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The anionic properties of electrolytes play a significant role in determining the electrochemical performance of VS2/SNC anodes, which is interpreted from the angle of solvation effect and corresponding electrode/electrolyte interface properties.

Nowadays X70 steel has been used as a core material for the construction of high-pressure, large-diameter and high-strength steel pipelines. The wall of piplines of an operating pipline system will deform in various degrees. Severe deformation will lead to microcracks or even a fracture. The ultrasonic flaw detection method is designed for the detection and evaluation of microcracks and defects caused by the deformation of X70 steel. However, an effective method to measure the stress level of X70 steel accurately is still lacking since the deformation of X70 steel often occurs within the elastic stage or plastic stage. Through conducting an experiment to study the tensile test of X70 steel specimens under a constant temperature, and measuring the propagation time and amplitude of ultrasonic LCR wave simultaneously to conclude the law of the propagation of ultrasonic LCR wave in elastic-plastic deformation of X70 steel, and determine the relationship between the propagation time and the amplitude of ultrasonic LCR wave and the deformation degree of X70 steel. According to the study, the deformation degree of X70 steel could be determined by the propagation time of ultrasonic LCR wave and the amplitude of the first wave: the level of stress could be calculated within the elastic stage and the deformation degree could be calculated within the plastic stage. The study results provide an effective support for the stress measurement of X70 steel by ultrasonic LCR wave.

All-solid-state batteries (SSBs) are prospective candidates for a range of energy accumulation systems, delivering higher energy densities compared to batteries which use liquid electrolytes. Amongst the numerous solid-state electrolytes (SEs), sulfide-based electrolytes in particular have received more attention given that they have a high ionic conductivity. However, the incompatibility between the electrode and SEs is still an ongoing challenge that leads to poor electrochemical performance. In this work, we focus on 1T-MoS2. It is well known that 1T metallic MoS2 is unstable even at room temperature. However, we showed that 1T-MoS2 can be stabilized at 600 °C for at least 2 h, and the 1T-MoS2-600 interlayer spacing expanded to 0.95 nm. The high crystallinity of the 1T phase is highly compatible with solid electrolytes and coupled with the increased interlayer spacing, so in the all-solid-state lithium-ion battery (ALLLIB), we achieved outstanding cycling performance. At the current density of 0.2 C (1 C = 670 mA g−1), this material delivered a capacity of 406 mA h g−1 after 50 cycles.

Herein, a heterostructural hexagonal@tetragonal GeO

Normally entrepreneur would raise fund from angel investors during the initial round. If the venture program was by then successful, the entrepreneur would then continue the fund-raising process from venture capitalist. By adopting the convertible preferred stock, we managed to construct the two-stage angel investment decision process. This research reveals the following: (1) The probability of the first stage’s success has negative relationships with levels of priority dividend in both first and second stages, as well as with the venture capitalist’s proportion of shares. (2) The probability of the second stage’s success has negative relationships with the venture capitalist’s proportion of shares and the dividend level of both first and second stage funding. (3) There has been a threshold of dividend distribution, which belongs to angel investor. While the level of angel investor’s shares is higher than the threshold, AN would decide to join the second phase of the program; otherwise, AN would exit the project at the end of the first stage.

While visual object detection with deep learning has received much attention in the past decade, cases when heavy intra-class occlusions occur have not been studied thoroughly. In this work, we propose a Non-Maximum-Suppression (NMS) algorithm that dramatically improves the detection recall while maintaining high precision in scenes with heavy occlusions. Our NMS algorithm is derived from a novel embedding mechanism, in which the semantic and geometric features of the detected boxes are jointly exploited. The embedding makes it possible to determine whether two heavily-overlapping boxes belong to the same object in the physical world. Our approach is particularly useful for car detection and pedestrian detection in urban scenes where occlusions often happen. We show the effectiveness of our approach by creating a model called SG-Det (short for Semantics and Geometry Detection) and testing SG-Det on two widely-adopted datasets, KITTI and CityPersons for which it achieves state-of-the-art performance. Our code is available at https://github.com/ChenhongyiYang/SG-NMS.

Monitoring population-level changes in diet could be useful for education and for implementing interventions to improve health. Research has shown that data from social media sources can be used for monitoring dietary behavior. We propose a scrape-by-location methodology to create food image datasets from Instagram posts. We used it to collect 3.56 million images over a period of 20 days in March 2019. We also propose a scrape-by-keywords methodology and used it to scrape ~30,000 images and their captions of 38 Kenyan food types. We publish two datasets of 104,000 and 8,174 image/caption pairs, respectively. With the first dataset, Kenya104K, we train a Kenyan Food Classifier, called KenyanFC, to distinguish Kenyan food from non-food images posted in Kenya. We used the second dataset, KenyanFood13, to train a classifier KenyanFTR, short for Kenyan Food Type Recognizer, to recognize 13 popular food types in Kenya. The KenyanFTR is a multimodal deep neural network that can identify 13 types of Kenyan foods using both images and their corresponding captions. Experiments show that the average top-1 accuracy of KenyanFC is 99% over 10,400 tested Instagram images and of KenyanFTR is 81% over 8,174 tested data points. Ablation studies show that three of the 13 food types are particularly difficult to categorize based on image content only and that adding analysis of captions to the image analysis yields a classifier that is 9 percent points more accurate than a classifier that relies only on images. Our food trend analysis revealed that cakes and roasted meats were the most popular foods in photographs on Instagram in Kenya in March 2019., Comment: Another version of the paper was submitted to the ACM International Conference on Multimedia (ACMMM2019)

Channel coordination is an optimal state with operation of channel. For achieving channel coordination, we present a quantity discount mechanism based on a fairness preference theory. Game models of the channel discount mechanism are constructed based on the entirely rationality and self-interest. The study shows that as long as the degree of attention (parameters) of retailer to manufacturer's profit and the fairness preference coefficients (parameters) of retailers satisfy certain conditions, channel coordination can be achieved by setting a simple wholesale price and fixed costs. We also discuss the allocation method of channel coordination profit, the allocation method ensure that retailer's profit is equal to the profit of independent decision-making, and manufacturer's profit is raised.

In this paper, we propose a new structure that combines Grey Prediction with Line of Sight (GP-LOS) to predict yaw angle in order to achieve path following for autonomous underwater vehicle (AUV). The proposed structure can be described by two stages. First, we use grey prediction to predict the position coordinates of the next time. Second, taking advantage of the principles of the Line of Sight, we can obtain the desired heading in advance. Then we take the classical PID as control algorithm of AUV because it has the advantage to be easily implemented and to provide reliable control performance. The simulation results have shown that the GP-LOS can achieve the path following effectively whether straight line or curve.

Crack-induced changes in the aeroelastic boundaries of an unswept composite wing are investigated. The bending-torsion couplings due to the unbalanced laminates and offset of the center of gravity are incorporated into the equation of motion. The edge crack, modeled with the local flexibility concept, introduces additional boundary conditions at the crack location. The fundamental modes of the intact and cracked beam are used in Galerkin's method, and the approximate solution for flutter and divergence speeds is obtained with steady and quasi-steady aerodynamic forces applied. Changes in flutter and divergence speeds (with respect to the crack ratio and its location, along with the fiber orientation) are compared. In many cases, the existence of an edge crack imposes detrimental effects on the aeroelastic boundaries, although it may increase the flutter and/or divergence speed when fibers are orientated at certain angles. The results may help composite wing designers in their aeroelastic tailoring and structural engineers in designing damage prognosis tools to predict the health status of, composite wing structures.

The aeroelastic characteristics of a cantilevered composite panel of large aspect ratio and with an edge crack are investigated. The panel consists of several fiber-reinforced composite plies, and is modeled with a one-dimensional beam vibrating in coupled bending and torsion. The fundamental mode shapes of the cracked cantilever are used to study the interaction between a crack and aerodynamic characteristics by employing Galerkin’s method. Variation of the divergence/flutter speed with respect to the crack ratio, its location as well as the fiber angle is investigated. The divergence/flutter speed is more sensitive to the bending-torsion coupling parameter than to the presence of the crack. The crack may or may not reduce the divergence/flutter speed, depending on the fiber orientation. The qualitative analysis may help the development of an online prognosis tool for an aircraft with large aspect-ratio unswept composite wings.

The coupled bending and torsional vibration of a fiber-reinforced composite cantilever with an edge surface crack is investigated. The model is based on linear fracture mechanics, the Castigliano theorem and classical lamination theory. The crack is modeled with a local flexibility matrix such that the cantilever beam is replaced with two intact beams with the crack as the additional boundary condition. The coupling of bending and torsion can result from either the material properties or the surface crack. For the unidirectional fiber-reinforced composite, analysis indicates that changes in natural frequencies and the corresponding mode shapes depend on not only the crack location and ratio, but also the material properties (fiber orientation, fiber volume fraction). The frequency spectrum along with changes in mode shapes may help detect a possible surface crack (location and magnitude) of the composite structure, such as a high aspect ratio aircraft wing. The coupling of bending and torsion due to a surface crack may serve as a damage prognosis tool of a composite wing that is initially designed with bending and torsion decoupled by noting the effect of the crack on the flutter speed of the aircraft.

The development, survivorship, and reproduction of brown citrus aphid, Toxoptera citricida (Kirkaldy), were evaluated at 8 constant temperatures (8, 10, 15, 20, 25, 28, 30, and 32°C). The developmental periods of immature stages ranged from 63.1 d at 8°C to 5.5 d at 30°C. The lower developmental threshold for the brown citrus aphid immature was estimated at 6.27°C. The upper temperature threshold of 31.17°C for development of nymph was determined from a nonlinear biophysical model. The percentage of survivorship of immature stages varied from 81 to 97% within the temperature range of 8–30°C. However, survivorship was reduced to 29% at 32°C. The average longevity of adult females ranged from 60.0 d at 10°C to 6.5 d at 32°C. The average progeny per female was 52.5 at 20°C and 7.5 at 32°C. The largest rm (0.3765) occurred at 28°C. Populations reared at 10 and 32°C had the smallest rm values of 0.0588 and 0.0960, respectively. The mean generation time of the population ranged from 51 d at 10°C to 8 d at 32°C. The optimal range of temperature for T. citricida population growth was 20–30°C. Several mathematical functions were used to quantify brown citrus aphid development, survivorship, reproduction and lifetable parameters in relation to temperatures.

The development, survivorship, and reproduction of buckthorn aphid, Aphis nasturtii Kaltenbach, were evaluated at 7 constant temperatures (10, 15, 20, 25, 27, 30, and 35°C). The developmental periods of immature stages ranged from 33.9 d at 10°C to 5.7 d at 30°C. The lower developmental threshold for the buckthorn aphid was estimated at 6.5°C. The upper temperature threshold of 35.6°C for development of nymph was determined from a nonlinear biophysical model. The percentages of survivorship of immature stages varied from 60.0 to 87.5% within the temperature range of 15-30°C. However, survivorships were reduced to 15.2 and 37.5% at 10 and 35°C, respectively. The average longevity of adult females ranged from 30.2 d at 15°C to 7.0 d at 35°C. The average progeny per female was 55.0 at 15°C and 5.5 at 35°C. At 10°C the majority of adults failed to reproduce. The largest r m (0.332) occurred at 27°C. Populations reared at 10 and 35°C had negative r m values of -0.022 and -0.014, respectively. The mean generation time of the population ranged from 40.3 d at 10°C to 8.3 d at 35°C. It was evident that extreme temperatures prolonged development, increased mortality of immatures, shortened adult longevity, and reduced fecundity. The optimal range of temperature for A. nasturtii population growth was 20-30°C.

Effects of 5 commercially grown vegetables on the development, survivorship, and reproduction of the whitefly Bemisia argentifolii Bellows & Perring were studied in the laboratory. The percentages of whitefly immatures that survived on eggplant, Solanum melongena L.; tomato, Lycopersicon esculentum Miller; sweet potato, Ipomoea batatus (L.) Lam; cucumber, Cucumis sativus L.; and garden bean, Phaseolus vulgaris L. at 25 ± 1°C were 88.7, 60.2, 67.5, 46.4, and 45.8%, respectively. The body lengths from 2nd to 4th instars among populations on these 5 host plants were not significantly different. Female nymphs were significantly larger than males. Developmental time from egg to adult ranged from 17.3 d on eggplant to 20.9 d on garden bean. The average number of eggs laid per female were 223.7, 167.5, 77.5, 66.0, and 83.5 on the above respective hosts, and female adults lived an average of 24.0, 20.5, 16.6, 9.8, and 13.4 d on these same hosts. The intrinsic rate of natural increase (rm) for B. argentifolii on eggplant was highest. Jackknife estimates of rm varied from 0.192 on eggplant to 0.120 on garden bean. The mean generation time of the population on these hosts ranged from 23.2 to 27.2 d at 25°C. Based on life-table analyses of whitefly populations, we concluded that eggplant was the most suitable host for B. argentifolii and garden bean the least suitable.

The development, survivorship, and reproduction of silverleaf whitefly, Bemisia argentifolii Bellows & Perring, on eggplant at 6 constant temperatures (15, 20, 25, 27, 30, and 35°C) were studied. The development time from egg to adult varied from 105 d at 15°C to 14 d at 30°C. A common developmental threshold for all immature stages was estimated at 12.5°C. A nonlinear biophysical model describing the relationship between development rate and temperature was constructed. Low- and high-temperature inhibitions of 15-18°C and 30-35°C, respectively, for development of immature stage were derived from the model. The survivorship from egg to adult was 89% at 25°C; at 15 and 35°C, the survival percentages were 40 and 37%, respectively. The average longevity of adult females ranged from 44 d at 20°C to 10 d at 35°C. The oviposition (eggs per female) of B. argentifolii varied from 324 at 20°C to 22 at 35°C. Life table parameters were determined at 5 constant temperatures (20, 25, 27, 30, and 35°C). The intrinsic rates of natural increase (r m ) for B. argentifolii at 25 and 27°C were the highest. Jackknife estimates of r m varied from 0.192 at 25 and 27°C to 0.073 at 35°C. The mean generation times of populations ranged from 46 d at 20°C to 18 d at 30°C. The adverse effects of high temperature (35°C) were evidenced by prolonged development, shortened adult longevity and reduced fecundity. The optimal range of temperature for B. argentifolii population growth was 20-30°C. The results of this study were compared with those reported for B. tabaci biotype B in Mississippi and B. tabaci biotype A in Arizona.

The main objective of this work is the development of an intelligent multisensor integration and fusion model that uses fuzzy logic. Measurement data from different types of sensors with different resolutions is integrated and fused together based on the confidence in them derived from information not typically used in traditional data fusion methods. Examples of such information are operating temperature, frequency range, fatigue cycles, etc. These are fed as additional inputs to a fuzzy inference system (FIS) that has predefined membership functions for each of these variables. The output of the FIS are weights that are assigned to the different sensor measurement data that reflect the confidence in the sensor’s behavior and performance. A modular approach is adopted for the data fusion system. It allows adding or deleting a sensor, along with its fuzzy logic controller (FLC), anytime without affecting the entire data fusion system. This paper presents a preliminary model that fuses the data from three different types of sensors that monitor the strain at a single location in a cantilever beam. This will be later extended to sensors that will be fixed at different locations on the same beam. The resulting model will then be tested on a sub-scale composite bridge for a smart structural health monitoring system. The results from the proposed work are a stepping stone towards the development of generic autonomous sensor models that are capable of data interpretation, self-calibration, data fusion from other sources and even learning so as to improve their performance with time.