Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Learning Neural Network Method Using Linear Eigenvalue Statistics for Schizophrenic EEG Data Classification

Electroencephalography (EEG) signals can be used as a neuroimaging indicator to analyze brain-related diseases and mental states, such as schizophrenia, which is a common and serious mental disorder. However, the main limiting factor of using EEG data to support clinical schizophrenia diagnosis lies in the inadequacy of both objective characteristics and effective data analysis techniques. Random matrix theory (RMT) and its linear eigenvalue statistics (LES) can provide an effective mathematical modeling method for exploring the statistical properties of non-stationary nonlinear systems, such as EEG signals. To obtain an accurate classification and diagnosis of schizophrenia, this paper proposes a LES-based deep learning network scheme in which a series of random matrixes, consisting of EEG data sliding window sampling and their eigenvalues, are employed as features for deep learning. Due to the fact that the performance of the LES-based scheme is sensitive to the LES’s test function, the proposed LES-based deep learning network is embedded with two ways of combining LES’s test functions with learning techniques: the first is to have the LES’s test function assigned, while, using the second way, the optimal LES’s test function should be solved in a functional optimization problem. In this paper, various test functions and different optimal learning methods were coupled in experiments. Our results revealed a binary classification accuracy of nearly 90% in distinguishing between healthy controls (HC) and patients experiencing the first episode of schizophrenia (FES). Additionally, we achieved a ternary classification accuracy of approximately 70% by including clinical high risk for psychosis (CHR). The LES-embedded approach yielded notably higher classification accuracy compared to conventional machine learning methods and standard convolutional neural networks. As the performance of schizophrenia classification is strongly influenced by test functions, a functional optimization problem was proposed to identify an optimized test function, and an approximated parameter optimization problem was introduced to limit the search area of suitable basis functions. Furthermore, the parameterization test function optimization problem and the deep learning network were coupled to be synchronously optimized during the training process. The proposal approach achieved higher classification accuracy rates of 96.87% between HC and FES, with an additional 89.06% accuracy when CHR was included. The experimental studies demonstrated that the proposed LES-based method was significantly effective for schizophrenic EEG data classification.
Exploring the effects of comminution level and natural antioxidant incorporation on the quality and oxidative stability of turkey meat system

Abstract This study aimed to explore the effects of different comminution degrees and the incorporation of a natural antioxidant on the quality attributes and oxidative reactions of turkey meat. Four distinctive turkey meat systems were established, namely: 3 mm minced treatment (M), 3 mm minced treatment with the addition of 200 ppm gallic acid equivalent Aloe vera (Aloe barbadensis Mill.) extract (MA), fine-ground treatment (FM), and fine-ground treatment with the addition of 200 ppm gallic acid equivalent Aloe vera extract (FMA). The evaluation encompassed an in-depth analysis of various quality parameters and the assessment of lipid-protein oxidation reactions throughout the storage period. The inclusion of Aloe vera extract (AE) increased the pH and b* values while simultaneously decreasing the L* and a* values. Conversely, increasing the degree of comminuting manifested an elevation in L* values, concomitant with a decline in a* values. Increased comminuting degree ratios were found to contribute to an exacerbation of oxidative reactions. Nonetheless, the strategic utilization of AE demonstrated its potential to effectively mitigate oxidative reactions during storage.
Spatial distribution of rainfall simulation and the cause analysis in China's Tianshan Mountains area

To study the rainfall spatial distribution laws as well as the formation mechanism in China's Tianshan Mountains area,this thesis built the mountain area rainfall estimation model and analyzed the rainfall causes with the partial least squares( PLS) and GIS technology,based on the DEM in the research areas as well as the data information from the meteorological stations. The result shows that the rainfall in the Tianshan Mountain area is showing an obvious longitudinal and latitudinal zonality,with more rainfall in the west section than that in the east,and more in the north slope( windward slope) than that in the south slope( leeward slope). We can find a linear increase of the rainfall in the research areas with an altitude of 4 000 m below,and then a rapid decrease,with the second maximum belt at around 5 500 m. The rainfall and the slope are positively associated when the slope is less than 50°. When the topography lifts,the rainfall will increase with the drops of temperature and the rise of humidity,which is the requirement for the rainfall formation in the mountain areas. Generally,the method of PLS can effectively solve the multiple correlation issues between the rainfall and various factors,the regression effect of the model being obvious. This model has certain adaptability for the mountain area rainfall simulation.
The Nonnormality of Coastal Ocean Flows around Obstacles, and Their Response to Stochastic Forcing

Abstract The effect of stochastic variability upon the wake downstream of a real headland is investigated. From observations taken at Bass Point, Australia, three sources of stochastic forcing are identified and quantified, these being variability in the wind stress, in the flow incident on the headland, and that generated in the flow by complex reef topography at the headland's tip. These sources of stochastic forcing are found to induce a significant degree of unsteadiness in a nonlinear numerical model of flow around Bass Point that, in the absence of stochastic forcing, simulates steady recirculating flow. The observed stochastic variability of the flow incident on the headland is found to be larger than is necessary to support unsteadiness in the simulated straining zone. The dynamics of the perturbation growth observed in the model are understood by applying the techniques of generalized stability theory to the associated tangent linear model (TLM). The eigenmodes of the TLM confirm that the system .
Exploring the Number of Web-Based Behavioral Health Coaching Sessions Associated With Symptom Improvement in Youth: Observational Retrospective Analysis

Background Rates of anxiety and depression have been increasing among children and adolescents for the past decade; however, many young people do not receive adequate mental health care. Digital mental health interventions (DMHIs) that include web-based behavioral health coaching are widely accessible and can confer significant improvements in youth anxiety and depressive symptoms. However, more research is necessary to determine the number of web-based coaching sessions that confer clinically significant improvements in anxiety and depressive symptoms in youth. Objective This study uses data from a pediatric DMHI to explore the number of web-based coaching sessions required to confer symptom improvements among children and adolescents with moderate or moderately severe symptoms of anxiety and depression. Methods We used retrospective data from a pediatric DMHI that offered web-based behavioral health coaching in tandem with self-guided access to asynchronous chat with practitioners, digital mental health resources, and web-based mental health symptom assessments. Children and adolescents who engaged in 3 or more sessions of exclusive behavioral health coaching for moderate to moderately severe symptoms of anxiety (n=66) and depression (n=59) were included in the analyses. Analyses explored whether participants showed reliable change (a decrease in symptom scores that exceeds a clinically established threshold) and stable reliable change (at least 2 successive assessments of reliable change). Kaplan-Meier survival analyses were performed to determine the median number of coaching sessions when the first reliable change and stable reliable change occurred for anxiety and depressive symptoms. Results Reliable change in anxiety symptoms was observed after a median of 2 (95% CI 2-3) sessions, and stable reliable change in anxiety symptoms was observed after a median of 6 (95% CI 5-8) sessions. A reliable change in depressive symptoms was observed after a median of 2 (95% CI 1-3) sessions, and a stable reliable change in depressive symptoms was observed after a median of 6 (95% CI 5-7) sessions. Children improved 1-2 sessions earlier than adolescents. Conclusions Findings from this study will inform caregivers and youth seeking mental health care by characterizing the typical time frame in which current participants show improvements in symptoms. Moreover, by suggesting that meaningful symptom improvement can occur within a relatively short time frame, these results bolster the growing body of research that indicates web-based behavioral health coaching is an effective form of mental health care for young people.
Face Anti-Spoofing Detection Using Structure-Texture Decomposition

A key area in computer vision and biometric authentication systems is detecting and classifying face anti-spoofing. The novel method for face anti-spoofing presented in this paper focuses on color invariant methods. The proposed approach consists to compare two different feature extraction methods: LBP and HOG, used with two different machine-learning models such as KNN and SVM. The suggested method improves the system's ability to discriminate by utilizing color properties and overcoming obstacles like varying lighting and image quality. The structure-texture decomposition is used as a feature that is used to improve the performance of the face anti-spoofing methods. After the experimental results using different techniques, we noticed that structure-texture decomposition can be a good feature for face anti-spoofing detection features.
SHEAR STRENGTH OF COMPACTED SOIL UNDER INFILTRATION CONDITION

Landslides in residual soil slopes are commonly induced by rainfall infiltration. These residual soils are typically in an unsaturated state with negative pore-water pressures or matric suctions since the groundwater tables in steep slopes are often deep. The net normal and shear stresses of the soil remain essentially constant during rainwater infiltration into the slope. Failure of the slope during rainfall can be primarily associated with the decrease in the matric suction of the soil. The objective of the study was to investigate the strength and deformation characteristics of a residual soil of the Bukit Timah Granitic Formation during infiltration that leads to slope failure. There were two modified direct shear apparatuses used. One apparatus was used for the determination of shear strength under controlled suction conditions while the other apparatus was used for shearing-infiltration tests. The shearing-infiltration test results were compared with the shear strength values obtained from the shearing tests under constant suction. The shearing-infiltration test results indicate a close relationship between the decreasing matric suction and the increasing displacement rate of the soil specimen. At the initial part of the infiltration process, there is a rapid reduction in matric suction that is accompanied by little movement in the soil. When failure of the soil is imminent, the soil movement will accelerate.
Recent Advances in Solar Drying Technology for Tomato Fruits: a Comprehensive Review

—The world's most popular and productive vegetable crop is the tomato fruit. The tomato is the second-most important vegetable crop in the world. Fresh tomatoes cannot be stored for a long time because they have a high moisture content (MC). The high MC acts as a suitable environment for the growth of fungi and molds, which leads to damage to stored tomatoes. So, MC must be decreased to a suitable level for storage and handling using a suitable drying method to maintain nutritional value and natural color properties. There are many drying methods that can be used for drying tomatoes. Throughout history, tomato drying methods have relied on direct sunlight, firewood, fossil fuels, and coal, leading to carbon emissions. These techniques are costly, unreliable, and unsanitary. There are now many modern methods, including solar drying, microwave drying, vacuum drying, infrared drying, freeze drying, oven drying, and various hybrid drying techniques. But hot air drying, solar drying (SD), sun tunnel drying (STD), microwave drying (MD), and freeze-drying (FD) are some of the popular methods of drying tomatoes for preservation. This paper aims to present the state-of-the-art solar energy technologies for tomato drying. The drying methods presented in the current paper are applicable to a wide range of vegetables and fruits.
Codes coupling optimization for absolute measurement

Absolute measurement has always been one of the important development directions of precision measurement, there are problems that the diffraction and mask working parameters are not considered in the positioning pulse analysis of the absolute code mask at present. Therefore, in order to solve the coupling optimal performance problem of absolute positioning code in actual work, an absolute code should be designed for working in the best parameter-model. In this paper, a multi-parameter model of absolute code working status is established, and the influence of working parameters on its positioning performance is analyzed respectively. The analysis shows that the distance and the angle between the mask and the grating, and the width of the unit code will affect the positioning accuracy. The three parameters restrict each other, and there is a coupling optimal solution. The optimal working state can be obtained through parameter analysis, so as to provide the design and installation parameter guidance of mask. The proposed research can help the practical application of absolute positioning measurement.
Climate change awareness in ELT: Ethnography in connected learning and ecojustice pedagogy

This research article explores the integration of climate change education in the context of English as a Foreign Language teacher education. As climate–induced disasters are on the rise, the mitigation of climate change necessitates an interdisciplinary approach to climate education. While science education has extensively addressed climate change education, the field of language education and literacy has not taken drastic measures to participate in climate change education. The article heeds the call for more research on climate change education in second/foreign language and literacy programs. Using the connected learning and ecojustice pedagogy, we engaged 20 preservice teachers in exploring climate change issues within a semester-long creative writing course. Analyses of classroom discussions, reflective writing, and picture book projects showed that the preservice teachers’ awareness of climate change issues developed as they delved into the projects that required their understanding of the global climate issues with the local connection. The article concludes by arguing that connected learning and ecojustice pedagogy support the development of climate change awareness while facilitating preservice teachers’ multimodal writing skills. It recommends that language educators engage in interdisciplinary participation to address climate change issues through curricular engagements that support multimodal-based climate literacy learning.
Description: Implementing Landsat TM and ETM to detect land cover and land use changes in the mount Cameroon region using the CVA technique with the tasselled cap as biophysical indicator

The continuous extraction of wood and the conversion of forest to small- and large-scale agricul- tural parcels is rapidly changing the land cover of the mount Cameroon region. The changes occur at varying spatial scales most often not more than 2ha for the small-scale subsistence farms and above 10ha for the extensive agricultural plantations of cocoa and palm. Given the importance of land use and land cover data in conservation planning, accurate and efficient techniques to provide up-to-date change information are required. A number of techniques for realising the detection of land cover dynamics using remotely sensed imagery have been formulated, tested and assessed with the results varying with respect to the change scenario under investigation, the information required and the imagery applied. In this study the Change Vector Analysis (CVA) technique was implemented on multitemporal multispectral Landsat data from the Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) sensors to mon- itor the dynamics of forest change in the mount Cameroon region. CVA was applied to multi-temporal data to compare the differences in the time-trajectory of the tasseled cap greenness and brightness for two successive time periods - 1987 and 2002. The tasseled cap was selected as biophysical indicator because it optimises the data viewing capabilities of vegetation, representing the basic types of land cover - vegetation, soil and water. Classes were created arbitrarily to predict the technique's potential in monitoring forest cover changes in the mount Cameroon region. The efficiency of the technique could not be fully assessed due to the inavailability of sufficient ground truth data. Assessment was based on the establishment of an error matrixofchangeversusno-change.Theoverallaccuracy was 70%. The technique nevertheless demonstrated immense potentials in monitoring forest cover change dynamics especially when complemented with field studies.
Statistics of ''Synoptic Circulation Weather'' in the North Sea as Derived from a Multiannual OGCM Simulation

A 15-yr simulation of an ocean general circulation model, exposed to atmospheric forcing as provided by the ECMWF reanalysis 1979‐93, is analyzed with respect to the statistics of the surface circulation of the North Sea on timescales of days to several weeks in winter. The first two EOFs of surface circulation are found to represent the bulk of the variability (72%). They are broadly consistent with the limited observational record. The first EOF represents regimes with one gyre flushing the entire North Sea, either with clockwise orientation (15% of time) or with counterclockwise orientation (30% of time). These regimes are excited by northeasterly and, respectively, southwesterly wind. The second EOF is representative for two opposite regimes with two bipolar patterns in the northern and southern part of the North Sea (45% of time). For a certain range of both EOFs coefficients, the North Sea circulation ceases (10% of time). The circulation of the North Sea in winter is highly variable; the regimes change frequently. Only 40% of the one-gyre regimes persist for longer than 5 days, and the bipolar pattern regimes rarely extend for more than 5 days.
Adaptive Channel Estimation for MIMO-OFDM Systems in Impulsive Noise Environments

Multiple-input multiple-output (MIMO) systems with orthogonal frequency-division multiplexing (OFDM) techniques provide a credible high-speed wireless communication system. To realize the efficiency of a MIMO-OFDM system, exact channel state information (CSI) is essential. To obtain CSI, several channel estimation algorithms were implemented in Gaussian noise environments. However, real wireless communication environments can be described by non-Gaussian impulsive noise. A robust adaptive channel estimation algorithm based on the maximum correntropy criterion (MCC) learning algorithm is proposed in this paper for a MIMO-OFDM communication system that operates in non-Gaussian impulsive noise environments. The proposed learning algorithm enables considerable improvement in the precision of mean square deviation (MSD) learning performance and in symbol error rate (SER) performance compared with the traditional least mean square (LMS) learning algorithm.
Diagnosis of Depression Based on New Features Extractive from the Frequency Space of the EEG

Major depressive disorder (MDD) is a prevalent psychotic disorder. Understanding the neurophysiological characteristics of MDD is very important in diagnostic and therapeutic applications. The use of electroencephalography (EEG) is being developed to identify brain activity mechanisms in MDD patients. The present study aimed to examine the efficacy of spectral features extracted from EEG signals based on reductionism and relativism methods in the diagnosis of MDD. Using the Welch analysis method, the best results were calculated with 6 optimal frequency features corresponding to 5 channels on 12-second segments. Using the K-Nearest Neighbor classifier, criteria such as accuracy, F1 score and Kappa were obtained as 86.79%, 85.71% and 0.73 respectively.
Identification of marine and continental aerosol sources in Paris using high resolution aerosol mass spectrometry

Major summertime aerosol emission sources in Paris were assessed using a high resolution time‐of‐flight aerosol mass spectrometer (HR‐ToF‐AMS). The application of positive matrix factorization (PMF) to the highly mass and time‐resolved AMS measurements allowed the identification of primary and secondary sources of organic (OA) and sulfate aerosols. Primary anthropogenic emissions contributed on average ~27% (14.7% cooking, 12% traffic) to the total organic mass, while the major contribution to the organic fraction was associated with secondary formation products. Low‐volatility oxygenated OA (LV‐OOA, 25.2%) and semi‐volatile oxygenated OA (SV‐OOA, 32.4%) factors were classified as SOA. An additional component with high S : C and O : C ratios was identified and attributed to marine emissions (marine organic aerosol, MOA), owing to its high correlation with methanesulfonic acid (R2 = 0.84) and contributing on average 15.7% to the total OA mass, even in the continental megacity of Paris. Non‐sea salt sulfate was apportioned by including both organic and sulfate ions in the PMF data matrix. This allowed apportionment of submicron sulfate to continental versus marine sources. A detailed source apportionment of PM1 combining AMS, aethalometer, and filter data is presented.
Faktor-faktor penentu repurchase intention pada produk shopping di e-commerce

Penelitian ini mempunyai tujuan inti untuk menguji baik pengaruh langsung maupun tidak langsung privacy concern, innovativeness, dan ease of use terhadap repurchase intention dengan e-satisfaction sebagai variabel mediasi. Model penelitian diujikan pada konteks produk shopping di e-commerce dengan melibatkan responden sejumlah 350 orang. Pengambilan sampel dilakukan melalui teknik purposive sampling. Analisis PLS-SEM dengan software SmartPLS 3.0 digunakan untuk menganalisis data. Hasil penelitian menunjukkan bahwa privacy concern, innovativeness, dan ease of use tidak mempunyai pengaruh langsung terhadap repurchase intention, e-satisfaction ditemukan mempunyai pengaruh terhadap repurchase intention. Hasil uji mediasi menunjukkan bahwa e-satisfaction tidak terbukti memediasi pengaruh privacy concern terhadap repurchase intention, sedangkan e-satisfaction terbukti memediasi pengaruh innovativeness dan ease of use terhadap repurchase intention.