Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial and Political Factors in Forest Resource Conflicts: The Eastern Mau Forest Case 1992–2014

Abstract This paper examines conflicts and forest resource politics in Eastern Mau, which is part of the Mau forest complex in Kenya. Conflict hotspots are mapped based on geo-coded transect walks, observation points derived from a combination of qualitative interviews, and informal conversations with local residents. Conflicts are categorized in terms of their temporal, spatial and substantial dimensions; and connections between conflict hotspots and forest resource politics are discussed. The study documents drivers of conflict such as forest and land resources access, political patronage and competition among ethnic groupings. Political patronage related to the voter sourcing seems to be a key underlying driver of forest and land use conflict. There is need to improve forest resources governance in order to water down patronage and reduce conflicts.
Transonic Store Separation Using Unstructured CFD With Dynamic Meshing

Time-accurate computational fluid dynamic simulations of a transonic store separation event have been performed. These simulations demonstrate the use of a dynamic unstructured tetrahedral mesh approach to this class of problem (as opposed to the typical overset grid approach). A combination of spring-based smoothing and local remeshing are employed with an implicit, second-order upwind accurate Euler solver. A six degree-of-freedom routine using a fourth-order multi-point time integration scheme is coupled with the flow solver to update the store trajectory information. The computational method is discussed, as well as advantages to this approach. Results presented and discussed include trajectory parameters and surface pressure distributions.
Mood analysis on spoken texts of teacher’s scaffolding talks in English subject : a study at eleventh grade of immersion students proficiency at MA Hasyim Asy’ari Bangsri Jepara in the academic year of 2017/2018

The research paper analyzed the mood system realized in the teacher’s scaffolding talks. This study aimed to explain and analyze the mood analysis on spoken texts realized on teacher’s scaffolding talk in English subject and to explain the dominant Mood system realized on teacher’s scaffolding talks in English Subject at eleventh grade of Immersion students proficiency at MA. Hasyim Asy’ari Bangsri Jepara in the academic year of 2017/2018. Beside, this research also explains the significances and the implementation of scaffolding talks in English language teaching. The type of this research is descriptive qualitative research. The object of the research is the teacher’s scaffolding talks. The subject of the research is of the teacher of English subject. Data source used in this research is the data transcriptions from the recording of teacher explanation in one meeting of English subject. First, the researcher made an interpretation of the data collected and the data focus on teacher’s scaffolding talks. Second, the data analyzed by using mood element analysis suggested by M.A.K Halliday. Third, the analysis based on the mood type either indicative or imperative mood. After that, the researcher found the dominant mood system appeared on the teacher’s scaffolding talks. The last is explaining the significances and implementation of scaffolding talks in English language teaching. The result of data analysis shows that there are 168 clauses of scaffolding talks analyzed. The dominant mood system of scaffolding talks in the research subject was declarative mood as it got 48%. As the declarative mood was closely related with statement in the speech functions, it means that the teacher tried her best to give information to guide her students. Besides, the scaffolding talks are expressions to guide students to understand the lessons.
Quantum neural networks versus conventional feedforward neural networks: an experimental study

This study investigates the capacity of quantum neural networks (QNNs) to function as fuzzy classifiers. For this purpose, QNNs are compared with multilayer feedforward neural networks (FFNNs). The experiments are performed on two-dimensional speech data and investigate a variety of issues involved in the training of QNNs. This experimental study verifies that QNNs are capable of representing and quantifying the uncertainty inherent in the training data. It is also shown that simple post-processing of the QNN outputs makes QNNs an attractive alternative to conventional FFNNs for pattern classification applications.
Characterization of Mental Models in a Virtual Reality-Based Multitasking Scenario

This research developed an empirical approach for characterizing user mental models in multitasking scenarios in virtual reality (VR). Participants were required to attend to and detect different types of perceptual events occurring randomly in time in a virtual locomotion environment (VLE) simulation while carrying on physical activity (walking on a treadmill). Three different forms of mental models for performing the perceptual task were hypothesized and expected response patterns for situation awareness (SA), task performance measures and mental workload ratings were compared with observed data. Results demonstrated the SA and task measures to be useful for identifying the occurrence of different mental models and to reveal response patterns in-line with hypotheses. However, in the multitasking scenario, the process of developing SA appeared to be substantially influenced by the physical and cognitive task demands; thus, the progressive development of highly accurate mental models of event distributions appeared to be restricted. Possible applications of the results of this study include development of training programs for mobile, visual inspection tasks, such as airport roving security patrols, for accurate mental model development and promoting detection of critical events.
Gamma-Poisson Distribution Model for Text Categorization

We introduce a new model for describing word frequency distributions in documents for automatic text classification tasks. In the model, the gamma-Poisson probability distribution is used to achieve better text modeling. The framework of the modeling and its application to text categorization are demonstrated with practical techniques for parameter estimation and vector normalization. To investigate the efficiency of our model, text categorization experiments were performed on 20 Newsgroups, Reuters-21578, Industry Sector, and TechTC-100 datasets. The results show that the model allows performance comparable to that of the support vector machine and clearly exceeding that of the multinomial model and the Dirichlet-multinomial model. The time complexity of the proposed classifier and its advantage in practical applications are also discussed.
HiPAR-DSP: a parallel VLIW RISC processor for real time image processing applications

Derived from a thorough analysis of a wide class of image processing algorithms' properties, a parallel RISC architecture has been developed. The architecture gains performance from data level parallelism as well as from instruction level parallelism. From the beginning of the concept phase, high-level programming capabilities have been one of the major design goals. Thus, there has been a steady interaction between the design of the software development toolkit-optimizing assembler and C++ compiler-and the architecture itself. The RISC-typical register files are one of the most critical elements as well concerning die size and clock frequency as the assembler's ability in VLIW scheduling. Running at 100 MHz (200 mm/sup 2/, 0.35 /spl mu/m CMOS) the processor reaches a sustained performance of more than 2 GOPS for a wide range of image processing algorithms.
Integrity of Phosphorus Adsorption in Forested Buffer Strips and Hardwood Forests After Earthworm Invasion

Exotic earthworm invasions have caused the degradation of Vermont's forest floors and changes in the nutrient cycling of forested areas. As climate change progresses, invasions by exotic species are predicted to spread. In Vermont, the most recent species of earthworm to invade are in the genus Amynthas. The change in nutrient cycling caused by exotic earthworm invasion will impact the phosphorus cycle in affected ecosystems. Phosphorus runoff is a serious concern for Vermont because it causes eutrophication and consequently algae blooms in Lake Champlain. It is predicted that climate change will increase the intensity of rainstorms which, in turn, will increase nutrient runoff, compounding the problem. This study evaluated if the presence of Amynthas spp. earthworms has an impact on the phosphorus adsorption capacity of forested areas, particularly of forested riparian buffers, which help prevent agricultural non-point source pollution. The phosphorus retention capacity was evaluated, as measured by the marginal phosphorus adsorption rate. The earthworm communities present were determined using morphological keys. It was found that the presence of Amynthas spp. earthworms did not reduce the phosphorus adsorption capacity of the soil, as initially hypothesized, and that the majority of studied riparian buffers had the ability to retain a significant amount of additional phosphorus.
Teaching Soft Skills to Business Students

Recent reports have suggested that many employees in the workforce today lack essential soft skills. This research analyzes the effectiveness of multiple classroom assignments for teaching soft skills in a Business Communication course. Five distinct pedagogical strategies were used in an effort to teach soft skills, including a self-analysis, an interview, a guest lecturer, a journal article, and a soft skills video. Results offer insights into students’ perceptions of the most helpful pedagogical approaches for teaching soft skills.
Is Excessive Prenatal Ultrasonography a Risk Factor for Autism? A Literature Review

For the past several decades, abdominal prenatal ultrasonography has been the most significant technology in obstetrics with a long-established application. However, the frequency, exposure time, thermal and cavitation exposure indices, and increased acoustic output of the ultrasonic waves may be harmful to the embryo/fetus and might increase susceptibility to Autism Spectrum Disorder (ASD). The increase in the prevalence of ASD is associated with an affluent ethnicity, high socioeconomic status, and high parental education where prenatal ultrasonography is readily available and affordable. Enhanced biophysical adverse effects may link the analogous increase in prenatal ultrasonography and autism, and prenatal ultrasonography may emerge as a risk factor for autism. Radiography usage provides historical evidence for this fact: the predominant past opinion was that exposure to X-rays during pregnancy caused no significant risk to a fetus. However, the association between X-ray exposure and childhood leukemia was only established 40 years after X-ray use began. This review focuses on excessive PUS usage and ASD development. Public Abstract Advancements in medical technology over the past several decades have made prenatal ultrasound more frequently accessible to expecting mothers during their pregnancy, especially for the affluent. A parallel development in health care is the increase in autism diagnoses (Autism Spectrum Disorder, or ASD) in children of affluent families. There is a general lack of studies of the impact of prenatal ultrasound on fetuses, especially around varying attributes such as frequency, duration of exposure, and thermal and cavitation indices. There is also a historical precedent set, where exposing fetuses to X-rays was not found to be harmful until it was linked to the development of childhood leukemia decades later. This paper seeks to establish a need to further study these attributes of prenatal ultrasound overuse and their possible impact on a developing fetus, with a special focus on the occurrence of Autism.
Optimal distributed leader election algorithm for synchronous complete network

We address the problem of electing a leader in a synchronous complete network consisting of n nodes, each node has a unique ID. We give a message-optimal algorithm which runs in O(k) time using O(kn/sup /spl alpha/(k)/) messages, where /spl alpha/(k)=1+(1/2/sup k/) and parameter k is a positive integer; in particular we get a constant time algorithm which uses O(n/sup 1+/spl epsiv//) messages.
Performance analysis of Kalman filter-based clock synchronization in IEEE 1588 networks

This paper analyzes the effects on synchronization performances of the accuracy of skew and offset estimates, the short and long-term stability of the unregulated clock, the rate at which timing information is exchanged within the network. The work makes use of a simulated clock based on the state-variable model, for which realistic parameters are obtained from experimental measurements of Allan variance plots for different kinds of clocks.
Compressional origin of the Naxos metamorphic core complex, Greece: Structure, petrography, and thermobarometry

The island of Naxos, Greece, has been previously considered to represent a Cordilleran-style metamorphic core complex that formed during Cenozoic extension of the Aegean Sea. Although lithospheric extension has undoubtedly occurred in the region since 10 Ma, the geodynamic history of older, regional-scale, kyanite- and sillimanite-grade metamorphic rocks exposed within the core of the Naxos dome is controversial. Specifically, little is known about the pre-extensional prograde evolution and the relative timing of peak metamorphism in relation to the onset of extension. In this work, new structural mapping is presented and integrated with petrographic analyses and phase equilibrium modeling of blueschists, kyanite gneisses, and anatectic sillimanite migmatites. The kyanite-sillimanite–grade rocks within the core complex record a complex history of burial and compression and did not form under crustal extension. Deformation and metamorphism were diachronous and advanced down the structural section, resulting in the juxtaposition of several distinct tectono-stratigraphic nappes that experienced contrasting metamorphic histories. The Cycladic Blueschists attained ∼14.5 kbar and 470 °C during attempted northeast-directed subduction of the continental margin. These were subsequently thrusted onto the more proximal continental margin, resulting in crustal thickening and regional metamorphism associated with kyanite-grade conditions of ∼10 kbar and 600–670 °C. With continued shortening, the deepest structural levels underwent kyanite-grade hydrous melting at ∼8–10 kbar and 680–750 °C, followed by isothermal decompression through the muscovite dehydration melting reaction to sillimanite-grade conditions of ∼5–6 kbar and 730 °C. This decompression process was associated with top-to-the-NNE shearing along passive-roof faults that formed because of SW-directed extrusion. These shear zones predated crustal extension, because they are folded around the migmatite dome and are crosscut by leucogranites and low-angle normal faults. The migmatite dome formed at lower-pressure conditions under horizontal constriction that caused vertical boudinage and upright isoclinal folds. The switch from compression to extension occurred immediately following doming and was associated with NNE-SSW horizontal boudinage and top-to-the-NNE brittle-ductile normal faults that truncate the internal shear zones and earlier collisional features. The Naxos metamorphic core complex is interpreted to have formed via crustal thickening, regional metamorphism, and partial melting in a compressional setting, here termed the Aegean orogeny, and it was exhumed from the midcrust due to the switch from compression to extension at ca. 15 Ma.
A Virtual Model of the Human Cervical Spine for Physics-based Simulation and Applications

INTRODUCTION used to create each body (C1 to T1). The reconstructed anatomical model is shown in Figure 1. In phase two, the reconstructed Skull-Cervical vertebrae-Upper torso geometry was imported into a physics-based multibody simulation software program (VisualNastran 4D, MSC) that was used to define the interconnecting joints and soft tissue elements. The facet joints were modeled as colliding biconvex surfaces at C1-C2 and a ball on a plane joint between vertebrae C2 through T1 (Figure 2). With emerging computer technology, there is an effort to integrate computer graphics, diagnostic imaging tools, and computer aided design and engineering analysis programs into biomedical research. The use of computer graphics to model the cervical spine provides the opportunity to create a virtual simulator for kinematics and kinetic testing of the cervical spine. The objective was to develop a graphics oriented interactive cervical spine model for physicsbased dynamic simulation.
Discrete nondeterministic modeling of biochemical networks

The ideas expressed in this work pertain to biochemical modeling. We explore our technique, the Nondeterministic Waiting Time algorithm, for modeling molecular signaling cascades. The algorithm is presented with pseudocode along with an explanation of its implementation. The entire source code can be found in the Appendices. This algorithm builds on earlier work from the lab of Dr. Andrei Paun, the advisor for this dissertation. We discuss several important extensions including: (i) a heap with special maintenance functions for sorting reaction waiting times, (ii) a nondeterminstic component for handling reaction competition, and (iii) a memory enhancement allowing slower reactions to compete with faster reactions. Several example systems are provided for comparisons between modeling with systems of ordinary differential equations, the Gillespie Algorithm, and our Nondeterministic Waiting Time algorithm. Our algorithm has a unique ability to exhibit behavior similar to the solutions to systems of ordinary differential equations for certain models and parameter choices, but it also has the nondeterministic component which yields results similar stochastic methods (e.g., the Gillespie Algorithm). Next, we turn our attention to the Fas-mediated apoptotic signaling cascade. Fas signaling has important implications in the research of cancer, autoimmune and neurodegenerative disorders. We provide an exhaustive account of results from the Nondeterminstic Waiting Time algorithm in comparison to solutions to the system
What We See and Why It Matters: How Competency in Visual Literacy can Enhance Student Learning

WHAT WE SEE AND WHY IT MATTERS: HOW COMPETENCY IN VISUAL LITERACY CAN ENHANCE STUDENT LEARNING Anneliese Tillmann Interdisciplinary Educational Studies, Illinois Wesleyan University In today’s world, we use more visuals than ever before. Research suggests that the balance between words and images has shifted considerably calling for new forms of literacy (Brumberger, 2011). Visual literacy goes above and beyond the traditional concepts of reading and writing, expanding literacy to include visuals. The analysis and review of current visual literacy research suggests teaching visual literacy is necessary for students to become capable of navigating the visually driven world in which we live. The research highlights the importance of incorporating visuals into the literacy curricula and explores practical uses of visual literacy in present day society. Findings suggest that developing the ability to create images will help students better learn to decipher, understand and communicate with images. If there is a better understanding of how and why visuals are developed, then the use of visuals can become more effective, ergo enhancing student learning.
Challenges and improvements in applying a particle image velocimetry (PIV) approach to granular flows

The particle image velocimetry (PIV) is a well-established non-invasive optical technique for measuring the velocity field in fluids. Recently, the PIV approach has been extended to granular flows, where the medium under investigation is composed of a discrete number of grains that are typically non-transparent and of super-millimetric size. Granular PIV (g-PIV) still represents a non-standard application, as some accuracy concerns arise. In particular, since granular flows can be highly sheared, the choice of appropriate interrogation windows for the PIV analysis is not trivial. As well, owing to the spatially-averaged nature of the PIV approach, the estimation of second-order statistics remains a very challenging task. Here, we report a laboratory investigation on dry granular flows composed of glass spheres in a rotating drum. The velocity measurements at the sidewall are obtained by using a window deformation multi-pass PIV approach, where the open-source code PIVlab has been specifically used. Different combinations of the number of PIV passes and of interrogation windows are investigated. A slightly modified version of PIVlab allowed us to carry out g-PIV calculations with an arbitrary number of passes (i.e. greater than 4). Comparisons among different analyses helped us to identify reliable settings for g-PIV applications.
Local-linear-fitting-based matting for joint hole filling and depth upsampling of RGB-D images

Abstract. We propose an approach for jointly filling holes and upsampling depth information for RGB-D images captured with common acquisition systems, where RGB color information is available at all pixel locations whereas depth information is only available at lower resolution and entirely missing in small regions referred to as “holes. ” Depth information completion is formulated as a minimization of an objective function composed of two additive terms. The first data fidelity term penalizes disagreement with the observed low-resolution data. The second regularization term penalizes weighted depth deviations from a local linear model in spatial coordinates, where the weights are experimentally determined to ensure consistency between the RGB color image and the estimated depth image. Analogous to techniques used for optimization formulations of image matting, the completed depth image is then obtained by solving a large sparse linear system of equations. We also propose a memory-efficient implementation of the proposed method based on the conjugate gradient method. Visual evaluation of results obtained with the proposed algorithm demonstrates that the method provides high-resolution depth maps that are consistent with the color images. Furthermore, the memory-efficient implementation significantly reduces memory requirements, allowing for computation of the upsampled, hole-filled depth maps for typical RGB-D images on normal workstation hardware. Quantitative comparisons demonstrate that the method offers an improvement in accuracy over the current state-of-the-art techniques for depth information completion. Importantly, statistical analysis, which we present in this paper, also reveals that prior evaluations of depth upsampling accuracy are potentially biased because the evaluations inappropriately used preprocessed hole-filled data as “ground truth. ” An implementation of the proposed algorithm can be accessed and executed through Code Ocean: https://codeocean.com/capsule/5103691/tree/v1.
State Ownership, Firm Specific Risk and Momentum Trading

This Dissertation is protected by copyright and/or related rights. It has been brought to you by ScholarWorks@UNO with permission from the rights-holder(s). You are free to use this Dissertation in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself.