Miracle-dz/UniFAR-FTUs huggingface dataset:
Morphometric Analysis of Sumatran, Kalimantan, and Javan Cyrtodactylus, which were Labelled as Cyrtodactylus marmoratus, Revealed Undescribed Species

Cyrtodactylus marmoratus is originally described based on the specimen from Java. Due to the marbled body colour pattern and the similarity in morphology, many specimens from outside of Java have been identified as C. marmoratus. The ongoing research, both molecular and morphological studies, showed that C. marmoratus is restricted to Java. The taxonomic status of specimens labelled as C. marmoratus from outside Java remains unresolved. Here, we study the morphometric and meristic of Javan, Sumatran, and Kalimantan Cyrtodactylus which were labelled as C. marmoratus to reveal their taxonomic status. We examined 11 morphometric and 19 meristic characters in 51 specimens at Museum Zoologicum Bogoriense (MZB) originally from, Java, Sumatra, and Kalimantan labelled as C. marmoratus as well as other recognized species. Principal Component Analysis (PCA) results show that C. marmoratus from Java differs from previous specimens which labelled as C. marmoratus from Sumatra and Kalimantan. The PCA results also show that C. marmoratus from Martabe differs from C. marmoratus Java which is supported by statistical analysis on interorbital, HeadW, HeadD, tubercle on the ventrolateral fold, dorsal tubercle and ventral scales. We hypothesized that Cyrtodactylus from Martabe is a suspected undescribed species-await formal description, and overall molecular analyses are needed for future study.
Analysis of a Low Power Inverting CMOS Schmitt Trigger Operating in Weak Inversion

This article analyzes the operation of a low power inverting CMOS Schmitt trigger in weak inversion. The analysis is based on an earlier proposed analytical model, which relates the hysteresis voltages to the transistors' dimensions, the supply voltage, and the temperature. The maximum error between the analytical and simulated transition voltages is below 10%, relative to the supply voltage. By optimizing the device sizes, the error reduces to 4%. The operation of the Schmitt trigger in weak inversion is also experimentally validated through an ASIC fabricated in AMS 0. 35μm CMOS process. The maximum error between the modeled and measured transition voltages is below 7%. Furthermore, the power consumption as a function of the supply voltage is analyzed. Overall, the proposed model may be used to optimize the operation of the analyzed Schmitt trigger circuit for low power operation.
Matrix-based algorithm for 4-Qubit reversible circuits synthesis

The mathematical model of the quantum reversible circuits is unitary matrix. Matrix can better reflect the quantum state evolution and further reflect the physical properties of the quantum computation. Matrix transformation based algorithm for 4-qubit reversible logic circuits synthesis is proposed in this paper. The algorithm skillfully uses the matrix representation of the quantum circuit and the circuit of the adjacent matrix to construct 4-qubit circuit with lower cost.
CasaPuNet: Channel Affine Self-Attention- Based Progressively Updated Network for Real Image Denoising

Recently, the popularity of deep learning has brought broad applications of computer vision technology in industrial information systems. However, the process of image acquisition will inevitably introduce noise, which may heavily degrade image visual quality. Most of the proposed denoising methods are nonblind and they have limited performance in removing real noise with different noise levels. To overcome this problem, we propose a deep convolutional neural network (CNN)-based blind model, i.e., channel affine self-attention (CASA) based progressively updated network (CasaPuNet) for real image denoising. First, we introduce degradation mapping module (DMM) to extract degradation information, which makes the remaining subnetwork of CasaPuNet perform nonblind denoising. Then, CasaPuNet adopts a multistage architecture, which resolves the large gap between the noisy input and clean output into several small gaps and eliminates these small gaps step by step through progressive inference. Finally, a novel CASA is designed to adaptively fuse the features from multiple stages according to input statistics. Specifically, CASA extracts channel information from different features and converts them into channel weights through an affine structure for adaptive adjustment. CASA brings a significant performance gain with a small number of parameters. Extensive experiments demonstrate that CasaPuNet outperforms state-of-the-art denoising methods both quantitatively and visually.
Dynamic Synchrophasor Estimation Based on Weighted Real-Valued Sinc Interpolation Method

Phasor measurement units (PMUs) are the cornerstone of intelligent sensing and monitoring of power systems. The superiority of the sinc interpolation function-based least squares (SLS) method, a signal processing algorithm for PMUs, has been proven in harmonic phasor estimation. However, the SLS-based estimators suffer from spectral interferences and heavy computational burden. To this end, a real-valued sinc weighted least squares (SWLS-R) method is proposed to track the dynamic synchrophasor accurately and quickly. First, the weighted vector, i.e., window function, is introduced to enhance the SLS estimator’s behavior by mitigating the impact of spectral interference. Then, the complex-valued system matrix is transformed into the real-valued system matrix to lower the computational burden by avoiding complex matrix multiplication. Finally, an explicit and fast solution is designed to calculate the pseudoinverse matrix of the SWLS-R, which aims to further improve computational efficiency. The accuracy and effectiveness of the proposed method are validated by simulations. These are designed, under steady and dynamic conditions, according to the M-class requirements of the Standard IEC/IEEE 60255-118-1.
An efficient SVDD-base boundary method for multiclass classification problems

In this article, we propose a new boundary method based on support vector domain description (SVDD) for multiclass classification problems. By utilizing the boundary around a class of data constructed by the SVDD, a new boundary distance measure, which gives the distance between a test object and the boundary around a class of data, can be determined by locating the nearest boundary point to the test object with the help of the geometrical interpretation of inner product. To decide which class the test object belongs to, a minimum distance classification is then carried out. Experimental results show that our method is robust to issues such as outliers and class imbalance, and overall it can achieve a comparable or better performance than other support vector-based classifiers for multiclass classification problems.
Wiener filter in the gyrator domain

The Wiener filter is the optimal filter according to the minimal mean square error criterion. For data distorted by noise with given spectral density, the Wiener filter is the optimal filter for restoring the noisy image. In this paper we will define a gyrator convolution operator and its optical implementation. The new generalized gyrator Wiener filter was defined using the new operator. The new filter was compared to the conventional Wiener filter and showed improved performance. An improvement of up to 40 dB in the mean square error at the optimal rotation angle in comparison to the conventional Wiener filter is demonstrated.
Rate theory and cooperative structural relaxation in amorphous polymer blends as revealed by a thermal sampling study

The thermal sampling technique is applied to investigate relaxation processes in amorphous blends and it was found that a distribution process exists. The relaxation kinetics are interpreted in terms of rate theory. A linear relation between activation enthalpy and entropy values is obtained, indicating that the compensation law is valid. The compensation plot revealed that the energy of the blend relaxation process comprises not only the energy required to move the segments along the reaction path but also the energy required to create these segments.
Poleable nanoparticles as fillers towards non-linear optically active actuators

A new type of poleable dielectric elastomer is introduced herein. The elastomer contains polymer nanoparticles with frozen molecular dipoles, which can be oriented at elevated temperatures in an electric field via poling. The aim is to provide a soft material with high, tunable optical properties suitable for actuator and flexible electronics applications. To that end poleable polymeric nanoparticles with high dipole concentrations and glass transition temperatures well above room temperature will be needed to be used as filler in an elastomer matrix. The synthesis and characterization of such particles is presented in this manuscript. Polyhydroxyethyl methacrylate (PHEMA) nanoparticles were synthesized using miniemulsion polymerization. The particles were loaded with 4-[ethyl (2-hydroxyethyl) amino]-4-nitrobenzene, usually called Disperse Red 1 (DR1), which has a large dipole moment (μ = 7.5 – 9.5 D). The maximum dipole loadings is limited by the solubility of the dipole in the monomer solutions prior to polymerization. All samples show a glass transition temperature around 95 °C. Secondary electron microscopy (SEM) revealed spherical particles, the size of which was confirmed by dynamic light scattering (DLS) measurements. A composite was prepared by dispersing the particles in polydimethyl siloxane (PDMS).
On the design of embedded solutions to banknote recognition

Abstract. Banknote recognition systems have many applications in the modern world of automatic monetary transaction machines. They are traditionally based on simple classifiers applied over manually selected areas. A new solution in this field, borrowed by content-based image retrieval (CBIR), which is based on dense scale-invariant feature transform features in a bag-of-words framework followed by a support vector machine (SVM) classifier, is explored. The proposed computer vision system for banknote recognition, on one hand, enables recognition at high accuracy and speed, and, on the other hand, provides basis for further applications, e.g., counterfeit detection and fitness test. This approach makes the system robust to various defects, which may occur during image acquisition or during circulation life of banknote. We implemented and tested on an embedded platform three state-of-the-art classification techniques [SVM, artificial neural network (ANN), and hidden Markov model (HMM)]. The comparative results are reported for accuracy with different sizes of the training datasets and with various types of datasets. In this framework, the SVM classifier outperforms ANN and HMM on the basis of speed and accuracy on our embedded platform.
An iterated forcing extension in which all a1 -dense sets of reals are isomorphic

AN ITERATED FORCING EXTENSION IN WHICH ALL א1-DENSE SETS OF REALS ARE ISOMORPHIC by Michael H. Vartanian If κ is an infinite cardinal, then X ⊆ R is called κ-dense if between any a < b, both in R, there are exactly κ elements of X. In these terms, a famous result of Cantor says that in every model of set theory all א0-dense sets of reals are isomorphic (to Q). This result cannot be directly extended, however, since for κ = א1 there exist models of set theory in which not all א1-dense sets are isomorphic. On the other hand, Baumgartner has shown by the method of iterated forcing that assuming the consistency of set theory, there does exist at least one model of set theory in which all א1-dense subsets are isomorphic. We present here a detailed, yet expository, account of Baumgartner’s result and discuss its relevance to the Proper Forcing Axiom of contemporary set theory.
Short-Term Demand Forecasting of Urban Online Car-Hailing Based on the K-Nearest Neighbor Model

Accurately forecasting the demand of urban online car-hailing is of great significance to improving operation efficiency, reducing traffic congestion and energy consumption. This paper takes 265-day order data from the Hefei urban online car-hailing platform from 2019 to 2021 as an example, and divides each day into 48 time units (30 min per unit) to form a data set. Taking the minimum average absolute error as the optimization objective, the historical data sets are classified, and the values of the state vector T and the parameter K of the K-nearest neighbor model are optimized, which solves the problem of prediction error caused by fixed values of T or K in traditional model. The conclusion shows that the forecasting accuracy of the K-nearest neighbor model can reach 93.62%, which is much higher than the exponential smoothing model (81.65%), KNN1 model (84.02%) and is similar to LSTM model (91.04%), meaning that it can adapt to the urban online car-hailing system and be valuable in terms of its potential application.
Iterative Development of the Caregiver Wellness After Traumatic Brain Injury Program (CG-Well)

Objectives: (1) To iteratively design a web/phone-based intervention to support caregivers of adults acutely following traumatic brain injury (TBI), Caregiver Wellness (CG-Well), and (2) to obtain qualitative and quantitative feedback on CG-Well from experts and caregivers to refine the intervention. Setting: A level I trauma and tertiary medical center. Participants: Convenience sample of a total of 19 caregivers and 25 experts. Design: Multistep prospective study with iterative changes to CG-Well: (1) developed intervention content based on qualitative feedback from a prior study and literature review; (2) obtained qualitative feedback from 10 experts; (3) refined content using a modified Delphi approach involving 4 caregivers and 6 experts followed by qualitative interviews with 9 caregivers; (4) designed CG-Well website and videos; and (5) obtained feedback on program acceptability, appropriateness, and feasibility from 6 caregivers and 9 experts. Interventions: CG-Well included content on TBI, self-care and support, and skill-building strategies delivered through a website and telephone calls. Main Outcome Measure(s): Qualitative data were analyzed using content analysis. Caregivers and experts completed Likert-type scales to rate module relevance, clarity, accuracy, utility and website acceptability, appropriateness, and feasibility (1 = strongly disagree to 5 = strongly agree). Means and standard deviations (SD) characterized ratings. Results: Qualitative findings were instrumental in designing and refining CG-Well. Ratings were positive for modules (means and SD for relevant [4.9, 0.33], clear [4.6, 0.53], accurate [4.9, 0.33], and useful [5, 0]) and the website (means and SD for acceptable [4.8, 0.36], appropriate [4.8, 0.35], and feasible [4.8, 0.36]). Conclusions: The iterative design process for CG-Well resulted in a highly acceptable program. An early-stage randomized controlled trial is underway to estimate treatment effects for a future well-powered clinical trial.
Stochastic approximation with non-additive measurement noise

The Robbins–Monro algorithm with randomly varying truncations for measurements with non-additive noise is considered. Assuming that the function under observation is locally Lipschitz-continuous in its first argument and that the noise is a φ-mixing process, strong consistency of the estimate is shown. Neither growth rate restriction on the function, nor the decreasing rate of the mixing coefficients are required.
Morphological and chemical characterization of cambucizeiro fruits

The cambucizeiro (Campomanesia phaea), belonging to the Myrtaceae family, is a native plant of the Brazilian Atlantic Forest. The description of the characteristics of the cambucizeiro fruits is important to support new genetic improvement works and its commercial exploitation, especially regarding the processing of the fruit. The present work aimed to perform the morphological and chemical characterization of the cambucizeiro fruits. Fifty-eight accessions, from different locations in the Atlantic Forest and Serra do Mar in the state of São Paulo, were collected, propagated by seeds and one specimen of each accessory is at the Seedling Production Center in São Bento do Sapucaí (SP). Forty fruits from each access were collected in May and submitted to the following analyses: longitudinal and transversal diameter, total fruit fresh mass, number and mass of seeds, total soluble solids, % citric acid, ratio, firmness, vitamin C and coloration. Fruit conformation varies intensely among accessions. The number of seeds is not a good indicator for the relation with the fruit mass, but the mass of one thousand seeds. Some accessions have high soluble solids content, but, on the other hand, the vast majority have fruits with high acidity. Cambuci is an excellent source of vitamin C. The fruits of the accessions are green in color, persisting an opaque shade when ripe.
Depth and thermal stability of dry etch damage in GaN Schottky diodes

GaN Schottky diodes were exposed to N2 or H2 inductively coupled plasmas prior to deposition of the rectifying contact. Subsequent annealing, wet photochemical etching, or (NH4)2S surface passivation treatments were examined for their effect on diode current–voltage (I–V) characteristics. We found that either annealing at 750 °C under N2, or removal of ∼500–600 A of the surface essentially restored the initial I–V characteristics. There was no measurable improvement in the plasma-exposed diode behavior with (NH4)2S treatments.
Privacy-Aware Secure Routing through Elliptical Curve Cryptography with Optimal RSU Distribution in VANETs

Vehicular Ad-Hoc Networks (VANETs) are the backbone of the intelligent transportation system, which consists of high-speed vehicles with huge dynamic mobility. The communication takes place with a vehicle-to-vehicle, vehicle to infrastructure, with traffic signals. The major flaw of this kind of network is that due to high mobility in VANETs, the communication overhead is so high that it directly affects the efficiency of the network. Security is also holding a vital role in VANETs. The attackers can easily capture vehicle details of this type. To overcome this drawback, security should also need to get improved. This paper introduces Elliptical Curve Cryptography with Generic Algorithm based Privacy-Aware Secure Routing (ECC-GA-PASR), which is the combination of two methods such as optimal RSU distribution and elliptical curve cryptography (ECC) based authentication. RSU distribution is optimized by using the generic algorithm (GA) as well as to improve the authentication in trusted authority (TA) ECC algorithm is used. By using these two concepts, the proposed method reduced the communication overhead and increased the security of the network. The simulation is conducted throughput NS2 and SUMO. The performance analysis is performed concerning vehicle count, varying speed, and malicious activities. The parameters that are concentrated for this performance analysis are energy efficiency, packet delivery ratio, overhead, and packet loss. The performance of the proposed method is calculated and compared with earlier works such as S-AODV, ES-AODV, and ECC-ACO-AODV. Compared with the earlier works, the proposed ECC-GA-PASR produced 15% better efficiency, 12% better packet delivery ratio, 50% lower overhead, and 30% lower packet loss.
SCIENTIA MEDICA

Aims: to evaluate the effects of 12 weeks of Pilates training (Classical Method) on cardiorespiratory fitness and heart rate responses of healthy sedentary women. Method: fifteen women (average age 29±4) performed 12 weeks of Pilates training for 60 minutes, three times per week (Pilates Group). Thirteen women (average age 29±5) as controls maintained their routine activities (Control Group). The exercises’ introduction was by the degree of difficulty with changes in the plane of movements; smaller base of support; spinal rotations and extensions; use of abdominal muscles in prone position. Heart rate was monitored and recorded during all sessions using a heart frequency meter. Results: ANOVA analysis results revealed significant difference in Pilates Group (p < 0.05) between pre and post measures of VO2peak (+13%), fat percentage (- 3.3%), free fat mass (+ 2.8 kg), and muscular endurance in the abdominal area (+61%), lower limbs (+75%) and upper limbs (+68%). The percent of Maximum Heart Rate achieved in the 6 th (79.25%) and 12 th (79.86%) weeks of training increased from the first week (73.4%). Physical fitness in Control Group remained unchanged. Conclusion: twelve weeks of Pilates training positively affected overall physical fitness in previously sedentary healthy women. Furthermore, the increase in VO2peak and achieved Maximum Heart Rate was significant even with no specific aerobic training.
The Effects of the Pragmatic Intervention Programme in Children with Autism Spectrum Disorder and Developmental Language Disorder

Purpose: Children with Autism Spectrum Disorder (ASD) and Developmental Language Disorder (DLD) frequently face pragmatic impairments which may result in learning, socialization, and mental health difficulties, therefore early intervention is crucial. In Portugal, the Pragmatic Intervention Programme (PICP) has been recently developed and validated, but its effects are unknown. This study aims to determine the effects of the PICP on preschool-age children with ASD or DLD with pragmatic impairments. Methods: A non-randomized controlled trial has been conducted. The children (n = 20) were assigned to the intervention (n = 11) or the control group (waiting list) (n = 9). Each child attended 24 PICP-based intervention sessions provided by a Speech and Language Therapist in kindergarten. The primary outcome measure was a Goal Attainment Scale (GAS) rated by parents and kindergarten teachers. Secondary outcomes include parent/teacher-reported communication skills (Escala de Avaliação de Competências Comunicativas) and an assessment of the child’s general language ability (Teste de Linguagem—Avaliação da Linguagem Pré-Escolar). Results: GAS results show that all the children in the intervention group made progress. Statistically significant differences between pre- and post-intervention assessments were found for all secondary outcomes. Conclusions: The main findings suggest that the PICP improves language in preschool-age children with ASD and DLD with pragmatic difficulties. Further research is needed to analyse the effects of the PICP for each neurodevelopmental disorder individually. These results are crucial and will contribute to future research and evidence-based practice.