Hasil untuk "Industrial productivity"

E. Dhanumalayan, Girish M. Joshi

V. Ponnusamy, D. Nguyen, J. Dharmaraja et al.

In recent years, lignin valorization is commercially an important and advanced sustainable process for lignocellulosic biomass-based industries, primarily through the depolymerization path. The conversion of the lignin moieties into biofuels and other high value-added products are still challenging to the researchers due to the heterogeneity and complex structure of lignin-containing biomass. Besides, the involvement of different microorganisms that carries varying metabolic and enzymatic complex systems towards degradation and conversion of the lignin moieties also discussed. These microorganisms are frequently short of the traits which are obligatory for the industrial application to achieve maximum yields and productivity. This review mainly focuses on the current progress and developments in the pretreatment routes for enhancing lignin degradation and also assesses the liquid and gaseous biofuel production by fermentation, gasification and hybrid technologies along with the biorefinery schemes which involves the synthesis of high value-added chemicals, biochar and other valuable products.

Lene Fjerbaek, K. Christensen, B. Norddahl

Elizabeth M. Caucutt, Thomas F. Cooley, Nezih Guner

Kai-Hua Wang, Muhammad Umar, R. Akram et al.

Abstract Countries around the world are making efforts to transform their industrial and economic structures in order to promote green growth, and environmentally adjusted multifactor productivity growth, that relies on cleaner and sustainable energy sources. With the Fourth Industrial Revolution coming into play, eco-friendly technologies have significantly improved and repaired the environmental conditions in modern economies. Many studies on the determining factors of green growth have attracted researchers and policymakers across the globe. However, thus far, no single study has reported the role of technological innovation, in the promotion of green growth. Therefore, this study examines the impact of technological innovation on green growth, in the presence of economic growth, globalization, research & development expenditures, and human capital between the periods of 1990 to 2018, with a multivariate framework in China. By using cointegration approaches, the results suggest that in the long-run, green growth depends on technological innovation, GDP, human capital, economic globalization, and R&D expenditures. Moreover, technological innovation is found to have a positive effect on green growth. On the policy side, any initiative that targets technological innovation, globalization, R&D, and human capital shall affect green growth. These policies should take approximately more than one year to start functioning.

Maulshree Singh, Rupali Srivastava, E. Fuenmayor et al.

One of the most promising technologies that is driving digitalization in several industries is Digital Twin (DT). DT refers to the digital replica or model of any physical object (physical twin). What differentiates DT from simulation and other digital or CAD models is the automatic bidirectional exchange of data between digital and physical twins in real-time. The benefits of implementing DT in any sector include reduced operational costs and time, increased productivity, better decision making, improved predictive/preventive maintenance, etc. As a result, its implementation is expected to grow exponentially in the coming decades as, with the advent of Industry 4.0, products and systems have become more intelligent, relaying on collection and storing incremental amounts of data. Connecting that data effectively to DTs can open up many new opportunities and this paper explores different industrial sectors where the implementation of DT is taking advantage of these opportunities and how these opportunities are taking the industry forward. The paper covers the applications of DT in 13 different industries including the manufacturing, agriculture, education, construction, medicine, and retail, along with the industrial use case in these industries.

G. J. Hahn

The Fourth Industrial Revolution – also known as Industry 4.0 (i4.0) – comprises the digitalisation of the industrial sector. This paper uses the theoretical lens of supply chain innovation (SCI) to investigate the implications of i4.0 on supply chain management. For these purposes, the method of structured content analysis is applied to more than 200 use cases of i4.0-enabled SCI introduced by both established and startup companies. i4.0-enabled SCI manifests along three dimensions: process, technology, and business architecture. The key findings of this study can be summarised as follows: first, i4.0-enabled SCI extends the initial focus on productivity improvements in SC processes towards scalability and flexibility. Second, extant i4.0 solutions rely mostly on analytics and smart things while omitting smart people technology and the human-centric approach associated with the i4.0 paradigm. Third, established companies adopt i4.0 merely to sustain their existing business architectures while startup companies radically change their operating models, relying heavily on data analytics and the platform economy. Consequently, established companies pursue a problem-driven, engineering-based approach to SCI while startup companies follow an ‘asset-light’, business-driven approach. Lastly, there are two distinct approaches to digitalising operational SC processes: platform-based crowdsourcing of standard processes and on-demand provision of customised services.

Zarmeena Khan, Fahed Javed, Zufishan Shamair et al.

G. Byrne, D. Dornfeld, I. Inasaki et al.

M. Weyrich, C. Ebert

Tolgahan Bardakci, Andreas Faes, Mutlu Beyazit et al.

Large Language Models (LLMs) are increasingly used to support software testing tasks, yet there is little evidence of their effectiveness for REST API testing in industrial settings. To address this gap, we replicate our earlier work on LLM-based REST API test amplification within an industrial context at one of the largest logistics companies in Belgium. We apply LLM-based test amplification to six representative endpoints of a production microservice embedded in a large-scale, security-sensitive system, where there is in-depth complexity in authentication, stateful behavior, and organizational constraints. Our experience shows that LLM-based test amplification remains practically useful in industry by increasing coverage and revealing various observations and anomalies.

Ilham Nassri, Hasnaa Harmouzi, Latifa Tahri et al.

Abstract Global warming, population growth, agricultural intensification, and rising industrial productivity have affected groundwater quality. However, assessing groundwater quality enables us to understand the risk of contamination better and protect these natural resources in the long term. The objectives of this study are to evaluate the physicochemical parameters of groundwater for human drinking purposes in North West of Morocco and to identify their geogenic and/or non-geogenic origins through the use of graphical diagrams (Piper and Schoeller Berkaloff) and by GIS interpolation tools to recognize the main geochemical processes governing groundwater quality. For this purpose, the physico-chemical parametrs analyzed of groundwater in the Bouregreg basin and the southern part of the Sebou basin, using 51 water points in the study region’s rural area. Specific physicochemical water parameters (pH, TH, TAC, and electrical conductivity), cations (K + , Na + , Ca2+, and Mg2+), and anions (Cl−, NO3 –, HCO3 – and SO2− 4) were measured and analyzed for human consumption. Piper diagrams and ArcGIS geoprocessing were used to identify groundwater hydrochemical facies and study the spatial distribution of estimated parameters concerning geology and anthropogenic factors. All the parameters measured were below the threshold values required by the WHO for human drinking water, except nitrates, recorded in one sample at a concentration of 88.75 mg/L. Piper and Schoeller Berkaloff diagrams show that strong acid (Cl−) and weak acid (HCO3 –) appear considerably over the other acids (NO3 – and SO2− 4). In contrast, most sources show no cation dominance. The Calcium and magnesium chloride facies (43%) and Calcium-magnesium bicarbonate facies (35%) are the most facies presnent in this stydy. Geoprocessing showed that the chemical composition of the various sampling points is governed mainly by lithological diversity, except for S35, which showed an anthropogenic origin.

Antoine Houssard

In the field of artificial intelligence (AI) research, there seems to be a rapprochement between academics and industrial forces. The aim of this study is to assess whether and to what extent industrial domination in the field as well as the ever more frequent switch between academia and industry resulted in the adoption of industrial norms and practices by academics. Using bibliometric information and data on scientific code, we aimed to understand academic and industrial researchers' practices, the way of choosing, investing, and succeeding across multiple and concurrent artifacts. Our results show that, although both actors write papers and code, their practices and the norms guiding them differ greatly. Nevertheless, it appears that the presence of industrials in academic studies leads to practices leaning toward the industrial side, but also to greater success in both artifacts, suggesting that if convergence is, then it is passing through those mixed teams rather than through pure academic or industrial studies.

Zongyun Zhang, Jiacheng Ruan, Xian Gao et al.

Industrial Anomaly Detection (IAD) is critical to ensure product quality during manufacturing. Although existing zero-shot defect segmentation and detection methods have shown effectiveness, they cannot provide detailed descriptions of the defects. Furthermore, the application of large multi-modal models in IAD remains in its infancy, facing challenges in balancing question-answering (QA) performance and mask-based grounding capabilities, often owing to overfitting during the fine-tuning process. To address these challenges, we propose a novel approach that introduces a dedicated multi-modal defect localization module to decouple the dialog functionality from the core feature extraction. This decoupling is achieved through independent optimization objectives and tailored learning strategies. Additionally, we contribute to the first multi-modal industrial anomaly detection training dataset, named Defect Detection Question Answering (DDQA), encompassing a wide range of defect types and industrial scenarios. Unlike conventional datasets that rely on GPT-generated data, DDQA ensures authenticity and reliability and offers a robust foundation for model training. Experimental results demonstrate that our proposed method, Explainable Industrial Anomaly Detection Assistant (EIAD), achieves outstanding performance in defect detection and localization tasks. It not only significantly enhances accuracy but also improves interpretability. These advancements highlight the potential of EIAD for practical applications in industrial settings.

Liang Yu

Generative AI (GenAI) applications are transforming software engineering by enabling automated code co-creation. However, empirical evidence on GenAI's productivity effects in industrial settings remains limited. This paper investigates the adoption of GenAI coding assistants (e.g., Codeium, Amazon Q) within telecommunications and FinTech domains. Through surveys and interviews with industrial domain-experts, we identify primary productivity-influencing factors, including task complexity, coding skills, domain knowledge, and GenAI integration. Our findings indicate that GenAI tools enhance productivity in routine coding tasks (e.g., refactoring and Javadoc generation) but face challenges in complex, domain-specific activities due to limited context-awareness of codebases and insufficient support for customized design rules. We highlight new paradigms for coding transfer, emphasizing iterative prompt refinement, immersive development environment, and automated code evaluation as essential for effective GenAI usage.

Maïté Michaud, Chalore Teepakorn, Véronique De Berardinis et al.

In complement to or as a replacement for environmentally-costly chemical transformations, biocatalytic synthesis is attracting increasing interest. To make it competitive, basic research on process engineering with continuous operations using immobilized enzymes must be pursued. Micro-fluidic reactors are generally operated with wall-immobilized enzymes, but their implementation at industrial scale requires both parallelization and characteristic dimension increases. As part of this scale-up, research is required on millimeter-scale reactors to assess their bio-performance and potential limitations. Here, we present the first study of a pillar-structured milli-reactor. We studied this in-flow bioreactor with immobilized nitrilase and numerically characterized it by CFD modeling. After 5 days of continuous operation, a mean space-time-yield of 0.80 mM.min-1 and a turnover number of 148 mgproduct.mgenz-1 were reached. These promising performance results and the model validation were then used in a numerical study to determine how the reactor’s performance could be optimized. Under strict laminar conditions, strategies like increasing the surface-to-volume ratio or distribution of the enzyme all over the developed reactor surfaces are the main characteristics contributing to conversion improvement. Pillar reactors have a greater scale-up potential than zigzag reactors, requiring lower pumping energies for a given conversion rate. Finally, we hypothesize that going to hydrodynamic conditions with instabilities combined with more active enzymes would be an interesting avenue for future investigation to reach higher levels of process intensification. Statement_of_novelty_and_significance: Micro-scale devices with wall-immobilized enzymes intensify mass transfers but the low productivity per unit requires complex parallelization to meet industrial throughput. This study proposes an experimental characterisation of a milli-size reactor coated with commercial polymethylmethacrylate beads on the surface. Compared to batch assays, the productivity has been intensified by 14-fold along with an increase of the space-time-yield of 27-fold. Reactor design engineering has been performed with CFD screening. Room for optimization has been elucidated with parameters including surface-to-volume ratio, enzyme distribution and liquid flow with instabilities using more active enzymes.

Yihui Wen, Guihuan Liu, Jinnan Wang et al.

Establishing a mechanism for the value realization of ecological products is crucial for promoting ecological development in China and for achieving the goals of carbon peaking and carbon neutrality. It also paves a new path for common prosperity for all the people and can provide effective support for achieving national strategic goals by 2035. This study sorts out the practical progress of the value realization of ecological products in China, and adopts methods such as literature analysis, current situation assessment, and strategic adaptability analysis to explore the core bottlenecks regarding the value realization, namely, difficulties in measurement, transaction, monetization, and mortgage. Under the national strategic frameworks of building a Beautiful China, achieving common prosperity, and attaining the carbon peaking and carbon neutrality goals, this study responds to the new requirements for developing new quality productivity and proposes the vision, goals, and tasks for the value realization of ecological products toward 2035, with the strategic mainline being "value goals‒basic systems‒policy tools‒industrial support‒supporting guarantees." It also puts forward a differentiated strategic layout and implementation paths for the value realization of ecological products in different regions, providing important support for the transformation of China's development concepts, modes, and drivers.

Atif Hussain, Rana Rizwan

This paper argues for the strategic treatment of artificial intelligence as a key industry within broader industrial policy framework of Pakistan, underscoring the importance of aligning it with national goals such as economic resilience and preservation of autonomy. The paper starts with defining industrial policy as a set of targeted government interventions to shape specific sectors for strategic outcomes and argues for its application to AI in Pakistan due to its huge potential, the risks of unregulated adoption, and prevailing market inefficiencies. The paper conceptualizes AI as a layered ecosystem, comprising foundational infrastructure, core computing, development platforms, and service and product layers, supported by education, government policy, and research and development. The analysis highlights that AI sector of Pakistan is predominantly service oriented, with limited product innovation and dependence on foreign technologies, posing risks to economic independence, national security, and employment. To address these challenges, the paper recommends educational reforms, support for local AI product development, initiatives for indigenous cloud and hardware capabilities, and public-private collaborations on foundational models. Additionally, it advocates for public procurement policies and infrastructure incentives to foster local solutions and reduce reliance on foreign providers. This strategy aims to position Pakistan as a competitive, autonomous player in the global AI ecosystem.

Dimitrios Kouzapas, Christos G. Panayiotou, Demetrios G. Eliades

Modern industrial systems require frequent updates to their cyber and physical infrastructures, often demanding considerable reconfiguration effort. This paper introduces the industrial Cyber-Physical Systems Description Language, iCPS-DL, which enables autonomic reconfigurations for industrial Cyber-Physical Systems. The iCPS-DL maps an industrial process using semantics for physical and cyber-physical components, a state estimation model, and agent interactions. A novel aspect is using communication semantics to ensure live interaction among distributed agents. Reasoning on the semantic description facilitates the configuration of the industrial process control loop. A Water Distribution Networks domain case study demonstrates iCPS-DL's application.

Wei Zhang, Xianfu Cheng, Yi Zhang et al.

Log parsing, a vital task for interpreting the vast and complex data produced within software architectures faces significant challenges in the transition from academic benchmarks to the industrial domain. Existing log parsers, while highly effective on standardized public datasets, struggle to maintain performance and efficiency when confronted with the sheer scale and diversity of real-world industrial logs. These challenges are two-fold: 1) massive log templates: The performance and efficiency of most existing parsers will be significantly reduced when logs of growing quantities and different lengths; 2) Complex and changeable semantics: Traditional template-matching algorithms cannot accurately match the log templates of complicated industrial logs because they cannot utilize cross-language logs with similar semantics. To address these issues, we propose ECLIPSE, Enhanced Cross-Lingual Industrial log Parsing with Semantic Entropy-LCS, since cross-language logs can robustly parse industrial logs. On the one hand, it integrates two efficient data-driven template-matching algorithms and Faiss indexing. On the other hand, driven by the powerful semantic understanding ability of the Large Language Model (LLM), the semantics of log keywords were accurately extracted, and the retrieval space was effectively reduced. Notably, we launch a Chinese and English cross-platform industrial log parsing benchmark ECLIPSE- BENCH to evaluate the performance of mainstream parsers in industrial scenarios. Our experimental results across public benchmarks and ECLIPSE- BENCH underscore the superior performance and robustness of our proposed ECLIPSE. Notably, ECLIPSE both delivers state-of-the-art performance when compared to strong baselines and preserves a significant edge in processing efficiency.