Miracle-dz/UniFAR-FTUs huggingface dataset:
Tensile Deformation Behaviors of Pure Ti with Different Grain Sizes under Wide-Range of Strain Rate

In this study, pure titanium equivalent to Grade 1 was subjected to tensile tests at strain rates ranging from 10−6 to 100 s−1 to investigate the relationship between its mechanical properties and its twinning and slip. Deformation properties and microstructures of samples having average grain sizes of 210 μm (Ti-210), 30 μm (Ti-30), and 5 μm (Ti-5) were evaluated. With increasing strain rates, the 0.2% proof stress and ultimate tensile strength increased for all samples; the fracture strain increased for Ti-210, decreased for Ti-5, and changed negligibly for Ti-30. Comparing high (100 s−1) and low (10−6 s−1) strain rates, twinning occurred more frequently in Ti-30 and Ti-210 at high strain rates, but the frequency did not change in Ti-5. The frequency of 1st order pyramidal slip tended to be higher in Ti-30 and Ti-5 at low strain rates. The higher ductility exhibited by Ti-210 at high strain rates was attributed to the high frequency of twinning. In contrast, the higher ductility of Ti-5 at low strain rates was attributed to the activity of the 1st order pyramidal slip.
A 1.2-A Calibration-Free Hybrid LDO With In-Loop Quantization and Auxiliary Constant Current Control Achieving High Accuracy and Fast DVS

This paper presents a high-current calibration-free analog-digital hybrid controlled LDO for large-area digital load. The proposed architecture has the advantages of an analog controller (continuous and high DC accuracy) with distributed digital power transistors (flexible and scalable for high current applications). Distinctive from the conventional digital LDOs that directly quantize the output voltage, the proposed LDO utilizes an error amplifier (EA) to pre-amplify the $\text{V}_{\mathrm {OUT}}$ error. Then, a 5-bit time-to-digital converter (TDC) quantizes the processed analog error signal subsequently transformed into a thermometer code that directly controls the distributed digital power transistors. This design can pull off high DC accuracy even without calibration. Besides, we implement an auxiliary constant current (ACC) circuit to solve reliability issues and to improve the stability under a large voltage dropout. Fabricated in a 28-nm bulk CMOS process with a 1.2-A load capability, the proposed LDO achieves 2- $\mu \text{V}$ /mA load regulation and close to 1.5% output accuracy. By employing a wide bandwidth EA and a fast TDC, the hybrid LDO can obtain a fast transient response. The measured undershoot is 70 mV with a 0.6-A load step within 10-ns edge time, and the output voltage can scale from 0.6 V to 0.9 V within 30 ns.
A Systematic Review on Emotion Recognition System Using Physiological Signals: Data Acquisition and Methodology

Emotion recognition systems (ERS) have become a popular research field to contribute to human-machine interaction in different areas. Different kinds of applications on ERS can serve different purposes. Artificial intelligence (AI) and the internet of things (IoT) are the technologies behind such applications. The main objective of this study is to enable researchers and developers to search for the most suitable options to develop an emotional state recognition system. More specifically, this paper presents work on ERS, which is built using physiological signals extracted from biosensors. It also presents details of how the extracted physiological signals are used to identify the user's emotional state. In this review, the sensors are categorized based on their modality: contact-based sensors and contactless sensors. Next, the ERS process is presented together with the reported results for each described technique. Articles from four different research databases were reviewed, of which 147 articles from 2009 to 2021 were referred to that are related to ERS using physiological signals. This paper should be significant for researchers developing systems that integrate human emotion recognition capability. The findings reported here can guide them in choosing suitable methods for their systems. Doi: 10.28991/ESJ-2022-06-05-017 Full Text: PDF
Optical response of one-dimensional (Si/SiO2)m photonic crystals

One-dimensional photonic crystals made of (Si/SiO2)m multilayers with m=2,…8 have been grown on SiO2 4-in. wafers by repeated polysilicon low-pressure chemical vapor deposition, oxidation, and wet etching steps. The poly-Si and SiO2 layers were about 220 and 660 nm thick, respectively, thus realizing λ/4 distributed Bragg reflectors. Spectroscopic ellipsometry in the 1.4–5 eV range was used to determine the dielectric function of poly-Si and the actual layer thicknesses, as well as to check the structural and compositional homogeneity of the structures. In order to measure the photonic crystal properties, specular reflectance and transmittance measurements were performed from 0.2 to 6 eV at different angles of incidence θ⩽50° and for transverse electric and transverse magnetic polarizations. The stop-bands characteristic of Bragg reflector multilayers appear up to the fifth order and become more pronounced with increasing m, reaching almost complete rejection for m=4 periods. The experimental spectra were.
CCENet: Cascade Class-Aware Enhanced Network for High-Resolution Aerial Imagery Semantic Segmentation

Semantic segmentation of high-resolution aerial images is a challenging task on account of interclass homogeneity and intraclass heterogeneity of land cover. Recent works have sought to mitigate this issue by exploiting pixelwise global contextual information using self-attention mechanism. However, the existing attention-based methods usually generate inaccurate object boundary segmentation results, as the self-attention model is embedded in high-level features with low resolution due to prohibitively computational complexity. Moreover, existing attention-based models ignore classwise contextual information from intermediate results, which leads to undesirable feature separability. To obtain discriminative feature as well as generate accurate segmentation boundaries, we present a novel segmentation framework, named cascade class-aware enhanced network (CCENet) for high-resolution aerial imagery. The proposed CCENet predicts segmentation results on multiple stages, and the result of the previous stage is used to refine object boundary details for the latter stage. To exploit the class-aware prior information in previous stage, we propose a lightweight class-aware enhanced module (CaEM) to grab the class-aware contextual dependencies. Specifically, CaEM first extracts a set of class representation of the land covers by global class pooling block and then reconstructs enhanced features using class relation measurement, which alleviates the interclass homogeneity and intraclass heterogeneity of ground objects in feature space. Quantitative and qualitative experimental results on three publicly available datasets demonstrate the superiority of our CCENet over other state-of-the-art methods in the items of high labeling accuracy and computation efficiency.
Performance Analysis of Full-Duplex Cooperative NOMA Short-Packet Communications

This paper presents the performance analysis for the two-user downlink cooperative non-orthogonal multiple access (C-NOMA) short-packet communication (SPC) over Rayleigh channels. The near user acts as a relay, which operates in full-duplex (FD) mode and employs decode-and-forward protocol, to forward the information to the far user. Two kinds of residual self-interference (RSI) models, which are modeled as being free of fading and following Rayleigh fading, are considered. We first derive the approximate closed-form expressions of average block error rate (BLER) at both users under these two RSI models. Then, simulations are provided to verify the validity of our theoretical analysis. Finally, we evaluate the BLER performance difference between long-packet and short-packet transmissions for FD C-NOMA and that between FD relaying and half-duplex (HD) relaying. The analysis and simulation results show that, for ultra-reliable and low-latency communication, we can use RSI link modeled as being free of fading to simplify the system analysis. Furthermore, compared to long-packet communication, for FD C-NOMA SPC have a narrow range of power allocation to achieve high-reliable transmission, and because BLER outperformance of FD relaying over HD relaying degrades when the RSI signal becomes strong, the optimal duplex mode for FD C-NOMA SPC is determined by the RSI signal and the target BLERs at both users.
The Relationship between Central Bank Auctions and Bill Market Liquidity

This paper investigates the relationship between central bank (reverse) auctions and bill market liquidity. The analysis includes data on the purchases of bills in the auctions by the Dutch Central Bank under the European Central Bank’s Pandemic Emergency Purchase Programme (PEPP). The results indicate that auctions contribute to smooth market functioning. Two ﬁndings stand out. First, by purchasing bills using auctions rather than bilaterally, the central bank increases the bid-to-cover ratio at bill issuance, especially in times of stress. Second, bills are of-fered at larger sizes and lower prices in central bank auctions near primary issuance.
An Agent-Based Dynamic Framework for Population Evacuation Management

Evacuating the population during crises to safe zones via optimal paths is vital. The evacuation planning process makes two main decisions: which shelter to reach and which path to take towards the chosen shelter. These decisions correspond to shelter allocation and traffic assignment problems, respectively. Many studies tackled these problems with a static formulation in the literature, while only a few considered a dynamic context. We conduct a comprehensive literature review and highlight that most studies independently solve these two problems while both are correlated with traffic conditions. To fill this gap, we propose a new framework to couple the shelter allocation problem (SAP) and the dynamic traffic assignment (DTA) problem and solve them. To capture traffic dynamics, we use a dynamic agent-based simulator. We assume the system determines the evacuees’ shelters to minimize the total evacuation time. However, each evacuee’s concern is reaching a shelter as fast as possible. Therefore, we formulate the DTA problem under stochastic user equilibrium (SUE) principles, i.e., every evacuee aims to minimize his own perceived travel time. We apply the proposed methodology to the network of Luxembourg City and compare its performance with other advanced methods that solve SAP and DTA separately. The comparison shows that solving the dynamic shelter allocation improves the mean evacuation time and significantly decreases the network clearance time compared to other methods with a fixed plan for SAP. The simulation results prove that considering the network state in the SAP can provide a more effective evacuation plan. Moreover, we perform a sensitivity analysis on optimization parameters and evaluate the computation cost of our methodology.
Potentialities of Complex Network Theory Tools for Urban Drainage Networks Analysis

Urban drainage networks (UDNs) represent important infrastructures to protect and maintain community health and safety. For these reasons, technicians and researcher are focusing more and more on topics related to vulnerability, resilience and monitoring for controlling illicit intrusions, contaminant and pathogenic spread. In the last years the complex network theory (CNT) is attracting attention as a new, useful and structured approach to analyze urban systems. The aim of this work is to evaluate potentialities of CNT approaches for UDNs vulnerability assessment and monitoring system planning. Limits and potentialities of applicability of CNT tools to UDNs are first provided evaluating the performances of standard centrality metrics. Then, it is proposed the use of tailored metrics embedding prior information, as intrinsic relevance of each node and pipe flow direction, which derive from the Horton's hierarchy and geometric data (pipe slope), respectively, without performing hydraulic simulations. The analysis is applied on two schematic literature networks of different complexity and to a real case‐study. The results suggest that vulnerability/resilience, monitoring design, contaminant and pathogenic spreads can be effectively analyzed using tailored metrics. Therefore, the proposed approach represents a complementary tool respect the more complex and computationally expensive methodologies and it is particular useful for large complex networks.
Effects of basal topography and ice-sheet surface slope in a subglacial glaciofluvial deposition model

Abstract We use a mathematical model to investigate the effect of basal topography and ice surface slope on transport and deposition of sediment within a water-filled subglacial channel. In our model, three zones of different behaviour occur. In the zone furthest upstream, variations in basal topography lead to sediment deposition under a wide range of conditions. In this first zone, even very small and gradually varying basal undulations (~5 m amplitude) can lead to the deposition of sediment within a modelled channel. Deposition is concentrated on the downstream gradient of subglacial ridges, and on the upstream gradient of subglacial troughs. The thickness and steepness of the ice sheet has a substantial impact on deposition rates, with shallow ice profiles strongly promoting both the magnitude and extent of sediment deposition. In a second zone, all sediment is transported downstream. Finally, a third zone close to the ice margin is characterised by high rates of sediment deposition. The existence of these zones has implications for esker formation and the dynamics of the subglacial environment.
First Isolation of Punique Virus from Sand Flies Collected in Northern Algeria

In the last decade, several phleboviruses transmitted by sand flies were detected in the Mediterranean countries, with the health impact of some of them being unknown. From September to October 2020, a total of 3351 sand flies were captured in Kherrata (Bejaia, northern Algeria) and identified by sex, grouped in 62 pools, which were tested for the presence of phlebovirus RNA using endpoint RT-PCR. Two pools (male and female, respectively) were positive. The genome sequencing and phylogenetic analysis showed that the two phleboviruses detected were closely related to the Punique virus (PUNV) isolated in Tunisia and detected in Algeria. Both PUNV strains were isolated on VERO cells from positive pools. Morphological identification of 300 sand flies randomly selected, showed a clear dominance of Phlebotomus perniciosus (98.67%). The dominance of this species in the study area was confirmed by PCR targeting the mitochondrial DNA. Our result represents the first isolation of PUNV and the second report in Algeria from two distinct regions which confirm its large circulation in the country and more broadly in North Africa. Further studies are needed to measure the impact on public health through seroprevalence studies in humans as well as animals and to investigate its potential involvement in neurological viral diseases.
Automated Identification of Coronary Arteries in Assisting Inexperienced Readers: Comparison between Two Commercial Vendors

Background: to assess the performance and speed of two commercially available advanced cardiac software packages in the automated identification of coronary vessels as an aiding tool for inexperienced readers. Methods: Hundred and sixty patients undergoing coronary CT angiography (CCTA) were prospectively enrolled from February until September 2021 and randomized in two groups, each one composed by 80 patients. Patients in group 1 were scanned on Revolution EVO CT Scanner (GE Healthcare), while patients in group 2 had the CCTA performed on Brilliance iCT (Philips Healthcare); each examination was evaluated on the respective vendor proprietary advanced cardiac software (software 1 and 2, respectively). Two inexperienced readers in cardiac imaging verified the software performance in the automated identification of the three major coronary vessels: (RCA, LCx, and LAD) and in the number of identified coronary segments. Time of analysis was also recorded. Results: software 1 correctly and automatically nominated 202/240 (84.2%) of the three main coronary vessels, while software 2 correctly identified 191/240 (79.6%) (p = 0.191). Software 1 achieved greater performances in recognizing the LCx (81.2% versus 67.5%; p = 0.048), while no differences have been reported in detecting the RCA (p = 0.679), and the LAD (p = 0.618). On a per-segment analysis, software 1 outperformed software 2, automatically detecting 942/1062 (88.7%) coronary segments, while software 2 detected 797/1078 (73.9%) (p < 0.001). Average reconstruction and detection time was of 13.8 s for software 1 and 21.9 s for software 2 (p < 0.001). Conclusions: automated cardiac software packages are a reliable and time-saving tool for inexperienced reader. Software 1 outperforms software 2 and might therefore better assist inexperienced CCTA readers in automated identification of the three main vessels and coronaries segments, with a consistent time saving of the reading session.
Exact diagonalization study of the antiferromagnetic spin-1/2 Heisenberg model on the square lattice in a magnetic field

We study the field dependence of the antiferromagnetic spin-1/2 Heisenberg model on the square lattice by means of exact diagonalizations. In the first part, we calculate the spin-wave velocity c, the spin stiffness rho(s), and the magnetic susceptibility chi(perpendicular to), and thus determine the microscopic parameters of the low-energy long-wavelength description. In the second part, we present a comprehensive study of dynamical spin-correlation functions for magnetic fields ranging from zero up to saturation. We find that, at low fields, magnons are well defined in the whole Brillouin zone but the dispersion is substantially modified by quantum fluctuations compared to the classical spectrum. At higher fields, decay channels open and magnons become unstable with respect to multimagnon scattering. Our results directly apply to inelastic neutron-scattering experiments.
A Novel Quality-Related Incipient Fault Detection Method Based on Canonical Variate Analysis and Kullback–Leibler Divergence for Large-Scale Industrial Processes

Quality-related fault detection is an effective way to ensure the stability of product quality and the safety of industrial processes. Quality abnormality is often caused by incipient faults, which propagate through the connectivity path among the equipment and subsystems, and eventually become significant faults that affect product quality and production efficiency. In this article, a quality-related incipient fault detection method for large-scale industrial process is proposed. First, a fault-relevant variable selection strategy based on Kullback–Leibler divergence (KLD) is proposed to obtain the optimal variable subsets of quality-related and quality-unrelated faults. Second, canonical variate analysis is applied to estimate the canonical variables from the collected processes data. Third, considering that KLD has a high sensitivity to incipient faults, it is used to quantify the dissimilarity between the distributions of canonical vectors before and after fault occurs. Finally, Bayesian inference is employed to fuse the detection results of all sub-blocks to get an intuitive global detection result. The advantages and validity of the proposed scheme are verified based on the Tennessee Eastman process.
Electrospun Biomimetic Nanofibrous Scaffolds: A Promising Prospect for Bone Tissue Engineering and Regenerative Medicine

Skeletal-related disorders such as arthritis, bone cancer, osteosarcoma, and osteoarthritis are among the most common reasons for mortality in humans at present. Nanostructured scaffolds have been discovered to be more efficient for bone regeneration than macro/micro-sized scaffolds because they sufficiently permit cell adhesion, proliferation, and chemical transformation. Nanofibrous scaffolds mimicking artificial extracellular matrices provide a natural environment for tissue regeneration owing to their large surface area, high porosity, and appreciable drug loading capacity. Here, we review recent progress and possible future prospective electrospun nanofibrous scaffolds for bone tissue engineering. Electrospun nanofibrous scaffolds have demonstrated promising potential in bone tissue regeneration using a variety of nanomaterials. This review focused on the crucial role of electrospun nanofibrous scaffolds in biological applications, including drug/growth factor delivery to bone tissue regeneration. Natural and synthetic polymeric nanofibrous scaffolds are extensively inspected to regenerate bone tissue. We focused mainly on the significant impact of nanofibrous composite scaffolds on cell adhesion and function, and different composites of organic/inorganic nanoparticles with nanofiber scaffolds. This analysis provides an overview of nanofibrous scaffold-based bone regeneration strategies; however, the same concepts can be applied to other organ and tissue regeneration tactics.
Inductive Coupler Analysis Based on Scattering Parameters With Nonstandard Terminal Impedance

A higher frequency helps to design a compact wireless power transfer system. Due to the parasitic effects and component nonlinearity, the increased frequency would depreciate the accuracy of the circuit model (CM). The scattering parameters (SPs) are powerful to avoid this issue and helpful to study the efficiency and voltage gain. However, the existing SP-based analysis would fail when the nonstandard terminal impedance is used. This article explores the limitation of the original SP-based analysis and, then, proposes a modified one with the help of $\bm{ABCD}$ and impedance matrices. For a system with a clear model, the modified SP-based analysis can get exactly the same results as the CM-based one in terms of calculation. In the experiment, a 1-MHz inductive power transfer system is built to show the benefit of the SP-based analysis with increased model uncertainty. Compared to the CM-based one, the largest error reduction is 17% for the efficiency and 8% for the voltage gain.
Spatial Distribution of Solar Energy (SE) and Comprehensive Potential Evaluation of Regional Development and Utilization Based on Deep Learning

: Energy is the fundamental driving force for social development and the material basis. In the social process from ancient times to the present, energy has always been closely related to it and is inseparable from time to time. With the continuous improvement of the level of human civilization, the role of energy has become more and more prominent, the scope of its influence has become wider and wider, and its role has become more and more important. The main purpose of this paper is to evaluate the spatial distribution of SE and the comprehensive potential of SE development and utilization based on deep learning algorithms. In order to more accurately and quantitatively describe the correlation between each parameter and photovoltaic power, this paper selects the Pearson correlation coefficient for quantitative analysis. Experimental research shows that irradiance and photovoltaic power have the greatest correlation, followed by temperature and wind speed. Through analysis, parameters such as irradiance, temperature, wind speed, wind direction and humidity can be selected for photovoltaic power prediction research.