Miracle-dz/UniFAR-FTUs huggingface dataset:
Neuroticism Is Prospectively Associated With 30-Month Changes in Broadband Internalizing Symptoms, but Not Narrowband Positive Affect or Anxious Arousal, in Emerging Adulthood

Elevated levels of Neuroticism/Negative Emotionality (N/NE) and, less consistently, lower levels of Extraversion/Positive Emotionality (E/PE) confer risk for pathological depression and anxiety. To date, most prospective-longitudinal research has narrowly focused on traditional diagnostic categories, creating uncertainty about the precise nature of these prospective associations. Adopting an explicitly hierarchical-dimensional approach, we examined the association between baseline variation in personality and longitudinal changes in broad and narrow internalizing-symptom dimensions in 234 emerging adults followed for 2.5 years, during the transition from older adolescence to early adulthood. N/NE was uniquely associated with increases in broadband internalizing—the core cognitive and affective symptoms that cut across the emotional disorders—and unrelated to the narrower dimensions of positive affect and anxious arousal that differentiate specific internalizing presentations. Variation in E/PE and several other Big Five traits was cross-sectionally but not prospectively related to longitudinal changes in specific internalizing symptoms. Exploratory personality-facet-level analyses provided preliminary evidence of more granular associations between personality and longitudinal changes in internalizing symptoms. These observations enhance the precision of models linking personality to internalizing illness, highlight the centrality of N/NE to increases in transdiagnostic internalizing symptoms during a key developmental chapter, and set the stage for developing more effective prevention and treatment strategies.
Seismicity patterns of earthquake swarms due to fluid intrusion and stress triggering

SUMMARY Earthquake swarms are often assumed to result from an intrusion of fluids into the seismogenic zone, causing seismicity patterns which significantly differ from aftershock sequences. But neither the temporal evolution nor the energy release of earthquake swarms is generally well understood. Because of the lack of descriptive empirical laws, the comparison with model simulations is typically restricted to aspects of the overall behaviour such as the frequency‐ magnitude distribution. However, previous investigations into a large earthquake swarm which occurred in the year 2000 in Vogtland/northwest Bohemia, Central Europe, revealed some well-defined characteristics which allow a rigorous test of model assumptions. In this study, simulations are performed of a discretized fault plane embedded in a 3-D elastic half-space. Earthquakes are triggered by fluid intrusion as well as by co-seismic and post-seismic stress changes. The model is able to reproduce the main observations, such as the fractal temporal occurrence of earthquakes, embedded aftershock sequences, and a power-law increase of the average seismic moment release. All these characteristics are found to result from stress triggering, whereas fluid diffusion is manifested in the spatiotemporal spreading of the hypocentres.
Research on Mental Robot Based on Knowledge Graph

Psychological counseling is one of the effective ways to solve psychological problems at present, and psychological counseling robot as a new interactive dialogue system tool (dialogue system refers to the system of dialogue with users through natural language processing) has significant research significance. In this paper, a psychological counseling robot based on natural language processing is developed by using Rasa framework and database Neo4j. The robot can assist users in psychological counseling, and help users better understand relevant knowledge by generating knowledge maps. Moreover, the psychological counseling robot has better semantic understanding ability and richer knowledge base. Aiming at the problem of low dialogue quality existing in functional components of Rasa framework, this paper analyzes the causes of this problem, and puts forward the corresponding algorithm model optimization and function improvement methods. In order to solve the problem of Chinese word recognition, the method based on rule template is used to improve the word recognition. In order to solve the problem of low accuracy of action prediction in dialogue management module, this system uses the cyclic neural network (RNN) model to improve the accuracy of action prediction. By constructing a sequence mapping model based on RNN, the original input was changed from sparse word vector to dense word vector, and experiments were designed to explore the key parameters affecting the experimental results, and then compared with the full-text retrieval model. Experimental results show that the answers generated by the model are about 10% more reasonable than those retrieved based on other similar models (e.g., Lucene).
Pioglitazone Protects Tubular Epithelial Cells during Kidney Fibrosis by Attenuating miRNA Dysregulation and Autophagy Dysfunction Induced by TGF-β

Excessive renal TGF-β production and pro-fibrotic miRNAs are important drivers of kidney fibrosis that lack any efficient treatment. Dysfunctional autophagy might play an important role in the pathogenesis. We aimed to study the yet unknown effects of peroxisome proliferator-activated receptor-γ (PPARγ) agonist pioglitazone (Pio) on renal autophagy and miRNA dysregulation during fibrosis. Mouse primary tubular epithelial cells (PTEC) were isolated, pre-treated with 5 µM pioglitazone, and then stimulated with 10 ng/mL TGF-β1 for 24 h. Male 10-week-old C57Bl6 control (CTL) and TGF-β overexpressing mice were fed with regular chow (TGF) or Pio-containing chow (20 mg/kg/day) for 5 weeks (TGF + Pio). PTEC and kidneys were evaluated for mRNA and protein expression. In PTEC, pioglitazone attenuated (p < 0.05) the TGF-β-induced up-regulation of Col1a1 (1.4-fold), Tgfb1 (2.2-fold), Ctgf (1.5-fold), Egr2 (2.5-fold) mRNAs, miR-130a (1.6-fold), and miR-199a (1.5-fold), inhibited epithelial-to-mesenchymal transition, and rescued autophagy function. In TGF mice, pioglitazone greatly improved kidney fibrosis and related dysfunctional autophagy (increased LC3-II/I ratio and reduced SQSTM1 protein content (p < 0.05)). These were accompanied by 5-fold, 3-fold, 12-fold, and 2-fold suppression (p < 0.05) of renal Ccl2, Il6, C3, and Lgals3 mRNA expression, respectively. Our results implicate that pioglitazone counteracts multiple pro-fibrotic processes in the kidney, including autophagy dysfunction and miRNA dysregulation.
Using Remote Sensing to Map Vegetation Density on a Reclaimed Surface Mine 1

The West Virginia Department of Environmental Protection, in a cooperative agreement with the Office of Surface Mining's Charleston Field Office, is evaluating the utility of high resolution satellite images for characterizing vegetation patterns on reclaimed surface mines. This paper details the results of the first phase of this project, which sought to determine whether satellite images could be used to estimate percentage vegetation cover. Depending on the post-mining land use, percent vegetation cover may be employed as a standard for evaluating re-vegetation success prior to bond release. However, the increasing size of surface operations, combined with labor intensive field techniques required to estimate percent cover, make implementing the standard difficult. This paper details a simple technique for estimating percent vegetation cover based on the widely-used Normalized Difference Vegetation Index (NDVI). NDVI exhibited a 0.96 correlation with percent vegetation cover for 34 reference samples collected on a 94 acre study area in southern West Virginia. Based on this relationship, a technique was developed that produced a mean error of 6.41% (+/- 2.68% at the 90% confidence level) when estimating percent cover for the 34 field sites.
First-Principles Calculation of Structural, Electronic, and Optical Properties of Cubic Perovskite CsPbF3

Lead halide perovskites have attracted considerable attention as one of the most promising materials for optoelectronic applications. The structural, electronic, and optical properties of the cubic perovskite CsPbF3 were studied using density functional theory in conjunction with plane waves, norm-conserving pseudopotentials, and Perdew-Berg-Erzenhof flavor of generalized gradient approximation. The obtained structural parameters are a good agreement with the experimentally measured and other’s theoretically predicted values. The obtained electronic band structure revealed that cubic CsPbF3 has a direct fundamental band gap of 2.99 eV at point R. The calculated energy band gaps at the high symmetry points agree with the other available theoretical results. The GW method is adapted to correct the underestimated fundamental energy gap value to 4.05 eV. The contribution of the different bands was analyzed from the total and partial density of states. The electron densities show that Cs and F have strong ionic bonds, whereas Pb and F have strong covalent bonds. The optical properties of CsPbF3 were calculated using the density functional perturbation theory and Kramers-Kronig relations. The wide and direct bandgap nature and the calculated optical properties imply that cubic CsPbF3 can be used in optical and optoelectronic devices for high frequencies visible and low frequencies ultraviolet electromagnetic radiation.
Theoretical Insight into the Effect of Phosphorus Oxygenation on Nonradiative Decays: Comparative Analysis of P-Bridged Stilbene Analogs

Incorporation of the phosphorus element into a π-conjugated skeleton offers valuable prospects for adjusting the electronic structure of the resulting functional π-electron systems. Trivalent phosphorus has the potential to decrease the LUMO level through σ*−π* interaction, which is further enhanced by its oxygenation to the pentavalent P center. This study shows that utilizing our computational analysis to examine excited-state dynamics based on radiative/nonradiative rate constants and fluorescence quantum yield (ΦF) is effective for analyzing the photophysical properties of P-containing organic dyes. We theoretically investigate how the trivalent phosphanyl group and pentavalent phosphine oxide moieties affect radiative and nonradiative decay processes. We evaluate four variations of P-bridged stilbene analogs. Our analysis reveals that the primary decay pathway for photoexcited bis-phosphanyl-bridged stilbene is the intersystem crossing (ISC) to the triplet state and nonradiative. The oxidation of the phosphine moiety, however, suppresses the ISC due to the relative destabilization of the triplet states. The calculated rate constants match an increase in experimental ΦF from 0.07 to 0.98, as simulated from 0.23 to 0.94. The reduced HOMO–LUMO gap supports a red shift in the fluorescence spectra relative to the phosphine analog. The thiophene-fused variant with the nonoxidized trivalent P center exhibits intense emission with a high ΦF, 0.95. Our prediction indicates that the ISC transfer is obstructed owing to the relatively destabilized triplet state induced by the thiophene substitution. Conversely, the thiophene-fused analog with the phosphine oxide moieties triggers a high-rate internal conversion mediated by conical intersection, leading to a decreased ΦF.
Pyrrhotites from the Strathcona Mine, Sudbury, Canada; A Thermomagnetic and Mineralogical Study

Pyrrhotites collected from sulfide concentrations in the irruptive and the underlying gneiss complex at the Strathcona Mine, Sudbury, were studied microscopically, by electron microprobe, thermomagnetic, X-ray, DTA and Moessbauer methods. The results show that two pyrrhotite types occur in this section through the orebody. The first is of composition (Fe,Ni) 7 S 8 with Ni [asymp] 0.35-0.50 wt. % and is ferrimagnetic up to 300-305 degrees C. X-ray powder data are in good agreement with those for "monoclinic" pyrrhotite with a 2A, 4C supercell. The second pyrrhotite is of composition (Fe,Ni) 9 S 10 with Ni [asymp] 0.68-1.01 wt. % and is ferrimagnetic between approximately 200 and 250 degrees C. X-ray powder data are in agreement with those for so-called "hexagonal" pyrrhotite with a 2A, 5C supercell. In the upper part of the section large amounts of both types of pyrrhotite occur intimately intergrown, but near the base of the irruptive there is an abrupt change to single-phase (Fe,Ni) 7 S 8 . Near the base of the orebody both types again become abundant. Electron microprobe analysis of the two pyrrhotite types and the associated pentlandite show greater concentrations of nickel in the pyrrhotites (of high susceptibility) and pentlandite towards the center of the section, with a concomitant decrease in the cobalt content of the pentlandite. The "hexagonal" pyrrhotite consistently shows an enrichment in residual nickel relative to the "monoclinic" type and is more readily etched by HI. Further studies of the two separate pyrrhotite types by Moessbauer spectroscopy and differential thermal analysis, together with the magnetic and X-ray data, suggest that each represents different, but definite, layer-order schemes of vacancies on the iron sites in the (001) planes. The Bertaut model for (Fe,Ni) 7 S 8 with the vacancies ordered in alternate iron atom planes parallel to (001), is consistent with these data and for (Fe,Ni) 9 S 10 a model with single and double filled planes alternating with those containing vacancies is proposed. Study of synthetic pyrrhotites containing low concentrations of nickel suggest that the nickel is evenly distributed over the magnetic sublattices in both types. This study suggests that variation in the original bulk composition of a sulfide melt is expressed as variation in the proportions of these essentially stoichiometric pyrrhotites and other co-existing sulfides.
Machine Learning Empowered Brain Tumor Segmentation and Grading Model for Lifetime Prediction

An uncontrolled growth of brain cells is known as a brain tumor. When brain tumors are accurately and promptly diagnosed using magnetic resonance imaging scans, it is easier to start the right treatment, track the tumor’s development over time, and select the best surgical techniques. This paper applies advanced and popular methods for preprocessing, segmentation, grading of tumors and lifetime prediction. On exploring various encoder-decoder architectures, UNet++ architecture was chosen for detecting brain tumor and obtained an accuracy of 98% and intersection over union score of 0.7483 during the testing phase. The segmented image is used to extract the radiomics features. Despite of several difficulties, data imbalance among the dataset is the common obstacle for survival prediction. To solve and increase the sample size and preserve the class distribution, the synthetic minority oversampling technique and adaptive synthetic approaches are used. After balancing the dataset, the important characteristics are selected using principal component analysis and tree-based feature selection techniques. The collected characteristics are used as input for machine learning techniques including stochastic gradient descent, decision tree, random forest, and support vector machine. The distinction between low-grade glioma and high-grade glioma is investigated as a binary classification. Accuracy, precision, recall, and F1score are used in the performance evaluation. The highest accuracy of 96% is achieved using stochastic gradient descent. Lifetime prediction of high-grade glioma patients is made using regression techniques: Linear, ridge, stochastic gradient descent, and extreme gradient boosting. We have obtained the least mean square error of 93726.45 using the extreme gradient boosting method. The proposed approach is contrasted with the most recent segmentation, grading, and lifetime prediction methods described in the literature.
Deep Neural Network-Based Fusion Localization Using Smartphones

Indoor location-based services (LBS) have tremendous practical and social value in intelligent life due to the pervasiveness of smartphones. The magnetic field-based localization method has been an interesting research hotspot because of its temporal stability, ubiquitousness, infrastructure-free nature, and good compatibility with smartphones. However, utilizing discrete magnetic signals may result in ambiguous localization features caused by random noise and similar magnetic signals in complex symmetric and large-scale indoor environments. To address this issue, we propose a deep neural network-based fusion indoor localization system that integrates magnetic and pedestrian dead reckoning (PDR). In this system, we first propose a ResNet-GRU-LSTM neural network model to achieve magnetic localization more accurately. Afterward, we put forward a multifeatured-driven step length estimation. A hierarchy GRU (H-GRU) neural network model is proposed, and a multidimensional dataset using acceleration and a gyroscope is constructed to extract more valid characteristics. Finally, more reliable and accurate pedestrian localization can be achieved under the particle filter framework. Experiments were conducted at two trial sites with two pedestrians and four smartphones. Results demonstrate that the proposed system achieves better accuracy and robustness than other traditional localization algorithms. Moreover, the proposed system exhibits good generality and practicality in real-time localization with low cost and low computational complexity.
A Survey on IoT Driven Smart Parking Management System: Approaches, Limitations and Future Research Agenda

In developing countries around the globe, the number of vehicle users has exceeded the optimum threshold and is increasing rapidly due to speedy economic growth. Consequently, roads are getting more congested in the cities. This has resulted in increased difficulties in meeting the minimum parking demands. Moreover, most of the parking lots are at the underground level of buildings. Due to the lack of natural light sources, there is a need for a massive power source to light up a large quantity of lighting equipment continuously. This results in waste of energy, equipment losses and causes a tremendous financial burden on property management. However, an IoT driven parking management can significantly aid in the utilization of parking resources. Availability of a sensor based smart parking spot with its location information identified and transmitted using wireless communication technology can immensely benefit the vehicle owners as well as parking spot authorities. It is a comprehensive solution both for the user and the owner of the parking space. Besides, it can fulfill the lighting requirement of the parking lot with minimized energy. Some eminent features of such IoT based systems are online reservations, user authentication for ensuring security, parking guidance, online payment, and energy saving. This paper reviews IoT driven smart parking management systems. It gives insights into various approaches for the parking system, related technologies, widely used components, communication standards and concerned system security issues. Besides, the paper gives researchers a direction for future work as it directs towards some open issues in this area.
A Large Therizinosauroid (Dinosauria: Theropoda) from the Early Cretaceous of Northwestern China

Abstract: We herein describe an associated partial postcranial skeleton of a theropod dinosaur discovered in the Lower Cretaceous Xinminpu Group of the Yujingzi Basin, in the Jiuquan area of Gansu Province, northwestern China. Features of its humerus, such as strongly expanded proximal and distal ends, a well developed medial tuberosity, distal condyles expressed on the humeral cranial surface, and a hypertrophied entepicondyle, definitively establish the therizinosauroid affinities of the specimen. It differs from other therizinosauroids in having a shallow, poorly demarcated glenoid fossa with a prominent rounded and striated tumescence on the dorsomedial surface of its scapular portion, and a pubis with a strongly concave cranial margin. It represents a new taxon, Suzhousaurus megatherioides gen. et sp. nov. Cladistic analysis recovers Suzhousaurus as the sister taxon of Nothronychus mckinleyi from the mid‐Cretaceous of western North America; together, they are basal members of the Therizinosauroidea, more derived than the Early Cretaceous Falcarius and Beipiaosaurus but less derived than Alxasaurus and the Therizinosauridae. Along with “Nanshiungosaurus” bohlini from possibly coeval beds in the Mazongshan area of northern‐most Gansu, Suzhousaurus represents one of the largest‐known Early Cretaceous therizinosauroids, demonstrating that this clade attained considerable body size early in its evolutionary history.
Enforcing the Opacity of Modular Discrete Event Systems Using Supervisory Control

Opacity is a security property that guarantees the confidentiality of secret information from a partial observer of the system. To address this problem, we propose a modular approach that uses Supervisory and Control Theory to design a global supervisor for Modular Discrete Event Systems (MDES). This approach assumes the attacker can observe the shared alphabet between modules, and takes into account the modular structure of the system; which significantly decreases computational complexity compared to monolithic methods. To achieve this, we introduce a reduced-complexity algorithm, based on the Hyper Symbolic Observation Graph. The HSOG is an abstraction graph that considers only events that have a direct impact on the opacity. This reduces the number of states to be considered, making the algorithm more efficient.
Factors Limiting Shelf Life of a Tomato–Oil Homogenate (Salmorejo) Pasteurised via Conventional and Radiofrequency Continuous Heating and Packed in Polyethylene Bottles

Salmorejo is a tomato–oil cold puree commercialized as a “fresh-like” product requiring mild pasteurisation and chill storage to reach a suitable shelf lifetime. The objective of this study was to study the factors which limit the shelf life of salmorejo pasteurised via conventional or radiofrequency continuous heating, packed in high-density polyethylene bottles, and kept at refrigeration. The pasteurised–chilled salmorejo reached a long shelf life (4 months) compared to that of pasteurised tomato juices or purees. Mesophilic and pathogenic bacteria were easily inhibited in this acidic product. Salmorejo mainly showed oxidative and subsequent sensory changes. Initial enzyme oxidation was associated with some adverse effects (loss of vitamin C and lipid oxidation) at the first month, although there were no sensory implications. Salmorejo remained stable at the physicochemical and sensory levels for the following 3 months, though colour and viscosity changes could be measured with instruments. Between the fourth and fifth month, salmorejo showed clear signs of deterioration, including changes in appearance (slight browning and loss of smooth surface), odour/flavour (loss of freshness and homogenisation), and consistency (thinning trend). The shelf life of salmorejo is limited by long-term oxidative deterioration and their sensory implications.