Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain-Controlled, AR-Based Home Automation System Using SSVEP-Based Brain-Computer Interface and EOG-Based Eye Tracker: A Feasibility Study for the Elderly End User

Over the past decades, brain-computer interfaces (BCIs) have been developed to provide individuals with an alternative communication channel toward external environment. Although the primary target users of BCI technologies include the disabled or the elderly, most newly developed BCI applications have been tested with young, healthy people. In the present study, we developed an online home appliance control system using a steady-state visual evoked potential (SSVEP)-based BCI with visual stimulation presented in an augmented reality (AR) environment and electrooculogram (EOG)-based eye tracker. The performance and usability of the system were evaluated for individuals aged over 65. The participants turned on the AR-based home automation system using an eye-blink-based switch, and selected devices to control with three different methods depending on the user’s preference. In the online experiment, all 13 participants successfully completed the designated tasks to control five home appliances using the proposed system, and the system usability scale exceeded 70. Furthermore, the BCI performance of the proposed online home appliance control system surpassed the best results of previously reported BCI systems for the elderly.
Analysis of infiltrated immune cells in left atriums from patients with atrial fibrillation and identification of circRNA biomarkers for postoperative atrial fibrillation

Background: Atrial fibrillation (AF) increases the risk of stroke and heart failure. Postoperative AF (POAF) increases the risk of mortality after cardiac surgery. This study aims to explore mechanisms underlying AF, analyze infiltration of immune cells in left atrium (LA) from patients with AF, and identify potential circular RNA (circRNA) biomarkers for POAF. Methods: Raw data of GSE797689, GSE115574, and GSE97455 were downloaded and processed. AF-related gene co-expression network was constructed using weighted gene correlation network analysis and enrichment analysis of genes in relevant module was conducted. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were applied to investigate pathways significantly enriched in AF group. Infiltration of immune cells was analyzed using single-sample GSEA. Differentially expressed genes (DEGs) between patients with or without AF were identified and competing endogenous RNA (ceRNA) networks of DEGs were constructed. To screen biomarkers for POAF, differentially expressed circRNAs (DEcircRNAs) between patients with or without POAF were identified. Intersection between DEcircRNAs and circRNAs in ceRNA networks of DEGs were extracted and circRNAs in the intersection were further screened using support vector machine, random forest, and neural network to identify biomarkers for POAF. Results: Three modules were found to be relevant with AF and enrichment analysis indicated that genes in these modules were enriched in synthesis of extracellular matrix and inflammatory response. The results of GSEA and GSVA suggested that inflammatory response-related pathways were significantly enriched in AF group. Immune cells like macrophages, mast cells, and neutrophils were significantly infiltrated in LA tissues from patients with AF. The expression levels of immune genes such as CHGB, HLA-DRA, LYZ, IGKV1-17 and TYROBP were significantly upregulated in patients with AF, which were correlated with infiltration of immune cells. ceRNA networks of DEGs were constructed and has_circ_0006314 and hsa_circ_0055387 were found to have potential predictive values for POAF. Conclusion: Synthesis of extracellular matrix and inflammatory response were main processes involved in development and progression of AF. Infiltration of immune cells was significantly different between patients with or without AF. Has_circ_0006314 and hsa_circ_0055387 were found to have potential predictive values for POAF.
Improving screening for major depressive disorder

ABSTRACT Background: Depression affects up to 20% of primary care patients and negatively affects patients' mental and physical health. Local Problem: At a primary care clinic, a review of 291 patient records revealed that no patients were being screened for depression using a validated and reliable tool. Methods: The problem was addressed through the implementation of a depression screening and management protocol based on the U.S. Preventive Services Task Force guidelines. Processes used were recommended by the American College of Preventive Medicine and Institute for Clinical Systems Improvement. Interventions: This project implemented a protocol to screen, treat, and manage patients with depression at this primary care clinic. Analysis compared preimplementation and postimplementation metrics, including the number of patients screened for depression, newly diagnosed with depression, offered antidepressants, offered referral, and managed with follow-up. Results: Implementing a depression screening and management protocol in this clinic significantly increased depression screenings, the percent of patients newly diagnosed with depression, and the number of patients offered treatment. Conclusion: This quality improvement (QI) project improved screening, diagnosis, and management of patients with depression in this primary care clinic. A future QI project for this clinic should focus on measures to track improvements in patients with depression at this primary care clinic.
Analysis of Development and Decline of Hypoxia by Using Monitoring Data Collected near The Tama River Estuary of Tokyo Bay

Hypoxia develops in bottom water in enclosed water bodies with limited water exchange and long water retention times, especially near the river mouth. The inner part of Tokyo Bay is an example. This study is focused on the development and decline of hypoxia around the Haneda Airport D-runway, which was constructed nearby the river mouth of the Tama River, and a comparison was made between monitoring data at a monitoring post at the Haneda Airport and those far off the river mouth nearby Kawasaki artificial island. Multiple sets of data have been incorporated, including water quality data collected every hour in monitoring posts by MLIT, and meteorological data of JMA. The hypoxia period and the average thickness of hypoxia were calculated, and the sudden recovery of DO was analyzed. The analysis revealed general characteristics of hypoxia development and its decline. Especially, three mechanisms of the DO recovery process were found to be dominated nearby the river mouth: strong and continued southerly wind, the intrusion of dense oceanic water, and extreme freshwater discharge of the Tama River associated with the typhoon. Year-to-year variations of hypoxia periods or volume at Haneda Airport showed no increasing tendency of hypoxia after the construction of the D-runway.
SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder

MicroRNA (miRNA)-disease association (MDA) prediction is critical for disease prevention, diagnosis, and treatment. Traditional MDA wet experiments, on the other hand, are inefficient and costly.Therefore, we proposed a multi-layer collaborative unsupervised training base model called SGAEMDA (Stacked Graph Autoencoder-Based Prediction of Potential miRNA-Disease Associations). First, from the original miRNA and disease data, we defined two types of initial features: similarity features and association features. Second, stacked graph autoencoder is then used to learn unsupervised low-dimensional representations of meaningful higher-order similarity features, and we concatenate the association features with the learned low-dimensional representations to obtain the final miRNA-disease pair features. Finally, we used a multilayer perceptron (MLP) to predict scores for unknown miRNA-disease associations. SGAEMDA achieved a mean area under the ROC curve of 0.9585 and 0.9516 in 5-fold and 10-fold cross-validation, which is significantly higher than the other baseline methods. Furthermore, case studies have shown that SGAEMDA can accurately predict candidate miRNAs for brain, breast, colon, and kidney neoplasms.
Responsive biomimetic hydrogel valve for microfluidics

A biomimetic hydrogel valve capable of directional flow control in response to the local fluid environment in microfluidic channels is described. The valve consists of a pair of photopolymerized hydrogel bistrips with different pH sensitivities, and becomes activated or deactivated reversibly based on local pH as a result of volume and shape changes. When activated at pH 8, the valve mimics anatomic venous valves both functionally and structurally, allowing fluid flow in one direction under forward pressure while restricting flow in the opposite direction. At an applied pressure of 0.71 psi, the forward flow rate was 140 μL/min, compared to a backward leakage rate of 2.3 μL/min.
A novel interrogation technique for time-division multiplexing fiber Bragg grating sensor arrays

A novel interrogation method with multi-switches channels for time-division multiplexing Fiber Bragg Grating (FBG) sensor system is introduced. An electric switches array controlled by a complex programmable logic device (CPLD) is used in this scheme to monitoring simultaneously each sensor of the FBG array according to the different transmition times of each sensor grating. In this paper, More than 10 FBG sensors are interrogated and demodulated simultaneously base on unbalanced Michelson interferometer. The sensing sensitivity of this system is 1.658 Deg per micro-strain, experimentally. The interrogation frequency of the system could reach 1 KHz.
Machine Learning for Emergency Management: A Survey and Future Outlook

Emergency situations encompassing natural and human-made disasters, as well as their cascading effects, pose serious threats to society at large. Machine learning (ML) algorithms are highly suitable for handling the large volumes of spatiotemporal data that are generated during such situations. Hence, over the years, they have been utilized in emergency management to aid first responders and decision-makers in such situations and ultimately improve disaster prevention, preparedness, response, and recovery. In this survey article, we highlight relevant work in this area by first focusing on the commonalities of emergency management applications and key challenges that ML algorithms need to address. Then, we present a categorization of relevant works across all the emergency management phases and operations, highlighting the main algorithms used. Based on our review, we conclude that ML algorithms can provide the basis for tackling different activities across the emergency management phases with a unified algorithmic framework that can solve a large set of problems. Finally, through the systematic literature review, we provide promising future directions for utilizing ML algorithms more effectively in emergency management applications. More importantly, we identify the need for better generalization of algorithms, improved explainability, and trustworthiness of ML algorithms with respect to the emergency management personnel, as well as more efficient ways of addressing the challenges associated with building appropriate datasets.
Extending Twomey’s Analytical Estimate of Nucleated Cloud Droplet Concentrations from CCN Spectra

A fundamental but approximate formula has been established by Twomey in order to compute the nucleated cloud droplet number concentration as a function of the vertical velocity and the CCN (cloud condensation nuclei) characteristics expressed by the C and k parameters. The derivation of such a relationship employs the widespread power-law dependence NCCN 5 of the total activable CCN number NCCN at a given supersaturation k Csy,w of water vapor over cloud droplets sy,w. In this paper the authors follow Twomey’s theoretical approach but using a more realistic four-parameter CCN activation spectrum as shaped by the physicochemical properties of the accumulation mode in a natural aerosol population. The analytical scheme is shown to improve the estimate of cloud droplet number concentration because it accounts for the limited availability of small-sized CN (condensation nuclei) with increasing supersaturation. A first validation and calibration of the new generic CCN activation spectrum is made by comparison with activation spectra, either produced by growing realistic lognormal distributions of CN or measured by a CCN spectrometer over the mid-Atlantic Ocean.
QUALITATIVE ANALYSIS OF COMPETITIVE-COOPERATIVE CELLULAR NEURAL NETWORKS WITH DELAY

Abstract This paper studies general Delayed Cellular Neural Networks (DCNNs) with competitive-cooperative configurations. It is demonstrated how such a configuration may be exploited to give a detailed characterization of the fixed point dynamics in DCNNs. Specifically, we show that by dividing the connection weights into inhibitory and excitatory type, it is always possible to embed a competitive-cooperative DCNN into an augmented cooperative delay system, and thus allows for the use of the powerful monotone dynamical system theory. In this way, we derive several simple sufficient conditions on guaranteed trapping regions and guaranteed componentwise (exponential) convergence of DCNNs. The results relate specific decay rate and trajectory bounds to system parameters and are therefore of practical significance in designing a DCNN with desired performance.
Optimal strategies in a risk selection investment model

We study the following stochastic investment model: opportunities occur randomly over time, following a renewal process with mean interarrival time d, and at each of them the decision-maker can choose a distribution for an instantaneous net gain (or loss) from the set of all probability measures that have some prespecified expected value e and for which his maximum possible loss does not exceed his current capital. Between the investments he spends money at some constant rate. The objective is to avoid bankruptcy as long as possible. For the case e>d we characterize a strategy maximizing the probability that ruin never occurs. It is proved that the optimal value function is a concave function of the initial capital and uniquely determined as the solution of a fixed point equation for some intricate operator. In general, two-point distributions suffice; furthermore, we show that the cautious strategy of always taking the deterministic amount e is optimal if the interarrival times are hyperexponential, and, in the case of bounded interarrival times, is optimal ‘from some point on’, i.e. whenever the current capital exceeds a certain threshold. In the case e = 0 we consider a class of natural objective functions for which the optimal strategies are non-stationary and can be explicitly determined.
A Bayesian Hierarchical Model Combination Framework for Real‐Time Daily Ensemble Streamflow Forecasting Across a Rainfed River Basin

The frequent occurrence of floods during the rainy season is one of the threats in rainfed river basins, especially in river basins of India. This study implemented a Bayesian hierarchical model combination (BHMC) framework to generate skillful and reliable real‐time daily ensemble streamflow forecast and peak flow and demonstrates its utility in the Narmada River basin in Central India for the peak monsoon season (July–August). The framework incorporates information from multiple sources (e.g., deterministic hydrological forecast, meteorological forecast, and observed data) as predictors. The forecasts were validated with a leave‐1‐year‐out cross‐validation using accuracy metrics such as BIAS and Pearson correlation coefficient (R) and probabilistic metrics such as continuous ranked probability skill score, probability integral transform (PIT) plots, and the average width of the 95% confidence intervals (AWCI) plots. The results show that the BHMC framework can increase the forecast skill by 40% and reduce absolute bias by at least 28% compared to the raw deterministic forecast from a physical model, the Variable Infiltration Capacity model. In addition, PIT and AWCI show that the framework can provide sharp and reliable streamflow forecast ensembles for short lead times (1–3‐day lead time) and provide useful skills beyond up to 5‐day lead time. These will be of immense help in emergency and disaster preparedness.
Cross-Talk between Transcriptome Analysis and Dynamic Changes of Carbohydrates Identifies Stage-Specific Genes during the Flower Bud Differentiation Process of Chinese Cherry (Prunus pseudocerasus L.)

Flower bud differentiation is crucial to reproductive success in plants. In the present study, RNA-Seq and nutrients quantification were used to identify the stage-specific genes for flower bud differentiation with buds which characterize the marked change during flower bud formation from a widely grown Chinese cherry (Prunus pseudocerasus L.) cultivar ‘Manaohong’. A KEGG enrichment analysis revealed that the sugar metabolism pathways dynamically changed. The gradually decreasing trend in the contents of total sugar, soluble sugar and protein implies that the differentiation was an energy-consuming process. Changes in the contents of D-glucose and sorbitol were conformed with the gene expression trends of bglX and SORD, respectively, which at least partially reflects a key role of the two substances in the transition from physiological to morphological differentiation. Further, the WRKY and SBP families were also significantly differentially expressed during the vegetative-to-reproductive transition. In addition, floral meristem identity genes, e.g., AP1, AP3, PI, AGL6, SEP1, LFY, and UFO demonstrate involvement in the specification of the petal and stamen primordia, and FPF1 might promote the onset of morphological differentiation. Conclusively, the available evidence justifies the involvement of sugar metabolism in the flower bud differentiation of Chinese cherry, and the uncovered candidate genes are beneficial to further elucidate flower bud differentiation in cherries.
Hierarchical Discriminant Analysis and Its Application

This article presents a new discriminant analysis method and tries to improve the accuracy of discriminant problems without reducing variables. This method establishes two layers of discriminant functions. For the first layer, a linear discriminant function is constructed to determine the sample type. Those samples’ type are not determined at the first layer, they will be distinguished as sub-samples, then a second layer of a nonlinear discriminant function will be established to determine the sub-sample type. The examples show that this method can handle discriminant problems of complex data, and results of this method are better than some common methods.
Scalable Robot Localization Using Incremental Map-Matching Techniques

Map-matching is an e ﬀ ective approach for robot localization using large-size landmark maps. Existing map-matching techniques typically address batch localization applications where the objective maps are given prior to the map-matching task. In contrast to the existing techniques, this paper addresses real-time localization applications where the maps can be incrementally updated even during the map-matching task. We present a solution that combines an incremental RANSAC map-matching with an ANN landmark database. Experiments using real data show promising results.
Development of Multimedia-Based Worksheets as a Teaching Material on Sub-Material of Invertebrates

The purpose of this study is to develop multimedia- based student worksheets in invertebrates sub materials and to describe both the response and scoring result of tenth grade students in Pontianak who learn to use the instructional materials. This research method is Research and Development. The development steps carried out are: 1) potential and problems, 2) information gathering, 3) product design, 4) design validation, 5) product design improvement and 6) product testing. In the product trial phase, a sample of 208 students was used. The validation from both material and media experts on multimedia- based student worksheets in invertebrate sub- materials was confirmed as valid. The student responses at schools with KTSP and curriculum 2013 on the use of multimedia- based student worksheets in Invertebrates sub material were in the fair positive and positive category. The average values obtained by students in schools that applied the KTSP and 2013 curriculum were 40.08 and 54.16 respectively. It was concluded that multimedia-based worksheets were appropriate to be used as teaching material in invertebrates sub material.
Effect of GGA on the half-metallicity of the itinerant ferromagnet CoS 2

The half-metallicity of ${\mathrm{CoS}}_{2},$ important for its possible use in spintronic applications, is investigated by means of density functional full-potential linearized augmented plane wave calculations within both the local spin-density approximation (LSDA) and the generalized gradient approximation (GGA). In contrast with earlier and our own predictions employing the LSDA, we find that ${\mathrm{CoS}}_{2}$ is clearly half-metallic with use of the GGA for the exchange-correlation functional. While the GGA induced modifications of the band structure are significant, they are limited to only the ${e}_{g\ensuremath{\downarrow}}$ state which is responsible for producing the half-metallicity, while leaving all other features the same as those obtained by LSDA. We address the recently measured reflectivity spectra and rectify the assignments given for their underlying optical transitions.
Monte Carlo and hypernetted chain equation of state for the square‐well fluid

Results of Monte Carlo and hypernetted chain theory calculations are reported for both the radial distribution function and the equation of state of the square‐well fluid for the particular case where the potential is cut off at 1. 5σ, where σ is the hard sphere diameter. At high densities, substantial errors are found in the hypernetted chain equation results at all temperatures. However, these errors are very nearly independent of temperature. The hypernetted chain theory accounts for the perturbing effects of the attractive forces with good accuracy despite appreciable errors in the treatment of the repulsive forces.
Characterization of Axial Flux Permanent Magnet Generator Under Various Geometric Parameters for Improved Performance of The Machine

An improved design of an axial flux permanent magnet machine is obtained using finite element method and sensitivity analyses. A 9-phase axial flux permanent magnet machine with one-rotor-two-stators configuration equipped with concentrated winding is investigated and condition for minimum cogging torque and voltage harmonics is deduced. Owing to three dimensional structure of axial flux permanent magnet machines, 3-D finite element model is used to model and analysis of the generator performance characteristics. Sensitivity of the machine is evaluated respect to variation of its geometric parameters