Miracle-dz/UniFAR-FTUs huggingface dataset:
A CMOS BD-BCI Incorporating Stimulation with Dual-Mode Charge Balancing and Time-Domain Pipelined Recording

Electrocorticography (ECoG)-based bi-directional brain-computer interfaces (BD-BCls) have drawn increasing attention due to: (1) the need for concurrent stimulation and recording to restore human sensorimotor functions [1] and (2) decent spatial resolution and signal fidelity along with clinical practicality. On the stimulation side, such BD-BCls may require > 10mA of biphasic current to elicit artificial sensation and > 20V of voltage compliance to accommodate various bio-impedances [1]. The charge mismatch between the two stimulation phases leads to voltage build-up, causing electrode corrosion and tissue damage. Existing charge balancing (CB) techniques, e.g., charge-pack injection (CP1) [2] and time-based charge balancing (TCB) [1], create CB current in the interpulse time interval, leading to unwanted secondary sensations and excessive stimulation artifacts (SAs). For recording, low input-referred noise (1RN) is necessary to acquire small neural signals (NSs) while a large dynamic range (DR) is required to accommodate large SAs. Existing recording systems employ either SAR [1] or continuous-time deltasigma (CT-$\Delta\Sigma$) [3] ADCs (Fig. 4). The former has limited DR due to the DAC mismatch, and the latter suffers from distortion caused by the large-amplitude sharp SAs within the loop delay. Although in [4], the sampling frequency of the $\Delta\Sigma-$ADC is adaptively varied to accommodate SAs, the required settling time is large. To address the above issues, this work presents an ECoG-based BD-BCI that includes: (1) a high-voltage (HV) stimulation system with dual-mode time-based charge balancing (DTCB) and (2) a high-dynamic-range (HDR) time-domain pipelined neural acquisition (TPNA) system.
An Efficient Soft-Input Soft-Output Decoder for Polar Codes in MIMO Iterative Detection System

Iterative detection and decoding (IDD) induces higher performance than separate detection and decoding schemes. However, IDD requires the exchange of soft information between the detector and decoder, thus decoders should be able to process soft input and provide soft output (SISO). Existing SISO polar decoders such as belief propagation (BP) and soft cancellation (SCAN) give poor block error rate (BLER) performance compared with cyclic redundancy check (CRC)-aided successive cancellation list (CA-SCL) decoders, whereas other more sophisticated SISO decoders have high complexity albeit good BLER performance. In this paper, an efficient SISO decoder that provides both high BLER performance and low complexity is proposed for polar codes in IDD system. Specifically, the proposed SISO decoder employs the sum-product algorithm over the sparse parity-check matrix representation of polar codes for lower complexity and uses the best candidates from list decoders for good error correction performance. We comprehensively evaluate the proposed SISO decoder performance in MIMO iterative system. Simulation results show that the proposed SISO decoder outperforms the existing SCAN and BP decoders and achieves similar BLER performance, but lower complexity compared with the state-of-the-art Soft List decoder.
A fast and effective maximum power point tracking method under various weather conditions for grid-connected photovoltaic systems

Photovoltaic systems (PV) are exposed to various weather conditions during the day and getting the maximum power of a PV system is one of its major challenges. While the conventional methods take time to track the maximum power accompanying the fluctuation around the maximum power. Consequently, several metaheuristic algorithms have been presented to overcome the fluctuating around the maximum power. On the other hand optimization methods will search into entire the PV curve to extract the maximum power. This leads to unnecessary search points during the uniform shading conditions which decrease the convergence speed towards the maximum power. In overcoming the tracking time issues as well as the capacity to distinguish between uniform and partial shading conditions, a quick and efficient tracking technique connected to the grid system was presented in this paper. The proposed system, has been tested using MATLAB/SIMULINK, which includes a boost converter to track the maximum power with a sampling time of 0.05 seconds, While a voltage source converter (VSC) is used to transfer the power generated by solar panels to the grid. The simulation results demonstrated that the proposed method was successfully implemented, with an average tracking time of 0.412 s for different weather conditions. Moreover, the comparison with the conventional method of perturb and observe (P& O) has been presented to evaluate the proposed method’s efficiency.
A Cluster Data Management Algorithm Based on Data Correlation of Wireless Sensor Networks

The nodes of wireless sensor networks(WSNs) are extremely power constrained,so energy-efficient data management and prolonging the networks lifetime are the major challenges in this researching area.With node calculation and analysis function,a cluster data management algorithm based on data correlation is presented for the network formed with clusters.Cluster head analyzes its member data correlation using error function and fuzzy function,and gains the integrative supportabilities of its members sensing data.Based on the integrative supportabilities,the members of the cluster are classified as conflict nodes,complementary nodes and reliable nodes,and the related nodes scheduling rules for the different types are given to reduce the energy consumption of the cluster and balance the energy consumption among members,and the data fusion mechanism of the cluster head is given.The simulation results demonstrate that the presented algorithm can realize the cluster data classification management,reduce the amount of the data transmission,and prolong the network lifetime.
Bianchi Type-III Cosmological Model with Linear Equation of State

We have studied Bianchi type-III cosmological model with linear equation of state (EoS) p = , where and are constants, in General Relativity. In order to obtain the solution of the field equations we have used the relation between shear scalar and expansion scalar . The geometrical and physical aspects of the model are also studied.
Bayesian Bounds For Parameter Estimation And Nonlinear Filteringtracking

Thank you for downloading bayesian bounds for parameter estimation and nonlinear filteringtracking. Maybe you have knowledge that, people have look numerous times for their favorite readings like this bayesian bounds for parameter estimation and nonlinear filteringtracking, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their computer.
Bandgap Engineering of Two-Dimensional Double Perovskite Cs4AgBiBr8/WSe2 Heterostructure from Indirect Bandgap to Direct Bandgap by Introducing Se Vacancy

Heterostructures based on layered materials are considered next-generation photocatalysts due to their unique mechanical, physical, and chemical properties. In this work, we conducted a systematic first-principles study on the structure, stability, and electronic properties of a 2D monolayer WSe2/Cs4AgBiBr8 heterostructure. We found that the heterostructure is not only a type-II heterostructure with a high optical absorption coefficient, but also shows better optoelectronic properties, changing from an indirect bandgap semiconductor (about 1.70 eV) to a direct bandgap semiconductor (about 1.23 eV) by introducing an appropriate Se vacancy. Moreover, we investigated the stability of the heterostructure with Se atomic vacancy in different positions and found that the heterostructure was more stable when the Se vacancy is near the vertical direction of the upper Br atoms from the 2D double perovskite layer. The insightful understanding of WSe2/Cs4AgBiBr8 heterostructure and the defect engineering will offer useful strategies to design superior layered photodetectors.
Photoplethysmogram Biometric Authentication Using a 1D Siamese Network

In the head-mounted display environment for experiencing metaverse or virtual reality, conventional input devices cannot be used, so a new type of nonintrusive and continuous biometric authentication technology is required. Since the wrist wearable device is equipped with a photoplethysmogram sensor, it is very suitable for use for nonintrusive and continuous biometric authentication purposes. In this study, we propose a one-dimensional Siamese network biometric identification model using a photoplethysmogram. To maintain the unique characteristics of each person and reduce noise in preprocessing, we adopted a multicycle averaging method without using a bandpass or low-pass filter. In addition, to verify the effectiveness of the multicycle averaging method, the number of cycles was changed and the results were compared. Genuine and impostor data were used to verify the biometric identification. We used the one-dimensional Siamese network to verify the similarity between the classes and found that the method with five overlapping cycles was the most effective. Tests were conducted on the overlapping data of five single-cycle signals and excellent identification results were observed, with an AUC score of 0.988 and an accuracy of 0.9723. Thus, the proposed biometric identification model is time-efficient and shows excellent security performance, even in devices with limited computational capabilities, such as wearable devices. Consequently, our proposed method has the following advantages compared with previous works. First, the effect of noise reduction and information preservation through multicycle averaging was experimentally verified by varying the number of photoplethysmogram cycles. Second, by analyzing authentication performance through genuine and impostor matching analysis based on a one-dimensional Siamese network, the accuracy that is not affected by the number of enrolled subjects was derived.
Intersection Analysis Using Computer Vision Techniques with SUMO

This paper presents intersection analysis using computer vision techniques with Simulation of Urban MObility (SUMO). At first, an efficient deep-visual tracking pipeline is proposed by using the off-the-shelf YOLO object detection architecture and cascading it with a discriminative correlation filter (CSRT) to produce reliable trajectories for traffic analysis of vehicles and pedestrians. While a variety of traffic measurements can be directly estimated from the extracted trajectories (e.g., speed, turning movement count), a method of incorporating turning movement count (TMC) within SUMO is proposed in order to mimic a realistic traffic flow for an observed intersection and its comprehensive analysis. Experimental evaluations on developed tracking system implies that YOLOv5 variant is the best for traffic cameras and after appropriate fine-tuning using the UNLV Pedestrian data-set, the YOLOv5 performance manifested a significant improvement with value of 0.62 in recall value. The tracking system is further employed for monitoring three other intersections in the downtown of Las Vegas and turning movement counts were estimated for peak hours of morning and evening time of one day 7:00-9:00 and 16:00-18:00) with 15 minutes intervals. Finally, the intersection design including traffic signals with estimated TMC are used to calibrate SUMO to provide critical parameters (e.g., lane density, travel time, occupancy) for traffic signal performance evaluation and comprehensive intersection analysis. The signal design treatment demonstrates significant improvement for travel times and simulation results indicates that turning left ratio is a crucial factor affecting the travel time of vehicles on each intersection leg.
Scattering Theory for One-Dimensional Schr¨ odinger Operators with Measures

The basic quantum scattering formalism for one-dimensional Schrödinger operators is generalized to potentials given as measures. This is done for direct scattering and inverse scattering. We see that most of the classical results for real potentials stay valid in the case of the measure-valued time-independent Schrödinger equation. Zusammenfassung Die grundlegenden Resultate der eindimensionalen Streutheorie für Schrödinger Operatoren werden für maßwertige Potentiale verallgemeinert. Dies wird für direkte und inverse Streuung durchgeführt und es stellt sich heraus, dass die meisten klassischen Resultate für reelle Potentiale gültig bleiben.
Cellular Protein Aggregates: Formation, Biological Effects, and Ways of Elimination

The accumulation of protein aggregates is the hallmark of many neurodegenerative diseases. The dysregulation of protein homeostasis (or proteostasis) caused by acute proteotoxic stresses or chronic expression of mutant proteins can lead to protein aggregation. Protein aggregates can interfere with a variety of cellular biological processes and consume factors essential for maintaining proteostasis, leading to a further imbalance of proteostasis and further accumulation of protein aggregates, creating a vicious cycle that ultimately leads to aging and the progression of age-related neurodegenerative diseases. Over the long course of evolution, eukaryotic cells have evolved a variety of mechanisms to rescue or eliminate aggregated proteins. Here, we will briefly review the composition and causes of protein aggregation in mammalian cells, systematically summarize the role of protein aggregates in the organisms, and further highlight some of the clearance mechanisms of protein aggregates. Finally, we will discuss potential therapeutic strategies that target protein aggregates in the treatment of aging and age-related neurodegenerative diseases.
"Should the use the Vampiricide Paste be Abolished in the Control of the Hematophagous Bat, the Main Transmitter of Rabies to Humans and Herbivores in Latin America?"

The Latin American livestock industry is strongly affected by the presence of the hematophagous bat Desmodus rotundus (É. Geoffroy, 1810) due to its effect on meat and milk production and the transmission of rabies to herbivores, causing the loss of over 50 million dollars a year [1]. Furthermore, this species has also an impact on public health because of spoliations and rabies transmission to humans [2]. Since the 1970’s, many Latin American countries implemented programs for the control of rabies transmitted by D. rotundus to herbivores and humans [3]. However, in these 50 years, the programs have had a small contribution to reducing the negative effects of rabies [3]. From the beginning, the population control of D. rotundus in these programs was carried out with an extensive use of vampiricide paste by two ways: Use around the wound on bovines e equines and topical application on bats [4,5].
Computational e!ects, algebraic theories and normalization by evaluation

This dissertation is concerned with modeling and reasoning about impure ML-like higherorder programs. Our work is based on the algebraic theories of computational effects proposed by Plotkin and Power. In particular, we present an extension from the algebraic value and effect theories to a fine-grained call-by-value intermediate language. Whilst this extension has a straightforward definition and is intuitively correct, the proof of its conservativeness requires extensive work. Before one is able to correctly reason about the terms in the value and effect theories and the corresponding terms in the intermediate language, it is necessary to effectively decide provable equality in the intermediate language. As a result, we spend a significant proportion of this dissertation on developing a suitable normalization by evaluation (NBE) algorithm to compute canonical normal forms in the intermediate language. The comparison of these normal forms provides us with the necessary decision procedure for proving the conservativity theorem. The NBE algorithm we define is a generalization of the usual presentations of NBE where normal forms are identified up to equality rather than modulo the given value and effect theories. However, the usual normalization results arise as special cases when the value and effect theories do not contain equations. We have also formalized the syntax of the intermediate language together with the formally verified NBE algorithm in the interactive theorem prover and functional programming language Agda.
TURBULENCE AND PROTON–ELECTRON HEATING IN KINETIC PLASMA

Analysis of particle-in-cell simulations of kinetic plasma turbulence reveals a connection between the strength of cascade, the total heating rate, and the partitioning of dissipated energy into proton heating and electron heating. A von Karman scaling of the cascade rate explains the total heating across several families of simulations. The proton to electron heating ratio increases in proportion to total heating. We argue that the ratio of gyroperiod to nonlinear turnover time at the ion kinetic scales controls the ratio of proton and electron heating. The proposed scaling is consistent with simulations.
Emerging Roles of Mesenchymal Stem/Stromal-Cell-Derived Extracellular Vesicles in Cancer Therapy

Despite the tremendous efforts of many researchers and clinicians, cancer remains the second leading cause of mortality worldwide. Mesenchymal stem/stromal cells (MSCs) are multipotent cells residing in numerous human tissues and presenting unique biological properties, such as low immunogenicity, powerful immunomodulatory and immunosuppressive capabilities, and, in particular, homing abilities. Therapeutic functions of MSCs are mediated mostly by the paracrine effect of released functional molecules and other variable components, and among them the MSC-derived extracellular vesicles (MSC-EVs) seem to be one of the central mediators of the therapeutic functions of MSCs. MSC-EVs are membrane structures secreted by the MSCs, rich in specific proteins, lipids, and nucleic acids. Amongst these, microRNAs have achieved the most attention currently. Unmodified MSC-EVs can promote or inhibit tumor growth, while modified MSC-EVs are involved in the suppression of cancer progression via the delivery of therapeutic molecules, including miRNAs, specific siRNAs, or suicide RNAs, as well as chemotherapeutic drugs. Here, we present an overview of the characteristics of the MSCs-EVs and describe the current methods for their isolation and analysis, the content of their cargo, and modalities for the modification of MSC-EVs in order for them to be used as drug delivery vehicles. Finally, we describe different roles of MSC-EVs in the tumor microenvironment and summarize current advances of MCS-EVs in cancer research and therapy. MSC-EVs are expected to be a novel and promising cell-free therapeutic drug delivery vehicle for the treatment of cancer.
An Elusive Target: Inhibitors of JC Polyomavirus Infection and Their Development as Therapeutics for the Treatment of Progressive Multifocal Leukoencephalopathy

Progressive multifocal leukoencephalopathy (PML) is a rare demyelinating disease caused by infection with JC Polyomavirus (JCPyV). Despite the identification of the disease and isolation of the causative pathogen over fifty years ago, no antiviral treatments or prophylactic vaccines exist. Disease onset is usually associated with immunosuppression, and current treatment guidelines are limited to restoring immune function. This review summarizes the drugs and small molecules that have been shown to inhibit JCPyV infection and spread. Paying attention to historical developments in the field, we discuss key steps of the virus lifecycle and antivirals known to inhibit each event. We review current obstacles in PML drug discovery, including the difficulties associated with compound penetrance into the central nervous system. We also summarize recent findings in our laboratory regarding the potent anti-JCPyV activity of a novel compound that antagonizes the virus-induced signaling events necessary to establish a productive infection. Understanding the current panel of antiviral compounds will help center the field for future drug discovery efforts.
Scores: Ordinal Data with Few Categories—How Should They Be Analyzed?

Scores, that is, ordinal data with few categories, are frequently encountered in practical biopharmaceutical research, but rarely in statistical textbooks and computer programs. This has caused inefficient and even incorrect procedures to appear in the literature, for example, application of the standard Chi-square test or dichotomization and subsequent evaluation of the resulting 2 × 2 table. Four different tests are recommended: 1. the Wilcoxon-Mann-Whitney test, 2. the Mantel-Haenszel test, 3. the arbitrary score test, and 4. all possible score tests (Streitberg-Röhmel), either asymptotic or preferably exactly based on permutational distributions. The discussion includes the relative merits of these tests, alternative procedures, and related areas such as effect size measures and sample size calculations. Two examples from the literature are used to demonstrate that test statistics and other information are easily obtained by using the personal computer (PC) program TESTIMATE.
An Assessment of Streambank Erosion Rates in Iowa

Streambank erosion is a major contributor to watershed suspended sediments and phosphorus exports in many regions, but in Iowa and other midwestern states, the load contribution from streambanks is not considered by state nutrient reduction strategies. The study’s objectives were to evaluate the annual bank erosion rates measured in Iowa using erosion pins and aerial imagery and assess how recession rates vary across space, time, and stream order. The overall goal was to determine whether there are global similarities to these streambank recession rates that could be generalized and scaled up for regional assessments using data from Iowa-based erosion pin studies and original research on stream migration rates. At the erosion pin sites, the recession rates averaged approximately 11 cm yr−1 in third-order streams and, when combined with stream migration analyses, we observed scaling associated with bank recession rates at longer time scales across a range of stream orders. More bank recession occurs in larger streams and rivers with greater discharge from larger watershed areas and an increase in stream power. Variations in these bank recession rates were observed in Iowa landform regions mainly due to differences in geology and the composition of the streambank sediments. The study’s results provide a temporal and spatial context for evaluating streambank recession in Iowa and the glaciated Midwest.