Miracle-dz/UniFAR-FTUs huggingface dataset:
Design optimization and manufacturing of holographic windows for daylighting applications in buildings

The function of holographic optical elements in daylighting applications is to redirect sunlight from the immediate window area into the rear of a room in order to illuminate the darker regions and to reduce glare. A prerequisite for the successful application of holograms in daylighting systems is the solution of the problems of white light diffraction and of uniform holographic properties across a large aperture. This paper presents theoretical and experimental investigations of these two problems. It will be shown that white light diffraction is possible and that uniform diffraction efficiencies over large apertures are attainable.
Optimization Design based on BP Neural Network and GA Method

This study puts forward one kind optimization controlling solution method on complicated system. At first modeling using neural network then adopt the real data to structure the neural network model of pertinence, make the parameter to seek to the neural network model excellently by mixing GA finally, thus got intelligence to the complicated system to optimize and control. The method can identify network configuration and network training methods. By adopting the number coding and effectively reducing the network size and the network convergence time, increase the network training speed. The study provides this and optimizes relevant MATLAB procedure which controls the method, so long as adjust a little to the concrete problem, can believe this procedure well the optimization of the complicated system controls the problem in the reality of solving.
Event Stream Super-Resolution via Spatiotemporal Constraint Learning

Event cameras are bio-inspired sensors that respond to brightness changes asynchronously and output in the form of event streams instead of frame-based images. They own outstanding advantages compared with traditional cameras: higher temporal resolution, higher dynamic range, and lower power consumption. However, the spatial resolution of existing event cameras is insufficient and challenging to be enhanced at the hardware level while maintaining the asynchronous philosophy of circuit design. Therefore, it is imperative to explore the algorithm of event stream super-resolution, which is a non-trivial task due to the sparsity and strong spatio-temporal correlation of the events from an event camera. In this paper, we propose an end-to-end framework based on spiking neural network for event stream super-resolution, which can generate high-resolution (HR) event stream from the input low-resolution (LR) event stream. A spatiotemporal constraint learning mechanism is proposed to learn the spatial and temporal distributions of the event stream simultaneously. We validate our method on four large-scale datasets and the results show that our method achieves state-of-the-art performance. The satisfying results on two downstream applications, i.e. object classification and image reconstruction, further demonstrate the usability of our method. To prove the application potential of our method, we deploy it on a mobile platform. The high-quality HR event stream generated by our real-time system demonstrates the effectiveness and efficiency of our method.
Estimize Bull speed using Back propagation

Now knowledge pre-processing, model and reasoning issues, power metrics, quality issues, post-processing of discovered structures, visualization, and on-line change is best challenge. In this paper Neural Network based forecasting of stock prices of selected sectors under Bombay Stock Exchange show that neural networks have the power to predict prices albeit the volatility in the markets(9). The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. Artificial Neural Networks are being counted as the wave of the future in computing. They are indeed self-learning mechanisms which don't require the traditional skills of a programmer. Back propagation is one of the approaches to implement concept of neural networks. Back propagation is a form of supervised learning for multi-layer nets. Error data at the output layer is back propagated to earlier ones, allowing incoming weights to these layers to be updated. It is most often used as training algorithm in current neural network applications. In this paper, we apply data mining technology to stock market in order to research the trend of price; it aims to predict the future trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier structure of the neural network, namely input layer, hidden layer and output layer. Finally, we get a better predictive model to improve forecast accuracy.
A NewMethod of Matrix Decomposition to Get the Determinants and Inverses of r-Circulant Matrices with Fibonacci and Lucas Numbers

We use a new method of matrix decomposition for r-circulant matrix to get the determinants of An � Circr(F1, F2, . . . , Fn) and Bn � Circr(L1, L2, . . . , Ln), where Fn is the Fibonacci numbers and Ln is the Lucas numbers. Based on these determinants and the nonsingular conditions, inverse matrices are derived. +e expressions of the determinants and inverse matrices are represented by Fibonacci and Lucas Numbers. In this study, the formulas of determinants and inverse matrices are much simpler and concise for programming and reduce the computational time.
The Role of Carbon Accounting in Managing the Climate Change Risks: Literature Review

Carbon accounting creates a competitive advantage for companies; henceforth, policymakers in companies and institutions nowadays are relying on carbon accounting to take decisions regarding climate change and environmental conservation. The development of carbon accounting would addeconomic value to companies and public institutions. The importance of this study is to demonstrate the significance of carbon accounting and how it relates to climate risk management.Besides, this study shows the impact of carbon accounting disclosure on raising the awareness of investors.
Feasibility of using calcined Patinopecten yessoensis shells for fluoride removal and investigation of the fluoride removal mechanism

This study assessed the applicability of seafood waste, Patinopecten yessoensis shells (PY), for fluoride removal. PY was modified by calcination to improve its fluoride removal capacity, and 800°C was the optimum calcination temperature its modification as a fluoride adsorbent. Fluoride adsorption by PY-800 was confirmed due to the formation of fluorite (CaF2) through X-ray diffraction analysis. The fluorine adsorption to PY-800 reached equilibrium within 2 h at an initial fluoride concentration of 50 mg/L, and a reaction time of 36 h was required to reach equilibrium at 200 mg/L. Both the pseudo-first-order and pseudo-second-order models described the kinetic adsorption data well. In isotherm studies, the fluoride adsorption of PY-800 was best suited to the Langmuir model. The enthalpy change of fluoride adsorption was 45.49 kJ/mol, ranging in the boundary between physical and chemical adsorption. Fluoride adsorption decreased from 113.13 to 86.01 mg/g as the pH increased from 3 to 11. Anions in the solution inhibited fluoride adsorption, and impacted other ions in the following order: chloride < sulfate < carbonate < phosphate. PY-800 has a higher adsorption capacity than other reported adsorbents, with a maximum of 159.62 mg/g. These results show that PY-800 recycled from food waste is highly efficient for fluoride removal.
Multi-Channel Allocation and Medium Access Control in Wireless Sensor Network

: The aggregate capacity of wireless mesh networks can be improved significantly by equipping each node with multiple interfaces and by using multiple channels in order to reduce the effect of interference. Since the number of available channels is limited, it is desired to allocate and reallocate channels on-demand. In this paper, a Cluster Channel Assignment (CCA) approach is proposed, to maximize the aggregate throughput by exploiting spatial reuse and local dynamic switching of the channels. A clustering approach is employed in order to maximize the network capacity while minimizing the interference and taking advantage of the possibility of reuse of channels among clusters.
Object Detection in Cluttered Environments with Sparse Keypoint Selection

In cases such as mobile robotic applications with limited computational resources, traditional approaches might be preferred over neural networks. However, open source solutions using traditional computer vision are harder to find than neural network implementations. In this work we address the task of object detection in cluttered environments in point clouds from RGB-D cameras. We compare several open source implementation available in the Point Cloud Library and present a novel and superior solution for this task. We further propose a novel sparse key-point selection approach that combines the advantages of uniform sampling and a dedicated keypoint detection algorithm. Our extensive evaluation shows the validity of our approach, which also improves the results of the compared methods. All code is available on our project repository: https://github.com/vseib/point-cloud-donkey.
Modeling a Fault Detection Predictor in Compressor using Machine Learning Approach based on Acoustic Sensor Data

Proper functioning of the air compressor ensures stability for many critical systems. The ill-effect of the breakdown caused by the wear and tear in the system can be mitigated if there exists an effective automated fault classification system. Traditionally, the simulation-based methods help to extend to identify the faults; however, those systems are not so effective enough to build real-time adaptive methods for the fault detection and its type. This paper proposes an effective model for the fault classification in the air compressor based on the realtime empirical acoustic sensor time-series data were taken on a sampling frequency of 50Khz. In the proposed work, the timeseries datais transformedinto the frequency domain using fast Fourier transforms,where half of the signals are considered due to its symmetric representation. Afterward, a masking operation is carried out to extract significant feature vectors fed to the multilayer perception neural network. The uniqueness of the proposed system is that it requires less trainable parameters, thus reduces the training time and imposes lower memory overhead. The model is benchmarked with performance metric accuracy, and it is found that the proposed masked feature setbased MLP-ANN exhibits an accuracy of 91.32%. In contrast, the LSTM based fault classification model gives only 83.12% accuracy, takes more training time, and consumes more memory. Thus, the proposed model is realistic enough to be considered a real-time monitoring system of the fault and control. However, other performance metrics like precision, recall, and F1-Score are also promising with the LSTM based fault classifier. Keywords—Air-compressor; fault detection; LSTM; multi-layer perception; ANN; acoustic sensor data
Electrically-activated, normally-closed diaphragm valves

Electrically activated, normally closed diaphragm valves have been fabricated using heated bimetallic diaphragms to provide the operating force. These valves have been designed to provide fully proportional control of flows in the range of 0-150 cm/sup 3//min at input pressures between 1 and 50 PSIG. The valves are batch-fabricated using silicon micromachining techniques. By combining these valves with pressure or flow sensing elements, closed-loop pressure or flow control is easily accomplished. <<ETX>>
The Convergence of Source Code and Binary Vulnerability Discovery -- A Case Study

Decompilers are tools designed to recover a high-level language representation (typically in C code) from program binaries. Over the past five years, decompilers have improved enormously, not only in terms of the readability of the produced pseudocode, but also in terms of similarity of the recovered representation to the original source code. Albeit decompilers are routinely used by reverse engineers in different disciplines (e.g., to support vulnerability discovery or malware analysis), they are not yet adopted to produce input for source-code static analysis tools. In particular, source code vulnerability discovery and binary vulnerability discovery remain today two very different areas of research, despite the fact that decompilers could potentially bridge this gap and enable source-code analysis on binary files. In this paper, we conducted a number of experiments on real world vulnerabilities to evaluate the feasibility of this approach. In particular, our measurements are intended to show how the differences between original and decompiled code impact the accuracy of static analysis tools. Remarkably, our results show that in 71% of the cases, the same vulnerabilities can be detected by running the static analyzers on the decompiled code, even though for several cases we observe a steep increment in the number of false positives. To understand the reasons behind these differences, we manually investigated all cases and we identified a number of root causes that affected the ability of static tools to 'understand' the generated code.
Impact of Carsharing on Household Vehicle Holdings

Carsharing has grown considerably in North America during the past decade and has flourished in metropolitan regions across the United States and Canada. The new transportation landscape offers urban residents an alternative to automobility, one without car ownership. As car-sharing has expanded, there has been a growing demand to understand its environmental effects. This paper presents the results of a North American carsharing member survey (N = 6,281). A before-and-after analytical design is established with a focus on carsharing's effects on household vehicle holdings and the aggregate vehicle population. The results show that carsharing members reduce their vehicle holdings to a degree that is statistically significant. The average number of vehicles per household of the sample drops from 0.47 to 0.24. Most of this shift constitutes one-car households becoming carless. The average fuel economy of carsharing vehicles used most often by respondents is 10 mi/gal more efficient than the average vehicle shed by respondents. The median age of vehicles shed by carsharing households is 11 years, but the distribution covers a considerable range. An aggregate analysis suggests that carsharing has taken between 90,000 and 130,000 vehicles off the road. This equates to 9 to 13 vehicles (including shed autos and postponed auto purchases) taken off the road for each carsharing vehicle.
Title : Human B cell lineages associated with germinal centers following influenza 1 vaccination are measurably evolving

6 Affiliations: 7 Department of Pathology, Yale School of Medicine, New Haven CT, 06520, USA 8 Department of Pathology and Immunology, Washington University School of Medicine, St 9 Louis, MO, 063110, USA 10 Worcester Polytechnic Institute, Worcester, MA, 01609, USA 11 Department of Immunobiology, Yale School of Medicine, New Haven, CT, 06520, USA 12 Department of Zoology, University of Oxford, Oxford, United Kingdom 13 The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy 14 Programs, Washington University School of Medicine, St Louis, MO, 63110 USA 15 Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New 16 Haven, CT, 06511, USA 17
A Complex Proteomic Response of the Parasitic Nematode Anisakis simplex s.s. to Escherichia coli Lipopolysaccharide

In Brief Anisakis simplex s.s. is a seafood-borne parasite that is involved in human infections. This experimental setup mimics the coexistence of helminth and gut bacteria in the host. Describing the mechanisms of the proteomic response of A. simplex to LPS can contribute to better understanding the parasite biology and more effective treatment of anisakiasis. Our findings indicate the complexity of the proteomic response of this parasite to LPS. Obtained results are valuable in planning future strategies for studying helminths.
New active-only grounded inductance simulator employing current-mode approach suitable for wide band operation

In this paper, an active-only grounded lossless inductance simulator operating in current-mode is presented. The circuit uses only a current operational amplifier (COA) and an operational transconductance amplifier (OTA). The novelty of the proposed circuit is that it uses a COA instead of a voltage operational amplifier (VOA) to take the wider frequency operation advantage of the current-mode approach. The simulation results obtained through SPICE with 0.5 μm standard CMOS technology verify that the designed circuit can be operated up to 30 MHz, which is much higher than the operation frequency of previously reported inductance simulators utilising VOAs. The inductance value of the presented circuit can be adjusted electronically between 3.9 μH and 37 μH via the biasing current of the OTA. A parallel resonance circuit application is also given validating the operation of the proposed inductance simulator.
On-line dynamic voltage instability prediction based on decision tree supported by a wide-area measurement system

In this study, a new decision tree-based method is proposed to assess the small disturbance (limited to load disturbance) voltage stability of power systems by only using synchronised phasors (synchrophasors) measured by phasor measurement units in a wide-area measurement system. To properly predict the voltage instability considering various load increase directions as well as load increase step sizes, a mathematical morphology algorithm is used to extract appropriate features from synchronised measured voltage phasors during the few seconds just after the disturbance. These features along with the pre-disturbance ones are then applied to a trained decision tree to predict the voltage instability of the power system. The proposed method is applied and tested on the Nordic32 test system. To examine the efficiency and accuracy of the proposed method, the dynamic behaviours of on-load tap changing transformers as well as the stator current limiters of synchronous generators are considered. The simulation results demonstrate that this method effectively predicts the voltage instability of the power system during the first two seconds just after the disturbance, for the test system.
Performance Analysis of Cryptography Algorithms in Wireless Sensor Network Based on Key Strength and Data Acceptance Probabilities

: A multitude of fields and applications now make use of wireless sensor networks. Yet, despite their versatility and usefulness, these types of networks come with two major drawbacks: Firstly, due to the nature of the fact that they are wireless, this type of networks are very susceptible to attacks and intruders. The second major issue concerns that the limited and irreplaceable resources used in these networks do not generally allow for heavy computation, secure data packing, and sender authentications over an extended period. For these reasons, the selection of a suitable cryptography algorithm that provides an acceptable security level while keeping the use of network resources to a minimum is a task of vital importance. In this paper, the development of a model that allows for the comparison and evaluation of the encryption algorithms used in wireless sensor networks is presented. The model proposed assesses algorithms along two metrics: key strength probability and data acceptance probability. The results from the evaluation of the model using a simulation are encouraging, and we suggest that the model we propose will aid network administrators to select the most appropriate encryption algorithm, namely that which exceeds the desired security level for their network while simultaneously requiring the lowest amount of network resources possible.
OP-MOLB150173 2694..2706

The fundamental asymmetry of female meiosis creates an arena for genetic elements to compete for inclusion in the egg, promoting the selfish evolution of centromere variants that maximize their transmission to the future egg. Such “female meiotic drive” has been hypothesized to explain the paradoxically complex and rapidly evolving nature of centromeric DNA and proteins. Although theoretically widespread, few cases of active drive have been observed, thereby limiting the opportunities to directly assess the impact of centromeric drive on molecular variation at centromeres and binding proteins. Here, we characterize the molecular evolutionary patterns of CENH3, the centromere-defining histone variant, in Mimulus monkeyflowers, a genus with one of the few known cases of active centromere-associated female meiotic drive. First, we identify a novel duplication of CENH3 in diploid Mimulus, including in lineages with actively driving centromeres. Second, we demonstrate long-term adaptive evolution at several sites in the N-terminus of CENH3, a region with some meiosis-specific functions that putatively interacts with centromeric DNA. Finally, we infer that the paralogs evolve under different selective regimes; some sites in the N-terminus evolve under positive selection in the pro-orthologs or only one paralog (CENH3_B) and the paralogs exhibit significantly different patterns of polymorphism within populations. Our finding of long-term, adaptive evolution at CENH3 in the context of centromere-associated meiotic drive supports an antagonistic, coevolutionary battle for evolutionary dominance between centromeric DNA and binding proteins.