Miracle-dz/UniFAR-FTUs huggingface dataset:
New Methodology for Evaluating Surface Quality of Experimental Aerodynamic Models Manufactured by Polymer Jetting Additive Manufacturing

The additive manufacturing (AM) applications have attracted a great deal of interest with regard to experimental aerodynamic studies. There is a need for a universal roughness scale that characterizes different materials used in aerodynamic research. The main purpose of this paper is identification of the potential of a material jetting AM process to produce accurate aerodynamic surfaces. A new methodology to evaluate the roughness of aerodynamic profiles (airfoils) was proposed. A very short-span wing artifact for preliminary tests and a long-span wing model were proposed for design of experiments. Different artifacts orientations were analyzed, maintaining the same surface quality on the upper and lower surface of the wing. A translucent polymeric resin was used for samples manufacturing by polymer jetting (PolyJet) technology. The effects of main factors on the surface roughness of the wing were investigated using the statistical design of experiments. Three interest locations, meaning the leading-edge, central, and trailing-edge zones, on the upper and lower surfaces of the airfoil were considered. The best results were obtained for a sample oriented at XY on the build platform, in matte finish type, with a mean Ra roughness in the range of 2 to 3.5 μm. Microscopy studies were performed to analyze and characterize the surfaces of the wing samples on their different zones.
Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects

Software engineering is one of the most significant areas, which extensively used in educational and industrial fields. Software engineering education plays an essential role in keeping students up to date with software technologies, products, and processes that are commonly applied in the software industry. The software development project is one of the most important parts of the software engineering course, because it covers the practical side of the course. This type of project helps strengthening students’ skills to collaborate in a team spirit to work on software projects. Software project involves the composition of software product and process parts. Software product part represents software deliverables at each phase of Software Development Life Cycle (SDLC) while software process part captures team activities and behaviors during SDLC. The low-expectation teams face challenges during different stages of software project. Consequently, predicting performance of such teams is one of the most important tasks for learning process in software engineering education. The early prediction of performance for low-expectation teams would help instructors to address difficulties and challenges related to such teams at earliest possible phases of software project to avoid project failure. Several studies attempted to early predict the performance for low-expectation teams at different phases of SDLC. This study introduces swarm intelligence -based model which essentially aims to improve the prediction performance for low-expectation teams at earliest possible phases of SDLC by implementing Particle Swarm Optimization-K Nearest Neighbours (PSO-KNN), and it attempts to reduce the number of selected software product and process features to reach higher accuracy with identifying less than 40 relevant features. Experiments were conducted on the Software Engineering Team Assessment and Prediction (SETAP) project dataset. The proposed model was compared with the related studies and the state-of-the-art Machine Learning (ML) classifiers: Sequential Minimal Optimization (SMO), Simple Linear Regression (SLR), Naïve Bayes (NB), Multilayer Perceptron (MLP), standard KNN, and J48. The proposed model provides superior results compared to the traditional ML classifiers and state-of-the-art studies in the investigated phases of software product and process development.
Self-Learning Spatial Distribution-Based Intrusion Detection for Industrial Cyber-Physical Systems

Thanks to the great advancement of cognitive computing, artificial intelligence, big data, and the Internet of Things (IoT) technologies, the fusion of the physical and virtual worlds is changing people’s lifestyles. Although the research and deployment of cyber-physical systems (CPSs) are notably promoted by cognitive computing, the reliability and large-scale application of CPSs are still significantly challenged by some security issues. Therefore, it is meaningful to clarify and address the weaknesses of current intrusion detection methods for CPSs and enhance the ability to identify, analyze, and predict to improve the performance of intrusion detection. In this article, we first propose a novel self-learning spatial distribution algorithm, named Euclidean distance-based between-class learning (EBC learning), which improves between-class learning by calculating the Euclidean distance (ED) among $k$ -nearest neighbors of different classes. In addition, a cognitive computing-based intrusion detection method named border-line SMOTE and EBC learning based on random forest (BSBC-RF) is also proposed based on the EBC learning for industrial CPSs. The experimental results over a real industrial traffic dataset show that the proposed EBC learning has strong spatial constraint capability and can improve the prediction and recognition performance. Compared with the eight state-of-the-art methods, the proposed method has an ACC exceeding 99.5%, false alarm rate (FAR) less than 0.06%, and $F1$ close to 0.99, which is still superior to other ones.
Sex and gender differences in the development of empathy

The topic of typical sex and gender difference in empathy is examined in both a developmental and neuroscientific perspective. Empathy is construed as a multi‐layered phenomenon with various degrees of complexity unfolding in ontogeny. The different components of empathy (i.e., affective, cognitive, and prosocial motivation) will be discussed as they interact and are expressed behaviorally. Significant sex/gender differences in empathy are discussed in relation to putative bottom‐up or top‐down processes underlying empathetic responses. The early onset and the pervasive presence of such sex/gender differences throughout the lifespan are further discussed in light of social and neurobiological modeling factors, including early socialization, brain's structural/functional variances, as well as genetics and hormonal factors.
The Effect of Financial Development on Energy Consumption: Evidence from Russia

This paper explores the effect of financial development, economic growth, and energy prices represented by consumer price index (CPI) on energy consumption in Russia by performing VECM, CCR, DOLS and FMOLS analyses to the annual data from 1995 to 2019. The findings of this empirical analysis reveal that financial development and economic growth have positive impact on energy consumption in Russia. Furthermore, the effects of energy prices expressed by CPI is revealed to be negative, which is consistent with the theory and expectations in practice. Based on the findings of this study, the nexus and impacts of financial development on energy consumption are discussed, as well as plausible explanations and policy implications.
Enhanced Terrestrial Carbon Export From East Antarctica During the Early Eocene

Terrestrial organic carbon (TerrOC) acts as an important CO2 sink when transported via rivers to the ocean and sequestered in coastal marine sediments. This mechanism might help to modulate atmospheric CO2 levels over short‐ and long‐ timescales (103–106 years), but its importance during past warm climates remains unknown. Here we use terrestrial biomarkers preserved in coastal marine sediment samples from Wilkes Land, East Antarctica (∼67°S) to quantify TerrOC burial during the early Eocene (∼54.4–51.5 Ma). Terrestrial biomarker distributions indicate the delivery of plant‐, soil‐, and peat‐derived organic carbon (OC) into the marine realm. Mass accumulation rates of plant‐ (long‐chain n‐alkane) and soil‐derived (hopane) biomarkers dramatically increase between the earliest Eocene (∼54 Ma) and the early Eocene Climatic Optimum (EECO; ∼53 Ma). This coincides with increased OC mass accumulation rates and indicates enhanced TerrOC burial during the EECO. Leaf wax δ2H values indicate that the EECO was characterized by wetter conditions relative to the earliest Eocene, suggesting that hydroclimate exerts a first‐order control on TerrOC export. Our results indicate that TerrOC burial in coastal marine sediments could have acted as an important negative feedback mechanism during the early Eocene, but also during other warm climate intervals.
Defective Cystinosin, Aberrant Autophagy−Endolysosome Pathways, and Storage Disease: Towards Assembling the Puzzle

Epithelial cells that form the kidney proximal tubule (PT) rely on an intertwined ecosystem of vesicular membrane trafficking pathways to ensure the reabsorption of essential nutrients—a key requisite for homeostasis. The endolysosome stands at the crossroads of this sophisticated network, internalizing molecules through endocytosis, sorting receptors and nutrient transporters, maintaining cellular quality control via autophagy, and toggling the balance between PT differentiation and cell proliferation. Dysregulation of such endolysosome-guided trafficking pathways might thus lead to a generalized dysfunction of PT cells, often causing chronic kidney disease and life-threatening complications. In this review, we highlight the biological functions of endolysosome-residing proteins from the perspectives of understanding—and potentially reversing—the pathophysiology of rare inherited diseases affecting the kidney PT. Using cystinosis as a paradigm of endolysosome disease causing PT dysfunction, we discuss how the endolysosome governs the homeostasis of specialized epithelial cells. This review also provides a critical analysis of the molecular mechanisms through which defects in autophagy pathways can contribute to PT dysfunction, and proposes potential interventions for affected tissues. These insights might ultimately accelerate the discovery and development of new therapeutics, not only for cystinosis, but also for other currently intractable endolysosome-related diseases, eventually transforming our ability to regulate homeostasis and health.
The Effect of NaCl Stress on the Response of Lettuce (Lactuca sativa L.)

In recent decades, increasing human pressure has caused the gradual deterioration of the physical and chemical properties of water and soil. Salinity is an important factor influencing the quality of water. The aim of this comprehensive research was to determine the effect of increasing concentrations of sodium chloride, which is a salinity inducer, on the yield, photosynthesis efficiency (expressed with chlorophyll fluorescence measurement) and content of selected nutrients in the leaves of hydroponically grown lettuce (Lactuca sativa L.). Experiments were conducted at the following concentrations of NaCl: 0 (control treatment), 10, 20, 40, and 60 mmol L−1. Studies were conducted in two independent seasons: spring and autumn. The plants exposed to NaCl stress modified their chemical composition by lowering the uptake of (for 60 mmol L−1 NaCl in relation to control): N (−11%), K (−35.7%), and Mg (−24.5%), while increasing the sodium content (+2400%). The Na:K ratio was significantly narrowed (from 76:1 to 2.6:1). The increase in the Cl level in the lettuce leaves may also have caused a decrease in the content of nitrates. As a result of disturbed ionic balance, the RWC was significantly reduced (−6.2%). As a result of these changes, the yield of the biomass of the aerial parts decreased (more than two-fold for the highest NaCl concentration in relation to control) whereas the dry matter content increased (+32%). The measurement of fluorescence showed significant changes at the PSII level. Salinity modified the energy flow rate (F0, FM, FV, FV/FM) as well as the specific energy flows through the reaction centre (ABS/RC, TR0/RC, ET0/RC, DI0/RC). The PSII functioning index, calculated on the basis of energy absorption (PIAbs), also changed. The salinity induced with NaCl significantly worsened the physiological reactions of the plants in the PSII, changed the ionic balance, which resulted in a significantly lower yield of the plants. Due to increasing water quality problems, it will be necessary to use, in agriculture on a much larger scale than before, saline water treatment systems (e.g., highly effective nanofiltration and/or reverse osmosis).
Cloud Removal in Optical Remote Sensing Imagery Using Multiscale Distortion-Aware Networks

Cloud layer contamination is a common problem in optical remote sensing (RS) images. Deep-learning-based cloud removal from RS imagery has attracted increasing attention in recent years. However, it remains challenging to exploit useful multiscale cloud-aware representations from cloud imagery due to the lack of effective modeling of cloud distortion effects and the weak feature representation capabilities of networks. To circumvent these challenges, we propose a multiscale distortion-aware cloud removal (MSDA-CR) network consisting of multiple cloud-distortion-aware representation learning (CDARL) modules combined in a multiscale grid architecture. Specifically, cloud distortion control functions (CDCFs) are defined and incorporated into the CDARL modules to adaptively model the distortion effects induced by cloud interference in the imaging process, with learnable parameters for the exploitation of distortion-restored representations. These representations are further distilled across different scales in the MSDA-CR network and integrated based on an attention mechanism to restore cloud-free images while retaining the spatial structures of ground objects. Extensive experiments on visible and multispectral RS datasets confirm the effectiveness of the proposed MSDA-CR network.
Segurança em Internet das Coisas: Uma Revisão da Literatura

A segurança em Internet das Coisas (IoT) ainda é um problema de pesquisa em aberto. A heterogeneidade de dispositivos, linguagens e protocolos cria uma grande superfície de ataque, e as limitações de processamento, memória e banda dos dispositivos embarcados limitam o uso de mecanismos tradicionais de segurança. Neste artigo, é apresentada uma revisão da literatura sobre a segurança em IoT, destacando vulnerabilidades e ataques que motivam as pesquisas, as contribuições da pesquisa brasileira, e identificação de lacunas de pesquisa para fomentar uma agenda de trabalhos futuros.
A Study on 
 ψ
 -Caputo-Type Hybrid Multifractional Differential Equations with Hybrid Boundary Conditions

In this research paper, we investigate the existence, uniqueness, and Ulam–Hyers stability of hybrid sequential fractional differential equations with multiple fractional derivatives of ψ -Caputo with different orders. Using an advantageous generalization of Krasnoselskii’s fixed point theorem, we establish results of at least one solution, whereas the uniqueness of solution is derived via Banach’s fixed point theorem. Besides, the Ulam–Hyers stability for the proposed problem is investigated by applying the techniques of nonlinear functional analysis. In the end, we provide an example to illustrate the applicability of our results.
Rivaroxaban with Aspirin Versus Aspirin for Peripheral Arterial Disease and Intermittent Claudication. Rationale and Design of the COMPASS CLAUDICATION Trial

Background The COMPASS trial demonstrated that in patients with peripheral arterial disease, the combination of rivaroxaban and aspirin compared with aspirin reduces the risk of major adverse limb events, but it is not known whether this combination can also improve symptoms in patients with intermittent claudication. The primary objective of this study is to evaluate the effect of the combination on claudication distance. Study design Eighty-eight patients with intermittent claudication will be randomized to receive rivaroxaban 2.5 mg twice daily plus aspirin 100 mg once daily or aspirin 100 mg once daily for 24 weeks. The primary outcome is the change in claudication distance from the baseline to 24 weeks, measured by 6 min walking test and treadmill test. The primary safety outcome is the incidence of major bleeding and clinically relevant non-major bleeding according to the International Society on Thrombosis and Hemostasis criteria. Summary The COMPASS CLAUDICATION trial will provide high-quality evidence regarding the effect of the combination of rivaroxaban and aspirin on claudication distance in patients with peripheral arterial disease.
Overlay Cognitive ABCom-NOMA-Based ITS: An In-Depth Secrecy Analysis

The upcoming Intelligent Transportation System (ITS) supported by sixth generation (6G) communication technologies is expected to face the great challenges of spectrum scarcity, large-scale connectivity, ultra-low latency, and various security threats. To mitigate these challenges and implement the ITS in practice, we propose an overlay cognitive ambient backscatter communication non-orthogonal multiple access (ABCom-NOMA) network for the ITS. Specifically, we elaborate on the secrecy performance the overlay cognitive ABCom-NOMA based on ITS in the presence of an eavesdropping vehicle by deriving the secrecy outage probability (SOP) between the primary network, overlay secondary network, and the eavesdropping vehicle of the considered networks, respectively. For comparison, the secrecy performance of secondary receiving vehicles is taken into account, and a series of numerical simulations by Monte-Carlo methods are carried out to investigate the secrecy performance. From the numerical results yielded by the simulations, we can conclude: 1) The secrecy performance of the proposed the overlay secondary network is superior to the one of the primary network; 2) The increasing of the power allocation factor yields a positive effect on the secrecy performance of the primary receiving vehicles but a negative effect on that of the secondary receiving vehicles.
LADRC-based attitude decoupling control for helicopter and parameters tuning

To meet the ADS-33E-PRF flight quality and effectively overcome the influence of external disturbance,an attitude control strategy based on a linear active disturbance rejection control( LADRC) is proposed for helicopter. Flight dynamics model of UH-60 A and the wind model were established. UH-60 A was trimmed for verifying accuracy of dynamic model and trim algorithm. Helicopter attitude decoupling control loop based on the single input / single output second-order LADRC controller was set up with stabilization feedback loop. The controller parameter tuning problem was transformed into constrained optimization problem in time and frequency domain according to quality requirement of ADS-33E-PRF. Combining H-infinity synthesis algorithm and steepest descent algorithm,the optimization of parameters was calculated. Quality assessment of attitude control was made and the control loop was applied to the attitude hold control simulation in atmospheric disturbance. The results of simulation and quality evaluation show that the attitude control system based on LADRC has good decoupling performance and capability of anti-disturbance.
Long-Term Memory for a Common Object RAYMOND S. NICKERSON AND MARILYN JAGER ADAMS

A series of experiments was done to determine how completely and accurately people remember the visual details of a common object, a United States penny. People were asked to: draw a penny from unaided recall; draw a penny given a list of its visual features; choose from among a list of possible features those which do appear on a penny; indicate what was wrong with an erroneous drawing of a penny; and select the correct representation of a penny from among a set of incorrect drawings. Performance was surprisingly poor on all tasks. On balance, the results were consistent with the idea that the visual details of an object, even a very familiar object, are typically available from memory only to the extent that they are useful in everyday life. It was also suggested that recognition tasks may make much smaller demands on memory than is commonly assumed.
ARMScope – the versatile platform for scanning probe microscopy systems

Scanning probe microscopy (SPM) since its invention in the 80’s became very popular in examination of many different sample parameters, both in university and industry. This was the effect of bringing this technology closer to the operator. Although the ease of use opened a possibility for measurements without high labour requirement, a quantitative analysis is still a limitation in Scanning Probe Microscopes available on the market. Based on experience of Nano-metrology Group, SPM still can be considered as a tool for quantitative examination of thermal, electrical and mechanical surface parameters. In this work we present an ARMScope platform as a versatile SPM controller that is proved to be useful in a variety of applications: from atomic-resolution STM (Scanning Tunnelling Microscopy) to Multi-resonance KPFM (Kelvin Probe force microscopy) to commercial SEMs (Scanning electron microscopes).
ICT-13-2016 Type of action : RIA Orchestration and Reconfiguration Control Architecture D 3 . 5 : Final operational real-time SDR platforms

This deliverable includes the implementation results obtained in Year 3 on the final operational real-time SDR platforms with focus on the data plane. A mapping of the real-time prototype developments to the showcase scenarios defined in WP2 and detailed explanations of the single contributions including availability in the testbed facilities are described in this final deliverable for WP3.
COMPARISON OF DIFFERENT COMMERCIAL SEROLOGY KITS FOR THE DETERMINATION OF SEROPOSITIVE TOXOPLASMOSIS IN CATTLE IN INDONESIA

This research aims to explain the evaluation of the differences in four commercial kits for the detection of serological toxoplasmosis used in Indonesia. The results of the study found that the toxoplasmosis seropositivity determined by the four commercial kits showed a significant difference (P0.05). Seropositive toxoplasmosis obtained using Pastorex, Toxotest, IDScreen, and Toxo Ab were 35.12%, 60.12%, 26.19%, and 10.12% respectively. IDScreen had a good agreement with Toxo Ab (Gwet's AC1= 0.623) and a moderate agreement with Pastorex (Gwet's AC1= 0.494-0.511). Toxotest had a low agreement with three commercial kits (Gwet's AC1= 0.2) but had a moderate agreement with western blotting (WB) and modified agglutination test (MAT) (Gwet's AC1= 0.458-0.557).
Handwritten Tamil Character Recognition Using ResNet

: This paper describes the recognition of Tamil language characters by using Residual Neural Network (ResNet). ResNet is a kind of Artificial Neural Network (ANN) which is typically consists of double or triple layers skips that has nonlinearities and batch normalization. Tamil is one among most traditional languages especially found on the southern regions of India. Recognizing a non-digitalized character in Tamil is a very difficult process because of its large and compound character set. In this paper we describe an offline character recognition approach using ResNet Architecture which explains the ResNet layers and the difficulty in gaining the digitalized character from the ResNet Layer using deep learning. This involves the data set training and pre-processing of all the Tamil characters. There are totally 256 characters in Tamil language in which most of the letters are almost similar and only a slight change can be seen only at the end for most of the characters, so recognizing a particular character is quite difficult and require highly trained datasets for distinguishing each and every characters.