Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing the Effects of Plasma Treatment Process Parameters on Fading of Cotton Fabrics Dyed with Two-Color Mix Dyes Using Bayesian Regulated Neural Networks (BRNNs)

ABSTRACT This study used Bayesian Regulated Neural Networks (BRNN) with 10-fold cross-validation to accurately forecast fading effects of plasma treatment on cotton fabrics for a given set of parameters. By training six independent BRNN models, a reduction in model complexity and an enhancement in generalizability to unknown datasets were achieved. The input comprises plasma treatment parameters and color measurements of the cotton fabric before fading, while the output comprises color measurements after fading. The plasma treatment parameters included color depth, air (oxygen) concentration, water content and treatment time. Color measurements included CIE L*a*b*C*h and K/S values. Furthermore, 162 datasets derived from two-color mixed-dye cotton fabrics were utilized for training and testing. The outcomes revealed superior prediction performance of the BRNN compared to the Levenberg-Marquardt Neural Networks, with R2 values approaching 1 and 82.35% to 94.12% of the sample predictions lying within the acceptable color difference range. Through global sensitivity analysis, the impact of treatment parameters on fading effects was quantified, providing a scientific basis for parameter adjustment. This study not only elucidated the mechanism of plasma treatment-induced fading but also offers effective prediction tools for the intelligent and digital development of the fashion clothing fading domain.
Tumor suppressor p 53 plays a key role in induction of both tristetraprolin and let-7 in human cancer cells

Tristetraprolin (TTP) and let-7 microRNA exhibit suppressive effects on cell growth through down-regulation of oncogenes. Both TTP and let-7 are often repressed in human cancers, thereby promoting oncogenesis by derepressing their target genes. However, the precise mechanism of this repression is unknown. We here demonstrate that p53 stimulated by the DNA-damaging agent doxorubicin (DOX) induced the expression of TTP in cancer cells. TTP in turn increased let-7 levels through down-regulation of Lin28a. Correspondingly, cancer cells with mutations or inhibition of p53 failed to induce the expression of both TTP and let-7 on treatment with DOX. Down-regulation of TTP by small interfering RNAs attenuated the inhibitory effect of DOX on let-7 expression and cell growth. Therefore, TTP provides an important link between p53 activation induced by DNA damage and let-7 biogenesis. These novel findings provide a mechanism for the widespread decrease in TTP and let-7 and chemoresistance observed in human cancers.
Design and Validation of Aerospace Control Systems Randomized and Robust Methods for Uncertain Systems using R-Ro M ulOC , with Applications to DEMETER Satellite Benchmark

DOI: 10.12762/2017.AL13-04 R-RoMulOC is a freely distributed toolbox aimed at making easily available to the users various optimization-based methods for dealing with uncertain systems. It implements both deterministic LMI-based results, which provide guaranteed performance for all values of the uncertainties, and probabilistic randomizationbased approaches, which guarantee performance for all values of the uncertainties except for a subset with arbitrary small probability measure. The paper is devoted to the description of these two approaches for analysis and control design when applied to a satellite benchmark proposed by the CNES, the French Space Agency. The paper also describes the modeling of the DEMETER satellite and its integration into the R-RoMulOC toolbox as a challenging test example. Design of state-feedback controllers and closed-loop performance analysis are carried out with the randomized and robust methods available in the R-RoMulOC toolbox.
Bayesian Inference of Temporal Specifications to Explain How Plans Differ Anonymous for Blind Review

Temporal logics are useful for describing dynamic system behavior, and have been successfully used as a language for goal definitions during task planning. Prior works on inferring temporal logic specifications have focused on “summarizing” the input dataset – i.e., finding specifications that are satisfied by all plan traces belonging to the given set. In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces. We formalize the concept of providing such contrastive explanations, then present a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic specifications. We demonstrate the efficacy, scalability, and robustness of our model for inferring correct specifications across various benchmark planning domains and for a simulated air
Interest and tax burden on corporations in the Czech industrial and banking sector after 2008

Abstract The present paper deals with the interest and tax burden of corporations in the Czech industrial and banking sector as well as with the identification of the differences between the two sectors, including the evaluation of their developmental trends in the period after the economic and financial crisis of 2008. The interest and tax burden on business entities is determined by negative cash flows that reduce the value of their assets and equity. The basis of the research is the analysis of both components of the financial burden on corporations in these sectors over the past eight years and the identification of factors influencing their capital structure and performance. According to DuPont's equation, the burden is expressed by interest and the tax reduction of corporation's earnings before interest and taxes. The amount of the financial burden depends on the macroeconomic environment where the corporations operate. Our analysis identified an asynchronous dependence between the real payments and changes in the interest and tax rates. The reduction of both rates has had a positive effect on the performance of Czech corporations and increased their capital resources. They became more attractive to foreign investors, and the return on invested capital improved. Based on the results of the analysis, we evaluated the differences in the performance of the average corporation in the banking and industrial sectors.
Identification of Missense Variants Affecting Carcass Traits for Hanwoo Precision Breeding

This study aimed to identify causal variants associated with important carcass traits such as weight and meat quality in Hanwoo cattle. We analyzed missense mutations extracted from imputed sequence data (ARS-UCD1.2) and performed an exon-specific association test on the carcass traits of 16,970 commercial Hanwoo. We found 33, 2, 1, and 3 significant SNPs associated with carcass weight (CW), backfat thickness (BFT), eye muscle area (EMA), and marbling score (MS), respectively. In CW and EMA, the most significant missense SNP was identified at 19,524,263 on BTA14 and involved the PRKDC. A missense SNP in the ZFAND2B, located at 107,160,304 on BTA2 was identified as being involved in BFT. For MS, missense SNP in the ACVR2B gene, located at 11,849,704 in BTA22 was identified as the most significant marker. The contribution of the most significant missense SNPs to genetic variance was confirmed to be 8.47%, 2.08%, 1.73%, and 1.19% in CW, BFT, EMA, and MS, respectively. We generated favorable and unfavorable haplotype combinations based on the significant SNPs for CW. Significant differences in GEBV (Genomic Estimated Breeding Values) were observed between groups with each favorable and unfavorable haplotype combination. In particular, the missense SNPs in PRKDC, MRPL9, and ANKFN1 appear to significantly affect the protein’s function and structure, making them strong candidates as causal mutations. These missense SNPs have the potential to serve as valuable markers for improving carcass traits in Hanwoo commercial farms.
Training Target domain reviews Source domain Review Aspect Aspect Review Extract Target domain Review Aspect Aspect Review Extract Domain classifier Sentiment classifier IATN Figure

Cross-domain sentiment classification refers to utilizing useful knowledge in the source domain to help sentiment classification in the target domain which has few or no labeled data. Most existing methods mainly concentrate on extracting common features between domains. Unfortunately, they cannot fully consider the effects of the aspect (e.g., the battery life in reviewing an electronic product) information of the sentences. In order to better solve this problem, we propose an Interactive Attention Transfer Network (IATN) for crossdomain sentiment classification. IATN provides an interactive attention transfer mechanism, which can better transfer sentiment across domains by incorporating information of both sentences and aspects. Specifically, IATN comprises two attention networks, one of them is to identify the common features between domains through domain classification, and the other aims to extract information from the aspects by using the common features as a bridge. Then, we conduct interactive attention learning for those two networks so that both the sentences and the aspects can influence the final sentiment representation. Extensive experiments on the Amazon reviews dataset and crowdfunding reviews dataset not only demonstrate the effectiveness and universality of our method, but also give an interpretable way to track the attention information for sentiment.
MINING AND VISUALIZATION USING INSURANCE CLAIMS DATA

OF THESIS INTERACTIVE CLINICAL EVENT PATTERN MINING AND VISUALIZATION USING INSURANCE CLAIMS DATA With exponential growth on a daily basis, there is potentially valuable information hidden in complex electronic medical records (EMR) systems. In this thesis, several efficient data mining algorithms were explored to discover hidden knowledge in insurance claims data. The first aim was to cluster three levels of information overload (IO) groups among chronic rheumatic disease (CRD) patients based on their clinical events extracted from insurance claims data. The second aim was to discover hidden patterns using three renowned pattern mining algorithms: Apriori, frequent pattern growth (FP-Growth), and sequential pattern discovery using equivalence classes (SPADE). The SPADE algorithm was found to be the most efficient method for the dataset used. Finally, a prototype system named myDietPHIL was developed to manage clinical events for CRD patients and visualize the relationships of frequent clinical events. The system has been tested and visualization of relationships could facilitate patient education.
A self-defeating interpersonal style predicts depression over and above the Big 5 personality trait constructs

Three hundred and fifty-three adult participants completed self-report measures assessing depression, the putative “Big 5” personality factors, socially desirable responding, and a new scale intended to measure the propensity to engage in a self-defeating interpersonal style of behavior (the SELF-DISS; Atkinson & Vernon, 2018). Our goals were to see whether SELF-DISS scores would significantly predict depression and whether they would do so over and above the proposed “Big 5” trait constructs using online recruitment of participants from the general population at large. The recruitment method, although expeditious, necessarily resulted in the self-selection of participants, thereby raising the possibility that some sampling bias may have been incorporated into the study. Nevertheless, the results obtained clearly supported both predictions.
Handling Children Dental Caries through Parents Awareness on Community Service

Elementary school children have been categorized as high risk caries group. Maintaining dental health of elementary school need parents’ role on making dental care decisions. Srondol Kulon Elementary School and Meteseh Elementary School are 2 elementary schools assisted by Poltekkes Kemenkes Semarang. Two schools located at urban and rural area therefore they have different characteristics of parents. Community service was carried out in 2 elementary schools with the aim to find differences of parental involvement in the promotion, prevention and treatment of children's dental health. Screening was done in class III, then their parents with children who have caries were counseled and educated about dental health. The parents were also given information about children's dental health plan treatment. Informed consent was given to parents in children who would be curated (fillings and extractions). Parent participation in Srondol Elementary School was higher than Meteseh. There was a change in the composition of caries prevalence, increasing in the number of filling permanent and milk teeth (f and F) and extracted (e). Parents' awareness of the importance of treating caries in children as early as possible is higher in Srondol Kulon Elementary School compared to SD Meteseh. Besides, the level of education and parents' socio-economic influenced parents' attitudes in giving decisions of dental treatments.
Relationship Trajectories: A Meta-Theoretical Framework and Theoretical Applications

Abstract This article introduces a metatheoretical framework—the Relationship Trajectories Framework—that conceptualizes how human mating relationships develop across their complete time span, from the moment two people meet until the relationship ends. The framework depicts relationships as arc-shaped evaluative trajectories that vary on five dimensions: shape (which includes ascent, peak, and descent), fluctuation, threshold, composition, and density. The framework can depict single trajectories in isolation or two partners’ trajectories with respect to each other (dyadic trajectories). Two theoretical models demonstrate the generative power of the framework—the relationship coordination and strategic timing (ReCAST) model and the sociosexuality trajectory model—both of which integrate close relationships and evolutionary psychological perspectives on mating. Finally, additional examples illustrate how the framework can generate new research questions about core relationships topics.
Revolutionizing Gene Therapy: Biomaterials as Enabling Tools for Targeted Delivery and Enhanced Efficacy

Bioengineered smart nano/biomaterials have emerged as promising tools for efficient and targeted gene delivery in gene therapy. Gene therapy has the potential to treat various genetic disorders, cancers, and infectious diseases by replacing, repairing, or regulating the expression of disease-causing genes. However, the success of gene therapy critically depends on the efficient delivery of therapeutic genes into target cells or tissues while minimizing off-target effects and toxicity. Bioengineered smart nano/biomaterials offer several advantages over conventional gene delivery vehicles, such as viral vectors and cationic lipids, including improved stability, biocompatibility, and tunable properties. This review provides an overview of bioengineered smart nano/biomaterials for gene delivery, including their design, fabrication, mechanisms of action, and applications in gene therapy. The design and fabrication of smart nano/biomaterials involve the synthesis and functionalization of various materials, such as polymers, lipids, peptides, and inorganic nanoparticles, to optimize their physicochemical properties and biological interactions. Mechanistically, smart nano/biomaterials can efficiently deliver DNA/RNA cargo into target cells by overcoming biological barriers, such as cellular membranes and endosomes, and facilitating nuclear entry and gene expression. Applications of smart nano/biomaterials in gene therapy include the treatment of genetic disorders, cancers, and infectious diseases, as well as targeted gene therapy for specific cell types or tissues. However, several challenges remain to be addressed before the clinical translation of smart nano/biomaterials for gene therapy, including safety, toxicity, and immunogenicity issues, optimization of delivery efficiency and specificity, and regulatory and ethical considerations. In conclusion, bioengineered smart nano/biomaterials hold great promise for the development of safe and effective gene therapies that can revolutionize the treatment of various diseases.
Organizational goal ambiguity and senior public managers’ engagement: does organizational social capital make a difference?

The Job Demands–Resources model highlights that aspects of the work environment that place high demands on employees are job stressors, which can reduce their levels of engagement with their work and organization. By contrast, social support for employees is a resource that can sustain their engagement and enable them to cope with high demands. We analyse the separate and combined effects of two key job demands and resources on the engagement of more than 4000 senior public managers in 10 European countries: organizational goal ambiguity, which may demotivate employees; and organizational social capital, which can prompt employees to share knowledge in constructive ways. The statistical results suggest that there is a negative relationship between goal ambiguity and engagement, and a positive one between social capital and engagement. Further analysis reveals that organizational social capital weakens the negative goal ambiguity–engagement relationship. Points for practitioners This study emphasizes the value of organizational social capital for enhancing senior managers’ engagement. Positive co-worker relationships lead senior public managers to feel more engaged with their work and organization, and protect that engagement from the damaging effects of organizational goal ambiguity. The findings we present therefore provide a strong basis for the introduction of policies and practices that support the development of social capital within public organizations.
Residual amplitude modulation and its mitigation in wedged electro-optic modulator

We theoretically analyze and experimentally investigate the dependence of residual amplitude modulation (RAM) on the beam radius within the electro-optic crystal (EOC), the wedge angle of the EOC and the overlap efficiency between the extraordinary and ordinary beams, and the overlap efficiency is determined by the distance from the wedge facet to the downstream polarizer. The results show that the RAM with the maximum optical path difference ∆ at the edge of light spot presents a sinc-like curve, and the magnitude of ∆ is directly proportional to the beam radius and the wedge angle. As a scaling factor, with the decrease of the overlap efficiency between the ordinary and extraordinary beams, the RAM can be further reduced. © 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Regulations on conflict of interest for higher civil servants and public officials - Enhancing a state’s legitimacy or just a pretty illusion?

OECD and other organizations argues in favor of the implementation of regulations on conflict of interest for higher civil servants and public officials, often including quarantine time for these employees when changing jobs. They do this with the argument that it will increase the population’s confidence in their governments, which again is claimed to be a cornerstone of the functioning of the states. The goal of this study is to investigate the connection between regulations on conflict of interest and state legitimacy. It is done so through a quantitative study on the OECD-countries, where it is almost even divided between the countries that have and do not have these regulations. The result of the quantitative study implies that regulations on conflict of interest do not have an impact on state legitimacy. Different explanations are presented, varying from the actual need of the regulations as a whole to the need of formalizing a normative concept.
Telemedicine in Elderly Hypertensive and Patients with Chronic Diseases during the COVID-19 Pandemic: A Systematic Review and Meta-Analysis

Background: One aspect of the distancing measures imposed in response to the COVID-19 pandemic is that telemedicine consultations have increased exponentially. Among these consultations, the assessment and follow-up of patients with chronic diseases in a non-presential setting has been strengthened considerably. Nevertheless, some controversy remains about the most suitable means of patient follow-up. Objective: To analyze the impact of the telemedicine measures implemented during the COVID-19 period on chronic patients. Material and Methods: A systematic review was carried out using the following databases: PubMed, Pro-Quest, and Scopus. The systematic review followed the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The search equation utilized descriptors sourced from the Medical Subject Headings (MeSH) thesaurus. The search equation was: “hypertension AND older AND primary care AND (COVID-19 OR coronavirus)” and its Spanish equivalent. Results: The following data were obtained: 14 articles provided data on 6,109,628 patients and another 4 articles focused on a study population of 9684 physicians. Telemedicine was less likely to be used by elderly patients (OR 0.85; 95% C.I. 0.83–0.88; p = 0.05), those of Asian race (OR 0.69; 95% C.I. 0.66–0.73; p = 0.05), and those whose native language was not English (OR 0.89; 95% C.I. 0.78–0.9; p = 0.05). In primary care, lower use of telemedicine was associated with residents of rural areas (OR 0.81; p = 0.05), patients of African American race (OR 0.65, p = 0.05), and others (OR 0.64; p = 0.05). A high proportion (40%) of physicians had no prior training in telemedicine techniques. The highest quality in terms of telephone consultation was significantly associated with physicians who did not increase their prescription of antibiotherapy during the pandemic (OR = 0.30, p = 0.05) or prescribe more tests (OR 0.06 p = 0.05), i.e., who maintained their former clinical criteria despite COVID-19. Conclusions: Telemedicine is of proven value and has been especially useful in the COVID-19 pandemic. A mixed remote–presential model is most efficient. Appropriate training in this area for physicians and patients, together with correct provision, is essential to prevent errors in implementation and use.
Measuring Global Economic Activity

A number of economic studies have used a proxy for world real economic activity derived from shipping costs. This measure turns out to depend on a normalization that has substantive consequences of which users of the index had been unaware prior to this paper. This paper further evaluates this and alternative measures in terms of treatment of trends, coherence with world output, and ability to predict commodity prices. I conclude that measures derived from world industrial production offer a better indicator of global real economic activity. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber. org.