Large volumes of product reviews generated by online users have important strategic value for product development. Prior studies often focus on the influence of reviews on customers' purchasing ...decisions through the word-of-mouth effect. However, little is known about how product developers respond to these reviews. This study adopts a big data analytical approach to investigate the impact of online customer reviews on customer agility and subsequently product performance. We develop a singular value decomposition-based semantic keyword similarity method to quantify customer agility using large-scale customer review texts and product release notes. Using a mobile app data set with over 3 million online reviews, our empirical study finds that review volume has a curvilinear relationship with customer agility. Furthermore, customer agility has a curvilinear relationship with product performance. Our study contributes to innovation literature by demonstrating the influence of firms' capability of utilizing online customer reviews and its impact on product performance. It also helps reconcile inconsistencies found in literature regarding the relationships among the three constructs.
The differential characteristics with high probability are critical for differential cryptanalysis. The process of searching such differential characteristics, especially the best one, is ...time-consuming. We believe that the modern hybrid computing systems can be used to accelerate the search process. However, to the best of our knowledge, the existing solutions are not designed for heterogeneous architectures. In this paper, we propose a parallel search algorithm for the best differential characteristic. Our method can be applied to any substitution–permutation network (SPN) block ciphers after making minor modifications. We implemented the proposed parallel search algorithm for PRESENT block cipher and also a sequential version, which based on the classic Matsui’s method, for comparison. The experimental result shows that the parallel algorithm using both CPU and GPU can achieve at least 4.4x and up to 18x speed-up compared to the sequential version.
Bactrocera cucurbitae (Coquillett) is an important pest of cucurbit crops and certain vegetables in Asia, the Middle East, Africa and Hawaii. Most studies on B. cucurbitae have focussed on the ...effects of prolonged high temperature and very few have examined the effects of short-term exposures to high-temperature on behaviour.
In this study, short-term of high-temperature treatments of 33°C, 37°C, 41°C and 45°C were maintained for 1-3hr, and long-term, variable high-temperature treatments were established that consisted of experienced one, two and three times high temperatures stages to 31°C, 33°C, 34°C, 35°C, 36°C, 37°C, 41°C and 45°C for 7hr. We compared the effects of the different high temperatures regimes changes treatments on the mating, oviposition and thermotactic taxis of the flies. The results showed that exposure to a 45°C/1hr treatment, delayed both initiation of mating and oviposition for 8 hr relative to the control but mating and was observed 41 times and oviposition 47 times. By comparison, in the control, mating commenced immediately and was observed 38.3 times and oviposition was observed 41.3 times. Under the other treatments, all the indices for the flies declined with the increase in temperature and duration of exposure.
Results showed that 1hr of exposure to 45°C significantly stimulated mating, oviposition and thermotactic behaviour of the flies. These results could improve our understanding of the mechanisms responsible for the population dynamics of B. cucurbitae during the high-temperature season.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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•The mesoscale kinetic Monte Carlo method is developed to simulate the sintering of NiO-YSZ composite anodes.•An artificial neural network accelerated method is developed to calibrate ...the Kinetic Monte Carlo model parameters.•Microstructure is resolved with high-accuracy using spherical particles to correct distortion in FIB-SEM 3D reconstruction.•The proposed framework is fully adaptable for sintering simulations of any other composite materials.
This study proposes a kinetic Monte Carlo (KMC) model for the sintering of two-phase composites using nickel oxide-yttria stabilized zirconia (NiO-YSZ) as a case study. An artificial neural network (ANN) assisted method is used to calibrate the KMC model parameters by comparing the simulated microstructures and the real microstructure reconstructed by focused ion beam scanning electron microscopy (FIB-SEM). It is demonstrated that the ANN calibrated KMC model can predict quantitatively the microstructure evolution of NiO-YSZ composite. The microstructural parameters such as volume fraction, specific surface area, and triple-phase boundary length density of sintered NiO-YSZ are well predicted within acceptable errors. The proposed framework can be adapted for the simulation of the microstructure evolution of any other composite electrodes for solid oxide fuel cells and other composites fabricated by sintering.
In this study, alumina ceramics with hierarchical pores were successfully fabricated using freeze casting. Experimental studies show that both the solid loading of the slurry and the thermal ...insulation layer at the interface of the slurry and cooling plate can influence the pore characteristics of cast samples. In order to examine the pore characteristics and evaluate the permeability of the freeze-cast samples fabricated under different conditions, a generative adversarial network (GAN) method was employed to reconstruct the three-dimensional (3D) microstructure from two-dimensional (2D) scanning electron microscopy (SEM) images of the samples. Furthermore, GAN 3D reconstruction was validated against X-ray tomography 3D reconstruction results. Based on the GAN reconstructed microstructures, the permeability and pore distribution of the various samples were analyzed. The sample cast with 35 wt.% solid loading shows an optimal permeability.
Images of original pupae of Zeugodacus cucurbitae (Coquillett) were normalized, grayed, and segmented to identify male and female pupae of this species via machine vision. The image of each pupa was ...divided into 25 small areas. The differences in surface texture features in each small area within 11 days were compared. The texture characteristics of both male and female pupae were screened by combining the eclosion of both sexes of Z. cucurbitae (Coquillett). Results indicated that the pectinate setae on the abdominal backplane could be used as a basis for the identification of the male and female pupa of Z. cucurbitae (Coquillett). Moreover, machine vision correctly identified these characteristics with an accuracy of 96.0%. This study lays a foundation for the identification of male and female pupae using machine vision and also for the comprehensive control of Z. cucurbitae (Coquillett).
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
With the vigorous development of the Internet of Things, 5G technology, and artificial intelligence, flexible wearable sensors have received great attention. As a simple and low-cost power supply in ...wearable sensors, the triboelectric nanogenerator (TENG) has a wide range of applications in the field of flexible electronics. However, most polymers are thermally poor conductors (less than 0.1 W/(m·K)), resulting in insufficient heat dissipation performance and limiting the development of TENG. In this study, a high-performance non-woven fabric TENG with strong thermal conductivity (0.26 W/m·K) was achieved by introducing ZrB
into the polyurethane (PU) matrix. The excellent output performance with an open circuit voltage (V
) of 347.6 V, a short circuit current (I
) of 3.61 μA, and an accumulated charge of 142.4 nC endows it with good sensitivity. The electrospun PU/ZrB
composites exhibit excellent sensing performance to detect body movements in situ, such as pressing, clapping, running, and walking. Moreover, the generated power can light up 224 LED bulbs as a demonstration of self-powering ability.
Imidacloprid is a neonicotinoid insecticide widely used in the production and cultivation of crops. In recent years, the extensive use of imidacloprid in agricultural production has resulted in large ...amounts of pesticide residues in agricultural products and the environment. Therefore, it is necessary to establish a rapid, accurate, sensitive and convenient method for detecting imidacloprid pesticide residues to ensure the safety of agricultural products and the environment. To clarify how to use the molecular imprinting method for the electrochemical rapid residue detection of imidacloprid. This paper selected reduced graphene oxide and gold nanoparticles as modifiers modified on screen-printed carbon electrodes (SPCE) chitosan as a functional monomer, and imidacloprid as template molecule to prepare molecularly imprinted polymer, and applied this sensor to the residue detection of imidacloprid. The results showed that the concentration of imidacloprid showed a good linear relationship with the peak response current, and the detection limit of imidacloprid was 0.5 μM, while the sensor had good repeatability and interference resistance. The recoveries of imidacloprid spiked on three samples, mango, cowpea and water, were in the range of 90–110% (relative standard deviation, RSD<5%), which proved the practicality and feasibility of the assay established in this paper. The results of this paper can be used as a basis for the research on the detection of imidacloprid pesticide residues in food or environment.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
The Mg–6Sn–3Al–1Zn (wt%) alloy was prepared by casting and then deformed by hot extrusion at 350 °C with different extrusion ratios (9:1, 13:1, 20:1), extrusion rate was 20 mm min
−1
. The ...microstructure of as-extruded alloy was analyzed by XRD and EBSD, the tensile properties were tested by a universal testing machine. The results showed that there underwent processing hardening and recrystallization softening simultaneously during the process thermal deformation. Dynamic recrystallization (DRX) mainly occurred at the grain boundary of the deformed grain, with the extrusion ratio increased, the volume fraction of the dynamic recrystallization grain of alloy increased. When the extrusion ratio was less than or equal to 13:1, the average grain size decreased with the extrusion ratio increased. When the extrusion ratio reached up to 20:1, the average grain size increased. {0001} basal plane texture formed after the alloy extruded, and it paralleled to the extrusion direction, the texture intensity decreased first and then increased as the extrusion ratio increased. With the extrusion ratio increased from 9:1 to 20:1, the tensile properties increased first and then declined. Among all the tested alloys, the alloy with the extrusion ratio of 13:1 exhibited the optimum mechanical properties, the yield strength, tensile strength and elongation of alloy was 320 MPa, 371 MPa and 13.5%, the texture strength of the alloy was 8.26, the average grain size was 1.5
μ
m.
•The combination of the discrete element method (DEM) and the mesoscale kinetic Monte Carlo method (KMC) is used to simulate the fabrication of the Ni-YSZ anodes.•Effective macroscopic properties of ...a porous Ni-YSZ anode are extracted from a representative numerical microstructure.•The deep learning model based on convolutional neural network is built to link the microstructures with five effective properties.•The deep learning framework is highly extendable for linking more macroscopic material properties to microstructures.
A deep learning based homogenization framework is proposed to link the microstructures of porous nickel/yttria-stabilized zirconia anodes in solid oxide fuel cells (SOFCs) to their effective macroscopic properties. A variety of microstructures are generated by the discrete element method and the meso‑scale kinetic Monte Carlo method. Then, the finite element method and the homogenization theory are used to calculate the effective elastic modulus (E), Poisson's ratio (υ), shear modulus (G) and coefficient of thermal expansion (CTE) of representative volume elements. In addition, the triple-phase boundary length density (LTPB) is also calculated. The convolutional neural network (CNN) based deep learning model is trained to find the potential relationship between the microstructures and the five effective macroscopic properties. The comparison between the ground truth and the predicted values of the new samples proves that the CNN model has an excellent predictive performance. This indicates that the CNN model could be used as an effective alternative to numerical simulations and homogenization because of its accurate and rapid prediction performance. Hence the deep learning-based homogenization framework could potentially accelerate the continuum modeling of SOFCs for microstructure optimization.
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