PLOS ONE: [sortOrder=DATE_NEWEST_FIRST, sort=Date, newest first, q=subject:"Production engineering"]PLOShttps://journals.plos.org/plosone/webmaster@plos.orgaccelerating the publication of peer-reviewed sciencehttps://journals.plos.org/plosone/search/feed/atom?sortOrder=DATE_NEWEST_FIRST&sort=Date,+newest+first&unformattedQuery=subject:%22Production+engineering%22All PLOS articles are Open Access.https://journals.plos.org/plosone/resource/img/favicon.icohttps://journals.plos.org/plosone/resource/img/favicon.ico2024-03-28T12:20:24ZDetection method of organic light-emitting diodes based on small sample deep learningHua QiuJin HuangYi-Cong FengPeng Rong10.1371/journal.pone.02976422024-02-05T14:00:00Z2024-02-05T14:00:00Z<p>by Hua Qiu, Jin Huang, Yi-Cong Feng, Peng Rong</p>
In order to solve the surface detection problems of low accuracy, low precision and inability to automate in the production process of late-model display panels, a little sample-based deep learning organic light-emitting diodes detection model SmartMuraDetection is proposed. First, aiming at the detection difficulty of low surface defect contrast, a gradient boundary enhancement algorithm module is designed to automatically identify and enhance defects and background gray difference. Then, the problem of insufficient little sample data sets is solved, Poisson fusion image enhancement module is designed for sample enhancement. Then, a TinyDetection model adapted to small-scale target detection is constructed to improve the detection accuracy of defects in small-scale targets. Finally, SEMUMaxMin quantization module is proposed as a post-processing module for the result images derived from network model reasoning, and accurate defect data is obtained by setting a threshold filter. The number of sample images in the experiment is 334. This study utilizes an organic light-emitting diodes detection model. The detection accuracy of surface defects can be improved by 85% compared with the traditional algorithm. The method in this paper is used for mass production evaluation in the actual display panel production site. The detection accuracy of surface defects reaches 96%, which can meet the mass production level of the detection equipment in this process section.A comprehensive guide to CAN IDS data and introduction of the ROAD datasetMiki E. VermaRobert A. BridgesMichael D. IannaconeSamuel C. HollifieldPablo MorianoSteven C. HespelerBill KayFrank L. Combs10.1371/journal.pone.02968792024-01-22T14:00:00Z2024-01-22T14:00:00Z<p>by Miki E. Verma, Robert A. Bridges, Michael D. Iannacone, Samuel C. Hollifield, Pablo Moriano, Steven C. Hespeler, Bill Kay, Frank L. Combs</p>
Although ubiquitous in modern vehicles, Controller Area Networks (CANs) lack basic security properties and are easily exploitable. A rapidly growing field of CAN security research has emerged that seeks to detect intrusions or anomalies on CANs. Producing vehicular CAN data with a variety of intrusions is a difficult task for most researchers as it requires expensive assets and deep expertise. To illuminate this task, we introduce the first comprehensive guide to the existing open CAN intrusion detection system (IDS) datasets. We categorize attacks on CANs including fabrication (adding frames, e.g., flooding or targeting and ID), suspension (removing an ID’s frames), and masquerade attacks (spoofed frames sent in lieu of suspended ones). We provide a quality analysis of each dataset; an enumeration of each datasets’ attacks, benefits, and drawbacks; categorization as real vs. simulated CAN data and real vs. simulated attacks; whether the data is raw CAN data or signal-translated; number of vehicles/CANs; quantity in terms of time; and finally a suggested use case of each dataset. State-of-the-art public CAN IDS datasets are limited to real fabrication (simple message injection) attacks and simulated attacks often in synthetic data, lacking fidelity. In general, the physical effects of attacks on the vehicle are not verified in the available datasets. Only one dataset provides signal-translated data but is missing a corresponding “raw” binary version. This issue pigeon-holes CAN IDS research into testing on limited and often inappropriate data (usually with attacks that are too easily detectable to truly test the method). The scarcity of appropriate data has stymied comparability and reproducibility of results for researchers. As our primary contribution, we present the Real ORNL Automotive Dynamometer (ROAD) CAN IDS dataset, consisting of over 3.5 hours of one vehicle’s CAN data. ROAD contains ambient data recorded during a diverse set of activities, and attacks of increasing stealth with multiple variants and instances of real (i.e. non-simulated) fuzzing, fabrication, unique advanced attacks, and simulated masquerade attacks. To facilitate a benchmark for CAN IDS methods that require signal-translated inputs, we also provide the signal time series format for many of the CAN captures. Our contributions aim to facilitate appropriate benchmarking and needed comparability in the CAN IDS research field.The establishment and application of quality gain-loss function when the loss of primary and cubic term is not ignored and the compensation quantity is constantBo WangXiaojuan LiJianyou ShiQikai LiXiangtian Nie10.1371/journal.pone.02959492023-12-18T14:00:00Z2023-12-18T14:00:00Z<p>by Bo Wang, Xiaojuan Li, Jianyou Shi, Qikai Li, Xiangtian Nie</p>
The traditional quality gain-loss function(QGLF) considers the case that the primary term loss cannot be ignored, does not consider the cubic term loss, but in practice the cubic term loss should not be ignored. In this paper, based on the existing QGLF model, the Taylor expansion is reserved to the third order, the determination of the quality loss coefficient is discussed and analyzed under the condition that the compensation quantity is constant, and the asymmetric cubic QGLF model is established, and uses an example of concrete mixture out of the machine slump during the dam concrete construction to analyze and discuss the relationship between the proposed model and the traditional quadratic QGLF, which verifies the rationality of the proposed model.Seasonal autoregressive integrated moving average (SARIMA) time-series model for milk production forecasting in pasture-based dairy cows in the Andean highlandsUri H. Perez-GuerraRassiel MacedoYan P. ManriqueEloy A. CondoriHenry I. GonzálesEliseo FernándezNatalio LuqueManuel G. Pérez-DurandManuel García-Herreros10.1371/journal.pone.02888492023-11-16T14:00:00Z2023-11-16T14:00:00Z<p>by Uri H. Perez-Guerra, Rassiel Macedo, Yan P. Manrique, Eloy A. Condori, Henry I. Gonzáles, Eliseo Fernández, Natalio Luque, Manuel G. Pérez-Durand, Manuel García-Herreros</p>
Milk production in the Andean highlands is variable over space and time. This variability is related to fluctuating environmental factors such as rainfall season which directly influence the availability of livestock feeding resources. The main aim of this study was to develop a time-series model to forecast milk production in a mountainous geographical area by analysing the dynamics of milk records thorough the year. The study was carried out in the Andean highlands, using time–series models of monthly milk records collected routinely from dairy cows maintained in a controlled experimental farm over a 9-year period (2008–2016). Several statistical forecasting models were compared. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percent Error (MAPE) were used as selection criteria to compare models. A relation between monthly milk records and the season of the year was modelled using seasonal autoregressive integrated moving average (SARIMA) methods to explore temporal redundancy (trends and periodicity). According to white noise residual test (Q = 13.951 and p = 0.052), Akaike Information Criterion and MAE, MAPE, and RMSE values, the SARIMA (1, 0, 0) x (2, 0, 0)<sub>12</sub> time-series model resulted slightly better forecasting model compared to others. In conclusion, time-series models were promising, simple and useful tools for producing reasonably reliable forecasts of milk production thorough the year in the Andean highlands. The forecasting potential of the different models were similar and they could be used indistinctly to forecast the milk production seasonal fluctuations. However, the SARIMA model performed the best good predictive capacity minimizing the prediction interval error. Thus, a useful effective strategy has been developed by using time-series models to monitor milk production and alleviate production drops due to seasonal factors in the Andean highlands.Intensified seed spices-based cropping systems for higher productivity, resource-use efficiency, soil fertility and profitability in arid and semi-arid regions of IndiaNarendra ChaudharyShiv LalRavindra SinghM. D. MeenaS. S. MeenaR. D. MeenaC. K. JangirV. BhardwajAsheesh Sharma10.1371/journal.pone.02929552023-10-18T14:00:00Z2023-10-18T14:00:00Z<p>by Narendra Chaudhary, Shiv Lal, Ravindra Singh, M. D. Meena, S. S. Meena, R. D. Meena, C. K. Jangir, V. Bhardwaj, Asheesh Sharma</p>
Coriander, fenugreek, nigella etc. are collectively known as seed spices. They are “<i>High value and low volume crops</i>” and considered cash crops for the growers of arid and semi-arid regions of India. Coriander, fenugreek and nigella are grown during the <i>rabi</i> season and take hardly 130–140 days to attain full maturity. In this context, farmers are not able to develop income from available arable land round the year, even though they have sufficient resources as well as manpower. Therefore, coriander, fenugreek and nigella-based cropping systems, four of each (total 12) were evaluated during 3 consecutive years (2019–20 to 2021–22) for their productivity, resource-use efficiency, economics and soil fertility. The results showed that among the seed spices-based cropping systems, maximum system productivity (5193 kg ha<sup>-1</sup>), production efficiency (18.81 kg ha<sup>-1</sup> day<sup>-1</sup>), water-use efficiency (2.31 kg ha<sup>-1</sup> mm<sup>-1</sup>), economic efficiency (11.85 US $ ha<sup>-1</sup> day<sup>-1</sup>), net return (3270 US $ ha<sup>-1</sup>), benefit:cost ratio (3.27) and available N (165.6 kg ha<sup>-1</sup>) were observed under nigella-green coriander-mungbean cropping system. Hence, seed spices growers are recommended to adopt nigella-green coriander-mungbean cropping system in order to realize better productivity, resource-use efficiency, soil fertility and profitability.Deformation rate of engineered wood flooring with response surface methodology and adaptive network-based fuzzy inference systemHuixiang Wang10.1371/journal.pone.02928152023-10-12T14:00:00Z2023-10-12T14:00:00Z<p>by Huixiang Wang</p>
Controlling the deformation rate is the key to improving the product quality of engineered wood flooring. In this work, the changes in the deformation rate of engineered wood flooring were in focus with cold-pressing, response surface methodology, and adaptive network-based fuzzy inference system were used to explore the relationship between deformation rate and processing parameters, including adhesive spreading rate, pressing time, and pressing pressure. According to the results, the deformation rate was positively related to pressing time, while it increased first and then decreased with both the increase of adhesive spreading rate and pressing pressure. Meanwhile, a mathematical model was developed, and the significant influence of each term on the deformation rate was analyzed. This model had high feasibility and can be used to describe the relationship between the deformation rate and processing parameters. Furthermore, an adaptive network-based fuzzy inference system model was established. It has higher accuracy than that of the response surface methodology model, and it can be used for predicting deformation rate and optimizing processing parameters. Finally, an optimal processing conditions with the lowest deformation rate was determined as follows: 147 g/m<sup>2</sup> adhesive spreading rate, 12s pressing time, and 1.2 MPa pressing pressure, and it hope to be adopted in the industrial processing of engineered wood flooring with respective of the higher product quality and lower production costs.Predicting sport fish mercury contamination in heavily managed reservoirs: Implications for human and ecological healthJesse M. LepakBrett M. JohnsonMevin B. HootenBrian A. WolffAdam G. Hansen10.1371/journal.pone.02858902023-08-22T14:00:00Z2023-08-22T14:00:00Z<p>by Jesse M. Lepak, Brett M. Johnson, Mevin B. Hooten, Brian A. Wolff, Adam G. Hansen</p>
Mercury (Hg) is a concerning contaminant due to its widespread distribution and tendency to accumulate to harmful concentrations in biota. We used a machine learning approach called random forest (RF) to test for different predictors of Hg concentrations in three species of Colorado reservoir sport fish. The RF approach indicated that the best predictors of 864 mm northern pike (<i>Esox lucius</i>) Hg concentrations were covariates related to salmonid stocking in each study system, while system-specific metrics related to productivity and forage base were the best predictors of Hg concentrations of 381 mm smallmouth bass (<i>Micropterus dolomieu</i>), and walleye (<i>Sander vitreus</i>). Protecting human and ecological health from Hg contamination requires an understanding of fish Hg concentrations and variability across the landscape and through time. The RF approach could be applied to identify potential areas/systems of concern, and predict whether sport fish Hg concentrations may change as a result of a variety of factors to help prioritize, focus, and streamline monitoring efforts to effectively and efficiently inform human and ecological health.A study on optimization of delayed production mode of iron and steel enterprises based on data miningZhiming ShiYisong LiChangxiang Lu10.1371/journal.pone.02787502023-01-18T14:00:00Z2023-01-18T14:00:00Z<p>by Zhiming Shi, Yisong Li, Changxiang Lu</p>
Delayed production mode has been adopted by an increasing number of process production enterprises as a method to realize mass customization of multi-products. This paper used the convolutional neural network-long short-term memory artificial neural network algorithm (C-LSTM) in data mining technology to analyze and determine factors that have an impact on delayed production mode in the internal and external production and operation of enterprises. Combined with the actual production situation of iron and steel enterprises, a quantitative model of the delayed production was constructed. Lastly, data from a large iron and steel enterprise with good operation was used to verify the validity of the proposed model and analyze key influencing factors. According to the research, in scenarios of considering PDP alone, considering CODP alone, considering both PDP and CODP, considering PDP and CODP and using data mining technology to model, the matching degree of these methods with the actual situation of the enterprise is 31.8%, 61.4%, 71.6% and 86.6%, respectively. The numerical analysis results of the model based on data mining technology show that in delayed production, when customer service level improves or the delay penalty coefficient increases, the optimal locations of the product differentiation point (PDP) and customer order decoupling point (CODP) move toward the end of production, and the total cost increases gradually. When the difference in production cost or benefit of early delivery between the candidate locations of PDP and CODP is small, optimal locations of PDP and CODP are close to the beginning of the general and dedicated production processes. With an increase of cost difference or early delivery benefit, the optimal locations of PDP and CODP jumped to the end stage of the general and dedicated production processes, and the total cost begins to decrease.Digital economy, industrial structure upgrading and green total factor productivity——Evidence in textile and apparel industry from ChinaXiangmei ZhuBin ZhangHui Yuan10.1371/journal.pone.02772592022-11-04T14:00:00Z2022-11-04T14:00:00Z<p>by Xiangmei Zhu, Bin Zhang, Hui Yuan</p>
According to the standard of GB/T4754-2017 Classification of National Economic Industry and the characteristics of the textile and apparel industry, the textile and apparel industry is divided into three categories: textile industry, clothing industry and chemical fiber manufacturing industry. Based on the panel data of the textile and apparel industry from 2010 to 2019, this paper measures green total factor productivity (GTFP) by using the unexpected output super efficiency SBM model and the ML index. On this basis, this paper empirically tests the impact of digital economy on the GTFP of textile and apparel industry, and the dual intermediary effects of rationalization of industrial structure and advanced industrial structure are discussed. The results show that: (1) The GTFP of the textile and apparel industry shows a fluctuating upward trend, but it is in a state of low growth. (2) Digital economy has a significant effect on promoting the GTFP. Among them, it has a positive effect on the improvement of GTFP in textile industry, but has no obvious effect on the clothing industry, and has a restraining effect on the chemical fiber manufacturing industry. (3) In the process of the impact of digital economy on GTFP, the rationalization of industrial structure has a partial intermediary effect, and the level of effect reaches 35.81%, while the advancement of industrial structure does not necessarily have a "structural dividend", and its influence on GTFP is non-linear. This paper enriches the research on the influencing factors of GTFP, and is also an effective supplement to the research on digital economy. The conclusions provide a reliable empirical basis for digital economy to help the textile and apparel industry pollution control, and also provide policy references for giving full play to the green value of digital economy.Study on the influence of COVID-19 on the growth of China’s small and medium-sized construction enterprisesWenbao WangWenhe LinZhenhua BaoXinyi DaiQiaohua Lin10.1371/journal.pone.02663152022-06-03T14:00:00Z2022-06-03T14:00:00Z<p>by Wenbao Wang, Wenhe Lin, Zhenhua Bao, Xinyi Dai, Qiaohua Lin</p>
The outbreak of COVID-19 at the beginning of 2020 had a significant impact on China’s economy, society, and citizens; it also had a negative impact on the development of the construction industry. In particular, small and medium-sized construction enterprises with low ability to withstand risk have been strongly impacted, aggravating a crisis of survival among these firms. The focus of this study is to analyze the impact of COVID-19 on the growth of small and medium-sized construction companies. Based on the characteristics of small and medium-sized construction enterprises, this paper establishes a growth evaluation index and builds a growth evaluation model based on factor analysis. Twenty-three construction enterprises listed on small and medium-sized enterprises board are selected as samples, and the quarterly data of 2019 and 2020 are used for empirical analysis. The results show that the epidemic has had a high short-term impact on construction enterprises, and the total output value of the construction industry in the first quarter of 2020 was 16% lower than that in the same period of last year. In the long run, the impact of the epidemic on the growth of small and medium-sized construction enterprises has been limited. In the first quarter of 2020, the growth score of enterprises decreased by only 1.95% year-over-year, and it was essentially flat in the second and third quarters. The epidemic has had little influence on solvency, tangible resources or intangible resources but a high short term influence on profitability, capital expansion and market expectations. The long-term impact is small; It is conducive to the improvement of enterprise operation ability. Finally, to both address the influence of the COVID-19 on small and medium-sized construction enterprises and to realize their transformation and upgrading, targeted suggestions are offered at the policy and enterprise levels. The results will help to understand the impact of the epidemic on the growth of construction enterprises, and provide decision support for the healthy and orderly development of the follow-up construction industry.Protein degradation sets the fraction of active ribosomes at vanishing growthLudovico CalabreseJacopo GrilliMatteo OsellaChristopher P. KempesMarco Cosentino LagomarsinoLuca Ciandrini10.1371/journal.pcbi.10100592022-05-02T14:00:00Z2022-05-02T14:00:00Z<p>by Ludovico Calabrese, Jacopo Grilli, Matteo Osella, Christopher P. Kempes, Marco Cosentino Lagomarsino, Luca Ciandrini</p>
Growing cells adopt common basic strategies to achieve optimal resource allocation under limited resource availability. Our current understanding of such “growth laws” neglects degradation, assuming that it occurs slowly compared to the cell cycle duration. Here we argue that this assumption cannot hold at slow growth, leading to important consequences. We propose a simple framework showing that at slow growth protein degradation is balanced by a fraction of “maintenance” ribosomes. Consequently, active ribosomes do not drop to zero at vanishing growth, but as growth rate diminishes, an increasing fraction of active ribosomes performs maintenance. Through a detailed analysis of compiled data, we show that the predictions of this model agree with data from <i>E. coli</i> and <i>S. cerevisiae</i>. Intriguingly, we also find that protein degradation increases at slow growth, which we interpret as a consequence of active waste management and/or recycling. Our results highlight protein turnover as an underrated factor for our understanding of growth laws across kingdoms.3D engineered human gingiva fabricated with electrospun collagen scaffolds provides a platform for <i>in vitro</i> analysis of gingival seal to abutment materialsWichurat SakulpaptongIsabelle A. ClairmonteBritani N. BlackstoneBinnaz LeblebiciogluHeather M. Powell10.1371/journal.pone.02630832022-02-03T14:00:00Z2022-02-03T14:00:00Z<p>by Wichurat Sakulpaptong, Isabelle A. Clairmonte, Britani N. Blackstone, Binnaz Leblebicioglu, Heather M. Powell</p>
In order to advance models of human oral mucosa towards routine use, these models must faithfully mimic the native tissue structure while also being scalable and cost efficient. The goal of this study was to develop a low-cost, keratinized human gingival model with high fidelity to human attached gingiva and demonstrate its utility for studying the implant-tissue interface. Primary human gingival fibroblasts (HGF) and keratinocytes (HGK) were isolated from clinically healthy gingival biopsies. Four matrices, electrospun collagen (ES), decellularized dermis (DD), type I collagen gels (Gel) and released type I collagen gels (Gel-R)) were tested to engineer lamina propria and gingiva. HGF viability was similar in all matrices except for Gel-R, which was significantly decreased. Cell penetration was largely limited to the top layers of all matrices. Histomorphometrically, engineered human gingiva was found to have similar appearance to the native normal human gingiva except absence of rete pegs. Immunohistochemical staining for cell phenotype, differentiation and extracellular matrix composition and organization within 3D engineered gingiva made with electrospun collagen was mostly in agreement with normal gingival tissue staining. Additionally, five types of dental material posts (5-mm diameter x 3-mm height) with different surface characteristics were used [machined titanium, SLA (sandblasted-acid etched) titanium, TiN-coated (titanium nitride-coated) titanium, ceramic, and PEEK (Polyetheretherketone) to investigate peri-implant soft tissue attachment studied by histology and SEM. Engineered epithelial and stromal tissue migration to the implant-gingival tissue interface was observed in machined, SLA, ceramic, and PEEK groups, while TiN was lacking attachment. Taken together, the results suggest that electrospun collagen scaffolds provide a scalable, reproducible and cost-effective lamina propria and 3D engineered gingiva that can be used to explore biomaterial-soft tissue interface.Heat transfer analysis of the mixed convective flow of magnetohydrodynamic hybrid nanofluid past a stretching sheet with velocity and thermal slip conditionsMuhammad RamzanAbdullah DawarAnwar SaeedPoom KumamWiboonsak WatthayuWiyada Kumam10.1371/journal.pone.02608542021-12-14T14:00:00Z2021-12-14T14:00:00Z<p>by Muhammad Ramzan, Abdullah Dawar, Anwar Saeed, Poom Kumam, Wiboonsak Watthayu, Wiyada Kumam</p>
The present study is related to the analytical investigation of the magnetohydrodynamic flow of <i>Ag</i> − <i>MgO</i>/ water hybrid nanoliquid with slip conditions via an extending surface. The thermal radiation and Joule heating effects are incorporated within the existing hybrid nanofluid model. The system of higher-order partial differential equations is converted to the nonlinear system of ordinary differential equations by interpreting the similarity transformations. With the implementation of a strong analytical method called HAM, the solution of resulting higher-order ordinary differential equations is obtained. The results of the skin friction coefficient, Nusselt number, velocity profile, and temperature profile of the hybrid nanofluid for varying different flow parameters are attained in the form of graphs and tables. Some important outcomes showed that the Nusselt number and skin friction are increased with the enhancement in Eckert number, stretching parameter, heat generation parameter and radiation parameter for both slip and no-slip conditions. The thermal profile of the hybrid nanofluid is higher for suction effect but lower for Eckert number, stretching parameter, magnetic field, heat generation and radiation parameter. For both slip and no-slip conditions, the hybrid nanofluid velocity shows an upward trend for both the stretching and mixed convection parameters.A heijunka study for the production of standard parts included in a customized finished productPaulina RewersJacek Diakun10.1371/journal.pone.02605152021-12-02T14:00:00Z2021-12-02T14:00:00Z<p>by Paulina Rewers, Jacek Diakun</p>
Efficient order execution plays a crucial role in the activity of every company. In production planning it is important to find a balance between the fluctuations of orders and stability of production flow regarding the company. One of the methods of achieving this goal is heijunka (production leveling). This paper presents a study of choosing the best variant of the production planning and control system for the production of standard parts. Three variants are investigated regarding delays in order delivery. The analysis of variants was conducted using a simulation method. The method of choosing the best variant for the production system being investigated is also proposed. The results show that the best variant is a mix of production leveling and production "for stock".Area efficient camouflaging technique for securing IC reverse engineeringMd. Liakot AliMd. Ismail HossainFakir Sharif Hossain10.1371/journal.pone.02576792021-11-04T14:00:00Z2021-11-04T14:00:00Z<p>by Md. Liakot Ali, Md. Ismail Hossain, Fakir Sharif Hossain</p>
Reverse engineering is a burning issue in Integrated Circuit (IC) design and manufacturing. In the semiconductor industry, it results in a revenue loss of billions of dollars every year. In this work, an area efficient, high-performance IC camouflaging technique is proposed at the physical design level to combat the integrated circuit’s reverse engineering. An attacker may not identify various logic gates in the layout due to similar image output. In addition, a dummy or true contact-based technique is implemented for optimum outcomes. A library of gates is proposed that contains the various camouflaged primitive gates developed by a combination of using the metal routing technique along with the dummy contact technique. This work shows the superiority of the proposed technique’s performance matrix with those of existing works regarding resource burden, area, and delay. The proposed library is expected to make open source to help ASIC designers secure IC design and save colossal revenue loss.