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Plants have evolved complex mechanisms to reprogram growth in response to drought stress. In herbaceous perennial plant species, the rhizome, which is normally an organ for propagation and food storage, can also support plant growth in stressful environments, and allows the plant to perennate and survive stress damage. However, the mechanisms that regulate rhizome growth in perennial herbs during abiotic stresses are unknown. Here, we identified a chrysanthemum (Chrysanthemum morifolium) DEAD-box RNA helicase gene, CmRH56, that is specifically expressed in the rhizome shoot apex. Knock down of CmRH56 transcript levels decreased the number of rhizomes and enhanced drought stress tolerance. We determined that CmRH56 represses the expression of a putative gibberellin (GA) catabolic gene, GA2 oxidase6 (CmGA2ox6). Exogenous GA treatment and silencing of CmGA2ox6 resulted in more rhizomes. These results demonstrate that CmRH56 suppresses rhizome outgrowth under drought stress conditions by blocking GA biosynthesis.

The paper aims to analyze the consumer joint choice behavior on fresh food purchase channels and terminal delivery services during major public health events, with the purpose of revealing the underlying influencing factors and behavioral characteristics. First, based on random utility maximization theory, the cross-nested logit model is formulated, which takes into account the influence of socioeconomic attribute factors, service attribute factors, risk perception attribute factors and trust perception attribute factors. Second, a questionnaire survey is conducted, and the obtained data are used to estimate the model parameters and perform an elasticity analysis of the utility variables. The parameter estimation results demonstrate that in the context of major public health events, consumers consider adjusting their attitudes toward e-commerce platforms first when the utility variables are altered, and fresh food purchase channels are easily replaced for consumers who choose unmanned equipment home delivery. The elasticity analysis results suggest that consumers are more willing to buy fresh food through community group-buying channels, are more sensitive to the convenience of the purchase process and are less concerned with delivery time. Although person-to-person contact increases the risk of infection, consumers still prefer attended terminal delivery services. Furthermore, consumers least agree with the effectiveness of body temperature detection methods in public places but feel that an effective way to increase consumer trust in enterprises is to strengthen personnel protection measures. [ABSTRACT FROM AUTHOR]

In this paper, a bi-population competition adaptive interior search algorithm (BCAISA) based on a reinforcement learning strategy is proposed for the classical flexible job shop scheduling problem (FJSP) to optimize the makespan. First, the scheduling solution is represented using a machine-job-based two-segment integer encoding method, and various heuristic rules are then applied to generate the initial population. Secondly, a bi-population mechanism is introduced to partition the population into two distinct sub-populations. These sub-populations are specifically tailored for machine assignment and operation permutation, employing different search strategies respectively, aiming to facilitate an efficient implementation of parallel search. A competition mechanism is introduced to facilitate the information exchange between the two sub-populations. Thirdly, the ISA is adapted for the discrete scheduling problem by discretizing a series of search operators, which include composition optimization, mirror search, and random walk. A Q-learning-based approach is proposed to dynamically adjust a key parameter, aiming to strike a balance between the capacity for global exploration and local exploitation. Finally, extensive experiments are conducted based on 10 well-known benchmark instances of the FJSP. The design of the experiment (DOE) method is employed to determine the algorithm’s parameters. Based on the computational results, the effectiveness of four improvement strategies is first validated. The BCAISA is then compared with fifteen published algorithms. The comparative data demonstrate that our algorithm outperforms other algorithms in 50% of benchmark instances. Additionally, according to the relative percentage deviation (RPD) from the state-of-the-art results, the BCAISA also exhibits superior performance. This highlights the effectiveness of our algorithm for solving the classical FJSP. To enhance the practical application, the scope of the ISA will be broadened in future work to more complex problems in real-world scenarios. [ABSTRACT FROM AUTHOR]

Energy-aware scheduling has been viewed as a feasible way to reduce energy consumption during the production process. Recently, energy-aware job shop scheduling problems (EAJSPs) have received wide attention in the manufacturing area. However, the majority of previous literature about EAJSPs supposed that all jobs are fabricated in the in-house workshop, while the outsourcing of jobs to some available subcontractors is neglected. To get close to practical production, the outsourcing and scheduling are simultaneously determined in an energy-aware job shop problem with outsourcing option (EAJSP-OO). To formulate the considered problem, a novel mathematical model is constructed to minimize the sum of completion time cost, outsourcing cost and energy consumption cost. Considering the strong complexity, a self-learning interior search algorithm (SLISA) is developed based on reinforcement learning. In the SLISA, a new Q-learning algorithm is embedded to dynamically select search strategies to prevent blind search in the iteration process. Extensive experiments are carried out to evaluate the performance of the proposed algorithm. Simulation results indicate that the SLISA is superior to the compared existing algorithms in more than 50% of the instances of the considered EAFJSP-OO problem. [ABSTRACT FROM AUTHOR]

Body image (BI) is a multidimensional construct that refers to one's perceptions of and attitudes toward one's own physical characteristics. Adolescence is a critical developmental stage in which concerns about BI increase. Therefore, the present cross-sectional study aimed to evaluate body image and aesthetic body shape standards in a sample of middle school students living in China. The researchers gathered demographic information, as well as height and weight data, for their study. They used a body silhouette to assess body image perception and body shape aesthetics and calculated two indexes: BIP, which measures the accuracy of self-perception and the estimation of bodily dimensions, and BIS, which indicates the difference between an individual's perceived and ideal body images. A total of 1585 students in three grades at two middle schools were included in the study (759 = female, mean age = 13.67 ± 0.90; 839 = male, mean age = 13.70 ± 0.90). The results showed that the BIP bias rate of middle school students was 55.7%, and the BI dissatisfaction rate was 81.0%. Females tended to overestimate their body shape and desire to be thinner compared to males. Students with a higher BMI grading were more prone to underestimating their body shape and aspiring to be thinner. Furthermore, 8.6% of students chose underweight as the ideal body type for boys, while 22.6% chose underweight as the ideal body type for girls. In conclusion, there are significant gender differences in the aesthetic standards of body shape, and adolescents believe that for women, a thin body shape is beautiful. [ABSTRACT FROM AUTHOR]

Public transportation is a crucial component of urban transportation systems, and improving passenger sharing rates can help alleviate traffic congestion. To enhance the punctuality and supply–demand balance of dedicated buses, we propose a hierarchical multi-objective optimization model to optimize bus guidance speeds and bus operation schedules. Firstly, we present an intelligent decision-making method for bus driving speed based on the mathematical description of bus operation states and the application of the Lagrange multiplier method, which improves the overall punctuality rate of the bus line. Secondly, we propose an optimization method for bus operation schedules that respond to passenger needs by optimizing departure time intervals and station schedules for supply–demand balance. The experiments were conducted in Future Science City, Beijing, China. The results show that the bus line's punctuality rate has increased to 90.53%, while the retention rate for platform passengers and the intersection stop rate have decreased by 36.22% and 60.93%, respectively. These findings verify the effectiveness and practicality of the proposed hierarchical multi-objective optimization model. [ABSTRACT FROM AUTHOR]

Chicken infectious anaemia virus (CIAV) has been identified as the causative agent of chicken infectious anaemia (CIA), causing huge economic losses to the poultry industry globally. In this study, a total of 573 clinical samples were collected from 197 broiler farms in 17 provinces of China during 2020-2021. Among them, 375 samples (375/573, 65.4%) were positive for CIAV by real-time PCR. The positive rate of CIAV detection between different regions of China ranged from 46.67% (North China) to 81.25% (Central China). The nucleotide sequences of the VP1 gene were obtained for 91 CIAV strains, whole genome sequencing was successful for 72 out of 91 strains. Phylogenetic analysis based on the VP1 gene revealed that 91 CIAV strains currently circulating in China belong to three genotypes (II, IIIa and IIIb), and most of the CIAV strains belong to genotype IIIa. Phylogenetic analysis of the whole genome showed that 71 CIAV strains belong to genotype IIIa, and one strain belongs to genotype II. Sequence analysis showed several amino acid substitutions in both the VP1, VP2 and VP3 proteins. Our results enhance the understanding of the molecular characterization of CIAV infection in China. RESEARCH HIGHLIGHTS A molecular systematic survey of CIAV in China during 2020-2021. CIAV genotype IIIa is the predominant genotype in China. [ABSTRACT FROM AUTHOR]

Keywords: Chrysanthemum; CRISPR/Cas9; eYGFPuv; flowering time; TFL1 EN Chrysanthemum CRISPR/Cas9 eYGFPuv flowering time TFL1 1519 1521 3 07/26/23 20230801 NES 230801 Chrysanthemum ( I Chrysanthemum morifolium i Ramat.) Developing a UV-visible reporter-assisted CRISPR/Cas9 gene editing system to alter flowering time in Chrysanthemum indicum When I CiPDS i is used as a visual marker to validate genome editing, chimerism is easily scorable, but scoring is time-consuming and laborious for most target genes without a visual mutant phenotype. In this regard, CRISPR/Cas9-mediated genome editing holds advantages in vector design and assembly, especially when targeting multiple genes, and has been widely used in many organisms. [Extracted from the article]

The energy-saving scheduling problem (ESSP) has gained increasing attention of researchers in the manufacturing field. However, there is a lack of studies on ESSPs in the assembly job shop environment. In contrast with traditional scheduling problems, the assembly job shop scheduling problem (AJSP) adds the additional consideration of hierarchical precedence constraints between different jobs of each final product. This paper focuses on developing a methodology for an energy-saving assembly job shop scheduling problem with job transportation times. Firstly, a mathematical model is constructed with the objective of minimizing total energy consumption. Secondly, an improved elephant herding optimization (IEHO) is proposed by considering the problem's characteristics. Finally, thirty-two different instances are designed to verify the performance of the proposed algorithm. Computational results and statistical data demonstrate that the IEHO has advantages over other algorithms in terms of the solving accuracy for the considered problem. [ABSTRACT FROM AUTHOR]

Plants have evolved complex mechanisms to reprogram growth in response to drought stress. In herbaceous perennial plant species, the rhizome, which is normally an organ for propagation and food storage, can also support plant growth in stressful environments, and allows the plant to perennate and survive stress damage. However, the mechanisms that regulate rhizome growth in perennial herbs during abiotic stresses are unknown. Here, we identified a chrysanthemum (Chrysanthemum morifolium) DEAD-box RNA helicase gene, CmRH56 , that is specifically expressed in the rhizome shoot apex. Knock down of CmRH56 transcript levels decreased the number of rhizomes and enhanced drought stress tolerance. We determined that CmRH56 represses the expression of a putative gibberellin (GA) catabolic gene, GA2 oxidase6 (CmGA2ox6). Exogenous GA treatment and silencing of CmGA2ox6 resulted in more rhizomes. These results demonstrate that CmRH56 suppresses rhizome outgrowth under drought stress conditions by blocking GA biosynthesis. [ABSTRACT FROM AUTHOR]

Energy-saving scheduling has been pointed out as an interesting research issue in the manufacturing field, by which energy consumption can be effectively reduced through production scheduling from the operational management perspective. In recent years, energy-saving scheduling problems in flexible job shops (ESFJSPs) have attracted considerable attention from scholars. However, the majority of existing work on ESFJSPs assumed that the processing of any two consecutive operations in a job cannot be overlapped. In order to be close to real production, the processing overlapping of consecutive operations is allowed in this paper, while the job transportation tasks are also involved between different machines. To formulate the problem, a mathematical model is set up to minimize total energy consumption. Due to the NP-hard nature, a new interior search algorithm (NISA) is elaborately proposed following the feature of the problem. A number of experiments are conducted to verify the effectiveness of the NISA algorithm. The experimental results demonstrate that the NISA provides promising results for the considered problem. In addition, the computational results indicate that the increasing transportation time and sub-lot number will increase the transportation energy consumption, which is largely responsible for the increase in total energy consumption. [ABSTRACT FROM AUTHOR]

Swine acute diarrhoea syndrome coronavirus (SADS‐CoV) is a newly identified swine enteropathogenic coronavirus that causes watery diarrhoea in neonatal piglets, leading to significant economic losses to the swine industry. Currently, there are no suitable serological methods to assess the infection of SADS‐CoV and effectiveness of vaccines, making an urgent need to exploit effective enzyme‐linked immunosorbent assay (ELISA) to compensate for this deficiency. Here, a recombinant plasmid that expresses the spike (S) protein of SADS‐CoV fused to the Fc domain of human IgG was constructed to generate recombinant baculovirus and expressed in HEK 293F cells. The S‐Fc protein was purified with protein G Resin, which retained reactivity with anti‐human Fc and anti‐SADS‐CoV antibodies. The S‐Fc protein was then used to develop an indirect ELISA (S‐iELISA) and the reaction conditions of S‐iELISA were optimized. As a result, the cut‐off value was determined as 0.3711 by analyzing OD450nm values of 40 SADS‐CoV‐negative sera confirmed by immunofluorescence assay (IFA) and western blot. The coefficient of variation (CV) of 6 SADS‐CoV‐positive sera within and between runs of S‐iELISA were both less than 10%. The cross‐reactivity assays demonstrated that S‐iELISA was non‐cross‐reactive with other swine viruses' sera. Furthermore, the overall coincidence rate between IFA and S‐iELISA was 97.3% based on testing 111 clinical serum samples. Virus neutralization test with seven different OD450nm values of the sera showed that the OD450nm values tested by S‐iELISA are positively correlated with the virus neutralization assay. Finally, a total of 300 pig field serum samples were tested by S‐iELISA and commercial kits of other swine enteroviruses showed that the IgG‐positive for SADS‐CoV, TGEV, PDCoV and PEDV was 81.7, 54, 65.3 and 6%, respectively. The results suggest that this S‐iELISA is specific, sensitive, repeatable and can be applied for the detection of the SADS‐CoV infection in the swine industry. [ABSTRACT FROM AUTHOR]

This paper presents a discrete animal migration optimization (DAMO) to solve the dual-resource constrained energy-saving flexible job shop scheduling problem (DRCESFJSP), with the aim of minimizing the total energy consumption in the workshop. A job-resource-based two-vector encoding method is designed to represent the scheduling solution, and an energy-saving decoding approach is given based on the left-shift rule. To ensure the quality and diversity of initial scheduling solutions, a heuristic approach is employed for the resource assignment, and some dispatching rules are applied to acquire the operation permutation. In the proposed DAMO, based on the characteristics of the DRCESFJSP problem, the search operators of the basic AMO are discretized to adapt to the problem under study. An animal migration operator is presented based on six problem-based neighborhood structures, which dynamically changes the search scale of each animal according to its solution quality. An individual updating operator based on crossover operation is designed to obtain new individuals through the crossover operation between the current individual and the best individual or a random individual. To evaluate the performance of the proposed algorithm, the Taguchi design of experiment method is first applied to obtain the best combination of parameters. Numerical experiments are carried out based on 32 instances in the existing literature. Computational data and statistical comparisons indicate that both the left-shift decoding rule and population initialization strategy are effective in enhancing the quality of the scheduling solutions. It also demonstrate that the proposed DAMO has advantages against other compared algorithms in terms of the solving accuracy for solving the DRCESFJSP. [ABSTRACT FROM AUTHOR]

For the job shop with variable processing speeds, the aim of energy saving and consumption reduction is implemented from the perspective of production scheduling. By analyzing the characteristics of the workshop, a multi-objective mathematical model is established with the objective of reducing the total energy consumption and shortening the makespan. A multi-objective discrete water wave optimization (MODWWO) algorithm is proposed for solving the problem. Firstly, a two-vector encoding method is adopted to divide the scheduling solution into two parts, which represent speed selection and operation permutation in the scheduling solution, respectively. Secondly, some dispatching rules are used to initialize the population and obtain the initial scheduling solutions. Then, three operators of the basic water wave optimization algorithm are redesigned to make the algorithm adaptive for the multi-objective discrete scheduling problem under study. A new propagation operator is presented with the ability of balancing global exploration and local exploitation based on individual rank and neighborhood structures. A novel refraction operator is designed based on crossover operation, by which each individual can learn from the current best individual to absorb better information. And a breaking operator is modified based on the local search strategy to enhance the exploitation ability. Finally, extensive simulation experiments demonstrate that the proposed MODWWO algorithm is effective for solving the considered energy-saving scheduling problem. [ABSTRACT FROM AUTHOR]

Design problems in structural engineering are often modeled as differential equations. These problems are posed as initial or boundary value problems with several possible variations in structural designs. In this paper, we have derived a mathematical model that represents different structures of beam-columns by varying axial load with or without internal forces including bending rigidity. We have also developed a novel solver, the LeNN-NM algorithm, which consists of weighted Legendre polynomials, and a single path following optimizer, the Nelder–Mead (NM) algorithm. To evaluate the performance of our solver, we have considered three design problems representing beam-columns. The values of performance indicators, MAD, TIC, NSE, and ENSE, are calculated for a hundred simulations. The outcome of our statistical analysis points to the superiority of the LeNN-NM algorithm. Graphical illustrations are presented to further elaborate on our claims. [ABSTRACT FROM AUTHOR]

In the area of production scheduling, some traditional indicators are always treated as the optimization objectives such as makespan, earliness/tardiness and workload, and so on. However, with the increasing amount of energy consumption, the low-carbon scheduling problem has gained more and more attention from scholars and engineers. In this paper, a low-carbon flexible job shop scheduling problem (LFJSP) is studied to minimize the earliness/tardiness cost and the energy consumption cost. In this paper, a low-carbon flexible job shop scheduling. Due to the NP-hard nature of the problem, a swarm-based intelligence algorithm, named discrete African buffalo optimization (DABO), is developed to deal with the problem under study effectively. The original ABO was proposed for continuous problems, but the problem is a discrete scheduling problem. Therefore, some individual updating methods are proposed to ensure the algorithm works in a discrete search domain. Then, some neighborhood structures are designed in terms of the characteristics of the problem. A local search procedure is presented based on some neighborhood structures and embedded into the algorithm to enhance its searchability. In addition, an aging-based population re-initialization method is proposed to enhance the population diversity and avoid trapping into the local optima. Finally, several experimental simulations have been carried out to test the effectiveness of the DABO. The comparison results demonstrate the promising advantages of the DABO for the considered LFJSP. [ABSTRACT FROM AUTHOR]

Alternative polyadenylation (APA) is a post-translational modification that occurs during mRNA maturation in humans. Studies suggested that abnormal APA events are associated with the genesis and progression of malignant tumors. Here, we aimed to comprehensively evaluate the prognostic value of APA events involved in breast cancer (BC). Both APA events and clinical information for BC patients were downloaded from The Cancer Genome Atlas (TCGA) database to identify prognosis-related APA events in BC. A total of 462 APA events and 374 APA events were shown to be significantly related to overall survival (OS) and relapse-free survival (RFS), respectively, of BC patients. The TCGA set was randomly divided into a training and a test set. Key prognosis-related APA events were selected by LASSO regression to build prediction signatures for OS and RFS by multivariate Cox regression analysis in the training, test, and whole set. BC patients were stratified into high-risk and low-risk groups based on median risk scores. Kaplan–Meier survival analysis demonstrated that low-risk groups had better OS and RFS than high-risk groups in all three sets. The time-dependent receiver operating characteristic (ROC) curves showed that our signatures had a good predictive ability for survival and recurrence for BC patients in all three sets. The independent prognostic indicators-based nomogram model had excellent performance and considerable net benefit for predicting the OS and RFS in BC. A PPI network was constructed between key prognosis and core regulators associated with APA, consisting of 48 nodes and 244 edges. Functional enrichment analysis also revealed their association with RNA processing and RNA synthesis. Collectively, our data indicate that prognostic signatures based on APA events may be powerful prognostic predictors for OS and RFS in BC. [ABSTRACT FROM AUTHOR]

The conventional production scheduling problem has mainly emphasized the time-related metrics, such as makespan, machine workload and tardiness/earliness, and so on. With the advent of the sustainable manufacturing, the green scheduling problem has been received more and more attention from scholars and researchers. In this paper, we investigate a green flexible job shop scheduling problem (GFJSP) with the consideration of environmental factors. To formulate the GFJSP problem, a mathematical model is first established to minimize the amount of total energy-consumption. To solve the model, a kind of improved African buffalo optimization (IABO) algorithm is proposed based on the characteristics of the problem. In the proposed IABO, a two-vector solution representation method is first designed, and a population initialization method is adopted to generate the initial solutions with certain quality and diversity. Based on the original ABO, several improvement strategies are introduced to enhance the performance of the algorithm, i.e., the modified individual learning mechanism and the aging-based re-initializaiton mechanism. In addition, in order to adapt our algorithm to the scheduling problem, a discrete individual updating method is developed to ensure the algorithm search directly in a discrete domain. Finally, a number of experiments have been conducted to test the performance of the proposed IABO algorithm. The simulation data demonstrate the effectiveness of the proposed IABO for the considered GFJSP. [ABSTRACT FROM AUTHOR]

The workshop scheduling has historically emphasized the production metrics without involving any environmental considerations. Low-carbon scheduling has attracted the attention of many researchers after the promotion of green manufacturing. In this paper, we investigate the low-carbon scheduling problem in a job shop environment. A mathematical model is first established with the objective to minimize the sum of energy-consumption cost and completion-time cost. A discrete genetic-grey wolf optimization algorithm (DGGWO) is developed to solve the problem in this study. According to the characteristics of the problem, a job-based encoding method is first employed. Then a heuristic approach and the random generation rule are combined to fulfill the population initialization. Based on the original GWO, a discrete individual updating method the crossover operation of the genetic algorithm is adopted to make the algorithm directly work in a discrete domain. Meanwhile, a mutation operator is adopted to enhance the population diversity and avoid the algorithm from getting trapped into the local optima. In addition, a variable neighborhood search is embedded to further improve the search ability. Finally, extensive simulations are conducted based on 43 benchmark instances. The experimental data demonstrate that the proposed algorithm can yield better results than the other published algorithms. [ABSTRACT FROM AUTHOR]

Workshop scheduling has mainly focused on the performances involving the production efficiency, such as times and quality, etc. In recent years, environmental metrics have attracted the attention of many researchers. In this study, an energy-efficient job shop scheduling problem is considered, and a grey wolf optimization algorithm with double-searching mode (DMGWO) is proposed with the objective of minimizing the total cost of energy-consumption and tardiness. Firstly, the algorithm starts with a discrete encoding mechanism, and then a heuristic algorithm and the random rule are employed to implement the population initialization. Secondly, a new framework with double-searching mode is developed for the GWO algorithm. In the proposed DMGWO algorithm, besides of the searching mode of the original GWO, a random seeking mode is added to enhance the global search ability. Furthermore, an adaptive selection operator of the two searching modes is also presented to coordinate the exploration and exploitation. In each searching mode, a discrete updating method of individuals is designed by considering the discrete characteristics of the scheduling solution, which can make the algorithm directly work in a discrete domain. In order to further improve the solution quality, a local search strategy is embedded into the algorithm. Finally, extensive simulations demonstrate the effectiveness of the proposed DMGWO algorithm for solving the energy-efficient job shop scheduling problem based on 43 benchmarks. [ABSTRACT FROM AUTHOR]

This paper aims to develop a hybrid grey wolf optimization algorithm (HGWO) for solving the job shop scheduling problem (JSP) with the objective of minimizing the makespan. Firstly, to make the GWO suitable for the discrete nature of JSP, an encoding mechanism is proposed to implement the continuous encoding of the discrete scheduling problem, and a ranked-order value (ROV) rule is used to conduct the conversion between individual position and operation permutation. Secondly, a heuristic algorithm and the random rule are combined to implement the population initialization in order to ensure the quality and diversity of initial solutions. Thirdly, a variable neighborhood search algorithm is embedded to improve the local search ability of our algorithm. In addition, to further improve the solution quality, genetic operators (crossover and mutation) are introduced to balance the exploitation and exploration ability. Finally, experimental results demonstrate the effectiveness of the proposed algorithm based on 23 benchmark instances. [ABSTRACT FROM AUTHOR]

• A mathematical model of the energy-conscious FJSP with transportation time and deterioration effect is built. • A modified animal migration optimization algorithm is elaborately designed by considering the characteristics of the problem. • Comprehensive computational experiments demonstrate that the MAMO algorithm is effective for the consider problem. In recent years, research on green manufacturing has received much attention to the increasing environmental problems. Production scheduling is viewed as an effective way of saving energy in manufacturing system from the operation management point of view. This paper investigates an energy-conscious flexible job shop scheduling problem with transportation time and deterioration effect simultaneously (ECFJSP-TD). To begin with, a mathematical model is built with the objective of optimizing total energy consumption. A modified animal migration optimization algorithm (MAMO) is elaborately designed by considering the characteristics of the problem. In the MAMO, a two-vector encoding is designed to represent the scheduling solution, and a left-shift decoding approach is proposed to ensure the operation permutation on each machine as compact as possible. In addition, a new animal migration operator is designed based on the problem-specific neighborhood structures, which can dynamically adjust the search scope along with the iteration process to balance global exploration and local exploitation. A new population updating operator is developed based on the crossover operation to generate new individuals by absorbing some information about the best individuals as well as two random individuals. Finally, comprehensive experiments are carried out to test the performance of the proposed MAMO algorithm. According to the comparison data, the MAMO is more effective for solving the considered problem than some published algorithms. [ABSTRACT FROM AUTHOR]

In recent decades, workshop scheduling has excessively focused on time-related indicators, while ignoring environmental metrics. With the advent of sustainable manufacturing, the energy-aware scheduling problem has been attracting more and more attention from scholars and researchers. In this study, we investigate an energy-aware flexible job shop scheduling problem to reduce the total energy consumption in the workshop. For the considered problem, the energy consumption model is first built to formulate the energy consumption, such as processing energy consumption, idle energy consumption, setup energy consumption and common energy consumption. Then, a mathematical model is established with the criterion to minimize the total energy consumption. Secondly, a modified migrating birds optimization (MMBO) algorithm is proposed to solve the model. In the proposed MMBO, a population initialization scheme is presented to ensure the initial solutions with a certain quality and diversity. Five neighborhood structures are employed to create neighborhood solutions according to the characteristics of the problem. Furthermore, both a local search method and an aging-based re-initialization mechanism are developed to avoid premature convergence. Finally, the experimental results validate that the proposed algorithm is effective for the problem under study. [ABSTRACT FROM AUTHOR]

In this paper, a novel improved whale optimization algorithm (IWOA), based on the integrated approach, is presented for solving the flexible job shop scheduling problem (FJSP) with the objective of minimizing makespan. First of all, to make the whale optimization algorithm (WOA) adaptive to the FJSP, the conversion method between the whale individual position vector and the scheduling solution is firstly proposed. Secondly, a resultful initialization scheme with certain quality is obtained using chaotic reverse learning (CRL) strategies. Thirdly, a nonlinear convergence factor (NFC) and an adaptive weight (AW) are introduced to balance the abilities of exploitation and exploration of the algorithm. Furthermore, a variable neighborhood search (VNS) operation is performed on the current optimal individual to enhance the accuracy and effectiveness of the local exploration. Experimental results on various benchmark instances show that the proposed IWOA can obtain competitive results compared to the existing algorithms in a short time. [ABSTRACT FROM AUTHOR]

Under the current environmental pressure, many manufacturing enterprises are urged or forced to adopt effective energy-saving measures. However, environmental metrics, such as energy consumption and CO2 emission, are seldom considered in the traditional production scheduling problems. Recently, the energy-related scheduling problem has been paid increasingly more attention by researchers. In this paper, an energy-efficient job shop scheduling problem (EJSP) is investigated with the objective of minimizing the sum of the energy consumption cost and the completion-time cost. As the classical JSP is well known as a non-deterministic polynomial-time hard (NP-hard) problem, an improved whale optimization algorithm (IWOA) is presented to solve the energy-efficient scheduling problem. The improvement is performed using dispatching rules (DR), a nonlinear convergence factor (NCF), and a mutation operation (MO). The DR is used to enhance the initial solution quality and overcome the drawbacks of the random population. The NCF is adopted to balance the abilities of exploration and exploitation of the algorithm. The MO is employed to reduce the possibility of falling into local optimum to avoid the premature convergence. To validate the effectiveness of the proposed algorithm, extensive simulations have been performed in the experiment section. The computational data demonstrate the promising advantages of the proposed IWOA for the energy-efficient job shop scheduling problem. [ABSTRACT FROM AUTHOR]

Object identifiers (IDs) are playing an increasingly important role in contemporary logistics resource integration processes. In particular, an increasing number of logistics enterprises have noted that unified object IDs (UOIDs) could facilitate effective communication. Numerous researchers have begun to study how to unify object IDs. However, their unification continues to be imperfect. Numerous factors contribute to this imperfection such as technology, economy and application aspects. In this study, the three most important economic factors that were addressed are outlined as follows: extra income, the investment cost created by using UOIDs, and the income loss caused by not using UOID. To study the influences of these three factors on decision-making, a model of UOID adoption strategies was established using evolutionary game theory. The findings are twofold: (1) Different parameter values could cause enterprises to use different evolutionarily stable strategies. These can be determined according to the enterprises’ own funds, other competitors’ strategies, and their logistics resource integration environment. (2) Enterprises participating in logistics resource integration should undertake in-depth cooperation with the help of UOIDs to accelerate the integration process and fulfill their integration goals. [ABSTRACT FROM AUTHOR]

Few studies have examined oral cancer-related mortality in Guangxi. This study aimed to explore the incidence and characteristics of oral cancer and to identify the risk factors for oral cancer-related mortality. The study was conducted to provide a reference for clinical treatment and to improve the survival rate of patients with oral cancer. A total of 271 patients with oral cancer who were treated in the Stomatology Hospital of Guangxi Medical University from 2016 to 2017 were selected as the research subjects. The follow-up lasted until the middle of 2021. The survival rate and mean survival time of 271 patients were calculated by the Kaplan-Meier method. Cox proportional hazard models and stratified analysis were used to explore the related factors that affect the mortality of patients. Nomogram plots were used to visualize the relationships among multiple variables. Among 271 patients with oral cancer, the 2-year and 5-year overall survival rates were 83.8% and 68.5% respectively. The results of multivariate analysis showed that, age, pathological type, surgery and readmission were significant factors affecting survival. When the above factors were incorporated into nomogram plots and stratified analysis, the results showed that the risk of death after treatment in patients with oral cancer aged > 55 years was 1.693 times higher than that in patients aged ≤ 55 years (HR, hazard ratio [HR] = 1.795, 95% confidence intervals [CI] = 1.073, 3.004). The risk of death after surgical treatment was 0.606 times higher than that without surgical treatment (HR = 0.590, 95% CI = 0.367, 0.948). Patients who were readmitted had a 2.340-fold increased risk of death compared with patients who were not readmitted (HR = 2.340, 95% CI = 1.267,4.321). Older age, surgery, and readmission were risk factors for mortality among patients with oral cancer. The median survival time of 271 patients with oral cancer was 52.0 months. Patients under the age of 55 years old and those who choose surgical treatment tend to have a better prognosis and a longer survival. Oral cancer-related mortality is affected by age, treatment mode, readmission, and other factors. All of these factors are worthy of clinical attention for their prevention and control., Competing Interests: The authors have no funding and conflicts of interest to disclose., (Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Vector control is a significant concern in maxillary distraction osteogenesis (DO). Distraction vector planning on the patient's 3D-printed skull phantom is more intuitive for surgeons and cost-efficient than virtual surgical planning. However, the accuracy of transferring the planned vector to intraoperative (vector transfer) according to the shape of the pre-bent footplate alone is relatively limited. The application of augmented reality (AR) in surgical navigation has been studied for years. However, few studies have focused on its role in maxillary DO vector transfer. This study aimed to evaluate the accuracy of AR surgical navigation combined with the pre-bent distractor in vector transfer by comparing it with the pre-bent distractor alone., Methods: Ten patients with maxillary hypoplasia were enrolled with consent, and three identical 3D-printed skull phantoms were manufactured based on per patient's corresponding pre-operative CT data. Among these, one phantom was for pre-operative planning ( n  = 10), while and the other two were for the AR+Pre-bending group ( n  = 10) and the Pre-bending group ( n  = 10) for the experimental surgery, respectively. In the Pre-bending group, the distraction vector was solely determined by matching the shape of footplates and maxillary surface. In the AR+Pre-bending group, the distractors were first confirmed to have no deformation. Then AR surgical navigation was applied to check and adjust the vector in addition to the steps as in the Pre-bending Group., Results: For the angular deviation of the distraction vector, the AR+Pre-bending group was significantly smaller than the Pre-bending group in spatial ( p  < 0.001), x-y plane ( p  = 0.002), and y-z plane ( p  < 0.001), and there were no significant differences in the x-z plane ( p  = 0.221). The AR+Pre-bending group was more accurate in deviations of the Euclidean distance ( p  = 0.004) and the y-axis ( p  = 0.011). In addition, the AR+Pre-bending group was more accurate for the distraction result., Conclusions: In this study based on 3D printed skull phantoms, the AR surgical navigation combined with the pre-bent distractor enhanced the accuracy of vector transfer in maxillary DO, compared with the pre-bending technique alone., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2022 Yuan, He, Jiang, Xie, Zhou and Huang.)

Plant organ abscission is regulated by multiple physiological and biochemical processes. However, the transcriptional, translational, and post-translational modifications occurring during organ abscission have not been systematically investigated. In this study, we report transcriptome, proteome, and ubiquitome data for the abscission zone (AZ) of rose petals collected during petal shedding. We quantified 40,506 genes, 6,595 proteins, and 2,720 ubiquitinated proteins in rose petal AZ. Our results showed that during petal abscission, 1,496 genes were upregulated and 2,199 were downregulated; 271 proteins were upregulated and 444 were downregulated; and 139 ubiquitination sites in 100 proteins were upregulated and 55 ubiquitination sites in 48 proteins were downregulated. Extracellular levels of cell component proteins were significantly increased, while levels within protoplasts were significantly decreased. During petal abscission, transcript levels of genes involved in defense response, transport, and metabolism changed significantly. Levels of proteins involved in the starch and sucrose metabolism and phenylpropanoid biosynthesis pathways were significantly altered at both the transcript and protein levels. The transcriptional and translational upregulation of peroxidase (POD), in the phenylpropanoid biosynthesis, pathway may be associated with deposition of lignin, which forms a protective layer during petal abscission. Overall, our data provide a comprehensive assessment of the translational and post-translational changes that occur during rose petal abscission., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Jiang, Jiang, Deng, Yuan, Liang, Li, Ma and Gao.)