Miracle-dz/UniFAR-FTUs huggingface dataset:
North-south asymmetry of ultra-low-frequency oscillations of Earth’s electromagnetic field

In the paper, we present the result of an experimental study of north-south asymmetry of ultralowfrequency electromagnetic oscillations IPCL. This study is based on observations made at Mirny Observatory (Antarctica). IPCLs are excited in the dayside sector of the auroral oval in the range 3–10 min periods and represent one of the most powerful types of oscillations of Earth's magnetosphere. These oscillations were discovered in the 1970s during IPhE AS USSR polar expeditions organized by Prof. V.A. Troitskaya. We have shown that IPCL activity in Mirny depends on the inclination (north-south asymmetry) of interplanetary magnetic field (IMF) lines to the plane of the geomagnetic equator before the front of the magnetosphere. The result suggests a controlling exposure of IMF on the magnetospheric oscillations and gives rise to the hypothesis that IPCL are forced oscillations of a nonlinear dynamical system whose major structural elements are dayside polar cusps. The paper is dedicated to the memory of Professor V.A. Troitskaya (1917–2010).
Cellular Therapy for Lung Cancer: Focusing on Chimeric Antigen Receptor T (CAR T) Cells and Tumor-Infiltrating Lymphocyte (TIL) Therapy

Simple Summary Lung cancer is the leading cause of cancer related mortality and morbidity in the United States and worldwide. The advent of Immunotherapy has significantly improved lung cancer prognosis. However, there is a huge unmet need for novel agents, as a significant number of patients do not have durable responses to immunotherapy. This review article highlights two such novel techniques—Chimeric antigen receptor (CAR) T-cell therapy and Tumor-infiltrating lymphocyte (TIL) therapy. Both these techniques utilize a patient’s own immune cells to fight against tumors. While CAR T-cell therapy requires genetic modification of a patient’s T cells to express receptors that can recognize and attack tumor cells rapidly, TILs involves extraction of immune cells from tumors and their proliferation in a laboratory before being infused back to the patient. Both these techniques are currently used in a clinical trial setting only. In this review, we discuss the limitations and future directions and potential for both these treatment strategies. Abstract Lung cancer is a leading cause of morbidity and mortality in the United States and worldwide. The introduction of immune checkpoint inhibitors has led to a marked improvement in the outcomes of lung cancer patients. Despite these advances, there is a huge unmet need for therapeutic options in patients who are not candidates for targeted or immunotherapy or those who progress after first-line treatment. With its high mutational burden, lung cancer appears to be an attractive target for novel personalized treatment approaches. In this review, we provide an overview of two adoptive cell therapy approaches–chimeric antigen receptors (CAR) T-cell therapy and Tumor-infiltrating lymphocytes (TILs) in lung cancer with an emphasis on current challenges and future perspectives. While both these therapies are still in the early phases of development in lung cancer and need more refinement, they harbor the potential to be effective treatment options for this group of patients with otherwise poor prognoses.
Moment exponential input‐to‐state stability of non‐linear switched stochastic systems with Lévy noise

The moment exponential input-to-state stability (ISS) problem for a class of non-linear switched stochastic systems is studied. A continuously differentiable Lyapunov function with indefinite derivative is introduced, which generalises classic Lyapunov function method. Two situations are considered: (i) synchronous switching, i.e. candidate controllers coincide with system modes; (ii) asynchronous switching, i.e. the candidate controllers have a lag to the switching of the system modes. By employing indefinite derivative Lyapunov function method and average dwell-time approach, sufficient conditions for moment exponential ISS of the systems are derived. Finally, an example is given to illustrate the effectiveness of the authors' results.
The Effect of Argument Driven Inquiry (ADI) Model on the Scientific Argumentation Ability of High School Students on the Topic of Light Waves

Scientific argumentation is the process of strengthening claims through critical analysis based on supporting evidence and logical reasons, where the evidence can include facts that can be accepted as truth. This study aims to describe the improvement of students' scientific argumentation abilities on the topic of light waves after applying the ADI learning model. This research uses the type of True Experimental with a Control Group Pretest and Posttest design. The data collection technique in this study was the test method. The results of this study indicate an increase in scientific argumentation abilities on the topic of light waves after applying the ADI learning model. The results of the scientific argumentation ability of experimental class students were higher than the control class, the experimental class got an n-gain of 0.60 with a medium category, while the control class got an n-gain of 0.26 with a low category. In the pretest, experimental, and control classes, students were at levels 1, 2, and 3. The posttest results of experimental class students who experienced increased scientific argumentation at levels 4 and 5. Meanwhile, the posttest results of students in the control class were relatively the same as the pretest results, namely at levels 1, 2, and 3. In addition, the Cohen's Effect Size analysis results showed a value of 0.88 in the strong criteria. It can be concluded the ADI learning model affects learning to improve students' scientific argumentation abilities of senior high school students on the topic of light waves.
A non-homogeneous boundary value problem of the sixth order Boussinesq equation in a quarter plane

The paper is concerned with an initial-boundary-value problem of the sixth order Boussinesq equation posed on a quarter plane with non-homogeneous boundary conditions: \begin{document}$\begin{equation}\label{0}\begin{cases}u_{tt}-u_{xx}+β u_{xxxx}-u_{xxxxxx}+(u^2)_{xx} = 0, \, \, \, \, \,\,\,\,\, \mbox{for }x>0\mbox{, }t>0, \\u(x, 0) = \varphi (x), u_t(x, 0) = ψ "(x), \\u(0, t) = h_1(t), u_{xx}(0, t) = h_2(t), u_{xxxx}(0, t) = h_3(t), \end{cases}\, \, \, \, \, \, \, \, \, \, (1)\end{equation}$ \end{document} where \begin{document}$β = ± 1$\end{document} . It is shown that the problem is locally well-posed in the space $H^s(\mathbb{R}^+)$ for any 0≤s \begin{document}$\frac{13}{2}$\end{document} with the initial data \begin{document}$ (\varphi, ψ)$\end{document} in the space \begin{document}$H^s(\mathbb{R}^+)× H^{s-1}(\mathbb{R}^+)$ \end{document} and the naturally compatible boundary data \begin{document}$\mbox{ $h_1∈ H_{loc}^{\frac{s+1}{3}}(\mathbb{R}^+)$, $h_2∈ H_{loc}^{\frac{s-1}{3}}(\mathbb{R}^+) \text{and}\,\,\, h_3∈ H_{loc}^{\frac{s-3}{3}}(\mathbb{R}^+)$}$ \end{document} with optimal regularity.
Room temperature CdZnTe and HgI/sub 2/ detectors for hard X-ray astrophysics

There is a great need in hard X-ray and gamma-ray astrophysics for large area, room temperature, lightweight, semiconductor detectors that have comparable energy resolution to germanium without the inconvenient cooling requirements. Recent improvements in the quality of cadmium zinc telluride (CdZnTe) and mercuric iodide (HgI/sub 2/) materials have brought room temperature semiconductors to the point where real astrophysics applications may seriously be considered. Future astrophysics instruments will be described, including PoRTIA, a small engineering prototype balloon instrument that will make material dependent background measurements at balloon altitudes. In addition to application oriented detector development, basic issues such as detector spectral performance, noise, thermal behavior and bulk transport properties in CdZnTe have also been studied. Preliminary results from this CdZnTe detector research will be presented. <<ETX>>
The relationships between reading mindsets, reading engagement, and reading comprehension performance in the Saudi EFL context

This study determines the relationship between reading mindsets and reading comprehension performance with the use of reading engagement as a mediating variable. It drew on Dweck’s (1999) mindset theory and employed a correlational research design to collect data from 567 EFL undergraduate students majoring in English in three public universities in Saudi Arabia. The data collection was conducted by distributing two questionnaires and administering a reading comprehension test. The collected data were analyzed using a two-phase structural equation modeling approach (i.e., measurement and structural models). The results indicated that there was a significant correlation between a growth reading mindset and reading engagement. Nevertheless, a significant yet negative correlation was revealed between a fixed reading mindset and reading engagement. Moreover, there was a significant and positive correlation between reading engagement and reading comprehension performance. Lastly, the findings showed that reading engagement served as a mediating variable in determining the correlation between reading mindsets and reading comprehension performance. The relevance of the current study stems from the dearth of research on reading mindsets. Also, previous studies have not focused on exploring the correlation between mindsets and performance in the EFL context. Therefore, the current study is expected to benefit EFL readers, instructors, and policymakers.
Barriers to Learning Healthcare-Associated Infections Prevention and Control during Clinical Practicum among Nursing Students in Korea: A Focus Group Study

Healthcare-associated infections (HAI) refer to infections that patients may acquire from healthcare facilities through nursing activities. Nursing students involved in patient care are regularly exposed to an array of pathogens and clinical practicum is essential for them to appropriately practice HAI prevention and control. This study aimed to explore the barriers to learning HAI prevention and control experienced by nursing students during their clinical practicum. A qualitative study was performed using focus group interviews. A total of 12 nursing students from South Korea, consisting of six third-year students and six fourth-year students, were enrolled. Both groups had taken clinical practicum courses. Data were collected using semi-structured questions and analyzed with conventional content analysis. Barriers experienced by the participants when learning HAI prevention and control were limited learning opportunities, inadequate infection control-related knowledge, inadequate practicum experience, and passive learning attitudes. Addressing the identified barriers would allow nursing students to effectively acquire infection control competencies during their clinical practicum.
Weak Convergence of Smoothed Empirical Processes

Let XI, X2, . be a sequence of i.i. d. random variables with common law P. The nth empirical measure is PD. = n - S1 bx= and its smoothed version is DPn, = 9n * P)n for a sequence of random signed measures an converging weakly in probability to zero. We give conditions for the weak convergence of the smoothed empirical processes ln(Pnf - P).
A search for pulsed high-energy non-thermal emission from the nova-like variable system AE Aquarii

AE Aquarii is an peculiar nova like variable system with emission observable in almost all wavelength regimes. The emission consists of both thermal and nonthermal components. The parameters and models for the non-thermal Synchrotron emission in the radio regime, as well as the optical and UV emission, have been extensively studied and presented since this enigmatic source was first studied in the 1930s. However, the emission processes and parameters towards the higher energies are still not clearly verified. The possibility of non-thermal pulsed emission in the VHE energies was mentioned in previous studies, as well as possible correlations between this pulsed emission and periods of enhanced optical activity. We searched for non-thermal high energy emission from AE Aquarii using archival data from Suzaku, Fermi and AGILE along with contemporaneous optical photometric data from Boyden Observatory and SAAO. Analysis of the parameters of optical flares resulted in signatures that was used to identify possible correlating features in the Fermi -LAT data. In addition, we found that given the presence of a thermal component in conjunction with a power-law towards higher energies in the Suzaku X-ray data, as well as a possible low-level pulse signal in the Fermi LAT data at the first harmonic of the white dwarf spin period, the possibility of non-thermal high energy emission from AE Aquarii cannot be excluded. The possible low-level signal in the Fermi -LAT data was as a result of a novel peak distribution analysis technique. Finally, an updated spectral energy distribution for AE Aquarii was completed to serve as a reference for future studies.
HIGHER ORDER MOMENTS OF MARKOV SWITCHING VARMA MODELS

In this paper we derive matrix formulae in closed form for higher order moments and give sufficient conditions for higher order stationarity of Markov switching VARMA models. We provide asymptotic theory for sample higher order moments which can be used for testing multivariate normality. As an application, we propose new definitions of multivariate skewness and kurtosis measures for such models, and relate them with the existing concepts in the literature. Our work completes the statistical analysis developed in the fundamental paper of Francq and Zakoïan (2001, Econometric Theory 18, 815–818) and relates with the concepts of multivariate skewness and kurtosis proposed by Mardia (1970, Biometrika 57, 519–530), Móri, Rohatgi, and Székely (1993, Theory of Probability and its Applications 38, 547–551), and Kollo (2008, Journal of Multivariate Analysis 99, 2328–2338). Under suitable assumptions, our results imply that the sample estimators of the skewness and kurtosis measures proposed by these authors are consistent and asymptotically normally distributed. Finally, we check our theory statements numerically via Monte Carlo simulations.
Pomegranate: A Source of Multifunctional Bioactive Compounds Potentially Beneficial in Alzheimer’s Disease

Pomegranate fruit (PF) is a fruit rich in nutraceuticals. Nonedible parts of the fruit, especially peels, contain high amounts of bioactive components that have been largely used in traditional medicine, such as the Chinese, Unani, and Ayurvedic ones, for treating several diseases. Polyphenols such as anthocyanins, tannins, flavonoids, phenolic acids, and lignans are the major bioactive molecules present in PF. Therefore, PF is considered a source of natural multifunctional agents that exert simultaneously antioxidant, anti-inflammatory, antitumor, antidiabetic, cardiovascular, and neuroprotective activities. Recently, several studies have reported that the nutraceuticals contained in PF (seed, peel, and juice) have a potential beneficial role in Alzheimer’s disease (AD). Research suggests that the neuroprotective effect of PF is mostly due to its potent antioxidant and anti-inflammatory activities which contribute to attenuate the neuroinflammation associated with AD. Despite the numerous works conducted on PF, to date the mechanism by which PF acts in combatting AD is not completely known. Here, we summarize all the recent findings (in vitro and in vivo studies) related to the positive effects that PF and its bioactive components can have in the neurodegeneration processes occurring during AD. Moreover, considering the high biotransformation characteristics of the nutraceuticals present in PF, we propose to consider the chemical structure of its active metabolites as a source of inspiration to design new molecules with the same beneficial effects but less prone to be affected by the metabolic degradation process.
α5-GABAA Receptor Modulation Reverses Behavioral and Neurophysiological Correlates of Psychosis in Rats with Ventral Hippocampal Alzheimer’s Disease-like Pathology

Of the 35 million people in the world suffering from Alzheimer’s Disease (AD), up to half experience comorbid psychosis. Antipsychotics, used to treat psychosis, are contraindicated in elderly patients because they increase the risk of premature death. Reports indicate that the hippocampus is hyperactive in patients with psychosis and those with AD. Preclinical studies have demonstrated that the ventral hippocampus (vHipp) can regulate dopamine system function, which is thought to underlie symptoms of psychosis. A viral-mediated approach was used to express mutated human genes known to contribute to AD pathology: the Swedish (K670N, M671L), Florida (I716V), and London (V717I) mutations of amyloid precursor protein and two mutations (M146L and L286V) of presenilin 1 specifically in the vHipp, to investigate the selective contribution of AD-like pathology in this region. We observed a significant increase in dopamine neuron population activity and behavioral deficits in this AD-AAV model that mimics observations in rodent models with psychosis-like symptomatologies. Further, systemic administration of MP-III-022 (α5-GABAA receptor selective positive allosteric modulator) was able to reverse aberrant dopamine system function in AD-AAV rats. This study provides evidence for the development of drugs that target α5-GABAA receptors for patients with AD and comorbid psychosis.
Next Point-of-Interest Recommendation Based on Joint Mining of Spatial-Temporal and Semantic Sequential Patterns

With the widespread use of the location-based social networks (LBSNs), the next point-of-interest (POI) recommendation has become an essential service, which aims to understand the user’s check-in behavior at the current moment by analyzing and mining the correlations between the user’s check-in behaviors within his/her historical trajectory and then recommending the POI that the user is most likely to visit at the next time step. However, the user’s check-in trajectory presents extremely irregular sequential patterns, such as spatial–temporal patterns, semantic patterns, etc. Intuitively, the user’s visiting behavior is often accompanied by a certain purpose, which makes the check-in data in LBSNs often have rich semantic activity characteristics. However, existing research mainly focuses on exploring the spatial–temporal sequential patterns and lacks the mining of semantic information within the trajectory, so it is difficult to capture the user’s visiting intention. In this paper, we propose a self-attention- and multi-task-based method, called MSAN, to explore spatial–temporal and semantic sequential patterns simultaneously. Specifically, the MSAN proposes to mine the user’s visiting intention from his/her semantic sequence and uses the user’s visiting intention prediction task as the auxiliary task of the next POI recommendation task. The user’s visiting intention prediction uses hierarchical POI category attributes to describe the user’s visiting intention and designs a hierarchical semantic encoder (HSE) to encode the hierarchical intention features. Moreover, a self-attention-based hierarchical intention-aware module (HIAM) is proposed to mine temporal and hierarchical intention features. The next POI recommendation uses the self-attention-based spatial–temporal-aware module (STAM) to mine the spatial–temporal sequential patterns within the user’s check-in trajectory and fuses this with the hierarchical intention patterns to generate the next POI list. Experiments based on two real datasets verified the effectiveness of the model.
Fine-Grained, Unsupervised, Context-based Change Detection and Adaptation for Evolving Categorical Data

Concept drift detection, the identification of changes in data distributions in streams, is critical to understanding the mechanics of data generating processes and ensuring that data models remain representative through time [2]. Many change detection methods utilize statistical techniques that take numerical data as input. However, many applications produce data streams containing categorical attributes. In this context, numerical statistical methods are unavailable, and different approaches are required. Common solutions use error monitoring, assuming that fluctuations in the error measures of a learning system correspond to concept drift [4]. There has been very little research, though, on context-based concept drift detection in categorical streams. This approach observes changes in the actual data distribution and is less popular due to the challenges associated with categorical data analysis. However, context-based change detection is arguably more informative as it is data-driven, and more widely applicable in that it can function in an unsupervised setting [4]. This study offers a contribution to this gap in the research by proposing a novel context-based change detection and adaptation algorithm for categorical data, namely Fine-Grained Change Detection in Categorical Data Streams (FG-CDCStream). This unsupervised method exploits elements of ensemble learning, a technique whereby decisions are made according to the majority vote of a set of models representing different random subspaces of the data [5]. These ideas are applied to a set of concept drift detector objects and merged with concepts from a recent, state-of-the-art,