Miracle-dz/UniFAR-FTUs huggingface dataset:
North Korean escapees' unthinkable journeys and the conceptual binaries of migration policy

There is a lack of empirical interview data on escapees from North Korea and the aim of this thesis is to address this omission. At a theoretical level, this thesis is concerned with North Korean escapees’ cross-border mobility to South Korea through a third country. It explores how North Koreans’ migration, often popularly described as the ‘Underground Railroad’, is undertaken through smuggling networks, documenting both the process of smuggling and the structure of its networks. Although academic research and human rights agencies’ reports have contributed to providing an overview of the plight of North Korean escapees, there has been very little empirical research on the actual migration experience of North Koreans. By conducting empirical research on the processes by which North Koreans move, and their lived experience throughout the migration process, my research addresses a gap in the academic research and literature on North Korean escapees. Empirical data on how and why North Koreans move across borders not only adds to the existing literature on North Koreans, but also draws attention to conceptual and theoretical weaknesses in dominant understandings of the line between ‘forced’ and ‘voluntary’ migration. Focusing on the actors involved in the brokerage and smuggling networks that facilitate North Koreans’ movement, this thesis highlights the complexities of ‘smuggling’ and shows how the category is far more complex than commonly assumed in either migration studies or popular discourse on the ‘Underground Railroad’. Particular attention is paid to the broad and divergent range of interests and motivations of different actors who facilitate the cross border movement of North Koreans. In doing so, the thesis shows that the categories ‘trafficking’, ‘smuggling’, ‘asylum-seeking’ and ‘migration’ do not describe neatly bounded and fundamentally distinct types of movement. It critically questions the way in which the boundaries between human trafficking and smuggling, and between forced and voluntary migration are conceptualized in migration policy, much of academic literature on migration, and in international human rights law. AUTHOR’S DECLARATION I declare that the work in this dissertation was carried out in accordance with the requirements of the University’s Regulations and Code of Practice for Research Degree Programmes and that it has not been submitted for any other academic award. Except where indicated by specific reference in the text, the work is the candidate’s own work. Work done in collaboration with, or with the assistance of, others, is indicated as such. Any views expressed in the dissertation are those of the author. SIGNED: . DATE:.
Non identification of structural change in non stationary AR(1) models

Abstract This article studies the identification problem in AR(1) models with a change in the AR parameter at an unknown time k 0. Consider the model where denotes the indicator function and is a sequence of i.i. d. random variables which are in the domain of attraction of the normal law with zero means and possibly infinite variances. Two cases were investigated: case (I) and case (II) where c is a fixed constant. We derived the limiting distributions of the least squares estimator of the break fraction τ 0( ) for the cases above under some mild conditions. The results showed that the change point (or k 0) is non identifiable for the aforementioned two cases by the least squares method. Monte Carlo experiments were conducted to examine the identification of τ 0 under finite sample situations. Our theoretical results are supported by the Monte Carlo experiments.
Usage of different neural networks in identification of plant types

Since introduction of neural networks into remote sensing they demonstrate good efficiency in remote sensing data analysis. This work is devoted to processing of multispectral (12 bands) images from Sentinel-2(A, B) satellites. Satellite images of areas in Krasnoyarsk Region and Khakassia with known vegetation types are used as task books to train neural networks. Trained neural networks have been reduced to determine which bands are significant for vegetation type identification. Reduction of trained neural network show that vegetation type can be determined from only four infrared bands without significant loses in performance in comparison with non-reduced neural network.
Observations of Greenhouse Gas Changes Across Summer Frontal Boundaries in the Eastern United States

Our knowledge about synoptic‐scale variations of atmospheric‐CO2 produced by interactions between midlatitude cyclones and regional‐scale surface fluxes remains limited due to the scarcity of observations. We synthesized observations of greenhouse gases (GHGs) with respect to frontal passages to understand how GHG distributions change vertically and horizontally during a synoptic event. We use the airborne in situ measurements of GHGs collected during the Atmospheric Carbon and Transport‐America summer 2016 field campaign. Using these measurements, we defined three metrics, (1) the differences in the GHG mole fractions across frontal boundaries in the atmospheric boundary layer (BL) and free troposphere (FT), (2) differences in the vertical contrasts in GHGs in warm and cold sectors, and (3) the size and magnitude of enhanced CO2 in the vicinity of frontal boundary. We found that frontal structures are clearly associated with spatially coherent and significant changes in GHG composition. Warm sector CO2 mole fractions [CO2] are higher than those in the cold sector. The cross‐frontal [CO2] contrasts are largest in the BL (5–30 ppm) with smaller differences in the FT (3–5 ppm). We found higher [CH4] in the BL in the warm sector than in the cold sector for 5 out of 11 cases. Analyses of vertical profiles revealed higher [CO2] in the FT than in the BL in the cold sector while opposite pattern in the warm sector. Average BL‐to‐FT [CO2] differences are 12 and −6 ppm in the warm and cold sectors, respectively. The observational analyses presented define new metrics involving horizontal and vertical GHG contrasts across fronts during summer which will be used to evaluate simulations of GHG transport.
Imprisoned Men: Masculinity Variability and Implications for Correctional Programming

ABSTRACT Prison is a place with an accumulation of men who espouse toughness and aggression, where those who deviate are punished and forced to renegotiate their thoughts on what it means to be a man. Behaviors not considered overtly masculine, such as receiving support in the form of correctional programming, may be off limits to men serving time. Guided by the larger masculinities and crime framework, we examine the perceptions of masculinity and its consequences through in-depth, semi-structured interviews with five incarcerated men in the state of Arizona. We focus on three questions: what does it mean to be a man? Does the prison experience alter this conceptualization? Does this conceptualization affect participation in correctional programming? Our results reveal that there is variation in how incarcerated men define what it means to be man, the incarceration experience matters for how incarcerated men define and express their masculinity, and what it means to be a man in prison indirectly impacts engagement in correctional programs.
Drinking and Driving

Driving while intoxicated causes many traffic accidents and deaths. Two decisions are closely related, whether to engage in heavy drinking, and to drive, conditional on heavy drinking. This paper reviews the extensive literature on heavy drinking, addiction, and driving after heavy drinking. Relevant public policies involve a combination of deterrence, incapacitation, and treatment. While there is empirical support for the rational addiction model applied to heavy drinking, some attributes of drinker-drivers differ from others (e.g., impulsivity in domains other than alcohol consumption, hyperbolic discounting). Policies most effective in reducing drinking and driving are alcohol excise taxes, minimum drinking age and zero tolerance laws for underage persons, dram shop and social host liability, and criminal sanctions overall. Empirical studies have not determined which specific criminal sanctions are most effective. A major impediment to criminal sanctions as a deterrent is that the probability of being stopped/arrested when driving while intoxicated is extremely low,
Building a high-quality reference genome assembly for the the eastern Mediterranean Sea invasive sprinter Lagocephalus sceleratus (Tetraodontiformes, Tetraodontidae)

The Tetraodontidae family encompasses several species which attract scientific interest in terms of their ecology and evolution. However, the genomic resources and especially reference assemblies are sparse for the members of the family. In this study, we focus on the silver-cheeked toadfish (Lagocephalus sceleratus) a well-known ‘invasive sprinter’ that has invaded and spread throughout the Eastern and part of the Western Mediterranean Sea from the Red Sea through the Suez Canal within a decade. We sequenced the genome of L. sceleratus using a single MinION flow cell for the main assembly, and Illumina reads for polishing the assembly. The resulted assembly consisted of 241 contigs (N50 = 11,3 Mb) with a total size of 360 Mb and yielded 98% BUSCO completeness. The high-quality genome assembly built here is expected to set the ground for future studies on this focal species’ invasive biology.
Aesthetic Facial Alterations Resulting from Osseous Chin Surgery

Osseous surgery of the chin is discussed and reviewed. Emphasis is placed on functional and aesthetic facial harmony. This requires concurrent evaluation of skeletal maxillary and mandibular disharmony, including the occlusion. Both reduction and advancement procedures are outlined and examples are presented. Complications, including chin ptosis and lack of cervicomandibular definition, are reviewed. Optimal results dictate that the timing of adjunctive facial cosmetic procedures be coordinated.
Modified Atom Search Optimization Based on Immunologic Mechanism and Reinforcement Learning

Atom search optimization algorithm has good searching ability and has been successfully applied to calculate hydrogeological parameters and groundwater dispersion coefficient. Since the atom search optimization algorithm is only based on the atom force motion model in molecular dynamics, it has some shortcomings such as slow search speed and low precision during the later stage of iteration. A modified atom search optimization based on the immunologic mechanism and reinforcement learning is proposed to overcome the abovementioned shortcomings in this paper. The proposed algorithm introduces a vaccine operator to better utilize the dominant position in the current atom population so that the speed, accuracy, and domain search ability of the atom search optimization algorithm can be strengthened. The reinforcement learning operator is applied to dynamically adjust the vaccination probability to balance the global exploration ability and local exploitation ability. The test results of 21 benchmark functions confirm that the performance of the proposed algorithm is superior to seven contrast algorithms in search accuracy, convergence speed, and robustness. The proposed algorithm is used to optimize the permutation flow shop scheduling problem. The experimental results indicate that the proposed algorithm can achieve better optimization results than the seven comparative algorithms, so the proposed algorithm has good practical application value.
Development of a Diagnostic Damkohler Number for Interpreting Laser-Induced Fluorescence Data in Turbulent Flames

Back-support pilot flames are commonly used for stabilization of turbulent, premixed flames in laboratory experiments. These pilot flames produce an adiabatic or super-adiabatic boundary to provide more favorable conditions for combustion. While the use of pilot flames is very common, it’s unclear how these pilot flames change the structure and behavior of the turbulent flames they stabilized. In our recent study of back-support pilot flame effects on flame structure and dynamics in interacting Bunsen flames, we found that the interpretation of laser-induced fluorescence diagnostics was not straightforward in highly strained backsupported flames. In particular, the extinction behavior of these flames is significantly different than in non-back-supported flames and very high rates of steady strain do not lead to extinction as would be the case without back-support. The goal of this study is to use a combination of experiment and simulation to understand the behavior of highly-strained back-supported flames and the interpretation of laser diagnostics under these conditions. In particular, we propose a “diagnostic Damköhler number” as a metric by which one can determine if a steady laminar flamelet concept is a realistic model for interpreting laserinduced fluorescence measurements.
Experimental plot size of passion fruit

Abstract The determination of the plot size is a practical matter pertinent to the experimental planning, and its optimal characterization allows to obtain higher precision and better quality in the results. Therefore, in this study, the main goal was to determine the plot size in experiments of passion fruit in two uniformity tests with Passiflora setacea and Passiflora alata. The experiment was constituted of a substrate at planting with 3 thirds of soil and 1 of barnyard manure. The soil was fertilizer with 3 kg of simple superphosphate and 0.5 kg of KCl by 1m³. Each species of Passiflora was considered a uniformity test with 40 basic units (BU). The evaluations of the experiments were done on 60 days after the transplant, noticing the tree’s height, stem’s diameter, number of leaves, number of buds, number of meristems and chlorophyll. Several plot sizes were simulated, in which each plant was first considered as a basic unit up to 40 plants per unit basic. For the estimation of optimum plot size, the maximum modified curvature method was used. The plot sizes varied with the specie, founding values as three to seven BU for Passiflora setacea and four BU to five for Passiflora alata.
A privacy‐preserving density peak clustering algorithm in cloud computing

Aiming at preventing the privacy disclosure of sensitive information, issues related to privacy protection in cloud computing have attracted the interest of researchers. To protect the privacy of users during clustering in a cloud computing environment, we present a privacy‐preserving density peak clustering (PPDPC) algorithm that neither discloses personal privacy information nor leaks the cluster centers. Our scheme contains two steps of density peak clustering: First, a cloud service provider calculates the cluster centers without knowing each participant's private data and without disclosing any cluster center information to the other participants, and second, participant allocation is secure and every participant is prevented from identifying the other members of the same cluster. Security analysis and comparison experiments show that the proposed PPDPC algorithm not only obtains good accuracy with respect to density peak clustering but also resists collusion attacks even if the cloud service provider is collaborating with all except one participant. Both theoretical analysis and experimental results confirm the security and accuracy of our method.
Racial Differences in Seasonal Variation in Election and Non-election Years in the Male to Female Ratio at Birth in the United States

Objective: In humans, male births exceed female births. This ratio is conventionally expressed to M/F and is influenced by a large number of factors, including stress. This study was carried out in order to ascertain whether the known seasonal variation in M/F in the United States (peaking in June) is affected by the quadrennial elections (November), and whether any such influences vary by race. Methods: Births by gender and by race for 2003-13 were obtained from the website of the Centers for Disease Control and Prevention for the four available races: White, Black/African American, Asian/Pacific Islander and American Indian/Alaska Native. Election years were 2004, 2008 and 2012. Seasonality tests were carried for the entire group and for White and Black/African American births. Results: This study analysed 45138496 live births (23102106 males, 22036390 females, M/F 0.51180). Overall, M/F was lowest in the election years rising, then falling again to the next election year (p=ns). This pattern was present for White and Asian/Pacific Islander births but not for Black/African American or American Indian/Alaskan Native births. Overall and for White births, only election year plus 3 (year just before election) showed seasonal variation (p < 0.01). Conclusion: Seasonality may have been disturbed/reduced in most years due to elections. Black births may have been unaffected due to chronic stress caused by socio-economic dampening of M/F trends.
Using real-time fluorescence and deformability cytometry and deep learning to transfer molecular specificity to label-free sorting

The identification and separation of specific cells from heterogeneous populations is an essential prerequisite for further analysis or use. Conventional passive and active separation approaches rely on fluorescent or magnetic tags introduced to the cells of interest through molecular markers. Such labeling is time- and cost-intensive, can alter cellular properties, and might be incompatible with subsequent use, for example, in transplantation. Alternative label-free approaches utilizing morphological or mechanical features are attractive, but lack molecular specificity. Here we combine image-based real-time fluorescence and deformability cytometry (RT-FDC) with downstream cell sorting using standing surface acoustic waves (SSAW). We demonstrate basic sorting capabilities of the device by separating cell mimics and blood cell types based on fluorescence as well as deformability and other image parameters. The identification of blood sub-populations is enhanced by flow alignment and deformation of cells in the microfluidic channel constriction. In addition, the classification of blood cells using established fluorescence-based markers provides hundreds of thousands of labeled cell images used to train a deep neural network. The trained algorithm, with latency optimized to below 1 ms, is then used to identify and sort unlabeled blood cells at rates of 100 cells/sec. This approach transfers molecular specificity into label-free sorting and opens up new possibilities for basic biological research and clinical therapeutic applications.
The components of health service marketing influencing health service quality for priests

The objectives of this research were to examine the components of health service marketing influencing health service quality for priests in the context of quantization and qualitative statistical analysis. The methodology used in the study included questionnaire and interview with the sample of 240 priest students from the Buddhist universities. Research findings showed that the result of the causal correlation analysis had a positive relationship between the components of health service marketing and the health service quality (0.778, Cronbach’s alpha 0.001). The most significant components of health service marketing were people, process, physical evidence, promotion (communication), place, product and service, and price (cost) respectively. Health service quality of the hospital comprised the responsiveness, reliability, assurance, empathy, and the tangibles.
Importance of considering the growth response after partial harvesting and economic risk of discounted net revenues when optimizing uneven-aged forest management

Because of the very high complexity of modern optimization models based on single trees, uncertainties are often disregarded. In this study, we present a modelling approach that allows partial harvesting but is still simple enough to consider risk. Our modelling approach investigates whether the inclusion of timber price uncertainty influences the harvesting schedule. The model considers positive growth response to the density reduction that follows harvesting. Testing the impact of uncertainty, we define the discounted net revenues of each harvest operation as random variables. We compare harvest scheduling both with and without the inclusion of uncertainty. We first model growth response based on a partial-harvest schedule, without integrating uncertainty from timber price fluctuations. The results show that harvesting tree cohorts at different times is financially optimal. We run the same model again, including the risk of timber price fluctuations. The inclusion of risk leads to slightly greater differences in recommended harvest timings. Because of the small difference observed, we conclude that it is unlikely that risk arising from fluctuating timber prices would strongly affect the results for more complex forest economic models concerning the optimal harvest schedules.
An Overview of Indian Dacine Fruit Flies (Diptera: Tephritidae: Dacinae: Dacini)

Fruit flies are distributed in all biogeographic regions except in extreme desert and polar areas, where their hosts are scarce or absent (Foote et al., 1993). They have highly diverse biology and larvae of many species develop in fruits, vegetables, flower heads, buds, seeds, stems, etc. Fruit flies belonging to tribe Dacini are of greatest horticultural relevance and its most species are phytophagous and many pertinent pest species have been studied extensively, due to damage they cause in plants of economic interest (Norrbom et al., 1999). Many invasive dacine flies are pests of horticultural crops worldwide, due to their wide climatic tolerance, polyphagous nature, high reproduction potential, multivoltine nature and high capacity for dispersal (Prokopy, 1977). Tribe Dacini contains around a fifth of all known species in the family Tephritidae (Schutze et al., 2017). An Overview of Indian Dacine Fruit Flies (Diptera: Tephritidae: Dacinae: Dacini)
A New Incremental Decision Tree Learning for Cyber Security based on ILDA and Mahalanobis Distance

A cyber-attack detection is currently essential for computer network protection. The fundamentals of protection are to detect cyber-attack effectively with the ability to combat it in various ways and with constant data learning such as internet traffic. With these functions, each cyber-attack can be memorized and protected effectively any time. This research will present procedures for a cyber-attack detection system Incremental Decision Tree Learning (IDTL) that use the principle through Incremental Linear Discriminant Analysis (ILDA) together with Mahalanobis distance for classification of the hierarchical tree by reducing data features that enhance classification of a variety of malicious data. The proposed model can learn a new incoming datum without involving the previous learned data and discard this datum after being learned. The results of the experiments revealed that the proposed method can improve classification accuracy as compare with other methods. They showed the highest accuracy when compared to other methods. If comparing with the effectiveness of each class, it was found that the proposed method can classify both intrusion datasets and other datasets efficiently.