Miracle-dz/UniFAR-FTUs huggingface dataset:
Three‐Dimensional Fourier Analysis of Atmospheric Gravity Waves and Medium‐Scale Traveling Ionospheric Disturbances Observed in Airglow Images in Hawaii Over Three Years

Three‐dimensional Fourier spectral analysis of airglow images is a useful technique to provide propagation characteristics of atmospheric gravity waves (AGWs) in the mesopause region and medium‐scale traveling ionospheric disturbances (MSTIDs) in the thermosphere observed at emission wavelengths of 557.7 nm (altitudes: 90–100 km for AGWs) and 630.0 nm (200–300 km for MSTIDs). However, a statistical study of spectral analysis of airglow images over years has not been done yet at Hawaii. Here, we report the first spectral analysis of AGWs and MSTIDs using airglow images obtained at Haleakala (20.71°N, 203.74°E), Hawaii, during the three years from 2013 to 2016. The power spectral density (PSD) of AGWs tends to be larger toward north to northeast in summer and from north to west and southward in winter. The eastward (summer) and westward (winter) preferences are consistent with a wind‐filtering mechanism of AGWs by a mesospheric jet. Some correspondence is found between the locations of tropospheric convection activities and AGW propagation directions, suggesting that these mesospheric AGWs are originated from the troposphere. The PSDs of MSTIDs are larger in solstice than in equinox. This PSD enhancement in solstice is consistent with a sporadic‐E (Es) layer occurrence in either northern or southern hemisphere, suggesting that the MSTIDs over Hawaii could be caused by a coupling process between the Perkins and Es layer instabilities. A small correlation was found between the PSDs of AGWs in the mesopause region and the PSDs of MSTIDs, suggesting that the MSTIDs are partly caused by AGWs from the mesopause region.
Three-Dimensional Landscape Pattern Characteristics of Land Function Zones and Their Influence on PM2.5 Based on LUR Model in the Central Urban Area of Nanchang City, China

China’s rapid urbanization and industrialization process has triggered serious air pollution. As a main air pollutant, PM2.5 is affected not only by meteorological conditions, but also by land use in urban area. The impacts of urban landscape on PM2.5 become more complicated from a three-dimensional (3D) and land function zone point of view. Taking the urban area of Nanchang city, China, as a case and, on the basis of the identification of urban land function zones, this study firstly constructed a three-dimensional landscape index system to express the characteristics of 3D landscape pattern. Then, the land-use regression (LUR) model was applied to simulate PM2.5 distribution with high precision, and a geographically weighted regression model was established. The results are as follows: (1) the constructed 3D landscape indices could reflect the 3D characteristics of urban landscape, and the overall 3D landscape indices of different urban land function zones were significantly different; (2) the effects of 3D landscape spatial pattern on PM2.5 varied significantly with land function zone type; (3) the effects of 3D characteristics of landscapes on PM2.5 in different land function zones are expressed in different ways and exhibit a significant spatial heterogeneity. This study provides a new idea for reducing air pollution by optimizing the urban landscape pattern.
An Interpretable Deep Learning Model for Speech Activity Detection Using Electrocorticographic Signals

Numerous state-of-the-art solutions for neural speech decoding and synthesis incorporate deep learning into the processing pipeline. These models are typically opaque and can require significant computational resources for training and execution. A deep learning architecture is presented that learns input bandpass filters that capture task-relevant spectral features directly from data. Incorporating such explainable feature extraction into the model furthers the goal of creating end-to-end architectures that enable automated subject-specific parameter tuning while yielding an interpretable result. The model is implemented using intracranial brain data collected during a speech task. Using raw, unprocessed timesamples, the model detects the presence of speech at every timesample in a causal manner, suitable for online application. Model performance is comparable or superior to existing approaches that require substantial signal preprocessing and the learned frequency bands were found to converge to ranges that are supported by previous studies.
Sleep and Social Wellness: Does Current Subjective and Objective Sleep Inform Future Social Well-Being?

Objectives: The present study aimed to investigate the link between sleep and broader social well-being. Specifically, the current study evaluated whether subjective and objective sleep indices were associated with subsequent social well-being. Methods: The archival data from the Midlife in the United States Study (MIDUS II and III, Project 1 and 4) were utilized for the current investigation. The participants completed cross-sectional surveys as part of their involvement in both study waves, 10 years apart. They were 213 adults, 59.6% female-identifying, with an average age of 56 years, who completed 8 days of sleep measurement via wrist actigraphy, sleep diary, as well as the PSQI. The participants also completed the measures of depressive symptoms and social well-being. Results: The actigraphic total sleep time, the diary-reported sleep quality, and the global sleep quality measured by the PSQI emerged as the significant predictors of social well-being over a 10-year period. Conclusions: The present study is an initial step in providing evidence for the importance of sleep for social functioning. Future research should attend to the association between past sleep behaviors and social functioning, specifically the mechanisms by which sleep is associated with social well-being as well as the temporal associations in an adult sample.
In-Field Citrus Disease Classification via Convolutional Neural Network from Smartphone Images

A high-efficiency, nondestructive, rapid, and automatic crop disease classification method is essential for the modernization of agriculture. To more accurately extract and fit citrus disease image features, we designed a new 13-layer convolutional neural network (CNN13) consisting of multiple convolutional layer stacks and dropout in this study. To address the problem created by the uneven number of disease images in each category, we used the VGG16 network module for transfer learning, which we combined with the proposed CNN13 to form a new joint network, which we called OplusVNet. To verify the performance of the proposed OplusVNet network, we collected 1869 citrus pest and disease images and 202 normal citrus images from the field. The experimental results showed that the proposed OplusVNet can more effectively solve the problem caused by uneven data volume and has higher recognition accuracy, especially for image categories with a relatively small data volume. Compared with the state of the art networks, the generalization ability of the proposed OplusVNet network is stronger for classifying diseases. The classification accuracy of the model prediction results was 0.99, indicating the model can be used as a reference for crop image classification.
Novel 3D-Printed Dressings of Chitosan–Vanillin-Modified Chitosan Blends Loaded with Fluticasone Propionate for Treatment of Atopic Dermatitis

In the present study, the blends of CS and Vanillin–CS derivative (VACS) were utilized for the preparation of printable inks for their application in three-dimensional (3D) printing procedures. Despite the synergic interaction between the blends, the addition of ι-carrageenan (iCR) as a thickening agent was mandatory. Their viscosity analysis was conducted for the evaluation of the optimum CS/VACS ratio. The shear thinning behavior along with the effect of the temperature on viscosity values were evident. Further characterization of the 3D-printed structures was conducted. The effect of the CS/VACS ratio was established through swelling and contact angle measurements. An increasing amount of VACS resulted in lower swelling ability along with higher hydrophobicity. Fluticasone propionate (FLU), a crystalline synthetic corticosteroid, was loaded into the CS/VACS samples. The drug was loaded in its amorphous state, and consequently, its in vitro release was significantly enhanced. An initial burst release, followed by a sustained release profile, was observed.
Plasminogen Activator Inhibitor-1 in Acute Myeloid Leukemia: Is It Useful in Predicting Response to Induction Remission Therapy?

BACKGROUND: Coagulation and fibrinolytic abnormalities are common in patients with acute myeloblastic leukemia (AML). Plasminogen activator inhibitor (PAI) activity is believed to be elevated during the initial diagnosis and relapse of AML patients. AIM: This study aimed to evaluate the levels of plasma PAI-1 activity in AML patients before and after remission induction. METHODS: Thirty AML patients and 20 healthy volunteers were included in this study. The patients were classified according to AML-FAB subtypes. All patients received 7+3 induction chemotherapy. They were evaluated for complete remission after induction chemotherapy and followed up for 6 months. RESULTS: PAI-1 activity was measured by ELISA immunoassay. PAI-1 activity was significantly higher in AML patients than the control group (P=0.016), whereas there was insignificant difference among patients in various AML subgroups (P>0.05). Before and after treatment, there was a significant difference in PAI-1 activity between patients with active disease and those at remission (P= 0.023 and <0.0001, respectively). CONCLUSION: High PAI-1 activity in AML patients is found to be associated with poor response to treatment.
Lessons from Very Long-Term, Very High-Precision Photoelectric Photometry

Traditional photoelectric techniques can achieve millirnagnitude preci sion if a few simple rules are scrupulously followed. We illustrate the prac tical limits of differential photometry obtained with a dedicated telescope and photometer. Using a highly homogeneous set of measurements of bright F, G, and K solar analog stars, we present the distribution of obser vational noise and intrinsic stellar variability over a typical observing season and over the entire 8-year program. Finally, we list Stromgren uvby stan dards which we found to vary slightly.
Free loop space homology of highly connected manifolds

Abstract We calculate the homology of the free loop space of ( n - 1 ) ${(n-1)}$ -connected closed manifolds of dimension at most 3 ⁢ n - 2 ${3n-2}$ ( n ≥ 2 ${n\geq 2}$ ), with the Chas–Sullivan loop product and loop bracket. Over a field of characteristic zero, we obtain an expression for the BV-operator. We also give explicit formulas for the Betti numbers, showing they grow exponentially. Our main tool is the connection between formality, coformality and Koszul algebras that was elucidated by the first author [6].
Bayesian DEJD model and detection of asymmetric jumps

News might trigger jump arrivals in financial time series. The "bad" and "good" news seems to have distinct impact. In the research, a double exponential jump distribution is applied to model downward and upward jumps. Bayesian double exponential jump-diffusion model is proposed. Theorems stated in the paper enable estimation of the model's parameters, detection of jumps and analysis of jump frequency. The methodology, founded upon the idea of latent variables, is illustrated with two empirical studies, employing both simulated and real-world data (the KGHM index). News might trigger jump arrivals in financial time series. The "bad" and "good" news seems to have distinct impact. In the research, a double exponential jump distribution is applied to model downward and upward jumps. Bayesian double exponential jump-diffusion model is proposed. Theorems stated in the paper enable estimation of the model's parameters, detection of jumps and analysis of jump frequency. The methodology, founded upon the idea of latent variables, is illustrated with two empirical studies, employing both simulated and real-world data (the KGHM index).
Parametric Optimization of Ball-Milled Bimetallic Nanoadsorbents for the Effective Removal of Arsenic Species

Arsenic (As) removal from portable water bodies using the nanotechnology-based adsorption technique offers a unique method to lower the As contamination below the World Health Organization's (WHO) maximum contaminant level (MCL). This work promotes a systematic methodological-based adsorption study by optimizing the different parameters that affect As removal using TiO2/γ-Fe2O3 nanocomposites (T/M NCs) prepared with the green, facile, and cost-effective ball milling method. The studies using X-ray Diffraction (XRD) illustrate the structural modifications with variations in the constituting T/M ratios, with high-resolution transmission electron microscopy (HRTEM) being used for the NC morphological studies. The optical characterization studies showed that bandgap tuning between 2–2.8 eV reduced the maghemite (γ-Fe2O3) content in the NCs and the elemental analysis confirmed the desired stoichiometry of the NCs. The magnetic measurements showed that the magnetic interaction among the particles tends towards exchange coupling behavior as the weight ratio of γ-Fe2O3 content decreases in the NCs. The adsorption studies using the most efficient NCs with an optimized condition (NC dose (8 g/L), contact time (15 min), As concentration (2 ppm), and pH (4)) resulted in a more than 99% removal of As species, suggesting the excellent behavior of the synthesized nanomaterial for water treatment and making it more economical than other competing adsorption techniques and materials.
Immunotherapy and Hepatocellular Cancer: Where Are We Now?

Simple Summary HCC is the third-leading cause of cancer death worldwide. Prior to the immunotherapy era, treatment of advanced HCC was dominated by antiangiogenic tyrosine kinase inhibitors, with a quite poor prognosis with a median survival time of around one year. In this review, we aimed to elaborate on all the advances in immunotherapy in HCC from the indisputable first-line therapy atezolizumab–bevacizumab in advanced HCC to promising new strategies. We highlight the new standard of care in advanced HCC, namely, the combination of immune checkpoint inhibitors (ICI) durvalumab–tremelimumab, and also discuss the different combinations of antiangiogenic drugs with ICI. We develop how the combination of ICI with locoregional therapies could become a future treatment in early and intermediate-stage HCC, and, finally, we outline several new cell-therapy-based strategies to follow in the coming years. Abstract Immunotherapy has demonstrated its effectiveness in many cancers. In hepatocellular carcinoma (HCC), promising results shown in the first phase II studies evaluating anti-PD-1 or anti-PD-L1 monotherapies resulted in their approval in the United States. Approval was not obtained in Europe; subsequent randomized studies in first- or second-line treatment did not confirm these initial results. However, first data with immunotherapy plus antiangiogenic treatments or dual immunotherapy combinations were positive. In this context, the combination of bevacizumab and atezolizumab took the lead. The IMbrave150 trial revealed an improved objective response rate (ORR), progression-free survival, and overall survival with this combination versus the previous standard, sorafenib. Subsequent results of dual immunotherapy with the anti-CTLA-4 and anti-PD-1 monotherapies tremelimumab and durvalumab (also superior to sorafenib monotherapy) confirmed the value of using a combination in first-line treatment. These significant therapeutic advances, and the increase in ORR, raise two main questions. Whereas response was very limited with previous treatments, the ORR reported with these new combinations are between 20% and 30%. This raises the question of whether immunotherapy (ICI single agent, combination of ICI with antiangiogenic agent or other antitumoral treatment) can be used in patients beyond those in BCLC group C, the traditional candidate group for systemic therapy. We have thus seen an increasing number of patients previously treated with trans-arterial chemoembolization (BCLC group B) receiving these new treatments, and we develop the results of several studies combining loco-regional therapies and immunotherapy-based systemic treatments. The other major question is that of how and when to use these medical treatments as “adjuvants” to interventional radiology or surgery; the results of several works are discussed for this purpose. In this review, we cover all of these points in a fairly comprehensive manner.
The sl2 Loop Algebra Symmetry of the Six-Vertex Model at Roots of Unity

We demonstrate that the six vertex model (XXZ spin chain) with Δ=(q+q-1)/2 and q2N=1 has an invariance under the loop algebra of sl2 which produces a special set of degenerate eigenvalues. For Δ=0 we compute the multiplicity of the degeneracies using Jordan–Wigner techniques.
Factors-based Asset Pricing Models: a literature review

In this paper we provide a literature review of the main factors-based asset pricing models, focusing in particular on factors related to firm characteristics. After presenting the Capital Asset Pricing Model, we describe first the most important empirical evidence that led to the well-known Fama-French three-factors model. Next, we highlight the most widely used multi-factors pricing models based on momentum, liquidity, investment and profitability, also outside the U.S. Finally, we discuss the ability of firm characteristics to predict the behavior of future stock returns.
Detecting underreport in real-world assessment contexts. The utility of multiple-scale indicators

Underreport of symptoms and personality characteristics is a relevant problem for psychological assessment. Nevertheless, most of the studies in this field use simulation designs. This study aims at comparing underreport prevalence in real-world samples of different contexts, using single-scale and multiple-scale underreport indicators from the Minnesota Multiphasic Personality Inventory–2 (MMPI-2) to identify the best one and assess its implications on the clinical scales scores. Using a Differential Prevalence Group design, 1438 participants were assessed with the MMPI-2, grouped in three samples from two non-clinical contexts (community and organizational) and one clinical context. The organizational sample had the highest prevalence of underreporting. Overall, multiple-scale indicator LKS ≥ T65 performed better at distinguishing these samples. Analysis of variance revealed that LKS ≥ T65 was also the only indicator in which participants doing underreport consistently varied from honest responders in the clinical scales scores, while also having lower probability of producing both type I and II errors. The existence of underreport has clear implication on the clinical scales results. The multiple-scale indicator is the most robust and should be used in the detection of underreporting. This is a relevant implication for psychological assessment in different contexts, mainly in the organizational context. La minimización de síntomas es un problema relevante para la evaluación psicológica. La mayoría de los estudios utilizan diseños de simulación. Este estudio tiene como objetivo comparar la prevalencia de la minimización de síntomas y sus implicaciones, utilizando indicadores de escala única y de escala múltiple del Inventario Multifásico de Personalidad de Minnesota-2 (MMPI-2) en muestras reales. Utilizando un diseño de Grupo de Prevalencia Diferencial se evaluaron 1438 participantes, agrupados en tres muestras: dos no clínicas (comunitario y organizacional) y una muestra clínica. La muestra organizacional tuvo la mayor prevalencia de minimización de síntomas. En general, el indicador de escala múltiple LKS ≥ T65 proporcionó los mejores resultados. El análisis de la varianza reveló que el LKS ≥ T65 era también el único indicador de diferenciación, en las escalas clínicas, de los participantes que realizaban o no la minimización de síntomas, a la vez que tenía una menor probabilidad de producir errores tipo I y II. La presencia de minimización tiene una clara implicación en las puntuaciones clínicas. El indicador de escalas múltiples es el más robusto en la detección de la minimización de síntomas y es relevante para la evaluación en diferentes contextos, principalmente en lo organizacional.
From Snake Venoms to Therapeutics: A Focus on Natriuretic Peptides

Snake venom is a cocktail of multifunctional biomolecules that has evolved with the purpose of capturing prey and for defense. These biomolecules are classified into different classes based on their functions. They include three-finger toxins, natriuretic peptides, phospholipases and metalloproteinases. The focus for this review is on the natriuretic peptide (NP), which is an active component that can be isolated from the venoms of vipers and mambas. In these venoms, NPs contribute to the lowering of blood pressure, causing a rapid loss of consciousness in the prey such that its mobility is reduced, paralyzing the prey, and often death follows. Over the past 30 years since the discovery of the first NP in the venom of the green mamba, venom NPs have shown potential in the development of drug therapy for heart failure. Venom NPs have long half-lives, different pharmacological profiles, and may also possess different functions in comparison to the mammalian NPs. Understanding their mechanisms of action provides the strategies needed to develop new NPs for treatment of heart failure. This review summarizes the venom NPs that have been identified over the years and how they can be useful in drug development.
Does Renewable Energy Consumption Mitigate Carbon Emissions for India?

Developing economies have an urge for development and energy is the lifeline of development. However, the consumption of non-renewable energy resources is emerging as a major concern towards environmental degradation. India is sailing in the same boat wherein the policy makers are trying to strike a balance between development and environment. Attainment of sustainable development that supports environment is vital, therefore, the Government of India is extensively promoting use of renewable energy resources. The present study is an attempt to empirically evaluate the impact of renewable energy consumption (REC) on India’s carbon emission over the period of 1990–2020 by employing autoregressive distributed lag (ARDL) model. To underline it, two model frameworks have been formed; one with environmental Kuznets curve (EKC) and another without EKC specification. The result for REC variable was found to be negative but insignificant with EKC framework, and negative and significant without EKC specifications indicating reduction in carbon emissions with the use of renewable energy resources. The results indicate that more extensive use of renewable energy resources might work towards making the REC coefficient more significant towards India’s environment.