenance fees. Sliding mode control is extensively applied to FTC because of its good robustness. Therefore, a sliding mode controller is constructed to guarantee the stability of the industrial plant which suffers multiple faults and uncertain disturbances. At the same time, most existing literature does not take into account several faults and uncertain disturbances. Firstly, employing generalized sliding mode method, we devise a sliding mode observer for evaluating state vector, actuator fault and sensor fault of the system. Secondly, according to the state estimation, we construct a sliding mode controller and prove its validity by Lyapunov's theorem. Our controller achieves satisfactory performance, and it is easier to be implemented in practical engineering than other controllers. Finally, we establish a SIMULINK model of blade and pitch system and make simulation experiments. Simulation outcomes validate the availability and practicability of our controller, which also provides a general scheme for fault estimation and FTC of other industrial plants. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for enhancing fault diagnosis in industrial equipment systems through the application of sensor network-driven knowledge graph-based in-context learning (KG-ICL). By focusing on the critical role of sensor data in detecting and isolating faults, we construct a domain-specific knowledge graph (DSKG) that encapsulates expert knowledge relevant to industrial equipment. Utilizing a long-length entity similarity (LES) measure, we retrieve relevant information from the DSKG. Our method leverages large language models (LLMs) to conduct causal analysis on textual data related to equipment faults derived from sensor networks, thereby significantly enhancing the accuracy and efficiency of fault diagnosis. This paper details a series of experiments that validate the effectiveness of the KG-ICL method in accurately diagnosing fault causes and locations of industrial equipment systems. By leveraging LLMs and structured knowledge, our approach offers a robust tool for condition monitoring and fault management, thereby improving the reliability and efficiency of operations in industrial sectors. [ABSTRACT FROM AUTHOR]

The paradigms of Industry 4.0 and Industrial Internet of Things (IIoT) require functional architectures to deploy and organize hardware and software taking advantage of modern digital technologies in industrial systems. In this sense, a lot of the literature proposes and describes this type of architecture with a conceptual angle, without providing experimental validation or with scarce details about the involved equipment under real operation. Aiming at overcoming these limitations, this paper presents the experimental application of an IIoT architecture divided into four functional layers, namely, Sensing, Network, Middleware and Application layers. Automation and IoT hardware and software are used to implement and apply the architecture. Special attention is put on the software Grafana, chosen in the top layer to deploy graphical user interfaces that are remotely accessible via web. A pilot microgrid integrating photovoltaic energy and hydrogen served as scenario to test and prove the suitability of the architecture in four application cases. [ABSTRACT FROM AUTHOR]

This study presents a novel algorithm for protocol reverse analysis of EtherCAT. The algorithm combines sequence alignment and the Pearson correlation coefficient. We utilize value distribution statistics and the bit flip rate algorithm to effectively partition the protocol fields. We propose a semantics analysis method based on sequence alignment when HMI data and EtherCAT messages have a direct correlation. Conversely, for circumstances where there exists a decoding relationship between HMI data and EtherCAT messages, a semantic analysis method is proposed that employs the Pearson correlation coefficient. We completed a reverse analysis of the EtherCAT messages from an industrial robot system. By comparing the experiment results with the protocol description document, we validated the effectiveness of the method. [ABSTRACT FROM AUTHOR]

With the deep implementation of the national "dual carbon" strategy, the development of a new power system dominated by renewable energy has accelerated significantly. Electrolytic aluminum load, as an important energy-intensive industrial resource, possesses response flexibility, providing a critical pathway for the efficient utilization of renewable energy. However, ensuring the safety of its production process during demand-side response remains a key challenge. This study systematically investigates the core production constraint of electrolytic aluminum load—electrolytic bath temperature—and its impacts on chemical reaction rates, current efficiency, and production equipment. A detailed coupling relationship between core production constraints and active power regulation is established. By quantifying the effects of temperature variation on the electrolytic aluminum production process, a demand-side response control cost model for electrolytic aluminum load is proposed. Additionally, a day-ahead scheduling model is developed with the objective of minimizing system operating costs while considering the participation of electrolytic aluminum load. Simulation results demonstrate that this method significantly reduces wind curtailment and load shedding while ensuring the safety of electrolytic aluminum production. It provides a novel approach for enhancing system economic efficiency, improving renewable energy utilization, and promoting the deep integration of power systems with industrial loads. [ABSTRACT FROM AUTHOR]

The fractional frequency offshore wind power system (FFOWPS) is a core technology for the long‐distance transmission and integration of the large‐scale offshore wind power. However, the risk of wide‐band oscillation in the FFOWPS has become increasingly prominent due to the inevitable presence of the power electronic devices. The traditional admittance of the frequency converter, which is a crucial component in the FFOWPS, may contain right‐half‐plane (RHP) poles when operating under various control modes. To address this deficiency, this paper establishes a four‐port small‐signal admittance model of a back‐to‐back frequency converter and demonstrates its interconvertibility with the traditional model. On this basis, the wide‐band oscillation analysis method for the FFOWPS is proposed, which explicitly describes the influence of the industrial frequency system, the fractional frequency system, and their coupling interactions on system stability. Simulation results in MATLAB/Simulink verify the proposed stability analysis method, highlight stability misjudgements associated with the traditional admittance, and analyse the impact of the coupling terms on system stability. [ABSTRACT FROM AUTHOR]

Solar photovoltaics, especially rooftop systems also called distributed solar photovoltaics, are crucial in the ongoing energy transition. Modeling these systems is vital to understanding their role in a decentralized energy system. While ground‐mounted photovoltaic power plants are easier to model, generalizing yield profiles for rooftop systems is challenging. This study aims to estimate yield loss effects for rooftop solar photovoltaic systems compared to optimized ground‐mounted systems. Anticipated yield losses are 18% for residential, 7% for commercial, and 4% for industrial rooftop systems. The impact on residential prosumers' viability is assessed by comparing prosumer system optimization results with and without yield losses. Results show a non‐uniform change in installed solar photovoltaic and battery capacities, with a tendency to compensate for reduced yields by increasing photovoltaic capacity by up to 20%, given favorable cost prospects by 2050. The annualized total cost of energy for prosumer households could therefore increase by up to 20% by 2050. Despite yield reductions, installing a solar photovoltaic prosumer system remains more favorable than relying entirely on‐grid electricity. This study highlights the importance of considering yield losses in rooftop solar photovoltaics and the significant role of prosumers despite identified yield losses. [ABSTRACT FROM AUTHOR]

This article presents research work on an intelligent system that was developed to monitor and continuously evaluate the quality of metal powder distribution in the laser powder bed fusion (LPBF) 3D printing process. The 3D printer that was used to carry out the work was equipped with an industrial vision system to capture images immediately after spreading powder on the work field. The powder distribution tests showed that the most common defects were identified as an insufficiently thick layer of powder applied to the working field (super elevation), unevenly distributed powder as a result of recoater vibration (so called recoater hopping), and its wear (so called recoater streaking). In the first stage of research, a set of training data (images) was collected. Then, the implementation of the machine learning process was prepared in the Roboflow environment. After that, the learning, validation, and prediction process was carried out several times using the selected machine learning model (YOLO model implemented in a Python environment) in order to select the most effective parameters. The study showed that deep machine learning can be effectively used to identify defects in powder distribution during the laser powder bed fusion (LPBF) process. [ABSTRACT FROM AUTHOR]

The integration of blockchain and federated learning (FL) has emerged as a promising solution to address data privacy and security challenges in Intelligent Unmanned Ports (IUPs). However, existing blockchain federated learning (BFL) frameworks encounter significant limitations, including high latency, inefficient data processing, and limited scalability, particularly in scenarios with sparse and distributed data. This paper introduces a novel multi-chain federated learning (MFL) framework to overcome these challenges. The proposed MFL architecture interconnects multiple BFL chains to facilitate the secure and efficient aggregation of data across distributed devices. The framework enhances privacy and efficiency by transmitting aggregated model updates rather than raw data. A low-frequency consensus mechanism is employed to improve performance, leveraging game theory for representative selection to optimize model aggregation while reducing inter-chain communication overhead. The experimental results demonstrate that the MFL framework significantly outperforms traditional BFL in terms of accuracy, latency, and system efficiency, particularly under the conditions of high data sparsity and network latency. These findings highlight the potential of MFL to provide a scalable and secure solution for decentralized learning in IUP environments, with broader applicability to other distributed systems such as the Industrial Internet of Things (IIoT). [ABSTRACT FROM AUTHOR]

Concern has been raised about the potential greenwashing/co-optation of regenerative agriculture (RA) due to a lack of consensus on its definition. While the academic literature has cataloged various approaches to defining RA, each definitional approach carries with it a relative concern for its likelihood for co-optation and the potential transformative power it can have within the sector. As the industrial agrifoods sector is taking interest in the field, lessons from the organic movement are worth highlighting. The corporate system has easily integrated the foundational pillar of growing food without chemicals, but left behind the pillars of alternative food distribution, and a focus on whole foods and unprocessed ingredients. Corporate interest in RA could be a major driver for scaled adaptation, yet it may lose its focus on the regeneration of agriculture resources, ecosystems functions, and the social systems required to reproduce the next generation of farmers. The greatest challenge is that the fundamental concern is a philosophical one, which entails a shift in how humans perceive the natural world and their role in it. As RA scales, will it hold to its values and remain obscure, or could its values merge with the predominant industrial system to have significance and affect real change in agriculture? [ABSTRACT FROM AUTHOR]

Daucus carota L. extract is attracting interest as a natural colorant alternative. However, the presence of anthocyanins (ACNs), which are sensitive to pH changes, limits its application. To tackle this issue, water-in-oil-in-water (W1/O/W2) double emulsions are emerging as innovative solutions. Nevertheless, the problem of reaching robust colorant systems for industrial use still needs to be overcome. One important target is to reach a high coloring power, minimizing its impact on the final product. In this context, the effect of colorant concentration and the volume of the primary emulsion, two routes to increase the colorant power, on color attributes and stability, an important feature to reach a marketable product, was studied. The optimal experimental design was conducted to two optimal solutions, whether through heightened colorant concentration or primary emulsion volume: a 41/59 (W1/O)/W2 ratio with 11 wt.% colorant, and a 48/52 (W1/O)/W2 ratio with 6 wt.% colorant, respectively. A subsequent assessment of color and physical emulsion stability over 30 days pointed out the solution with the lower colorant concentration (6 wt.%) as the one with better performance (L*: 44.11 ± 0.03, a*: 25.79 ± 0.01, D4;3: 9.62 ± 0.1 µm, and CI: 14.55 ± 0.99%), also minimizing the permeability of the colorant to the outer aqueous phase. Overall, these optimized emulsions offer versatile coloring solutions suitable for various industrial applications, such as food matrices and functional cosmetics. [ABSTRACT FROM AUTHOR]

Civil society organizations (CSO) engaged in food systems work have grown substantially in number, scale, and scope. Many of these CSOs are seizing opportunities to engage with the globalized industrial food system in efforts to promote greater equity and sustainability and have realized that addressing issues at a systemic level demand engaging with governance. Despite increasing scholarly attention to food systems and to CSO engagement with governments, the diversity of governance arrangements remains understudied. In this paper, we explore the meanings and perspectives of food systems governance from the standpoint of CSOs leaders across Canada and Indigenous territories. Drawing on 70 semi-structured interviews, we argue that CSOs play a central role in advancing food systems governance by how they frame and act on food issues. We point to CSOs understandings and engagement with food systems governance as broader and more nuanced than what has previously been documented in the literature. A more discerning and comprehensive understanding of how CSO actors describe and advance food systems governance helps articulate how CSOs are scaling-up place-based work, modelling new forms of governance, and ultimately, impacting decision-making structures. [ABSTRACT FROM AUTHOR]

This introduction examines the debates on late industrialism and the processes of ruination in light of the historical and social transformations that have been shaping Southern Italy. Through the lens of cultural anthropology, it highlights the enduring impact of state-led industrialization on the region, drawing critical attention to the co-existence of conditions of abandonment, uncertainty, and disillusionment of environmental degradation, as well as the renewed meanings of territories and the imaginaries of possible futures. The articles in this special issue are grounded in ethnographic research in Sardinia, Sicily, and Apulia - territories where weakened industry is contradictorily intertwined with new development narratives. The authors examine the practices, narratives, sentiments and moral ambivalences that shape these territories and their stories, shedding light on how concrete groups of people make meaning of modern ruins and live in and with ruined spaces. [ABSTRACT FROM AUTHOR]

Uranium extraction from seawater (UES) is crucial for ensuring the sustainable development of nuclear power and has seen significant advancements in recent years. However, natural seawater is a highly complex biogeochemical system, characterized by an extremely low uranium (U) concentration (≈3.3 µg L−1), abundant competitive ions, and significant marine biological pollution, making UES a formidable challenge. This review addresses the challenges encountered in UES and explores potential methods for enhancing the industrial UES system, including membrane separation, electrochemistry, photocatalysis, and biosorption. Additionally, several representative marine tests are summarized and restrictive factors of large‐scale UES are analyzed. Finally, the further development of UES from laboratory to industry applications is promoted, with a focus on technological innovation. The goal is to stimulate innovative ideas and provide fresh insights for the future development of the UES system, bridging the gap between laboratory research and industrial implementation. [ABSTRACT FROM AUTHOR]

This article explores the experience of ruination in one of Europe’s largest petrochemical hubs, located in southeastern Sicily. Analyzing a public demonstration that blends an eco-cultural outing with a protest march, I focus on a military zone near old petrochemical plants and deteriorating structures associated with the mid-twentieth-century modernist ideology. The article looks at how local activists interact with this residual materiality during the walk, creating a deep sensory engagement and fostering a renewed understanding of the site’s potential for transformation. The concept of ‘scratching’ describes the tactics used to scrape away the disturbing layers of the late industrial landscape, revealing alternatives that transcend current limitations. [ABSTRACT FROM AUTHOR]

The Ascomycete genus Fusarium , first introduced by Link in 1809, currently consists of 431 species and 3558 unclassified isolates and hybrids (according to NCBI Taxonomy lists). Collectively, these fungi have diverse lifestyles and infection cycles exploiting a wide range of environments, hosts, ecological niches, and nutrient sources. Here, we carried out a pan- Fusarium species review to describe and explore the glamorous, and the less attractive niches, exploited by pathogenic and endophytic species. We survey species that infect plant, human, animal and/or invertebrate hosts, free-living non-pathogenic species dwelling in land, air or water-based natural ecosystems, through to those species that exploit human-modified environments or are cultivated in industrial production systems. Fully sequenced, assembled and annotated reference genomes are already available for 189 Fusarium species, many at chromosome scale. In addition, for some of the world's most important species extensive single species pangenomes or closely related formae speciales genome clusters are readily available. Previous comparative genomics studies have focussed on taxonomically restricted clusters of Fusarium species. We now investigate potential new relationships between these vastly contrasting Fusarium biologies, niches and environmental occupancies and the evolution of their respective genomes. [ABSTRACT FROM AUTHOR]

AbstractAugmented Reality (AR) enables an immersive understanding of industrial operating behavior in a mixed environment of reality and virtual. Hence, an AR-guided navigation system for industrial wiring is proposed. The objective is to establish an interactive learning platform for the digital transmission of wiring skills to improve efficiency and quality. Firstly, the mapping relationship from virtual to real is constructed, and a reasonable spatial error threshold is set to reduce the error. Then, the visual positioning method is used to calibrate the spatial position errors of virtual parts to optimize the AR guidance effect. Finally, the virtual operation is accurately mapped to the real space for guidance. The results show that AR-guided wiring reduces operating time by 22% and errors by 74% compared to real wiring. Compared to general AR-guided wiring, the calibrated wiring exhibits an 80% reduction in operating errors. As a result, the virtual and real spatial calibration contributes to efficient AR-guided wiring for accurate digital training. [ABSTRACT FROM AUTHOR]

Due to the high costs associated to experimental testing of hydrogen (H 2) explosions, numerical simulations play an essential role in the design of safe industrial systems. In this work, we investigate the use of the Thickened Flame Model (TFM) to perform simulations of H 2 deflagrations. Specific issues arise when considering hydrogen: (i) due to differential diffusion, the flame is very sensitive to stretch; (ii) instabilities of the flame front are generated and lead to an increased flame propagation speed. Both of these phenomena are affected by flame thickening. First, the flame sensitivity to stretch is artificially increased by the thickening. The recently developed Ma-TFM model proposes a correction of diffusive fluxes to reach the correct Markstein length and is selected in this work. Secondly, thermo-diffusive and hydrodynamic flame instabilities are also altered by thickening. By computing the dispersion relationship for a hydrogen/air mixture, we show that the instability scales are proportional to the thickening factor F. A simple subgrid scale (SGS) model, based on the computations of laminar unstable 2-D and 3-D flames, is then considered to take into account the effect of instabilities. The Ma-TFM and SGS instability models are applied together for the first time on a semi-industrial vented lean hydrogen deflagration, leading to promising results. In particular, it is found that considering 3-D unstable flames is necessary to accurately predict the experimental pressure peaks. [Display omitted] • Stretch corrected Thickened Flame Model coupled to a model for flame instabilities. • Thermodiffusive instability scales are proportional to the thickening factor. • Successful simulation of a semi-industrial vented hydrogen deflagration. • 3-D nature of thermo-diffusive instabilities needs to be considered. [ABSTRACT FROM AUTHOR]

Purpose: In industrial applications, couplings are widely utilised, primarily to connect prime movers to process machines. However, coupling must contend with various induced moments, additional load toque, stress under vibrations, and misalignment during operation. One of the most significant challenges it encounters is shaft misalignment. Furthermore, due to misalignment, a cyclic load generates additional moments at the bearing, escalating time-varying bearing reactions at the bearing cap. Methods: The bearing cap vibrations are caused by this time-varying reaction. The dynamics of a two-jaw elastomeric coupling are evaluated in this context for three sixty-degree rotations while in a predetermined misalignment position. Results: The analysis reveals that the load on the jaws of coupling follows a cosine cyclic curve. Findings also reveal that certain misalignment angles increase vibration amplitude, with critical thresholds that affect coupling lifespan and machinery performance. As a result, the load induced at the motor shaft appears to fluctuate. The maximum bearing cap vibrations found as 0.00014 mm. The vibration pattern of a single coupling rotation is identified as periodic. This pattern is expected to become aperiodic as the induced jaw load cyclic pattern becomes distorted due to shaft pulling during coupling rotation. A comprehensive analysis will be expounded upon in the following sections. Shaft tugging can result from extreme misalignment. Because the second shaft pulled it inline when one shaft rotated fully. Conclusions: This study helps to improve industrial machinery reliability by elucidating complex dynamics of coupling behaviour. Further research should focus on adaptive control strategies that dynamically adjust to misalignment to strengthen industrial systems against vibration-induced failures. The findings could also be applied to more industrial scenarios by analyzing different elastomeric materials. [ABSTRACT FROM AUTHOR]

In recent decades, standardisation has become crucial in Industrial Automation Systems, promoting interoperability among devices from different manufacturers. Nonetheless, the portability of manufacturing engineering tools continues to present a significant challenge. AutomationML (IEC 62714 -5) has played a role in formulating strategies that leverage O-PAS Standard Function Blocks. The O-PAS specification includes several standards, such as IEC 62443, IEC 61499, IEC 61131–3, OPC-UA (Open platform communications unified architecture), DCS (Distributed control system), and PLC (Programmable logic controller). This highlights the necessity of integrating new technologies with legacy systems to achieve enhanced interoperability. In this context, this work proposes the development of User-Defined Function Blocks (UDFB) that adhere to O-PAS standards and can be implemented across any compliant system. The proposed methodology is aligned with O-PAS standards, leading to the inclusion of UDFB information in XML format within the Function Block Library and AutomationML. [ABSTRACT FROM AUTHOR]

In medium voltage and high-power drive applications, pulse width modulation (PWM) techniques are widely used to achieve effective speed control of AC motors. In real-time, an industrial drive system requires reduced hardware complexity and low computation time. The reliability of the AC drive can be improved with the FPGA (field programmable gate array) hardware equipped with digital controllers. To improve the performance of AC drives, a new FPGA-based Wavect real-time prototype controller (Xilinx ZYNQ-7000 SoC) is used to verify the effectiveness of the controller. These advanced controllers are capable of reducing computation time and enhancing the drive performance in realtime applications. The comparative performance analysis is carried out for the most commonly used voltage source inverter (VSI)-based PWM techniques such as sinusoidal pulse width modulation (SPWM) and space vector pulse width modulation (SVPWM) for three-phase, two-level inverters. The comparative study shows the SVPWM technique utilizes DC bus voltage more effectively and produces less harmonic distortion in terms of higher output voltage, flexible control of output frequency, and reduced harmonic distortion at output voltage for motor control applications. The simulation and hardware results are verified and validated by using MATLAB/Simulink software and FPGA-based Wavect real-time controller respectively. [ABSTRACT FROM AUTHOR]

The decarbonization of industrial energy systems which comprise different networks (such as steam, water, electric power, fuel sources) is crucial for mitigating climate change and achieving sustainability goals. This paper presents a comprehensive methodology integrated in an open-source in-house tool for the coupled design and operation optimization of energy systems in industrial settings. The proposed approach integrates advanced optimization techniques with modeling of energy systems including properties like mass flow and temperature to simultaneously optimize both design parameters and operational strategies. The methodology encompasses the optimized integration of various energy technologies, such as renewable energy technologies, energy storage, and power-to-heat technologies while considering changing operational conditions and variable energy demand and supply. A multi-objective optimization framework is employed to balance conflicting targets, such as minimizing greenhouse gas emissions, operational costs, and ensuring system reliability. The in-house tool application considering a case study based on a food industry process demonstrates the effectiveness of the proposed approach in significantly reducing carbon footprints as well as operational and investment costs compared to traditional low-fidelity methods incorporated in commercial tools. The optimized concept achieved through the in-house tool has shown 8.5% less emission (EMI) compared to the optimized designs of the commercial tool. It shows 36% reduction in CO2 emissions compared to the existing facility of the case study. The optimized energy concept can be implemented in the existing facility with a payback period of 4.6 years. The outcomes of the selected use-case highlight the importance of coordinated design and operation decisions in achieving optimal performance and sustainability in industrial energy systems. It also shows an ideal workflow for making optimized design decisions to decarbonize industry with novel energy concepts. Thus, this work provides a robust foundation for future research and practical applications aimed at accelerating the transition towards low-carbon industrial processes. [ABSTRACT FROM AUTHOR]

The steam turbine and its digital electro-hydraulic (DEH) control system constitute vital elements within thermal power generation. However, the complexity of the on-site environment and the high production costs of the equipment hinder users, especially novices, from fully understanding and mastering the operation mechanisms and production processes. In the realm of emerging technologies, the digital twin stands out as a powerful tool for enhancing industrial training and learning for students and operators in this field. This paper details the design and implementation of a web-based digital twin steam turbine system. Initially, a pioneering web-based digital twin architecture is proposed, featuring high-fidelity equipment modeling, web-based immersive 3D displays, algorithm design and networked implementation, and data-driven model synchronization. Subsequently, the functionalities and benefits of the digital twin system in facilitating industrial training are explained, covering aspects such as steam turbine cognitive learning, DEH system simulation learning, and condition monitoring. Finally, a case study in a real thermal power plant is presented to demonstrate the practicability and effectiveness of this web-based digital twin system. This research endeavors to contribute valuable insights and potential solutions to the growing field of web-based digital twin applications in industry. [ABSTRACT FROM AUTHOR]

Autonomous Guided Vehicles (AGVs) play an important role in the automation of material handling and transportation tasks in modern industrial and logistics systems. However, suboptimal path planning and longer waiting times at charging stations significantly affect the operational efficiency of these vehicles. To address these challenges, we leverage the capabilities of Graph Neural Networks (GNN) to find the optimal paths for AGVs. In this paper, we propose GRouteNet, a GNN-based model that effectively finds the shortest path for AGVs. The proposed model utilizes the message-passing mechanism of GNN to determine the neighbor nodes and then aggregates this information to find the shortest path. We compare the results of GRouteNet with a couple of existing state-of-the-art pathfinding models and show that the path length computed by GRouteNet is up to 45% shorter compared to the existing models. Furthermore, we propose a Shortest Charging Time First (SCTF) scheduling algorithm to reduce the long waiting times in the queues at the charging stations. The proposed SCTF algorithm prioritizes the charging of AGVs based on their charging time and charges the AGVs with the shortest charging time first. We compare the results of SCTF with the first-come-first-serve scheduling algorithm and show that SCTF reduces the waiting times at the charging stations by up to 42%. [ABSTRACT FROM AUTHOR]

Carbon disclosures are essential for investors to evaluate firms' efforts to combat climate change. In this study, we focus on a specific type of carbon disclosures—namely, emission allowance disclosures—that capture how firms implement emission reductions under the European Union emission trading system (EU ETS). Given the continuously changing institutional features of the EU ETS and the lack of authoritative guidance on how firms should account for emission allowances in their financial statements, it is exceedingly difficult for investors to understand numerical emission allowance disclosures and to compare them across—and even within—firms. Motivated by this criticism, we hypothesize and find that textual disclosures complementing numerical emission allowance disclosures are associated with lower information asymmetries between firms and their investors. Further analyses show that textual disclosures that: (i) describe the accounting approach, and (ii) contain institutional information on the EU ETS, respectively, are particularly important for improving the information environment of numerical emission allowance disclosures. Overall, our findings suggest that text can improve the understanding of numbers in a carbon disclosure context. Therefore, our study not only contributes to the (industrial ecology) literature but also has important implications for regulators, policymakers, investors, financial analysts, and firms. [ABSTRACT FROM AUTHOR]

The integration of metals/semiconductor metals of varying chemical properties into quantum dots has produced quantum dot derivatives with superior adsorption capacities and this facilitates their selectivity in the removal of a wide range of dyes and other toxic micropollutants. Quantum dots and their derivatives can be produced via a wide array of synthetic routes and they are also susceptible to numerous post-synthetic modification possibilities, all aimed at enabling selectivity in wastewater treatment measures. This work highlights the comparative advantages of various synthetic routes for quantum dots. Topical research reports on how thermal, oxidative and photolytic stability could be effectively managed to enhance optimum performance in targeted applications. Based on the aforementioned, quantum dots essentially become very attractive for an extensive variety of applications in numerous fields due to their easy dispersion in gaseous, liquid and solid media. Consequently, quantum dots and their derivatives can be invaluable candidates for integration into microfiltration systems for industrial wastewater treatments. [ABSTRACT FROM AUTHOR]

China's need for skilled workers to upgrade its industrial system is increasingly urgent due to global economic competition. Despite massive expansion of its Vocational Education and Training system over the past two decades, a significant skilled labour deficit remains. Current debates on the inefficacy of vocational education focuses on the lack of institutional synergies between education and industry but often overlooks the inherent conflicts within the vocational education system and micro-level skill formation processes. Our ethnographic research in two upper-secondary vocational schools unveils a disturbing trend: the rapid massification of vocational education, which prioritizes student enrolment numbers over educational quality, fails to address the demand for skill formation and skilled labour. It inadvertently generates adverse consequences for both administrative management and classroom instruction. Skill development frequently falls to after-school professional associations and an exclusive group of elite students. Meanwhile, reform initiatives, such as the craftsmanship campaign and skill competitions, benefit only a few without improving the employment prospects for the wider student population. These findings call for greater attention to the inherent tensions within China's Vocational Education and Training system. Only when there is a deeper understanding of the underlying causes of ineffective skill formation in vocational schools can China achieve its national goal of industrial upgrades. [ABSTRACT FROM AUTHOR]

This review article indicates the untapped edible termites' nutritional potential and viability as a sustainable resource for future industrial applications. The global quest for sustainable and nutrient-rich food sources has intensified recently. Edible termites are a promising solution to address these challenges. Our study delves into the nutritional composition of edible termites, highlighting their rich protein content, essential amino acids, and valuable micronutrients. We assess their potential as a sustainable alternative for addressing malnutrition and meeting the dietary needs of a growing global population. Regarding industrial applications, we scrutinize the feasibility of incorporating edible termites into food production, their economic viability, scalability, and technological aspects of integrating their products into existing industrial systems. The findings indicate that in the midst of global food insecurity, incorporating edible termites into our industrial practices emerges as a promising avenue for fostering sustainability and resilience in the face of evolving environmental and nutritional challenges. [ABSTRACT FROM AUTHOR]

Serious games are instructional tools that harmonise different pedagogical approaches and pursue specific behavioural and learning outcomes. Characterised by flexibility and complexity, serious games can draw from a vast number of elements and game mechanics. We argue that such adaptability requires specific pedagogical competencies from designers to avoid using game elements counterproductively for user learning. The literature shows lack of studies based on this perspective: if the influence of single instructional techniques has been studied, the impact of pedagogical expertise is missing, and this represents the knowledge gap this study intends to bridge. This paper reports on an experimental study of a serious game that is part of a course in Industrial Systems Engineering. Through collaborative efforts between an engineering professor and a pedagogical expert rooted in neuroeducation and syntheses of meta-analyses tied to learning achievement, the serious game was redesigned. This pre-test-post-test control and experimental group design study investigates how designers' pedagogical competencies can impact learning outcomes, motivation, and decision-making processes. The pedagogically revised serious game outperforms the original one in motivation and, in specific subgroups, in learning performance, highlighting the importance of combining pedagogical and engineering insights. [ABSTRACT FROM AUTHOR]

Since polyhydroxyalkanoates (PHAs) are biodegradable, bioresourced and biocompatible, they are versatile biopolymers having huge applicability in food packaging, biomedicine, drug delivery systems, production of biofuels and many other fields. With the use of waste carbon sources or renewable feedstocks, their process of production can be made cost effective. Moreover, just by exploiting PHA synthesis enzymes, they can also be produced in vivo i.e., without employing microbial cell. Thanks to this adaptable PHA nanogranular technology, it also has become possible to immobilize a huge variety of enzymes, creating a novel and promising set of immobilization techniques. In comparison to soluble enzymes, immobilized enzymes have a number of significant benefits, like increased stability, higher catalytic activity, extended shelf life, recycling for repeated use and easier purification of the product, however, showing some limitations (like improper orientation, substrate diffusion and enzyme deactivate etc) as well. Due to directed display and a high surface area to volume ratio, the PHA based enzyme immobilization platform offers flexibility, stability and excellent functionality. The nano or micro beads of PHA produced in vitro , act as matrix for enzyme immobilization being both economically appealing and potentially applicable in a variety of process circumstances. The developments in synthetic biology and material science to produce more effective, adaptable PHA-enzyme systems for industrial and environmental applications, are future prospects for enzyme immobilization on PHA. It is important to optimize enzyme-PHA interactions for enhanced efficiency and cost effective industry level production processes. This review highlights the importance and great utility of PHA for enzyme immobilization. It mainly focuses on the in vivo and in vitro enzyme immobilization studies done so far, using latest technologies to enhance the properties of immobilized enzymes. [Display omitted] • Polyhydroxyalkanoates (PHAs) being biodegradable, biocompatible and highly versatile, are promising candidates for enzyme immobilization. • There exist a lot of scope for development of various applications of PHA-immobilized enzymes in different industries. • This review addresses the two approaches for enzyme immobilization i.e. in vivo and in vitro , detailing advantages and challenges of each method. • It evaluates efficiency, scalability, enzyme activity, cost-effectiveness and environmental impact of both approaches in various sectors. [ABSTRACT FROM AUTHOR]

The application of the Internet of Things (IoT) in industrial systems and other new technological advancements led to the development of the Industrial Internet of Things (IIoT). IIoT can help overcome the shortcomings of the conventional monitoring and control system while enabling enterprises to create a unified monitoring system to automate operations, provide a safe working environment, enforce compliance effectively, and regulate environmental issues. Given the advantages that IoT brings to the forefront, it makes sense that so many mining companies have raised their investment following the implementation of IoT-enabled solutions in their organisations. To increase safety, productivity, and environmental sustainability in mining operations, this article examines the current state of information technology in the mineral industry with an emphasis on the implications and challenges brought on by the technological diversity of various systems and devices used in those activities. The study contributes valuable insights into the integration of IoT technologies in the mining industry, highlighting its potential to improve safety, efficiency, and sustainability. The paper paves the way for future research and development efforts aimed at overcoming the challenges of adopting IIoT in mining operations by identifying gaps and proposing a comprehensive IoT architecture. The innovations of the study are encapsulated in its approach to detailing the application of IoT technologies in enhancing mining operations and the establishment of an overall IIoT architecture suitable for the general operations in the mining industry. [ABSTRACT FROM AUTHOR]

Root cause diagnosis is able to find the propagation path of faults timely when the fault occurs. Therefore, it is of key significance in the maintenance and fault diagnosis of industrial systems. A commonly used method for root cause diagnosis is causal analysis method. In this work, a causal analysis method Extended Convergent Cross Mapping (ECCM) algorithm is used for root cause diagnosis of industry, however, it has difficulties in dealing with large amounts of steady state data and obtaining accurate propagation paths. Therefore, a causal analysis method based on Kernel Independent Component Analysis (KICA) and ECCM is proposed in this study to deal with the above problems. First, the KICA algorithm is used to detect faults to get the transition process data. Second, the ECCM algorithm is used to construct causal relationship among variables based on the transition process data to construct the fault propagation path diagram. Finally, the effectiveness of the proposed KICA-ECCM algorithm is tested by using the Tennessee Eastman Process and Industrial Process Control Test Facility platform. Compared with the ECCM and GC algorithm, the KICA-ECCM algorithm performs better in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Inspection and maintenance activities are effective ways to reveal and restore the health conditions of many industrial systems, respectively. Most extant works on inspection and maintenance optimization problems have assumed that systems operate under a time-invariant demand. Such a simplified assumption is oftentimes violated by a changeable market environment, seasonal factors, and even unexpected emergencies. In this article, with the aim of minimizing the expected total cost associated with inspections, maintenance, and unsupplied demand, a dynamic inspection and maintenance scheduling model is proposed for Multi-State Systems (MSSs) under a time-varying demand. Non-periodic inspections are performed on the components of an MSS and imperfect maintenance actions are dynamically scheduled based on the inspection results. By introducing the concept of decision epochs, the resulting inspection and maintenance scheduling problem is formulated as a Markov Decision Process (MDP). The Deep Reinforcement Learning (DRL) method with a Proximal Policy Optimization (PPO) algorithm is customized to cope with the "curse of dimensionality" of the resulting sequential decision problem. As an extra input feature for the agent, the category of decision epochs is formulated to improve the effectiveness of the customized DRL method. A six-component MSS, along with a multi-state coal transportation system, is given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

To need of accurate motor fault diagnosis in industrial system, we propose a fault diagnosis framework that utilizes motor current and electromagnetic signals, combining them with a self-attention-enhanced capsule network for enhanced signal analysis and accuracy. Firstly, the original signal extracted by multiple sensors is constructed into a symmetric point mode (SDP) image, and the visual fault information of different sensors and fusion signals of different motion health states are obtained by the proposed multi-channel image fusion method. Then, the capsule network, combined with self-attention, extracts spatial features from the high-dimensional tensor of the multi-channel fused image for adaptive recognition and extraction. Subsequently, advanced feature vector information is obtained through softmax for diagnosis. Diagnosis results of several datasets indicate that the developed diagnosis framework with compressed image information can availably identify 8 kinds of motor fault states under various loads, and the fault diagnosis rate is as high as 99.95 %, it is helpful for low cost and high-speed diagnosis of motors. In addition, by learning multiple sensor signals in the same state, it obtains stronger robustness and effectiveness than a single signal model. • The symmetric dot pattern (SDP) is used to convert a one-dimensional signal into a two-dimensional visual image. • A multi-sensor fusion (MSF) method based on multi-channel SDP images is proposed. • A multi-channel capsule network based on self-attention mechanism is developed. • The proposed method is verified by fusion diagnosis of multi-channel isomorphic spatial information and heterogeneous information. [ABSTRACT FROM AUTHOR]

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Investigating vibrations in mechanical transmission systems to detect and identify the causes of abnormal vibrations remains a prominent focus for researchers. Within industrial transmission systems, rolling bearings play a pivotal role. Utilizing the envelope spectrum of vibration signals to diagnose faults in rolling bearings proves highly reliable due to the bearings' mechanical properties. Under constant shaft rotational speed, most envelope analysis methods can accurately identify faults at the characteristic frequency of rolling bearings. However, numerous transmission systems operate at variable rotational speeds due to technological demands and load fluctuations. In such scenarios, the vibration signals of rolling bearings undergo frequency modulation, rendering conventional vibration analysis methods like envelope spectrum analysis ineffective. Timefrequency analysis is a valuable diagnostic tool for identifying faults in rolling bearings under these conditions. Nonetheless, its practical application, particularly with large-scale data, necessitates high-performance computers capable of processing extensive multi-dimensional matrices. To overcome this challenge, this paper introduces a novel tool based on the Synchrosqueezing Transform (SST) and other resampling techniques. The proposed method additionally incorporates advanced techniques for extracting frequency curves from nonstationary signals to detect the fault characteristic frequencies (FCFs) of rolling bearings. The effectiveness of this approach is showcased through both a simulated example and an experimental instance. Furthermore, a comprehensive comparison of the novel method with existing techniques is provided. [ABSTRACT FROM AUTHOR]

Static random-access memory (SRAM) is in great demand due to the development of portable electronics and its growing popularity in system-on-chip and advanced very-large-scale-integration (VLSI) of integrated circuits. Given the fact that memory takes up to 70% of total chip area, SRAM cell optimization has become a focus of research. In this work, a six-transistor SRAM (6T-SRAM) cell is designed using core insulator double-gate (CIDG) MOSFETs due to their reduced short-channel effects (SCEs). We showed in our previous work that the CIDG MOSFETs provide better digital performance than DG MOSFETs and are negligibly affected by quantum effects. In nanoscale MOSFET technologies, the continual scaling of the supply voltage (VDD) and dimensions of transistors adversely affects the stability of SRAM cells, i.e., read noise margin (RNM) and write noise margin (WNM). The 6T-SRAM cell designed using CIDG MOSFETs improves the WNM by 3.478% and 2.1837% for cell ratio (CR) and pull-up ratio (PR) equal to 0.7, respectively, as compared to a conventional DG 6T-SRAM cell. Further, its RNM and WNM have been found to be superior as compared to a DG MOSFET SRAM by 6.19% and 3.99%, respectively, for equally sized transistors, i.e., CR and PR equal to 1. The CIDG SRAM cell occupies approximately 4 times lower layout area as compared to TSMC's 20-nm high-density SRAM cell. The findings of present study confirm that CIDG SRAM cells can be used in applications requiring higher stability and density. The proposed SRAM utilizing a CIDG can be integrated into a radio-frequency (RF) transceiver circuit for an industrial wireless system. [ABSTRACT FROM AUTHOR]

Since its origin in 15th century European imperialism, the globe has been an object of conquest involving regimes of territorial exclusion and various forms of land abstraction now known as nationalism, colonialism, capitalism, and industrialism. Coming to think like the Earth system and generating politics grounded in it could pose a welcome disruption of these systematically controlling orders only if such planetary thinking is grounded in a nondominating orientation. We propose that this grounding be geological wonder, the open consideration of Earth as a system exceeding human narcissism. Following Dipesh Chakrabarty's distinction between the globe and the planet, we articulate planetarism in an emerging cosmology decentering human life while emphasizing human limits. Globality, by contrast, is a modern, narcissistic formation that geological wonder unsettles. We draw on Martha C. Nussbaum's politics of wonder to articulate a postglobal, disruptive virtue. [ABSTRACT FROM AUTHOR]

In today's expanding cities, pipeline networks are becoming an essential part of the industrial infrastructure. Monitoring these pipelines autonomously is becoming increasingly important. Inspecting pipelines for cracks is one specific task that poses a huge burden on humans. Undetected cracks may pose multi-dimensional risks. In this paper, we introduce the Pipeline Leak Identification Emergency Robot Swarm (PLIERS) system, an industrial system that deploys Internet-of-Things (IoT), robotics, and neural network technologies to detect cracks in emptied water and sewage pipelines. In PLIERS, a swarm of robots inspect emptied pipelines from the inside to detect cracks, collect images of them, and register their locations. When the images are taken, they are fed into a cloud-based module for analysis by a convolutional neural network (CNN). The CNN is used to detect cracks and identify their severity. Through extensive training and testing, the CNN model performance showed promising scores for accuracy (between 80% and 90%), recall (at least 95%), precision (at least 95%), and F1 (at least 96%). Additionally, through the careful design of a prototype for a water/sewage pipeline structure with several types of cracks, the robots used managed to exchange information among themselves and convey crack images to the cloud-based server for further analysis. PLIERS is a system that deploys modern technologies to detect and recognize cracks in pipeline grids. It adds to the efforts of improving instrumentation and measurement approaches by using robots, sensory, IoT principles, and the efficient analysis of CNNs. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT) domain, vast numbers of smart devices are interconnected, generating large volumes of data requiring advanced management mechanisms. One major challenge in smart environments is the ability to accurately distinguish and categorize the various types of objects within these systems. To address this issue, the study introduces a recurrent neural network (RNN) model designed for classifying data from smart home devices. Using a dataset from Kaggle, the research outlines the processes of data collection, loading, normalization, and model development. The RNN, enhanced with long short-term memory (LSTM) layers, was trained and evaluated, showing notable improvements in training and validation accuracy over ten epochs. The model achieved a test accuracy of 83.25%, a loss of 35.4%, a precision of 85%, and a recall of 81%. The evaluation of the model on the test set includes a detailed analysis using ROC curves, area under the curve (AUC) scores for multi-class classification, and a confusion matrix. With an AUC score of 0.9896, the model demonstrated exceptional performance in accurately classifying IoT device categories. These results suggest that the LSTM-equipped RNN offers strong learning efficiency and generalization, making it a highly suitable approach for IoT device classification. Additionally, the article explores the concept of IoT and reviews recent advancements in using deep learning models across various IoT sectors, including smart homes, industrial systems, and healthcare. Future research could aim to improve the model's real-time processing abilities and scalability and incorporate a wider variety of IoT data types to enhance its practical applications and expand its utility across more IoT environments. [ABSTRACT FROM AUTHOR]

Integration of the Internet of Things (IoT) in industrial settings necessitates robust cybersecurity measures to mitigate risks such as data leakage, vulnerability exploitation, and compromised information flows. Recent cyberattacks on critical industrial systems have highlighted the lack of threat analysis in software development processes. While existing threat modeling frameworks such as STRIDE enumerate potential security threats, they often lack detailed mapping of the sequences of threats that adversaries might exploit to apply cyberattacks. Our study proposes an enhanced approach to systematic threat modeling and data flow-based attack scenario analysis for integrating cybersecurity measures early in the development lifecycle. We enhance the STRIDE framework by extending it to include attack scenarios as sequences of threats exploited by adversaries. This extension allows us to illustrate various attack scenarios and demonstrate how these insights can aid system designers in strengthening their defenses. Our methodology prioritizes vulnerabilities based on their recurrence across various attack scenarios, offering actionable insights for enhancing system security. A case study in the automotive industry illustrates the practical application of our proposed methodology, demonstrating significant improvements in system security through proactive threat modeling and analysis of attack impacts. The results of our study provide actionable insights to improve system design and mitigate vulnerabilities. [ABSTRACT FROM AUTHOR]