Factory Settings Research Articles

Energy-efficient distributed permutation flow shop scheduling problem using a multi-objective whale swarm algorithm

Abstract Production scheduling is of great significance in improving production effectiveness while the energy-efficient problem is one of most concerned problems for researchers and manufacturers. Thus, this study investigates the energy-efficient distributed permutation flow shop scheduling problem (DPFSP) with the objectives of makespan and energy consumption. The DPFSP is an extension of permutation flow shop problem (PFSP) considering a set of identical factories. This paper presents a multi-objective mixed integer programming model based on the three sub-problems: allocating jobs among factories, scheduling the jobs in each factory and determining speed upon each job. A multi-objective whale swarm algorithm (MOWSA) is proposed to solve this energy-efficient DPFSP. A new problem-dependent local search is developed to improve the exploitation capability of MOWSA. Moreover, the updating exploitation mechanism is presented to enhance energy efficiency without affecting production efficiency. Finally, the extensive comparison experiments are designed to demonstrate the effectiveness of proposed MOWSA, problem-dependent local search and updating exploitation mechanism. The results indicate the effectiveness of MOWSA and the superior performance over NSGA-II, SPEA2, PAES and MDEA, and also demonstrate that the proposed algorithm can significantly reduce the energy consumption compared with other algorithms.

Giberellik Asit ve Salisilik Asit Uygulamalarının Tuz Stresi Altında Yetiştirilen Kolzanın (Brassica napus L.) Bazı Fiziksel ve Kimyasal Özellikleri Üzerine Etkileri

Tarımsal üretimde, bitki büyümesi ve verimi üzerindeki sınırlayıcı faktörlerden biri toprak tuzluluğudur. Bitki büyüme düzenleyiciler, tuz stresinin olumsuz etkilerini azaltmada büyük öneme sahiptir. Bu çalışma, tuzlu koşullar altında salisilik asit (SA) ve giberellik asidin (GA3) kolzadaki (Brassica napus L.) büyüme ve bazı fizyolojik karakterlere etkilerini belirlemek amacıyla sera şartlarında yürütülmüştür. Bu amaçla, tesadüf parsellerinde faktöriyel deneme desenine göre üç SA (0, 0,5 ve 1 mM) ve GA3 (0, 50 ve 100 mg l-1) ile dört NaCl (0, 50, 100 ve 150 mM) dozu uygulanarak üç tekerrürlü olarak kurulmuştur. Çalışmada bitki boyu, yan dal sayısı, yaprak alanı, yaprak sayısı, elektrolit sızıntısı, klorofil içeriği ve yaprak turgoru kaybı incelenmiştir. Tuz uygulamaları, kontrol ile karşılaştırıldığında dikkate alınan parametrelerin tümünü önemli ölçüde azaltmıştır. Tuzluluk, kontrol bitkilerine kıyasla, bitki boyu ve yaprak alanında sırasıyla %63 ve %67 oranında önemli bir azalmaya neden olmuştur. Diğer yandan, elektrolit sızıntısı, NaCl seviyelerinin artmasıyla önemli düzeyde (altı kat) artmıştır. Giberellik asit bitki boyu önemli ölçüde arttırmış ve diğer parametrelerde önemli bir değişim olmaksızın yaprak turgorunun kaybını azaltmıştır. Sonuçlar SA ve GA3'ün tuzluluk stresine maruz kalan bitkilere uygulanmasının kolzanın vejetatif büyümesini önemli ölçüde etkilemediğini göstermiştir. Bununla birlikte, GA3 tuzluluğun olumsuz etkisinde kısmi bir azalmaya neden olmuştur.

Numerical simulation of the effect of crater morphology for the prediction of surface roughness on electrical discharge textured Ti6Al4V

Extensive efforts (mathematical, numerical, and analytical) have been earlier tried to replicate the EDM process in its true nature. In the present work, the surface roughness (Ra) of the electrical discharge textured Ti6Al4V has got mathematically modeled. The parametric influence on the morphology of the developed craters got elucidated at distinct process conditions. The influential factors, viz. current (I, 2–10 A) and pulse on time (Ton, 100–300 μs), were varied to conduct a full factorial set of simulations and experimentations, keeping voltage (V, 30 V) as a constant. The I and V possessed dominant influence for material removal along the radial and axial directions (respectively), whereas the impact of Ton remained uniform in both the directions. Besides, two distinct considerations (based on the positioning of multi-overlapping craters) for prediction of Ra got proposed; the secondary craters superimposing with the primary along its circumference were found to fetch convincing results [with a maximum possible error (average) of 16%]. Simulation and experimentation results were compared using Ra data, crater morphological images, and radius to depth ratio (Rrd) plots. The outcomes were in close concordance with acceptable tolerances. The variation got attributed to the uncontrollable/disregarded factors (formation of recast layer and short circuit ensuing debris interference), prevalent in the case of in situ work environments.

Comparison of pulsed light inactivation kinetics and modeling of Escherichia coli (ATCC-29055), Clostridium sporogenes (ATCC-7955) and Geobacillus stearothermophilus (ATCC-10149)

Pulsed light (PL) inactivation kinetics of Escherichia coli K-12, Clostridium sporogenes and Geobacillus stearothermophilus were evaluated under different treatment conditions. The PL system was factory set to operate at three pulses per second with a pulse width of 360 μs exposing samples placed on one of the 9 trays on a rack. Two PL parameters were evaluated in the study: number of pulses (a time factor) and the tray position (a spatial distance factor) both influencing the amount of light energy absorbed. As expected, the level of microbial inactivation increased with an increase in the number of pulses (from 1 to 15) and decreased with an increase in the Spatial distance (Tray # 1 to 9) away from the light source. Both the number of pulses and spatial distance as well as their interactions were found to have a significant effect (P < 0.05) on the extent of microbial inactivation. Vegetative cells of E. coli were most sensitive to PL treatment with a maximum 5 logarithmic reductions on Tray 1 after a 12-pulse treatment (4 s). G. stearothermophilus was more resistant to PL than C. sporogenes. Overall, the PL treatments (12–15 pulses) achieved a minimum four logarithmic reductions in the populations of all three microorganisms on the top tray at doses still below 12 J/cm2, the FDA-approved limit.

Discrete ordinate and P1-based approximations of heater transparency on radiation-convection of four separate gases in factory setting

Industrial ventilation and heat dissipation time are important factors due to high temperature and emissivity coefficients of heat source conducive to radiation and convection produced indoors. The main aim of this study was to investigate the separate effects of four different gases (air; nitrogen, N2; carbon dioxide, CO2; and water vapor) on radiation/convection in an industrial complex with a heat source. The heat source surface is opaque or semi-transparent and two radiation models, P1 and discrete ordinate (DO), are compared in different Grashof numbers (Gr), ranging from 108 to 1011. Surface transparency has the most and the least effects on CO2 and water vapor, respectively. Water vapor has efficient natural ventilation at all heater lengths. High absorption coefficient (ap) at high Gr prevents dampening effects of radiative relative to convective parameters. The radiation model type for precise numerical simulation is indispensable with ap reduction. The opaque surface is most efficient to transfer energy only in the temperature distribution specific area, while the semi-transparent surface concentrates on temperature rise around the heater itself. The Nusselt of radiation (Nur) in P1 is more than ^DO model for all gases. Incident radiation for CO2 and water vapor follows a complete descending trend with progressive distance from heater, while a peakand trough trend is observed for N2 and air.

Plant Growth Nullifies the Effect of Increased Water‐Use Efficiency on Streamflow Under Elevated CO2 in the Southeastern United States

AbstractPlant response to elevated CO2 concentration is known to increase leaf‐level water‐use efficiency through a reduction in stomatal opening. Recent studies have emphasized that increased plant water‐use efficiency can ameliorate the impact of drought due to climate change. However, there is a potentially counterbalancing impact due to the increased leaf area. We investigate long‐term trends (1951 to 2015) of observed streamflow in the Southeastern United States (SE US) and quantify the contribution of major drivers of streamflow changes using single factor climate modeling experiments from Community Land Model Version 5 (CLM5). The SE US streamflow observations do not exhibit a trend, which is in agreement with the CLM5 control experiment. Using the factorial set of CLM5 experiments, we find that increased leaf area under elevated CO2 leads to decreased runoff and completely counteracts increased runoff due to water‐use efficiency gains under elevated CO2 and land‐use change.

Experimental analysis on solar powered mobile robot as the prototype for environmentally friendly automated transportation

An unmanned vehicle can be deployed in a dull area such as a repetitive task of a transport vehicle in a factory setting which can be considered as automated transportation. The current problem faced by the transportation sectors are the reduction of fossil fuel availability, therefore, solar-powered automated transport vehicle is a great alternative. This paper discussed the experimental analysis of solar-powered mobile robot as the prototype for environmentally friendly automated transportation. The solar cell output is used to charge the capacitor banks functioning as the substitute for a battery system. The robot moves from one station to another imitating the stations in a factory scenario. The solar-powered robot is equipped with a voltage sensor to ensure efficient charging time. The charging time was conducted in 5 days to show the charging fluctuation affected by external factors such as weather condition. The highest average charging in a day is 4.96 V during 100.687 Lux of irradiance received by the solar cell. The highest power required during the loaded task is 0.0121 W, and during unloaded is 0.0106 W. The experiment results show that the proposed method is effective for environmentally friendly automated transportation.

Optimizing the u411 automated urinalysis instrument for veterinary use

The Cobas u411 Analyzer (Roche Diagnostics) is an automated, reflectance photometry-based urinalysis instrument designed for use with Roche's CHEMSTRIP 10UA technology and human urine samples. We aimed to optimize and validate the Cobas u411 Analyzer for use in canine and feline urinalysis. Patient urine samples presenting to the Clinical Pathology Laboratory at the Colorado State University Veterinary Teaching Hospital were analyzed with the Cobas u411 and by manual readings in parallel. Initially, 223 canine and 83 feline urine samples were run using the u411 factory settings. Following comparisons with manual results, and evaluation for directional bias, adjustments to the reflectance values were made in the instrument's programming. An additional 183 canine and 95 feline samples were run using the adjusted settings. Total urine protein concentrations were measured in 48 samples and used to generate receiver operating characteristic curves for the protein test pad. Following adjustments in reflectance programming, concordance between u411 and manual results was increased by 17.7% for protein, 11.7% for ketones, and 4.5% for bilirubin. Concordances for pH, glucose, and blood were not substantially changed. Discordance for all analytes was ≤3%. Canine and feline samples had similar levels of discordance, though marginal concordance was higher in dogs for ketones, bilirubin, and blood. Adjustments to the reflectance programming of the Cobas u411 Analyzer improved concordance with manual results for canine and feline samples. This instrument has the potential to greatly increase both efficiency and consistency of urinalysis procedures in higher throughput veterinary diagnostic laboratories.

Modeling and multi-neighborhood iterated greedy algorithm for distributed hybrid flow shop scheduling problem

As economic globalization, large manufacturing enterprises build production centers in different places to maximize profit. Therefore, scheduling problems among multiple production centers should be considered. This paper studies a distributed hybrid flow shop scheduling problem (DHFSP) with makespan criterion, which combines the characteristic of distributed flow shop scheduling and parallel machine scheduling. In the DHFSP, a set of jobs are assigned into a set of identical factories to process. Each job needs to be through same route with a set of stages, and each stage has several machines in parallel and at least one of stage has more than one machine. For solving the DHFSP, this paper proposes two algorithms: DNEH with smallest-medium rule and multi-neighborhood iterated greedy algorithm. The DNEH with smallest-medium rule constructive heuristic first generates a seed sequence by decomposition and smallest-medium rule, and then uses a greedy iteration to assign jobs to factories. In the iterated greedy algorithm, a multi-search construction is proposed, which applies the greedy insertion to the factory again after inserting a new job. Then, a multi-neighborhood local search is utilized to enhance local search ability. The proposed algorithms are evaluated by a comprehensive comparison, and the experimental results demonstrate that the proposed algorithms are very competitive for solving the DHFSP.

ПРОФЕССИОНАЛЬНЫЕ ТИПЫ И ЦЕННОСТНЫЕ ОРИЕНТАЦИИ ЛИЧНОСТИ

The basic method is applied to the study of the typology of personal values. A basis is a complete, ordered, measurable set of elements. In our case, the elements are personality types. Completeness is determined by a factorial dichotomous typological set. Orderliness is determined by a single dichotomous multi-dimensional basic word structure of a set of types (a single dichotomous multi-dimensional-typological basis or «word basis»). The measurability of types is associated with a single dichotomous numerical multidimensional basic evaluation symmetric scale or with a «numerical basis». Completeness is related to the traditional dichotomous factor analysis of typological information (it resembles the factor strategy of Kettell in his typology of personality qualities). Orderliness sets a dichotomous basic multidimensionality in verbal typological information. Measurability makes it possible to create basic multidimensional methods based on a dichotomous symmetric numerical scale estimation of types. Multidimensional measuring typological basis of personality is a combination of several one-dimensional measuring typological bases with a single personal dichotomy «social-individual». In typology of information about the values of the personality is possible to allocate three levels of consistency. The first level of consistency corresponds to the traditional chaotic-eclectic dichotomous factor representation of information about personal values. The second level of consistency corresponds to functional structural coherence in the verbal dichotomous multidimensional basic regulation of the types of values of the individual. The third level of consistency corresponds to the project-dimensional dichotomous multidimensional basic implementation of the estimated-numerical measurement of the types of values of the individual. This research strategy is related to all three levels of systemicity, it is the basis of our dichotomous factor multidimensional base measurement typological concept, and it defines the strategy and stages of this research. In this article, the typology of personal values is checked for «basicity». For this purpose, based on the principle of semantic proximity, one-to-one correspondence («typological isomorphism») is established between the typology of personal values and professional personality types according to D. Holland. The article uses the method of «basic superimposition» of these two typologies, which provides a semantic community of these two typologies and generates such a psychological concept as «professional values».

ХАРАКТЕРИСТИКИ ПОДСТАНОВОК ФАКТОРИАЛЬНЫХ МНОЖЕСТВ И КРИТЕРИИ ВЫБОРА ОДИНОЧНЫХ ПОДСТАНОВОК

The substitution and permutation function, which are presented in the article as elements of a number of factorial sets, are the key functions of cryptographic systems that provide diffusion and mixing of information. A new scale of notation is proposed while analyzing this problem. This is the notation scale of a number of factorial sets. This scale of notation helps to index the elements of a number of factorial sets and establish a one-to-one correspondence between the number and a specific type of substitution. This allows analyzing substitutions characteristics systematically. This paper presents the basic concepts of a number of the factorial sets. It is noted that the permutations of the factorial sets form symmetric permutation groups, and specific permutations (when raised to a power) form cyclic groups. The group axioms are fulfilled for the permutations of a number of factorial sets. Also, the definition domain, the group operation of multiplication, and identical and inverse substitutions are given for them. The number of independent cycles, decrement, inverse, parity and sign are common characteristics of the substitutions of a number of factorial sets. The criteria for choosing single substitutions with the best characteristics are proposed.

Effects of working years in cold environment on the musculoskeletal system and carpal tunnel symptoms.

Musculoskeletal disorders are the most common work-related health problems. The risk of musculoskeletal disorders is increased by working in a cold environment. The present study aims to investigate the effects of working years on the body. We examined the effects of the working years on the musculoskeletal system and carpal tunnel syndrome symptoms of the individuals who were engaged in cutting, shredding, packaging, bagging and transportation operations at 9 degrees and lower ambient temperatures in a poultry factory in Turkey. Nordic Musculoskeletal Questionnaire (NMQ) was used to evaluate musculoskeletal symptoms of the individuals. Boston Carpal Tunnel Syndrome Questionnaire (BCTQ) was used to investigate the carpal tunnel symptoms. Individuals grouped according to their total working years in a cold factory setting. Groups were formed as less than two years, between two and five years, between six and nine years, and ten years or more. This study showed that the increase in working years in a cold factory setting might lead to a feeling of pain and discomfort in various regions of the body, disrupt ordinary works due to the pain felt (p<0.05). This study also showed that the severity of the symptoms felt because carpal tunnel syndrome worsened, especially when an individual has been working for more than ten years in cold. Working in the cold for a long time may result in long-term health effects on the human body.

Teaching maintenance plan development in a learning factory environment

The use of learning factories for education in maintenance concepts is limited, despite the important role maintenance plays in the effective operation of organizational assets. A training programme in a learning factory environment is presented where a combination of gamification, classroom training and learning factory applications was used to introduce students to the concepts of maintenance plan development, asset failure characteristics and the costs associated with maintenance decision-making. The programme included a practical task to develop a maintenance plan for different advanced manufacturing machines in a learning factory setting. The programme stretched over a four-day period and demonstrated how learning factories can be effectively utilized to teach management related concepts in an interdisciplinary team context, where participants had no, or very limited, previous exposure to these concepts.

Added value of a virtual approach to simulation-based learning in a manufacturing learning factory

More and more learning factories (LF) are set up supporting the vision of Industry 4.0 connectivity and automation levels; thus, digital twins, virtual and augmented reality are emerging tools. Literature review is the basis for the discourse on possible constraints and opportunities these tools have for the cognitive learning processes in a simulation. State of the art give insight in the lack of research on value of digital learning activities in learning factory setting. This paper is a concept description, giving input to the community on aspects to be considered regarding the use VR/AR/digital twins in a learning factory context.

Understanding the Relationship Between Real-Time Thermal Imaging and Thermal Damage Estimate During Magnetic Resonance–Guided Laser Interstitial Thermal Therapy

Magnetic resonance-guided laser interstitial thermal therapy is a minimally invasive procedure that uses intraoperative magnetic resonance thermometry (MRT) to generate a thermal damage estimate (TDE) of the ablative area. This study aimed to compare areas produced by the MRT heat map with the system-generated TDE produced by Visualase software. All ablations were performed using the Visualase laser ablation system. MRT heat map and TDE were quantified using MATLAB version R2014a. TDE was compared with the summed area of green, yellow, and red areas (heat map 63.9 [HM63.9]) and the summed area of light blue, green, yellow, and red areas (heat map 50.4 [HM50.4]) produced by the MRT heat map. Fifty-six patients undergoing magnetic resonance-guided laser interstitial thermal therapy were examined. Mean TDE produced was 236 mm2 (SEM= 9.5). Mean HM63.9 was 231 mm2 (SEM= 8.7), and mean HM50.4 was 370 mm2 (SEM= 12.8). There was no significant difference between TDE and HM63.9 (P= 0.51). There was a significant difference between TDE and HM50.4 (P<0.001) and between HM63.9 and HM50.4 (P < 0.001). The system-generated TDE consistently remains contained within the boundaries of the MRT heat map. At standard factory settings, TDE and the area produced within the periphery of HM63.9 are similar in magnitude. The light blue portion of the MRT heat map may serve as an additional means of predicting when critical structures may be at risk during laser ablation if exposed to further thermal stress.

The effects of organic and inorganic phosphorus amendments on the biochemical attributes and active microbial population of agriculture podzols following silage corn cultivation in boreal climate

Phosphorus (P) is the second most important macronutrient that limits the plant growth, development and productivity. Inorganic P fertilization in podzol soils predominantly bound with aluminum and iron, thereby reducing its availability to crop plants. Dairy manure (DM) amendment to agricultural soils can improve physiochemical properties, nutrient cycling through enhanced enzyme and soil microbial activities leading to improved P bioavailability to crops. We hypothesized that DM amendment in podzol soil will improve biochemical attributes and microbial community and abundance in silage corn cropping system under boreal climate. We evaluated the effects of organic and inorganic P amendments on soil biochemical attributes and abundance in podzol soil under boreal climate. Additionally, biochemical attributes and microbial population and abundance under short-term silage corn monocropping system was also investigated. Experimental treatments were [P0 (control); P1: DM with high P2O5; P2: DM with low P2O5; P3: inorganic P and five silage-corn genotypes (Fusion RR, Yukon R, A4177G3RIB, DKC 23-17RIB and DKC 26-28RIB) were laid out in a randomized complete block design in factorial settings with three replications. Results showed that P1 treatment increased acid phosphatase (AP-ase) activity (29% and 44%), and soil available P (SAP) (60% and 39%) compared to control treatment, during 2016 and 2017, respectively. Additionally, P1 treatments significantly increased total bacterial phospholipids fatty acids (ΣB-PLFA), total phospholipids fatty acids (ΣPLFA), fungi, and eukaryotes compared to control and inorganic P. Yukon R and DKC 26-28RIB genotypes exhibited higher total bacterial PLFA, fungi, and total PLFA in their rhizospheres compared to the other genotypes. Redundancy analyses showed promising association between P1 and P2 amendment, biochemical attributes and active microbial population and Yukon R and DKC 26-28RIB genotypes. Pearson correlation also demonstrated significant and positive correlation between AP-ase, SAP and gram negative bacteria (G−), fungi, ΣB-PLFA, and total PLFA. Study results demonstrated that P1 treatment enhanced biochemical attributes, active microbial community composition and abundance and forage production of silage corn. Results further demonstrated higher active microbial population and abundance in rhizosphere of Yukon R and DKC 26-28RIB genotypes. Therefore, we argue that dairy manure amendment with high P2O5 in podzol soils could be a sustainable nutrient source to enhance soil quality, health and forage production of silage corn. Yukon R and DKC 26-28RIB genotypes showed superior agronomic performance, therefore, could be good fit under boreal climatic conditions.

A novel mathematical optimisation model for the scheduling of activities in modular construction factories

Modular construction has been reported to lead to a number of advantages when contrasted with conventional construction approaches, including time and cost savings, along with reduced environmental impacts. Given that the operations involved in modular construction take place within a controlled environment, an integral factor that impacts the productivity of works involved is the timely scheduling of the associated activities. Unlike conventional on-site construction methods, operations in modular construction take place in a sequential and linear manner, with large dependence on adequate resource allocation to specific workstations. In a bid to address the lack of mathematical optimisation methods that target the scheduling of operations in a modular factory setting, this paper proposes a novel mixed integer non-linear programming model for optimising the scheduling of modular construction activities on the shop floor of modular factories. The impact of scheduling of resources, such as overhead cranes, and the distance between stations to which activities are scheduled are factored in the proposed method. The solution approach presented for solving the scheduling problem involves the linearisation of the model to enable the use of efficient off-the-shelf solvers. A realistic case study is implemented, and an extensive computational experiment is also conducted to test the robustness and tractability of the proposed method. The performance of the model is also contrasted with a commonly adopted heuristic, with the model producing project durations reductions that are 43% less than that of the heuristic.

Wearable cognitive assistants in a factory setting: a critical review of a promising way of enhancing cognitive performance and well-being

Rapid technological innovations are constantly influencing the complexification and automatization of the work lines pushing human operators to use diverse cognitive processes for supervising complex industrial machines. This urges factories to offer wearable cognitive assistants to human operators to analyze, integrate and maintain a considerable amount of information. The aim of this review is twofold. First, we borrow theoretical elements from the working memory literature to question the way these wearable cognitive assistants could optimize human operators’ cognitive load. Second, we argue that Technology Acceptance Model (TAM) and Job Characteristics Model (JCM) may theoretically predict the effectiveness of cognitive wearable assistants in enhancing the person–job fit, namely their cognitive performance and well-being. A critical review method was used to collect and summarize the most studied models associated with application of wearable devices in the workplace. Our review suggests that the current literature on working memory give useful insights concerning the way in which information should be displayed to operators to optimize the efficiency of wearable cognitive assistants. Moreover, JCM suggests original explanations on the way they can facilitate access to information and in turn increase job satisfaction. Finally, a small number of studies that used TAM with wearable devices in an industrial setting provide some interesting theoretical and empirical evidence on the acceptance of wearable cognitive assistants. As a conclusion, we argue that using wearable cognitive assistants properly would enhance both cognitive performance and well-being of human operators through promoting the person–job fit.

Effects of Silicon and Zinc Nanoparticles on Growth, Yield, and Biochemical Characteristics of Rice

Silicon (Si) and Zn are beneficial for improving plant growth and human health. Fortifying rice (Oryza sativa L.) with Si and Zn can correct deficiencies of these elements in humans who consume rice. The present study evaluated the effects of different Si and Zn application forms as nanoparticles (NPs) foliar application and soil application (traditional fertilizers) on agronomic performance, grain yield (GY), Si and Zn accumulation, and protein content in rice tissue. The experiment was performed as a randomized complete block design with a factorial set of treatments that included three Si treatments (0, soil‐Si, nano‐Si) and three Zn treatments (0, soil‐Zn, nano‐Zn) with three replicates in two experimental farms (Mazandaran, Iran). The results indicated that Si and Zn applications by both NPs (300 g ha−1) and soil application (9 kg Zn ha−1 and 392 kg Si ha−1) ameliorated the yield components, yield, and nutrient accumulation in rice plant tissue. Application of nano‐Zn, nano‐Si, soil‐Zn, and soil‐Si significantly increased GY by 12.6, 9.5, 9.2, and 6.9%, respectively, above the control. Application of Si and Zn through NPs had greater effects than soil form for some experimental parameters, such as fortification of rice grains. Overall, our results suggest that Si and Zn applications as NPs could increase GY, reduce fertilizer costs and environmental pollution, and enrich rice grains with Si and Zn through improving agronomic and physiological traits, leading to higher GY and nutrients accumulation in grain.Core Ideas Application of Si and Zn increased the agronomic parameters and grain yield. There was no significant interaction among Si and Zn on grain yield. The nano‐Zn foliar spray had better effects than soil‐Zn application for grain Zn accumulation.

Studies to Predict Maintenance Time Duration and Important Factors From Maintenance Workorder Data

Maintenance Work Orders (MWOs) are a useful way ofrecording semi-structured information regarding maintenanceactivities in a factory or other industrial setting. Analysisof these MWOs could provide valuable insights regardingthe many facets of reliability, maintenance, and planning.Information such as which maintenance activities consumethe most work hours, identification of problem machines,and spare parts needs can all be inferred to some degreefrom well documented MWOs. However, before one canderive insights, it is first necessary to transform the data inthe MWOs (generally some form of natural language) intosomething more suitable for computer analysis. NIST previouslydeveloped a computer aided tagging system that allowsfor the quick identification of key concepts within the naturallanguage of the MWOs, and a protocol for categorizingthese concepts as solutions, problems, or items. Using thisannotation method, this paper investigates machine learningmethods to gain insights about work hours needed for variousmaintenance activities. Through these methods, it ispossible to explain the factors captured in the MWOs thathave the strongest relationship with the duration of maintenanceactions. The workflow of this research is to firstbuild strong data driven models to classify the duration ofany maintenance activity based on the language and conceptsgathered from the associated MWO. Sensitivity analysis ofthe inputs to these classifiers can then be used to determinerelationships and factors influencing maintenance activities.This paper investigates two machine learning models - aneural network classifier and a decision tree classifier. Inputfeatures for the classifier were the annotated concept tags for solutions, problems and items derived from MWOs of anactual manufacturer. This process for gaining insights can begeneralized to various applications in the maintenance andPHM communities.