Miracle-dz/UniFAR-FTUs huggingface dataset:
Selenium accumulation in wheat (Triticum aestivum L) as affected by coapplication of either selenite or selenate with phosphorus

ABSTRACT The objectives of this pot experiment were to study the effects of P fertilizer applied with either selenate or selenite on (1) selenium (Se) uptake and translocation in wheat (Triticum aestivum L) and (2) Se fractions in calcareous soil. The experiment consisted of three factors: (1) amount of phosphorus (P) fertilizer (0, 1 and 2 g kg−1); (2) type of Se fertilizer (selenate and selenite); and (3) amount of Se fertilizer (0, 1 and 2 mg kg−1). The results indicate that P fertilizer significantly increased wheat biomass. Selenite significantly reduced wheat biomass, whereas the effects of selenate were not significant. In the selenite treatments, P fertilizer significantly reduced soil pH, causing declines in soluble soil Se and exchangeable Se and increases in iron (Fe) oxide-bound Se and organic matter-bound Se. The Se accumulation coefficient, the Se translocation coefficient and the Se content of wheat roots, stems, leaves and spikes all declined when P was applied to selenite-fertilized soil. In the selenate treatments, P application significantly reduced soil exchangeable Se, Fe oxide-bound Se and organic matter-bound Se. The wheat Se content, Se accumulation coefficient and Se translocation coefficient all increased when P fertilizer was applied to selenate-fertilized soil. In conclusion, P fertilizer activated Fe oxide-bound Se and organic matter-bound Se, resulting in an increase in wheat Se concentrations in selenate-fertilized, calcareous soil. In selenite-fertilized soil, however, P fertilizer reduced soil pH, thus promoting the transformation of soluble Se and exchangeable Se to either Fe oxide-bound Se or organic matter-bound Se. These changes inhibited Se absorption and translocation in selenite-fertilized wheat, thus reducing Se uptake by wheat.
Macroscopic transports in a rotational system with an electromagnetic field

In this work, we explore macroscopic transport phenomena associated with a rotational system in the presence of an external orthogonal electromagnetic field. Simply based on the lowest Landau level approximation, we derive nontrivial expressions for chiral density and various currents consistently by adopting small angular velocity expansion or Kubo formula. While the generation of anomalous electric current is due to the pseudo gauge field effect of the spin-rotation coupling, the chiral density and current can be simply explained with the help of Lorentz boosts. Finally, Lorentz covariant forms can be obtained by unifying our results and the magnetovorticity effect.
Non-asymptotic Performances of Robust Markov Decision Processes

In this paper, we study the non-asymptotic performance of optimal policy on robust value function with true transition dynamics. The optimal robust policy is solved from a generative model or oﬄine dataset without access to true transition dynamics. In particular, we consider three diﬀerent uncertainty sets including the L 1 , χ 2 and KL balls in both ( s, a ) -rectangular and s -rectangular assumptions. Our results show that when we assume ( s, a ) -rectangular on uncertainty sets, the sample complexity is about
A Design of Monitoring System for Low-Voltage Distribution Station

In order to improve the low-voltage distribution station’s stability and safety, a monitoring system for this station based on the STM32 microprocessor is designed. The system can collect data on the environmental and switch status of multiple power distribution stations in the partial area through NB-loT Technology, then report it to the cloud platform server in the form of CoAP protocol with XTEA encryption algorithm. The cloud platform server transmits the data to the monitoring platform, categorizes and stores the node data. After on-site installation and deployment, the self-designed plug device can reduce the complexity of the plug-in. The system possesses the characteristics of instantaneity, automatic networking and high security, which can meet the industrial requirements well.
Protecting Individual Interests across Clusters: Spectral Clustering with Guarantees

Studies related to fairness in machine learning have recently gained traction due to its ever-expanding role in high-stakes decision making. For example, it may be desirable to ensure that all clusters discovered by an algorithm have high gender diversity. Previously, these problems have been studied under a setting where sensitive attributes, with respect to which fairness conditions impose diversity across clusters, are assumed to be observable; hence, protected groups are readily available. Most often, this may not be true, and diversity or individual interests can manifest as an intrinsic or latent feature of a social network. For example, depending on latent sensitive attributes, individuals interact with each other and represent each other’s interests, resulting in a network, which we refer to as a representation graph. Motivated by this, we propose an individual fairness criterion for clustering a graph G that requires each cluster to contain an adequate number of members connected to the individual under a representation graph R . We devise a spectral clustering algorithm to ﬁnd fair clusters under a given representation graph. We further propose a variant of the stochastic block model and establish our algorithm’s weak consistency under this model. Finally, we present experimental results to corroborate our theoretical ﬁndings.
COMPARISON OF METHODS FOR EVALUATING UPPER LIMB STRENGTH BY HAND-HELD DYNAMOMETRY

ABSTRACT Introduction The upper limbs are segments of the human body responsible for primary activities of daily life, and the muscles are essential structures for performing these activities. There have been few studies on intra- and inter-examiner reliability of the hand-held dynamometer (HHD) in healthy subjects, and none have been published that compare dynamometric evaluation methods in the main muscles in this segment. Objective Evaluate intra-examiner and inter-examiner assessment reliability of the hand-held dynamometry of upper limb muscles in healthy individuals, as well as comparing the assessment reliability between fixed and non-fixed methods. Methods Healthy subjects aged over 18 years were recruited for the study. The isometric contraction for ten muscle groups of the dominant upper limb was tested. For the fixed method, we used a system of suction cups, connected to the HHD by an inelastic belt. For the non-fixed method, the examiner supported the device by hand. The isometric contraction was sustained for three seconds. Each measurement was repeated three times, considering the highest value obtained. The reliability was calculated using the intraclass correlation coefficient (ICC). The dispersion between measurements was expressed by a Bland-Altman plot. Results The sample consisted of 25 volunteers, all right-handed. The intra-examiner ICC was 0.89-0.99 for the non-fixed method, and 0.43 to 0.85 for the fixed method. Inter-examiner reliability showed equivalent behavior. This study showed that evaluation of upper limb muscle strength using an isometric dynamometer has excellent intra-examiner and inter-examiner reliability. The supine position was chosen due to the need to propose a feasible protocol for clinical practice that could be replicated for the majority of publics and in different environments. The non-fixed method showed better reliability overall, demonstrating the feasibility of this tool without the need for adaptations, additional devices, or increased operating costs for this evaluation. Conclusion Comparison between the fixed and non-fixed HHD methods demonstrated superiority of the non-fixed method in terms of reliability. Level of evidence II; Investigation of a diagnostic exam - Development of diagnostic criteria with consecutive patients.
Crack path comparisons of a mixed phase‐field fracture model and experiments in punctured EPDM strips

Working on quasi‐static phase‐field fracture modeling in nearly incompressible solids for crack propagation is a challenging task. To avoid arising locking effects therein, a mixed form for the solid displacement equation is developed, resulting in two unknowns: a displacement field and a hydro‐static pressure variable. In order to fulfil an inf‐sup condition, stable Taylor‐Hood elements are employed for the displacement‐pressure system. The irreversibility condition of the crack evolution is handled by help of a primal‐dual active set method. To get both a sharper crack and reasonable computational costs, adaptive meshes are used based on a predictor‐corrector scheme. The crack paths from the numerical simulations are compared on the experimentally observed crack paths in carbon black filled ethylene propylene diene monomer (EPDM) rubber strips. The punctured EPDM strips with a hole and a given notch at different heights are stretched till total failure.
Zero-Shot Personalized Speech Enhancement Through Speaker-Informed Model Selection

This paper presents a novel zero-shot learning approach towards personalized speech enhancement through the use of a sparsely active ensemble model. Optimizing speech denoising systems towards a particular test-time speaker can improve performance and reduce run-time complexity. However, test-time model adaptation may be challenging if collecting data from the test-time speaker is not possible. To this end, we propose using an ensemble model wherein each specialist module denoises noisy utterances from a distinct partition of training set speakers. The gating module inexpensively estimates test-time speaker characteristics in the form of an embedding vector and selects the most appropriate specialist module for denoising the test signal. Grouping the training set speakers into non-overlapping semantically similar groups is non-trivial and ill-defined. To do this, we first train a Siamese network using noisy speech pairs to maximize or minimize the similarity of its output vectors depending on whether the utterances derive from the same speaker or not. Next, we perform k-means clustering on the latent space formed by the averaged embedding vectors per training set speaker. In this way, we designate speaker groups and train specialist modules optimized around partitions of the complete training set. Our experiments show that ensemble models made up of low-capacity specialists can outperform high-capacity generalist models with greater efficiency and improved adaptation towards unseen test-time speakers.
Comparison of Single-Event Transients in an Epitaxial Silicon Diode Resulting From Heavy-Ion-, Focused X-Ray-, and Pulsed Laser-Induced Charge Generation

Heavy-ion, focused X-ray, and pulsed laser single-event transient (SET) experiments are performed on a silicon epitaxial diode. Collected charge, transient rise times, and transient fall times are calculated and compared between different sources. The transient shape characteristics depend on the source (ion, X-ray, or laser), even when similar amounts of charge are generated. The observed differences are examined and explained in terms of basic charge collection mechanisms.
Least squares Monte Carlo methods in stochastic Volterra rough volatility models

In stochastic Volterra rough volatility models, the volatility follows a truncated Brownian semi-stationary process with stochastic vol-of-vol. Recently, efficient VIX pricing Monte Carlo methods have been proposed for the case where the vol-of-vol is Markovian and independent of the volatility. Following recent empirical data, we discuss the VIX option pricing problem for a generalized framework of these models, where the vol-of-vol may depend on the volatility and/or not be Markovian. In such a setting, the aforementioned Monte Carlo methods are not valid. Moreover, the classical least squares Monte Carlo faces exponentially increasing complexity with the number of grid time steps, whilst the nested Monte Carlo method requires a prohibitive number of simulations. By exploring the infinite dimensional Markovian representation of these models, we device a scalable least squares Monte Carlo for VIX option pricing. We apply our method firstly under the independence assumption for benchmarks, and then to the generalized framework. We also discuss the rough vol-of-vol setting, where Markovianity of the vol-of-vol is not present. We present simulations and benchmarks to establish the efficiency of our method.
Slash or burn: Power line and vegetation classification for wildfire prevention

Electric utilities are struggling to manage increasing wildfire risk in a hotter and drier climate. Utility transmission and distribution lines regularly ignite destructive fires when they make contact with surrounding vegetation. Trimming vegetation to maintain the separation from utility assets is as critical to safety as it is difficult. Each utility has tens of thousands of linear miles to manage, poor knowledge of where those assets are located, and no way to prioritize trimming. Feature-enhanced convolutional neural networks (CNNs) have proven effective in this problem space. Histograms of oriented gradients (HOG) and Hough transforms are used to increase the salience of the linear structures like power lines and poles. Data is frequently taken from drone or satellite footage, but Google Street View offers an even more scalable and lower cost solution. This paper uses $1,320$ images scraped from Street View, transfer learning on popular CNNs, and feature engineering to place images in one of three classes: (1) no utility systems, (2) utility systems with no overgrown vegetation, or (3) utility systems with overgrown vegetation. The CNN output thus yields a prioritized vegetation management system and creates a geotagged map of utility assets as a byproduct. Test set accuracy with reached $80.15\%$ using VGG11 with a trained first layer and classifier, and a model ensemble correctly classified $88.88\%$ of images with risky vegetation overgrowth.
Comparison of Transforming Growth Factor 1 (TGF-β1) Expression in Various Lysate Platelet-Rich Fibrin (L-PRF) Concentrations on Human Dental Pulp Stem Cell Differentiation

Objective: To compare Transforming growth factor-β1 (TGF-β1) expression in various L-PRF concentrations on the hDPSC differentiation process. Material and Methods: hDPSC cell cultures were subjected to serum starvation by reducing FBS levels in the hDPSC culture media. Lysate PRF was obtained from the PRF gel, which was then incubated at 4°C for 24 h. The supernatant was dried, transferred to a 2-ml Eppendorf tube, and stored at −20°C. The evaluation of TGF-β1 expression in 1%, 5%, 10%, and 25% L-PRF samples and 10% FBS (control) during the process of hDPSC differentiation was quantified using an ELISA reader on day 7. The expression of TGF-β1 was subjected to a one-way ANOVA test, followed by Bonferroni’s post hoc test with significant values (p<0.05). Results: Significant differences were noted in TGF-β1 expression between 1%, 5%, 10%, and 25% L-PRF and the control group (10% FBS). The highest TGF-β1 expression occurred with 25% L-PRF (0.645 ± 0.048), followed by 10% L-PRF (0.461 ± 0.035), 10% FBS (0.374 ± 0.013), 5% L-PRF (0.275 ± 0.045), and the lowest expression was with 1% LPRF (0.160 ± 0.045). Conclusion: The best result of TGF-B1 expression in hDPSC differentiation was in the 25% L-PRF group.
Deploying Micro-PMUs With Channel Limit in Reconfigurable Distribution Systems

In the presence of distributed energy resources (DERs), distribution networks (DNs) operate in multiple configurations. Maintaining complete observability is essential for the monitoring of the network during operation in any configuration. This article proposes a methodology for the optimal deployment of micro-phasor measurement units (<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>PMUs) in a DN operating in multiple configurations. A composite objective function has been formulated to minimize the number of <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>PMUs while maximizing measurement redundancy. The optimal solution has been obtained considering the <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>PMU with or without channel limits. The constraints have been formed simultaneously to cover all operational configuration of the network while eliminating repetition. The proposed methodology has been illustrated on an eight-node reconfigurable network. It has also been verified on several reconfigurable DNs, having 33, 34, 69, 118, 123, and 167 nodes. A comparison between the proposed methodology and recently published approaches has been made. The constraints for zero-injection nodes with various network configuration has also been considered to find optimal <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>PMU deployment. The obtained optimal deployment of <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>PMUs with or without channel limit satisfies the criteria of complete topological observability, topological in-dependency, and maximizes the sum of topology measurement redundancy.
MCTG: Multi-frequency continuous-share trading algorithm with GARCH based on deep reinforcement learning

Making profits in stock market is a challenging task for both professional institutional investors and individual traders. With the development combination of quantitative trading and reinforcement learning, more trading algorithms have achieved significant gains beyond the benchmark model Buy&Hold (B&H). There is a certain gap between these algorithms and the real trading decision making scenarios. On the one hand, they only consider trading signals while ignoring the number of transactions. On the other hand, the information level considered by these algorithms is not rich enough, which limits the performance of these algorithms. Thus, we propose an algorithm called the Multi-frequency Continuous-share Trading algorithm with GARCH (MCTG) to solve the problems above, which consists of parallel network layers and deep reinforcement learning. The former is composed of three parallel network layers, respectively dealing with different frequencies (five minute, one day, one week) data, and day level considers the volatilities of stocks. The latter with a continuous action space of the reinforcement learning algorithm is used to solve the problem of trading stock shares. Experiments in different industries of Chinese stock market show our method achieves more extra profit comparing with basic DRL methods and bench model.
Domestic Activities Clustering From Audio Recordings Using Convolutional Capsule Autoencoder Network

Recent efforts have been made on domestic activities classification from audio recordings, especially the works submitted to the challenge of DCASE (Detection and Classification of Acoustic Scenes and Events) since 2018. In contrast, few studies were done on domestic activities clustering, which is a newly emerging problem. Domestic activities clustering from audio recordings aims at merging audio clips which belong to the same class of domestic activity into a single cluster. Domestic activities clustering is an effective way for unsupervised estimation of daily activities performed in home environment. In this study, we propose a method for domestic activities clustering using a convolutional capsule autoencoder network (CCAN). In the method, the deep embeddings are learned by the autoencoder in the CCAN, while the deep embeddings which belong to the same class of domestic activities are merged into a single cluster by a clustering layer in the CCAN. Evaluated on a public dataset adopted in DCASE- 2018 Task 5, the results show that the proposed method outperforms state-of-the-art methods in terms of the metrics of clustering accuracy and normalized mutual information.
News Kaleidoscope: Visual Investigation of Coverage Diversity in News Event Reporting

When a newsworthy event occurs, media articles that report on the event can vary widely-a concept known as coverage diversity. To help investigate coverage diversity in event reporting, we de-velop a visual analytics system called News Kaleidoscope. News Kaleidoscope combines several backend language processing techniques with a coordinated visualization interface. Notably, News Kaleidoscope is tailored for visualization non-experts, and adopts an analytic workflow based around subselection analysis, whereby second-level features of articles are extracted to provide a more detailed and nuanced analysis of coverage diversity. To robustly evaluate News Kaleidoscope, we conduct a trio of user studies. (1) A study with news experts assesses the insights promoted for our targeted journalism-savvy users. (2) A follow-up study with news novices assesses the overall system and the specific insights pro-moted for journalism-agnostic users. (3) Based on identified system limitations in these two studies, we refine News Kaleidoscope's design and conduct a third study to validate these improvements. Results indicate that, for both news novice and experts, News Kalei-doscope supports an effective, task-driven workflow for analyzing the diversity of news coverage about events, though journalism expertise has a significant influence on the user's insights and take-aways. Our insights developing and evaluating News Kaleidoscope can aid future tools that combine visualization with natural language processing to analyze coverage diversity in news event reporting.
A Survey of Knowledge Level about Pediatric oral/dental Health among Pediatricians

Pediatricians play a central role in children’s health care and are the first line of care for oral/dentalconditions. The level of pediatrician knowledge about oral/dental health is not accurately determined.In this cross-sectional study, a previously adapted questionnaire was used to evaluate the level of oral/dental health knowledge among pediatricians affiliated with medical universities in Tehran, Iran. The datawere summarized through descriptive statistics and central index tests. In this study, 263 pediatricians(49.4% male) completed the questionnaire. The mean age was 43.9 years. Only 17 individuals (6.5%) hadcompleted a course of oral/dental health during their residency. The mean score was 5.9 ± 1.7. Questionnairescores were similar between male and female pediatricians. Time from graduation and completion of anoral/dental health course was not associated with better performance according to the questionnaire. Thelevel of knowledge about oral/dental health among pediatricians is inadequate. Most pediatricians havenot completed a training course on this matter. These findings highlight the need for integrated trainingprograms to improve pediatrician knowledge about oral/dental health.