Generic Methodology Research Articles

High-temperature heat pumps in industrial heating networks: A study on energy use, emissions, and economics

Industrial heat significantly contributes to global primary energy use and primarily relies on fossil-fuel combustion. Recovering residual heat in the industry offers a means to reduce overall energy use. However, the temperature and amount of residual heat available varies widely across industrial sites. Some may have a large amount of residual heat available at relatively high temperatures, while others may not have any residual heat available. Furthermore, for most industries, the residual heat available is at temperatures below 100 °C, while the heat demands are at higher temperatures. Hence, clustering these industries by industrial heating networks, using high-temperature heat pump integration, could offer a promising solution. Research on this concept regarding energy use, emissions and economics is however lacking in the literature. This study however distinguishes and subsequently compares three different heat network configurations in terms of their energy use, carbon emissions and financial appraisal. These configurations include a ‘consumer based heat upgrading network’, a ‘supplier based heat upgrading network’ and a ‘supplier based heat upgrading network with an additional hot water network’. For this purpose a generic methodology is developed, using first and second law principles completed with empirical data for performance and costs. The methodology is applied to data collected from ten companies clustered within the North Sea Port, Ghent (Belgium). The results indicate that the first configuration exhibits the most efficient use of energy and consequently also has the lowest carbon emissions. In addition it also has the lowest levelized cost of heat. This configuration shows, depending on maximum supply temperature of the heat pump, a potential reduction in carbon emissions ranging from 70 % to 80 % in comparison to natural gas boilers. Considering low gas prices, a positive financial appraisal is difficult without carbon taxation. On the other hand, an evaluation during the energy crisis of 2021–2022 indicates that the even without carbon taxation, the levelized cost of heat decreases by 19 % compared to a gas boiler at a maximum heat pump temperature of 160 °C. It was also found that in scenarios of dynamic energy prices a hybrid configuration of a consumer based heat upgrading network and a natural gas boiler could lower the LCOH compared to the individual solutions, by up to 7.6 % compared to the best individual solution. This is done by activating the technology with the lowest operational cost in each time frame.

Análisis espacio-temporal del proceso de urbanización en la ciudad de Santa Tecla, El Salvador

This study aims to analyze the urbanization process in the municipality of Santa Tecla and propose a generic methodology with free tools so that the population can monitor the spatio-temporal process of urbanization in the municipalities of the metropolitan area of San Salvador using Sentinel satellite images and the current regulatory planning instrument as a reference. The evolution of the 30-year temporal space (1991-2021) of the five-year urban sprawl of the municipality of Santa Tecla was analyzed using Landsat satellite data and contrasted with current regulatory instruments or reference techniques. NDVI and NDBI indices calculated from Sentinel-2 satellite data and applying a hybrid image classification methodology, conflicts in real land use were identified with high resolution. The study concluded that the process in the municipality of Santa Tecla has been disorderly in the last 30 years, which has suspected the risk for the population, because although there is a legal framework for land use planning, its application has been flexible. A hybrid method of classification based on NDVI and NDBI from satellite images has been used to simplify the manual inspection of the area in a given period of time with an acceptable margin of error. ILIA: Investigaciones Latinoamericanas en Ingeniería y Arquitectura, No. 01, 2024: 53-60.

Identification and application of quality elements, indicators, and criteria for assessment of impact on habitats in marine Natura 2000 areas

The EU Habitat Directive adopted in 1992, requires member states of the European Union to protect species and habitats considered to be of ‘Community Interest’ and listed in annexes to the directive. The appropriate environmental assessment of “plans and projects” is an important part of the conservation process. Despite several amendments and guidelines supporting the implementation of the Habitat Directive, science based operational procedures, indicators, and impact criteria for assessing potential negative impacts on marine Natura 2000 areas are still lacking. The lack of a generic and operational methodology complicates the management of plans and projects with potential impact on marine Natura 2000 areas. In this study, generic methods for the assessment of marine aquaculture in the inner Danish waters in relation to Natura 2000 areas was developed and applied for assessment of nine existing marine fin fish farms, in accordance with the latest methodological guidance on the provisions of Article 6(3) and (4) of the Habitat Directive. The applied methodology is based on high resolution 3D hydrodynamic- and ecosystem modelling (MIKE by DHI), that describes the dynamical physical, chemical, and biogeochemical processes and changes of marine ecosystems in time and space. To our knowledge, this is the first study that formulates operational biological quality elements, key indicators, concrete and generic impact criteria, and assessment procedures for operational assessment across several distinct marine habitat types. The method represents a generic, operational, transparent, and science-based assessment tool, that simplifies management, and is widely applicable for quantification of environmental impacts from various marine activities and eutrophication related pressures across geographical zones and different marine habitat types in marine Natura 2000 areas.

Aptamer-Based Enforced Phosphatase-Recruiting Chimeras Inhibit Receptor Tyrosine Kinase Signal Transduction.

Aberrant phosphorylation of receptor tyrosine kinases (RTKs) is usually involved in tumor initiation, progression, and metastasis. However, developing specific and efficient molecular tools to regulate RTK phosphorylation remains a considerable challenge. In this study, we reported novel aptamer-based chimeras to inhibit the phosphorylation of RTKs, such as c-Met and EGFR, by enforced recruitment of a protein tyrosine phosphatase receptor type F (PTPRF). Our studies revealed that aptamer-based chimeras displayed a generic and potent inhibitory effect on RTK phosphorylation induced by growth factor or auto-dimerization in different cell lines and modulated cell biological behaviors by recruiting PTPRF. Furthermore, based on angstrom accuracy of the DNA duplex, the maximum catalytic radius of PTPRF was determined as ∼25.84 nm, providing a basis for the development of phosphatase-recruiting strategies. Taken together, our study provides a generic methodology not only for selectively mediating RTK phosphorylation and cellular biological processes but also for developing novel therapeutic drugs.

Chemical Mobilization-Based Capillary Isoelectric Focusing-Mass Spectrometry Using the nanoCEasy Interface for Pharmaceutical Protein Analysis.

Capillary isoelectric focusing (CIEF) coupled with electrospray ionization mass spectrometry (ESI-MS) is regarded as an outstanding approach for protein and proteoform analysis, combining a high-resolution separation technique and an enhanced detection technique. The few so-far developed CIEF-ESI-MS approaches exhibit limitations regarding sensitivity and separation performance. Here, we report a new generic methodology for CIEF-ESI-MS based on chemical mobilization, leading to highly efficient separation. This new integrated methodology relies on exchanging catholyte, initially introduced in the nanoCEasy interface in the focusing step, with sheath liquid (SL) in order to chemically mobilize the analytes into the ESI-MS system. The CIEF-MS method is evaluated by separation of a peptide set, model proteins, and monoclonal antibody charge variants. The effect of various parameters including master mixture composition, field strength, catholyte, SL composition, focusing time, and capillary conditions is optimized and discussed. Excellent separation performance can be achieved with a pI resolution down to 0.1 pH unit. The mobilization reproducibility is demonstrated with "migration time" RSDs below 10%. Additionally, the chemical mobilization is compared with the pressure assistance-chemical mobilization method, demonstrating that even a small pressure causes a strong decrease in separation performance, which clearly indicates the benefit of the chemical mobilization-based method. The applicability and separation power of the developed method are further exhibited by separation of Fc-conjugated insulins (mass = 62 kDa) differing in only one amino acid.

Approach for sizing by optimization of an electrical drive considering a multiphysics and multidynamics behaviour

PurposeThis paper aims to describe a modelling and solving methodology of a (static converter–electric motor–control) system for its sizing by optimization, considering the dynamic thermal heating of the machine.Design/methodology/approachThe electrical drive sizing model is composed of two simulators (electrical and thermal) that are co-simulated with a master−slave relationship for the time step management. The computation is stopped according to simulation criteria.FindingsThis paper details a methodology to represent and size an electrical drive using a multiphysics and multidynamics approach. The thermal simulator is the master and calls the electrical system simulator at a fixed exchange time step. The two simulators use a dedicated dynamic time solver with adaptive time step and event management. The simulation automatically stops on pre-established criteria, avoiding useless simulations.Research limitations/implicationsThis paper aims to present a generic methodology for the sizing by optimization of electrical drives with a multiphysics approach, so the precision and computation time highly depend on the modelling method of each components. A genetic multiobjective optimization algorithm is used.Practical implicationsThe methodology can be applied to size electrical drives operating in a thermally limited zone. The power electronics converter and electrical machine can be easily adapted by modifying their sub-model, without impacting the global model and simulation principle.Originality/valueThe approach enables to compute a maximum operating duration before reaching thermal limits and to use it as an optimization constraint. These system considerations allow to over constrain the electrical machine, enabling to size a smaller machine while guaranteeing the same output performances.

Applying Large Language Models for Intelligent Industrial Automation

This paper explores the transformative potential of Large Language Models (LLMs) in industrial automation, presenting a comprehensive framework for their integration into complex industrial systems. We begin with a theoretical overview of LLMs, elucidating their pivotal capabilities such as interpretation, task automation, and autonomous agent functionality. A generic methodology for integrating LLMs into industrial applications is outlined, explaining how to apply LLM for task-specific applications. Four case studies demonstrate the practical use of LLMs across different industrial environments: transforming unstructured data into structured data as asset administration shell model, improving user interactions with document databases through conversational systems, planning and controlling industrial operations autonomously, and interacting with simulation models to determine the parametrization of the process. The studies illustrate the ability of LLMs to manage versatile tasks and interface with digital twins and automation systems, indicating that efficiency and productivity improvements can be achieved by strategically deploying LLM technologies in industrial settings.

Lifecycle battery carbon footprint analysis for battery sustainability with energy digitalization and artificial intelligence

As an indispensable component and intermediate bridge, electrochemical battery as an indispensable component is essential for power supply reliability, stability, grid-friendly interaction, sustainability with e-transportation and building electrification. However, the lifecycle carbon intensity of electrochemical batteries is uncertain throughout lifecycle battery-related activities. In this study, a generic methodology is proposed to accurately quantify the lifecycle carbon intensity of electrochemical batteries. A cross-scale multi-stage analytic platform with inter-disciplinary and trans-disciplinary is formulated, involving battery materials (anode, cathode, electrolyte), charging/discharging behaviours, cascade battery utilization, recycling, and reproduction. A case study on a zero-energy district in subtropical Guangzhou indicates that lifetime EV battery carbon intensity is +556 kg CO2,eq/kWh for the scenario with pure fossil fuel-based grid reliance, while the minimum carbon intensity of EVs at −860 kg CO2,eq/kWh can be achieved for the solar-wind supported scenario. The grid mandatory EVs charging will slightly increase the battery carbon intensity to −617.2 kg CO2,eq/kWh, and the exclusion of embodied carbon on both solar PV and wind turbines will increase the battery carbon intensity to −583.8 kg CO2,eq/kWh. The proposed approach and formulated platform can enable synthetical and comprehensive analysis on battery sustainability, throughout integrated cross-disciplinary approaches for 2060 carbon neutrality in China.

Explainable proactive control of industrial processes

One of the goals of Industry 4.0 is the adoption of data-driven models to enhance various aspects of the manufacturing process, such as monitoring equipment conditions, ensuring product quality, detecting failures, and preparing optimal maintenance plans. However, many machine-learning algorithms require a large amount of training data to reach desired performance. In numerous industrial applications, such data is either not available or its acquisition is a costly process. In such cases, simulation frameworks are employed to replicate the behavior of real-world facilities and generate data for further analysis. Simulation frameworks typically provide high-quality data but are often slow which can be problematic when real-time decision-making is required. Control approaches based on simulation-based data commonly face challenges related to inflexibility, particularly in dynamic production environments undergoing frequent reconfiguration and upgrades. This paper introduces a method that seeks to strike a balance between the reliance on simulated data and the limited robustness of simulation-based control methods. This balance is achieved by supplementing available data with additional expert knowledge, enabling the matching of similar data sources and their combination for reuse. Furthermore, we augment the methods with an explainability layer, facilitating collaboration between the human expert and the AI system, leading to informed and actionable decisions. The performance of the proposed solution is demonstrated through a case study on gas production from an underground reservoir resulting in reduced downtime, heightened process reliability, and enhanced overall performance. This paper builds upon our conference paper (Kuk et al., 2023), addressing the same problem with an extended, more generic methodology, and presenting entirely new results.

Climate Change Scenarios for Impact Assessment: Lower Zab River Basin (Iraq and Iran)

Selecting appropriate climate change scenarios is crucial, as it influences the outcomes of climate change impact studies. Several storylines could be used to investigate the sensitivity of water resource schemes to weather variability and improve policymakers’ adaptation strategies. This study proposes a comprehensive and generic methodology for assessing the future climate change impact on semi-arid and arid zones at the basin scale by comparing delta perturbation scenarios to the outcomes of seven collections of GCMs (general circulation models). The findings indicate that the two scenarios predicted nearly identical declines in average reservoir discharges over a monthly timescale. Consequently, their maximum values are almost similar. The projected decrease in the streamflow for the period 2080–2099 is approximately 48%—the same as the ratio from the delta perturbation scenario of Future16 (a 30% precipitation decrease and a 30% potential evapotranspiration increase). Furthermore, delta perturbation scenarios allow the impacts of model sensitivity to climate change to be clearly identified in relation to GCM scenarios. Delta perturbation scenarios allow for an extensive collection of possible climate changes at the regional scale. In addition, delta perturbation scenarios are simpler to create and use; therefore, they might complement GCM scenarios.

Enabling Health Information Recommendation Using Crowdsourced Refinement in Web-Based Health Information Applications: User-Centered Design Approach and EndoZone Informatics Case Study.

In the digital age, search engines and social media platforms are primary sources for health information, yet their commercial interests-focused algorithms often prioritize irrelevant content. Web-based health applications by reputable sources offer a solution to circumvent these biased algorithms. Despite this advantage, there remains a significant gap in research on the effective integration of content-ranking algorithms within these specialized health applications to ensure the delivery of personalized and relevant health information. This study introduces a generic methodology designed to facilitate the development and implementation of health information recommendation features within web-based health applications. We detail our proposed methodology, covering conceptual foundation and practical considerations through the stages of design, development, operation, review, and optimization in the software development life cycle. Using a case study, we demonstrate the practical application of the proposed methodology through the implementation of recommendation functionalities in the EndoZone platform, a platform dedicated to providing targeted health information on endometriosis. Application of the proposed methodology in the EndoZone platform led to the creation of a tailored health information recommendation system known as EndoZone Informatics. Feedback from EndoZone stakeholders as well as insights from the implementation process validate the methodology's utility in enabling advanced recommendation features in health information applications. Preliminary assessments indicate that the system successfully delivers personalized content, adeptly incorporates user feedback, and exhibits considerable flexibility in adjusting its recommendation logic. While certain project-specific design flaws were not caught in the initial stages, these issues were subsequently identified and rectified in the review and optimization stages. We propose a generic methodology to guide the design and implementation of health information recommendation functionality within web-based health information applications. By harnessing user characteristics and feedback for content ranking, this methodology enables the creation of personalized recommendations that align with individual user needs within trusted health applications. The successful application of our methodology in the development of EndoZone Informatics marks a significant progress toward personalized health information delivery at scale, tailored to the specific needs of users.

Sustainable and smart electric bus charging station deployment via hybrid spherical fuzzy BWM and MULTIMOORA framework

This study aims to assist public bus operators in locating electric bus charging station (EBCS) facilities from a smart and sustainable view. The selection of the most suitable EBCSs from various possible candidates involves a sophisticated decision-making procedure in terms of several contradictory criteria with imprecise information. The novelty of the study resides in exploring the EBCS site selection problem with spherical fuzzy sets (SFSs), which have shown remarkable effectiveness in limiting information loss by seizing ambiguous, and uncertain data. In this regard, a novel best–worst method (BWM) incorporating Multi-objective optimization via full multiplicative form ratio analysis (MULTIMOORA) methodology in the spherical fuzzy context is proposed to choose the optimal locations for EBCSs. The integrated framework combines the adaptability of the spherical fuzzy BWM (SF-BWM) for determining the criteria weights with the convenience of spherical fuzzy MULTIMOORA (SF-MULTIMOORA) approach for ranking the alternatives. A case study for Istanbul is provided to substantiate the propounded technique and to confirm its viability and efficiency. In the course of making a decision, a four-level hierarchical structure consisting of five main and 22 sub-criteria is built and the comparison matrices are reviewed by a panel of seven experts. A sensitivity analysis is executed, and the results demonstrate that the propositioned approach produces outcomes that are quite robust and consistent. Hence, the findings of this research can benefit public bus operators in choosing the ideal sites for electric charging stations. Finally, the formulated generic methodology is also easily applicable to diverse and complex multiple-criteria problems in the spherical fuzzy domain.

Selection and Aggregation of Conformal Prediction Sets

Conformal prediction is a generic methodology for finite-sample valid distribution-free prediction. This technique has garnered a lot of attention in the literature partly because it can be applied with any machine learning algorithm that provides point predictions to yield valid prediction regions. Of course, the efficiency (width/volume) of the resulting prediction region depends on the performance of the machine learning algorithm. In the context of point prediction, several techniques (such as cross-validation) exist to select one of many machine learning algorithms for better performance. In contrast, such selection techniques are seldom discussed in the context of set prediction (or prediction regions). In this article, we consider the problem of obtaining the smallest conformal prediction region given a family of machine learning algorithms. We provide two general-purpose selection algorithms and consider coverage as well as width properties of the final prediction region. The first selection method yields the smallest width prediction region among the family of conformal prediction regions for all sample sizes but only has an approximate coverage guarantee. The second selection method has a finite sample coverage guarantee but only attains close to the smallest width. The approximate optimal width property of the second method is quantified via an oracle inequality. As an illustration, we consider the use of aggregation of nonparametric regression estimators in the split conformal method with the absolute residual conformal score. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

A quantitative weight-of-evidence method for confidence assessment of adverse outcome pathway networks: A case study on chemical-induced liver steatosis

The field of chemical toxicity testing is undergoing a transition to overcome the limitations of in vivo experiments. This evolution involves implementing innovative non-animal approaches to improve predictability and provide a more precise understanding of toxicity mechanisms. Adverse outcome pathway (AOP) networks are pivotal in organizing existing mechanistic knowledge related to toxicological processes. However, these AOP networks are dynamic and require regular updates to incorporate the latest data. Regulatory challenges also persist due to concerns about the reliability of the information they offer. This study introduces a generic Weight-of-Evidence (WoE) scoring method, aligned with the tailored Bradford-Hill criteria, to quantitatively assess the confidence levels in key event relationships (KERs) within AOP networks. We use the previously published AOP network on chemical-induced liver steatosis, a prevalent form of human liver injury, as a case study. Initially, the existing AOP network is optimized with the latest scientific information extracted from PubMed using the free SysRev platform for artificial intelligence (AI)-based abstract inclusion and standardized data collection. The resulting optimized AOP network, constructed using Cytoscape, visually represents confidence levels through node size (key event, KE) and edge thickness (KERs). Additionally, a Shiny application is developed to facilitate user interaction with the dataset, promoting future updates. Our analysis of 173 research papers yielded 100 unique KEs and 221 KERs among which 72 KEs and 170 KERs, respectively, have not been previously documented in the prior AOP network or AOP-wiki. Notably, modifications in de novo lipogenesis, fatty acid uptake and mitochondrial beta-oxidation, leading to lipid accumulation and liver steatosis, garnered the highest KER confidence scores. In conclusion, our study delivers a generic methodology for developing and assessing AOP networks. The quantitative WoE scoring method facilitates in determining the level of support for KERs within the optimized AOP network, offering valuable insights into its utility in both scientific research and regulatory contexts. KERs supported by robust evidence represent promising candidates for inclusion in an in vitro test battery for reliably predicting chemical-induced liver steatosis within regulatory frameworks.

Linking methodologies to assess climate impacts and circular economy strategies along supply chains

This paper introduces a generic hybrid methodology for assessing economic and environmental impacts along interconnected supply chain markets, considering both the material/product level and the market mechanisms that govern resource allocation. The methodology is illustrated by considering a competitive market and benevolent social planner variant of a supply chain equilibrium (SCE) model, applied to the specific case of single-use and reusable plastic bottles in Belgium. The benevolent social planner optimises a societal welfare function that includes all relevant revenues and costs along the plastic bottle supply chains – including external environmental damage costs. These costs comprise climate damage and are incorporated into the SCE model by combining life cycle assessment data with economic data. The main advantage of this model setup is that it better represents policy reality because it allows to study the effectiveness and efficiency of the simultaneous implementation of multiple circular strategies, their interactions, and possible rebound effects. The findings indicate that when all climate costs are internalised simultaneously, the competitive market solution approaches that of the benevolent social planner. Yet, when policymakers have fewer instruments at their disposal in relation to the number of environmental externalities, the welfare impact of competitive market solutions is limited or even negative. Moreover, key circular economy practices such as material intensity reduction and green design are particularly lagging behind compared to their welfare-optimising levels.

A Prime-Order Group with Complete Formulas from Even-Order Elliptic Curves

This paper describes a generic methodology for obtaining unified, and then complete formulas for a prime-order group abstraction homomorphic to a subgroup of an elliptic curve with even order. The method is applicable to any curve with even order, in finite fields of both even and odd characteristic; it is most efficient on curves with order equal to 2 modulo 4, dubbed "double-odd curves". In large characteristic fields, we obtain doubling formulas with cost as low as 1M + 5S, and the resulting group allows building schemes such as signatures that outperform existing fast solutions, e.g. Ed25519. In binary fields, the obtained formulas are not only complete but also faster than previously known incomplete formulas; we can sign and verify in as low as 18k and 27k cycles on x86 CPUs, respectively.

Generic methodology to prevent food contamination by soil born legacy POPs in free range livestock

Government monitoring commonly includes regulating POPs in animal feed and products of animal origin, with many countries setting Maximum Residue Levels (MRLs) to ensure safe tolerable concentrations. However, these MRLs do not address the presence of most POP families in soil, where concentrations can be much higher due to the contaminants' strong affinity and persistence in comparison to other environmental matrices. Extensive damage to food and production systems during a pollution incident causing soil contamination by POPs lead to severe economic and social consequences for the affected area. To mitigate these effects, it is crucial to implement necessary measures for consumer protection while also focusing on rehabilitating conditions for food production, tailored to both commercial farms and private holders. In this context, the present work aims to develop and test a methodology for assessing the tolerable concentration of the most cancerogenic legacy POPs in soil for various livestock animals in diverse rearing systems ensuring the safety of food of animal origin. Therefore, we summarize existing knowledge about the risk of POP transfer in different livestock breeding systems via soil exposure, and modeling via a backward calculation from the MRLs the corresponding tolerable quantity of POPs that may be ingested by animals in the considered rearing system. Results of these simulations showed that soil ingestion is a predominant contamination pathway, which is a central factor in the risk assessment of POP exposure on livestock farms, especially in free-range systems. In field conditions of POP exposure, low productive animals may be more susceptible to uptake through soil than high-yielding animals, even if the feed respected MRLs. Results show that PCDD/Fs revealed the lowest security ratio for low productive dairy cows (1.5) compared to high productive ones (52). Laying hens with a productivity of 45% show also as a high sensitivity to POPs exposure via soil ingestion. Indeed, their security ratio for PCDD/Fs, lindane and DDT were 3, 2 and 1, respectively. In perspective, proposed methodology can be adapted for assessing the risk of industrial POPs newly listed in the Stockholm Convention. In practice, it could be useful for food producers to apprehend their own risk of chemical contamination.

Application of computer vision techniques for 3D matching and retrieval of archaeological objects

Background As cultural institutions embark in projects oriented to digitise art and archaeological collections in three dimensions, the need for developing means to access the resulting 3D models has become imperative. Shape recognition techniques developed in the field of computer vision can help in this task. Methods This paper describes the implementation of three shape descriptors, specifically shape distributions, reflective symmetry and spherical harmonics as part of the development of a search engine that retrieves 3D models from an archaeological database without the need of using keywords as query criteria. Use case The usefulness of this system is obvious in the context of cultural heritage museums, where it is essential to provide automatic access to archaeological and art collections. The prototype described in this paper uses, as study case, 3D models of archaeological objects belonging to Museo del Templo Mayor, a Mexican institution that preserves one of the largest collections of Aztec cultural heritage. Conclusions This work is part of an ongoing project focused on creating generic methodologies and user-friendly computational tools for shape analysis for the benefit of scholars and students interested in describing, interpreting and disseminating new knowledge about the morphology of cultural objects.

RG-GAN: Dynamic Regenerative Pruning for Data-Efficient Generative Adversarial Networks

Training Generative Adversarial Networks (GAN) to generate high-quality images typically requires large datasets. Network pruning during training has recently emerged as a significant advancement for data-efficient GAN. However, simple and straightforward pruning can lead to the risk of losing key information, resulting in suboptimal results due to GAN’s competitive dynamics between generator (G) and discriminator (D). Addressing this, we present RG-GAN, a novel approach that marks the first incorporation of dynamic weight regeneration and pruning in GAN training to improve the quality of the generated samples, even with limited data. Specifically, RG-GAN initiates layer-wise dynamic pruning by removing less important weights to the quality of the generated images. While pruning enhances efficiency, excessive sparsity within layers can pose a risk of model collapse. To mitigate this issue, RG-GAN applies a dynamic regeneration method to reintroduce specific weights when they become important, ensuring a balance between sparsity and image quality. Though effective, the sparse network achieved through this process might eliminate some weights important to the combined G and D performance, a crucial aspect for achieving stable and effective GAN training. RG-GAN addresses this loss of weights by integrating learned sparse network weights back into the dense network at the previous stage during a follow-up regeneration step. Our results consistently demonstrate RG-GAN’s robust performance across a variety of scenarios, including different GAN architectures, datasets, and degrees of data scarcity, reinforcing its value as a generic training methodology. Results also show that data augmentation exhibits improved performance in conjunction with RG-GAN. Furthermore, RG-GAN can achieve fewer parameters without compromising, and even enhancing, the quality of the generated samples. Code can be found at this link: https://github.com/IntellicentAI-Lab/RG-GAN

Data-driven carbon emission accounting for manufacturing systems based on meta-carbon-emission block

In the context of carbon neutrality and sustainable manufacturing, there is an urgent need for carbon emission accounting in the manufacturing industry, especially in high-energy, low-efficiency, and high-carbon emission manufacturing systems. However, the dynamics and coupling of carbon emissions caused by the multiple characteristics of manufacturing systems increase the challenges of carbon emission accounting. To this end, this research aims to develop a generic methodology to reveal the carbon emission characteristics and implement carbon emission accounting for manufacturing systems, so as to support the lean diagnosis and management of carbon emission hotspots. Firstly, the concept of the “meta-carbon-emission block” consisting of both static and variable carbon emission blocks is proposed to characterize the carbon emission behaviors of each device in various operating states. Then, a data-driven carbon emission accounting approach based on the meta-carbon-emission block is presented to calculate the carbon emission and diagnose the carbon emission hotspots. Finally, a typical manufacturing system, that is, a laser welding system for butt welding of aluminum alloy 6061 with multi-source of the carbon emission, multi-device of the processing system, multi-state of the sub-device, and multi-stage of the production, is conducted to validate the proposed approach, and the results revealed the effectiveness and practicality of this approach. This research provides an appropriate theoretical and methodological basis for carbon emission accounting of manufacturing systems and also supports the diagnosis and reduction of carbon emissions for manufacturing industries.