Improve Production Efficiency Research Articles

Layered oxide cathodes: A comprehensive review of characteristics, research, and development in lithium and sodium ion batteries

AbstractLayered oxide materials are widely used in the field of energy storage and conversion due to their high specific energy, high efficiency, long cycle life, and high safety. Herein, We summarize the latest research progress in the field of layered metal oxide cathode materials from three aspects: challenges faced, failure mechanisms, and modification methods. We also compare the characteristics of lithium‐based layered oxides and sodium‐based layered oxides, and predict future development directions. The layered oxide cathode materials for sodium‐ion batteries and lithium‐ion batteries exhibit overall structural and operational similarities. There are also some differences, such as lattice parameters and application extent. Sodium‐ion battery cathode materials need to explore new materials and address structural instability issues, while lithium‐ion batteries require finding alternative materials and improving production efficiency. Future challenges for both types of materials include enhancing capacity and cycle performance, elucidating deep mechanisms, reducing costs, and improving resource sustainability. Future development should focus on balancing cycle stability and charge cut‐off voltage to meet the growing demand for battery applications.

Review of the Proteomics and Metabolic Properties of Corynebacterium glutamicum.

Corynebacterium glutamicum (C. glutamicum) has become industrially important in producing glutamic acid and lysine since its discovery and has been the subject of proteomics and central carbon metabolism studies. The proteome changes depending on environmental conditions, nutrient availability, and stressors. Post-translational modification (PTMs), such as phosphorylation, methylation, and glycosylation, alter the function and activity of proteins, allowing them to respond quickly to environmental changes. Proteomics techniques, such as mass spectrometry and two-dimensional gel electrophoresis, have enabled the study of proteomes, identification of proteins, and quantification of the expression levels. Understanding proteomes and central carbon metabolism in microorganisms provides insight into their physiology, ecology, and biotechnological applications, such as biofuels, pharmaceuticals, and industrial enzyme production. Several attempts have been made to create efficient production strains to increase productivity in several research fields, such as genomics and proteomics. In addition to amino acids, C. glutamicum is used to produce vitamins, nucleotides, organic acids, and alcohols, expanding its industrial applications. Considerable information has been accumulated, but recent research has focused on proteomes and central carbon metabolism. The development of genetic engineering technologies, such as CRISPR-Cas9, has improved production efficiency by allowing precise manipulation of the metabolic pathways of C. glutamicum. In addition, methods for designing new metabolic pathways and developing customized strains using synthetic biology technology are gradually expanding. This review is expected to enhance the understanding of C. glutamicum and its industrial potential and help researchers identify research topics and design studies.

Failure Mode and Effect Analysis (FMEA) and Root Cause Analysis (RCA) for Urea Fertilizer Production Risk Mitigation

The production of urea fertilizer is one of the most important processes in the agricultural industry, but it cannot be separated from various risks that can interfere with its smooth operation. This research aims to identify and mitigate the risks involved in urea fertilizer production using the Failure Mode and Effect Analysis (FMEA) and Root Cause Analysis (RCA) methods. The results show that some of the major risks in urea fertilizer production include equipment failure, raw material supply disruption, and operational errors. By applying FMEA and RCA, the company can identify the root causes of the problems and develop effective mitigation strategies. The implementation of these mitigation actions is expected to improve production efficiency and reduce downtime, thus ensuring better production continuity.

Detection and Tracking of Underwater Fish Using the Fair Multi-Object Tracking Model: A Comparative Analysis of YOLOv5s and DLA-34 Backbone Models

Modern aquaculture utilizes computer vision technology to analyze underwater images of fish, contributing to optimized water quality and improved production efficiency. The purpose of this study is to efficiently perform underwater fish detection and tracking using multi-object tracking (MOT) technology. To achieve this, the FairMOT model was employed to simultaneously implement pixel-level object detection and re-identification (Re-ID) functions, comparing two backbone models: FairMOT+YOLOv5s and FairMOT+DLA-34. The study constructed a dataset targeting the popular black porgy in Korean aquaculture, using underwater video data from five different environments collected from the internet. During the training process, the FairMOT+YOLOv5s model rapidly reduced train loss and demonstrated stable performance. The FairMOT+DLA-34 model showed better results in ID tracking performance, with an accuracy of 44.1%, an IDF1 of 11.0%, an MOTP of 0.393, and an IDSW of 1. In contrast, the FairMOT+YOLOv5s model recorded an accuracy of 43.8%, an IDF1 of 14.6%, an MOTP of 0.400, and an IDSW of 10. The results of this study indicate that the FairMOT+YOLOv5s model demonstrated higher IDF1 and MOTP scores compared to the FairMOT+DLA-34 model, while the FairMOT+DLA-34 model showed superior performance in ID tracking accuracy and had fewer ID switches.

Meta-analysis of association between Just-in-Time system and firm performance: moderating effects of National Culture differences

PurposeThe effectiveness of the Just-in-Time (JIT) production system in non-Japanese contexts is a topic of diverse findings. This study conducts a meta-analysis of empirical research on JIT and its relationship with performance, focusing on studies published since 1995. Additionally, it examines the moderating influence of National Culture (NC) values on JIT outcomes.Design/methodology/approachA total of 59 empirical studies with 211 effects and 17,008 observations from 18 countries are meta-analyzed. A meta-regression using hierarchical linear modeling (HLM) is performed to explore how four dimensions of National Culture (NC) moderate the impact. (viz. institutional collectivism, uncertainty avoidance future orientation, and power distance,) based on the Global Leadership and Organizational Behavior Effectiveness (GLOBE) culture model.FindingsThe meta-analysis results show that improved production efficiency, product quality and reduced wastes achieved through JIT deployments translate into the overall performance of organizations. The meta-regression results shed light on how local cultures influence the effectiveness of JIT across different countries.Originality/valueThe findings of meta-analysis have implications for multinational manufacturers in realizing efficacy of JIT. The research adds to the international operations management literature by examining how NC values influence strategies and decisions in operations management.

Assessing the need for the adoption of digitalization in Indian small and medium enterprises

Abstract Manufacturing has experienced an immense transformation since the emergence of Industry 4.0. Digitalization has substantially improved production efficiency and product quality, and innovative business models have emerged. The backbone of emerging countries like India comprises small and medium enterprises (SMEs) that provide jobs for millions of families. Small and medium-sized businesses (SMBs) account for around 31% of the nation’s GDP and 43% of 2021–2022 exports, both significant contributors to economic growth when there is a substantial labour pool. Therefore, they are often known as India’s growth engine. Unfortunately, in today’s highly competitive digital market, they fail to keep pace with technological innovations and keep their employees up to date. This article discusses the significance of digitalization for India’s SMEs, addressing the hurdles and the methods to remove them. New consumer markets have been established due to government programs like Make in India, Digital India, and E-Government.

Prediction of Bonding Strength of Heat-Treated Wood Based on an Improved Harris Hawk Algorithm Optimized BP Neural Network Model (IHHO-BP)

In this study, we proposed an improved Harris Hawks Optimization (IHHO) algorithm based on the Sobol sequence, Whale Optimization Algorithm (WOA), and t-distribution perturbation. The improved IHHO algorithm was then used to optimize the BP neural network, resulting in the IHHO-BP model. This model was employed to predict the bonding strength of heat-treated wood under varying conditions of temperature, time, feed rate, cutting speed, and grit size. To validate the effectiveness and accuracy of the proposed model, it was compared with the original BP neural network model, WOA-BP, and HHO-BP benchmark models. The results showed that the IHHO-BP model reduced the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by at least 51.16%, 40.38%, and 51.93%, respectively, while increasing the coefficient of determination (R2) by at least 10.85%. This indicates significant model optimization, enhanced generalization capability, and higher prediction accuracy, better meeting practical engineering needs. Predicting the bonding strength of heat-treated wood using this model can reduce production costs and consumption, thereby significantly improving production efficiency.

Selection indices for residual feed intake derived from milk spectra

Improving production efficiency and minimizing the environmental impact of dairy farming are 2 important goals of the dairy industry. Achieving these objectives requires improving the feed-to-milk conversion efficiency. One way to achieve this goal is through genetic selection. However, measuring feed efficiency in commercial herds is currently not feasible. As such, we conducted a study to evaluate the genetic accuracy of various selection indices derived from Fourier transform mid-infrared (FTIR)-spectra or milk composition. We use 7,793 weekly records on 537 genotyped cows (78,964 SNPs), with information on residual feed intake (RFI), and FTIR-spectra. We fitted various types of selection indexes using the complete FTIR-spectra of milk samples. The estimated heritability of RFI was 0.12 ± 0.02. The accuracy of indirect selection using the FTIR-spectra was maximized using a principal components selection index (0.16 ± 0.07), followed by a Lasso-type penalized selection index (0.14 ± 0.06). We determined that an index based on milk spectral data recorded on ~25 daughters produced a progeny average with an accuracy comparable to direct phenotypic selection for RFI. We conclude that indirect selection for RFI using FTIR-spectra data can be effective for sires with progeny; however, future studies with a larger sample size are needed to validate these results.

Microwave drying coupled with convective air and steam for efficient dehydration of extruded zeolite honeycomb monolith

Zeolite honeycomb monoliths (ZHMs) as key adsorbents or catalysts are widely applied in industries, for which, however, the popularization is limited by its low production efficiency due to the long and careful drying process of the fragile ZHMs green body. Here we address this challenge by developing a microwave drying strategy coupled with convective air and steam. The individual microwave, microwave-air, and microwave-air-steam drying characteristics on the honeycomb and slab monoliths of 13X zeolite were systematically studied, and the drying quality-microwave power/air temperature/humidity relationship was correlated. The results showed that microwave achieves rapid dehydration of ZHMs, eliminating 29.94 % relative mass of water within only 660 s. However, the uncontrollable microwave adsorbing inhomogeneity and thermal accumulation over the hydrophilic ZHM resulted in overburning, cracking and deformation issues. Introducing convective air at room temperature eliminates overburning and cracking phenomena, and after coupled with 110 g/m3 steam, the irregular deformation of ZHMs was further mitigated (shrinkage rate reduced from 2.10 % to 1.11 %) with even increased drying efficiency by 4.47 %. The advantageous mechanism of microwave-air-steam coupling drying was revealed as the combined effect of the heat and mass transfer through the honeycomb hole enhanced by convective air and the heating and dehydration homogeneity improved by uniform microwave absorbing on surface-condensable steam. The efficacy of the optimized drying strategy was demonstrated by significantly shortened time for scale-up production of 1 m3 ZHMs from nearly 1 month to 2 days. This work affords new insights for improving production efficiency of high-quality ZHMs and presents microwave-air-steam drying process as a platform to advancing structured material preparation.

Construction of digital twin workshop integrated with edge computing and deep learning

Abstract This study addresses the challenges of real-time data synchronization and big data processing in the construction of digital twin workshops under the background of intelligent manufacturing. A solution that integrates edge computing with deep learning technology is proposed. Through a multi-layered, modular framework structure, this solution achieves real-time synchronous mapping and interaction between physical workshops and virtual workshops, effectively improving production efficiency, reducing maintenance costs, and ensuring product quality. The edge computing layer in the research is responsible for preprocessing heterogeneous data from multiple sources and making rapid response decisions. Simultaneously, deep learning algorithms are employed to conduct in-depth analysis, forecasting, and optimization of the production process. Case studies demonstrate that this solution can significantly improve equipment utilization and optimize workpiece scheduling, validating its application value in actual manufacturing environments.

Exploration of the application of artificial intelligence in modern agricultural production Take orchard management as an example

In the context of increasing global agricultural challenges, the application of artificial intelligence technology in the agricultural field is increasingly a trend, especially in the production of agricultural products, intelligent identification technology has shown the potential to significantly improve production efficiency and optimize yield and quality. By the mid-21st century, demand for food production is expected to reach 50 percent, and there will be enormous pressure to achieve this goal with traditional agricultural technologies, which could be achieved through the application of artificial intelligence. The application of artificial intelligence technology in modern agricultural production will be analyzed in detail in this paper, the guidance of future research fields will be proposed, and the existing challenges and technical problems will be identified and discussed in order to promote the deepening and wide application of intelligent agriculture. This paper specifically discusses examples of applications in orchard management, pest detection, and automated harvesting and summarizes the effectiveness and obstacles of these techniques.

Edukasi Keuangan dan Pemasaran untuk Peternak Jangkrik: Kunci Kesuksesan UMKM

This community service activity aims to enhance the knowledge of cricket farmers in financial recording and marketing strategies to help their businesses grow effectively. Conducted on May 25, 2024, this event took place at a cricket farming UMKM (Micro, Small, and Medium Enterprises) located on Jln. Benda Permai II, Pd Benda, Pamulang District, South Tangerang. The activity is a collaboration between Sekolah Tinggi Ilmu Ekonomi Ganesha (STIE GANESHA) and local farmers. The program ran from 08:00 to 12:00 WIB and aimed to apply knowledge and research findings to improve community welfare. The participating lecturers conducted research covering digital marketing, financial analysis, and human resource development. The results of this research are expected to improve production efficiency, marketing, and financial management of UMKM. The main problems faced by the farmers include limited access to bank loans and a lack of strategies to increase sales. Through this program, STIE Ganesha is committed to supporting and developing the potential of local UMKM, while also contributing positively to the welfare of the South Tangerang community.The results of this activity showed an increase in financial and marketing literacy among cricket farming UMKM owners. Jhandriansyah, a dim sum entrepreneur and cricket farmer, expressed his gratitude for the guidance provided, stating that the collaboration with STIE Ganesha greatly helped his business grow

A forecasting model with hybrid bidirectional long short-term memory for mooring line responses of semi-submersible offshore platforms

Semi-submersible offshore platforms are often produced in the deep sea far away from land, which results in high operating costs and difficult safety maintenance due to the harsh and complex working conditions. Adopting a neural network model for online accurate prediction of mooring tension of semi-submersible offshore platforms can adjust the ballast according to the advance of mooring tension change to keep the platform in a relatively smooth movement amplitude and improve production efficiency. In addition, it can also prevent catastrophic accidents caused by anchor chain breakage due to excessive mooring tension. However, the measured mooring tension of semi-submersible offshore platforms is characterized by complex nonlinear non-stationarity, which leads to insufficient prediction accuracy of a single prediction model. This paper develops a novel online prediction model of mooring tension for semi-submersible offshore platforms based on empirical modal decomposition (EMD), convolutional neural network (CNN), and bidirectional long and short-term memory neural network (BiLSTM). The proposed model is validated using the measured data of the first semi-submersible offshore platform in the South China Sea during operation. The experimental results show that the proposed model outperforms other comparative models currently mainstream for mooring tension prediction. In addition, the proposed method can also be used to predict complex nonlinear data in the field of ship and ocean engineering.

Supply Chain Value-added Strategy for the Rice Industry in Jilin Province

This paper explores the application of digital technology in enhancing the rice supply chain management in Jilin Province. By integrating modern agricultural services and internet-based models, the study highlights how digital advancements can improve production efficiency, reduce costs, and optimise resource allocation. The research also emphasises the importance of constructing a value-addition system based on the lifecycle theory of rice crops, which can maximise value at each stage of the supply chain. The study addresses risk control and cost management challenges, proposing a value optimisation model incorporating traditional value innovation and new dynamics. The findings suggest that digital technology presents significant value addition and optimisation opportunities in the rice industry, ultimately leading to a more resilient and profitable supply chain. This paper provides practical solutions for overcoming existing challenges and unlocking new potentials in the rice supply chain, ensuring its growth and competitiveness in the evolving market environment.

Industrial restructuring and corporate income distribution gap

This study employs regression analysis to investigate the crucial role of industrial restructuring in reducing the income distribution gap within the enterprise. The findings show the following. First, adjusting the industrial structure has substantially increased the average wage levels for companies and their employees, particularly for rank-and-file workers, while exerting little influence on executive compensation. Second, industrial restructuring indirectly boosts the income growth of companies and their employees by fostering technological advancements, improving production efficiency, and expanding sales volume. Finally, the positive impact of industrial restructuring on corporate income distribution is more pronounced in state-owned enterprises and competitive sectors.

Effects of isoleucine, lysine, valine, and a group of nonessential amino acids on mammary amino acid metabolism in lactating dairy cows

Intracellular AA regulate milk protein synthesis within the mammary gland by modifying mammary plasma flow (MPF) and AA transporter activity. Amino acid transporters catalyze translocation using Na+ gradient, substrate gradient (uniporters), and exchange mechanisms; further, they exhibit specificity for individual AA or groups of AA with similar side-chain properties within each transport system. Nonessential AA are actively transported through Na+-dependent transporters and, thus, are often used as intracellular currencies for EAA transport through exchange transporters. Therefore, it was hypothesized that individual EAA supplementation would compete with other EAA for shared transporters, and supplementation with Ala, Gln, and Gly would stimulate EAA transport through exchange transporters. Ten primiparous lactating dairy cows were divided into 2 groups based on milk production and were randomly assigned to treatment sequences within 2 balanced 5 × 5 Latin squares by group. Period length was 14 d. Treatments were 9-d jugular infusions of (1) saline; (2) 34.5 g of Val per day; (3) a ratio of 32.7 g of Ala to 40 g of Gln to 26.7 g of Gly per day (AQG); (4) 43 g of Lys per day; or (5) 33.5 g of Ile per day. All cows were fed a common base diet formulated to contain 15.0% CP. Infusions of Ile, Lys, or AQG did not affect milk protein or milk production; however, Val infusion decreased both. The effects of Val infusion on milk protein production appeared to be partially driven by decreased DMI. The decline in milk protein percentage indicated that milk lactose production was also affected. Additionally, Val infusion increased MPF efficiency (MPF/milk; L/L) by approximately 44%. Infusion of Val tended to decrease or decreased mammary net uptakes of Lys, Leu, Met, and total AA. Infusion of Ile tended to increase its mammary net uptakes but did not affect any other AA. Infusions of Lys and AQG did not affect any mammary net uptakes. Infusion of Val tended to decrease Phe and total NEAA mammary clearance rates. Infusion with AQG stimulated Tyr clearance rates and tended to decline system N mammary clearance rates. Ratios of branched-chain AA mammary uptake to milk protein output (U:O) did not differ from 1 for Val-infused cows, which indicated that little intramammary catabolism was occurring. Additionally, the average NEAA U:O in response to all treatments except Val was 0.70, but Val-infused cows had NEAA U:O that averaged 0.09, indicating increased synthesis within the glands. The effects of Val on mammary net clearance rates of multiple EAA support the incorporation of AA limitations in ration optimizers to prevent AA imbalances. It is possible that oversupplementation of EAA other than Val may also decrease DMI and mammary activity. Identifying efficiency apexes for each of the EAA will allow more precise diet formulation and supplementation, leading to improved production efficiency.

Expression of ovine MYH3 gene and its polymorphisms and association analysis with growth traits in Hu sheep

The growth rate of sheep determines their productivity; the faster the growth rate, the higher the productivity. Sheep growth is usually measured by body weight, which is an important indicator of overall growth, and body size, which reflects body shape and structure. Therefore, in sheep production, the selection and improvement of growth traits is one of the important means to improve production efficiency. In this study, 857 males of Hu sheep were selected to record growth traits, including body weight, height, length, and chest, and cannon circumference at 80, 100, 120, 140, 160 and 180 days of age, as well as live weight before slaughter and carcass weight. Correlation analyses conducted between these growth traits at different stages were significantly correlated with both live weight before slaughter and carcass weight. And the strongest correlation was observed for body weight, while body height showed the weakest correlation. The myosin heavy chain 3 (MYH3) gene in the MYH gene family was selected as the target gene, and a synonymous mutation (MYH3 g.33834192A>G) in the exon 12 region of MYH3 was identified by polymerase chain reaction (PCR) and Sanger sequencing. Further association analysis using competitive allele-specific PCR (KASPar) technique showed that the MYH3 g.33834192A>G locus was significantly (P < 0.05) associated with growth traits (including body weight from 80 to 160 days of age, body height at 80 and 100 days of age, body length at 100 and 140 days of age, chest circumference at 80, 100, 160, and 180 days of age, cannon circumference at 80–140 days of age and carcass weight). Among them, the dominant genotype was GG. Meanwhile, the tissue expression results indicated that the highest level of MYH3 gene expression was found in muscle tissues. Therefore, this locus can be used as a candidate molecular genetic marker for improving growth traits in Hu sheep.

Long Short‐Term Memory‐Based Multi‐Robot Trajectory Planning: Learn from MPCC and Make It Better

The current trajectory planning methods for multi‐robot systems face challenges due to high computational burden and inadequate adaptability in complex constrained environments, obstructing efficiency improvements in production and logistics. This article presents an innovative solution by integrating model predictive contouring control (MPCC) and long short‐term memory (LSTM) networks for real‐time trajectory planning of multiple mobile robots. Based on the datasets generated by MPCC, a customized LSTM network is constructed to learn the collaborative planning behavior from these datasets offline, subsequently producing smooth and efficient trajectories online with a low computational burden. Moreover, a hybrid control scheme, incorporating a lidar‐based safety evaluator, avoids unexpected collision risks by switching to MPCC when necessary, ensuring the overall safety and reliability of the multi‐robot system. The proposed hybrid LSTM method is implemented and tested in the robot operating system (ROS) within diverse constrained scenarios. Experimental results demonstrate that the hybrid LSTM method achieves ≈6% enhancements in trajectory productivity and a reduced computational burden of roughly 75% compared to MPCC, thereby providing a promising solution for local multi‐robot trajectory planning in logistics transportation tasks.

Effect of cleaning parameters on the molten pool shape and molten slag dynamics in the corner scarfing process of the casting slab

In the steel industry, improving the hot charging and heat delivery ratio in the continuous casting of slabs and thick slabs can effectively reduce carbon emissions, lower energy consumption, and improve production efficiency. However, this puts very strict requirements on the surface quality of continuous-casting slab. Flame scarfing (or cleaning) technology is currently an important process in the metallurgy field, which can effectively remove impurities and crack defects on the surface and corners of continuous casting slab to ensure the quality and performance of the final product. Under the action of cleaning flame and iron oxide reaction heat, a cleaning molten pool is formed in the corner of the casting slab, and the shape and depth of the molten pool have an important influence on the cleaning effect. A three-dimensional mathematical model is developed to study the effect of slab cleaning speed and oxygen flow rate on the shape of the molten pool in the corners for the corner flame cleaning process. Simultaneously, a two-phase flow volume of fluid (VOF) model for gas-slag interactions is employed to examine the motion behavior of the molten slag. The results showed that when the operating conditions changed, the cleaning speed increased by 1 m/min, the length of the wide and narrow sides decreased by approximately 9.2 mm, and the depth of the corner pool decreased by approximately 5.3 mm. Moreover, a slag-blocking device was designed to mitigate the issue of slag splashing effectively based on the numerical simulation results compared with and without the slag-blocking device. These study works provide beneficial theoretical support and practical guidance for refining and optimizing flame-cleaning techniques.

Trade policy uncertainty and capacity utilisation rate: Firm‐level evidence from China's WTO accession

AbstractThis paper was one of the first to explore the impact of trade policy uncertainty (TPU) reduction on Chinese firm capacity utilisation rate from the perspective of trade policy variation. Based on a quasi‐natural experiment of the implementation of Permanent Normal Trade Relation (PNTR) after China's WTO accession, this paper adopts the difference‐in‐difference (DID) method to conduct empirical analysis, and finds that reduction in TPU significantly raises firm capacity utilisation rate. The mechanism tests show that export expansion is an important channel through which reduction in TPU raises firm capacity utilisation rate, especially for firms exporting to the United States; reduction in TPU also raises firm capacity utilisation rate through the improvement of production efficiency; in addition, reduction in TPU raises firm capacity utilisation rate along the downstream linkages, whereas the effect of the upstream linkages is insignificant. Finally, this paper takes a step further to conduct empirical analysis at industry level, the results show that reduction in TPU also significantly raises manufacturing aggregate capacity utilisation rate, and the improvement of resource reallocation efficiency (especially the exit of the firms with backward production capacity) plays an important role. Our study was helpful to understand the driving forces of the changes in Chinese manufacturing capacity utilisation rate in recent years, and it also provides new ideas for solving over‐capacity.