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Odorant-binding protein (OBP) is a potential target for developing insect behavior control agents due to its properties in transporting semiochemicals. In this study, 12 novel jasmonic acid (JA) derivatives were rationally designed and synthesized based on the binding features between

In the era of Industry 4.0, intelligent algorithms provide an effective way to make design methods more sustainable through mining people’s demands continuously, especially in the field of evaluating and predicting the user preferences of phasic or interim design schemes. Vehicle-mounted unmanned aerial vehicles (UAVs) are of significance in mobility experience and traffic surveys. However, as a new type of product, UAVs lack general rules in modeling design and the process of development decision making presents some fuzzy characteristics, which make the evolution and iteration of modeling design more complex. Based on the theories of Kansei Engineering, this study utilized support vector regression (SVR) to establish a correlation model between design factors and preference degree. Because the perceptual evaluation knowledge is fuzzy and uncertain, the paper applied cross-validation and grid search methods to find the optimal parameters. The parameters of the SVR model were adjusted to meet the need for stable learning and for endurance of the noise from subjective experience data to improve the prediction effect and generalization ability. In addition, by means of the Kano model, the customers’ cognition of demand types was quantified to obtain the prioritization of UAV modeling design elements, as well as to compare with the preference scores to validate the feasibility of this research. It was found that the SVR model proposed in the study could effectively predict user preference (R2 = 0.763, RMSE = 0.057). For the UAVs with a higher preference score, the modeling characteristics were consistent with the attractive, one-dimensional or must-be quality elements in the results of the Kano model, which verified the reliability of the study. The conclusion is expected to provide a sustainable design method for vehicle-mounted UAVs commonly used in citizen travel and outdoor activities.

The study analyzed the drivers’ take-over behaviors in intelligent cars when driving on urban roads and tried to find reasonable dimensions of the human-machine interface. Firstly, the main driving assistance functions in the process of take-over were analyzed based on the entropy theory, and the weight values of each function for the consumer’s purchase intention were calculated. Secondly, we explored the perceived comfortable dimensions of the interactive components under typical interaction modes. By means of experiments using a within-subjects design, the initial population of the evolutionary computation was obtained. The evolutionary mechanism of dimensions driven by users’ perception was constructed with a genetic algorithm. After debugging the parameters of the model, we verified the rationality of the model and evolved appropriate dimensions. Finally, the validity of the evolved dimensions was proved by a controlled experiment and paired-sample t-test. The results indicated that the completion time of most take-over tasks under the HMI with the evolved dimensions was significantly shorter, which ensured the HMI could be more conducive to the take-over quality and traffic efficiency.

At a macroscopic level, concepts such as “top spin,” “back spin,” and “rolling” are commonly used to describe the collision of balls and surfaces. Each term refers to an aspect of the coupling of rotational motion during the collision of a spherical particle with a planar surface. In this paper, we explore the mechanisms of energy transfer involving the collision of a rotating sphere and a surface using a model of frictional interactions developed for a granular material. We present explicit analytical treatments for the scattering and derive expressions for two important limiting classes: energy conserving collisions and collisions subject to rapid transverse dissipation.

In the context of the “Internet +” era, more and more schools are beginning to pay attention to online teaching models, and the country has specific requirements for the promotion of online and offline hybrid teaching models. After the outbreak of COVID-19, online teaching has been promoted. The “Internet Community and Interactive Cultural Creativity” course uses digitalization and informatization as the main teaching methods in teaching. A hybrid teaching model combining online teaching using Wisdom Tree platform and offline traditional face-to-face teaching has been established. Through the construction of a teaching situation integrating context, interaction, experience, evaluation and feedback, problem-oriented to improve students' awareness of active learning, cultivate the spirit of innovation, and obtain significant teaching results.

Interdisciplinary research methods bring about more opportunities for designers to understand the users and predict some problems that hard to clarify by traditional methods. This study takes product design of the elder-friendly low-speed vehicles as an example, discussing the rationality of some quantitative methods introduced from other fields. The collected data include the subjects’ operations’ finishing time and finger moving times. By means of multiple linear regression and logistic regression, senior people’s driving behavioral model is built to clarify their operational characteristics. According to the results, a prototype design is proposed and then assessed by analytic hierarchy process. The set of feasible methods used in the research process is sorted out for solving other similar issues targeting relative product design.

In-vehicle human–machine interface (HMI) mainly refers to the T-shaped panel system with instruments, centre console, gear lever and other components installed. For intelligent vehicles, the high level of intelligent interconnection may to some extent make drivers lack situational safety awareness and reduce the usability of the system. Thus, this study attempted to establish a relationship between design features and system usability of the in-vehicle panels. From the perspective of visual ergonomics, the panels were deconstructed into design features to determine 36 samples to be studied. After dividing each sample into four areas of interest (AOI), eye movement and subjective preference data were collected to quantify the user experience. Artificial neural network (ANN) and support vector machine (SVM) were used in the study. Nevertheless, conventional learning algorithms often underwent deficiencies in accuracy and robustness in the detection of multifarious kinds of panels. Therefore, the parameters of the two models were tuned to deal with the noise common in user experience data. The determinant coefficients, mean-square errors and mean relative errors of the two models showed that the SVM model had a higher accuracy, smaller error and was more stable in the learning of user experience of HMI design features, which could provide a method for the layout design and evaluation of T-shaped instrument panel.

A sustainable human-machine interface design has been highlighted for shared cars which is environmentally friendly. To improve people’s perceptual, psychological, and behavioral experience in shared cars, this study revealed the relationship between modeling forms of the instrument panel and interaction performance. Modeling forms include the panel layout and the central screen installation type. After classifying existing panel layout designs into four kinds, this study relied on System Usability Scale (n = 182) to score them and clarify the usability of each kind. The one with the best usability (the symmetrical driver-oriented layout) was identified and ANOVA was used to judge the significance of the difference. Then, three central screen installation types were analyzed and sorted by means of analytic hierarchy process. Based on the above analysis for perceptual preference, behavioral experiments were carried out (n = 60) in intelligent vehicles equipped with the two advantageous screens (all-in-one type and semi-detached type) to analyze electrocardiograph data and workload of typical interaction behaviors. The logit model showed that when interacting with the SD-AIO panel (the panel of symmetrical driver-oriented layout with an all-in-one type screen), tension level was often lower in both driving and secondary tasks. Besides, we explored how the heart rate of specific tasks influenced the total completion time. The conclusion confirmed the advantages of SD-AIO panel, which could contribute to a sustainable interaction with high traffic efficiency.

In recent years, an important development in flood management has been the focal shift from flood protection towards flood risk management. This change greatly promoted the progress of flood control research in a multidisciplinary way. Moreover, given the growing complexity and uncertainty in many decision situations of flood risk management, traditional methods, e.g., tight-coupling integration of one or more quantitative models, are not enough to provide decision support for managers. Within this context, this paper presents a beneficial methodological framework to enhance the effectiveness of decision support systems, through the dynamic adaptation of support regarding the needs of the decision-maker. In addition, we illustrate a loose-coupling technical prototype for integrating heterogeneous elements, such as multi-source data, multidisciplinary models, GIS tools and existing systems. The main innovation is the application of model-driven concepts, which put the system in a state of continuous iterative optimization. We define the new system as a model-driven decision support system (MDSS ). Two characteristics that differentiate the MDSS are as follows: (1) it is made accessible to non-technical specialists; and (2) it has a higher level of adaptability and compatibility. Furthermore, the MDSS was employed to manage the flood risk in the Jingjiang flood diversion area, located in central China near the Yangtze River. Compared with traditional solutions, we believe that this model-driven method is efficient, adaptable and flexible, and thus has bright prospects of application for comprehensive flood risk management.

A well-balanced Godunov-type finite volume algorithm is developed for modelling free-surface shallow flows over irregular topography with complex geometry. The algorithm is based on a new formulation of the classical shallow water equations in hyperbolic conservation form. Unstructured triangular grids are used to achieve the adaptability of the grid to the geometry of the problem and to facilitate localised refinement. The numerical fluxes are calculated using HLLC approximate Riemann solver, and the MUSCL-Hancock predictor–corrector scheme is adopted to achieve the second-order accuracy both in space and in time where the solutions are continuous, and to achieve high-resolution results where the solutions are discontinuous. The novelties of the algorithm include preserving well-balanced property without any additional correction terms and the wet/dry front treatments. The good performance of the algorithm is demonstrated by comparing numerical and theoretical results of several benchmark problems, including the preservation of still water over a two-dimensional hump, the idealised dam-break flow over a frictionless flat rectangular channel, the circular dam-break, and the shock wave from oblique wall. Besides, two laboratory dam-break cases are used for model validation. Furthermore, a practical application related to dam-break flood wave propagation over highly irregular topography with complex geometry is presented. The results show that the algorithm can correctly account for free-surface shallow flows with respect to its effectiveness and robustness thus has bright application prospects.

Single-objection function cannot describe the characteristics of the complicated hydrologic system. Consequently, it stands to reason that multiobjective functions are needed for calibration of hydrologic model. The multiobjective algorithms based on the theory of nondominate are employed to solve this multiobjective optimal problem. In this paper, a novel multiobjective optimization method based on differential evolution with adaptive Cauchy mutation and Chaos searching (MODE-CMCS) is proposed to optimize the daily streamflow forecasting model. Besides, to enhance the diversity performance of Pareto solutions, a more precise crowd distance assigner is presented in this paper. Furthermore, the traditional generalized spread metric (SP) is sensitive with the size of Pareto set. A novel diversity performance metric, which is independent of Pareto set size, is put forward in this research. The efficacy of the new algorithm MODE-CMCS is compared with the nondominated sorting genetic algorithm II (NSGA-II) on a daily streamflow forecasting model based on support vector machine (SVM). The results verify that the performance of MODE-CMCS is superior to the NSGA-II for automatic calibration of hydrologic model.

Overexposure to manganese (Mn) has been known to induce neuronal death and neurodegenerative symptoms. However, the precise mechanisms underlying Mn neurotoxicity remain incompletely understood. In the present study, we established a Mn-exposed rat model and found that downregulation of wild type p53-induced phosphatase 1 (Wip1) might contribute to p53 activation and resultant neuronal apoptosis following Mn exposure. Western blot and immunohistochemical analyses revealed that the expression of Wip1 was markedly decreased following Mn exposure. In addition, immunofluorescence assay demonstrated that Mn exposure led to significant reduction in the number of Wip1-positive neurons. Accordingly, the expression of Mdm2 was progressively decreased, which was accompanied with markedly increased expression of p53, as well as the ratio of Bax/Bcl-xl. Furthermore, we showed that Mn exposure decreased the viability and induced apparent apoptosis in NFG-differentiated neuron-like PC12 cells. Importantly, the expression of Wip1 decreased progressively, whereas the level of cellular p53 and the ratio of Bax/Bcl-xl were elevated, which resembled the expression of the proteins in animal model studies. Depletion of p53 significantly ameliorated Mn-mediated cytotoxic effect in PC12 cells. In addition, ectopic expression of Wip1 attenuated Mn-induced p53 signaling as well as apoptosis in PC12 cells. Finally, we observed that depletion of Wip1 augmented Mn-induced apoptosis in PC12 cells. Collectively, these findings suggest that downregulated Wip1 expression plays an important role in Mn-induced neuronal death in the brain striatum via the modulation of p53 signaling.

In the modern society, barcode has integrated into people's daily life. Nowadays, a simple 1-dimension barcode cannot bear the loadings of complicated information, and the 2-dimension barcode occupies the barcode end-market in no time with the coming the Internet of Things (IOT) era. As the times require emerges the colorcode technology with the bottleneck of 2-dimension barcode such as techs, terminals, information capacity, etc. On the market adaptability, the colorcode owns more advantages than the existent 1 & 2-dimentsion barcode, especially features on the efficiency, convenience, reliability, and inexpensive of recognition with the brand new method of information carrier and reader method adopted. However, there are several technological issues that need perfecting for the widespread use of colorcode, especially the color distortion resulted error code is one of the most important topic of colorcode recognition. Therefore, this document started from the colorcode recognition issue, and then discussed the encountered problems in the colorcode recognition with the principles of physics, chromatology, etc. applied, and proposed corresponding normalized solutions, which are intended to improve on the error correcting capability and extend the range of application.