Miracle-dz/UniFAR-FTUs huggingface dataset:
Biochar improves the adaptability of Vicia faba L in cadmium contaminated soil

ABSTRACT Cadmium (Cd) is widely present in pesticides and fertilizers, which represent a major problem in crop production. Biochar is recognized for its ability to modify Cd availability and reduce plant phytotoxicity. In this work, a controlled lab-experiment was done in order to assess the effect of biochar on Cd uptake, growth and oxidative stress of Vicia faba L plants. For that, in addition to the control (CK), soils were exposed to both concentrations of Cd (Cd1: 0.35 mg kg−1 and Cd2: 0.7 mg kg−1). Then, bean seeds were cultivated in presence or absence of biochar (2%) for 60 days. Cd uptake was assessed in both shoots and roots. Growth and physiological parameters (length, fresh matter (FM), dry matter (DM), Chlorophyll a and b (Chl a and Chl b) were determined after harvesting. Oxidative stress, apprehended by Peroxidase (PRX) and Glutathione-S-Transferase (GST) activities and Malondialdehydes (MDA) accumulation, was evaluated. Then, biochar reduced Cd uptake in roots and shoots. Also, under biochar effect growth and physiological parameters were enhanced. Oxidative stress was reduced regarding PRX and GST activities and MDA accumulation. Our results suggest the efficiency of biochar to reduce Cd uptake and plant phytotoxicity of Vicia faba L in Cd polluted soils, therefore its application in soil remediation strategies.
Support vector machine parallelized remote sensing image classification algorithm based on big data

Abstract. With the development of big data technology, machine learning classification methods have been widely used in the classification and recognition of remote sensing images. For remote sensing big data, how to quickly and efficiently use machine learning classification algorithms to classify remote sensing images is an urgent problem. It is a general term for the theory, method, technology, and activities of obtaining valuable information based on massive remote sensing data sets, synthesizing auxiliary data from other sources, and using big data thinking and means. The purpose of this paper is to study the support vector machine (SVM) parallelized remote sensing image classification algorithm based on big data. We propose a parallel nesting of GPU in MPI multiprocesses based on the big data framework, which can more effectively improve the calculation processing speed and build a high-performance SVM parallel computing framework based on the big data framework. The optimization problem of SVM considers both empirical risk and structural risk minimization and requires maximum edge distance when constructing hyperplane decision boundaries, so there is ample space between the interval boundaries to accommodate the test samples. Based on this framework, we improve the machine learning SVM algorithm and realize the high-performance parallel computing of SVM classification algorithm on this platform. It is an efficient hybrid parallel mode to nest GPU in MPI multiprocess in parallel. When the number of nodes is 2, 4, and 6, the speedup of the SVM classification algorithm is 1.52, 2.24, and 2.55.
Low-Voltage and Low-Power True-Single-Phase 16-Transistor Flip-Flop Design

A low-voltage and low-power true single-phase flip-flop that minimum the total transistor count by using the pass transistor logic circuit scheme is proposed in this paper. Optimization measures lead to a new flip-flop design with better various performances such as speed, power, energy, and layout area. Based on post-layout simulation results using the TSMC CMOS 180 nm and 90 nm technologies, the proposed design achieves the conventional transmission-gate-based flip-flop design with a 53.6% reduction in power consumption and a 63.2% reduction in energy, with 12.5% input data switching activity. In order to further the performance parameters of the proposed design, a shift-register design has been realized. Experimental measurements at 0.5 V/0.5 MHz show that this proposed design reduces power consumption by 47.3% while achieving a layout area reduction of 30.5% compared to the conventional design.
Bayesian Analysis of Linear and Non‐Linear Population Models by Using the Gibbs Sampler

Abstract : A fully Bayesian analysis of linear and nonlinear population models has previously been unavailable, as a consequence of the seeming impossibility of performing the necessary numerical Integrations in the complex multi- parameter structures typically arising in such models. It is demonstrated that, for a variety of linear and nonlinear population models, a fully Bayesian analysis can be implemented in a straightforward manner using the Gibbs sampler. The approach is illustrated with examples involving challenging problems of outliers and mean-variance relationships in population modelling.
Murine Models of Endometriosis: A Systematic Review

Objectives: This review inspects the usage of animal models and the practical solutions of this method for different challenges of endometriosis. The objectives of the study are to determine and compare the histopathology, biomarkers, and development of endometrial lesions in murine homologous and heterologous endometriosis models. Methods: The literature search was performed on PubMed, Scopus, Web Cochrane, and EMBASE from January 1990 to January 2019. Experimental articles in which the establishment of the endometriosis model had been proven through the examination of size, weight, number of implants, adhesion, histologic score, and altered biomarker were eligible for inclusion. Results: Based on type of induction, articles were categorized into two groups: heterologous-induced method (n=5) and autologousinduced method (n=13). In general, in case of establishing the heterologous induction method is less reliable than the autologous induction method. Conclusions: Using human endometrial tissues for endometrial inductions is possible in heterologous models under immunosuppression, which is more suitable for therapeutic studies, but time limitation considerations are mandatory for this type of model. Homologous endometriosis inductions cause larger endometrial lesions, biomarkers, and reproduction rate changes similar to those occurring in humans. Similarities make this method more appropriate for pathogenesis and genetic studies and also observe the impact of endometriosis on the next generation. Choosing an appropriate model for the induction of endometriosis is dependent on the purpose of each study.
Hierarchical Core Decomposition in Parallel: From Construction to Subgraph Search

The model of k-core discovers a novel hierarchical structure of a network, which has been widely applied in various areas, e.g., sociology, biology, and brain science. Based on the containment relations of k-cores with different $k$, the hierarchical core decomposition (HCD) of a graph formalizes the hierarchy of all k-cores for each possible $k$ • HCD is effective in locating high-quality subgraphs (e.g., densest subgraph search) and exploring particular network phenomena (e.g., user engagement study). However, existing solutions of HCD are still not efficient enough, for both the hierarchy construction and the subgraph search on the hierarchy. In this paper, we propose the first parallel construction algorithm PHCD for HCD, using a new union-find-based paradigm, and the first parallel algorithm PBKS to search high-quality subgraphs from the hierarchy with respect to various community scoring metrics. We prove the problem of hierarchy construction is $\mathcal{P}$-complete (difficult to parallelize effectively). Despite the negative result, our PHCD has a near-linear time cost, and PBKS is time-optimal in score computation for most community metrics. Extensive experiments are conducted on 10 real-world networks, where our proposed parallel algorithms significantly outperform the existing solutions, for both the hierarchy construction and the subgraph search.
Cascade Control System‐Based Cost Effective Combination of Tensor Product Model Transformation and Fuzzy Control

This paper proposes the combination of tensor product (TP)‐based model transformation and of fuzzy control as a cost effective cascade control system (CS) structure. A novel two‐step design approach is suggested. A parallel distributed compensation technique is used in the first step in the TP‐based design of the inner state feedback control loops which is next characterized by simple models using equivalent state feedback matrices. The modulus optimum method and the modal equivalent principle are used in the second step to design the Mamdani PI‐fuzzy controller in the outer control loops. The cost effective feature of the original cascade CS combination concerns the simplicity of the CS structure, of the controllers and of the design approach. The presentation is focused on the level control in horizontal three tank systems where two cascade CS structures are separately designed to control two water levels. The efficiency of the cascade CS structure is proved by real‐time experimental results for a laboratory three tank system. Comparisons with the Mamdani PI‐fuzzy CS and with the cascade combination of TP and PI CS are included.
Collaboration in Mathematics Teacher Education: the What, How, and Why of Mathematical Modeling

In this paper, we share our collaboration across the disciplines of mathematics and mathematics education to develop and implement a mathematical modeling task for prospective secondary mathematics teachers. Through this collaboration, we identified three key components of mathematical modeling: the what, how, and why. In this paper, we outline these components from the literature and how each framed our development and implementation of the Sprinkler Task in our mathematics content and mathematics methods courses for secondary teachers. These three components show that mathematical modeling is a particularly fruitful space for collaboration between the disciplines of mathematics and mathematics education in teacher education.
Non-Reciprocal Sub-Micron Waveguide Raman Amplifiers, Towards Loss-Less Silicon Photonics

Optical amplification in silicon photonic integrated circuits remains a challenge and stimulated Raman scattering has been proposed as a means of achieving optical gain in silicon. In this work, we experimentally investigate silicon Raman amplifiers optimized for fabrication with open-access foundry sub-micron silicon platform. We discuss and experimentally validate the importance of considering the non-reciprocal Raman-gain by using counter-propagating or co-propagating pumps and probes, different amplifier lengths, input pump powers and nonlinear loss values. We demonstrate a Raman-assisted loss-less optical circuit in a 1.2-cm-length waveguide that reaches zero net-gain with only 60 mW continuous-wave pumping. A 5.5 dB stimulated Raman scattering (SRS) gain yielding 0.5 dB net-gain is also demonstrated with a 115 mW pump in a 3.25-cm waveguide. Furthermore, we examine the nonlinear loss of silicon waveguides to estimate free carrier lifetime (FCL) with different bias voltages applied to the waveguide p-i-n structure. Accounting for non-reciprocal Raman amplification by using the two dataset of co- and counter-propagating SRS gain, we extract the Raman gain coefficient of this 220 nm thick silicon photonic waveguide. We perform the curve fitting over the whole input pump power range using the extracted FCL, and over the low power range where only linear loss is expected. We find a good agreement in the extracted Raman gain coefficients. We use these key parameters as input to a silicon photonic Raman amplifier model to find the optimum performance based on the available footprint and pump power.
Physical, psychological, academic, and sexual abuse of university students in five Latin American countries: characteristics and association with student satisfaction: a cross-sectional study

Background: A high rate of student abuse has been reported in various studies; however, few studies substantiate the seriousness of the issue in Latin American contexts. This study aimed to characterize the abuse of university students in five Latin American countries and its association with student satisfaction. Methods: An analytical transversal study was conducted, in which 2,141 undergraduate students from Paraguay, Ecuador, Colombia, Venezuela, and Panama were surveyed. A validated questionnaire was applied to evaluate four types of abuse (Cronbach's Alpha: 0.90): psychological, physical, academic, and sexual. In addition, students’ perception of satisfaction of four items in the university environment (Cronbach's Alpha: 0.85) was also analyzed. Results: It was found that the older students were, the more dissatisfied they were with “logistics” (aPR: 1.02; 95% CI: 1.00-1.04; p-value; 0.014) and the top-third segment of those who perceived “sexual abuse” were more dissatisfied with the teaching provided by their universities (aPR: 1.23; 95% CI: 1.00-1.52; p-value: 0.048). Moreover, those who were most dissatisfied with the agreements were part of the top-third segment of those who perceived sexual abuse (aPR: 1.16; 95% CI: 1.03-1.32; p-value: 0.016) and those in the country of Panama were less dissatisfied with “agreements” (RPa: 0.70; 95% CI: 0.66-0.073; p-value<0.001). The countries of Colombia (aPR: 0.08; IC95%: 0.01-0.59; p-value: 0.014) and Panama (aPR: 0.57; 95% CI: 0.52-0.64; p-value<0.001) were less dissatisfied with investigation programs. Conclusions: The perception of ‘sexual abuse’ was the only risk factor statistically associated with dissatisfaction with teaching and agreements.
Interactive network visualization on the integration of mindsets and sustainability – creating conditions for emergence through a relational narrative

Transdisciplinary research processes often involve the integration of knowledge and stakeholders from various backgrounds. Here, we introduce the story of the research project AMA (A Mindset for the Anthropocene) on the role of mindsets in the context of sustainability and present an interactive visualization tool that we developed for stakeholder mapping and research communication. Through this platform, we provide access and navigation to everyone interested in this field of research and we have simultaneously created a channel for all stakeholders to co-create content. Here, we describe the design and functionalities of the platform and the participatory way it was developed as part of our stakeholder engagement. We discuss upon how such a design allows for reflection of potential biases in transdisciplinary research processes and simultaneously catalyzing self-organization in stakeholder networks.
Pyrimidines-Based Heterocyclic Compounds: Synthesis, Cytoxicity Evaluation and Molecular Docking

A variety of structurally different pyrimidines were synthesized. Elemental analysis, FT-IR, 1H NMR, and 13C NMR spectroscopy were used to confirm the chemical structures of all prepared compounds. The synthesized pyrimidines were screened against the growth of five human cancer cell lines (prostate carcinoma PC3, liver carcinoma HepG-2, human colon cancer HCT-116, human breast cancer MCF-7, human lung cancer A-549), and normal human lung fibroblasts (MRC-5) using MTT assay. Most of the screened pyrimidines have anti-proliferative activity on the growth of the PC3 cell line. Compounds 3b and 3d were more potent than the reference vinblastine sulfate (~2 to 3 × fold) and they can be considered promising leads for treating prostate cancer disease. Moreover, the screened compounds 3b, 3f, 3g, 3h, and 5 were assessed according to the values of their selectivity index (SI) and were found to be more selective and safer than vinblastine sulfate. Furthermore, using in silico computational tools, the physicochemical properties of all pyrimidine ligands were assessed, and the synthesized compounds fall within the criteria of RO5, thus having the potential to be orally bioavailable.
Inertial electrostatic confinement: Theoretical and experimental studies of spherical devices

Inertial Electrostatic Confinement (IEC) is a means to confine ions for fusion purposes with electrostatic fields in a converging geometry. Its engineering simplicity makes it appealing when compared to magnetic confinement devices. It is hoped that such a device may one day be a net energy producer, but it has near term applications as a neutron generator. We study spherical IECs (SIECs), both theoretically and experimentally. Theoretically, we compute solutions in the free molecular limit and map out regions in control parameter space conducive to the formation of double potential wells. In addition, several other observables are mapped in the control parameter space. Such studies predict the threshold for the phenomena of “core splitting” to occur when the fractional well depth (FWD) is ~70%-80%. With respect to double potential wells, it is shown that an optimal population of electrons exists for double well formation. In addition, double well depth is relatively insensitive to space charge spreading of ion beams. Glow discharge devices are studied experimentally with double and single Langmuir probes. The postulated micro-channeling phenomenon is verified with density measurements along a micro-channel and along the radius where micro-channels are absent. In addition, the measurements allow an evaluation of the neutrality of micro-channels and the heterogeneous structure of “Star Mode”. It is shown that, despite visual evidence, micro-channeling persists well into “Jet” mode. In addition, the threshold for the “Star” mode to “Jet” mode transition is obtained experimentally. The studies have revealed new techniques for estimating tangential electric field components and studying the focusing of ion flow.
Sensor development and integration for robotized laser welding

Laser welding requires fast and accurate positioning of the laser beam over the seam trajectory. The task of accurate positioning of the laser tools is performed by robotic systems. It is therefore necessary to teach the robot the path it has to follow. Seam teaching is implemented in several ways: Offline Programming, Manual Point-to-point, Sensor Guided. The first two processes are time consuming; the second requires constant human interaction, whereas the last one is fast and fully automated. The most commonly used seam detection sensors are based on optical triangulation with a single structured light line. Such sensors impose restrictions on the laser tool orientation in relation to the seam trajectory. The shape of the seam trajectory in combination with the required speed, can force the robot into positioning errors due to robot dynamics. Furthermore, closed looped seam trajectories such as circles or rectangles are not possible to be taught with such sensors. A solution to these problems is given by the seam detection sensor of the integrated laser welding head. The developed perimetric sensor allows the detection and following of seam trajectories without restrictions on the relative position of the welding head. This reduces positioning errors due robot dynamics and allows looped seam trajectories. The developed integrated laser welding head fulfills two additional sensing functionalities: weld inspection and process monitor. The weld inspection sensor measures the weld surface properties and provides quality estimation according to ISO13919. The process monitor sensor allows the detection of the keyhole formation in full penetration welding. The integration of these sensors into one compact laser welding head required the fusion of their optical paths. Automated calibration procedures have been developed for the system’s integration with robots. Finally, a user friendly sensor software application has been developed that allows access and overview of all the sensors’ processes and handles all required data communications.
An FDTD model of scattering from meteor head plasma

We have developed a three‐dimensional finite difference time domain (FDTD) model of scattering of radar waves from meteor head plasma. The model treats the meteor head plasma as a cold, collisional, and magnetized plasma, and solves Maxwell's equations and the Langevin equation simultaneously and self‐consistently in and around the plasma. We use this model to investigate scattering of radar waves from a meteor head (the “head echo”) under a range of plasma densities, meteor scale sizes, and wave frequencies. In this way we relate the radar cross section (RCS) to these variable parameters. We find that computed RCS disagrees with previous analytical theory at certain meteor sizes and densities, in some cases by over an order of magnitude. We find that the calculated meteor head RCS is monotonically related to the “overdense area” of the meteor, defined as the cross‐section area of the part of the meteor where the plasma frequency exceeds the wave frequency. These results provides a physical measure of the meteor size and density that can be inferred from measured RCS values from ground‐based radars. Meteoroid mass can then be inferred from the meteor plasma distribution using established methods.
Comprehensive analysis of the potential cuproptosis-related biomarker LIAS that regulates prognosis and immunotherapy of pan-cancers

Lipoic acid synthetase (LIAS) has been demonstrated to play a crucial role in the progression of cancer. Exploring the underlying mechanisms and biological functions of LIAS could have potential therapeutic guidance for cancer treatment. Our study has explored the expression levels and prognostic values of LIAS in pan-cancer through several bioinformatics platforms, including TIMER2.0, Gene Expression Profiling Interactive Analysis, version 2 (GEPIA2.0), and Human Protein Atlas (HPA). We found that a high LIAS expression was related to the good prognosis in patients with kidney renal clear cell carcinoma (KIRC), rectum adenocarcinoma (READ), breast cancer, and ovarian cancer. Inversely, a high LIAS expression showed unfavorable prognosis in lung cancer patients. In addition, the genetic alteration, methylation levels, and immune analysis of LIAS in pan-cancer have been evaluated. To elucidate the underlying molecular mechanism of LIAS, we conduct the single-cell sequencing to implicate that LIAS expression was related to hypoxia, angiogenesis, and DNA repair. Thus, these comprehensive pan-cancer analyses have conveyed that LIAS could be potentially significant in the progression of various cancers. Moreover, the LIAS expression could predict the efficacy of immunotherapy in cancer patients.