Miracle-dz/UniFAR-FTUs huggingface dataset:
Bioaccumulation of Heavy Metals by Wild Plants Growing on Copper Mine Spoils in China

Abstract In the present study, the wild vegetation associated with copper (Cu) mine spoils was investigated at five sites in eastern China. A collection of 49 plant species (members of 48 genera and 30 families) was studied. All the aboveground parts (shoots or leaves) of the samples were analyzed for total concentrations of Cu, zinc (Zn), lead (Pb), and cadmium (Cd). The average Cu concentrations in plants at different deserted mining sites showed an increasing trend with the age of mine spoils. Commelina communis, Polygonum macrathum, Elsholtzia haichowensis, and Arthraxon prionodes collected at different sites had significantly higher concentrations of Cu than other plant species, with the highest Cu concentrations of 361, 286, 186, and 181 mg kg−1, respectively. These plant species also had significantly higher Zn, Pb, and Cd concentrations than other species studied. Results suggest that these plants could be good candidates for use in the revegetation and phytoremediation of copper mine spoils.
Power-Efficiency Evolution of Capacitive Sensor Interfaces

Recent years have witnessed an improvement in the energy efficiency of capacitive sensor interfaces by more than three orders of magnitude. This article reviews the architectural and circuit innovations that have contributed to this progress. The fundamental limit on the energy consumption of capacitive sensor interfaces is discussed, as well as the widely used figure-of-merit (FoM). Interfaces based on period modulation feature simple circuitry, but their power efficiency at higher resolution deteriorates. Those employing <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> modulation achieve high resolution with improved efficiency but require operational transconductance amplifiers that do not easily scale with process and supply voltage. Interfaces using successive approximation techniques feature mostly digital circuitry achieving good power efficiency at medium resolution. To achieve higher resolution, they can also be employed as the front-end in a hybrid architecture, where a back-end based on <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> modulation or a voltage-controlled oscillator (VCO) performs a fine measurement on the front-end’s residue, resulting in high resolution and excellent energy efficiency simultaneously.
Comparative Assessment of Process Mining for Supporting IoT Predictive Security

The growth of the Internet-of-Things (IoT) has been characterized by the large-scale deployment of sensors and connected objects. These ones are integrated with other Internet resources in order to elaborate more complex systems and applications. Security management is a major challenge for these systems due to their complexity, their heterogeneity and the limited resources of their devices. In this article we evaluate the exploitability and performance of a process mining approach for detecting misbehaviors in such systems. We describe the considered architecture and detail its operation, from the generation of behavioral models to the detection of potential attacks. We formalize several alternative commonly-used detection methods, including elliptic envelope, support-vector machine, local outlier factor, and isolation forest techniques. After presenting a proof-of-concept prototype, we quantify comparatively the benefits and limits of our process mining solution combined with data pre-processing, through extensive experiments based on different industrial datasets.
A contribution to the determination of “reactive” and “total” mercury in sea water

Abstract A method for the determination of “reactive” and “total” mercury in sea water by Atomic Fluorescence Spectroscopy with a Hg‐radiofrequency electrodeless discharge lamp to measure Hg levels below 0.05 ng is described. UV photo‐oxidation to degrade organic mercury compounds is used. Concentrations of reactive and total mercury respectively in the range of 0.4–9 and 1–30 ng [ are measured.
Proportional–Integral Synchronization for Nonidentical Wireless Packet-Coupled Oscillators With Delays

Precise timing among wireless sensor nodes is a key enabling technology for time-sensitive industrial wireless sensor networks (WSNs). However, the accuracy of timing is degraded by manufacturing tolerance, aging of crystal oscillators, and communication delays. This article develops a framework of packet-coupled oscillators (PkCOs) to characterize the dynamics of communication and time synchronization of clocks in WSNs. The nonidentical clock is derived to describe the embedded clock's behavior accurately. A proportional–integral (PI) packet coupling scheme is proposed for synchronizing networked embedded clocks, while, scheduling wireless Sync packets to different slots for transmission. It also possesses the feature of automatically eliminating the effects of unknown processing delay, which further improves the synchronization performance. The rigorous theoretical analysis of PI-based PkCOs is presented via studying a closed-loop time synchronization system. The performance of PI-based PkCOs is evaluated on a hardware testbed of IEEE 802.15. 4 WSN. The experimental results show that the precision of the proportional-integral PkCOs protocol is as high as 60 $\mu s$ (i.e., 2 ticks) for 32.768 kHz crystal oscillator-based clocks.
The X-Hall Sensor: Toward Integrated Broadband Current Sensing

This article presents the X-Hall sensor, a viable sensing architecture for implementing a silicon-integrated, broadband, current/magnetic sensor. The X-Hall sensor overcomes the bandwidth limit of the state-of-the-art Hall sensors by replacing the spinning-current technique with dc-biased-based passive offset compensation. In this way, the X-Hall architecture removes the methodological bandwidth limit due to the spinning-current technique and allows for exploiting the Hall probe up to its practical limit, which is set by the parasitic capacitive effects. Moreover, the X-Hall architecture allows pushing the practical bandwidth limit at higher frequencies due to both the removal of the switches inherent in the spinning-current approach and a specially designed analog front end. To this end, a differential-difference current-feedback amplifier (DDCFA) is proposed as an analog front end in the X-Hall sensor. A prototype of the proposed X-Hall architecture is implemented in bipolar-CMOS–DMOS (BCD) 0.16- $\mu \text{m}$ silicon technology to experimentally assess the performance of the X-Hall architecture. The passive offset compensation implemented into the X-Hall architecture is frequency-independent and preserves an adequate offset reduction performance, though less efficient than the spinning-current technique operated at low frequency. Experimental dynamic tests on the prototype identify the presence of an additive parasitic dynamic perturbation due to the package that prevents from fully exploiting the X-Hall prototype up to its designed bandwidth limit. However, the implementation of a postdeemphasis digital filter allows us to mitigate for the dynamic perturbation and experimentally achieve a sensor bandwidth of 4 MHz, which is the broadest bandwidth ever demonstrated by a purely Hall-effect based sensor.
Attention-deficit/hyperactivity disorder as a risk factor for fractures

©Copyright 2020 by Anatolian Journal of Family Medicine Available online at www.anatoljfm. org INTRODUCTION Attention deficit hyperactivity disorder (ADHD) is defined as attention deficit, hyperactivity, and behavioral and impulse control problems that are inappropriate for age and developmental level. According to the symptom types, ADHD has three sub-presentations: "predominantly inattentive presentation", "predominantly hyperactive/impulsive presentation" and "combined presentation" with the characteristics of the two presentations. [1] ADHD is 2-5 times more common in boys than girls and in the clinical manifestation, hyperactivity and impulsivity are prominent in boys and attention deficit symptoms are prominent in girls. [1, 2]
Learning-Based Resource Partitioning in Heterogeneous Networks With Multiple Network Operators

In heterogeneous network, it is important to mitigate cross-tier interference. Resource partitioning is the one of solution to reduce interference. However, most of studies on partitioning assumed that there was only one network operator to cooperate with. In this letter, we study the network selection problem of small access points in heterogeneous networks, which are provided by multiple network operators. We model the multiple network operators scenario as a congestion game. To solve the equilibrium point of suggested game, we analyze some features of proposed model such as potential game property, smoothed best response dynamics and logit equilibrium. Then, we propose a reinforcement learning algorithm that can reach logit equilibrium in distributed way. Moreover, we also suggest the adjustment of learning parameters to enhance adaptability. By means of simulations, it is shown that proposed algorithm has near-optimal performance in view of throughput, fairness and adaptability.
The effect of a nighttime zoo event on spider monkey (Ateles geoffroyi) behavior

Abstract The relationship between zoo animals, particularly nonhuman primates, and visitors is complex and varies by species. Adding complexity to this relationship is the trend for zoos to host events outside of normal operating hours. Here, we explored whether a late-night haunted-house style event influenced the behavior of spider monkeys. We conducted behavioral observations both on event nights and nights without the event. The spider monkeys were active and outside more frequently on event nights compared to the control nights indicating that their typical nighttime behavior was altered. However, it is difficult to definitively conclude whether the behavioral changes were a result of the event being aversive or enriching. Our findings suggest that zoos should conduct behavioral observations of and collect physiological data from their animals, especially if they are sensitive to environmental changes, when implementing new events, including those occurring outside of normal operating hours to ensure high levels of animal welfare.
Effects of varieties and boron on growth and yield of summer tomato

A study was conducted to investigate the effects of varieties and boron (B) levels on growth and yield of summer tomato (Lycopersicon esculentum Mill.) at the Germplasm Centre in the Department of Horticulture, Patuakhali Science and Technology University, Patuakhali during the period from May, 2018 to September, 2018. Fifteen treatments were comprising (i) three summer tomato varieties (BARI hybrid tomato 4, 8 and 10) and (ii) five levels of boron as boric acid (0, 1, 2, 3 and 5 kg B ha-1) in all combinations. Randomized complete block design with three replications was used in the earthen pot (0.79 ft3) experimentation. The effects of varieties and boron levels showed significant variations (p <0.05) on growth and yield of summer tomato at different days after transplanting. Among the varieties at final count plant-1, delayed flowering (32.6 days), the highest plant height (93.8 cm), number of leaves (99.93), no. of branches (26.27), no. of flower clusters (18.53), no. of flowers (82.73), no. of fruits (51.87), longest fruit length (41.87 mm) and maximum fruit width (48.0 mm), weight of individual fruit (55.71 g) and total weight of fruits (2892.88 g) were observed in BARI hybrid tomato 8. In contrast, the lowest plant height (87.3 cm), no. of leaves (86.47), no. of branches (24.06), no. of flower clusters (15.87), no. of flowers (66.07), no. of fruits (37.33), weight of individual fruit (43.60 g) and total weight of fruits (1630.57 g) were found in BARI hybrid tomato 4; and early flowering (31.93 days), shortest fruit length (33.07 mm) and maximum fruit width (34.60 mm) were noticed in BARI hybrid tomato 10. Among the boron levels at final count plant-1, early flowering (29.67 days), the maximum no. of flower clusters (18.44), no. of flowers (89.11), no. of fruits (46.22) and total weight of fruits (2364.29 g) were recorded in 2 kg B ha-1 treatment; the maximum plant height (96.50 cm), no. of leaves (102.89), no. of branches (28.11), longest fruit length (42.89 mm) and maximum fruit width (46.78 mm) and weight of individual fruit (51.74 g) were obtained in 3 kg B ha-1 treatment. Conversely, delayed flowering (34.67 days), minimum plant height (83.50 cm), no. of leaves (87.56), no. of branches (21.78), no. of flower clusters (15.89), no. of flowers (63.56), no. of fruits (40.33), shortest fruit length (31.78 mm) and minimum fruit width (34.67 mm), weight of individual fruit (47.47 g) and total weight of fruits (1936.00 g) were recorded in control (0 kg B ha-1) treatment. Our results suggest that the inclusion of B (2–3 kg ha-1) with the current fertilization practice will enhance the growth and yield of summer tomato grown at AEZ (agro-ecological zone) 13 while BARI hybrid tomato 8 could be recommended as one of the promising varieties.
On Compensating Actuator Delays in Consensus Control Under Undirected Graphs

This article studies delay compensation in consensus control under undirected graphs via output feedback. The dynamics of each node involved in the network is subject to an unknown bounded actuator delay. A transport partial differential equation is employed to model the dynamics of the delay state, and a filter with adaptive control gains is proposed using the available relative output information of neighboring nodes. Based on the state of the filter, the estimation delay state, and the delay estimation, we propose two distributed adaptive output feedback consensus protocols. The first one employs a delay-adaptive predictor and guarantees local consensus, whereas the second one, replacing the delay estimation by a constant, is proven to be a global consensus controller provided that the delay mismatch is in a small compact set. Simulation results performed on a group of neutrally stable systems are presented to validate the effectiveness of the proposed scheme.
Application of a Zircaloy/Steam Oxidation Model to a CFD Code and its Validation against a CANDU Fuel Channel Experiment: CS28-2

Oxidation of a Zircaloy cladding exposed to high-temperature steam is an important phenomenon in the safety analysis of CANDU reactors during a postulated loss-of-coolant accident (LOCA), since a Zircaloy/steam reaction is highly exothermic and results in hydrogen production. As part of a computational fluid dynamics (CFD) simulation of the CS28-2 high-temperature experiment for this accident analysis, two Zircaloy/steam reaction models based on a parabolic rate law are implemented in a commercial CFD code (CFX-10) through a user FORTRAN. It is confirmed that the present oxidation models for the CFX-10 reproduce the results of each empirical correlation in the verification tests well. Then the CFX-10 predictions of a temperature rise and hydrogen production due to Zircaloy/steam oxidation are compared with the results of the CS28-2 experiment. From these validation processes, it is shown that the Urbanic-Heidrick model, which is widely used in CANDU fuel channel codes, is also applicable to a CFX-10 simulation of Zircaloy/steam oxidation in a CANDU fuel channel.
Study on the Large Batch Size Training of Neural Networks Based on the Second Order Gradient

Large batch size training in deep neural networks (DNNs) possesses a well-known 'generalization gap' that remarkably induces generalization performance degradation. However, it remains unclear how varying batch size affects the structure of a NN. Here, we combine theory with experiments to explore the evolution of the basic structural properties, including gradient, parameter update step length, and loss update step length of NNs under varying batch sizes. We provide new guidance to improve generalization, which is further verified by two designed methods involving discarding small-loss samples and scheduling batch size. A curvature-based learning rate (CBLR) algorithm is proposed to better fit the curvature variation, a sensitive factor affecting large batch size training, across layers in a NN. As an approximation of CBLR, the median-curvature LR (MCLR) algorithm is found to gain comparable performance to Layer-wise Adaptive Rate Scaling (LARS) algorithm. Our theoretical results and algorithm offer geometry-based explanations to the existing studies. Furthermore, we demonstrate that the layer wise LR algorithms, for example LARS, can be regarded as special instances of CBLR. Finally, we deduce a theoretical geometric picture of large batch size training, and show that all the network parameters tend to center on their related minima.
Nearest neighbour considerations in Stillinger-Weber type potentials for diamond

Results of a preliminary investigation into the effect of varying the interaction cutoff on the bulk properties of diamond using a Stillinger-Weber (SW) type potential for C (Diamond) are presented. The interaction cutoff is varied over a range that includes and excludes the second-nearest neighbours. Whilst the original SW potential for silicon only included first-nearest neighbours inside the interaction cut-off, subsequent parameterizations for carbon (diamond) have also included second-nearest neighbours. Elastic and vibration properties of diamond were calculated over a range of cutoff distances used and the results show that certain lattice properties exhibit an approximately linear dependence on the interaction cut-off.
Size and shape controlled LiMnPO4 nanocrystals by a supercritical ethanol process and their electrochemical properties

In this paper, we report the preparation of systematically size and shape controlled LiMnPO4 nanocrystals under supercritical fluid conditions. The effect of different reaction conditions such as the reaction time, temperature, surfactant and precursor concentration on the size and shape of the LiMnPO4 nanocrystals was studied in detail. It was noticed that shorter reaction time and lower reaction temperature facilitated the formation of crystalline LiMnPO4 nanocrystals with size ∼10 nm. The nanocrystals ranging from 7 to 24 nm were obtained by controlling different reaction conditions. The formation mechanism for the LiMnPO4 nanocrystals is proposed based on the obtained results. The effect of nanosize on the electrochemical properties of LiMnPO4 nanocrystals was studied. Improved electrochemical performance was observed for ∼20 nm sized LiMnPO4 after conductive carbon coating. This study indicates the importance of LiMnPO4 nanocrystals below 50 nm size in improving the electrochemical performance of LiMnPO4 cathodes.
Large‐Area Ordered Quantum‐Dot Monolayers via Phase Separation During Spin‐Casting

We investigate a new method for forming large‐area (> cm2) ordered monolayers of colloidal nanocrystal quantum dots (QDs). The QD thin films are formed in a single step by spin‐casting a mixed solution of aromatic organic materials and aliphatically capped QDs. The two different materials phase separate during solvent drying, and for a predefined set of conditions the QDs can assemble into hexagonally close‐packed crystalline domains. We demonstrate the robustness and flexibility of this phase‐separation process, as well as how the properties of the resulting films can be controlled in a precise and repeatable manner. Solution concentration, solvent ratio, QD size distribution, and QD aspect ratio affect the morphology of the cast thin‐film structure. Controlling all of these factors allows the creation of colloidal‐crystal domains that are square micrometers in size, containing tens of thousands of individual nanocrystals per grain. Such fabrication of large‐area, engineered layers of nanoscale materials brings the beneficial properties of inorganic QDs into the realm of nanotechnology. For example, this technique has already enabled significant improvements in the performance of QD light‐emitting devices.
Grounding Artificial Intelligence in the Origins of Human Behavior

Recent advances in Artificial Intelligence (AI) have revived the quest for agents able to acquire an open-ended repertoire of skills. However, although this ability is fundamentally related to the characteristics of human intelligence, research in this field rarely considers the processes that may have guided the emergence of complex cognitive capacities during the evolution of the species. Research in Human Behavioral Ecology (HBE) seeks to understand how the behaviors characterizing human nature can be conceived as adaptive responses to major changes in the structure of our ecological niche. In this paper, we propose a framework highlighting the role of environmental complexity in open-ended skill acquisition, grounded in major hypotheses from HBE and recent contributions in Reinforcement learning (RL). We use this framework to highlight fundamental links between the two disciplines, as well as to identify feedback loops that bootstrap ecological complexity and create promising research directions for AI researchers.
Robot skills learning with Riemannian manifolds: Leveraging geometry-awareness in robot learning, optimization and control

Humans exhibit outstanding learning and adaptation capabilities while performing various types of manipulation tasks. When learning new skills, humans are able to extract important information by observing examples of a task and efficiently refine a priori knowledge to master new tasks. However, learning does not happen in isolation, and the planning and control abilities of humans also play a crucial role to properly execute manipulation tasks of varying complexity. In the last years, a lot of attention has been devoted to the problem of providing robots with close-to-human-level abilities. In this context, this thesis proposes to enhance robot learning and control capabilities by introducing domain knowledge into the corresponding models. Our approaches, built on Riemannian manifolds, exploit the geometry of non-Euclidean spaces, which are ubiquitous in robotics to represent rigid-body orientations, inertia matrices, manipulability ellipsoids, or controller gain matrices. We initially consider the problem of transferring skills to a robot and propose a probabilistic framework to learn symmetric positive definite (SPD) matrices from demonstrations. Given a learned reference trajectory in the form of SPD matrices, the goal of the robot is to reproduce the task by tracking this sequence using appropriate controllers. This challenge is tackled in the second part of this thesis. We focus on a specific application and propose a complete geometry-aware framework to learn, control and transfer posture-dependent task requirements from humans to robots via manipulability profiles. The third part of this thesis focuses on refining the skills learned by the robot and on adapting them to new situations by introducing a geometry-aware Bayesian optimization framework. Overall, this thesis proves that geometry-awareness is crucial for successfully learning, controlling and refining non-Euclidean parameters in addition to providing a proper mathematical treatment of the different problems. Finally, the last part of this work shows that domain knowledge can be introduced not only through geometry-awareness, but also through structure-awareness, which may be considered either as prior models or as intrinsically present in the data. With its final part, this thesis emphasizes that domain knowledge, that can be introduced into robot learning algorithms through various means, may be significant for providing robots with close-to-humans abilities.