Predicting Purchase Proneness of Anonymous User in Mobile Commerce

In recent years, mobile commerce is developing rapidly because of the popularity of mobile devices. However, for the difficulty of the mobile device input, the users of the e-commerce websites usually don’t log on the website when they are browsing, which resulting in a situation that a large number of website visitors are anonymous users. In order to increase sales revenue and expand market share, an effective prediction of anonymous users’ purchases proneness is very helpful in providing targeted marketing strategy for website to induce anonymous users to purchase. In the past, customer segmentation was mainly analyzed and modeled by customers’ historical data. But the history data of anonymous users can’t be obtained on mobile commerce sites. This method is difficult to put into management practice. In order to solve this problem, this paper proposes a method based on random forest of using user clickstream data to forecast purchase proneness in real time. This method includes two stages: the model training part and the user purchasing proneness prediction part. In the model training part, a classifier based on random forest algorithm is trained. In the users\u27 predicting part, the classifier is used to predict the user\u27s purchase proneness in real time. The method proposed can be effectively applied in the real-time prediction of anonymous users\u27 purchasing proneness, and the results of prediction will help enterprises implement the marketing measures in real time

Research on the Construction of Sales Forecasting Model of Fashion Products Based on Feature Representation of Multimodal and Deep Learning

By improving the accuracy of sales forecasting, this paper provides support for fashion product sales enterprises to make better inventory management and operational decisions. The deep neural network is introduced into the construction of multimodal features, and the internal structure of different modes, such as historical sales features, picture features, and basic attribute features of products, are fully considered, and finally the sales forecasting model of fashion products based on multimodal feature fusion is constructed. In addition, combined with the actual data of the enterprise, the proposed model is compared with the exponential regression model and shallow neural network model. The paper finds that multimodal features and deep learning representation method has better performance than traditional methods (exponential regression and shallow neural network) in the task of predicting sales of fashion products. The results help enterprises use the deep learning method and the data of multiple modal to make accurate sales forecast

Environmental Impacts of Artisanal Gold Mining: A case study of Nkaseim Community - Ghana

Artisanal gold mining [AGM] has numerous socio-economic benefits to communities involved. However, the four environmental variables or constructs used revealed that AGM significantly affects the environment. In other words; despite a source of livelihood; AGM communities are besieged with issues such as water pollution, land degradation and air pollution which affect almost all facets of the environment including humans. Most miners have no idea about alternative technologies to make AGM sustainable, yet Ghana ranks next to South Africa as the highest producers of gold in Africa. This paper, therefore, discusses the consequences of AGM for stakeholders to regularize AGM activities; and adopt alternative methods to mitigate the damage caused to the environment. Keywords: Artisanal gold mining, environmental impacts, regularize, pollutio

Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware Learning

Group-level emotion recognition (GER) is an inseparable part of human behavior analysis, aiming to recognize an overall emotion in a multi-person scene. However, the existing methods are devoted to combing diverse emotion cues while ignoring the inherent uncertainties under unconstrained environments, such as congestion and occlusion occurring within a group. Additionally, since only group-level labels are available, inconsistent emotion predictions among individuals in one group can confuse the network. In this paper, we propose an uncertainty-aware learning (UAL) method to extract more robust representations for GER. By explicitly modeling the uncertainty of each individual, we utilize stochastic embedding drawn from a Gaussian distribution instead of deterministic point embedding. This representation captures the probabilities of different emotions and generates diverse predictions through this stochasticity during the inference stage. Furthermore, uncertainty-sensitive scores are adaptively assigned as the fusion weights of individuals' face within each group. Moreover, we develop an image enhancement module to enhance the model's robustness against severe noise. The overall three-branch model, encompassing face, object, and scene component, is guided by a proportional-weighted fusion strategy and integrates the proposed uncertainty-aware method to produce the final group-level output. Experimental results demonstrate the effectiveness and generalization ability of our method across three widely used databases.Comment: 11 pages,3 figure

Pathologically Activated Neuroprotection via Uncompetitive Blockade of \u3cem\u3eN\u3c/em\u3e-Methyl-d-aspartate Receptors with Fast Off-rate by Novel Multifunctional Dimer Bis(propyl)-cognitin

Uncompetitive N-methyl-d-aspartate (NMDA) receptor antagonists with fast off-rate (UFO) may represent promising drug candidates for various neurodegenerative disorders. In this study, we report that bis(propyl)-cognitin, a novel dimeric acetylcholinesterase inhibitor and γ-aminobutyric acid subtype A receptor antagonist, is such an antagonist of NMDA receptors. In cultured rat hippocampal neurons, we demonstrated that bis(propyl)-cognitin voltage-dependently, selectively, and moderately inhibited NMDA-activated currents. The inhibitory effects of bis(propyl)-cognitin increased with the rise in NMDA and glycine concentrations. Kinetics analysis showed that the inhibition was of fast onset and offset with an off-rate time constant of 1.9 s. Molecular docking simulations showed moderate hydrophobic interaction between bis(propyl)-cognitin and the MK-801 binding region in the ion channel pore of the NMDA receptor. Bis(propyl)-cognitin was further found to compete with [3H]MK-801 with a Ki value of 0.27 μm, and the mutation of NR1(N616R) significantly reduced its inhibitory potency. Under glutamate-mediated pathological conditions, bis(propyl)-cognitin, in contrast to bis(heptyl)-cognitin, prevented excitotoxicity with increasing effectiveness against escalating levels of glutamate and much more effectively protected against middle cerebral artery occlusion-induced brain damage than did memantine. More interestingly, under NMDA receptor-mediated physiological conditions, bis(propyl)-cognitin enhanced long-term potentiation in hippocampal slices, whereas MK-801 reduced and memantine did not alter this process. These results suggest that bis(propyl)-cognitin is a UFO antagonist of NMDA receptors with moderate affinity, which may provide a pathologically activated therapy for various neurodegenerative disorders associated with NMDA receptor dysregulation

Effect of reduced energy density of close-up diets on metabolites, lipolysis and gluconeogenesis in Holstein cows

Objective An experiment was conducted to determine the effect of reduced energy density of close-up diets on metabolites, lipolysis and gluconeogenesis in cows during the transition period. Methods Thirty-nine Holstein dry cows were blocked and assigned randomly to three groups, fed a high energy density diet (HD, 1.62 Mcal of net energy for lactation [NEL]/kg dry matter [DM]), a medium energy density diet (MD, 1.47 Mcal NEL/kg DM), or a low energy density diet (LD, 1.30 Mcal NEL/kg DM) prepartum; they were fed the same lactation diet to 28 days in milk (DIM). All the cows were housed in a free-stall barn and fed ad libitum. Results The reduced energy density diets decreased the blood insulin concentration and increased nonesterified fatty acids (NEFA) concentration in the prepartum period (p0.05). The dietary energy density had no effect on mRNA abundance of insulin receptors, leptin and peroxisome proliferator-activated receptor-γ in adipose tissue, and phosphoenolpyruvate carboxykinase, carnitine palmitoyltransferase-1 and peroxisome proliferator-activated receptor-α in liver during the transition period (p>0.05). The HD cows had higher mRNA abundance of hormone-sensitive lipase at 3 DIM compared with the MD cows and LD cows (p = 0.001). The mRNA abundance of hepatic pyruvate carboxykinase at 3 DIM tended to be increased by the reduced energy density of the close-up diets (p = 0.08). Conclusion The reduced energy density diet prepartum was effective in controlling adipose tissue mobilization and improving the capacity of hepatic gluconeogenesis postpartum

Boosting the efficiency of inverted quantum dot light-emitting diodes by balancing charge densities and suppressing exciton quenching through band alignment.

We report an inverted and multilayer quantum dot light emitting diode (QLED) which boosts high efficiency by tuning the energy band alignment between charge transport and light emitting layers. The electron transport layer (ETL) was ZnO nanoparticles (NPs) with an optimized doping concentration of cesium azide (CsN3) to effectively reduce electron flow and balance charge injection. This is by virtue of a 0.27 eV upshift of the ETL's conduction band edge, which inhibits the quenching of excitons and preserves the superior emissive properties of the quantum dots due to the insulating characteristics of CsN3. The demonstrated QLED exhibits a peak current efficiency, power efficiency and external quantum efficiency of up to 13.5 cd A-1, 10.6 lm W-1 and 13.4% for the red QLED, and correspondingly 43.1 cd A-1, 33.6 lm W-1 and 9.1% for green, and 4.1 cd A-1, 2.0 lm W-1 and 6.6% for the blue counterparts. Compared with QLEDs without optimization, the performance of these modified devices shows drastic improvement by 95.6%, 39.4% and 36.7%, respectively. This novel device architecture with heterogeneous energy levels reported here offers a new design strategy for next-generation high efficiency QLED displays and solid-state lighting technologies