Production Efficiency Research Articles

Agricultural carbon emissions in China: measurement, spatiotemporal evolution, and influencing factors analysis

IntroductionThe agricultural sector is the second largest emitter of greenhouse gases, accounting for 23% of global anthropogenic carbon emissions. Analysis of the basic state of carbon emissions from China's agriculture is helpful to achieve carbon reduction targets.MethodsAgricultural carbon emissions were calculated using the emission factor method, based on data from the China Rural Statistical Yearbook and various provincial statistical yearbooks. To analyze spatial patterns, the standard deviation ellipse method and the center of gravity migration model were employed, uncovering the migration path of agricultural carbon emissions. Regional disparities and the driving factors of agricultural carbon emissions were further examined using the Theil index and the Logarithmic Mean Divisia Index (LMDI) model.ResultsThe analysis indicated that the emissions center has gradually shifted towards the central and western regions, reflecting changes in agricultural production activity areas. Intraregional differences are the primary contributors to the imbalance in agricultural carbon emissions, with pronounced disparities in grain production and consumption balance regions. Key influencing factors include agricultural production efficiency, adjustments in agricultural industrial structure, economic structure and output, and urbanization levels. The economic output effect and urbanization effect are identified as the main drivers of increased carbon emissions, while declining production efficiency has hindered emission reduction efforts.ConclusionThe findings provide valuable insights for regional management and policymaking in China's agricultural sector, highlighting the need to enhance production efficiency and optimize agricultural structure to reduce emissions.

Chip Appearance Inspection Using Machine Learning Techniques

In recent years, the increasing demand for chips has put forward higher requirements for chip production quality and production efficiency. After the chip manufacturing is completed before leaving the factory, it needs to be detected after packaging, which makes the development of chip detection and localization system difficult due to the small size and large number of chips and the high precision requirements for them. Therefore, this paper aims to design a fast, highprecision chip detection and positioning system for the positioning and detection needs of the chip packaging process, the chip detection and positioning system for in-depth research, to achieve the accurate positioning of the chip as well as the deformation produced by the detection of the main work is as follows: the first chapter is mainly about why this paper is mainly aimed at discovering the problems in the chip packaging process. The second chapter is about how to take pictures of the chip. Chapter 3 focuses on the pre-processing of the pictures. Chapter 4 focuses on determining whether the chip is deformed through different machine learning and deep learning techniques. Finally, the conclusion section summarizes the whole paper.

The impact of hydroclimate-driven periodic runoff on hydropower production and management

This study evaluates the impact of hydroclimate-driven periodic runoff on hydropower operations and production, with a focus on how the forecasted biennial periodicity of runoff time series could affect the efficiency of hydropower generation. Hydrologic stochastic processes are utilized to forecast long-term runoff, and seven hydroclimate scenarios are developed to be input into a production management model, allowing for an analysis of how periodic hydroclimate variations influence hydropower management and output. The results reveal that the biennial alternation between wet and dry years is a key factor affecting hydropower operations in the Dalälven River Basin. Notable differences between wet- and dry-year scenarios were observed in terms of power efficiency, production output, and forecasting accuracy. Operating hydropower systems based on dry-year runoff forecasts in wet years results in a 1.63% decrease in production efficiency and a reduction of 9,104 MWh in power generation. Conversely, applying wet-year forecasts in dry years slightly boosts production efficiency by 0.31% and increases power generation by 7,832 MWh. Scenarios that adhere to biennial periodicity offer the highest forecasting accuracy, particularly when applying dry-year forecasts in dry years in winter and spring, which produce the most precise predictions. In contrast, using dry-year forecasts in wet years results in the lowest forecasting accuracy.

Ready-to-Cook Foods: Technological Developments and Future Trends-A Systematic Review.

Ready-to-cook (RTC) foods can significantly improve the cooking experience of busy or unskillful consumers, based on production involving technical combinations of food processing and packaging. Initialized by a market survey of 172 products in Beijing, this systematic review analyzes RTC foods' development status according to ingredients, packaging, and storage conditions to further clarify the scope of RTC foods. The working principles and efficacy of various food processing techniques, such as washing, cutting, marinating, and frying, and packaging design or innovations such as modified atmosphere packaging (MAP) were both summarized in detail, with attention to their ability to extend shelf life, reduce safety risks, and maximize production efficiency in RTC food production. The cutting-edge technologies that may potentially apply in the RTC food processing or packaging sector were compared with current approaches to visualize the direction of future developments. In conclusion, we have observed the specific pattern of RTC food varieties and packaging formats in the Beijing market and revealed the advancements in RTC food technologies that will continue playing a critical role in shaping this growing market, while challenges in scalability, cost-efficiency, and sustainability remain key areas for future research. The data and perspectives presented will articulate the conceptions and existing challenges of RTC food, foster consumer perception and recognition of similar products, and deliver useful guidance for stakeholders interested in such products.

3D printer fabrication of boron doped Cu-MWCNT/PLA electrodes: Investigation of its efficiency in electrocatalytic hydrogen production

In this study, three-dimensional (3D) electrodes based on MWCNT-Cu/PLA with high electrocatalytic efficiency, large surface area and different amounts of nano‑boron doped MWCNT-Cu/PLA were fabricated using Fused Granular Fabrication (FGF) method with a 3D printer. When FE-SEM images are examined, MWCNT fibers and copper particles are seen more clearly on the surface due to the formation of more dense conductive networks between the conductive carbon fibers trapped in the PLA structure due to the increasing amount of boron. When XRD results are examined, it is observed that the characteristic peaks belonging to the face-centered cubic structure of Cu are dominant and the crystal size increases as MWCNT addition and boron doping increase. When Raman spectra were analyzed to investigate the internal structural changes of MWCNTs, three characteristic bands corresponding to the D band (defect), G band (graphite band) and G' band (D overtone) of MWCNTs were determined and it was observed that the ID/IG ratio decreased with increasing boron doping compared to MWCNT-Cu/PLA electrode. The B1s spectrum of the 3D 0.8B@MWCNT-Cu/PLA electrode confirmed the presence of boron with the peak corresponding to B@C bonds at 191.2 eV. The obtained 3D electrocatalysts were used as cathodes in an electrolysis cell in aqueous solution containing 1 M KOH and their catalytic efficiency in hydrogen evolution reaction (HER) was tested. In order to increase the activity of the 3D electrodes, unlike the literature, they were activated by electrochemical activation process without using organic solvent and HER measurements of the activated electrodes were performed. Before activation, the charge transfer resistance (Rct) value of the Cu/PLA electrode, which was 6219.00 Ω cm−2, decreased to 681.90 Ω cm−2 after 5 % MWCNT doping. It is seen that with boron doping, a greater decrease in Rct value is realized, reaching values of 66.50–74.56 Ω cm−2. The cathodic Tafel slopes show that the Volmer reaction is the rate-determining step for HER and the rate-determining step is the electrochemical adsorption of hydrogen on the metal surface. In addition, energy efficiency tests of the 3D electrodes in HER were also performed and electrochemically active surface areas were determined. Thus, this study has taken an important step toward using 3D electrocatalysts, which are produced with more cost and more flexible designs, unlike previous production techniques, for hydrogen energy production.

Enhanced MPPT Based on Zebra Optimization Algorithm for Control of a Partially Shaded Photovoltaic System

Objective: The objective of this paper is to adapt the Zebra Optimization Algorithm (ZOA) to track the Global Maximum Power Point (GMPP) of a PV system when subjected to partial shading conditions. Theoretical Framework: Photovoltaic (PV) systems are increasingly being used due to their clean nature and low maintenance cost. However, solar cells exhibit a nonlinear relationship between current and voltage, which necessitates the use of Maximum Power Point Tracking (MPPT) algorithms to ensure the system operates at its full potential. Bypass diodes are integrated into PV modules to protect solar cells from damage during partial shading, but this leads to a distorted multi-peak Power-Voltage (P-V) curve. This distortion presents a challenge for algorithms to distinguish between Global Maximum Power Point (GMPP) and Local Maximum Power Points (LMPPs). Method: The Zebra Optimization Algorithm (ZOA) is applied to track the GMPP of a PV system under partial shading conditions. Simulations were conducted using the MATLAB/Simulink platform to evaluate the algorithm’s performance. The focus was on ensuring fast and accurate tracking of the GMPPT under rapid changes in solar irradiance levels. Results and Discussion: Simulation results demonstrated that the ZOA-based MPPT algorithm effectively tracked the GMPPT of the PV system, even under rapid variations in solar irradiance. The algorithm proved its accuracy and speed in distinguishing the GMPP from LMPPs, addressing the challenges posed by the distorted P-V curve. Research Implications: The successful application of the ZOA for tracking the GMPP in partial shading conditions offers a significant improvement for PV systems. This can lead to enhanced efficiency in energy production and more reliable performance in real-world scenarios, where shading and other environmental factors frequently affect solar panels. Originality/Value: This study introduces the adaptation of the Zebra Optimization Algorithm (ZOA) in MPPT for PV systems, addressing the issue of distinguishing between GMPP and LMPPs under partial shading. The approach demonstrates both innovation and practical value in improving the efficiency of PV systems in challenging environmental conditions.

Modulating Core Polarity in Metal-free Covalent Organic Frameworks for Selective Electrocatalytic Hydrogen Peroxide Production.

Tuning the charge density at the active site to balance the adsorption ability and reactivity of oxygen is extremely significant for driving a two-electron oxygen reduction reaction (ORR) to produce hydrogen peroxide (H2O2). Herein, we have highlighted the influence of intermolecular polarity in covalent organic frameworks (COFs) on the efficiency and selectivity of electrochemical H2O2 production. Different C3 symmetric building blocks have been utilized to regulate the charge density at the active sites. The benzene-cored COF, which exhibits reduced polarity than the triazine-cored COF, displayed enhanced performance in H2O2 production, achieving 93.1% selectivity for H₂O₂ at 0.4 V with almost two-electron transfer and a faradaic efficiency of 90.5%. In-situ electrochemical Raman spectroscopy and scanning electrochemical microscopy (SECM) were employed to confirm H₂O₂ generation and analyze spatial reactivity patterns. These techniques provided detailed insights into localized catalytic behavior, emphasizing the influence of core polarity.

Modulating Core Polarity in Metal‐free Covalent Organic Frameworks for Selective Electrocatalytic Hydrogen Peroxide Production

Tuning the charge density at the active site to balance the adsorption ability and reactivity of oxygen is extremely significant for driving a two‐electron oxygen reduction reaction (ORR) to produce hydrogen peroxide (H2O2). Herein, we have highlighted the influence of intermolecular polarity in covalent organic frameworks (COFs) on the efficiency and selectivity of electrochemical H2O2 production. Different C3 symmetric building blocks have been utilized to regulate the charge density at the active sites. The benzene‐cored COF, which exhibits reduced polarity than the triazine‐cored COF, displayed enhanced performance in H2O2 production, achieving 93.1% selectivity for H₂O₂ at 0.4 V with almost two‐electron transfer and a faradaic efficiency of 90.5%. In‐situ electrochemical Raman spectroscopy and scanning electrochemical microscopy (SECM) were employed to confirm H₂O₂ generation and analyze spatial reactivity patterns. These techniques provided detailed insights into localized catalytic behavior, emphasizing the influence of core polarity.

Polyoxometalate‐Derived Photocatalysts Enabling Progress in Hydrogen Evolution Reactions

AbstractPhotocatalytic water splitting is capable of converting abundant solar energy into environmentally friendly and renewable chemical energy, presenting a promising solution to alleviate the energy crisis and combat environmental pollution. The development of high‐performance photocatalysts is crucial for significantly improving the efficiency of the hydrogen evolution reaction (HER) involved. Polyoxometalate (POM)‐derived materials, known for their tunable compositions, diverse structures, electron storage/release capabilities, as well as quasi‐semiconductor photochemical properties, serve as highly efficient catalysts in sustainable photosynthesis. This comprehensive review navigates the latest advancements in the assembly strategies and HER performance of POM‐based crystalline materials. It also discusses the composite materials formed by infiltrating POM into metal‐organic frameworks (MOF) and examines the roles of transition metal compounds derived from polyoxometalates, such as sulfides and carbides, in photocatalytic HER. Emphasis is placed on the prospects for the future development of POM‐based compounds as photocatalysts, along with several strategies and outlooks that could facilitate their progress. POM‐derived materials are believed to have significant potential to enhance hydrogen production efficiency while maintaining thermal stability in HER processes.

Microorganisms in Fermented Foods and Their Effects on the Human Body

In recent years, microorganisms in fermented foods have shown an important role in regulating human health. Studies have shown that probiotics have significant health benefits in fermented foods, such as improving gut health, stimulating immune function, and promoting metabolism, and mental health. Specifically, lactic acid bacteria in yogurt can increase the number of beneficial bacteria in the intestines and reduce the growth of pathogenic bacteria, and yeasts such as Saccharomyces cerevae have multiple health effects on intestinal microbiota protection and anti-inflammation. In addition, various enzymes and metabolites produced by Aspergillus during fermentation process can improve the nutritional value of food. However, although lots of research has been done, the understanding of the microbial mechanism under different fermentation conditions is still insufficient. In this article, the types and influencing factors of microorganisms in yogurt, wine and seasoner were reviewed. The effects of temperature, pH value, fermentation time and other conditions on microbial activity and product quality were analyzed. The results showed that suitable fermentation conditions could significantly increase microbial activity, improve product flavor and health function, and provide a theoretical basis for optimizing the production process of fermented food. The article provides a theoretical basis for improving the fermented food production process, thus helping to improve product quality and market competitiveness. However, this article has some limitations like the lack of understanding of interactions between different microorganisms. Future research should focus on examining the collaborative relationship between these microbes to improve the health benefits and production efficiency of fermented foods.

Study on High-quality Development Strategy of Rural Economy Under the Strategy of Agriculture, Rural Areas and Farmers

With the in-depth implementation of the "three rural" strategy, the high-quality development of rural economy has become an important goal to achieve rural revitalization. This paper starts with the current situation of rural economic development, analyzes the main challenges facing rural economy, including infrastructure construction lag, unreasonable industrial structure and brain drain and innovation ability. On this basis, the core strategy of high-quality development of rural economy is put forward from three aspects: industrial revitalization, ecological revitalization and talent revitalization. By optimizing the industrial structure and promoting the integration of the primary, secondary and tertiary industries, the agricultural production efficiency is improved; Emphasizing green agriculture and environmental governance to promote sustainable development; Attention should be paid to attracting high-quality talents and improving rural education and training systems to promote innovation and entrepreneurship in rural areas. Through comprehensive measures, we will strive to provide systematic support for the high-quality development of the rural economy and help the smooth implementation of the rural revitalization strategy.

Digital twin-enhanced robotic system for remote diesel engine assembly defect inspection

Purpose This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork. Design/methodology/approach This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period. Findings Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers. Originality/value The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

The Economic Impact of Successful Businesspeople: The Dual Effect of Wealth Creation and Spending

This article explores the complex impact of successful businesspeople on others and society through wealth creation and spending in their economic lives. The article begins by defining the concept of a “successful businesspeople” and points out its diversity in different historical and political contexts. Then, from an economic perspective, this paper analyzes how successful businesspeople can positively impact society by promoting economic growth, improving production efficiency, creating jobs, and donating to philanthropy in their different roles as investors, producers, consumers, donors, and employers. However, the article also points out the negative externalities that can be brought about by successful businesspeople, such as environmental damage, labor problems, data privacy violations, and public health risks. By citing historical and modern examples, such as Andrew · Carnegie, Henry · Ford, Jeff · Bezos, and Elon · Musk, this article further elaborates on how the economic behavior of successful businessmen has both positive and negative effects, and emphasizes the importance of understanding these complex relationships.

System efficiency of methylmercury production, an important indicator for evaluating the long-term risk of mercury: From a case study in the Jiaozhou Bay to global coastal seas

With the implementation of the Minamata Convention, total Hg (THg) in coastal seas are expected to be reduced. However, methylmercury (MeHg) levels in aquatic environments depend not only on THg, but also the system efficiency of MeHg production (represented by MeHg/THg ratio in seawater) whose variations with time remain unclear. By choosing the Jiaozhou Bay (JZB) as a typical coastal system, combined with the published data from the global coastal systems, system efficiency of MeHg production in coastal seas were investigated. The mass budget of MeHg showed that the in situ production and degradation are the major source and sink of MeHg in the JZB. The relationships of MeHg in the seawater and fish with MeHg/THg in seawater indicate that system efficiency of MeHg production may control the risk of MeHg in coastal systems. The sulfate and nitrate in seawater, organic matter in sediment, biotic methylation, and transport of inorganic Hg from the seawater to sediment were identified to be critical parameters and processes for the MeHg/THg in global coastal seas. The findings of this study highlight the importance of monitoring the system efficiency of MeHg production and its controlling processes and parameters for evaluating the long-term risk of Hg.

Does bank FinTech spur productivity of SMEs?

ABSTRACT Financial technology (FinTech) has promoted the popularization of intelligent banking services and profoundly impacted the development of small and medium-sized enterprises (SMEs). Based on manually collected over 20,000 data on FinTech patents of banks and data from Chinese A-share listed firms in the Growth Enterprise Market board from 2010 to 2020, we investigate the effects of Bank FinTech on the total factor productivity (TFP) of SMEs. Our results demonstrate that Bank FinTech significantly boosts the TFP of SMEs. The conclusion remains unchanged after conducting a series of robustness tests. Furthermore, we find that strengthening external financial regulation and internal digitalization in SMEs enhance the driving effect of FinTech on the TFP of SMEs. Moreover, heterogeneity analysis indicates that Bank FinTech is particularly beneficial for promoting TFP in non-state-owned SMEs, SMEs with large sizes and old age, and SMEs in the East and with dense bank branches. Our research offers empirical evidence on how Bank FinTech influences SMEs’ production efficiency, which is crucial for the technological catch-up of SMEs and for enhancing the ability of Bank FinTech to serve the real economy.

Maintaining Agricultural Production Profitability—A Simulation Approach to Wheat Market Dynamics

Profitability is a complex notion, intertwined with theoretical, socio-economic, and modern economic considerations. While traditionally linked to monopoly advantages, contemporary views broaden this to include shareholder value creation. In agriculture, profitability faces impediments such as rising input costs, market volatility, and economic disparities, emphasizing technological innovation, market conditions, and policy as crucial determinants. This study presents an econometric simulation model to analyze and forecast wheat production profitability in Poland from 2023 to 2027, providing insights into production efficiencies, market dynamics, and policy impacts. Using a system of recursive equations, the model forecasts profitability, integrating econometric techniques with expert insights. It examines variables like resource use, production levels, and price changes, validated through FADN data. The findings reveal that price relations and production efficiencies are the key to profitability. The model highlights market volatility, particularly through “price scissors”, as the main factor influencing profitability. Favorable price conditions significantly boost profitability and incentivize production. The model is a critical tool for analyzing wheat profitability, highlighting the influence of market and policy changes. Future work could extend the model’s use to other regions or crops, incorporating advanced technologies to improve accuracy. This study offers valuable insights for agricultural economics, aiding stakeholders in strategic decision-making.

Engineering Halomonas bluephagenesis for synthesis of polyhydroxybutyrate (PHB) in the presence of high nitrogen containing media

The trade-offs exist between microbial growth and bioproduct synthesis including intracellular polyester polyhydroxybutyrate (PHB). Under nitrogen limitation, more carbon flux is directed to PHB synthesis while growth is inhibited with diminishing overall carbon utilization, similar to the suboptimal carbon utilization during glycolysis-derived pyruvate decarboxylation. This study reconfigured the central carbon network of Halomonas bluephagenesis to improve PHB yield theoretically and practically. It was found that the downregulation of glutamine synthetase (GS) activity led to a synchronous improvement on PHB accumulation and cell growth under nitrogen non-limitation condition, increasing the PHB yield from glucose (g/g) to 85% of theoretical yield, PHB titer from 7.6 g/L to 12.9 g/L, and from 51 g/L to 65 g/L when grown in shake flasks containing a rich N-source, and grown in a fed-batch cultivation conducted in a 7-L bioreactor also containing a rich N-source, respectively. Results offer better metabolic balance between glucose conversion efficiency and microbial growth for economic PHB production.

Comparative Analysis of Pretreatment Methods for Fruit Waste Valorization in Euglena gracilis Cultivation: Impacts on Biomass, β-1,3-Glucan Production, and Photosynthetic Efficiency.

This study explored the sustainable valorization of fruit waste extracts from sugarcane bagasse (SB), banana peel (BP), and watermelon rind (WR) for Euglena gracilis biomass and β-1,3-glucan production. The extracts were prepared using water extraction (WE), high-temperature and pressure treatment (HTP), and dilute sulfuric acid treatment (DSA). The DSA-treated extracts consistently yielded the best results. E. gracilis cultured in SB-DSA showed the highest cell density with a 2.08-fold increase compared to the commercial HUT medium, followed by BP-DSA (1.35-fold) and WR-DSA (1.70-fold). Photosynthetic pigment production increased significantly, with chlorophyll a yield being highest in SB-DSA (1.90-fold increase). The chlorophyll a/b ratio and total carotenoid content also improved, indicating enhanced light-harvesting capacity and photoprotection. Photosynthetic efficiency, measured by chlorophyll fluorescence, notably improved. The maximum quantum yield of PSII (Fv/Fm) increased by up to 25.88% in SB-DSA, suggesting reduced stress and improved overall photosynthetic health. The potential photochemical efficiency (Fv/F0) showed even greater improvements: up to 40.53% in SB-DSA. Cell morphology analysis revealed larger cell aspect ratios, implying a more active cellular physiological state. β-1,3-glucan yield also increased by 23.99%, 12.92%, and 23.38% in SB-DSA, BP-DSA, and WR-DSA, respectively. This study demonstrates the potential of pretreated fruit waste as a cost-effective and sustainable medium for E. gracilis cultivation, offering the dual benefits of waste valorization and high-value compound production. These findings contribute to the development of more efficient biorefinery processes and align with the circular economy principles in food biotechnology.

Optimizing biomass production and photochemical efficiency of in vitro-grown Capsicum annuum L. using gas-permeable membrane flasks

Optimizing biomass production and photochemical efficiency of in vitro-grown Capsicum annuum L. using gas-permeable membrane flasks

D2-SPDM: Faster R-CNN-Based Defect Detection and Surface Pixel Defect Mapping with Label Enhancement in Steel Manufacturing Processes

The steel manufacturing process is inherently continuous, meaning that if defects are not effectively detected in the initial stages, they may propagate through subsequent stages, resulting in high costs for corrections in the final product. Therefore, detecting surface defects and obtaining segmentation information is critical in the steel manufacturing industry to ensure product quality and enhance production efficiency. Specifically, segmentation information is essential for accurately understanding the shape and extent of defects, providing the necessary details for subsequent processes to address these defects. However, the time-consuming and costly process of generating segmentation annotations poses a significant barrier to practical industrial applications. This paper proposes a cost-efficient segmentation labeling framework that combines deep learning-based anomaly detection and label enhancement to address these challenges in the steel manufacturing process. Using ResNet-50, defects are classified, and faster region convolutional neural networks (faster R-CNNs) are employed to identify defect types and generate bounding boxes indicating the defect locations. Subsequently, recursive learning is performed using the GrabCut algorithm and the DeepLabv3+ model based on the generated bounding boxes, significantly reducing annotation costs by generating segmentation labels. The proposed framework effectively detects defects and accurately defines them, even in randomly collected images from the steel manufacturing process, contributing to both quality control and cost reduction. This study presents a novel approach for improving the quality of the steel manufacturing process and is expected to enhance overall efficiency in the steel manufacturing industry.