Miracle-dz/UniFAR-FTUs huggingface dataset:
PENINGKATAN PRESTASI BELAJAR MAHASISWA MELALUI PENDEKATAN SAVI PADA MATA KULIAH CURRICULUM AND MATERIAL DEVELOPMENT

Penelitian ini merupakan Penelitian Tindakan Kelas (PTK) yang bertujuan untuk menganalisis efektifitas penerapan pendekatan Somatis, Auditori, Visual, dan Intelektual (SAVI) dalam meningkatkan prestasi belajar mahasiswa pada Mata KuliahCurriculum and Material Design Development (CMD). PTK tersebut dilaksanakan dalam tiga siklus melalui pendekatan kualitatif-kuantitatif untuk mendapatkan data penelitian. Subjek penelitian adalah mahasiswa Program Studi Pendidikan Bahasa Inggris FKIP UMSU. Data kuantitatif diperoleh melalui pemberian tes berupa pemahaman konsep kurikulum dan pengembangan desain materi pembelajaran, sedangkan data kualitatif didasarkan pada hasil observasi aktivitas mahasiswa dan dosen dalam pembelajaran. Hasil analisis data menunjukkan bahwa prestasi belajar mahasiswa mengalami peningkatan pada setiap siklusnya dengan tingkat keberhasilan 24,24% pada siklus I, 51,52 % pada siklus II, dan mencapai 87,89% pada siklus III. Dengan demikian dapat disimpulkan bahwa pendekatan SAVI dapat meningkatkan prestasi belajar mahasiswa, khususnya dalam pembelajaranCMD.
Forward and Backward Visual Fusion Approach to Motion Estimation with High Robustness and Low Cost

We present a novel low-cost visual odometry method of estimating the ego-motion (self-motion) for ground vehicles by detecting the changes that motion induces on the images. Different from traditional localization methods that use differential global positioning system (GPS), precise inertial measurement unit (IMU) or 3D Lidar, the proposed method only leverage data from inexpensive visual sensors of forward and backward onboard cameras. Starting with the spatial-temporal synchronization, the scale factor of backward monocular visual odometry was estimated based on the MSE optimization method in a sliding window. Then, in trajectory estimation, an improved two-layers Kalman filter was proposed including orientation fusion and position fusion . Where, in the orientation fusion step, we utilized the trajectory error space represented by unit quaternion as the state of the filter. The resulting system enables high-accuracy, low-cost ego-pose estimation, along with providing robustness capability of handing camera module degradation by automatic reduce the confidence of failed sensor in the fusion pipeline. Therefore, it can operate in the presence of complex and highly dynamic motion such as enter-in-and-out tunnel entrance, texture-less, illumination change environments, bumpy road and even one of the cameras fails. The experiments carried out in this paper have proved that our algorithm can achieve the best performance on evaluation indexes of average in distance (AED), average in X direction (AEX), average in Y direction (AEY), and root mean square error (RMSE) compared to other state-of-the-art algorithms, which indicates that the output results of our approach is superior to other methods.
Bathymetry and Geomorphology of Shelikof Strait and the Western Gulf of Alaska

We defined the bathymetry of Shelikof Strait and the western Gulf of Alaska (WGOA) from the edges of the land masses down to about 7000 m deep in the Aleutian Trench. This map was produced by combining soundings from historical National Ocean Service (NOS) smooth sheets (2.7 million soundings); shallow multibeam and LIDAR (light detection and ranging) data sets from the NOS and others (subsampled to 2.6 million soundings); and deep multibeam (subsampled to 3.3 million soundings), single-beam, and underway files from fisheries research cruises (9.1 million soundings). These legacy smooth sheet data, some over a century old, were the best descriptor of much of the shallower and inshore areas, but they are superseded by the newer multibeam and LIDAR, where available. Much of the offshore area is only mapped by non-hydrographic single-beam and underway files. We combined these disparate data sets by proofing them against their source files, where possible, in an attempt to preserve seafloor features for research purposes. We also attempted to minimize bathymetric data errors so that they would not create artificial seafloor features that might impact such analyses. The main result of the bathymetry compilation is that we observe abundant features related to glaciation of the shelf of Alaska during the Last Glacial Maximum including abundant end moraines, some medial moraines, glacial lineations, eskers, iceberg ploughmarks, and two types of pockmarks. We developed an integrated onshore–offshore geomorphic map of the region that includes glacial flow directions, moraines, and iceberg ploughmarks to better define the form and flow of former ice masses.
A NUMERICAL ASSESSMENT OF ATMOSPHERIC BOUNDARY LAYER SIMULATION INSIDE TWO DIFFERENT BOUNDARY LAYER WIND TUNNELS

A new large scale wind tunnel is under development at METU Center for Wind Energy (RÜZGEM). This wind tunnel is a closed-loop multi-purpose wind tunnel with a 3 m x 7 m x 20 m boundary layer test section. Inside this test section the atmospheric boundary layer (ABL) will be simulated using the spire-roughness element technique in order to represent different terrain exposures (or categories) as defined by American Society of Civil Engineers (ASCE). Since no experimental data are available yet, Computational Fluid Dynamics (CFD) will be implemented as a tool in order to provide an initial assessment for the simulation of the ABL. However, in order to validate the CFD approach, another wind tunnel test case from literature will be used for comparison. This wind tunnel has 1.82 m x 1.82 m x 9.8 m test section. Four different test cases have been simulated and the results show reasonable agreement between the experiments and numerical results in terms of velocity profiles, power law exponents and boundary layer parameters.
Aesthetics and Clarity in Information Visualization: The Designer’s Perspective

Designers are increasingly involved in creating ‘popular’ data visualizations in mass media. Scientists in the field of information visualization propose collaborations between designers and scientists in popular data visualization. They assume that designers put more emphasis on aesthetics than on clarity in their representation of data, and that they aim to convey subjective, rather than objective, information. We investigated designers’ criteria for good design for a broad audience by interviewing professional designers and by reviewing information design handbooks. Additionally, we investigated what might make a visualization aesthetically pleasing (attractive) in the view of the designers. Results show that, according to the information designers, clarity and aesthetics are the main criteria, with clarity being the most important. They aim to objectively inform the public, rather than conveying personal opinions. Furthermore, although aesthetics is considered important, design literature hardly addresses the characteristics of aesthetics, and designers find it hard to define what makes a visualization attractive. The few statements found point at interesting directions for future research.
Performance evaluation of two filter materials in intermittent sand filtration system

The environment is a treasure that needs to be protected from point and diffuse sources of pollution. Most wastewater treatment plants cannot attain 100% efficiencies and this call for tertiary treatment process before discharging final treated wastewater into the environment. The study focused on harnessing the locally available materials, sand and granulated Palm Kernel Shell (gPKS) as filters for treatment of wastewater from constructed wetland using intermittent filtration system in the laboratory. The filter depth and hydraulic loading rate was 650 mm and 0.135 L min -1 , respectively. Applied wastewater was effluent from the Constructed Wetland (CW) of wastewater treatment plant University of Lagos. The filtration system was dosed intermittently 6 hourly for 12 weeks. Experimental results showed that pH of the effluent from gPKS filter medium was slightly reduced to the influent due to acidic nature of the gPKS while effluent from sand filter slightly increased. The five day biochemical oxygen demand (BOD5) removal efficiency for the gPKS and sand filters were 59.2 and 69.08% respectively, while the corresponding average Dissolve Oxygen (DO) were 2.9 ± 0.6 mg L -1 and 3.4 ± 0.345. The percentage removal of E. coli in sand and gPKS filters are 69.34% and 87.49% respectively.
Depositional framework of the Gwna Group mélanges of Anglesey and North-West Wales, UK

The Gwna Group contains chaotic, disrupted melange of unknown genetic history. Publications that speculate on Gwna Group formation often cast the melange in a pivotal role: the regional co-location of blueschist and greenschist with the Gwna Group has inspired hypotheses of tectonic melange formation during accretionary orogen subduction, yet no specific published studies of the Gwna Group exist. Detailed field study of the Gwna Group has distinguished primary features from those overprinted by later events, over four defined outcrop belts within which combined stratigraphic relationships are determined. The Gwna Group is emphatically redefined as a sedimentary, gravity flow deposit; • Four basal unconformities demonstrate sedimentary superposition. • Pre-Sturtian molar tooth microspar is described in abiotic Neoproterozoic dolo-limestone clasts within the Gwna Group – the first account in Britain. • A regionally ubiquitous, internally conformable coherent sedimentary system of three main lithofacies is interbedded within repeated megacycles. • The sediments record early Palaeozoic passive margin evolution. New U-Pb zircon investigations and meta-analysis of published ages show the Gwna Group began to accumulate near the Gondwanan margin during early Cambrian rifting and crucially demonstrate no genetic relationship with the Penmynydd Zone as Gwna Group deposition followed exhumation of the blueschists at 560 - 550 Ma.
Artificial Neural Network (ANN) Approach to Modelling of Selected Nitrogen Forms Removal from Oily Wastewater in Anaerobic and Aerobic GSBR Process Phases

Paper presents artificial neural network models (ANN) approximating concentration of selected nitrogen forms in wastewater after sequence batch reactor operating with aerobic granular activated sludge (GSBR) in the anaerobic and aerobic phases. Aim of the study was to determine parameters conditioning effectiveness of selected nitrogen forms removal in GSBR reactor process phases. Models of artificial neural networks were developed separately for N-NH4, N-NO3 and total nitrogen concentration in particular process phases of GSBR reactor. In total, 6 ANN models were presented in this paper. ANN models were made as multilayer perceptron (MLP), which were learned using the Broyden-Fletcher-Goldfarb-Shanno algorithm. Developed ANN models indicated variables the most influencing of particular nitrogen forms in aerobic and anaerobic phase of GSBR reactor. Concentration of estimated nitrogen form at the beginning of anaerobic or aerobic phase, depending on ANN model, in all ANN models influenced approximated value. Obtained determination coefficients varied from 0.996 to 0.999 and were depending on estimated nitrogen form and GSBR process phase. Hence, developed ANN models can be used in further studies on modeling of nitrogen forms in anaerobic and aerobic phase of GSBR reactors.
Bayesian Pitch Tracking Based on the Harmonic Model

Fundamental frequency is one of the most important characteristics of speech and audio signals. Harmonic model-based fundamental frequency estimators offer a higher estimation accuracy and robustness against noise than the widely used autocorrelation-based methods. However, the traditional harmonic model-based estimators do not take the temporal smoothness of the fundamental frequency, the model order, and the voicing into account as they process each data segment independently. In this paper, a fully Bayesian fundamental frequency tracking algorithm based on the harmonic model and a first-order Markov process model is proposed. Smoothness priors are imposed on the fundamental frequencies, model orders, and voicing using first-order Markov process models. Using these Markov models, fundamental frequency estimation and voicing detection errors can be reduced. Using the harmonic model, the proposed fundamental frequency tracker has an improved robustness to noise. An analytical form of the likelihood function, which can be computed efficiently, is derived. Compared to the state-of-the-art neural network and non-parametric approaches, the proposed fundamental frequency tracking algorithm reduces the mean absolute errors and gross errors by 15\% and 20\% on the Keele pitch database and 36\% and 26\% on sustained /a/ sounds from a database of Parkinson's disease voices under 0 dB white Gaussian noise. A MATLAB version of the proposed algorithm is made freely available for reproduction of the results\footnote{An implementation of the proposed algorithm using MATLAB may be found in \url{this https URL}
Attack Provenance Tracing in Cyberspace: Solutions, Challenges and Future Directions

With the increasing damage of APT attacks, the modern world has moved from individual hackers for fun to nation-wide cybercriminals for strategic advantage or profit. These APT attacks are often prolonged and have multiple stages, and they usually utilize zero-day or one-day exploits to be penetrating and stealthy. As a result, there is an urgent need to detect and investigate APT attacks. Among all kinds of security techniques, provenance tracing is regarded as a promising and important approach for attack investigation, as it discloses the root cause, the path, and the results of attacks. However, the existing techniques either suffer from the limitation of only focusing on the log type, or have non-trivial space and runtime overhead, which hinder their wide applications in practice. In this article, we provide a comprehensive survey of provenance tracing technologies in the most recent literature. Following the overview of each scheme, we present the key technical features of them and then compare the state-of-the-art solutions in terms of both security and performance. Finally, we propose and discuss several potential future research directions.
GIS approaches to understanding connections and movement through space

The overarching theme of this work is an exploration of ways in which GIS can be used to analyse connections or movement through space in a wide variety of contexts. The published work focuses upon the application of both network and raster-based techniques at a variety of scales. A review is provided summarising the breadth of applications that currently use GIS to model connectivity or movement through space. This is followed by a series of published work in this field. This includes both raster and network approaches to assessing journey-time exposure to air pollution; exploring the impact of artificial lighting on gap crossing thresholds of bats; examining the presence of food deserts in rainforest cities; assessing urban accessibility and its influence on social vulnerability to climate shocks; and understanding of the impact of segregation on everyday patterns of mobility. With a diverse range of application areas and variety of spatial scales ranging from 2 - 605,000km2, this published work highlights the ways in which GIS can be implemented in new ways to improve understanding of connections and/or movement through space.
Projected Impact of Sea Level Rise on Nigeria’s Coastal City of Calabar in Cross River State

This study projects the impact of sea level rise on Nigeria’s coastal city of Calabar in Cross River State. Data for the study were obtained from both secondary and primary sources through the use of the internet and questionnaire administration respectively. The data were analysed using the geographical information systems (GIS), frequency tables and percentages. The results revealed that at 0.3 m rise in sea level, about 4.56% of the total land area will be covered with flood water. This would affect 159 houses, with approximately 1,431 persons. Further, at 3.0 m rise in sea level, about 10.10% of the area will be flooded, affecting 2012 houses with an estimated population of 18,108 persons. Again, the resilience of the residents to the vagaries of flooding by sea level rise is generally low due to very low income and lack of awareness. The study concluded that Calabar City is vulnerable to the impact of sea level rise which is primarily caused by climate change. Therefore, awareness campaign on the impact of flooding by sea level rise on coastal communities should be carried out by the appropriate agencies of government in the State.
Wind Turbine Blade Design with Computational Fluid Dynamics Analysis

Although there are many blade profile have been improved for use in aviation and energy sector, there is still needed blade profiles which have higher performance especially the commercial horizontal axis wind turbine efficiency is taken into account. The purpose of this study is to obtain the new blade profiles which have higher lift (CL) and drag (CD) coefficients for wind turbine making geometric modifications on several NACA wing profile systematically. For this purpose, the performance of NACA and developed new profiles have been compared with each other using computational fluid dynamics analysis and it is seen that the new developed profiles have higher performance than NACA profiles. Later on, according to the Blade Element Momentum Theory (BEM Theory) turbine blades are designed with developed new profiles and 3-dimensional CFD analyses are performed. Increase in torque in the wind turbine is determined.
Zooplankton community density in relation to water level fluctuations and inorganic turbidity in an Amazonian lake, "Lago Batata", State of Pará, Brazil

This research concems the pattems of variations in the zooplankton community density in Batata Lake, State of Pará, Braztl. Four collections were made during one year at distinct phases of the regional hydrological cycle. Together with the zooplankton community density, the water temperature, transparency, pH, concentration of suspended solids, chlorophyll-a and dissolved oxygen were determined. The chief objectives of the work were the observation of the spatial and temporal variations of the zooplankton community density and their relationships to the ambient variables measured; and the identification of the possible influence on these organisms of the fine particulate clays, tailings from bauxite processing, that have been discarded into the lake for ten years. The temporal fluctuations and the reduction in plankton density observed in the affected area of the lake are discussed, considering factors such as water level fluctuation and changes in the quantity of suspended inorganic matter and the direct consequences for the zooplankton, as well as effects through interactive mechanisms with other ambient variables such as phytoplankton primary production.
A genetic algorithm solution to the gram-schmidt image fusion

ABSTRACT There is no such thing as ‘the best image fusion method’ in terms of both spectral and spatial fidelity. This fact encourages the researchers to develop more advanced approaches in order to optimally transfer the spatial details without distorting the colour content. Component substitution (CS)-based image fusion methods have been proven to produce sharper images but suffer from colour distortion. The aim of this study was to modify the CS-based Gram-Schmidt (GS) fusion method with the aid of the Genetic Algorithm (GA) to further improve its colour preservation performance. The GA was used to estimate a weight for each multispectral (MS) band. The obtained band weights were used to generate a low-resolution panchromatic (PAN) band, which plays a significant role in the performance of the GS method. The performance of the proposed approach was compared not only against the conventional GS, but also against widely-used CS-based, multiresolution analysis (MRA)-based and colour-based (CB) image fusion methods. The results indicated that the proposed GA-based approach produced spectrally and spatially superior results compared to the other methods used.
Economic benefits of soil and water conservation measures at the sub-catchment scale in the northern Highlands of Ethiopia

Soil and water conservation (SWC) interventions are needed to control rainfall-driven erosion, and profitability of SWC measures at the sub-catchment scale emerges as the principal reason for their adoption. This study carried out a cost–benefit analysis of SWC measures in mountainous agricultural catchments. Physical data were obtained through field measurements of discharge, sediment and nutrient loss at the sub-catchment scale with and without SWC measures. The major cost benefits of various measures implemented in the study area were quantified using net present value (NPV), and direct market prices were employed in valuing the cost of items required for crop production. The results revealed that sediment loss decreased by 8.78 Mg ha−1 y−1 (46.8%) due to SWC measures, and the cost of production inputs, such as fertilizer (urea) and lime, was reduced by $17.97 ha−1 y−1 and $3.63 ha−1 y−1, respectively. Furthermore, crop yield was enhanced by 13% for teff, 19.4% for sorghum and 19.42% for chickpeas, which is equivalent to economic returns of $102, $96.9 and $140.25 ha−1 y−1, respectively. The total discounted cost of SWC interventions was about $331.74. This includes establishment costs, maintenance costs, input costs and the costs resulting from lost productive land. In sum, the total discounted benefits of SWC measures were the enhancement of crop production, a reduction in lime requirement and a reduction in the loss of total nitrogen and sediment, estimated at about $809.42. Hence, it is possible to deduce that SWC measures reduced nutrient depletion and greatly improved crop yield with a NPV of $477.68 ha−1. The results strongly suggest that SWC measures in the study sub-catchment, as well as in nearby areas with a similar indigenous SWC adoption strategy, topographic conditions and agroclimatic characteristics, should be maintained.
Toward Efficient Image Representation: Sparse Concept Discriminant Matrix Factorization

The key ingredients of matrix factorization lie in basic learning and coefficient representation. To enhance the discriminant ability of the learned basis, discriminant graph embedding is usually introduced in the matrix factorization model. However, the existing matrix factorization methods based on graph embedding generally conduct discriminant analysis via a single type of adjacency graph, either similarity-based graphs (e.g., Laplacian eigenmaps graph) or reconstruction-based graphs (e.g., ${L}_{1}$ -graph), while ignoring the cooperation of the different types of adjacency graphs that can better depict the discriminant structure of original data. To address the above issue, we propose a novel Fisher-like criterion, based on graph embedding, to extract sufficient discriminant information via two different types of adjacency graphs. One graph preserves the reconstruction relationships of neighboring samples in the same category, and the other suppresses the similarity relationships of neighboring samples from different categories. Moreover, we also leverage the sparse coding to promote the sparsity of the coefficients. By virtue of the proposed Fisher-like criterion and sparse coding, a new matrix factorization framework called Sparse concept Discriminant Matrix Factorization (SDMF) is proposed for efficient image representation. Furthermore, we extend the Fisher-like criterion to an unsupervised context, thus yielding an unsupervised version of SDMF. Experimental results on seven benchmark datasets demonstrate the effectiveness and efficiency of the proposed SDMFs on both image classification and clustering tasks.