Miracle-dz/UniFAR-FTUs huggingface dataset:
Sleep quantity and variability during the first semester at university: implications for well-being and academic performance

ABSTRACT Research has demonstrated the importance of sleep quality for university students, although work is limited with respect to how sleep during the first semester of school impacts well-being and academic outcomes. The current study sought to examine the role of sleep quantity and variability over the first semester of university on GPA and happiness. Students (n = 97) from a first-semester psychology course completed weekly assessments of sleep and well-being (happiness and anxiety) for 15 weeks, and they gave permission for researchers to collect their GPA and SAT data from the registrar. Average weekly sleep quantity was associated with greater weekly happiness and less anxiety. Weekly sleep variability was associated with lower happiness and more anxiety. Even when accounting for sleep quantity and initial start-of-semester measures, sleep variability predicted worse GPA and happiness for students at the end-of-semester. In summary, sleep consistency may be an important target for health professionals working within the university setting. This study contributes to the literature on the importance not only of sleep but also of sleep consistency, during a major life transition.
Posterior Intraparietal Sulcus Mediates Detection of Salient Stimuli Outside the Endogenous Focus of Attention

Abstract Visual consciousness is shaped by the interplay between endogenous selection and exogenous capture. If stimulus saliency is aligned with a subject’s attentional priorities, endogenous selection will be facilitated. In case of a misalignment, endogenous selection may be compromised as attentional capture is a strong and automatic process. We manipulated task-congruent versus -incongruent saliency in a functional magnetic resonance imaging change-detection task and analyzed brain activity patterns in the cortex surrounding the intraparietal sulcus (IPS) within the Julich-Brain probabilistic cytoarchitectonic mapping reference frame. We predicted that exogenous effects would be seen mainly in the posterior regions of the IPS (hIP4–hIP7–hIP8), whereas a conflict between endogenous and exogenous orienting would elicit activity from more anterior cytoarchitectonic areas (hIP1–hIP2–hIP3). Contrary to our hypothesis, a conflict between endogenous and exogenous orienting had an effect early in the IPS (mainly in hIP7 and hIP8). This is strong evidence for an endogenous component in hIP7/8 responses to salient stimuli beyond effects of attentional bottom-up sweep. Our results suggest that hIP7 and hIP8 are implicated in the individuation of attended locations based on saliency as well as endogenous instructions.
Forced Normalization Revisited: New Concepts About a Paradoxical Phenomenon

The phenomenon of Forced Normalization (FN) was first described by Landolt in 1953, who described the disappearance of epileptiform discharges in the EEG of patients with epilepsy, concomitant with the development of psychotic symptoms. Later, Tellenbach coined the term “alternative psychosis” referring specifically to the alternation between clinical phenomena. Finally, in 1991, Wolf observed a degenerative process involved in the phenomenon, which he called “paradoxical normalization. ” Initially, FN was explained through experimental models in animals and the demonstration of the kindling phenomenon, in its electrical and pharmacological subdivisions. At this stage of research on the epileptic phenomenon, repetitive electrical stimuli applied to susceptible regions of the brain (hippocampus and amygdala) were considered to explain the pathophysiological basis of temporal lobe epileptogenesis. Likewise, through pharmacological manipulation, especially of dopaminergic circuits, psychiatric comorbidities began to find their basic mechanisms. With the development of new imaging techniques (EEG/fMRI), studies in the area started to focus on the functional connectivity (FC) of different brain regions with specific neuronal networks, which govern emotions. Thus, a series of evidence was produced relating the occurrence of epileptic discharges in the limbic system and their consequent coactivation and deactivation of these resting-state networks. However, there are still many controversies regarding the basic mechanisms of network alterations related to emotional control, which will need to be studied with a more homogeneous methodology, in order to try to explain this interesting neuropsychiatric phenomenon with greater accuracy.
Temperature Estimation Using Lumped-Parameter Thermal Network With Piecewise Stator-Housing Modules for Fault-Tolerant Brake Systems in Highly Automated Driving Vehicles

With the increased interest in intelligent transportation, the need for fault-tolerant systems has also increased. In this paper, we propose a piecewise stator-housing module (PSM) and construct a lumped-parameter thermal network (LPTN) that can be used in a fault-tolerant system based on the PSM. The proposed LPTN model considers not only radial and axial heat transfer but also tangential heat transfer; therefore, even if only one of the two circuits is running on a fault-tolerant motor (dual winding motor), the coil temperature can be estimated. To verify the proposed model, three winding-type motors are tested with varying current values during normal and fault operations, and the test and analysis results are in good agreement. Additionally, the usefulness of the proposed model is demonstrated by comparing the temperatures of both the conventional and proposed LPTNs in the event of a brake system fault during braking operation in a virtual traffic jam simulation. This simulation demonstrates that temperature estimation of the motor is important for motor design because the brake operation time is dependent on the motor temperature. Furthermore, the system performance or size can be determined by accurately predicting the temperature, even in the event of a fault in the brake system, where the fault-tolerant motor is used, thus keeping the driver safe. The proposed LPTN can be used in brake systems and in other systems that utilize fault-tolerant dual winding motors.
Association between Metabolic Syndrome and Microvascular Complications in Chinese Adults with Type 1 Diabetes Mellitus

Background Both type 1 diabetes mellitus (T1DM) and metabolic syndrome (MetS) are associated with an elevated risk of morbidity and mortality yet with increasing heterogeneity. This study primarily aimed to evaluate the prevalence of MetS among adult patients with T1DM in China and investigate its associated risk factors, and relationship with microvascular complications. Methods We included adult patients who had been enrolled in the Guangdong T1DM Translational Medicine Study conducted from June 2010 to June 2015. MetS was defined according to the updated National Cholesterol Education Program criterion. Logistic regression models were used to estimate the odds ratio (OR) for the association between MetS and the risk of diabetic kidney disease (DKD) and diabetic retinopathy (DR). Results Among the 569 eligible patients enrolled, the prevalence of MetS was 15.1%. While female gender, longer diabetes duration, higher body mass index, and glycosylated hemoglobin A1c (HbA1c) were risk factors associated with MetS (OR, 2.86, 1.04, 1.14, and 1.23, respectively), received nutrition therapy education was a protective factor (OR, 0.46). After adjustment for gender, age, diabetes duration, HbA1c, socioeconomic and lifestyle variables, MetS status was associated with an increased risk of DKD and DR (OR, 2.14 and 3.72, respectively; both P<0.05). Conclusion Although the prevalence of MetS in adult patients with T1DM in China was relatively low, patients with MetS were more likely to have DKD and DR. A comprehensive management including lifestyle modification might reduce their risk of microvascular complications in adults with T1DM.
Short‐ and Long‐Term Clinical Outcomes for Patients With Takotsubo Syndrome and Patients With Myocardial Infarction: A Report From the Swedish Coronary Angiography and Angioplasty Registry

Background Takotsubo syndrome (TS) is a potentially life‐threatening acute cardiac syndrome with a clinical presentation similar to myocardial infarction and for which the natural history, management, and outcome remain incompletely understood. Our aim was to assess the relative short‐term mortality risk of TS, ST‐segment–elevation myocardial infarction (STEMI), and non‐STEMI (NSTEMI) and to identify predictors of in‐hospital complications and poor prognosis in patients with TS. Methods and Results This is an observational cohort study based on the data from the SCAAR (Swedish Coronary Angiography and Angioplasty Registry). We included all patients (n=117 720) who underwent coronary angiography in Sweden attributed to TS (N=2898 [2.5%]), STEMI (N=48 493 [41.2%]), or NSTEMI (N=66 329 [56.3%]) between January 2009 and February 2018. We compared patients with TS to those with NSTEMI or STEMI. The primary end point was all‐cause mortality at 30 days. Secondary outcomes were acute heart failure (Killip Class ≥2) and cardiogenic shock (Killip Class 4) at the time of angiography. Patients with TS were more often women compared with patients with STEMI or NSTEMI. TS was associated with unadjusted and adjusted 30‐day mortality risks lower than STEMI (adjusted hazard ratio [adjHR], 0.60; 95% CI, 0.48–0.76; P<0.001), but higher than NSTEMI (adjHR, 2.70; 95% CI, 2.14–3.41; P<0.001). Compared with STEMI, TS was associated with a similar risk of acute heart failure (adjHR, 1.26; 95% CI, 0.91–1.76; P=0.16) but a lower risk of cardiogenic shock (adjHR, 0.55; 95% CI, 0.34–0.89; P=0.02). The relative 30‐day mortality risk for TS versus STEMI and NSTEMI was higher for smokers than nonsmokers (adjusted P interaction STEMI=0.01 and P interaction NSTEMI=0.01). Conclusions The 30‐day mortality rate in TS was higher than in NSTEMI but lower than STEMI despite a similar risk of acute heart failure in TS and STEMI. Among patients with TS, smoking was an independent predictor of mortality.
Mapping Wetland Plant Communities Using Unmanned Aerial Vehicle Hyperspectral Imagery by Comparing Object/Pixel-Based Classifications Combining Multiple Machine-Learning Algorithms

Understanding the spatial patterns of plant communities is important for sustainable wetland ecosystem management and biodiversity conservation. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral data with high spatial resolution have become ideal for accurate classification of wetland plant communities. In this article, four dominant plant communities (Phragmites australis, Typha orientalis, Suaeda glauca, and Scirpus triqueter) and two unvegetated cover types (water and bare land) in the Momoge Ramsar wetland site were classified. This was achieved using UAV hyperspectral images and three object- and pixel-based machine-learning classification algorithms [random forest (RF), convolutional neural network (CNN), and support vector machine (SVM)]. First, spectral derivative analysis, logarithmic analysis, and continuum removal analysis identified the wavelength at which the greatest difference in reflectance occurs. Second, dimensionality reduction of hyperspectral images was conducted using principal component analysis. Subsequently, an optimal feature combination for community mapping was formed based on data transformation (spectral features, vegetation indices, and principal components). Image objects were obtained by segmenting the optimum object feature subsets. Finally, distribution maps of communities were produced by using three machine-learning classification algorithms. Our results reveal that object-based image analysis outperforms pixel-based methods, with overall accuracies (OAs) of 80.29–87.75%; RF has the highest OA of 87.75% (Kappa = 0.864), followed consecutively by CNN (OA = 83.31%, Kappa = 0.829) and SVM (OA = 80.29%, Kappa = 0.813). Phragmites australis dominates the plant community (55.9%) at the study area, followed by Typha orientalis (16.2%), Suaeda glauca (16.2%), and Scirpus triqueter (4.6%). The results highlight the importance of spectral transformation features in red-edge regions. The mapping results will help establish basic information for subsequent studies involving habitat suitability assessment at this study site.
Effect of Salt, Nutrients, and Native Microbe Additions on Common Dune Restoration Grasses

ABSTRACT Clark, G.S. ; Busch, M.H. , and Crawford, K.M. , 2021. Effect of salt, nutrients, and native microbe additions on common dune restoration grasses. Journal of Coastal Research, 37(5), 1025–1034. Coconut Creek (Florida), ISSN 0749-0208. Sand dunes are a valuable resource that shelters coastal communities and wildlife. Unfortunately, their survival is threatened by commercial development and erosion accelerated by climate change. Restoring sand dunes is challenging because of the difficulty in establishing vegetation in such a hostile environment. Restoration practitioners have responded by choosing stress-tolerant native species, adding nutrients, or incorporating native microbial communities into transplanted soils, but little is known about how these practices interact. In other systems, native microbes sometimes increase plant performance and salt tolerance. Nutrient additions promote growth but may inhibit development of beneficial fungi and disproportionately benefit some plant species at the expense of community diversity. To explore these interactions, two grasses commonly used in dune restorations—Panicum amarum and Uniola paniculata—were grown in a greenhouse and exposed to soil with and without native microbiota and to a range of nutrient and salinity treatments. Native microbial additions had little impact on live, aboveground biomass or the salt tolerance of the nursery-grown plants. Nutrient additions did not inhibit beneficial fungi. In fact, the density of extraradical hyphae in the soil increased by an average of 31% across all plants subject to a balanced, slow-release fertilizer. The two grasses responded differently to the salinity and nutrient treatments. Under a 2.5% salinity treatment, average live aboveground biomass for P. amarum fell 52%, compared with a 5.5% increase for U. paniculata. None of the nutrient additions affected U. paniculata; however, a combination of nitrogen and phosphorus increased P. amarum live, aboveground biomass by 356%. The strong response of P. amarum to fertilization offers a path to rapid growth of dune vegetation, but it may foster that species' dominance, which could lower the plant community's aggregate resistance to salinity.
Power Grids as Complex Networks: Resilience and Reliability Analysis

Power grids are cyber-physical systems and can be modelled as network systems where individual units (generators, busbars and loads) are interconnected through physical and cyber links. Network components (nodes/edges) may undergo intentional and/or random failures. In catastrophic cases, a failure initiating from a small set of these components can quickly propagate through the whole network, leading to a cascade of failures that might force a deep whole-grid blackout. Often network components have different vitality and protecting some is more critical than others. This manuscript aims to provide a focused overview of modelling power grids as complex networks and their resilience and reliability analysis. We also perform a critical review of vitality metrics and their precision in power grid resilience analysis. The review is accompanied by some simulations on benchmark and real power grids to show the applicability of these concepts in studying resilience.
Robust Community Detection in Graphs

Community detection in network-type data provides a powerful tool in analyzing and understanding real-world systems. In fact, community detection approaches aim to reduce the network’s dimensionality and partition it into a set of disjoint clusters or communities. However, real networks are usually corrupted with noise or outliers which affect the detected community structure quality. In this paper, a new robust community detection algorithm that is capable of recovering a clean or a smoothed version of the corrupted graph and detecting the correct community structure is introduced. The proposed approach combines robust principal component analysis (RPCA) and symmetric nonnegative matrix factorization (SymNMF) in a single optimization problem. The proposed problem is solved under the framework of alternating direction methods of multipliers (ADMM). In particular, the corrupted adjacency matrix is decomposed into a low-rank and sparse components using RPCA and the community structure is detected by applying SymNMF to the extracted low-rank component. Extensive experiments that have been conducted on real and simulated binary and weighted networks show that the proposed approach significantly outperforms existing algorithms in detecting the correct community structure even in grossly corrupted networks.
Study on Resistive Switching Property of Ti Doped Novel NiO Thin Films

Ti doped nickel oxide thin films have been fabricated by sol-gel dip-coating process using nickel acetate and tetrabutyl titanate as source materials. The effect of the amount of Ti dopant on the surface roughness, optical, chemical state and electrical properties of NiO: Ti thin films was observed by atomic force microscopy (AFM), Uv-vis spectroscopy, X-ray photoelectron spectroscopy(XPS) and I-V measurement, respectively. Results show that the Ti doping is an effective ways to improve the resistive switching behaviors and it is a convenient way to understand the mechanism of the resistive switching behaviors.
High speed synchrotron X-ray diffraction experiments resolve microstructure and phase transformation in laser processed Ti-6Al-4V

The microstructures of Ti-6Al-4V following laser processing depend primarily on the phase transformation of β to α, but their development is constrained by the rapidly changing temperature in the small fusion zone. In-situ synchrotron X-ray diffraction was utilized to probe the rapid phase evolution in single melt tracks with high angular and temporal resolution. Both fully martensitic and mixed α+α′+β microstructures were confirmed by microscopy. Cooling rates were inferred from the lattice parameter history and complementary thermal simulation. It was found that the threshold cooling rate for fully martensitic transformation is in the range between 2900 and 6500°C/s. IMPACT STATEMENT High-speed synchrotron X-ray diffraction during operando laser processing suggests a new threshold between martensitic and diffusional phase transformation in Ti-6Al-4V occurring at higher cooling rates than previously reported.
Optical Fiber SPR Sensor With Surface Ion Imprinting for Highly Sensitive and Highly Selective Ni2+ Detection

A reflective optical fiber nickel ion (Ni<sup>2+</sup>) sensor based on surface plasmon resonance (SPR) and surface ion imprinting was proposed and demonstrated. The sensor is made of multimode optical fiber with a core diameter of 600 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> and coated with a gold film for an exciting SPR effect. To realize highly sensitive and highly selective detection of Ni<sup>2+</sup>, graphene oxide (GO) and Ni<sup>2+</sup>-imprinted Chitosan (IP-CS) are subsequently combined on the sensor surface. The GO can not only act as the intermediate binder between the Au film and the CS but also increase the sensitivity of the sensor. The Ni<sup>2+</sup> holes on the IP-CS can realize the specific capture of Ni<sup>2+</sup>. The experimental results show that the sensitivity of the sensor to Ni<sup>2+</sup> is 5.220 nm/[lg(mg/L)] with a limit of detection of <inline-formula> <tex-math notation="LaTeX">$2.393\times 10^{-3}$ </tex-math></inline-formula> mg/L, and the selectivity of the sensor is significantly improved after imprinting. Besides, the sensor has good stability and repeatability, and fast responsiveness.
Anomaly Detection of Network Streams via Dense Subgraph Discovery

We consider cyber security as one of the most significant technical challenges in current times. One of the main tasks is to detect anomalous patterns in the network streams as soon as they appear. In order to solve the above problem, previous propositions use statistical or machine learning-based methods to detect anomalous patterns in the network streams. However, these solutions incur significant low efficiency and precision due to the frequent recomputation of the results from scratch and unreasonable assumptions. In graph theory, dense subgraphs can be used to model the anomalous patterns if we abstract the network streams as a dynamic graph. This motivates us to explore dense subgraph discovery under the scenario where the network is updating. In this paper, we propose a graph-based framework, referred to as SAD, towards continuous dense subgraph discovery over network streams. In specific, we design an auxiliary data structure that is a concise representation of intermediate results, and its execution model allows a fast incremental maintenance strategy. In this way, we can detect anomalous patterns in the network streams in near real-time. Experiments demonstrate that SAD can not only get a higher accuracy of 90.2% but also faster than $11.4\times$ times compared to the state-of-the-art anomaly detection algorithms.
Correlation of SPME-GC-MS Volatile Compounds with Descriptive Sensory Odor Analysis of Whole Wheat and Quinoa Flours in Accelerated Storage

Correlation of SPME-GC-MS Volatile Compounds with Descriptive Sensory Odor Analysis of Whole Wheat and Quinoa Flours in Accelerated Storage Sarah Snow Turner Department of Nutrition, Dietetics, and Food Science, BYU Master of Science The acceptability of whole grain flours during storage varies widely, as does the estimated shelf life of such flours, in part because acceptability is typically determined using subjective human sensory testing. Research is needed to establish more objective measures of acceptability. This study correlated the quantitative results of a descriptive odor sensory panel with volatile compounds determined by solid-phase microextraction-gas chromatograph-mass spectrometry (SPME-GC-MS). Whole wheat flour and quinoa flour were held at 40°C for up to 24 weeks to accelerate changes occurring during storage. Samples were collected every 4 weeks and placed in frozen storage. Thawed samples were then evaluated using SPME-GC-MS and descriptive sensory odor analysis. Significant correlations were found between 1-hexanol, 2pentylfuran, phenol, hexanoic acid, and hexanal volatiles of whole wheat flour and the odor descriptor cardboard/stale. This indicates that SPME-GC-MS can be used as a less expensive, less time-consuming, more precise method to determine the acceptability of whole wheat flour during storage. Significant correlations were not present in the quinoa flour data, suggesting that SPME-GC-MS may not be preferable to human sensory odor analysis in determining acceptability of stored quinoa flour.