Miracle-dz/UniFAR-FTUs huggingface dataset:
In vivo genome-wide CRISPR screens identify SOCS1 as intrinsic checkpoint of CD4+ TH1 cell response

Description Inactivation of SOCS1 optimizes adoptive T cell therapy including human CAR-T cell composition and efficacy. A key to the CAR The therapeutic effects of adoptive T cell therapy (ATCT) are influenced by limited clonal expansion of antigen-experienced CD4+ T cells, which restrict their own proliferation after transfer. The cell-intrinsic factors that regulate clonal proliferation of antigen-experienced CD4+ T cells are not well defined. Sutra Del Galy et al. performed an in vivo genome-wide CRISPR screen to identify genes that controlled antigen-specific CD4+ T cell proliferation after transfer. They identified SOCS1 as a critical node that functions downstream of IFN-γ and IL-2 signaling to inhibit CD4+ T cell proliferation, survival, and effector function. Inactivation of SOCS1 in human CAR-T cells improved antitumor responses, indicating that SOCS1 is a major negative checkpoint in ATCT, representing an attractive target for optimizing CAR-T cell therapies. Although CD8+ T cells undergo autonomous clonal proliferation after antigen stimulation in vivo, the expansion of activated CD4+ T cells is limited by intrinsic factors that are poorly characterized. Using genome-wide CRISPR-Cas9 screens and an in vivo system modeling of antigen-experienced CD4+ T cell recruitment and proliferation during a localized immune response, we identified suppressor of cytokine signaling 1 (SOCS1) as a major nonredundant checkpoint imposing a brake on CD4+ T cell proliferation. Using anti–interleukin-2 receptor (IL-2R) blocking antibodies, interferon-γ receptor (IFN-γR) knockout mice, and transcriptomic analysis, we show that SOCS1 is a critical node integrating both IL-2 and IFN-γ signals to block multiple downstream signaling pathways abrogating CD4+ T helper 1 (TH1) cell response. Inactivation of SOCS1 in both murine and human CD4+ T cell antitumor adoptive therapies restored intratumor accumulation, proliferation/survival, persistence, and polyfunctionality and promoted rejection of established tumors. However, in CD8+ T cells, SOCS1 deletion did not affect the proliferation but rather improved survival and effector functions, which allowed for optimal therapeutic outcome when associated with SOCS1 inactivation in CD4+ T cells. Together, these findings identify SOCS1 as a major intracellular negative checkpoint of adoptive T cell response, opening new possibilities to optimize CAR-T cell therapy composition and efficacy.
The detection of the state of health of lead-acid batteries

The authors have tested, for about 6 years, four different types of lead-acid batteries under cycling, floating and self-discharge conditions. A correlation has been established between the operating data (current, voltage) and the capacity measured during these tests by a fuzzy cluster approach. The data sets were categorised in three fuzzy clusters. These three fuzzy clusters: (1) starting phase (new phase); (2) working phase; and (3) capacity drop phase should able to describe the ageing behaviour of the battery sufficiently. For all fuzzy analysed data, they found a continuous correlation between the fuzzy classifications and the operation time of the battery, which themselves correlate to the development of the battery state of health. This fuzzy technique allows the prediction of the future behaviour of the battery by measuring the operating parameters (current, voltage) without explicitly checking the capacity, if the fuzzy set is determined once as a kind of calibration curve.
Variability Modeling and Statistical Parameter Extraction for CMOS Devices

Semiconductor technology has been scaling down at an exponential rate for many decades, yielding dramatic improvements in power, performance and cost, year after year. Today’s advanced CMOS transistors have critical dimensions well below 24nm. This means that controlling the manufacturing process is increasingly difficult. Process and material fluctuations cause device and circuit characteristics to deviate from design goals, and introduce significant device-to-device variability due to spatial variations across silicon wafers. Accurate modeling of these spatial process variations has become critical to both foundries and circuit designers that seek optimal power/speed/area balance. To understand the nature of spatial process variations, we first carried out a comprehensive variability analysis of data measured from thousands of variability-sensitized test structures, including ring oscillators, SRAM bit cells and their internal transistors. We manufactured these test chips using early stage 90nm and 45nm commercial semiconductor processes. We proposed a hierarchical variability model to capture the systematic and random components of device parameter variations across silicon wafers, and across chips. The detailed decomposition of the process variation profile reveals significant across-wafer systematic component for the delay and leakage of ring oscillators, and across-chip systematic component for the read/write margins of SRAM bit cells, as well as their internal transistors. The proper modeling of each hierarchical component proved to be crucial for the accurate estimation of the statistics of device performance distribution and its parametric yield.The knowledge gained about process variation from carefully designed test structures was leveraged into estimating the variation and parametric yield of new devices and circuits. This was accomplished by improved the statistical compact model parameter extraction methodology, and by proposing a stepwise parameter selection method. We used a normalized notional confidence interval and, and the sum of squares of fitting residuals as extraction and fitting quality criteria. This allowed us to determine the essential model parameters for accurate fitting over a large number of transistors. We applied this methodology to EKV and PSP with both simulated and experimental data, demonstrating its effectiveness. Finally, we combined the results from statistical parameter extraction with the hierarchical spatial variability model. This, compared to traditional methods, produced much-improved estimates of device performance and manufacturing yield.
ReactNet: self-adjusting architecture for networked systems

Providers today run numerous applications on their networks with diverse quality of service requirements. An appealing vision to deal with the resulting complexity of network operation, is to give more control to the network, allowing it to become more autonomous and to dynamically "self-adjust", to meet its requirements. This paper presents an architecture, ReactNet, to realize this vision, by leveraging two enabling technologies. First, we use programmable dataplanes and P4 to get accurate information about the traffic patterns the network currently serves. Second, we leverage Machine Learning (ML) techniques to process this information and react to the network changes dynamically.
English Teaching Practice with Multimodal Pedagogy for Chinese Non-state-owned College Students

This study aims to explore specific methods in College English teaching in Non-state-owned colleges under the framework of multi-modal pedagogy, with which teachers can create authentic contexts and use multiple modalities to stimulate student auditory and visual senses. In vocabulary teaching, teachers can use vivid pictures and supplement the knowledge of root and etymology; in grammar teaching, teachers can introduce videos, provide authentic corpora resources, and advocate independent induction; in facilitating text comprehension, teachers may design activities according to student personal experience, and encourage students to see through the appearance to perceive the essence; in composition guidance, teachers may refer corpora and other AI writing correction platforms. With the facilitation of Chaoxing Super Star online learning system, teachers can realize the integration of online and offline resources in and out of class, enhancing student learning motivation and moral education.
Location of partial discharge source on distribution line by measuring emitted pulse-train electromagnetic waves

This paper presents a new system designed for detecting the pulse-train electromagnetic waves emitted from partial discharge. The authors utilize a VHF wideband antenna array as electromagnetic sensors and calculate the time delay between them in order to locate partial discharge in 3 dimensions. The standard cross correlation and smoothed coherence transform are used and compared to estimate the time delay. The time delay is then statistically calculated from a lot of pulses in one time of detection. The proposed system shows the effectiveness in the detection and location of partial discharge, and enhances the reliability of a method. A field experiment is performed to approach the concept of this method.
Reliability investigations for flip-chip on flex using different solder materials

Flip chip assembly on flexible organic substrates is facing increasing interest. In consumer products such as mobile phones or pagers, the assembly of the drivers of the LCD displays can be realized this way. Also there is a growing demand for Chip Size Packages (CSP). As a low cost version, CSPs based on flexible interposers have been developed. Especially for this application the reliability of the assemblies is important. Little is known about the aging behavior of these packages with different solder materials such as Pb-Sn and Au-Sn on standard tape metallizations as Cu-Au or Cu-Ni-Au. The impact of the bumping technology on the reliability is also a subject. Electroplated bumps are studied in comparison with meniscus bumps. Based on electroless Ni bumping, this cost effective technology is especially suited for flip chip on flex as the solder volume deposited is very small. In order to make useful predictions about the reliability of a metallurgical system it is necessary to understand the basic reactions involved. The scope of the investigations presented in this paper is to gain the data on interdiffusion in these systems, on the formation and growth of intermetallic phases. The impact of the presence of intermetallics and voids on the mechanical reliability is determined. The test program includes annealing of the flip chip assemblies at different temperatures and thermal cycling. Electrical measurements (Daisy chain) and shear tests are performed to determine the electrical and mechanical degradation of the solder joints.
Electric Spring-Based Power Flow Routers for Economic Enhancement of the Microgrid Cluster Based on Smart Homes

High penetrations of intermittent renewable energy sources (RES) in smart homes necessitate the power flow management. The recently proposed electric springs (ES), as alternatives to the conventional energy storage systems (ESS), can regulate the power flow with smaller battery capacities as compared to that of the ESS. This greatly increases the economic value of smart homes, as batteries of the domestic ESS are quite expensive. In this paper, a concept of having the ES equipped with communicate ports that connect with a home user’s portable devices, hereon known as ES-based power flow routers (PFR), to mitigate the distribution line power loss, is proposed. By raising the power utilization, the economic value can be further enhanced. Case studies are carried out in MATLAB/SIMULINK to verify the effectiveness of ES in stabilizing a five-bus home microgrid, instead of using the conventional ESS. Besides, it is validated that the ES-based PFR can save substantial payments of electricity bills through the reduction of the distribution power loss.
Deterministic Gaussian Sampling With Generalized Fibonacci Grids

We propose a simple and efficient method to obtain unweighted deterministic samples of the multivariate Gaussian density. It allows to place a large number of homogeneously placed samples even in high-dimensional spaces. There is a demand for large high-quality sample sets in many nonlinear filters. The Smart Sampling Kalman Filter (S2KF), for example, uses many samples and is an extension of the Unscented Kalman Filter (UKF) that is limited due to its small sample set. Generalized Fibonacci grids have the property that if stretched or compressed along certain directions, the grid points keep approximately equal distances to all their neighbors. This can be exploited to easily obtain deterministic samples of arbitrary Gaussians. As the computational effort to generate these anisotropically scalable point sets is low, generalized Fibonacci grid sampling appears to be a great new source of large sample sets in high-quality state estimation.
Prognosis of Transformer Health through Oil Data Analysis

Transformer oil test data are commonly used to monitor transformer health. By comparing test data with IEC 60422 recommended values, transformer oil condition in terms of good, fair and poor can be interpreted. This paper demonstrates the importance of sampling temperature in interpreting moisture and breakdown voltage results. A potential issue with IEC 60422 is also shown by the mismatch in condition interpreted based on dielectric dissipation factor and resistivity. Also, possible combination of interfacial tension and acidity is suggested in representing transformer ageing.
Impact of Price-Elastic Heat Demand on the Equilibrium of Distribution Power Market

With the development of the power market, pricing-based demand response is becoming a promising mechanism in the distribution network, where some flexible load demand such as heat demand varies according to the real-time locational marginal price (LMP). Such responsive load may leave a complex impact on the power market, as responsive load and LMP will affect each other, and eventually reaches an equilibrium. As a result, the sensitivity of the LMP becomes harder to analyze because the sensitivity is related to the variation of the equilibrium point. In this paper, we point out that the challenge of such sensitivity analysis actually comes from the fact that the pricing-based demand response explicitly introduces dual variables into the optimization problem, which has not yet been considered in the existing sensitivity studies. Based on such analysis, an advanced form of sensitivity analysis is proposed to acquire the variation of the LMP with load demand when considering demand response in the distribution system.
NASA Contractor Report 203877 (1997) Spectral and Spectral-Element Methods: Lecture Notes in High Performance Computational Physics

This is an introduction to spectral and spectral-element methods for the numerical simulation of incompressible and compressible hydrodynamics. The theory behind the methods is presented as well as some examples of working codes. The work is reasonably self-contained in that there is included a discussion of numerical quadrature, weighted residual methods, Fast Fourier Transforms and time marching schemes. The work is based on a set of lectures by the author for the NASA Summer School for High Performance Computational Physics, held at Goddard Space Flight Center. NASA CR203877: Spectral and Spectral-Element Methods ii
VEGA and MOGA an Approach to Multi-Objective Optimization

VEGA and MOGA are most common approaches to solve a multi-objective optimization problem. Multi- objective optimization problem arises when we have more than one conflicting objectives, for example: In two objectives the task is to maximize the first objective, while to minimize the other, then there exists no single solution while A set of solutions(trade-offs) is found in which we can never say any solution is better than others with additional information. The fundamental principle of VEGA and MOGA are presented with manual calculations. We also present the advantage and disadvantage of each approach.
Distributed Voltage Control in Distribution Network with Area Partition

The deployment of distributed generations(DGs) poses increasing burden to sensing and communication in centralized voltage control. To address this issue, a distributed voltage control method is proposed in this paper. In the proposed method, the whole distribution network is separated into several areas and each area is equipped with a sub-controller. The idea of alternating direction method of multipliers(ADMM) is used for the distributed voltage control design by exchanging information of boundary variables between two adjacent areas. In this way, the requirements for data collection and communication is reduced. Case studies on a modified IEEE 33 bus system with three area partition demonstrate the performance of the proposed distributed voltage control method.
Vehicle license plate recognition based on geometry restraints and multi-feature decision

Vehicle license plate (VLP) recognition is of great importance to many traffic applications. Though researchers have paid much attention to VLP recognition there has not been a fully operational VLP recognition system yet for many reasons. This paper discusses a valid and practical method for vehicle license plate recognition based on geometry restraints and multi-feature decision including statistical and structural features. In general, the VLP recognition includes the following steps: the location of VLP, character segmentation, and character recognition. This paper discusses the three steps in detail. The characters of VLP are always declining caused by many factors, which makes it more difficult to recognize the characters of VLP, therefore geometry restraints such as the general ratio of length and width, the adjacent edges being perpendicular are used for incline correction. Image Moment has been proved to be invariant to translation, rotation and scaling therefore image moment is used as one feature for character recognition. Stroke is the basic element for writing and hence taking it as a feature is helpful to character recognition. Finally we take the image moment, the strokes and the numbers of each stroke for each character image and some other structural features and statistical features as the multi-feature to match each character image with sample character images so that each character image can be recognized by BP neural net. The proposed method combines statistical and structural features for VLP recognition, and the result shows its validity and efficiency.
Early Warning System for Physical Distancing Detection in the Prevention of COVID-19 Spread

The widespread of the COVID-19 virus poses a threat to human health; hence, the prevention of the Covid-19 spread needs to immediately be realized. Physical distancing is a way that can reduce the COVID-19 spread. However, human negligence in implementing physical distancing due to the lack of strict supervision often occurs. Many studies, one of which is Camera Machine learning-based system, have attempted to solve this problem, but they focused on detection accuracy without considering device mobility that, in fact, is needed to detect physical distancing. We proposed a system that can provide early warnings against physical distancing negligence on the constrained computers. The system was built using a constrained computer and a camera with high mobility to facilitate its movement. The system used Tensorflow Lite as a framework to do machine learning and the SSD MobileNet pretrain model was used to classify human detection. Test scenarios applied included distance accuracy and detection accuracy. The system had accuracy in detecting physical distancing negligence with 86% accuracy and 87% F -1 Score. The system built can run on a constrained computer by used 4.59 MB of memory that is 0.001 % of total memory, and the cumulative utilization of four cores was 139%. The system performs 10%better in accuracy than a similar related work.
Integrated mission planning architectures for unmanned underwater vehicles

Deployment and operational capability of unmanned underwater vehicles (UUVs) can be enhanced by leveraging mission planning technologies that have been developed for cruise missiles (CM), unmanned air vehicle (UAV) control, and space applications. The authors discuss the architecture, design philosophy, and application of command, control, communication, classification, and information (C/sup 4/I) technology to future UUV operations. They provide a framework of the approach for an integrated UUV and mission planning architecture. This approach has resulted from past CM and UAV work, but is tailored to the unique UUV environment. In particular, the unique UUV environment consists of low bandwidth communications, extended mission times, and limited space available for the UUV planning and controlling element (UPCE). The architecture shown illustrates a low-cost/low-risk functional approach to the planning, control, and monitoring of UUV systems. The intent of this architecture is to provide the UUV with sufficient autonomy to require minimum attention by the mission planner and controller. <<ETX>>
Modeling and Control of Isolated Modular Hydrokinetic Energy Systems

This paper assesses the performance of renewable energy generation systems based on hydrokinetic turbines and permanent magnet synchronous generators (PMSG). For this purpose, in each unit the turbine is modeled through its power curve and the PMSG model is based the well-known dq-frame reference approach. The electronic interface with the load is composed of a boost converter, used for maximum power point tracking (MPPT), and a buck converter for DC bus voltage stabilization. An electric power generation system is then composed by three hydrokinetic generators and its behavior is tested for variable load and river speed. The results demonstrate that these systems can provide support for electric energy generation with high efficiency, keeping constant the load voltage. Also, it is possible to confirm that the parallel operation of these units at different points of a river can be realized due to control systems. Finally, the simulation allows to visualize that normal operation of generation units can be maintained even when one of them operates without the MPPT algorithm.
Antenna technologies for multimedia mobile satellite communications

Today's space industry is living in a fast growing phase of its history, due to the enormous success of wireless cellular telephony and the worldwide diffusion of the Internet. In the field of satellite personal communication networks (S-PCNs), the three main non-geostationary systems (Iridium, Globalstar, ICO), together with some regional GEO systems (e.g. AceS, Thuraya), triggered a substantial breakthrough in satellite active phased array techniques. Major advancements were achieved in printed array technologies; receive and transmit RF modules miniaturization and performances; passive Butler matrices and beam-forming networks in multi-layer, printed-circuit technology; digital beam-forming and signal processing techniques; thermo-mechanical design and integration techniques. Moreover, the above mentioned regional GEO satellites have qualified in orbit unfurlable reflectors of up to 15 meter diameter. Further antenna developments are likely to be required for the future provision of Third Generation (3G) mobile communications via satellite (S-UMTS), as a result of more stringent requirements in terms of link performance (G/T, EIRP) and frequency reuse. The forthcoming "broadband wave", i.e. commercial broadband multimedia communications satellites (a mix of LEO, medium Earth orbit and geostationary orbit spacecraft), operating at Ku-band and Ka-band, will rely on the development of both multibeam reflector antennas and of active multiple beam antennas in a direct-radiating array configuration.