Miracle-dz/UniFAR-FTUs huggingface dataset:
Baricitinib protects mice from sepsis-induced cardiac dysfunction and multiple-organ failure

Sepsis is one of the major complications of surgery resulting in high morbidity and mortality, but there are no specific therapies for sepsis-induced organ dysfunction. Data obtained under Gene Expression Omnibus accession GSE131761 were re-analyzed and showed an increased gene expression of Janus Kinase 2 (JAK2) and Signal Transducer and Activator of Transcription 3 (STAT3) in the whole blood of post-operative septic patients. Based on these results, we hypothesized that JAK/STAT activation may contribute to the pathophysiology of septic shock and, hence, investigated the effects of baricitinib (JAK1/JAK2 inhibitor) on sepsis-induced cardiac dysfunction and multiple-organ failure (MOF). In a mouse model of post-trauma sepsis induced by midline laparotomy and cecal ligation and puncture (CLP), 10-week-old male (n=32) and female (n=32) C57BL/6 mice received baricitinib (1mg/kg; i.p.) or vehicle at 1h or 3h post-surgery. Cardiac function was assessed at 24h post-CLP by echocardiography in vivo, and the degree of MOF was analyzed by determination of biomarkers in the serum. The potential mechanism underlying both the cardiac dysfunction and the effect of baricitinib was analyzed by western blot analysis in the heart. Trauma and subsequent sepsis significantly depressed the cardiac function and induced multiple-organ failure, associated with an increase in the activation of JAK2/STAT3, NLRP3 inflammasome and NF- κβ pathways in the heart of both male and female animals. These pathways were inhibited by the administration of baricitinib post the onset of sepsis. Moreover, treatment with baricitinib at 1h or 3h post-CLP protected mice from sepsis-induced cardiac injury and multiple-organ failure. Thus, baricitinib may be repurposed for trauma-associated sepsis.
Students' Interest In Making Table Cloths Learning Using Fantasy Embroidery

This study started from the problem that students' interest in the competence of making tablecloths using fantasy embroidery was still lacking. The purpose of this study was to express students' interest in learning about the competence to learn the skills of making tablecloths using fantasy embroidery at MTsN Payakumbuh, Payakumbuh City. The method used in this research is the descriptive method. The population in this study was class VIII students, amounting to 198 students. The sample in this study is part of the population obtained by 40 students. The indicators in this study are the strength of students' motives, attention, and feelings of pleasure towards arts and culture subjects in learning the skills of making tablecloths using fantasy embroidery. This research instrument is in the form of a questionnaire, and the data analysis technique uses percentages. The results showed that the interest of class VIII students in arts and culture subjects in learning the skills of making tablecloths using fantasy embroidery at MTsN Payakumbuh was in the high category with an average score of 3.6 with an achievement rate of 72.5% of respondents. Student interest from the aspect of motive power obtained an average score of 3.36 with a respondent's level of achievement of 67.14%, including the high category, and student interest from the aspect of attention with obtained an average score of 3.72 with a respondent's achievement level of 74.5% including the high category and student interest in terms of feeling happy with obtained an average score of 3. 8 with an achievement level of 75.9% respondents included in the high category. It is recommended that students increase their interest in learning, and the teacher directs students more to growing students' interest in learning.
Gut microbiota is associated with spatial memory and seed-hoarding behavior of South China field mice (Apodemus draco)

Background Scatter-hoarding animals store food in multiple locations within their home range and rely on spatial memory for subsequent localization and retrieval. The relationship between memory and scatter-hoarding behavior has been widely demonstrated, but the association of gut microbiota with spatial memory and seed-hoarding behavior of animals remains unclear. Methods In this study, by using enclosure behavior tests, memory tests including an object location test (OLT) and a novel object recognition test (NORT), and fecal microbiota transplantation (FMT) experiment, we evaluated the role of gut microbiota in affecting the memory and seed-hoarding behavior of rodents. According to their scatter-hoarding intensity, South China field mice (Apodemus draco) were divided into scatter-hoarding group (SG) and non-scatter-hoarding group (NG). Results We found that the SG performed better than the NG in the NORT. FMT from SG donor mice altered the NG recipient mice’s gut microbiota structure. Further tests demonstrated FMT from SG donor mice increased memory of NG recipient mice in laboratory tests and seed larder hoarding intensity of NG recipient mice in enclosures. Conclusion Our results suggest gut microbiota could modulate the memory and seed-hoarding behavior of animals.
A Useful and Effective Method for Selecting a Smart Controller for SDN Network Design and Implement

Software Defined Networking (SDN) is a modern network architectural model that manages network traffic using software. SDN is a networking scenario that modifies the conventional network design by combining all control features into a single place and making all choices centrally. Controllers are the "brains" of SDN architecture since they are responsible for making control decisions and routing packets at the same time. The capacity for centralized decision-making on routing improves the performance of the network. SDN's growing functionality and uses have led to the development of many controller systems. Every SDN controller idea or design must prioritize the control plane since it is the most crucial part of the SDN architecture. Studies have been done to examine, analyze, and evaluate the relative advantages of the many controllers that have been created in recent years. In this paper, finding the perfect controller based on derived needs (for example, the controller must have a "Java" or "Python" interface), a matching process compares controller features with requirements.
Identifying dynamical structures in the physical space of stochastic processes

Characterizing dynamical patterns in the (physical) state space of stochastic processes can be a challenging task. From two visualization techniques, the observable-representation and k-means clustering, a unified framework to identify such structures is developed. The only information required is the system transition matrix R (a quantity that can be directly accessed from experimental data). The approach is illustrated through the analysis of random searches for targets distributed in patchy environments. The protocol —for R constructed from a typical tracked long trajectory— is able to reveal the shape and locations of all the landscape patches. The method constitutes a valuable new tool to study the underlying geometry of general stochastic processes.
Performance Analysis of the PMD Camboard Picoflexx Time-of-Flight Camera for Markerless Motion Capture Applications

The PMD Camboard Picoflexx Time-of-Flight (ToF) camera is evaluated against the Microsoft Kinect V2 to assess its performance in the context of markerless motion capture system for human body kinematics measurements. Various error sources such as the warm-up time, the depth distortion, the amplitude related error, the signal-to-noise ratio, and limitations such as their dependence on the illumination pattern and on the target distance, are studied and compared. The Picoflexx device is also compared to the Kinect V2 in relation to the quality of shape reconstructions, to assess its adequateness in modeling human body segments, and human body kinematics measurements. The final result of this paper is definitely useful to the research community, demonstrating that, even if the Picoflexx performs lower than the Kinect concerning the measurement performances, its behavior in estimating the volume of the body segments, the angles at the joints for human body kinematics, and the angle at the ankle in assisted walking applications is definitely satisfactory. These results are extremely significant to obtain accurate estimates of the parameters of the human body models in markerless motion capture applications, especially in laboratory-free environments, where compactness, lightweight, wireless connection, and low-power consumption are of outmost importance.
Sm/Co Magnetic Materials: A Recycling Strategy Using Modifiable Hydrophobic Deep Eutectic Solvents Based on Trioctylphosphine Oxide

Hydrophobic deep eutectic solvents (HDES) are widely used as extractants. Usually, when preparing HDES, only the extraction ability of one component is taken into account, with the second serving as an “inert” component, whose effect on the extraction process is not taken into account. The present study demonstrates the possibility of controlling the selectivity of a hydrophobic deep eutectic solvent based on trioctylphosphine oxide (TOPO) by varying the substance that acts as a hydrogen bond donor, but which does not have an extractive ability. In the course of the work, the influence of the “inert” component on the physicochemical and extraction properties of HDES was confirmed by experimental, spectroscopic, and also calculation methods. A number of phenols with different structural features were chosen as the HDES’ hydrogen bond donors to modify: phenol (Ph), para-tert-butylphenol (PTBP) and thymol (Th). Using the example of separation of the Sm/Co pair, the influence of the structure of a hydrogen bond donor on the extraction ability of a hydrophobic deep eutectic solvent was established, where the degree of extraction of Sm (III) increased in the series Th:TOPO < PTBP:TOPO < Ph:TOPO. HDES based on TOPO and phenols can potentially be used to separate Sm and Co from the process leach solutions generated during the hydrometallurgical processing of waste SmCo magnets.
Solving partially observable agent‐intruder games with an application to border security problems

In this paper, we introduce partially observable agent‐intruder games (POAIGs). These games model dynamic search games on graphs between security forces (an agent) and an intruder given possible (border) entry points and high value assets that require protection. The agent faces situations with dynamically changing, partially observable information about the state of the intruder and vice versa. The agent may place sensors at selected locations, while the intruder may recruit partners to observe the agent's movement. We formulate the problem as a two‐person zero‐sum game, and develop efficient algorithms to compute each player's optimal strategy. The solution to the game will help the agent choose sensor locations and design patrol routes that can handle imperfect information. First, we prove the existence of ϵ‐optimal strategies for POAIGs with an infinite time horizon. Second, we introduce a Bayesian approximation algorithm to identify these ϵ‐optimal strategies using belief functions that incorporate the imperfect information that becomes available during the game. For the solutions of large POAIGs with a finite time horizon, we use a solution method common to extensive form games, namely, the sequence form representation. To illustrate the POAIGs, we present several examples and numerical results.
AttAuth: An Implicit Authentication Framework for Smartphone Users Using Multimodality Data

Smartphones have become the most important devices for users to communicate and interact with different forms of media, and at the same time stored a large amount of sensitive and private data. The security and protection of such data has become increasingly critical. As the sensor technology rapid developed, the diversity of sensors on smartphones has greatly increased (e.g., motion sensor and touchscreen sensor), empowering the smartphones to provide continuous and implicit user authentication by capturing behavioral biometrics. Unfortunately, it remains a challenge to make full use of the data from the multimodality sensors to provide accurate authentication. Toward this end, in this article, we develop an implicit authentication (IA) framework AttAuth which explores organic integration of such multimodality data to authenticate smartphone users through the usage session. Specifically, AttAuth first develops a series of data processing techniques to process the multichannel motion sensor data and the discrete touchscreen data. Then, a temporal and channelwise attention-based temporal and channelwise attention recurrent neural network (TCA-RNN) is developed to build authentication model, which allows to jointly model continuously monitored motion sensor data and irregularly recorded discrete touchscreen data effectively. By generating a guidance vector based on touch events, TCA-RNN guides the temporalwise attention mechanism on the processed multichannel motion sensor data and outputs authentication results. We evaluate AttAuth on a real-world multimodality smartphone usage data set of 100 users. Extensive experiments demonstrate that AttAuth achieves the state-of-the-art authentication accuracy. Additional experiments are provided to examine the applicability of AttAuth in terms of running overheads and sensitivity to various application scenarios.
Gas Sensor Array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning

With the development of gas sensor arrays and computational technology, machine olfactory systems have been widely used in environmental monitoring, medical diagnosis, and other fields. The reliable and stable operation of gas sensing systems depends heavily on the accuracy of the sensors outputs. Therefore, the realization of accurate gas sensor array fault diagnosis is essential to monitor the working status of sensor arrays and ensure the normal operation of the whole system. The existing methods extract features from a single dimension and require the separate training of models for multiple diagnosis tasks, which limits diagnostic accuracy and efficiency. To address these limitations, for this study, a novel fault diagnosis network based on multi-dimensional feature fusion, an attention mechanism, and multi-task learning, MAM-Net, was developed and applied to gas sensor arrays. First, feature fusion models were applied to extract deep and comprehensive features from the original data in multiple dimensions. A residual network equipped with convolutional block attention modules and a Bi-LSTM network were designed for two-dimensional and one-dimensional signals to capture spatial and temporal features simultaneously. Subsequently, a concatenation layer was constructed using feature stitching to integrate the fault details of different dimensions and avoid ignoring useful information. Finally, a multi-task learning module was designed for the parallel learning of the sensor fault diagnosis to effectively improve the diagnosis capability. The experimental results derived from using the proposed framework on gas sensor datasets across different amounts of data, balanced and unbalanced datasets, and different experimental settings show that the proposed framework outperforms the other available methods and demonstrates good recognition accuracy and robustness.
Marathi text summarization through NLP and deep learning mechanism

Every day, an ever-increasing amount of people gain access to the internet platform. This has proven to be efficient in creating cost-effective internet platform deployments and applications. The growth in the amount of people using the platform has resulted in a rise in the quantity of information accessible on the internet in the form of news, media, and other forms of communication. This causes evaluating and comprehending a significant amount of textual information a very challenging task. For the objective of generating textual summaries for Marathi texts, an effective and trustworthy approach is required. Through the use of machine learning methods, a successful strategy for extracting summary for the Marathi text has been generated for this objective. To obtain the Marathi text summary, the proposed method uses feature extraction as well as deep belief networks and decision tree methodologies. The experimentation was carried out on the performance of the Term Frequency-Inverse Document Frequency (TF-IDF) in the stopword elimination procedure, along with the evaluation of the summarization outcome which achieves a Mean Absolute Error (MAE) of 2.8 for the stopword removal approach through TF-IDF technique and a precision of 95.49% with an accuracy of 92.76%.
Investigating Substance Use via Reddit: Systematic Scoping Review

Background Reddit’s (Reddit Inc) large user base, diverse communities, and anonymity make it a useful platform for substance use research. Despite a growing body of literature on substance use on Reddit, challenges and limitations must be carefully considered. However, no systematic scoping review has been conducted on the use of Reddit as a data source for substance use research. Objective This review aims to investigate the use of Reddit for studying substance use by examining previous studies’ objectives, reasons, limitations, and methods for using Reddit. In addition, we discuss the implications and contributions of previous studies and identify gaps in the literature that require further attention. Methods A total of 7 databases were searched using keyword combinations including Reddit and substance-related keywords in April 2022. The initial search resulted in 456 articles, and 227 articles remained after removing duplicates. All included studies were peer reviewed, empirical, available in full text, and pertinent to Reddit and substance use, and they were all written in English. After screening, 60 articles met the eligibility criteria for the review, with 57 articles identified from the initial database search and 3 from the ancestry search. A codebook was developed, and qualitative content analysis was performed to extract relevant evidence related to the research questions. Results The use of Reddit for studying substance use has grown steadily since 2015, with a sharp increase in 2021. The primary objective was to identify tendencies and patterns in various types of substance use discussions (52/60, 87%). Reddit was also used to explore unique user experiences, propose methodologies, investigate user interactions, and develop interventions. A total of 9 reasons for using Reddit to study substance use were identified, such as the platform’s anonymity, its widespread popularity, and the explicit topics of subreddits. However, 7 limitations were noted, including the platform’s low representativeness of the general population with substance use and the lack of demographic information. Most studies use application programming interfaces for data collection and quantitative approaches for analysis, with few using qualitative approaches. Machine learning algorithms are commonly used for natural language processing tasks. The theoretical, methodological, and practical implications and contributions of the included articles are summarized and discussed. The most prevalent practical implications are investigating prevailing topics in Reddit discussions, providing recommendations for clinical practices and policies, and comparing Reddit discussions on substance use across various sources. Conclusions This systematic scoping review provides an overview of Reddit’s use as a data source for substance use research. Although the limitations of Reddit data must be considered, analyzing them can be useful for understanding patterns and user experiences related to substance use. Our review also highlights gaps in the literature and suggests avenues for future research.
Determining the Differences in Hurricane Perception and Evacuation Behavior in the Elderly of South Carolina

v CHAPTER 1: INTRODUCTION 1 1.1 Overview 1 1.2 Research Objectives 3 1.3 Structure of the Document 4 CHAPTER 2: LITERATURE REVIEW 6 2.1 Overview 6 2.2 Evacuation: Warning/Response and Action Processes 7 2.3 Evacuation: Factors and Influences 14 2.4 The Elderly in Disaster 24 2.5 Summary and Conclusions 32 CHAPTER 3: METHODOLOGY 34 3.1 Overview 34 3.2 Theoretical Framework 34
Turbulent Flow Analysis with Banach and Sobolev Spaces in the LES Method Incorporating the Smagorinsky Subgrid-Scale Model

The mathematical analysis, applied in this work, serves as a pillar for a broader investigation on the regularity of the Navier-Stokes Equations. In this context, this investigation marks a significant step forward in the advancement of the Smagorinsky model coupled with the LES methodology, resulting, based on the Banach and Sobolev Spaces, a new theorem that points the way to the construction of an anisotropic viscosity model (not yet discussed in the present work). At first, the effort dedicated here aims to present a more comprehensive mathematical analysis, promoting a more leveled understanding of the challenge posed by the regularity of the Navier-Stokes equations.
Exploring spatiotemporal trends and impacts of health resources and services on under-5 mortality in West African countries, 2010–2019: a spatial data analysis

Background West African countries experienced the highest under-5 mortality rate (U5MR), the lowest life expectancy, and the poorest economic development. This study aimed to explore the spatiotemporal trend of U5MR and spatial spillover effects of health resources and services to provide a basis for establishing health policies and international cooperative mechanisms in West Africa. Methods We retrieved data from the World Health Organization’s Global Health Observatory, the United Nations Human Development Report, and the Global Burden of Disease Study 2019. Joinpoint regression analysis and Moran’s I method were used to examine the temporal trend and spatial dependence of U5MR, respectively. Spatial regression analysis was used to examine the spatial spillover effects. Results The long-term downward trends in U5MR were divided into multiple segments by two or three change points in 2013, 2014, or 2015, and the annual percentage change after 2015 was higher than before 2015. Global Moran’s I was positive, significantly indicating positive spatial autocorrelation, which increased from 0.2850 (p = 0.0210) to 0.3597 (p = 0.0080). Based on spatial regression analysis, human development index (HDI), physicians density, nurses and midwives density, health center density, percentage of infants lacking immunization for diphtheria and measles, and coverage rate of at least one antenatal visit had negative spatial spillover effects on U5MR. HDI had the strongest negative correlation (β = −0.0187 to −0.1054, p < 0.0001). Current health expenditure (CHE) per capita had positive spatial spillover effects on U5MR. Conclusion This study revealed the spatiotemporal trend of U5MR in West African countries and spatial spillover effects of health resources and services. Promoting economic development, increasing health human resources, health expenditure, vaccination rate, antenatal care coverage, and the proportion of health professionals attending births not only reduced the local U5MR but also exerted spatial spillover effects on adjacent countries. The West African Health Organization may consider regional spillover mechanisms to develop regional health policy and intervention cooperation mechanisms, which will contribute to achieving the sustainable development goal on U5MR, Africa Agenda 2063, and universal health coverage.