Miracle-dz/UniFAR-FTUs huggingface dataset:
L A TEX More Realistic 3D object Detection

Point clouds are an important geometric data structure consisting of points with x y z coordinates. The 3DETR model is a good base model which employs the trans-former’s encoder decoder structure in object detection us-ing point clouds. While this is a good base it is un-optimized and trained on unrealistic datasets. In this work it is optimized for and trained on a realistic dataset.
DMSO_A_377404 2771..2780

Purpose: Females with polycystic ovary syndrome (PCOS) are found to have hormonal and metabolic alterations. This study investigated the efficacy of the flavonolignan silibinin in restoring the metabolic alterations associated with letrozole-induced PCOS in rats. Methods: The study allocated 42 albino rats into two groups. The first group was a normal control group (n=12) in which only the vehicle was given. The second group, the PCOS group (n=30), received letrozole (1 mg/kg/day) orally for 21 days. On day 21, six animals from the first group and six animals from the second group were sacrificed to confirm the development of PCOS, and the rest of the animals (n=24) in the second group were distributed equally into four groups: the PCOS group received vehicle, the metformin (MET) group received 300 mg/kg metformin orally, and the low-dose silibinin (LD-100) and high-dose silibinin (HD-200) groups received 100 and 200 mg/kg silibinin intraperitoneally, respectively. Blockade of the estrus cycle in the diestrus phase, hyperglycemia, and body weight elevation were related to a positive PCOS induction. An oral glucose tolerance test (OGTT) was also carried out for all animals on day 21 and on the last day of the experiment (day 40) to investigate the effect of silibinin on insulin resistance. The rats’ lipid profile, insulin level, estrus cycle patterns, body weight, and weights of the ovaries and uterus were also measured on day 40. Results: A 19-day silibinin treatment led to the restoration of regular estrus cyclicity, reduced the glucose spike in OGTT analysis, and alleviated insulin resistance in PCOS rats. There was a statistically non-significant decrement in insulin level and lipid profile in the treatment groups. Conclusion: Silibinin flavonolignan ameliorated some metabolic and reproductive alterations associated with PCOS. This could be related to the decreased insulin resistance, and antiandrogenic and phytoestrogenic activity of silibinin. Further study with longer term therapy is recommended to clarify more potential effects of silibinin and its mechanism of action in PCOS.
Special Section on New Era of Transporter Science: Unraveling the Functional Role of Orphan Transporters — Minireview Transcriptional Regulation of Solute Carrier Drug Transporters

Facilitated transport is necessitated for large size, charged, and/or hydrophilic drugs to move across the membrane. The drug transporters in the solute carrier (SLC) superfamily, mainly including organic anion-transporting polypeptides, organic anion transporters, organic cation transporters, organic cation/carnitine transporters, peptide transporters, and multidrug and toxin extrusion proteins, are critical facilitators of drug transport and distribution in human body. The expression of these SLC drug transporters is found in tissues throughout the body, with high abundance in the epithelial cells of major organs for drug disposition such as intestine, liver, and kidney. These SLC drug transporters are clinically important in drug absorption, metabolism, distribution, and excretion. The mechanisms underlying their regulation have been revealing in recent years. Epigenetic and nuclear receptor – mediated transcriptional regulation of SLC drug transporters has particularly attracted much attention. This review focuses on the transcriptional regulation of major SLC drug transporter genes. Revealing the mechanisms underlying the transcription of these critical drug transporters will help us understand pharmacokinetics and pharmacodynamics, ultimately improving drug therapeutic effectiveness while minimizing drug toxicity. conditions, which is necessary to clarify the pharmacological response, adverse effects, and drug-drug interactions for clinically used drugs.
Learning to Rank

Let X denote the query space and [m] := {1 . m} the finite target space. Let perm(m) denote the set of m! permutations of [m]. The ranking problem is to learn a model Πθ(x) ≈ Π∗(x) where Π∗(x) ⊂ perm(m) is the set of gold rankings for each x ∈ X . The model can be parameterized as an input-target score function sθ(x, y) ∈ R from which rankings can be extracted as Πθ(x) = {π ∈ perm(m) : sθ(x, π(1)) ≥ · · · ≥ sθ(x, π(m))}. For annotation, it is not practical to manually sort all m targets per query, thus we typically assume partial “pointwise” annotation: humans assign gold scores s∗(x, y) ≥ 0 for some query-target pairs x, y. The score can be graded (e.g., 0–4 where 0 is least relevant and 4 is highly relevant) or binary (e.g., 0 if irrelevant and 1 if relevant).
ReadAlong Studio: Practical Zero-Shot Text-Speech Alignment for Indigenous Language Audiobooks

While the alignment of audio recordings and text (often termed “forced alignment”) is often treated as a solved problem, in practice the process of adapting an alignment system to a new, under-resourced language comes with significant challenges, requiring experience and expertise that many outside of the speech community lack. This puts otherwise “solvable” problems, like the alignment of Indigenous language audiobooks, out of reach for many real-world Indigenous language organizations. In this paper, we detail ReadAlong Studio, a suite of tools for creating and visualizing aligned audiobooks, including educational features like time-aligned highlighting, playing single words in isolation, and variable-speed playback. It is intended to be accessible to creators without an extensive background in speech or NLP, by automating or making optional many of the specialist steps in an alignment pipeline. It is well documented at a beginner-technologist level, has already been adapted to 30 languages, and can work out-of-the-box on many more languages without adaptation.
The BATSE experiment on the Compton Gamma Ray Observatory: status and some early results.

The Burst and Transient Source Experiment (BATSE) on the Compton Gamma Ray Observatory (GRO) is a sensitive all-sky detector system. It consists of eight uncollimated detectors at the corners of the spacecraft which have a total energy range of 15 keV to 100 MeV. The primary objective of BATSE is the detection, location, and study of gamma ray bursts and other transient sources. The instrement also has considerable capability for the study of pulsars, solar flares, and other discrete high energy sources. The experiment is now in full operation, detecting about one gamma ray burst per day. A brief description of the on-orbit performance of BATSE is presented, along with examples of early results from some of the gamma ray bursts.
Antifatigue effect of naringin on improving antioxidant capacity and mitochondrial function and preventing muscle damage

The aim of this study was to explore effects of naringin (Nar) on antifatigue ability; the weight-loaded and non-loading swimming tests were performed. Compared with the control group, dietary supplementation of Nar significantly prolonged the weight-loaded swimming time to exhaustion of mice (P < 0.01). Nar significantly reduced the serum lactic acid (LD) level (P < 0.05) and lactate dehydrogenase (LDH) activity (P < 0.001), while increased the serum non-esterified free fatty acids (NEFA) level (P < 0.001). In addition, Nar significantly increased the liver glycogen and muscle glycogen contents (P < 0.05) and the phosphoenolpyruvate carboxykinase (PEPCK) (P < 0.01) and glucokinase (GCK) mRNA levels (P < 0.001) in liver and gastrocnemius (GAS) muscle. Furthermore, Nar significantly improved the antioxidant capacity, mitochondrial function, and muscle mitochondrial fatty acid β-oxidation (P < 0.05), and decreased inflammation and muscle damage–related gene expression (P < 0.05). These findings suggested that Nar can improve antifatigue effect by enhancing antioxidant capacity and mitochondrial function and preventing muscle damage.
Two-Stage Vehicle Routing Optimization for Logistics Distribution Based on HSA-HGBS Algorithm

Aiming at the problems of complex urban road network, low efficiency of logistics distribution, and the difficulty of large-scale logistics distribution area division and routing planning, this paper proposes a two-stage logistics distribution vehicle routing optimization (VRP) method based on the establishment of a multi-factor complex road network constrained logistics distribution mathematical model. Considering the complex traffic elements and road network topological structure in logistics and distribution, in the first stage, a heuristic simulated annealing (HSA) distribution region partitioning algorithm is proposed with the objective of balancing vehicle task load to divide the urban logistics distribution network under complex road networks, so as to reduce the region scale and path search cost. In the second stage of route decision making, aiming at minimizing the total cost of logistics distribution, combining the VRP problem with complex road network conditions, a heuristic path search method combined with complex road network model constraints is proposed. In this stage, a hybrid genetic beam search(HGBS) algorithm is used to plan the path nodes, reduce the randomness of the model in the initial search for paths by heuristic genetic algorithms, then combine with Beam Search methods to reduce the space and time used for the search, and use optimization algorithms to improve the accuracy of independent sub-region routing optimization and the rationality of overall physical distribution route selection. Finally, the proposed method is validated in this paper with two practical cases. The experimental results show that the two-stage decision-making algorithm proposed in this paper has certain advantages in partitioning schemes, minimizing total cost and iteration times. Through comparison, the optimization ability of this method for logistics distribution networks is proved.
Label Oriented Hierarchical Attention Neural Network for Short Text Classification

: Text classification task is a very common task in natural language processing. The conventional text classification task model often uses word bag model or representation model, but the existing model usually deals with long text classification task, which is not suitable for short text classification. Short text features are relatively fewer and often need more sophisticated feature extraction, and the role of keywords play great importance roles in the classification of short text. In this paper, we propose a label-oriented hierarchical attention mechanism network for short text classification. The model achieves better results on public data sets of Tiao and Weibo, compared with convolutional neural network CNN, GATE control unit neural network GRU, gate control unit neural network fusion convolutional neural network GRU-CNN and translation model Transfomer. It is proved that the model has good performance. Our model has two significant advantages: (1) it is a hierarchical structure consisting of two levels of attention mechanisms, which facilitates interpretation and analysis; (2) Compared with other architectures, this model can be used to extend multi-label text classification tasks. In addition, it can be used for long-term importance analysis in many industrial scenarios.
Regulatory Effects of Lycium barbarum Extract and Isolated Scopoletin on Atopic Dermatitis-Like Skin Inflammation

Lycium barbarum and scopoletin are widely used in oriental Eastern medicine and are often consumed as teas. In this study, proinflammatory cytokines expressed in human keratinocytes (HaCaT) were induced by skin diseases caused by 2,4-dinitrochlorobenzene (DNCB) and tumor necrosis factor alpha (TNF-α)/interferon gamma (IFN-γ). The inhibitory activity of L. barbarum EtOH extract (LBE) and scopoletin on proinflammatory cytokines and chemokines was investigated. In the DNCB-induced animal model, oral administration of LBE inhibited skin lesions and proinflammatory cytokines and chemokines and showed inhibitory effects in vitro. Additionally, as a result of examining the efficacy of scopoletin isolated from L. barbarum, scopoletin in HaCaT cells showed inhibitory effects on proinflammatory cytokines and chemokines. It shows promise in the treatment of chronic skin diseases.
Disentangled Graph Contrastive Learning With Independence Promotion

Self-supervised learning for graph neural networks has attracted considerable attention and shows notable successes in graph representation learning. However, the formation of a real-world graph typically arises from highly complex interactions of many latent factors. The existing self-supervised learning methods for GNNs are inherently holistic and neglect the entanglement of the latent factors, resulting in suboptimal learned representations for downstream tasks and difficult to be interpreted. Learning disentangled graph representations with self-supervised learning poses great challenges and remains largely ignored by the existing literature. In this paper, we introduce Independence Promoted Disentangled Graph Contrastive Learning (IDGCL) method, which can learn disentangled graph-level representations with self-supervision. In particular, we first identify the latent factors of the input graph and derive its factorized representations. Then we propose a factor-wise discrimination objective in a contrastive learning manner, which can force the factorized representations to independently reflect the expressive information from different latent factors. To further promote the independence between the representations, we employ the Hilbert-Schmidt Independence Criterion to eliminate the dependence among different representations, which is effectively integrated into the self-supervised framework as a regularizer. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of our method against several state-of-the-art baselines.
THE EFFECT OF PERCEIVED ORGANIZATIONAL SUPPORT, MEANING OF WORK AND PROACTIVE PERSONALITY ON EMPLOYEE CREATIVITY (STUDY ON EMPLOYEES OF HARNO SURYA NGEMPLAK BOTHI)

This study aims to determine the effect of perceived organizational support, work meaning and proactive personality on employee creativity. The results of data analysis in this study are that there is an influence of Perceived Organizational Support on Employee Creativity, there is an influence of Job Meaning on Employee Creativity and there is an influence of Proactive Personality on Employee Creativity, and also from the results of the F test analysis, the results show that Perceived Organizational Support, Work Meaning and Personality Proactive jointly affects Employee Creativity. Another result of this study is that the coefficient of determination (R2) produces a value of 0.59 or 59%. This means that 59% of employee creativity in Harno Surya Ngemplak Bothi Kartasura employees is influenced by the variables of Organizational Support Perception, Work Meaning and Proactive Personality while the remaining 41% is influenced by other variables not examined in this study. Keywords: Perceived Organizational Support, Meaning Of Work, Proactive Personality, Employee Creativity
USE OF MOSQUITO REPELLENT: A COMPARATIVE ANALYSIS BETWEEN SLUM AND PLANNED RESIDENTIAL AREAS OF KHULNA CITY CORPORATION IN THE SOUTH-WEST BANGLADESH

People in developing countries like Bangladesh often ignore the harmful effect of using mosquito repellents on health as well as environment. This is a cross-sectional study among adult individuals selected from planned residential (60) and slum (60) areas of Khulna City Corporation in the south-west Bangladesh considering the user (64) and non-user (56) of repellents chosen through applying a stratified systematic random sampling method. Most of the surveyed people use mosquito repellents at evening and at night while sleeping. Poor slum people mainly use local brand coil, while a comparatively high income earner planned residential people use variety of repellents. This study finds that the planned residential people spend more on repellent purchase per month than slum people. The smoke congestion of living room due to use of repellent is more evident in slum areas compared to planned residential areas. Feeling sick due to this smoke is also more evident in the slum areas compared to planned residential areas. Similarly, the severity of disease due to use of mosquito repellent is higher in the slum areas compared to planned residential areas. The planned residential area people are more aware about the negative health effect of using mosquito repellents. The ordered logit estimation result shows that disease awareness of using repellent is higher among educated and planned residential people in comparison with less educated and slum people. Hence, activities under different public health program might be develop to enhance awareness, especially among poor, less educated slum people toward the negativity of using repellents and encourage them towards using alternative means for preventing mosquito.
Embedded Machine Learning Using Microcontrollers in Wearable and Ambulatory Systems for Health and Care Applications: A Review

The use of machine learning in medical and assistive applications is receiving significant attention thanks to the unique potential it offers to solve complex healthcare problems for which no other solutions had been found. Particularly promising in this field is the combination of machine learning with novel wearable devices. Machine learning models, however, suffer from being computationally demanding, which typically has resulted on the acquired data having to be transmitted to remote cloud servers for inference. This is not ideal from the system’s requirements point of view. Recently, efforts to replace the cloud servers with an alternative inference device closer to the sensing platform, has given rise to a new area of research Tiny Machine Learning (TinyML). In this work, we investigate the different challenges and specifications trade-offs associated to existing hardware options, as well as recently developed software tools, when trying to use microcontroller units (MCUs) as inference devices for health and care applications. The paper also reviews existing wearable systems incorporating MCUs for monitoring, and management, in the context of different health and care intended uses. Overall, this work addresses the gap in literature targeting the use of MCUs as edge inference devices for healthcare wearables. Thus, can be used as a kick-start for embedding machine learning models on MCUs, focusing on healthcare wearables.
Gevrey Smoothing Effect for Solutions of the Non-Cutoff Boltzmann Equation in Maxwellian Molecules Case

In this paper we study the Gevrey regularity for the weak solutions to the Cauchy problem of the non-cutoff spatially homogeneous Botlzmann equation for the Maxwellian molecules model with the singularity exponent $s\in (0,1)$. We establish that any weak solution belongs to the Gevrey spaces for any positive time.
Islamic Populism in Rural Indonesia: An Agrarian Change Approach

A massive demonstration in Jakarta called “Aksi Bela Islam” (Action to Defend Islam) marks a continuity of the Islamist currents in post-New Order Indonesia. Many observers called it “Islamic populism”, a populist, cross-class alliance on behalf of the Islamic masses or “ummah” against capitalist development that has marginalized Muslims in the struggle for access to economic and political resources. However, despite this refreshing approach, many studies on Islamic populism still concentrate on the state (instead of capital) and the urban areas in explaining the development of Islamic populism. This article, therefore, offers a different approach to analyzing Islamic populism through the understanding of capitalism as a social relation and shifts to the countryside as its empirical basis by focusing on the case study of Bulak village in West Java. By combining insights from the literature on agrarian change and populism as a political strategy and adopting qualitative methods namely in-depth interviews (including oral history) and field observation, this article found that contemporary Islamic populism in Indonesia is a result of the specific development of capitalist relations in the context of rural agrarian change. In addition, it also found that Islamic populism is not a phenomenon confined to the urban areas, since it also spreads to the countryside. Moreover, Islamic populism in the countryside has distinctiveness, related to context, social background, and the ways it is mobilized.
Analysis of Long-Term Utah Temperature Trends Using Hilbert-Haung Transforms

Analysis of Long-Term Utah Temperature Trends Using Hilbert-Haung Transforms Brent H. Hargis Department of Civil Engineering, BYU Master of Science We analyzed long-term temperature trends in Utah using a relatively new signal processing method called Empirical Mode Decomposition (EMD). We evaluated the available weather records in Utah and selected 52 stations, which had records longer than 60 years, for analysis. We analyzed daily temperature data, both minimum and maximums, using the EMD method that decomposes non-stationary data (data with a trend) into periodic components and the underlying trend. Most decomposition algorithms require stationary data (no trend) with constant periods and temperature data do not meet these constraints. In addition to identifying the long-term trend, we also identified other periodic processes in the data. While the immediate goal of this research is to characterize long-term temperature trends and identify periodic processes and anomalies, these techniques can be applied to any time series data to characterize trends and identify anomalies. For example, this approach could be used to evaluate flow data in a river to separate the effects of dams or other regulatory structures from natural flow or to look at other water quality data over time to characterize the underlying trends and identify anomalies, and also identify periodic fluctuations in the data. If these periodic fluctuations can be associated with physical processes, the causes or drivers might be discovered helping to better understand the system. We used EMD to separate and analyze long-term temperature trends. This provides awareness and support to better evaluate the extremities of climate change. Using these methods we will be able to define many new aspects of nonlinear and nonstationary data. This research was successful and identified several areas in which it could be extended including data reconstruction for time periods missing data. This analysis tool can be applied to various other time series records.