Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation and Dynamic Volatility Spillover between Green Investing Market, Coal Market, and CO2 Emissions: Evidence from Shenzhen Carbon Market in China

With the continuous expansion scale of carbon market and the development of carbon trading mechanism, carbon emission right, as a new financial asset, is being brought into the category of asset allocation by more and more investors. As the burning of coal is the major source of carbon dioxide, China is facing serious ecological and environmental problems, which restrict the development of low-carbon economy. In order to reach the carbon dioxide emission reduction targets and promote the development of green investment market, the carbon market should be a good emission reduction measure. The correlation and dynamic volatility spillover among coal, carbon, and green investing markets are becoming a hot topic for current research. The paper applies both VAR-GARCH-DCC and VAR-GARCH-BEKK models to draw some significant conclusions. (1) The green investment market, coal market, and Shenzhen carbon market show obvious time-varying correlation, and the volatility of the green investment market is higher. (2) There is a bidirectional Granger causality between green investing and coal markets. (3) The investment portfolio and hedging mechanism of the market are established to reduce the risk and help investors obtain higher returns.
Type III cutaneous atypical meningioma of the scalp

Meningiomas occur rarely in extracranial sites, including the skin, where they may pose a diagnostic challenge because of their histopathologic overlap with several other spindle‐cell tumors. Cutaneous meningiomas are divided into type I (congenital), type II (ectopic), and type III (via direct extension) lesions. We present a rare case of atypical meningioma of the skin in a 71‐year‐old woman who presented with a painful and enlarging lesion on the left central frontal scalp. Biopsy showed bone and soft tissue with involvement of a spindle cell neoplasm, consisting of whorled nests with atypical features, including variably increased mitotic index, areas of hypercellularity, and sheeted architecture. The overall findings were consistent with an atypical meningioma (World Health Organization grade 2). Atypical meningiomas constitute only 5% to 15% of all meningiomas. Magnetic resonance imaging of the skull later demonstrated a left frontal tumor consistent with an atypical meningioma that had eroded through the skull. Dermatopathologists should consider cutaneous meningioma as a differential diagnosis of spindle‐cell neoplasms of the skin and subcutaneous tissue in head and neck.
An Efficient Multilevel Probabilistic Model for Abnormal Traffic Detection in Wireless Sensor Networks

Wireless sensor networks (WSNs) are low-cost, special-purpose networks introduced to resolve various daily life domestic, industrial, and strategic problems. These networks are deployed in such places where the repairments, in most cases, become difficult. The nodes in WSNs, due to their vulnerable nature, are always prone to various potential threats. The deployed environment of WSNs is noncentral, unattended, and administrativeless; therefore, malicious attacks such as distributed denial of service (DDoS) attacks can easily be commenced by the attackers. Most of the DDoS detection systems rely on the analysis of the flow of traffic, ultimately with a conclusion that high traffic may be due to the DDoS attack. On the other hand, legitimate users may produce a larger amount of traffic known, as the flash crowd (FC). Both DDOS and FC are considered abnormal traffic in communication networks. The detection of such abnormal traffic and then separation of DDoS attacks from FC is also a focused challenge. This paper introduces a novel mechanism based on a Bayesian model to detect abnormal data traffic and discriminate DDoS attacks from FC in it. The simulation results prove the effectiveness of the proposed mechanism, compared with the existing systems.
Subgroup analysis for multi-response regression

: Correctly identifying the subgroups in a heterogeneous population has gained increasing popularity in modern big data applications since studying the heterogeneous effect can eliminate the impact of individual differences and make the estimation results more accurate. Despite the fast growing literature , most existing methods mainly focus on the heterogeneous univariate regression and how to precisely identify subgroups in face of multiple responses remains unclear. Here , we develop a new methodology for heterogeneous multi-response regression via a concave pairwise fusion approach , which estimates the coefficient matrix and identifies the subgroup structure jointly. Besides , we provide theoretical guarantees for the proposed methodology by establishing the estimation consistency. Our numerical studies demonstrate the effectiveness of the proposed method.
Predicting Current Potential Distribution and the Range Dynamics of Pomacea canaliculata in China under Global Climate Change

Simple Summary Pomacea canaliculata is one of the 100 worst invasive alien species in the world, causing significant effects and harm to native species, ecological environment, human health, and social economy. In this study, we used species distribution modeling (SDM) methods to predict the potential distribution of P. canaliculata in China and found that with climate change, there would be a trend of expanding and moving northward in the future. Abstract Pomacea canaliculata is one of the 100 worst invasive alien species in the world, which has significant effects and harm to native species, ecological environment, human health, and social economy. Climate change is one of the major causes of species range shifts. With recent climate change, the distribution of P. canaliculata has shifted northward. Understanding the potential distribution under current and future climate conditions will aid in the management of the risk of its invasion and spread. Here, we used species distribution modeling (SDM) methods to predict the potential distribution of P. canaliculata in China, and the jackknife test was used to assess the importance of environmental variables for modeling. Our study found that precipitation of the warmest quarter and maximum temperature in the coldest months played important roles in the distribution of P. canaliculata. With global warming, there will be a trend of expansion and northward movement in the future. This study could provide recommendations for the management and prevention of snail invasion and expansion.
Color measurement of animal source foods

Rapid and objective assessment of food color is necessary in quality control. The color evaluation of animal source foods using a computer vision system (CVS) and a traditional colorimeter is examined. With the same measurement conditions, color results deviated between these two approaches. The color returned by the CVS had a close resemblance to the perceived color of the animal source foods, whereas the colorimeter returned not typical colors. The effectiveness of the CVS is confirmed by the study results. Considering these data, it could be concluded that the colorimeter is not representative method for color analysis of animal source foods, therefore, the color read by the CVS seemed to be more similar to the real ones.
Distinct Glucocorticoid Receptor Actions in Bone Homeostasis and Bone Diseases

Glucocorticoids (GCs) are steroid hormones that respond to stress and the circadian rhythm. Pharmacological GCs are widely used to treat autoimmune and chronic inflammatory diseases despite their adverse effects on bone after long-term therapy. GCs regulate bone homeostasis in a cell-type specific manner, affecting osteoblasts, osteoclasts, and osteocytes. Endogenous physiological and exogenous/excessive GCs act via nuclear receptors, mainly via the GC receptor (GR). Endogenous GCs have anabolic effects on bone mass regulation, while excessive or exogenous GCs can cause detrimental effects on bone. GC-induced osteoporosis (GIO) is a common adverse effect after GC therapy, which increases the risk of fractures. Exogenous GC treatment impairs osteoblastogenesis, survival of the osteoblasts/osteocytes and prolongs the longevity of osteoclasts. Under normal physiological conditions, endogenous GCs are regulated by the circadian rhythm and circadian genes display oscillatory rhythmicity in bone cells. However, exogenous GCs treatment disturbs the circadian rhythm. Recent evidence suggests that the disturbed circadian rhythm by continuous exogenous GCs treatment can in itself hamper bone integrity. GC signaling is also important for fracture healing and rheumatoid arthritis, where crosstalk among several cell types including macrophages and stromal cells is indispensable. This review summarizes the complexity of GC actions via GR in bone cells at cellular and molecular levels, including the effect on circadian rhythmicity, and outlines new therapeutic possibilities for the treatment of their adverse effects.
Prediction of Changeable Eddy Structures around Luzon Strait Using an Artificial Neural Network Model

Mesoscale eddies occur frequently in the Luzon Strait and its adjacent area, and accurate prediction of eddy structure changes is of great significance. In recent years, artificial neural network (ANN) has been widely applied in the study of physical oceanography with the continuous accumulation of satellite remote sensing data. This study adopted an ANN approach to predict the evolution of eddies around the Luzon Strait, based on 25 years of sea level anomaly (SLA) data, 85% of which are used for training and the remaining 15% are reserved for testing. The original SLA data were firstly decomposed into spatial modes (EOFs) and time-dependent principal components (PCs) by the empirical orthogonal function (EOF) analysis. In order to calculate faster and save costs, only the first 35 PCs were selected as predictors, whereas their variance contribution rate reached 96%. The results of predicted reconstruction indicated that the neural network-based model can reliably predict eddy structure evaluations for about 15 days. Importantly, the position and variation of four typical eddy events were reconstructed, and included a cyclone eddy event, an eddy shedding event, an anticyclone eddy event, and an abnormal anticyclone eddy event.
Research on Power Knowledge Platform Method Based on Knowledge Graph

The knowledge graph also known as intelligent database, is an intelligent knowledge management system combining artificial intelligence technology and traditional database. Introducing knowledge graph into power knowledge platform will enable the platform to obtain the ability to mine useful knowledge from a large number of texts, so as to integrate scattered knowledge in power field, improve knowledge utilization and serve users in the platform. This paper starts with the design of intelligent power knowledge platform, that is, the four unification of role, scene, function and knowledge will be realized. The system architecture planning of power knowledge platform is completed, which can be divided into four layers: knowledge acquisition, knowledge base, application layer and customer layer. Based on this, the main function of the knowledge management platform is designed, and the non-functional parameters of the system, including security, expansibility, and reliability parameters are configured. It has important engineering practical significance and engineering application value.
A Preliminary Cross-Sectional Assessment on Exclusive Breastfeeding among Women Attending a Postnatal Clinic at the Holy Family Hospital, Techiman, Ghana

Exclusive breastfeeding (EBF) for the first six months of life has remained low in Ghana, despite strong evidence of benefits to babies/infants that supports the practice. This study is a preliminary assessment of the adherence to EBF among mothers attending the postnatal clinic. Methods: The study was cross-sectional, involving 85 breastfeeding mothers who attended the postnatal clinic at the Holy Family Hospital, Techiman. A structured questionnaire was used to collect data on socio-demographic information and adherence to EBF. A total of 85 mothers and their babies (50 females and 35 males) participated in the study. Out of this total, 45.9% (39/85) were exclusively breastfed, while 54.1% (46/85) were non-exclusively breastfed. About 17.6% of the mothers with Junior High School (JHS) education exclusively breastfed their babies compared to those with Senior High School (SHS) (15.3%) and tertiary (12.9%) (p > 0.05). Babies who were not exclusively breastfed were 12.9% (11/85), 17.6% (15/85), and 23.5% (20/85) for mothers with JHS, SHS, and tertiary education respectively (r = 0.182, p = 0.096). The distribution of weight classification among exclusive and non-exclusively breastfed babies was significantly different (χ2 = 13.583, p 0.001). The findings of this study provide a preliminary assessment of adherence to EBF in Techiman, Ghana. Exclusive breastfeeding practices appear suboptimal. Education and working status of mothers negatively impact adherence on exclusive breastfeeding. Interventions emphasizing the continuous education of mothers could promote exclusive breastfeeding.
A proibição de chute após a cobrança do lateral ou escanteio modifica o comportamento de jogadores de futsal da categoria sub-09?

O estudo objetivou comparar as propriedades de interação e as finalizações em jogos de futsal da categoria sub-09, com a permissão de gol (CT) e sem a permissão de gol (ST), por meio da “tiradinha” (chute direto ao gol após a cobrança de lateral). Foram analisados 10 jogos formais de futsal, seis ST e quatro CT. A Social Network Analysis e a análise notacional foram utilizadas para as análises. Os resultados apontaram maiores interações entre os jogadores nos jogos CT. Observou-se maior número de finalizações em situações originadas dos laterais e escanteios nos jogos CT. Conclui-se que a presença da “tiradinha”, nos jogos de futsal, na categoria sub-09, leva a um aumento da interação entre os jogadores e das finalizações provenientes desta regra. Apesar dos jogos ST apresentarem menores valores nas interações do que CT, sugere-se que treinadores possam utilizar esta regra para estimular a progressão dos jogadores na quadra.
Análise da aplicação de sistemas de recomendação no contexto educacional: revisão sistemática da literatura nacional

Os sistemas de recomendação, apresentados como uma possibilidade de personalização de informações, podem funcionar no contexto educacional para a construção do processo de aprendizagem e formação de usuários, visando o envio de dados (materiais, tipos de conteúdo e estratégias de aprendizagem) que são de interesse. Este trabalho teve como objetivo a construção de uma visão geral dos estudos nacionais que tratam dessa implementação, e também da validação e aplicação de sistemas de recomendação no âmbito educacional nos últimos 7 anos. A análise da revisão sistemática mostrou o modelo Felder-Silverman Learning Styles (FSLSM) e o padrão de metadados IEEE-LOM como as tecnologias mais utilizadas, bem como as técnicas de validação de projetos mais frequentes e outros aspectos gerais do planejamento e do desenvolvimento de sistemas. Os resultados apresentados apontam que, apesar da carência de estudos específicos acerca do uso de sistemas de recomendação para cursos de graduação, a relevância de recursos que melhorem a experiência acadêmica e facilitem o processo de aprendizagem recebe cada vez mais destaque.
Optimal Resource Allocation for Quality-of-Service in D2D Communication Underlying Imperfect CSI

Device-to-device (D2D) communication has become a forefront technology to deal with the escalating demand for wireless network traffic. It allows devices in proximity to transmit data directly without the intervention of the base station for both uplink and downlink procedures. Devices in D2D communication can communicate either in underlay or overlay modes. The overlay D2D communication has a dedicated spectrum, hence reduces the interferences and spectral efficiency. In contrast, the underlay mode improves spectral efficiency with resource reuse. It increases the data rate, power control, and energy efficiency, but can experience co-channel and cross-channel interferences. To mitigate these issues, we propose a Heuristic-based optimal resource allocation scheme for the D2D communication network under imperfect channel state information (CSI). Both cellular and D2D users use the orthogonal frequency division multiple access (OFDMA) scheme for uplink and downlink resources. The proposed scheme significantly improves the average network data rate and optimizes the energy of the cellular network.
Human Amniotic Epithelial Cells Secretome: Components, Bioactivity, and Challenges

Human amniotic epithelial cells (hAECs) derived from placental tissue have received significant attention as a promising tool in regenerative medicine. Several studies demonstrated their anti-inflammatory, anti-fibrotic, and tissue repair potentials. These effects were further shown to be retained in the conditioned medium of hAECs, suggesting their paracrine nature. The concept of utilizing the hAEC-secretome has thus evolved as a therapeutic cell-free option. In this article, we review the different components and constituents of hAEC-secretome and their influence as demonstrated through experimental studies in the current literature. Studies examining the effects of conditioned medium, exosomes, and micro-RNA (miRNA) derived from hAECs are included in this review. The challenges facing the application of this cell-free approach will also be discussed based on the current evidence.
Inverse kinematic optimization of 7R humanoid arm with joint limits

To solve the kinematic optimization problem of the 7R humanoid arm,the closed-form solutions to the inverse kinematics based on the arm angle parameter were established.The range of the feasible arm angle corresponding to joint limits was determined using analytical methods.Besides,a new function was defined,which is used to evaluate the joint angles,and the optimal indexes of joint limits avoidance were transformed into virtual torques.Based on this,the self-optimizing method was proposed to solve the kinematic optimization problem of joint limits avoidance.Simulations demonstrate that the proposed method can determine the feasible range of the arm angle accurately and implement the kinematic optimization of the humanoid arm efficiently.
Towards Understanding Confusion and Affective States Under Communication Failures in Voice-Based Human-Machine Interaction

We present a series of two studies conducted to understand user's affective states during voice-based human-machine interactions. Emphasis is placed on the cases of communication errors or failures. In particular, we are interested in understanding “confusion” in relation with other affective states. The studies consist of two types of tasks: (1) related to communication with a voice-based virtual agent: speaking to the machine and understanding what the machine says, (2) non-communication related, problem-solving tasks where the participants solve puzzles and riddles but are asked to verbally explain the answers to the machine. We collected audio-visual data and self-reports of affective states of the participants. We report results of two studies and analysis of the collected data. The first study was analyzed based on the annotator's observation, and the second study was analyzed based on the self-report.
Effect of Ulinastatin Combined with Xingnaojing Injection on Severe Traumatic Craniocerebral Injury and Its Influence on Oxidative Stress Response and Inflammatory Response

Objective This study is aimed at exploring the effect of ulinastatin combined with Xingnaojing injection on severe traumatic craniocerebral injury and its influence on oxidative stress response and inflammatory response in patients. Methods A total of 100 patients with severe traumatic craniocerebral injury admitted to our hospital from January 2018 to January 2020 were selected and equally assigned into a study group (50 cases) and a control group (50 cases) according to a random sampling method. Patients in study group received treatment of ulinastatin combined with Xingnaojing injection, while those in control group were treated with ulinastatin only. The study compared the two groups on the oxidative stress response, inflammatory response, the therapeutic effect, and the incidence rate of adverse reactions. Results It is observed that patients in study group obtained lower levels of free cortisol (FC) and norepinephrine (NE) in the serum and higher level of total thyroxine (TT4) after treatment compared with those in control group with significant difference (P < 0.05); in the meantime, they were examined to have significantly fewer oxidative stress response products, lower serum inflammatory factor level, and serum indicator levels of craniocerebral injury as opposed to those in control group, suggesting significant differences (P < 0.05); study group demonstrated higher treatment response rate and lower incidence rate of adverse reactions compared with control group with a significant difference (P < 0.05). Conclusion The study found that ulinastatin combined with Xingnaojing infection has a significant effect in the treatment of severe traumatic craniocerebral injury, which can reduce the degree of craniocerebral injury and the level of inflammatory factors in the serum of patients. It is worthy of being promoted and applied clinically.
COVID-19 Spread Forecasting, Mathematical Methods vs. Machine Learning, Moscow Case

To predict the spread of the new coronavirus infection COVID-19, the critical values of spread indicators have been determined for deciding on the introduction of restrictive measures using the city of Moscow as an example. A model was developed using classical methods of mathematical modeling based on exponential regression, the accuracy of the forecast was estimated, and the shortcomings of mathematical methods for predicting the spread of infection for more than two weeks. As a solution to the problem of the accuracy of long-term forecasts for more than two weeks, two models based on machine learning methods are proposed: a recurrent neural network with two layers of long short-term memory (LSTM) blocks and a 1-D convolutional neural network with a description of the choice of an optimization algorithm. The forecast accuracy of ML models was evaluated in comparison with the exponential regression model and one another using the example of data on the number of COVID-19 cases in the city of Moscow.
Minsum node-disjoint paths with time-varying delay functions

The fault tolerance of telecommunication networks is the ability to maintain service continuity in case of accident and attack. Designing disjoint paths to ensure fault tolerance of networks is regarded as one of the most important research problem. Especially in time-dependent networks which is used to the model that the weight of each link varies with time, answering shortest disjoint paths queries and plans in real time is considered to be one of major challenges in many industrial applications. Motivated by this observation, in the first in and first out (FIFO) time-varying network, we investigate the problem of finding two time-varying node-disjoint paths from the source to the destination with the departure time such that the total time-varying delay of the paths is minimised. Based on the time-varying interlacing path in different time-intervals, we develop an effective algorithm to obtain the optimal solutions in time .