Miracle-dz/UniFAR-FTUs huggingface dataset:
A Power Management System for Hybrid Energy Harvesting From Multiple Multitype Sources and Ultrawide Range Source Tracking

This article presents a power management system (PMS) for hybrid energy harvesting from multiple multitype sources. Three key features of the proposed approach are 1) a simple architecture for hybrid energy harvesting from multiple multitype sources by sharing a single inductor, 2) a backup power controller (BPC) for recycling surplus energy, and 3) an efficient boost–buck conversion (B2C) mode for tracking multiple sources within a single cycle. The power stages of the PMS consist of an active bias-flip rectifier (ABFR) for ac sources, three input low-voltage boost converters (3LVBC), and a high-voltage buck converter for dc sources. The B2C mode efficiently combines the operation of the 3LVBC and HVBC. By sharing an inductor in B2C mode, the ABFR increases the overall input power (PIN) by aggregating the power from the hybrid sources. The proposed PMS operates under four modes depending on the load conditions detected by the BPC. This approach improves the average harvesting efficiency using the auxiliary output paths and two storage nodes. For per-source maximum power point tracking, adaptive impedance matching and perturb and observe techniques are used. The integrated circuit (IC) part of the PMS is fabricated in a 180 nm process. The measured tracking efficiency is higher than 92% for a wide source resistance range from 2 Ω to 2 kΩ (1000× range). An end-to-end efficiency of up to 92.5% is achieved. The energy harvesting from multiple multitype sources significantly extends the output power range from 0.053 μW to 132600 μW. All the functions of the IC are realized with only 900 nW.
Congestive Heart Failure Detection From ECG Signals Using Deep Residual Neural Network

The early and accurate detection of congestive heart failure (CHF) using an electrocardiogram (ECG) is of great significance for improving the survival rate of patients. Existing approaches show limited detection accuracy as they fail to capture the temporal ECG dynamics. Also, these methods lack model transparency and are often difficult to interpret. This article proposes a novel end-to-end diagnostic attention-based deep residual recurrent neural network (DA-DRRNet) that effectively captures the temporal dynamics and extracts high-level attentive representations for accurate CHF detection. Specifically, we first employ a recurrent neural network (RNN) layer to encode the temporal dynamics from the raw ECG beats. Then, multilayered RNNs with residual connections are incorporated to extract high-level feature representations hierarchically. The residual connections allow gradients in deep RNN to propagate effectively, thereby improving the network’s representation ability. Finally, an attention module identifies the hidden vectors corresponding to the diagnostically prominent ECG characteristics to form an attentive representation for improved CHF detection. Using ECG signals from the three publicly available datasets (BIDMC-CHF, PTBDB, and MIT-BIH NSRDB), the proposed method achieves an impressive accuracy of 98.57% and nearly 100% for beat-level and 24-h record-level diagnosis, respectively. Notably, the analysis of learned attention weights demonstrates that the proposed model focuses on the clinically relevant ECG features that characterize CHF. This model transparency and improved detection results advance research in this field and provide a reliable and transparent diagnostic system for CHF analysis.
Carleman Linearization for Control and for State and Disturbance Estimation of Nonlinear Dynamical Processes

Abstract In this paper, techniques based on Carleman linearization are presented which are applicable to the design of controllers and state estimators for nonlinear dynamical systems. Originally, Carleman linearization has been developed to transform sets of polynomial ordinary differential equations into infinite dimensional linear system representations. After choosing suitable finite orders for truncation of Carleman linearization, finite dimensional linear and bilinear system representations are obtained for which the design of controllers and state estimators can be performed in a straightforward way. To visualize the practical applicability of the presented procedures, state as well as disturbance estimation for a linear axis driven by pneumatic muscle actuators is studied.
Chain equivalences for symplectic bases, quadratic forms and tensor products of quaternion algebras

We present a set of generators for the symplectic group which is different from the well-known set of transvections, from which the chain equivalence for quadratic forms in characteristic 2 is an immediate result. Based on the chain equivalences for quadratic forms, both in characteristic 2 and not 2, we provide chain equivalences for tensor products of quaternion algebras over fields with no nontrivial 3-fold Pfister forms. The chain equivalence for biquaternion algebras in characteristic 2 is also obtained in this process, without any assumption on the base-field.
The Seiberg–Witten equations on end‐periodic manifolds and an obstruction to positive scalar curvature metrics

By studying the Seiberg–Witten equations on end‐periodic manifolds, we give an obstruction on the existence of positive scalar curvature metric on compact 4‐manifolds with the same homology as S1×S3 . This obstruction is given in terms of the relation between the Frøyshov invariant of the generator of H3(X;Z) with the 4‐dimensional Casson invariant λSW(X) defined in [Mrowka, Ruberman and Saveliev, J. Differential Geom. 88 (2011) 333–377]. Along the way, we develop a framework that can be useful in further study of the Seiberg‐Witten theory on general end‐periodic manifolds.
Multidisciplinary Insights into the Structure–Function Relationship of the CYP2B6 Active Site

Cytochrome P450 2B6 (CYP2B6) is a highly polymorphic human enzyme involved in the metabolism of many clinically relevant drugs, environmental toxins, and endogenous molecules with disparate structures. Over the last 20-plus years, in silico and in vitro studies of CYP2B6 using various ligands have provided foundational information regarding the substrate specificity and structure–function relationship of this enzyme. Approaches such as homology modeling, X-ray crystallography, molecular docking, and kinetic activity assays coupled with CYP2B6 mutagenesis have done much to characterize this originally neglected monooxygenase. However, a complete understanding of the structural details that make new chemical entities substrates of CYP2B6 is still lacking. Surprisingly little in vitro data has been obtained about the structure–function relationship of amino acids identified to be in the CYP2B6 active site. Since much attention has already been devoted to elucidating the function of CYP2B6 allelic variants, here we review the salient findings of in silico and in vitro studies of the CYP2B6 structure–function relationship with a deliberate focus on the active site. In addition to summarizing these complementary approaches to studying structure–function relationships, we note gaps/challenges in existing data such as the need for more CYP2B6 crystal structures, molecular docking results with various ligands, and data coupling CYP2B6 active site mutagenesis with kinetic parameter measurement under standard expression conditions. Harnessing in silico and in vitro techniques in tandem to understand the CYP2B6 structure–function relationship will likely offer further insights into CYP2B6-mediated metabolism. SIGNIFICANCE STATEMENT The apparent importance of cytochrome P450 2B6 (CYP2B6) in the metabolism of various xenobiotics and endogenous molecules has grown since its discovery with many in silico and in vitro studies offering a partial description of its structure–function relationship. Determining the structure–function relationship of CYP2B6 is difficult but may be aided by thorough biochemical investigations of the CYP2B6 active site that provide a more complete pharmacological understanding of this important enzyme.
L moment diagrams should replace product moment diagrams

It is well known that product moment ratio estimators of the coefficient of variation Cν, skewness γ, and kurtosis κ exhibit substantial bias and variance for the small (n ≤ 100) samples normally encountered in hydrologic applications. Consequently, L moment ratio estimators, termed L coefficient of variation τ2, L skewness τ3, and L kurtosis τ4 are now advocated because they are nearly unbiased for all underlying distributions. The advantages of L moment ratio estimators over product moment ratio estimators are not limited to small samples. Monte Carlo experiments reveal that product moment estimators of Cν and γ are also remarkably biased for extremely large samples (n ≥ 1000) from highly skewed distributions. A case study using large samples (n ≥ 5000) of average daily streamflow in Massachusetts reveals that conventional moment diagrams based on estimates of product moments Cν, γ, and κ reveal almost no information about the distributional properties of daily streamflow, whereas L moment diagrams based on estimators of τ2, τ3, and τ4 enabled us to discriminate among alternate distributional hypotheses.
Generalized line bundles on primitive multiple curves and their moduli

In this paper, we study generalized line bundles over $C_n$, a primitive multiple curve of arbitrary multiplicity $n$, where $n$ is a positive integer. In particular, we give a structure theorem for them and we characterize their semistability in terms of $n-1$ integral invariants, the indices. These results are used to describe the irreducible components that contain stable generalized line bundles in the Simpson moduli space of semistable sheaves of generalized rank $n$ and fixed generalized degree on $C_n$. We compute also the dimension of the Zariski tangent space to this moduli space in a point representing a generic generalized line bundle (any generalized line bundle for $n=3$). In the case $n=1$ all the results are completely trivial, while the case $n=2$ has already been treated by Chen and Kass.
The generalized weighted mean of correlated quantities

An analysis is presented of the generalized weighted mean of a set of quantities, some or all of which may be correlated, and the variance associated with that mean. The cases covered specifically are (a) two correlated quantities, (b) a general number of quantities, two of which are correlated and (c) a general number of quantities, all of which are correlated. For (c), two instances are treated, namely where the quantity variances are equal and unequal. Consistency of the data representing realizations of the quantities concerned is shown to be important in terms of appreciating the results obtained.
Forward Modeling of the Corona of the Sun and Solar-like Stars: From a Three-dimensional Magnetohydrodynamic Model to Synthetic Extreme-Ultraviolet Spectra

A forward model is described in which we synthesize spectra from an ab initio three-dimensional MHD simulation of an outer stellar atmosphere, where the coronal heating is based on braiding of magnetic flux due to photospheric footpoint motions. We discuss the validity of assumptions such as ionization equilibrium and investigate the applicability of diagnostics like the differential emission measure inversion. We find that the general appearance of the synthesized corona is similar to the solar corona and that, on a statistical basis, integral quantities such as average Doppler shifts or differential emission measures are reproduced remarkably well. The persistent redshifts in the transition region, which have puzzled theorists since their discovery, are explained by this model as caused by the flows induced by the heating through braiding of magnetic flux. While the model corona is only slowly evolving in intensity, as is observed, the amount of structure and variability in Doppler shift is very large. This emphasizes the need for fast coronal spectroscopic observations, as the dynamical response of the corona to the heating process manifests itself in a comparably slow evolving coronal intensity but rapid changes in Doppler shift.
Space‐Charge Limited Current from Plasma‐Facing Material Surface

We have derived an exact theoretical expression for the space‐charge limited current from the solid surfaces adjacent to plasmas that is applicable for an arbitrary sheath voltage. Our expression shows that the spacecharge limited current tends to saturate with the sheath voltage. This new formula is evaluated by 1‐D Particle in Cell (PIC) simulation and experiment, and is in a good agreement with the simulation and experimental results. We have also obtained an analytical equation fitted to the new formula based on conventional Child‐Langmuir formula by taking into account a more sophisticated dependence of the electrode potential and the plasma density through the effect of Debye shielding and a sheath expansion due to increased voltage across the sheath. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Land-Cover Classification With High-Resolution Remote Sensing Images Using Interactive Segmentation

Deep convolutional neural network (CNN) has been increasingly applied in interpretation of remote sensing image such as automatically mapping land cover. Although the automatic CNN method achieves relatively high accuracy, there are still many misclassified areas. Considering that it is still far from practical application, this paper proposes a semi-automatic auxiliary scheme for land cover classification whose core idea is to use an interactive segmentation network. To infer the rough positions and categories of objects, a CNN is relied on to classify images in a patch-wise manner. Then an interactive segmentation method is proposed by accepting user-clicks on the inside and outside of object to guide the model for the segmentation task in the patches. This model also introduces different interactive modules to better integrate features of different scales. In addition, we create a large-scale sample library containing five common land cover categories which covers Jiangsu Province, China, and includes both aerial and satellite imagery. On our sample, we gave a thorough evaluation of most recent deep learning-based methods. The experimental results shown by our interactive segmentation also far outperform the recent semantic segmentation method, which provides a reference for semi-automatic land cover mapping.
Effectiveness of dynamic neuromuscular stabilisation for improving trunk control in hemiplegic stroke: A scoping mini review

Stroke is one of the leading causes of mortality and morbidity in the adult population, resulting in significant cognitive and sensorimotor impairments affecting one-half of the body in most patients. The limitations that are attributed to trunk impairment affect the postural and respiratory functions due to either spasticity or weakness of trunk muscles, including the diaphragm. Physiotherapy is effective in interdisciplinary stroke management, with approaches utilising the principles of plasticity. This review focused on briefing the pathomechanical aspects of trunk impairment in hemiplegic stroke. The proposed mechanisms of Dynamic Neuromuscular Stabilization (DNS) as a treatment for hemiplegic stroke were analysed, and the existing research evidence for the efficacy of DNS in improving trunk control among stroke participants was critically reviewed. The findings substantiate the need for high-quality trials, emphasising study design, subset size, reflective outcomes, and regulated follow-ups.
Aguas residuales de la industria pesquera: Retos y oportunidades en la recuperación de proteínas y péptidos con alto valor biológico y funcional- Una revisión

Las aguas residuales que provienen de las industrias del enlatado o de las reductoras de pescados, son una fuente rica en una gran variedad de compuestos químicos de interés industrial y de otros altamente perjudiciales para el medio ambiente. Con el fin de reducir el impacto negativo de estos efluentes en los ecosistemas marinos, se han desarrollado diversas técnicas para establecer procedimientos de remoción de los sólidos y recuperación de los nutrientes, como las proteínas para aplicaciones en la industria alimentaria, agroquímica y farmacéutica. Estas proteínas son concentradas mediante una variedad de métodos, siendo el más utilizado la ultrafiltración. Sin embargo, son varias las técnicas de recuperación y modificación de los productos pesqueros que mejoran las cualidades nutricionales, funcionales y biológicas de los concentrados de proteínas en las aguas residuales. En esta revisión, se examinan y se discuten las técnicas no sólo para el tratamiento de los sólidos de las aguas residuales de la industria del enlatado y de la reductora de pescado, sino también de los compuestos químicos presentes y de otros fluidos resultado del procesamiento de los productos de la pesca, con especial mención del uso de la ultrafiltración para la obtención de las proteínas destinadas a las industrias citadas al inicio.
Approximate Ultrametricity for Random Measures and Applications to Spin Glasses

In this documentname, we introduce a notion called “approximate ultrametricity,” which encapsulates the phenomenology of a sequence of random probability measures having supports that behave like ultrametric spaces insofar as they decompose into nested balls. We provide a sufficient condition for a sequence of random probability measures on the unit ball of an infinite‐dimensional separable Hilbert space to admit such a decomposition, whose elements we call clusters. We also characterize the laws of the measures of the clusters by showing that they converge in law to the weights of a Ruelle probability cascade. These results apply to a large class of classical models in mean field spin glasses. We illustrate the notion of approximate ultrametricity by proving a conjecture of Talagrand regarding mixed p‐spin glasses that is known to imply a prediction of Dotsenko‐Franz‐Mézard. © 2017 Wiley Periodicals, Inc.
On strongly almost lacunary statistical $A$-convergence defined by Musielak-Orlicz function

We study some new strongly almost lacunary statistical $A$-convergent sequence space of order $\alpha$ defined by a Musielak-Orlicz function. We also give some inclusion relations between the newly introduced class of sequences with the spaces of strongly almost lacunary $A$-convergent sequence of order $\alpha$. Moreover we have examined some results on Musielak-Orlicz function with respect to these spaces.
Decision problems of finite automata design and related arithmetics

1. Motivation. Many variants of the notion of automaton have appeared in the literature. We find it convenient here to adopt the notion of E. F. Moore [7]. Inasmuch as Rabin-Scott [9] adopt this notion, too, it is convenient to refer to [9] for various results presumed here. In particular, Kleene's theorem [5, Theorems 3, 5] is used in the form in which it appears in [9]. It is often perspicacious to view regular expressions, and this notion is used in the sense of [3]. In general, we are concerned with the problems of automatically designing an automaton from a specification of a relation which is to hold between the automaton's input sequences and determined output sequences. These "design requirements" are given via a formula of some kind. The problems with which we are concerned have been described in [1]. With respect to particular formalisms for expressing "design requirements" as well as the notion of automaton itself, the problems are briefly and informally these: (1) to produce an algorithm which when it operates on an automaton and a design requirement produces the correct answer to the question "Does this automaton satisfy this design requirement? ", or else show no such algorithm exists; (2) to produce an algorithm which operates on a design requirement and produces the correct answer to the question "Does there exist an automaton which satisfies this design requirement? ", or else show no such algorithm exists; (3) to produce an algorithm which operates on a design requirement and terminates with an automaton which satisfies the requirement when one exists and otherwise fails to terminate, or else show no such algorithm exists. Interrelationships among problems (1), (2), (3) will appear in the paper [1]. This paper will also indicate the close connection between problem (1) and decision problems for truth of sentences of certain arithmetics. The paper [1 ] will also make use of certain results concerning weak arithmetics already obtained in the literature to obtain answers to problems (1) and (3). Thus
Lifting linear preferential attachment trees yields the arcsine coalescent

We consider linear preferential attachment trees which are specific scale-free trees also known as (random) plane-oriented recursive trees. Starting with a linear preferential attachment tree of size $n$ we show that repeatedly applying a so-called lifting yields a continuous-time Markov chain on linear preferential attachment trees. Each such tree induces a partition of $\{1, \ldots, n\}$ by placing labels in the same block if and only if they are attached to the same node in the tree. Our main result is that this Markov chain on linear preferential attachment trees induces a partition valued process which is equal in distribution (up to a random time-change) to the arcsine $n$-coalescent, that is the multiple merger coalescent whose $\Lambda$ measure is the arcsine distribution.
Measurement of Falling Film Thickness Around a Horizontal Tube Using a Laser Measurement Technique

A new optical method for the non-intrusive measurement of falling film thickness on the perimeter of horizontal tubes is described. The technique uses a high-speed digital video camera to capture the images of the liquid interface that are illuminated by a laser sheet, with the contrast enhanced by a fluorescent powder in the fluid. The results are compared to those predicted by the Nusselt falling film theory, showing relatively good agreement around the upper perimeter of the tube but much poorer agreement on the lower perimeter. The corresponding effects on heat transfer have also been estimated.
Defenders of the Faith? How shifting social cleavages and the rise of identity politics are reshaping right-wing populists’ attitudes towards religion in the West

ABSTRACT From the display of oversized crosses at the United States Capitol riots, to a new rhetoric centred on defence of the ‘Judaeo-Christian West’ in Europe: right-wing populist movements on both sides of the Atlantic are intensifying their use of Christian symbols and language. Many observers have interpreted such rhetoric as symptomatic of a conservative religious resurgence against secular liberalism and multiculturalism. However, several indicators suggest a more complicated relationship between the populist Right, religion, and secularisation. For instance, in the United States Donald Trump was perceived to be the least religious Republican party candidate in recent history, while in Europe church attendance remains a strong predictor for not voting for right-wing populist parties. Deploying a demand- and supply-side framework to understand the socio-demographic roots behind the rise of right-wing populist movements and the motives behind their references to Christianity, this contribution posits that right-wing populists primarily employ Christianity as a cultural identity marker to mobilise voters around a new post-religious identity cleavage. However, they often remain distanced from Christian doctrine, beliefs, and institutions, and instead seek to combine cultural references to Christianity with secular policies, suggesting a secularisation of Christian symbols rather than a resurgence of religion in western politics.