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Abstract The job shop scheduling problem (JSSP) is critical for building one smart factory regarding resource management, effective production, and intelligent supply. However, it is still very challenging due to the complex production environment. Besides, most current research only focuses on classical JSSP, while flexible JSSP (FJSSP) is more usual. This article proposes an effective method, GAILS, to deal with JSSP and FJSSP based on genetic algorithm (GA) and iterative local search (ILS). GA is used to find the approximate global solution for the JSSP instance. Each instance was encoded into machine and subtask sequences. The corresponding machine and subtasks chromosome could be obtained through serval-time gene selection, crossover, and mutation. Moreover, multi-objects, including makespan, average utilization ratio, and maximum loading, are used to choose the best chromosome to guide ILS to explore the best local path. Therefore, the proposed method has an excellent search capacity and could balance globality and diversity. To verify the proposed method's effectiveness, the authors compared it with some state-of-the-art methods on sixty-six public JSSP and FJSSP instances. The comparative analysis confirmed the proposed method's effectiveness for classical JSSP and FJSSP in makespan, average utilization ratio, and maximum loading. Primarily, it obtains optimal-like solutions for several instances and outperforms others in most instances.

We present research on the role of multiple quantum well periods in extended short-wavelength infrared InGaAs/InAsPSb type-I LEDs. The fabricated LEDs consisted of 6, 15, and 30 quantum well periods, and we evaluated the structural properties and device performance through a combination of theoretical simulations and experimental characterization. The strain and energy band offset was precisely controlled by carefully adjusting the composition of the InAsPSb quaternary material, achieving high valence and conduction band offsets of 350 meV and 94 meV, respectively. Our LEDs demonstrated a high degree of relaxation of 94–96 %. Additionally, we discovered that the temperature-dependent dark current characterization attributed to generation-recombination and trap-assign tunneling, with trap-assign tunneling being more dominant at lower current injections. Electroluminescence analysis revealed that the predominant emission mechanism of the LEDs originated from localized exciton and free exciton radiative recombination, which the 30 quantum wells LED exhibited the highest contribution of the localized exciton/free exciton radiative recombination. We observed that increasing the quantum well periods from 6 to 15 led to an increase in the 300 K electroluminescence intensity of the LED. However, extending the quantum well period to 30 resulted in a decline in emission intensity due to the degradation of the epitaxial film quality.

Manufacturing is a broad field with different types of production processes. Therefore, specific processes can accommodate multiple parallel machines operating simultaneously in some production environments. This assumption is particularly crucial in factory scheduling for industries such as textile, aircraft, and semiconductor manufacturing. The assumption proposed in this paper is differ from FJSP in that they are closer to the real world by allowing different machines to perform the same multiple processes. In this paper, a new approach for solving the flexible job shop scheduling problem has been proposed, which is referred to as the flexible job shop scheduling problem in a parallel machine environment with a variable group. We proposed a hybrid genetic algorithm-based variable neighborhood search algorithm (GA-VNS) to solve this problem, where the multiple parallel machines can operate the same operations simultaneously and are grouped as a variable group. The GA-VNS algorithm combines the global searching ability of GA with the local searching ability of VNS to fully reflect the condition of the parallel machines with variable groups. The objective functions of the algorithm are to minimize the production makespan and to improve facility usage. Multiple simulation experiments are conducted using forty-seven test instances to assess the feasibility and effectiveness of the new approach. This study is expected to reduce processing time and production costs for Small and Medium Enterprises (SMEs) with low cost and high efficiency compared to existing systems, such as the manufacturing execution system, enterprise resource planning, and job shop scheduling system.

This manuscript proposed an effective hybrid method based on multi-level convolutional neural network (ML-CNN) and iterative local search (ILS) to solve job shop scheduling problems (JSSP) with small scale training samples and less time. In the proposed method, ML-CNN is proposed to find the global path of JSSP instance from the genetic algorithm (GA); ML-CNN learned global path is fed into ILS to search for the best local path, it is the key to attach excellent adaptivity. In order to find the global path from optimal solutions, the proposed ML-CNN treated the JSSP as some sub-classification tasks first. Each subclass corresponding to one suboperation prioritizes a particular machine according to the production environment. Significantly, the proposed ML-CNN designed two level inputs to represent production statutes. The detailed-level input channel records fourteen detailed statutes, while the system-level input channel records four systematic statutes. Two level inputs are fed into one dimensional (1-D) CNN to extract rich hidden features to predict the priority of each suboperation using a support vector machine (SVM) classifier. At last, the global path (priority sequences) could be obtained, which is encoded as the input of ILS to find the best local path. The authors trained and tested the proposed method on 82 public JSSP instances. The results indicated that the proposed method could obtain optimal solutions for small scale instances and outperform others regarding makespan and computation time for large scale JSSP instances.

Photoacoustic imaging is limited by a lack of contrast agents which can enable combined molecular and physiological imaging at depth. Here the authors address these limitations by developing and validating a contrast agent based on targeted liposomes loaded with J-aggregated indocyanine green dye.

Abstract Background The nucleotide-binding site–leucine-rich repeat (NBS-LRR) genes are important for plant development and disease resistance. Although genome-wide studies of NBS-encoding genes have been performed in several species, the evolution, structure, expression, and function of these genes remain unknown in radish (Raphanus sativus L.). A recently released draft R. sativus L. reference genome has facilitated the genome-wide identification and characterization of NBS-encoding genes in radish. Results A total of 225 NBS-encoding genes were identified in the radish genome based on the essential NB-ARC domain through HMM search and Pfam database, with 202 mapped onto nine chromosomes and the remaining 23 localized on different scaffolds. According to a gene structure analysis, we identified 99 NBS-LRR-type genes and 126 partial NBS-encoding genes. Additionally, 80 and 19 genes respectively encoded an N-terminal Toll/interleukin-like domain and a coiled-coil domain. Furthermore, 72% of the 202 NBS-encoding genes were grouped in 48 clusters distributed in 24 crucifer blocks on chromosomes. The U block on chromosomes R02, R04, and R08 had the most NBS-encoding genes (48), followed by the R (24), D (23), E (23), and F (17) blocks. These clusters were mostly homogeneous, containing NBS-encoding genes derived from a recent common ancestor. Tandem (15 events) and segmental (20 events) duplications were revealed in the NBS family. Comparative evolutionary analyses of orthologous genes among Arabidopsis thaliana, Brassica rapa, and Brassica oleracea reflected the importance of the NBS-LRR gene family during evolution. Moreover, examinations of cis-elements identified 70 major elements involved in responses to methyl jasmonate, abscisic acid, auxin, and salicylic acid. According to RNA-seq expression analyses, 75 NBS-encoding genes contributed to the resistance of radish to Fusarium wilt. A quantitative real-time PCR analysis revealed that RsTNL03 (Rs093020) and RsTNL09 (Rs042580) expression positively regulates radish resistance to Fusarium oxysporum, in contrast to the negative regulatory role for RsTNL06 (Rs053740). Conclusions The NBS-encoding gene structures, tandem and segmental duplications, synteny, and expression profiles in radish were elucidated for the first time and compared with those of other Brassicaceae family members (A. thaliana, B. oleracea, and B. rapa) to clarify the evolution of the NBS gene family. These results may be useful for functionally characterizing NBS-encoding genes in radish.

Short-Term Load Forecasting (STLF) is one critical assignment regarding the power supply and demand in the smart grid. Multi-step STLF provides strong evidence for decision-making to achieve consistent, quick supply and reduce direct or indirect cost. However, most of the current research only focuses on one-step STLF, which cannot satisfy the human-beings needs. Besides, short-term consumption fluctuates significantly in different periods and people, which increases the difficulty of forecasting. In this paper, we present a novel deep model named multi-channel long short-term memory (LSTM) with time location (TL-MCLSTM) in a multiple output strategy to forecast the multi-step short-term power consumption. The proposed model contains three channels: power consumption, time location, and customer behavior channels, respectively. Power consumption channel reflects the change and general trend of use; Time location channel reflects the hidden pattern of customer habits, which records the information consisting of time, day of the week, holidays. Moreover, we combine a convolution autoencoder and k-means to identify the type of behavior at the customer behavior channel. Power consumption and time location channels are trained individually through the LSTM as it has excellent memory function. Extracted features from LSTM in power consumption and time location channels are combined with customer behavior as comprehensive features to forecast. We designed, trained, and verified our proposed deep model on two nature data sets, and compared with other leading deep learning-based methods. The comparative studies have confirmed the effectiveness and priority of TL-MCLSTM for multi-step short-term consumption forecasting.

Short-term power consumption forecasting plays a critical role in the process of building the smart grid. However, it is very challenging as the power consumption series has strong randomness and volatility. In this paper, the authors proposed a novel domain fusion deep model based on convolutional neural network (CNN), long short-term memory (LSTM), and discrete wavelet transform (DWT) to deal with this task accurately. The proposed deep model has two channels: raw power consumption and DWT. The raw power consumption channel corresponding to time-domain feature extraction while the DWT channel is frequency domain. They extract time-domain and frequency-domain features individually by using CNN. CNN extracted time-domain, and frequency-domain features are merged as time-frequency fusion features, which fully reflect the changing power consumption trend. The time-frequency fusion features are fed into LSTM to mining the features which have a long-time dependency. The comprehensive features are the fusion of time-domain and frequency-domain features with a long-time dependency, which are utilized for power consumption forecasting. The proposed method is evaluated on two public nature data sets related to power consumption with multiple metrics. The comparative experimental analysis has confirmed the state-of-the-art performance of the proposed method for short-term power consumption forecasting.

This paper studied the synergistic effects of catalyst mixtures on biomass catalytic pyrolysis in comparison with the single catalyst in a microwave reactor and a TGA. In general, positive synergistic effects were identified based on increased mass loss rate, reduced activation energy, and improved bio-oil quality compared to the case with a single catalyst at higher catalyst loads. 10KP/10Bento (a mixture of 10% K3PO4 and 10% bentonite) increased the mass loss rate by 85 and 45% at heating rates of 100 and 25°C/min, respectively, compared to switchgrass without catalyst. The activation energy for 10KP/10Bento and 10KP/10Clino (a mixture of 10% K3PO4 and 10% clinoptilolite) was slightly lower or similar to other catalysts at 30 wt.% load. The reduction in the activation energy by the catalyst mixture was higher at 100°C/min than 25°C/min due to the improved catalytic activity at higher heating rates. Synergistic effects are also reflected in the improved properties of bio-oil, as acids, aldehydes, and anhydrosugars were significantly decreased, whereas phenol and aromatic compounds were substantially increased. 30KP (30% K3PO4) and 10KP/10Bento increased the content of alkylated phenols by 341 and 207%, respectively, in comparison with switchgrass without catalyst. Finally, the use of catalyst mixtures improved the catalytic performance markedly, which shows the potential to reduce the production cost of bio-oil and biochar from microwave catalytic pyrolysis.

Lignin is known to limit the enzyme-mediated hydrolysis of biomass by both restricting substrate swelling and binding to the enzymes. Pretreated mechanical pulp (MP) made from Aspen wood chips was incubated with either 16% sodium sulfite or 32% sodium percarbonate to incorporate similar amounts of sulfonic and carboxylic acid groups onto the lignin (60 mmol/kg substrate) present in the pulp without resulting in significant delignification. When Simon’s stain was used to assess potential enzyme accessibility to the cellulose, it was apparent that both post-treatments enhanced accessibility and cellulose hydrolysis. To further elucidate how acid group addition might influence potential enzyme binding to lignin, Protease Treated Lignin (PTL) was isolated from the original and modified mechanical pulps and added to a cellulose rich, delignified Kraft pulp. As anticipated, the PTLs from both the oxidized and sulfonated substrates proved less inhibitory and adsorbed less enzymes than did the PTL derived from the original pulp. Subsequent analyses indicated that both the sulfonated and oxidized lignin samples contained less phenolic hydroxyl groups, resulting in enhanced hydrophilicity and a more negative charge which decreased the non-productive binding of the cellulase enzymes to the lignin.

The complex and heterogeneous polyphenolic structure of lignin confers recalcitrance to plant cell walls and challenges biomass processing for agroindustrial applications. Recently, significant efforts have been made to alter lignin composition to overcome its inherent intractability. In this work, to overcome technical difficulties related to biomass recalcitrance, we report an integrated strategy combining biomass genetic engineering with a pretreatment using a bio-derived deep eutectic solvent (DES). In particular, we employed biomass from an Arabidopsis line that expressed a bacterial hydroxycinnamoyl-CoA hydratase-lyase (HCHL) in lignifying tissues, which results in the accumulation of unusual C6C1 lignin monomers and a slight decrease in lignin molecular weight. The transgenic biomass was pretreated with renewable DES that can be synthesized from lignin-derived phenols. Biomass from the HCHL plant line containing C6C1 monomers showed increased pretreatment efficiency and released more fermentable sugars up to 34% compared to WT biomass. The enhanced biomass saccharification of the HCHL line is likely due to a reduction of lignin recalcitrance caused by the overproduction of C6C1 aromatics that act as degree of polymerization (DP) reducers and higher chemical reactivity of lignin structures with such C6C1 aromatics. Overall, our findings demonstrate that strategic plant genetic engineering, along with renewable DES pretreatment, could enable the development of sustainable biorefinery.

Abstract A key design consideration in developing contrast agents is obtaining distinct, multiple signal changes in diseased tissue. Plasmonic gold nanoparticles (Au NPs) have been developed as contrast agents due to their strong surface plasmon resonance (SPR). This study aims to demonstrate that stimuli-responsive plasmonic Au nanoclusters (Au NCs) can be used as a contrast agent for optical coherence tomography (OCT) in detecting early-stage cancer. Au NPs were clustered via acid-cleavable linkers to synthesize Au NCs that disassemble under mildly acidic conditions into individual Au NPs, simultaneously diminishing SPR effect (quantified by scattering intensity) and increasing Brownian motion (quantified by Doppler variance). The acid-triggered morphological and accompanying optico-physical property changes of the acid-disassembling Au NCs were confirmed by TEM, DLS, UV/Vis, and OCT. Stimuli-responsive Au NCs were applied in a hamster check pouch model carrying early-stage squamous carcinoma tissue. The tissue was visualized by OCT imaging, which showed reduced scattering intensity and increased Doppler variance in the dysplastic tissue. This study demonstrates the promise of diagnosing early-stage cancer using molecularly programmable, inorganic nanomaterial-based contrast agents that are capable of generating multiple, stimuli-triggered diagnostic signals in early-stage cancer.

Background/purposeFew studies have examined the risk factors for gallbladder (GB) disease in young adults. This study aimed to evaluate risk factors for GB disease in young adults based on big data in Korea.MethodsAll participants underwent routine checkup at the Korea Medical Institute from June 2014 to May 2015. After excluding 677 individuals with missing information in records, 724,114 individuals (435,635 men, 288,479 women) were finally included. The definition of abnormal GB finding included stones, sludge, polyps, and adenomyomatosis detected using ultrasonography. All statistical analyses were performed using SAS software version 9.2.ResultsOverall, 27,130 (17.5%) individuals were diagnosed as having abnormal GB finding in the young age group (N = 154,463, aged 20-39 years). In men, significant differences in low-density lipoprotein (LDL) and cholesterol levels were observed between the abnormal GB finding group and normal GB group (p < 0.05). In women, a significant difference in smoking history was noted between the abnormal GB finding group and normal GB group (p < 0.05). The prevalence rate of GB stones was 1.9% (27,979/154,463) in the young age group. High body mass index (BMI), large thigh circumference, and low high-density lipoprotein (HDL) level in women and low HDL level in men were independent risk factors for the presence of GB stones (p < 0.05).ConclusionIn this study, obesity-related factors (BMI, waist size, thigh circumference, and cholesterol, LDL, and HDL levels) correlated with GB disease in the young generation of Korea.

[This corrects the article DOI: 10.1371/journal.pone.0211480.].

Lignin is a major component of lignocellulosic biomass along with cellulose and hemicellulose, currently underutilized with the bulk of technical lignin being combusted for heat generation in plant. From the technoeconomic perspective, the success of a future biorefinery is highly dependent on lignin valorization. However, structural complexity, heterogeneity of lignin, and several undesirable reactions associated with the nature of lignin and process conditions obstruct the effective utilization of lignin. Lignin condensation has been a long-lasting problem from conventional to recent biorefineries, which makes the recovered lignin more difficult to decompose. Also, the undesirable condensation and repolymerization during the depolymerization of lignin is more problematic as this significantly limits its ability to be depolymerized to low molecular weight products at high yields. To solve this problem, which ultimately enables maximizing lignin utilization, there have been many efforts in various aspects. In this review, we focus on the undesirable reactions occurring during the fractionation and depolymerization process of lignin and introduce recent efforts to suppress or minimize those unwanted reactions. The aim of all these efforts is to maximize lignin conversion to low molecular weight products.

Small gold nanoparticles (sAuNPs,

Small gold nanorods (GNRs) with longitudinal plasmon absorption in the near-infrared window (700–900 nm) are of great interest for in vivo optical applications (e.g., photothermal therapy) and for their high-payload-to-carrier ratio for drug delivery. Common synthetic strategies for GNR production afford spherical and cubical nanoparticles in addition to the desired GNRs. Thus, several methods have been proposed for the selective separation of GNRs from the reaction by-products. For example, centrifugation has been used to separate the high aspect ratio (AR) GNRs (AR > 4). However, it is difficult to separate small sized GNRs with low AR (AR ≤ 4) that are particularly promising for biomedical applications. Here, we describe a simple and fast procedure for the separation of small GNRs with AR of 4, and length of 28 nm from reaction by-products. The shape separation is achieved through centrifugation according to the following steps: • Isolation of all gold products of the reaction from the excess of cetyltrimethylammonium bromide through a first cycle of centrifugation. • Optimization of the speed and the time of centrifugation for the separation of GNRs from the reaction by-products. • Shape separation of GNRs through a second cycle of centrifugation. The effectiveness of this procedure is documented.

People of developing countries especially from rural area are commonly exposed to high levels of household pollution for 3–7 hours daily using biomass in their kitchen. Such biomass produces harmful smoke and makes indoor air pollution. Community based cross-sectional study was performed to identify effects of indoor air pollution (IAP) by simplified measurement approach in Sunsari district of Nepal. Representative samples of 157 housewives from household, involving more than 5 years in kitchen were included by cluster sampling. Data was analyzed by SPSS and logistic regression was applied for the statistical test. Most (87.3%) housewives used biomass as a cooking fuel. Tearing of eyes, difficulty in breathing and productive cough were the main reported health problems and traditional mud stoves and use of unrefined biomass were statistically significant (p 2) with health problems related to IAP. The treatment cost and episodes of acute respiratory infection (ARI) was >2 folders higher in severe IAP than mild IAP. Simplified measurement approach could be helpful to measure IAP in rural area. Some effective intervention is suggested to reduce the severe level of IAP considering women and children.

A case-control study in the Republic of Korea evaluated the protective effectiveness of the hantavirus vaccine. Point estimates showed increasing effectiveness with increasing numbers of doses received: 25% for one dose, 46% for two doses, and 75% for three doses. All 95% confidence intervals overlapped zero; therefore, the findings could be due to chance.

The physical growth and cognitive development of elementary school children are very crucial and this group is large in number but has little research dedicated to it. The physical growth and cognitive development of children occur simultaneously and can be measured by body mass index (BMI) and intelligence quotient (IQ). Previous studies could not sufficiently focus on both aspects. The aim of this study was to identify determinants of BMI and IQ of students in two elementary schools in the Humla district of Nepal. Two randomly selected elementary schools and all children available there (n = 173) participated in the study. BMI was calculated with the objective of proper measurement of height and weight of the children. Likewise, the updated universal nonverbal intelligence test (UNIT) was applied for IQ. Descriptive statistics, t-test, analysis of variance and multiple linear regressions were used when appropriate. Study findings showed that one-tenth of the children had grade 2 thinness (-2SD) and about one-third had poor IQ (

The university course scheduling problem (UCSP) aims at optimally arranging courses to corresponding rooms, faculties, students, and timeslots with constraints. Previously, the university staff solved this thorny problem by hand, which is very time-consuming and makes it easy to fall into chaos. Even some meta-heuristic algorithms are proposed to solve UCSP automatically, while most only utilize one single algorithm, so the scheduling results still need improvement. Besides, they lack an in-depth analysis of the inner algorithms. Therefore, this paper presents a novel and practical approach based on Tabu search and simulated annealing algorithms for solving USCP. Firstly, the initial solution of the UCSP instance is generated by one construction heuristic algorithm, the first fit algorithm. Secondly, we defined one union move selector to control the moves and provide diverse solutions from initial solutions, consisting of two changing move selectors. Thirdly, Tabu search and simulated annealing (SA) are combined to filter out unacceptable moves in a parallel mode. Then, the acceptable moves are selected by one adaptive decision algorithm, which is used as the next step to construct the final solving path. Benefits from the excellent design of the union move selector, parallel tabu search and SA, and adaptive decision algorithm, the proposed method could effectively solve UCSP since it fully uses Tabu and SA. We designed and tested the proposed algorithm in one real-world (PKNU-UCSP) and ten random UCSP instances. The experimental results confirmed its effectiveness. Besides, the in-depth analysis confirmed each component's effectiveness for solving UCSP. [ABSTRACT FROM AUTHOR]