Miracle-dz/UniFAR-FTUs huggingface dataset:
Exopolysaccharide is the potential effector of Lactobacillus fermentum PS150, a hypnotic psychobiotic strain

Psychobiotics are a class of probiotics that confer beneficial effects on the mental health of the host. We have previously reported hypnotic effects of a psychobiotic strain, Lactobacillus fermentum PS150 (PS150), which significantly shortens sleep latency in experimental mice, and effectively ameliorate sleep disturbances caused by either caffeine consumption or a novel environment. In the present study, we discovered a L. fermentum strain, GR1009, isolated from the same source of PS150, and found that GR1009 is phenotypically distinct but genetically similar to PS150. Compared with PS150, GR1009 have no significant hypnotic effects in the pentobarbital-induced sleep test in mice. In addition, we found that heat-killed PS150 exhibited hypnotic effects and altered the gut microbiota in a manner similar to live bacteria, suggesting that a heat-stable effector, such as exopolysaccharide (EPS), could be responsible for these effects. Our comparative genomics analysis also revealed distinct genetic characteristics in EPS biosynthesis between GR1009 and PS150. Furthermore, scanning electron microscopy imaging showed a sheet-like EPS structure in PS150, while GR1009 displayed no apparent EPS structure. Using the phenol-sulfate assay, we found that the sugar content value of the crude extract containing EPS (C-EPS) from PS150 was approximately five times higher than that of GR1009, indicating that GR1009 has a lower EPS production activity than PS150. Through the pentobarbital-induced sleep test, we confirmed the hypnotic effects of the C-EPS isolated from PS150, as evidenced by a significant reduction in sleep latency and recovery time following oral administration in mice. In summary, we utilized a comparative approach to delineate differences between PS150 and GR1009 and proposed that EPS may serve as a key factor that mediates the observed hypnotic effect.
Performance Measurement Through Caching in Named Data Networking Based Internet of Things

Named Data Networking (NDN) is considered the future of Internet architecture, providing a realistic solution for data delivery using a caching module in an Internet of Things (IoT) based environment. However, a major challenge of the caching module is data redundancy, which decreases the overall caching performance by caching similar data at numerous locations in an NDN-based IoT scenario. Moreover, the latency and stretch are maximized due to high redundant caching operations. Several attempts have been made by the research community to provide an enhanced solution to overcome such issues. However, the caching module still requires efficient enhancement. This study provides critical insights into earlier caching strategies. To solve the problems of these caching strategies, an enhanced caching strategy is proposed, named Priority-based Content Popularity-Aware (PCPA) Caching Strategy, which is evaluated by comparing its performance with some of the novel NDN-based IoT caching strategies. The proposed caching strategy outperforms the comparing strategies in terms of latency, hop count, cache hit ratio and energy consumption.
Multiple subspaces separation in case of camera motion

We explore the problem of subspace clustering. Given a set of data samples approximately drawn from a union of multiple subspaces, our goal is to cluster the samples into respective subspaces, and also remove possible outliers. We propose an Approximated Robust PCA Clustering (ARPCAC) method that involves extracting the point trajectories only induced by object motion, from the pool of all motions induced by objects and camera motion, and then projecting them onto a 5-dimensional space, using PowerFactorization. Our algorithm can be used to segment multiple motions in video and furthermore, is extended to the problem of face clustering. Conducted experiments demonstrate state-of-the-art performance.
The Cooling of The Neutron Star in The Cassiopeia A Supernova Remnant

A young neutron star cools mainly via neutrino emission from the star’s core. Thus, the thermal evolution of neutron stars reflects changes in their core, constraining temperature-sensitive properties such as the composition of the core and the envelope of the neutron star. The neutron star in the Cassiopeia A supernova remnant (Cas A NS) is the youngest known, at only 330 years old. Heinke and Ho reported a drop of 3.6% in its surface temperature (21% drop in observed flux) from Chandra ACIS-S X-ray data on the Cas A NS between 2000 and 2009. This is the only young neutron star to have been observed to cool over time, permitting a clearer picture of its thermal evolution. This drop was interpreted as enhanced neutrino emission due to a superfluid transition in the core [40], [58]. Here I present analysis of data from another Chandra detector (HRCS) over the same time period to test the cooling rate. I used the best current estimates of the effective area of this detector and the spectrum of the Cas A NS to infer the countrates corresponding to various NS temperatures, and thus to compute the temperature drop from the countrate change. The temperature drop inferred from HRC-S observations is more uncertain, but is significantly less than the temperature drop inferred from ACIS-S. Observations using Chandra’s other detectors suffer a variety of systematic uncertainties. The result suggests that the temperature drop could be half as large as originally suggested, but that the Cas A NS’s temperature is indeed decreasing. The hardest arithmetic to master is that which enables us to count our blessings.
Free Vibration of Thin Spherical Shells

April Bryan No. 7 Jack Trace, Enterprise Chaguanas Trinidad and Tobago aprilbr@gmail.com ASME Membership since 2003 ABSTRACT This research introduces a new approach to analytically derive the differential equations of motion of a thin spherical shell. The approach presented is used to obtain an expression for the relationship between the transverse and surface displacements of the shell. This relationship, which is more explicit than the one that can be obtained through use of the Airy Stress Function, is used to uncouple the surface and normal displacements in the spatial differential equation for transverse motion. The associated Legendre polynomials are utilized to obtain analytical solutions for the resulting spatial differential equation. The spatial solutions are found to exactly satisfy the boundary conditions for the simply supported and the clamped hemispherical shell. The results to the equations of motion indicate that the eigenfrequencies of the thin spherical shell are independent of the azimuthal coordinate. As a result, there are several mode shapes for each eigenfrequency. The results also indicate that the effects of midsurface tensions are more significant than bending at low mode numbers, but become negligible as the mode number increases.
A Resizable Mini-batch Gradient Descent based on a Randomized Weighted Majority

Determining the appropriate batch size for mini-batch gradient descent is always time consuming as it often relies on grid search. This paper considers a resizable mini-batch gradient descent (RMGD) algorithm-inspired by the randomized weighted majority algorithm-for achieving best performance in grid search by selecting an appropriate batch size at each epoch with a probability defined as a function of its previous success/failure and the validation error. This probability encourages exploration of different batch size and then later exploitation of batch size with history of success. At each epoch, the RMGD samples a batch size from its probability distribution, then uses the selected batch size for mini-batch gradient descent. After obtaining the validation error at each epoch, the probability distribution is updated to incorporate the effectiveness of the sampled batch size. The RMGD essentially assists the learning process to explore the possible domain of the batch size and exploit successful batch size. Experimental results show that the RMGD achieves performance better than the best performing single batch size. Furthermore, it attains this performance in a shorter amount of time than that of the best performing. It is surprising that the RMGD achieves better performance than grid search.
A Data Enhancement Model for Intrusion Detection In Smart Home

Smart home devices differ in data throughput and attack frequency. However, some devices lack sufficient attack data, resulting in small sample sizes that pose a challenge to training intrusion detection models. Therefore, this paper proposes a data enhancement model DE-CWGAN (Data Enhancement Based on Conditional Wasserstein Generative Adversarial Networks) to generate effective training samples for small sample data. The DE-CWGAN model uses CWGAN as a method for data enhancement. The sampled data are used as real data samples to train the CWGAN, and the generator is used to produce generated data samples (fake data). Wasserstein distance is used to calculate the loss between fake and real data, and the generator is adjusted based on this loss to generate fake data i.e closer to the real data. Add the fake data to the original dataset to expand the dataset. The experimental results show that the DE-CWGAN model effectively fills sparse attack records, and the machine learning classification algorithm achieve improved accuracy after training with enhanced data.
Dropping off the bandwagon: a puzzle for the resource mobilisation perspective on civil society contestation networks

ABSTRACT This paper builds on an implicit assumption of resource mobilisation theory in order to explain why civil society organisations stay on policy bandwagons. It demonstrates that, after the initial decision to engage with a policy issue, organisational maintenance explains ongoing contestation better than the structural factors which encourage them to get involved. It also adds three new findings which contribute to the existing literature on attention cascades: (1) at the actor level, the effect of salience on contestation is often short-lived; (2) organisational maintenance encourages factionalisation within groups of like-minded policy opponents; and (3) that survival strategies adapted to domestic political opportunity structures can limit CSOs’ capacity to participate in European policy debates. The argument is tested using measures of network centrality from an online forum (Twitter) as a predictor for ongoing participation in campaigns against the Transatlantic Trade and Investment Partnership. The findings are of immediate relevance to trade scholars seeking to understand the reactions of civil society organisations to contestation; interest group scholars focused on the determinants of issue prioritisation; and EU integration scholars seeking to explain variation in the domestication of EU policy issues.
Exploring Hypergraph of Earnings Call for Risk Prediction (Student Abstract)

In financial economics, studies have shown that the textual content in the earnings conference call transcript has predictive power for a firm's future risk. However, the conference call transcript is very long and contains diverse non-relevant content, which poses challenges for the text-based risk forecast. This study investigates the structural dependency within a conference call transcript by explicitly modeling the dialogue between managers and analysts. Specifically, we utilize TextRank to extract information and exploit the semantic correlation within a discussion using hypergraph learning. This novel design can improve the transcript representation performance and reduce the risk of forecast errors. Experimental results on a large-scale dataset show that our approach can significantly improve prediction performance compared to state-of-the-art text-based models.
ORGANIZATION MANAGEABILITY ENHANCED THROUGH TOPOLOGICAL MODULAR FORMS

Organizational manageability is a crucial aspect of business management, requiring a combination of forecasting, planning, organizing, implementing, controlling and decision-making. Topological modular forms study the properties of objects that are invariant under certain types of transformations and the authors search for and identify a set of key factors that are essential to the organizational manageability (both stable and unstable) and create a framework that captures these factors. Organizational manageability is highly complex and multifaceted field that requires the integration of mentioned elements. In order to simplify incommensurable complexity, authors offer the hypothesis that differentiating the approach to manageability in the two distinct situations "steady and familiar condition" and "unsteady and with considerable uncertainty condition" is effective. Discrimination between those two situations is essential for business success and requires a deep understanding of market trends, customer needs, design of organization and usage of resources. By mastering the principles of organizational manageability based on mentioned classification of situations, businesses can improve their performance, increase their competitiveness and achieve their goals more effectively.
A Tool for Generating Controllable Variations of Musical Themes Using Variational Autoencoders with Latent Space Regularisation

A common musical composition practice is to develop musical pieces using variations of musical themes. In this study, we present an interactive tool which can generate variations of musical themes in real-time using a variational autoencoder model. Our tool is controllable using semantically meaningful musical attributes via latent space regularisation technique to increase the explainability of the model. The tool is integrated into an industry standard digital audio workstation - Ableton Live - using the Max4Live device framework and can run locally on an average personal CPU rather than requiring a costly GPU cluster. In this way we demonstrate how cutting-edge AI research can be integrated into the exiting workflows of professional and practising musicians for use in the real-world beyond the research lab.
Rotational and Infrared Spectroscopy of Ethanimine: A Route toward Its Astrophysical and Planetary Detection

Ethanimine, a possible precursor of amino acids, is considered an important prebiotic molecule and thus may play important roles in the formation of biological building blocks in the interstellar medium. In addition, its identification in Titan’s atmosphere would be important for understanding the abiotic synthesis of organic species. An accurate computational characterization of the molecular structure, energetics, and spectroscopic properties of the E and Z isomers of ethanimine, CH3CHNH, has been carried out by means of a composite scheme based on coupled-cluster techniques, which also account for extrapolation to the complete basis-set limit and core-valence correlation correction, combined with density functional theory for the treatment of vibrational anharmonic effects. By combining the computational results with new millimeter-wave measurements up to 300 GHz, the rotational spectrum of both isomers can be accurately predicted up to 500 GHz. Furthermore, our computations allowed us to revise the infrared spectrum of both E- and Z-CH3CHNH, thus predicting all fundamental bands with high accuracy.
Balance Gait Controller for a Bipedal Robotic Walker With Foot Slip

Low-friction ground conditions present a navigation challenge for bipedal robotic locomotion. While robots might traverse slippery surfaces by carefully planning trajectories, balance recovery from unexpected slip remains a challenge. We present a motion and gait control design for bipedal robotic walkers under foot slip. Slipping dynamics are explicitly considered as a part of the walker's dynamic model. A two-mass inverted pendulum model is presented to capture the ankle actuation effect and used to determine the gait recovery stepping location. A whole-body balance controller is then applied to realize the stepping task. The integration of the abstracted inverted pendulum model and the multi-link model helps to build a whole-body operational space design to compute the controlled joint torques. We design a five-link planar walking robot and implement the control system on the platform. A comprehensive set of walking experiments are presented, demonstrating the performance of the controller for walking on both high-, low-, and extremely low-friction ground surfaces. The experimental results confirm that explicit consideration of foot slip improves the performance and yields a stable gait on a low-friction ground surface.
Comparison: Binomial model and Black Scholes model

The Binomial Model and the Black Scholes Model are the popular methods that are used to solve the option pricing problems. Binomial Model is a simple statistical method and Black Scholes model requires a solution of a stochastic differential equation. Pricing of European call and a put option is a very difficult method used by actuaries. The main goal of this study is to differentiate the Binominal model and the Black Scholes model by using two statistical model - t-test and Tukey model at one period. Finally, the result showed that there is no significant difference between the means of the European options by using the above two models.
Health Promotion Behaviors and Psychosocial Factors Among Middle-Aged Women in Saudi Arabia

Introduction Middle-aged women experience several changes that can affect their biological and psychosocial health. A healthy lifestyle is a fundamental requirement for achieving optimal health. Objectives To examine the impact of psychosocial factors on health promotion behaviors among middle-aged women in Saudi Arabia. Methods A cross-sectional, descriptive, correlational study design was used. One hundred and eighteen middle-aged women completed the demographic questionnaire, Stunkard Figure Rating Scale, Perceived Stress Scale, and Health-Promoting Lifestyle Profile-II from September 2, 2020 to February 15, 2021. Multiple regression analysis was performed to estimate the impact of psychosocial factors on health promotion behaviors. Results Participants “sometimes” engaged in health promotion behaviors. Spiritual growth scored the highest, whereas physical exercise scored the lowest. Most of the participants were dissatisfied with their body image and reported moderate levels of perceived stress. The participants were more likely to report a preferred body image that was smaller than their perceived body image. Perceived stress and body image were significantly correlated with the total score for health promotion behaviors. Perceived stress level, age, and menopause status were significant predictors of health promotion behaviors. The model significantly predicted all the health promotion behavior subscales, excluding nutrition. Conclusion There were relationships between certain psychosocial predictors and health-promoting behaviors. This study can guide healthcare professionals in promoting middle adult women's adoption of health-promoting behaviors. More research can be conducted in Saudi Arabia to understand the health promotion behaviors of middle-aged women.
Multimodal Feature Integration on Manifold for Traffic Applications

In the fields of traffic modeling, objects (i.e., various vehicles) are usually represented using multimodal features. However, two problems remain unsolved on how to utilize these multimodal features better: 1) The missing features because of noise and 2) The curse of dimensionality. In this paper, we addressed these two problems by integrating the multimodal features on the Grassmann manifold. By defining grouping criteria on the multimodal features, the feature vectors are grouped into a set of subspaces, and are further treated as a point on the Grassmann manifold. To deal with the missing features L2-Hausdorff distance, a metric that compares multimodal feature vectors of different number of subspaces, is computed first. And then a kernel matrix is computed. Furthermore, based on the kernel matrix, we proposed a supervised as well as an unsupervised feature selection criterion to obtain t h e representative features on in the Reproducing Kernel Hilbert Space (RKHS). This has alleviated the curse of dimensionality to a significant extent. Experimental results on three different multimodal datasets show that the proposed feature integration technique can outperform the state-of-the-art methods
Altered MicroRNA Maturation in Ischemic Hearts: Implication of Hypoxia on XPO5 and DICER1 Dysregulation and RedoximiR State

Ischemic cardiomyopathy (ICM) is associated with abnormal microRNA expression levels that involve an altered gene expression profile. However, little is known about the underlying causes of microRNA disruption in ICM and whether microRNA maturation is compromised. Therefore, we focused on microRNA maturation defects analysis and the implication of the microRNA biogenesis pathway and redox-sensitive microRNAs (redoximiRs). Transcriptomic changes were investigated via ncRNA-seq (ICM, n = 22; controls, n = 8) and mRNA-seq (ICM, n = 13; control, n = 10). The effect of hypoxia on the biogenesis of microRNAs was evaluated in the AC16 cell line. ICM patients showed a reduction in microRNA maturation compared to control (4.30 ± 0.94 au vs. 5.34 ± 1.07 au, p ˂ 0.05), accompanied by a deregulation of the microRNA biogenesis pathway: a decrease in pre-microRNA export (XPO5, FC = −1.38, p ˂ 0.05) and cytoplasmic processing (DICER, FC = −1.32, p ˂ 0.01). Both processes were regulated by hypoxia in AC16 cells (XPO5, FC = −1.65; DICER1, FC = −1.55; p ˂ 0.01; Exportin-5, FC = −1.81; Dicer, FC = −1.15; p ˂ 0.05). Patients displayed deregulation of several redoximiRs, highlighting miR-122-5p (FC = −2.41, p ˂ 0.001), which maintained a good correlation with the ejection fraction (r = 0.681, p ˂ 0.01). We evidenced a decrease in microRNA maturation mainly linked to a decrease in XPO5-mediated pre-microRNA export and DICER1-mediated processing, together with a general effect of hypoxia through deregulation of biogenesis pathway and the redoximiRs.
Synchronization Instability Mechanism and Damping Enhancement Control for DFIG-Based Wind Turbine During Grid Faults

Doubly fed induction generator (DFIG)-based wind turbines (WTs) are prone to lose synchronization with the power grid during weak grid faults, and the grid synchronization instability phenomenon features the frequency oscillations or divergence of phase-locked loop (PLL). However, the instability mechanism of PLL caused by other parts dynamics of DFIG-based WTs remains unclear. Thus, this article investigates how the PLL dynamics are affected by the current control loops of the grid-side converter (GSC) and rotor-side converter (RSC) in terms of grid synchronization. Firstly, a small-signal model is developed, where the transfer function of PLL is cascaded with another transfer function that represents the dynamics of other control loops in the open-loop transfer function of this model. Then, the instability mechanism is elaborated by analogy with the Heffron–Phillips model of synchronous generator, which indicates that the current control loops of GSC and RSC deteriorate the synchronization stability by providing negative damping. Based on it, the GSC-based stabilizer for damping enhancement is proposed for DFIG-based WT to improve synchronization stability, which takes full advantages of the GSC capacity potential. Finally, the theoretical analysis and proposed method are validated by experiments.