Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-Particle Optical Imaging for Ultrasensitive Bioanalysis

The quantitative detection of critical biomolecules and in particular low-abundance biomarkers in biofluids is crucial for early-stage diagnosis and management but remains a challenge largely owing to the insufficient sensitivity of existing ensemble-sensing methods. The single-particle imaging technique has emerged as an important tool to analyze ultralow-abundance biomolecules by engineering and exploiting the distinct physical and chemical property of individual luminescent particles. In this review, we focus and survey the latest advances in single-particle optical imaging (OSPI) for ultrasensitive bioanalysis pertaining to basic biological studies and clinical applications. We first introduce state-of-the-art OSPI techniques, including fluorescence, surface-enhanced Raman scattering, electrochemiluminescence, and dark-field scattering, with emphasis on the contributions of various metal and nonmetal nano-labels to the improvement of the signal-to-noise ratio. During the discussion of individual techniques, we also highlight their applications in spatial–temporal measurement of key biomarkers such as proteins, nucleic acids and extracellular vesicles with single-entity sensitivity. To that end, we discuss the current challenges and prospective trends of single-particle optical-imaging-based bioanalysis.
Polaroid SX-70 camera system: an optical-mechanical-chemical tightly-coupled design

Abstract. We survey the design of the Polaroid SX-70 camera system, to exhibit what it means to design a tightly-coupled system (rather than a loosely coupled or modular one). The optics, the mechanics of the camera, and the chemistry of the film-picture unit are described as being adapted to each other so that we have a system, and the system as a whole is optimized with superfluous refinements avoided. We then discuss the picture-making experience inherent in such a system.
A GRASP for a real-world scheduling problem with unrelated parallel print machines and sequence-dependent setup times

We consider a real-world scheduling problem arising in the colour printing industry. The problem consists in assigning print jobs to a heterogeneous set of flexographic printer machines and finding a processing sequence for the jobs assigned to each machine. The machines are characterised by a limited sequence of colour groups and can equip additional components (e.g. embossing rollers and perforating rolls) to process jobs that require specific treatments. The process to equip a machine with an additional component or to clean a colour group takes a long time, with the effect of significantly raising the setup times. The aim is to minimise a weighted sum of total weighted tardiness and total setup time. The problem derives from the activities of an Italian food packaging company. To solve it, we developed a greedy randomised adaptive search procedure equipped with several local search procedures. The excellent performance of the algorithm is proved by extensive computational experiments on real-world instances, for which it produced good-quality solutions within a limited computing time. The algorithm is currently in use at the company to support their weekly scheduling decisions.
CASPAR: Cloud-based Alzheimer’s, Schizophrenia and Parkinson’s Automatic Recognizer

Alzheimer’s Disease (AD), Parkinson’s Disease (PD) and SchiZophrenia (SZ) are the principal brain dysfunctions, whose diagnosis is still challenging due to their complex clinical manifestations. Structural Magnetic Resonance (MR) is an effective way to diagnose brain dysfunctions by catching structural (like the ones caused by AD and PD) or connectivity (like the ones caused by SZ) alterations. As far as we know, we were the first to propose an end-to-end Convolutional Long Short-Term Memory (ConvLSTM)-based framework, ConvLSTM4AD, and to develop an improved version of it, Brain-on-Cloud, for AD automatic recognition from MR whole-brain scans. This research aims to extend their applicability to other brain dysfunctions and verify their efficiency through the development of CASPAR, a cloud-based AD, PD and SZ automatic recognizer that employs a neural network, whose principal layer is a ConvLSTM layer, fed with MR whole-brain scans. To this aim, we selected unenhanced MR scans from the ADNI database, the PPMI database and the COBRE respectively for AD, PD and SZ, and preprocessed them. Next, we divided the preprocessed MR scans in train, validation and test sets, and performed train data augmentation. Then, we fed the preprocessed MR scans to the ConvLSTM-based neural network for automatic feature extraction and classification on cloud. Eventually, we evaluated the neural network performance on test data. CASPAR achieved very promising results in automatically recognizing AD (average sensitivity of 96.43%), PD (average sensitivity of 95.48%) and SZ (average sensitivity of 100.00%), even outperforming recent state-of-the-art frameworks. With the development of CASPAR, we demonstrated that a cloud-based and ConvLSTM-based framework that works well for AD automatic recognition, works well also for PD and SZ automatic recognition, paving the way to a fully-integrated system for simultaneous automatic recognition of multiple brain dysfunctions from MR whole-brain scans.
Crack Unet: Crack Recognition Algorithm Based on Three-Dimensional Ground Penetrating Radar Images

Three-dimensional (3D) ground-penetrating radar is an effective method for detecting internal crack damage in pavement structures. Inefficient manual interpretation of radar images and high personnel requirements have substantially restrained the generalization of 3D ground-penetrating radar. An improved Crack Unet model based on the Unet semantic segmentation model is proposed herein for 3D ground-penetrating radar crack image processing. The experiment showed that the MPA, MioU, and accuracy of the model were improved, and it displayed better capacity in the radar image crack segmentation task than current mainstream algorithms do, such as deepLabv3, PSPNet, and Unet. In the test dataset without cracks, Crack Unet is on the same level as deepLabv3 and PSPNet, which can meet engineering requirements and display a significant improvement compared with Unet. According to the ablation experiment, the MPA and MioU of Unet configured with PMDA, MC-FS, and RS modules were larger than those of Unet configured with one or two modules. The PMDA module adopted by the Crack Unet model showed a higher MPA and MioU than the SE module and the CBAM module did, respectively. The results show that the Crack Unet model has a better segmentation ability than the current mainstream algorithms do in the task of the crack segmentation of radar images, and the performance of crack segmentation is significantly improved compared with the Unet model. The Crack Unet model has excellent engineering application value in the task of the crack segmentation of radar images.
Arithmetic properties of l-regular overpartitions

Recently, Andrews introduced the partition function C¯k,i(n) as the number of overpartitions of n in which no part is divisible by k and only parts ≡±i(modk) may be overlined. He proved that C¯3,1(9n + 3) and C¯3,1(9n + 6) are divisible by 3. Let A¯l(n) be the number of overpartitions of n into parts not divisible by l. In this paper, we call the overpartitions enumerated by the function A¯l(n) l-regular overpartitions. For A¯3(n) and A¯4(n), we obtain some explicit results on the generating function dissections. We also derive some congruences for A¯l(n) modulo 3, 6 and 24 which imply the congruences for C¯3,1(n) proved by Andrews. By introducing a rank of vector partitions, we give a combinatorial interpretation of the congruences of Andrews for C¯3,1(9n + 3) and C¯3,1(9n + 6).
MEDIATED PERFECT BODY IMAGES AND WOMEN: UNDERSTANDING ROLE OF SOCIAL MEDIA IN CREATING PRESSURES ON YOUNG WOMEN FACING HEIGHT, WEIGHT AND COMPLEXION STIGMA

The mediated images of perfect body create concerns for its consumers. Such mediated images set some ideal and unrealistic standards of body features especially about weight, height and complexion that marginalize the individuals. The stigmatization of body features and beauty standards posits certain expectations in society. The social media is crucially becoming responsible for propagating the ideal and perfect body images due to its availability and wide consumption. So, this research study tends to understand the role of social media in creating pressures on young women and also how the lives of women facing weight, height and complexion stigma are being affected due to different kind of pressures. The exploratory research design with in-depth interviews as research method was opted. The research study revealed that women generally face lots of pressures due to mediated perfect body images and they also face body shaming when they are not meeting the expected beauty standards. The women find mediated body images very stereotypical and there are many stigma associated with body images. The practices of body shaming create certain pressures on women from families, peers and social media. Such pressures includes psychological and physical pressures that creates marital issues, low self-esteem, lack of confidence, negative feelings, insecurities, self- complexes, depression, loneliness, anxiety and eating disorders. Keywords: Mediated body images, Social media, Social pressures, Stigma, Women
Astatine-211-Labeled Gold Nanoparticles for Targeted Alpha-Particle Therapy via Intravenous Injection

Alpha-particle radiotherapy has gained considerable attention owing to its potent anti-cancer effect. 211At, with a relatively short half-life of 7.2 h, emits an alpha particle within a few cell diameters with high kinetic energy, which damages cancer cells with high biological effectiveness. In this study, we investigated the intravenous injection of 211At-labeled gold nanoparticles (AuNPs) for targeted alpha-particle therapy (TAT). Different kinds of surface-modified gold nanoparticles can be labeled with 211At in high radiochemical yield in 5 min, and no purification is necessary. The in vivo biodistribution results showed the accumulation of 5 nm 211At-AuNPs@mPEG at 2.25% injection dose per gram (% ID/g) in tumors within 3 h via the enhanced permeability and retention (EPR) effect. Additionally, we observed a long retention time in tumor tissues within 24 h. This is the first study to demonstrate the anti-tumor efficacy of 5 nm 211At-AuNPs@mPEG that can significantly suppress tumor growth in a pancreatic cancer model via intravenous administration. AuNPs are satisfactory carriers for 211At delivery, due to simple and efficient synthesis processes and high stability. The intravenous administration of 5 nm 211At-AuNPs@mPEG has a significant anti-tumor effect. This study provides a new framework for designing nanoparticles suitable for targeted alpha-particle therapy via intravenous injection.
Wafer-scale layer transfer of GaAs and Ge onto Si wafers using patterned epitaxial lift-off

We have developed a wafer-scale layer-transfer technique for transferring GaAs and Ge onto Si wafers of up to 300 mm in diameter. Lattice-matched GaAs or Ge layers were epitaxially grown on GaAs wafers using an AlAs release layer, which can subsequently be transferred onto a Si handle wafer via direct wafer bonding and patterned epitaxial lift-off (ELO). The crystal properties of the transferred GaAs layers were characterized by X-ray diffraction (XRD), photoluminescence, and the quality of the transferred Ge layers was characterized using Raman spectroscopy. We find that, after bonding and the wet ELO processes, the quality of the transferred GaAs and Ge layers remained the same compared to that of the as-grown epitaxial layers. Furthermore, we realized Ge-on-insulator and GaAs-on-insulator wafers by wafer-scale pattern ELO technique.
Japanese species of Ormosia Rondani (Diptera, Limoniidae): revision of the subgenera Oreophila Lackschewitz and Parormosia Alexander

﻿Abstract Japanese species of the subgenera Oreophila Lackschewitz and Parormosia Alexander of the genus Ormosia Rondani (Limoniidae) are revised. Two new species Ormosia (Oreophila) komazawai Kato & Kolcsár, sp. nov. and Ormosia (Parormosia) phalara Kato & Kolcsár, sp. nov. are described. The identities of all Japanese species of the two subgenera are clarified and redescribed with images of habitus and wings, and drawings of male and female terminalia. The first DNA barcode sequences of the species Ormosia (Parormosia) diversipes Alexander and Ormosia (Parormosia) phalara Kato & Kolcsár, sp. nov. are also provided. A key to, and distribution maps of, the Japanese species are provided.
Modeling of bouncing of a single clean bubble on a free surface

Bouncing of a rectilinearly rising bubble on a free surface is modeled with the use of a simple mass-spring system. The most striking characteristic of the present model is that it is based on an equation of motion of the system that consists of two springs connected in series; hence, we can account for the restoring forces of both the bubble and free surfaces. This equation of motion is coupled with a conservation equation of energy that is taking into account an exchange between the surface energy due to deformations of both the bubble and free surfaces and the kinetic energy of the bubble. With the use of these equations, the contact time, i.e., the time duration of a bubble contacting a free surface, is determined to be a half of the characteristic period of the oscillator. We also conducted experiments of a single clean bubble in ultrapure water bouncing on a flat free surface in order to verify the present model. We found that results obtained from the model agree well with the experimentally obtain.
Minimax Risk Over Hyperrectangles, and Implications

Consider estimating the mean of a standard Gaussian shift when that mean is known to lie in an orthosymmetric quadratically convex set in l 2 . The minimax risk among linear estimates is within 25% of the minimax risk among all estimates. The minimax risk among truncated series estimates is within a factor 4.44 of the minimax risk. This implies that the difficulty of estimation ― a statistical quantity ― is measured fairly precisely by the n-widths ― a geometric quantity. If the set is not quadratically convex, as in the case of l p -bodies with p<2, things change appreciably. Minimax linear estimators may be out-performed arbitrarily by nonlinear estimates
Early Detection and Control of the Next Epidemic Wave Using Health Communications: Development of an Artificial Intelligence-Based Tool and Its Validation on COVID-19 Data from the US

Social media networks highly influence on a broad range of global social life, especially in the context of a pandemic. We developed a mathematical model with a computational tool, called EMIT (Epidemic and Media Impact Tool), to detect and control pandemic waves, using mainly topics of relevance on social media networks and pandemic spread. Using EMIT, we analyzed health-related communications on social media networks for early prediction, detection, and control of an outbreak. EMIT is an artificial intelligence-based tool supporting health communication and policy makers decisions. Thus, EMIT, based on historical data, social media trends and disease spread, offers an predictive estimation of the influence of public health interventions such as social media-based communication campaigns. We have validated the EMIT mathematical model on real world data combining COVID-19 pandemic data in the US and social media data from Twitter. EMIT demonstrated a high level of performance in predicting the next epidemiological wave (AUC = 0.909, F1 = 0.899).
A Robust Voltage Control of DAB Converter With Super-Twisting Sliding Mode Approach

This article presents a supertwisting sliding mode control (ST-SMC) for the isolated dc-dc bi-directional dual active bridge (DAB) power converter for enhanced output voltage regulation. The sliding mode control (SMC) is well-known for its robustness and improved dynamic performance for a nonlinear system. However, it includes the drawback of chattering and degradation of tracking performance. Here, the suggested ST-SMC scheme incorporates the advantages of a sliding mode controller and subsequently eliminates the problem of chattering. The average model of DAB under single phase-shift modulation is considered for controller design, which simplifies the design process. Thus, improved dc-link voltage stability and high robustness for accurate reference voltage tracking are obtained. Further, the proposed scheme provides a robust control for parameter uncertainties as well as the steady-state output voltage contains less ripple when compared to the classical SMC and proportional-integral controller. The experimental results for a 2 kW DAB converter are provided to validate the effectiveness and the robustness of the proposed strategy.
Prediction of Scalp EEG Waveforms from Forehead Electrodes Using Convolutional Neural Networks to Improve Signal-to-Noise Ratio

The electroencephalogram (EEG) is a non-invasive method for measuring brain activity, and event-related potentials (ERPs)—EEG responses observed to be time-locked to events—have been used for brain-computer interfaces (BCI) in real-world environments. An EEG is generally measured from electrodes placed on the scalp. However, it is not suitable for daily use because the preparation time is relatively long, and the electrodes are likely to cause discomfort to the user. EEG measurements from disposable electrodes placed on the forehead (forehead EEG) have been used to mitigate this disadvantage. However, because many ERP components used in BCI show the maximal voltage on the scalp, the signal-to-noise ratio (SNR) of ERPs obtained from a forehead EEG is low, which may affect the reliability of a BCI system. To address this shortcoming, we propose convolutional neural networks that predict the EEG signal measured from electrodes placed on the scalp (scalp EEG) from forehead EEG. In the study, we focused on predicting the mismatch negativity (MMN) responses, and single-trial scalp EEG at Fz was predicted from three forehead EEG measures (Fpz, horizontal, and vertical electrooculograms). Data were measured while nine subjects performed a passive auditory oddball task. To evaluate the proposed model, the mean squared error (MSE) between the observed single-trial EEG at Fz and the predicted single-trial EEG from three forehead EEG measures was calculated, as well as the MSE between the observed ERP difference wave (deviant – standard) at Fz and the difference wave predicted from three forehead EEG measures within the time window in which MMN was observed. The result showed that within the time window in which MMN was observed, the MSE between the ERP difference wave at Fz and the ERP difference wave predicted from three forehead EEG measures was significantly smaller than the MSE between the ERP difference wave at Fz and the ERP difference wave at the forehead (Fpz). This indicates that the proposed neural network improved the SNR of the forehead EEG for predicting ERP responses at the scalp and could lead to enhancing the usefulness of forehead EEG for BCI use in daily life.
A method of discretization for the Lagerström-Cole model equation

A method of discretization for a non linear singularly perturbed boundary value problem is considered. It involves a certain number of steps, one of them including the application of Petrov-Galerkin finite element methods. The resulting scheme is called adjoint method scheme and is in some way related to Niijima's scheme (cf. [ 11 ] ). It is proved that this discretization provides existence and uniqueness of solution for a problem defined by the Lagerstr o m- Cole model equation. Finally some numerical experiments compare the results obtained when the adjoint method scheme is used, as well as when Niijima's scheme or a direct finite element discretization are applied.
The Origin of Stereoselectivity in the Hydrogenation of Oximes Catalyzed by Iridium Complexes: A DFT Mechanistic Study

Herein the reaction mechanism and the origin of stereoselectivity of asymmetric hydrogenation of oximes to hydroxylamines catalyzed by the cyclometalated iridium (III) complexes with chiral substituted single cyclopentadienyl ligands (Ir catalysts A1 and B1) under acidic condition were unveiled using DFT calculations. The catalytic cycle for this reaction consists of the dihydrogen activation step and the hydride transfer step. The calculated results indicate that the hydride transfer step is the chirality-determining step and the involvement of methanesulfonate anion (MsO−) in this reaction is of importance in the asymmetric hydrogenation of oximes catalyzed by A1 and B1. The calculated energy barriers for the hydride transfer steps without an MsO− anion are higher than those with an MsO− anion. The differences in Gibbs free energies between TSA5−1fR/TSA5−1fS and TSB5−1fR/TSB5−1fS are 13.8/13.2 (ΔΔG‡ = 0.6 kcal/mol) and 7.5/5.6 (ΔΔG‡ = 1.9 kcal/mol) kcal/mol for the hydride transfer step of substrate protonated oximes with E configuration (E−2a−H+) with MsO− anion to chiral hydroxylamines product R−3a/S−3a catalyzed by A1 and B1, respectively. According to the Curtin–Hammet principle, the major products are hydroxylamines S−3a for the reaction catalyzed by A1 and B1, which agrees well with the experimental results. This is due to the non-covalent interactions among the protonated substrate, MsO− anion and catalytic species. The hydrogen bond could not only stabilize the catalytic species, but also change the preference of stereoselectivity of this reaction.
Hawkes process marked with topics and its application to Twitter data analysis

On Twitter, complex networks of information propagation are observed. In this article, we propose a method to model the time interval of user's tweets by combining the marked Hawkes process and the Latent Dirichlet Allocation method. We also propose a method that quantifies how individual topics in tweets excite future tweets and visualize the results as a topic-wise Hawkes graph. As an application to actual data, we analyze the tweets from the American, Chinese, and British embassies from February 1, 2020, to September 27, 2020, a period that roughly corresponds to the initial outbreak of the COVID-19 pandemic.
A Prototype for Mexican Sign Language Recognition and Synthesis in Support of a Primary Care Physician

Few hearing people know and use Mexican Sign Language (MSL). Consequently, this is the main barrier between people who having total or partial hearing loss and hearing people. This study proposes a system that recognizes and animates in real time a set of signs belonging to the semantic field of general medicine consultation services. Therefore, a linkage between a hearing doctor and a deaf patient can be established in a non-intrusive way and with easy dynamic interaction. Our main contribution is a bidirectional translator system for Mexican Sign Language in the context of primary care health services, in addition to basic signs to fingerspell alphabet and numbers as a complement to provide personal information such as name, age, etc. The recognition module uses a Microsoft Kinect sensor to obtain sign trajectories and images to feed hidden Markov Models (HMMs) for processing sign samples in real time. The experiments showed the recognition of 82 different signs by 22 participants. As a result, accuracy and F1 scores average rates of 99% and 88%, respectively, were obtained.