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Objective: To investigate the feasibility of pre- and post-encounter patient-reported outcome (PRO) measure collection within an outpatient integrative health and medicine (IHM) clinic and to characterize factors associated with successful completion. Methods: We conducted a retrospective review of 27,464 outpatient IHM encounters including 9,520 chiropractic; 8,237 acupuncture; 5,847 massage; 2,345 IHM consultation; and 1,515 osteopathic manipulation treatment encounters at four clinics offering IHM over 18 months. Patients were asked to complete paper questionnaires rating pain, anxiety, and stress from 0–10 immediately pre- and post-encounter. Generalized linear mixed effect regression models were used to examine the relationship between demographic, clinical, and operational covariates and completing (1) pre-encounter and (2) paired (i.e., pre and post) PROs. Results: Patients (N = 5587, mean age 49 years, 74% white, 77% female) generally presented for musculoskeletal conditions (81.7%), with a chief complaint of pain (55.1%). 21,852 (79.6%) encounters were among patients who completed pre-encounter PROs; 11,709/21,852 (53.6%) completed subsequent post-encounter PROs. Odds of PRO completion were more impacted by provider, operational, and clinical-level factors than patient factors. Covariates associated with increased odds of pre-encounter PRO completion included being female, having additional IHM encounters, and having a pain or anxiety complaint. Covariates associated with increased odds of paired PRO completion included being aged 31–40 vs. 51–60 years and having additional IHM encounters. Conclusion: Implementing a paper-based PRO collection system in outpatient IHM is feasible; however, collecting post-encounter PROs was challenging. Future endeavors should leverage the electronic health record and patient portals to optimize PRO collection and engage patients and clinical providers. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive exploration of topic modeling methods tailored for large language model (LLM) using data obtained from Web of Science and LexisNexis from June 1, 2020, to December 31, 2023. The data collection process involved queries focusing on LLMs, including "Large language model," "LLM," and "ChatGPT." Various topic modeling approaches were evaluated based on performance metrics, including diversity and coherence. latent Dirichlet allocation (LDA), nonnegative matrix factorization (NMF), combined topic models (CTM), and bidirectional encoder representations from Transformers topic (BERTopic) were employed for performance evaluation. Evaluation metrics were computed across platforms, with BERTopic demonstrating superior performance in diversity and coherence across both LexisNexis and Web of Science. The experiment result reveals that news articles maintain a balanced coverage across various topics and mainly focus on efforts to utilize LLM in specialized domains. Conversely, research papers are more concise and concentrated on the technology itself, emphasizing technical aspects. Through the insights gained in this study, it becomes possible to investigate the future path and the challenges that LLMs should tackle. Additionally, they could offer considerable value to enterprises that utilize LLMs to deliver services. [ABSTRACT FROM AUTHOR]

Introduction: Pregnancy increases the risk of periodontitis due to the increase in progesterone and estrogen. Moreover, periodontitis during pregnancy is associated with development of pregnancy and birth related complications. The aim of this study is to determine, whether periodontal treatment during pregnancy can reduce systemic inflammation and lower the risk of adverse pregnancy and birth related outcomes. Methods and analysis: The PROBE study is a non-randomized controlled intervention study conducted among 600 pregnant women with periodontitis. The women will be recruited among all pregnant women at two Danish hospitals in Region Zealand during their nuchal translucency scan and will subsequently be screened for periodontitis. The intervention group includes 300 pregnant women, who will be offered state-of-the-art periodontal treatment during pregnancy. The control group includes additional 300 pregnant women, who will be offered periodontal treatment after giving birth. Outcome measures include periodontal measures, inflammatory, hormonal and glycaemic markers as well as the prevalence of preterm birth risk, low birth weight and risk markers of gestational diabetes mellitus (GDM) and preeclampsia that will be collected from all screened women and further during pregnancy week 20 and pregnancy week 35 for women enrolled in the intervention. Ethics and dissemination: The study's findings will be published in peer reviewed journals and disseminated at national and international conferences and through social media. The PROBE study is designed to provide important new knowledge as to whether periodontal treatment during pregnancy can reduce the prevalence of complications related to pregnancy and birth. Clinical trials registration: The study was registered on clinicaltrials.gov (NCT06110143). [ABSTRACT FROM AUTHOR]

Background: Global evidence on psychosis is dominated by studies conducted in Western, high-income countries. The objectives of the Study of Context Of Psychoses to improve outcomes in Ethiopia (SCOPE) are (1) to generate rigorous evidence of psychosis experience, epidemiology and impacts in Ethiopia that will illuminate aetiological understanding and (2) inform development and testing of interventions for earlier identification and improved first contact care that are scalable, inclusive of difficult-to-reach populations and optimise recovery. Methods: The setting is sub-cities of Addis Ababa and rural districts in south-central Ethiopia covering 1.1 million people and including rural, urban and homeless populations. SCOPE comprises (1) formative work to understand care pathways and community resources (resource mapping); examine family context and communication (ethnography); develop valid measures of family communication and personal recovery; and establish platforms for community engagement and involvement of people with lived experience; (2a) a population-based incidence study, (2b) a case-control study and (2c) a cohort study with 12 months follow-up involving 440 people with psychosis (390 rural/Addis Ababa; 50 who are homeless), 390 relatives and 390 controls. We will test hypotheses about incidence rates in rural vs. urban populations and men vs. women; potential aetiological role of khat (a commonly chewed plant with amphetamine-like properties) and traumatic exposures in psychosis; determine profiles of needs at first contact and predictors of outcome; (3) participatory workshops to develop programme theory and inform co-development of interventions, and (4) evaluation of the impact of early identification strategies on engagement with care (interrupted time series study). Findings will inform development of (5) a protocol for (5a) a feasibility cluster randomised controlled trial of interventions for people with recent-onset psychosis in rural settings and (5b) two uncontrolled pilot studies to test acceptability, feasibility of co-developed interventions in urban and homeless populations. [ABSTRACT FROM AUTHOR]

How do author perceptions match up to the outcomes of the peer-review process and perceptions of others? In a top-tier computer science conference (NeurIPS 2021) with more than 23,000 submitting authors and 9,000 submitted papers, we surveyed the authors on three questions: (i) their predicted probability of acceptance for each of their papers, (ii) their perceived ranking of their own papers based on scientific contribution, and (iii) the change in their perception about their own papers after seeing the reviews. The salient results are: (1) Authors had roughly a three-fold overestimate of the acceptance probability of their papers: The median prediction was 70% for an approximately 25% acceptance rate. (2) Female authors exhibited a marginally higher (statistically significant) miscalibration than male authors; predictions of authors invited to serve as meta-reviewers or reviewers were similarly calibrated, but better than authors who were not invited to review. (3) Authors' relative ranking of scientific contribution of two submissions they made generally agreed with their predicted acceptance probabilities (93% agreement), but there was a notable 7% responses where authors predicted a worse outcome for their better paper. (4) The author-provided rankings disagreed with the peer-review decisions about a third of the time; when co-authors ranked their jointly authored papers, co-authors disagreed at a similar rate—about a third of the time. (5) At least 30% of respondents of both accepted and rejected papers said that their perception of their own paper improved after the review process. The stakeholders in peer review should take these findings into account in setting their expectations from peer review. [ABSTRACT FROM AUTHOR]

Paper mulberry pollen, declared a pest in several countries including Pakistan, can trigger severe allergies and cause asthma attacks. We aimed to develop an algorithm that could accurately predict high pollen days to underpin an alert system that would allow patients to take timely precautionary measures. We developed and validated two prediction models that take historical pollen and weather data as their input to predict the start date and peak date of the pollen season in Islamabad, the capital city of Pakistan. The first model is based on linear regression and the second one is based on phenological modelling. We tested our models on an original and comprehensive dataset from Islamabad. The mean absolute errors (MAEs) for the start day are 2.3 and 3.7 days for the linear and phenological models, respectively, while for the peak day, the MAEs are 3.3 and 4.0 days, respectively. These encouraging results could be used in a website or app to notify patients and healthcare providers to start preparing for the paper mulberry pollen season. Timely action could reduce the burden of symptoms, mitigate the risk of acute attacks and potentially prevent deaths due to acute pollen-induced allergy. [ABSTRACT FROM AUTHOR]

Threatened shark species are caught in large numbers by artisanal and commercial fisheries and traded globally. Monitoring both which shark species are caught and sold in fisheries, and the export of CITES-restricted products, are essential in reducing illegal fishing. Current methods for species identification rely on visual examination by experts or DNA barcoding techniques requiring specialist laboratory facilities and trained personnel. The need for specialist equipment and/or input from experts means many markets are currently not monitored. We have developed a paper-based Lab-on-a-Chip (LOC) to facilitate identification of three threatened and CITES-listed sharks, bigeye thresher (Alopias superciliosus), pelagic thresher (A. pelagicus) and shortfin mako shark (Isurus oxyrinchus) at market source. DNA was successfully extracted from shark meat and fin samples and combined with DNA amplification and visualisation using Loop Mediated Isothermal Amplification (LAMP) on the LOC. This resulted in the successful identification of the target species of sharks in under an hour, with a working positive and negative control. The LOC provided a simple "yes" or "no" result via a colour change from pink to yellow when one of the target species was present. The LOC serves as proof-of-concept (PoC) for field-based species identification as it does not require specialist facilities. It can be used by non-scientifically trained personnel, especially in areas where there are suspected high frequencies of mislabelling or for the identification of dried shark fins in seizures. [ABSTRACT FROM AUTHOR]

Anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV) is a chronic, relapsing-remitting condition associated with increased morbidity. Previous research has shown patients with AAV report high levels of fatigue, pain, depression and anxiety. Over recent years successful work has been carried out to improve clinical outcomes, resulting in reduced mortality and end stage kidney disease (ESKD). Despite this, little work has been done to better understand the role of the patient within this condition. The prevalence of AAV is increasing and to date, there is a shortage of specific tools that assess and measure key features relating to patient reported outcomes (PROs). This protocol details how we can better understand the lived experiences of those with AAV through the development of a disease specific, patient reported outcome measure (PROM), to be used in clinic practice. This will allow us to recognise and validate PROs and the impact the disease and its treatment has on patients' health related quality of life (HRQoL). In addition, we aim to identify potential differences in PRO's between demographics, organ involvement and treatment subgroups in AAV as well as outcomes relating to the patient experience. Patients from a single centre in the UK will be recruited to take part in the exploratory qualitative study which will include focus groups and semi-structured interviews. The inclusion criteria comprise anyone with a diagnosis of AAV and willing to participate, including those who have active or relapsing disease, those are economically active, unemployed, retired and patients receiving renal replacement therapy. The aim of the project is to identify key issues patients experience in relation to their disease and its management and how these can be better assessed in a new PROM developed for use in the clinic setting. This will enable better delivery of individualised care and inform shared decision making, while also serving as a platform for future research looking at PROs in other glomerulonephritides. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of our study was to determine the order in which science and health researchers read scientific papers, their reasons for doing so and the perceived difficulty and perceived importance of each section. Study design and setting: An online survey open to science and health academics and researchers distributed via existing research networks, X (formerly Twitter), and LinkedIn. Results: Almost 90% of respondents self-declared to be experienced in reading research papers. 98.6% of the sample read the abstract first because it provides an overview of the paper and facilitates a decision on continuing to read on or not. Seventy-five percent perceived it to be the easiest to read and 62.4% perceived it to be very important (highest rank on a 5-point Likert scale). The majority of respondents did not read a paper in the IMRAD (Introduction, Methods, Results And Discussion) format. Perceived difficulty and perceived importance influenced reading order. Conclusion: Science and health researchers do not typically read scientific and health research papers in IMRAD format. The more important a respondent perceives a section to be, the more likely they are to read it. The easier a section is perceived, the more likely it will be read. We present recommendations to those teaching the skill of writing scientific papers and reports. [ABSTRACT FROM AUTHOR]

In the era of rapid product iteration, companies need simple and effective methods to guide the entire process of product innovation design and enhance their product innovation capabilities. Most research focused on improving one or several steps in the product design process. Although some scholars have proposed methods that guided the entire process, they combined more than three different theories, which increased the difficulty of theoretical learning and the complexity of practical implementation. This paper proposed a product innovation design process composed of three theoretical methods: Kano, Axiomatic Design (AD), and Theory of the Solution of Inventive Problems (TRIZ). This new process guided the entire product design process with fewer theoretical methods, reducing the difficulty of learning and implementation. The paper demonstrated the effectiveness of this method through the design practice of a portable two-wheeled self-balancing vehicle. Additionally, the discussion section explored the method's potential from the design management perspective. [ABSTRACT FROM AUTHOR]

This paper studies the recycling and remanufacturing mode and sales channel issues in the closed-loop supply chain. Specifically, this study establishes an e-commerce closed-loop supply chain consisting of a manufacturer and an e-commerce platform, and divides the recycling model into recycling by the manufacturer or recycling by the platform. Considering two common sales models in e-commerce platforms: the resale model and agency model, combined with the recycling model, four different research scenarios are formed. We use backward induction to solve the Stackelberg game problem and explore the remanufacturing and channel strategies of the manufacturer and the e-commerce platform. The research results show that for the manufacturer, under the same recycling model, when consumers' preference for remanufactured products and the sensitivity of recycling volume to recycling prices are low, he will prefer the resale model. Under the same sales model, the manufacturer always prefers the recycling model in which he is responsible for recycling. However, the choice of platform is contrary to that of the manufacturer. In the resale model, both the manufacturer and the platform will choose to recycle by themselves, which cannot achieve a win-win situation. Under the agency model, when consumers' preference for remanufactured products is high and the sensitivity coefficient of recycling volume to recycling price is low, supply chain members can achieve a win-win situation, and the scope of the win-win situation decreases as the unit production cost of new products increases. In addition, rising consumer preference for remanufactured products will lead to lower consumer surplus. [ABSTRACT FROM AUTHOR]

Background: Accuracy of molecular tools for the identification of parasites that cause human cutaneous leishmaniasis (CL) could largely depend on the sampling method. Non-invasive or less-invasive sampling methods such as filter paper imprints and cotton swabs are preferred over punch biopsies and lancet scrapings for detection methods of Leishmania based on polymerase chain reaction (PCR) because they are painless, simple, and inexpensive, and of benefit to military and civilian patients to ensure timely treatment. However, different types of samples can generate false negatives and there is a clear need to demonstrate which sample is more proper for molecular assays. Methodology: Here, we compared the sensitivity of molecular identification of different Leishmania (Viannia) species from Peru, using three types of sampling: punch biopsy, filter paper imprint and lancet scraping. Different composite reference standards and latent class models allowed to evaluate the accuracy of the molecular tools. Additionally, a quantitative PCR assessed variations in the results and parasite load in each type of sample. Principal findings: Different composite reference standards and latent class models determined higher sensitivity when lancet scrapings were used for sampling in the identification and determination of Leishmania (Viannia) species through PCR-based assays. This was consistent for genus identification through kinetoplastid DNA-PCR and for the determination of species using FRET probes-based Nested Real-Time PCR. Lack of species identification in some samples correlated with the low intensity of the PCR electrophoretic band, which reflects the low parasite load in samples. Conclusions: The type of clinical sample can directly influence the detection and identification of Leishmania (Viannia) species. Here, we demonstrated that lancet scraping samples consistently allowed the identification of more leishmaniasis cases compared to filter paper imprints or biopsies. This procedure is inexpensive, painless, and easy to implement at the point of care and avoids the need for anesthesia, surgery, and hospitalization and therefore could be used in resource limited settings for both military and civilian populations. Author summary: Human cutaneous leishmaniasis affects low-income populations living in places far from health services. The early sampling and detection of the parasite are necessary for timely treatment, however there are no uniform sampling criteria, thus the sensitivity of molecular tests may vary due to various factors such as the type of sample, the time of the disease and the parasite load in the lesion. In this study, we compared the performance of three sampling methods for molecular identification of the genus and Leishmania (Viannia) species in Peru. Several analytical methods, including composite reference standards and latent class models, suggested that lancet scraping might be the best approach for parasite genus detection by kDNA-PCR and for parasite species determination by FRET probes-based Nested Real-Time PCR. [ABSTRACT FROM AUTHOR]

Background and aims: As a part of the framework of the EU-funded Energy efficiency through Behavior CHANge Transition (ENCHANT) project, the present paper intends to provide a "Research Protocol" of a web-based trial to: (i) assess the effectiveness of behavioral intervention strategies––either single or in combination––on electricity saving, and (ii) unravel the psychological factors contributing to intervention effectiveness in households across Europe. Methods and materials: Six distinct interventions (i.e., information provision, collective vs. individual message framing, social norms, consumption feedback, competitive elements, and commitment strategies) targeting electricity saving in households from six European countries (i.e., Austria, Germany, Italy, Norway, Romania, and Türkiye) are evaluated, with an initial expected samples of about 1500 households per country randomly assigned to 12 intervention groups and two control groups, and data is collected through an ad-hoc online platform. The primary outcome is the weekly electricity consumption normalized to the last seven days before measurement per person per household. Secondary outcomes are the peak consumption during the last day before measurement and the self-reported implementation of electricity saving behaviors (e.g., deicing the refrigerator). The underlying psychological factors expected to mediate and/or moderate the intervention effects on these outcomes are intentions to save electricity, perceived difficulty of saving energy, attitudes to electricity saving, electricity saving habit strength, social norms to save electricity, personal norms, collective efficacy, emotional reaction to electricity consumption, and national identity. The intervention effectiveness will be evaluated by comparing psychological factors and consumption variables before and after the intervention, leading to a 14 (groups including 2 control groups) × 6 (time) mixed factorial design, with one factor between (group) and one factor within subjects (time)–6 measurements of the psychological factors and 6 readings of the electricity meters, which gives then 5 weeks of electricity consumption. Results: Data collection for the present RCT started in January 2023, and by October 2023 data collection will conclude. Discussion: Upon establishing feasibility and effectiveness, the outcomes of this study will assist policymakers, municipalities, NGOs, and other communal entities in identifying impactful interventions tailored to their unique circumstances and available resources. Researchers will benefit from a flexible, structured tool that allows the design, implementation and monitoring of complex interventions protocols. Crucially, the intervention participants will benefit from electricity saving strategies, fostering immediate effectiveness of the interventions in real-life contexts. Trial registration: This trial was preregistered in the Open Science Framework: https://osf.io/9vtn4. [ABSTRACT FROM AUTHOR]

Background: Research on gender inequality is crucial as it unveils the pervasive disparities that persist across various domains, shedding light on societal imbalances and providing a foundation for informed policy-making. Aim: To investigate gender differences in scientometric indices among faculty members in dental schools across Iran. This included overall data and speciality-specific data. Methods: The publication profiles of academic staff in all dental schools were examined using the Iranian Scientometric Information Database (ISID, http://isid.research.ac.ir). Variables analyzed were working field, academic degree, the total number of papers, papers per year, total number of citations, percentage of self-citation, h-index, g-index, citations per paper, gender, university type, number of years publishing, proportion of international papers, first-author papers, and corresponding-author papers. Mann-Whitney and Kruskal-Wallis nonparametric tests were used to analyze the relationship between background characteristics and scientometric indicators. The extracted data were analyzed using R v4.0.1. Results: The database included 1850 faculty members, of which about 60% (1104 of 1850) were women. Men (n = 746) had a higher number of papers (6583 vs. 6255) and citations (60410 vs. 39559) compared with women; 234 of the 376 faculty members with no papers were women. Almost half of the women (N = 517 of 1104) were in Type 2 universities, and nearly half of the men (N = 361 of the 746) were faculty members at Type 1 universities (Type 1 universities ranking higher than Type 2 and 3 universities). The medians of scientometric indices were higher in men, except for self-citation percentage (0 (IQR = 2) vs. 0 (IQR = 3), P = 0.083), international papers percentage (0 (IQR = 7.5) vs. 0 (IQR = 16.7), P<0.001). The proportion of corresponding-author papers was more than 62% higher in women (25 (IQR = 50) vs. 15.4 (IQR = 40), P<0.001). Men had a two-fold higher median h-index (2 (IQR = 4) vs. 1 (IQR = 3), P<0.001). Restorative dentistry and pediatric dentistry had the highest men-to-women ratios (1.5 for both). Dental materials and oral and maxillofacial surgery showed the lowest men-to-women ratios (0.42 and 0.5, respectively). Conclusions: Women made up the majority of dental faculty members in Iran. Nevertheless, men showed better scientometric results in several significant indices. Having identified scientometric information reflecting differences across faculty members, further research is now needed to better understand the drivers of these differences. [ABSTRACT FROM AUTHOR]

Spatial interaction models with spatial origin-destination (OD) filters are powerful tools to characterize trip flows in space, which is a classic and important problem in regional science. To the authors' knowledge, existing studies adopting OD filters mostly specify the spatial dependence as an autoregressive process, which may not be the full picture of spatial effects. To examine the problem, this paper proposes the hypotheses that 1) spatial OD dependences can take place in both the spatial autoregressive term and the spatial error term in a spatial interaction model. 2) Estimating a spatial autoregressive model with spatial autoregressive disturbances (SARAR) model with OD filters would disentangle where the spatial dependence exists and by how much. 3) The marginal effects obtained from SARAR models would be preferred to analysts when SARAR models outperform spatial autoregressive (SAR) models and spatial error models (SEM) from the statistical point of view. To assess these hypotheses, this paper specifies, estimates, and applies SARAR models with OD filters to investigate trip distributions. By comparing against alternative models, this paper investigates the estimation results in SAR, SEM and SARAR models using an empirical data collected from Hangzhou, China. The contribution of this paper is to be the first in developing an SARAR model with OD filters for trip distribution analyses and examining its performance. [ABSTRACT FROM AUTHOR]

Sketch maps are valuable tools used across various disciplines including spatial cognition, environmental psychology, and spatial reasoning. A common approach to evaluate sketch maps in research is to align and compare them with metric maps. However, sketch maps are highly abstract and contain generalized information causing difficulty in their alignment. Current approaches to study sketch maps cannot handle generalized information. They require a one-on-one correspondence between features in the metric map and features in the sketch map. But memory is often generalized. This paper makes two contributions to the research on sketch maps: (i) we present an algorithmic approach to detect generalization in sketch maps (ii) we present an online tool that creates a generalized metric map corresponding to features in sketch maps. Previously, we identified nine types of generalization in sketch maps. In this paper, we develop formal operators to detect these generalizations and implement them as an online tool. We evaluated our algorithm with a set of 11 sketch maps containing 84 instances of generalization. The results indicated that our algorithm consistently detects instances of generalization in sketch maps. [ABSTRACT FROM AUTHOR]

Introduction: Peer-reviewed literature is commonly used to assess academic progress and research excellency. However, representation in authorship of global health publications is biased and unfair. In order to shed light on current gaps towards attaining gender equality in scientific production and shift power asymmetries in global health research, we conducted an assessment of authorship trends from 1972 to 2021 with a focus on gender and geographic representation in scientific articles authored or co-authored by researchers affiliated with UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP). Methods: We searched PubMed, Web of Science, and HRP public reports for publications where at least one author was affiliated with HRP. Our main outcome measures were author gender and location of author affiliation, classified by region and country income group. We used descriptive statistics to characterize the publications under analysis as well as the total number of authors from the included papers. We applied a logistic regression model to explore associations between author gender and other characteristics of published articles and a time series analysis to assess how time can influence the inclusion of women as authors in a publication. Python and R were used for all analyses. Results: A total of 1,484 publications with 14,424 listed authors representing 5,950 unique authors were included in our analysis: 42.5% were female, 35.1% male, and 22.4% unknown (p<0.0001). First authorship was more likely female (56.9%) and from a high-income country (74.6%, p<0.0001) while last authorship was mostly male (53.7%) also from a high-income country (82.5%, p<0.0001). Females more frequently published papers using qualitative data (61.4%) and reviews/estimates (59.4%) while men published more case control (70.7%) and randomised controlled studies (53.0%), p<0.0001. The adjusted odds of there being a female author increased 4% for every additional year that passed. Conclusion: While there are more females authoring articles as compared to the past, they are still lagging behind with regards to seniority and prestige. Likewise, female representation is closely tied to what institution they are affiliated with and where that institution is located. Global health research institutions need to actively promote change by ensuring women are included in research and research outputs, giving them opportunities to lead. [ABSTRACT FROM AUTHOR]

As of now, more than 60 years have passed since the first determination of protein structures through crystallography, and a significant portion of protein structures can be predicted by computers. This is due to the groundbreaking enhancement in protein structure prediction achieved through neural network training utilizing extensive sequence and structure data. However, substantial challenges persist in structure prediction due to limited data availability, with antibody structure prediction standing as one such challenge. In this paper, we propose a novel neural network architecture that effectively enables structure prediction by reflecting the inherent combinatorial nature involved in protein structure formation. The core idea of this neural network architecture is not solely to track and generate a single structure but rather to form a community of multiple structures and pursue accurate structure prediction by exchanging information among community members. Applying this concept to antibody CDR H3 loop structure prediction resulted in improved structure sampling. Such an approach could be applied in the structural and functional studies of proteins, particularly in exploring various physiological processes mediated by loops. Moreover, it holds potential in addressing various other types of combinatorial structure prediction and design problems. Author summary: In this paper, we propose a new architecture that aims to improve upon protein structure prediction algorithms like AlphaFold or RoseTTAFold by considering the combinatorial nature of protein structure formation. Such an architecture, reflecting the physical principles of nature, is expected to yield beneficial results, particularly in scenarios with limited structure and sequence information. Named ComMat, this architecture does not focus on a single structure but rather on a set of multiple structures—a community—simultaneously. In this process, combinatorial exploration of protein structure is encouraged through information exchange among community members. ComMat is an instance that integrates this idea within the structure module of AlphaFold. Applying ComMat to antibody CDR H3 loop structure prediction yielded outstanding results in structure sampling and prediction when tested on the IgFold set and compared with IgFold and AlphaFold-Multimer. It confirmed that improved structure sampling stems from effective structural exploration. The proposed concept here could potentially be used in the development of various other combinatorial protein structure prediction and protein design methods. [ABSTRACT FROM AUTHOR]

Fulfilling corporate social responsibility (CSR) is crucial for society's sustainable development. Executives' academic experience significantly affects their awareness of social responsibility, value orientation, professional ability, and network resources. Thus, it is critical in CSR-related corporate decision-making. This paper explores the impact of executives' academic experience on the fulfillment of CSR. It focuses on non-financial, Special treatment enterprises (ST), and ST* enterprises listed in the A-share market from 2012 to 2021. It utilizes a fixed-effects analysis model to examine the relationship between executives' academic experience and CSR fulfillment. The CSR score and executives' academic experience were positively correlated. This paper also explores the intermediary role of compensation incentives and the moderating effect of marketization level. Both compensation incentives and the level of marketization positively moderated the relationship between executives' academic experience and CSR fulfillment. Meanwhile, the robustness results showed that the experimental findings still held after replacing the explained and explanatory variables. This paper contributes to the advancement of the Upper Echelons Theory and provides empirical evidence for the society's sustainable development. [ABSTRACT FROM AUTHOR]

Advanced Persistent Threat (APT) attacks are causing a lot of damage to critical organizations and institutions. Therefore, early detection and warning of APT attack campaigns are very necessary today. In this paper, we propose a new approach for APT attack detection based on the combination of Feature Intelligent Extraction (FIE) and Representation Learning (RL) techniques. In particular, the proposed FIE technique is a combination of the Bidirectional Long Short-Term Memory (BiLSTM) deep learning network and the Attention network. The FIE combined model has the function of aggregating and extracting unusual behaviors of APT IPs in network traffic. The RL method proposed in this study aims to optimize classifying APT IPs and normal IPs based on two main techniques: rebalancing data and contrastive learning. Specifically, the rebalancing data method supports the training process by rebalancing the experimental dataset. And the contrastive learning method learns APT IP's important features based on finding and pulling similar features together as well as pushing contrasting data points away. The combination of FIE and RL (abbreviated as the FIERL model) is a novel proposal and innovation and has not been proposed and published by any research. The experimental results in the paper have proved that the proposed method in the paper is correct and reasonable when it has shown superior efficiency compared to some other studies and approaches over 5% on all measurements. [ABSTRACT FROM AUTHOR]

Recent research in economics and sociology demonstrates the existence of significant occupational segregation by sexual orientation and gender identity and differences in a range of labor market outcomes, such as hiring chances, earnings, and leadership positions. In this paper, we examine one possible cause of these differences that is associated with the disadvantaged position of sexual and gender minorities in the labor market: LGBTQ* individuals' choices aimed at avoiding possible discrimination. This paper examines LGBTQ* people's relative importance of income, time, promotion prospects, an LGBTQ*-friendly work environment, and diversity management in the decision for or against a job. Based on a discrete choice experiment conducted in a large online sample recruited through social media in Germany (N = 4,507), an LGBTQ*-friendly work climate accounted, on average, for 33.8 percent of respondents' decisions which is comparable with the relative importance of income. Overtime, a diversity management on company level and promotion prospects are less important in the job decision process of LGBTQ* people. While the results show only small differences by sexual orientation, they show group-specific preferences by gender identity. An LGBTQ*-friendly work climate is more important for cisgender women of the LGBTQ* community and gender minorities than for cisgender men of the LGBTQ* community. In contrast, income is less important for gender minorities and cisgender women of the LGBTQ* community than for cisgender men of the LGBTQ* community. [ABSTRACT FROM AUTHOR]

In traditional supply chain finance, the financing of enterprise mainly relies on the credit segmentation of the core enterprise, resulting in a short trust transmission radius and poor financing ability. The development of Internet technology, while expanding financing channels, has also seen an increasing severity in issues such as information fraud and data breaches, which has further aggravated the trust crisis in supply chain finance. This paper integrates blockchain technology into the industrial internet platform and analyzes the applicability of both in empowering supply chain financial trust. Then a supply chain financial trust framework, which emphasizes information sharing, data security, and trust circulation, is proposed. Furthermore, combined with the theories of Funk-SVD and entropy value, this paper designs a global trust evaluation mechanism that facilitates the trust circulation in supply chain finance and proposes a recommendation algorithm for global trust. With the testing conducted using the Epinions dataset, it is found that the algorithm proposed in this paper has a strong data dimensionality reduction and concentration ability, especially for large sample data, it can obtain more accurate evaluation values with less space occupation, thus enhancing the trust circulation ability of supply chain finance. Finally, the paper puts forward specific policy recommendations for the implementation of the supply chain finance information mechanism, aiming to better improve the financing accessibility of enterprises in supply chain, particularly small and medium-sized enterprises. [ABSTRACT FROM AUTHOR]

This paper aims to verify the impact of capital structure on business model innovation and firm performance and the mediating effect of business model innovation. We use the data of the Chinese growth enterprises market (GME) listed high-tech firms from 2016 to 2022 as a dynamic panel data model with the system–generalized method of moments (sys-GMM), adopting return on asset and earning per share as firm performance. Our results show that capital structure has a lag effect on firm performance. The total debt ratio in the last period has a significant non-linear impact on the performance and business model innovation level of nowadays, presenting a U-shaped relationship. The first-order lag short-term debt ratio positively improves firm performance. Business model innovation significantly promotes better firm performance, and business model innovation does exist in the mediating effect between enterprise capital structure and its performance. These results remain robust to different sample sizes or proxy variables. This paper proposes some suggestions for firm operations and government policies based on the findings. [ABSTRACT FROM AUTHOR]

Noise pollution is one of the consequences of urbanization that can cause environmental disturbances in urban areas. Urban ecosystems provide noise reduction services through Urban Green infrastructures (UGIs). Many studies have been conducted to evaluate and model traffic noise, but none have addressed the flow, supply, and demand of noise reduction ecosystem services. The main purpose of this paper is to present a new methodology for estimating flow, supply, and demand for noise reduction in Hamadan city that has not been mentioned in any paper so far. UGIs were classified into six main categories: agricultural lands, gardens, parks, abandoned lands, single trees, and street trees. A total of 57 sampling stations for sound measurement were made in August 2018. The current map of noise pollution (flow) was created using the Kriging method. The amount of supply was measured up to a distance of 50 meters from the main roads based on two approaches (the distance effect and the sound barrier effect). To quantify the demand, the current sound intensity level in the noise-sensitive land uses was compared with standards. Zonal statistics was used for spatial analysis of supply-demand in the urban neighborhood as a working unit. Results showed that at distances of 5m, 10m, 15m, and 20m, the average noise reduction was found to be 1.61, 2.83, 3.92, and 5.33 dB, respectively. Sound barriers at distances of 5m and 10m resulted in an average sound reduction of 1.61 and 2.83 dB, respectively. Individual trees, strip trees, abandoned lands, parks, and gardens led to a decrease in traffic noise by 0.3, 1, 0.1, 3.5, and 4.5 dB, respectively. The clustering analysis revealed a significant spatial clustering of noise pollution in Hamedan. The results and new methodology of this research can be used in similar areas to estimate the supply and demand of noise reduction and also for decision-makers to take management actions to increase supply and meet the demand for noise reduction service. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019(COVID-19) has caused widespread and significant harm globally. In order to address the urgent demand for a rapid and reliable diagnostic approach to mitigate transmission, the application of deep learning stands as a viable solution. The impracticality of many existing models is attributed to excessively large parameters, significantly limiting their utility. Additionally, the classification accuracy of the model with few parameters falls short of desirable levels. Motivated by this observation, the present study employs the lightweight network MobileNetV3 as the underlying architecture. This paper incorporates the dense block to capture intricate spatial information in images, as well as the transition layer designed to reduce the size and channel number of the feature map. Furthermore, this paper employs label smoothing loss to address the inter-class similarity effects and uses class weighting to tackle the problem of data imbalance. Additionally, this study applies the pruning technique to eliminate unnecessary structures and further reduce the number of parameters. As a result, this improved model achieves an impressive 98.71% accuracy on an openly accessible database, while utilizing only 5.94 million parameters. Compared to the previous method, this maximum improvement reaches 5.41%. Moreover, this research successfully reduces the parameter count by up to 24 times, showcasing the efficacy of our approach. This demonstrates the significant benefits in regions with limited availability of medical resources. [ABSTRACT FROM AUTHOR]

Although adaptive cancer therapy shows promise in integrating evolutionary dynamics into treatment scheduling, the stochastic nature of cancer evolution has seldom been taken into account. Various sources of random perturbations can impact the evolution of heterogeneous tumors, making performance metrics of any treatment policy random as well. In this paper, we propose an efficient method for selecting optimal adaptive treatment policies under randomly evolving tumor dynamics. The goal is to improve the cumulative "cost" of treatment, a combination of the total amount of drugs used and the total treatment time. As this cost also becomes random in any stochastic setting, we maximize the probability of reaching the treatment goals (tumor stabilization or eradication) without exceeding a pre-specified cost threshold (or a "budget"). We use a novel Stochastic Optimal Control formulation and Dynamic Programming to find such "threshold-aware" optimal treatment policies. Our approach enables an efficient algorithm to compute these policies for a range of threshold values simultaneously. Compared to treatment plans shown to be optimal in a deterministic setting, the new "threshold-aware" policies significantly improve the chances of the therapy succeeding under the budget, which is correlated with a lower general drug usage. We illustrate this method using two specific examples, but our approach is far more general and provides a new tool for optimizing adaptive therapies based on a broad range of stochastic cancer models. Author summary: Tumor heterogeneities provide an opportunity to improve therapies by leveraging complex (often competitive) interactions of different types of cancer cells. These interactions are usually stochastic due to both individual cell differences and random events affecting the patient as a whole. The new generation of cancer models strive to account for this inherent stochasticity, and adaptive treatment plans need to reflect it as well. In optimizing such treatment, the most common approach is to maximize the probability of eventually stabilizing or eradicating the tumor. In this paper, we consider a more nuanced version of success, maximizing the probability of reaching these therapy goals before the cumulative burden from the disease and treatment exceed a chosen threshold. Importantly, our method allows computing such optimal treatment plans efficiently and for a range of thresholds at once. If used on a high-fidelity personalized model, our general approach could potentially be used by clinicians to choose the most suitable threshold after a detailed discussion of a specific patient's goals (e.g., to include the trade-offs between toxicity and quality of life). [ABSTRACT FROM AUTHOR]

Blockchain-based applications are becoming more and more widespread in business operations. In view of the shortcomings of existing enterprise blockchain evaluation methods, this paper proposes a multi-source heterogeneous blockchain data quality evaluation model for enterprise business activities, so as to achieve efficient evaluation of business activity information consistency, credibility and value. This paper proposes a multi-source heterogeneous blockchain data quality assessment method for enterprise business activities, aiming at the problems that most of the data in enterprise business activities come from different data sources, information representation is inconsistent, information ambiguity between the same block chain is serious, and it is difficult to evaluate the consistency, credibility and value of information. The method firstly proposes an entity information representation method based on the Representation learning for fusing entity category information (CEKGRL) model, which introduces the triad structure of related entities in blockchain, then associates them with enterprise business activity categories, and carries out similarity calculation through contextual information to achieve blockchain information consistency assessment. After that, a trustworthiness characterization method is proposed based on information sources, information comments, and information contents, to obtain the trustworthiness assessment of the business. Finally, based on the information trustworthiness characterization, a value assessment method is introduced to assess the total value of business activity information in the blockchain, and a blockchain quality assessment model is constructed. The experimental results show that the proposed model has great advantages over existing methods in assessing inter-block consistency, intra-block activity information trustworthiness and the value of blockchain. [ABSTRACT FROM AUTHOR]

The efficiency and responsiveness of supply chains are vitally dependent on inventory replenishment and transportation decisions. In this paper, we study a supply chain consisting of a single retailer ordering seasonal products within the newsvendor framework. The primary objective of the paper is to investigate the retailer's decision-making process, aimed at determining the optimal replenishment quantities and selecting the appropriate mix and size of the truck fleet. Initially, we formulate a mathematical model where the retailer exclusively manages a limited fleet of its own trucks for inbound transportation of a single seasonal product. In this context, we determine a lower breakeven point for the fixed transportation cost than what has been previously proposed in the literature. Subsequently, we examine a commonly encountered transportation scenario where the retailer has the opportunity to expand its fleet size by leasing trucks from the external market. The outcomes of the numerical example indicates that the flexibility resulting from the utilization of different types of trucks can lead to reduced overall costs. We also address the practical transportation problem of efficiently shipping various seasonal products solely with the retailer's own trucks. For this complex problem, we propose an optimal solution procedure based on Lagrangian method. We show that the joint replenishment of multiple products results in cost savings and enhances utilization of the trucks' capacities. [ABSTRACT FROM AUTHOR]

Background: Ankylosing spondylitis(AS) is a chronic inflammatory rheumatic disease that leads to a reduced quality of life. Exercise appears to be one of the promising modes of intervention. The aim of this study was to review the available evidence and compare the effectiveness of different exercises in relieving symptoms of AS. Methods: We searched the Pubmed, WOS, EMbase, CNKI, Cochrane Library, and Scopus databases. The search has spanned from the creation of the database until September 15, 2023. We extracted the first author, year of article publication, sample information, intervention, duration of intervention, and outcome indicators from the literature that met the inclusion criteria. The Cochrane Risk Assessment Tool was used to assess the risk of bias for included studies. I² was used to judge the consistency of the included studies. Egger's test and Begg's test were used to judge whether there was significant publication bias. Forest plots were used to compare intervention effects and SUCRA was provided to rank the effects of the interventions. This study has been registered in PROSPERO(No. CRD42024518522). Results: After screening, 10 papers matched the inclusion criteria for this study, and the total sample size of the 10 papers was 623. Upon analysis, the papers included in this study did not have significant publication bias (Begg's Test P = 0.210) and had good consistency (P>0.05). The direct comparisons showed that Running, Pilates, Stretching, Yoga, and Tai Chi were more effective interventions than traditional therapies. The effect sizes, confidence intervals, and number of studies for each intervention are shown below: Running [MD -1.90 (95% CI -3.14,-0.66) n = 1], Pilates [MD -1.70 (95% CI -2.90,-0.51) n = 1], Stretching [MD -1.54 (95% CI -2.21,-0.88) n = 4], Yoga [MD -1.24 (95% CI -2.18,-0.30) n = 1], Tai Chi [MD -0.78 (95% CI -1.44,-0.12) n = 2], Exergame[MD -0.80 (95% CI -1.99,0.39) n = 1], Swiss balls[MD -1.07 (95% CI -2.58,0.44) n = 1]. The indirect comparisons showed that the range of effect sizes for each sport intervention intersected the null line. Based on cumulative probability, the order of effectiveness of different exercises in relieving AS symptoms is Running, Pilates, Stretching, Yoga, Tai Chi, Exergame, and Swiss ball. Conclusion: Running, Pilates, Stretching, Yoga, and Tai Chi provided significant relief from AS symptoms. Exergame and Swiss ball were not statistically significant in relieving AS symptoms. There were no significant differences in the effectiveness of different exercise interventions in relieving AS symptoms. Running may have the most beneficial effect on alleviating AS symptoms. [ABSTRACT FROM AUTHOR]

Debris flow risk assessment can provide some reference for debris flow prevention and control projects. In risk assessment, researchers often only focus on the impact of objective or subjective indicators. For this purpose, this paper proposed a weight calculation method based on t-distribution and linear programming optimization algorithm (LPOA). Taking 72 mudslides in Beichuan County as an example, this paper used analytic hierarchy process (AHP), entropy weight method (EWM) and variation coefficient method (VCM) to obtain the initial weights. Based on the initial weights, weight intervals with different confidence levels were obtained by t-distribution. Subsequently, the final weights were obtained by LOPA in the 90% confidence interval. Finally, the final weights were used to calculate the risk score for each debris flow, thus delineating the level of risk for each debris flow. The results showed that this paper's method can avoid overemphasizing the importance of a particular indicator compared to EWM and VCM. In contrast, EWM and VCM ignored the effect of debris flow frequency on debris flow risk. The assessment results showed that the 72 debris flows in Beichuan County were mainly dominated by moderate and light risks. Of these, there were 8 high risk debris flows, 24 medium risk debris flows, and 40 light risk debris flows. The excellent triggering conditions provide favorable conditions for the formation of high-risk debris flows. Slightly and moderate risk debris flows are mainly located on both sides of highways and rivers, still posing a minor threat to Beichuan County. The proposed fusion weighting method effectively avoids the limitations of single weight calculating method. Through comparison and data analysis, the rationality of the proposed method is verified, which can provide some reference for combination weighting method and debris flow risk assessment. [ABSTRACT FROM AUTHOR]

This paper uses data from the American Voices Project, an interview study based on a random population sample, to explore the relationship between assault experiences and how people narrate their lives. Using quantitative sentiment analysis, we find that survivors of assault express significantly greater negative sentiment when asked to tell their life stories. These negative sentiments are observable throughout the entire interview, including before questions of assault are asked. Survivors of assault narrate their experiences with more anger, disgust, fear, and sadness, and less anticipation, joy, and trust than those who do not report assault experiences. We provide evidence that the negative sentiment associated with sexual violence is greater than having lost a parent and less than having experienced a significant financial hardship within the last month. We contextualize these findings with a qualitative evaluation of the interview transcripts, further substantiating our finding. Overall, this paper suggests that sexual violence is part of what we have called, drawing inspiration from the work of Beth Richie, a "matrix of harm" that structures people's lives. While our finding is consistent with what we might expect given the negative life experiences and sequalae associated with sexual assault, it has important implications. Sentimental differences in narrating life experience are an important yet relatively understudied phenomenon, and experiences of assault are rarely asked about yet may be consequential to both quantitative and qualitative accounts of social processes. [ABSTRACT FROM AUTHOR]

CNN has demonstrated remarkable performance in EEG signal detection, yet it still faces limitations in terms of global perception. Additionally, due to individual differences in EEG signals, the generalization ability of epilepsy detection models is week. To address this issue, this paper presents a cross-patient epilepsy detection method utilizing a multi-head self-attention mechanism. This method first utilizes Short-Time Fourier Transform (STFT) to transform the original EEG signals into time-frequency features, then models local information using Convolutional Neural Network (CNN), subsequently captures global dependency relationships between features using the multi-head self-attention mechanism of Transformer, and finally performs epilepsy detection using these features. Meanwhile, this model employs a light multi-head attention mechanism module with an alternating structure, which can comprehensively extract multi-scale features while significantly reducing computational costs. Experimental results on the CHB-MIT dataset show that the proposed model achieves accuracy, sensitivity, specificity, F1 score, and AUC of 92.89%, 96.17%, 92.99%, 94.41%, and 96.77%, respectively. Compared to the existing methods, the method proposed in this paper obtains better performance along with better generalization. [ABSTRACT FROM AUTHOR]

This paper aims to explore the impact of China's value-added tax (VAT) credit refunds policy on the enterprises' labor demand through a paradigm combining ex-ante analysis and ex-post test. By introducing the VAT credit refunds into the production-decision model of the enterprise, calibrating the parameters and conducting the dynamic effects tests using the data of Chinese A-share listed enterprises, this paper finds that the labor employment of the pilot enterprises exhibits a V-shaped fluctuation trend. In the initial implementation of the policy, due to the existence of layoff costs, iso-cost line of the enterprise bends, which results in that the enterprise with a capital-labor substitution elasticity greater than 1 will not reduce labor hiring, as it has already deployed labor force before the implementation of the policy. When the enterprise enter the next production cycle where the labor force can be freely allocated, the labor employment of the enterprise with a capital-labor elasticity of substitution greater than 1 will decline compared to that without the policy. In the long run, as output increases, the labor demand will recover. The results of ex-post test are consistent with that of the ex-ante analysis. Additionally, heterogeneity test reveals that the greater the elasticity of capital-labor substitution of the sub-industry is, the more severe the degree of the V-shaped fluctuation is. Following the implementation of the policy, the continuous increase in enterprise output and capital stock verifies the relevant transmission mechanism. This study provides a more detailed perspective for comprehensively understanding the impact of VAT credit refunds policy on employment. [ABSTRACT FROM AUTHOR]

Using a multilevel modelling approach to analyse a novel dataset of academic publications at all business schools in 11 European countries, this paper finds that the influence of organisational- and country-level contextual factors on researchers varies considerably based on the type of institution and the development level of the country they are located in. At the organisational-level, we find that greater spatial connectivity–operationalised through proximity to nearby business schools, rail stations, and airports–is positively related to scientific research volume and public dissemination (news mentions). While this result is significant only for high-income countries (above EU-average 2018 GDP per capita), this is likely because the low-income countries (below EU-average 2018 GDP per capita) examined here lack a 'critical mass' of well-connected universities to generate observable agglomeration effects. At the country-level, the results indicate that in high-income countries, less prestigious schools benefit from higher rates of recent international immigration from any foreign country, providing a direct policy pathway for increasing research output for universities that aren't already well-known enough to attract the most talented researchers. In low-income countries, recent immigration rates are even stronger predictors of research performance across all levels of institutional prestige; more open immigration policies would likely benefit research performance in these countries to an even greater extent. Finally, the paper's results show that, in low-income countries, a composite measure of a country's quality of life (including self-rated life satisfaction, health, working hours, and housing overcrowding) is positively related to research outcomes through its interaction with school prestige. This suggests that the lower a country's quality of life, the more researchers are incentivised to produce higher levels of research output. While this may in part reflect the greater disparities inherent in these countries' economic systems, it is noteworthy–and perhaps concerning–that we have observed a negative correlation between country-level quality of life and research performance in low-income countries, which is particularly felt by researchers at less prestigious institutions. [ABSTRACT FROM AUTHOR]

Accurately modeling artificial boundary conditions and wave inputs is paramount for numerical simulations of wave scattering in semi-infinite domains within seismic engineering. Traditionally, analysts focused on one- or two-dimensional free-field problems to determine wave inputs, primarily for vertically incident plane waves or obliquely incident waves parallel to two axes. However, these methods were inadequate for handling arbitrary incident directions in three-dimensional scenarios. This paper proposes a method for modeling seismic wave incidents in arbitrary directions. The basic theory of viscoelastic boundaries is leveraged, and a plane containing an arbitrary incident direction and the vertical coordinate axis is selected to establish a two-dimensional plane coordinate system. The two-dimensional free-field problem in this coordinate system is derived using the transfer matrix method. Subsequently, displacement, velocity, and stress are converted into the coordinate system where the three-dimensional calculation model is located, providing input for the three-dimensional scattering problem. Furthermore, the implementation of transmitting boundary conditions and viscoelastic boundary wave inputs is presented to enable incident wave scattering problems at any angle of the plane. The effect of oblique-incidence soil-structure dynamic interaction is also discussed, focusing on the parallel technology method adopted in this paper. With the relatively mature technology route and method, together with nuclear power systems and large-span deep-water bridge models, through examples of comparative analysis, qualitative and quantitative analyses are made on the impact on the soil mass, foundation, and structure when the seismic wave is an oblique incident. [ABSTRACT FROM AUTHOR]

Currently, digital transformation is having various impacts on enterprises around the world, including the green innovation. However, the current literature on the relationship between digitalization and green innovation in enterprises is scarce. What is the relationship between them, and whether heterogeneous environmental regulation has mediating effects, are questions that are worth exploring. Using a sample of listed manufacturing enterprises in China, this paper empirically tests the impact of digital transformation on enterprise green innovation. The results show that: (1) Digital transformation has a significant positive impact on green innovation, including green innovation output and green innovation capability. (2) Diverse environmental regulation may have mediating effects of digital transformation's influence on green innovation. (3) After a number of robustness tests, the conclusions are still valid. This paper can provide a reference for developing green development strategies for manufacturing enterprises. [ABSTRACT FROM AUTHOR]

The demographic structure is an important factor influencing the development of the services industry. As the country with the world's most serious aging problem, China's service industry structure is likely to undergo profound changes in response to the rapid demographic transition. Therefore, this paper examines the effect of population aging on the development of the service industry in the context of China's accelerating population aging. The study found that: (1) Population aging has a significant "inverted U" effect on the development of the services industry. (2) The impact of population aging on the development of the service industry has obvious regional and industry heterogeneity. The study of regional heterogeneity found that population aging in economically developed regions has a more obvious effect on the development of the service industry than in economically less developed regions. Industry heterogeneity studies found that population aging has an obvious promotional effect on the development of medical and other rigid demand industries, while the effect on other non-rigid demand industries is not significant. (3) The threshold effect test found that when the degree of population aging exceeds the threshold, the stimulating effect of population aging on the development of the services industry is no longer significant. The research in this paper provides useful insights into the likely response to changes in the industrial structure of the services industry, and offers some implications for countries with similar demographic profiles to China. [ABSTRACT FROM AUTHOR]

With the accelerated development of the technological power of society, aerial images of drones gradually penetrated various industries. Due to the variable speed of drones, the captured images are shadowed, blurred, and obscured. Second, drones fly at varying altitudes, leading to changing target scales and making it difficult to detect and identify small targets. In order to solve the above problems, an improved ASG-YOLOv5 model is proposed in this paper. Firstly, this research proposes a dynamic contextual attention module, which uses feature scores to dynamically assign feature weights and output feature information through channel dimensions to improve the model's attention to small target feature information and increase the network's ability to extract contextual information; secondly, this research designs a spatial gating filtering multi-directional weighted fusion module, which uses spatial filtering and weighted bidirectional fusion in the multi-scale fusion stage to improve the characterization of weak targets, reduce the interference of redundant information, and better adapt to the detection of weak targets in images under unmanned aerial vehicle remote sensing aerial photography; meanwhile, using Normalized Wasserstein Distance and CIoU regression loss function, the similarity metric value of the regression frame is obtained by modeling the Gaussian distribution of the regression frame, which increases the smoothing of the positional difference of the small targets and solves the problem that the positional deviation of the small targets is very sensitive, so that the model's detection accuracy of the small targets is effectively improved. This paper trains and tests the model on the VisDrone2021 and AI-TOD datasets. This study used the NWPU-RESISC dataset for visual detection validation. The experimental results show that ASG-YOLOv5 has a better detection effect in unmanned aerial vehicle remote sensing aerial images, and the frames per second (FPS) reaches 86, which meets the requirement of real-time small target detection, and it can be better adapted to the detection of the weak and small targets in the aerial image dataset, and ASG-YOLOv5 outperforms many existing target detection methods, and its detection accuracy reaches 21.1% mAP value. The mAP values are improved by 2.9% and 1.4%, respectively, compared with the YOLOv5 model. The project is available at https://github.com/woaini-shw/asg-yolov5.git. [ABSTRACT FROM AUTHOR]

Although an increasing number of studies have examined issues relating to the preservation and development of intangible cultural heritage (ICH), there has been limited research on how tourists perceive ICH. Moreover, UNESCO asserts that the concept of "authenticity" is not applicable to ICH, and so far, no valid instrument for measuring tourists' subjective perceptions of ICH has been developed, even though their perceptions play a very important role in the preservation and development of ICH. Therefore, this paper aims to develop a measurement scale for ICH image, using both qualitative and quantitative research methods. Participant observation, semi-structured in-depth interviews, secondary data collection, and a literature review were conducted to generate the initial scale items, and then the main surveys were conducted to collect data for the model tests. Four dimensions were extracted by exploratory factor analysis: transmission, localization, vitality, and association. The reliability and validity of the measurement model were demonstrated through confirmatory factor analysis. We further determined that the transmission, vitality, and association of ICH image have a positive impact on tourists' revisit intention. The paper highlights the crucial role of ICH image in sustainable tourism development. The theoretical and managerial implications of the study are discussed, followed by suggestions for future research. [ABSTRACT FROM AUTHOR]

Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell and are found in the brain of all vertebrates. While traditionally viewed as being supportive of neurons, it is increasingly recognized that astrocytes play a more direct and active role in brain function and neural computation. On account of their sensitivity to a host of physiological covariates and ability to modulate neuronal activity and connectivity on slower time scales, astrocytes may be particularly well poised to modulate the dynamics of neural circuits in functionally salient ways. In the current paper, we seek to capture these features via actionable abstractions within computational models of neuron-astrocyte interaction. Specifically, we engage how nested feedback loops of neuron-astrocyte interaction, acting over separated time-scales, may endow astrocytes with the capability to enable learning in context-dependent settings, where fluctuations in task parameters may occur much more slowly than within-task requirements. We pose a general model of neuron-synapse-astrocyte interaction and use formal analysis to characterize how astrocytic modulation may constitute a form of meta-plasticity, altering the ways in which synapses and neurons adapt as a function of time. We then embed this model in a bandit-based reinforcement learning task environment, and show how the presence of time-scale separated astrocytic modulation enables learning over multiple fluctuating contexts. Indeed, these networks learn far more reliably compared to dynamically homogeneous networks and conventional non-network-based bandit algorithms. Our results fuel the notion that neuron-astrocyte interactions in the brain benefit learning over different time-scales and the conveyance of task-relevant contextual information onto circuit dynamics. Author summary: Astrocytes, a non-neuronal cell type, constitute a significant portion of all cells in the brain, yet our understanding of their involvement in neural computation remains limited. In this paper, we use computational modeling to examine how astrocytes may interact with neurons to expand the types of activity and, ultimately, computations that neurons can produce. Two features of astrocytes make them interesting in this context. First, a single astrocyte is able to modulate dozens of neurons and synaptic connections between them. Furthermore, this modulation occurs at slower temporal scales than the neural activity itself, thus meaning that astrocytes could have long-lasting effects on neural activity and connectivity. Our computational models capture both of these features. Using these models, we show that networks with astrocytes can more readily adapt to slowly varying task parameters versus those with neurons alone. We further analyze the models to understand, in mathematical terms, why such effects may arise. Our results shed light on the potential computational role of this common, but enigmatic type of brain cell. [ABSTRACT FROM AUTHOR]

During the machine vision inspection of the inner section of bottle caps within pharmaceutical packaging, the unique conca bottom and convex side walls often create obstructions to the illumination. Consequently, this results in challenges such as irregular background and diminished feature contrast in the image, ultimately leading to the misidentification of defects. As a solution, a vision system characterized by a Low-Angle and Large Divergence Angle (LALDA) is presented in this paper. Using the large divergence angle of LED, combined with low-angle illumination, a uniform image of the side wall region with bright-field characteristics and a uniform image of inner circle region at the bottom with dark-field characteristics are obtained, thus solving the problems of light being obscured and brightness overexposure of the background. Based on the imaging characteristics of LALDA, a multi-channel segmentation (MCS) algorithm is designed. The HSV color space has been transformed, and the image is automatically segmented into multiple sub-regions by mutual calculation of different channels. Further, image homogenization and enhancement are used to eliminate fluctuations in the background and to enhance the contrast of defects. In addition, a variety of defect extraction methods are designed based on the imaging characteristics of different sub-regions, which can avoid the problem of over-segmentation in detection. In this paper, the LALDA is applied to the defect detection inside the cap of capsule medicine bottle, the detection speed is better than 400 pcs/min and the detection accuracy is better than 95%, which can meet the actual production line capacity and detection requirements. [ABSTRACT FROM AUTHOR]

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively. [ABSTRACT FROM AUTHOR]

In ischaemic stroke, a large reduction in blood supply can lead to the breakdown of the blood-brain barrier and to cerebral oedema after reperfusion therapy. The resulting fluid accumulation in the brain may contribute to a significant rise in intracranial pressure (ICP) and tissue deformation. Changes in the level of ICP are essential for clinical decision-making and therapeutic strategies. However, the measurement of ICP is constrained by clinical techniques and obtaining the exact values of the ICP has proven challenging. In this study, we propose the first computational model for the simulation of cerebral oedema following acute ischaemic stroke for the investigation of ICP and midline shift (MLS) relationship. The model consists of three components for the simulation of healthy blood flow, occluded blood flow and oedema, respectively. The healthy and occluded blood flow components are utilized to obtain oedema core geometry and then imported into the oedema model for the simulation of oedema growth. The simulation results of the model are compared with clinical data from 97 traumatic brain injury patients for the validation of major model parameters. Midline shift has been widely used for the diagnosis, clinical decision-making, and prognosis of oedema patients. Therefore, we focus on quantifying the relationship between ICP and midline shift (MLS) and identify the factors that can affect the ICP-MLS relationship. Three major factors are investigated, including the brain geometry, blood-brain barrier damage severity and the types of oedema (including rare types of oedema). Meanwhile, the two major types (stress and tension/compression) of mechanical brain damage are also presented and the differences in the stress, tension, and compression between the intraparenchymal and periventricular regions are discussed. This work helps to predict ICP precisely and therefore provides improved clinical guidance for the treatment of brain oedema. Author summary: Midline shift has been widely employed in clinical settings to estimate the severity of post-stroke brain oedema. However, the midline shift is affected by many factors and its relationship with intracranial pressure has been proven to vary among patients. In this paper, we utilise a computational tool to investigate the midline shift under large brain deformation from a mechanical perspective. The Augmented Lagrange Method is for the first time employed to solve the large deformation of oedema brains with midline shifts over 5 mm. In the model validation, a reasonable agreement between clinical data and simulation results are obtained. This paper models three different factors that can affect midline shift during oedema development, including brain geometry, blood-brain barrier damage severity and type of oedema. Meanwhile, we discuss the scenarios that can lead to unexpectedly small midline shifts and present quantitative results for a better understanding of brain mechanics during oedema. [ABSTRACT FROM AUTHOR]

Computer-aided design (CAD) models can now be directly converted into products and structures. One technique to realize such approach is through Additive Manufacturing (AM). AM is relatively new manufacturing technology in which products are manufactured by layering various materials like rubber, metal, ceramic, composites, and polymers. However, the use of this technology requires consideration of its associated cost to ensure its competitiveness. In this paper, a simplified mathematical cost model is suggested. The model considers the main components of costs. The model formula utilizes expenses related to the pre-processing, main processing, and the post-processing operations. To validate the model, it is tested to estimate the cost of medical implants manufacturing using AM technique. In many cases, medical implants require unique or dedicated design for each patient. Hence cost estimation will help to assess and estimate the required financial resources for such operations. A case study is provided in this paper to estimate the manufacturing cost of a finger's phalanges bone, with metal implant using AM technique. The developed model may be described as Activity Based Costing (ABC). The model is introduced to estimate the cost of parts produced using AM technique. Although the model is developed to suit custom implant manufacturing using AM technique, its use may also be adapted to suit the manufacturing of many other parts and products. The developed model is aiming to achieve several tasks namely assigning cost drivers to each activity, estimating the cost of individual actions, allocating overhead expenses, calculating the overall production cost, and establishing an acceptable selling price. It assists companies in computing the cost of custom implants for customers, enhancing the accuracy of production cost estimates, and ultimately boosting profitability. [ABSTRACT FROM AUTHOR]

Media information plays an essential role in the stock market. Recent financial research has verified that media information could shock stock price by influencing investors' expectation. Now, a new type of interactive media, called Digital Interactive Media (DIM), is popular in Chinese stock market and becomes the main channel for investors to understand listed companies. Unlike general news media or investor forums, DIM enables direct interaction between listed companies and investors. In the modern society where digital economy is booming, media information would largely affect investors' decisions. Therefore, it is urgent to use natural language processing (NLP) technology to deconstruct the massive questions and answers (Q&A) interactive information in DIM and extract valuable factors that affect stock prices and stock performances to explore the influence mechanism of digital interactive information on stock performances. This paper firstly uses web crawling technology to obtain approximately 110000 Q&A text information from the digital interactive platform ('Panoramic Network') from 2015 to 2021. Then we use big data text analysis technology and emotional quantification technology to extract valuable influencing factors from the massive text. A Multiple Linear Regression (MLR) model was created to explore specific influence mechanism of digital interactive information on stock price performance. The empirical results show that the emotions implicit in investors' questions do not significantly impact stock performance. However, the emotions and attitudes of the answers by listed companies can significantly affect corresponding stock prices, which indirectly confirms the Proximate Cause Effect of behavioral finance. This effect is particularly evident in the stock prices on the current trading day and the next trading day. In the Robustness Test, this paper replaces dependent variable and adds relevant control variables, and the conclusion remains valid. In the Endogeneity Test, this paper selects sample data before the launch of Panorama Network in 2014 as a comparison, and uses a Difference-in-Difference (DID) model to prove the significant impact of the launch of Panorama Network on Chinese stock market. In the Heterogeneity Test, the paper classifies the market value, region, and industry of listed companies and regressed the sub samples, once again confirming the reliability of the empirical conclusions. The results of Robustness Test, Endogeneity Test, and Heterogeneity Test conducted in this paper all support empirical conclusions. [ABSTRACT FROM AUTHOR]

In this paper, we focus on a dynamic Cournot game in the market with a nonlinear (isoelastic) demand function. In our model, there are N competing firms featured by nonlinear cost functions, which enhances our model's resemblance to real-world scenarios. Firstly, we focus on the homogeneous case where firms' marginal costs change at equal rates. We establish analytical expressions of the market supply at equilibrium and perform comparative static analysis. In addition, we investigate the local stability under different economies of scale and show that there could be multiple stable equilibria if firms face economies of scale. The heterogeneous case where firms' marginal costs change at distinct rates is much more complex, thus we investigate the duopoly game with only two firms involved. Methods of symbolic computations such as triangular decomposition and partial cylindrical algebraic decomposition are employed in the analytical investigations of the equilibrium, which is nearly impossible by using the pencil-and-paper approach since the closed-form equilibrium is quite complicated. According to the computational results, we derive that two stable positive equilibria may coexist if both firms face economies of scale. Additionally, we conduct preliminary numerical simulations and find two different types of complex dynamics of the model considered in this paper: complex trajectories such as periodic and chaotic orbits may appear; the topological structure of the basins of attraction may be complex. [ABSTRACT FROM AUTHOR]

Listening to music is a crucial tool for relieving stress and promoting relaxation. However, the limited options available for stress-relief music do not cater to individual preferences, compromising its effectiveness. Traditional methods of curating stress-relief music rely heavily on measuring biological responses, which is time-consuming, expensive, and requires specialized measurement devices. In this paper, a deep learning approach to solve this problem is introduced that explicitly uses convolutional neural networks and provides a more efficient and economical method for generating large datasets of stress-relief music. These datasets are composed of Mel-scaled spectrograms that include essential sound elements (such as frequency, amplitude, and waveform) that can be directly extracted from the music. The trained model demonstrated a test accuracy of 98.7%, and a clinical study indicated that the model-selected music was as effective as researcher-verified music in terms of stress-relieving capacity. This paper underlines the transformative potential of deep learning in addressing the challenge of limited music options for stress relief. More importantly, the proposed method has profound implications for music therapy because it enables a more personalized approach to stress-relief music selection, offering the potential for enhanced emotional well-being. [ABSTRACT FROM AUTHOR]

In recent years, blockchain technology has developed rapidly and has been widely used in medical, financial, energy and other fields. However, in the process of practical application, each blockchain is a small independent ecosystem, with all transactions and operations limited to the chain, resulting in a large number of mutually heterogeneous to independent blockchains. It presents challenges for cross-chain interactions, cross-organization data sharing, and cross-blockchain expansion, and hinders the wider application of blockchain technology. In addition, the traditional digital signature method based on elliptic curve cipher faces the threat of being cracked by quantum computing attacks. To solve the aforementioned problems, this paper proposed a blockchain smart contract technique based on quantum computing attack resistance(BSCTQCAT). The technique first introduces the digital signature of the lattice cipher into the blockchain to resist the quantum search algorithm attack. Then, based on the smart contract authentication scheme, the nodes on multiple heterogeneous chains are organized into an identity agent layer P2P network, through which transactions on the chain will establish a credible identity management and message authentication mechanism between different chains, solving the current problem that each chain is difficult to communicate with each other. In this paper, the performance of the algorithm is evaluated by simulating the Bitcoin transaction scenario and analyzing the experimental data. [ABSTRACT FROM AUTHOR]

Background: Iron deficiency anemia (IDA) is a prevalent hematological complication associated with gastrointestinal (GI) cancers due to an increased loss of iron and decreased iron absorption. The purpose of this systematic review is to evaluate the use of parenteral iron to treat IDA in patients with GI cancer. Methods: PubMed, Cochrane, EMBASE, CINHAL and Scopus were searched from January 1, 2010 to September 29, 2023 with no language restrictions. We excluded editorials, case reports, abstracts, conference papers, and poster presentations. Studies were included if they discussed IDA, GI neoplasms, use of iron supplementation (with or without erythropoietin-stimulating agents [ESAs]), defined anemia and had an adult patient population. We assessed the efficacy of parenteral iron in comparison to other iron supplementation methods when treating IDA in patients with GI cancer. The Cochrane Risk of Bias Tool 2 (RoB 2) and the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) assessment tools were used to assess the quality of the included studies. Moreover, the Cochrane Effective Practice and Organization data collection form was used to collect pertinent study information. Results: Our search yielded 3,969 studies across all databases. Twenty-one studies were included (6 randomized control trials; 15 non-randomized studies). Of the 15 studies evaluating hemoglobin (Hb) response, seven studies found an increase in Hb levels when patients were treated with IV iron. The 14 studies evaluating red blood cell (RBC) transfusion rates found conflicting differences in RBC transfusion needs when treated with IV iron. Studies analyzing health related outcomes typically found an increase in quality of life and decreased post-operative complications. Discussion: This review demonstrates improved outcomes of IDA in patients with GI cancer treated with IV iron instead of other iron supplementation methods. Timely diagnosis and appropriate IDA management can greatly improve quality of life in this patient population, especially if myelosuppressive chemotherapy is required. [ABSTRACT FROM AUTHOR]

China has experienced rapid development in the digital economy. Using data from 30 provinces in China between 2011 and 2017, this paper constructs a two-way fixed effects model to study the effects and mechanisms of the digital economy development on social insurance funds revenue. An increase of one unit in digital economy development led to a 0.56% increase in basic endowment insurance funds revenue and a 0.33% increase in basic health insurance funds revenue. The digital economy increased the social insurance funds revenue by promoting employment and increasing income. Furthermore, the effects of digital economic development on social insurance funds revenue were heterogeneous for different levels of economic development and urbanization. The conclusions stood after robustness tests by changing the method of weighting the digital economy indicators and using instrumental variables. This paper confirmed the positive role of the development of the digital economy in increasing the revenue of social insurance funds from the perspective of quantitative research and explored the mechanisms in depth. In order to increase social insurance funds revenue, it is essential to accelerate the development of the digital economy, especially in regions with lower economic development and urbanization, and to address the needs of the technically unemployed and those engaged in flexible employment. [ABSTRACT FROM AUTHOR]