Miracle-dz/UniFAR-FTUs huggingface dataset:
Major Components of Aerosols in North China: Desert Region and the Yellow Sea in the Spring and Summer of 1995 and 1996.

Abstract Aerosol samples were collected from the northwest China desert region (i.e., Minqin), a coastal suburb area (i.e., Qingdao), and an island from the Yellow Sea (i.e., Qianliyan), respectively, in spring and summer of 1995 and 1996. Samples were analyzed for major components (Al, Ca, K, Mg, and Na), carbon (RAC), and sulphur (RAS). The results show that concentration of aerosols change considerably in time and space. The application of a three end-member-mixing model indicates that dust-dominated materials contribute up to 80%–90% of total aerosols when the cold fronts pass through over the Yellow Sea. The crust-dominated aerosols carried by cold front systems may well reduce the percentage concentrations of pollutant and sea salt over the Yellow Sea. The sea salt and regional aerosols become dominant fractions in atmosphere in summer when the dust storms expire in desert regions and the southeast monsoon starts in the subtropical Pacific Ocean.
Comparison of electrical and optical characteristics in gas-phase and gas-liquid phase discharges

This paper presents an AC-excited argon discharge generated using a gas-liquid (two-phase) hybrid plasma reactor, which mainly consists of a powered needle electrode enclosed in a conical quartz tube and grounded deionized waterelectrode. The discharges in the gas-phase, as well as in the two-phase, exhibit two discharge modes, i.e., the low current glow-like diffuse mode and the high current streamer-like constrict mode, with a mode transition, which exhibits a negative resistance of the discharges. The optical emission spectral analysis shows that the stronger diffusion of the water vapor into the discharge region in the two-phase discharges boosts up the generation of OH (A–X) radicals, and consequently, leads to a higher rotational temperature in the water-phase plasma plume than that of the gas-phase discharges. Both the increase of the power input and the decrease of the argon flow rate result in the increase of the rotational temperature in the plasma plume of the water-phase discharge. The stable two-phase discharges with a long plasma plume in the water-phase under a low power input and gas flow rate may show a promising prospect for the degradation of organic pollutants, e.g., printing and dyeing wastewater, in the field of environmental protection.
Generalization of Zero-Knowledge Proof of Polynomial Equality

In this paper, we are interested in a generalization of zero-knowledge interactive protocols between prover and verifier, especially to show that the product of an encrypted polynomial and a random polynomial, but published by a secure commitment scheme was correctly computed by the prover. To this end, we provide a generalized protocol for proving that the resulting polynomial is correctly computed by an encrypted polynomial and another committed polynomial. Further we show that the protocol is also secure in the random oracle model. We expect that our generalized protocol can play a role of building blocks in implementing secure multi-party computation including private set operations.
Associated to Certain Shift Spaces

In (24) Matsumoto associated to each shift space (also called a subshift) an Abelian group which is now known as Mat- sumoto's K0-group. It is defined as the cokernel of a certain map and resembles the first cohomology group of the dynamical system which has been studied in for example (2), (28), (13), (16) and (11) (where it is called the dimension group). In this paper, we will for shift spaces having a certain property (¤), show that the first cohomology group is a factor group of Matsumoto's K0-group. We will also for shift spaces having an additional property (¤¤), describe Matsumoto's K0-group in terms of the first cohomol- ogy group and some extra information determined by the left special elements of the shift space.
Application Study on Fuzzy Multiscale Edge Detection Alg orithm

A fuzzy multiscale edge detection algorithm of im ag e based on wavelet multiresolution analysis is presented in this paper. The dyad ic wavelet transform is employed to produce the multiscale representation of ima ge, and fuzzy decision method is applied in wavelet domain of different scales. The information of images with different scales can be synthesized effectively, and an optimal result of edge detection can be acquired. The paper compares the performances of the new algorithm with the Canny algorithm and Mallats algori thm according to the concrete image signals. The results prove the feasibility a nd effectiveness of our algorithm over these other methods.
8.—Bifurcation and Asymptotic Bifurcation for Non-compact Nonsymmetric Gradient Operators

Synopsis The first part of this paper is devoted to a study of the classical bifurcation problem in a Hilbert space, under the assumption that the operators involved are gradient operators, but not necessarily compact. Our approach to the problem was introduced by Krasnosel'skii, but here we show that his assumption about the compactness of the operators can be replaced by a much weaker Lipschitz type condition, without affecting the generality of his conclusions. The rest of the paper is concerned with the analogous problem when the operator is knownto be asymptotically linear rather than Fréchet differentiable. Indeed, we show that this question can always be reduced to the first case, after some manipulation. After this manipulation the new operator is found to be a Fréchet differentiable gradient operator, and so we can invoke the results of the first part. This manipulation is in the spirit of that of [11] but is necessarily different.
Low-energy-spread laser wakefield acceleration using ionization injection with a tightly focused laser in a mismatched plasma channel

An improved ionization injection scheme for laser wakefield acceleration using a tightly focused laser pulse, with intensity near the ionization threshold to trigger the injection in a mismatched plasma channel, has been proposed and examined via 3D particle-in-cell (PIC) simulations. In this scheme, the key to achieving a very low energy spread is shortening the injection distance through the fast diffraction of the tightly focused laser. Furthermore, the oscillation of the laser envelope in the mismatched plasma channel can induce multiple low-energy-spread injections with an even distribution in both space and energy. The envelope oscillation can also significantly enhance the energy gain of the injected beams compared to the standard non-evolving wake scenario due to the rephasing between the electron beam and the laser wake. A theoretical model has been derived to precisely predict the injection distance, the ionization degree of injection atoms/ions, the electron yield as well as the ionized charge for given laser–plasma parameters, and such expressions can be directly utilized for optimizing the quality of the injected beam. Through 3D PIC simulations, we show that an injection distance as short as tens of microns can be achieved, which leads to ultrashort fs, few pC electron bunches with a narrow absolute energy spread around 2 MeV (rms). Simulations also show that the initial absolute energy spread remains nearly constant during the subsequent acceleration due to the very short bunch length, and this indicates that further acceleration of the electron bunches up to the GeV level may lead to an electron beam with an energy spread well below 0.5%. Such low-energy-spread electron beams may have potential applications for future coherent light sources driven by laser–plasma accelerators.
An Improved Entropy Minimization Method for Correction of Intensity Inhomogeneity in MRI Data

Intensity inhomogeneity (ⅡH) is often encountered in MRI image, which may influence unfavorably human diagnosis and computer auto-process. Due to the fact that intensity bias field is usually smooth and changing slowly, an improved method based on entropy minimization is proposed for this problem in this paper. The method does not require manual interaction or prior knowledge. First, we extract the characteristic points by a fast fuzzy C-mean clustering algorithm from degraded image, which reflect the rude direction of bias field. Then, we get the parameters of the initialization of bias field by quadric surface fitting technique. The algorithm of Particle Swarm Optimization, which has the ability of study, is used to find the optimization of minimized image entropy. Using the proposed method, the correction of simulated MRI (from Brain Web database) and real MRI images are suitable for observation, which provided accurate image data for diagnoses.
Obesity is an Independent Risk Factor for In-Hospital Mortality among Patients Admitted for Heart Failure: A National Study

Background Obesity is a major risk factor for developing chronic conditions such as cardiovascular disease and diabetes. However, the phenomenon of "obesity paradox" has been reported over the past two decades which makes the relationship between BMI and inpatient mortality unclear. Methods This study identified adult patients (aged 18 years or older) who were admitted to hospitals for the primary cause of heart failure during 2017-2019 from the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) database. Baseline characteristics (i.e., weight status, age, gender, race, Elixhauser score) at the time of admission and the comorbidities were collected. Associations between weight status and in-hospital mortality were examined using logistic regression models that adjusted for individual comorbidities and global risk measures. The likelihood of patients developing each disease comorbidity under different obesity states was examined using logistic regression and the odds were compared across all the disease comorbidities. Results The study identified 204,970 hospital admissions with 4,290 (2.1%) deaths during the hospitalization and 200,680 (97.9%) live discharges. Analysis that did not adjust for individual comorbidities demonstrated the paradox. However, when adjusting for individual comorbidities and global risk measures and compared to the normal-weight patients, those who had higher BMI had an increased risk for in-hospital mortality. BMI of 35-39.9 group had a 26.5% higher likelihood of in-hospital mortality (OR=1.265, 95% CI: 1.066 - 1.503); BMI of 40-69.9 groups was 61.0% to 83.8% higher odds to die in hospital (OR ranged from 1.610 to 1.838, 95% CI varied); patients with a BMI of 70 and above had higher odds of in-hospital mortality (OR=3.144, 95%CI: 2.351 - 4.203). Conclusion Obesity is an independent risk factor for in-hospital mortality among patients who were admitted for heart failure. Adjustment for individual comorbidities resolves the obesity paradox. Patients with obesity have a different spectrum of diseases compared to non-obese patients, which may lead to the obesity paradox and bias in the inpatient outcome evaluation.
Cancer-Associated Thrombosis: Risk Assessment, Prevention, and Treatment

The risk of developing venous thromboembolism (VTE) is four to seven times higher in patients with cancer than in those without. At JADPRO Live 2022, presenters discussed risk factors and assessing patients for VTE, as well as how to protect patients against VTE in both the inpatient and outpatient clinic settings. They reviewed selecting an appropriate anticoagulation treatment, including the choice of agent and duration of treatment for the patient with cancer, and finally the steps needed to assess and treat patients experiencing therapeutic anticoagulation failure.
Partitioning and subsampling statistics in compartment-based quantification methods

The precision of compartment-based quantification methods is subject to multiple effects, of which partitioning and subsampling play a major role. Partitioning is the process of aliquoting the sample liquid and consequently the contained target molecules, whereas subsampling denotes the fact that usually only a portion of a sample is analyzed. In this work, we present a detailed statistical description comprising the effects of partitioning and subsampling on the relative uncertainty of the test result. We show that the state-of-the-art binomial model does not provide accurate results for the level of subsampling present when analyzing the nucleic acid content of single specific cells. Hence, in this work we address partitioning and subsampling effects separately and subsequently combine them to derive the relative uncertainty of a test system and compare it for single cell content analysis and body fluid analysis. In point-of-care test systems the area for partitioning and detection is usually limited, which means that a trade-off between the number of partitions (related to a partitioning uncertainty) and the amount of analyzed volume (related to a subsampling uncertainty) might be inevitable. In case of low target concentration, the subsampling uncertainty is dominant whereas for high target concentration, the partitioning uncertainty increases, and a larger number of partitions is beneficial to minimize the combined uncertainty. We show, that by minimizing the subsampling uncertainty in the test system, the quantification uncertainty of low target concentrations in single cell content analysis is much smaller than in body fluid analysis. In summary, the work provides the methodological basis for a profound statistical evaluation of partitioning and subsampling effects in compartment-based quantification methods and paves the way towards an improved design of future digital quantification devices for highly accurate molecular diagnostic analysis at the point-of-care.
A smart solution for the vibration suppression in cables for the electric power distribution

Vibration reduction in mechanical systems is an open issue. Indeed the stresses associated to the dynamic amplifications can affect systems’ performances and life cycle time. Nowadays the possibility to integrate sensors, actuators and control logics, in order to create a smart structure able to reduce vibration levels in all the operating conditions, represents a very attractive solution. This paper presents a comparison between two control strategies for vibration suppression in flexible structures: the Direct Velocity Feedback (DVF) and the Active Tuned Mass Damper (ATMD). As test case, the model of a smart cable for electric power distribution has been investigated.
A Reconstruction Method for Compressed Sampling in Shift-Invariant Spaces

A traditional sampling method is that the signal should be sampled at a rate exceeding twice the highest frequency. This is based on the assumption that the signal occupies the entire bandwidth. In practice, however, many signals are sparse so that only part of the bandwidth is used. Compressed sampling has been developed for low-rate sampling of continuous time sparse signals in shift-invariant spaces generated by m kernels with period T . However, in general the reconstruction of compressed sampling signals is unstable. To reconstruct the signal, continuous reconstruction is replaced by generalized inverse. In this paper, periodic non-uniform sampling and the reconstruction of functions in shift-invariant spaces are discussed, the unique sparse expression is obtained by using the minimal L 1 norm. Also, necessary condition and error of reconstruction were analyzed. Finally, the method was validated via simulation and it was shown that the method was effective.
A Rare Case of Cluster Headache Occurring Exclusively During Sleep Without Autonomic Symptoms and Agitation: A Case Report and Literature Review

As a primary headache disorder, cluster headache (CH) is a severe unilateral headache that recurs at certain times of the year, such as during season changes. It is characterized by autonomic symptoms, such as ipsilateral lacrimal and nasal discharge, as well as an inability to stay still during headache attacks. We report a rare case of CH in a 67-year-old male who presented with a severe right-sided headache lasting 30 minutes to one hour and occurring only during sleep. The headache resolved within five minutes after the subcutaneous injection of sumatriptan and was not accompanied by any autonomic symptoms or clear agitation.
Advancing the Care of Women With HER2-Positive Metastatic Breast Cancer

HER2 is overexpressed and/or amplified in approximately 20% of breast cancers. It is a clinically aggressive subtype; however, the introduction of targeted therapies has greatly improved survival rates. At JADPRO Live 2022, presenters discussed recent updates to clinical practice for HER2-positive metastatic breast cancer, and how to interpret emerging evidence on HER2-low data. They also highlighted best practices for monitoring and managing side effects for patients on these therapies.
A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them

Shapley values originated in cooperative game theory but are extensively used today as a model-agnostic explanation framework to explain predictions made by complex machine learning models in the industry and academia. There are several algorithmic approaches for computing different versions of Shapley value explanations. Here, we focus on conditional Shapley values for predictive models fitted to tabular data. Estimating precise conditional Shapley values is difficult as they require the estimation of non-trivial conditional expectations. In this article, we develop new methods, extend earlier proposed approaches, and systematize the new refined and existing methods into different method classes for comparison and evaluation. The method classes use either Monte Carlo integration or regression to model the conditional expectations. We conduct extensive simulation studies to evaluate how precisely the different method classes estimate the conditional expectations, and thereby the conditional Shapley values, for different setups. We also apply the methods to several real-world data experiments and provide recommendations for when to use the different method classes and approaches. Roughly speaking, we recommend using parametric methods when we can specify the data distribution almost correctly, as they generally produce the most accurate Shapley value explanations. When the distribution is unknown, both generative methods and regression models with a similar form as the underlying predictive model are good and stable options. Regression-based methods are often slow to train but produce the Shapley value explanations quickly once trained. The vice versa is true for Monte Carlo-based methods, making the different methods appropriate in different practical situations.
Absolutely Stable Discrete Time Crystals

We show that interacting bosons on a ring which are driven periodically by a rotating lattice potential can support absolutely stable discrete time crystals. The absolute stability is demonstrated by an exact mapping of discrete time crystal states to low-lying eigenstates of a time-independent model that reveals spontaneous breaking of space translation symmetry. The mapping ensures that there are no residual time-dependent terms that could lead to heating of the system and destruction of discrete time crystals. With the help of the Bethe ansatz solutions we also analyze periodically kicked bosons where the mapping is approximate only and cannot guarantee the absolute stability of discrete time crystals. However, the kicked boson model shows a richer interplay between time and space symmetry breaking.
Accelerating Quantum Algorithms with Precomputation

Real-world applications of computing can be extremely time-sensitive. It would be valuable if we could accelerate such tasks by performing some of the work ahead of time. Motivated by this, we propose a cost model for quantum algorithms that allows quantum precomputation; i.e., for a polynomial amount of ``free'' computation before the input to an algorithm is fully specified, and methods for taking advantage of it. We analyze two families of unitaries that are asymptotically more efficient to implement in this cost model than in the standard one. The first example of quantum precomputation, based on density matrix exponentiation, could offer an exponential advantage under certain conditions. The second example uses a variant of gate teleportation to achieve a quadratic advantage when compared with implementing the unitaries directly. These examples hint that quantum precomputation may offer a new arena in which to seek quantum advantage.
Point particle E-models

We show that the same algebraic data that permit to construct the Lax pair and the $r$-matrix of an integrable non-linear $\sigma$-model in $1+1$ dimensions can be also used for the construction of Lax pairs and of $r$-matrices of several other non-trivial integrable theories in $1+0$ dimension. We call those new integrable theories the point particle ${\cal E}$-models, we describe their structure and give their physical interpretation. We work out in detail the point particle ${\cal E}$-models associated to the bi-Yang-Baxter deformation of the $SU(N)$ principal chiral model. In particular, for each complex flag manifold we thus obtain a two-parameter family of integrable models living on it.
Towards Automatic Identification of Globally Valid Geometric Flat Outputs via Numerical Optimization

Differential flatness enables efficient planning and control for underactuated robotic systems, but we lack a systematic and practical means of identifying a flat output (or determining whether one exists) for an arbitrary robotic system. In this work, we leverage recent results elucidating the role of symmetry in constructing flat outputs for free-flying robotic systems. Using the tools of Riemannian geometry, Lie group theory, and differential forms, we cast the search for a globally valid, equivariant flat output as an optimization problem. An approximate transcription of this continuum formulation to a quadratic program is performed, and its solutions for two example systems achieve precise agreement with the known closed-form flat outputs. Our results point towards a systematic, automated approach to numerically identify geometric flat outputs directly from the system model, particularly useful when complexity renders pen and paper analysis intractable.