Hasil untuk "Industrial productivity"

Sutthinee Keawmaungkom, Supatra Patrawoot, Panithi Wiroonpochit et al.

Prevulcanization of natural rubber (NR) latex is a key process in producing diverse rubber products, as it governs their mechanical performance. Conventional sulfur prevulcanization is widely used owing to its simplicity and low cost, yet it poses environmental and health concerns due to zinc-based accelerators and sulfur compounds. This study compared five prevulcanization processes (sulfur-based; UV irradiation from fluorescent lamps, UV-Flu; UV from light emitting diodes, UV-LED; electron beam, EB; X-ray irradiation) using life cycle analysis (LCA) and life cycle cost analysis (LCCA). Laboratory experiments established the life cycle inventory (LCI) for processes that were then scaled up to industrial production scenarios. Measurements confirmed that all processes produced films that met ASTM requirements (Standard D3578–19). The LCA showed that EB irradiation minimized the environmental burdens because of short irradiation times and high throughput. X-ray prevulcanization resulted in the highest impact, driven by a high energy requirement and low productivity. UV-LED outperformed UV-Flu, reflecting higher efficiency of LED lamps and their longer life compared to fluorescent lights. The LCCA revealed sulfur-based process to be the most economic (US$ 1.48 kg−1), followed by UV-LED (US$ 4.38 kg−1) and the EB (US$ 10.56 kg−1). The X-ray process was prohibitively expensive (US$ 203.83 kg−1) and environmentally the most burdensome. Overall, the UV-LED and EB processes were most sustainable, especially if these technologies were developed further to reduce energy input and the hardware costs.

Maoguo Wu, Yihao Liu, Linsong Huang et al.

Utilizing a panel dataset of 273 prefecture-level cities in China from 2000 to 2019, this study evaluates the impact of the low-carbon city pilot (LCCP) policy on green total factor productivity (GTFP). This study calculates GTFP via a hybrid function model of the epsilon-based measure. Using a staggered difference-in-differences framework, we found that the LCCP policy improves GTFP. Through its implementation, the LCCP policy exerts an increasingly positive impact. We identify promoting industrial structure optimization and technological innovation as two plausible underlying mechanisms. Heterogeneity analysis finds that the impact is more pronounced in central and western China, as well as in low-level administrative and low-marketization cities. This study provides empirical evidence for the optimization of the LCCP policy and the transformation of the low-carbon economy, and provides a basis for policy-making.

GENG Lijia, XU Lei, TAN Xiaoxuan et al.

This paper establishes the Ningbo Marine New Quality Productivity Index from six aspects, namely the strength of the marine economy, characteristic marine industries, marine scientific and technological innovation, marine ecological environment, internal and external circulation of the marine economy, and marine common prosperity, and conducts a quantitative assessment of the development level of marine new-quality productivity from 2020 to 2023. The research results show that the level of Ningbo’s marine new quality productivity has been rising at an accelerated pace, with an average annual growth rate of 17.2%. Both the quantity and quality of the scale of the marine economy, industries, science and technology, ecology, and people’s livelihood have been improved. Based on the construction requirements of a modern marine city and the actual situation of Ningbo’s marine economic development, it is found that Ningbo still faces bottlenecks and constraints in the modern marine industrial system, marine scientific and technological innovation, the transformation of scientific and technological achievements and capital market services. Given this, this paper puts forward a series of policy recommendations, such as giving full play to industrial and locational advantages and continuously optimizing the development ecology of marine talents, to support the construction of Ningbo as a modern marine city.

Huda Enaya Mahood, Virginia Sarropoulou, Thalia Tsapraili et al.

<i>Washingtonia filifera</i> is important for its ecological, economic, cultural, horticultural, ornamental, and medicinal potential. Elicitation of in vitro cultures presents a promising and efficient method for the large-scale production of valuable bioactive compounds. This study assessed the effect of chitosan concentration (0, 20, 40, 60, 80, 100 mg L⁻¹) on biomass growth [fresh weight (FW), dry weight (DW)] and phytochemical profile [total phenolic content (TPC), total flavonoid content (TFC), DPPH antioxidant activity, total phenolic productivity (TPP), total flavonoid productivity (TFP)] in <i>W. filifera</i> callus and cell suspension cultures. Among different plant growth regulator combinations tested, 3 mg L⁻¹ 2,4-D + 0.5 mg L⁻¹ 2ip gave higher callus induction (90%) (MS medium, 12 weeks). A maximum growth curve (FW: 180 mg) of cell suspension culture was achieved 7 weeks after initiation (shaker at 90 rpm for 24 h). Cell suspension exhibited higher FW, DW, TPC, TFC, DPPH, TPP, and TFP than callus, while flavonoid production was higher than phenolic production. FW and DW were higher in both systems, with 40 mg L⁻¹ chitosan. Chitosan at 60 mg L⁻¹ best enhanced the phytochemical profile of both the 4-week solidified callus and the 7-week liquid cell suspension (TPC: 29.9 and 32.1 mg GAE g⁻¹ DW; TFC: 40.5 and 56.1 mg QE g⁻¹ DW; TPP: 969.2 and 1122.6 mg L⁻¹; TFP: 1313.9 and 1521.7 mg L⁻¹; DPPH: 87.4 and 92.3%), respectively, while 40 mg L⁻¹ chitosan was equally effective regarding DW, TFC, and TFP in cell suspension. Chitosan elicitation provides a powerful strategy to upregulate phenolic and flavonoid biosynthesis in <i>W. filifera</i> in vitro systems, conferring superior antioxidant potential. The identification of peak elicitation parameters (chitosan concentration, exposure time) allows for the targeted enhancement of bioactive compound yields, suggesting a viable path for industrial bioproduction and commercialization in pharmaceuticals, nutraceuticals, and functional foods, leveraging bioreactor technology for efficient scale-up.

Qi Yuan, Shuo Ma, Shanji Yao

Artificial intelligence (AI) provides novel technological pathways and research perspectives to mitigate global carbon emissions. This study empirically examines the impact of AI on carbon productivity utilizing panel data from 286 prefecture-level cities in China, covering the period from 2003 to 2021. The results indicate that AI enhances urban carbon productivity (CP). Mechanism analysis reveals that AI indirectly improves carbon productivity via industrial optimization and innovation promotion impacts, with environmental regulation (ER) and internet penetration (IP) rates serving as positive moderating factors in this process. A subsequent study reveals that the influences of AI, human capital (HC), and financial development (Fin) on carbon productivity display threshold effects marked by escalating marginal returns. Heterogeneity research indicates that the impact of AI on carbon production differs markedly across various resource endowments, city sizes, regions, and urban agglomerations. This study’s conclusions provide novel theoretical frameworks for implementing AI technology in carbon emission reduction and furnish critical insights for advancing low-carbon transitions.

Najimov Iskander, Adilbaev Ismail, Kamalov Maqset et al.

The current situation of energy consumption in agricultural systems and the problems existing in traditional farming practices are proposed to achieve higher energy efficiency, lower environmental impact, and improved economic viability. This paper aimed to develop a comprehensive framework that integrates principles of the industrial economy with agricultural practices to achieve significant energy savings. To meet the increased sustainability and productivity demands of modern agricultural enterprises, the capability of energy management systems must improve. One important technique for doing this is to redesign energy allocation frameworks, the optimization models that agricultural stakeholders follow to perform resource distribution and efficiency assessments. In this study, the Analytical Hierarchy Process (AHP) method in agricultural energy efficiency evaluation combines the decision-support system method based on multi-criteria analysis, comparative weighting techniques, and hierarchical structuring. The research results show that the constructed model can systematically analyze the comparative effectiveness of energy-efficient strategies, and renewable energy integration is more economically viable, more environmentally sustainable, and more operationally efficient in agricultural energy management. Analyzing this energy optimization trend reveals that solar energy adoption has been the largest contributor to sustainable energy transition in agriculture, with drip irrigation systems and biomass energy solutions (e.g., solar-powered irrigation, wind-assisted grain drying) leading the way, but that traditional fossil-fuel-based farming machinery has faded in recent decades.

Kiana Jafari Meimandi, Gabriela Aránguiz-Dias, Grace Ra Kim et al.

As industry reports claim agentic AI systems deliver double-digit productivity gains and multi-trillion dollar economic potential, the validity of these claims has become critical for investment decisions, regulatory policy, and responsible technology adoption. However, this paper demonstrates that current evaluation practices for agentic AI systems exhibit a systemic imbalance that calls into question prevailing industry productivity claims. Our systematic review of 84 papers (2023--2025) reveals an evaluation imbalance where technical metrics dominate assessments (83%), while human-centered (30%), safety (53%), and economic assessments (30%) remain peripheral, with only 15% incorporating both technical and human dimensions. This measurement gap creates a fundamental disconnect between benchmark success and deployment value. We present evidence from healthcare, finance, and retail sectors where systems excelling on technical metrics failed in real-world implementation due to unmeasured human, temporal, and contextual factors. Our position is not against agentic AI's potential, but rather that current evaluation frameworks systematically privilege narrow technical metrics while neglecting dimensions critical to real-world success. We propose a balanced four-axis evaluation model and call on the community to lead this paradigm shift because benchmark-driven optimization shapes what we build. By redefining evaluation practices, we can better align industry claims with deployment realities and ensure responsible scaling of agentic systems in high-stakes domains.

Meng Li, Yu Zhang, Leshan Li

The Industry 4.0 refers to a industrial ecology which will merge the information system, physical system and service system into an integrate platform. Since now the industrial designers either conceive the physical part of products, or design the User Interfaces of computer systems, the new industrial ecology will give them a chance to redefine their roles in R&D work-flow. In this paper we discussed the required qualities of industrial designer in the new era, according to an investigation among Chinese enterprises. Additionally, how to promote these qualities though educational program.

Supun Dissanayaka, Alexander Ferrein, Till Hofmann et al.

The RoboCup Logistics League is a RoboCup competition in a smart factory scenario that has focused on task planning, job scheduling, and multi-agent coordination. The focus on production logistics allowed teams to develop highly competitive strategies, but also meant that some recent developments in the context of smart manufacturing are not reflected in the competition, weakening its relevance over the years. In this paper, we describe the vision for the RoboCup Smart Manufacturing League, a new competition designed as a larger smart manufacturing scenario, reflecting all the major aspects of a modern factory. It will consist of several tracks that are initially independent but gradually combined into one smart manufacturing scenario. The new tracks will cover industrial robotics challenges such as assembly, human-robot collaboration, and humanoid robotics, but also retain a focus on production logistics. We expect the reenvisioned competition to be more attractive to newcomers and well-tried teams, while also shifting the focus to current and future challenges of industrial robotics.

Huike Zou, Haiyang Yang, Yindu Su et al.

Identifying attribute values from product profiles is a key task for improving product search, recommendation, and business analytics on e-commerce platforms, which we called Product Attribute Value Identification (PAVI) . However, existing PAVI methods face critical challenges, such as cascading errors, inability to handle out-of-distribution (OOD) attribute values, and lack of generalization capability. To address these limitations, we introduce Multi-Value-Product Retrieval-Augmented Generation (MVP-RAG), combining the strengths of retrieval, generation, and classification paradigms. MVP-RAG defines PAVI as a retrieval-generation task, where the product title description serves as the query, and products and attribute values act as the corpus. It first retrieves similar products of the same category and candidate attribute values, and then generates the standardized attribute values. The key advantages of this work are: (1) the proposal of a multi-level retrieval scheme, with products and attribute values as distinct hierarchical levels in PAVI domain (2) attribute value generation of large language model to significantly alleviate the OOD problem and (3) its successful deployment in a real-world industrial environment. Extensive experimental results demonstrate that MVP-RAG performs better than the state-of-the-art baselines.

Yixuan Sun, Teng Zhang, Baolei Zhang et al.

The industrial economy occupies a crucial position in China’s national economy, and industrial eco-efficiency (IEE) as a significant indicator of regional green development levels. Balancing the positive interaction between industrial economy and resource environment, and enhancing ecological efficiency in industrial development are vital for achieving sustainable regional economic development. This study measures the IEE of 115 cities in coastal China based on panel data of industrial resources and the environment factors. Subsequently, it further analyzes the influencing mechanisms and future trends of IEE. The results indicate that the overall IEE in coastal China is on an upward trend, with higher efficiency values in provinces and regions characterized by faster economic development and better environmental conditions. Significant changes in spatial patterns are observed, with the gaps between cities narrowing and a “multi-core” development model emerging. Factors such as per capita GDP, the ratio of industrial pollution control investment to GDP, innovation index, the proportion of foreign direct investment to GDP, and industrial labor productivity significantly positively influence IEE. In contrast, the proportion of industrial added value to GDP, urbanization rate, and the number of industrial enterprises exhibit notable negative inhibitory effects. Moreover, the interaction effect between industrialization level and other factors is most significant. In the future, IEE is expected to continue improving, although the sustainability of these changes appears weak. These findings reveal the potential impact mechanisms of resource consumption and environmental pollution caused by industrial activities on economic benefit output. This study provides a scientific basis for optimizing energy development layout, enhancing the comprehensive utilization of energy resources, and improving ecological compensation and protection mechanisms.

Md Sazol Ahmmed, Sriram Praneeth Isanaka, Frank Liou

The manufacturing industry continues to suffer from inefficiency, excessively high prices, and uncertainty over product quality. This statement remains accurate despite the increasing use of automation and the significant influence of Industry 4.0 and AI on industrial operations. This review details an extensive analysis of a substantial body of literature on artificial intelligence (AI) and Industry 4.0 to improve the efficiency of material processing in manufacturing. This document includes a summary of key information (i.e., various input tools, contributions, and application domains) on the current production system, as well as an in-depth study of relevant achievements made thus far. The major areas of attention were adaptive manufacturing, predictive maintenance, AI-driven process optimization, and quality control. This paper summarizes how Industry 4.0 technologies like Cyber-Physical Systems (CPS), the Internet of Things (IoT), and big data analytics have been utilized to enhance, supervise, and monitor industrial activities in real-time. These techniques help to increase the efficiency of material processing in the manufacturing process, based on empirical research conducted across different industrial sectors. The results indicate that Industry 4.0 and AI both significantly help to raise manufacturing sector efficiency and productivity. The fourth industrial revolution was formed by AI, technology, industry, and convergence across different engineering domains. Based on the systematic study, this article critically explores the primary limitations and identifies potential prospects that are promising for greatly expanding the efficiency of smart factories of the future by merging Industry 4.0 and AI technology.

Liang Chu, Liang Cheng, Yulong Gao et al.

With the widespread promotion of the concept of green development, China’s green credit policy system has been established, developed, and gradually improved during the past decade. Against the background of the country’s vigorous development of green finance, this finance has had an increasingly important influence on agricultural green total factor productivity (GTFP). In this study, we took 30 provinces (autonomous regions or municipalities directly under central government control) in China as research samples (Hong Kong, Macao, Taiwan, and Tibet were not included due to a lack of data). The time period from 2010 to 2020 was selected as the research period, given that 2010 was the year when the development stage of China’s green finance was first initiated. Through in-depth analysis of the spatial correlations of agricultural GTFP in China and the influences of green finance on agricultural GFTP, we constructed a research framework with multiple dimensions, including green credit, green bonds, green insurance, green investment, and carbon finance. We then systematically studied the influences of green finance on agricultural GTFP. Our results showed that: (1) The development levels of green finance and agricultural GTFP in China were high, but there were not able differences among provinces, with higher agricultural GTFP in northern China and lower agricultural GTFP in central China; (2) green finance had the greatest promoting effect in western China, a weaker promoting effect in central China, and the weakest promoting effect in eastern China; and (3) green finance can indirectly promote improvements in agricultural GTFP by promoting the upgrading of industrial structure, driving technological progress, and optimizing energy consumption structure. Our work not only provides valuable reference data and suggestions for the green and sustainable development of China’s agriculture but also academic support for the development of China’s agricultural economy.

Lorenzo Pasotti, Ilaria Massaiu, Paolo Magni et al.

Poly-γ-glutamic acid (γ-PGA) is an attractive biopolymer for medical, agri-food, and environmental applications. Although microbial synthesis by <i>Bacilli</i> fed on waste streams has been widely adopted, the obtainment of efficient sustainable production processes is still under investigation by bioprocess and metabolic engineering approaches. The abundant glycerol-rich waste generated in the biodiesel industry can be used as a carbon source for γ-PGA production. Here, we studied fermentation performance in different engineered <i>Bacillus subtilis</i> strains in glycerol-based media, considering a <i>swrA⁺ degU32^Hy</i> mutant as the initial producer strain and glucose-based media for comparison. Modifications included engineering the biosynthetic <i>pgs</i> operon regulation (replacing its native promoter with P_hyspank), precursor accumulation (<i>sucCD</i> or <i>odhAB</i> deletion), and enhanced glutamate racemization (<i>racE</i> overexpression), predicted as crucial reactions by genome-scale model simulations. All interventions increased productivity in glucose-based media, with P_hyspank-<i>pgs</i> ∆<i>sucCD</i> showing the highest γ-PGA titer (52 g/L). Weaker effects were observed in glycerol-based media: ∆<i>sucCD</i> and P_hyspank-<i>pgs</i> led to slight improvements under low- and high-glutamate conditions, respectively, reaching ~22 g/L γ-PGA (26% increase). No performance decrease was detected by replacing pure glycerol with crude glycerol waste from a biodiesel plant, and by a 30-fold scale-up. These results may be relevant for improving industrial γ-PGA production efficiency and process sustainability using waste feedstock. The performance differences observed between glucose and glycerol media also motivate additional computational and experimental studies to design metabolically optimized strains.

Anton Sergeev, Victor Minchenkov, Aleksei Soldatov

Manual operations remain essential in industrial production because of their flexibility and low implementation cost. However, ensuring their quality and monitoring execution in real time remains a challenge, especially under conditions of high variability and human-induced errors. In this paper, we present an AI-based control system for tracking manual assembly and propose a novel methodology to evaluate its overall efficiency. The developed system includes a multicamera setup and a YOLOv8-based detection module integrated into an experimental stand designed to replicate real production scenarios. The evaluation methodology relies on timestamp-level comparisons between predicted and actual execution stages, using three key metrics: Intersection over Union (IoU), Mean Absolute Scaled Error (MASE), Residual Distribution histograms. These metrics are aggregated into a unified efficiency index E_total for reproducible system assessment. The proposed approach was validated on a dataset of 120 assemblies performed at different speeds, demonstrating high segmentation accuracy and identifying stage-specific timing deviations. The results confirm the robustness of the control system and the applicability of the evaluation framework to benchmark similar solutions in industrial settings.

Jiaqi Lei, Liang Hu, Yi Bu et al.

Previous researches on the Information retrieval (IR) field have focused on summarizing progress and synthesizing knowledge and techniques from individual studies and data-driven experiments, the extent of contributions and collaborations between researchers from different communities (e.g., academia and industry) in advancing IR knowledge remains unclear. To address this gap, this study explores several characteristics of information retrieval research in four areas: productivity patterns and preferred venues, the relationship between citations and downloads, changes in research topics, and changes in patterns of scientific collaboration, by analyzing 53,471 papers published between 2000 and 2018 from the Association for Computing Machinery (ACM) Digital Library dataset. Through the analysis and interpretation on empirical datasets, we find that academic research, industry research, and collaborative research between academia and industry focused on different topics. Among the collaboration models, Academia-Industry Collaboration is more oriented towards large teamwork. Collaborative networks between researchers in academia and industry suggest that the field of information retrieval has become richer over time in terms of themes, foci, and sub-themes, becoming a more diverse field of study.

Sandeep Gupta

Secure and efficient communication to establish a seamless nexus between the five levels of a typical automation pyramid is paramount to Industry 4.0. Specifically, vertical and horizontal integration of these levels is an overarching requirement to accelerate productivity and improve operational activities. Vertical integration can improve visibility, flexibility, and productivity by connecting systems and applications. Horizontal integration can provide better collaboration and adaptability by connecting internal production facilities, multi-site operations, and third-party partners in a supply chain. In this paper, we propose an Edge-computing-based Industrial Gateway for interfacing information technology and operational technology that can enable Industry 4.0 vertical and horizontal integration. Subsequently, we design and develop a working prototype to demonstrate a remote production-line maintenance use case with a strong focus on security aspects and the edge paradigm to bring computational resources and data storage closer to data sources.

Marco V. Lara-Fiallos, Dayana T. Montalvo-Villacreses, Rosario C. Espín-Valladares et al.

Background: Luffa cylindrica has numerous domestic and industrial uses. For example, this natural fiber supports immobilization through the covalent bonding of an inulinase. In addition, fructose syrup from crude inulin obtained from prickly pear (Opuntia ficus-indica) can be obtained in a plug-flow mini reactor with this immobilized inulinase on L. cylindrica. Results: A central composite design of experiments was used to maximize the enzymatic fructose production from crude inulin obtained from Opuntia ficus-indica in a plug-flow mini reactor. The experiments explore temperature (between 45 and 55°C), pH (4.0–5.0), and feed flow (0.1–0.2 ml/min). After verifying the adequacy of the quadratic model for productivity, it was maximized to find the optimal condition. It was at 49.97°C, 4.6 and 0.20 ml/min for the temperature, pH, and flow, respectively. Under the optimal condition, the quadratic model suggested a productivity of 2.456 ± 0.015 mg/h. Three validation experiments confirmed the validity of the model. Conclusions: The results confirmed the suitability of L. cylindrica as support for the immobilization of inulinase.How to cite: Lara-Fiallos MV, Montalvo-Villacreses DT, Espín-Valladares RC, et al. Optimization of fructose-rich syrups production from Opuntia ficus-indica inulin using immobilized inulinase on Luffa cylindrical. Electron J Biotechnol 2023;66. https://doi.org/10.1016/j.ejbt.2023.03.007.

Nwosu Obinnaya Chikezie Victor

Artificial intelligence (AI) can revolutionize the development industry, primarily electrical and electronics engineering. By automating recurring duties, AI can grow productivity and efficiency in creating. For instance, AI can research constructing designs, discover capability troubles, and generate answers, reducing the effort and time required for manual analysis. AI also can be used to optimize electricity consumption in buildings, which is a critical difficulty in the construction enterprise. Via machines gaining knowledge of algorithms to investigate electricity usage patterns, AI can discover areas wherein power may be stored and offer guidelines for enhancements. This can result in significant value financial savings and reduced carbon emissions. Moreover, AI may be used to improve the protection of creation websites. By studying statistics from sensors and cameras, AI can locate capacity dangers and alert workers to take suitable action. This could help save you from injuries and accidents on production sites, lowering the chance for workers and enhancing overall safety in the enterprise. The impact of AI on electric and electronics engineering productivity inside the creation industry is enormous. AI can transform how we layout, build, and function buildings by automating ordinary duties, optimising electricity intake, and enhancing safety. However, ensuring that AI is used ethically and responsibly and that the advantages are shared fairly throughout the enterprise is essential.

Lasse Matthias Reinpold, Lukas Peter Wagner, Felix Gehlhoff et al.

To achieve a highly agile and flexible production, it is envisioned that industrial production systems gradually become more decentralized, interconnected, and intelligent. Within this vision, production assets collaborate with each other, exhibiting a high degree of autonomy. Furthermore, knowledge about individual production assets is readily available throughout their entire life-cycles. To realize this vision, adequate use of information technology is required. Two commonly applied software paradigms in this context are Software Agents (referred to as Agents) and Digital Twins (DTs). This work presents a systematic comparison of Agents and DTs in industrial applications. The goal of the study is to determine the differences, similarities, and potential synergies between the two paradigms. The comparison is based on the purposes for which Agents and DTs are applied, the properties and capabilities exhibited by these software paradigms, and how they can be allocated within the Reference Architecture Model Industry 4.0. The comparison reveals that Agents are commonly employed in the collaborative planning and execution of production processes, while DTs typically play a more passive role in monitoring production resources and processing information. Although these observations imply characteristic sets of capabilities and properties for both Agents and DTs, a clear and definitive distinction between the two paradigms cannot be made. Instead, the analysis indicates that production assets utilizing a combination of Agents and DTs would demonstrate high degrees of intelligence, autonomy, sociability, and fidelity. To achieve this, further standardization is required, particularly in the field of DTs.