Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Approach for Analyzing the Effects of Almen Intensity on the Residual Stress and Hardness of Shot-Peened (TiB + TiC)/Ti–6Al–4V Composite: Deep Learning

In the present study, the experimental data of a shot-peened (TiB + TiC)/Ti–6Al–4V composite with two volume fractions of 5 and 8% for TiB + TiC reinforcements were used to develop a neural network based on the deep learning technique. In this regard, the distributions of hardness and residual stresses through the depth of the materials as the properties affected by shot peening (SP) treatment were modeled via the deep neural network. The values of the TiB + TiC content, Almen intensity, and depth from the surface were considered as the inputs, and the corresponding measured values of the residual stresses and hardness were regarded as the outputs. In addition, the surface coverage parameter was assumed to be constant in all samples, and only changes in the Almen intensity were considered as the SP process parameter. Using the presented deep neural network (DNN) model, the distributions of hardness and residual stress from the top surface to the core material were continuously evaluated for different combinations of input parameters, including the Almen intensity of the SP process and the volume fractions of the composite reinforcements.
Influence of Testosterone in Neglected Tropical Diseases: Clinical Aspects in Leprosy and In Vitro Experiments in Leishmaniasis

Neglected tropical diseases encompass a group of chronic and debilitating infectious diseases that primarily affect marginalized populations. Among these diseases, leprosy and leishmaniasis are endemic in numerous countries and can result in severe and disfiguring manifestations. Although there have been reports indicating a higher incidence of leprosy and leishmaniasis in males, the underlying factors contributing to this observation remain unclear. Therefore, the objective of this study was to examine both clinical and experimental evidence regarding the role of testosterone in leprosy and leishmaniasis. A prospective clinical study was conducted to compare the clinical forms of leprosy and assess circulating testosterone levels. Additionally, the impact of testosterone on Leishmania amazonensis-infected macrophages was evaluated in vitro. The findings demonstrated that serum testosterone levels were higher in women with leprosy than in the control group, irrespective of the multi- or pauci-bacillary form of the disease. However, no differences in testosterone levels were observed in men when comparing leprosy patients and controls. Interestingly, increasing doses of testosterone in macrophages infected with L. amazonensis resulted in a higher proportion of infected cells, decreased CD40 expression on the cell surface, elevated expression of SOCS1, and decreased expression of IRF5. These findings provide biological evidence to support the influence of testosterone on intracellular infections, though the interpretation of clinical evidence remains limited.
Identification of Students' Online Learning Readiness (KBO) in the Post-Pandemic Period

This study aims to identify online learning readiness (KBO) students of FKIP University of Mataram. This type of research is quantitative with a descriptive approach. The study used a KBO questionnaire instrument which was compiled with a scoring system. Analysis of the data used is the Pearson product moment correlation to see the correlation between KBO scores and academic abilities possessed by students. The results showed that students' online learning readiness (KBO) was at a score of less than 80, meaning that students' online learning readiness was still not very good. Meanwhile, according to the Pearson product moment correlation data analysis, it shows that the student correlation value from JIP is 0.815. For students from JMIPA it is 0.10. Meanwhile, students from JPBI and JIPS got a correlation value of 0.386 and 0.59. Based on these data, it can be concluded that JIP and JIPS students have a strong correlation between academic ability and students' KBO scores. While JMIPA and JPBI, students' academic ability and KBO scores have a low level of correlation.
MLGL: An R Package Implementing Correlated Variable Selection by Hierarchical Clustering and Group-Lasso

The MLGL R-package, standing for Multi-Layer Group-Lasso, implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data. A sparsity assumption is made that is, only a few variables are assumed to be relevant for predicting the response variable. In this context, the performance of classical Lasso-based approaches strongly deteriorates as the redundancy strengthens. The proposed approach combines variables aggregation and selection in order to improve interpretability and performance. First, a hierarchical clustering procedure provides at each level a partition of the variables into groups. Then, the set of groups of variables from the different levels of the hierarchy is given as input to group-Lasso, with weights adapted to the structure of the hierarchy. At this step, group-Lasso outputs sets of candidate groups of variables for each value of regularization parameter. The versatility offered by MLGL to choose groups at different levels of the hierarchy a priori induces a high computational complexity. MLGL however exploits the structure of the hierarchy and the weights used in group-Lasso to greatly reduce the final time cost. The final choice of the regularization parameter – and therefore the final choice of groups – is made by a multiple hierarchical testing procedure.
A time-aware hyperlink-induced topic search-based reputation evaluation method for optimal manufacturing service recommendation in distributed peer-to-peer networks

With the explosive growth of manufacturing services on a global scale, it has become an enormous challenge to recommend optimal manufacturing service in distributed manufacturing environments. Reputation evaluation is a key step to make optimal manufacturing service recommendation. However, the reputation values calculated through conventional reputation evaluation methods are static and local, and have a severe impact on the effectiveness of manufacturing service recommendation. This article presents a novel and effective reputation evaluation method for optimal manufacturing service recommendation in distributed peer-to-peer networks. The main contribution of the proposed approach is that it makes it possible to calculate the global and dynamic reputation values of enterprises by employing a time-aware hyperlink-induced topic search algorithm. This algorithm not only disseminates the reputation value of transaction enterprises, but also takes account of the temporal dimension in the process of computing reputation values. A concrete example demonstrates the feasibility of calculating reputation values of the proposed method, and an evaluation experiment reveals that both of recall rate and precision rate are improved, employing our proposed method for recommending manufacturing service.
An Intelligent System With Reduced Readout Power and Lightweight CNN for Vision Applications

An always-on intelligent system comprising of an image sensor requires continuous functioning of each pixel. This includes sensing the illumination content of the scene and also the conversion of the analog values into their digital representations. Therefore, power consumption during analog to digital conversion and computational cost at the image sensor module become critical while designing a system that is always-on and incorporates intelligence near the sensor module. This work focuses on the inherent property of the ADC for converting the analog pixel values to digital values by taking a defined number of analog-to-digital converter (ADC) cycles. The design factors considered are 1) Power saving due to reduced ADC conversion cycles for each pixel; 2) The reduced bit-precision of the processing unit to reduce hardware cost; 3) The dataflow design through hls4ml, which produces parallel computational modes for low latency CNN architectures. The proposed work implements two lightweight CNN models with reduced parameters as compared to the original architectural models of VGG16 (like) and SqueezeNet (like) which are trained in Qkeras and deployed on Zynq UltraScale+ MPSoC board. In addition, the design pipeline is validated on the MobileNetV2 and GhostNet architectures to demonstrate its generalization ability. A detailed analysis shows that limiting the number of ADC bits from 8 to 4 reduces the mean accuracy merely from 50.3 to 49.17 for VGG16 (like) and 67.83 to 67.80 for SqueezeNet (like) model, however, the readout power is significantly reduced from 140.45 mW to 7.7 mW for STL-10 dataset with $96\times96$ image resolution. Additional experiments are conducted with CIFAR-10 and mini-ImageNet datasets for classification and with Oxford-IIIT Pet Dataset for segmentation. The proposed work, thus, provides empirical evidence that a reasonable performance for intelligent vision tasks with power saving can be achieved by tuning CNN models to work with reduced ADC bit precision.
An Empirical Study on The Calendar Effect of The Shanghai Index in China

The premise of traditional financial theory is efficient market theory and rational man hypothesis, while the market anomaly which can not be explained by traditional financial theory such as the calendar effect poses a great challenge to traditional financial theory. This paper uses the daily closing price data of the Shanghai Composite Index from December 19, 1999, to May 6, 2022, to investigate the calendar effect of the logarithmic return of the Shanghai Composite Index. The research results show that China's Shenzhen stock market is inefficient and has a negative Tuesday effect, which empirically proves the non-efficiency of China's stock market and the calendar effect in behavioral finance in China's stock market.
Fourier method for first order differential equations with involution and groups of operators

In the paper we study a mixed problem for a first-order differential equation with an involution. It is written by means of a differential operator with an involution acting in the space functions square integrable on a finite interval. We construct a similarity transform of this operator in an operator being an orthogonal direct sum of an operator of finite rank and operators of rank 1. The method of our study is the method of similar operators. Theorem on similarity serves as the basis for constructing groups of operators, whose generator is the original operator. We write out asymptotic formulae for groups of operators. The constructed group allows us to introduce the notion of a mild solution, and also to describe the mild solutions to the considered problem. This serves to justify the Fourier method. Almost periodicity of bounded mild solutions is established. The proof of almost periodicity is based on the asymptotic representation of the spectrum of a differential operator with an involution.
Harmonic Analysis of Quasi-2-Level Medium Voltage Super-Switch

This article focuses on the harmonic analysis of the quasi-2-level super-switch (Q2L SS), considering different delay times and the number of steps. The harmonic performance is investigated in-depth using double fourier integral transformation analysis, simulations, and experiments. According to the analysis, increasing the number of steps facilitates a smoother transition between the negative and positive states of the output voltage. As a result, the harmonic performance of the Q2L SS is significantly improved. On the other hand, an increased number of steps sets the boundaries to the switching frequency and the maximum modulation index. Moreover, multistep Q2L SS requires more capacitors, which can significantly increase the volume of the converter. The study establishes the ground basis for understanding the Q2L SS operation in the harmonic domain. It can be used for the future research in the domain of Q2L SS and approaches for designing EMI filters for Q2L SS-based converters.
Hierarchies for Constrained Partial Spanning Problems in Graphs

The problem of minimal cost partial spanning trees is well known as the NP-complete Steiner problem in graphs. If there are constraints to respect by using some edges and nodes in the spanning structure or constraints on the required end to end (QoS) properties of the partial spanning structure, then the optimal structure can be different from a partial spanning tree. This paper present simple hierarchical spanning structures which correspond generally to the optimal solution. To illustrate the optimality of spanning hierarchies, two problems in communication networks, which are very usefull for multicast routing, are analyzed. In the first problem, the constraints are related to the physical capacity of the nodes in WDM networks. Namely, the analyzed example presents the case of sparse splitter nodes in optical networks. In the second example, the multi-constrained multicast routing is analyzed. In both cases, the optimal routing structure is a hierarchy. Since its computation is NP-complete, after the presentation of the computation difficulties some ideas on heuristic algorithms are discussed.
Extracting a Consistency Operator from a Constraint Satisfiability Function and Application to Partially Defined Constraints

Very often, it is easier to specify the set of solutions of a constraint than its ﬁltering algorithm. In this paper, we propose a method to automatically derive a ﬁltering algorithm from a constraint expressed by its satisﬁability function. Many global constraints can be easily described declaratively as a tree which compactly encodes a disjunction of alternatives. For such a tree, we give a class of ﬁltering algorithms based on the notion of extension to sets or to intervals. This technique is applied to decision trees and neural network in the case of partially deﬁned constraints .
Panorama des principales approches relevant du «cadre H∞»

Cet article a pour but de mettre en perspective les approches utilisant les outils developpes dans le cadre lineaire a partir du formalisme H∞. Les aspects lies a la notion de robustesse y sont plus particulierement soulignes, a la lumiere de l'experience acquise au cours des travaux effectues dans le Service Automatique de Supelec.
Lorentz Force Distribution With Local Curvature and Near-Field Calculation of Racetrack Coil EMAT

Electromagnetic acoustic transducer (EMAT) has been widely studied and applied because it can generate ultrasonic waves in metal without coupling agents. It usually consists of one or more sets of permanent magnets and coils, the working principle is that Lorentz force in skin depth of conductive material caused by the electromagnetic induction, and generating ultrasonic waves in the structure. When ultrasonic is propagating in a solid, the sound pressure is not stable in the near field. If there are factors to be measured within this field, they may not be identified accurately. For EMAT, Lorentz force is used as the excitation mechanism, and its distribution on the curved surface is not uniform, which is comprehensively affected by the local curvature of the surface, EMAT structure, and other factors. In this article, the influence of structure parameters of racetrack coil EMAT and curved part on the Lorentz force distribution was studied, and the calculation model of near field is established by Lorentz force distribution. In order to further verify the validity of the calculation model, the finite element model was established and the laws of near-field length, the size of EMAT, and the local curvature of curved parts are obtained. The racetrack coil EMATs that were consistent with the theoretical analysis were made and the experiments were carried out. The relationships between beam focusing ability, sizes of EMAT, and the radius of curved parts are summarized by calculating amplitude, and the correctness of the near-field calculation method is coherently proved by finite element verification and experiments.
SPICA, a new 6T visible beam combiner for CHARA: science, design and interfaces

We present the recent developments preparing the construction of a new visible 6T beam combiner for the CHARA Array, called SPICA. This instrument is designed to achieve a large survey of stellar parameters and to image surface of stars. We first detail the science justification and the general idea governing the establishment of the sample of stars and the main guidance for the optimization of the observations. After a description of the concept of the instrument, we focus our attention on the first important aspect: optimizing and stabilizing the injection of light into single mode fibers in the visible under partial adaptive optics correction. Then we present the main requirements and the preliminary design of a 6T-ABCD integrated optics phase sensor in the H-band to achieve long exposures and reach fainter magnitudes in the visible.
Modelo con enfoque en el Technology Enhanced Learning para la exteriorización del conocimiento para la efectividad de la enseñanza

La exteriorización y la gestión del conocimiento es un tema de investigación recurrente y de importancia significativa en el ámbito educativo, su abordaje fue parte del trabajo de los filósofos en tiempos de Platón y sigue vigente hasta nuestros días. En este artículo se consideró retomar la temática, a partir de la generación de un modelo con enfoque en Technology Enhanced Learning para la exteriorización del conocimiento que tuvo como objetivo apoyar en específico a los profesores que imparten los cursos propedéuticos, puntualmente en la materia de Programación en la Facultad de Informática de la Universidad Autónoma de Querétaro, mediante la externalización del conocimiento tácito de los profesores expertos en el área de Programación de la Facultad, identificando estrategias de aprendizaje y herramientas basadas en tecnologías de información y comunicación que apoyan al proceso de enseñanza-aprendizaje con la finalidad de cumplir los objetivos de aprendizaje que se plantean para cada tema específico del curso propedéutico. A partir de la implementación del modelo generado se obtuvieron resultados favorables como el mejoramiento de la práctica docente en los profesores y el aumento en la obtención de conocimientos de los estudiantes, alcanzando en mejor y mayor medida los objetivos planteados.
Gain-scheduled $H_\infty$ loop-shaping autopilot design for spin-stabilized canard-guided projectiles

This article is dedicated to the design of a complete guidance & control system for the roll/pitch/yaw-channels of a 155 mm dual-spin projectile equipped with nosemounted trajectory correction canards. The projectile airframe parameter-dependent nonlinear model including aerodynamic and actuator/sensor uncertainty descriptions is given and the subsequently computed linearized models necessary for autopilot design are presented. The pitch/yaw-channel dynamics linearized system is useful for highlighting important properties specific to these dynamics, in particular in relation with the parameter vector dimension and the sensor position. The computation of a linear structured controller for the nose roll-axis and of a gain-scheduled structured compensator for the airframe pitch/yaw-axes, using an H∞ loop-shaping design approach, is detailed with the assessment of the obtained performance and robustness properties. Finally, various guided flight nonlinear 7DoF simulation results are exposed for the purpose of evaluating over the projectile flight envelope the effectiveness of the proposed guidance & control scheme.
DSFA: cross-scene domain style and feature adaptation for landslide detection from high spatial resolution images

ABSTRACT Rapid and accurate landslide inventory mapping is significant for emergency rescue and post-disaster reconstruction. Nowadays, deep learning methods exhibit excellent performance in supervised landslide detection. However, due to differences between cross-scene images, the performance of existing methods is significantly degraded when directly applied to another scene, which limits the application of rapid landslide inventory mapping. In this study, we propose a novel Domain Style and Feature Adaptation (DSFA) method for cross-scene landslide detection from high spatial resolution images, which can leverage labeled source domain images and unlabeled target domain images to mine robust landslide representations for different scenes. Specifically, we mitigate the large discrepancy between domains at the dataset level and feature level. At the dataset level, we introduce a domain style adaptation strategy to shift landslide styles, which not only bridges the domain gap, but also increases the diversity of landslide samples. At the feature level, adversarial learning and domain distance minimization are integrated to narrow large feature distribution discrepancies for learning domain-invariant information. In addition, to avoid information omission, we improve the U-Net3+ model. Extensive experimental results demonstrate that DSFA has superior detection capability and outperforms other methods, showing its great application potential in unsupervised landslide domain detection.
The beaming of subhalo accretion

Abstract We examine the infall pattern of subhaloes onto hosts in the context of the large-scale structure. We find that the infall pattern is essentially driven by the shear tensor of the ambient velocity field. Dark matter subhaloes are preferentially accreted along the principal axis of the shear tensor which corresponds to the direction of weakest collapse. We examine the dependence of this preferential infall on subhalo mass, host halo mass and redshift. Although strongest for the most massive hosts and the most massive subhaloes at high redshift, the preferential infall of subhaloes is effectively universal in the sense that its always aligned with the axis of weakest collapse of the velocity shear tensor. It is the same shear tensor that dictates the structure of the cosmic web and hence the shear field emerges as the key factor that governs the local anisotropic pattern of structure formation. Since the small (sub-Mpc) scale is strongly correlated with the mid-range (∼ 10 Mpc) scale - a scale accessible by current surveys of peculiar velocities - it follows that findings presented here open a new window into the relation between the observed large scale structure unveiled by current surveys of peculiar velocities and the preferential infall direction of the Local Group. This may shed light on the unexpected alignments of dwarf galaxies seen in the Local Group.
A High-Efficiency High-Voltage-Tolerant Buck Converter With Inductor Current Emulator for Battery-Powered IoT Devices

This article proposes a high-voltage-tolerant stacked buck converter compatible with a Li-ion battery. The proposed converter utilizes a stacked power stage with only low-voltage transistors (1.5 V) to safely convert 2.8–4.2 V battery voltage to 1 V output. The converter adopts pulse frequency modulation control in the discontinuous conduction mode to reduce power losses at light load. To maximize power efficiency for a wide range of operating conditions, the converter utilizes an inductor current emulator (ICE), which adaptively controls the peak inductor current against input voltage and load variations. The proposed converter chip was fabricated in 28-nm fully depleted silicon on insulator (FDSOI) and achieved a peak efficiency of 70% at <italic>V</italic><sub>IN</sub> = 3.6 V (nominal Li-ion voltage), <italic>V</italic><sub>OUT</sub> = 1 V, and <italic>I</italic><sub>LOAD</sub> = 1 μA. By optimizing the peak inductor current depending on <italic>V</italic><sub>IN</sub> with ICE, the efficiency can be further improved up to 4.1% when <italic>V</italic><sub>OUT</sub> = 1 V and <italic>I</italic><sub>LOAD</sub> = 10 <italic>μ</italic>A, while ensuring small efficiency variation of 5.6% over the battery voltage range. The converter efficiently regulates 1 V output from 2.8–4.2 V input at 500 nA-1 mA load with stacked 1.5 V transistors, suitable for battery-powered IoT devices.