The Secret to Popular Chinese Web Novels: A Corpus-Driven Study

What is the secret to writing popular novels? The issue is an intriguing one among researchers from various fields. The goal of this study is to identify the linguistic features of several popular web novels as well as how the textual features found within and the overall tone interact with the genre and themes of each novel. Apart from writing style, non-textual information may also reveal details behind the success of web novels. Since web fiction has become a major industry with top writers making millions of dollars and their stories adapted into published books, determining essential elements of "publishable" novels is of importance. The present study further examines how non-textual information, namely, the number of hits, shares, favorites, and comments, may contribute to several features of the most popular published and unpublished web novels. Findings reveal that keywords, function words, and lexical diversity of a novel are highly related to its genres and writing style while dialogue proportion shows the narration voice of the story. In addition, relatively shorter sentences are found in these novels. The data also reveal that the number of favorites and comments serve as significant predictors for the number of shares and hits of unpublished web novels, respectively; however, the number of hits and shares of published web novels is more unpredictable

The Impact of Customer Profile and Customer Participation on Customer Relationship Management Performance

There are two main sources of knowledge about customers: customer profile and customer participation. The companies use information technologies to analyze the customer profiles and extract tacit knowledge about customer via customer participation. The result of this experiment demonstrates that the use of customer profile improves customers’ perception on goods quality and increase the effectiveness of Customer Relationship Management (CRM). In addition, customer participation can improve customers’ perception on goods quality and enhance performance of CRM through perceived participation. The result indicates that the customer profiles and customer participation are two crucial factors for companies to maintain customer relationship

Experiencing the difference between a virtual and in-person fit session

This research explored the concept of using avatars as fit models. Participants who had 2D/3D computer-aided design skills developed two basic garments and virtually draped the garments on two avatars, one was the program’s default avatar and the second was a personal avatar captured through a 3D body scan. The garments were also assembled in muslin and fit during a live session. Photographs of the three scenarios documented the static fit of the garments. Overall, the participants were satisfied with the evaluation of the virtual garment on their personal avatar, though disappointed that the avatar itself was not a cleaner image. There was some difference in the fit results between the personal avatar and the live session which was due to fabric appearance and difference in draping. Further research, and advancement in virtual textile rendering, needs to take place as this affects the visual fit on the avatar. Virtual fit has the potential to be a time- and cost-saving practice for industry.Design, Housing and Merchandisin

A dual interpolation boundary face method for 3D elasticity

The dual interpolation boundary face method (DiBFM) proposed recently has been successfully applied to solve various problems in two dimensions. Compared with the conventional boundary element method (BEM), it has been proved that the DiBFM has the advantages of higher accuracy, convergence rate and computational efficiency. In addition, the DiBFM is suitable to unify the conforming and nonconforming elements in the BEM implementation, as well as to approximate both continuous and discontinuous fields. Moreover, there are no geometric errors by the DiBFM in the computational process. In this paper, the DiBFM is extended successfully to solve the elasticity problems in three-dimensions (3D) with formulations derived in details. A number of numerical examples are presented in order to validate the accuracy and convergence rate of the proposed method

Stateless Two-Stage Multiple Criteria Scheduling in Nuclear Medicine

Examination in nuclear medicine exhibits scheduling difficulties due to its intricate clinical issues, such as varied radiopharmaceuticals for different diseases, machine preparation and length of scan, and patients’ and hospital’s criteria and/or limitations. Many scheduling methods exist but are limited for nuclear medicine. In this paper, we present stateless two-stage scheduling to cope with multiple criteria decision making. The first stage mostly deals with patients’ conditions. The second stage concerns more the clinical condition and its correlations with patients’ preference which presents more complicated intertwined configurations. A greedy algorithm is proposed in the second stage to determine the (time slot and patient) pair in linear time. The result shows practical and efficient scheduling for nuclear medicine

Carbon monoxide may enhance bile secretion by increasing glutathione excretion and Mrp2 expression in rats

AbstractBackgroundNitric oxide (NO) donors have been reported to induce choleresis via an increased excretion of glutathione. The effects of another gas molecule, carbon monoxide (CO), on bile formation are, however, inconsistent among previous reports. We investigated the sequential changes of bile output and the biliary contents in rats with or without CO supplementation to elucidate the mechanism of CO on bile excretion.MethodsDichloromethane (DCM) was gastrically fed to male Sprague–Dawley rats to yield CO by liver biotransformation. The rats were divided into DCM-treated (n = 7), DCM plus L-NAME-treated (n = 6), and corn oil-treated-(n = 8) groups. Bile samples were collected hourly to examine the flow rate and bile content. Serum levels of nitrite and nitrate 4 hours after DCM supplementation with or without NO synthase (NOS) inhibition were measured by capillary electrophoresis. The expression of hepatic inducible NOS was evaluated by Western blotting 6 hours after DCM administration.ResultsLevels of carboxyhemoglobin rose to around 10% at 4 hours after DCM supplementation and were maintained until the end of the experiments. Bile flow increased after DCM supplementation and was associated with a concomitant increase of biliary glutathione and higher hepatic multidrug resistance-associated protein 2 (Mrp2) expression. Hepatic inducible NOS expression and serum nitrate/nitrite levels were also increased. Treatment with an NOS inhibitor (L-NAME) abolished the CO-induced glutathione excretion and choleresis, but not Mrp2 expression.ConclusionThe present study demonstrated that CO enhanced biliary output in conjunction with NO by increasing the biliary excretion of glutathione. The increment in biliary glutathione was associated with an increased expression of hepatic Mrp2

Preserving Fairness Generalization in Deepfake Detection

Although effective deepfake detection models have been developed in recent years, recent studies have revealed that these models can result in unfair performance disparities among demographic groups, such as race and gender. This can lead to particular groups facing unfair targeting or exclusion from detection, potentially allowing misclassified deepfakes to manipulate public opinion and undermine trust in the model. The existing method for addressing this problem is providing a fair loss function. It shows good fairness performance for intra-domain evaluation but does not maintain fairness for cross-domain testing. This highlights the significance of fairness generalization in the fight against deepfakes. In this work, we propose the first method to address the fairness generalization problem in deepfake detection by simultaneously considering features, loss, and optimization aspects. Our method employs disentanglement learning to extract demographic and domain-agnostic forgery features, fusing them to encourage fair learning across a flattened loss landscape. Extensive experiments on prominent deepfake datasets demonstrate our method's effectiveness, surpassing state-of-the-art approaches in preserving fairness during cross-domain deepfake detection. The code is available at https://github.com/Purdue-M2/Fairness-GeneralizationComment: Accepted by The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024

GeneAlign: a coding exon prediction tool based on phylogenetical comparisons

GeneAlign is a coding exon prediction tool for predicting protein coding genes by measuring the homologies between a sequence of a genome and related sequences, which have been annotated, of other genomes. Identifying protein coding genes is one of most important tasks in newly sequenced genomes. With increasing numbers of gene annotations verified by experiments, it is feasible to identify genes in the newly sequenced genomes by comparing to annotated genes of phylogenetically close organisms. GeneAlign applies CORAL, a heuristic linear time alignment tool, to determine if regions flanked by the candidate signals (initiation codon-GT, AG-GT and AG-STOP codon) are similar to annotated coding exons. Employing the conservation of gene structures and sequence homologies between protein coding regions increases the prediction accuracy. GeneAlign was tested on Projector dataset of 491 human–mouse homologous sequence pairs. At the gene level, both the average sensitivity and the average specificity of GeneAlign are 81%, and they are larger than 96% at the exon level. The rates of missing exons and wrong exons are smaller than 1%. GeneAlign is a free tool available at