Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on Online Health Community Users’ Information Demands Based on the BERT-LDA Model

As the economy and society develop and the standard of living improves, people’s health awareness increases and the demand for health information grows. This study introduces an advanced BERT-LDA model to conduct topic-sentiment analysis within online health communities. It examines nine primary categories of user information requirements: causes, symptoms and manifestations, examination and diagnosis, treatment, self-management and regulation, impact, prevention, social life, and knowledge acquisition. By analyzing the distribution of positive and negative sentiments across each topic, the correlation between various health information demands and emotional expressions is investigated. The model established in this paper integrates BERT’s semantic comprehension with LDA’s topic modeling capabilities, enhancing the accuracy of topic identification and sentiment analysis while providing a more comprehensive evaluation of user information demands. This research furthers our understanding of users’ emotional reactions and presents valuable insights for delivering personalized health information in online communities.
Design and analysis of nanowire p-type MOSFET coaxially having silicon core and germanium peripheral channel

In this work, a nanowire p-type metal–oxide–semiconductor field-effect transistor (PMOSFET) coaxially having a Si core and a Ge peripheral channel is designed and characterized by device simulations. Owing to the high hole mobility of Ge, the device can be utilized for high-speed CMOS integrated circuits, with the effective confinement of mobile holes in Ge by the large valence band offset between Si and Ge. Source/drain doping concentrations and the ratio between the Si core and Ge channel thicknesses are determined. On the basis of the design results, the channel length is aggressively scaled down by evaluating the primary DC parameters in order to confirm device scalability and low-power applicability in sub-10-nm technology nodes.
Risk Analysis in Energy Program Management

Risk management is one of the best precautionary interventions undertaken in various sectors to minimize the consequence that accrues whenever risks or crises occur. Risk assessment is the first step in the risk management process, and it leads to risk identification, which is followed by risk evaluation to understand the magnitude and possible impact of the risk in case it occurs. Risk assessment and management in the energy sector are not exceptional since risks related to energy are usually catastrophic and lead to loss of life and destruction of the environment. The increase in risks related to energy use and the complexity of managing such risks necessitates the adoption of appropriate risk assessment and risk evaluation mechanisms to guarantee high-quality speculation and forecasting. This report focuses on the use of a semi-quantitative risk assessment approach and the threat-based risk assessment approach. Each of these techniques uses a different paradigm but they all offer invaluable findings regarding risks in the energy sector to enable effective prioritization of such risks when designing interventions.
A survey on sentiment classification algorithms, challenges and applications

Abstract Sentiment classification is the process of exploring sentiments, emotions, ideas and thoughts in the sentences which are expressed by the people. Sentiment classification allows us to judge the sentiments and feelings of the peoples by analyzing their reviews, social media comments etc. about all the aspects. Machine learning techniques and Lexicon based techniques are being mostly used in sentiment classification to predict sentiments from customers reviews and comments. Machine learning techniques includes several learning algorithms to judge the sentiments i.e Navie bayes, support vector machines etc whereas Lexicon Based techniques includes SentiWordnet, Wordnet etc. The main target of this survey is to give nearly full image of sentiment classification techniques. Survey paper provides the comprehensive overview of recent and past research on sentiment classification and provides excellent research queries and approaches for future aspects
Order Invariant Tests for Proper Calibration of Multivariate Density Forecasts

Established tests for proper calibration of multivariate density forecasts based on Rosenblatt probability integral transforms can be manipulated by changing the order of variables in the forecasting model. We derive order invariant tests. The new tests are applicable to densities of arbitrary dimensions and can deal with parameter estimation uncertainty and dynamic misspecification. Monte Carlo simulations show that they often have superior power relative to established approaches. We use the tests to evaluate GARCH-based multivariate density forecasts for a vector of stock market returns.
Structure maximale pour la somme des carrés d'une contingence aux marges fixées ; une solution algorithmique programmée

Nous commencons par analyser l'approche que nous appelons «analytique» ou une borne majorant la borne exacte est fournie par une formule mathematique, symetrique par rapport aux deux marges. Nous montrons que cette approche est necessairement basee sur une application de l'inegalite de Schwarz, mais concue dans un cadre logique. La solution originale que nous proposons pour construire la configuration optimale et la borne exacte associee, consiste d'abord a remplacer la notion de formule par celle beaucoup plus generale d'algorithme recursif dont le point de depart est le couple des marges du tableau de contingence.
Job satisfaction, organizational commitment and burnout in teachers in Mexico

The study's objective was to determine the relationships between the proposed variables that impact to the Job Satisfaction, Organizational Commitment and Burnout through the Psychological Empowerment mediator variable. The variables proposed as impact factors were Structural Empowerment and Remunerations for the case of teachers from public middle-higher education institutions in Mexico. The study had a non-experimental design, being a correlational and causal research. The measurement instrument was applied to a sample of 167 teachers, whose data were analyzed using the structural equations method. The findings show which of these direct and indirect effects are generating significant effects. Subsequently, the analysis of the contrast and impact differences between the segmentation groups was proposed: by gender, marital status, schooling and employment status. One limitation was to carry out the cross-sectional study, with the data collected in a single moment. It is recommended to continue with the analysis of Burnout in other contexts and organizational areas.
Very high energy afterglow of binary neutron star mergers

The joint detection of GW170817 and a short gamma-ray burst (GRB) has provided the first direct evidence that binary neutron star (BNS) merger produces GRB. Recently and unprecedentedly, very-high-energy ( 0.1–10 TeV) afterglow emission were reported from a few GRBs (e
The Impacts of Slc19a3 Deletion and Intestinal SLC19A3 Insertion on Thiamine Distribution and Brain Metabolism in the Mouse

The Thiamine Transporter 2 (THTR2) encoded by SLC19A3 plays an ill-defined role in the maintenance of tissue thiamine, thiamine monophosphate, and thiamine diphosphate (TDP) levels. To evaluate the impact of THTR2 on tissue thiamine status and metabolism, we expressed the human SLC19A3 transgene in the intestine of total body Slc19a3 knockout (KO) mice. Male and female wildtype (WT) and transgenic (TG) mice were fed either 17 mg/kg (1×) or 85 mg/kg (5×) thiamine hydrochloride diet, while KOs were only fed the 5× diet. Thiamine vitamers in plasma, red blood cells, duodenum, brain, liver, kidney, heart, and adipose tissue were measured. Untargeted metabolomics were performed on the brain tissues of groups with equivalent plasma thiamine. KO mice had ~two- and ~three-fold lower plasma and brain thiamine levels than WT on the 5× diet. Circulating vitamers were sensitive to diet and equivalent in TG and WT mice. However, TG had 60% lower thiamine but normal brain TDP levels regardless of diet, with subtle differences in the heart and liver. The loss of THTR2 reduced levels of nucleic acid and amino acid derivatives in the brain. Therefore, mutation or inhibition of THTR2 may alter the brain metabolome and reduce the thiamine reservoir for TDP biosynthesis.
Gene Ontology Analysis Highlights Biological Processes Influencing Responsiveness to Biological Therapy in Psoriasis

Psoriasis is a chronic, immune-mediated and inflammatory skin disease. Although various biological drugs are available for psoriasis treatment, some patients have poor responses or do not respond to treatment. The aim of the present study was to highlight the molecular mechanism of responsiveness to current biological drugs for psoriasis treatment. To this end, we reviewed previously published articles that reported genes associated with treatment response to biological drugs in psoriasis, and gene ontology analysis was subsequently performed using the Cytoscape platform. Herein, we revealed a statistically significant association between NF-kappaB signaling (p value = 3.37 × 10−9), regulation of granulocyte macrophage colony-stimulating factor production (p value = 6.20 × 10−6), glial cell proliferation (p value = 2.41 × 10−5) and treatment response in psoriatic patients. To the best of our knowledge, we are the first to directly associate glial cells with treatment response. Taken together, our study revealed gene ontology (GO) terms, some of which were previously shown to be implicated in the molecular pathway of psoriasis, as novel GO terms involved in responsiveness in psoriatic disease patients.
Influence of tracking duration on the privacy of individual mobility graphs

ABSTRACT Location graphs, compact representations of human mobility without geocoordinates, can be used to personalise location-based services. While they are more privacy-preserving than raw tracking data, it was shown that they still hold a considerable risk for users to be re-identified solely by the graph topology. However, it is unclear how this risk depends on the tracking duration. Here, we consider a scenario where the attacker wants to match the new tracking data of a user to a pool of previously recorded mobility profiles, and we analyse the dependence of the re-identification performance on the tracking duration. We find that the re-identification accuracy varies between 0.41% and 20.97% and is affected by both the pool duration and the test-user tracking duration, it is greater if both have the same duration, and it is not significantly affected by socio-demographics such as age or gender, but can to some extent be explained by different mobility and graph features. Overall, the influence of tracking duration on user privacy has clear implications for data collection and storage strategies. We advise data collectors to limit the tracking duration or to reset user IDs regularly when storing long-term tracking data.
A Combinatorial Model of Lagrangian Skeleta

Author(s): Zorn, Alex | Advisor(s): Nadler, David | Abstract: We investigate a collection of posets- combinatorial arboreal singularities- which are the strata posets of the arboreal singularities constructed by David Nadler. Nadler demonstrated that any Lagrangian skeleton admits a non-characteristic deformation into a skeleton with only arboreal singularities, suggesting that arboreal singularities form the basis for a combinatorial theory of Lagrangian skeleta.In this document, we introduce a form of combinatorial data called a `cyclic structure', which is essentially codimension-one data with compatibility conditions in codimensions two and three. We develop a comprehensive theory of isomorphisms of combinatorial arboreal singularities and cyclic arboreal singularities (singularities equipped with a cyclic structure). We show that a cyclic structure determines (up to quasi-equivalence) a sheaf of dg-categories on a combinatorial arboreal singularity, and investigate combinatorial properties of this sheaf. We describe a class of `arboreal moves', which are local mutations of combinatorial arboreal spaces preserving this sheaf of categories. Finally, we discuss how this combinatorial picture is related to the geometric understanding of Lagrangian embeddings of arboreal singularities.
A Statistical Study of Pulsar Proper Motions

This thesis is about a possible statistical bias in the measurement of pulsar proper motion. This bias is similar to the Lutz & Kelker bias in parallax measurements. To investigate this bias, a likelihood analysis was conducted concerning the pulsar proper motion. The likelihood distribution was constraint using the measured parallax, upper and lower distance limits and radio flux of a pulsar as well as the distribution of pulsar three-dimensional velocity, obtained by Hobbs et al. [2005]. The resulting likelihood distribution was analysed, using self-written python methods. For this two different samples of pulsars were used. In summary the results were, that for a single pulsar the bias is insignificant for all pulsars out of the samples, since either the measurement precision of the pulsar proper motion is to high for the bias to occur or the measurement was insignificant to make a statement on the bias. Alternatively, for a larger sample of proper motion measurements, the measured proper motion tend to be underestimated. This is rather surprising, since a distance measurements of a pulsar tend to be underestimated as well. Another result from the analysis is, that for a large value with a significance of two to four σ, there is the possibility, that the measured proper motion is inconsistent with the corrected biased value obtained by this analysis. Then again we find no evidence in the data that such in a case exists in the data available.
Weighted-Diffusion Distance Measurement Ranking for Pedestrian Re-Identification

Pedestrian re-identification (Re-ID) is a very difficult problem due to complex and changeable environment. Although we can extract discriminative features, simple metric algorithms tend to yield their unreliable matching results. In order to achieve higher matching rate, we propose a more reliable histogram comparison method called weighted-diffusion distance. When calculating the cumulative difference between spatial vector histograms on the basis of high-dimensional diffusion distance, lacking the representation of differences between internal elements results in less desired accuracy. Thus, greater weight should be given to those of the corresponding element values that are same or similar. After these operations, the proposed weighted-diffusion distance is computed. Then we use it to complete multi-feature fusion, and establish a similarity measurement function, which is used to measure the similarity between two pedestrians. At last, pedestrian Re-ID experiments with different cameras are performed and the results show that the proposed method can achieve much better results than some known methods.
Use of Bull Urine, Catfish Testis and Goat Testis to Replace Alcohol, Fishmeal and Steroid Hormone for the Production of Mono-Sex Tilapıa Fry

One-week old swim-up fry of Nile tilapia (Oreochromis niloticus) were used for the trial. Six dietary feeds were assigned during the sex-reversal period – the standard sex-reversal diet without 17-α-methyltestosterone (MT) (C1), standard sex-reversal diet (C2), alcohol-replaced with bull urine included standard sex-reversal diet (BU), half MT-replaced with goat testis (GT), or catfish testis (CT1), all MT-replaced with catfish testis (CT2). Catfish or goat testis to fully replace the fishmeal and half of MT yielded acceptable ratio of the male population (96.6±1.0 and 94.8±0.9%, respectively), comparable to that produced from the standard diet (98.4±0.5%) (P>0.05). Bull urine can replace alcohol additionally required in the sex-reversal feed making process without compromising the fish sex efficiency. These results suggested the potential of the alternative natural products, bull urine, catfish, and goat testes, over the synthetic chemical conventionally used in the feed for sex-reversal of Tilapia.
Study of power-law activity distributions in a spiking neural network model

Neuronal avalanches are cascades of bursts of activity observed primarily in the superficial cortical layers, the distribution of which fits a power law well. Motivated by the observation, we study how a power-law activity distribution emerges in a spiking neural network model. Specifically, we clarify the fundamentals of the phenomenon by applying a general theory of scale-free behavior, introduced to explain the power-law degree distribution in a brain network, and disclose that two kinds of fluctuations in spiking dynamics serve as the essential mechanism for the phenomenon. It is shown that the scale-free behavior arises from a Markov process or a Fokker-Planck diffusion in one dimension and how the power-law exponent of the activity distribution is determined depending on several factors, including the time bin. Finally, we also explain the scale-free behavior observed in the statistics of activity lifetimes.
Prevalence of Hepatitis B Virus among Renal Dialysis Patients in Diyala Province, Iraq

Background: One of the main public health challenges in the globe is the prevalence of the hepatitis B virus (HBV). Objectives: The curent study aimed to determine the prevalence of hepatitis B virus in renal dialysis patients in Diyala province, Iraq. Methods: Ninety of renal dialysis patients attended Ibn-Sina Dialysis Center were screened for HBsAg, HBc IgG, HBc IgM and HBe Ab using ELISA technique, and then positive results were retested by conventional PCR for detection of HBs gene and HB core gene. Demographic data of study population were recorded. Simple statistical analysis was done using SPSS Version 25. Results: Among studied samples, the seroprevalence of HBsAg, Anti-HBc Ab, Anti-HBc IgM and Anti-HBe Ab were 4.4%, 17.8%, 2.2% and 5.6% respectively. The results of conventional PCR showed that only 1(1.1%) of HBs gene at 417 bp in renal dialysis patients were positive. Whereas, the HBc gene at 791 bp was not detected in all included study population. Conclusion: The prevalence of HBV was relatively high among renal dialysis in Diyala province/Iraq.
Exploiting the Synchronization of Nonlinear Dynamics to Secure Distributed Consensus

Distributed cooperative multi-agent operations, which are emerging as effective solutions in countless application domains, are prone to eavesdropping by malicious entities due to their exposure on the network. Moreover, in several cases, agents are reluctant to disclose their initial conditions (even to legitimate neighbors) due to their sensitivity to private data. Providing security guarantees against external readings by malicious entities and the privacy of exchanged data while allowing agents to reach an agreement on some shared variables is an essential feature to foster the adoption of distributed protocols. In this article, we propose to implement a secure and privacy-preserving consensus strategy that exploits, for this purpose, the performance of synchronization of nonlinear continuous-time dynamical systems. This is achieved by splitting the initial conditions into two information fragments, one of which is subject to nonlinear manipulation. In this way, the information being exchanged in the network will always be subject to the influence of nonlinear dynamics. However, by exploiting the ability of such dynamics to synchronize, the combination of the two fragments still converges to a weighted average of each node's actual initial conditions. Furthermore, due to the dependence of the hidden dynamics on a coordinate transformation known only to the legitimate nodes, message security is ensured even once consensus is reached; our approach relies on the assumption that a secure communication channel is available during an initialization phase. The article is complemented by a simulation campaign aimed at numerically demonstrating the effectiveness of the proposed approach.
Self-Supervised Seismic Data Interpolation via Frequency Extrapolation

Antialiasing seismic data interpolation algorithms can reconstruct sparse seismic data into dense data, which helps obtaining high-precision migration images and accurately locate reservoirs. Nonlinear seismic interpolation techniques based on deep learning (DL) have become popular in recent years. The majority of supervised techniques, however, depend on large volumes of labeled datasets, which are rarely available for field data interpolation. To overcome the limitations of the labeled data requirement in supervised learning, some unsupervised interpolation techniques, such as the well-known deep image prior (DIP), have been developed. However, these unsupervised techniques often have poor generalization. In order to avoid the complete labeled data requirement and achieve an accurate interpolation result efficiently, we propose a novel self-supervised interpolation via frequency extrapolation (SIFE) algorithm for regularly missing seismic data. The proposed SIFE mainly contains two steps: aliasing-free low-frequency complete data reconstruction via the Nyquist sampling theorem and high-frequency data recovery via self-supervised frequency extrapolation. In the first step, a low-frequency filter is adopted to obtain aliasing-free sparse data, which can be interpolated into dense low-frequency data via the Nyquist sampling theorem. In the second step, the low-frequency filtered observation data is mapped to its original full-band observed data via self-supervised learning for frequency extrapolation. After the training convergence, we can obtain an optimized network that can map the reconstructed low-frequency data at the missing locations to the corresponding full-band seismic data via frequency extrapolation, i.e., reconstructing the missing seismic traces. Numerical examples with synthetic and field data show the superiority of the proposed SIFE when compared with DIP.
TRPM2: bridging calcium and ROS signaling pathways—implications for human diseases

TRPM2 is a versatile and essential signaling molecule that plays diverse roles in Ca2+ homeostasis and oxidative stress signaling, with implications in various diseases. Research evidence has shown that TRPM2 is a promising therapeutic target. However, the decision of whether to activate or inhibit TRPM2 function depends on the context and specific disease. A deeper understanding of the molecular mechanisms governing TRPM2 activation and regulation could pave the way for the development of innovative therapeutics targeting TRPM2 to treat a broad range of diseases. In this review, we examine the structural and biophysical details of TRPM2, its involvement in neurological and cardiovascular diseases, and its role in inflammation and immune system function. In addition, we provide a comprehensive overview of the current knowledge of TRPM2 signaling pathways in cancer, including its functions in bioenergetics, oxidant defense, autophagy, and response to anticancer drugs.