Hasil untuk "Industrial productivity"

N. Maier, P. Franco, W. Lindner

R. Florida

R. Reis, A. Zydney

Sanjeet Mehariya, R. Goswami, Obulisamy Parthiba Karthikeysan et al.

C. Dimkpa, P. Bindraban

Kumera Neme, A. Nafady, S. Uddin et al.

Ensuring food security in developing countries is highly challenging due to low productivity of the agriculture sector, degradation of natural resources, high post farming losses, less or no value addition, and high population growth. Researchers are striving to adopt newer technologies to enhance supply to narrow the food demand gap. Nanotechnology is one of the promising technologies that could improve agricultural productivity via nano fertilizers, use of efficient herbicides and pesticides, soil feature regulation, wastewater management, and pathogen detection. It is equally beneficial for industrial food processing with enhanced food production with excellent market value, elevated nutritional and sensing property, improved safety, and better antimicrobial protection. Nanotechnology can also reduce post-farming losses by increasing the shelf life with the aid of nanoparticles. However, further investigation is required to solve the safety and health risks associated with the technology.

N. Crafts

Dingteng Wang, Ningning Li, Guoan Liang et al.

The digital transformation of industrial clusters, while enhancing productivity, has exacerbated the corporate digital divide, particularly for small and medium-sized enterprises (SMEs). Digital inclusion, which aims to bridge this divide, exhibits public-good attributes and positive externalities, yet its co-construction is often hindered by the "Olson's dilemma" arising from the conflict between individual and collective rationality. Platform enterprises, occupying the "bridging point" of structural holes within clusters, are posited to act as opinion leaders capable of orchestrating ecosystem co-creation. Through a theoretical simulation approach, there employs a "small-world + opinion leader" complex network framework to model the evolutionary dynamics of digital inclusion ecosystem co-construction. Integrating a public goods game model with a Fermi learning algorithm enhanced by a random surfer mechanism, we simulate strategic interactions among cluster firms under controlled parameter conditions. Monte Carlo simulations reveal a distinct threshold effect under simulated conditions: cooperation emerges sustainably only when the investment return coefficient surpasses the critical value defined by the number of game participants. Furthermore, the platform's connection ratio and strategic commitment emerge as pivotal factors; maintaining an investment strategy significantly promotes cooperation, whereas free-riding by the platform exerts a strong negative demonstration effect. While traditional platform governance tools like rewards and punishments can mitigate cooperation decay, they cannot reverse collective free-riding trends in our model. Crucially, a binding collective agreement, featuring pre-commitment of costs and redistribution of unused funds, emerges as the most effective mechanism for resolving the cooperation dilemma within the simulation. These theoretically derived findings offer insights for policymakers, platform enterprises, and SMEs in fostering collaborative and inclusive digital ecosystems within industrial clusters, though empirical validation remains necessary.

M. Benedetti, Valeria Vecchi, Simone Barera et al.

Interest in bulk biomass from microalgae, for the extraction of high-value nutraceuticals, bio-products, animal feed and as a source of renewable fuels, is high. Advantages of microalgal vs. plant biomass production include higher yield, use of non-arable land, recovery of nutrients from wastewater, efficient carbon capture and faster development of new domesticated strains. Moreover, adaptation to a wide range of environmental conditions evolved a great genetic diversity within this polyphyletic group, making microalgae a rich source of interesting and useful metabolites. Microalgae have the potential to satisfy many global demands; however, realization of this potential requires a decrease of the current production costs. Average productivity of the most common industrial strains is far lower than maximal theoretical estimations, suggesting that identification of factors limiting biomass yield and removing bottlenecks are pivotal in domestication strategies aimed to make algal-derived bio-products profitable on the industrial scale. In particular, the light-to-biomass conversion efficiency represents a major constraint to finally fill the gap between theoretical and industrial productivity. In this respect, recent results suggest that significant yield enhancement is feasible. Full realization of this potential requires further advances in cultivation techniques, together with genetic manipulation of both algal physiology and metabolic networks, to maximize the efficiency with which solar energy is converted into biomass and bio-products. In this review, we draft the molecular events of photosynthesis which regulate the conversion of light into biomass, and discuss how these can be targeted to enhance productivity through mutagenesis, strain selection or genetic engineering. We outline major successes reached, and promising strategies to achieving significant contributions to future microalgae-based biotechnology.

M. Narayani, Smita Srivastava

Garcia Juan-Carlos, Varas Michelle, Chang Lidia et al.

Manual material handling (MMH) tasks are one of the leading causes of musculoskeletal disorders (MSDs) in industrial and logistics environments. This study proposes a hybrid approach that integrates the NIOSH lifting equation with artificial intelligence (AI) technologies, with the aim of optimizing ergonomic evaluation in MMH processes. The developed system combines real-time posture detection through computer vision, using MediaPipe Pose, with automated calculation of the Lifting Index (LI) based on the parameters of the NIOSH method. The research was carried out with a sample of 30 workers in a logistics center, evaluated over a four-week period. The validity of the system was verified by comparing its results with manual expert evaluations, achieving a Pearson’s correlation coefficient of 0.96. The findings showed a 44. 1% reduction in the average LI and 60% decrease in reported incidents. In addition, surveys indicated an improvement in workers’ perception of fatigue and musculoskeletal discomfort. These findings demonstrate that integrating AI with established ergonomic methods provides a practical, accurate and scalable solution to mitigate ergonomic risks, enhancing workplace safety and productivity in logistics operations.

Ahmed Chennana, Ahmed Chaouki Megherbi, Noureddine Bessous et al.

Abstract In engineering applications, the bearing faults diagnosis is essential for maintaining reliability and extending the lifespan of rotating machinery, thereby preventing unexpected industrial production downtime. Prompt fault diagnosis using vibration signals is vital to ensure seamless operation of industrial system avert catastrophic breakdowns, reduce maintenance costs, and ensure continuous productivity. As industries evolve and machines operate under diverse conditions, traditional fault detection methods often fall short. In spite of significant research in recent years, there remains a pressing need for improve existing methods of fault diagnosis. To fill this research gap, this research work aims to propose an efficient and robust system for diagnosing bearing faults, using deep and Shallow features. Through the evaluated experiments, our proposed model Multi-Block Histograms of Local Phase Quantization (MBH-LPQ) showed excellent performance in classification accuracy, and the audio-trained VGGish model showed the best performance in all tasks. Contributions of this work include: Combine the proposed Shallow descriptor, derived from a novel hand-crafted discriminative features MBH-LPQ, with deep features obtained from VGGish pre-trained of Convolutional Neural Network (CNN) using audio spectrograms, by merging at the score level using Weighted Sum (WS). This approach is designed to take advantage of the complementary strengths of both feature models, thus enhancing overall bearing fault diagnostic performance. Furthermore, experiments conducted to verify the approach’s performance is assessed based on fault classification accuracy demonstrated a significant accuracy rate on two different noisy datasets, with an accuracy rate of 98.95% and 100% being reached on the CWRU and PU datasets benchmark, respectively.

Nimet Elmacıoğlu, Salih Özdemir

Amaç: Bu çalışmanın amacı, Türkiye’nin 2007-2019 dönemi çevresel dirençliliğini değerlendirerek ülkenin iklim zorluklarına karşı direnç ve uyum kapasitesini incelemek ve sürdürülebilir iklim politikalarına katkı sağlamaktır.Yöntem: Çalışmada Türkiye’nin çevresel dirençliliğini değerlendirmek için EUROSTAT’dan elde edilen çevresel dirençlilik göstergeleri, Çok Kriterli Karar Verme (ÇKKV) yöntemlerinden veri odaklı MEREC ve COPRAS ile analiz edilmektedir. Ayrıca analizin tutarlılığını sağlamak amacıyla karşılaştırmalı analiz ve Borda yöntemi gerçekleştirilmektedir.Bulgular: Analiz sonucunda elde edilen bulgulara göre Türkiye'nin çevresel dirençliliğinin belirleyici göstergeleri olarak ana biyolojik çeşitlilik alanları, enerji verimliliği ve yenilenebilir enerji kaynaklarının payı kriterleri belirlenmiştir. Performans sıralamaları dikkate alındığında ise Türkiye’nin çevresel dirençliliği son yıllarda iyileşme göstermiştir. Özgünlük: Bu çalışma, Türkiye'nin çevresel dirençliliğine ilişkin kapsamlı bir genel bakış sunarak hem güçlü hem de zayıf yönlerini vurgulamaktadır. Bu bulgular, politika yapıcılar için Türkiye'nin çevresel dirençliliğini artırmayı hedefleyen müdahalelerin geliştirilmesine yönelik değerli bilgiler sağlamaktadır. Ayrıca çevresel dirençlilik yaklaşımının benimsemesi ülkenin iklim değişikliğini ele alan sürdürülebilir kalkınma politikalarını şekillendirmesine olanak tanımaktadır.

Mehmet Ali Cevheri, Nihat Küçük

Amaç: Bu çalışma, Türkiye tarım sektörünün 1961–2021 dönemindeki toplam faktör verimliliğini (TFV) ölçmek, teknik etkinlik değişimi (TEC) ile teknolojik etkinlik değişimi (TC) gibi TFV bileşenlerinin zaman içinde gösterdiği performansı etkileyen faktörleri analiz etmek amacıyla gerçekleştirilmiştir. Yöntem: Araştırmada Veri Zarflama Analizi (VZA) ile Malmquist TFV Endeksi hesaplanmış; Sabit Getiriler Altında Ölçek (CRS) ve Değişken Getiriler Altında Ölçek (VRS) varsayımlarına göre girdi ve çıktı odaklı analizler gerçekleştirilmiştir. Bulgular: Türkiye’nin 1961-2021 dönemi tarımsal verimliliği, girdi odaklı teknik verimlilikte düşüş (TEC, 1,010’dan 0,959’a) ve çıktı odaklı verimlilikte artış (TEC, 0.990’dan 1,040’a) göstermiştir. Teknolojik değişim (TC), karışık etkilerle son dönemde girdi odaklı olarak 1,060’a yükselmiş, çıktı odaklı olarak da 0,949’a gerilemiştir. Malmquist toplam faktör verimliliği (MTFV) endeksi yaklaşık 1 civarında sabit kalmış ve nispeten sınırlı bir iyileşme göstermiştir. Bu durum, teknoloji kullanımının artırılması, tarımsal yayım ve danışmanlık hizmetlerinin geliştirilmesi ve girdi verimliliğinin artırılması gibi kapsayıcı politikaların uygulanarak tarımsal büyümenin sürdürülmesi gerektiğini göstermektedir. Özgünlük: Çalışma, Türkiye tarım sektörünün 61 yıllık uzun dönemini kapsayan ilk Malmquist TFV analizlerinden biri olması ve 61 yıllık dönemi 10’ar yıllık periyodlarla değerlendirmesi bakımından özgün bir katkı sağlamaktadır.

Kwangryul Baek, Duk-Hyoung Kim, Jooyeon Jeong et al.

Microalgae are versatile organisms capable of converting CO2, H2O and sunlight into fuel and chemicals for domestic and industrial consumption. Thus, genetic modifications of microalgae for enhancing photosynthetic productivity and biomass and bio-products generation are crucial for both academic and industrial applications. However, targeted mutagenesis in microalgae with CRISPR-Cas9 is limited. Here we report, a one-step transformation of Chlamydomonas reinhardtii by the DNA-free CRISPR-Cas9 method rather than plasmids that encode Cas9 and guide RNAs. Outcome was the sequential CpFTSY and ZEP two-gene knockout and the generation of a strain constitutively producing zeaxanthin and showing improved photosynthetic productivity.

Kentaro Inokuma, Kiyotsuna Toyohara, Tomoya Hamada et al.

Abstract Cellobiose has received increasing attention in various industrial sectors, ranging from food and feed to cosmetics. The development of large-scale cellobiose applications requires a cost-effective production technology as currently used methods based on cellulose hydrolysis are costly. Here, a one-pot synthesis of cellobiose from sucrose was conducted using a recombinant Pichia pastoris strain as a reusable whole-cell biocatalyst. Thermophilic sucrose phosphorylase from Bifidobacterium longum (BlSP) and cellobiose phosphorylase from Clostridium stercorarium (CsCBP) were co-displayed on the cell surface of P. pastoris via a glycosylphosphatidylinositol-anchoring system. Cells of the BlSP and CsCBP co-displaying P. pastoris strain were used as whole-cell biocatalysts to convert sucrose to cellobiose with commercial thermophilic xylose isomerase. Cellobiose productivity significantly improved with yeast cells grown on glycerol compared to glucose-grown cells. In one-pot bioconversion using glycerol-grown yeast cells, approximately 81.2 g/L of cellobiose was produced from 100 g/L of sucrose, corresponding to 81.2% of the theoretical maximum yield, within 24 h at 60 °C. Moreover, recombinant yeast cells maintained a cellobiose titer > 80 g/L, even after three consecutive cell-recycling one-pot bioconversion cycles. These results indicated that one-pot bioconversion using yeast cells displaying two phosphorylases as whole-cell catalysts is a promising approach for cost-effective cellobiose production.

Jiang Liu, Qianyu Zhao

With the continuous advancement of industrialization, global environmental problems are becoming increasingly severe. Maintaining economic growth while improving the environment has been an important issue for many countries, especially developing countries. As industry is a major source of environmental pollution, industrial green transformation and upgrading have become particularly important. In the era of the digital economy (DE), there is a new path for industrial green transformation and upgrading. Based on provincial data on industry from 2008 to 2021, a difference-in-differences (DID) model was constructed to analyze the environmental and economic benefits. New pathways for trade-offs between environmental improvement and economic growth in China are presented. In addition, new ideas are concerning global environmental issues and economic issues in the DE are presented. The present study indicates that the DE has reduced the intensity of pollution emissions and elevated total factor productivity (TFP), which has helped to promote industrial green transformation and upgrading. Further mechanism testing showed that the DE has promoted industrial green transformation and upgrading by improving the utilization of energy and resources and promoting technological innovation. Considering the utilization of energy and resources, the DE has decreased China’s total industrial consumption of energy and coal, reduced industrial water consumption, and reduced the share of coal consumption and increased the share of clean energy consumption in China’s total industrial energy consumption; these effects have optimized the efficiency and structure of China’s energy utilization to contribute to the green transformation and upgrading of industry. Regarding technological innovation, the development of the DE has increased industrial innovation output and R&D input. Furthermore, it has promoted innovation with respect to green processes, accelerating technological innovation, and realized industrial green transformation and upgrading.

F. Scherer

Stefan Roos, Francesc Barbera Flichi, Laia Ortiz-Membrado et al.

This study investigates the use of Electron Beam Powder Bed Fusion (PBF-EB) spot melting on SDSS 2507, a material known for its high tensile strength, corrosion resistance, and welding properties. Spot melting, a localized melting technique, utilize a stationary beam to create melt pools before rapidly repositioning to form new ones, offering precise control over material properties in additive manufacturing. We conducted a design of experiments with 32 samples and analysed them using SEM, XRD, EBSD, optical microscopy, nanoindentation and statistical modelling.Elevated PBF-EB processing temperatures significantly influence microstructure and phase composition. XRD analysis identified the presence of the detrimental sigma phase. EBSD analysis revealed a composition primarily consisting of austenite (63 %) and sigma phase (33.5 %), with residual ferrite (3.5 %). Statistical modelling demonstrated that a combination of spot-time, spot distance, focus offset, and layer thickness was the most reliable predictor for density while area energy proved to be the most accurate predictor for hardness, with R2adj values of 0.766 and 0.802, respectively.Our study confirms that PBF-EB is capable of processing SDSS 2507 through spot melting, resulting in high-density samples at high productivity rates. The presence of sigma phase shows a need for post build heat treatment to achieve the desired phase distribution. This research enhances the understanding of the process and also holds promise for industrial applications.

Shutters Shade T, Waters Keith

The structures of regional economies play a critical role in determining both a region’s productivity and its resilience to shocks. We extend previous work on the regional occupation and skills structure by analyzing the effect of a region’s industry structure. We operationalize the concept of economic structure by constructing a network of interdependent economic components, employing ecological techniques of co-occurrence analysis to infer interactions between industries. For each U.S. metropolitan statistical area, we create an aggregate measure of economic tightness that captures the degree of interconnectedness among a region’s industries. We find that industry tightness, which we find is partly driven by rare industry pairs, is positively correlated with a region’s economic productivity, negatively correlated with a region’s change in productivity following the Great Recession. This study contributes to an understanding of the tradeoff between productivity and resilience, which is intended to help policy makers that face similar real-world tradeoffs.