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In dealing with the expensive multiobjective optimization problem, some algorithms convert it into a number of single-objective subproblems for optimization. At each iteration, these algorithms conduct surrogate-assisted optimization on one or multiple subproblems. However, these subproblems may be unnecessary or resolved. Operating on such subproblems can cause server inefficiencies, especially in the case of expensive optimization. To overcome this shortcoming, we propose an adaptive subproblem selection (ASS) strategy to identify the most promising subproblems for further modeling. To better leverage the cross information between the subproblems, we use the collaborative multioutput Gaussian process surrogate to model them jointly. Moreover, the commonly used acquisition functions (also known as infill criteria) are investigated in this article. Our analysis reveals that these acquisition functions may cause severe imbalances between exploitation and exploration in multiobjective optimization scenarios. Consequently, we develop a new acquisition function, namely, adaptive lower confidence bound (ALCB), to cope with it. The experimental results on three different sets of benchmark problems indicate that our proposed algorithm is competitive. Beyond that, we also quantitatively validate the effectiveness of the ASS strategy, the CoMOGP model, and the ALCB acquisition function.

Yong Lu,1 Yiping Wang,1 Can Shen,1 Jianping Luo,1 Wanjun Yu2 1Laboratory Medicine Center, The Affiliated People’s Hospital of Ningbo University, Ningbo, 315040, People’s Republic of China; 2Department of Respiration, The Affiliated People’s Hospital of Ningbo University, Ningbo, 315040, People’s Republic of ChinaCorrespondence: Wanjun Yu, Email nbyyywj@163.comBackground: The aim of this study was to analyse changes in influenza detection rates of the influenza seasons 2017/2018, 2018/2019, 2019/2020, and 2020/2021 and the changes in personal awareness of protection during the COVID-19 pandemic.Methods: This retrospective study included patients tested for influenza virus A and B from November 2017 to March 2021 at the Affiliated People’s Hospital of Ningbo University (Ningbo, China). Influenza virus A and B tested by direct RT-PCR. A small group of 100 regular participants in influenza virus detection were surveyed on the use of protective measures in four different influenza seasons.Results: There were 14,902, 14,762, 25,070, and 1107 tests of influenza virus A and B in the four influenza periods, for total positive rates of 32.45%, 35.77%, 29.40%, and 0.54%, respectively. In the two periods of four influenza seasons, from November to January, the total number of influenza samples was 8530, 4980, 22,925, 868; from February to March, the number of tests was 6372, 9782, 2145, 239. Total number of tests and positive rate decreased significantly from February/March onwards of the 2019/2020 season, coinciding with the beginning of COVID-19. The proportion of people taking protective measures also increased during the 2019/20 and 2020/21 flu seasons.Conclusion: The influenza virus has a high incidence in this area. The diagnosis rate of influenza decreased after the start of the COVID-19 pandemic. The COVID-19 pandemic had an important impact on the detection rates for influenza virus.Keywords: influenza, COVID-19, pandemic

In order to fundamentally solve the hidden danger of dust explosion in grain silos, a new fully hydraulic intelligent screw clearing machine for grain silos was studied. The rotation of the spiral shaft and the movement of the whole machine were controlled by the hydraulic system, which realized that there was no electrical component in the internal actuator and the safety was higher. This paper introduces the structure and composition of the fully hydraulic intelligent screw clearing machine for grain silos and studies its key technologies. Based on the above achievements, the first domestic fully hydraulic intelligent screw clearing machine for grain silos is developed and applied to the grain silos of Xiamen Port Hailong Wharf. The application further compares and analyzes the main performance parameters of the fully hydraulic intelligent screw clearing machine and the electric screw clearing machine for grain silos. The research shows that the fully hydraulic intelligent screw clearing machine for grain silos can effectively reduce the operation of personnel entering the warehouse and realize safer, intelligent and efficient clearing operations.

In collaboration with 12 other institutions, the Meteorological Observation Center of the China Meteorological Administration undertook a comprehensive marine observation experiment in the South China Sea using the Yilong-10 high-altitude large unmanned aerial vehicle (UAV). The Yilong-10 UAV carried a self-developed dropsonde system and a millimeter-wave cloud radar system. In addition, a solar-powered unmanned surface vessel and two drifting buoys were used. The experiment was further supported by an intelligent, reciprocating horizontal drifting radiosonde system that was deployed from the Sansha Meteorological Observing Station, with the intent of producing a stereoscopic observation over the South China Sea. Comprehensive three-dimensional observations were collected using the system from 31 July to 2 August, 2020. This information was used to investigate the formation and development processes of Typhoon Sinlaku (2020). The data contain measurements of 21 oceanic and meteorological parameters acquired by the five devices, along with video footage from the UAV. The data proved very helpful in determining the actual location and intensity of Typhoon Sinlaku (2020). The experiment demonstrates the feasibility of using a high-altitude, large UAV to fill in the gaps between operational meteorological observations of marine areas and typhoons near China, and marks a milestone for the use of such data for analyzing the structure and impact of a typhoon in the South China Sea. It also demonstrates the potential for establishing operational UAV meteorological observing systems in the future, and the assimilation of such data into numerical weather prediction models.

Many multimodal multiobjective optimization algorithms aim to find as many Pareto optimal solutions as possible while the performance in the objective space is despised. More seriously, some algorithms even overemphasize the diversity of solution set in the decision space at the cost of convergence. How to improve convergence and diversity simultaneously is an important issue when solving multimodal multiobjective optimization problems. In this paper, we propose an evolutionary multiobjective optimization algorithm with a decomposition strategy in the decision space (EMO-DD). A decision subregion allocation and diversity archive preservation methods are proposed to promote the diversity of solutions in the decision space. Meanwhile, a bi-objective optimization problem is formulated for screening for solutions with great convergence and diversity. Combining a modified mating selection method, well-performed solutions both on the convergence and diversity are preserved and inherited. The performance of EMO-DD is compared with five state-of-the-art algorithms on fifteen test problems. The experimental results show that EMO-DD can solve multimodal multiobjective optimization problems, and can improve the performance of the solution set in both the decision and objective spaces.

Resource Allocation (RA) is one of the important factors in network function virtualization (NFV) deployment. As physical (PHY) layer resources are limited, e.g., transmitted energy and channel uses, the RA problem at the PHY layer for NFV deployment has become a fast-growing problem, especially for supporting ultra-reliable and low-latency communications (URLLC). Moreover, different nodes in NFV have different requirements for end-to-end communication, e.g., a controller has more stringent reliability requirements than does a logical node. There is a need for efficient and robust RA algorithms at the PHY layer for NFV deployment. To illustrate these challenges, we consider an up-link (UL) transmission protocol for NFV deployment, in which wireless transmissions with short packets are considered, and both the packet length and the transmission power are adjustable. Then, for three NFV deployment scenarios, we formulate three RA problems as three optimization problems to obtain the optimal parameters. Since these optimization problems are highly non-convex and they include excessive constraint conditions, the global optimal solutions are hard to obtain and are even infeasible for the conventional heuristic algorithms due to their low convergence efficiency. To address these problems, in this paper, the intelligent scheme of the modified shuffled frog-leaping algorithm (MSFLA) based on improved extremal optimization (EO) is applied to design RA algorithms. Three RA algorithms are designed for three NFV deployment scenarios to evaluate the quality of the solutions produced by the MSFLA-EO scheme. We perform simulations of three proposed RA algorithms in terms of various performance parameters. The experimental results are encouraging and demonstrate the efficiency of the proposed RA algorithms.

Cross water–air interface acoustic and electromagnetic integrated communication (AEIC) technology refers to an underwater speaker that excites the water surface micro-amplitude wave (WSAW) on the water’s surface, and millimeter wave radar detects the vibrations of the WSAW to realize the transmission of information. The research on cross-media communication meets many challenges due to the large amplitude of the water surface disturbance and the small amplitude of the WSAW. In this paper, a novel sea wave interference suppression method based on improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet transform (WT) is presented. This method divides the phase change into different intrinsic mode functions (IMFs) and obtains a reconstructed scale of the WSAW signal through wavelet decomposition and correlation procession to separate the WSAW signal and the sea wave interference. It is proved to be better than the reference filtering method by experiment. By using this novel method, the bit error rate (BER) of the communication system can be reduced effectively.

Axial extrusion-connection technology is one of the important connection technologies for hydraulic piping systems, with high sealing performance and mechanical strength. In this paper, the finite-element-modeling method is used to simulate the experimental process of the connection strength of the axial extrusion joint. The generation mechanism and calculation method of the connection strength are analyzed. To optimize the joint strength, orthogonal testing and grey correlation analysis are used to analyze the influencing factors of joint strength. The key factors affecting joint strength are obtained as the friction coefficient μ1, μ2 between joint components and the groove angle θ1 of the fittings body. The back-propagation (BP) neural-network algorithm is used to establish the connection-strength model of the joint and the genetic algorithm is used to optimize it. The optimal connection strength is 8.237 kN and the optimal combination of influencing factors is 0.2, 0.4 and 76.8°. Compared with the prediction results of the neural-network genetic algorithm, the relative error of the finite-element results is 3.9%, indicating that the method has high accuracy. The results show that the extrusion-based joining process offers significant advantages in the manufacture of high-strength titanium tubular joints.

To offer the possibility to achieve the teleoperation of electric overhead travelling (EOT) crane by human operators and avoid in-site hazardous operations, this study presented a technical prototype of tele-operated overhead handling (TOH) crane system using a three-axial robot. To evaluate the usability of the proposed TOH crane system, this study aimed to investigate the satisfaction of human operators during teleoperation. Specifically, we surveyed the subjective feedback of a novice group including 21 University participants and an expert group including 11 professional EOT crane operators regarding the teleoperation system. The subjective feedback was collected through a designed 7-point Likert questionnaire comprised of 11 standard questions. A typical side-to-side handling task condition was tested. The feedback of the novices in each dimension was compared with the experts. The feedback of the experts was used to evaluate the reproducibility of the prototype. The results showed that although most of the novices and experts (over 80%) agreed that the prototype TOH crane system was simple to learn and operate, the positioning assistance displaying critical information on the visual display interface (VDI) such as the relative location of items in relation to the loading area and the alignment, was crucial for the novices’ teleoperations. 91% of the experts gave positive feedback on the reproducibility of the technical prototype. The results of prototyping demonstrated the system features and explored possibilities before the in-site constructions. The results of the investigation of operators’ subjective feedback provided suggestions for developing the features of TOH cranes in the future.

Machine recovery is met from time to time in real-life production. Rescheduling is often a necessary procedure to cope with it. Its instability gauges the number of changes to the existing scheduling solutions. It is a key criterion to measure a rescheduling solution's quality. This work aims at solving a flexible job shop problem with machine recovery, which arises from the scheduling and rescheduling of pump remanufacturing systems. In their scheduling phase, the objective is to minimize makespan. In their rescheduling phase, two objectives are to minimize both instability and makespan. By introducing two novel local search operators into the original Jaya algorithm, this work proposes an improved Jaya algorithm to solve it. It performs experiments on ten different-scale cases of real-life remanufacturing environment. The results show that the improved Jaya is effective and efficient for solving a flexible job shop scheduling and rescheduling problems. It can effectively balance instability and makespan in a rescheduling phase.

The major difficulty in multi-objective optimization evolutionary algorithms (MOEAs) is how to find an appropriate solution which is able to converge towards the true Pareto Front with high diversity. In order to strengthen the selection pressure of the algorithms, indicator-based algorithms have been proposed to handle many-objective optimization problems (MaOPs), among which binary addition quality indicator I e + is superior to other indicators in terms of low computational complexity. However it often has edge effects which degrade the performance of MOEA. In this work, we devise a new MOEA approach, which is able to combine binary addition quality indicator I e + with direction vector (EDV), to address MaOPs. At the same time, an efficient resource allocation strategy is developed to improve the diversity distribution of solutions. Simulation results are presented to show that EDV outperforms state-of-the-art approaches in all problems considered in this paper, and takes a great advantage in solving the black box problem.

Neural Architecture Search (NAS) is studied to automatically design the deep neural network structure, freeing people from heavy network design tasks. Traditional NAS based on individual performance evaluation needs to train many networks generated by the search, and compare the performance of the networks according to their accuracy, which is very time-consuming. In this study, we propose to use a two-category comparator based random forest model as a surrogate to estimate the accuracy of the networks. thereby reducing heavy network training process and greatly saving search time. Instead of directly predicting the accuracy of each network, we propose to compare the relative performance between each two networks in our proposed two-category comparator. Furthermore, we implement the modeling process of the surrogate model in the sampling space of the original training data, which further accelerates the search process of the network in the NAS. Experimental results show that our proposed NAS framework can greatly reduce the search time, while the accuracy of the obtained network is comparable to that of other state-of-the art NAS algorithms.

Neural architecture search (NAS) is an exciting new field in automating machine learning. It can automatically search for the architecture of neural networks. But the current NAS has extremely high requirements for hardware equipment and time costs. In this work, we propose a predictor based on Radial basis function neural network (RBFNN) as a surrogate model of Bayesian optimization to predict the performance of neural architecture. The existing work does not consider the difficulty of directly searching for neural architectures that meet the performance requirements of NAS in real-world applications. Meanwhile, NAS needs to execute multiple times independently when facing multiple similar tasks. Therefore, we further propose a multi-task learning surrogate model with multiple RBFNNs. The model not only functions as a predictor, but also learns knowledge of similar tasks jointly. The performance of NAS is improved by processing multiple tasks simultaneously. Also, the current NAS is committed to searching for very high-performance networks and does not take into account that neural architectures are limited by device memory during actual deployment. The scale of architecture also needs to be considered. We use a multi-objective optimization algorithm to simultaneously balance the performance and the scale, and build a multi-objective evolutionary search framework to find the Pareto optimal front. Once the NAS is completed, decision-makers can choose the appropriate architecture for deployment according to different performance requirements and hardware conditions. Compared with existing NAS work, our proposed MT-ENAS algorithm is able to find a neural architecture with competitive performance and smaller scale in a shorter time.

Quality K-12 education is essential for a student’s success in college and future career. In this paper, we proposed a Random Forest-based algorithm to identify the principal factors affecting the academic success of students in K-12 education across various states in the United States. Our model outperforms other machine learning-based models like Lasso and ElasticNet, reaching an MSE of 0.910. Grid search is used to automatically search for a set of optimal hyperparameters and we concluded that enrollment number, the year when data is recorded and capital outlay expenditure per student have the strongest effect on student’s academic success.

Video super-resolution (SR) aims at generating high-resolution (HR) frames from consecutive low-resolution (LR) frames. The challenge is how to make use of temporal coherence among neighbouring LR frames. Most previous works use motion estimation and compensation based models. However, their performance relies heavily on motion estimation accuracy. In this paper, we propose a multi-scale pyramid 3D convolutional (MP3D) network for video SR, where 3D convolution can explore temporal correlation directly without explicit motion compensation. Specifically, we first apply 3D convolution into a pyramid subnet to extractmulti-scale spatial and temporal features simultaneously from the LR frames, such that it can handle various sizes of motions. We then feed the fused feature maps into an SR reconstruction subnet, where a 3D sub-pixel convolution layer is used for up-sampling. Finally, we append a detail refinement subnet based on the encoder-decoder structure to further enhance texture details of the reconstructed HR frames. Extensive experiments on benchmark datasets and real-world cases show that the proposed MP3D model outperforms state-of-the-art video SR methods in terms of PSNR/SSIM values, visual quality and temporal consistency, respectively.

Multiple-related tasks can be learned simultaneously by sharing information among tasks to avoid tabula rasa learning and to improve performance in the no transfer case (i.e., when each task learns in isolation). This study investigates multitask learning with conditional neural process (CNP) networks and proposes two multitask learning network models on the basis of CNPs, namely, the one-to-many multitask CNP (OMc-MTCNP) and the many-to-many MTCNP (MMc-MTCNP). Compared with existing multitask models, the proposed models add an extensible correlation learning layer to learn the correlation among tasks. Moreover, the proposed multitask CNP (MTCNP) networks are regarded as surrogate models and applied to a Bayesian optimization framework to replace the Gaussian process (GP) to avoid the complex covariance calculation. The proposed Bayesian optimization framework simultaneously infers multiple tasks by utilizing the possible dependencies among them to share knowledge across tasks. The proposed surrogate models augment the observed dataset with a number of related tasks to estimate model parameters confidently. The experimental studies under several scenarios indicate that the proposed algorithms are competitive in performance compared with GP-, single-task-, and other multitask model-based Bayesian optimization methods.

Polysaccharides are the most important group of active ingredients in the orchid Dendrobium officinale. Previous studies have focused on D. officinale polysaccharides during the pre-harvest period, but little is known about the changes in the amounts and activities of polysaccharides in D. officinale stems during the postharvest period. In this study, we evaluated the physiological properties of D. officinale stems during postharvest storage for 90 days at ambient temperature (25 °C) and at low temperatures (4 °C and 0 °C). The contents of polysaccharides, mannose, and glucose first increased then decreased during all storage conditions (storage at 25 °C, 4 °C and 0 °C). The carbohydrate and starch contents decreased considerably as the storage period extended, while cellulose content showed the opposite trend. Polysaccharides from D. officinale stored at ambient temperature exhibited lower antioxidant activity in vitro than polysaccharides from D. officinale stored at low temperatures. The transcript levels of genes encoding α-amylases and β-amylases were related to the degree of starch degradation, and application of an amylase inhibitor prevented starch degradation and polysaccharide accumulation. These results suggested that the increase in polysaccharide content in D. officinale stems stored under low temperatures was related to the decrease in starch content. Our results show that low-temperature storage is a useful approach to enhance the content of polysaccharides and extend the shelf-life of D. officinale.

Dendrobium officinale is a precious traditional Chinese medicinal herb because it is abundant in mannose-containing polysaccharides (MCPs). GDP-mannose transporter (GMT), which translocates GDP-mannose into the Golgi lumen, is indispensable for the biosynthesis of MCPs. In this study, we found that the dominant polysaccharides in D. officinale were MCPs in a range of varieties and different physiological phases. After a positive correlation between the accumulation of mannose and the transcript levels of candidate GMT genes was found, three GMT genes (DoGMT1-3) were identified in D. officinale. DoGMT1, DoGMT2 and DoGMT3 exhibited the highest transcript level in stem that an organ for MCPs storage. All three DoGMT proteins were targeted to Golgi apparatus, and had a GDP binding domain (GXL/VNK) that was homologous to a specially characterized GMT protein GONST1 in Arabidopsis thaliana. Moreover, DoGMT1, DoGMT2 and DoGMT3 complemented a GDP-mannose transport-defective yeast mutant (vrg4-2), meanwhile they also demonstrated a higher GDP-mannose uptake activity. Therefore, we conclude that DoGMT1, DoGMT2 and DoGMT3 are able to transport GDP-mannose while the expression patterns of these genes correspond to the accumulation of MCPs in D. officinale. These findings support the importance of GMT genes from D. officinale in the biosynthesis of MCPs.

To deal with the multi-objective optimization problems (MOPs), a meta-heuristic based on an improved shuffled frog leaping algorithm (ISFLA) which belongs to memetic evolution is presented. For the MOPs, both diversity maintenance and searching effectiveness are crucial for algorithm evolution. In this work, modified calculation of crowding distance to evaluate the density of a solution, memeplex clustering analyses based on a grid to divide the population, and new selection measure of global best individual are proposed to ensure the diversity of the algorithm. A multi-objective extremal optimization procedure (MEOP) is also introduced and incorporated into ISFLA to enable the algorithm to evolve more effectively. Finally, the experimental tests on thirteen unconstrained MOPs and DTLZ many-objective problems show that the proposed algorithm is flexible to handle MOPs and many-objective problems. The effectiveness and robustness of the proposed algorithm are also analyzed in detail.

The issue of integrating preference information into multi-objective optimization is considered, and a multi-objective framework based on decomposition and preference information, called indicator-based MOEA/D (IBMOEA/D), is presented in this study to handle the multi-objective optimization problems more effectively. The proposed algorithm uses a decomposition-based strategy for evolving its working population, where each individual represents a subproblem, and utilizes a binary quality indicator-based selection for maintaining the external population. Information obtained from the quality improvement of individuals is used to determine which subproblem should be invested at each generation by a power law distribution probability. Thus, the indicator-based selection and the decomposition strategy can complement each other. Through the experimental tests on seven many-objective optimization problems and one discrete combinatorial optimization problem, the proposed algorithm is revealed to perform better than several state-of-the-art multi-objective evolutionary algorithms. The effectiveness of the proposed algorithm is also analyzed in detail.

This study presents a new technique for typhoon observations that is based on dropsondes launched by long-distance rockets instead of research aircraft. This technology was developed by Shanghai Typhoon Institute of China Meteorological Administration in collaboration with China Aerospace Science and Industry Corporation in the past three years. On October 3, 2015, this meteorological rocket was successfully launched for the first time into an offshore strong typhoon (Mujigae, 2015) located east of Hainan Island. Four dropsondes were subsequently deployed within 6 min by the rocket when it flew into the inner core of Mujigae from the launching city, Wanning in Hainan Province that was located 200 km away from the storm. Real-time data from the dropsondes were sent back to the observation station on the ground. The detailed profiles of wind, temperature and humidity were obtained, showing high quality according to comparisons with the near collocated GPS radio-sondes data. These dropsonde profiles captured some fine-scale wind structure in the Typhoon Mujigae with may be related to large eddies. This field experiment described in this study implied a potential use of this new dropsondes-deployment technique for future TC research and operation. The success of the field campaign with rocket-carrying dropsondes could provide an effective and direct observation technique for rapid and accurate access to the fine-scale structure of typhoon interior (different areas, but at the same time), and provide a new way to achieve the typhoon targeting sensitivity analysis related to forecasting. Showing the advantages of long-range, high-quality and high-precision, this new technique, when deployed from high altitudes could improve TC model initial condition and provide accurate estimates of storm intensity of offshore typhoons, resulting in improved operational TC track and intensity predictions, as proved by previous studies that used aircraft-deployed dropsondes. Under the support of the Economic and Social Commission for Asia and the Pacific (ESCAP) and World Meteorology Organization (WMO) Typhoon Committee, STI together with HKO organized the Experiment of Typhoon Intensity Change in Coastal Area (EXOTICCA) as an international cooperation project. This project carried out field experiments using rocket-platform sounding detection technology and other new typhoon field detection utilities. In the coming few years, EXOTICCA will also implement observations using man aircraft, unman aircraft, long-distance rockets and GPS sounding in South China Sea. It is believed that the new rocket deployment technology presented in this study when used in future field experiment will improve the track and intensity forecasts of typhoons.

UDP glucose 4-epimerase (UGE), an enzyme with significant impacts on sugar metabolism, catalyzes the reversible inter-conversion between UDP-glucose and UDP-galactose. However, very little is known about whether UGE plays a critical role in the accumulation of water-soluble polysaccharide (WSP) and its relationship to abiotic stress tolerance. Here, DoUGE from D. officinale, encoding UGE localized in the cytoplasm, was initially cloned and analyzed. DoUGE exhibited highly tissue-specific expression patterns. The highest expression was in the stems of seedlings and adult plants. The content of WSPs ranged from 168.43 to 416.12 mg g−1 DW from developmental stages S1 to S4, the highest value being in S3. DoUGE was expressed throughout S1 to S4, with a maximum in S3. This trend was similar in three cultivated varieties (T10, T32-5 and T636). There was a positive correlation between DoUGE expression and the content of WSPs (R 2 = 0.94; p

BACKGROUND Carotid atherosclerosis (CA) is a common disease in middle-aged and elderly people, which is closely related to cardiovascular and cerebrovascular disease. In this study, we investigated the benefits of the electrocardiogram (ECG)-based R wave pulse wave index (ERWVI) for the diagnosis of CA. MATERIAL AND METHODS According to CA examinations by color Doppler ultrasound, patients were assigned to positive and negative groups. The ECG R wave-Pulse wave transit time (ERWPTT) was obtained by synchronously collecting ECG signals (R wave in ECG) and the time variations in maximum finger pulse oxygen (DOP) on the ECG monitor. RESULTS ERPWI was positively correlated with sex, age, BMI, diastolic/systolic blood pressure, fasting blood glucose, uric acid, cholesterol and triglyceride levels, LDL-cholesterol, non-alcoholic fatty liver disease (NAFLD), creatinine, and homocysteine, and was negatively correlated with HDL-cholesterol (P

The purpose of this paper is to reflect on the design method and process of extravehicular activity (EVA) spacesuit interface design, which is mainly dominated by engineering. Studies in the literature focus on “physical device,” but research on “interactive behavior” is insufficient. This study also proposes a change in the design strategy of an EVA spacesuit’s interface from the design of “things” to the design of “behavior”. Interaction design takes people’s “interaction behavior” as its design object, and the interface itself are the intermediary of the interaction process. This paper analyzes the influencing factors and principles of the design of the interface of the spacesuit through case studies and analogy analysis. Results of this study can help designers comprehensively research the relationship among humans, actions, intermediary (tools), goals, and scenes involving the interactive design of a spacesuit.

The aim of this study is to advance the current understanding of human-centred design (HCD) and its toolkits effectively applied in designing and developing spacesuits for extravehicular activity (EVA). This study intends to address how HCD supports the design and development of the EVA Spacesuit through an in-depth case study on the ‘Feitian’ EVA Spacesuit. Data were primarily collected through a series of in-depth interviews with senior staff of the Astronaut Center of China. This study mainly contributes two aspects of knowledge: (1) knowledge of aerospace product design process based on HCD principle, including design strategy and design process knowledge; (2) user knowledge: user needs and ergonomic factors involved in the design of EVA Spacesuit products were comprehensively summarised. The results of this study can contribute towards promoting a humanised design of the EVA Spacesuit.

The effect of ultrasonic treatment was evaluated to develop an efficient preparation method of cellulose with controllable molecular weight (Mw) and preferable functional properties. Our results showed that the physical approach could prominently reduce the mean Mw of cellulose from 68.07 to 6.82 KDa via 30‐hr ultrasonic treatment. The morphology and structure of the samples were characterized using Fourier transform infrared spectroscopy, X‐ray diffraction, scanning electron microscope (SEM), and laser diffraction, indicating a slight change in structure and notable modification in morphology caused by ultrasonic treatment. Interestingly, the ultrasonic‐treated cellulose exhibited much better physicochemical properties. For example, with a 30‐hr ultrasonic treatment, the cholesterol‐binding capacities of cellulose were increased about 2.42‐fold at pH 7.0 and 1.96‐fold at pH 2.0. The findings demonstrated that cellulose treated by ultrasonic processing could have a great potential for applications in the biomedical and food industries. PRACTICAL APPLICATIONS: As a major source of insoluble fiber, cellulose has already been recommended for its possible benefits to human health, leading to numerous efforts for its applications in food industry and biomedical engineering. In this work, we successfully controlled Wm of cellulose via ultrasonic treatment, and the ultrasonic‐treated cellulose exhibited improved functional and physicochemical properties. The positive effect of ultrasound may be useful for functional modification and utilization of cellulose.

The plant late embryogenesis abundant (LEA) proteins are abundant in seeds, play an important role in various abiotic stresses. However, there is still no information on genome-wide identification of LEA genes in orchids and their function in callus formation is almost unknown. In this study, the LEA genes from two orchids (Phalaenopsis equestris and Dendrobium officinale), were genome-wide identified, classified and characterized. A total of 57 and 59 LEA genes were identified in the genomes and these were divided into 8 and 9 groups for P. equestris and D. officinale, respectively. The LEA_1 and LEA_4 genes from P. equestris and D. officinale showed strong expression in seeds, but were significantly down-regulated in flowers and absent in vegetative organs (leaves, stems and roots). In addition, the LEA_1 and LEA_4 genes from D. officinale were abundant in the protocorm-like body (PLB) stage, while weak signals that were detected in in vitro shoots could not be detected in plantlets. The expression of these genes highlights PLBs in orchids are somatic embryos. The DoLEA36 from LEA_4 and DoLEA43 from LEA_1 were further characterized. The GFP signal of the DoLEA36-GFP fusion protein was only detected in the cytoplasm, while the GFP signal of the DoLEA43-GFP fusion protein was detected in both the cytoplasm and nucleus. This indicates that DoEA36 localizes in the cytoplasm while DoLEA43 localizes in both the cytoplasm and nucleus. Both DoLEA36 and DoLEA43 stimulated callus formation in transgenic Arabidopsis. The percentage of callus formation from 35S::DoLEA43 transgenic lines was higher than in wild type plants in two callus induction methods. Our results provide comprehensive information about the LEA gene family in orchids and genetic evidence for the involvement of LEA genes in the induction of callus, which may reveal their positive role in the maintenance of PLBs in orchids.

A novel many-objective particle swarm optimization (PSO) algorithm called IDMOPSO is presented in this study to robustly and effectively address many-objective optimization problems (MaOPs). IDMOPSO is based on a performance indicator and direction vectors. A selection strategy based on the quality indicator Ie+ and Pareto dominance for personal best (pbest) particles is proposed to ensure the convergence and diversity of the algorithm and enhance the capability of local exploration. An external archive based on Ie+ and direction vectors is used to preserve the diversity of non-dominated solutions found in the search process. A multi-global optimal (gbest) particle selection method is developed to increase global search ability and ensure the particles' diversity. This method allows each particle to be assigned to a different gbest particle. This method differs from the traditional method, wherein only one gbest particle is allocated for the whole population of PSO. We aim to design a robust multi-objective evolutionary algorithm to deal with MaOPs. Extensive comparative experiments on DTLZ and DTLZ−1 problems with varied numbers of objectives show that IDMOPSO is effective and flexible in addressing MaOPs. The influences and effectiveness of the proposed strategies are also analyzed in detail.

Osteosarcoma (OS) is a common primary malignancy in adolescents and children. MicroRNAs (miRNAs or miRs) can regulate the progression of OS. Herein, we explored the target genes and effects of miR‑9 in OS. Cell growth, colony formation and cell cycle were respectively examined using a cell counting kit‑8 (CCK‑8), crystal violet staining and flow cytometry. The target gene of miR‑9 was predicted according to the MicroRNA.org website. Luciferase activity was examined using a dual luciferase reporter gene assay kit. The corresponding factors levels were analyzed by carrying out reverse transcription‑quantitative PCR (RT‑qPCR) and western blot analysis. A mouse model of OS was also established and the volume and weight of the tumors of the mice with OS were measured. The levels of p16 in the mice with OS were detected by immunohistochemistry (IHC). The data revealed a high expression of miR‑9 and a low expression of p16 in the OS tissue. p16 was found to be the target gene for miR‑9 in OS. miR‑9 depletion decreased the proliferation and colony formation of Saos‑2 cells by arresting the cells at the G1 phase, accompanied by the downregulation of cyclin A, cyclin D1 and c‑Myc expression levels. Moreover, miR‑9 depletion inhibited the phosphorylation of p38, c‑Jun N‑terminal kinase (JNK) and extracellular signal‑regulated kinase (ERK). In vivo, miR‑9 depletion decreased the tumor volume and weight and increased p16 expression in the mouse tumor tissues. Nevertheless, p16 silencing reversed the suppressive effects of miR‑9 inhibitors on OS cells. On the whole, the findings of this study substantiate that miR‑9 depletion suppresses cell proliferation by targeting p16 in OS and by mediating the activation of the ERK/p38/JNK pathway.

Late embryogenesis abundant (LEA) proteins are widely involved in plant stress responsive, while their involvement in callus formation is largest unknown. In this study, we identified and conducted expression analysis of the LEA genes from Phalaenopsis equestris and Dendrobium officinale, and characterized a LEA gene from D. officinale. A total 57 and 59 LEA genes were identified in P. equestris and D. officinale, respectively. A phylogenetic analysis showed that AtM, LEA_5 and Dehydrin groups were absent in both orchids. LEA_1 group genes were strongly expressed in seeds, significantly down-regulated in flowers, and absent in vegetative organs (leaves, stems and roots) in both orchids. Moreover, LEA_1 and LEA_4 group genes from D. officinale were abundant in the protocorm-like body stage and were dramatically up-regulated in response to abscisic acid and salinity stress. A LEA_1 gene (DoLEA43) was selected for further functional analysis. DoLEA43 protein was localized in the cytoplasm and nucleus, and its promoter contained a WUN-motif that was modulated by wounding. Overexpression of DoLEA43 in Arabidopsis enhanced callus induction, causing changes to callus formation-related genes such as WIND1. Our results indicate the involvement of LEA genes in the induction of callus, which provide insights into plant regeneration.

Display Omitted Novel meta-heuristic to the multi-objective optimisation problem.The multi-objective optimization algorithm compared with other work in the literature.The algorithm possesses outstanding performance. In addition to dominance-based and decomposition-based algorithms, performance indicator-based algorithms have been widely used and investigated in the field of evolutionary multi-objective optimisation. This study proposes a multi-objective artificial bee colony optimisation method called e -MOABC based on performance indicators to solve multi-objective and many-objective problems. The proposed algorithm develops an external archive on the basis of both Pareto dominance and preference indicators to save the non-dominated solutions produced in each generation. The population of the presented algorithm includes employed bees, onlooker bees, and scout bees. Employed bees adjust their trajectories according to the information provided by other employed bees. Motivated by employed bees, onlooker bees select food sources to update their positions according to a power law probability, with which the food sources with high quality have a high probability to be selected for exploration. The quality of food sources is calculated on the basis of the quality indicator I e + . Scout bees dispose of food sources with poor quality. The proposed algorithm proves to be competitive in dealing with multi-objective and many-objective optimisation problems in comparison with other state-of-the-art algorithms for CEC09, LZ09, and DTLZ test instances.

Shionone is a triterpenoid component derived from the herbal medicine Aster tataricus, and it has been reported to possess marked anti-inflammatory properties. The activation of NLRP3 inflammasome plays an important role in cystitis, and the effect of Shionone on NLRP3 inflammasome-dependent pyroptosis remains unclear. In this study, we established an interstitial cystitis (IC) rat model and SV-HUC-1 cell model with CYP or LPS + ATP treatment to mimic inflammation response and induce NLRP3 inflammasome activation. Shionone treatment significantly attenuated the bladder wet weight, score of edema and hemorrhage, enhanced the viability of SV-HUC-1 cell, decreased the rate of pyroptosis. Moreover, Shionone reduced the expression of NF-κB, NLRP3, ASC, Pro-caspase-1, Caspase-1, GSDMD, GSDMD-N at the mRNA and protein levels both in rat and SV-HUC-1 cell model, demonstrating NLRP3 inflammasome pathway was blocked and pyroptosis degree was reduced. These results indicated that Shionone could alleviate interstitial cystitis in Rat model and enhancing the viability of SV-HUC-1 cells via NF-κB/NLRP3/GSDMD-N pathway, which illustrated that Shionone could be used as a drug candidate for the treatment of interstitial cystitis.

This paper proposes a Gaussian process (GP) based co-sub-Pareto front surrogate augmentation strategy for evolutionary optimization of computationally expensive multiobjective problems. In the proposed algorithm, a multiobjective problem is decomposed into a number of subproblems, the solution of each of which is used to approximate a portion or sector of the Pareto front (i.e., a subPF). Thereafter, a multitask GP model is incorporated to exploit the correlations across the subproblems via joint surrogate model learning. A novel criterion for the utility function is defined on the surrogate landscape to determine the next candidate solution for evaluation using the actual expensive objectives. In addition, a new management strategy for the evaluated solutions is presented for model building. The novel feature of our approach is that it infers multiple subproblems jointly by exploiting the possible dependencies between them, such that knowledge can be transferred across subPFs approximated by the subproblems. Experimental studies under several scenarios indicate that the proposed algorithm outperforms state-of-the-art multiobjective evolutionary algorithms for expensive problems. The parameter sensitivity and effectiveness of the proposed algorithm are analyzed in detail.

This paper proposes a novel hybrid shuffled frog leaping algorithm (HSFLA) for vehicle routing problem with time windows (VRPTW). The diversity control strategy is developed to construct the memeplexes of the HSFLA and avoid ending the search prematurely. The modified clone selection procedure is presented to improve the quality of the solutions and bring more diversity to the population. Improved and extended extremal optimization (EO) with alternative move operators is also introduced to the exploitation of the algorithm. Furthermore, the adaptive soft time windows penalty measure is proposed to allow the existence of infeasible solutions in the evolution process. Our approach is estimated and compared with other state-of-the-art heuristics using Solomon and Cordeau VRPTW test sets. The experimental results show that the presented algorithm is very effective for handling VRPTW.

The aim was to assess prognostic value of serum 25-hydroxyvitamin D (25[OH] D) levels in older Chinese patients with hip fracture. From June, 2012 to February, 2014, older patients with hip fracture were included. Serum 25(OH) D levels were measured at admission. The functional evaluation at the time of discharge was performed by the Barthel Index. In the 66 patients with an unfavorable outcome, serum 25(OH) D levels were lower compared with those with a favorable outcome. In multivariate analyses, there was an increased risk of unfavorable outcome associated with serum 25(OH) D levels ≤20 ng/ml (OR 5.25, 95% CI: 3.12–8.16). Our data supported an association between serum 25[OH] D levels at admission and short-term prognosis in Chinese older patients with hip fracture.

Aims Osteosarcoma (OS) is the most common primary bone malignancy that affects adolescents. Although great attention has been paid to the diagnosis of and therapy for OS, the 5-year survival rate of patients with this disease remains poor. MicroRNAs are small non-coding RNAs involved in pathogenesis and progression of human malignancies. MiR-139 has been implicated in several human cancers. However, the role played by miR-139 in pathogenesis of human OS has remained largely unknown. Main methods Realtime PCR was used to detect the expression of miR-139. CCK-8, BrdU-ELISA and ApoTox-Glo™ Triplex assay was employed to detect the proliferation and apoptosis of osteosarcoma cells. Realtime PCR, Western Blotting and luciferase report assays were conducted for the target genes analysis. Key findings The expression of miR-139 was reduced while the expression of forkhead-box P2 (FOXP2) was induced in both OS tissue and cell lines. The reduced level of miR-139 was correlated with tumor size, clinical stage and distant metastasis. Overexpression of miR-139 inhibited the expression of FOXP2, which suppressed cell growth, but induced apoptosis. Further, we confirmed that FOXP2 was a direct target of miR-139 by luciferase reporter assay. Knockdown of FOXP2 resulted in reduced levels of cell proliferation, but increased levels of apoptosis in vitro. Significance These findings suggest that miR-139 plays a suppressive role in the regulation of OS cell proliferation and migration via directly targeting FOXP2, which might be a potential clinical diagnostic or predictive biomarker for human OS.

The WRKY family, one of the largest families of transcription factors, plays important roles in the regulation of various biological processes, including growth, development and stress responses in plants. In the present study, 63 DoWRKY genes were identified from the Dendrobium officinale genome. These were classified into groups I, II, III and a non-group, each with 14, 28, 10 and 11 members, respectively. ABA-responsive, sulfur-responsive and low temperature-responsive elements were identified in the 1-k upstream regulatory region of DoWRKY genes. Subsequently, the expression of the 63 DoWRKY genes under cold stress was assessed, and the expression profiles of a large number of these genes were regulated by low temperature in roots and stems. To further understand the regulatory mechanism of DoWRKY genes in biological processes, potential WRKY target genes were investigated. Among them, most stress-related genes contained multiple W-box elements in their promoters. In addition, the genes involved in polysaccharide synthesis and hydrolysis contained W-box elements in their 1-k upstream regulatory regions, suggesting that DoWRKY genes may play a role in polysaccharide metabolism. These results provide a basis for investigating the function of WRKY genes and help to understand the downstream regulation network in plants within the Orchidaceae.

Cloud computing aims to provide dynamic leasing of server capabilities as scalable virtualized services to end users. However, data centers hosting cloud applications consume vast amounts of electrical energy, thereby contributing to high operational costs and carbon footprints. Green cloud computing solutions that can not only minimize the operational costs but also reduce the environmental impact are necessary. This study focuses on the Infrastructure as a Service model, where custom virtual machines (VMs) are launched in appropriate servers available in a data center. A complete data center resource management scheme is presented in this paper. The scheme can not only ensure user quality of service (through service level agreements) but can also achieve maximum energy saving and green computing goals. Considering that the data center host is usually tens of thousands in size and that using an exact algorithm to solve the resource allocation problem is difficult, the modified shuffled frog leaping algorithm and improved extremal optimization are employed in this study to solve the dynamic allocation problem of VMs. Experimental results demonstrate that the proposed resource management scheme exhibits excellent performance in green cloud computing.