Hasil untuk "Industrial productivity"

Yang Liu, Yanlin Yang, Huihui Li et al.

The digital economy is an important engine to promote sustainable economic growth. Exploring the mechanism by which the digital economy promotes economic development, industrial upgrading and environmental improvement is an issue worth studying. This paper takes China as an example for study and uses the data of 286 cities from 2011 to 2019. In the empirical analysis, the direction distance function (DDF) and the Global Malmquist-Luenberger (GML) productivity index methods are used to measure the green total factor productivity (GTFP), while Tobit, quantile regression, impulse response function and intermediary effect models are used to study the relationship among digital economy development, industrial structure upgrading and GTFP. The results show that: (1) The digital economy can significantly improve China’s GTFP; however, there are clear regional differences. (2) The higher the GTFP, the greater the promotion effect of the digital economy on the city’s GTFP. (3) From a dynamic long-term perspective, the digital economy has indeed positively promoted China’s GTFP. (4) The upgrading of industrial structures is an intermediary transmission mechanism for the digital economy to promote GTFP. This paper provides a good reference for driving green economic growth and promoting the environment.

Siying Yang, Fengshu Liu

Kasra Ekrad, Bjarne Johansson, Inés Alvarez Vadillo et al.

Interoperability across industrial automation systems is a cornerstone of Industry 4.0. To address this need, the OPC Unified Architecture (OPC UA) Publish-Subscribe (PubSub) model offers a promising mechanism for enabling efficient communication among heterogeneous devices. PubSub facilitates resource sharing and communication configuration between devices, but it lacks clear guidelines for mapping diverse industrial traffic types to appropriate PubSub configurations. This gap can lead to misconfigurations that degrade network performance and compromise real-time requirements. This paper proposes a set of guidelines for mapping industrial traffic types, based on their timing and quality-of-service specifications, to OPC UA PubSub configurations. The goal is to ensure predictable communication and support real-time performance in industrial networks. The proposed guidelines are evaluated through an industrial use case that demonstrates the impact of incorrect configuration on latency and throughput. The results underline the importance of traffic-aware PubSub configuration for achieving interoperability in Industry 4.0 systems.

Yanqing Xie, Wenjing Zhang

IntroductionThe Consistency Evaluation Policy of Generic Drugs is a major quality-oriented regulatory reform in China’s pharmaceutical manufacturing industry. Whether and how this policy facilitates the integration of the innovation chain and the industrial chain at the enterprise level remains insufficiently examined. This study evaluates the policy effect and investigates potential mechanisms.MethodsThis study used panel data on A-share listed pharmaceutical enterprises from 2013 to 2023. Enterprises were treated as the micro-level carriers of both the innovation chain and the industrial chain, and a enterprise-level index was constructed to measure their integration. A difference-in-differences (DID) design was employed to estimate the impact of the Consistency Evaluation Policy of Generic Drugs. Mechanism analyses focused on government subsidies and market concentration, and heterogeneity was assessed by market demand and total factor productivity (TFP).ResultsThe Consistency Evaluation Policy of Generic Drugs significantly promoted the integration of the innovation chain and the industrial chain. Mechanism tests suggested that the effect operated through two channels: increased government subsidies and higher market concentration. The positive effect was stronger among enterprises facing larger market demand. Moreover, the effect was significant for enterprises with higher TFP, while it was not statistically significant for enterprises with lower TFP.DiscussionThese findings suggest that policy implementation can be strengthened by (1) improving the depth and precision of the Consistency Evaluation Policy of Generic Drugs, (2) enhancing the targeting of government subsidies and supporting an appropriate degree of industry concentration where warranted, and (3) adopting differentiated guidance to stimulate enterprise vitality through multiple measures.

Xia Cheng, Yan Luo, Minghua Dong et al.

Industrial hemp has an excellent tolerance for lead (Pb) and accumulation capacity. Further improving the Pb tolerance in industrial hemp is of great interest for its future application in phytoremediation. Present study was performed to evaluate the various Pb contaminated soils (normal soil; Pb spiked soil using Pb(NO3)2 and Pb polluted mine soil) with the Pb level 1300 mg kg−1 and various treatments of the vermicompost (VC) and cerium oxide nanoparticles (CeO2 NPs), (T0 = no VC and CeO2-NPs; T1 = CeO₂ NPs (30 mg L−1); T2 = VC (5 % w/w of soil); and T3 = T1 + T2 on the Pb accumulation and hemp productivity. The findings indicated that Pb stress (artificially spiked and natural contamination) led to significant reduction in the growth, biomass, and physiological traits of hemp. The Pb polluted mine soil exhibits more harmful impacts in comparison to artificially Pb spiked soil. The sole application of CeO2-NPs leads to less pronounced enhancements in growth and development parameters at rapid growth and harvesting stage in comparison with soil applied VC treatment under the treated and untreated Pb-stressed plants. Combined application of CeO2-NPs and VC effectively reduced the malondialdehyde contents (41.34 %), increased the soluble protein (62.35 %) and soluble sugar (29.97 %) as compared to control group. Moreover, the co-active effect of VC and CeO2-NPs also had the prospects of reducing Pb accumulation in difference tissues by enhancing physiological resiliency in hemp. Particularly, combined use of VC and CeO2-NPs counteracted the adverse effect of Pb stress by boosting growth, biomass, enzymatic antioxidants, and osmoprotectants through limiting the Pb accumulation. Use of organic amendments (VC) and metallic oxide NPs (CeO2-NPs) holds promising tool for mitigating the Pb stress, offering a practical and viable approach for hemp production.

Chongmei Wang, Lei Wang

Green credit is an important financial policy tool to solve environmental pollution problems. Improving industrial green total factor productivity (IGTFP) is the key to promote industrial green development. Our study adopts provincial data from 2005 to 2020 to investigate the influence of green credit (GC) on IGTFP. We find that GC significantly improves IGTFP on the whole, industrial structure upgrading and green innovation are the two key impact paths. Threshold model tests show that with the increase of GC, human capital and R&D intensity, the promoting effects of GC on IGTFP are significantly enhanced. Heterogeneity tests indicate that the promoting effect of GC on IGTFP was further enhanced after 2016, GC significantly promotes IGTFP in eastern China, but it is not obvious in central and western China. Besides, the promoting effect of GC on IGTFP is significantly enhanced with the increase of IGTFP. Our research shows that the government should further optimize the green credit system and play the role of green credit in promoting green innovation and industrial structure upgrading.

Changhua Shao, Han Dong, Yuan Gao

To explore the connotation and development level of China’s new quality productivity, this paper constructs an index system based on innovation, greenness, and productivity. This system is used to describe the development level of China’s new quality productivity. Using relevant data from 30 provincial administrative regions in China from 2011 to 2021, the entropy weight-TOPSIS method was employed to measure the index system. The development level of new quality productivity in China and its four major economic regions was analyzed through the three dimensions of the index system. Additionally, this paper examines the impact of new quality productivity on China’s industrial restructuring and integrates environmental regulation to elucidate the interaction mechanisms among these factors. An econometric regression model is further constructed to verify the effect of new quality productivity on industrial structural change and to examine the moderating role of environmental regulation. The results of this study show that there is a regional imbalance in the level of development of new quality production in China, with the level of development of new quality productivity in the eastern region being significantly higher than that in the central, western, and northeastern regions. However, on the whole, the new quality productivity of the four major regions has been in a state of continuous improvement during the period under investigation, and the spatial gap has been constantly decreasing. The benchmark regression coefficients, sys-GMM regression coefficients, and diff-GMM regression coefficients for new quality productivity and industrial rationalization are −0.6228, −0.1121, and −0.0439, respectively, and they are negatively correlated. The regression coefficients of the sys-GMM and diff-GMM of the interaction terms of environmental regulation and new quality productivity are −0.0051 and −0.0045, and there is a negative moderating effect of environmental regulation between new quality productivity and industrial structure rationalization. The benchmark regression coefficient, the sys-GMM regression coefficient, and the diff-GMM regression coefficient of new quality productivity and industrial upgrading are 2.5179, 0.7525, and 0.3572, respectively, and there is a positive correlation between the two. The regression coefficients of sys-GMM and diff-GMM for the interaction terms of environmental regulation and new quality productivity are 0.0380 and −0.0167, and there is a positive moderating effect of environmental regulation between new quality productivity and industrial structure upgrading.

Amaratou Mahamadou Saley, Thierry Moyaux, Aïcha Sekhari et al.

The convergence of the Internet of Things (IoT) and Industry 4.0 has significantly enhanced data-driven methodologies within the nuclear industry, notably enhancing safety and economic efficiency. This advancement challenges the precise prediction of future maintenance needs for assets, which is crucial for reducing downtime and operational costs. However, the effectiveness of data-driven methodologies in the nuclear sector requires extensive domain knowledge due to the complexity of the systems involved. Thus, this paper proposes a novel predictive maintenance methodology that combines data-driven techniques with domain knowledge from a nuclear equipment. The methodological originality of this paper is located on two levels: highlighting the limitations of purely data-driven approaches and demonstrating the importance of knowledge in enhancing the performance of the predictive models. The applicative novelty of this work lies in its use within a domain such as a nuclear industry, which is highly restricted and ultrasensitive due to security, economic and environmental concerns. A detailed real-world case study which compares the current state of equipment monitoring with two scenarios, demonstrate that the methodology significantly outperforms purely data-driven methods in failure prediction. While purely data-driven methods achieve only a modest performance with a prediction horizon limited to 3 h and a F1 score of 56.36%, the hybrid approach increases the prediction horizon to 24 h and achieves a higher F1 score of 93.12%.

Jazmín Vanessa Pérez-Pazos, Deimer Fuentes-Cassiani, Sol-Mara Regino et al.

The production of high-quality cassava planting material is a key strategy for mitigating the spread of pests and diseases. To promote the adoption of such strategies by farmers, it is essential to strengthen local capacities through knowledge transfer and the incorporation of innovative technologies, such as tunnels for rapid propagation (TxRPs), which have been successfully implemented in various international contexts. This study appraised the performance of four industrial cassava (Manihot esculenta Crantz) varieties—Corpoica Tai, Corpoica Belloti, Corpoica Ropain, and Corpoica Sinuana—under tunnel conditions at two locations on the Caribbean coast of Colombia. Planting material consisted of mini-cuttings (7–9 months old) with three buds. Five successive harvest cycles were assessed by measuring key growth parameters, including plant height, leaf number, SPAD (Soil Plant Analysis Development) chlorophyll index, leaf area, and biomass (dry weight and nutrient content). Environmental conditions within the tunnels, such as temperature and humidity, were regulated to promote rapid sprouting and accelerated growth of the cuttings. However, sprouting, vigor, and overall growth performance varied by variety. All four cassava varieties produced high-quality cuttings (>20 mm in diameter and >6 leaves), suitable for further seedling propagation. Cutting vigor increased across cycles, with productivity rising from over 60 cuttings/m² in the first cycle to more than 180 cuttings/m² by the fifth. Substrate mixtures enhanced both physical and chemical soil properties, depending on the source (CRT or CBL). The addition of coco peat or sand effectively minimized environmental impacts by preventing substrate compaction. The findings demonstrate the potential of tunnel-based systems to accelerate the production of high-quality cassava planting material, supporting improved productivity and sustainability in cassava cultivation for both farmers and industry stakeholders.

Islam Sayah, Ibtissem Chakroun, Claudio Gervasi et al.

Bacterial nanocellulose (BNC) has gained considerable interest over the last decade due to its unique properties and versatile applications. However, the low yield and the high production cost significantly limit its industrial scalability. The proposed study explores the isolation of new BNC producers from date palm sap and the use of date waste extract as a sustainable carbon source to improve BNC productivity. Results revealed three potential BNC producers identified as <i>Komagataeibacter</i> sp. IS20, <i>Komagataeibacter</i> sp. IS21, and <i>Komagataeibacter</i> sp. IS22 with production yield of 1.7 g/L, 0.8 g/L and 1.8 g/L, respectively, in Hestrin-Schramm (HS) medium. The biopolymer characterization indicated the presence of type I cellulose, a high thermal stability, and a highly dense network made of cellulose nanofibrils for all BNC samples. The isolate IS22, showing the highest productivity, was selected for an optimization procedure using a full factorial design with date waste extract as a carbon source. The BNC yield increased to 6.59 g/L using 4% date waste extract and 2% ethanol after 10 days of incubation compared to the standard media (1.8 g/L). Two probiotic strains, including <i>Bacillus subtilis</i> (BS), and <i>Lactobacillus plantarum</i> (LP) were successfully encapsulated into BNC matrix through a co-culture approach. The BNC-LP and BNC-BS composites showed antibacterial activity against <i>Pseudomonas aeruginosa</i>. BNC–probiotic composites have emerged as a promising strategy for the effective delivery of viable probiotics in a wide range of applications. Overall, this study supports the use of date waste extract as a sustainable carbon source to enhance BNC productivity and reduce the environmental footprint using a high-yielding producer (IS22). Furthermore, the produced BNC demonstrated promising potential as an efficient carrier matrix for probiotic delivery.

Pablo N. Refolio-Samperi, Elena Adaschewski, Dieter Hanelt et al.

The present study evaluated a two-stage process of Haematococcus sp. to enhance the nutritional value by astaxanthin and fatty acid accumulation. Initial screening of different growth media during the green stage, focusing on enhanced biomass yield, showed the maximum growth using flory medium with a biomass yield of 0.991 g L−1 at the 30th day. Bold’s basal medium (BBM) exhibited the second highest biomass yield of 0.856 g L−1 at the 21th day. Due to faster growth, BBM presented the highest recorded biomass productivity of 0.040 g L−1 day−1, an increase of 21.2 % higher than flory and 207.7 % higher than the standard WHM medium. In the red stage, focused on maximizing astaxanthin yield, high-temperature stress was found to be the most effective stressor, leading to a significant increase in astaxanthin production by 217 % in comparison to the control. Interestingly, this stress condition also enhanced the total cellular fatty acids accumulation by 82.4 % over the control. However, a reduction in stearic acid (18:0) and alpha-linolenic acid (18:3n3) proportions under stress conditions was observed, suggesting the induction of metabolic shifts which involve reallocation of resources towards astaxanthin biosynthesis. These findings demonstrate a successful optimization strategy for Haematococcus sp. cultivation, which could be applied in industrial settings to enhance astaxanthin yield while reducing the production costs by avoiding vitamin supplementation, thereby helping in sustainable bio-based economy development.

Zhihong Yang, Yang Shen

As an integration of artificial intelligence and advanced manufacturing technology, intelligent manufacturing has realized the innovation of manufacturing mode and created conditions for the green development of industry. After constructing a theoretical framework between intelligent manufacturing and industrial green total factor productivity, this paper uses panel data of 30 provinces in China from 2006 to 2020, and expresses the level of intelligent manufacturing with industrial robot density, to discuss the economic effects and mechanisms of intelligent manufacturing. The results show that intelligent manufacturing has a positive effect on industrial green total factor productivity, and the panel quantile regression model indicates that there is an increasing marginal effect. With the quantile points going from low to high, the coefficient and statistical significance become larger. Human capital is the mechanism for intelligent manufacturing to improve industrial green total factor productivity. Green technology innovation and producer service industry agglomeration have strengthened the positive effect. There is also heterogeneity in the effect, and the stronger the effect in regions launched local pilot schemes for carbon emissions trading and industrial green transformation development policy. In order to give full play to the technological dividend and empower sustainable industrial development, the paper argues that we need to accelerate the integration of artificial intelligence and manufacturing technology, thus improving the level of industrial intelligence and empowering green development.

Eric Guiffo Kaigom

Metarobotics aims to combine next generation wireless communication, multi-sense immersion, and collective intelligence to provide a pervasive, itinerant, and non-invasive access and interaction with distant robotized applications. Industry and society are expected to benefit from these functionalities. For instance, robot programmers will no longer travel worldwide to plan and test robot motions, even collaboratively. Instead, they will have a personalized access to robots and their environments from anywhere, thus spending more time with family and friends. Students enrolled in robotics courses will be taught under authentic industrial conditions in real-time. This paper describes objectives of Metarobotics in society, industry, and in-between. It identifies and surveys technologies likely to enable their completion and provides an architecture to put forward the interplay of key components of Metarobotics. Potentials for self-determination, self-efficacy, and work-life-flexibility in robotics-related applications in Society 5.0, Industry 4.0, and Industry 5.0 are outlined.

Francisco de Arriba-Pérez, Silvia García-Méndez, Javier Otero-Mosquera et al.

New technologies such as Machine Learning (ML) gave great potential for evaluating industry workflows and automatically generating key performance indicators (KPIs). However, despite established standards for measuring the efficiency of industrial machinery, there is no precise equivalent for workers' productivity, which would be highly desirable given the lack of a skilled workforce for the next generation of industry workflows. Therefore, an ML solution combining data from manufacturing processes and workers' performance for that goal is required. Additionally, in recent times intense effort has been devoted to explainable ML approaches that can automatically explain their decisions to a human operator, thus increasing their trustworthiness. We propose to apply explainable ML solutions to differentiate between expert and inexpert workers in industrial workflows, which we validate at a quality assessment industrial workstation. Regarding the methodology used, input data are captured by a manufacturing machine and stored in a NoSQL database. Data are processed to engineer features used in automatic classification and to compute workers' KPIs to predict their level of expertise (with all classification metrics exceeding 90 %). These KPIs, and the relevant features in the decisions are textually explained by natural language expansion on an explainability dashboard. These automatic explanations made it possible to infer knowledge from expert workers for inexpert workers. The latter illustrates the interest of research in self-explainable ML for automatically generating insights to improve productivity in industrial workflows.

Carlos Alberto Durigan Junior, André Taue Saito, Daniel Reed Bergmann et al.

The main objective of this paper is to Identify which macroe conomic factors and industrial indexes influenced the total Brazilian banking spread between March 2011 and March 2015. This paper considers subclassification of industrial activities in Brazil. Monthly time series data were used in multivariate linear regression models using Eviews (7.0). Eighteen variables were considered as candidates to be determinants. Variables which positively influenced bank spread are; Default, IPIs (Industrial Production Indexes) for capital goods, intermediate goods, du rable consumer goods, semi-durable and non-durable goods, the Selic, GDP, unemployment rate and EMBI +. Variables which influence negatively are; Consumer and general consumer goods IPIs, IPCA, the balance of the loan portfolio and the retail sales index. A p-value of 05% was considered. The main conclusion of this work is that the progress of industry, job creation and consumption can reduce bank spread. Keywords: Credit. Bank spread. Macroeconomics. Industrial Production Indexes. Finance.

Haiming Yao, Yunkang Cao, Wei Luo et al.

Image anomaly detection plays a pivotal role in industrial inspection. Traditional approaches often demand distinct models for specific categories, resulting in substantial deployment costs. This raises concerns about multi-class anomaly detection, where a unified model is developed for multiple classes. However, applying conventional methods, particularly reconstruction-based models, directly to multi-class scenarios encounters challenges such as identical shortcut learning, hindering effective discrimination between normal and abnormal instances. To tackle this issue, our study introduces the Prior Normality Prompt Transformer (PNPT) method for multi-class image anomaly detection. PNPT strategically incorporates normal semantics prompting to mitigate the "identical mapping" problem. This entails integrating a prior normality prompt into the reconstruction process, yielding a dual-stream model. This innovative architecture combines normal prior semantics with abnormal samples, enabling dual-stream reconstruction grounded in both prior knowledge and intrinsic sample characteristics. PNPT comprises four essential modules: Class-Specific Normality Prompting Pool (CS-NPP), Hierarchical Patch Embedding (HPE), Semantic Alignment Coupling Encoding (SACE), and Contextual Semantic Conditional Decoding (CSCD). Experimental validation on diverse benchmark datasets and real-world industrial applications highlights PNPT's superior performance in multi-class industrial anomaly detection.

Mykhailo Koshil, Tilman Wegener, Detlef Mentrup et al.

Visual inspection, or industrial anomaly detection, is one of the most common quality control types in manufacturing. The task is to identify the presence of an anomaly given an image, e.g., a missing component on an image of a circuit board, for subsequent manual inspection. While industrial anomaly detection has seen a surge in recent years, most anomaly detection methods still utilize knowledge only from normal samples, failing to leverage the information from the frequently available anomalous samples. Additionally, they heavily rely on very general feature extractors pre-trained on common image classification datasets. In this paper, we address these shortcomings and propose the new anomaly detection system AnomalousPatchCore~(APC) based on a feature extractor fine-tuned with normal and anomalous in-domain samples and a subsequent memory bank for identifying unusual features. To fine-tune the feature extractor in APC, we propose three auxiliary tasks that address the different aspects of anomaly detection~(classification vs. localization) and mitigate the effect of the imbalance between normal and anomalous samples. Our extensive evaluation on the MVTec dataset shows that APC outperforms state-of-the-art systems in detecting anomalies, which is especially important in industrial anomaly detection given the subsequent manual inspection. In detailed ablation studies, we further investigate the properties of our APC.

Abolfazl Kazzazi, Amir Mohammad khani

<p>This study aims to investigate the unique features of the food supply chain, examining the impact of food supply chain integration, consisting of internal integration, supplier and customer, the quality of food products and product innovation capability. Managers need to understand the importance of supplier and customer integration when responding to supply chain risk and company uncertainty. The data were collected from 168 managers active in the food industry in Tehran province. The partial least squares tool (SmartPLS 3.0) was used to analyze the data using Structural Equation Modeling (SEM) technique. The results show that there is a strong relationship between uncertainty and supply chain integration including customer, supplier and internal integration. The findings indicate that customer integration and supplier integration are critical factors in improving product quality in the food supply chain. The results can be related to the prominent role of customer relations and contact in the development of innovation capabilities in manufactured products, which has also been approved by some previous studies. Additionally, analyzing the various dimensions of supply chain integration separately revealed that internal integration is a capability factor for external integration. This study can help businesses in the food industry understand the value-creating roles of food supply chain integration and provide valuable guidance for them to decide how to meet the various challenges and manage food supply chain integration in order to improve product quality and product innovation capability.</p>

E. Shahri, P. Pedreiras, L. Almeida

The increasing prominence of concepts such as Smart Production and Industrial Internet of Things (IIoT) within the context of Industry 4.0 has introduced a new set of requirements for the engineering of industrial systems, including support for dynamic environments, timeliness guarantees, support for heterogeneity, interoperability and reliability. These requirements are further exacerbated at the network level by the notable rise in the number and variety of devices involved. To stay competitive in this ever-changing industrial landscape while boosting productivity, it is vital to meet those requirements, combining established protocols with emerging technologies. Software-Defined Networking (SDN) is the forefront traffic management paradigm that offers flexibility for complex industrial networks, enabling efficient resource allocation and dynamic reconfiguration. Message Queuing Telemetry Transport (MQTT) is a low-overhead protocol of the application layer that is gaining popularity in the scope of the IoT and IIoT. However, its Quality-of-Service (QoS) policies do not support timeliness requirements. This article presents a framework that seamlessly integrates SDN and MQTT, enhancing network management flexibility while satisfying real-time requirements found in industrial environments. It leverages the User Properties of MQTTv5 to allow specifying real-time requirements. MQTT traffic is intercepted by a Network Manager that extracts real-time information and instructs an SDN controller to deploy corresponding network reservations. MQTT traffic across multiple edge networks is propagated by selected brokers using multicasting. Extensive experiments validate the proposed approach, demonstrating its superiority over MQTT and Direct Multicast-MQTT (DM-MQTT) DM-MQTT in latency reduction. A response time analysis, validated experimentally, emphasizes robust performance across metrics.

Mohammad Erfatpour, Dustin MacLean, Rachid Lahlali et al.

The ovule is a plant structure that upon fertilization, transforms into a seed. Successful fertilization is required for optimum crop productivity and is strongly affected by environmental conditions including temperature and precipitation. Climate change refers to sustained changes in global or regional climate patterns over an extended period, typically decades to millions of years. These shifts can result from natural processes like volcanic eruptions and solar radiation fluctuations, but in recent times, human activities—especially the burning of fossil fuels, deforestation, and industrial emissions—have accelerated the pace and scale of climate change. Human-induced climate change impacts the agricultural sector mainly through global warming and altering weather patterns, both of which create conditions that challenge agricultural production and food security. With food demand projected to sharply increase by 2050, urgent action is needed to prevent the worst impacts of climate change on food security and allow time for agricultural production systems to adapt and become more resilient. Gaining insights into the female reproductive part of the flower and seed development under extreme environmental conditions is important to oversee plant evolution, agricultural productivity, and food security in the face of climate change. This review summarizes the current knowledge on plant reproductive development and the effects of temperature and water stress, soil salinity, elevated carbon dioxide, and ozone pollution on the female reproductive structure and development across grain legumes, cereal, oilseed, and horticultural crops. It identifies gaps in existing studies for potential future research and suggests suitable mitigation strategies for sustaining crop productivity in a changing climate.