Miracle-dz/UniFAR-FTUs huggingface dataset:
Distinct clusters of movement entropy in children’s exploration of a virtual reality balance beam

Although assessing motor competence is vital to advancing current understandings of motor development and its significance in various fields, no consensus exists on how the construct should be operationalised and measured. Existing approaches to assessing motor competence in children typically involve applying qualitative and/or quantitative scoring procedures in which children’s performance is evaluated according to certain levels of assessment-specific task performance dependent upon predefined sets of instructions and procedures. Building upon ecological dynamics as a framework, different levels of motor competence can be identified in children’s attempts to coordinate their degrees of freedom while trying to complete the interactive task and environmental constraints. Given the dynamic, nonlinear features of that coordinating process, assessments need to consider the inherit structure of inter- and intra-individual variability in patterns of movement. Against that background, we investigated 7–10-year-old children’s (n = 58) whole-body joint kinematics as they freely explored a balance beam in a virtual reality playground. Specifically, we used exploratory cluster analysis to examine the discriminatory capability of utilising joint-specific sample entropy as a window into individual differences in movement coordination that emerged from children’s exploration of the constraints embedded in the virtual task. Among the results, three clusters of children with distinct profiles of movement variability emerged, all of which showed heterogeneous levels of repeatability in joint movements in combination with the level of spatiotemporal exploration on the balance beam that could not be explained by between-cluster differences in age and gender distributions. Those findings suggest that entropy from whole-body movements can be used to cluster children into distinct groups with different profiles regarding the structure of movement variability, which can inform new understandings and the development of gross motor competence assessments for children.
Histologic Inflammation can Predict Future Clinical Relapse in Ulcerative Colitis Patients in Endoscopic Remission

Abstract Background In ulcerative colitis (UC), endoscopic improvement, defined as a Mayo Endoscopic Score (MES) of 0 or 1, is a target of treatment. The aim of our study was to evaluate the risk of clinical relapse between patients with an MES of 0 or 1 and determine if histologic activity using the Robarts Histopathologic Index (RHI) was predictive of clinical relapse. Methods UC patients with an MES score of 0 or 1, no prior colectomy, and at least 1 year of outpatient follow-up after colonoscopy were included. Demographic, clinical characteristics, and clinical relapse were retrospectively collected. Biopsy specimens were read by a gastrointestinal pathologist. Primary outcome was defined as a composite of relapse requiring change in medical therapy, new steroid use, UC-related hospitalization, and/or colectomy. Results Four hundred and forty-five UC patients were identified. Ninety-five percent of patients with MES 0 were in histologic remission by the RHI whereas only 35% of patients with MES 1 were in histologic remission. Twenty-six percent of patients experienced a clinical relapse; patients with MES 1 or RHI > 3 were significantly more likely to relapse (P < .01) compared to patients with MES 0 or RHI ≤ 3. When patients were stratified into 4 groups (MES 0, RHI ≤ 3; MES 0, RHI > 3; MES 1, RHI ≤ 3; MES 1, RHI > 3) and adjusted for age and sex, RHI > 3 was predictive of relapse (P = .008). Conclusions UC patients with endoscopic improvement have a high rate of clinical relapse over time. Histologic activity is a predictor of clinical relapse.
RANCANG BANGUN APLIKASI TRACKING BERBASIS WEBSITE PADA PT ABCDE

PT. ABCDE is a service company operating in the field of goods delivery and online purchases. Currently, PT. ABCDE aims to improve its services and quality standards in various aspects within the company, including the utilization of information technology. Currently, the existing services do not utilize a tracking application to obtain real-time information about the current location of the goods, causing a lack of transparency and inconsistencies in data across different departments. Additionally, the current process of generating delivery notes is manual, time-consuming, and leads to delays for drivers in delivering goods. Therefore, there is a need for a tracking application that records and displays real-time information about the current location of the goods, inputted by the drivers. Additionally, the application should automate the generation of delivery notes to reduce delays. This system will be designed using the PHP programming language and the Laravel framework, utilizing a MySQL server database that allows access from anywhere and anytime. The benefits obtained from this system include facilitating each department, especially the administrative team and owner, in checking the current location of the goods, and ensuring transparency of data across different departments to avoid any misinformation.
The development of fire-induced damage functions for forest recreation activity in Alberta, Canada

This study develops an intertemporal fire damage function for forest recreation activity in the eastern slopes region of the Canadian Rocky Mountains. The methodology employed combined revealed-stated preference data in which the behavioral response variable was annual camping trip frequencies. Photographs were used to portray changes in stand ages and related changes in trip frequencies. The data were analysed using negative binomial count data models. Unlike previous studies employing similar methods, a random effects specification was used to develop trip demand parameters. The results suggest that fires initially decrease annual trips from ~2.56 to 1.0 after the burn. As the stand ages, the effect of the fire decreases until ~12 years after the fire when the trip frequencies recover to about their previous ‘old-growth’ levels. This function is different from others described in the literature for similar mountain ecosystems in North America.
Varieties of Anti‐Globalism: The Italian Government’s Evolving Stance on the EU’s Investment Screening Mechanism

In 2017, Italy, France, and Germany jointly supported the setting up of an EU-wide investment screening mechanism to strengthen the EU’s capacity to screen and eventually block foreign investments. In a few months, however, the Italian government changed position dramatically, shifting from leading supporter to staunchest opposer of this pol-icy initiative. Such a change of positioning was decisive in both watering down the initial proposal and moving for-ward with the idea of a looser mechanism coordinating national investment screening activities. This article develops an explanation of the Italian government’s changing negotiating stance. We develop an argument that stresses how two factors combined to produce this puzzling outcome. First, we stress the role of political parties as drivers of governments’ foreign economic policy choices. More specifically, we show that the preferences of the parties form-ing the Italian government after the 2018 general elections (the Lega Nord and the Five Star Movement) were crucial in shaping Italy’s evolving stance on this important issue. Second, we highlight the implications of the tension that exists between two different “varieties” of anti-globalism. While “self-proclaimed” anti-globalist political parties usu-ally combine a traditional critique of globalization and opposition to further political integration in the EU, they may be forced to prioritize one over the other when they prove incompatible. In this context, we show how Italian anti-globalist parties’ choice to prioritize anti-Europeanism over anti-globalism led them to prefer strengthening domes-tic-level institutions to screen FDIs rather than allowing the EU to acquire new powers.
Demosaicing using Artificial Neural networks

The problem of color image enhancement and the specific case of color demosaicing which involves reconstruction of color images from sampled images, is an under-constrained problem. Using single-channel restoration techniques on each color-channel separately results in poorly reconstructed images. It has been shown that better results can be obtained by considering the cross-channel correlation. In this paper, a novel approach to demosaicing is presented, using learning schemes based on Artificial Neural Networks. Thus the reconstruction parameters are determined specifically for predefined classes of images. This approach improves results for images of the learned class, since the variability of inputs is constrained (within the image class) and the parameters are robust due to the learning process. Three reconstruction methods are presented in this work. Additionally, a selection method is introduced, which combines several reconstruction methods and applies the best method for each input. The first reconstruction method presented in this work is the Perceptron method which considers linear restoration models. Reconstruction using the Perceptron method yields excellent results in low-frequency regions of the image. However, in the high-frequency regions (especially in desaturated color regions) the results of the Perceptron network are not satisfactory. This is shown to be due to the linearity and the uniformity of the Perceptron network. Restoration using the Backpropagation method yields relatively good results in high-frequency regions. However, in the low-frequency regions the results are not satisfactory since it fails to reconstruct the correct color of the region. The Selector method is shown to be useful in combining both of the previous methods in one reconstruction scheme which works in both cases of low and high frequency regions. The Quadratic Perceptron method is a new development extending the Perceptron network in order to overcome the limitations of the latter network and provide a better solution for the reconstruction in the high-frequency regions.
The Impact of the Chesapeake Bay Program on Pennsylvania Application Rates for the Environmental Quality Incentive Program

This paper presents an application of matching on propensity score to evaluate the impact of the Chesapeake Bay Program (CBP) on farmers' willingness to participate in the United States Department of Agriculture Environmental Quality Incentive Program (EQIP). One goal of the CBP is to reduce agricultural nutrient loadings to Chesapeake Bay. Achievement of this goal will require increased farm adoption of conservation practices to limit and reduce the levels of residual nutrients the bay. One expectation of this investigation is that the CBP's agricultural related research and educational outreach programs directed to the farm community has a positive effect on farmers' willingness to enroll in conservation programs. Furthermore, the CBP funding to support county watershed technicians who assist farm operators with adoption of conservation practices is expected to also have a positive impact on farmers' willingness to enroll in EQIP. EQIP is the leading federal conservation program for cost-share funding for structural, vegetative, and land management practices to reduce agricultural non-point source pollution. The study is limited in scope. The analysis is conducted over one state, Pennsylvania, and analyzes the impact of the CBP only on one program, EQIP. The study is not presented as a comprehensive evaluation of the CBP's impact on all farm conservation programs administered within the Chesapeake Bay watershed. However, the estimation method used for this study can be extended to other farm conservation programs.
An Analysis of the Board of Directors Network Relations Regarding Determination of their Tax Policies Planning

Tax is one of the primary sources of government revenue which is a principal part of the government budget and is considered an inevitable part of corporate payments. The Board of directors is one of the chief decision-making groups in determining corporate tax avoidance. Therefore, the relationships seen in the form of a social network between the board members of various companies can affect the tax policy of companies. The primary purpose of this study is to examine the structure of relationships between companies based on their joint board members and to detect the relationship between the companies position in the network of board members' relations with their tax planning activities. The statistical population of this research was all companies listed on the Tehran Stock Exchange from 2011 to 2020. The social network analysis approach developed on graphic techniques based on graph theory and regression analysis was used to conduct the research analysis. The research results show that some companies active in the Tehran Stock Exchange have a better position than other companies in the network. In addition, evidence showed that the status and position of companies in the network of relationships could affect the extent of their tax avoidance.
Expression Pattern of Trace Amine-Associated Receptors during Differentiation of Human Pluripotent Stem Cells to Dopaminergic Neurons

Trace amine-associated receptors (TAARs), which were discovered only in 2001, are known to be involved in the regulation of a spectrum of neuronal processes and may play a role in the pathogenesis of a number of neuropsychiatric diseases, such as schizophrenia and others. We have previously shown that TAARs also have interconnections with the regulation of neurogenesis and, in particular, with the neurogenesis of dopamine neurons, but the exact mechanisms of this are still unknown. In our work we analyzed the expression of TAARs (TAAR1, TAAR2, TAAR5, TAAR6, TAAR8 and TAAR9) in cells from the human substantia nigra and ventral tegmental areas and in human pluripotent stem cells at consecutive stages of their differentiation to dopaminergic neurons, using RNA sequencing data from open databases, and TaqMan PCR data from the differentiation of human induced pluripotent stem cells in vitro. Detectable levels of TAARs expression were found in cells at the pluripotent stages, and the dynamic of their expression had a trend of increasing with the differentiation and maturation of dopamine neurons. The expression of several TAAR types (particularly TAAR5) was also found in human dopaminergic neuron-enriched zones in the midbrain. This is the first evidence of TAARs expression during neuronal differentiation, which can help to approach an understanding of the role of TAARs in neurogenesis.
Ocular Microbiota of Severe Meibomian Gland Dysfunction (Chronic Dry Eyes) after Intense Pulsed Light (IPL)

Ocular IPL therapy has recently been widely used for MGD, especially for patients not showing improvement with traditional therapies (warm compresses and lid scrubs) to clean debris and reduce bacterial overgrowth. Insights on the ocular microbiome and quantitative microbiome in MGD after a course of IPL could provide useful data on bacterial community monitoring and associated mechanisms linked with IPL. Ocular swabs were obtained from a severe MGD patient and age-sex matched healthy for metagenomics, followed by 16S rRNA gene sequencing and qPCR. Of 10 samples, including left and right eyes (el, er) of severe MGD females before (Db) and after 2-4 IPLs (Da2, Da3, and Da4) and the matched non-MGD females (H), both of ~40 years Using 16S rRNA gene sequencing as microbiota and combined 16S rRNA gene qPCR as quantitative microbiota revealed significant disperse in the microbiome structures of Db compared with Da and H (HOMOVA, p<0.001). Bacterial Propionibacterium acnes and unclassified taxa in the family Propionibacteriaceae and order Actinomycetales represented the core Db microbiota and were reduced after 2-4 IPLs in Da, making the Da microbiome and clinical (mucocutaneous junction, corneal, and conjunctival fluorescein score) closer to H (NMDS with Pearson’s correlation, p<0.05). The recovery of the Da microbiome also allowed Da metabolic potentials to be closer to H. Our findings first demonstrated the ocular microbiome dysbiosis in severe MGD, dispersed from Da and H, in Thai subjects, correlated with bacterial quantity and clinical MGD, including the mucocutaneous junction process. The results additionally provided taxa representing Db vs. Da and H and preliminarily underlie the idea that IPL could improve dysbiosis in the MGD microbiome. Doi: 10.28991/ESJ-2023-07-05-015 Full Text: PDF
Activity Patterns of Howler Monkeys in Captivity

Howler monkeys are a diurnal genus of New World monkey whose diet may impact their frequency of activity and patterns of behavior. This study investigates this potential relationship by examining the activity frequency of black howler monkeys in captivity, where food is readily available. Captive howler monkeys were found to spend 77% of their time resting, 7% foraging or feeding, and 13% locomoting. When compared to wild specimens, these findings suggest that captivity and easy access to food are associated with more time resting, less time locomoting, and less time feeding or foraging. Future research should focus on investigating the effects of food accessibility on other primates in captivity to better understand how it influences their behavior and improve standards of captive primate care.
Making a Beamline for Deep UV Spectroscopy

U ltrafast spectroscopy offers a way to capture the light-response of an object at the speed that its atoms and electrons move. The most effective techniques make use of light in the optical and extreme UV bands, where ultrashort pulses are easiest to produce. Now a team has developed a technique that uses a miniature beamline to deliver pulses in the “deep-UV” range [1]. As demonstrated in experiments with diamond, the new spectroscopic method can reveal ultrafast behavior in dielectric materials, which are being considered for future high-speed electronics applications.
ARCH-COMP23 Category Report: Continuous and Hybrid Systems with Linear Continuous Dynamics

We present the results of the ARCH1 2023 friendly competition for formal verification of continuous and hybrid systems with linear continuous dynamics. In its seventh edition, three tools participated to solve nine different benchmark problems in the category for linear continuous dynamics (in alphabetical order): CORA, JuliaReach, and Verse. This report is a snapshot of the current landscape of tools and the types of benchmarks they are particularly suited for. Due to the diversity of problems, we are not ranking tools, yet the presented results provide one of the most complete assessments of tools for the safety verification of continuous and hybrid systems with linear continuous dynamics up to this date.
Produção de Casulos do Bicho-da-Seda (Bombyx mori L.) e sua relação com características morfológicas e bromatológicas da folha de amoreira (Morus SPP.)

The objective of this study was to relate silkworm cocoons yield with morphological and bromatological characteristics of mulberry leaves. Ten mulberry cultivars, Calabresa, Korin, IZ 40, IZ 64, IZ 5/2, IZ 13/6, IZ 15/7, IZ 23/3, IZ 56/4 and IZ 57/2 were evaluated. Leaf samples from two levels of insertion on branches were collected, referring to superior (5th leaf) and median levels (15th leaf). The results obtained suggest that the leaf thickness, dry matter, crude protein, crude fiber and mineral matter percentages influenced the cocoon weight. The cultivars IZ 13/6, IZ 23/3 and Korin were better than IZ 57/2 regarding the cocoons yield. The leaves from superior level of insertion showed higher nutritive value than leaves from median level.
Interactive Dairy Goat Image Segmentation for Precision Livestock Farming

Simple Summary Deep-learning-based algorithms have achieved great success in intelligent dairy goat farming. However, these algorithms require a large load of image annotation to obtain a decent performance. The existing annotation of dairy goat images heavily relies on non-intelligent tools such as Labelme, which makes it extremely inefficient and time-consuming to obtain a high-quality annotation result, hindering the application and development of deep-learning algorithms in intelligent dairy goat farming. In this study, we explore an interactive annotation method based on deep-learning algorithms for dairy goat image annotation, which significantly reduces the annotation workload of the user. Specifically, it only takes 7.12 s on average to annotate a dairy goat image with our developed annotation tool, five times faster than Labelme, which takes an average of 36.34 s per instance. Abstract Semantic segmentation and instance segmentation based on deep learning play a significant role in intelligent dairy goat farming. However, these algorithms require a large amount of pixel-level dairy goat image annotations for model training. At present, users mainly use Labelme for pixel-level annotation of images, which makes it quite inefficient and time-consuming to obtain a high-quality annotation result. To reduce the annotation workload of dairy goat images, we propose a novel interactive segmentation model called UA-MHFF-DeepLabv3+, which employs layer-by-layer multi-head feature fusion (MHFF) and upsampling attention (UA) to improve the segmentation accuracy of the DeepLabv3+ on object boundaries and small objects. Experimental results show that our proposed model achieved state-of-the-art segmentation accuracy on the validation set of DGImgs compared with four previous state-of-the-art interactive segmentation models, and obtained 1.87 and 4.11 on mNoC@85 and mNoC@90, which are significantly lower than the best performance of the previous models of 3 and 5. Furthermore, to promote the implementation of our proposed algorithm, we design and develop a dairy goat image-annotation system named DGAnnotation for pixel-level annotation of dairy goat images. After the test, we found that it just takes 7.12 s to annotate a dairy goat instance with our developed DGAnnotation, which is five times faster than Labelme.
Environmental Change of Saemangeum Tideland based on Remote Sensing Method

Reclamation project is very efficient development plan which saves the sea to create new land in case of Republic of Korea where the population density is high and urbanization is still in progress. However, reclamation project is necessary to be managed consistently because to install the artificial structures in natural terrain may cause the environmental change. Thus, in this study, we suggested the remote sensing method as the efficient management plan about large-scale reclamation project and derived quantitative environmental change based on this method.
Tropical Waves Are Key Drivers of Extreme Precipitation Events in the Central Sahel

Extreme precipitation events (EPE) are often associated with severe floods and significant damages in Central Sahel. To better understand their formation and improve their forecasts, we investigate the sub‐seasonal drivers of EPEs. A composite analysis reveals that moist, cyclonic and upper‐level divergence anomalies are found on average as a result of several tropical waves. The equatorial Rossby wave (ER) dominates at large scale providing a moist and convectively‐active anomaly over the northern Sahel together with a smaller‐scale African Easterly Wave (AEW). The Madden‐Julian Oscillation provides upper‐level divergence anomalies and a Kelvin wave increases convection during the EPE. Statistics show the prevalence of AEW and emphasize ER as a key driver of EPE. The co‐occurrences of several tropical waves, especially those involving AEW, ER, and Kelvin waves, increase the probability of EPE. Monitoring these tropical waves combinations could improve EPEs forecasts.