Miracle-dz/UniFAR-FTUs huggingface dataset:
Mood Symptoms and Chronic Fatigue Syndrome Due to Relapsing-Remitting Multiple Sclerosis Are Associated with Immune Activation and Aberrations in the Erythron

Background: Multiple sclerosis (MS) is a chronic autoimmune and neuroinflammatory disease of the central nervous system characterized by peripheral activation of immune-inflammatory pathways which culminate in neurotoxicity causing demyelination of central neurons. Nonetheless, the pathophysiology of relapsing-remitting MS (RRMS)-related chronic fatigue, depression, anxiety, cognitive impairments, and autonomic disturbances is not well understood. Objectives: The current study aims to delineate whether the remitted phase of RRMS is accompanied by activated immune-inflammatory pathways and if the latter, coupled with erythron variables, explain the chronic fatigue and mood symptoms due to RRMS. Material and Methods: We recruited 63 MS patients, 55 in the remitted phase of RRMS and 8 with secondary progressive MS, and 30 healthy controls and assessed erythron variables and used a bio-plex assay to measure 27 serum cytokines. Results: A significant part of the MS patients (46%) displayed activation of the immune-inflammatory response (IRS) and compensatory immune response (CIRS) systems, T helper (Th)1 and Th-17 cytokine profiles. Remitted RRMS patients showed increased chronic fatigue, depression, anxiety, physiosomatic, autonomic, and insomnia scores, which could partly be explained by M1 macrophage, Th1, Th-17, growth factor, and CIRS activation, as well as aberrations in the erythron including lowered hematocrit and hemoglobin levels. Conclusions: Around 50% of remitted RRMS patients show activation of immune-inflammatory pathways in association with mood and chronic-fatigue-like symptoms. IRS and CIRS activation as well as the aberrations in the erythron are new drug targets to treat chronic fatigue and affective symptoms due to MS.
Cost-effectiveness of the second COVID-19 booster vaccination in the United States

ABSTRACT Background: On March 29, 2022, the United States (US) authorized the second booster dose of COVID-19 vaccine for individuals aged 50 years and older. To date, the cost-effectiveness of the second booster strategy remains unassessed. Methods: We developed a decision-analytic SEIR-Markov model by five age groups (0-4yrs with 18,827,338 individuals, 5-11yrs with 28,584,443 individuals, 12-17yrs with 26,154,652 individuals, 18-49yrs with 138,769,369 individuals, and 50+yrs with 119,557,943 individuals) to evaluate the cost-effectiveness of the second COVID-19 booster vaccination (administered 4 months after the first booster dose) over an evaluation period of 180 days in the US, from a healthcare system perspective. Results: Implementing the second booster strategy among individuals aged 50+ years would cost US$807 million but reduce direct medical care costs by $1,128 million, corresponding to a benefit-cost ratio of 1.40. This strategy would also result in a gain of 1,048 QALYs during the 180 days, indicating it was cost-saving. Probabilistic sensitivity analysis demonstrated that the probability of being cost-effective with the strategy was 68%. Further, vaccinating individuals aged 18-49 years with the second booster would result in an additional gain of $1,566 million and 2,276 QALYs. Expanding vaccination to individuals aged 12-17 years would result in an additional gain of $15 million and 89 QALYs. Coverage of the first booster vaccination in age groups under 12 was too low to consider the administration of the second booster. If the social interaction between all age groups was severed, vaccination expansion to 18-49yrs and 12-17yrs would no longer be cost-effective. Conclusion: The second booster strategy was likely to be effective and cost-effective in reducing the disease burden of the COVID-19 pandemic. Expanding the second booster strategy to 18-49yrs and 12-17yrs remains cost-effective due to their social contacts with the older age group. Keywords: COVID-19; Second booster; Cost-effective analysis; SEIR-Markov model; Age groups
Algorithm for computing Jordan basis

We propose a novel algorithm to compute a Jordan basis (JB) for an arbitrarily given square matrix. The algorithm is based on the fact that a JB for a linear transformation f is obtained by extending a JB for the restriction of f to its range R ( f ). The main ingredient of the algo-rithm is singular value decomposition, and that ensures backward-stability of the algorithm. To enhance the practical utility, we also introduce an automatic mechanism into the algorithm such that it outputs all possible Jordan structures close to the exact one of the input matrix
Accumulation of health complaints is associated with persistent musculoskeletal pain two years later in adolescents: The Fit Futures study

There is limited knowledge on the association between different health complaints and the development of persistent musculoskeletal pain in adolescents. The aims of this study were to assess whether specific health complaints, and an accumulation of health complaints, in the first year of upper-secondary school, were associated with persistent musculoskeletal pain 2 years later. We used data from a population-based cohort study (the Fit Futures Study in Norway), including 551 adolescents without persistent musculoskeletal pain at baseline. The outcome was persistent musculoskeletal pain (≥3 months) 2 years after inclusion. The following self-reported health complaints were investigated as individual exposures at baseline: asthma, allergic rhinitis, atopic eczema, headache, abdominal pain and psychological distress. We also investigated the association between the accumulated number of self-reported health complaints and persistent musculoskeletal pain 2 years later. Logistic regression analyses estimated adjusted odds ratios (ORs) with 95% confidence intervals (CIs). At the 2-year follow-up, 13.8% (95% CI [11.2–16.9]) reported persistent musculoskeletal pain. Baseline abdominal pain was associated with persistent musculoskeletal pain 2 years later (OR 2.33, 95% CI [1.29–4.19], p = 0.01). Our analyses showed no statistically significant associations between asthma, allergic rhinitis, atopic eczema, headache or psychological distress and persistent musculoskeletal pain at the 2-year follow-up. For the accumulated number of health complaints, a higher odds of persistent musculoskeletal pain at the 2-year follow-up was observed for each additional health complaint at baseline (OR 1.33, 95% CI [1.07–1.66], p = 0.01). Health care providers might need to take preventive actions in adolescents with abdominal pain and in adolescents with an accumulation of health complaints to prevent development of persistent musculoskeletal pain. The potential multimorbidity perspective of adolescent musculoskeletal pain is an important topic for future research to understand the underlying patterns of persistent pain conditions in adolescents.
Glycemic control among patients with type 2 diabetes in a low resource setting in Rwanda: a prospective cohort study

Introduction diabetes is a leading cause of death, disability, and high healthcare costs, especially among patients with poor glycemic control. Providing decentralized diabetes care to patients in low-income countries remains a major challenge. We aimed to assess hemoglobin A1C (HbA1c) level of patients enrolled in primary-level non-communicable disease clinics of Rwamagana, Rwanda, and identify predictors associated with a) change in HbA1c level over a 6-month period or b) achieving HbA1c <7%. We also explored whether living in a community with a home-based care practitioner was associated with HbA1c-related outcomes. Methods we conducted structured interviews and HbA1c testing among patients with type 2 diabetes at baseline and after six months. Multivariable linear regression and multivariable logistic regression were used. Results hundred and thirty (130) participants enrolled at baseline, and 123 patients remained in the study after six months. At baseline, 26% of patients had HbA1c <7%. After 6-months, 37% of patients had HbA1c <7%. Factors correlated with the greatest improvements in HbA1c were having HbA1c >9% at baseline, while factors associated with having HbA1c <7% after six months included older age and having HbA1c <7% at baseline. We did not find significant associations between home-based care practitioners and improvement in HbA1c level or achieving HbA1c <7% Conclusion the number of patients with well-controlled glycemia improved over time during this study but was still low overall. Care provided by home-based care practitioners was not associated with six-month HbA1c outcomes. Enhanced care is needed to achieve glycemia control in primary healthcare settings.
Does charitable giving reduce firms’ willingness to invest in green innovation?

While corporate charitable giving(CG) can help firms obtain external innovation resource support, it can also crowd out internal innovation resources. The purpose of this study is to clarify the mechanism of CG and government green subsidies(GS) on green innovation(GI). In this regard, we integrated signaling theory and principal-agent theory to provide a new theoretical perspective for simultaneously focus on the impact of external resource acquisition and internal resource allocation on GI. We conducted a threshold regression analysis on the balanced panel data of 863 listed companies of China from 2016 to 2019 to clarify the input boundary between the promoting and inhibiting effects of corporate CG on corporate GI. And we further explored the relationship between GS and GI under the effect of different CG thresholds. Our findings indicate that there is an inverted U-shaped threshold effect of CG on GI. The impact of GS on GI shows a decreasing marginal benefit as the intensity of CG increases. Based on the findings, we propose corresponding countermeasures for the management of enterprises and the government.
Mean-Field Method for Analysis of Effects of Ordering and Absorption of Flexible-Chains in Surface Polymer Layers

The proposed model employed is an adsorbed flexible macromolecule with an arbitrary contour length placed in an effective mean field created by intermolecular interactions with other polymer chains. It is shown that there exists a first order phase transition of the chain from the absorbed state to elongated one. This transition is accompanied by а sharp growing of fluctuations, a presence of intermediate metastable states, and jumps of degree of elongation, energy and heat-capacity of chains.
A Semi-Supervised Contrastive Learning Approach to Alzheimers Disease Diagnostics using Convolutional Autoencoders

Alzheimers Disease (AD) is a neurodegenerative disease that progressively deteriorates memory and cognitive abilities. PET 18F-AV45 (florbetapir) is a common imaging modality used to characterize the distribution of beta-amyloid deposits in the brain, however interpretation may be subjective and the misdiagnosis rate of AD ranges from 12-23%. Automated algorithms for PET 18F-AV45 interpretation including those derived from deep learning may facilitate more objective and accurate AD diagnosis. A total of 1232 PET AV45 scans (207 - AD; 1025 - normal) were obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI). A semi-supervised deep learning framework was developed to differentiate AD and normal patients. The framework consists of an autoencoder (AE), a contrastive learning loss, and a categorical classification head. A contrastive learning paradigm is used to improve the discriminative properties of latent feature vectors in multidimensional space. Upon five-fold cross-validation, the best-performing semi-supervised contrastive model achieved validation accuracy of 82% to 86%. Secondary analysis included visualization of intermediate activations, classification report verification, and principal component analysis (PCA) of latent feature vectors. The training process yielded optimal converging losses for all three loss frameworks. A deep learning model can accurately diagnose AD using PET 18F-AV45 scans. Such models require large amounts of labeled data during training. The use of a semi-supervised contrastive learning objective and AE regularizer helps to improve model performance, especially when dataset sizes are constrained. Latent representations extracted by the model are visually clustered strongly with the addition of a contrastive learning mechanism.
System design of the compact IR space imaging system MIRIS

Multi-purpose Infra-Red Imaging System (MIRIS) is the main payload of the Korea Science and Technology Satellite-3 (STSAT-3), which is being developed by Korea Astronomy & Space Science Institute (KASI). MIRIS is a small space telescope mainly for astronomical survey observations in the near infrared wavelengths of 0.9~2 μm. A compact wide field (3.67 x 3.67 degree) optical design has been studied using a 256 x 256 Teledyne PICNIC FPA IR sensor with a pixel scale of 51.6 arcsec. The passive cooling technique is applied to maintain telescope temperature below 200 K with a cold shutter in the filter wheel for accurate dark calibration and to reach required sensitivity, and a micro stirling cooler is employed to cool down the IR detector array below 100K in a cold box. The science mission of the MIRIS is to survey the Galactic plane in the emission line of Paschen-α (Paα, 1.88 μ;m) and to detect the cosmic infrared background (CIB) radiation. Comparing the Paα map with the Hα data from ground-based surveys, we can probe the origin of the warm-ionized medium (WIM) of the Galaxy. The CIB is being suspected to be originated from the first generation stars of the Universe and we will test this hypothesis by comparing the fluctuations in I (0.9~1.2 um) and H (1.2~2.0 um) bands to search the red shifted Lyman cutoff signature. Recent progress of the MIRIS imaging system design will be presented.
Medical Image Segmentation and Reconstruction Based on Bayesian Level Set Method and Marching Cubes Algorithm

We propose an approach, integrating Bayesian level set method with modified marching cubes algorithm for brain tissue and tumor segmentation and surface reconstruction. First, we extend the level set method based on the Bayesian risk to three-dimensional segmentation. Then, the three-dimensional Bayesian level set method is used to segment solid three-dimensional targets (e.g., tissue, whole brain, or tumor) from serial slice of medical images. Finally, the modified marching cubes algorithm is used to continuously reconstruct the surface of targets. Since each step can definitely obtain an appropriate treatment by statistical tests, the tissue and tumor segmentation and surface reconstruction are expected to be satisfied.
Exploring the motivational states of English learning among Chinese EFL learners at tertiary-level: A perspective of Directed Motivational Currents

Under the framework of Directed Motivational Currents (DMCs), the present study aimed to explore the motivational dynamics of Chinese tertiary-level EFL learners’ English learning and to identify the possible parameters that influence Chinese EFL learners’ DMC-typed motivational states in their English learning. Data were collected from 10 focal Chinese tertiary-level EFL learners through reflective journal, trajectory equivalent modeling and semi-structured interview over a two-semester-long IELTS training course. The collected data were examined with thematic analysis and the findings indicated that: (1) Chinese tertiary-level EFL learners experienced the clear DMC-typed motivational surges during their journey of English learning; (2) Chinese tertiary-level EFL learners’ DMCs states were affected by various contextual factors which could be examined under three major themes, namely important others, instructional elements, and the exam pressure. The findings not only add to the literature on the validity of DMCs construct in the Chinese EFL context, but offer implications about how to facilitate the DMC-typed motivations in the classroom language instruction.
A multi-category scanning acoustic image dataset: design, collection, and evaluation

Scanning Acoustic Microscopy (SAM) is an essential tool for the non-destructively deriving depth-specific information, which can detect the internal morphology and defect location of Integrated Circuit (IC) sensitively. When SAM equipment works, it will generate a large number of acoustic scanning images, which provide data support for defect detection algorithm. However, due to lack of professional researchers to uniformly sort and classify these acoustic scanning data and the deficiency of standard and available acoustic scanning image datasets, it is impossible to carry out the research on intelligent detection algorithm. In order to solve the above problems, a novel Multi-category Scanning Acoustic Image (MSAI) database is presented in this paper. MSAI database including 4565 acoustic scanning images acquired from 52 products in four packaging structures (BGA, QFN, SOIC and SOP). In order to prove the availability of MSAI database, four typical convolutional neural networks are used to identify and classify the MSAI database. The experimental results show that the VGG-16 model achieves the best classification performance in IC packaging structures grading, which show a train accuracy of 99.58% and a test accuracy of 99.44%, and all network models show a good inter-class separability on MSAI database.
Efficient relative pose estimation with an RGB-D camera in indoor environment

In this paper, we propose an efficient framework to estimate the motion of a robot when it is navigating indoors. Particularly, we mainly focus on the case where the robot is mounted with an RGB-D camera, and the environments have a lot of planar structures such as the floor, ceiling and walls. The novelties of our method lie in three-folds. First, an efficient normal vector extraction method for planes is proposed to fully make use of the planar structures. Without fitting planes, we only need to use the inverse-depth induced histograms to get the dominant planar structures as well as the normal of the planes. Second, since the robot is roaming indoors, we assume that the environment satisfies the weak Manhattan world constraint, i.e., the floor and ceiling are perpendicular to the walls. It is reasonable in our real world. Based on this assumption, we can calculate the relative camera motion by aligning the current local frame with respect to the world coordinate. Third, we present real-world RGB-D datasets that satisfy the weak Manhattan world constraint including 5,737 images, which make contributions to the community. Extensive experimental results show a very promising performance of our method in terms of accuracy, robustness and efficiency, especially in large-scale lowtextured scenes.
An optimization algorithm for TSP-like problem in warehouse picking

In order to solve the problem of the order picking optimization in warehouse line, this study puts forward the concept of class TSP (Travelling Salesman Problem) based on a model using graph theory knowledge modelling. With the aid of the thought of classical TSP, it puts forward three principles, based on a fixed starting point (not repeatable) integer programming model; The solution of the model is also based on a simple optimization algorithm (vertex translocation method) of the classical TSP, and the appropriate modification is made. For the problem of unfixed starting point, the global traversal algorithm is added. Through the reconstruction of the line sequence, a TSP-like problem is successfully transformed into a TSP, and a model is finally established, which is a prominent innovation point of this paper. The calculation method of adjacency matrix W is given in the postscript.
Online learning effect analysis based on machine learning

Online learning has grown in popularity due to the advancement of Internet technology, and how to accurately assess its efficacy has emerged as a major challenge. Online learning provides advantages and benefits in terms of enhancing people’s learning effectiveness and cognitive approach, but it also faces numerous obstacles as a new learning technique and educational model. To increase the efficacy of online learning, we assess and investigate the existing state, shortcomings, learning effects, and behavioral evaluation of online learning in this research.
Research on a new improved UAV anti-jamming and optimization model

In complex environments, UAV wireless sensing networks suffer from path loss, incomplete channels, swarm access security and other problems, and the signal fading and packet loss rates are very obvious. In order to solve the problem of user control data security, this paper gives full play to the characteristics of blockchain technology and builds a blockchain-based UAV cluster anti-interference communication network in UAV cluster applications to achieve antiinterference performance. Meanwhile, Markov decision process is used to optimize and realize the research of UAV airground integrated radio wave anti-interference.
A Technique and a Scale for Loudness Measurement

A method of loudness scaling is presented for obtaining a true ratio scale of loudness. Two separate loudness functions are required, one based on equisection judgments and the other based on fractionation judgments. Interrelation of these two scales allows determination of all constants necessary for a ratio scale. The method provides validation of the assumptions underlying its use. Experimental data are also presented which show that (1) the method is valid for individual observers; (2) differences between loudness scales of different observers are considerably reduced with the method; and (3) the loudness function obtained with this method is much flatter than the standard scale now used. It is then shown that this new loudness scale is in better agreement with other data on equal loudness intervals than the standard scale. It is also shown that this scale is in agreement with monaural‐binaural loudness data and data on loudness of multicomponent tones.
Outlier Detection Using Support Vector Machines

실생활에서 얻어지는 자료에서 근사함수를 구성하기 위하여 모델링을 하기 전에 측정된 원자료로부터 이상치를 제거하는 것이 필요하다. 기존의 이상치 진단의 방법들은 시각화나 최대 잔차들을 이용해왔다. 그러나 종종 다차원의 입력자료를 가지는 비선형 함수에 대한 이상치 진단은 좋지 않은 결과를 얻었다. 다차원 입력자료를 갖는 비선형 함수에 대한 전형적인 서포트 벡터 회귀에 기초한 이상치 진단방법들은 좋은 수행능력을 얻어지지만, 계산비용이나 모수들의 보정 등의 실질적인 문제점들을 가지고 있다. 본 논문에서 계산비용을 감소하고 이상치의 문턱을 적절히 정의하는 서포트 벡터 회귀를 이용한 이상치 진단의 실질적인 방법을 제안한다. 제안한 방법을 실제자료들에 적용하여 타당성을 보일 것이다.
Comparison of two-classification models based on neural network for DGA domain name detection

Many malware families could generate a huge number of pseudo-random domain names through DGAs (Domain Generation Algorithm). Using DGA domain name to take DDoS (Distributed Denial of Service) attacks makes network defenses more difficult. So detection of DGA domain name has become an important research in network security, and methods based on neural network have been explored. By extracting different character features of domain name in character-level word embedding, this paper compared the performance between CNN (Convolutional Neural Network) and Bi-LSTM (Bi-Directional Long Short-Term Memory) in two-classification of DGA domain name. Experiment results show that using character features including semantic features could improve the performance of neural network, and there is little difference between CNN and Bi-LSTM in DGA domain name detection.