Miracle-dz/UniFAR-FTUs huggingface dataset:
Species-specific differences in antagonism of APOBEC3 proteins by HIV-2 and SIVsmm Vif proteins

SIVsmm infecting sooty mangabeys has been transmitted to humans on at least nine independent occasions, giving rise to HIV-2 groups A to I. SIVsmm isolates replicate in human T cells and seem capable of overcoming major human restriction factors without adaptation. However, only groups A and B are responsible for the HIV-2 epidemic in Sub-Saharan Africa and it is largely unclear whether adaptive changes were associated with significant spread in humans. To address this, we examined the sensitivity of infectious molecular clones (IMCs) of five HIV-2 strains (4 group A and one AB recombinant) and representatives of five different SIVsmm lineages to inhibition by type I interferon (IFN) and various APOBEC3 proteins. We confirmed that SIVsmm strains replicate in primary human CD4+ T cells. However, SIVsmm replication was highly variable, typically lower relative to HIV-2 isolates and almost entirely prevented by type I IFN treatment. Viral propagation was generally dependent on intact vif genes, highlighting the need for efficient counteraction of APOBEC3 proteins. On average, SIVsmm strains were significantly more susceptible to inhibition by human APOBEC3D, F, G and H than HIV-2 IMCs. For example, human APOBEC3F reduced infectious virus yield of SIVsmm by ∼80% but achieved only ∼40% in the case of HIV-2. Functional and mutational analyses of human, sooty mangabey and rhesus macaque derived alleles revealed that an R128T polymorphism in APOBEC3F is important for species-specific counteraction by HIV-2 and SIVsmm Vif proteins. In addition, we found that changes of Y45H and T84S in SIVsmm Vif increase its ability to antagonize human APOBEC3F. Altogether, our results show that SIVsmm Vifs show some intrinsic activity against human ABOBEC3 proteins, but HIV-2 Vifs acquired adaptive changes to efficiently clear this barrier in the human host. AUTHOR SUMMARY SIVs infecting African monkey species do not infect humans, with one notable exception. SIVsmm from sooty mangabeys managed to cross the species barrier to humans on at least nine independent occasions. This is because SIVsmm strains seem capable of overcoming many innate defense mechanisms without adaptation and that their Vif proteins are active against human APOBEC3 proteins. Here, we show that replication of SIVsmm is highly variable in human CD4 T cells and more sensitive to interferon inhibition compared to HIV-2. While different lineages of SIVsmm were capable of counteracting human APOBEC3 proteins in a Vif-dependent manner, they were significantly more susceptible to inhibition by APOBEC3D/F/G/H compared to HIV-2. Mutational analyses revealed an R128T substitution in APOBEC3F and a T84S change in Vif are relevant for species-specific counteraction by HIV-2 and SIVsmm. Altogether, our results support that HIV-2 group A adapted to humans prior to or during epidemic spread.
On the convergence of augmented Lagrangian strategies for nonlinear programming

Augmented Lagrangian (AL) algorithms are very popular and successful methods for solving constrained optimization problems. Recently, global convergence analysis of these methods has been dramatically improved by using the notion of sequential optimality conditions. Such conditions are necessary for optimality, regardless of the fulfillment of any constraint qualifications, and provide theoretical tools to justify stopping criteria of several numerical optimization methods. Here, we introduce a new sequential optimality condition stronger than previously stated in the literature. We show that a well-established safeguarded Powell–Hestenes–Rockafellar (PHR) AL algorithm generates points that satisfy the new condition under a Lojasiewicz-type assumption, improving and unifying all the previous convergence results. Furthermore, we introduce a new primal–dual AL method capable of achieving such points without the Lojasiewicz hypothesis. We then propose a hybrid method in which the new strategy acts to help the safeguarded PHR method when it tends to fail. We show by preliminary numerical tests that all the problems already successfully solved by the safeguarded PHR method remain unchanged, while others where the PHR method failed are now solved with an acceptable additional computational cost.
HeartQuake

This work presents HeartQuake, a low cost, accurate, non-intrusive, geophone-based sensing system for extracting accurate electrocardiogram (ECG) patterns using heartbeat vibrations that penetrate through a bed mattress. In HeartQuake, cardiac activity-originated vibration patterns are captured on a geophone and sent to a server, where the data is filtered to remove the sensor's internal noise and passed on to a bidirectional long short term memory (Bi-LSTM) deep learning model for ECG waveform estimation. To the best of our knowledge, this is the first solution that can non-intrusively provide accurate ECG waveform characteristics instead of more basic abstract features such as the heart rate using bed-mounted geophone sensors. Our extensive experimental results with a baseline dataset collected from 21 study participants and a longitudinal dataset from 15 study participants suggest that HeartQuake, even when using a general non-personalized model, can detect all five ECG peaks (e.g., P, Q, R, S, T) with an average error of 13 msec when participants are stationary on the bed. Furthermore, clinically used ECG metrics such as the RR interval and QRS segment width can be estimated with errors 3 msec and 10 msec, respectively. When additional noise factors are present (e.g., external vibration and various sleeping habits), the estimation error increases, but can be mitigated by using a personalized model. Finally, a qualitative study with 11 physicians on the clinically perceived quality of HeartQuake-generated ECG signals suggests that HeartQuake can effectively serve as a screening tool for detecting and diagnosing abnormal cardiovascular conditions. In addition, HeartQuake's low-cost and non-intrusive nature allow it to be deployed in larger scales compared to current ECG monitoring solutions.
Modeling Interprocessor Communication and Performance Scalability for Distributed Deep Learning Systems

While deep learning applications become popular, the design of deep learning systems is a critical task to unleash the computing power of underlying systems. Aside from the computing hardware, the computer networking is also a key factor that affects the delivered performance. When considering a large and complex model, the scalability of the system highly depends on the design of the networks, as well as the software behaviors. In this paper, we propose a profile-data-guided performance prediction method to estimate the performance of the system with desired high-speed interconnects, based on the profiling data obtained in a previous run. In particular, we leverage the open-source profiling tool, SOFA, for characterizing the software activities of deep learning software running in a computer cluster, and the characterized information is used to build the performance model for the model training process. When estimating the performance, SOFA is used to capture the performance critical factors for the model to make the predictions. To evaluate the proposed method, four popular deep learning models are adopted in our experiments, ResNet50, Inception3, Alexnet, and VGG16, where a computer cluster formed by four nodes is used to profile the training of the above models on TensorFlow. We ran the scalability analysis to analyze the size of the cluster, and the suitable computer networks for the models. By comparing the predicted data and those measured on the cluster, our model achieves up to 95% accuracy in most of the cases, with the maximum error rate of 10%.
Study on relationship between cell proliferation of uterus leiomyoma and epidermal growth factor-receptor

Objective To investigate the relationship between cell proliferation and epidermal growth factor-receptor level in uterine leiomyoma. Method DI, SPF, PI in 50 cases of and EGF-R level in 28 cases of leiomyoma were measured by FCM and in myometrium as control. Results SPF, PI and EGF-R levels in leiomyoma were significantly higher than those in the myometrium (P < 0.05, or P < 0.01). There was positive correlation for EGF-R level versus PI and SPF in leiomyoma (P < 0.05), but of no significant difference for DI between leiomyoma and myometrium. Conclusion Higher concentration of EGF-R is closely linked with excessive prolfierative activity of leiomyoma cells.
Comparison of Analytical Models of Transformer Leakage Inductance: Accuracy Versus Computational Effort

A fast and accurate model of the transformer leakage inductance is crucial for optimization-based design of galvanically isolated converters. Analytical models are fastly executable, and therefore, especially suitable for such optimizations. This article compares several analytical leakage inductance per unit length models with respect to the accuracy and computational effort. The considered models are applicable to E-Core and U-Core transformers. 2D FEM simulations are used as a benchmark to evaluate the model accuracy, whereas the computation time is extracted as an indicator for computational effort. Six different transformer prototypes provide the geometries for the comparison. Based on the conducted comparisons, Roth's model is the most accurate. Rogowski's model is the fastest low-error model. Margueron's model is the most versatile as it takes the finite permeability of the core into account. The conducted comparisons lay the foundation for accurate and fast Double-2D modeling of the transformer leakage inductance as it is executed for the two main cross sections of E-core and U-core transformers: inside the transformer window, and outside the transformer window. This article is accompanied by a supplementary document summarizing the equations of Roth's and Margueron's model.
An Optimized Turning Gait Strategy for Walking Quadruped Robot

In this paper, an optimized turning gait strategy is proposed to achieve the turning behavior for the walking quadruped robot and a new feasible gait turning angle model is presented to simplify the design process according to the stability and performance requirements of quadruped robot during turning. First, a foot trajectory is designed in local part reference frame by the synthesis and decomposition of kinematics to reduce the impact on the ground and simplify calculations. The homogeneous coordinate transformation and inverse kinematics are used to achieve turning motion of quadruped robot. Next, we establish the feasible gait turning angle range model based on the stability and kinematics constraints of quadruped robot, and a feasible turning gait can be achieved easily from this model. Finally, the simulation results are given to show the efficiency and validity of the proposed methods.
Load Frequency Regulation using Linear Active Disturbance Rejection Control Technique

In this paper a Linear Active Disturbance Rejection Control (LADRC) technique is utilized to control the fluctuation in frequency for single area and two- area interconnected power system. LADRC consists of feedback controller and Extended State Observer. The total disturbance comprising of internal ambiguity and external perturbation in the plant is estimated in real time with the help of ESO and these estimated total disturbance is compensated by feedback controller. The parameters of LADRC are simplified into two bandwidths using pole placement technique i.e. observer bandwidth and controller bandwidth. The Particle Swarm Optimization algorithm is utilized to find out these two bandwidth and then the optimized value is used in LADRC controller. The effectiveness of the proposed LADRC technique in LFC problem is authenticated by the recently reported technique in the literature. The disturbance rejecting capability and the robustness toward the parameter variations of the proposed method are also shown by the extensive simulation results.
Machine Learning Price Prediction on Green Building Prices

In the era of Industry 4.0, Machine Learning models have become increasingly popular and influential as they are often used to solve different prediction and classification problems in various industries and including the real estate industry. However, to obtain the best combination of these approaches for a good of Green Building (GB) price predictor model, it is important to be identify and require extensive empirical experiments work by identifying the best parameters configurations, techniques, and algorithms. GB is known as a potential approach to improve the performance of the building. Where in Malaysia involving five distinctly different assessment criteria namely, Energy Efficiency (EE), Indoor Environment Quality (EQ), Sustainable Site Planning & Management (SM), Material & Resources (MR), Water Efficiency (WE). This paper provides a report of an empirical study that model building price prediction based on GB dataset that covered Kuala Lumpur District, Malaysia. The experiments used five common algorithms namely Linear Regression, Decision Tree, Random Forest, Ridge and Lasso that tested on a set of real estate building datasets. The result showed the Decision Tree Regressor outperforms the other four algorithms on the test dataset.
ESO-Based Adaptive Robust Control of 3-Axis Winding of Carbon Fiber

Structured and unstructured uncertainties always exist in physical servo systems and degrade their tracking accuracy. Meanwhile, there is a phenomenon that the coupling and mutual restraint exist in the axis of winding device. In this paper, we could apply a practical method named extended state observer (ESO) based adaptive robust control (ARC) to high-precision motion control of 3-Axis Winding of Carbon Fiber. The proposed controller accounts for not only the structured uncertainties (i.e., parametric uncertainties) but also the unstructured uncertainties (i.e., nonlinear friction, external disturbances, and/or unmodeled dynamics) and coupling. Adaptive control for the structured uncertainty and ESO for the unstructured uncertainty are designed for compensating themselves respectively and integrated together via a feedforward cancellation technique. The controller is guaranteed by a feedback robust law and theoretically guarantees the performance of prescribed tracking when we are in the face of various uncertainties, which is very important for high-precision control of motion systems. At the same time, the expectation trajectory of the two rotating motors are triggered by the control output of the linear motor to ensure the cooperativity between the motors.
An Efficient Three-Phase Resonant DC-Link Inverter With Low Energy Consumption

A novel three-phase resonant dc-link inverter with low energy consumption is presented to achieve efficient operation of the inverter. When the auxiliary resonant circuit is used to make the input voltage across bridge-arms become zero during every switching period, the main switching devices could achieve zero-voltage switching turn-on and zero-current switching turn-off. When the metal oxide semiconductor field effect transistor (MOSFET) or the insulated gate bipolar transistor (IGBT) is used as the main switching device, the lossless switching could be completed, and the capacitive turn-on loss caused by the internal junction capacitance of MOSFET and the turn-off loss caused by the tail current of IGBT are eliminated. The working process of the circuit and the realization condition of the soft-switching are analyzed. The experimental results on the three-phase prototype of 2.5 kW show that the switching devices achieve the soft-switching and the performance of the inverter is improved. Therefore, the novel topology is of great significance for improving the efficiency of the inverter.
Zipper classification and defect detection based on computer vision

At present, the research progress of zipper classification and defect detection based on computer vision is slow in China. Generally, manual method is used to detect the appearance quality of zipper products, which is not only inefficient, but also low reliability. This paper proposes a reliable and accurate method. In the actual industrial production, there are various types, shapes and colors of zippers, and in the actual situation, lighting and other factors have a great impact on the detection effect. In view of the above situation, this paper proposes an adaptive region growing algorithm and template matching algorithm. By region growing algorithm, the background is removed to obtain the complete inner region of the zipper. Then, the deflection angle of the zipper is corrected by the smallest external rectangle of the zipper. Finally, the zipper type is determined by template matching algorithm. For defect detection, firstly, the complete zipper is extracted by morphological method, then the defect location is determined by searching the area of connected domain, and finally the defect is determined by gray value comparison. The experimental results show that the above method has high accuracy and execution efficiency in zipper classification and defect detection.
Unequal Access Latency Random Access Protocol for Massive Machine-Type Communications

In this paper, we propose a novel multi-slot pilot allocation (MSPA) random access scheme with unequal access latency (UAL) protection for user equipments (UEs) in massive machine-type communications (mMTC). In order to provide UAL protection, we allocate the UEs into different groups according to their UAL requirements, where the higher priority groups can access in a stringent latency under the required access failure probability (AFP) requirement; while the lower priority groups are able to access with predetermined AFP by allocated multi-slot. Specifically, our generalized UAL-MSPA random access protocol is incorporated into two considered UAL protection schemes, i.e., the independent UAL scheme and the expanded UAL scheme. We derive the closed-form expressions to the AFP and the sum throughout for the investigated UAL schemes. By utilizing these analytical results, a joint parameter optimization problem is formulated for obtaining the minimum access latency under the desired AFP requirements. Detailed simulations validate our analytical results and confirm the efficiency of our proposed UAL schemes. Finally, the UAL-MSPA random access protocol with joint optimized parameters outperforms the existing protocols in terms of achieving high sum throughput and shortening the access latency of low priority group.
Digital Multispectral Images Superimposition Based on Preliminary Calibration or Contour Analysis: Advantages and Disadvantages

The article considers two approaches to solving the problem of multispectral images superimposition in vision systems: based on the search for key points correspondences and preliminary photogrammetric calibration using a universal test object. Shows two variants of the test object design. The advantages and disadvantages of superimposition of the visible and infrared images by two strategies are analyzed.
Design proposal of low noise automotive power supply for high-end audio systems

The paper deals with design and simulation analysis of the switched-mode power supply suited for high-end audio systems within automotive applications. As the focus is given on specific requirements, which are needed within the automotive segment, low voltage input must be considered. The presented approach describes design of the main circuit topology and its parameters. Key operational characteristics are very high-efficiency demand and very high-power density. The performance of proposed design is condition by high-current operation, thus thermal management is key indicator related to requirements on high-efficiency operation. Secondary features of proposed converter must consider high-end demands on audio systems, which are fast dynamics to load change and low noise of output voltage. Almost switched-mode power supplies are defined by high noise of the output voltage, presented approach proposes also solution how to overcome these negative issues.
Quality and reliability of electricity supply

Article presents problems related to disturbances in power networks. The issues will be presented on a global and local level. Disturbing the quality of power affects the correct operation of electric devices. The consequence of disturbances in the quality of energy and interruptions in the supply of electricity are significant financial losses. In particularly hazardous cases, human life may also be endangered. The authors present the causes that affect the deterioration of the efficiency parameters of electricity. Particular attention will be given to recipients which require high reliability of power supply. There will be presented results of research carried out at various points of the power system. The sources of interference will be indicated. The results of the electric energy quality tests for selected power generators will be introduced. Authors submitted ways to improve the quality of electricity. The article will also present the problem of ensuring continuity of electricity power supply. This issue is especially important in case of major failures in power systems (blackouts). System failures are a threat to the proper functioning of the international energy systems. In the summary, authors are going to introduce conclusions regarding the quality of energy in power grids supplying industrial and municipal (individual) customers.
Multi-level Evolutionary Genetic Algorithm for Solving VRPSPD Problem

In the field of industrial production and distribution, in order to meet the production needs of production enterprises, it is necessary to consider the customer’s need for simultaneous picking and delivery in the classic vehicle routing optimization problem, which we called VRPSPD(vehicle routing problem with simultaneous pick-up and delivery). In order to minimize the transportation cost and rationally use the distribution vehicle to balance the workload, establishing a VRPSPD target model that considers both the transportation cost and minimizing the maximum transport length difference between distribution lines, and constructing an improved genetic algorithm(GA) based on the characteristics of the model. The algorithm use the RMPCIM insertion method to improve the global search ability and accelerate the iterative process. At the same time, a new multi-level evolution iterative process is adopted in the population iteration to improve the superiority of the algorithm. The accuracy of the algorithm is verifified by the simulation of 18 sets of Solomon exextended instances. Compared with different algorithms in the literature,the result shows that the algorithm can weigh the objective function and obtain the optimal objective function.
Obstacle-Avoidance Distributed Optimal Coordination of Multiple Euler-Lagrangian Systems

This paper investigates the problem of obstacle avoidance in the distributed optimal coordination for multiple uncertain Euler-Lagrangian (EL) systems. The main challenge focuses on the co-design of obstacle avoidance mechanism and distributed optimization strategy. To address it, a novel safety barrier function in the closed form of path integrals is constructed. By combining distributed optimization algorithm and adaptive tracking control, a distributed coordination algorithm is proposed. Based on Lyapunov method and boundedness analysis for the barrier function, it is proven that the global convergence and collision avoidance of the EL systems can be guaranteed in the presence of parametric uncertainties.