Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the interior structure of gaseous giant exoplanets with machine learning techniques

Context. Characterizing the interiors of gaseous giant exoplanets is currently one of the main objectives in exoplanetary sciences. In particular, the planetary heavy-element mass provides a critical constraint on planet formation from exoplanetary systems. However, gas giant exoplanets show large diversities in thermal states and their interior properties vary across a wide magnitude range. Forward modeling of their interiors exhibits a larger degeneracy with respect to rocky exoplanets. Aims. We applied machine learning techniques based on mixture density networks (MDNs) to investigate the interiors of gaseous giant exoplanets. We aim to provide a well-trained MDN for quick and efﬁcient predictions. Methods. Based on our current knowledge of gas giants in the Solar System, we discussed an effect of model uncertainties on planetary interiors and presented a data set for gas giants with masses between 0.1 and 10 Jupiter masses using two-layer interior models. Then, MDNs were constructed to train the generated data set and their performance was evaluated in order to achieve a well-trained one. Results. The MDN using planetary mass and radius as inputs exhibits the well-known degeneracy of interior models. The surface temperature of a planet bears constraints on the thermal state of planetary interiors, and adding it as additional input considerably breaks the degeneracy of possible interior structures. The MDN with inputs of mass, radius, and surface temperature is found to show excellent performance in predicting the interior properties of gaseous giant exoplanets, although these interior properties span over a very wide range. We also applied the well-trained MDN to four gas giants in the Solar System and beyond. The MDN predictions are in good agreement with the interior model solutions within the observational and systematic uncertainties. Conclusions. We offer a convenient and powerful tool available online providing knowledge of the interiors of gaseous giant exo-planets in addition to rocky exoplanets, which could be helpful for our understanding of planet formation in diverse protoplanetary environments.
Assessing the generational impact of COVID-19 using National Transfer Accounts (NTAs)

An important aspect of the current COVID-19 crisis is that not all age groups are equally affected by the pandemic. To account for the generational impact of COVID- 19, a dynamic overlapping generations model with realistic demography, human capital and NTAs is constructed. The COVID-19 crisis is modelled through two unexpected and temporary negative shocks: an economic shock that reduces labour income, and a demographic shock that increases the mortality hazard rates of those infected. The model is applied to 12 countries for which full NTA data are available. Results are presented for two extreme fiscal policies: one in which governments compensate workers for 0% (without fiscal support) of their total labour income losses due to the pandemic, and another in which governments compensate workers for 100% (with fiscal support) of these losses. In addition, I analyse the impact of these policies on public debt. The results show that COVID-19 is affecting the financial situations of people aged 25 to 64 and their children more than those of older people. By compensating workers for their income losses, the economic impact of COVID-19 has been more evenly distributed across cohorts, reducing the burden on people aged zero to 64, and increasing the burden on people aged 65 and older. Moreover, the simulation results show that a 1% decline in labour income leads to an average increase in the debt-to-total labour income ratio of between 1.2% (without fiscal policy) and 1.6% (with fiscal policy).
Driving intentions identification and behaviors prediction in car lane change

In order to realize the auxiliary driving and achieve the goal of improving the active safety,an integrated model based on the artificial neural network and the hidden Markov chain was proposed to identify and predict the driving intentions and behaviors.Such a model was established with the double-lane change and straight line data obtained from driving simulator.This model was used to identify and predict the driving intentions and behaviors under this three condition,such as emergency turning,normal turning,and straight line running.Experimental results show that the model can accurately identify the current driving intention and accurately predict the next driving behavior under a given speed.
Mitochondrial Processing Peptidases—Structure, Function and the Role in Human Diseases

Mitochondrial proteins are encoded by both nuclear and mitochondrial DNA. While some of the essential subunits of the oxidative phosphorylation (OXPHOS) complexes responsible for cellular ATP production are synthesized directly in the mitochondria, most mitochondrial proteins are first translated in the cytosol and then imported into the organelle using a sophisticated transport system. These proteins are directed mainly by targeting presequences at their N-termini. These presequences need to be cleaved to allow the proper folding and assembly of the pre-proteins into functional protein complexes. In the mitochondria, the presequences are removed by several processing peptidases, including the mitochondrial processing peptidase (MPP), the inner membrane processing peptidase (IMP), the inter-membrane processing peptidase (MIP), and the mitochondrial rhomboid protease (Pcp1/PARL). Their proper functioning is essential for mitochondrial homeostasis as the disruption of any of them is lethal in yeast and severely impacts the lifespan and survival in humans. In this review, we focus on characterizing the structure, function, and substrate specificities of mitochondrial processing peptidases, as well as the connection of their malfunctions to severe human diseases.
Network intrusion detection based on ensemble classification and feature selection method for cloud computing

Cloud computing security is the most critical factor for providers, cloud users, and organizations. The various novel approaches apply host‐based or network‐based methods to increase cloud security performance and detection rate. However, due to the virtual and distributed environment of the cloud, conventional network intrusion detection systems (NIDS) have been unreliable in handling these security attacks. Therefore, we design a methodology that incorporates feature selection and classification using ensemble techniques to provide efficient and accurate intrusion detection to address these problems. This proposed model combines the three most effective feature selection techniques (gain‐ratio, chi‐squared, and information gain) to offer a qualifying result and four top classifiers (SVM, LR, NB, and DT) using enhanced weighted majority voting. Moreover, we proposed an experimental technique using a new dataset called Honeypot. All experiments utilized three datasets: Honeypots, Kyoto, and NSL: KDD. In addition, the results of this experimental study were compared with other approaches and performed the statistical significance analysis. Finally, the results reveal that the proposed intrusion detection based on the Honeypot dataset was better and more efficient than other methods because we have an accuracy of 98.29%, FAR of 0.012%, DR of 97.9%, and AUC = 0.9921.
Effects of Verbal Encouragements on Selected Measures of Physical Fitness and Subjective Effort Perception in Young High School Students

: This study aimed to examine the effects of verbal encouragements (VE) on linear sprint speed, jump height, change of direction (CoD) speed, and subjective perception of effort among high school students. Twenty-three high school male students volunteered to participate in this study (age = 19.70 ± 1.06 years). Participants were evaluated during two separate sessions (first session with VE and second session without VE) in a randomized order. The teacher provided VE during each physical test. Jump height [squat jump (SJ), countermovement jump (CMJ), five jump test (FJT)], linear sprint speed (10-m and 30-m sprint), and CoD speed (30-m with CoD) were performed during the two sessions. The rating of perceived exertion (RPE) was recorded after each session. Results indicated that VE induced moderate but sig-nificant improvement in 10-m (ES = 0.71, P = 0.023) and 30-m (ES = 0.76, P = 0.016) sprint speed. However, the 30-m sprint with CoD did not change across conditions (P > 0.05). For jump tests, significant moderate-to-large increases were observed under the VE compared with the non-VE condition (ES = 0.65 to 0.90; P = 0.005 to 0.037). Findings showed a large increase in effort perception following VE compared to the non-VE condition (ES = 1.54, P < 0.001). In conclusion, the findings of this study indicated that VE has a positive impact on measures of physical fitness but increases effort perception.
Protective efficacy of Capsicum frutescens fruits in pancreatic, hepatic and renal cell injury and their attenuation of oxidative stress in diabetic Wistar rats

According to our insight, it is the first investigation based on a literature review to establish the oxidative stress defense and hypoglycemic effects of Capsicum frutescens fruits. The anti-hyperglycemic effect of acetone extract of the fruits of C. frutescens (CFA) was imposed at 250mg/kg and 500mg/kg for 21 days in diabetic-induced Wistar rats. The rats were divided into five groups as control, diabetic, diabetic receiving glibenclamide, and two diabetic receiving CFA-treatment groups. Beginning on the 4th day and up to the 21st day of therapy, CFA at 250mg/kg and 500mg/kg demonstrated a substantial decrease in Blood Glucose Levels (BGL). CFA at 500mg/kg led to a considerable decrease in Low-Density Lipoprotein (LDL) cholesterol and a substantial increase in High-Density Lipoprotein (HDL) cholesterol, showing that CFA possesses anti-diabetic properties. Histopathological examinations reveal hepatic, pancreatic, and renal regularization. As a result, the current study clearly demonstrated more important antidiabetic activity of CFA extracts.
Computer Modelling and Simulation

Background: Computer simulations can be briefly described as the use of computers to represent the dynamic responses of a tested system, by observing the behavior of another, artificial, system modeled on basis of the test system. Physical modeling has been practiced unconsciously since the beginning of the human species, that is, from the moment when the human mind began to understand all the complexity of phenomena and things that surrounded it in nature. Objective : The aim of this paper is to analyze the implementation of computer's simulation and modeling for educational agenda. Methods : This article is a review of the entire to use models, modeling and simulation by using compiters and ICT for teaching system in undergraduate, postgraduate and continuing education using published information, as well as professional papers and publications about this topic. The simulation methodology is based on computer science, statistics, numerical mathematics, operational research and artificial intelligence, but today it is sufficiently coherent and developed that it can rightly be called a separate scientific discipline. The system model represents a simplified and idealized (abstract) image of a real system. In other words, a model is a description of a real system with all its characteristics that are relevant from our point of view. Results and Discussion: Application of computer simulations in science: Different simulations are used to meet specific requirements of the scientific field and the problem such as: Numerical simulation of a differential equation, which cannot be solved analytically. This category includes theories dealing with continuous systems, such as phenomena in physical cosmology, fluid dynamics ; Stochastic simulation, used for discrete systems, where changes occur on the basis of probability, and cannot be explained by equations. These include genetic changes, and biochemical and genetic regulatory processes; Modeling of molecules and their behavior for the purpose of creating drugs; CFD–Computational Fluid Dynamics. –computers are used to perform calculations, which describe the behavior of liquids and gases; Blue Brain project; Cognitive architecture and Movement of parasites in the human body. Conclusion: This paper presents explanations and examples of the application of computer simulations in order to solve everyday problems encountered, both in medicine and all other branches of science. Conclusion: Computer simulations are an invaluable blend of nature and technology, and are one of the main sources of hope for understanding and improving the world we live in.
Active Flutter Suppression and Aeroelastic Response of Functionally Graded Multilayer Graphene Nanoplatelet Reinforced Plates with Piezoelectric Patch

This paper investigates the aeroelastic flutter and vibration reduction of functionally graded (FG) multilayer graphene nanoplatelets (GPLs) reinforced composite plates with piezoelectric patch subjected to supersonic flow. Activated by the control voltage, the piezoelectric patch can generate the active mass and active stiffness that can accordingly increase the base plate’s stiffness and mass. As a result, it changes the GPLs reinforced plate’s dynamic characteristics. The motion equation of the plate-piezoelectric system is derived through the Hamilton principle. Based on the modified Halpin–Tsai model, the effects of graphene nanoplatelets weight fraction and distribution pattern on the dynamic behaviors of the plate are numerically studied in detail. The result illustrates that adding a few amounts of grapheme nanoplatelets can effectually enhance the aeroelastic properties of the plates. Two kinds of control strategies, including the displacement and acceleration feedback control, are applied to suppress the occurrence of the flutter of the plate. It shows that the displacement and acceleration feedback control can improve the critical flutter Mach number of the plate by attaching active stiffness and active mass, respectively. Furthermore, the combined displacement and acceleration feedback control has a better control effect than that of considering only one of them.
Part of Speech Tagging Pada Teks Bahasa Indonesia dengan BiLSTM + CNN + CRF dan ELMo

Part of Speech Tagging atau POS Tag merupakan salah satu proses untuk mengelompokkan kata berdasarkan kelas kata seperti: kata benda, kata kerja, atau kata sambung. Kegunaan POS Tag antara lain dapat bermanfaat pada analisis sentimen, pengenalan entitas bernama, dan konversi teks ke suara. Dalam melakukan POS Tag, jika dilakukan secara manual dapat menghabiskan banyak waktu, oleh karena itu dibuatlah sistem berbasis machine learning untuk mengotomatisasi proses ini. Pada penelitian ini, dilakukan POS Tag dengan menerapkan transfer learning dengan model Embedding from Language Model (ELMo). Model ELMo cukup populer digunakan pada dataset bahasa Inggris karena dapat memberi hasil akurasi yang memuaskan namun pada dataset bahasa Indonesia belum ada paper yang membahas tentang model ini. Melalui penelitian ini ingin dilihat bagaimana performa ELMo pada dataset bahasa Indonesia. Model yang digunakan untuk menyelesaikan masalah POS Tag adalah model berbasis BiLSTM. Pada penelitian ini, juga ingin dilihat bagaimana performa model jika ditambahkan CNN setelah BiLSTM. Selain itu, juga diteliti bagaimana performa dari tiap lapisan ELMo. Dari penelitian ini diperoleh bahwa metode BiLSTM + CNN + CRF dengan embedding ELMo lapisan pertama memiliki akurasi terbaik, dengan nilai 95.62%. Selain itu, diperoleh bahwa penambahan CNN setelah BiLSTM dapat meningkatkan akurasi serta mengurangi overfitting pada masalah POS Tag bahasa Indonesia.
Classification of the weather images with the proposed hybrid model using deep learning, SVM classifier, and mRMR feature selection methods

Abstract As in many fields, the use of artificial intelligence methods in the classification of weather images will be very useful. In this study, a data set consisting of five classes such as cloudy, foggy, rainy, shine, and sunrise was used. A hybrid model has been developed to classify the images in the dataset. First of all, the features of the images in the dataset are obtained by using MobilenetV2, Densenet201, and Efficientnetb0 architectures, which are the most popular Convolutional Neural Network (CNN) architectures. These features are combined and optimized so that these optimized features are classified in the Support Vector Machine (SVM) classifier, one of the most popular classifier methods in machine learning. As a result, the developed hybrid model has outperformed the existing pre-trained architectures in the study. In addition, it has been proven that classification by concatenating the features obtained with CNN architectures is a successful method.
Digital Life Data in the Clinical Whitespace

In our increasingly digital world, aspects of our lives are encoded in the routine interactions we have with technology. Over the past few years, psychologists and technologists have been exploring what possibilities these digital life data might hold for improving mental health and well-being. Here I examine some of the recent advances in this field, particularly in the use of language data; consider the ethical and pragmatic implications of this technology; and examine a few areas where I believe these advances could significantly alter the way in which mental health and well-being are approached. This technology holds special promise for providing information about a patient’s life in between clinical encounters, in the clinical whitespace.
Associations Between Prenatal Exposure to Serotonergic Medications and Biobehavioral Stress Regulation: Protocol for a Systematic Review and Meta-analysis

Background Up to 20% of mothers experience antenatal depression and approximately 30% of these women are treated with serotonergic psychotropic pharmacological therapy during pregnancy. Serotonergic antidepressants readily cross the placenta and the fetal blood-brain barrier, altering central synaptic serotonin signaling and potentially altering serotonin levels in the developing fetal brain. Objective The aim of this study is to assess the impact of prenatal exposure to serotonergic antidepressants, accounting for maternal mood disturbances, on markers of stress regulation during childhood. Methods We will follow PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and will search MEDLINE, Embase, CINAHL, PsycINFO, and ClinicalTrials.gov for full-length studies that assessed physiological (eg, cortisol level, heart rate variability, salivary amylase, pupillary size, C-reactive protein) indices of stress regulation in children of pregnant people who were treated with a serotonergic antidepressant at any point during pregnancy. We will assess the quality of observational studies using the Newcastle-Ottawa Scale and the quality of experimental studies using the Cochrane risk-of-bias tool. When possible, we will conduct a random-effects meta-analysis. If meta-analysis is not possible, we will conduct a narrative review. If a sufficient number of studies are found, we will perform subgroup analysis and assess outcomes measured by drug class, dose, trimester of exposure, and child’s age and gender. Results We registered our review protocol with PROSPERO (International Prospective Register of Systematic Reviews; CRD42021275750), completed the literature search, and initiated title and abstract review in August 2021. We expect to finalize this review by April 2022. Conclusions Findings should identify the impact of prenatal antidepressant effects on stress regulation and distinguish it from the impact of prenatal exposure to maternal mood disturbances. This review should inform decisions about serotonergic antidepressant use during pregnancy. Trial Registration PROSPERO CRD42021275750; https://www.crd. york.ac. uk/prospero/display_record. php? RecordID=275750 International Registered Report Identifier (IRRID) PRR1-10.2196/33363
Hybrid cylindrical dielectric resonator antenna with monopole for tri‐polarized operation

A wideband tri‐polarized antenna is presented in this letter. The proposed hybrid structure consists of a cylindrical dielectric resonator (DR) and a copper cylinder. Tri‐polarized operation is achieved by exciting the HE11δx and HE11δy modes of the DR, and the λ/4 monopole mode of the copper cylinder. The resonant frequency of the monopole mode is well tuned to work cooperatively with the two HE11δ modes. Only one single substrate layer is required to integrate the three balanced feedings. A prototype of the proposed antenna is built and tested. The measured overlapping impedance bandwidth of the three ports is 410 MHz (2.38–2.79 GHz), and the isolation among the three ports is also less than −27 dB across the bandwidth. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:635–638, 2016
Cyber Security Management of Access Controls in Digital Ecosystems and Distributed Environments

Resource and information protection plays a significant role in engaging the enterprises to actively participate inside a Digital Ecosystem or collaborative distributed digital environment. However, current developments of such security mechanisms for protecting these resources are still in their infancy. Most of the proposed protection frameworks do not provide a scalable and effective mechanism for multiple interacting entities in particular. This is even a greater issue when multiple resources are exchanged and shared in an open and dynamic environment. This paper proposes a distributed mechanism for individual enterprises to manage their own authorization processes and information access permissions with the aim of providing rigorous protection of enterprise resources.
Assessing Performance of the Generalized Exponential Model in the Presence of the Interval Censored Data with Covariate

This study aims to extend the generalized exponential model (GEM) to include covariates in the presence of interval-censored data. The maximum likelihood estimator (MLE) was obtained for the parameter of the model formulated. Afterward, a thorough simulation study was carried out to evaluate the estimator's performance based on the values of bias, standard error (SE), and root mean square error (RMSE). The result indicated that the (SE) and (RMSE) decrease with the increase in sample sizes and decrease in censoring proportions. Finally, the performance of the Wald confidence interval estimation technique for the GE model with interval-censored data covariate was assessed by a coverage probability study at several censoring proportions and different sample sizes.