Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial and Temporal Characteristics of Dust Storms and Aeolian Processes in the Southern Balkash Deserts in Kazakhstan, Central Asia

Sand and dust storms are hazardous to the environment and have a significant role in desertification. Under the influence of climate change and human activities, dust storms and aeolian processes have been common phenomena in the Southern Balkash deserts in Kazakhstan, Central Asia. However, knowledge gaps on spatial and temporal characteristics of dust storms and aeolian process in the Southern Balkash deserts still exist. Therefore, in present study, meteorological observations and numerous cartographic materials were used to identify the powerful sources with the highest frequency of dust storms and aeolian processes in the Southern Balkash deserts. The result showed that the Southern Balkash deserts were covered mainly by transverse parabolic sands (48%), dome dunes (24%), and transverse dome dunes (23%), where the aeolian processes occurred to a significant degree. Significant and strong degrees of aeolian processes occurred in most of the Southern Balkash deserts. The eastern part of the Taukum and the northern part of the Zhamankum and Karakum deserts were prone to aeolian processes to a substantial degree. The Moiynkum, Bestas, Saryesikatyrau, and Taukum deserts had the most frequent storms, occuring, on average, 17 to 43 days/per year. The occurrence of dust storms has been of a stable decreasing trend since the 1990s, except for 2008–2009. Aeolian dust in the Southern Balkash deserts flowed mainly from the western and southwestern to the eastern and northeastern. The results of the present study shed light on the temporal and spatial characteristics of dust storms and aeolian processes in the Southern Balkash deserts. This is of great importance in helping to monitor and predict dust storms and motion patterns of aeolian dust in this region.
Finite-Time Control Method for Uncertain Teleoperation Systems with Time-Varying Delays and Disturbance

In this paper, a non-singular terminal sliding surface method which includes arctan function (ANTSM) and mixed-type errors is proposed for the serious effects of internal uncertainties and unknown additional disturbance on the stability as well as the control performance of teleoperation systems. The suggested sliding surface’s finite temporal stability is ensured through stability analysis. In addition, the compensation bound for online estimation is proposed to cope with the performance degradation caused by time-varying delays introduced by communication channel. To improve the transparency of the master, force prediction is combined with RBF neural-network (RBFNN) to reconstruct the environmental force with unknown impedance structure, avoiding the direct transmission of force signals. Finally, dual three degrees of freedom (3 DOFs) manipulators based on geomagic touch models are used to verify the validity of designed controllers.
Identification of printed pattern repeat and its dimensions by image analysis

Abstract A fast method for the determination of the repeat of printed patterns and its dimensions with the help of an image analysis system has been developed. Repeat is the periodicity of any pattern. Modification or reproduction of patterns requires the identification or extraction of the repeat and its dimensions. The developed method in this research is based on the distance matching function. Distance matching function is a derivative of the co-occurrence matrix. The co-occurrence matrix constitutes one of the most important algorithms capable of identifying the repeat. The method developed in this work makes the rapid and exact identification of a range of repeats on non-pile fabrics possible. The range covers simple repeats as well as more complex ones such as compound, staggered and chessboard repeats.
The prosody of surprise questions in Estonian

This paper examines the role of prosody in a little-studied type of non-canonical questions: syntactically and lexically canonical interrogative sentences that have been uttered by the speaker in order to express surprise. The study compares Estonian surprise questions with string-identical information-seeking questions elicited by means of context descriptions. The materials comprise 1,008 utterances by 21 speakers. It is concluded that the prosody of the examined utterances has three roles that are relevant to the expression of surprise by ordinary interrogative sentences. First, the enhanced prosodic realisation of the utterances as manifested in a longer duration, a wider pitch range, and a more frequent occurrence of upstepped pitch accents conveys emotional expressivity. Second, lower pitch along with the creaky voice quality signals that the utterances are not canonical questions, while the main prosodic correlate of information-seeking questions is high pitch. Phonological pitch accents and boundary tones, however, are not used to distinguish between surprise questions and information-seeking questions. Third, the nuclear accent placement signals an information structure that is associated with the expression of incongruity or counterexpectation: the focal accent can evoke an alternative (set) that arises from the speaker’s expectations.
Representing Objects in Map Building Using A Semantic SLAM Scheme

In order to make the robot understand the scene better and the human-robot interaction more friendly, a lightweight semantic SLAM system is proposed in this paper. Our system uses a LIDAR combined with a camera to obtain object location in SLAM and determines object information such as category with a CNN-based object detector. The object identities are represented in the form of joint probability distributions that are updated by Bayes’ theorem. The FPA algorithm is used to obtain parameter valuation with maximum posterior probability so as to determine the modeling feature parameters of objects on the map. The similarity between objects is measured with the Bhattacharyya coefficient to avoid duplicate modeling of identified objects in map updating. The proposed system is simulated in Gazebo and an effective scheme balancing accuracy and computation cost is obtained. Finally, experiments on a small indoor robot demonstrate the effectiveness of our method.
Postoperative Recovery of Gait Function at Early Phase Is Delayed in Patients with Spinal Tumors with Impairment of the Joint Position Sense in the Big Toe: A Retrospective Cohort Study

Introduction We investigated the effect of preoperative joint position sense in the big toe on the postoperative recovery of gait function after spinal tumor surgery. Methods Seventy-three patients with spinal tumors who underwent surgery at our hospital between 2014 and 2019 and could be followed for at least 6 months after surgery were included. The patients were divided into the cervical spinal (41 cases) and thoracic spinal (32 cases) groups according to the localization of the tumor. These groups were further classified into an Impaired group (cervical spinal, 34 cases; thoracic spinal, 19 cases) and an Intact group (cervical spinal, 7 cases; thoracic spinal, 13 cases) according to the presence or absence of preoperative joint position sense in the big toe. The amount of change in ambulatory function from the preoperative period to 3 and 6 months postoperatively was compared between the Impaired and Intact groups within each tumor localization category. Results Impaired preoperative joint position sense in the big toe in patients undergoing thoracic spinal tumor surgery delayed the recovery of gait function in the early postoperative period. Conclusions In patients with thoracic spinal tumor surgery, the absence of preoperative joint position sense in the big toe delayed the recovery of postoperative gait function.
Target-driven machine learning-enabled virtual screening (TAME-VS) platform for early-stage hit identification

High-throughput screening (HTS) methods enable the empirical evaluation of a large scale of compounds and can be augmented by virtual screening (VS) techniques to save time and money by using potential active compounds for experimental testing. Structure-based and ligand-based virtual screening approaches have been extensively studied and applied in drug discovery practice with proven outcomes in advancing candidate molecules. However, the experimental data required for VS are expensive, and hit identification in an effective and efficient manner is particularly challenging during early-stage drug discovery for novel protein targets. Herein, we present our TArget-driven Machine learning-Enabled VS (TAME-VS) platform, which leverages existing chemical databases of bioactive molecules to modularly facilitate hit finding. Our methodology enables bespoke hit identification campaigns through a user-defined protein target. The input target ID is used to perform a homology-based target expansion, followed by compound retrieval from a large compilation of molecules with experimentally validated activity. Compounds are subsequently vectorized and adopted for machine learning (ML) model training. These machine learning models are deployed to perform model-based inferential virtual screening, and compounds are nominated based on predicted activity. Our platform was retrospectively validated across ten diverse protein targets and demonstrated clear predictive power. The implemented methodology provides a flexible and efficient approach that is accessible to a wide range of users. The TAME-VS platform is publicly available at https://github.com/bymgood/Target-driven-ML-enabled-VS to facilitate early-stage hit identification.
An algorithm to find minimal cuts of coherent fault-trees with event-classes, using a decision tree

A new algorithm, the Carrasco-Sune minimal cuts (CS-MC) algorithm for computing the minimal cuts of s-coherent fault trees is presented. Input events of the fault tree are assumed classified into classes, where events of the same class are indistinguishable. This allows capturing some symmetries which some systems exhibit. CS-MC uses a decision tree. The search implemented by the decision tree is guided by heuristics which try to make the CS-MC algorithm as efficient as possible. In addition, an irrelevance test on the inputs of the fault tree is used to prune the search. The performance of CS-MC is illustrated and compared with the basic top-down and bottom-up algorithms using a set of fault trees, some of which are very difficult. The CS-MC performs very well even in the difficult examples, and the memory requirements of CS-MC are small.
The Mechanism of Ochratoxin Contamination of Artificially Inoculated Licorice Roots

Ochratoxin (OT) contamination of medicinal herbs is a serious threat to human health. This study was performed to investigate the mechanism of OT contamination of licorice (Glycyrrhiza sp.) root. Licorice root samples were cut into eight parts, which were placed separately on sucrose-free Czapek Dox agar medium, inoculated with the spores of ochratoxigenic Aspergillus westerdijkiae. After incubation for 10 and 20 days, the OT contents of the samples were determined by high-performance liquid chromatography, and microtome sections prepared from the samples were analyzed by desorption electrospray ionization tandem mass spectrometry, to visualize OT localization. The same sections were further examined by light microscopy and scanning electron microscopy, to investigate the path of fungal mycelial penetration of the inner roots. OT concentrations tended to increase from the upper- to the middle-root parts. OTs were located in cut areas and areas of cork layer damage; they were not present in the undamaged cork layer, indicating that the structure of this layer prevents OT contamination of the licorice root.
Bayes and Empirical Bayes Inference in Changepoint Problems

We generalize the approach of Liu and Lawrence (1999) for multiple changepoint problems where the number of changepoints is unknown. The approach is based on dynamic programming recursion for efficient calculation of the marginal distribution of the data with the hidden parameters integrated out. For the estimation of the hyperparameters, we propose to use Monte Carlo EM when training data are available. The samples from the posterior obtained by our algorithm are independent, getting rid of the convergence issue associated with the MCMC approach. We illustrate our approach on limited simulations and some real data set.
Optical properties of edible oils within spectral range from 300 to 2500 nm determined by double optical pathlength transmission method

The optical properties of nine different samples of edible oils were experimentally determined by double optical pathlength transmission method in the spectral range from 300 to 2500 nm between 20°C and 80°C. The index of refraction exhibits little difference from the different types of edible oils. The index of absorption of all brands of studied edible oils decreased with the increasing temperature in the spectral range from 500 to 1100 nm. The variation of index of absorption was found to be insensitive to the increasing of temperature in the spectral range from 300 to 500 nm and from 1100 to 2500 nm. The absorption peaks locating at 900, 1210, 1400, 1750, 2150, and 2300 nm of the measured edible oils were corresponding to the result of overtones and combinations of C─H stretching vibration of various chemical groups. A difference of index of absorption was observed among the peanut oil, soybean oil, and sunflower oil in the spectral range from 300 to 500 nm.
Flow and Forced-Convection Characteristics of Turbulent Flow Through Parallel Plates with Periodic Transverse Ribs

ABSTRACT Two general turbulence models, the standard k–ϵ model and the Reynolds stress model (RSM), were used to predict the forced convection of a fully developed turbulent flow through an assembly of two horizontally oriented parallel plates in the Reynolds number range 22,000 < Re D < 94,000. The upper smooth plate was thermally insulated, whereas the bottom plate, attached with rectangular-cross-sectional ribs perpendicular to the mean air flow, was provided with a uniform heat flux. The ribs were uniformly spaced with the pitch-to-height ratio of p/e = 4, a height-to-hydraulic-diameter ratio of e/D = 0.25, and a width-to-height ratio of w/e = 2. The numerical approaches were based on the finite-volume technique. A second-order upwind scheme was applied in the calculation and a very fine mesh density was arranged in the regions near the wall boundaries. The SIMPLE algorithm was adopted to handle the pressure–velocity coupling in the calculation. Local Nusselt number distribution along the heated bottom ribbed surface was investigated, which was validated against corresponding experimental results conducted by Lorenz et al. [1]. It was found that in the simulation of the turbulent forced convection in this two-dimensional channel with a ribbed surface, the standard k–ϵ model had superiority over the Reynolds stress model. An anticlockwise vortex was found in the downstream region of a rib by using either of the two models; however, the length and relative strength of the vortex predicted by these two models were significantly different. Recirculating flow pattern was formed in the cavity between two adjacent ribs, while no reattachment of the mainstream flow was observed at the present pitch-to-height ratio of p/e = 4.
A State Transition Algorithm based Convolutional Neural Network Optimization Method to Froth Flotation Monitoring

The deep Convolutional Neural Networks (CNNs) have achieved significant success in vision applications of various process industry. Typically, the optimization methods for training CNNs are mostly based on gradient via Back-Propagation (BP) algorithm. However, such methods always suffer from local minimum, slow convergence and over-fitting problems, which means the training process need considerable data and computational resource. Therefore, a state transition algorithm (STA) based CNN optimization framework is proposed in this paper. The convolutional filters of the CNN are layer-wise optimized by STA. In particular, the features which are extracted by CNN are connected with a linear classifier. And the parameters of last layer can be calculated by a non-iterative method, which means the training time is reduced dramatically. Finally, the proposed framework is successfully tested on a common image classification dataset and applied to the froth flotation monitoring application. Experimental results have demonstrated that the proposed optimization framework can achieve higher classification accuracy and has stronger stability than traditional method.
Activation of the PI3K/AKT/mTOR Pathway in Cajal–Retzius Cells Leads to Their Survival and Increases Susceptibility to Kainate-Induced Seizures

Cajal–Retzius cells (CRs) are a class of transient neurons in the mammalian cortex that play a critical role in cortical development. Neocortical CRs undergo almost complete elimination in the first two postnatal weeks in rodents and the persistence of CRs during postnatal life has been detected in pathological conditions related to epilepsy. However, it is unclear whether their persistence is a cause or consequence of these diseases. To decipher the molecular mechanisms involved in CR death, we investigated the contribution of the PI3K/AKT/mTOR pathway as it plays a critical role in cell survival. We first showed that this pathway is less active in CRs after birth before massive cell death. We also explored the spatio-temporal activation of both AKT and mTOR pathways and reveal area-specific differences along both the rostro–caudal and medio–lateral axes. Next, using genetic approaches to maintain an active pathway in CRs, we found that the removal of either PTEN or TSC1, two negative regulators of the pathway, lead to differential CR survivals, with a stronger effect in the Pten model. Persistent cells in this latter mutant are still active. They express more Reelin and their persistence is associated with an increase in the duration of kainate-induced seizures in females. Altogether, we show that the decrease in PI3K/AKT/mTOR activity in CRs primes these cells to death by possibly repressing a survival pathway, with the mTORC1 branch contributing less to the phenotype.
Landslide Sensitivity Mapping Based on One-Dimensional Residual Convolutional Neural Networks

Landslide is a common geological disaster. Landslide sensitivity mapping (LSM) is the key technology for landslide monitoring, early warning and risk assessment. Deep learning shows good performance in feature extraction. This paper proposes a one-dimensional residual convolution neural network (1DRCNN), which takes Yunyang County, Chongqing City, China, located in the Three Gorges Reservoir area as the research area. Extracting 12 evaluation factors from multi-source remote sensing data and building a training set. The spatial probability of landslide occurrence is quantitatively predicted by the proposed model, and finally, the landslide sensitivity mapping is generated. Compared with the common machine learning models SVM and logistic regression, the results show that the AUC (area under curve) and accuracy of 1D-RCNN are 0.9860 and 93.38%, respectively, which proving that this method is effective and can provide a reference for disaster prevention and reduction.
Turbulence in induction plasma modelling

This paper describes a k- epsilon turbulent model with two-dimensional representation of electromagnetic fields for the simulation of fluid flow and heat transfer in an induction plasma. A comparative analysis is made with an earlier study using one-dimensional electromagnetic fields representation. The results reveal the simultaneous presence of both laminar and turbulent flow regimes in the discharge. The present model was found to cope well with such variations in the local laminar/turbulent diffusivity and could be used to predict the details of the flow and temperature fields in the discharge under predominantly laminar (low Reynolds number) or turbulent (high Reynolds number) conditions.
Chandra measurements of the X-ray core and cluster of 3C 220.1

We report results of an 18-ks exposure with the ACIS instrument on Chandra of the powerful z=0.62 radio galaxy 3C 220.1. The X-ray emission separates into cluster gas of emission-weighted kT∼5 keV, 0.7–12 keV luminosity (to a radius of 45 arcsec) of 5.6×1044 erg s−1 and unresolved emission (coincident with the radio core). While the extended X-ray emission is clearly thermal in nature, a straightforward cooling-flow model, even in conjunction with a point-source component, is a poor fit to the radial profile of the X-ray emission. This is despite the fact that the measured properties of the gas suggest a massive cooling flow of ∼130 M⊙ yr−1, and the data show weak evidence for a temperature gradient. The central unresolved X-ray emission has a power-law spectral energy index α∼0.7 and 0.7–12 keV luminosity of 1045 erg s−1, and any intrinsic absorption is relatively small. The two-point spectrum of the core emission between radio and X-ray energies has αrx=0.75. Since this is a flatter spectrum than seen in other sources where the X-ray emission is presumed to be radio-related, regions close to the active galactic nucleus (AGN) in this source may dominate the central X-ray output, as is believed to be the case for lobe-dominated quasars. Simple unification models would be challenged if this were found to be the case for a large fraction of high-power radio galaxies.
The Necessity of Implant Removal after Fixation of Thoracolumbar Burst Fractures—A Systematic Review

Background: Thoracolumbar burst fractures are a common traumatic vertebral fracture in the spine, and pedicle screw fixation has been widely performed as a safe and effective procedure. However, after the stabilization of the thoracolumbar burst fractures, whether or not to remove the pedicle screw implant remains controversial. This review aimed to assess the benefits and risks of pedicle screw instrument removal after fixation of thoracolumbar burst fractures. Methods: Data sources, including PubMed, EMBASE, Cochrane Library, Web of Science, Google Scholar, and Clinical trials.gov, were comprehensively searched. All types of human studies that reported the benefits and risks of implant removal after thoracolumbar burst fractures, were selected for inclusion. Clinical outcomes after implant removal were collected for further evaluation. Results: A total of 4051 papers were retrieved, of which 35 studies were eligible for inclusion in the review, including four case reports, four case series, and 27 observational studies. The possible risks of pedicle screw removal after fixation of thoracolumbar burst fractures include the progression of the kyphotic deformity and surgical complications (e.g., surgical site infection, neurovascular injury, worsening pain, revision surgery), while the potential benefits of pedicle screw removal mainly include improved segmental range of motion and alleviated pain and disability. Therefore, the potential benefits and possible risks should be weighed to support patient-specific clinical decision-making about the removal of pedicle screws after the successful fusion of thoracolumbar burst fractures. Conclusions: There was conflicting evidence regarding the benefits and harms of implant removal after successful fixation of thoracolumbar burst fractures, and the current literature does not support the general recommendation for removal of the pedicle screw instruments, which may expose the patients to unnecessary complications and costs. Both surgeons and patients should be aware of the indications and have appropriate expectations of the benefits and risks of implant removal. The decision to remove the implant or not should be made individually and cautiously by the surgeon in consultation with the patient. Further studies are warranted to clarify this issue. Level of evidence: level 1.