Display “Why” Higher than “How”: How Display Positioning Affects Construal Level

Prior research has shown that vertical position of an item is important in both an offline and an online digital context. However, findings in the digital context are inconsistent and atheoretical. Recent psychology research has observed that looking up vs. down can shift processing style (abstract vs. concrete) because looking up (down) tends to associate with observing distant (proximal) stimuli. Based on this insight, we propose that when looking at an object displayed on the top (bottom) of a webpage, users will process the object in a relatively abstract (concrete) way. Further, according to the fit hypothesis in the construal level theory, we propose that advertising with low-level (vs. high-level) construals will be more persuasive when it appears at the bottom (vs. on the top) of the webpage. An initial study has been conducted to examine our propositions. Two future studies using eye-tracking technology are proposed to provide more stringent evidence

Effects of e-Commerce Websites’ Auditory Features on Consumers’ Appreciation for Innovative Products

It is has been established that sensory interactions, including sight, hearing, smell, taste and touch, can affect consumers’ consumption decisions. While various sensory interactions are devised in the offline context, e-commerce has to rely primarily on vision and hearing due to its inability to access other sensory. Previous IS literature has documented the substantial effects of various visual features. However, very few studies have examined auditory features. Drawing on the recent observation that medium noises enhance people’s abstractive thinking and creativity, this study tries to investigate this topic from a novel perspective that ambient sounds can promote users’ appreciation of innovative products when shopping online. The preliminary results of a lab experiment show that medium noise or music can improve participants’ likelihood of buying innovative products over traditional products, and noise brings other negative effects (e.g. bad mood), while music do not. Theoretical and practical contributions are discussed

An improved method for predicting CO2 minimum miscibility pressure based on artificial neural network

 The CO2 enhanced oil recovery (EOR) method is widely used in actual oilfields. It is extremely important to accurately predict the CO2 minimum miscibility pressure (MMP) for CO2-EOR. At present, many studies about MMP prediction are based on empirical, experimental, or numerical simulation methods, but these methods have limitations in accuracy or computation efficiency. Therefore, more work needs to be done. In this work, with the results of the slim-tube experiment and the data expansion of the multiple mixing cell methods, an improved artificial neural network (ANN) model that predicts CO2 MMP by the full composition of the crude oil and temperature is trained. To stabilize the neural network training process, L2 regularization and Dropout are used to address the issue of over-fitting in neural networks. Predicting results show that the ANN model with Dropout possesses higher prediction accuracy and stronger generalization ability. Then, based on the validation sample evaluation, the mean absolute percentage error and R-square of the ANN model are 6.99 and 0.948, respectively. Finally, the improved ANN model is tested by six samples obtained from slim-tube experiment results. The results indicate that the improved ANN model has extremely low time cost and high accuracy to predict CO2 MMP, which is of great significance for CO2-EOR.Cited as: Dong, P., Liao, X., Chen, Z., Chu, H. An improved method for predicting CO2 minimum miscibility pressure based on artificial neural network. Advances in Geo-Energy Research, 2019, 3(4): 355-364, doi: 10.26804/ager.2019.04.0

INVESTIGATING CONSUMERS’ REDEMPTION RESPONSES THROUGH THE INTERPLAY BETWEEN MESSAGE FRAMING AND PSYCHOLOGICAL DISTANCE IN MOBILE ADVERTISEMENT DESIGN

On the one hand, the increasing digitalization of commerce has put local owner operated retail outlets (LOOROs) under pressure to adapt their business models to the new technological and competitive environment as well as to the changing shopping habits of their customers. On the other hand, it also offers potential competitive advantages for them. This paper investigates the retailers’ perception of the competition and their perception of customer expectations, combined with a survey of the current use of digitalized services and the LOOROs readiness to increase the usage of digitalized services. Our results confirm that the perception of competitive pressure and customer expectations has a positive influence on LOOROs’ readiness to adopt new technologies and business models. But a significant number of the surveyed retailers underestimate the expectations of their customers and are reluctant to add digital services to their business portfolio. While our key findings are relevant insights for all LOOROs on their journey towards digitalization, our findings provide even more significant insights for all digital service providers aiming to take a slice of the still substantial market shares of LOOROs in rural areas

Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments

Deep learning-based physical-layer secret key generation (PKG) has been used to overcome the imperfect uplink/downlink channel reciprocity in frequency division duplexing (FDD) orthogonal frequency division multiplexing (OFDM) systems. However, existing efforts have focused on key generation for users in a specific environment where the training samples and test samples obey the same distribution, which is unrealistic for real world applications. This paper formulates the PKG problem in multiple environments as a learning-based problem by learning the knowledge such as data and models from known environments to generate keys quickly and efficiently in multiple new environments. Specifically, we propose deep transfer learning (DTL) and meta-learning-based channel feature mapping algorithms for key generation. The two algorithms use different training methods to pre-train the model in the known environments, and then quickly adapt and deploy the model to new environments. Simulation results show that compared with the methods without adaptation, the DTL and meta-learning algorithms both can improve the performance of generated keys. In addition, the complexity analysis shows that the meta-learning algorithm can achieve better performance than the DTL algorithm with less time, lower CPU and GPU resources

Analysis on the interactions between the first introns and other introns in mitochondrial ribosomal protein genes

It is realized that the first intron plays a key role in regulating gene expression, and the interactions between the first introns and other introns must be related to the regulation of gene expression. In this paper, the sequences of mitochondrial ribosomal protein genes were selected as the samples, based on the Smith-Waterman method, the optimal matched segments between the first intron and the reverse complementary sequences of other introns of each gene were obtained, and the characteristics of the optimal matched segments were analyzed. The results showed that the lengths and the ranges of length distributions of the optimal matched segments are increased along with the evolution of eukaryotes. For the distributions of the optimal matched segments with different GC contents, the peak values are decreased along with the evolution of eukaryotes, but the corresponding GC content of the peak values are increased along with the evolution of eukaryotes, it means most introns of higher organisms interact with each other though weak bonds binding. By comparing the lengths and matching rates of optimal matched segments with those of siRNA and miRNA, it is found that some optimal matched segments may be related to non-coding RNA with special biological functions, just like siRNA and miRNA, they may play an important role in the process of gene expression and regulation. For the relative position of the optimal matched segments, the peaks of relative position distributions of optimal matched segments are increased during the evolution of eukaryotes, and the positions of the first two peaks exhibit significant conservatism

Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments

Deep learning-based physical-layer secret key generation (PKG) has been used to overcome the imperfect uplink/downlink channel reciprocity in frequency division duplexing (FDD) orthogonal frequency division multiplexing (OFDM) systems. However, existing efforts have focused on key generation for users in a specific environment where the training samples and test samples follow the same distribution, which is unrealistic for real-world applications. This paper formulates the PKG problem in multiple environments as a learning-based problem by learning the knowledge such as data and models from known environments to generate keys quickly and efficiently in multiple new environments. Specifically, we propose deep transfer learning (DTL) and meta-learning-based channel feature mapping algorithms for key generation. The two algorithms use different training methods to pre-train the model in the known environments, and then quickly adapt and deploy the model to new environments. Simulation and experimental results show that compared with the methods without adaptation, the DTL and meta-learning algorithms both can improve the performance of generated keys. In addition, the complexity analysis shows that the meta-learning algorithm can achieve better performance than the DTL algorithm with less cost

Quantitative analysis of multi-components by single marker method combined with UPLC-PAD fingerprint analysis based on saikosaponin for discrimination of Bupleuri Radix according to geographical origin

Background: Saikosaponins are regarded as one of the most likely antipyretic constituents of Bupleuri Radix, establishing a comprehensive method that can reflect both the proportion of all constituents and the content of each saikosaponin is critical for its quality evaluation.Methods: In this study, the combination method of quantitative analysis of multiple components with a single marker (QAMS) and fingerprint was firstly established for simultaneous determination of 7 kinds of saikosaponins in Bupleuri Radix by ultra-high performance liquid chromatography (UPLC).Results: The results showed that saikosaponin d was identified as the optimum IR by evaluating the fluctuations and stability of the relative calibration factors (RCFs) under four different conditions. The new QAMS method has been confirmed to accurately quantify the 7 kinds of saikosaponins by comparing the obtained results with those obtained from external standard method and successfully classify the 20 batches of Bupleuri Radix from 8 provinces of China. The experimental time of fingerprint was significantly reduced to approximate 0.5 h through UPLC-PAD method, a total of 17 common peaks were identified.Conclusion: The QAMS-fingerprint method is feasible and reliable for the quality evaluation of Bupleuri Radix. This method could be considered to be spread in the production enterprises of Bupleuri Radix