Miracle-dz/UniFAR-FTUs huggingface dataset:
Multitype Highway Mobility Analytics for Efficient Learning Model Design: A Case of Station Traffic Prediction

The provincial highway transportation system supports substantial cross-city transitions of people and logistics, where the prediction tasks in terms of station/road traffic, urban transitions, and individual traveling are crucial for boosting data intelligence. However, to achieve efficient prediction model design, the predictability analytics with data is the basis, but has not been sufficiently investigated in the existing literature yet. To bridge this gap, in this paper, we study one large-scale dataset collected from one provincial highway transportation system, which contains totally 21,685,765 vehicles and 351,766,743 transaction records, and conduct a comprehensive mobility analytics on its predictable performance. We first investigate the station traffic by mining its spatio-temporal correlations, then examine the multi-type urban transition flows (i.e., people flows and logistics) by demystifying the difference and similarity between the two types of behaviors, and finally analyze the uncertainty of individual traveling behaviors in terms of the destination and arriving time. After that, in accordance with the analytical findings, we cast a case study of data-driven model design for station traffic prediction. Specifically, a novel learning model is devised, named STAR, i.e., Spatio-Temporal Attention based pRediction model, which consists of station outflow/inflow temporal embedding components and spatio-temporal attention blocks to push the limit of prediction capability. Extensive experiments corroborate the efficacy of the proposed STAR.
EGraph: Efficient Concurrent GPU-Based Dynamic Graph Processing

In many applications of the analysis of dynamic graph, many Timing iterative Graph Processing (TGP) jobs usually need to be generated for the processing of the corresponding snapshots of the dynamic graph to obtain the results at different points of time. For high throughput of such applications, it is expected to run the TGP jobs on the GPU concurrently. Although many GPU-based systems have been recently developed, for out-of-GPU-memory dynamic graph processing, this concurrent way suffers from significant data access overhead due to a large volume of data transfer between CPU and GPU and the interference between these concurrently running jobs, which eventually incurs low GPU utilization ratio. In this work, we observed that the TGP jobs have strong temporal and spatial similarity when they access different snapshots for their own processing as most parts of the snapshots are the same and only a few parts are changing with time. It creates ideal opportunities for efficient concurrent execution of the TGP jobs by dramatically reducing CPU-GPU graph data transfer cost. Based on this observation, we develop the first GPU-based dynamic graph processing system EGraph, which can be integrated into the existing out-of-GPU-memory static graph processing systems to enable them to efficiently support concurrent execution of TGP jobs on dynamic graphs with the help of GPU accelerators. Different from the existing approaches, we propose in EGraph an effective Loading-Processing-Switching (LPS) execution model. It is able to effectively reduce the overhead of CPU-GPU data transfer and ensures a higher GPU utilization ratio for efficient execution of the TGP jobs by fully utilizing the data access similarity between the TGP jobs. Experimental results show that the existing GPU-accelerated systems achieve performance improvements of 2.3-3.5 times after being integrated with EGraph.
Tutorial and Perspectives on MAGICAL: A Silicon-Proven Open-Source Analog IC Layout System

MAGICAL is an open-source system for analog and mixed-signal (AMS) circuit layout synthesis. Using custom place-and-route and constraint extraction algorithms, MAGICAL provides a fully-automated layout implementation flow. MAGICAL 1.0 has been proven in silicon with a 40-nm 1GS/s $\Delta \Sigma $ ADC. The source code has been released to enable broad usage. Recently, MAGICAL has also been extended to cover more circuit classes such as SAR-ADC. This tutorial/perspective paper describes the overall MAGICAL framework and algorithms. We also provide a tutorial on how to use and extend MAGICAL and discuss future research directions for AMS layout design automation.
Forced housing mobility and mental wellbeing: evidence from Australia

Abstract This article examines the links between forced housing mobility and the mental wellbeing of Australians in an era of heightened risks in both labour and housing markets. Specifically, we examine how the links between forced housing mobility and mental wellbeing may vary according to states of employment and housing tenure insecurity. Using the 2001–2018 Household, Income and Labour Dynamics in Australia Survey, we implement hybrid models across four mental wellbeing dimensions and uncover three key findings. First, there is strong evidence that forced moves impair mental wellbeing. Second, the adverse wellbeing impacts of forced moves are greater for those experiencing employment insecurity than those in secure employment. Third, forced moves can depress the wellbeing of both owner purchasers and private renters, but the wellbeing penalty is greater in the case of the former. Overall, our analysis emphasises the importance of harnessing housing as a policy instrument for promoting wellbeing. Our findings also highlight the need for policies that mitigate loss of home ownership and reforms that improve tenure security for renters.
Analysis of power quality for photovoltaic systems connected to the grid

From the advancement of alternative power generation systems, in conjunction with its technical feasibility for direct connection to the conventional power grid, diversification of global energy has been growing with the introduction of distributed energy production which results in a new need adaptation to allow the connection with these systems, in addition to determination of power quality control parameters that fall within the limits of operation and supply are extremely important for ensuring compliance with the power quality levels, as well as the optimization of generation potential that is critical to predict the behavior and meet production expectations. This requirement makes the utilities and the responsible agencies take steps to enable the monitoring and effective correction of these systems, making them efficient and avoiding any risks or damage to the growing demand for energy supply. To contribute to the power quality control processes, this paper aims to proposed use of passive filters to reduce the harmonic distortion levels injected into the grid from computer modeling to simulate the conditions of a real photovoltaic (PV) system connected to the grid, and the use of energy analyzers designed to measure the quantities of interest and subsequent comparison of the data obtained in the field for improved reliability of the final result.
Parallel Sponge-based Authenticated Encryption with Side-Channel Protection and Adversary-Invisible Nonces

Since its birth in 2000, Authenticated Encryption (AE) has been a hot research topic, and many new features have been proposed to boost its security or performance. The block cipher was the dominant primitive in constructing AE schemes, followed by stream ciphers and compression functions until the sponge construction emerged in 2011. Sponge-based AE schemes provide functional characteristics such as parallelizability, incrementality, and online. They also offer security features for protection against active or passive adversaries. Currently, there exist parallel sponge-based AE schemes, but they are not protected against simple power analysis (SPA) and differential power analysis (DPA). On the other hand, sponge-based AE schemes that protect against such attacks are serial and cannot be parallelized. Furthermore, sponge-based AE schemes handle the nonces in a way that could allow misuse. So, sponge-based AE schemes that hide the nonce from adversaries are also an open problem. This work aims to bridge these gaps by proposing a parallel sponge-based AE with side-channel protection and adversary-invisible nonces (PSASPIN), using parallel fresh rekeying and the duplex mode of the sponge construction. A leveled implementation is used to implement the key generation part using a pseudorandom function (PRF) based on the Galois field multiplication. The data processing (the rekeyed) part is implemented using the sponge-based duplex mode. Finally, the security proof of the proposed scheme is provided using game-based theory according to the PRP/PRF switching lemma, and its performance is analyzed.
Albumin Binds Doxorubicin via Self–Assembling Dyes as Specific Polymolecular Ligands

Congo red (CR) type self–assembled ribbon–like structures (SRLS) were previously shown to interact with some proteins, including albumin. SRLS also complex with some drugs with a flat, ring–shaped structure with aromatic characteristics, intercalating them into their ribbon structure. The combination of interaction with proteins and drug binding by SRLS enables the use of such systems for immunotargeting. It is especially interesting in the case of chemotherapeutic agents. The present experiments aimed to show that the model carrier system composed of supramolecular albumin and Congo red efficiently binds doxorubicin (Dox) and that the drug can be released at reduced pH. The presented results come from the studies on such complexes differing in the molar ratio of CR to Dox. The following methods were used for the analysis: electrophoresis, dialysis, gel filtration, spectral analysis, and analysis of the size of the hydrodynamic radius using the dynamic light scattering method (DLS). The applied methods confirmed the formation of the CR–Dox complex, with large dimensions and changed properties compared with free CR. The presented results show that albumin binds both CR and its complex with Dox. Various CR–Dox molar ratios, 5:1, 2:1, and 1:1, were analyzed. The confirmation of the possibility of releasing the drug from the carriers thus formed was also obtained. The presented research is important due to the search for optimal solutions for the use of SRLS in drug immunotargeting, with particular emphasis on chemotherapeutic agents.
Escherichia coli tRNA 2-Selenouridine Synthase (SelU): Elucidation of Substrate Specificity to Understand the Role of S-Geranyl-tRNA in the Conversion of 2-Thio- into 2-Selenouridines in Bacterial tRNA

The bacterial enzyme tRNA 2-selenouridine synthase (SelU) is responsible for the conversion of 5-substituted 2-thiouridine (R5S2U), present in the anticodon of some bacterial tRNAs, into 5-substituted 2-selenouridine (R5Se2U). We have already demonstrated using synthetic RNAs that transformation S2U→Se2U is a two-step process, in which the S2U-RNA is geranylated and the resulting geS2U-RNA is selenated. Currently, the question is how SelU recognizes its substrates and what the cellular pathway of R5S2U→R5Se2U conversion is in natural tRNA. In the study presented here, we characterized the SelU substrate requirements, identified SelU-associated tRNAs and their specific modifications in the wobble position. Finally, we explained the sequence of steps in the selenation of tRNA. The S2U position within the RNA chain, the flanking sequence of the modification, and the length of the RNA substrate, all have a key influence on the recognition by SelU. MST data on the affinity of SelU to individual RNAs confirmed the presumed process. SelU binds the R5S2U-tRNA and then catalyzes its geranylation to the R5geS2U-tRNA, which remains bound to the enzyme and is selenated in the next step of the transformation. Finally, the R5Se2U-tRNA leaves the enzyme and participates in the translation process. The enzyme does not directly catalyze the R5S2U-tRNA selenation and the R5geS2U-tRNA is the intermediate product in the linear sequence of reactions.
Temperature compensation of the responsivity of avalanche photodiodes in free-space optical communication systems

In the paper, the temperature compensation of the responsivity of avalanche photodiode (APD) applied in the wireless optical communication link receiver is solved. Communication systems of this type are exposed to a wide range of operating temperatures, which markedly affect APD properties. The simplest method of compensating the temperature effects on responsivity is a corresponding change in the reverse voltage applied to the diode. The paper gives a description and an example of the application of the compensation method to the APDs of the firm Perkin Elmer. The method makes use of a temperature-dependent element in the feedback loop of switched mode power supply.
High-Speed Terahertz Band Radio-Over-Fiber System Using Hybrid Time-Frequency Domain Equalization

The radio-over-fiber (RoF) orthogonal frequency division multiplexing (OFDM) system at Terahertz (THz) band has been a promising solution to meet the demand for high capacity and flexibility. The signals transmitted in the RoF system will suffer severe impairments caused by the wireless multi-path effect, optical fiber transmission, and photoelectric device response. The time-frequency domain equalization (TFDE) based on the two-dimension convolution (Conv2D) neural network (NN) is proposed to resist the linear and nonlinear impairments of the RoF-OFDM system. Furthermore, complex convolution is applied to compensate for the complex channel response. Compared with several baseline models, the detailed performance improvement and model size discussion of the complex-valued Conv2D NN TFDE are investigated. With the aid of the complex-valued Conv2D NN TFDE, the 16 Gbaud 16-ary quadrature amplitude modulation (16QAM) signals are delivered over 54.6 m wireless distance and 20 km standard single mode fiber. The line rate of this RoF system achieves 53.5 Gbit/s.
Hybrid Predictive Modeling for Charging Demand Prediction of Electric Vehicles

In recent years, the supply of electric vehicles, which are eco-friendly cars that use electric energy rather than fossil fuels, which cause air pollution, is increasing. Accordingly, it is emerging as an urgent task to predict the charging demand for the smooth supply of electric energy required to charge electric vehicle batteries. In this paper, to predict the charging demand, time series analysis is performed based on two types of frames: One is using traditional time series techniques such as dynamic harmonic regression, seasonal and trend decomposition using Loess, and Bayesian structural time series. The other is the most widely used machine learning techniques, including random forest and extreme gradient boosting. However, the tree-based machine learning approaches have the disadvantage of not being able to capture the trend, so a hybrid strategy is proposed to overcome this problem. In addition, the seasonal variation is reflected as the feature by using the Fourier transform which is useful in the case of describing the seasonality patterns of time series data with multiple seasonality. The considered time series models are compared and evaluated through various accuracy measures. The experimental results show that the machine learning approach based on the hybrid strategy generally achieves significant improvements in predicting the charging demand. Moreover, when compared with the original machine learning method, the prediction based on the proposed hybrid strategy is more accurate than that based on the original machine learning method. Based on these results, it can find out that the proposed hybrid strategy is useful for smoothly planning future power supply and demand and efficiently managing electricity grids.
Bifunctional Luneburg-Eaton Lens Fabricated of 3-D-Printed Anisotropic Medium

Luneburg lens (LL) and Eaton lens (EL) are well known as optical and microwave components. This letter proposes a bifunctional Luneburg-Eaton lens (LEL) fabricated by three-dimensional (3-D)-printed anisotropic medium. The spherical LEL is designed as a LL for incident vertical polarization and as an EL for incident horizontal polarization through the use of anisotropic dielectric. In order to realize a good continuous refractive index, the proposed lens structure is divided into many cells, and the equivalent permittivity of each unit is calculated by using the equivalent media theory. The prototype of LEL is designed and excited by X-band waveguide WR-90, and the ideal results are achieved. The gain of the antenna is from 15.7 to 16.4 dBi in X-band with the LEL functioning as a LL, and the waveguide can have a beam deflection of set angle with the LEL functioning as an EL.
Influence of RAFT Agent on the Mechanism of Copolymerization of Polypropylene Glycol Maleinate with Acrylic Acid

Studies have shown the possibility of synthesizing new polymers based on polypropylene glycol maleate with acrylic acid in the presence of a RAFT agent (2-Cyano-2-propyl dodecyl trithiocarbonate CPDT). The effect of RAFT agent concentration on network density has been shown to be connected with product yield. Herein, the composition of the obtained copolymers was determined using FTIR spectrometry in combination with the chemometric method of partial least squares (or projection to latent structures). To investigate the synthesized hydrogels, the degrees of equilibrium swelling was studied. The resulting objects were characterized by infrared spectroscopy. The surface morphology of the polymers was studied and the pore sizes were estimated using scanning electron microscopy. The structure of the test samples was confirmed by NMR spectroscopy. The thermal stability of crosslinked polymers was determined using thermogravimetry.
Asymptotically autonomous dynamics for non-autonomous stochastic 2D g-Navier–Stokes equation in regular spaces

This work is a continuation of our previous work [Li et al., Commun. Pure Appl. Anal. 19, 3137 (2020)] on the regular backward compact random attractor. We prove that under certain conditions, the components of the random attractor of a non-autonomous dynamical system can converge in time to those of the random attractor of the limiting autonomous dynamical system in more regular spaces rather than the basic phase space. As an application of the abstract theory, we show that the backward compact random attractors [[Formula: see text] is precompact for each [Formula: see text]] for the non-autonomous stochastic g-Navier–Stokes ( g-NS) equation is backward asymptotically autonomous to a random attractor of the autonomous g-NS equation under the topology of [Formula: see text].
MANAGEMENT OF TOOTH EXTRUSION AS A COMPLICATION DUE TO INTERMAXILLARY FIXATION: TWO CASE REPORTS

Abstract: One of the most common and trusted methods for mandibular fracture management is the intermaxillary fixation(IMF) with Erich arch bar with the application of circumdental wiring. Twisting a wire around a tooth conveys little feel as to its tightness and it can lead to tooth extrusion or avulsion. Anterior teeth affected more because of having single root and no occlusal contact with opposite teeth. The aim of this report to present the treatment with orthodontic button and elastics of two patients have a rarely seen complication of anterior tooth extrusion caused by loosening of circumdental wires of Erich arch bar during the IMF. Keywords : complication, intraoperative; jaw fixation technics; maxillomandibular fixation; orthodontic extrusion Intermaksiller Fiksasyona Bagli Bir Komplikasyon Olan Dis Ekstruzyonun Tedavisi: Iki Vaka Ozet: Mandibula kiriklarinin tedavisi icin en yaygin ve guvenilir metotlardan biri sirkumdental telleme ile yapilan Erich ark barlar kullanilan intermaksiller fiksasyon(IMF) islemidir. Disin cevresinde teli sarmak hassas bir his gerektirir ve diste ekstruzyona ve avulsiyona neden olabilir. Anterior disler, tek koklu olmalari ve karsit dis ile okluzal kontaga sahip olmamalari sebebiyle daha cok etkilenirler. Bu raporun amaci; iki hastada IMF sirasinda sirkumdental tellerin gevsemesi sebebiyle olusan nadir olarak gorulen bir komplikasyon olan anterior dis ekstruzyonunun ortodontik buton ve elastikler ile tedavisini sunmaktir. Anahtar kelimler: Komplikasyon, Intraoperatif; Cene sabitleme teknikleri; Alt-ust cene sabitlemesi, Ortodontik cekis
Automation in Cars: Voice Controlled Car Assistant System and Automatic Breaking System – A Review

The purpose of this project is to build a Voice Controlled Car Assistant System and Automatic Breaking System. A Voice Controlled Car is an advanced robotic vehicle that can be operated by the power of voice commands. It is based on an Arduino microcontroller, motor drivers, and a Bluetooth module. The Arduino hardware is an open-source micro-controller kit used to build digital devices. In our project, we will design the hardware of the Voice Controlled Robotic Car first, then use our previous knowledge of programming to code the entire work. The code will then be simulated on IDE software, and then interfaced with the hardware. An android device with a Bluetooth application is used to control the control unit in coordination with the Bluetooth device, and a Bluetooth module is used to capture and read the voice commands. We choose this project because automation has become a significant part of our lives and also has a broad range of applications in the engineering field. Automation plays a vital role in the development of new technology.
Validating the Negative Emotion Regulation Inventory Measure of Emotion Regulation for Children in Preadolescence

Emotion modulation is achieved through various strategies. We further validated a measure of emotion regulation (ER), the Negative Emotion Regulation Inventory (NERI), designed by Zimmermann and Iwanski (2014) to assess emotions and ER strategies in situations intended to induce sadness, fear, or anger. U.S. children 9–14 years old (n = 105) completed an adapted version of the NERI and measures of anxiety symptoms and attachment security. We replicated Zimmermann and Iwanski’s (2014) finding that ER strategies (adaptive regulation, social support seeking, dysfunctional rumination, expressive suppression) varied by emotion situation. We extended their study by showing that specific ER strategies were related to both anxiety symptoms and secure parent-child attachment. This cross-cultural replication and extension study provided some evidence for the reliability and validity of the adapted NERI in U.S. preadolescents.
Intelligent Transport Systems (ITS)

The term Intelligent Transport Systems (ITS) refers to any technology applied to transport and infrastructure to transfer information between systems, and to transport users, for improved safety, efficiency and environmental outcomes. This is a fast evolving field that includes stand-alone applications such as traffic management systems, information and warning systems installed in individual vehicles, as well as applications involving vehicle to infrastructure and vehicle to vehicle communications. Many ITS applications combine some or all of the above with Smartphone applications and GPS devices to enable transport users to make informed decisions.