Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling the isotopic evolution of snowpack and snowmelt: Testing a spatially distributed parsimonious approach

Use of stable water isotopes has become increasingly popular in quantifying water flow paths and travel times in hydrological systems using tracer‐aided modeling. In snow‐influenced catchments, snowmelt produces a traceable isotopic signal, which differs from original snowfall isotopic composition because of isotopic fractionation in the snowpack. These fractionation processes in snow are relatively well understood, but representing their spatiotemporal variability in tracer‐aided studies remains a challenge. We present a novel, parsimonious modeling method to account for the snowpack isotope fractionation and estimate isotope ratios in snowmelt water in a fully spatially distributed manner. Our model introduces two calibration parameters that alone account for the isotopic fractionation caused by sublimation from interception and ground snow storage, and snowmelt fractionation progressively enriching the snowmelt runoff. The isotope routines are linked to a generic process‐based snow interception‐accumulation‐melt model facilitating simulation of spatially distributed snowmelt runoff. We use a synthetic modeling experiment to demonstrate the functionality of the model algorithms in different landscape locations and under different canopy characteristics. We also provide a proof‐of‐concept model test and successfully reproduce isotopic ratios in snowmelt runoff sampled with snowmelt lysimeters in two long‐term experimental catchment with contrasting winter conditions. To our knowledge, the method is the first such tool to allow estimation of the spatially distributed nature of isotopic fractionation in snowpacks and the resulting isotope ratios in snowmelt runoff. The method can thus provide a useful tool for tracer‐aided modeling to better understand the integrated nature of flow, mixing, and transport processes in snow‐influenced catchments.
The Wits Intelligent Teaching System: Detecting student engagement during lectures using convolutional neural networks

To perform contingent teaching and be responsive to students' needs during class, lecturers must be able to quickly assess the state of their audience. While effective teachers are able to gauge easily the affective state of the students, as class sizes grow this becomes increasingly difficult and less precise. The Wits Intelligent Teaching System (WITS) aims to assist lecturers with real-time feedback regarding student affect. The focus is primarily on recognising engagement or lack thereof. Student engagement is labelled based on behaviour and postures that are common to classroom settings. These proxies are then used in an observational checklist to construct a dataset of engagement upon which a CNN based on AlexNet is successfully trained and which significantly outperforms a Support Vector Machine approach. The deep learning approach provides satisfactory results on a challenging, real-world dataset with significant occlusion, lighting and resolution constraints.
A Chopping Switched-Capacitor RF Receiver With Integrated Blocker Detection

A 0.1–0.6 GHz chopping switched-capacitor (SC) RF receiver (RX) with an integrated blocker detector features a tunable center frequency, a programmable filter order, and a high out-of-band (OB) linearity. RF impedance matching, high-order OB interferer filtering, and flicker-noise chopping are achieved with passive SC circuits only. The 34–80 mW 65-nm RX achieves 35-dB gain, +31 dBm OB-third-order input intercept point, +15 dBm B1dB, and 4.6–9 dB NF. The 0.2-mW integrated blocker detector detects large OB blockers with only 1-<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> response time. The filter order can be adapted to blocker power with the blocker detector.
The Effects of Exercise for the Prevention of Overuse Anterior Knee Pain

Background: Anterior knee pain (AKP) is the most common activity-related injury of the knee. The authors investigated the effect of an exercise intervention on the incidence of AKP in UK army recruits undergoing a 14-week physically arduous training program. Hypothesis: Modifying military training to include targeted preventative exercises may reduce the incidence of AKP in a young recruit population. Study Design: Randomized controlled trial; Level of evidence, 1. Methods: A single-blind cluster randomized controlled trial was performed in 39 male and 11 female training groups (median age: 19.7 years; interquartile range, 17-25) undergoing phase 1 of army recruit training. Each group was randomly assigned to either an intervention (n = 759) or control (n = 743) protocol. The intervention consisted of 4 strengthening and 4 stretching exercises completed during supervised physical training lessons (7 per week). The control group followed the existing training syllabus warm-up exercises. The primary outcome was a diagnosis of AKP during the 14-week training program. Results: Forty-six participants (3.1%; 95% confidence interval [CI], 2.3-4.1) were diagnosed with AKP. There were 36 (4.8%; 95%CI, 3.5-6.7) new cases of AKP in the control group and 10 (1.3%; 0.7-2.4) in the intervention group. There was a 75% reduction in AKP risk in the intervention group (unadjusted hazard ratio = 0.25; 95% CI, 0.13-0.52; P < .001). Three participants (0.4%) from the intervention group were discharged from the military for medical reasons compared to 25 (3.4%) in the control group. Conclusion: A simple set of lower limb stretching and strengthening exercises resulted in a substantial and safe reduction in the incidence of AKP in a young military population undertaking a physical conditioning program. Such exercises could also be beneficial for preventing this common injury among nonmilitary participants in recreational physical activity.
Depth estimation of objects with known geometric model for IBVS using an eye-in-hand camera

One of the most crucial and challenging issues in Image-Based Visual Servoing (IBVS) is the derivation of accurate depth information of feature points, which is indispensable in the calculation of the image Jacobian. For an object without a known geometric model, it is difficult to retrieve the depth information of feature points using a single camera. However, in many application scenarios such as industrial manufacturing processes, geometric models of artificial objects such as polyhedrons are known in advance. As a result, this paper aims at developing an estimation algorithm that can be used to retrieve the depth information of an object with a known geometric model. Several computer simulations and real experiments have been conducted to verify the effectiveness of the proposed depth estimation algorithm. In addition, a 6-DOF industrial robot is used to perform an eye-in-hand IBVS task with the aid of the proposed depth estimation algorithm.
Smart Charging Strategy for Electric Vehicle Charging Stations

Although the concept of transportation electrification holds enormous prospects in addressing the global environmental pollution problem, in reality the market penetration of plug-in electric vehicles (PEVs) has been very low. Consumer concerns over the limited availability of charging facilities and unacceptably long charging periods are major factors behind this low penetration rate. From the perspective of the electricity grid, a longer PEV peak load period can potentially overlap with the residential peak load period, making energy management more challenging. A suitably designed charging strategy can help to address these concerns, which motivated us to conduct this research. In this paper, we present a smart charging strategy for a PEV network that offers multiple charging options, including ac level 2 charging, dc fast charging, and battery swapping facilities at charging stations. For a PEV requiring charging facilities, we model the issue of finding the optimal charging station as a multiobjective optimization problem, where the goal is to find a station that ensures the minimum charging time, travel time, and charging cost. We extend the model to a metaheuristic solution in the form of an ant colony optimization. Simulation results show that the proposed solution significantly reduces waiting time and charging cost.
The lifetime of inositol 1,4,5-trisphosphate in single cells

In many eukaryotic cell types, receptor activation leads to the formation of inositol 1,4,5-trisphosphate (IP3) which causes calcium ions (Ca) to be released from internal stores. Ca release was observed in response to the muscarinic agonist carbachol by fura-2 imaging of N1E-115 neuroblastoma cells. Ca release followed receptor activation after a latency of 0.4 to 20 s. Latency was not caused by Ca feedback on IP3 receptors, but rather by IP3 accumulation to a threshold for release. The dependence of latency on carbachol dose was fitted to a model in which IP3 synthesis and degradation compete, resulting in gradual accumulation to a threshold level at which Ca release becomes regenerative. This analysis gave degradation rate constants of IP3 in single cells ranging from 0 to 0.284 s-1 (0.058 +/- 0.067 s-1 SD, 53 cells) and a mean IP3 lifetime of 9.2 +/- 2.2 s. IP3 degradation was also measured directly with biochemical methods. This gave a half life of 9 +/- 2 s. The rate of IP3 degradation sets the time frame over which IP3 accumulations are integrated as input signals. IP3 levels are also filtered over time, and on average, large-amplitude oscillations in IP3 in these cells cannot occur with period < 10 s.
Counting Triangles in Large Graphs by Random Sampling

The problem of counting triangles in graphs has been well studied in the literature. However, all existing algorithms, exact or approximate, spend at least linear time in the size of the graph (except a recent theoretical result), which can be prohibitive on today's large graphs. Nevertheless, we observe that the ideas in many existing triangle counting algorithms can be coupled with random sampling to yield potentially sublinear-time algorithms that return an approximation of the triangle count without looking at the whole graph. This paper makes these random sampling algorithms more explicit, and presents an experimental and analytical comparison of different approaches, identifying the best performers among a number of candidates.
Design, implementation and experimental evaluation of a wireless fall detector

Due to demographic reasons the percentage of elderly citizens in developed countries is rapidly increasing and as a consequence prevention of age-related problems is getting more important. Falls are amongst the most frequent causes for injuries to the aging population and many efforts have been concentrated in fall detection and prevention. This paper presents the design, implementation and experimental analysis of a portable wireless fall detector.
Monitoring Bridge Dynamic Responses Using Fiber Bragg Grating Tiltmeters

In bridge health monitoring, tiltmeters have been used for measuring rotation and curvature; however, their application in dynamic parameter identification has been lacking. This study installed fiber Bragg grating (FBG) tiltmeters on the bearings of a bridge and monitored the dynamic rotational angle. The dynamic features, including natural frequencies and mode shapes, have been identified successfully. The innovation presented in this paper is the first-time use of FBG tiltmeter readings to identify the natural frequencies of a long-span steel girder bridge. The identified results have been verified using a bridge finite element model. This paper introduces a new method for the dynamic monitoring of a bridge using FBG tiltmeters. Limitations and future research directions are also discussed in the conclusion.
SHOP: An Integrated Scheme for SCTP Handover Optimization in Multihomed Environments

Multihoming feature enables stream control transmission protocol (SCTP) to be a handover and mobility scheme for mobile users in multihomed environment. However, there are two flaws deteriorating SCTP handover performance. First, the congestion control mechanism of SCTP makes entire association experience a slow-start phase after handover, thus leading to a sudden-shrink of throughput. Second, the diversity of round-trip time (RTT) between separate paths incurs packet reordering and likely to trigger the spurious fast-retransmit, which degrades performance of reliable transport, too. This paper presents SHOP: an integrated scheme for SCTP handover optimization. By means of available bandwidth estimation technologies, SHOP avoids the slow-start phase after handover through configuring proper congestion control variables of new primary path. Meanwhile, postpone-handover based on RTT measurement is put forward in SHOP to minimize the negative effects of packet reordering. Detailed simulation results show the effectiveness of proposed scheme.
Distributed Humidity Sensing in PMMA Optical Fibers at 500 nm and 650 nm Wavelengths

Distributed measurement of humidity is a sought-after capability for various fields of application, especially in the civil engineering and structural health monitoring sectors. This article presents a method for distributed humidity sensing along polymethyl methacrylate (PMMA) polymer optical fibers (POFs) by analyzing wavelength-dependent Rayleigh backscattering and attenuation characteristics at 500 nm and 650 nm wavelengths. Spatially resolved humidity sensing is obtained from backscatter traces of a dual-wavelength optical time domain reflectometer (OTDR). Backscatter dependence, attenuation dependence as well as the fiber length change are characterized as functions of relative humidity. Cross-sensitivity effects are discussed and quantified. The evaluation of the humidity-dependent backscatter effects at the two wavelength measurements allows for distributed and unambiguous measurement of relative humidity. The technique can be readily employed with low-cost standard polymer optical fibers and commercial OTDR devices.
Recovering complex non-rigid 3D structures from monocular images by union of nonlinear subspaces

Non-rigid structure from motion (NRSfM) is a well-known challenging task due to its inherent ambiguities. Most existing approaches rely on kinds of low-rank linear subspaces assumption to make the problem well-constrained. In this paper, we make two contributions. First, we empirically present that relying on the assumption, 3D shapes lie on a union of non-linear subspaces, can better model the complex non-rigid motion than its linear counterparts. Second, we introduce the nonlinear low-rank representation as a regularizer to the objective function for NRSfM and show that it can be solved by alternating direction multiplier method (ADMM). Our experiments demonstrate that our method yields more accurate reconstruction and more reasonable clustering results than state-of-the-art methods, on CMU MoCap and UMPM datasets.
Reconstruction of highly structured image by entropy optimization

We propose an image reconstruction approach for highly structured image based on entropy optimization, extending the idea of sparsity with minimal number of non-zero elements to the concept of minimum entropy and simple structure. Minimum entropy signals have unique characteristics of low uncertainty, simple structure, and high concentration, and are usually sufficiently sparse. However, not all low-entropy signals can be linearly sparsified. Compared to a sparse signal in L0 or L1 norm having one dominant mode centered around 0, a highly structured signal with minimum entropy tends to be concentrated in one or more dominant modes, not necessarily around 0. We study histogram entropy for finite discrete signal and vector entropy for continuous real or complex signal, and propose two algorithms to seek the optimal signal with minimum entropy or low theoretic dimension, subject to the reconstruction constraint. Experiments are carried out on 1-D finite discrete signal, 2-D halftone, industrial and medical images to demonstrate the efficacy.
Robust shape regularity criteria for superpixel evaluation

Regular decompositions are necessary for most superpixel-based object recognition or tracking applications. So far in the literature, the regularity or compactness of a superpixel shape is mainly measured by its circularity. In this work, we first demonstrate that such measure is not adapted for super-pixel evaluation, since it does not directly express regularity but circular appearance. Then, we propose a new metric that considers several shape regularity aspects: convexity, balanced repartition, and contour smoothness. Finally, we demonstrate that our measure is robust to scale and noise and enables to more relevantly compare superpixel methods.
Electrospun Nanofibers of Polypyrrole-Poly(Acrylonitrile-co-Vinyl Acetate)

The oxidative polymerization of pyrrole (Py) by cerium(IV) on Poly(Acrylonitrile- co-Vinyl Acetate) matrix was performed. Nanofibers were obtained from this solution by the electrospinning method. A new absorption band was observed corresponding to the CH in plane vibration of polypyrrole (PPy) by Fourier transform infrared-attenuated total reflectance spectrophotometric analysis. A linear relationship was determined between the absorbance ratios of functional groups corresponding to the conjugated polymeric units and initial Py concentrations in the presence and absence of Py. Scanning electron microscope images indicated that the diameters of nanofibers were dependent on PPy content and that the average nanofiber diameters were reduced with increasing the initially added Py concentration. The alternating current conductivity of nanofibers was increased with frequency, particularly in the higher frequency region (> 105 Hz).
Advances in noninvasive myelin imaging

Myelin is important for the normal development and healthy function of the nervous system. Recent developments in MRI acquisition and tissue modeling aim to provide a better characterization and more specific markers for myelin. This allows for specific monitoring of myelination longitudinally and noninvasively in the healthy brain as well as assessment of treatment and intervention efficacy. Here, we offer a nontechnical review of MRI techniques developed to specifically monitor myelin such as magnetization transfer (MT) and myelin water imaging (MWI). We further summarize recent studies that employ these methods to measure myelin in relation to development and aging, learning and experience, and neuropathology and psychiatric disorders. © 2017 The Authors. Developmental Neurobiology Published by Wiley Periodicals, Inc. Develop Neurobiol 78: 136–151, 2018
Chlordetect: Commercial Calcium Aluminate Based Conductimetric Sensor for Chloride Presence Detection

Chloride presence affects different environments (soil, water, concrete) decreasing their qualities. In order to assess chloride concentration this paper proposes a novel sensor for detecting and measuring it. This sensor is based on electric changes of commercial monocalcium aluminate (CA) when it interacts with chloride aqueous solutions. CA is used as a dielectric material between two coplanar capacitors. The geometry proposed for this sensor allows to assess the chloride content profile, or to make four times the same measurement. Besides, the experimental design gives us the possibility of study not just the chloride effect, but also the time and some geometric effects due to the sensor design. As a result, this sensor shows a limit of detection, sensitivity, and response time: 0.01 wt % Cl− and 0.06 wt % Cl−, and 2 min, respectively, comparable with other non invasive techniques as optical fibre sensors.
SSPP-DAN: Deep domain adaptation network for face recognition with single sample per person

Real-world face recognition using a single sample per person (SSPP) is a challenging task. The problem is exacerbated if the conditions under which the gallery image and the probe set are captured are completely different. To address these issues from the perspective of domain adaptation, we introduce an SSPP domain adaptation network (SSPP-DAN). In the proposed approach, domain adaptation, feature extraction, and classification are performed jointly using a deep architecture with domain-adversarial training. However, the SSPP characteristic of one training sample per class is insufficient to train the deep architecture. To overcome this shortage, we generate synthetic images with varying poses using a 3D face model. Experimental evaluations using a realistic SSPP dataset show that deep domain adaptation and image synthesis complement each other and dramatically improve accuracy. Experiments on a benchmark dataset using the proposed approach show state-of-the-art performance.
Gesture recognition through HITEG data glove to provide a new way of communication

Hands gestures recognition, by means of measuring apparatus, can provide a new way of human-computer interaction. Controlling different devices or speaking through a speech synthesizer can be time saving as well as an aid for impaired persons. In this work we performed the classification of 20 different gestures, evaluating three different methodologies: Support Vector Machines, Mahalanobis and Euclidean based classifiers.
Quantitative assessment of relative changes of immunohistochemical staining by light microscopy in specified anatomical regions

Despite the advent of ever newer microscopic techniques for the study of the distribution of macromolecules in biological tissues, the enzyme‐based immunohistochemical (IHC) methods are still used widely and routinely. However, the acquisition of reliable conclusions from the pattern of the reaction products of IHC procedures is hindered by the regular need for subjective judgments, in view of frequent inconsistencies in staining intensity from section to section or in repeated experiments. Consequently, when numerical comparisons are required, light microscopic morphological descriptions are commonly supplemented with analytical data (e.g. from Western blot analyses); however, these cannot be directly associated with accurate structural information and can easily be contaminated with data from outside the region of interest. Alternatively, to eliminate the more or less subjective evaluation of the results of IHC staining, procedures should be developed that correct for the variability of staining through the use of objective criteria. This paper describes a simple procedure, based on digital image analysis methods and the use of an internal reference area on the analyzed sections, that reduces the operator input and hence subjectivity, and makes the relative changes in IHC staining intensity in different experiments comparable. The reference area is situated at a position of the section that is not affected by the experimental treatment, or a disease condition, and that can therefore be used to specify the baseline of the IHC staining. Another source of staining variability is the internal heterogeneity of the object to be characterized, which means that identical fields can never be analyzed. To compensate for this variability, details are given of a systematic random sampling paradigm, which provides simple numerical data describing the extent and strength of IHC staining throughout the entire volume to be characterized. In this integrated approach, the figures are derived by pooling relative IHC staining intensities from all sections of the series from a particular animal. The procedure (1) eliminates the problem arising from the personal assessment of the significance of the IHC staining intensity, (2) does not depend on the precise dissection of the tissue on a gross scale and (3) considerably reduces the consequences of limited, arbitrary sampling of the region of interest for IHC analysis. The quantification procedure is illustrated by data from an experiment in which inflammatory reactions in the murine spinal cord, measured as microglial activation, were followed by IHC after the lesion of the sciatic nerve.