Miracle-dz/UniFAR-FTUs huggingface dataset:
28.4 A 400mVpp 92.3 dB-SNDR 1kHz-BW 2nd-Order VCO-Based ExG-to-Digital Front-End Using a Multiphase Gated-Inverted Ring-Oscillator Quantizer

Next-generation wearable devices will enable clinical-grade, continuous ExG (ECG, EEG, EMG, etc.) biopotential monitoring, providing medical professionals with valuable longitudinal data outside of hospital settings. These devices must be ultra-low power $(\lt 10 \mu W)$ to enable long battery life while accurately digitizing sub-kHz, $\mu V-$level ExG signals in the presence of large motion and/or stimulation artifacts $(\gt 100$ mV) with high input-impedance $(Z_{in}\gt 10M\Omega)$ to avoid signal attenuation. Achieving such performance with conventional PGA + ADC architectures is challenging due to the conflicting low-power and $\gt 90$ dB dynamic-range (DR) requirements [1]. To address this, several direct digitization analog front-ends (AFEs) have been reported [2]–[4]. While these offer wide DR, they typically have low input-impedance $(\lt 5 M\Omega)$ and/or low power-efficiency (FoM $\le 172$ dB). This paper presents a scalable $2^{nd}-$order voltage-controlled oscillator (VCO)-only $\Delta \sum$ ADC that achieves 92.3dB SNDR in a 1kHz bandwidth using a mismatch tolerant, multiphase gated-inverted ring-oscillator (GIRO) quantizer with dynamic power-scaling. The ADC uses an impedance-booster to maintain $\gt 50 M\Omega$ input-impedance over the entire bandwidth while consuming $4.25 \mu W$ during nominal operation and $5.8 \mu W$ in the presence of artifacts resulting in an FoM of 174.7dB.
Crystal structure of decameric peroxiredoxin (AhpC) from Amphibacillus xylanus

Introduction. Peroxiredoxins (Prxs) act as antioxidants to protect organisms against reactive oxygen species. In Escherichia coli, Prx is responsible for the reduction of endogenously generated hydrogen peroxides. In mammalian cells, the enzyme is essential for tumor suppression. Prxs are currently divided into three classes: typical 2-Cys, atypical 2-Cys, and 1-Cys Prxs. All Prxs contain one conserved cysteine in the N-terminal region (peroxidatic cysteine, Cys47 in Amphibacillus xylanus AhpC), which is the site of oxidation. 2-Cys Prx contains an additional conserved cysteine near the C-terminal region (resolving cysteine, Cys166). The oxidized peroxidatic cysteine rapidly forms an intermolecular disulfide bond with the resolving cysteine of the other subunit, which is subsequently reduced with electrons supplied by reducing agents such as thioredoxin. To date, the crystal structures of seven eukaryotic and bacterial Prxs have been determined (homodecamer or homodimer). A. xylanus, isolated from alkaline compost, lacks a respiratory system and the heme proteins catalase and peroxidase, and has the same growth rate and cell yield under strictly anaerobic and aerobic conditions. A typical 2-Cys Prx from this facultatively anaerobic bacterium (A. xylanus AhpC) plays an essential role in reducing oxygen species coupled with the flavoprotein, A. xylanus NADH oxidase. Here we report the crystal structure of A. xylanus AhpC in its oxidized and decameric form.
Probing Homotopy Continuation Method for Solving Nonlinear Magnetic Network Equations

Nonlinear magnetic network is an effective model to solve the magnetic field of the motor. For the strong nonlinearity of magnetic network parameters, it is difficult to solve the nonlinear magnetic network equations. To overcome the divergent issue for solving the nonlinear magnetic network equations, a new method, the homotopy continuation method, is proposed. Based on the benchmark model of Testing Electromagnetic Analysis Methods Problem 20 (TEAM P20), the two-dimensional nonlinear magnetic network model is established. Next, homotopy equations of nonlinear magnetic network equations are derived, and they are solved by the homotopy continuation method. So that solving the nonlinear magnetic network equations is transformed into solving a set of homotopy equations. Then, the magnetic flux density of iron core with different saturation levels is calculated using the simple iteration method, the relaxation iteration method, Newton-Raphson method, and the homotopy continuation method, respectively. The results and convergence performance of the four methods are compared. It is proved that the proposed method can effectively expand the convergence domain and simplify the calculation process. The magnetic flux density calculated by the homotopy continuation method is compared with the finite element method in the supersaturated state, and the results are in good agreement. The relative error of the magnetic flux density of the core is less than 5%, which verifies the correctness of the proposed algorithm.
Organocatalytic Enantioselective Approach to the Synthesis of Verbalactone and (R)-Massoialactone

The organocatalytic enantioselective synthesis of verbalactone and (R)-massoialactone is described. The requisite stereogenic centers of the target molecules were constructed using L-proline-catalyzed α-aminoxylation and Horner-Wadsworth-Emmons (HWE) olefination. Yamaguchi macrolactonization and ring-closing metathesis were employed as key steps in the syntheses.
Global Consistent Graph Convolutional Network for Hyperspectral Image Classification

While semisupervised methods based on graph convolutional networks (GCNs) can achieve good results in hyperspectral image (HSI) classification, their performance is limited as they rely only on spatial–spectral similarity when constructing local adjacency graphs. Moreover, relying on local adjacency graphs can limit the ability of the methods to ensure the consistency of global feature in complex hyperspectral remote sensing environments. Using the spatial–spectral information is typically not sufficient for providing a reliable similarity measurement to construct a global graph when the spectral variability is large and the spatial distance is long in intraclass pixels. To address this issue, this article presents a novel globally consistent GCN (GCGCN) for HSI classification. According to the proposed method, a local reliable initial graph is first built from inherent spatial–spectral information by considering this graph as a variable to optimize. Then, adaptive global high-order neighbors are explored to capture the underlying rich spatial contextual information by utilizing the graph topological consistent connectivity instead of the common strategy of using only the spatial–spectral similarity measurement. Finally, the adaptive global high-order graph structure and two-layer networks are combined to achieve the global feature smoothing of the same class samples and maintain high global feature consistency. The proposed GCGCN method is evaluated on three real HSI data sets to demonstrate its superiority compared to ten different classification methods. Moreover, the proposed GCGCN method achieves state-of-the-art classification results on three data sets in terms of four classification evaluation metrics, including overall accuracy (OA), kappa coefficient (KC), average accuracy (AA), and class accuracy (CA).
A Fast, Automatic Risk Detector for COVID-19

With its lethal spread to more than 200 countries, COVID-19 has brought a global crisis, affecting more than 3 crore people across the world. Viruses don’t have a cure, and this makes the population vulnerable and heavily rely on preventing the infection. Hence, following the rules of social distancing and wearing a face mask are two very essential approaches to fight against this pandemic. Motivated by this notion, this work proposes a deep learning-based framework for automating the detection of risk due to COVID19. The proposed framework utilizes YOLOv3 object detector to detect whether a person has worn a mask. In case of absence of mask, to categorize the level of risk, the person’s age category is estimated, and the result of the risk detector is displayed on the image with a bounding box. In case of multiple boxes, the framework also calculates the distance between them to check whether the rules of social distancing are being followed. The result of the YOLOv3 model is compared with popular state-of-the-art model, Faster Regionbased Convolutional Neural Network. From the experimental analysis, it is concluded that YOLOv3 object detector displays best results with respect to the trade-off between speed and accuracy.
On the fractal dimensions of some transient soil properties

SUMMARY Spatial variation of soil properties can be studied by use of the concept of fractals, and the overall roughness of a surface characterized by its fractal dimension. Soil surface strength was recorded by both cone penetrometer and field shear vane, along a series of transects, at 10 cm spacings. In the natural state, such data gave fractal dimensions very close to 2, implying an almost totally random data set, and only the deliberate action of cultivations introduced an order of persistence. Microtopography, measured by a profile meter at 1 cm intervals, had fractal dimensions of 1.7 on two separate sites. Problems are identified specifically in the choice of a suitable model for fitting the semi-variogram, and in the collection of an efficient data set.
Un nouvel acteur dans la détection hypothalamique du glucose : les canaux Transient Receptor Potential Canonical (TRPC)

L’hyperglycemie est detectee et integree au niveau de l’hypothalamus medio-basal (MBH) qui inhibe la prise alimentaire et declenche la secretion d’insuline. Le MBH renferme des neurones specialises gluco-sensibles (GS) qui detectent directement ou indirectement des variations de la concentration extracellulaire en glucose.Dans une premiere etude, nous suggerons que la detection indirecte du glucose par les neurones GS hypothalamiques repose sur la liberation d’endozepines par les astrocytes, un gliotransmetteur connu pour inhiber la prise alimentaire en reponse a l’hyperglycemie. Nous travaux montrent que les endozepines activent specifiquement les neurones a pro-opiomelanocortine (POMC) du MBH pour generer leur effet anorexigene.Dans une seconde etude, nous montrons que la detection directe de l’hyperglycemie implique les neurones hypothalamiques dits « high gluco-excited » (HGE). Grâce a des approches pharmacologiques et genetiques, nous mettons en evidence que les canaux redox sensibles Transient Receptor Potential Canonical 3 et 4 (TRPC3/4) sont fondamentaux pour la detection du glucose par les neurones HGE in vitro, la stimulation de la secretion d’insuline et la diminution de la prise alimentaire en reponse a l’hyperglycemie cerebrale in vivo. De plus, nos travaux demontrent que les canaux TRPC3 du MBH jouent un role clef dans le controle de l’homeostasie energetique.Les travaux de cette these permettent de mettre en evidence deux nouveaux mecanismes de detection hypothalamique de l’hyperglycemie : l’un reposant sur l’implication des canaux TRPC3/4 dans les neurones HGE et l’autre proposant les endozepines astrocytaires comme relai du signal « glucose » aux neurones POMC.
Biomechanical Ambulatory Assessment of 3D Knee Angle Using Novel Inertial Sensor-Based Technique

Three-dimensional (3D) knee angle measurement is one of the key measures in human gait analysis. Inertial sensor capable of measuring joint motion under unconstrained conditions is a practical tool for clinical evaluation and rehabilitation. An inertial measurement unit (IMU) consisting of accelerometer and gyroscope allows orientation measurement in 3D with an additional sensor (i.e., magnetometer). However, ferromagnetic interference negatively affects the performance of magnetometer and thus reduces measurement accuracy. In this study, a technique based on nonlinear autoregressive neural network with exogenous inputs (NARX) is presented to measure 3D segmental orientation during gait without the use of magnetometer. With IMUs attached to the thigh and shank, 3D knee angles in long-distance treadmill walking were computed and validated against an optical motion analysis system as the gold standard. Pseudo-integrator (PI) was also compared to the reference system for benchmarking. The learning capability of NARX was further assessed with the comparison of complementary filter (CF) to the reference system. The proposed NARX model was shown to outperform PI with biases between −3.5° and −0.2°, and root mean square errors between 4.5° and 2.5°. Results demonstrated the capability of NARX in providing accurate estimates of 3D knee joint angle while avoiding interference as encountered in systems incorporating magnetometer, suggesting that NARX is feasible to computing long-term ambulatory measurements of body segment orientation and 3D joint angles.
Learning Richer Features in Deep CNN for Object Detection

Recently developed object detectors employ a Deep Convolutional Neural Network (DCNN) by adding the number of feature layers with a pyramidal shape. Owing the occurrence of Feature Pyramid Network (FPN), the representation of the DCNN’s ability was largely improved. Although these object detectors with feature pyramid achieve encouraging results, they have some limitations that didn’t fully explore richer information in the deep layers, which is essential for the Object Detection task. In this work, we propose Multi-Level Spatial Pyramid Pooling module to construct more effective feature network for detecting objects of different scales. It is an integration of three pooling schemes of different kernel sizes. Besides, we design novel backbone based Darknet as our feature extractor. First, we fuse multi-level features extracted by new backbone followed by FPN style as the basic features. Then we feed the fused feature to the module to generate richer semantic feature maps at different scales. Finally, we gather up the richer feature maps in the former step for the detectors. To evaluate the effectiveness of the proposed method, experiments are conducted on two major benchmarks, which is PASCAL VOC 2007 and MS COCO dataset, and demonstrates that the ML-SPP module achieve comparable results with high efficiency.
A 1.25 μJ per Measurement Ultrasound Rangefinder System in 65 nm CMOS for Explorations With a Swarm of Sensor Nodes

This paper presents an ultrasound rangefinder system able to find relative distances among energy-constrained sensor nodes. The nodes build a swarm that is operated in collision and multipath rich environments. A new distance measurement technique combining Wake-up and Frequency Modulated Continuous Wave (FMCW) is proposed to enable the ranging while neglecting the echoes from passive reflectors in the environment. The building blocks of the sensor nodes comprise a transmitter, a wake-up receiver, and a ranging receiver, all implemented in a 65 nm CMOS technology. The transmitter includes two switched-capacitor converters and an output multiplexer to generate a four-level driving signal and broadcast either a wake-up sequence or a digitally synthesized ultrasound Chirp. The transmitter dissipates <inline-formula> <tex-math notation="LaTeX">$0.43~\mu \text{J}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${0.82~\mu \text {J}}$ </tex-math></inline-formula> to broadcast the wake-up signal and the Chirp, respectively. A mixer first architecture is exploited in the wake-up receiver to reduce the always-on power consumption of the nodes. The ranging receiver uses a heterodyne architecture suited for the FMCW. The power consumption of the wake-up receiver and ranging receiver is 23.6 nW and <inline-formula> <tex-math notation="LaTeX">$0.56~\mu \text{W}$ </tex-math></inline-formula>, respectively. The proposed rangefinder is experimentally characterized up to a 1 m distance in air and dissipates <inline-formula> <tex-math notation="LaTeX">$1.25~\mu \text {J}$ </tex-math></inline-formula> per measurement, achieving a resolution of 18.7 mm at 0.55 m.
CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings

The alarmingly high mortality rate and increasing global prevalence of cardiovascular diseases (CVDs) signify the crucial need for early detection schemes. Phonocardiogram (PCG) signals have been historically applied in this domain owing to its simplicity and cost-effectiveness. In this article, we propose CardioXNet, a novel lightweight end-to-end CRNN architecture for automatic detection of five classes of cardiac auscultation namely normal, aortic stenosis, mitral stenosis, mitral regurgitation and mitral valve prolapse using raw PCG signal. The process has been automated by the involvement of two learning phases namely, representation learning and sequence residual learning. Three parallel CNN pathways have been implemented in the representation learning phase to learn the coarse and fine-grained features from the PCG and to explore the salient features from variable receptive fields involving 2D-CNN based squeeze-expansion. Thus, in the representation learning phase, the network extracts efficient time-invariant features and converges with great rapidity. In the sequential residual learning phase, because of the bidirectional-LSTMs and the skip connection, the network can proficiently extract temporal features without performing any feature extraction on the signal. The obtained results demonstrate that the proposed end-to-end architecture yields outstanding performance in all the evaluation metrics compared to the previous state-of-the-art methods with up to 99.60% accuracy, 99.56% precision, 99.52% recall and 99.68% F1- score on an average while being computationally comparable. This model outperforms any previous works using the same database by a considerable margin. Moreover, the proposed model was tested on PhysioNet/CinC 2016 challenge dataset achieving an accuracy of 86.57%. Finally the model was evaluated on a merged dataset of Github PCG dataset and PhysioNet dataset achieving excellent accuracy of 88.09%. The high accuracy metrics on both primary and secondary dataset combined with a significantly low number of parameters and end-to-end prediction approach makes the proposed network especially suitable for point of care CVD screening in low resource setups using memory constraint mobile devices.
Genesis of electric field assisted microparticle assemblage in a dielectric fluid

Abstract Oscillatory motions of charged particles inside a liquid medium have been explored under the influence of an electric field emulating field-induced particle-laden fluid flows. The properties of the surrounding fluid are found to play key roles in the kinetics of such a particle aggregation process. While the weakly conducting or insulating liquids promote high-frequency oscillations of charged particles followed by a quick assemblage, the viscosity and relative permittivity of the liquid play significant roles in modulating the time scale. In fact, the origin of such motions in a multi-particle system is very similar to a system with a single charged particle wherein the particle gathers charge from one of the electrodes before moving towards the other of opposite polarity. Interestingly, in the multi-particle system, an unprecedented charge reversal is observed wherein a charged particle reverses its direction of motion after colliding with another particle of opposite polarity. Experiments together with simulations further reveal that, while the equal-sized particles undergo an electric field driven ‘elastic’ collision and show synchronized motions with nearly similar speeds of approach and separation, the motions of unequal-sized particles are rather non-uniform after undergoing an ‘inelastic’ collision. Importantly, the simulations with two-particle systems uncover the presence of counter-rotating vortices surrounding the charged particles. The results reported not only usher the genesis of the chain-like assemblage in the multi-particle systems but also open up the possibility of the generation of on-demand power-law liquid properties through ‘chaining’ or ‘layering’ of the charged particles.
The chemical character of the Late Caledonian Donegal Granites, Ireland, with comments on their genesis

ABSTRACT The Late Caledonian granites of Donegal are all intruded into metasediments of the Dalradian Supergroup of Neoproterozoic age, which were metamorphosed and deformed during the Grampian Phase of the Caledonian orogeny at c. 470–460 m.y. They were intruded in a singular pulse well after the main tectonic event, apparently peaking at 407–402 m.y. ; importantly after the strong collision of Laurentia with Baltica on closure of the Iapetus Ocean. The plutons are mainly made up of granodiorite and granite, and are all 'I' type, but different to Cordilleran ‘I’ types of the eastern Pacific margin. Major element chemistry indicates they are high-K calc-alkaline rocks with a large range in SiO2 content. However three of the plutons (Fanad, Thorr, Ardara), have very high Ba and Sr contents, even higher than Mainland Scotland counterparts; they are high Ba–Sr plutons. Three plutons (Barnesmore, Rosses, Trawenagh Bay) are evolved and are low-Ba–Sr types, while one (Main Donegal) has atypical, intermediate characteristics. The origin of the magmas is still much debated; here we suggest slab breakoffon Iapetus Ocean closure accounts for the special compositions of these magmas and the other major features of Late Caledonian granitic magmatism, including the singular intrusion peak and the associated appinite–lamprophyre suite.
Department of Administration and Organization Theory

i DEDICATION . iii ACKNOWLEDGEMENT . iv LIST OF TABLES . viii LIST OF FIGURES . ix LIST OF ACRONYMS . x INTRODUCTION . 1 1.0 Background . 1 1.1 Statement of Problem . 3 1.2 Research Objectives . 4 1.3 Research Questions . 4 1.4 Significance of the Master Thesis . 5 1.5 Scope of the Master Thesis . 5 1.6 Organization of the Master Thesis . 5 CHAPTER TWO. 7 CONTEXT OF THE STUDY . 7 2.0 Introduction . 7 2.1 Brief Description of West Africa . 7 2.2 Overview of the ECOWAS Commission . 8 2.3 Organizational Structure of The ECOWAS Commission . 13 2.3.
Exploring the plutonic-volcanic link: a zircon U-Pb, Lu-Hf and O isotope study of paired volcanic and granitic units from southeastern Australia

Abstract The relationship between plutonic and volcanic rocks is central to understanding the geochemical evolution of silicic magma systems, but it is clouded by ambiguities associated with unravelling the plutonie record. Here we report an integrated U-Pb, O and Lu-Hf isotope study of zircons from three putative granitic-volcanic rock pairs from the Lachlan Fold Belt, southeastern Australia, to explore the connection between the intrusive and extrusive realms. The data reveal contrasting petrogenetic scenarios for the S- and I-type pairs. The zircon Hf-O isotope systematics in an 1-type dacite are very similar to those of their plutonie counterpart, supporting an essentially co-magmatic relationship between these units. The elevated δ18O of zircons in these I-type rocks confirm a significant supracrustal source component. The S-type volcanic rocks are not the simple erupted equivalents of the granites, although the extrusive and plutonie units can be related by open-system magmatic evolution. Zircons in the S-type rocks define covariant εΗf—βO arrays that attest to mixing or assimilation processes between two components, one being the Ordovician metasedimentary country rocks, the other either an I-type magma or a mantle-derived magma. The data are consistent with models involving incremental melt extraction from relatively juvenile magmas undergoing open-system differentiation at depth, followed by crystal-liquid mixing upon emplacement in shallow magma reservoirs, or upon eruption. The latter juxtaposes crystals with markedly different petrogenetic histories and determines whole-rock geochemical and textural properties. This scenario can explain the puzzling decoupling between the bulk rock isotope and geochemical compositions commonly observed for granite suites.