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BackgroundWith increasing gaps between the rich and poor, potential risk factors for class conflict have attracted increasing attention from researchers. Although cognitive factors are known to be significant predictors of class-conflict behavior, limited attention has been paid to competence stereotypes of the upper class. When considering economic inequality, people pay more attention to competence stereotypes of the upper class, which may have adverse effects. This study aimed to investigate the relationship between competence stereotypes held by the lower class about the upper class and class conflict, and to test the mediating role of intergroup envy in this relationship and the moderating role of upward social mobility belief.MethodsData were collected from a convenience sample from a comprehensive university in China. Based on scores on subjective and objective class scales, 284 lower-class college students (103 males and 181 females) aged 18–24 were selected to participate (both their subjective and objective scores were lower than 3 points). Their endorsement of upper-class competence stereotypes, intergroup envy, upward social mobility beliefs, and class conflict were measured using a well-validated self-report questionnaire.ResultsThe main data were analyzed using correlation analysis, the SPSS macro PROCESS (Model 7), and simple slope analysis. The results show a significant positive correlation between competence stereotypes held by lower-class college students toward the higher class and class conflict, and this connection was mediated by intergroup envy. Moreover, the indirect effect of intergroup envy on this link was moderated by upward social mobility beliefs; this effect was stronger for college students with lower upward social mobility beliefs.ConclusionThis study broadens our understanding of how and when competence stereotypes among the lower class concerning the upper class are related to class conflict. Researchers and policymakers should pay special attention to competence stereotypes of the upper class, especially intergroup envy and class conflict among lower-class individuals with lower levels of upward social mobility beliefs.

This study explored a new model of Prostate Imaging Reporting and Data System (PIRADS) and adjusted prostate-specific antigen density of peripheral zone (aPSADPZ) for predicting the occurrence of prostate cancer (PCa) and clinically significant prostate cancer (csPCa). The demographic and clinical characteristics of 853 patients were recorded. Prostate-specific antigen (PSA), PSA density (PSAD), PSAD of peripheral zone (PSADPZ), aPSADPZ, and peripheral zone volume ratio (PZ-ratio) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The calibration and discrimination abilities of new nomograms were verified with the calibration curve and area under the ROC curve (AUC). The clinical benefits of these models were evaluated by decision curve analysis and clinical impact curves. The AUCs of PSA, PSAD, PSADPZ, aPSADPZ, and PZ-ratio were 0.669, 0.762, 0.659, 0.812, and 0.748 for PCa diagnosis, while 0.713, 0.788, 0.694, 0.828, and 0.735 for csPCa diagnosis, respectively. All nomograms displayed higher net benefit and better overall calibration than the scenarios for predicting the occurrence of PCa or csPCa. The new model significantly improved the diagnostic accuracy of PCa (0.945 vs 0.830, P < 0.01) and csPCa (0.937 vs 0.845, P < 0.01) compared with the base model. In addition, the number of patients with PCa and csPCa predicted by the new model was in good agreement with the actual number of patients with PCa and csPCa in high-risk threshold. This study demonstrates that aPSADPZ has a higher predictive accuracy for PCa diagnosis than the conventional indicators. Combining aPSADPZ with PIRADS can improve PCa diagnosis and avoid unnecessary biopsies.

The intention expresses the user’s preference for document structure division. Intention-guided document structure division is an important task in the field of text mining. To achieve this goal, deep semi-supervised document clustering provides a promising solution to personalized document clustering. However, traditional deep semi-supervised clustering models suffer from the problem of the limited number of constraints which is insufficient for intention-guided document clustering. Moreover, documents normally have various emphases on their representations to reflect different structural opinions. In this paper, we proposed an intention-guided deep semi-supervised document clustering model, namely IGSC, to divide document structure based on a small amount of user-provided supervised information. IGSC designs a deep metric learning network to solve the above problems. The deep metric learner explores the user’s global intention and outputs an intention matrix. The intention is explored from the small amount user provided pairwise constraints and is used to guide the representation learning. Moreover, IGSC uses the intention matrix to guide the clustering process, to get the clustering results that best meet the user’s intention. This paper compares IGSC with a number of document clustering models on four real-world text datasets, namely Reu-10k, BBC, ACM, and Abstract. The results show that IGSC evidently improves the clustering performance and outperforms the best result of benchmark models with 7% on average. The comparison with other models and the visualization results can demonstrate that IGSC is effective.

Piezoelectric wind energy harvesters based on flow-induced vibration are attracting widespread attention because they can provide long-term sustainable power to wireless sensor network nodes. In order to improve the harvesting performance, this paper proposes a multi-interference local pressure modulation mechanism, and a three-plate multi-interference structure is applied for verification. Computational fluid simulations indicate that this three-plate multi-interference structure can effectively combine the double wake flow effect of the upstream double plates and the high-pressure feature in front of the downstream single plate. So that the local pressure of the fluid field can be effectively modulated to generate a significant lift force on the bluff body. Experiments show that this structure can promote the VIV(vortex-induced vibration)-based energy harvester to exhibit galloping vibration. Moreover, compared to the cases with only a single downstream plate or a double-plate upstream structure, lower cut-in wind speed and higher harvesting efficiency can be achieved by using the multi-interference structure. When the horizontal distance of the downstream plate, the horizontal and vertical offset distances of the upstream plates are set as 0.5D, 0.5D and 1.5D (D: windward width of the bluff body), respectively, the harvester can generate the highest output power of about 1.4 mW at a wind speed of 5 m/s. By introducing a spring–damper coupling mechanism, the synchronous movement of two side-by-side energy harvesters can be realized, thereby realizing the synchronous output of voltage and increasing the output power per unit volume.

Association studies investigating miRNA in relation to diseases have consistently shown significant alterations in miRNA expression, particularly within inflammatory pathways, where they regulate inflammatory cytokines, transcription factors (such as NF-κB, STAT3, HIF1α), and inflammatory proteins (including COX-2 and iNOS). Given that endometriosis (EMS) is characterized as an inflammatory disease, albeit one influenced by estrogen levels, it is natural to speculate about the connection between EMS and miRNA. Recent research has indeed confirmed alterations in the expression levels of numerous microRNAs (miRNAs) in both endometriotic lesions and the eutopic endometrium of women with EMS, when compared to healthy controls. The undeniable association of miRNAs with EMS hints at the emergence of a new era in the study of miRNA in the context of EMS. This article reviews the advancements made in understanding the pathological role of miRNA in EMS and its association with EMS-associated infertility. These findings contribute to the ongoing pursuit of developing miRNA-based therapeutics and diagnostic markers for EMS.