Miracle-dz/UniFAR-FTUs huggingface dataset:
Radial Electron Temperature and Density Measurements Using Thomson Scattering System in GAMMA 10/PDX

A Thomson scattering (TS) system in GAMMA 10/PDX has been developed for the measurement of radial profiles of electron temperature and density in a single plasma and laser shot. The TS system has a large solid angle optical collection system and high-sensitivity signal detection system. The TS signals are obtained using four-channel high-speed digital oscilloscopes controlled by a Windows PC. We designed the acquisition program for six oscilloscopes to obtain 10-Hz TS signals in a single plasma shot, following which the time-dependent electron temperatures and densities can be determined. Moreover, in order to obtain larger TS signal intensity in the edge region, we added a second collection mirror. The radial electron temperatures and densities at six radial positions in GAMMA 10/PDX were successfully obtained.
Determination of Antioxidant, Anti-Alzheimer, Antidiabetic, Antiglaucoma and Antimicrobial Effects of Zivzik Pomegranate (Punica granatum)—A Chemical Profiling by LC-MS/MS

Zivzik pomegranate (Punica granatum) has recently sparked considerable interest due to its nutritional and antioxidant properties. To evaluate the antioxidant capacities of P. granatum juice, ethanol (EEZP), and water (WEZP) extracts from peel and seed, the antioxidant methods of 2,2′-azino-bis-3-ethylbenzthiazoline-6-sulphonic acid radical (ABTS•+) scavenging, 1,1-diphenyl-2-picrylhydrazyl free radical (DPPH•) scavenging, Fe3+-2,4,6-tris(2-pyridyl)-S-triazine (TPTZ) reducing, Fe3+ reducing, and Cu2+ reducing methods were used. The antioxidant capacities of samples were compared with the most commonly used synthetic antioxidants, i.e., BHA, BHT, α-tocopherol, and Trolox. In terms of setting an example, the IC50 values of EEZP for ABTS•+ and DPPH• scavenging activities were found to be lower than standards, at 5.9 and 16.1 μg/mL, respectively. The phenolic and flavonoid contents in EEZP peel were 59.7 mg GAE/g and 88.0 mg QE/g, respectively. Inhibition of α-glycosidase, α-amylase, acetylcholinesterase, and human carbonic anhydrase II (hCA II) enzymes was also investigated. EEZP demonstrated IC50 values of 7.3 μg/mL against α-glycosidase, 317.7 μg/mL against α-amylase, 19.7 μg/mL against acetylcholinesterase (AChE), and 106.3 μg/mL against CA II enzymes. A total of 53 phenolic compounds were scanned, and 30 compounds were determined using LC-MS/MS. E. coli and S. aureus bacteria were resistant to all four antibiotics used as standards in hospitals.
A Soft Robot Driven by a Spring-Rolling Dielectric Elastomer Actuator with Two Bristles

Confined space searches such as pipeline inspections are widely demanded in various scenarios, where lightweight soft robots with inherent compliance to adapt to unstructured environments exhibit good potential. We proposed a tubular soft robot with a simple structure of a spring-rolled dielectric elastomer (SRDE) and compliant passive bristles. Due to the compliance of the bristles, the proposed robots can work in pipelines with inner diameters both larger and smaller than the one of the bristles. Firstly, the nonlinear dynamic behaviors of the SRDE were investigated experimentally. Then, we fabricated the proposed robot with a bristle diameter of 19 mm and then studied its performance in pipelines on the ground with inner diameters of 18 mm and 20 mm. When the pipeline’s inner diameter was less than the outer diameter of the bristles, the bristles remained in the state of bending and the robot locomotion is mainly due to anisotropic friction (1.88 and 0.88 body lengths per second horizontally and vertically, respectively, in inner diameter of 18 mm and 0.06 body length per second in that of 16 mm). In the case of the pipeline with the larger inner diameter, the bristles were not fully constrained, and a small bending moment applied on the lower bristle legs contributed to the robot’s locomotion, leading to a high velocity (2.78 body lengths per second in 20 mm diameter acrylic pipe). In addition, the robot can work in varying geometries, such as curving pipes (curve radius ranges from 0.11 m to 0.31 m) at around two body lengths per second horizontally and on the ground at 3.52 body lengths per second, showing promise for pipeline or narrow space inspections.
Causal architecture, complexity and self-organization in time series and cellular automata

All self-respecting nonlinear scientists know self-organization when they see it: except when we disagree. For this reason, if no other, it is important to put some mathematical spine into our floppy intuitive notion of self-organization. Only a few measures of self-organization have been proposed; none can be adopted in good intellectual conscience. To find a decent formalization of self-organization, we need to pin down what we mean by organization. The best answer is that the organization of a process is its causal architecture—its internal, possibly hidden, causal states and their interconnections. Computational mechanics is a method for inferring causal architecture—represented by a mathematical object called the e-machine—from observed behavior. The e-machine captures all patterns in the process which have any predictive power, so computational mechanics is also a method for pattern discovery. In this work, I develop computational mechanics for four increasingly sophisticated types of process—memoryless transducers, time series, transducers with memory, and cellular automata. In each case I prove the optimality and uniqueness of the e-machine's representation of the causal architecture, and give reliable algorithms for pattern discovery. The e-machine is the organization of the process, or at least of the part of it which is relevant to our measurements. It leads to a natural measure of the statistical complexity of processes, namely the amount of information needed to specify the state of the E-machine. Self-organization is a self-generated increase in statistical complexity. This fulfills various hunches which have been advanced in the literature, seems to accord with people's intuitions, and is both mathematically precise and operational.
Glomus mosseae improved the adaptability of alfalfa (Medicago sativa L.) to the coexistence of cadmium-polluted soils and elevated air temperature

The coexistence of heavy metal-polluted soils and global warming poses serious threats to plants. Many studies indicate that arbuscular mycorrhizal fungi (AMF) can enhance the resistance of plants to adverse environments such as heavy metals and high temperature. However, few studies are carried out to explore the regulation of AMF on the adaptability of plants to the coexistence of heavy metals and elevated temperature (ET). Here, we investigated the regulation of Glomus mosseae on the adaptability of alfalfa (Medicago sativa L.) to the coexistence of cadmium (Cd)-polluted soils and ET. G. mosseae significantly enhanced total chlorophyll and carbon (C) content in the shoots by 15.6% and 3.0%, respectively, and Cd, nitrogen (N), and phosphorus (P) uptake by the roots by 63.3%, 28.9%, and 85.2%, respectively, under Cd + ET. G. mosseae significantly increased ascorbate peroxidase activity, peroxidase (POD) gene expression, and soluble proteins content in the shoots by 13.4%, 130.3%, and 33.8%, respectively, and significantly decreased ascorbic acid (AsA), phytochelatins (PCs), and malondialdehyde (MDA) contents by 7.4%, 23.2%, and 6.5%, respectively, under ET + Cd. Additionally, G. mosseae colonization led to significant increases in POD (13.0%) and catalase (46.5%) activities, Cu/Zn-superoxide dismutase gene expression (33.5%), and MDA (6.6%), glutathione (22.2%), AsA (10.3%), cysteine (101.0%), PCs (13.8%), soluble sugars (17.5%), and proteins (43.4%) contents in the roots and carotenoids (23.2%) under ET + Cd. Cadmium, C, N, G. mosseae colonization rate, and chlorophyll significantly influenced shoots defenses and Cd, C, N, P, G. mosseae colonization rate, and sulfur significantly affected root defenses. In conclusion, G. mosseae obviously improved the defense capacity of alfalfa under ET + Cd. The results could improve our understanding of the regulation of AMF on the adaptability of plants to the coexistence of heavy metals and global warming and phytoremediation of heavy metal-polluted sites under global warming scenarios.
Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data

The automated extraction of critical information from electronic medical records, such as oncological medical events, has become increasingly important with the widespread use of electronic health records. However, extracting tumor-related medical events can be challenging due to their unique characteristics. To address this difficulty, we propose a novel approach that utilizes Generative Adversarial Networks (GANs) for data augmentation and pseudo-data generation algorithms to improve the model’s transfer learning skills for various tumor-related medical events. Our approach involves a two-stage pre-processing and model training process, where the data is cleansed, normalized, and augmented using pseudo-data. We evaluate our approach using the i2b2/UTHealth 2010 dataset and observe promising results in extracting primary tumor site size, tumor size, and metastatic site information. The proposed method has significant implications for healthcare and medical research as it can extract vital information from electronic medical records for oncological medical events.
Study of the α and β relaxations on a commercial poly(vinyl chloride) by thermally stimulated creep and depolarization current techniques

Thermally stimulated creep and depolarization current measurements have been carried out in a temperature range between −180 and 120 °C, on a pure commercial poly(vinyl chloride). Mechanical and dielectric relaxation times involved in α and β relaxations are obtained by fractional stresses/polarizations procedure. The distribution of the Arrhenius‐like kinetic parameters, deduced from the relaxation times, is interpreted in terms of compensation laws. From the obtained results, a same molecular origin can be assumed for the mechanical and dielectric behavior in both α and β relaxations. Moreover, the compensation law analysis leads to two different relaxations modes, likely associated to two kinds of thermally activated molecular motions, in β relaxation. In the α region, the values of found kinetic parameters point out a high degree of cooperativeness of the involved molecular motions. Depolarization curve in this region shows a high temperature peak, not found in the mechanical one, whose evolution with.
Machine Learning-Based Flexural Capacity Prediction of Corroded RC Beams with an Efficient and User-Friendly Tool

Steel corrosion poses a serious threat to the structural performance of reinforced concrete (RC) structures. Thus, this study evaluates the flexural capacity of RC beams through machine learning (ML)-based techniques with six parameters used as input features: beam width, beam effective depth, concrete compressive strength, reinforcement ratio, reinforcement yield strength, and corrosion level. Four single and ensemble ML models are evaluated; namely, decision tree, support vector machine, adaptive boosting, and gradient boosting. Hyperparameters of each model were optimized using grid search and K-fold cross-validation with root mean squared error used as the performance index. The predictive performance of each model was assessed using four statistical performance metrics. The analysis results demonstrated that the decision tree model exhibited overfitting and limited generalization ability. The adaptive boosting model also had a slight overfitting issue. In addition, the support vector machine reported comparable accuracy to that of adaptive boosting. Conversely, the proposed gradient boosting ensemble model achieved the best performance with strong generalization ability, as indicated by its lowest mean absolute error of 2.78 kN.m, mean absolute percent error of 13.40%, and root mean squared error of 3.56 kN.m, and the highest coefficient of determination of 97.30% on the test dataset. The optimized gradient boosting model has been deployed into a graphical user interface, allowing for practical implementation of the model and enabling fast, efficient, and intelligent prediction of the flexural capacity of corroded RC beams.
Perturbation analysis and optimization of a flow controlled manufacturing system

We use Stochastic Fluid Models (SFM) to capture the operation of threshold-based flow control policies in manufacturing systems without resorting to detailed discrete event models. By applying Infinitesimal Perturbation Analysis (IPA) to a SFM of a workcenter we derive gradient estimators of throughput and buffer overflow metrics with respect to flow control parameters. It is shown that these gradient estimators are unbiased and independent of distributional information of supply and service processes involved. Moreover, they can be implemented using actual system data, which enables us to develop simple iterative schemes for adjusting thresholds (hedging points) on line so as to optimize an objective function that trades off throughput and buffer overflow costs.
On continuous rings and self injective rings

0. Throughout this paper we assume that every ring has a unit element. A module is called injective if it is a direct summand of every extension module. A ring is said to be left self injective if it is iniective as the left module over itself. The main results we shall show in the present paper are the following: Let S be a left self injective ring. Then S/N(S) is also left self injective, where N(S) denotes the Jacobson radical of S. Any system of orthogonal idempotents of S/N(S) can be lifted to a system of orthogonal idempotents of S. This theorem about orthogonal idempotents can be proved under a somewhat weaker assumption than the left self injectivity of S. In fact, it is enough to suppose that S is a ring satisfying the following two conditions: 0.1. CONDITION. For any left ideal A there is an idempotent e such that Se is an essential extension of A.
Exosome-Based Liquid Biopsy Approaches in Bone and Soft Tissue Sarcomas: Review of the Literature, Prospectives, and Hopes for Clinical Application

The knowledge of exosome impact on sarcoma development and progression has been implemented in preclinical studies thanks to technological advances in exosome isolation. Moreover, the clinical relevance of liquid biopsy is well established in early diagnosis, prognosis prediction, tumor burden assessment, therapeutic responsiveness, and recurrence monitoring of tumors. In this review, we aimed to comprehensively summarize the existing literature pointing out the clinical relevance of detecting exosomes in liquid biopsy from sarcoma patients. Presently, the clinical utility of liquid biopsy based on exosomes in patients affected by sarcoma is under debate. The present manuscript collects evidence on the clinical impact of exosome detection in circulation of sarcoma patients. The majority of these data are not conclusive and the relevance of liquid biopsy-based approaches in some types of sarcoma is still insufficient. Nevertheless, the utility of circulating exosomes in precision medicine clearly emerged and further validation in larger and homogeneous cohorts of sarcoma patients is clearly needed, requiring collaborative projects between clinicians and translational researchers for these rare cancers.
Computational Analysis of Potential Vorticity and Quasi-Geostrophic Approximation for the Quasi-Hydrostatics Thermal Rotating Shallow Water Equations

A novel idealized and incompressible fluid flow governing model is introduced, namely, the two-dimensional quasi-hydrostatics thermal rotating shallow water equations. The potential vorticity for the new model is computationally analyzed through the Hamiltonian formulation of the Eulerian variables using the noncanonical Poisson brackets. Both similarities and differences are observed between the new and standard shallow models.
AGE DATING GALAXY GROUPS IN THE MILLENNIUM SIMULATION

We study galaxies drawn from the semi-analytic models of Guo et al. (2011) based on the MillenniumSimulation. We establish a set of four observationally measurable parameters which can be used incombination to identify a subset of galaxy groups which are old, with a very high probability. We thereforeargue that a sample of fossil groups selected based on the luminosity gap will result in a contaminatedsample of old galaxy groups. By adding constraints on the luminosity of the brightest galaxy, and itso set from the group luminosity centroid, we can considerably improve the age-dating.Key words: journals: galaxies: groups : general { galaxies groups: evolution { groups: old or young{galaxies: structure1. INTRODUCTIONIn the hierarchical structure formation scenario the ageperseverance for galaxy systems is not trivial, as in thisparadigm, more massive galaxy systems such as galaxyclusters are formed through the mergers of smallergalaxy systems and are therefore generally young galaxysystems. In fact, the answer to the question of age de-termination should come from cosmological simulationswhere the evolutionary history of galaxy systems canbe studied. Cosmological dark matter simulations andthe implemented semi-analytic galaxy models o er toolsneeded to develop an insight into this subject. An ob-servational method for the identi cation of old galaxygroups was developed in a pioneering study by Ponmanet al. (1994). According to the convention introducedby Jones et al. (2003), fossil groups have a magnitudegap of at least 2 magnitudes within 0.5 virial radii andL
Exploring Prosodic Features Modelling for Secondary Emotions Needed for Empathetic Speech Synthesis

A low-resource emotional speech synthesis system for empathetic speech synthesis based on modelling prosody features is presented here. Secondary emotions, identified to be needed for empathetic speech, are modelled and synthesised in this investigation. As secondary emotions are subtle in nature, they are difficult to model compared to primary emotions. This study is one of the few to model secondary emotions in speech as they have not been extensively studied so far. Current speech synthesis research uses large databases and deep learning techniques to develop emotion models. There are many secondary emotions, and hence, developing large databases for each of the secondary emotions is expensive. Hence, this research presents a proof of concept using handcrafted feature extraction and modelling of these features using a low-resource-intensive machine learning approach, thus creating synthetic speech with secondary emotions. Here, a quantitative-model-based transformation is used to shape the emotional speech’s fundamental frequency contour. Speech rate and mean intensity are modelled via rule-based approaches. Using these models, an emotional text-to-speech synthesis system to synthesise five secondary emotions-anxious, apologetic, confident, enthusiastic and worried-is developed. A perception test to evaluate the synthesised emotional speech is also conducted. The participants could identify the correct emotion in a forced response test with a hit rate greater than 65%.
Feeding Problems in Young Children: A Cross-Sectional Study in Sweden

Objectives: To describe the prevalence of feeding problems (FPs) in children aged 10, 18, and 36 months who visited Swedish Child Health Services. Methods: Parents of children attending regular 10-, 18-, and 36-month visits at the child health care centers (CHCCs) in Sweden answered a questionnaire including a Swedish version of the Behavioral Pediatrics Feeding Assessment Scale (BPFAS) as well as demographic questions. CHCCs were stratified according to a sociodemographic index. Results: Parents of 238 girls (115) and boys (123) completed the questionnaire. Using international thresholds for FP detection, 8.4% of the children had a total frequency score (TFS) indicating FP. Based on the total problem score (TPS), the result was 9.3%. The mean score for all children was 62.7 for TFS (median 60; range 41–100), and 2.2 for TPS (median 0; range 0–22). Children aged 36 months had a significantly higher average TPS score than younger children, but TFS scores did not differ by age. There were no significant difference in gender, parents’ education, or sociodemographic index. Conclusion: Prevalence numbers found in this study are similar to those found in studies with BPFAS in other countries. Children 36 months of age had a significantly higher prevalence of FP than children aged 10 and 18 months. Young children with FP should be referred to health care specializing in FP and PFD. Creating awareness of FP and PFD in primary care facilities and child health services may facilitate early detection and intervention for children with FP.
Design of a Chaotic Index Modulation Aided Frequency Diverse Array Scheme for Directional Modulation

Different from traditional phased array (PA), frequency diverse array (FDA) employs an additional frequency offset to its array element so that the transmitted signal is angle-range-dependent. Therefore, FDA can be utilized to realize directional modulation (DM) in both angle and range dimensions. However, traditional DM is mainly realized through optimization methods, which is time-consuming. This article proposes an analytical method to address this problem. For a transmitter with <inline-formula><tex-math notation="LaTeX">$M+1$</tex-math></inline-formula> elements, the proposed approach adopts a chaotic sequence to map out the frequency offsets and the weights of the first <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> elements. Then, the weight of the last element can be determined in an analytical way. Numerical simulations are implemented to validate the proposed approach.
A Novel Differential Dual-Band Bandpass Filter Based on Stepped-Impedance Double-Sided Parallel-Strip Line

Abstract A novel differential dual-band bandpass filter based on a stepped-impedance double-sided parallel-strip line is proposed in this article. In order to suppress the common-mode signal, an H-shaped ground plane is inserted. The simulated and measured results show that the proposed filter operated at 2.4 GHz and 5.8 GHz with the insertion losses of 3.2 dB and 3.5 dB, respectively. Also, the common-mode signal is suppressed to below −30 dB from 1 GHz to 7.5 GHz.
Green-Synthesized Graphene for Supercapacitors—Modern Perspectives

Graphene is a unique nanocarbon nanostructure, which has been frequently used to form nanocomposites. Green-synthesized graphene has been focused due to environmentally friendly requirements in recent technological sectors. A very important application of green-synthesized graphene-based nanocomposite has been observed in energy storage devices. This state-of-the-art review highlights design, features, and advanced functions of polymer/green-synthesized graphene nanocomposites and their utility in supercapacitor components. Green graphene-derived nanocomposites brought about numerous revolutions in high-performance supercapacitors. The structural diversity of conjugated polymer and green graphene-based nanocomposites has facilitated the charge transportation/storage capacity, specific capacitance, capacitance retention, cyclability, and durability of supercapacitor electrodes. Moreover, the green method, graphene functionality, dispersion, and matrix–nanofiller interactions have affected supercapacitance properties and performance. Future research on innovative polymer and green graphene-derived nanocomposites may overcome design/performance-related challenging factors for technical usages.