Miracle-dz/UniFAR-FTUs huggingface dataset:
Disturbance Observers for Rigid Mechanical Systems: Equivalence, Stability, and Design

Mechanical (direct-drive) systems designed for high-speed and high-accuracy applications require control systems that eliminate the influence of disturbances like cogging forces and friction. One way to achieve additional disturbance rejection is to extend the usual (P(I)D) controller with a disturbance observer. There are two distinct ways to design, represent, and implement a disturbance observer, but in this paper it is shown that the one is a generalization of the other. A general systematic design procedure for disturbance observers that incorporates stability requirements is given. Furthermore, it is shown that a disturbance observer can be transformed into a classical feedback structure, enabling numerous well-known tools to be used for the design and analysis of disturbance observers. Using this feedback interpretation of disturbance observers, it will be shown that a disturbance observer based robot tracking controller can be constructed that is equivalent to a passivity based controller. By this equivalence not only stability proofs of the disturbance observer based controller are obtained, but it also provides more transparent controller parameter selection rules for the passivity based controller.
Multi-target data association approach for vehicle tracking in road situation

Data association was an important content in Multi-target tracking. Typical algorithms to deal with such problems are the joint probabilities data association filter (JPDAF) proposed by Bar-Shalom and his team. The basis of JPDAF is the calculus of the joint probabilities between the measurements and the tracks. The algorithm assigns weights for reasonable measurements and uses a weighted centroid of those measurements to update the track. Based on the evidence theory and fuzzy mathematics, a new data association method between the tracks and the measurements is proposed in the paper. The mass function is determined by using fuzzy mathematics, and a belief matrix is produced according to the evidence combination rule, then the decision is made by means of the maximum belief. The Monte Carlo simulation results indicate that the new method has a good association capacity. Compared with cheap JPDAF method, the new method has improved the track accuracy.
Feasibility Study on the Classification of Persimmon Trees’ Components Based on Hyperspectral LiDAR

Intelligent management of trees is essential for precise production management in orchards. Extracting components’ information from individual fruit trees is critical for analyzing and understanding their general growth. This study proposes a method to classify persimmon tree components based on hyperspectral LiDAR data. We extracted nine spectral feature parameters from the colorful point cloud data and performed preliminary classification using random forest, support vector machine, and backpropagation neural network methods. However, the misclassification of edge points with spectral information reduced the accuracy of the classification. To address this, we introduced a reprogramming strategy by fusing spatial constraints with spectral information, which increased the overall classification accuracy by 6.55%. We completed a 3D reconstruction of classification results in spatial coordinates. The proposed method is sensitive to edge points and shows excellent performance for classifying persimmon tree components.
The Expression Patterns of Exogenous Plant miRNAs in Chickens

(1) Background: MicroRNAs (miRNAs) are involved in a variety of biological processes, such as cell proliferation, cell differentiation, and organ development. Recent studies have shown that plant miRNAs may enter the diet and play physiological and/or pathophysiological roles in human health and disease; however, little is known about plant miRNAs in chickens. (2) Methods: Here, we analyzed miRNA sequencing data, with the use of five Chinese native chicken breeds and six different tissues (heart, liver, spleen, lung, kidney, and leg muscle), and used Illumina sequencing to detect the expression of plant miRNAs in the pectoralis muscles at fourteen developmental stages of Tibetan chickens. (3) Results: The results showed that plant miRNAs are detectable in multiple tissues and organs in different chicken breeds. Surprisingly, we found that plant miRNAs, such as tae-miR2018, were detectable in free-range Tibetan chicken embryos at different stages. The results of gavage feeding experiments also showed that synthetic tae-miR2018 was detectable in caged Tibetan chickens after ingestion. The analysis of tae-miR2018 showed that its target genes were related to skeletal muscle organ development, regulation of mesodermal cell fate specification, growth factor activity, negative regulation of the cell cycle, and regulation of growth, indicating that exogenous miRNA may regulate the development of chicken embryos. Further cell cultures and exogenous miRNA uptake assay experiments showed that synthetic wheat miR2018 can be absorbed by chicken myoblasts. (4) Conclusions: Our study found that chickens can absorb and deposit plant miRNAs in various tissues and organs. The plant miRNAs detected in embryos may be involved in the development of chicken embryos.
Bifurcation analysis of a singular nonlinear Sturm–Liouville equation

In this paper we study existence of positive solutions to the following singular nonlinear Sturm–Liouville equation where α > 0, p > 1 and λ are real constants. We prove that when 0 1 or when ½ 1, and when ½ 1, we show that a second branch of continuous positive solution can be found to the left of the first eigenvalue of the operator ℒα under the boundary condition limx→0 u(x) = 0. Finally, when α ≥ 1, the operator ℒα has no eigenvalues under its canonical boundary condition at the origin, and we prove that in fact there are no positive solutions to the equation, regardless of λ ∈ ℝ and p > 1.
The role of lactate metabolism-related LncRNAs in the prognosis, mutation, and tumor microenvironment of papillary thyroid cancer

Background Lactate, a byproduct of glucose metabolism, is primarily utilized for gluconeogenesis and numerous cellular and organismal life processes. Interestingly, many studies have demonstrated a correlation between lactate metabolism and tumor development. However, the relationship between long non-coding RNAs (lncRNAs) and lactate metabolism in papillary thyroid cancer (PTC) remains to be explored. Methods Lactate metabolism-related lncRNAs (LRLs) were obtained by differential expression and correlation analyses, and the risk model was further constructed by least absolute shrinkage and selection operator analysis (Lasso) and Cox analysis. Clinical, immune, tumor mutation, and enrichment analyses were performed based on the risk model. The expression level of six LRLs was tested using RT-PCR. Results This study found several lncRNAs linked to lactate metabolism in both The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets. Using Cox regression analysis, 303 lactate LRLs were found to be substantially associated with prognosis. Lasso was done on the TCGA cohort. Six LRLs were identified as independent predictive indicators for the development of a PTC prognostic risk model. The cohort was separated into two groups based on the median risk score (0.39717 -0.39771). Subsequently, Kaplan-Meier survival analysis and multivariate Cox regression analysis revealed that the high-risk group had a lower survival probability and that the risk score was an independent predictive factor of prognosis. In addition, a nomogram that can easily predict the 1-, 3-, and 5-year survival rates of PTC patients was established. Furthermore, the association between PTC prognostic factors and tumor microenvironment (TME), immune escape, as well as tumor somatic mutation status was investigated in high- and low-risk groups. Lastly, gene expression analysis was used to confirm the differential expression levels of the six LRLs. Conclusion In conclusion, we have constructed a prognostic model that can predict the prognosis, mutation status, and TME of PTC patients. The model may have great clinical significance in the comprehensive evaluation of PTC patients.
Sapphire resonator for the measurement of surface impedance of high-temperature superconducting thin films

A technique using cavity perturbation of a cooled sapphire disk resonator has been developed to measure the surface impedance Zs=Rs+iXs of high-temperature superconducting thin films. The resonator is excited in the TE011 mode at 14.4 GHz and induces a-b plane screening currents in the sample. The resonator and its enclosure are maintained at 4.2 K while the sample is heated independently on a movable sapphire rod. The thermal isolation of the sample and resonator leaves the measurement virtually free of systematic error and permits the use of a superconducting niobium shield. The combination of low dielectric loss sapphire and a superconducting enclosure results in typical unloaded quality factors (Q) of several million. The ability to move the sample makes the sensitivity variable, allowing surface resistance values from 5 μΩ to several Ω to be measured. Background loss can also be accurately determined at the time of experiment by withdrawing the sample from the influence of the resonator. In addition,.
An Efficient Safety-Oriented Car-Following Model for Connected Automated Vehicles Considering Discrete Signals

With the rapid development of Connected and Automated Vehicle (CAV) technology, limited self-driving vehicles have been commercially available in certain leading intelligent transportation system countries. When formulating the car-following model for CAVs, safety is usually the basic constraint. Safety-oriented car-following models seek to specify a safe following distance that can guarantee safety if the preceding vehicle were to brake hard suddenly. The discrete signals of CAVs bring a series of phenomena, including discrete decision-making, phase difference, and discretely distributed communication delay. The influences of these phenomena on the car-following safety of CAVs are rarely considered in the literature. This paper proposes an efficient safety-oriented car-following model for CAVs considering the impact of discrete signals. The safety constraints during both normal driving and a sudden hard brake are incorporated into one integrated model to eliminate possible collisions during the whole driving process. The mechanical delay information of the preceding vehicle is used to improve car-following efficiency. Four modules are designed to enhance driving comfort and string stability in case of heavy packet losses. Simulations of a platoon with diversified vehicle types demonstrate the safety, efficiency, and string stability of the proposed model. Tests with different packet loss rates imply that the model could guarantee safety and driving comfort in even poor communication environments.
Pandemic preparedness and response-related content to integrate into the MSW curricula: implications for provider burnout

ABSTRACT The COVID-19 pandemic, a first in many generations disaster, has highlighted gaps globally among graduated social work providers. This qualitative study of (N = 12) inpatient social workers who provided care during the pandemic, strives to suggest specific disaster-related content to inform the MSW curricula. Thematic analysis suggested including: 1) self-care in a prolonged disaster; 2) responding to nuances of the job; 3) expansion and integration of practicum specialties; 4) preparing for the effects of another pandemic; 5) advocating for yourself; and 6) the public’s view of social workers. Findings could potentially inform the content that is taught to MSW students post-pandemic.
An Adaptive Data Collection Procedure for Call Prioritization

Abstract We propose an adaptive data collection procedure for call prioritization in the context of computer-assisted telephone interview surveys. Our procedure is adaptive in the sense that the effort assigned to a sample unit may vary from one unit to another and may also vary during data collection. The goal of an adaptive procedure is usually to increase quality for a given cost or, alternatively, to reduce cost for a given quality. The quality criterion often considered in the literature is the nonresponse bias of an estimator that is not adjusted for nonresponse. Although the reduction of the nonresponse bias is a desirable goal, we argue that it is not a useful criterion to use at the data collection stage of a survey because the bias that can be removed at this stage through an adaptive collection procedure can also be removed at the estimation stage through appropriate nonresponse weight adjustments. Instead, we develop a procedure of call prioritization that, given the selected sample, attempts to minimize the conditional variance of a nonresponse-adjusted estimator subject to an overall budget constraint. We evaluate the performance of our procedure in a simulation study.
RNS processor using moduli sets of the form 2n±1

In recent years, research on residue number systems (RNS) has targeted larger dynamic ranges to explore their inherent parallelism further. In this paper, we start from the traditional 3‐moduli set {2n,2n−1,2n+1} , with an equivalent 3 n ‐bit dynamic range, and propose balanced horizontal extensions by using modulus of the form 2n,2n−1 and 2n+1 to scale the dynamic range and enhance the parallelism according to the requirements. A method to design the reverse conversion by using it is also presented. A case study of multiplier‐accumulator (MACs) in cascade for filtering applications has been analyzed to validate the RNS processor using the proposed moduli set and reverse conversion architecture. Experimental results show that the proposed approaches achieve a significant speedup compared with the state‐of‐the‐art cases for the same dynamic range purposes.
Torque Ripple Reduction of Brushless DC Motor

Brushless DC motor(BLDCM) have a wide range of applications,easy to control,but its weakness is large torque ripple.Through the analysis of HPWM-LON modulation method for brushless DC motor′s influence of electromagnetic torque during phase change,an improved pulse width modulation method was proposed,the combination of HON-LON and HPWM-LON.First of all,according to three phase current equations of commutation,motor electromagnetic torque can be get;second,compare commutation motor electromagnetic torque and steady-state motor electromagnetic torque,commutation electromagnetic torque ripple can be deduced;Finally,through the control on-all time of conducting state MOSFET,motor electromagnetic torque ripple can be minimum.The experiment proves the proposed improved pulse width modulation method can effectively restrain the torque ripple and improve system performance.
Computer-Aided Drug Design and Synthesis of Rhenium Clotrimazole Antimicrobial Agents

In the context of the global health issue caused by the growing occurrence of antimicrobial resistance (AMR), the need for novel antimicrobial agents is becoming alarming. Inorganic and organometallic complexes represent a relatively untapped source of antibiotics. Here, we report a computer-aided drug design (CADD) based on a ‘scaffold-hopping’ approach for the synthesis and antibacterial evaluation of fac-Re(I) tricarbonyl complexes bearing clotrimazole (ctz) as a monodentate ligand. The prepared molecules were selected following a pre-screening in silico analysis according to modification of the 2,2′-bipyridine (bpy) ligand in the coordination sphere of the complexes. CADD pointed to chiral 4,5-pinene and 5,6-pinene bipyridine derivatives as the most promising candidates. The corresponding complexes were synthesized, tested toward methicillin-sensitive and -resistant S. aureus strains, and the obtained results evaluated with regard to their binding affinity with a homology model of the S. aureus MurG enzyme. Overall, the title species revealed very similar minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values as those of the reference compound used as the scaffold in our approach. The obtained docking scores advocate the viability of ‘scaffold-hopping’ for de novo design, a potential strategy for more cost- and time-efficient discovery of new antibiotics.
Extremely fast Holocene coastal landscape evolution in the Kachchh Upland (NW India): Clues from a multidisciplinary review

ABSTRACT The coastal landscape of the Kachchh Upland (KU) region (NW-India) changed over the last few thousand years from a shallow marine gulf to a salty desert (1-4 meters asl). In this area, bordered to the south by the Northern Hill Range (NHR), the tectonic-climatic interaction triggered the sea level fall from +2/4 m circa (6000-2000 BP) to zero. An ancient river pattern deposited a tidally regulated delta area during the sea level fall that stopped 2000-3000 years ago due to tectonic activity and a dry climate. Deltaic-alluvial fans (DAF) in front of the NHR suggest that the KU's tectonic activity led to fast landscape evolution. We explored such drastic changes by integrating scientific information from a multidisciplinary literature review, identifying terraces and DAFs, and inferring faults through landform recognition, quantitative morphometry, and field surveys. Our interpretation, summarized in a map, provides new information on active processes along the NHR.
Surface ozone pollution in China: Trends, exposure risks, and drivers

Introduction Within the context of the yearly improvement of particulate matter (PM) pollution in Chinese cities, Surface ozone (O3) concentrations are increasing instead of decreasing and are becoming the second most important air pollutant after PM. Long-term exposure to high concentrations of O3 can have adverse effects on human health. In-depth investigation of the spatiotemporal patterns, exposure risks, and drivers of O3 is relevant for assessing the future health burden of O3 pollution and implementing air pollution control policies in China. Methods Based on high-resolution O3 concentration reanalysis data, we investigated the spatial and temporal patterns, population exposure risks, and dominant drivers of O3 pollution in China from 2013 to 2018 utilizing trend analysis methods, spatial clustering models, exposure-response functions, and multi-scale geographically weighted regression models (MGWR). Results The results show that the annual average O3 concentration in China increased significantly at a rate of 1.84 μg/m3/year from 2013 to 2018 (160 μg/m3) in China increased from 1.2% in 2013 to 28.9% in 2018, and over 20,000 people suffered premature death from respiratory diseases attributed to O3 exposure each year. Thus, the sustained increase in O3 concentrations in China is an important factor contributing to the increasing threat to human health. Furthermore, the results of spatial regression models indicate that population, the share of secondary industry in GDP, NOx emissions, temperature, average wind speed, and relative humidity are important determinants of O3 concentration variation and significant spatial differences are observed. Discussion The spatial differences of drivers result in the spatial heterogeneity of O3 concentration and exposure risks in China. Therefore, the O3 control policies adapted to various regions should be formulated in the future O3 regulation process in China.
Eliminating or Shortening the Calibration for a P300 Brain–Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study

A brain-computer interface (BCI) measures and analyzes brain activity and converts it into computer commands to control external devices. Traditional BCIs usually require full calibration, which is time-consuming and makes BCI systems inconvenient to use. In this study, we propose an online P300 BCI spelling system with zero or shortened calibration based on a convolutional neural network (CNN) and big electroencephalography (EEG) data. Specifically, three methods are proposed to train CNNs for the online detection of P300 potentials: (i) training a subject-independent CNN with data collected from 150 subjects; (ii) adapting the CNN online via a semisupervised learning/self-training method based on unlabeled data collected during the user’s online operation; and (iii) fine-tuning the CNN with a transfer learning method based on a small quantity of labeled data collected before the user’s online operation. Note that the calibration process is eliminated in the first two methods and dramatically shortened in the third method. Based on these methods, an online P300 spelling system is developed. Twenty subjects participated in our online experiments. Average accuracies of 89.38%, 94.00% and 93.50% were obtained by the subject-independent CNN, the self-training-based CNN and the transfer learning-based CNN, respectively. These results demonstrate the effectiveness of our methods, and thus, the convenience of the online P300-based BCI system is substantially improved.
On the Multilayer Planning of Filterless Optical Networks With OTN Encryption

With enhanced cost-effectiveness, filterless optical networks (FONs) have been considered as a promising candidate for future optical infrastructure. However, as the transmission in FON relies on the “select-and-broadcast” scenario, it is more vulnerable to eavesdropping. Therefore, encrypting the communications in FONs will be indispensable, and this can be realized by introducing the optical transport network (OTN) encryption technologies that leverage high-speed encryption cards (ECs) to protect the integrity of OTN payload frames. In this paper, we study the problem of security-aware multilayer planning of FONs with OTN encryption. We first formulate a mixed integer linear programming (MILP) model (i.e., w-MILP) to solve the problem exactly. Then, to reduce the time complexity of problem-solving, we transform w-MILP into two correlated MILP models for establishing fiber trees for an FON (t-MILP) and planning flows in the fiber trees (s-MILP), respectively. The optimization in t-MILP is further transformed into a weighted set partitioning problem, which can be solved time-efficiently. As for s-MILP, we propose a polynomial-time approximation algorithm based on linear programming (LP) relaxation and randomized rounding. Extensive simulations verify the performance of our proposals.
A Note on the Asymptotic Distributions of Unit Root Tests in the Additive Outlier Model With Breaks

This note discusses tests for a unit root allowing the possibility of a onetime change in the intercept and/or the slope of the trend function in the additive outlier model considered in Perron (1989). We discuss and correct an error in the stated asymptotic distributions of the tests in this case. We propose a simple modification of the procedure which yields statistics having the same asymptotic distributions as stated in Perron (1989). We also discuss the adequacy of the asymptotic approximations and various extensions to the case where the breakpoint is unknown with corresponding asymptotic critical values.
X-Ray Crystal Structure Analysis of Low Temperature Phase of SrTiO3

The crystal structure analysis of the low temperature phase of SrTiO 3 has been made at 77 K using the method of X-ray oscillation photograph. The space group is determined as I4/mcm and the rotational shift of oxygen octahedra with respect to Sr ion frame is by 1.6°; this is slightly larger than those estimated from a previous ESR experiment, neutron diffraction study and from X-ray lattice parameter measurement. Observed temperature parameters are in reasonable agreement with previously reported ones estimated from a rigid shell model.