Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of an Anionic Surfactant Addition on Solubility of Humic Acid in Acid-Alkaline Solutions

The solubility of humic acid (HA) is a very important factor in the study of its interaction with metal ions and radionuclides. The solubility of HA as a function of hydrogen ion concentration was studied. It has been found that chemical behaviour of HA is similar to other polyelectrolytes. Solubility depends on pH, ionic strength, and many other factors. Unmonotonous character of the solubility curve in relationship with pH may be connected with conformation changes of HA saturated solutions. The results show that the addition of anionic surfactant to HA saturated solution can influence the solubility curve of HA. A large fraction of soil organic carbon and waterdissolved organic carbon is constituted by humic sub­ stances. Humic substances are complex, heterogeneous molecules which are operationally defined on the ba­ sis of solubility: fulvic acid is completely soluble in aqueous solution, while humic acid is soluble in al­ kali but precipitates in acid solutions. A knowledge of the solubility characteristics of HA is important for understanding the behaviour of and relationship between humic substances in soil and natural waters [1]. In solution, HA behaves as micellar colloids. The colloid particles have a negative charge and this iso­ electric point is in the acid region. It has been found that chemical properties of HA are similar to the other acidic polyelectrolytes. The solubility of HA depends on pH, ionic strength, and many other factors [2]. In view of the fact that HA is a relatively high-molecularmass species containing polar and nonpolar moieties [3], we speculate that there occurs a partition-like in­ teraction of the surfactants with the soil HA. The pur­ pose of the present work is to study the solubility of HA at different pH values and influence of an anionic surfactant addition on its solubility curve.
R Genes - The Natural Arsenal of Plants and their Role in Plant-Pathogen Interactions

Efforts have been on for several years now in introgressing disease resistant genes into elite cultivars through conventional breeding. With the rapid strides made in the development of transgenic plants as well as in the understanding of disease resistance at Ihe molecular level, it is now possible 10 devise more efficient strategies for the development of disease-resistant varieties. An attempt has been made in this article describe the advances made in the basic understanding of plant disease resistance genes (R genes) - the plant's natural arsenal against pathogens - and discuss the application of this knowledge towards enhancing disease resistance in crops. A number of R genes have been cloned and characterized in tenns of their structure and function. The models proposed for interactions of R proteins and Avr proteins have now been supported by adequat~ molecular evidence. Both host and non-host resistance R genes have been used to engineer disease resistance in crop plants. Although aUf current knowledge on molecular basis of plant-pathogen interactions is only fragmentary, given the advances being made, it is reasonable to expect that in the years to come, we would be able to devise suitable strategies that will provide effective and eco-friendly protection against plant diseases.
Review-based Question Generation with Adaptive Instance Transfer and Augmentation

While online reviews of products and services become an important information source, it remains inefficient for potential consumers to exploit verbose reviews for fulfilling their information need. We propose to explore question generation as a new way of review information exploitation, namely generating questions that can be answered by the corresponding review sentences. One major challenge of this generation task is the lack of training data, i.e. explicit mapping relation between the user-posed questions and review sentences. To obtain proper training instances for the generation model, we propose an iterative learning framework with adaptive instance transfer and augmentation. To generate to the point questions about the major aspects in reviews, related features extracted in an unsupervised manner are incorporated without the burden of aspect annotation. Experiments on data from various categories of a popular E-commerce site demonstrate the effectiveness of the framework, as well as the potentials of the proposed review-based question generation task.
Anti-fungal activity of Capsicum frutescence and Zingiber officinale against key post-harvest pathogens in citrus

Alcoholic extracts of Capsicum frutescence (Chilly) and Zingiber officinale (Ginger) (ranging between 500 and 3000 ppm) were tested for antifungal activity in vitro on Penicillium digitatum, Aspergillus niger and Fusarium sp isolated from naturally infected citrus fruit. The water extracts served as control and it was observed that the alcoholic extracts concentrations were more effective than the water extract control in showing antifungal activity (P<0.05) against the test pathogens. The results also indicated that Capsicum frutescence showed 100% inhibition of colony diameter (cm) at 3000 ppm. Hence, the results of the present investigations indicate the plant extracts posses’ antifungal activity that can be exploited as an ideal treatment for future plant disease management to eliminate fungal spread.
Molecular basis of acid ceramidase de fi ciency in a neonatal form of

a Genetic Department, National Health Institute Doutor Ricardo Jorge (INSARJ), Oporto, Portugal b Institute of Biomedical Sciences Abel Salazar, Oporto University, Portugal c Laboratoire de Biochimie Métabolique, Institut Fédératif de Biologie, CHU Purpan, Toulouse, France d INSERM UMR1037, CRCT, Université Paul Sabatier, Toulouse, France e Neurometabolic Laboratory, Klinik für Kinderund Jugendmedizin, University of Tübingen, Tübingen, Germany f Health Sciences Faculty, University Fernando Pessoa, Oporto, Portugal
Pyannote.Audio: Neural Building Blocks for Speaker Diarization

We introduce pyannote.audio, an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to build speaker diarization pipelines. pyannote.audio also comes with pre-trained models covering a wide range of domains for voice activity detection, speaker change detection, overlapped speech detection, and speaker embedding – reaching state-of-the-art performance for most of them.
Strategies for Design-Build in Korea Using System Dynamics Modeling

Increasingly adopted by both public and private organizations, design-build (DB) has become a favored construction project delivery system, outperforming other systems in terms of cost, schedule, and quality. However, DB has been especially criticized by the public sector for practicing subjective evaluation, for requiring excessive resources, and for providing only limited accessibility to small and medium-sized contractors. In Korea, similar critiques have been raised, as these qualities have prevented public owners from benefiting from the potential advantages of DB. In order to address these challenging issues, the present research systematically analyzes the characteristics of the DB delivery system in Korea. Based on industry surveys and an extensive literature review, a qualitative system dynamics model is developed and used to propose and test hypothetical DB policy alternatives which are expected to enhance DB performance. Furthermore, after the appropriate customization processes, these research findings can also be applied to the industry settings of different countries.
On the Convergence of Stochastic Primal-Dual Hybrid Gradient

In this paper, we analyze the recently proposed stochastic primal-dual hybrid gradient (SPDHG) algorithm and provide new theoretical results. In particular, we prove almost sure convergence of the iterates to a solution and linear convergence with standard step sizes, independent of strong convexity constants. Our assumption for linear convergence is metric subregularity, which is satisfied for smooth and strongly convex problems in addition to many nonsmooth and/or nonstrongly convex problems, such as linear programs, Lasso, and support vector machines. In the general convex case, we prove optimal sublinear rates for the ergodic sequence and for randomly selected iterate, without bounded domain assumptions. We also provide numerical evidence showing that SPDHG with standard step sizes shows favorable and robust practical performance against its specialized strongly convex variant SPDHG-$\mu$ and other state-of-the-art algorithms including variance reduction methods and stochastic dual coordinate ascent.
On-Device Machine Learning: An Algorithms and Learning Theory Perspective

The predominant paradigm for using machine learning models on a device is to train a model in the cloud and perform inference using the trained model on the device. However, with increasing number of smart devices and improved hardware, there is interest in performing model training on the device. Given this surge in interest, a comprehensive survey of the field from a device-agnostic perspective sets the stage for both understanding the state-of-the-art and for identifying open challenges and future avenues of research. However, on-device learning is an expansive field with connections to a large number of related topics in AI and machine learning (including online learning, model adaptation, one/few-shot learning, etc.). Hence, covering such a large number of topics in a single survey is impractical. This survey finds a middle ground by reformulating the problem of on-device learning as resource constrained learning where the resources are compute and memory. This reformulation allows tools, techniques, and algorithms from a wide variety of research areas to be compared equitably. In addition to summarizing the state-of-the-art, the survey also identifies a number of challenges and next steps for both the algorithmic and theoretical aspects of on-device learning.
LSTM-Sharp: An Adaptable, Energy-Efficient Hardware Accelerator for Long Short-Term Memory

The effectiveness of LSTM neural networks for popular tasks such as Automatic Speech Recognition has fostered an increasing interest in LSTM inference acceleration. Due to the recurrent nature and data dependencies of LSTM computations, designing a customized architecture specifically tailored to its computation pattern is crucial for efficiency. Since LSTMs are used for a variety of tasks, generalizing this efficiency to diverse configurations, i.e., adaptiveness, is another key feature of these accelerators. In this work, we first show the problem of low resource-utilization and adaptiveness for the state-of-the-art LSTM implementations on GPU, FPGA and ASIC architectures. To solve these issues, we propose an intelligent tiled-based dispatching mechanism that efficiently handles the data dependencies and increases the adaptiveness of LSTM computation. To do so, we propose LSTM-Sharp as a hardware accelerator, which pipelines LSTM computation using an effective scheduling scheme to hide most of the dependent serialization. Furthermore, LSTM-Sharp employs dynamic reconfigurable architecture to adapt to the model's characteristics. LSTM-Sharp achieves 1.5x, 2.86x, and 82x speedups on average over the state-of-the-art ASIC, FPGA, and GPU implementations respectively, for different LSTM models and resource budgets. Furthermore, we provide significant energy-reduction with respect to the previous solutions, due to the low power dissipation of LSTM-Sharp (383 GFLOPs/Watt).
PHYTOEXTRACTION OF CADMIUM AND ZINC FROM AN OXISOL CONTAMINATED WITH HEAVY METALS

PHYTOEXTRACTION OF CADMIUM AND ZINC FROM AN OXISOL CONTAMINATED WITH HEAVY METALS In a greenhouse experiment, the ability of sunflower (Helianthus annuus L.), castor bean (Ricinus communis L.), tobacco (Nicotiana tabacum L.), and Amazon pepper (Capsicum baccatum L.) to extract Cd and Zn from an oxisol contaminated with heavy metals was evaluated. The experiment was carried out in a complete ramdomized block design in a factorial scheme consisted of 4 species x 3 rates of metals x 2 rates of EDTA, with 3 replicates and using plastic pots with 3 L capacity. Heavy metals were applied to the soil, as inorganic salts, at rates, in mg kg-1: dose 0: control (no metals added); dose 1: – combined application of 0.75 of Cd; 6.25 of Pb; 20.0 of Cu; 8.75 of Ni and 62.5 of Zn; and dose 2 – combined application of 1.5 of Cd; 12.5 of Pb; 40.0 of Cu; 17.5 of Ni and 125,0 of Zn, with and without EDTA addition of 1 mmol kg-1. Plant tops were harvested from 48 to 100 days after sowing or seedlings transplant to the pots. EDTA addition decreased dry matter yield of all species, although it contributed to the increase of shoot Cd concentration on castor bean and sunflower plants, and had no effect on Zn shoot concentration for all species under investigation. Tobbaco was the most efficient specie in extracting Zn and Cd from the contaminated soil. A great number of sunflower, Amazon pepper and tobacco cropping will be necessary for soil decontamination, if it receives the addition of EDTA at rate of 1 mmol kg-1.
Chapter 11 Nanofibers and Electrospinning Method

As a result of their peculiar features like extremely high surface area to weight ratio, low density, as well as high pore volume and controllable pore size, which may not be present in other structures, nanofibers have taken centre stage in nanotechnology. The indicated properties, consequently, make non-woven nanofibers as appropriate materials for wide-spread applications. Electrospinning, because of its high productivity, simplicity, low cost, reproducibility and its potentialities of being utilised at the industrial level is regarded as one of the most potential processes in nanotechnology. This method implies the application of high voltage electric field aiming to extract very thin fibres from a polymeric fluid stream (solution or melt) potentially deliverable through a millimetre-scale needle. Electrospinning, as a technique, is reliant on various processing standards like solution properties and processing parameters. Consequently, altering these parameters could exert a considerable degree of influence on the nanofiber size, shape and morphology. Thus, by controlling those parameters well, specific fibres can be produced to benefit various applications.
Reduction of a damped, driven Klein–Gordon equation into a discrete nonlinear Schrödinger equation: Justification and numerical comparison

We consider a discrete nonlinear Klein–Gordon equation with damping and external drive. Using a small amplitude ansatz, one usually approximates the equation using a damped, driven discrete nonlinear Schrödinger equation. Here, we show for the first time the justification of this approximation by finding the error bound using energy estimate. Additionally, we prove the local and global existence of the Schrödinger equation. Numerical simulations are performed that describe the analytical results. Comparisons between discrete breathers of the Klein–Gordon equation and discrete solitons of the discrete nonlinear Schrödinger equation are presented.
Biased Ligands for Better Cardiovascular Drugs: Dissecting GPCR Pharmacology G-Protein-Dependent and –Independent Signaling Pathways and their Impact on Cardiac Function Compartmentalization of b-Adrenergic Signals in Cardiomyocytes G-Protein-Coupled Receptor Kinases (GRK) as Therapeutic Targets in 

Drug discovery efforts targeting G-protein-coupled receptors (GPCR) have been immensely successful in creating new cardiovascular medicines. Currently marketed GPCR drugs are broadly classified as either agonists that activate receptors or antagonists that prevent receptor activation by endogenous stimuli. However, GPCR couple to a multitude of intracellular signaling pathways beyond classical G-protein signals, and these signals can be independently activated by biased ligands to vastly expand the potential for new drugs at these classic targets. By selectively engaging only a subset of a receptor’s potential intracellular partners, biased ligands may deliver more precise therapeutic benefit with fewer side effects than current GPCR-targeted drugs. In this review, we discuss the history of biased ligand research, the current understanding of how biased ligands exert their unique pharmacology, and how research into GPCR signaling has uncovered previously unappreciated capabilities of receptor pharmacology. We focus on several receptors to illustrate the approaches taken and discoveries made, and how these are steadily illuminating the intricacies of GPCR pharmacology. Discoveries of biased ligands targeting the angiotensin II type 1 receptor and of separable pharmacology suggesting the potential value of biased ligands targeting the -adrenergic receptors and nicotinic acid receptor GPR109a highlight the powerful clinical promise of this new category of potential therapeutics. (Circ Res. 2011;109:205-216.)
CIRCULATING P53 ANTIBODIES IN PATIENTS WITH COLORECTAL CANCER: RELATION TO CLINICOPATHOLOGICAL FEATURES AND TUMOR MARKERS

Background: P53 antibodies (p53 Abs) have been detected in the serum of a proportion of colorectal cancer (CRC) patients. The development of these antibodies is associated with the presence of mutant oncoprotein p53. However, it is not yet known at which stage during CRC progression p53Abs appear in serum. Aim of the Work: To identify the prevalence of p53 Abs in CRC and their association with clinicopathological features of the tumor. Patients and Methods: The study included 30 CRC patients (16 males and 14 females) their age ranged between 20-73 years. All patients were subjected to full clinical examination and laboratory studies including tumor markers (CEA, CA 19.9). Colonoscopy was done for pathological grading and staging of the tumor according to TNM and Dukes’ staging system. P53 antibodies in patients’ sera were detected using ELISA technique. Results: We found that 16.6 % of our CRC patients had positive anti-p53 Abs. No association was observed between the presence of these Abs and clinicopathological features including age, tumor location, tumor size, T-stage, Dukes’ staging or even distant metastasis. A statistically significant association was found between the presence of p53 Abs and lymph node status (p < 0.001) and with histological grading (p=0.031). Conclusion: The presence of p53 Abs in sera of patients with CRC indicates a more advanced tumor histopathological stage and could be utilized as a complementary prognostic tool to colonoscopy in high risk patients.
The Physics of Fun: Quantifying Human Engagement into Playful Activities

Engaging in playful activities, such as playing a musical instrument, learning a language, or performing sports, is a fundamental aspect of human life. We present a quantitative empirical analysis of the engagement dynamics into playful activities. We do so by analyzing the behavior of millions of players of casual video games and discover a scaling law governing the engagement dynamics. This power-law behavior is indicative of a multiplicative (i.e., ``happy-get-happier'') mechanism of engagement characterized by a set of critical exponents. We also find, depending on the critical exponents, that there is a phase transition between the standard case where all individuals eventually quit the activity and another phase where a finite fraction of individuals never abandon the activity. The behavior that we have uncovered in this work might not be restricted only to human interaction with videogames. Instead, we believe it reflects a more general and profound behavior of how humans become engaged in challenging activities with intrinsic rewards.
Evaluation of Atherosclerotic Risk Factors in Patients with Essential Hypertension and Coronary Arteries Disease with or without Renal Artery Stenosis

I Klinika Choroby Wieńcowej, Instytut Kardiologii w Warszawie Klinika Nadciśnienia Tętniczego, Instytut Kardiologii w Warszawie Zakład Biochemii Klinicznej, Instytut Kardiologii w Warszawie II Zakład Radiologii Klinicznej Akademii Medycznej w Warszawie Klinika Niewydolności Serca, Instytut Kardiologii w Warszawie Zakład Epidemiologii, Prewencji Chorób Układu Krążenia i Promocji Zdrowia, Instytut Kardiologii w Warszawie
Remark on a gap of inviscid shear flow instability between infinite stripe and periodic box

Bedrossian and Masmoudi (2015) showed nonlinear stability of 2D inviscid parallel shear flows in the infinite stripe $(\mathbb{R}/2\mathbb{Z})\times\mathbb{R}$. In this paper we exhibit a gap of instability (stability) mechanism between the infinite stripe case and periodic boxes cases.
QCD baryogenesis

We explore a simple model which naturally explains the observed baryon asymmetry of the Universe. In this model the strong coupling is promoted to a dynamical quantity, which evolves through the vacuum expectation value of a singlet scalar field that mixes with the Higgs field. In the resulting cosmic history, QCD confinement and electroweak symmetry breaking initially occur simultaneously close to the weak scale. The early confinement triggers the axion to roll toward its minimum, which creates a chemical potential between baryons and antibaryons through the interactions of the $\eta'$ meson, resulting in spontaneous baryogenesis. The electroweak sphalerons are sharply switched off after confinement and the baryon asymmetry is frozen in. Subsequently, evolution of the Higgs vacuum expectation value (which is modified in the confined phase) triggers a relaxation to a Standard Model-like vacuum. We identify viable regions of parameter space, and describe various experimental probes, including current and future collider constraints, and gravitational wave phenomenology.
Bulk Heterojunction Solar Cell Devices Prepared with Composites of Conjugated Polymer and Zinc Oxide Nanorods

ZnO nanorods (Nrods) with ~20ź50źnm lengths were synthesized using an aqueous solution of zinc acetate and glacial acetic acid. Bulk heterojunction solar cells were fabricated with the structure of indium tin oxide (ITO)/polyethylenedioxythiophene doped with polystyrene-sulfonic acid (PEDOT:PSS)/ZnO-Nrods + polymer/electron transport layer (ETL)/Al. Current density-voltage characterization of the resulting cells showed that, by adding an ETL and using polymers with a low band gap energy, the photoactive layer surface morphology and the device performance can be dramatically improved.
The auditory time resolution in bottlenose dolphins: Behavioral experiments versus auditory evoked potential methods

Non‐invasive auditory evoked potentials (AEP) methods are now widely used to study dolphins' hearing because some auditory characteristics can be obtained much faster compare to behavioral methods. The bottlenose dolphin auditory time resolution assessed using evoked potentials responses to a double click, amplitude modulated tone and periodic click is generally believed to be around 300 microseconds. This assessment is claimed to be in full agreement with behavioral measurements. The intention of this paper is to reevaluate behavioral results which are believed to support AEP methods in light of numerous behavioral results indicative of the bottlenose dolphin time resolution as high as 20‐30 microseconds. We found that as long as there are differences in waveforms, bottlenose dolphins are able to discriminate between very short (as short as a bottlenose dolphin sonar click) signals with identical energy spectra as well as between brief noise signals with random energy spectra. Auditory evoked responses d.