Search

Abstract Understanding human endometrial dynamics in the establishment of endometrial receptivity remains a challenge, which limits early diagnosis and treatment of endometrial-factor infertility. Here, we decode the endometrial dynamics of fertile women across the window of implantation and characterize the endometrial deficiency in women with recurrent implantation failure. A computational model capable of both temporal prediction and pattern discovery is used to analyze single-cell transcriptomic data from over 220,000 endometrial cells. The time-series atlas highlights a two-stage stromal decidualization process and a gradual transitional process of the luminal epithelial cells across the window of implantation. In addition, a time-varying gene set regulating epithelium receptivity is identified, based on which the recurrent implantation failure endometria are stratified into two classes of deficiencies. Further investigation uncovers a hyper-inflammatory microenvironment for the dysfunctional endometrial epithelial cells of recurrent implantation failure. The holistic characterization of the physiological and pathophysiological window of implantation and a computational tool trained on this temporal atlas provide a platform for future therapeutic developments.

Abstract Although patients with alpha-fetoprotein-negative hepatocellular carcinoma (AFPNHCC) have a favorable prognosis, a high risk of postoperative recurrence remains. We developed and validated a novel liver fibrosis assessment index, the direct bilirubin-gamma-glutamyl transpeptidase-to-platelet ratio (DGPRI). DGPRI was calculated for each of the 378 patients with AFPNHCC who underwent hepatic resection. The patients were divided into high- and low-score groups using the optimal cutoff value. The Lasso-Cox method was used to identify the characteristics of postoperative recurrence, followed by multivariate Cox regression analysis to determine the independent risk factors associated with recurrence. A nomogram model incorporating the DGPRI was developed and validated. High DGPRI was identified as an independent risk factor (hazard ratio = 2.086) for postoperative recurrence in patients with AFPNHCC. DGPRI exhibited better predictive ability for recurrence 1–5 years after surgery than direct bilirubin and the gamma-glutamyl transpeptidase-to-platelet ratio. The DGPRI-nomogram model demonstrated good predictive ability, with a C-index of 0.674 (95% CI 0.621–0.727). The calibration curves and clinical decision analysis demonstrated its clinical utility. The DGPRI nomogram model performed better than the TNM and BCLC staging systems for predicting recurrence-free survival. DGPRI is a novel and effective predictor of postoperative recurrence in patients with AFPNHCC and provides a superior assessment of preoperative liver fibrosis.

Heavy metals in water bodies pose a significant threat to both ecology and human well-being. Therefore, the development of green and sustainable adsorbents for water remediation is essential. This study presents a crosslinked chitosan composite structure with multiple layers, which was generated by adjusting the ratio of chitosan to wheat straw–derived magnetic biochar in the composite. The adsorption behavior of the composite under different conditions was revealed by varying the Cr(VI) concentration, pH, and temperature. The composite exhibited a maximum adsorption of 121.8 mg g−1 for Cr(VI) in water, which was 11.9 times higher than that of magnetic biochar. It maintained a residual performance of 78.6 % after seven cycles, higher than most chitosan-based composites. The reusability was improved via hydrochloric acid protonation of functional groups and layer-by-layer exposure of the composite structure. This study provides new insights into the application of composites derived from chitosan and wheat straw, an agricultural waste, as sustainable adsorbents.

Background: Osteosarcoma, a tumor that originates from bone cells, has a poor prognosis and a high degree of malignancy. Anlotinib, a small-molecule multi-target tyrosine kinase inhibitor (TKI), is the first-line drug in treating osteosarcoma, especially in late-stage osteosarcoma. However, patients often develop resistance after using anlotinib for a certain period, which poses a challenge to its further clinical application. Recently, several TKIs, for instance regorafenib and cabozantinib, have showed clinical interest in treating osteosarcoma and target both vascular endothelial growth factor receptor (VEGFR) and mesenchymal epithelial transition factor (c-MET). Therefore, the identification of new TKI warrants further investigation. Methods: We performed CCK8 aasays to confirm that BMS-794833 sensitization osteosarcoma cells to anlotinib. Bioinformatics analysis and rescue experiments showed that the reduce of resistance were dependent on the VEGFR/Ras/CDK2 pathway. Cell line based xenograft model were used to demonstrate that BMS-794833 and anlotinib could synergistically treat OS. Results: Here, we found that BMS-794833 reduced anlotinib resistance in osteosarcoma by targeting the VEGFR/Ras/CDK2 pathway. CCK8 assay showed that BMS-794833 significantly improved the resistance of osteosarcoma cells to anlotinib. The results of rescue experiments showed that the regulatory effects of BMS-794833 on the proliferation and drug resistance of osteosarcoma cells were dependent on the VEGFR/Ras/CDK2 pathway. In addition, BMS-794833 affected the resistance of osteosarcoma cells to anlotinib through epithelial–mesenchymal transition (EMT) and apoptosis pathways. More importantly, BMS-794833 and anlotinib exerted synergistic therapeutic effects against osteosarcoma in vivo. Conclusion: Altogether, this study reveals a new (VEGFR)-targeting drug that can be combined with anlotinib for the treatment of osteosarcoma, which provides an important theoretical basis for overcoming anlotinib resistance.

Wayfinding in hospitals today is a significant challenge for urban residents, especially for the elderly. This study investigated the perceptions and attitudes of the elderly toward existing hospital signage systems to identify the wayfinding needs in the healthcare environment. This study collected 762 elderly participants' perceptions and personal preferences regarding 12 features of the existing signage systems in three hospitals in the Yuexiu, Haizhu, and Liwan districts of Guangzhou using a questionnaire methodology. The study further explored the differences in perceptions and preferences for signage based on the gender, age, and educational level of the elderly participants. The findings indicate that most of the elderly participants experienced becoming lost in the hospital; they typically chose to ask others for directions first, followed by using the signage system. Most of the elderly participants had positive attitudes toward the current hospital signage system. Furthermore, they emphasized the importance of the signage system's graphics, texts, colors, and updates, which directly affects the readability and comprehensibility of signs. We found gender differences in perceptions and attitudes toward signage; male participants had more positive attitudes toward the hospital signage systems than female participants. Additionally, consistent with previous findings, the older the age of participants, the less comprehension they had regarding signage graphic symbols. We also found that the more educated elderly participants were, the more understanding of signage they had. At the same time, however, they were less satisfied, which is possibly because the more educated they were, the more aware they were of signage issues.

This study aimed to investigate the molecular mechanisms of periodontitis and identify key immune-related biomarkers using machine learning and Mendelian randomization (MR). Differentially expressed gene (DEG) analysis was performed on periodontitis datasets GSE16134 and GSE10334 from the Gene Expression Omnibus (GEO) database, followed by weighted gene co-expression network analysis (WGCNA) to identify relevant gene modules. Various machine learning algorithms were utilized to construct predictive models, highlighting core genes, while MR assessed the causal relationships between these genes and periodontitis. Additionally, immune infiltration analysis and single-cell sequencing were employed to explore the roles of key genes in immunity and their expression across different cell types. The integration of machine learning, MR, and single-cell sequencing represents a novel approach that significantly enhances our understanding of the immune dynamics and gene interactions in periodontitis. The study identified 682 significant DEGs, with WGCNA revealing seven gene modules associated with periodontitis and 471 core candidate genes. Among the 113 machine learning algorithms tested, XGBoost was the most effective in identifying periodontitis samples, leading to the selection of 19 core genes. MR confirmed significant causal relationships between CD93, CD69, and CXCL6 and periodontitis. Further analysis showed that these genes were correlated with various immune cells and exhibited specific expression patterns in periodontitis tissues. The findings suggest that CD93, CD69, and CXCL6 are closely related to the progression of periodontitis, with MR confirming their causal links to the disease. These genes have potential applications in the diagnosis and treatment of periodontitis, offering new insights into the disease’s molecular mechanisms and providing valuable resources for precision medicine approaches in periodontitis management. Limitations of this study include the demographic and sample size constraints of the datasets, which may impact the generalizability of the findings. Future research is needed to validate these biomarkers in larger, diverse cohorts and to investigate their functional roles in the pathogenesis of periodontitis.

BackgroundChatbots have become ubiquitous in our daily lives, enabling natural language conversations with users through various modes of communication. Chatbots have the potential to play a significant role in promoting health and well-being. As the number of studies and available products related to chatbots continues to rise, there is a critical need to assess product features to enhance the design of chatbots that effectively promote health and behavioral change. ObjectiveThis scoping review aims to provide a comprehensive assessment of the current state of health-related chatbots, including the chatbots’ characteristics and features, user backgrounds, communication models, relational building capacity, personalization, interaction, responses to suicidal thoughts, and users’ in-app experiences during chatbot use. Through this analysis, we seek to identify gaps in the current research, guide future directions, and enhance the design of health-focused chatbots. MethodsFollowing the scoping review methodology by Arksey and O'Malley and guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist, this study used a two-pronged approach to identify relevant chatbots: (1) searching the iOS and Android App Stores and (2) reviewing scientific literature through a search strategy designed by a librarian. Overall, 36 chatbots were selected based on predefined criteria from both sources. These chatbots were systematically evaluated using a comprehensive framework developed for this study, including chatbot characteristics, user backgrounds, building relational capacity, personalization, interaction models, responses to critical situations, and user experiences. Ten coauthors were responsible for downloading and testing the chatbots, coding their features, and evaluating their performance in simulated conversations. The testing of all chatbot apps was limited to their free-to-use features. ResultsThis review provides an overview of the diversity of health-related chatbots, encompassing categories such as mental health support, physical activity promotion, and behavior change interventions. Chatbots use text, animations, speech, images, and emojis for communication. The findings highlight variations in conversational capabilities, including empathy, humor, and personalization. Notably, concerns regarding safety, particularly in addressing suicidal thoughts, were evident. Approximately 44% (16/36) of the chatbots effectively addressed suicidal thoughts. User experiences and behavioral outcomes demonstrated the potential of chatbots in health interventions, but evidence remains limited. ConclusionsThis scoping review underscores the significance of chatbots in health-related applications and offers insights into their features, functionalities, and user experiences. This study contributes to advancing the understanding of chatbots’ role in digital health interventions, thus paving the way for more effective and user-centric health promotion strategies. This study informs future research directions, emphasizing the need for rigorous randomized control trials, standardized evaluation metrics, and user-centered design to unlock the full potential of chatbots in enhancing health and well-being. Future research should focus on addressing limitations, exploring real-world user experiences, and implementing robust data security and privacy measures.

The transformation of social development modes has led to profound changes in the pattern of intangible cultural heritage, while simultaneously posing significant challenges to its preservation. The rapid development of artificial intelligence (AI) technology has brought new development opportunities in various research fields. This study intends, by constructing and evaluating a theoretical model, to investigate whether AI-generated cultural and creative products can promote the sustainability of intangible cultural heritage. The central focus of this research is to measure the effectiveness of AI technologies in promoting the sustainability of intangible cultural heritage. The context of the research design is rooted in the attention, interest, search, action, and share (AISAS) model, incorporating theories of perceived value and cultural identity, to forecast the long-term viability of AI-generated cultural and creative products in the promotion of intangible cultural heritage. This research was conducted in Tianjin, China and carried out using quantitative methods, a questionnaire survey, and the accidental sampling method, taking a sample of 291 participants for analysis. The results show that 1) the attraction of and interest and participation in AI-generated Yangliuqing New Year Print cultural and creative products have a positive effect on perceived value; 2) the purchase and sharing of these products have a positive impact on cultural identity; 3) the perceived value has a positive impact on cultural identity; and 4) cultural identity has a positive impact on the sustainability of intangible cultural heritage. This study contributes to the theoretical development and practical application of the AISAS model and offers valuable insights into the future development trajectory of intangible cultural heritage, thereby promoting its sustainability. The limitations of this study are its small sample size and geographical restrictions. In future studies, the sample size will be expanded and will include more regions for data analysis.

Osteosarcoma (OS) is a highly malignant tumor with a poor prognosis and a growing incidence. LncRNAs and microRNAs control the occurrence and development process of osteosarcoma through ceRNA patterns. The LPAR3 gene is important in cancer cell proliferation, apoptosis and disease development. However, the regulatory mechanism of the ceRNA network through which LPAR3 participates in osteosarcoma has not been clarified. Herein, our study demonstrated that the AP003352.1/miR-141-3p axis drives LPAR3 expression to induce the malignant progression of osteosarcoma. First, the expression of LPAR3 is regulated by the changes in AP003352.1 and miR-141-3p. Similar to the ceRNA of miR-141-3p, AP003352.1 regulates the expression of LPAR3 through this mechanism. In addition, the regulation of AP003352.1 in malignant osteosarcoma progression depends to a certain degree on miR-141-3p. Importantly, the AP003352.1/miR-141-3p/LPAR3 axis can better serve as a multi-gene diagnostic marker for osteosarcoma. In conclusion, our research reveals a new ceRNA regulatory network, which provides a novel potential target for the diagnosis and treatment of osteosarcoma.

BackgroundScholars have used data from in-person interviews, administrative systems, and surveys for sexual violence research. Using Twitter as a data source for examining the nature of sexual violence is a relatively new and underexplored area of study. ObjectiveWe aimed to perform a scoping review of the current literature on using Twitter data for researching sexual violence, elaborate on the validity of the methods, and discuss the implications and limitations of existing studies. MethodsWe performed a literature search in the following 6 databases: APA PsycInfo (Ovid), Scopus, PubMed, International Bibliography of Social Sciences (ProQuest), Criminal Justice Abstracts (EBSCO), and Communications Abstracts (EBSCO), in April 2022. The initial search identified 3759 articles that were imported into Covidence. Seven independent reviewers screened these articles following 2 steps: (1) title and abstract screening, and (2) full-text screening. The inclusion criteria were as follows: (1) empirical research, (2) focus on sexual violence, (3) analysis of Twitter data (ie, tweets or Twitter metadata), and (4) text in English. Finally, we selected 121 articles that met the inclusion criteria and coded these articles. ResultsWe coded and presented the 121 articles using Twitter-based data for sexual violence research. About 70% (89/121, 73.6%) of the articles were published in peer-reviewed journals after 2018. The reviewed articles collectively analyzed about 79.6 million tweets. The primary approaches to using Twitter as a data source were content text analysis (112/121, 92.5%) and sentiment analysis (31/121, 25.6%). Hashtags (103/121, 85.1%) were the most prominent metadata feature, followed by tweet time and date, retweets, replies, URLs, and geotags. More than a third of the articles (51/121, 42.1%) used the application programming interface to collect Twitter data. Data analyses included qualitative thematic analysis, machine learning (eg, sentiment analysis, supervised machine learning, unsupervised machine learning, and social network analysis), and quantitative analysis. Only 10.7% (13/121) of the studies discussed ethical considerations. ConclusionsWe described the current state of using Twitter data for sexual violence research, developed a new taxonomy describing Twitter as a data source, and evaluated the methodologies. Research recommendations include the following: development of methods for data collection and analysis, in-depth discussions about ethical norms, exploration of specific aspects of sexual violence on Twitter, examination of tweets in multiple languages, and decontextualization of Twitter data. This review demonstrates the potential of using Twitter data in sexual violence research.

Underground coal mining will inevitably cause serious ground deformation, and therefore, preventive mining-induced deformation analysis (MIDA) is of great importance in assisting mining planning and decision-making. Current web-based Geographic Information System (webGIS)-based applications usually use 2D GIS data and lack a holistic framework. This study presents a multi-scalar oblique photogrammetry-supported unified 3D webGIS framework for MIDA applications to fill this gap. The developed web platform uses multiple open-source JavaScript libraries, and the prototype system provides user-friendly interfaces for GIS data collecting and corresponding database establishment, geo-visualization and query, dynamic prediction, and spatial overlapping analysis within the same framework. The proposed framework was tested and evaluated in the Qianyingzi mining area in eastern China. The results demonstrated that multi-scalar oblique photogrammetry balances data quality and acquisition efficiency and provides a good source of GIS datasets, and the web-based platform has a good absolute and relative spatial accuracy verified by two types of validation data. Practical application results proved the feasibility and reliability of the system. The developed web-based MIDA prototype system attains an appealing performance and can be easily extended to similar geoscience applications.

With the fast development of energy storage technology, more applications of Energy Storage Devices (ESDs) have been found in rail transportation in recent years. This paper aims to address the optimal sizing problem of on-board Hybrid Energy Storage Devices (HESDs) which are installed to assist train traction and recover the regenerative braking energy. On-board HESDs combining Li-ion battery and supercapacitor can further enhance the capacity and instant power rating. In this paper, a mixed integer linear programming (MILP) model is proposed to minimize the economic cost in terms of energy consumption and installation, and the degradation cost of on-board HESDs considering the long-term train operation constrained by the initial investment of on-board HESDs. Train operation is found to be highly related to characteristics of on-board HESDs including the maximum power, capacity, and state of health (SOH). By changing the investment ratio between Li-ion battery and supercapacitor, the energy-saving rate and economic cost for various investment ratios have been obtained. Compared with the results of train optimal control with Li-ion battery only, supercapacitor only and no on-board HESDs, the results indicate an energy-saving rate up to 25.59%, from the perspective of the long-term train operation. When the allowable capital cost is relaxed from 20 k $\$ $ to 60 k $\$ $ , the cost per kilometer is reduced from 0.55 USD/km to 0.53 USD/km, showing higher capital cost is closely linked to higher cost reduction in the long-term train operation.

Open-source software is used by almost all technology companies and has become an integral part of the technical infrastructure of digital capitalism. Generally, developers of open-source software have been viewed as a social movement at odds with the capitalist profit motive. This idealized view has been challenged as companies have made significant investments in open-source since the early 2000s. Current research frames corporate participation in open-source as fundamentally extractive in nature, failing to account for these sizable investments. Through a historical analysis of Google's Chromium browser project, we provide another way to understand corporate participation in open-source. This article takes a material political economic approach to argue that control of open-source projects can grant companies logistical power that enables them to influence standards and shape the Internet as an infrastructure for digital capitalism.

Energy is an essential topic owing to the severe environmental problems faced worldwide, especially in underdeveloped rural areas. Energy literacy is closely related to energy consumption and conservation behaviors in daily life. Although the energy attitudes and behaviors of communities can determine the sustainable development of rural tourism, less attention has been paid to the energy literacy of rural communities. This study adopted a measurable scale to compare the energy literacy levels between 188 questionnaires from rural residents in tourism communities and 195 questionnaires from non-tourism communities both in the Jizhou District, China. A theoretical model was constructed to investigate the relationships between the variables. Additionally, this study verified the influence of rural tourism activities on communities’ energy literacy by comparing different villages. The results showed that rural communities have high levels of energy literacy in the knowledge, attitude, and behavioral sectors. Positive relationships between knowledge and attitude and between attitudes and behaviors were estimated. However, the positive impact of knowledge on behavior was inconspicuous. The most important theoretical contribution of this study is the confirmation of the significant differences in energy literacy between traditional and rural tourism destination communities, thus proving the improvement in communities’ energy literacy induced by tourism development. Finally, this study presents practical implications for policymakers.

To overcome the shortcoming of conventional transarterial chemoembolization (cTACE) like high systemic release, a novel droplet-based flow-focusing microfluidic device was fabricated and the biocompatible poly(lactic-co-glycolic acid) (PLGA) magnetic drug-eluting beads transarterial chemoembolization (TACE) microspheres with tunable size and shell thickness were prepared via this device. Paclitaxel, as a model active, was loaded through O/O/W emulsion method with high efficiency. The size and the shell thickness vary when adjusting the flow velocity and/or solution concentration, which caters for different clinical requirements to have different drug loading and release behavior. Under the designed experimental conditions, the average diameter of the microspheres is 60 ± 2 μm and the drug loading efficiency has reached 6%. The drug release behavior of the microspheres shows the combination of delayed release and smoothly sustained release profiles and the release kinetics differ within different shell thickness. The microspheres also own the potential of magnetic resonance imaging (MRI) visuality because of the loaded magnetic nanoparticles. The microsphere preparation method and device we proposed are simple, feasible, and effective, which have a good application prospect.

Biodegradable microspheres have been widely used in the field of medicine due to their ability to deliver drug molecules of various properties through multiple pathways and their advantages of low dose and low side effects. Poly (lactic-co-glycolic acid) copolymer (PLGA) is one of the most widely used biodegradable material currently and has good biocompatibility. In application, PLGA with a specific monomer ratio (lactic acid and glycolic acid) can be selected according to the properties of drug molecules and the requirements of the drug release rate. PLGA-based biodegradable microspheres have been studied in the field of drug delivery, including the delivery of various anticancer drugs, protein or peptide drugs, bacterial or viral DNA, etc. This review describes the basic knowledge and current situation of PLGA biodegradable microspheres and discusses the selection of PLGA polymer materials. Then, the preparation methods of PLGA microspheres are introduced, including emulsification, microfluidic technology, electrospray, and spray drying. Finally, this review summarizes the application of PLGA microspheres in drug delivery and the treatment of pulmonary and ocular-related diseases.

Metamaterial perfect absorbers (MPAs) have been widely studied due to their distinctive tunable electromagnetic properties. Conventional MPAs mostly focus on realizing perfect absorption in a single direction, which leads to their limitations for various practical applications. We propose a bidirectional MPA with the ability of switching the ultra-wideband and dual-narrowband absorption. An inversion algorithm is applied to extract the normalized impedance of the absorber for explaining the physical mechanism. Simulation results clearly manifest that the average absorptivity of 93.8% and 99.5% could be achieved both over an ultra-wide spectral range of 1200 nm and at the specific wavelengths of 589 nm and 1097 nm with polarization independence and angle insensitiveness, which are led by the multiple excitations of different resonance modes induced by the metal–insulator-metal (MIM) Fabry-Perot (F-P) cavities. Our results are desirable for the integration of multiple optical applications such as energy harvesting, thermal emission, perfect cloaking and nanoparticle detecting.

Background: It has not been fully elucidated whether the change of the uterus flora is correlated to impaired fecundity. This case-control study aimed to analyze the differences in uterus microbial flora between women with post-cesarean section (CS) scar diverticulum (PCSD) (CS group) and women after vaginal delivery (control group), exploring the correlation between differentially expressed microbial flora and inflammation.Methods: Infertile women who underwent hysteroscopy were enrolled in this case-control study. The swab samples were classified into four subgroups: CS cervix group, CS endometrium group, control cervix group, and control endometrium group. The total DNA obtained from 16 women (a total of 31 samples, the cervix or endometrium) was extracted for 16S recombinant DNA (rDNA) analysis. The Luminex platform was used to detect the abundance of 34 kinds of local inflammatory cytokines in 32 endometrium samples, and the correlation between microbial flora and inflammatory cytokines was analyzed.Results: The alpha and beta diversity analysis indicated that the microbial diversity was higher in the CS group compared to the control group, especially in endometrium tissues. The heatmaps revealed that the microbial flora structure differs at each level of the phylum-class-order-family-genus among the groups. The analysis of four of the most prominently changed microbial flora revealed that Lactobacillus in the cervix was significantly higher in the control group when compared with the cesarean section group (P < 0.05). Furthermore, Proteobacteria and Neisseriaceae had a higher abundance in the CS groups, especially in the cervical tissue (P < 0.05), while Staphylococcaceae increased only in the CS endometrium tissue (P < 0.05). Next, these women were re-divided into the high- and low-Staphylococcaceae, and the abundance of 34 kinds of local inflammation cytokines was compared between groups. It was found that there was a positive correlation between Staphylococcaceae and IL-2, and a negative correlation between Staphylococcaceae and IL-8 (P < 0.05).Conclusion: The present results suggest that the disrupted uterus microbiota composition in women with CS may be closely associated with local inflammation. The interplay between the microbiota and the immune system may be linked to clinical disorders. The potential mechanisms require further exploration.

Chinese New Year prints constitute a significant component of the country’s cultural heritage and folk art. Yangliuqing New Year prints are the most important and widely circulated of all the different kinds of New Year prints. Due to a variety of factors including societal change, industrial structure change, and economic development, New Year prints, which were deeply rooted in agricultural society, have been adversely impacted, and have even reached the brink of disappearance. With the protection and effort from the government and researchers, New Year prints can finally be preserved. However, the underlying problems remain, such as receiving little attention, a singular product form, and being unable to keep up with the times, especially among the younger generation. In this paper, the researchers first processed Yangliuqing New Year prints through the GANs model. Then, the image is segmented by binarization and color extraction of images from the Pop art dataset by the K-Means algorithm, followed by colorizing the binarized and segmented image. Finally, usable high-quality Pop art style Yangliuqing New Year prints are generated. The generated images are used in the development of cultural and creative products. Questionnaires were then distributed based on the empirical research scale. The results of this study are as follows: 1. The method proposed in this study can generate high-quality Pop art style New Year prints. 2 Using Pop art style New Year print images in the design of cultural and creative products is popular among the younger generation, and they possess a great propensity to purchase. This study solves the problems encountered by the current cultural heritage of New Year prints, and broadens the artistic expression forms and product categories, and provides research ideas for the cultural heritage of the same type that is facing similar problems. In the future, researchers will continue to explore the incorporation of AI technology in New Year prints to stimulate the vitality of traditional cultural heritage.

In high frequency AC (HFAC) distribution system, the resonant inverter is used to improve power quality and keep the stability of the output AC voltage. Aiming at the problems of poor output power quality and slow transient performance caused by unreasonable filter parameter design and load change during inverter operation, a single-phase H-bridge LCLC resonant inverter based on analog circuit controller implement is introduced in this paper for HFAC power distribution system (PDS). In this study, to design harmonic compensator and analyze the responsiveness of the inverter, it is necessary to analyze the output voltage total harmonic distortion (THD) of LCLC resonant inverter and the performance of the open loop system in detail. On the one hand, a proportional-integral-resonant (PIR) controller is designed to maintain the zero static error of the voltage output and suppress the output voltage THD of LCLC resonant inverter. On the other hand, an integral controller combines with phase-shift modulation (PSM) method is presented to effectively improve the transient performance of resonant inverter and provide the fixed frequency of the output voltage. On the basis of the above, the experimental prototype is implemented with the output AC voltage root mean square of 28 V, and the output voltage frequency for resonant inverter is equal to switching frequency. A rated output power of 130 W experimental platform is built to verify the effectiveness of the theoretical analysis, control strategy, and modulation method.

Abstract Background Since pre-exposure prophylaxis (PrEP) is mainly prescribed to high-risk uninfected individuals, consistent condom use (CCU) continues to be recommended as an inexpensive, feasible, practical and acceptable way to prevent the general population from acquiring and transmitting HIV through sexual intercourse. The objective of this cross-sectional study was to compare the relative importance of various determinants of CCU among sexually experienced undergraduates in mainland China so as to assess and subsequently to suggest ways to eliminate inequities in its use. Method From September 10, 2018, to January 9, 2019, an anonymous self-administered online questionnaire was voluntarily completed by 12,750 participants distributed across 30 provinces in mainland China (except for Tibet). The present analysis was restricted to 2054 sexually experienced undergraduates. Pearson’s chi-square test and Logistic regression models were chosen to analyze the factors associated with CCU. Results The overall rate of CCU was 61.3% [95% confidence interval (CI) = 59.2–63.4%]. CCU was inequitably distributed since enabling factors exerted greater effects than predisposing and need variables. Compared with heterosexual men, heterosexual women [adjusted odds ratio (AOR) = 0.78, 95% confidence interval (CI):0.64–0.96)], non-heterosexuals men (AOR = 0.64, 95% CI:0.45–0.92) and women (AOR = 0.68, 95% CI:0.47–0.99) were less prone to using condoms consistently. Those with more resources [i.e., higher levels of self- efficacy for condom use (AOR = 2.86, 95% CI:2.35–3.49) and being knowledgeable of the national AIDS policy (AOR = 1.50, 95% CI:1.23–1.82)], and those with lower need for condoms [i.e., late initiation of sexual activity (AOR = 1.34, 95% CI:1.09–1.64) and single sexual partner (AOR = 1.68,95% CI:1.21–2.33)] were more likely to be consistent condom users. Conclusions In order to increase consistency of condom use and simultaneously reduce the remaining inequities, a comprehensive intervention measure should be taken to target heterosexual women, non-heterosexual men and women, and those with higher need for condoms, improve their condom use self- efficacy and raise their awareness of the national AIDS policy.

To improve the energy-efficiency of transport systems, it is necessary to investigate electric trains with on-board hybrid energy storage devices (HESDs), which are applied to assist the traction and recover the regenerative energy. In this paper, a time-based mixed-integer linear programming (MILP) model is proposed to obtain the energy-saving operation for electric trains with different constraints of on-board HESDs, such as their capacity, initial state of charge (SOC), and level of degradation. The proposed integrated power flow model based on the train longitudinal dynamics, power split of on-board HESDs, and line impedance is discretized and linearized, aiming to minimize the net energy consumption (NEC). The results reveal that on-board HESDs with a higher capacity does not necessarily lead to a higher energy-saving rate; a lower or excessive initial SOC could undermine the energy-saving potential; considering the long-term train operation, the degradation of the Li-ion battery will influence the energy-saving operation for electric trains, as well as result in an energy-saving rate that ranges from 41.57% to 31.90%. The practical data from Guangzhou Metro Line 7 were applied in the simulations, which enhanced the practicality and effectiveness of the proposed method.

Considering the unknown compound interference in manufacturing systems, the finite-time tracking and synchronization performance of the multi-motor system significantly affects the production safety, reliability, and quality, which can be considered a multi-agent system with unmeasured speed and uncertainty. In recent years, the synchronous control schemes of the multi-motor system have grown to maturity, but the research on the speed sensorless finite-time consensus tracking control remains to be extended. This paper proposes an observer-based leader–follower consensus tracking control for the synchronous coordination control of the multi-motor system. The speed and position of all motors can be tracked by consensus in a finite time when only some motors realize partial interaction. First, a finite-time observer is designed to estimate the unknown composite disturbance and unmeasurable speed variable of each motor. Second, the distributed finite-time consensus tracking control protocol is designed using the observed value and local information interaction. The stability of the overall closed-loop system is theoretically analyzed based on Lyapunov theory and graph theory, which shows that the consensus tracking error converges to an arbitrary small neighborhood of zero, and all signals are globally bounded in finite time. Finally, simulation results are provided to illustrate the effectiveness of the proposed control method.

With increasing concerns on transportation decarbonization, fuel cell hybrid trains (FCHTs) attract many attentions due to their zero carbon emissions during operation. Since fuel cells alone cannot recover the regenerative braking energy (RBE), energy storage devices (ESDs) are commonly deployed for the recovery of RBE and provide extra traction power to improve the energy efficiency. This paper aims to minimize the net hydrogen consumption (NHC) by co-optimizing both train speed trajectory and onboard energy management using a time-based mixed integer linear programming (MILP) model. In the case with the constraints of speed limits and gradients, the NHC of co-optimization reduces by 6.4% compared to the result obtained by the sequential optimization, which optimizes train control strategies first and then the energy management. Additionally, the relationship between NHC and employed ESD capacity is studied and it is found that with the increase of ESD capacity, the NHC can be reduced by up to 30% in a typical route in urban railway transit. The study shows that ESDs play an important role for FCHTs in reducing NHC, and the proposed time-based co-optimization model can maximize the energy-saving benefits for such emerging traction systems with hybrid energy sources, including both fuel cells and ESD.

Demand response can reduce peak load, transfer part of peak load to valley load, and reduce system risk at peak time. In this study, a calculation model of power grid loss risk is proposed, which is suitable for evaluating the risk changes caused by the demand response, and calculating the corresponding risk value under the demand response load curve. In the calculation of demand response load, considering the impact of two measures of time-of-use (TOU) price and incentive measure, the TOU and incentive load curve are obtained based on the user's maximum benefit. In order to gain the corresponding load risk index, Monte Carlo simulation method is used, demand response load curve is taken as input, and load point fault time, load point fault rate and expected energy not serve indexes are introduced. Moreover, compared with the risk ratio of the original load curve, the simulation results verify the effectiveness of the proposed risk model, and prove that the demand response measures can transfer the risk of partial high load and balance the risk distribution.

In this work, quinoa straw (QS) is considered as a sustainable biomass resource to produce adsorbent materials for wastewater treatment. Two materials, a porous carbon material derived from QS (PCQS) and a Fe _3 O _4 -containing composite material based on the PCPS (Fe _3 O _4 @PCQS), were prepared. PCQS was prepared via carbonization and subsequent chemical activation of the QS using NaOH. Thereafter, PCQS was characterized by SEM, TEM, XRD, IR, XPS, and N _2 adsorption-desorption analysis. As a carbon material with heterogeneous pores, PCQS has a BET specific surface area of 3435.21 m ^2 g ^−1 , which is about 175 times higher than that of the precursor QS (19.60 m ^2 g ^−1 ). The PCQS had an adsorption capacity of 1778.1 mg g ^−1 toward rhodamine B (RhB), and the adsorption followed pseudo-second-order kinetics and the Freundlich isotherm model. The PCQS was further modified by synthesizing Fe _3 O _4 magnetic nanoparticles on the surface of PCQS to give Fe _3 O _4 @PCQS. The adsorption capacity of Fe _3 O _4 @PCQS toward RhB reached 1156.2 mg g ^−1 , and it could be rapidly separated from water by applying an external magnetic field. The PCQS and Fe _3 O _4 @PCQS exhibited acceptable reusability which was evaluated through ten successive adsorption/desorption cycles. In summary, the adsorption capacities of PCQS and Fe _3 O _4 @PCQS toward RhB are comparable with most current adsorbents, including the graphene-based materials, which shows that QS is a promising biomass feedstock to prepare carbon-based materials and composites.