Miracle-dz/UniFAR-FTUs huggingface dataset:
ASVspoof 2015 : Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan

The ASVspoof initiative follows on from the first special session in spoofing and countermeasures for automatic speaker verification (ASV) held during the 2013 edition of INTERSPEECH in Lyon, France [1]. The vision behind that first edition was to promote the consideration of spoofing, to encourage the development of anti-spoofing countermeasures and to gather a community to design, collect and distribute standard databases with standard evaluation protocols and metrics. Such an initiative is deemed vital in order to address weaknesses in research methodologies common to the majority of previous work. In the past, both spoofing attacks and countermeasures have generally been developed with full knowledge of a particular ASV system used for vulnerability assessments [2]. Similarly, countermeasures have been developed with full knowledge of the spoofing attack which they are designed to detect. This is clearly unrepresentative of the real use case scenario in which neither the specific attack, much less the specific algorithm, can ever be known a priori. It is thus likely that the prior work has as much over-exaggerated the threat of spoofing as it has the performance of countermeasures. The ASVspoof challenge has been designed to address these shortcomings and to support, for the first time, independent assessments of vulnerabilities to spoofing and of countermeasure performance. While preventing as much as possible the inappropriate use of prior knowledge, the challenge aims to stimulate the development of generalised countermeasures with potential to detect varying and unforeseen spoofing attacks. The first evaluation, ASVspoof 2015, is being held within the scope of a special session at INTERSPEECH 2015 and with a focus on spoofing detection. Expertise in ASV is therefore not a prerequisite to participation in the 2015 edition. ASVspoof 2015 participants are invited to develop spoofing detection algorithms and to submit results for a freely available standard database and according to the standard protocol and metrics described in this evaluation plan.
Modeling Physiology of Crop Development, Growth and Yield

Part I: Background 1. What is a crop model? 2. Fundamental Guides in Constructing Crop Models 3. Evaluation of Model to Meet Objectives 4. Applications of Crop Models 5. Status of Crop Modeling Part II: Potential Production Models 6. Phenology - Temperature 7. Phenology - Temperature & Photoperiod 8. Phenology - Vernalization 9. Crop Leaf Area 10. Dry Matter Production 11. Dry Matter Distribution and Yield Formation 12. A Model for Potential Production Part III: Water-Limited Models 13. Soil Water 14. Soil Water Balance 15. Plant Responses to Soil Water Deficit and Excess 16. A Model for Water-Limited Conditions Part IV: Nitrogen-Limited Models 17. Plant Nitrogen Budget 18. Soil Nitrogen Balance 19. A Model for Nitrogen-Limited Conditions Appendices.
Effectiveness of a dietary education program upon hypertensive clients' information

Objective: To determine the effectiveness of a dietary educational program upon the hypertensive client’sinformation.Methodology: The sample of the study consists of (60) hypertensive clients who attended Al Anbar Health Office –AlAndalus Health Center during the period of the study from the14th of March 2010 to the 30th of September 2010. Thesample is divided into two groups; study group (30) hypertensive clients who received dietary educational programand control group (30) hypertensive clients who received a routine care from the center. Three major instrumentswere used; these instruments included (1) knowledge test which was applied on both groups pretest and eight weekspost-test (2) questions to know what they eat in the last 24 hours in the main meals and in between meals (snacks)which were applied on both groups pretest and eight weeks post-test (3) Body Mass Index measurement was appliedon both groups pre-test and eight weeks post-test.Results: The results show that there is highly significant difference concerns the decrease of BMI in post- test forhypertensive clients in the study group than the control group marked improvement in dietary status of the studygroup compared to the control group. The results of our study also show that there is a significant difference betweenstudy group and control group in post-test regarding the adequate grain vegetable fruit. Meat, poultry, and fish (MPF)and Nuts, seeds, and legumes (NSL) intake daily.Recommendations: The study recommends that all hypertensive clients should be involved in the application ofnutritional educational programme which was developed in this study.
Research on Lane Line Detection Based on Around View System

: Traditional lane detection methods are limited by factors such as camera position and perspective, and often encounter issues such as false detection and missed detection. This article conducts research on lane detection methods from the perspective of multi camera BEV, and proposes a circular lane detection method based on convolutional neural networks (CNN). In order to solve the occlusion problem perceived from traditional forward looking perspectives, an around system was constructed using multiple cameras, and a multi classification semantic segmentation network was innovatively designed to predict obstructions, greatly reducing the false detection rate of obstructed lane lines. After verification, the algorithm proposed in this article can achieve good lane line detection results in different environments.
Connecting Communities Using New Media: The Sharing Stories Project

The Kelvin Grove Urban Village (KGUV) is a diverse inner-city master-planned development in Brisbane, Australia, established through a partnership between Queensland University of Technology (QUT) and the Queensland Government's Department of Housing. The 16-hectare KGUV has been home to indigenous people, military and educational institutions, but very few residents. This chapter examines two phases of Sharing Stories, an applied research project that has brought together urban studies, public history and new media. It seeks to synchronise the various opportunities and challenges arising within each discipline in order to capture community memory, both past and present.
The Influence of Prior Experience on Entrepreneurs' Decision-making Logic: The Moderating Effect of Environmental Uncertainty

: The effectiveness of entrepreneurs' decision-making significantly influences the outcomes of their ventures. Therefore, understanding how entrepreneurs enhance entrepreneurial performance through effective decision-making has become a focal point in academic research. This study investigates the role of experience in shaping entrepreneurs' decision-making approaches in uncertain environments, specifically examining three aspects of experience: entrepreneurial experience, managerial experience, and industry experience. Utilizing a research sample of 203 domestic entrepreneurs, we explore the nature of experience and its impact on decision-making logic. The findings indicate that entrepreneurs with substantial entrepreneurial experience tend to employ both causation and effectuation in the entrepreneurial process. On the other hand, entrepreneurs with extensive managerial experience predominantly adopt a causation-based decision-making approach. Interestingly, industry experience does not significantly influence the decision-making logic of entrepreneurs. Additionally, we observe that environmental uncertainty negatively moderates the relationship between management experience and the use of causation and effectuation.
Archaeological Site and Reservoir Shoreline Stabilization Using Wetland Plants and Bioengineering, Rice Reservoir, Wisconsin

Abstract : This technical note describes a case study in which wetland plants and biocngineering treatments were used to protect an archeological site on an eroded reservoir shoreline and to stabilize the shoreline from further erosion. The site is called the Robinson Site and is located on Rice Reservoir in northern Wisconsin, about 26 km (16 miles) southeast of Rhinelander.
E-government Program Implementation in Public Elementary Schools in Sorsogon, Philippines

: E-government Program Implementation in Public Elementary Schools in Sorsogon, Philippines. Objectives: This study determined the e-government programs, extent, and challenges in Public Elementary Schools in Sorsogon, Philippines. Methods: This study utilized the mixed-methods sequential-explanatory design in which the major design is quantitative and minor is qualitative research where one thousand elementary teachers and sixty-eight elementary school heads at Sorsogon West Ditrict, Sorsogon City, Philippines were selected using stratified random (percentage) sampling. Data was collected through document analysis, survey questionnaire
MULTIPLE TIME SCALE ANALYSIS OF CLIMATE VARIATION IN MACAU DURING THE LAST 100 YEARS

The multiple time scale climate changes are studied and calculated with statistical analysis and wavelet transformation on the basis of daily series of observed data over the period 1901-2007 in Macau.The result shows that statistically significant oscillations with 2 to 5 years of period generally exist in the series of climate variables(e.g.annual mean surface air temperature and precipitation as well as evaporation etc.),but with obvious locality in time domain.The variation of annual mean surface air temperature has a quasi 60-year period.The phases of the 60-year variation approximately and consistently match that of Atlantic Multidecadal Oscillation(AMO). The oscillations of seasonal mean surface air temperature in summer and winter have the periods of quasi 30-year and quasi 60-year,respectively.These two periods of oscillations have statistically significant correlation with Pacific decadal oscillation(PDO) and AMO,individually.The multidecadal variations of the precipitation of the annually first flood period and annual evaporation are dominated by periods of quasi 30-year and quasi 50-year,respectively.
Synoptic fingerprints of Zonda wind from a statistical prediction model

Zonda wind is a typical downslope windstorm over the eastern slopes of the Central Andes in Argentina, which produces extremely warm and dry conditions and has substantial socioeconomic impacts. In this study, we propose a new statistical model for Zonda prediction based on the “synoptic fingerprints” of atmospheric diagnostic variables from ERA5. The model combines principal component analysis (PCA) and logistic regression to establish a relationship between the observed occurrence and the PCA loading component of a predictor variable. This approach enables us to determine the probability of Zonda occurrence at selected stations and identify the synoptic structure features (fingerprints) associated with Zonda events. The obtained fields successfully discriminate between Zonda and non‐Zonda events, suggesting that the available information in the reanalysis data is sufficient for predicting the presence of Zonda. The synoptic fingerprints generated by the model reveal a cross‐barrier pressure gradient resulting from a negative geopotential height anomaly at low levels. The cross‐barrier flow remains unimpeded by the Andes, leading to forced vertical motions on the windward side, accompanied by cooling and precipitation. On the lee side, sinking motions, warming and drying are observed, further facilitated by favourable mid‐ and upper‐level conditions that establish the Zonda wind. The model performs comparably to previous research, with the best results achieved using low‐level variables as predictors. Key performance measures, including the area under the receiver operating curve (ROC) (AUC) of 0.9468 and a Brier score lower than 0.1, demonstrate the model's effectiveness. Using a 0.5 threshold, the accuracy, F1 score and correct alarm ratio (CAR) all exceed 88%, with a probability of detection (POD) higher than 90%. Studies on this type of downslope windstorm are scarce in South America, making this work a significant contribution to understanding synoptic‐scale atmospheric structures associated with Zonda occurrences.
Accelerated differentiation of human induced pluripotent stem cells into regionally specific dorsal and ventral spinal neural progenitor cells for application in spinal cord therapeutics

Spinal cord injury can attenuate both motor and sensory function with minimal potential for full recovery. Research utilizing human induced pluripotent stem cell (hiPSC) -derived spinal cell types for in vivo remodeling and neuromodulation after spinal cord injury has grown substantially in recent years. However, the majority of protocols for the differentiation of spinal neurons are lengthy, lack the appropriate dorsoventral or rostrocaudal specification, and are not typically replicated in more than one cell line. Furthermore, most researchers currently utilize hiPSC-derived motor neurons for cell transplantation after injury, with very little exploration of spinal sensory neuron transplantation. The lack of studies that utilize sensory populations may be due in part to the relative scarcity of dorsal horn differentiation protocols. Building upon our previously published work that demonstrated the rapid establishment of a primitive ectoderm population from hiPSCs, we describe here the production of a diverse population of both ventral spinal and dorsal horn progenitor cells. Our work creates a novel system allowing dorsal and ventral spinal neurons to be differentiated from the same intermediate ectoderm population, making it possible to construct the dorsal and ventral domains of the spinal cord while decreasing variability. This technology can be used in tandem with biomaterials and pharmacology to improve cell transplantation for spinal cord injury, increasing the potential for neuroregeneration.
Detection and Diagnosis of Inter-Turn Short Circuit Faults of PMSM for Electric Vehicles Based on Deep Reinforcement Learning

The fault diagnosis of electric vehicle motors is one of the exciting topics, and machine learning-based artificial intelligence proved its worth in this field. The primitive methods of machine learning, such as the support vector machine (SVM) and Artificial nural network (ANN) suffered from feature extraction problems, the efficiency of the system depended on the quality of these extracted features until deep learning and deep neural networks came to solve This problem, Although the efficient performance of the deep neural network, it needs excellent experience in selecting parameters and building the structure of the neural network. The emergence of deep reinforcement learning, capable of handling raw data directly and constructing end-to-end systems to link raw fault data with its corresponding mode, represents a significant advancement in the field of machine learning. Furthermore, deep reinforcement learning exhibits greater intelligence compared to previous methods. In this Research, Deep reinforcement learning will be applied in diagnosing inter-turn short circuit faults and finding the level of fault in the built-in wheel permanent magnet synchronous motor of electric vehicles. The proposed method proved highly effective for detecting faults, with an efficiency of 99.9 % and has a promising future in building a general system capable of predicting faults in the early stages.
Real‐time 3D multi‐pedestrian detection and tracking using 3D LiDAR point cloud for mobile robot

Mobile robots are used in modern life; however, object recognition is still insufficient to realize robot navigation in crowded environments. Mobile robots must rapidly and accurately recognize the movements and shapes of pedestrians to navigate safely in pedestrian‐rich spaces. This study proposes real‐time, accurate, three‐dimensional (3D) multi‐pedestrian detection and tracking using a 3D light detection and ranging (LiDAR) point cloud in crowded environments. The pedestrian detection quickly segments a sparse 3D point cloud into individual pedestrians using a lightweight convolutional autoencoder and connected‐component algorithm. The multi‐pedestrian tracking identifies the same pedestrians considering motion and appearance cues in continuing frames. In addition, it estimates pedestrians' dynamic movements with various patterns by adaptively mixing heterogeneous motion models. We evaluate the computational speed and accuracy of each module using the KITTI dataset. We demonstrate that our integrated system, which rapidly and accurately recognizes pedestrian movement and appearance using a sparse 3D LiDAR, is applicable for robot navigation in crowded spaces.
Research and Implementation of Inpainting Algorithms for Old Photos

: In view of the scratches and red spots caused by the impact of time, environment and shooting equipment on the old photos, this paper uses MATLAB as image processing simulation software to carry out the research and implementation of image inpainting algorithm. By comparing and analyzing the repair effect and operational efficiency of the three basic models based on partial differential equations: BSCB model, TV model, and CDD model, the method of introducing weights into the TV model is selected to perform simple repair on color photos and black and white photos with scratches and red dots, and the repair results of old photos are presented through the MATLAB GUI interface. The experimental results show that the introduction of weight values in the TV model not only has a significant effect on solving the problem of unidirectional extension of the equal illuminance line in the TV model, but also has a good effect on repairing scratches and red dots in small-scale color and black and white photos.
Thermal Analysis of Longitudinal Fin with Temperature-Dependent Properties and Internal heat Generation by a Novel Intelligent Computational Approach Using Optimized Chebyshev Polynomials

Abstract In this work, heat transfer in a longitudinal rectangular fin with temperature-dependent thermal properties and internal heat generation is studied and more accurate results obtained in respect of the previous investigations. The advanced heat transfer models have been used to study the effects of thermo-geometric parameters, coefficient of heat transfer and thermal conductivity parameters on the temperature distribution, heat transfer and thermal performance of the longitudinal rectangular fin. It is applied a novel intelligent computational approach for searching the solution. In order to achieve this aim, the governing equation is transformed into an equivalent problem whose boundary conditions are such that they are convenient to apply reformed version of Chebyshev polynomials of the first kind. These Chebyshev polynomials based functions construct approximate series solution with unknown weights. The mathematical formulation of optimization problem consists of an unsupervised error which is minimized by tuning weights via interior point method. The trial approximate solution is validated by imposing tolerance constrained into optimization problem.
Deductive Thematic Analysis of Educational Leadership Styles in Indonesian Religious School

This research aims to reveal the principal leadership styles of Christian religious secondary schools in Indonesia. This study used a qualitative descriptive approach. The informants in the study were teachers, vice principal, and school principal in a Christian religious secondary school. The data was collected through semi-structured interviews. Subsequently, the data was analyzed using deductive thematic analysis, and its validity was checked using triangulation. The study results showed that the principal implemented five leadership styles in a Christian religious secondary school: paternalistic, charismatic, servant, transformational, and autocratic. This study indicates that leadership styles are urgently needed to improve Christian religious education quality; however, they are still limited in the literature. Therefore, the Ministry of Religion Affairs, particularly the Directorate General for Christian Community Guidance, should create a concise policy on leadership competency for Christian religious school principals. Furthermore, each principal in Christian religious schools should carry out various leadership styles properly to enhance management and leadership practices in their educational organizations.
The Barriers to Seeking Mental Health Services at King Saud Bin Abdulaziz University for Health Sciences

Background: The mental health of college students, especially medical students, is a major issue worldwide. Depression and anxiety are among the top causes of death among people aged 15-29 years old. Mental health disorders, especially mood disorders such as depression and anxiety, are common among university students. Psychological problems can negatively impact academic performance and life satisfaction. The earlier mental illness is detected, the better the treatment and outcome. The aim of this study is to determine the barriers among students to seeking mental health services. Methods: This cross-sectional questionnaire-based study was carried out at King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia The cross-sectional questionnaire-based study involved 434 students: 72.1% men and 27.9% women. This study had a response rate of 28.5% and responses were gathered in May 2023. Results: The top three barriers identified in this study were “Feeling that my problems are not important,” “Concern that no one will understand my problems,” and “Difficulty with access to care. ” The majority of students reported feeling “I feel reluctance a little” to use mental health services; 31.4% chose “I need it a little” when asked whether they needed to use mental health services. Furthermore, 34.8% of students reported having mild anxiety, and 34.1% reported having mild depression. Conclusion: Mental illness is a serious issue, which is why medical students should be aware of it to improve their quality of life and reduce the stress and obstacles they face. Medical schools should address awareness of mental illness and how to approach a clinic. This is important for students to succeed and overcome the psychological difficulties that might affect academic performance. In addition, recognizing the barriers will help achieve better outcomes in seeking help and utilizing existing services.
Experimental and Numerical Analysis and Prediction of Ground Vibrations Due to Heavy Haul Railway Viaduct

The ground vibration wave induced by the viaduct section of the heavy freight wagons is transformed into the ground vibration problem under the condition of point source excitation after it is transmitted to soil through pier. When the speed and axle load of wagon become larger and larger, the impact on the surrounding buildings will also increase. In this paper, the ground vibration around Shenshan Village in section of Shuo-Huang railway line is monitored and numerical analyzed; the 3D numerical model of the Bridge-Pier-Field-House system is established. The relationship between peak vibration velocity (PPV) and the distance to the pier caused by heavy freight wagons at different speeds and different type of wagons is analyzed. The power function relationship between the two when measuring line perpendicular to the centerline of railway is verified. Based on the modified Sodev’s equation, the relationship between PPV of ground points and wagon speed, axle load, and soil properties is proposed, and the value of every parameter in the formula is discussed in detail. The concept of energy index is put forward for the first time in the formula, and the relationship between energy index and wagon speed and wagon weight is analyzed by regression analysis. The accuracy of the calculation model and prediction formula is verified by comparing field test results; an analytical method is proposed to predict the ground vibration induced by viaduct.