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The high-tech industrial convergence of high-tech manufacturing and knowledge-intensive business services is crucial for building a modern industrial system and effectively improving regional green innovation efficiency (GIE). Using data on 30 Chinese provinces between 2006 and 2020, non-angular and non-radial SBM-DEA model and the coupling coordination degree model are used to evaluate China's reginal GIE and high-tech industrial convergence, respectively. Based on the fixed effect model, threshold model, intermediary effect model, and moderating effect model to explain whether and under what conditions high-tech industrial convergence may foster GIE, the following results were obtained. (1) High-tech industrial convergence can be significantly improved and exerts a noticeable threshold effect on GIE. Specifically, GIE will improve when the level of high-tech industrial convergence exceeds a critical threshold. (2) Heterogeneity analysis presents that the impact of high-tech industrial convergence on GIE is the largest in the eastern region, followed by the central region and the smallest in the western region. (3) Mechanism analysis identified regional innovation capacity and industrial structure upgrading as the two effective channels through which high-tech industrial convergence can improve GIE. (4) Further analysis showed that industrial digitalization exerts a positive moderating effect and a threshold effect on the relationship between the high-tech industrial convergence position and GIE. This paper proposes relevant policy recommendations for better high-tech industrial convergence and regional GIE. [ABSTRACT FROM AUTHOR]

Agriculture is the main human land use that modifies the environment. The industrial paradigm is the current predominant agricultural model in the Pampean region of Argentina. Its sustainability is being questioned since the detection of soil degradation and water and food pollution. We aimed to assess if alternative farming systems foster soil health-indicated by soil fauna-compared to industrial systems in the Pampean region of Argentina. We sampled soil meso and macrofauna in fields under agroecology (AEC), organic farming (ORG), industrial agriculture (IND) and semi-natural grasslands (GRA). Most functionally relevant faunal groups, ecosystem engineers, litter transformers and predators were more abundant in AEC and ORG than in IND. Mean earthworm biomass was 8 and 2 times greater in AEC and ORG than in IND, respectively. Our results suggest a better development of nutrient cycling and soil structure formation in the alternative systems than in the industrial; together with a better preservation of soil health, crucial for achieving sustainability. We think that recognizing the negative impact of the current model on soil health is a necessary step to promote a change toward agricultural systems that are more socially and environmentally sustainable, in Argentina and also in other Latin American countries that face similar situations. [ABSTRACT FROM AUTHOR]

This paper compares concentrated and distributed reactive power compensation to improve the power factor at the point of common connection (PCC) of an industrial electrical system (IES) with harmonics. The electrical system under study has a low power factor, voltage variation, and harmonics caused by motors operating at low loads and powered by variable-speed drives. The designed compensation system mitigates harmonics and reduces electrical losses with the shortest payback period. Four solutions were compared, considering concentrated and distributed compensation with capacitor banks and harmonic filters. Although the cost of investment in concentrated compensation is lower than that of distributed compensation, a higher reduction in electrical losses and a lower payback period are obtained with distributed compensation. Although the lowest payback period was obtained with distributed compensation with capacitor banks (0.4 years), it is not recommended in the presence of harmonics because the effects of current harmonics significantly reduce the useful life of these elements. For this reason, distributed compensation with harmonic filters obtained a payback period of 0.6 years, and it was proposed as the best solution. These results should be considered in projects aimed at power factor compensation in IESs with harmonics. The concentrated compensation of the capacitor bank at the PCC is proposed because of the lower investment cost and ease of installation. However, the advantages of distributed compensation with harmonic filters have not been evaluated. An energy efficiency approach is used to analyze the impact of the location methods of the power factor compensation equipment on the electrical losses of the IES. [ABSTRACT FROM AUTHOR]

The present research presents a method for scaling up a continuous treatment system with bacterial cellulose biomass for the removal of contaminants on a large industrial scale from effluents loaded with chromium (VI). This consisted of a laboratory-scale modeling process of the chromium (VI) adsorption processes, which would provide the necessary parameters to build a system on an industrial scale. The research also involved designing, modeling and developing scale models for the treatment of water contaminated with chromium (VI) through bacterial cellulose biomass. The results of the model indicated the specific route for the construction of a treatment system on an industrial scale, with the experimental data adjusted to achieve this objective. The pilot scale prototype was built using 450 g of biomass, including elution processes, with the data obtained from the aforementioned processes. In general, the excellent efficiency of the two models at different scales, together with the excellent elution results, suggests that this prototype could be presented to polluting industries for the treatment of water from different industrial effluents, being an advanced biotechnology for the treatment of industrial wastewater. [ABSTRACT FROM AUTHOR]

Feature toggles and configuration options are techniques to include or exclude functionality in software. The research contributions to these two techniques have most often been focused on either one of them. However, focusing on the similarities of these two techniques and the use of a common terminology may enable a combined family of research on software configuration (a term we use to encompass both techniques) and prevent duplication of effort. The goal of this study is to aid researchers in conducting a family of research on software configuration by extending an existing model of software configuration that provides a common terminology for feature toggles and configuration options in research studies. We started with Siegmund et al.'s Model of Software Configuration (MSC), which was developed based on configuration option-related resources. We extend the MSC by qualitative analysis of feature toggle-related resources. From our analysis, we proposed MSCv2 and evaluated it through its application on publications and an industrial system. Our results indicate researchers studying the same system may provide different definitions of software configuration in publications, similar research questions may be answered repeatedly because of a lack of a clear definition of software configuration, and having an MSC may enable generalized research on this family of research. [ABSTRACT FROM AUTHOR]

Copyright of Arab Journal for Scientific Publishing is the property of Research & Development of Human Recourses Center (REMAH) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The extensive body of research dedicated to optimizing energy consumption and efficiency in the manufacturing sector demonstrates a significant and well-established legacy. Despite a peak of publications in this field over recent years, the subject of reusing residual energy is only infrequently discussed. Where authors target this topic, research is often exclusively directed towards specialized applications or industries. In this article, an initial attempt of approaching residual energy reclamation in industrial manufacturing in a structured and universal manner is made. By employing a systematic literature review and design science research, a universal tool chain for decomposing individual industrial manufacturing systems to successfully reclaim and reintegrate residual energy is developed. A comprehensive overview of technologies available for energy conversion in industrial scenarios and their corresponding efficiency ranges are presented in the form of a table, called the energy conversion overview (ECO) table. The main contribution poses a multistep sequential framework guiding through identifying, assessing, harnessing, reusing, and validating residual energy in manufacturing systems. As a universal tool, the Industrial Recommissioning of Residual Energy (IRRE) framework is empowering its adopters to systematically approach residual energy recovery in their individual context by a universal tool. The application of both tools is showcased in a case study from the large-aircraft carbon fiber manufacturing industry. [ABSTRACT FROM AUTHOR]

The passages composed by Nietzsche around the time he spent at Sorrento reflect an engagement with the anarcho-utopian socialist milieu into which he had been introduced by Malwida von Meysenbug. The "Sorrentino politics" that appear in Human, All Too Human I and II and later works need to be understood in the context of an affirmative form of political thought that could remedy the pessimism and nihilism that he finds in the politics of all sides. Nietzsche argues that the monarchical state, modern industrialism, and the restricted ownership of capital and property all undermine the goal of creating a life-affirming culture for Europe. He also provides a criticism of a utopian teleology of equality in the future – whether religious or socialist – that imposes an objective notion of purpose. Nietzsche rejects the Schopenhauerian pessimism of resignation while affirmatively engaging with the thought of Alexander Herzen and Guiseppe Mazzini. [ABSTRACT FROM AUTHOR]

Copyright of Investigaciones Regionales is the property of Asociacion Espanola de Ciencia Regional and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The deep integration of information technology and process industry production systems makes system failure increasingly multi‐source and multi‐scale. In contrast to conventional hazard methods, system theoretic process analysis (STPA) can analyze the hazards in system control processes from the perspective of interactions among the system components. Theoretically, this method offers advantages that are better suited for modern production systems. However, as of now, the integration between STPA and process industrial production systems is still lacking. To address this issue, this study improved the original STPA method. First, we propose the "5 flows" concept for the process industrial cyber‐physical systems. The systems are described using multilevel flow modeling (MFM). This leads to the development of the MSTPA method, which is specifically designed to analyze the cyber‐physical hazards in process industrial production systems. Subsequently, the cyber‐physical hazards of a fluidized‐bed catalytic cracking unit are analyzed in detail using the MSTPA method as an example. The results show that MSTPA can identify cyber‐physical hazards in multiple dimensions. It is proved that, compared with the original STPA and traditional hazard methods, the MSTPA method can better identify cyber‐physical hazards in process industrial production systems. [ABSTRACT FROM AUTHOR]

Copyright of Publishing Journal is the property of Wuhan University, School of Information Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current object detection algorithm based on CNN makes it difficult to effectively capture the characteristics of subtle defects in online industrial product packaging bags. These defects are often visually similar to the texture or background of normal product packaging bags, and the model cannot effectively distinguish them. In order to deal with these challenges, this paper optimizes and improves the network structure based on YOLOv8 to achieve accurate identification of defects. First, in order to solve the long-tail distribution problem of data, a fuzzy search data enhancement algorithm is introduced to effectively increase the number of samples. Secondly, a joint network of FasterNet and SPD-Conv is proposed to replace the original backbone network of YOLOv8, which effectively reduces the computing load and improves the accuracy of defect identification. In addition, in order to further improve the performance of multiscale feature fusion, a weighted bidirectional feature pyramid network (BiFPN) is introduced, which effectively enhances the model's ability to detect defects at different scales through the fusion of deep information and shallow information. Finally, in order to reduce the sensitivity of the defect position deviation, the NWD loss function is used to optimize the positioning performance of the model better and reduce detection errors caused by position errors. Experimental results show that the FSNB_YOLOv8 model proposed in this paper can reach 98.8% mAP50 accuracy. This success not only verifies the effectiveness of the optimization and improvement of this article's model but also provides an efficient and accurate solution for surface defect detection of industrial product packaging bags on artificial assembly systems. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to examine the behavior of certain industrial systems under the conditions of product warranty policy. Given this policy, we are interested in considering all the parameters of the system in order to study them and show their impact on the proposed system. In order to increase the efficiency of the system and activate the product warranty policy, the system will undergo a preliminary examination for any system failure occurrence to determine the reasons that led to the failure. After completing the initial inspection, the mechanic will decide whether it is a user-caused failure or a manufacturing defect to determine whether or not the repair of the broken appliance is covered by the manufacturer's contractual warranty policy. All times in the system are assumed as a negative exponential distribution. The expressions for some measures of system effectiveness such as reliability, mean time to system failure (MTSF), and availability are derived using the supplementary variable technique and Laplace transform. Sensitivity analysis and a relative sensitivity are then carried out in order to recognize how each system parameter affects performance measures. Finally, some numerical examples are designed and performed to show how the change of each system parameter affects system performance. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts. [ABSTRACT FROM AUTHOR]

Most advanced hydrogen evolution reaction (HER) catalysts show high activity under alkaline conditions. However, the performance deteriorates at a natural and acidic pH, which is often problematic in practical applications. Herein, a rhenium (Re) sulfide–transition‐metal dichalcogenide heterojunction catalyst with Re‐rich vacancies (NiS2‐ReS2‐V) has been constructed. The optimized catalyst shows extraordinary electrocatalytic HER performance over a wide range of pH, with ultralow overpotentials of 42, 85, and 122 mV under alkaline, acidic, and neutral conditions, respectively. Moreover, the two‐electrode system with NiS2‐ReS2‐V1 as the cathode provides a voltage of 1.73 V at 500 mA cm−2, superior to industrial systems. Besides, the open‐circuit voltage of a single Zn–H2O cell with NiS2‐ReS2‐V1 as the cathode can reach an impressive 90.9% of the theoretical value, with a maximum power density of up to 31.6 mW cm−2. Moreover, it shows remarkable stability, with sustained discharge for approximately 120 h at 10 mA cm−2, significantly outperforming commercial Pt/C catalysts under the same conditions in all aspects. A series of systematic characterizations and theoretical calculations demonstrate that Re vacancies on the heterojunction interface would generate a stronger built‐in electric field, which profoundly affects surface charge distribution and subsequently enhances HER performance. [ABSTRACT FROM AUTHOR]

The near-eye display (NED) systems, designed to project content into the human eye, are pivotal in the realms of augmented reality (AR) and virtual reality (VR), offering users immersive experiences. A small volume is the key for a fashionable, easy-to-wear, comfortable NED system for industrial and consumer use. Freeform surfaces can significantly reduce the system volume and weight while improving the system specifications. However, great challenges still exist in further reducing the volume of near-eye display systems as there is also a limit when using only freeform optics. This paper introduces a novel method for designing compact freeform NED systems through a powerful optical-digital joint design. The method integrates a geometrical freeform optical design with deep learning of an image compensation neural network, addressing off-axis nonsymmetric structures with complex freeform surfaces. A design example is presented to demonstrate the effectiveness of the proposed method. Specifically, the volume of a freeform NED system is reduced by approximately 63% compared to the system designed by the traditional method, while still maintaining high-quality display performance. The proposed method opens a new pathway for the design of a next-generation ultra-compact NED system. [ABSTRACT FROM AUTHOR]

The application of electrical capacitance tomography (ECT) for monitoring of industrial processes has been studied and proposed by many researchers. Examples can be found in monitoring of multiphase flows or mixing processes in reactors. Demonstrations of the functionality based on lab and test rig measurements have proven the potential of the proposed principles. This paper discusses the application of an ECT system in a heavy industries application. The harsh operating conditions in the industrial environment pose several challenges for the application of sophisticated measurement technology. This work addresses key aspects for the application of an ECT measurement system in such an environment. Specifically the electrical system design and the influence of the temperature are addressed. Relevant parameters and possible solutions are discussed. Furthermore the application of ECT as instrument for mass flow metering in pneumatic conveying processes is addressed. Supportive measurement studies from test rig experiments and comparative simulation studies are presented. Therefore, the paper provides a concise discussion on the application of ECT under harsh operating conditions, as well as the use of ECT as a measurement device for process measurement. The work concludes with a presentation of a measurement system in an industrial plant in which the proposed concepts were succesfully implemented. [ABSTRACT FROM AUTHOR]

At present, electric vehicle (EV) development has been widely accepted as a coping strategy by countries encountering environmental issues. However, there remain some uncertainties surrounding the future of China's EV industry. The super-network provides an effective solution to the research on optimising subject decisions in a multilevel network model. On this basis, a closed-loop super-network model of the EV industrial chain system is proposed, which involves four different participants: EV manufacturer, sales company, consumer market, and recycling company. Furthermore, the variables are extracted based on practice, the behavioural strategies of each participant are analysed, and the dynamic equilibrium is determined with variational inequalities. Through numerical analyses, a further study is conducted on the main influencing factors and the extent of their impact on the dynamic equilibrium of the system. According to the results, the main research questions are answered, and some suggestions are made on the formulation of appropriate regulations. [ABSTRACT FROM AUTHOR]

Monitoring while drilling (MWD) is a crucial task in mining operations. Accurately measuring drill and rock-related operating parameters can significantly reduce the cost of drilling operations. This study explores the potential of monitoring drilling specific energy (SE) and optimizing drilling operations by processing vibroacoustic signals generated while drilling. For this purpose, 30 samples of different rocks, are used for drilling tests. During the drilling process, the acoustic and vibration signals are recorded and analyzed in the time, frequency, and time–frequency domains., and parameters related to the resulting spectra are extracted. After obtaining the vibroacoustic parameters for drilling, the relationship between them and the drilling SE was investigated. There is evidence that the progression of SE contributes to the magnitude of rock drilling vibroacoustic features, which could be employed to indicate energy conditions during drilling. Results obtained in this study have the potential to be used as the basis for an industrial monitoring system that can detect excessive energy consumption and advise the user of the end of the bit's useful life. This method can be an intelligent technique for measuring the behavior of real-time drilling operations based on the SE simply by installing vibroacoustic sensors on the drilling machines. [ABSTRACT FROM AUTHOR]

The Great Central Gas Consumers’ Company (GCGCC) was established by the metropolitan middle class to address the detrimental effects of gas industry monopoly through the promotion of market competition. It revealed that the middle class staunchly advocated for the use of liberalism to safeguard their own rights and interests, coinciding with Britain’s wider acceptance of liberalism and echoing the clarion call of the era of liberalism. Government engagement and interference in the formation of the GCGCC, as well as the ensuing disputation around it, could be seen as a manifestation of the principle of liberalism rather than a violation thereof. It reflected that liberalism was neither the absence of government nor total laissez-faire. The fact that the old gas companies adjusted their positions and compromised in favour of liberal principles demonstrated the more flexible response mechanisms of the modern industrial system to industrial and social problems, as well as the significant influence of liberalism on the development of the British industrial sector and society as a whole. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) is a pivotal driving force behind the intelligent transformation of the global industrial system, bringing about a gradual shift in production methods. However, due to users completing remote access and data transmission through open channels, ensuring user legitimacy and data security has become an important factor in IIoT. In addition, resource limited users or devices need to collect and publish corresponding messages while also timely processing instant messages obtained from the network. Leveraging edge computing technology effectively mitigates the transmission time of messages, addressing the challenges of high computing pressure and prolonged response time associated with cloud server computing. Therefore, investigating authentication key agreement based on edge computing in the IIoT environment holds substantial theoretical significance and practical importance. This article introduces an IIoT authentication that ensures both security and efficiency by integrating elliptic curve cryptography and three-factor authentication. Within this scheme, the user and the edge server accomplish security authentication. Through security analysis, the proposed scheme is proven to meet the essential security requirements. Performance analysis shows that it achieves higher security, supports more functions, and achieves lower computational and communication costs compared to related schemes. [ABSTRACT FROM AUTHOR]

Considering Industry 4.0 directions, followed by recent Industry 5.0 principles, interest in integrating legacy systems in industrial manufacturing has emerged. Due to the continuous evolution of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), as well as the rapid extension of the scope and adoption of broader technologies, such integration has become feasible. Even though newly developed equipment provides easier interoperability, the replacement of legacy systems highly impacts cost and sustainability, which usually extends to the entire production process, the operators and the maintenance team, and sometimes even the robustness of the production process. Ensuring the interoperability of legacy systems is a problematic task, being dependent on technologies and development techniques and specific industrial domain particularities. This paper considers strategies to ensure the interoperability of legacy systems in a building-management system scenario where local structures are approached using both industrial protocols and web-based contexts. The solution is built following the Industry 5.0 pillars (sustainability, human focus, resilience) and conceives the entire data acquisition and supervisory solution to be flexible, open-source, resilient, and under the control of company engineers. The chosen environment for interfacing and supervision is Node-RED, enabling IoT and IIoT tools, together with a complete orientation toward digital transformation. This way, it is possible to construct a final result that enhances security while bridging outdated protocols and technologies, eliminating compatibility risks in the context of the evolutionary IIoT, ensuring critical process functions are possible, and aiding operators in complying with regulations governing building-management system (BMS) operations, thus solving the challenges that arise in the complex task of adopting the IoT backbone of digital transformation in relation to the integration of legacy equipment. The obtained solution is tested in an automotive industry building-management system, and the results demonstrate its performance, reliability, and high customizability in a context of openness and low cost. [ABSTRACT FROM AUTHOR]

The adoption of artificial intelligence into industrial settings promises notable enhancements in productivity, quality, efficiency, competitiveness, and innovations. However, transitioning AI models from concept to full-scale industrial applications involves various complexities and challenges. These challenges are not only technical but also extend into the ethical and regulatory realms, calling for a comprehensive approach to AI integration. This paper examines the diverse hurdles faced during developing and deploying AI applications in the industrial domain. It addresses challenges in collecting the right data, construction of AI models, and ensuring that these models work accurately and responsibly when deployed in real industrial environment. Furthermore, the paper presents strategic recommendations, underscoring the necessity of ethical considerations and regulatory compliance to effectively overcome these obstacles. We provide guidelines aimed at maximizing AI's benefits in industrial environments while minimizing potential risks. [ABSTRACT FROM AUTHOR]

High-polluting industries are an important part of China's industrial system. The transformation of high-polluting enterprises into sustainable trajectories is key not only to ensuring ' six-aspect stability' and ' six-aspect security,' but also to promoting ' new momentum,' stimulating ' new vitality,' and ultimately achieving the ' dual carbon' goals. In the context of green development, high-polluting enterprises may face increasing development risks. However, implementing digital transformation is an essential way for these enterprises to address development difficulties. Due to the greening virtue of digital transformation, green finance could play a beneficial role in promoting the digital transformation of these enterprises by providing internal incentives and strategic resources. Based on a matched dataset of Chinese prefectural cities and listed enterprises from 2011 to 2021, this study measured the green finance development index and investigated the effects and related mechanisms both theoretically and empirically. The results showed that: (1) Green finance significantly improved the digitization level of high-polluting enterprises, and the improvement effect showed stability and persistence, indicating a strong consistency between the goals of low-carbon transformation and digital transformation in the development of green finance. (2) The documented driving effect of green finance on the digital transformation of high-polluting enterprises was mainly reflected in the improvement effect on the digitalization of management, and the effect on the digitalization of application-oriented technologies in enterprises needed to be further explored. (3) Mechanism analyses demonstrated that green finance promoted the digitalization level of high-polluting enterprises by improving their information transparency and green technological innovation. (4) The improvement effect of green finance on high-polluting enterprises' digitalization level was influenced by multiple external and internal conditions. Specifically, green finance could effectively lessen the negative impact of economic policy uncertainty on high-polluting enterprises' digitalization level. Government-enterprise correlation deterred the enhancement effect of green finance development on the digitalization of high-polluting enterprises. The present study provides theoretical support and policy insights for improving the green finance system and guiding the digital transformation of high-polluting enterprises. [ABSTRACT FROM AUTHOR]

Immense research efforts on graphene or graphene oxide have led to the formation of unique nanocarbon derived nanomaterials. Graphene and graphene oxide have been reinforced in polymeric matrices to form high performance nanocomposites. Significant applications of polymer nanocomposites with graphene or graphene oxide were experiential for nanofiber formation and ensuing membranes. This overview highlights design, essential features, and potential of graphene or graphene oxide derived nanocomposite nanofibrous membranes for water remediation permeation towards contaminates, salts, toxins, microbials, and other separation purposes. Here, polymer filled graphene or graphene oxide nanocomposites have been processed into nanofibers using appropriate techniques such as electrospinning, wet spinning, template method, etc. Afterwards, polymer/graphene and polymer/graphene oxide nanofiber nanocomposites were applied to form the nanocomposite membranes using appropriate techniques like solution processing, casting methods, infiltration, etc. Consequently, high performance membranes have been researched for technological purposes, especially water management competence. Future research on polymer/graphene nanofibrous membranes may lead to highly efficient systems for commercial and industrial level uses. [ABSTRACT FROM AUTHOR]

The wide-ranging applications of the Internet of Things (IoT) show that it has the potential to revolutionise industry, improve daily life, and overcome global challenges. This study aims to evaluate the performance scalability of mature industrial wireless sensor networks (IWSNs). A new classification approach for IoT in the industrial sector is proposed based on multiple factors and we introduce the integration of 6LoWPAN (IPv6 over low-power wireless personal area networks), message queuing telemetry transport for sensor networks (MQTT-SN), and ContikiMAC protocols for sensor nodes in an industrial IoT system to improve energy-efficient connectivity. The Contiki COOJA WSN simulator was applied to model and simulate the performance of the protocols in two static and moving scenarios and evaluate the proposed novelty detection system (NDS) for network intrusions in order to identify certain events in real time for realistic dataset analysis. The simulation results show that our method is an essential measure in determining the number of transmissions required to achieve a certain reliability target in an IWSNs. Despite the growing demand for low-power operation, deterministic communication, and end-to-end reliability, our methodology of an innovative sensor design using selective surface activation induced by laser (SSAIL) technology was developed and deployed in the FTMC premises to demonstrate its long-term functionality and reliability. The proposed framework was experimentally validated and tested through simulations to demonstrate the applicability and suitability of the proposed approach. The energy efficiency in the optimised WSN was increased by 50%, battery life was extended by 350%, duplicated packets were reduced by 80%, data collisions were reduced by 80%, and it was shown that the proposed methodology and tools could be used effectively in the development of telemetry node networks in new industrial projects in order to detect events and breaches in IoT networks accurately. The energy consumption of the developed sensor nodes was measured. Overall, this study performed a comprehensive assessment of the challenges of industrial processes, such as the reliability and stability of telemetry channels, the energy efficiency of autonomous nodes, and the minimisation of duplicate information transmission in IWSNs. [ABSTRACT FROM AUTHOR]

In the studies of unit commitment or optimal power flow, to formulate a mixed-integer linear programming model that can be efficiently solved with commercial solvers, it is necessary to approximate the quadratic cost curves of thermal units as piecewise linear (PWL) functions. The conventional approach involves evenly spaced piecewise linear (ES-PWL) interpolation, which often results in relatively large approximation errors. In order to reduce the error, this paper proposes a more accurate PWL method for approximating the quadratic cost functions of thermal units. The method employs a linear least-squares fit instead of linear interpolation within each subinterval and introduces a one-terminal-constraint approach to ensure the continuity of the piecewise function. Subsequently, a straightforward equation is derived, applicable to the widely used ES-PWL interpolation, with the potential to enhance the accuracy of the approximation. Mathematical verification attests that the proposed method substantially diminishes the squared 2-norm error, less than 37.5% of the error associated with ES-PWL interpolation. Subsequent numerical investigations are carried out on a 10-unit system, the IEEE RTS-79, and a real industrial system. The findings validate that all the approximation errors of the proposed method are within 37.5% of the errors associated with the ES-PWL interpolation, meaning that a unit commitment solution that closely approximates the outcome of the quadratic function is obtained. The computational time is also acceptable. [ABSTRACT FROM AUTHOR]

Distributed systems have been widely used in many safety‐critical areas. Any abnormalities (e.g., service interruption or service quality degradation) could lead to application crashes or decrease user satisfaction. These things may cause serious economic losses. Among the various quality assurance approaches for distributed systems, log‐based anomaly detection (LAD) has become a popular research topic. Its popularity relates to system logs being able to record and reveal important run‐time information. This paper presents a general LAD framework for distributed systems. Log grouping and feature‐pattern mining are two crucial LAD components that impact on the anomaly‐detection effectiveness. We also present a systematic survey of techniques in these two directions; propose classification frameworks for log grouping and feature patterns; and summarize four log‐grouping techniques and five feature patterns (which refer to invariant relationships among logs that can be used for anomaly detection). To evaluate their applicability, we report on the findings when applying existing techniques to Ray, a popular industrial distributed system. Based on these findings, several open issues are identified, which provide potential guidance for future research and development. [ABSTRACT FROM AUTHOR]

The burgeoning demand for effective predictive maintenance in industrial systems necessitates accurate prognostics of equipment's Remaining Useful Life (RUL). In particular, the prediction of RUL plays a crucial role in MOSFET devices for many applications to prevent failures and maintenance schedule optimization. Motivated by the potential requirement for more than what traditional models can provide, this study sought to improve upon these estimates. This research aimed to solve the problem of accurately estimating RUL for MOSFET devices, since these systems are characterized by substantial uncertainties and non-linear processes. To address this issue, we introduced a highperformance prognostic model that promises to take advantage of both the Adaptive Particle Filter (APF) and Gaussian Process Regression (GPR), which is called as PF-GPR method. The model used a genetic algorithm to control when and how many particles were adaptively resampled applying different weighted strategies (mean or median) in response of the stochastic system deterioration. We designed and conducted a series of experiments based on the PF-GPR approach with two objectives: i) to benchmark different systematic resampling schemes; ii) demonstrate that even in case of small number (i.e. FloatTensor section {newString 10}0/75k). Systematic comparisons with standard particle filtering techniques showed the model performed well in tracking RUL as it degraded through wear degrees and estimated prediction errors were obtained. The results showed that the PFGPR model significantly outperformed traditional methods, in particular their adpative resampling version based on the median. The PF-GPR median approach performed consistently better with respect to true RUL approximation and had the lowest RMSE for all time points. These results highlight the improved strength of prediction power for equipment RULs using our provided approach, thus reinforcing that real-world predictive maintenance applications are possible by said model. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis of nonstationary processes are crucial for ensuring the safety of industrial production systems. However, the nonstationarity of process data poses multifaceted challenges to them. First, conventional stationary fault detection methods encounter difficulties in discerning evolving trends within nonstationary data. Secondly, the majority of current nonstationary fault detection methods directly extract features from all variables, rendering them susceptible to redundant interference. Moreover, nonstationary trends possess the capacity to conceal and modify the correlations among variables. Coupled with the smearing effect of faults, it is challenging to achieve accurate fault diagnosis. To address these challenges, this paper proposes sparse Wasserstein stationary subspace analysis (SWSSA). Specifically, a ℓ 2 , p -norm constraint is introduced to endow the stationary subspace model with excellent sparse representation capability. Furthermore, recognizing that fault variables within the sparse stationary subspace influence only a limited subset of stationary sources, this paper proposes a novel contribution analysis method based on local dynamic preserving projection (LDPP), termed LDPPBC, which can effectively mitigate the smearing effect on nonstationary fault diagnosis. LDPPBC establishes a LDPP matrix by extracting the latent positional information of fault variables within the stationary subspace. This allows LDPPBC to selectively analyze the contributions of variables within the latent fault subspace to achieve precise fault diagnosis while avoiding the interference of variable contributions from the fault-free subspace. Finally, the superiority of the proposed method is thoroughly validated through a numerical simulation, a continuous stirred tank reactor, and a real industrial roaster. • This method can realize fault diagnosis under the influence of nonstationarity. • The smearing effect of faults is effectively mitigated. • Through mathematical deduction, the convergence of the algorithm is proven. [ABSTRACT FROM AUTHOR]

Copyright of Agricultural Outlook (1673-3908) is the property of Institute of Agricultural Information, Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study proposes a conceptual design of green hydrogen production via proton exchange membrane electrolysis powered by a floating solar photovoltaic system. The system contributes to industrial decarbonization in which hydrogen blending with natural gas is proposed as an approach to smooth the energy transition. The proposed design addresses the challenge of supplying a continuous flow-rate of green hydrogen, which is typically demanded by industrial end users. This study particularly considers a realistic area required for the installation of a floating solar photovoltaic system. To enable the green hydrogen production of 7.5 million standard cubic feet per day, the required structure includes the floating solar photovoltaic system and Li-ion batteries with the nominal capacities of 518.4 megawatts and 780.8 megawatt-hours. This is equivalent to the requirement for 1 524 765 photovoltaic modules and 3718 Li-ion batteries. The assessment confirms the technical viability of the proposed concept of green hydrogen production, transportation and blending. While the present commercialization is hindered by economics due to a high green hydrogen production cost of USD 26.95 per kg, this green hydrogen pathway is expected to be competitive with grey hydrogen produced via coal gasification and via natural gas steam reforming by 2043 and 2047, respectively. [ABSTRACT FROM AUTHOR]

This study develops a new controller for an industrial crane system in a three-dimensional space. First, the dynamic model of the industrial crane system with two subsystems, the tower crane and the overhead crane is presented. A bidirectional mapping is established between the system's input and output, allowing for efficient trajectory generation. Additionally, the design process explicitly considers the system's kinematic constraints, ensuring safe and feasible motions. This designed trajectory serves as an input for Model Predictive Control (MPC). The MPC is designed with the dual objectives of trajectory tracking and payload anti-swing. Finally, simulations are conducted and the results are compared with those of other control strategies under different cases to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Although capacitive deionization (CDI) technology has been studied intensively for more than 20 years, its commercialization remains in the initial stage, which is partly caused by the insufficient knowledge exchange between academia and industry. This concept reviews multiple scaling‐up efforts in the CDI technology for treating real water streams, following a case‐by‐case fashion. While the cell architecture in pilot scales is limited to the membrane CDI, highlighting the necessary role of ion‐exchange components during scaling‐ups, different ways of electrode stacking, i. e. monopolar and bipolar, are available. The performance indicators of these CDI systems when treating real water streams are summarized to gain insights into industrial practices. Importantly, key discrepancies in cell components and performances between pilot‐scale and lab‐scale studies are emphasized. The main challenges in large‐scale CDI systems for industrial purposes are discussed, providing hints for a better integration of research and applications. [ABSTRACT FROM AUTHOR]

The excessive growth of carbon emissions (CO2E) from industrial energy use not only exacerbates global warming and severely curbs the sustainable development of the economy and society. As a high energy-consuming sector second only to the fossil energy division, the power and heavy division, China's chemical industry should have received more attention for its CO2E. However, there are limited literatures on energy CO2E in China's chemical sector at present. Based on this fact, this current paper uses the energy utilization approach, the input–output analysis approach, and the extended structural decomposition method to evaluate the energy-related CO2E of China's chemical sector from 2007 to 2017. (1) China's chemical sector energy-related CO2E showed a trend of first growth and then a slow decline, demonstrating that the rapid growth of China's chemical sector energy-related CO2E has been effectively controlled; However, it should be noted that the chemical industry is still dominated by high-CO2E energy-related CO2E at the current stage. (2) Input structure and energy intensity effects have a reduced influence on the growth of energy-related CO2E in China's chemical sector. This is due to upgrading energy use technology and optimizing the generalized technology progress rate in the chemical sector. (3) Energy structure and final demand effects have encouraged the growth of the chemical sector's energy-related CO2E. It shows that the industrial system's demand for chemical products is constantly expanding, and the chemical products still have the characteristics of high carbonization. Also, the chemical sector's supply-side energy utilization structure has not been significantly enhanced. [ABSTRACT FROM AUTHOR]

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Copyright of China Forest Products Industry is the property of China Forest Products Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Generally, thermal inputs dominate the food processing industry for food preservation. Pulsed electric field (PEF) is one of the most promising nonthermal microorganism‐killing techniques. The most important factors in PEF processing are electric field strength and treatment duration. At the laboratory level, encouraging results are reported; however, industrialization raises the cost of the command charging power supply and the high‐speed electrical switch. In this review, the results of previous experimental studies on PEFs and proposed future research directions in this field are discussed. There is currently no successful PEF processing system for industrial applications. Those who wish to promote the industrial application of the PEF processing system face a significant barrier in the form of the system's high initial cost of installation. Innovative developments in high‐voltage pulse technology will reduce the cost of pulse generation and make PEF processing competitive with thermal processing methods. [ABSTRACT FROM AUTHOR]

As a key enabling technology of Industry 4.0, Digital Twin (DT) has been widely applied to various industrial domains covering different lifecycle phases of products and systems. To fully realize the Industry 4.0 vision, it is necessary to integrate multiple relevant DTs of a system according to a specific mission. This requires integrating all available data, information and knowledge related to the system across its entire lifecycle. It is a challenging task due to the high complexity of modern industrial systems. Semantic technologies such as ontology and knowledge graphs provide potential solutions by empowering DTs with augmented cognitive capabilities. The Cognitive Digital Twin (CDT) concept has been recently proposed which reveals a promising evolution of the current DT concept towards a more intelligent, comprehensive, and full lifecycle representation of complex systems. This paper reviews existing studies relevant to the CDT concept, and further explores its definitions and key features. To facilitate CDT development, a reference architecture is proposed based on the RAMI4.0 and some other existing architectures. Moreover, some key enabling technologies and several application scenarios of CDT are introduced. The challenges and opportunities are discussed in the end to boost future studies. [ABSTRACT FROM AUTHOR]

Copyright of Food & Fermentation Industries is the property of Food & Fermentation Industries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)