Miracle-dz/UniFAR-FTUs huggingface dataset:
Electroencephalogram Based Depression Detection Using Ensemble Approach

Numerous people Shave lately been diagnosed with mental diseases such as depression all around the globe. The conventional methodology for distinguishing depression depends on the clinical questionnaire, which relies upon the patients’ reactions as well as detecting their behavior exercises. Recent advances in EEG and processing speed, along with well-trained ML models, have resulted in a potent method for diagnosing psychological disorders like depression, anxiety, and many more. Signals captured by the EEG equipment can measure potential changes between different areas of the cortex. EEG readings are a useful technique for diagnosing depression and are said to be more accurate than other signals. In this paper, we implemented the ensemble approach to explore depression recognition. In an experiment, 53 individuals took part, including 24 depressed patients and 29 healthy controls. We divided the extracted features from the EEG data into two: linear features and non-linear features. In this study, we developed an ensemble approach for detecting depression from EEG signals. We extracted linear and nonlinear features from the EEG data and used dimensionality reduction with LDA to improve classification performance. Our approach achieved a 91% accuracy rate in identifying MDD patients, outperforming previous studies. Our findings suggest that EEG-based machine learning is a promising new method for diagnosing depression. Future studies are needed to validate our approach in larger clinical trials.
Comparison between WKB and Wavelet Approach for Analytical Calculation of Tunneling Currents in Schottky Barrier Field-Effect Transistors

The tunneling current in Schottky barrier field-effect transistors (SBFETs) is calculated by two analytical approaches. The Wentzels-Kramer-Brillouin (WKB) approximation and a method based on the wavelet transform are verified in comparison to numerical calculations using the non-equilibrium Green's function (NEGF). The results show that the WKB approximation overestimates the tunneling current for the onset of the tunneling current in the transfer characteristics of the device, especially at low temperatures. In contrast, the wavelet approach gives results which are in good agreement to the NEGF calculations. The conclusions are manifested by comparison to measurements performed on ultra-thin body and buried oxide SOI SBFETs at a temperature of 5.7 K. Therefore, the wavelet transform could be superior to the WKB approximation for the derivation of a compact device model.
Soft Sensing Brace for Monitoring Knee Joint Kinetics and Kinematics During Squatting

Exoskeletons have been developed to increase joint performance by providing assistance torque. Soft exosuits, which use light and soft materials for human-robot interaction, are a growing trend in the orthosis. However, wearable motion capture technology for biomechanical data collection remains a challenge. Traditional measurement systems such as motion capture camera (Mocap), inertial measurement units (IMUs), and ground reaction force and moment (GRF&M) are computationally intensive and collected in restricted laboratory environments. To address this challenge, we propose a sensing brace to monitor knee joint kinetics using an elastic strain sensor and a pressure-based mechanomyography (pMMG) sensor. The pMMG sensor improves comfort, reliability, linearity, and durability, and does not need to attach directly to the skin. Linear regression model of knee angle and moment are conducted with high reliability which showed $p-value < 0.05$ and $R^{2}$ is over 0.98. We used a linear regression model to estimate knee angle and torque during squatting with $\text{RMSE} =4.4\ deg$ in knee angle estimation and $\text{RMSE}=3.4\ Nmm/kg$ in knee moment estimation.
Guideline-based survey of outpatient COPD management by pulmonary specialists in Germany

Background: Little is known about the role of guidelines for the practical management of chronic obstructive pulmonary disease (COPD) by office-based pulmonary specialists. The aim of this study was to assess their outpatient management in relation to current guideline recommendations for COPD. Methods: A nationwide prospective cross-sectional COPD questionnaire survey in the form of a multiple-choice questionnaire was sent to 1000 office-based respiratory specialists in Germany. The product-neutral questions focused on routine COPD management and were based on current national and international COPD guideline recommendations being consistent in severity classification and treatment recommendations. Results: A total of 590 pulmonary specialists (59%) participated in the survey. Body plethysmography was considered the standard for diagnosis (65.9%), followed by spirometry (32%). Most respondents were able to cite the correct spirometric criteria for classifying moderate (87%) to very severe COPD (77%). A quarter of the respondents equated the World Health Organization (WHO) definition of chronic bronchitis with COPD. Notably, most participants preferred the updated national COPD guidelines (51.4%) to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines (40.2%). Improvement of functional exercise capacity and quality of life were considered the two most relevant treatment goals; whereas impact on mortality was secondary. Treatment of COPD largely complied with the guidelines. However, a significant percentage of the pulmonary specialists differed in their assessment of the benefits of various therapeutic measures from evidence-based results. Referral for pulmonary rehabilitation was uncommon, regardless of the severity of COPD. Conclusion: The findings of this large national survey suggest that most pulmonary specialists adhere to the current COPD guideline recommendations in daily practice. However, physicians’ knowledge of guidelines is not sufficient as the sole benchmark when assessing their implementation in day-to-day practice. Necessary changes in the health care system must include more effective ways to transfer knowledge to clinical practice and to give access to interventions of proven clinical benefit.
TagClass: A Tool for Extracting Class-Determined Tags from Massive Malware Labels via Incremental Parsing

VirusTotal is widely used for malware annotation by providing malware labels from a large set of anti-malware engines. A long-standing challenge in using these inconsistent labels is extracting class-determined tags. In this paper, we present Tagclass,a tool based on incremental parsing to associate tags with their corresponding family, behavior, and platform classes. Tagclasstreats behavior and platform tags as locators and achieves incremental parsing by introducing and iterating the following two algorithms: 1) location first search, which hits family tags using locators, and 2) co-occurrence first search, which finds new locators by family tags. Experiments across two benchmark datasets indicate Tagclassoutperforms existing methods, improving the parsing accuracy by 21% and 28%, respectively. To the best of our knowledge, Tagclassis the first tag class-determined malware label parsing tool, which would pave the way for research on crowdsourcing malware annotation. Tagclasshas been released to the community 11https://github.com/crowdma/tagclass.
Converters Control Roles Selection for Operating Hybrid AC/DC Grids with Enhanced Small-signal Stability Performance

In hybrid AC/DC grids, Interconnecting Power Converters (IPCs) can assume different control roles, mainly among grid-following and grid-forming. The various possibilities of roles assignment are in this study indicated as Converter Control Roles Configurations (CCRCs). Their number can become large, as it grows both with the number of considered control roles and the number of IPCs in the system. This adds complexity to the studies aimed at assessing the stability of hybrid AC/DC grids operation, as all the possible CCRCs have to be considered. This paper presents a methodology for selecting a subset of the CCRCs that allows the operation of the system with enhanced small-signal stability performance. Data mining techniques are employed for clustering the CCRCs according to their stability performance behavior in the system operable space and revealing explicitly the discovered pattern through a knowledge extraction process. To exemplify the proposed methodology, a test system made of three AC sub-grids and two DC sub-grids is used and a small-signal stability performance indicator measuring the voltage deviation in the DC grids is considered.
Elite China: Luxury Consumer Behavior in China

Acknowledgments ix Foreword xi Introduction: Understand China's Elite, Understand China's Future xiii Chapter 1: Luxury Consumption and China's Elite 1 Chapter 2: The Confused Concept of "Luxury" 19 Chapter 3: Consumer Value Systems in 21st-Century China 31 Chapter 4: A Typology of Chinese Luxury Consumers 69 Chapter 5: Habits, Lifestyles, Locations 99 Chapter 6: Opportunities for Chinese Luxury Brands 141 Conclusion 179 Appendix 195 Index 203
Integrating NARX Neural Network with K-S Test for Accurate Partial Discharge Detection in Transformers

Condition monitoring of transformers is crucial in preventing unexpected outages and equipment damage due to the ageing and deterioration processes. However, the traditional methods of setting alarm thresholds are problematic due to the dynamic changes in the operational and environmental conditions of transformers. This paper proposes a novel approach to partial discharge (PD) detection by integrating the Nonlinear Auto Regressive eXogenous (NARX) neural network with the Two-Sample Kolmogorov-Smirnov (K-S) test for online monitoring of a partial discharge index, called Peak to Average Power Ratio (PAPR). The proposed model aims to enhance the accuracy of PD detection by increasing the chances of identifying normal process behavior and reducing false positive alerts. The results of the study demonstrate the effectiveness of the NARX-K-S integration in improving the accuracy of partial discharge detection and provide a new direction for the condition monitoring of transformers.
Towards a Change Taxonomy for Machine Learning Systems

Machine Learning (ML) research publications commonly provide open-source implementations on GitHub, allowing their audience to replicate, validate, or even extend machine learning algorithms, data sets and metadata. However, thus far little is known about the degree of collaboration activity happening on such ML research repositories, in particular regarding (1) the degree to which such repositories receive contributions from forks, (2) the nature of such contributions (i
Nature in fragments : the legacy of sprawl

PrefaceAcknowledgmentsContributorsPart I Biodiversity and the Genesis of Sprawl 1. The Context and Causes of Sprawl, by Barbara L. Lawrence2. The Impacts of Sprawl on Biodiversity, by Elizabeth A. Johnson and Michael W. KlemensPart II Sprawl, Ecosystems, and Processes 3. Freshwater Wetland Biodiversity in an Urbanizing World, by Nicholas A. Miller and Michael W. Klemens4. Ecosystems, Disturbance, and the Impact of Sprawl, by Seth R. Reice5. Bees, Pollination, and the Challenges of Sprawl, by James H. Cane6. Effects of Urbanization on Decomposer Communities and Soil Processes in Forest Remnants, by Margaret M. Carreiro7. Sprawl and Disease, by Fred W. Koontz and Peter DaszakPart III Sprawl and Species 8. Sprawl and Species with Limited Dispersal Abilities, by Diane L. Byers and Joseph C. Mitchell9. Sprawl and Highly Mobile or Wide-Ranging Species, by Justina C. Ray10. Species that Benefit from Sprawl, by Stephen DeStefano and Elizabeth A. JohnsonPart IV Identifying and Meeting the Challenges of Sprawl 11. Maintaining Connectivity in Urbanizing Landscapes, by M. A. Sanjayan and Kevin R. Crooks12. The Economics of Biodiversity in Urbanizing Ecosystems, by Stephen Farber13. Conserving Biodiversity Through State and Regional Planning, by Jessica Wilkinson, Sara Vickerman, and Jeff Lerner14. Integrating Conservation of Biodiversity into Local Planning, by Jayne Daly and Michael W. Klemens15. Building Public Awareness About the Effects of Sprawl on Biodiversity, by Cynthia Coffin and Jane Elder16. Creating a Framework for Change, by Michael W. Klemens and Elizabeth A. JohnsonIndex
Child Neuropsychology : A Journal on Normal and Abnormal Development in Childhood and Adolescence

The Cognitive Profile of Preschool-Aged Children with Neurofibromatosis Type 1 Jennifer Sangster a , E. Arthur Shores a b , Sharon Watt a b & Kathryn N. North b c a Department of Psychology , Macquarie University , New South Wales, Australia b Institute for Neuroscience and Muscle Research, The Children's Hospital at Westmead , New South Wales, Australia c Discipline of Paediatrics and Child Health, Faculty of Medicine, University of Sydney Published online: 25 May 2010.
Improvement of System Identification using N4SID and DBSCAN Clustering for Monitoring of Electromechanical Oscillations

Poorly damped electromechanical modes could lead to stability problems or islanding of the electrical grid. Thus, it is important for power grid operators to be able to observe and monitor critical modes. In this article, an algorithm from the system identification using N4SID and clustering of eigenvalues using DBSCAN was developed for online monitoring. Traditional N4SID requires model order as input which was avoided by the usage of clustering on intervals of model orders. The true modes are clustered while the noise is filtered thus benefiting the estimates. The results were promising when tested on both the simulated and real PMU data. The algorithm responds quickly in finding the new modes after a topological change in system with good accuracy. The modes obtained using the real PMU data were coherent with previous studies. Preliminary results favour rotor angle and rotor speed as input signals to the algorithm.
Distributed Control for Accurate Voltage/Frequency Regulation and Power Sharing in a Microgrid

Owing to uncertainties arising from the intermittent nature of distributed generating resources, communication constraints, and parameter variations in the dynamics, control of a microgrid is challenging. This paper presents the design of a multi-agent-based distributed adaptive controller for a microgrid. The paper presents the design of an adaptive controller to regulate the voltage and frequencies of the distributed generating sources at nominal values in the face of the uncertainties mentioned above. The MG setup consists of four distributed generating (DG) sources of 35kW each. The performance of the proposed distributed controller is compared with a secondary voltage controller [14] by pursuing simulations using MATLAB/Simulink and then in real-time using OPAL-RT. The simulation and experimental analysis envisage that the proposed controller exhibits faster convergence and handles sudden load change and plug-and-play operations compared to the secondary voltage controller.
This Land Was Theirs: A Study of Native North Americans

Each chapter ends with Additional Sources and a Selected Bibliography CHAPTER 1. QUESTIONS ABOUT NATIVE AMERICANS Bison and Americans Indians in Popular Culture Who Is a Native American? Population Figures Where Did Native Americans Originate? Precontact History North of Mexico: An Overview How Have Native American Cultures Been Studied? What Do We Know of Native American Languages? Grouping Tribes CHAPTER 2. INDIAN-NON-INDIAN RELATIONS Early Contact Destruction and Displacement of Peoples U.S. Treaties Administration of U.S. Indian Affairs Canada, the First Nations Greenland in Brief Landmark U.S. Indian Policies Forces Fostering Native American Identity Recent History Native Americans and Anthropologists Comparing Cultures CHAPTER 3. THE NETSILIK: SEAL HUNTING AND SNOWHOUSE ESKIMOS Aboriginal Life Historical Developments Recent Changes CHAPTER 4. THE CHIPEWYAN: SUBARCTIC HUNTERS Aboriginal Life Early History Becoming Modern at Snowdrift (Lutselk'e, Lutsel K'e) Contemporary Lutselk'e CHAPTER 5. THE LOWER KOOTENAI: PLATEAU FISHERS AND HUNTERS Aboriginal Life Historical Changes Select Comparisons: Bonners Ferry Kootenai (BFK) and Creston Kootenay (CK) CHAPTER 6. THE WESTERN SHOSHONE: SUBDUERS OF THE HIGH DESERT Aboriginal Life Early Historical Developments Later History The Battle Mountain Colony CHAPTER 7. THE CROW: PLAINS RAIDERS AND BISON HUNTERS Baseline Ethnography Early History Later History Other Dimensions CHAPTER 8. THE CAHUILLA: GATHERERS IN THE DESERT Aboriginal Life Historic Changes The Agua Caliente Band CHAPTER 9. THE TLINGIT: ALASKAN SALMON FISHERS Aboriginal Life Early History Later History CHAPTER 10. THE HOPI: FARMERS OF THE DESERT Aboriginal Life Early History Later History Emergence of the Modern Hopi Additional Considerations CHAPTER 11. THE NAVAJO: TRANSFORMATIONS AMONG A DESERT PEOPLE The Background Traditional Life The Comparatively Recent Past Modern Developments Additional Issues and Concerns CHAPTER 12. THE IROQUOIS: WARRIORS AND FARMERS OF THE EASTERN WOODLANDS Early Historic Life Later History The Six Nations Reserve Nationalism and Sovereignty CHAPTER 13. THE EASTERN CHEROKEE: FARMERS OF THE SOUTHEAST People, Population, and Language Early Historic Life Early History Later History: The Eastern Band of Cherokee The Contemporary Scene CHAPTER 14. THE NATCHEZ: SOPHISTICATED FARMERS OF THE DEEP SOUTH Aboriginal Life Ceremonies Tattooed Serpent's Oration Recorded Natchez History CHAPTER 15. OVERVIEWS Indian Casino Highlights Federal Administration Urban Indians Unrecognized Tribes Diabetes and Native Americans Indians as Environmentalists Tribalism National Museum of the American Indian Tribal Sovereignty Summarizing Thoughts Appendix A: A Sample of Widespread Artifact Types Appendix B: Resources about Native Americans
Power hardware-in-the-loop implementation for multi-infeed grid-connected voltage source converters

With the increasing complexity of modern power systems, mainly due to the high penetration of power electronics, it is necessary to have available adequate tools and testing environments to analyse different oscillatory phenomena. Due to the costs and risks involved for testing real equipment, a common approach is the use of real-time simulations. In this context, this paper presents in detail the experience of setting up a power hardware-in-the-loop (PHIL) facility to study multi-infeed voltage source converter (VSC) based networks. Moreover, it shows the effectiveness of the testing environment to observe oscillatory phenomena. For this purpose, a case study was implemented by connecting VSCs with grid following control structures through long impedance paths to the network. The PHIL configuration enabled the observation of oscillatory phenomena and the identification of its causes, as described in the literature, through the analysis of the frequency spectrum of the logged measurements at the point of common coupling, where the real-time simulator interfaces the real equipment.
Using SHAP Values to Validate Model’s Uncertain Decision for ML-based Lightpath Quality-of-Transmission Estimation

We apply Quantile Regression (QR) for lightpath quality-of-transmission (QoT) estimation with the aim of identifying uncertain decisions and then exploit Shapley Additive Explanations (SHAP) to quantify lightpath features’ importance by means of SHAP values and validate the model’s decisions in a post-processing phase. Numerical results show that our approach can eliminate more than 98% of false predictions and that SHAP values can validate up to 90% of the model's uncertain decisions.
Estimation of Differential Graphs via Log-Sum Penalized D-Trace Loss

We consider the problem of estimating differences in two Gaussian graphical models (GGMs) which are known to have similar structure. The GGM structure is encoded in its precision (inverse covariance) matrix. In many applications one is interested in estimating the difference in two precision matrices to characterize underlying changes in conditional dependencies of two sets of data. Most existing methods for differential graph estimation are based on a lasso penalized loss function. In this paper, we analyze a log-sum penalized D-trace loss function approach for differential graph learning. An alternating direction method of multipliers (ADMM) algorithm is presented to optimize the objective function. Theoretical analysis establishing consistency in estimation in high-dimensional settings is provided. We illustrate our approach using a numerical example where log-sum penalized D-trace loss significantly outperforms lasso-penalized D-trace loss as well as smoothly clipped absolute deviation (SCAD) penalized D-trace loss.
Syntactic Computations in the Language Network: Characterizing Dynamic Network Properties Using Representational Similarity Analysis

The core human capacity of syntactic analysis involves a left hemisphere network involving left inferior frontal gyrus (LIFG) and posterior middle temporal gyrus (LMTG) and the anatomical connections between them. Here we use magnetoencephalography (MEG) to determine the spatio-temporal properties of syntactic computations in this network. Listeners heard spoken sentences containing a local syntactic ambiguity (e.g., “… landing planes …”), at the offset of which they heard a disambiguating verb and decided whether it was an acceptable/unacceptable continuation of the sentence. We charted the time-course of processing and resolving syntactic ambiguity by measuring MEG responses from the onset of each word in the ambiguous phrase and the disambiguating word. We used representational similarity analysis (RSA) to characterize syntactic information represented in the LIFG and left posterior middle temporal gyrus (LpMTG) over time and to investigate their relationship to each other. Testing a variety of lexico-syntactic and ambiguity models against the MEG data, our results suggest early lexico-syntactic responses in the LpMTG and later effects of ambiguity in the LIFG, pointing to a clear differentiation in the functional roles of these two regions. Our results suggest the LpMTG represents and transmits lexical information to the LIFG, which responds to and resolves the ambiguity.
EXPERIENCE ON WINTER TIME ROAD ACCIDENTS

Every third road accident in Finland occurs when the roads are covered with snow, slush or ice in winter time conditions. To improve trafficability and road safety winter service has been intensified especially on the main roads. In this article some research results from kymi road administration district are presented. In this study special features of winter condition accidents were identified. One of the most important findings in the study was that about every other fatal accident in the district in winter time occurred when the temperature was -2 to +1 degrees celsius. The authors report that in winter time conditions morning rush hours cause problems, heavy vehicles are in trouble more often than passenger cars, and slippery conditions often come as a surprise. It is expected that on the basis of the study the road district staff will be able to direct and schedule winter service countermeasures in a more effective way. (TRRL)