Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Demanding Mental Tasks on Electrodermal Activity and Heart Rate During Physical Activity: A Pilot Study

This paper presents the development of a virtual rehabilitation game and mental load with different difficulty blocks. The game was controlled with body tracking and physiological signals - acrlong ECG and acrlong EDA - were recorded throughout the session. Several parameters - acrlong HR (HR), acrlong HRV (HRV), acrlong SCL (SCL), acrlong SCR (SCR), energy expenditure. - were extracted from these signals to check mental load influence on them. Mental load was found to affect the variation in kinetic power and instantaneous heart rate; a Support Vector Machine with linear kernel was trained with these two variables and an 82.3% accuracy rate was obtained. Furthermore, the mental load was reflected in the number of errors made by the volunteers, in the selection time and in the number of rounds in the game.
Coefficients of powers of univalent functions

Let S be the usual class of normalized univalent functions. We write and consider the behaviour of the upper bounds An (λ) of the |an(λ)| for fψ S. If f(z) is the Koebe function an (λ)= bn (λ) where For λ = 1 de Branges has recently proved Bieberbach's conjecture that An (λ)= bn (λ) and his method extends at once to the case λ > 1. The result is false for n = 2 and λ > 1. It is reasonable to compare An (λ) and bn (λ)for λ 1 We conjecture that KK(λ) = 1 for ½ ⩽ λ ⩽ 1, and K(λ) > 1, for λ < ½ The work is related to recent results of Baemstein and Pommerenke. If k = λ-1 is an integer, then the functions are the k-symmetric functions in S
Oxidative Stress, Inflammatory, Angiogenic, and Apoptotic molecules in Proliferative Diabetic Retinopathy and Diabetic Macular Edema Patients

The aim of this study is to evaluate molecules involved in oxidative stress (OS), inflammation, angiogenesis, and apoptosis, and discern which of these are more likely to be implicated in proliferative diabetic retinopathy (PDR) and diabetic macular edema (DME) by investigating the correlation between them in the plasma (PLS) and vitreous body (VIT), as well as examining data obtained from ophthalmological examinations. Type 2 diabetic (T2DM) patients with PDR/DME (PDRG/DMEG; n = 112) and non-DM subjects as the surrogate controls (SCG n = 48) were selected according to the inclusion/exclusion criteria and programming for vitrectomy, either due to having PDR/DME or macular hole (MH)/epiretinal membrane (ERM)/rhegmatogenous retinal detachment. Blood samples were collected and processed to determine the glycemic profile, total cholesterol, and C reactive protein, as well as the malondialdehyde (MDA), 4-hydroxynonenal (4HNE), superoxide dismutase (SOD), and catalase (CAT) levels and total antioxidant capacity (TAC). In addition, interleukin 6 (IL6), vascular endothelial growth factor (VEGF), and caspase 3 (CAS3) were assayed. The VITs were collected and processed to measure the expression levels of all the abovementioned molecules. Statistical analyses were conducted using the R Core Team (2022) program, including group comparisons and correlation analyses. Compared with the SCG, our findings support the presence of molecules involved in OS, inflammation, angiogenesis, and apoptosis in the PLS and VIT samples from T2DM. In PLS from PDRG, there was a decrease in the antioxidant load (p < 0.001) and an increase in pro-angiogenic molecules (p < 0.001), but an increase in pro-oxidants (p < 0.001) and a decline in antioxidants (p < 0.001) intravitreally. In PLS from DMEG, pro-oxidants and pro-inflammatory molecules were augmented (p < 0.001) and the antioxidant capacity diminished (p < 0.001), but the pro-oxidants increased (p < 0.001) and antioxidants decreased (p < 0.001) intravitreally. Furthermore, we found a positive correlation between the PLS-CAT and the VIT-SOD levels (rho = 0.5; p < 0.01) in PDRG, and a negative correlation between the PSD-4HNE and the VIT-TAC levels (rho = 0.5; p < 0.01) in DMEG. Integrative data of retinal imaging variables showed a positive correlation between the central subfield foveal thickness (CSFT) and the VIT-SOD levels (rho = 0.5; p < 0.01), and a negative correlation between the CSFT and the VIT-4HNE levels (rho = 0.4; p < 0.01) in PDRG. In DMEG, the CSFT displayed a negative correlation with the VIT-CAT (rho = 0.5; p < 0.01). Exploring the relationship of the abovementioned potential biomarkers between PLS and VIT may help detecting early molecular changes in PDR/DME, which can be used to identify patients at high risk of progression, as well as to monitor therapeutic outcomes in the diabetic retina.
Specific innovativeness, digital entrepreneurship, and female entrepreneurs

ABSTRACT How does digital entrepreneurship help innovative, women entrepreneurs in emerging economies? Using strategic choice theory, we predict that specific innovativeness improves new venture performance, and that digital entrepreneurship enables innovativeness and increases its effect on performance. We also predict that innovative female entrepreneurs benefit more than male ones from a high degree of digital entrepreneurship. We confirm our hypotheses using primary data of entrepreneurs from Ghana (N = 199) and Vietnam (N = 223). We also conducted a series of robustness tests to validate our findings. We conclude that strategic choices among new ventures yield best results if bounded by prior strategic choice and specific innovativeness, enabled by a high degree of digital entrepreneurship, and enacted by women.
A neuropsychodynamic view of dreaming

ABSTRACT Functional neuroimaging and neurophysiological techniques open fascinating new perspectives on the psychodynamic understanding of dreams. The article offers a review of the recent relevant neuroscientific and psychoanalytic literature, including the “inferential brain” model’s recent revolutionary approach. This model proposes a correlation between the neurophysiological and the clinical role of dreaming. A growing framework of animal and human research converge on the concept of the evolutionary role of dreaming in maintaining brain homeostasis. Functional recordings show that the same brain structures and circuits are involved in dreaming as well as waking states, thus proving that dreams are a particular form of consciousness, where the brain’s synaptic architecture is remodeled through memory retrieval and reconsolidation. The partial deactivation of cortical prefrontal regions during dreaming facilitates the emergence of emotional contents belonging to subcortical and limbic structures, allowing emotional self-regulation and a better emotional balance during waking states. According to the inferential brain model, a specific function of dreaming would be to organize and update a virtual reality model which informs both the body and the external world of the organism’s expectations. Moreover, the iconic and metaphorical language of dreams is neither bizarre and meaningless per se, nor does it hide a latent content to be revealed through interpretations. Instead, the manifest content of the dream needs to be “unraveled” into its emotional roots. In conclusion, within a framework which includes psychoanalysis, theoretical and affective neurosciences, dreams represent the via regia not to the unconscious but to self-awareness and emotional self/regulation.
A Research on the Mathematical Problem Solving Beliefs of Mathematics, Science and Elementary Pre-Service Teachers in Turkey in terms of Different Variables

In this study, we aimed to report the results of the statistical analysis that was performed to determine the beliefs about mathematical problem solving of the mathematics, science and elementary school pre-service teachers and the results of whether gender and the teaching field they studied have any effect on problem solving beliefs. A total of 567 mathematics, science and elementary school pre-service teachers participated in the research. Mathematical Problem Solving Beliefs Instrument applied to these pre-service teachers. In conclusion, there could not be found any significant differences between the beliefs of pre-service teachers and their genders or their education field in which they were studying. It has been understood that the beliefs about mathematical problem solving of pre-service teachers need to develop.
ON DISTANCE ESTIMATORS OF DENSITY IN RANDOMLY DISTRIBUTED FORESTS

SUMMARY In various journals a number of methods are described which utilize spacing distances instead of fixed-area plots for estimating plant densities. Some of these methods are studied in this paper. Maximum likelihood (ML) estimators for the forest density are given and the distributions and moments of these estimators are derived. Other distance estimators of density have been used in the past, but they do not have the advantages of ML estimators, and it even seems difficult to determine their moments. A distance method has the advantage over a fixed plot method that the sample size does not depend upon the density being measured. There are many practical difficulties in using them, however, and some of these are discussed. The trees in a natural forest will not be distributed uniformly at random, and some care must be exercised in using a distance method. The difficulty can sometimes be overcome by using a stratified sampling method. The model is described in terms of trees and forests, but it is clear that it can be applied to many different types of problem.
Wnt/β‐catenin signaling pathway inhibitors, glycyrrhizic acid, solanine, polyphyllin I, crocin, hypericin, tubeimoside‐1, diosmin, and rutin in medicinal plants have better binding affinities and anticancer properties: Molecular docking and ADMET study

Abstract Wnt/β‐catenin signaling pathway plays a role in cancer development, organogenesis, and embryogenesis. The abnormal activation promotes cancer stem cell renewal, proliferation, and differentiation. In the present study, molecular docking simulation and ADMET studies were carried out on selected bioactive compounds in search of β‐catenin protein inhibitors for drug discovery against cancer. Blind docking simulation was performed using PyRx software on Autodock Vina. β‐catenin protein (PDB ID: 1jdh) and 313 bioactive compounds (from PubChem database) with selected standard anticancer drugs were used for molecular docking. The ADMET properties of the best‐performing compounds were calculated using SwissADME and pkCMS web servers. The results obtained from the molecular docking study showed that glycyrrhizic acid, solanine, polyphyllin I, crocin, hypericin, tubeimoside‐1, diosmin, and rutin had the best binding interactions with β‐catenin protein based on their binding affinities. Glycyrrhizic acid and solanine had the same and lowest binding energy of −8.5 kcal/mol. This was followed by polyphyllin I with −8.4 kcal/mol, and crocin, hypericin, and tubeimoside‐1 which all had a binding energy of 8.1 kcal/mol. Other top‐performing compounds include diosmin and rutin with binding energy of −8.0 kcal/mol. The ADMET study revealed that the following compounds glycyrrhizic acid, solanine, polyphyllin I, crocin, hypericin, tubeimoside‐1, diosmin, rutin, and baicalin all violated Lipinski's rule of 5 which implies poor oral bioavailability. However, based on the binding energy score, it was suggested that these pharmacologically active compounds are potential molecules to be tested against cancer.
Advanced Fabrication of Vacuum Electron Devices

RF source scientist and engineers continuously push the envelope with new designs, striving for improved performance with higher efficiency, higher frequency, greater bandwidth, increased gain, smaller size, lower voltage, and myriad other parameters required for ever more demanding applications. Invariably, it becomes more challenging to achieve the required fabrication and assembly performance with increasing complexity and precision. This publication reviews recent development on advanced fabrication technologies and describes the current state of the art in machining, assembly, and alignment capabilities.
Electrokinetic cells powered by osmotic gradients: an analytic survey of asymmetric wall potentials and hydrophobic surfaces

Nowadays, the fabrication of microelectromechanical systems has given rise to several studies whose main purpose is to obtain the greater benefit of micro-nano scales, putting special interest in the improvement of the design of such devices. One of several applications is harvesting energy due to electrokinetic phenomena, more specifically, streaming potential. Nonetheless, there is a lack of theoretical studies encompassing coupled asymmetries in both slip conditions and electric potentials (these being associated with the chemical and physical characteristics of the surfaces). By virtue of the previous explanation, ideal assumptions based on the symmetry of some variables must be reconsidered, especially when manufacturing symmetric flat surfaces on a tiny scale is quite difficult to achieve. This work presents a theoretical study in power generation, exploiting streaming potentials produced by an asymmetric membrane which in turn prompts a flux inside a microchannel made of two flat parallel surfaces. The driving force in this electrokinetic battery is the osmotic gradient on both sides of the membrane. The model uses the Debye–Hückel approximation together with the appropriate asymmetric boundary conditions for both slips and potentials on the surfaces. The main variables of interest, such as the dimensionless horizontal velocity component, the pressure field, and the average streaming potential, were estimated.
Exploiting Virtual Array Diversity for Accurate Radar Detection

Using millimeter-wave radars as a perception sensor provides self-driving cars with robust sensing capability in adverse weather. However, mmWave radars currently lack sufficient spatial resolution for semantic scene understanding. This paper introduces Radatron++, a system leverages cascaded MIMO (Multiple-Input Multiple-Output) radar to achieve accurate vehicle detection for self-driving cars. We develop a novel hybrid radar processing and deep learning approach to leverage the 10× finer angular resolution while combating unique challenges of cascaded MIMO radars. We train and evaluate Radatron++ with a novel cascaded radar dataset. Radatron++ achieves 93.9% and 58.5% Average Precisions with 0.5 and 0.75 Intersection over Union thresholds respectively in 2D bounding box detection, outperforming prior work using low-resolution radars by 9.3% and 18.1% respectively.
The Contribution of Fe(III) Reduction to Soil Carbon Mineralization in Montane Meadows Depends on Soil Chemistry, Not Parent Material or Microbial Community

The long‐term stability of soil carbon (C) is strongly influenced by organo‐mineral interactions. Iron (Fe)‐oxides can both inhibit microbial decomposition by providing physicochemical protection for organic molecules and enhance rates of C mineralization by serving as a terminal electron acceptor, depending on redox conditions. Restoration of floodplain hydrology in montane meadows has been proposed as a method of sequestering C for climate change mitigation. However, dissimilatory microbial reduction of Fe(III) could lead to C losses under increased reducing conditions. In this study, we explored variations in Fe‐C interactions over a range of redox conditions and in soils derived from two distinct parent materials to elucidate biochemical and microbial controls on soil C cycling in Sierra Nevada montane meadows. Soils derived from basalt showed greater rates of Fe(III)‐reduction at increasing soil moisture levels than granitic soils. Increases in Fe(III) reduction, however, were only associated with elevated rates of C mineralization in one basalt soil. Known Fe(III)‐reducing taxa were present in all samples but neither the relative abundance nor richness of Fe(III)‐reducers corresponded with measured rates of Fe(III) reduction. Under reducing conditions, Fe(III)‐reduction was only coupled to C mineralization in the soil with the greatest amount of Fe‐oxide bound C. However, Fe‐oxide ‐bound C was below theoretical limits for C sorption onto Fe‐oxides and not detectable in all soils. Overall, our results suggest that “what's there” in terms of soil chemistry may be a more important driver of C mineralization coupled to Fe(III) reduction than “who's there” in the microbial community.
Contrastive Learning-Based Audio to Lyrics Alignment for Multiple Languages

Lyrics alignment gained considerable attention in recent years. State-of-the-art systems either re-use established speech recognition toolkits, or design end-to-end solutions involving a Connectionist Temporal Classification (CTC) loss. However, both approaches suffer from specific weaknesses: toolkits are known for their complexity, and CTC systems use a loss designed for transcription which can limit alignment accuracy. In this paper, we use instead a contrastive learning procedure that derives cross-modal embeddings linking the audio and text domains. This way, we obtain a novel system that is simple to train end-to-end, can make use of weakly annotated training data, jointly learns a powerful text model, and is tailored to alignment. The system is not only the first to yield an average absolute error below 0.2 seconds on the standard Jamendo dataset but it is also robust to other languages, even when trained on English data only. Finally, we release word-level alignments for the JamendoLyrics Multi-Lang dataset.
Age-of-Information-Based Performance of Ultrasonic Communication Channels for Nanosensor-to-Gateway Communication

The successful connection between in-body nanosensors and gateway devices outside the human body enables the identification of diseases, before symptoms even appear. In this paper, we investigate the timely detection of abnormalities using nanosensors flowing in the blood vessels and reporting to external wearable devices. We develop analytic solutions to evaluate the information freshness using the average peak age of information (PAoI) metric. We model the mobility of nanosensors in the blood flow as a random process through a Markov chain. The communication capabilities with external devices to report detected abnormalities are modeled as a linear time-variant (LTV) channel. Besides, for communication with external devices, we study the impact of the mobility of nanosensors when using ultrasonic waveforms and integrate it with the PAoI formulation. In addition to a previously proposed analytical model, we perform large-scale simulations using Blood Voyager Simulator (BVS)) and the network simulator ns-3. As primary metrics, we use the bit error rate (BER), the packet error rate (PER), and the average PAoI. The results give clear insights into the impact of the position of the external monitor. We also illustrate that local communication performance almost does not influence the average PAoI.
SARdBScene: Dataset and Resnet Baseline for Audio Scene Source Counting and Analysis

This paper introduces a first of its kind dataset for audio scene analysis (ASA) and presents a baseline approach for audio source counting. SARdBScene is developed to promote research for audio source counting, as a relatively new ASA task, and present a comprehensive dataset that covers a variety of scenarios and audio-based tasks. It contains 80 hours of audio scene mixtures depicting four distinct environments with detailed annotations that make it a unique collection of curated data in the audio analysis landscape. Our baseline approach using ResNet establishes state-of-the-art results of 77.3% and 85.7% accuracy for audio source counting up to 12 sources and speaker counting up to 4 speakers, respectively.
Agile Radio Map Prediction Using Deep Learning

In this paper, we introduce a runtime-efficient radio frequency (RF) map prediction method based on UNet convolutional neural networks (CNNs), trained on a large-scale 3D maps dataset. The proposed method calculates the line-of-sight maps and feeds them as input for the UNet CNN. A special Kullback–Leibler divergence loss function is adopted, enabling the proposed method to minimize both error’s mean and variance. The performance of our model is evaluated in the context of the 2023 IEEE ICASSP Signal Processing Grand Challenge, namely, the First Pathloss Radio Map Prediction Challenge. The evaluation results demonstrate that the proposed method achieves an average normalized root-mean-square error (RMSE) of 0.045 with an average of 14 milliseconds (ms) runtime.
Gross energy levels in practical diets for the Amazon river prawn postlarvae, Macrobrachium amazonicum

The present study evaluated the effect of different energy levels (15.07, 15.91, 17.17, 18.00, and 19.68 kJ g-1 of diet) on isoproteic practical diets for Macrobrachium amazonicum during the nursery phase on the zootechnical performance and proximate composition of the whole body. Postlarvae with a mean weight of 0.043 ± 0.01 g were stored in 25 experimental units, at 150 animals per m2, in a completely randomized design with five treatments and five replicates. The post-larvae were evaluated at the end of the experimental period according to the pre-established performance parameters. The analysis of variance for regression (α = 0.05) allowed us to estimate that the maximum final weight was observed in the treatment with 16.74 kJ g-1. In contrast, the lowest apparent feed conversion was observed at the 16.43 kJ g-1 level. The best values were found in 17.29 and 17.50 kJ g-1, respectively, for the energy and protein retention rates. As the best fit of the equation was presented for the energy retention ratio (R2 = 0.63), which approached the treatment of 17.17 kJ g-1, this energy level is recommended in postlarvae diets.
Grit Competency, Growth Mindset, and Mental Ability among Filipino High School Students

Educators and mental health professionals have promoted social and emotional learning (SEL) as the foundation for safe and positive education. SEL factors such as grit competency and growth mindset were the critical factors in this research, along with the student’s mental ability level. Grit competency pertains to one’s ability to work hard, overcome setbacks, finish tasks, and set long-term goals. At the same time, a growth mindset refers to believing intelligence and ability can change with persistence and practice and believing interests and behaviors can positively change with effort and learning. This research aimed to determine the grit and growth mindset levels of 137 all-female Filipino senior students from Quezon City and test their relationship with mental ability. A standardized test measured grit competency and growth mindset, while the Otis-Lennon School Ability Test, 7th Edition (OLSAT-7), measured cognitive ability. Results signified a low and negative relationship between grit competency and mental ability, with r = -. 152 and p = 0.039 < 0.05. Similarly, there was a low and negative relationship between a growth mindset and cognitive ability with a value of r = -. 157 and p = 0.034 < 0.05. Keywords: Grit Competency; Growth Mindset; Mental Ability; High School Students.