Miracle-dz/UniFAR-FTUs huggingface dataset:
International Journal of Health Geographics Case-control Geographic Clustering for Residential Histories Accounting for Risk Factors and Covariates

Background: Methods for analyzing space-time variation in risk in case-control studies typically ignore residential mobility. We develop an approach for analyzing case-control data for mobile individuals and apply it to study bladder cancer in 11 counties in southeastern Michigan. At this time data collection is incomplete and no inferences should be drawn – we analyze these data to demonstrate the novel methods. Global, local and focused clustering of residential histories for 219 cases and 437 controls is quantified using time-dependent nearest neighbor relationships. Business address histories for 268 industries that release known or suspected bladder cancer carcinogens are analyzed. A logistic model accounting for smoking, gender, age, race and education specifies the probability of being a case, and is incorporated into the cluster randomization procedures. Sensitivity of clustering to definition of the proximity metric is assessed for 1 to 75 k nearest neighbors.
The Effects of Personalized Nudges on Cognitively Disengaged Student Behavior in Low-Stakes Assessments

In educational settings, students rely on metacognitive processes to determine whether or not to exert effort. We investigated ways to minimize cognitively disengaged responses (i.e., not-fully-effortful responses) during a low-stakes mathematics assessment. Initially, we established theory-driven time thresholds for each item to detect such responses. We then administered the test to 800 eighth-graders across three conditions: (a) control (n = 271); (b) instruction (n = 267); and (c) nudge (n = 262). In the instruction condition, students were told to exert their best effort before starting the assessment. In the nudge condition, students were prompted to give their best effort following each first-attempt response that was both incorrect and not-fully-effortful. Therefore, students had multiple opportunities to adjust their level of effort. Nudges, but not effort instruction, significantly reduced students’ not-fully-effortful responses. Neither the nudges nor the effort instruction significantly impacted performance. In a post-test survey, most students reported that they received nudges whenever they did not know the answer (55%). Overall, these findings suggest that while nudges reduce cognitively disengaged responses, most students appear to strategically modulate their level of effort based on self-monitoring their knowledge and response effort.
Fast-Track Authority and International Trade Negotiations

We develop a simple model of trade relations in which legislators with different stakes in import-competing and export industries decide whether to grant fast-track authority (FTA) to the president, giving up the power to amend international trade agreements. We show that strategic delegation motives are key to understanding FTA votes, which involve a decision between alternative country representatives: the executive or the majority in Congress. We then examine the determinants of all votes by US congressmen on FTA since the introduction of this institutional procedure in 1974. Our empirical analysis provides strong support for the predictions of the model.
News from the WOCE IPO

Firstly I have been sending off to Academic Press the completed Chapters for the “WOCE” book now titled “Ocean Circulation and Climate – Observing and Modelling the Global Ocean”. Of the 33 Chapters only about 3 need a little more work before they are submitted. All will be completed by the time this newsletter appears on your desks. Unfortunately I still cannot give you information about the price of the book. That will be fixed in September when the publishers have put it all together. It has been an enormous undertaking for the authors, for staff in Hobart and for Roberta Boscolo and Jean Haynes in the WOCE IPO. Gerold Siedler and John Church and I as editors are pleased to see it finally coming together.
The evaluation of a two-dimensional sediment transport and bed morphology model based on the Seymour River

ii TABLE OF CONTENTS . iii LIST OF TABLES . v LIST OF FIGURES . vi LIST OF EQUATIONS . viii LIST OF VARIABLES . ix ACKNOWLEDGEMENTS. xi DEDICATION . xii 1. INTRODUCTION . 1 2. BACKGROUND . 3 2.1. FLOW MODELLING . 3 2.1. 1. One-Dimensional Hydrodynamic Modelling . 3 2.1. 2. Two-Dimensional Hydrodynamic Modelling . 4 2.2. MORPHOLOGICAL AND SEDIMENT TRANSPORT MODELLING . 5 2.2.1. One-Dimensional Sediment Transport Modelling . 6 2.2.2. Two-Dimensional Sediment Transport Modelling . 6 2.2.3. Three-Dimensional Sediment Transport Modelling . 6 2.3. HABITAT MODELLING . 7 2.3.1. Habitat Suitability Curves . 7 2.3.2. One-Dimensional Habitat Modelling (PHABSIM ) . 8 2.3. 3. River2D Habitat . 10 2.4. NUMERICAL MODELLING USE IN RIVER RESTORATION . 11 3. RIVER2D AND R2DM . 14 3.1. RIVER2D HYDRODYNAMICS . 14 3.2. RIVER2D MORPHOLOGY (R2DM) . 15 4. SEYMOUR RIVER STUDY SITE . 23 4.1. WATERSHED BACKGROUND . 23 4.2. HYDROLOGY . 24 4.3. SEYMOUR RIVER MORPHOLOGY . 26
Comparative Transcriptome Analysis between Resistant and Susceptible Pakchoi Cultivars in Response to Downy Mildew

Downy mildew caused by the obligate parasite Hyaloperonospora brassicae is a devastating disease for Brassica species. Infection of Hyaloperonospora brassicae often leads to yellow spots on leaves, which significantly impacts quality and yield of pakchoi. In the present study, we conducted a comparative transcriptome between the resistant and susceptible pakchoi cultivars in response to Hyaloperonospora brassicae infection. A total of 1073 disease-resistance-related differentially expressed genes were identified using a Venn diagram. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that these genes were mainly involved in plant−pathogen interaction, plant hormone signal transduction, and other photosynthesis-related metabolic processes. Analysis of the phytohormone content revealed that salicylic acid increased significantly in the resistant material after inoculation with Hyaloperonospora brassicae, whereas the contents of jasmonic acid, abscisic acid, and 1-aminocyclopropane-1-carboxylic acid decreased. Exogenous salicylic acid treatment also significantly upregulated Hyaloperonospora brassicae-induced genes, which further confirmed a crucial role of salicylic acid during pakchoi defense against Hyaloperonospora brassicae. Based on these findings, we suggest that the salicylic-acid-mediated signal transduction contributes to the resistance of pakchoi to downy mildew, and PAL1, ICS1, NPR1, PR1, PR5, WRKY70, WRKY33, CML43, CNGC9, and CDPK15 were involved in this responsive process. Our findings evidently contribute to revealing the molecular mechanism of pakchoi defense against Hyaloperonospora brassicae.
The Effects of Stereoscopic Depth on Vigilance Task Performance and Cerebral Hemodynamics

The current study examined the effect of stereoscopic depth cues on vigilance performance and cerebral hemodynamics as reflected in cerebral bloodflow velocity (CBFV). During a 40 min continuous vigil, participants took the role of a flight engineer in a simulated fuel transfer task involving a circular gauge in which a vertical line was embedded. In a 2D condition, critical signals for detection were cases in which the vertical line was tilted slightly to the right. In a 3D condition, a stereoscopic display projected a 3D image in which critical signals were cases wherein the vertical line appeared to be located in front of the circular gage. The overall level of signal detections was greater in the 3D than in the 2D condition. Moreover, detection scores in the 2D condition showed the vigilance decrement, a temporal decline over time, while those in the 3D condition maintained relative stability. In both conditions, CBFV was greater in the right than in the left cerebral hemisphere and declined significantly over time. The results provide the the initial demonstration that 3D displays can enhance performance in tasks requiring sustained attention and that right hemispheric control may be involved in vigilance performance with both 2D and 3D stimuli.
THE EFFECT OF FINANCIAL TECHNOLOGY, ONLINE SHOPPING, AND SELF-CONTROL ON CONSUMPTIVE BEHAVIOR

Technological advances have now been felt in business, particularly in financial services companies. The financial services industry innovates in financial services, namely establishing financial technology. This study aims to determine the influence of financial technology, online shopping, and self-control on the consumptive behavior of Generation Z in Yogyakarta. The samples in this study were 520 respondents taken by sampling technique using non-probability sampling with purposive sampling method. The data obtained is then processed using the Smart PLS statistical tool. The results showed that online shopping has a negative influence on consumptive behavior, while financial technology and self-control show a positive impact on consumptive behavior.
Science Literacy Assessment Instrument for Additives and Addictive Substances: Development, Validation, and Rasch Model Analysis

Science literacy is one of the areas of basic literacy that PISA focuses on. Science literacy is essence of learning in the 21st century. The purpose of this study is to develop and validate instruments to assess science literacy in the area of additives and addictive substances. The researcher refers to the modified PISA framework and Bloom's Revised Taxonomy. The developed instrument was tested for reliability and validity using the Aiken V method and the Rasch model. The research respondents consisted of 47 students from two junior high schools: a public school and a private school that has been accredited A. The results of the analysis showed that the instrument has validity. The results of the analysis showed that of the 25 items given there were 24 valid items, the resulting Cronbach's alpha value of 0.86 showed very good criteria, it can be concluded that the instrument has high validity and good reliability. This instrument is expected to be used to measure the quality of education and science literacy of students in Indonesia.
Leveraging Explainable Artificial Intelligence for Understanding the Effect of Model Capacity on Training Dataset Size

Automated Image classification has seen substantial growth in recent times. However, several applications suffer from the limited availability of training data, such as the classification of medical images, where data collection is mostly limited by privacy concerns of human subjects. As a result, to compensate for the limited availability of training data, most of these applications employ custom-made lightweight architectures. While state-of-the-art deep models for computer vision applications usually exploit architectures with huge model capacity. However, the increase in model complexity and size necessarily doesn't guarantee better performance on small medical datasets. We study this phenomenon in the context of medical images, where several existing studies report that most sophisticated deep networks for computer vision trained on large datasets such as Image Net do not generalize on medical image applications, due to huge model capacity, subsequently leading to overfitting on smaller training datasets. In this research, we exploit explainable artificial intelligence to analyze the features learned by state-of-the-art deep models for smaller medical image training datasets and contrast them with the features learned for larger medical training datasets. In particular, we exploit Shapley Additive explanations (SHAP) features to perform a qualitative comparison of feature relevance maps and understand how different standard models when trained with different training sizes understand discriminative image patterns to perform classification. Furthermore, we also compare SHAP features on scenarios in which the same model focuses on images belonging to different classes. Experiments on two datasets of different sizes have been presented to understand the dependence of model complexity on the number of samples in the training dataset. Results demonstrate that simpler models learn generalizable SHAP features that allow them to perform better on small datasets, unlike larger models when trained on smaller datasets. Likewise, bigger models when trained on larger datasets learn more distinctive and diverse features that allow them to outperform smaller models.
Effectiveness of the Online Election via E-Voting Based on Community Response

The election of RT/RW leaders is a regular event that takes place in every village across Indonesia. Similarly, the election process was carried out at RW 23 Permata Biru housing area, located in Cinunuk Village, Cileunyi District, Bandung City Regency, West Java Province, Indonesia. The leadership of RT/RW is elected every 5 years, and in this instance, the election process occurred in November-December 2021, determining new leaders for the 2022-2027 term. However, the election faced a challenge due to the restrictions imposed by the Indonesian government related to the Covid-19 pandemic. Gathering in large groups was not permitted to curb the spread of Covid-19. Despite these circumstances, the transition of RT/RW leaders was considered crucial to avoid potential conflicts within the community. As a result, an alternative solution was implemented, conducting an online election using the E-Voting application. Through explanations and community outreach in RW 23, the people were receptive to the idea of conducting the election online, considering the ongoing Covid-19 pandemic. RW 23 comprises 732 families, divided into 10 RTs. The election process took place in two stages: first, the election of the leader of RW 23, followed by the election of the leaders of the RTs. The selection of the RW 23 leader began with the formation of a committee, followed by the dissemination of rules, candidate selection, and further outreach. The election itself was conducted through the E-Voting application. The E-Voting software was created using Google Form Apps, with the assistance of Microsoft Excel. Community members accessed the E-Voting application through their PCs and smartphones. Despite the challenges, the election of the RT/RW leaders proceeded smoothly and safely. As a result, a new team of RT/RW leaders was formed for RW 23, serving during the 2022-2027 period. Keywords: online election, leader rt/rw, pandemic, Covid-19, e-voting
Beneficial and biocontrol effects of Trichoderma atroviride, a dominant species in white birch rhizosphere soil

White birch (Betula platyphylla Suk.) is a typical pioneer tree species that is important in forest restoration in northern China, Japan, and Korea. In the present study, 37 isolates were obtained from B. platyphylla rhizosphere soils in Heilongjiang Province; they were identified as T. pleuroticola (3 isolates), T. virens (2 isolates), T. hamatum (8 isolates), T. atroviride (21 isolates, dominant species) and T. asperelloides (3 isolates). Stress tolerance tests (salt, alkali, and nutritional stress that simulated saline alkali or barren soil) and confrontation assays (with four pathogens) were performed to determine which isolates had good biocontrol ability in barren soil; the results show that T. atroviride was outstanding. Then, in order to determine the effect of T. atroviride on plants and soil, Gynura cusimbua seeds were sown and treated with a T. atroviride spore suspension, as was unsown soil. The seedlings treated using T. atroviride had significantly greater height, stem diameter, soluble protein content, soluble sugar content, and malonaldehyde (MDA) content and their catalase (CAT) activity was also significantly increased. In addition, when the plants were inoculated with Alternaria alternata, the plants treated using T. atroviride had stronger CAT activity, significantly higher soluble protein content and soluble sugar content, and significantly lower MDA content, which indicates stronger resistance and less injury caused by the pathogen. In addition, T. atroviride not only increased the content of available nitrogen and available phosphorus in the soil, but also promoted G. cusimbua seedlings’ absorption of available nitrogen and available phosphorus. Thus, the characteristics of T. atroviride may make it the main factor that helps B. platyphylla colonise cut-over lands. T. atroviride, a promising biocontrol candidate, can be used in agriculture and forestry.
The effects of information framing on self-protective behavior: Evidence from the COVID-19 vaccine uptake

Objectives The Healthy China 2030 strategy outlines the government's plans for healthcare reform, emphasizing the need for increased awareness about infectious diseases to prevent and fight future infections. Information campaigns can be used as a medium to raise awareness and encourage citizens’ willingness to protect themselves against diseases, such as COVID-19. Extant studies have found that individual health behavior decision-making can be changed under different information frames. However, limited evidence is available about emerging infectious diseases. Based on the Prospect Theory and Theory of Planned Behavior, the impact of information frames on self-protective behavior—vaccination against COVID-19 is investigated in this study. Methods A 2(gain/loss frame)*2(factual/emotional frame) intergroup experimental design was designed to explore the effects of different information frames. 228 valid participants in China were recruited and the experiment was performed online. Results First, the gain frame was more effective in promoting public self-protection behavior than the loss frame under information frame intervention. Compared with the factual frame, the emotional frame is more effective in reducing individual risk perception. Second, perceptual behavior control has masking effects on self-protection behavior under the influence of the gain/loss frame. Third, age, subjective norms, attitudes, and the gain frame, have predictive effects on self-protection behavior. Conclusions This study provides empirical evidence on the impact of information framing interventions on public self-protection behavior during the COVID-19 pandemic and provides important practical implications for public administrators and media practitioners.
Treatment Adherence in Multiple Sclerosis: The Effect of Drug Administration Methods and Neuropsychiatric Comorbidities

Objectives: Multiple sclerosis (MS) is a chronic neurodegenerative disease affecting young adults. While disease-modifying drugs (DMDs) help manage and decelerate disease progression, adherence to these treatments is a significant challenge. This study aimed to investigate the relationship between treatment adherence and different drug administration methods in MS patients in the context of neuropsychiatric evaluations. Methods: A prospective cohort study was conducted at the University Hospital of Neurology Clinic using the 2017 revised McDonald criteria for MS diagnosis. Demographic data, MS-related metrics, and DMDs were recorded. Participants were stratified based on their DMDs into oral, injectable, and infusion treatments. The MS Treatment Adherence Questionnaire, Beck Depression Inventory, and State-Trait Anxiety Inventory were administered. Results: Of the 89 patients, treatment adherence was 45%. There were significant differences in side effect scores between the non-adherent and adherent groups for both oral and injectable DMDs. The Beck Depression Inventory average score was 12.49±9.81, while the State-Trait Anxiety Inventory average scores for STAI1 and STAI2 were 38.95±10.41 and 47.89±10.66, respectively. Significant differences were observed in disease duration, the average expanded disability status scale score, and the average STAI score. Conclusion: Adherence rates varied with the method of drug administration, with oral treatments having 34.4% adherence and injectable treatments having 53.4%. Factors like perceived efficacy, depression, and anxiety influenced treatment adherence. No significant correlations were found between demographic factors like age, gender, or education and adherence rates. Treatment adherence is crucial in managing MS. This study highlights the role of drug administration methods and neuropsychiatric comorbidities in influencing adherence. A comprehensive assessment considering these factors is vital in choosing an appropriate DMD for MS patients
Transfer Learning-Based Framework Enhanced by Deep Generative Model for Cold-Start Forecasting of Residential EV Charging Behavior

Reliable smart charging requires forecasting the charging behavior of EVs. Deep learning algorithms could present a solution. However, deep neural networks (DNNs) require a large number of samples for training. Although there are available datasets for EV charging, such data is not available for newly committed EVs with limited historical charging records. Therefore, forecasting the charging behavior of a new EV owner suffers from the cold-start forecast problem. This paper aims to forecast two essential parameters of charging events for new EVs: plug-out hour and required energy. To this end, a transfer learning-based framework is proposed to address the cold-start forecast. In the proposed framework, we freeze the middle layers of the pre-trained DNNs and make a shortcut between the output layer and input layer during the backpropagation. Moreover, this paper proposes to use a deep generative model, i.e., generative adversarial network (GAN), to improve the accuracy of forecasting. Plug-out hours of charging events are primarily forecasted by the proposed framework and then the plug-out hour is used as an auxiliary feature to forecast the required energy. Numerical results prove the effectiveness of the proposed framework where forecasting results are improved by more than 31% for the plug-out hour and 34% for the required energy compared to alternative machine learning and deep learning algorithms.
Fractal analysis of fingerprints

Fractal Analysis of Fingerprints by John C. Deal Master of Science in Electrical Engineering West Virginia University Matthew Valenti, Ph.D., Chair Current methods for comparing fingerprints have weaknesses that have opened them to criticism. Current methods concentrate on the comparison of minutia in the print either manually or with the assistance of a computer algorithm. This causes these methods to depend highly on the presence of minutia and their relationship to one another. Absence or rotations of minutia can prevent current methods form making accurate comparisons. The goal of this process is to develop a new method for analyzing fingerprints that addresses many of the concerns with current methods. The developed process uses an iterated function sequence (IFS) to convert the image of a fingerprint into a fractal pattern. The input for the IFS is constructed by a random walk through the image. Once a fingerprint is converted into a fractal pattern, the fractals can be used to make comparisons. Fractals are well defined mathematical objects that make them far easier to compare than fingerprints themselves. This process addresses many of the issues with current methods. This method is global in nature and thus it is not dependent on a set number of minutiae. Moreover, the rules for the random walk are constructed so as to make the fractal produced invariant of orientation of the print. This method offers a new fast way to compare images. This method can be used to increase confidence, both in court and public opinion, in the use of fingerprints as identification. It can offer both an independent and/or supplemental method to the current ones used.