Miracle-dz/UniFAR-FTUs huggingface dataset:
Formation Mechanism of Arcuate Tectonic Structures around Northeast Tibetan Plateau: Insight from 3‐D Numerical Modeling

The arcuate tectonic structure is a prominent feature of the northeast Tibetan plateau, the dynamics of which is still not well constrained. In this study, 3‐D high‐resolution numerical models are systematically conducted to investigate the arcuate tectonics and dynamics. The models highly emphasize the significance of lateral heterogeneities, indicating that the pre‐existing strong blocks within the deforming plate strongly control the formation of arcuate tectonics. The laterally changing thickness of sediment strata or varying geometry of inherited faults within the deforming plate can also contribute but does not play crucial roles. On the other hand, the non‐uniform shape of the leading edge of the indenting plate only leads to the formation of arcuate tectonics close to the indenter. Finally, the numerical models are systematically compared with the arcuate tectonic structures around the northeast Tibetan plateau and other possible regions.
Interpretable Saliency Map for Deep Reinforcement Learning

Deep reinforcement learning (deep RL) achieved big successes with the advantage of deep learning techniques, while it also introduces the disadvantage of the model interpretability. Bad interpretability is a great obstacle for deep RL to be applied in real situations or human-machine interaction situations. Borrowed from the deep learning field, the techniques of saliency maps recently become popular to improve the interpretability of deep RL. However, the saliency maps still cannot provide specific and clear enough model interpretations for the behavior of deep RL agents. In this paper, we propose to use hierarchical conceptual embedding techniques to introduce prior-knowledge in the deep neural network (DNN) based models of deep RL agents and then generate the saliency maps for all the embedded factors. As a result, we can track and discover the important factors that influence the decisions of deep RL agents.
Sum Rate and Reliability Analysis for Power-Domain Nonorthogonal Multiple Access (PD-NOMA)

Nonorthogonal multiple access (NOMA) is seen as an important technology for tomorrow’s Internet-of-Things (IoT) systems. In uplink power-domain NOMA (PD-NOMA), allocating the uplink transmit power of the IoT devices is important to maximize both the sum rate and the reliability of devices. However, it is challenging to optimize the uplink transmit power when the received signal power is affected by a random fading channel. Hence, in this article, the problem of uplink transmit power assignment is studied for a wireless network with PD-NOMA that serves uplink IoT services. This is posed as a problem of determining the target received signal power at the base station (BS) so that the reliability and upper bound of sum rate of the users are jointly maximized, where the received signal power at the BS is unknown to the devices due to Nakagami-<inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading channel. To find an optimal allocation of the lower and higher target received power values for the devices using PD-NOMA, the reliability and upper bound of sum rate are derived in terms of target received power values and power difference threshold. For a special case of Nakagami-<inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading channel, the theoretical analysis shows that the highest reliability and the highest upper bound of sum rate are achieved, when the target received power values are highest. For a general Nakagami-<inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading channel, simulation results show that there is a tradeoff between reliability and sum-rate upper bound and, thus, allocation of lower and higher target received power values is necessary to satisfy the communication requirements of IoT devices. Moreover, for a special case of Nakagami-<inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading channel, simulation results show that the derived optimal transmit power achieves the optimal sum-rate upper bound and reliability, and the target received power values of two devices must be highest for the maximum upper bound of sum rate and reliability. Furthermore, in simulation results, increasing the lower and higher target received power values increases both the upper bound of sum rate and reliability.
Effect of Medium Composition on In vitro Seed Germination and Plant Development in Kentucky Bluegrass (Poa pratensis L. cv. Evora)

: In the current study, the effects of De Greef & Jacobs (DG), Linsmaier & Skoog (LS), Murashige & Skoog (MS), and Schenk & Hildebrandt (SH) media were tested on seed germination and plant development in Poa pratensis cv. Evora. The highest germination rate (83±2.74%) was found on SH medium, whereas LS medium gave the lowest (46±4.18%) germination rate. The statistically same leaf numbers were recorded from SH (2.10±0.27) and DG (2.12±0.18) media. SH and DG media gave 4.28±0.28 cm and 4.16±0.31 cm mean leaf lengths, respectively. SH medium gave the maximum mean root number (3.09±0.26). However, the LS medium gave the lowest mean root number (1.84±0.10). The longest roots (1.43±0.19 cm) were observed in the plants grown in SH medium. However, DG medium had the minimum mean root length (0.81±0.08 cm). In conclusion, SH medium should be preferred over the other medium tested in in vitro tissue culture studies on the species to increase the biomass production yield. The efficacy of SH medium in the in vitro propagation of P. pratensis could be increased using growth promoters. cm) vermiştir. Sonuç olarak, biyokütle üretimini artırmak için bu tür üzerinde yapılan in vitro doku kültürü çalışmalarında test edilen diğer besiyerlerine kıyasla SH besiyeri tercih edilmelidir. SH ortamının P. pratensis 'in in vitro gelişimindeki etkinliği büyüme destekleyicileri kullanılarak artırılabilir. Anahtar kelimeler: Makroelementler; mikroelementler; doku kültürü; çim; vitaminler.
China’s Carbon Neutrality Policy: Objectives, Impacts and Paths

5 China’s Carbon Neutrality Policy: Objectives, Impacts and Paths by DONG Liang, MIAO Gaoyi and WEN Weigang China will face various challenges and multiple opportunities in achieving carbon neutrality due to the impact of COVID-19 and the nature of its economic activities. Its specific path towards achieving carbon peak and carbon neutrality should be coordinated with the requirements of world economic situation and its long-term economic reforms. It will also increase policy space to adjust its industrial structure and economic model towards green recovery.
Algorithm to Generate Trajectories in a Robotic Arm Using an LCD Touch Screen to Help Physically Disabled People

In the last two-decade, robotics has attracted a lot of attention from the biomedical sectors, to help physically disabled people in their quotidian lives. Therefore, the research of robotics applied in the control of an anthropomorphic robotic arm to people assistance and rehabilitation has increased considerably. In this context, robotic control is one of the most important problems and is considered the main part of trajectory planning and motion control. The main solution for robotic control is inverse-kinematics, because it provides the angles of robotic arm joints. However, there are disadvantages in the algorithms presented by several authors because the trajectory calculation needs an optimization process which implies more calculations to generate an optimized trajectory. Moreover, the solutions presented by the authors implied devices where the people are dependent or require help from other people to control these devices. This article proposes an algorithm to calculate an accuracy trajectory in any time of interest using an LCD touch screen to calculate the inverse-kinematics and get the end-point of the gripper; the trajectory is calculated using a novel distribution function proposed which makes an easy way to get fast results to the trajectory planning. The obtained results show improvements to generate a safe and fast trajectory of an anthropomorphic robotic arm using an LCD touch screen allowed calculating short trajectories with minimal fingers moves.
Classification of Chinese Hotel Reviews Based on GCN

Text classification is a fundamental part of natural language processing and can help with many downstream tasks, such as emotion analysis, question and answer systems, and recommendation systems. The graph convolution neural network has the natural superiority in the non - Euclidean space data. For Chinese text data, there is a lot of correlation between the data, using the graph convolutional neural network for text classification can achieve good results. In our experiment, we use a simple one-hot encoding of the word vector to process our words and use of the graph convolutional neural network can achieve 94.24% accuracy in our data set.
Product disassembly sequence planning: state-of-the-art, challenges, opportunities and future directions

Disassembly sequence planning (DSP) has gained active research interest since the 90s due to increasing environmental awareness and stricter regulations regarding end-of-life (EOL) strategies of used products. DSP is a subset of disassembly research that focuses on the systematic separation of constituent parts in a product. Despite tremendous efforts from researchers worldwide, DSP remains a challenging research area due to a variety of reasons, e.g. vast variety and complexity of products and uncertainties in the EOL conditions of products. Numerous survey papers have been published from time to time to offer up-to-date insights into this field. Researchers have continuously proposed new solutions to solve the DSP problem in tandem with the advancements in computing and the introduction of new concepts, such as virtual reality. This paper aims to provide a state-of-the-art survey of DSP research in the last 12 years. The research progress and achievements in DSP are summarised from three perspectives, namely, product representation models, sequencing algorithms and methodology validation. Lastly, the challenges and potential research directions in DSP are elaborated followed by conclusion.
Helminthofauna of ruminants and solipeds in the Center for Breeding Steppe Animals “Orenburg Tarpaniya”

During the autumn-winter-spring season of 2016-2017, the Center for Breeding Steppe Animals (Orenburg region) conducted coprological surveys of herbivores. Identified contamination strongylata have yaks and camels at a low infestation intensity. These animals were not dehelmintizated in the autumn period. By spring, the strongylate infestation intensity in camels increased. The kiangs and Przewalski’s horses kept here were subjected to autumn dehelmintization. In these animals, helminths were not detected during the entire study period. It is recommended to carry out planned dehelmintization of animals kept in small protected areas.
A Compact Textile Antenna with a Defected Ground Structure for S, C, X and Ku Band Applications

ABSTRACT The design and analysis of a compact textile antenna, exhibiting wideband operation from 2.59 to 6.67 GHz and from 10.18 to 12.98 GHz, supporting multiple communication services in S, C, X and Ku bands, is presented in this paper. The multiband and wideband operations are comprehended in a single antenna structure by introducing Triple Notch Circular Patch Antenna (TNCPA) with a Defected Ground Structure (DGS). The reduced return loss, safe electromagnetic radiation and high gain are realized using the presented geometry. The prototype of TNCPA with the Quarter Ground Structure (QGS) functioning as DGS is fabricated using a wearable cotton substrate of dimension 28 × 28 mm2 and the measured results linger close to the simulated results with stable radiation performance.
Distributed Power Sharing Control for Islanded Single-/Three-Phase Microgrids With Admissible Voltage and Energy Storage Constraints

This article investigates power sharing and power quality improvement issues of islanded single-/three-phase microgrids (S/T-MGs) where both sources and loads are unbalanced. A hierarchical distributed control approach is proposed, which consists of 1) a phase-independent virtual synchronous generator (P-VSG) control used for primary control of distributed generators (DGs), 2) a distributed secondary power flow regulator used for power sharing control among DGs and among phases, and 3) a distributed secondary voltage regulator used for voltage restoration and power quality improvement. Compared with conventional methods, the proposed control has several salient features: 1) the P-VSG control allows for independent and flexible power control and voltage regulation for each phase and accurate phase shifts; 2) distributed containment control proposed in the secondary power control and voltage regulation layer guarantees admissible output phase powers, voltage profiles and power quality; 3) the constraint operator developed for the secondary controllers makes charging/discharging power of the energy storage system (ESS) within permitted values; 4) communication delays are also considered in the proposed distributed approach. Simulation results are presented to demonstrate the proposed control method.
An exploratory study of foster child stress and behavioral health during Hurricane Irma

ABSTRACT This study provides an exploration of foster child stress and behavioral health during and after Hurricane Irma. Results from a survey of 50 foster parents indicated that foster child stress was significantly higher when court hearings were cancelled/rescheduled, foster parents perceived delayed progress toward case goals, and foster parents had higher hurricane-related stress. Anxiety and aggression were the most frequently mentioned ways in which foster child stress was manifested during and after the storm. These findings provide an introductory exploration that has implications for future research on foster care during natural disasters.
A novel unknown input interval observer for systems not satisfying relative degree condition

In this article, a novel unknown input interval observer (UIIO) is proposed for systems not satisfying the relative degree condition. In interval estimation, estimation errors are required not only positive but also convergent. For this purpose, some works combined unknown input observer with interval estimation, namely UIIO where by virtue of some transformations, some states are expressed as functions of outputs and other states which are decoupled from the unknown input. Nevertheless, complete decoupling can be guaranteed when the systems satisfy relative degree condition, which does not hold for some practical systems. Although some works have been done to relax the requirement on relative degree condition, they usually construct auxiliary outputs using high‐order differentiators which necessitates that the signals are smooth enough as well as that their derivatives are bounded, limiting the application of existing approaches and also increasing the computational cost. Motivated by the discussions above, a novel UIIO scheme combining the interval observation technique with adaptive sliding mode is proposed, without the relative degree condition and free from differentiating signals, hence the design process is simplified and the computational cost is decreased. Finally, simulations are conducted to verify the effectiveness of the proposed scheme.
Functional Analysis of SARS-CoV-2 Proteins in Drosophila Identifies Orf6-induced Pathogenicity Attenuated by Selinexor

Background: SARS-CoV-2 causes COVID-19 with a widely diverse disease profile that affect many different tissues. The mechanisms underlying its pathogenicity in host organisms remain unclear. Animal models for study the pathogenicity of SARS-CoV-2 proteins are lacking. Methods: Using bioinformatic analysis, we showed that the majority of the virus-host interacting proteins are conserved in Drosophila. Therefore, we generated a series of transgenic lines for individual SARS-CoV-2 genes and used the Gal4-UAS system to express them in various tissues to study their pathogenicity. Results: We found that the Nsp6, Orf6 and Orf7a transgenic flies displayed reduced trachea branching and muscle deficits resulting in “held-up” wing phenotype and poor climbing ability. Furthermore, muscle tissue in these flies showed dramatically reduced mitochondria. Since Orf6 was found to bind nucleopore proteins XPO1, we tested Selinexor, a drug that inhibits XPO1, and found that it could attenuated the Orf6-induced lethality and tissue-specific phenotypes in flies. Conclusions: Our studies here established new Drosophila models for studying the function of SARS-CoV2 genes, identified Orf6 as a highly pathogenic protein in various tissues, and demonstrated the effects of Selinexor for inhibiting Orf6 toxicity with an in vivo model system.
How to Make Digitalization Better Serve an Increasing Quality of Life?

Modern people live in the era of knowledge and digitalization supposed to increase their quality of life. Nevertheless, digital technologies are only the instruments in the development and transformation of social-economic processes and their usage per se does not ensure only positive effects, which much depends on goals, conditions, institutes, etc. Thus, digitalization has an unambiguous influence on many social-economic processes and needs a wise policy to provide smooth progress and well-being for everybody. This study aims to design and test appropriate tools for managing digitalization to direct this process on increasing the quality of life. For this purpose we analyzed: (1) correlation to identify interrelations between digitalization and quality of life; (2) the potential of using the visualization matrix method to identify and monitor national trends of digitalization in the context of quality of life. We found: (1) close correlation between subjective and objective indicators of quality of life and between the quality of life and digitalization; (2) the two-dimensional matrix turned out to be a relevant visual tool that embraces specific two-way relationships between human development and digitalization. In combination with statistical and qualitative methods, this tool has wide prospects for managing digitalization in the context of social progress and increasing quality of life.
Birth Weight in Relation to Post-Natal Growth Patterns as Predictor of Arterial Stiffness and Central Hemodynamics in Young Adults from a Population-based Study

Objective: Our aim was to examine the impact of mismatch patterns reflecting pre-and post-natal growth conditions on markers of arterial stiffness and central hemodynamics in young adults. Methods: In all, 1056 participants from Malmö Offspring Study, 484 men and 572 women (age-range 18–44 years), were included. All participants were stratified into four subgroups based on low (≤0) or high (>0) Birth Weight z -score (BWz) and low (≤ median) or high (> median) Body Mass Index (BMI) at 20 years age (BMI20). All participants underwent carotid-femoral Pulse Wave Velocity (PWV) measurement and pulse wave analysis with Sphygmocor. Additionally, 24-h ambulatory blood pressure data was recorded in a subgroup of 184 participants. Results: Systolic Blood Pressure (SBP), central SBP (cSBP) and Diastolic Blood Pressure (DBP), and 24-h night-time SBP was higher ( p
Visualization of integrin molecules by fluorescence imaging and techniques

Integrin molecules are transmembrane αβ heterodimers involved in cell adhesion, trafficking, and signaling. Upon activation, integrins undergo dynamic conformational changes that regulate their affinity to ligands. The physiological functions and activation mechanisms of integrins have been heavily discussed in previous studies and reviews, but the fluorescence imaging techniques -which are powerful tools for biological studies- have not. Here we review the fluorescence labeling methods, imaging techniques, as well as Förster resonance energy transfer assays used to study integrin expression, localization, activation, and functions.
Classification of Pituitary Tumor and Multiple Sclerosis Brain Lesions through Convolutional Neural Networks

Automatic classification of Brain Tumor and brain Lesions has become a very important step in the field of medical image analytics. The machine learning/Deep learning approaches are playing a tremendous role in the field of medical imaging classification, due to the drastic changes in the field of computing power and image analytics techniques. The deep learning, which is the subfield of machine learning, is playing the major role in the automatic classification of Magnetic Resonance Images (MRIs) having various brain abnormalities. Convolutional Neural Networks are widely used for the classification and detection of various brain disorders. In this research paper, Convolutional Neural Networks are designed with considering various learning parameters for the classification of Multiple Sclerosis Brain Lesions and Pituitary Tumor. In the proposed research, T1-weighted Contrast-enhanced Magnetic Resonance images are preprocessed with various image-preprocessing approaches such as to resize the images, to convert the images into suitable image format so that the experimental work can be performed with deep learning in the Matlab environment. The Experiment is conducted with the dataset of Multiple Sclerosis and Pituitary Tumor each of having 718 and 930T1-weighted MRI images respectively. The experimental results we achieved 99.7% classification accuracy of pituitary Tumor, and 99.2% accuracy of Multiple Sclerosis brain Lesions. The average accuracy of both classifications is 99.55%. The precision of the classification of Pituitary Tumor is 99.7, recall value is 99.7 and the f1_score of the classification is 99.7%. Similarly, the Precision of the classification of Multiple Sclerosis Brain Lesions is 99.15%, the recall value is 99.15%, and the f1_score is 99.15%. The purposed approach of the Convolutional Neural Network architecture exhibited outstanding performance as compared to other research outcomes.
Evaluation of sampling criteria for the detection of GM soybeans in bulk

The aim of this study was to develop a sampling procedure to quantify genetically-modified Round-up Ready (GM RR) soybeans in a lot. Seven lots (35 kg each) fortified with three different percentages of GM RR soybeans (4 lots at 1%, 2 lots at 10%, and 1 lot at 5%) were prepared. Four sampling procedures were carried out on each lot. Different numbers and sizes of increment samples were collected from the lots tested and prepared by homogenising the sample with water. Separate analyses were carried out using quantitative Real Time PCR of split aliquots. The sampling procedures that used ten increment samples provided the best results, in terms of precision and accuracy.
A global two-layer meta-model for response statistics in robust design optimization

ABSTRACT Robust design optimization (RDO) of large-scale engineering systems is computationally intensive and requires significant CPU time. Considerable computational effort is still required within conventional meta-model assisted RDO frameworks. The primary objective of this article is to minimize further the computational requirements of meta-model assisted RDO by developing a global two-layered approximation based RDO technique. The meta-model in the inner layer approximates the response quantity and the meta-model in the outer layer approximates the response statistics computed from the response meta-model. This approach eliminates both model building and Monte Carlo simulation from the optimization cycle, and requires considerably fewer actual response evaluations than a single-layered approximation. To demonstrate the approach, two recently developed compressive sensing enabled globally refined Kriging models have been utilized. The proposed framework is applied to one test example and two real-life applications to illustrate clearly its potential to yield robust optimal solutions with minimal computational cost.