Miracle-dz/UniFAR-FTUs huggingface dataset:
Label‐free atherosclerosis diagnosis through a blood drop of apolipoprotein E knockout mouse model using surface‐enhanced Raman spectroscopy validated by machine learning algorithm

Abstract The direct preventative detection of flow‐induced atherosclerosis remains a significant challenge, impeding the development of early treatments and prevention measures. This study proposes a method for diagnosing atherosclerosis in the carotid artery using nanometer biomarker measurements through surface‐enhanced Raman spectroscopy (SERS) from single‐drop blood samples. Atherosclerotic acceleration is induced in apolipoprotein E knockout mice which underwent a partial carotid ligation and were fed a high‐fat diet to rapidly induce disturbed flow‐induced atherosclerosis in the left common carotid artery while using the unligated, contralateral right carotid artery as control. The progressive atherosclerosis development of the left carotid artery was verified by micro‐magnetic resonance imaging (micro‐MRI) and histology in comparison to the right carotid artery. Single‐drop blood samples are deposited on chips of gold‐coated ZnO nanorods grown on silicon wafers that filter the nanometer markers and provide strong SERS signals. A diagnostic classifier was established based on principal component analysis (PCA), which separates the resultant spectra into the atherosclerotic and control groups. Scoring based on the principal components enabled the classification of samples into control, mild, and severe atherosclerotic disease. The PCA‐based analysis was validated against an independent test sample and compared against the PCA‐PLS‐DA machine learning algorithm which is known for applicability to Raman diagnosis. The accuracy of the PCA modification‐based diagnostic criteria was 94.5%, and that of the machine learning algorithm 97.5%. Using a mouse model, this study demonstrates that diagnosing and classifying the severity of atherosclerosis is possible using a single blood drop, SERS technology, and machine learning algorithm, indicating the detectability of biomarkers and vascular factors in the blood which correlate with the early stages of atherosclerosis development.
On the Geometry of the Space of Monopole-Clusters

We review the results pertaining to the space of monopole-clusters, Mk,l, which was first proposed by Roger Bielawski. In particular, it has a pseudo-hyperk¨ahler metric which approximates the metric of the moduli space of SU(2)-monopoles on R 3 with exponential accuracy. We define actions of the groups R 3 , T 2 and SO(3) on Mk,l, and show that they are all isometry groups. In the case (k, l) = (1, 2), we express the monopole-clusters in terms of elliptic functions, and verify that they approach the true monopoles with rate inversely proportional to the separation distance between the clusters. For some SO^(2) ⊂ SO(3), the subgroups of SO^(2) × T 2 that admit a fixed point in the asymptotic region of M1,2 will be classified; their fixed point sets will be parametrized in terms of real coordinates and hence are manifolds. Finally, we compute the induced metric on an axially symmetric manifold in such family of manifolds, and show that it is asymptotically flat.
The universal impossibility of photonic quantum nonlocality

The theoretical concept of photonic quantum nonlocality cannot be implemented physically because of the quantum Rayleigh scattering of single photons. Physical scrutiny of landmark experiments (December 2015, M. Giustina, et al. Phys. Rev. Lett. 115, 250401, and L. K. Shalm, et al., Phys. Rev. Lett. 115, 250402) is undertaken. These articles reported that measured outcomes were fitted with quantum states possessing a dominant component of non-entangled photons, thereby contradicting their own claim of quantum nonlocality. With probabilities of photon detections lower than 0.1%, the alleged quantum nonlocality cannot be classified as a resource for developing quantum computing devices, despite recent publicity. Experimental evidence of a feasible process for quantum-strong correlations has been identified (M. Iannuzzi, et al., Phys. Lett. A, 384 (9), 126200, 2020) in terms of correlations between independent and multi-photon states evaluated as Stokes vectors on the Poincaré sphere. As single-photon sources are not needed, the design and implementation of quantum computing operations and other devices will be significantly streamlined.
Management of Esophago-Gastric Junction Carcinoma: A Narrative Multidisciplinary Review

Simple Summary The management of esophagogastric junction (EGJ) cancer is multidisciplinary, and due to its specificity, requires a high-volume center with high medical and surgical expertise. The aim of this narrative review was to critically analyze the evidence and guidelines in the literature and examine the tips and tricks in surgical and medical treatment to increase the long-term outcome of patients with this kind of tumor. Abstract Esophagogastric junction (EGJ) carcinoma represents a specific site of disease, given the opportunities for multimodal clinical care and management and the possibilities of combined treatments. It encompasses various clinical subgroups of disease that are heterogeneous and deserve different treatments; therefore, the guidelines have progressively evolved over time, considering the evidence provided by clinical trials. The aim of this narrative review was to summarize the main evidence, which orientates the current guidelines, and to collect the main ongoing studies to address existing gray areas.
Smoothing Transition Autoregressive (STAR) Models with Ordinary Least Squares and Genetic Algorithms Optimization

In this paper we present, propose and examine additional membership functions as also we propose least squares with genetic algorithms optimization in order to find the optimum fuzzy membership functions parameters. More specifically, we present the tangent hyperbolic, Gaussian and Generalized bell functions. The reason we propose that is because Smoothing Transition Autoregressive (STAR) models follow fuzzy logic approach therefore more functions should be tested. Some numerical applications for S&P 500, FTSE 100 stock returns and for unemployment rate are presented and MATLAB routines are provided.
On matrix-vector product based sub-quadratic arithmetic complexity schemes for field multiplication

Using an efficient way to compute the product of a Toeplitz matrix and a vector, a sub-quadratic arithmetic complexity scheme for field multiplication over binary extension fields has recently been proposed by Fan and Hasan. The scheme has been developed using a shifted polynomial basis for the representation of the field elements and has been stated to be applicable to the conventional polynomial basis, although with an increased gate delay for some field defining polynomials. The conventional polynomial basis is widely used in practice and is part of different recommendations and standards for cryptographic applications. In this article, we give additional details of the sub-quadratic complexity algorithm for the case of the polynomial basis by presenting efficient transformation matrices for a class of low weight field defining polynomials, namely trinomials.
La ideología de la nueva gestión pública desde la mirada de los directivos de las universidades chilenas: un estudio de casos múltiples

El presente artículo examina los efectos de la interiorización de las ideas de la nueva gestión pública entre los directivos de las universidades chilenas, analizando tanto su interpretación de este proceso, como las principales tensiones que perciben en la implementación de los nuevos sistemas internos de seguimiento y rendición de cuentas. El análisis muestra que los directivos se conforman en su acción a los principios de la nueva gestión pública, a la vez que manifiestan críticas en torno al uso de nuevos indicadores, con independencia de su institución. El artículo finaliza con un resumen e identifica posibles áreas de estudio sobre el tema.
Digital therapeutics in the clinic

Abstract Digital therapeutics are emerging as a new form of therapeutic interventions. Unlike conventional therapeutics, digital therapeutics deliver interventions directly to patients using an evidence‐based, clinically evaluated software to treat, manage, or prevent diseases. Digital therapeutics manifest in diverse forms such as web‐based applications, mobile applications on smart devices, virtual reality, and video games. As its own product category for FDA approval, digital therapeutics can function as stand‐alone treatments or in combination with conventional therapeutics to improve adherence and/or efficacy. Here, we review the clinical landscape of digital therapeutics. We summarize FDA‐approved products and their clinical use, overview >300 ongoing clinical trials, and discuss challenges for their clinical translation and strategies to overcome the same.
Coarse Graining the φ4 Model: Landau-Ginzburg Potentials from Computer Simulations

We discuss the problem of how to calculate Landau and Landau-Ginzburg free energies for lattice spin models from computer simulations. In setting up a proper simulation algorithm, emphasis is placed on the coarse grained nature of these potentials, which must be take into account by any suitable simulation approach. The development of theory and simulation results is reviewed and the results of a novel Monte Carlo algorithm using Fourier amplitudes are presented, which partly confirm and sharpen the assumptions on the temperature behavior of the Landau-Ginzburg coefficients made in the literature.
Atmospheric Circulation Effects on Wind Speed Variability at Turbine Height

Abstract Mean monthly wind speed at 70 m above ground level is investigated for 11 sites in Minnesota for the period 1995–2003. Wind speeds at these sites show significant spatial and temporal coherence, with prolonged periods of above- and below-normal values that can persist for as long as 12 months. Monthly variation in wind speed primarily is determined by the north–south pressure gradient, which captures between 22% and 47% of the variability (depending on the site). Regression on wind speed residuals (pressure gradient effects removed) shows that an additional 6%–15% of the variation can be related to the Arctic Oscillation (AO) and Nino-3.4 sea surface temperature (SST) anomalies. Wind speeds showed little correspondence with variation in the Pacific–North American (PNA) circulation index. The effect of the strong El Nino of 1997/98 on the wind speed time series was investigated by recomputing the regression equations with this period excluded. The north–south pressure gradient remains the primary .
Image Denoising using Angular Packet Representation

We describe a new method for removing noise from digital images based on the select of the best angular decomposition for image denoising. We propose a new representation that we namely call the angular packet representation, defined by a recursive directional decomposition. The result is a dictionary of different tight frames, which provides a theoretical tool to select a best representation for image denoising by minimizing an estimated error to improve image denoising.
Emergency use of nebulized milrinone, a phosphodiesterase 3 inhibitor, in status asthmaticus: a case report in Suriname

Data indicate the benefits of phosphodiesterase inhibitors as an add-on treatment in severe asthma, but mainly because of the limited tolerability, no compound reaches the market. Low- and middle-income countries (LMIC) face the challenge of successfully treating status asthmaticus, due to little or no access to expensive drug regimens. We describe a case of a 25-year-old man admitted to the ICU with status asthmaticus where nebulized milrinone was used as a last resort as there was no improvement with optimal standard management. After initiation of nebulized milrinone the patient could be smoothly weaned from ventilation and discharged.
ON BLACKWELL'S MINIMAX THEOREM AND THE COMPOUND DECISION METHOD

Blackwell (1956a) proved a minimax theorem for games with a vector loss and characterized sets such that a player has a strategy under which, whatever strategy the other player uses, the average payoff approaches the set. Based on this result, Blackwell (1956b) described a strategy for a sequence of plays of a game, under which the average loss approaches the Bayes risk with respect to the relative frequencies of the opponent's actions. In both cases, the distance of the average loss from a set, in repeated plays of the game, was proved to converge to 0 with probability one. In both cases, we show that the rate of the convergence is better than (n/ log 1+t n) �1/2 for every positive t, obtain bounds for the L2 norm of the distance and extend the results to cases in which past losses and actions are only estimated.
The Robustness of Consensus in Network-Based Non-Bayesian Learning Models

This paper provides a formal characterization of non-Bayesian updating functions that yield asymptotic consensus in strongly connected networks. Extreme reluctance, a property of opinion formation that has been experimentally validated recently by Lorenz, Rauhut, Schweitzer and Helbing (2011) is shown to be crucial for asymptotic consensus.The paper further analyzes the robustness of asymptotic consensus when agents are subject to probabilistic mistakes while forming their opinions. A sufficient condition on the stochastic process of errors is characterized such that asymptotic consensus holds almost surely.As corollaries of the general theorems, new results to the literature on (i) political opinion formation with partisans, and (ii) cognitive dissonance are provided. Partisan and/or cognitive dissonant agents do not perpetuate sharp differences of opinions within social networks. Despite their presence, asymptotic consensus occurs in strongly connected networks.
COVID-19 Prognosis from Chest X-ray Images by using Deep Learning Approaches: A Next Generation Diagnostic Tool

Global public health is overwhelmed due to the ongoing Corona Virus Disease (COVID-19). As of October 2022, the causative virus SARS-CoV-2 and its multiple variants have infected more than 600 million confirmed cases and nearly 6.5 million fatalities globally. The main objective of this reported study is to understand the COVID-19 infection better from the chest X-ray (CXR) image database of COVID-19 cases from the dataset of CXR of normal, pneumonia and COVID-19 patients. Deep learning approaches like VGG-16 and LSTM models were used to classify images as normal, pneumonia and COVID-19 impacted by extracting the features. It has been observed during the COVID-19 pandemic peaks that large number of patients could not avail medical beds and were seen stranded outdoors. To address such health emergency situations with limited available bed and scarcity of expert physicians, computer-aided analysis could save precious lives through early screening and appropriate care. Such computer-based deep-learning strategy could help during future pandemics, especially when the available health resources and the need for preventive measures to take do not match the burden of a disease.
A Case Study of Trade-Wind Rainbands and Their Interaction with the Island-Induced Airflow

Abstract A case study of trade-wind rainbands observed on 22 August 1990 during the Hawaiian Rainband Project is presented. It shows that the interaction between the morning rainbands and the island-induced airflow is important for the evolution of the rainbands. In the early morning of 22 August, there are two convective periods, 0400–0600 and 0700–0900 HST (Hawaii standard time), on the windward side of the island of Hawaii. For both periods, preexisting rain cells are observed in the trade-wind flow at least 40 km upstream of the island and move westward toward the island. At night and in the early morning, the offshore flow opposes the trade winds resulting in a convergent region over the area immediately upstream of the island. As the first group of rain cells (0400–0600 HST) moves toward the island, the low-level convergent airflow provides a favorable kinematic background for the enhancement of the coming rain cells. These rain cells merge in the convergent zone and become a well-defined rainband.
Design-based metrology: beyond CD/EPE metrics to evaluate printability performance

Process-window (PW) evaluation is critical to assess the lithography process quality and limitations. Usual CD-based PW gives only a partial answer. Simulations such as Tachyon LMC (Lithography Manufacturability Check) can efficiently overcome this limitation by analyzing the entire predicted resist contours. But so far experimental measurements did not allow such flexibility. This paper shows an innovative experimental flow, which allows the user to directly validate LMC results across PW for a select group of reference patterns, thereby overcoming the limitations found in the traditional CD-based PW analysis. To evaluate the process window on wafer more accurately, we take advantage of design based metrology and extract experimental contours from the CD-SEM measurements. Then we implement an area metric to quantify the area coverage of the experimental contours with respect to the intended ones, using a defined “sectorization” for the logic structures. This ‘sectorization’ aims to differentiate specific areas on the logic structures being analyzed, such as corners, line-ends, short and long lines. This way, a complete evaluation of the information contained in each CD-SEM picture is performed, without having to discard any information. This solution doesn’t look at the area coverage of an entire feature, but uses a ‘sectorization’ to differentiate specific feature areas such as corners, line-ends, short and long lines, and thus look at those area coverages. An assessment of resist model/OPC quality/process quality at sub nm-level accuracy is rendered possible.
The de-excited energy of electron capture in accreting neutron star crusts

When a daughter nucleus produced by electron capture takes part in a level transition from an excited state to its ground state in accreting neutron star crusts, thermal energy will be released and heat the crust, increasing crust temperature and changing subsequent carbon ignition conditions. Previous studies show that the theoretical carbon ignition depth is deeper than the value inferred from observations because the thermal energy is not sufficient. In this paper, we present the de-excited energy from electron capture of rp-process ash before carbon ignition, especially for the initial evolution stage of rp-process ash, by using a level-to-level transition method. We find the theoretical column density of carbon ignition in the resulting superbursts and compare it with observations. The calculation of the electron capture process is based on a more reliable level-to-level transition, adopting new data from experiments or theoretical models (e.g., large-scale shell model and proton-neutron quasi-particle random phase approximation). The new carbon ignition depth is estimated by fitting from previous results of a nuclear reaction network. Our results show the average de-excited energy from electron capture before carbon ignition is ∼0.026 MeV/u, which is significantly larger than the previous results. This energy is beneficial for enhancing the crust's temperature and decreasing the carbon ignition depth of superbursts.
The Effects of Active Shootings on 4-H Youth and Families

The effects of active shootings should be a priority to provide needed assistance to 4-H youth and families in coping with their social-emotional well-being. Exposure to such violence can lead to lasting impacts on youth that can affect behavior. Addressing this sensitive topic is crucial in ensuring that Extension professionals are prepared to meet the needs of youth and families. Higher rates of depression, aggression, to name a few, are a result of having witnessed such events as a shooting. Providing training for Extension personnel can aid in reducing the amount of PTSD and other social-emotional trauma.
2.6- and 4-W E-Band GaN Power Amplifiers With a Peak Efficiency of 22% and 15.3%

In this letter, we report two high-power gallium nitride (GaN) power amplifiers (PAs) in the Satcom <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-band (71–86 GHz) with an output power of 2.6 and 4 W, designed by incorporating an ultralow-loss ON-chip integrated power combiner. The first one is a three-stage four-way combining (unit) PA, and the second one is an eight-way combining balanced PA. The unit PA produces a saturated output power (<inline-formula> <tex-math notation="LaTeX">$P_{\mathrm{SAT}}$ </tex-math></inline-formula>) of 34.2 dBm (2.6 W), a peak power-added-efficiency (PAE) of 22%, and an associated power gain of 16.2 dB at 74 GHz. This performance was partly made possible by the design and optimization of the low-loss integrated power combiner, which minimized the losses in the matching networks. In addition, the balanced PA produces a <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm{SAT}}$ </tex-math></inline-formula> of 36 dBm (4 W), <inline-formula> <tex-math notation="LaTeX">$P_{\text {1 dB}}$ </tex-math></inline-formula> of 35.6 dBm (3.63 W), with an associated PAE of 15.3% at 80 GHz. To the best of the authors’ knowledge, this is the highest output power (4 W) and the highest PAE (22%) for a PA > 2.5 W reported in any of the III–V technologies at <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-band.
A mathematical model to forecast solar PV performance

ABSTRACT This work focuses on developing a mathematical model to forecast the performance of solar PV. In the present scenario, solar photovoltaic performance has been envisaged by considering the impact of limited variables subject to certain assumptions and conditions and the current work drives the attention toward the effect of temperature, relative humidity, wind velocity, and panel aging effects. There hasn’t been any breakthrough in the previous research so far while collectively analyzing the efficiency loss in solar photovoltaic, due to the aforementioned parameters. Hence, this work aims to develop a mathematical model in view of the above-mentioned parameters. This work has been experimented using MATLAB software and online simulation tools are used to validate the results. The results are favorable with the deviation of efficiency between the online simulation tool and the proposed mathematical model being 2–4%. Furthermore, a real-time case study has been carried out at a solar power plant installed at Thiagarajar College of Engineering, Madurai, Tamilnadu, to validate the proposed mathematical model. It is observed that the absolute difference in power between the simulation and the actual meter reading is between 30 and 60 Watts with an RMSE between 5 and 16% and MPE of 675 Watts.
Inflation driven by exponential non-minimal coupling of inflaton with gravity

We consider a modified gravity model of the form $ f(R,\phi)=R e^{h(\phi)R} $, where $\phi$ is a non-interacting massive scalar field. We show that the conformal transformation of this model to Einstein frame leads to non-canonical kinetic term and negates the advantage of the Einstein frame. We obtain exact solutions for the background and show that the model leads to inflation with exit. We obtain scalar and tensor power-spectrum in Jordan frame and show that the model leads to blue-tilt. We discuss the implications of the same in the light of cosmological observations.