Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrical properties of polymer/expanded graphite nanocomposites with low percolation

Expanded graphite (EG)-filled poly(phenylene sulfide) (PPS) nanocomposites prepared by a simple method exhibited an electrical conductivity percolation threshold of 1 wt% (0.6 vol%). In contrast to this, the percolation threshold of micrometre-sized graphite flake filled PPS composites prepared by the same method was 5 wt%. The electrical conductivity for 2 wt% nanocomposites increased 14 orders of magnitude higher than that of pure PPS. The dielectric constants and dissipation factor of nanocomposites were measured in a frequency range from 100 kHz to 15 MHz. The dielectric constant of the nanocomposites with more than 1 wt% EG showed a strong dependence on EG content and frequencies. The dielectric constants for 2 wt% and 10 wt% nanocomposites measured at 1 MHz were increased more than 102 and 105 times, respectively. Similarly, the dissipation factor was increased. Scanning electron microscopy showed a three-dimensional conducting network of EG across the PPS matrix which may be the reason for the significant improvement in electrical properties. The significantly increased electrical conductivity, dielectric constant and dissipation factor of the nanocomposites are favourable for their use in antistatic and electromagnetic interference shielding applications.
Stochastic Higher-Order Independent Component Analysis for Hyperspectral Dimensionality Reduction

Hyperspectral imaging is a remote sensing technique that measures the spectrum of each pixel in the image of a scene. Various methods have been developed to reduce the spectral dimension of hyperspectral images in order to facilitate their analysis. Independent Component Analysis (ICA) is a class of algorithms which extract statistically independent features. FastICA, is one of the most used ICA algorithms because it is simple and fast. However, FastICA often finds irrelevant stationary points (e.g., minima instead of maxima) and is not scalable as it uses at each iteration the whole set of pixels. In this paper, we present a new stochastic algorithm, called SHOICA, which smoothly approximates the non-convex loss functions of ICA using higher-order Taylor minorizers. Because SHOICA guarantees ascent of its objective function, it identifies (local) maxima. Moreover, because SHOICA is stochastic, it facilitates minibatching and thus is scalable and appropriate for large datasets. Hence, we show that our method is faster and the extracted features are better than those of FastICA. The quality of features extracted, as well as time and epochs required by FastICA and SHOICA are compared practically, on dimensionality reduction and classification tasks of real hyperspectral images.
Convolutional Network Research for Defect Identification of Productor Appearance Surface

The accurate and rapid identification of surface defects is an important element of product appearance quality evaluation, and the application of deep learning for surface defect recognition is an ongoing hot topic. In this paper, a lightweight KD-EG-RepVGG network based on structural reparameterization is designed for the identification of surface defects on strip steel as an example. In order to improve the stability and accuracy in the recognition of strip steel surface defects, an efficient attention network was introduced into the network, and then a Gaussian error linear activation function was applied in order to prevent the neurons from being set to zero during neural network training, leaving neuron parameters without being updated. Finally, knowledge distillation is used to transfer the knowledge of the RepVGG-A0 network to give the lightweight model better accuracy and generalization capability. The outcomes of the experiments indicate that the model has a computational and parametric volume of 22.3 M and 0.14 M, respectively, in the inference phase, a defect recognition accuracy of 99.44% on the test set, and a single image detection speed of 2.4 ms, making it more suitable for deployment in real engineering environments.
Do Not Forget: Exploiting Stability-Plasticity Dilemma to Expedite Unsupervised SNN Training for Neuromorphic Processors

This paper presents a novel early training termination technique that significantly improves the training speed and energy efficiency of unsupervised learning-based spiking neural networks (SNNs) by skipping redundant training samples. To achieve early termination, we leveraged the key observation that unsupervised SNNs tend to stably maintain previously learned information and systematically analyze the spike firing activity of the network during training. To make a training termination decision, we exploit the difference between the number of spikes generated by the previous and current input training samples. Our termination algorithm is adopted in an SNN using the spike-timing-dependent plasticity (STDP) learning rule for a pattern classification application. The proposed scheme made an early termination decision with insignificant accuracy performance loss by adequately ignoring redundant training samples. Specifically, it enhances the training speedup and energy efficiency by up to 5.07 × and 5.14 × , respectively, with less than 1 percent points (pp) accuracy loss compared to the baseline counterparts by skipping up to 80% of the training samples. Additionally, when employed in a VLSI-based neuromorphic chip environment, it exhibits up to 4.95 × better energy efficiency than the baseline.
Bayesian inference for bisexual galton-watson processes

In this work, an approach to the Bayesian estimation in a bisexual Galton-Watson process is considered. First we study an important parametric case assuming offspring distribution belonging to the bivariate series power family of distributions and then, we continue to investigate the nonparametric case. In both situations, Bayes estimators under weighted squared error loss function, for means, variances and covariance of the off spring distribution are obtained. For the superadditive case, the Bayes estimation of the asymptotic growth rate is also considered. Illustrative examples are given.
Variation of residual stresses during cross-rolling

Residual stresses in cross-rolled polycrystalline copper and low carbon steel are studied. Cross-rolling modifies both residual stresses and crystallographic texture. Consequently, it can be used in order to improve the final properties of a material by introducing a given state of internal stress and by imposing more symmetry to the texture.
Human disturbance compresses the spatiotemporal niche

Significance Anthropogenic landscape change may disrupt invisible facets of biodiversity such as species interactions. Here, we use data from an automated biodiversity monitoring scheme to demonstrate that, across multiple types of species interactions, human disturbance is associated with increased co-occurrence probability of wildlife species. This suggests that human disturbance effectively compresses the space and time that is shared by communities, which may strengthen species interactions and precipitate far-ranging ecological consequences, since exposure to competitors or predators is linked to negative physiological and demographic outcomes.
Genome-Wide Analysis of Circular RNAs Reveals circCHRNG Regulates Sheep Myoblast Proliferation via miR-133/SRF and MEF2A Axis

As relatively new members of the non-coding RNA family, circRNAs play important roles in a variety of biological processes. However, the temporal expression pattern and the function of circRNAs during sheep skeletal muscle development remains unclear. This study aimed to identify circRNAs related to sheep skeletal muscle development and explore their roles in myoblast proliferation. The circRNA expression profiles of longissimus dorsi of sheep from F90, L30, and A3Y were obtained by the RNA-seq method. The function and mechanisms of the novel circCHRNG in muscle satellite cell proliferation were explored using CCK-8 assay, Western blot, qPCR, and dual-luciferase reporter assay. We identified 12,375 circRNAs, including 476, 133, and 233 DEcircRNAs found among three comparative groups. KEGG results showed that DEcircRNAs were enriched in muscle contraction, the regulation of cell proliferation, and the AMPK, insulin, and PI3K-Akt signaling pathways. Notably, a novel circRNA, termed circRNA CHRNG, acts as a miR-133 sponge to promote skeletal muscle satellite cell proliferation. Our study provides a systematic description of circRNAs of ovine skeletal muscle across fetal, lamb, and adult stages. GO and KEGG analyses showed that DEcircRNAs were enriched in multiple pathways associated with muscle development, such as the PI3K-Akt and AMPK signaling pathways. In addition, we propose that circCHRNG acts as a miR-133 sponge to upregulate the expression levels of SRF and MEF2A, thereby promoting myoblast proliferation.
Exploring user profiles for healthy gamification

Gamification in healthcare is still not widespread. One way to facilitate application of gamification is to understand the user via user profiles. This study introduces an easier way to categorize the user profiles in three groups instead of six (Hexad). Most of the survey's to categorize user groups are done with adolescents or students. This study takes a much more realistic sample of 274 Dutch households. The results show that designing within gamified mechanics based on a user profiles main motivation is an effective strategy for personalization. Based on these findings we propose a new user profiles model with three user profiles for healthcare that are mainly autonomy driven (Blue), competence driven (Red) and purpose driven (Green). Furthermore, the results indicate a more generic path towards including essential elements to create a basic understanding, get users involved, and provide them with content fitting their motivations. Further study is needed to show if the model can be used in various environments. The first test at an energy provider shows positive results.
Evaluation of the English Version of the Smartphone Application-Based Addiction Scale (SABAS) among an Adolescent Sample

The increased use of smartphones among adolescents has highlighted the need to distinguish between problematic and non-problematic smartphone use. To date, there are a lack of short, easy-to-use, and valid psychometric tools to assess smartphone addiction. The primary aim of the present study was to conduct the first psychometric assessment and evaluation of the Smartphone Application-Based Addiction Scale (SABAS) among English-speaking adolescents. Participants were 1175 students recruited from across four different high schools in New Zealand, of which 1031 completed all questions and were used in the final analyses. Several psychometric tests were conducted to ascertain reliability and validity. The SABAS had high internal consistency. Consistent with earlier validation studies, the SABAS displayed weak-moderate, positive relationships with symptoms of depression (PHQ-2), anxiety (GAD-2), and sleep quality. Overall, the SABAS is a sound, unidimensional scale with robust psychometric properties and is a short and easy-to-use measure that can be used confidently among English-speaking adolescents.
Blended, Cross-Domain Algorithms for Improved Hydrographic DEMs

Hydrographic data are increasingly fused together with above-water remote sensing imagery such as from photogrammetry, Lidar, multispectral and acoustic data. This is facilitating a need to accommodate cross-domain tools and to visualize results in two-, three- and four-dimensional environments. However, added value is emerging in these sophisticated and evolving solutions. Presented here is one such result where bathymetry is augmented using a Poisson surface produced in similar manner as in Lidar processing, gaming and simulation. The bathymetry data originate from a hydrographic survey that was conducted to evaluate sonar hardware and sonar mount performance. This exceptionally high-quality survey from Lake Tahoe includes a variety of bottom types and steep slopes, coupled with oversampling to produce multibeam point densities ranging between hundreds to tens of thousands of points per meter. Conventional workflows producing a digital elevation model struggled to produce even a half-meter digital elevation model. We decided to introduce the Screened Poisson Reconstruction Surface into the workflow, and the resulting output is a variable density digital elevation model exceeding the resolution of a conventional approach.
Evaluation of deep learning approaches for dermoscopic evaluation of vascular features of port-wine stain birthmarks

Dermoscopy is a useful tool for observing the vascular profile of port-wine stain (PWS) birthmarks. However, due to the complicity of the vascular profile, there is a lack of consensus on the classification of dermoscopic features of PWS vessels. This study investigated the potentials of deep learning-assisted methods in the classification of dermoscopy image-based of PWS vascular profiles. The classified images were used as training samples, and the RegNet network with better classification effect was selected to establish the migration learning method. The results showed that the accuracy of the RegNet network on the validation set was 82.63%. The preliminary study suggests that deep learning assisted PWS vascular contour type classification is feasible.
Preventive Medicine via Lifestyle Medicine Implementation Practices Should Consider Individuals’ Complex Psychosocial Profile

Noncommunicable chronic diseases are associated with lifestyle behaviors. Psychological and social factors may influence the adoption of such behaviors. Being mentally and physically energized or fatigued may influence the intention–behavior gap of healthy lifestyle adoption accordingly. We investigated the associations of age, sex, lifestyle behaviors, mood, and mental and physical energy and fatigue at both the trait and state levels. The participants (N = 670) completed questionnaires assessing their sleep, mood, mental and physical state energy and fatigue, physical activity, mental workload, and diet. The ordinary least squares regression models revealed an overlap between the mental state and trait energy levels for males who consume polyphenols, have a high mental workload, and sleep well. Being younger, having a high stress level, bad sleep habits, and being confused and depressed were associated with high mental fatigue. Physical energy and fatigue shared the same commonalities with the previous results, with greater discrepancies observed between the state and trait indicators compared to that between mental energy and fatigue. Diet and stress management seem to be predictors of high physical energy, and females report higher physical fatigue levels. Health care professionals should consider this psychosocial complex profiling in their differential diagnosis and when one is implementing lifestyle behavioral changes to address the facets of preventive medicine, wellness, and health promotion.
Lactate Threshold and Psychomotor Fatigue Threshold in Hot Conditions: Suggestions for Soccer Players Participating in the Qatar World Cup 2022

The study aimed at finding relationships between lactate threshold and psychomotor fatigue threshold during incremental exercise in thermo-neutral climate conditions and conditions for the 2022 FIFA World Cup in Qatar simulated in an environmental test chamber. The study included 24 soccer players aged 21.02 ± 3.22 years old. The following procedures were performed: The incremental exercise test to mark lactate concentration—LA (mmol·l−1); Psychomotor test to determine choice reaction time; Designation of the lactate threshold (TLA) and psychomotor fatigue threshold (TPF). Climate conditions: The procedure was performed twice in the climatic chamber: (1) in thermo-neutral conditions—TNC (ambient temperature 20.5 °C and relative air humidity 58.7%), (2) after 7 days—in Qatar conditions—QC (28.5 ± 1.92 °C) and (58.7 ± 8.64%). It was confirmed that the TPF, which reflects the highest efficiency of CNS functioning, occurs at a higher running speed than the TLA. The temperature of 28.5 °C with 58.7% humidity, which is the lower limit of heat stress, causes the psychomotor fatigue threshold to appear at a lower running speed than in thermoneutral conditions. The data recorded in this work may help to understand the specificity of physiological and psychomotor reactions to various climatic conditions.
“Unordered” Versus “Ordered” Characters

Multistate morphological characters have generally been treated as either «unordered» or «ordered» in phylogenetic analyses using parsimony. Because ordering relations do not apply to the states of characters treated under these methods, I prefer «maximally connected» character to «unordered» character and «minimally connected» character to «ordered» character. This paper formally defines the two character types, compares their properties, and considers the consequences of the two methods for both resolution and congruence. The results demonstrate that minimally connected characters increase resolution relative to maximally connected characters. Minimally connected characters do not, however, necessarily increase congruence among data sets
Estimation in a problem of fractional integration

In this paper we study the problem of estimating an unknown function f L2(0, 1) observed with some fractional Brownian noise. This problem is equivalent to estimation in the inverse problem of fractional integration. Since we are interested in boundary effects, we consider nonperiodic functions which belong to some Sobolev ball. Using a spline basis we construct an estimator and give its exact asymptotic risk. As an inverse problem this framework presents some interest. Indeed, the best basis for the functions is not the best one for the operator.
Efficient Real-Time Tracking of Satellite Components Based on Frame Matching

In order to obtain the satellite’s in-orbit attitude information, it is necessary to track the satellite components in satellite video sequences. To solve the problem of low illumination and target occlusion in space environment, we propose an efficient satellite component tracking technique based on Rethinking Space-Time Networks with Improved Memory Coverage (STCN). We classify the pixels in the query frame by feature matching network that establishes the corresponding relationship between the frames. Unlike STCN, we reduce the contribution of background region in feature matching and enhance the robustness of the model in low illumination environment, thus improving the segmentation results. For lost targets due to the overturning and occlusion of satellite components, a position information encoder module is designed to further raise the tracking performance of the model. In addition, we present a local matching module to upgrade the existing feature matching methods. Experiments demonstrate that compared to STCN, our method heightens the tracking performance (J&F) by 10.1% and can achieve multi-object recognition at 15+ FPS.
Bioactive Properties of Extracts from Plectranthus barbatus (Coleus forskohlii) Roots Received Using Various Extraction Methods

The aim of this study was to verify various extraction methods: shaking water bath extraction (SWA), ultrasound-assisted extraction (UAE) and microwave assisted extraction (MAE), and their parameters to optimize the extraction yield as well as maximize the concentration of polyphenols in Plectranthus barbatus extracts. Extracts were obtained from dried roots of P. barbatus in various degrees of fragmentation and analyzed for content of polyphenols, antioxidant capacity and flavonoids. Additionally, phenolic compounds in extracts were analyzed using the UHPLC–DAD–ESI–MS/MS method. The conducted research showed that roots of P. barbatus are rich in polyphenolic compounds. A total of 15 phenolic compounds, belonging to the group of phenolic acids and their derivatives, were identified. The extraction yield was similar for all extraction methods and averaged 31%. Irrespective of the extraction method, the yield was the lowest in the case of using 80% ethanol as the solvent. The extracts obtained from the finer fraction were characterized by a higher antioxidant capacity as well as a higher concentration of polyphenolic compounds including flavonoids. UAE seems to be the most effective method for extraction of polyphenols from P. barbatus roots. Regardless of the extraction method, ethanol was a better extractant than distilled water. All ethanolic extracts were characterized by a high antioxidant capacity. The 80% ethanol solution was considered the best solvent for the extraction of flavonoids, while the 40% and 60% ethanol solutions were sufficient for the effective extraction of polyphenolic compounds in general.
Quality Analysis of iOS Applications with Focus on Maintainability and Security

We use mobile apps on a daily basis and there is an app for everything. We trust these applications with our most personal data. It is therefore important that these apps are as secure and well usable as possible. So far most studies on the maintenance and security of mobile applications have been done on Android applications. We do, however, not know how well these results translate to iOS.This research project aims to close this gap by analysing iOS applications with regards to maintainability and security. Regarding maintainability, we analyse code smells in iOS applications, the evolution of code smells in iOS applications and compare code smell distributions in iOS and Android applications. Regarding security, we analyse the evolution of the third-party library dependency network for the iOS ecosystem. Additionally, we analyse how publicly reported vulnerabilities spread in the library dependency network.Regarding maintainability, we found that the distributions of code smells in iOS and Android applications differ. Code smells in iOS applications tend to correspond to smaller classes, such as Lazy Class. Regarding security, we found that the library dependency network of the iOS ecosystem is not growing as fast as in some other ecosystems. There are less dependencies on average than for example in the npm ecosystem and, therefore, vulnerabilities do not spread as far.
Fractional Coupling of Primary and Johari–Goldstein Relaxations in a Model Polymer

A polymer model exhibiting heterogeneous Johari–Goldstein (JG) secondary relaxation is studied by extensive molecular-dynamics simulations of states with different temperature and pressure. Time–temperature–pressure superposition of the primary (segmental) relaxation is evidenced. The time scales of the primary and the JG relaxations are found to be highly correlated according to a power law. The finding agrees with key predictions of the Coupling Model (CM) accounting for the decay in a correlation function due to the relaxation and diffusion of interacting systems. Nonetheless, the exponent of the power law, even if it is found in the range predicted by CM (0<ξ<1), deviates from the expected one. It is suggested that the deviation could depend on the particular relaxation process involved in the correlation function and the heterogeneity of the JG process.