Miracle-dz/UniFAR-FTUs huggingface dataset:
International bureaucrats in the UN Security Council debates: A speaker-topic network analysis

ABSTRACT Intergovernmental deliberations in the United Nations Security Council (UNSC) are typically considered the realm of sovereign nation states. We challenge this position by studying the role of the UN Secretariat in UNSC debates, focusing on the debates on Afghanistan (1995–2017). We combine natural language processing with a network theoretical perspective to observe speaker position, topic introduction, and topic evolution and we complement this analysis with an illustrative case study. The quantitative analysis shows that UN officials take an overall impartial position but that they do, at times, introduce and promote their own topics putting them in the position to shape the debate. The qualitative case study selects one ‘bureaucratic topic’ to confirm bureaucratic agency. Combined, our methods allow to study the role of speakers in a debate and show that the UN bureaucracy acted as an autonomous speechmaker even in a venue were bureaucratic agency seems unlikely – the UNSC.
Haptic perception mechanism of softness - second report, relationship between stiffness of an object and softness sensation

A palpation is a useful method to detect cancer and to evaluate skin or under skin conditions. However, the palpation is a skillful method. Doctor's mental and physical conditions affect diagnoses by the palpation. Additionally, it is hard to share the feeling of palpation with other people because palpation is subjective skill. Therefore, the development of the sensor that can measure the skin and under skin condition of the body like softness as a doctor's palpation is demanded. In this paper, we focus on a haptic perception mechanism of human for evaluating the softness. Six objects are made to conduct sensory tests. The silicone rubber part of the objects has two kinds of thickness and three kinds of Young's modulus. Relationships between physical properties of evaluated objects and the softness that human feel in active touch are investigated. After that, to control the contact condition between objects and finger, a passive touch evaluation system is fabricated. Using the system, relationships between the stiffness of the objects and evaluated haptic softness are investigated under passive touch condition.
A machine learning approach to Structural Health Monitoring with a view towards wind turbines

The work of this thesis is centred around Structural Health Monitoring (SHM) and is divided into three main parts. The thesis starts by exploring di�erent architectures of auto-association. These are evaluated in order to demonstrate the ability of nonlinear auto-association of neural networks with one nonlinear hidden layer as it is of great interest in terms of reduced computational complexity. It is shown that linear PCA lacks performance for novelty detection. The novel key study which is revealed ampli�es that single hidden layer auto-associators are not performing in a similar fashion to PCA. The second part of this study concerns formulating pattern recognition algorithms for SHM purposes which could be used in the wind energy sector as SHM regarding this research �eld is still in an embryonic level compared to civil and aerospace engineering. The purpose of this part is to investigate the e�ectiveness and performance of such methods in structural damage detection. Experimental measurements such as high frequency responses functions (FRFs) were extracted from a 9m WT blade throughout a full-scale continuous fatigue test. A preliminary analysis of a model regression of virtual SCADA data from an o�shore wind farm is also proposed using Gaussian processes and neural network regression techniques. The third part of this work introduces robust multivariate statistical methods into SHM by inclusively revealing how the in uence of environmental and operational variation a�ects features that are sensitive to damage. The algorithms that are described are the Minimum Covariance Determinant Estimator (MCD) and the Minimum Volume Enclosing Ellipsoid (MVEE). These robust outlier methods are inclusive and in turn there is no need to pre-determine an undamaged condition data set, o�ering an important advantage over other multivariate methodologies. Two real life experimental applications to the Z24 bridge and to an aircraft wing are analysed. Furthermore, with the usage of the robust measures, the data variable correlation reveals linear or nonlinear connections.
A clinically valid simulator with tactile sensing to train specialists to perform cochlear implantation

Cochlear implantation is a maximally invasive surgical procedure aimed at overcoming human inaudibility and providing the sensation of sound to thousands of severely deaf recipients worldwide. Specialists require extensive training to perform the surgery, yet traditional approaches such as cadaver dissection and device insertion can prove costly. Alternative training schemes have not been developed, however surgical simulators that offer force feedback during anatomical model manipulation may provide the answer. In the work, a novel approach to medical education is presented. It combines haptic technology and computer visualisation to recreate cochlear implantation in a virtual environment. The surgical simulator provides visual and haptic rendering during cochlear implant insertion into a virtual model of the human Scala Tympani. As the user inserts the sub-sampled array into a three-dimensional, reproducible representation of the Scala Tympani, collisions between the electrode and Scala Tympani walls are detected. In response, real-time forces are delivered back through the haptic device in a closed loop control system. Insertion studies are performed to evaluate the cochlear implant insertion process. Electrode array trajectories and output forces are monitored during device insertion into a synthetic model of the Scala Tympani. The force, torque and position data produced from the experiments are used in the final stage of work for simulator validation. A three-dimensional, surface description of the human Scala Tympani is derived from measured data and parameterised for future reproduction. It is visualised in a virtual environment, the Reachin Application Programming Interface, where visual and haptic rendering is implemented to make the insertion process interactive. Algorithms are
Assessment of remaining fatigue life based on temperature-evolution measurements

ABSTRACT Assessing the remaining fatigue life of various engineering products is crucial to their long-term use. Temperature increases can be promising indicators of the remaining fatigue life since fatigue loading increases the material temperature. This study uses the cumulative temperature-increase method, in which fatigue failures are assumed to occur when the temperature increase in a material under fatigue loading accumulates up to a certain specific value. The conventional method fails to consider the temperature increases unrelated to fatigue damage; here, we attribute such increases to the viscoelastic behaviour (i.e. internal friction). To improve the estimation accuracy of the remaining fatigue life, the conventional method was modified to consider the non-fatigue-related temperature increases. Carbon steel bar specimens were subjected to torsional loading, and their remaining fatigue lives were assessed based on their cumulative temperature increases. Then, the accuracies of the modified and conventional methods were quantitatively evaluated in comparison with their actual remaining fatigue lives. The modified method could better estimate the remaining fatigue life than the conventional method, as evidenced by the lower average relative errors in the modified method compared with the conventional method. As the total cumulative temperature increase is affected by the loading history, future studies must clarify the influence of the loading history and consider it in the assessment of the remaining fatigue life.
The Mediating Role of Organizational Happiness in the Relationship Between Work Engagement and Life Satisfaction: A Study on Teachers

This study aims to examine the mediating role of organizational happiness in the relationship between teachers' work engagement and life satisfaction. The study involved 767 teachers, 557 women (73%) and 210 men (27%). Data from the study were collected using the Work Engagement Scale, the Organisational Happiness Scale, and the Life Satisfaction Scale. Descriptive statistics, Pearson product moment correlation coefficient, path analysis and bootstrapping procedure were used in the analysis of the data to test the significance of the mediating effect. As a result of the study, it was found that there is a significant positive relationship between work engagement, organizational happiness and life satisfaction. The bootstrapping result showed that organizational happiness has a partially mediating role in the relationship between work engagement and life satisfaction. It is thought that the results obtained can be used to increase the life satisfaction of teachers. In addition, it can be said that the results obtained from the research will be a guide for future studies.
Improving Quality of Care in Virtual Visits: Heuristic Evaluations of the User Interface in Telehealth

The COVID-19 pandemic has expedited the growing rate of reliance on telehealth. Beyond the benefits of telehealth for patients and healthcare providers during atypical circumstances, the changes prompted by the pandemic have possibly altered the position of telehealth to the point that virtual communication will become the new normal. Therefore, the importance of designing an interface to facilitate user interaction with the system and consequently with one another is of utmost importance. Building on Nielsen’s usability heuristics and mapping them to the six aspects of quality of care introduced by the Institute of Medicine, we identify the design features that have the highest impact on the quality of care. Our analysis shows that error prevention and recovery is the most influential design feature, followed by help and documentation, visibility of the system, match between the system and the real world, flexibility and efficiency of use, and user control and freedom.
Stereo vision and rover navigation software for planetary exploration

NASA's Mars Exploration Rover (MER) missions will land twin rovers on the surface of Mars in 2004. These rovers will have the ability to navigate safely through unknown and potentially hazardous terrain, using autonomous passive stereo vision to detect potential terrain hazards before driving into them. Unfortunately, the computational power of currently available radiation hardened processors limits the amount of distance (and therefore science) that can be safely achieved by any rover in a given time frame. We present overviews of our current rover vision and navigation systems, to provide context for the types of computation that are required to navigate safely. We also present baseline timing results that represent a lower bound in achievable performance (useful for systems engineering studies of future missions), and describe ways to improve that performance using commercial grade (as opposed to radiation hardened) processors. In particular, we document speedups to our stereo vision system that were achieved using the vectorized operations provided by Pentium MMX technology. Timing data were derived from implementations on several platforms: a prototype Mars rover with flight-like electronics (the Athena Software Development Model (SDM) rover), a RAD6000 computing platform (as will be used in the 2003 MER missions), and research platforms with commercial Pentium III and Sparc processors. Finally, we summarize the radiation effects analysis that suggests that commercial grade processors are likely to be adequate for Mars surface missions, and discuss the level of speedup that may accrue from using these instead of radiation hardened parts.
Complete chloroplast genome of Baccaurea ramiflora (phyllanthaceae), a promising underutilized species

Abstract Baccaurea ramiflora is a high economic value for the exploitation and utilization of wild fruit tree species with edible and medicinal values in Southeast Asia. In this study, the complete chloroplast genome sequence of B. ramiflora was assembled and characterized. The chloroplast genome was 161,089 bp in length, consisting of a large single copy (LSC) of 89,515 bp and a small single copy (SSC) region of 18,826 bp, which were separated by a pair of 26,374 bp inverted repeat (IR) regions. The overall GC content was 36.71%. A total of 132 genes, including 84 protein-coding genes, 37 tRNA genes, and 8 rRNA genes were identified. A low intraspecies variation within B. ramiflora was found with 22 single nucleotide polymorphisms (SNPs) and 17 insertions and deletions (INDELs). Phylogenetic tree reconstructed by 14 chloroplast genomes revealed that B. ramiflora clusters together with species of Phyllanthus, Glochidion, and Flueggea.
Characteristics that Predict Phishing Susceptibility: A Review

Phishing attack countermeasures have previously relied on technical solutions or user training. As phishing attacks continue to impact users resulting in adverse consequences, mitigation efforts may be strengthened through an understanding of how user characteristics predict phishing susceptibility. Several studies have identified factors of interest that may contribute to susceptibility. Others have begun to build predictive models to better understand the relationships among factors in addition to their prediction power, although these studies have only used a handful of predictors. As a step toward creating a holistic model to predict phishing susceptibility, it was first necessary to catalog all known predictors that have been identified in the literature. We identified 32 predictors related to personality traits, demographics, educational background, cybersecurity experience and beliefs, platform experience, email behaviors, and work commitment style.
Knowledge discovery for behavioral patterns in wireless sensor networks

The research consolidated in this thesis is motivated by the recent evolvement of wireless technologies and microelectronic devices, which instigated the emergence of Wireless Sensor Networks (WSNs). Many WSN-based applications have come about; these applications are related, but not limited, to the fields of military, environment and health care. Research in WSNs is still in its early stages, and efforts have been put forward to design fast, reliable, and fault-tolerant protocols that guarantee acceptable levels of quality for events delivery, to meet the limited capabilities of sensor nodes and the effects of unreliable wireless communication. In this thesis, we focus on the design of a Knowledge-based framework for extracting behavioral patterns regarding sensor nodes from WSNs. Three types of behavioral patterns are introduced: Sensor Association Rules, Coverage-based Rules and Sensor Chronological Patterns. The proper steps in the Knowledge Discovery process that pertain to the extraction of the behavioral patterns are defined. These steps are: (i) a formal definition of the required ‘knowledge’; (ii) the data preparation stage that covers the communication aspects of the process of preparing data that is needed to extract these patterns; (iii) the data mining techniques that are essential for extracting the required patterns. A set of schemes have been proposed to attain these steps, and meet the critical properties of WSNs. In contrast to other techniques, the proposed behavioral patterns are mainly about the sensor nodes, instead of the area under monitoring. The direct application of the proposed patterns is enhancing the performance of WSNs by participating in the resource management process of sensor nodes, and reducing the undesired cons of wireless communication; thus improving the Quality of Service of WSNs. Several experiments have been conducted, using synthetic and real data, to report about the performance of proposed schemes.
Understanding the Impact of Soil Moisture on Precipitation Under Different Climate and Meteorological Conditions: A Numerical Sensitivity Study Over the CONUS

Accurate quantitative precipitation forecasts are essential for water resources management, agriculture, and other applications. Soil moisture is among the most important boundary conditions affecting the overlying atmosphere. It can affect precipitation either directly via evapotranspiration, affecting the moisture supply to the atmosphere, or indirectly, changing sensible heat fluxes, which can affect the structure of storms and their associated distributions of precipitation. In this study we investigate the impact of soil moisture uncertainty on simulated precipitation over the conterminous United States (CONUS) with the Weather Research and Forecasting model. We selected two 30‐day periods to conduct simulation scenarios with 3‐km horizontal grid spacing over the CONUS, where the different simulations were identical except for their initial soil moisture and temperature states which were derived from three different products: Global Forecast System analyses, the National Center for Atmospheric Research (NCAR) continuously cycling ensemble Kalman filter (EnKF) data assimilation system, and the North American Land Data Assimilation System (NLDAS‐2). Results indicate different characteristics of the soil moisture–precipitation relationship during the cold and warm periods and under different climate and meteorological conditions. Better representation of initial soil moisture does not consistently provide an improvement in the simulation of precipitation. Our results suggest that soil moisture affects both the magnitude and spatial distribution of precipitation. The impact of soil moisture on precipitation is more pronounced under drier soil and strong thermal and weak synoptic forcing, while in areas with complex terrain the soil moisture‐precipitation coupling becomes weaker.
Microwave receivers with electronic warfare applications

This book is a treatise on EW receivers that is relevant to you if you are just looking for a top-level insight into EW receivers or need to know the intricate details of cause and effect behavior in EW receiver theory and design. The book begins with a general discussion of EW receivers in their functional context and then goes into just the right amount of detail about the theoretical characteristics common to all receiver designs. Examples of this include receiver design issues impacting probability of detection, false alarm rate, and dynamic range performance. Tsui then describes the important characteristics of key emitter parameters to be measured by an EW receiver. He goes on to systematically cover each of the types of EW receivers by dedicating a chapter to each in well-organized and exquisite detail.
The Significance of Aggregation Methods in Functional Group Modeling

The growth of forests and the feedbacks between forests and environmental changes are central issues in the planetary carbon cycle, global climate change, and basic plant ecology. A challenge to understanding both growth and feedbacks from local to global scales is that many critical metabolic processes vary among species. An innovation in solving this challenge is the recognition that species can be lumped into “functional groups” based on metabolic similarity, and these functional groups can then be studied in computational models that simulate ecosystem function. Despite the vast resources devoted to functional group studies and the progress made by them, an important logical and biological question has not been formally addressed, “How do the groupings alter the results of modeling studies? ” To what extent do modeling results depend on the choices made in aggregating taxa into functional groups. Here, we consider the effects of using different aggregation strategies in simulating the carbon dynamics of a deciduous forest. Understanding the impacts that aggregation strategy has on efforts to simulate regional-to-global-scale forest dynamics offers insights into both ecosystem regulation and model function and addresses this central problem in the study of carbon dynamics.
Lane changing models for arterial traffic

Driving behavior models for lane-changing and acceleration form an integral component of microscopic traffic simulators and determine its value in evaluation of different traffic management strategies. The state-of-art model for lane changing adopts a two-level framework: the first level involves a latent or unobserved choice of a target lane; the second level models the acceptance of adjacent gaps in the direction of the target lane. While this modeling approach has several advantages over past works, it assumes drivers to execute lane change within the same time step in which gap was found to be acceptable. In other words, under time steps typically adopted in model applications, the lane change duration is assumed to be negligibly small. However, past works report average lane change durations to the order of 5-6 seconds. Besides this practical maneuvering requirement, the assumption fails further in moderate or low density traffic conditions with ample gap sizes or low speed conditions, where lane changing maneuver can take longer than average. The work outlined in this thesis proposes an extension to the twolevel framework for lane changing models through a third level that explicitly models the lane change duration. Traffic conditions in the driver’s neighborhood that are likely to influence lane change duration are accounted for in the third level. The extended model is applied to data obtained from video observations on traffic on a stretch of an arterial corridor in California. Apart from possessing distinctive features including signalized intersections and multiple access locations that result in lower average speeds, the arterial dataset used in this study represents a relatively low density scenario in terms of gap availability, thereby presenting an ideal test-bed for the proposed model extension. Since arterial datasets have not received predominant attention in literature, this work uncovers some traffic aspects not encountered in past studies. The model is estimated using a sample of the overall dataset available in the form of disaggregate vehicle trajectories. The estimated model is implemented in a microscopic traffic simulator MITSIMLab, and model validation is done using aggregated traffic data. Estimation and validation results showcase the improved modeling capabilities achieved through the proposed extension. Thesis Supervisor: Moshe E. Ben-Akiva Title: Edmund K. Turner Professor of Civil and Environmental Engineering