Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing of Groundwater Contamination by Toxic Elements through Multivariate Statistics and Spatial Interpolation, Wadi Fatimah, Western Arabian Shield, Saudi Arabia

Groundwater is present in the shallow alluvial and fractured bedrocks aquifers of wadi Fatimah, west central Arabian Shield, Saudi Arabia. The impact of human activities and natural processes on groundwater quality is one of the major serious problems for sustain- able development in this area. 80 well-representative groundwater samples were analyzed for the total 17 water quality descriptors (va- riables) including toxic elements which are not considered in traditional techniques for water quality assessments. This study was to assess the levels of groundwater contamination of toxic elements and their spatial distribution in the region, and to identify possible anthropogenic and natural sources of contamination in the area. Multivariate statistical techniques were performed in this study. Cluster analysis in both R and Q modes was used. R-mode analysis is interpreted and there are three major groups. Q-mode shows three main groups related to the same geological formation. Factor analysis revealed that the first three factors account for 85% of the total variance. Factor 1 revealed high loading of Al, Li, B and Cd, suggested interaction between water and geological formation. The second factor represented high loading of As, Pb and Hg, suggested human and agricultural activities. Factor three represented high loading of Si and Fe, which may be resulted from water/weather rock interactions.
Modeling of human velocity habituation for a robotic wheelchair

This work proposes a model for human habituation while riding a robotic wheelchair. We present and describe the concept of human navigational habituation which we define as the human habituation to repetitively riding a robotic wheelchair. The approach models habituation in terms of preferred linear velocity based on the experience of riding a wheelchair. We argue that preferred velocity changes as the human gets used to riding on the wheelchair. Inexperienced users initially prefer to ride at a slow moderate pace, however the longer they ride they prefer to speed up to a certain comfort level and find initial slower velocities to be tediously “too slow” for their experience level. The proposed habituation model provides passenger preferred velocity based on experience. Human biological measurements, galvanic skin conductance, and participant feedback demonstrate the preference for habituation velocity control over fixed velocity control. To our knowledge habituation modeling is new in the field of autonomous navigation and robotics.
Automating test cases generation: from xtUML system models to QML test models

The scope of Model-Driven Engineering is not limited to Model-Based Development (MBD), i.e. the generation of code from system models, but involves also Model-Based Testing (MBT), which is the automatic generation of efficient test procedures from corresponding test models. Both MBD and MBT include activities such as model creation, model checking, and use of model compilers for the generation of system/test code. By reusing these common activities, the efficiency of MBT can be significantly improved for those organizations which have already adopted MBD, since one of the major efforts in MBT is the creation of test models. In this work, we propose to exploit modeling activity efforts by deriving test models from system models. In this respect, we present a case study in which the Executable and Translatable UML system models are used for automatically generating test models in the QTronic Modeling Language using horizontal model transformations. In turn, the derived artefacts can be used to produce test cases which result to be appropriate for the system under development.
Improved Generalization by Adding both Auto-Association and Hidden-Layer-Noise to Neural-Network-Based-Classifiers

We propose a novel method for learning that improves generalization in classifiers based on neural networks. The proposed method consists of (1) adding auto-associative learning and (2) simultaneously adding independent noise to the hidden layer of the neural-network. We verify this method with the classification problem of faces under variable illumination. Considering the interpolation for untrained samples as the key aspect of generalization, we expect that in our method, neural-classifiers will (1) learn (nearly) principal components of trained samples by auto-association, and will (2) generate and learn the variated samples from trained samples (along the axes of nearly principal components) by added noise, which leads both to increased amount of trained samples and (hopefully) to improved generalization
Inhibition Accompanies Reference-Frame Selection

Spatial relational terms are ambiguous because they can be defined by different and sometimes conflicting frames of reference. Previous research has suggested that multiple reference frames are simultaneously active before a reference frame is selected. Two experiments examined the on-line selection of a reference frame to determine whether it is assisted by inhibition. These experiments used a negative-priming paradigm in which access to a reference frame was assessed on trial n when that reference frame was either available but not selected or not available on trial n – 1. Significant negative priming was observed; it operated along the axis of the reference frame, encompassing both endpoints. In addition, reference-frame selection seems to be independent of object selection. We cast these findings within the view of negative priming as an inhibitory mechanism, and discuss their implications for the use of spatial relations.
Generation of Subdivision Hierarchies for Efficient Occlusion Culling of Large Polygonal Models

Efficient handling of large polygonal scenes has always been a challenging task. In recent years, view-frustum and occlusion culling have drawn a lot of attention for reducing the complexity of those scenes. The problem of how to efficiently organize such scenes for fast image synthesis is widely neglected, although the answer heavily affects the overall performance. In this paper, we present three novel algorithms for efficient scene subdivision and compare those with another already available algorithm for subdividing general polygonal models into a hierarchy of sub-models for an occlusion culling application. While the latter is available as a commercial product, the three other approaches introduce new algorithms for scene subdivision which achieve significant better results.
Periodontal Proteomics: Wonders Never Cease!

Proteins are vital parts of living organisms, as they are integral components of the physiological metabolic pathways of cells. Periodontal tissues comprise multicompartmental groups of interacting cells and matrices that provide continuous support, attachment, proprioception, and physical protection for the teeth. The proteome map, that is, complete catalogue of the matrix and cellular proteins expressed in alveolar bone, cementum, periodontal ligament, and gingiva, is to be explored for more in-depth understanding of periodontium. The ongoing research to understand the signalling pathways that allow cells to divide, differentiate, and die in controlled manner has brought us to the era of proteomics. Proteomics is defined as the study of all proteins including their relative abundance, distribution, posttranslational modifications, functions, and interactions with other macromolecules, in a given cell or organism within a given environment and at a specific stage in the cell cycle. Its application to periodontal science can be used to monitor health status, disease onset, treatment response, and outcome. Proteomics can offer answers to critical, unresolved questions such as the biological basis for the heterogeneity in gingival, alveolar bone, and cemental cell populations.
A method for web documents clustering based on dynamic concept

Conceptual clustering can be used to solve the lack of domain knowledge or incompleteness. According to the organizational structure and characteristics of web documents subject information, and based on the conceptual clustering techniques, the paper proposes and implementes the dynamic concept clustering algorithm and merge algorithm for web documents clustering, and proves the superiority of these algorithms by analyzing the efficiency and clustering accuracy.
Tracking Control of a Leg Rehabilitation Machine Driven by Pneumatic Artificial Muscles Using Composite Fuzzy Theory

It is difficult to achieve excellent tracking performance for a two-joint leg rehabilitation machine driven by pneumatic artificial muscles (PAMs) because the system has a coupling effect, highly nonlinear and time-varying behavior associated with gas compression, and the nonlinear elasticity of bladder containers. This paper therefore proposes a T-S fuzzy theory with supervisory control in order to overcome the above problems. The T-S fuzzy theory decomposes the model of a nonlinear system into a set of linear subsystems. In this manner, the controller in the T-S fuzzy model is able to use simple linear control techniques to provide a systematic framework for the design of a state feedback controller. Then the LMI Toolbox of MATLAB can be employed to solve linear matrix inequalities (LMIs) in order to determine controller gains based on the Lyapunov direct method. Moreover, the supervisory control can overcome the coupling effect for a leg rehabilitation machine. Experimental results show that the proposed controller can achieve excellent tracking performance, and guarantee robustness to system parameter uncertainties.
A New Configuration of Two-Wheeled Inverted Pendulum: A Lagrangian-Based Mathematical Approach

Abstract— This work presents a novel design of two-wheeled vehicles. The proposed design provides the vehicle with more flexibility in terms of the increased degrees of freedom which enable the vehicle to enlarge its working space. The additional translational degree of freedom (DOF), offered by the linear actuator, assists any attached payload to reach higher levels as and when required. The model of the system mimics the scenario of double inverted pendulum on a moving base. However, it is further complicated due to the addition of a one more (DOF). As adding more degrees of freedom to the system increases the degree of complexity, Lagrangian dynamic formulation is used, due to its relative simplicity, to derive the system dynamics. The new developed configurations is of great importance in various applications including self balance robots, wheelchairs on two wheels, stability analysis of multi segment gaits and multi links cranes etc. In order to maintain the system nonlinear characteristics, the system model is derived with the consideration of the joints friction based on the Coulomb friction model. An investigation is carried out on the impact of the joints damping on the stability of the system. Keywords- Lagrangian formulation, modelling and simulation , double inverted pendulum.
Investigating integration challenges and solutions in global software development

One of the major challenges of Global Software Development (GSD) is associated with overcoming integration problems that remain hidden throughout development and surface at the end of a project. Incompatibilities and other integration complexities can lead to extra costs, delays, lowered quality and even failure of a GSD project. A solid integration strategy can be an effective solution to overcome integration challenges, but this requires a good understanding of what causes failure and how the failures can be mitigated. The aim of this study is to investigate integration problems that occurred in different phases of GSD, and successful integration practices with their relative importance through an extensive literature review and a Delphi survey. As a result, a prioritized list of failure and success factors is obtained. The findings are applicable for planning new projects at an early stage of GSD or improving ongoing projects.
Neutrophils Promote Mycobacterial Trehalose Dimycolate-Induced Lung Inflammation via the Mincle Pathway

Trehalose 6,6′-dimycolate (TDM), a cord factor of Mycobacterium tuberculosis (Mtb), is an important regulator of immune responses during Mtb infections. Macrophages recognize TDM through the Mincle receptor and initiate TDM-induced inflammatory responses, leading to lung granuloma formation. Although various immune cells are recruited to lung granulomas, the roles of other immune cells, especially during the initial process of TDM-induced inflammation, are not clear. In this study, Mincle signaling on neutrophils played an important role in TDM-induced lung inflammation by promoting adhesion and innate immune responses. Neutrophils were recruited during the early stage of lung inflammation following TDM-induced granuloma formation. Mincle expression on neutrophils was required for infiltration of TDM-challenged sites in a granuloma model induced by TDM-coated-beads. TDM-induced Mincle signaling on neutrophils increased cell adherence by enhancing F-actin polymerization and CD11b/CD18 surface expression. The TDM-induced effects were dependent on Src, Syk, and MAPK/ERK kinases (MEK). Moreover, coactivation of the Mincle and TLR2 pathways by TDM and Pam3CSK4 treatment synergistically induced CD11b/CD18 surface expression, reactive oxygen species, and TNFα production by neutrophils. These synergistically-enhanced immune responses correlated with the degree of Mincle expression on neutrophil surfaces. The physiological relevance of the Mincle-mediated anti-TDM immune response was confirmed by defective immune responses in Mincle−/− mice upon aerosol infections with Mtb. Mincle-mutant mice had higher inflammation levels and mycobacterial loads than WT mice. Neutrophil depletion with anti-Ly6G antibody caused a reduction in IL-6 and monocyte chemotactic protein-1 expression upon TDM treatment, and reduced levels of immune cell recruitment during the initial stage of infection. These findings suggest a new role of Mincle signaling on neutrophils during anti-mycobacterial responses.
Ad-Hoc Network Unicast in Time O(log log n) using Beamforming

We investigate the unicast problem for ad-hoc networks in the plane using MIMO techniques. In particular, we use the multi-node beamforming gain and present a self-synchronizing algorithm for the necessary carrier phase synchronization. First, we consider $n$ nodes in a grid where the transmission power per node is restricted to reach the neighboring node. We extend the idea of multi-hop routing and relay the message by multiple nodes attaining joint beamforming gain with higher reception range. In each round, the message is repeated by relay nodes at dedicated positions after a fixed waiting period. Such simple algorithms can send a message from any node to any other node in time $\mathcal{O}(\log \log n - \log \lambda)$ and with asymptotical energy $\mathcal{O}(\sqrt{n})$, the same energy an optimal multi-hop routing strategy needs using short hops between source and target. Here, $\lambda$ denotes the wavelength of the carrier. For $\lambda \in \Theta(1)$ we prove a tight lower time bound of $\Omega(\log \log n)$. Then, we consider $n$ randomly distributed nodes in a square of area $n$ and we show for a transmission range of $\Theta(\sqrt{\log n})$ and for a wavelength of $\lambda = \Omega(\log^{-1/2}n)$ that the unicast problem can be solved in $\mathcal{O}(\log \log n)$ rounds as well. The corresponding transmission energy increases to $\mathcal{O}(\sqrt{n} \log n)$. Finally, we present simulation results visualizing the nature of our algorithms.
Automated Coloring of Panchromatic Orthoimagery

Realistic rendering of outdoor terrain requires both that the geometry of environment be modeled accurately and that appropriate texturing be laid down on top of that geometry. We describe an approach for automatic coloring of panchromatic aerial orthoimagery. The method is able to remove shading and shadowing effects in the original image so that shading and shadowing appropriate to variable times of day and year can be added. The method we present is based on pattern recognition principles. It classiﬁes regions in the original panchromatic orthoimagery into classes that are subsequently colored with user selected color palette. The method requires very little user interaction and is robust. The user only needs to select a few training point for each class that are later used in the pattern recognition and classiﬁcation step. We also present an alternative method that is even simpler and requires no user intervention.
A Dirty Model for Multitask Learning ( Appendices )

The proofs of our three main theorems are in appendices E, F an d G respectively. We collate the machinery needed to prove these in earlier appendices. In the first appendix B, we restate our method for convenience and set up some notation. In the second appendix C, we state the optimality c onditions characterizing the solution of the proposed optimization problem. The next appendix D is an important se c ion of the appendix: it sets out our resume for proving our theorems via a primal-dual certificate construction. Su ch certificate proof techniques have been used in many such high-dimensional analyses [3, 5]. We have to provide a s lightly more delicate construction because of the two interplaying parameter components in our optimization pro blem. Finally, in appendices E-G, we prove theorems 13 respectively by showing that the primal-dual witness cons truction succeeds under the conditions of the respective theorems. Appendix H describe our experimental setup and re sults in detail. Finally, Appendix I details the coordinate descent algorithm used for solving the dirty model optimiza tion.
How Consumers Respond to Environmental Certification and the Value of Energy Information

The ENERGY STAR certification is a voluntary labeling that favors the adoption of energy efficient products. In the US appliance market, the label is a coarse summary of otherwise readily accessible information. Using micro-data of the US refrigerator market, I develop a structural demand model and find that consumers respond to certification in different ways. Some consumers have a large willingness to pay for the label, well beyond the energy savings associated with certified products; others appear to pay attention to electricity costs, but not to the certification, and still others appear to be insensitive to both electricity costs and ENERGY STAR. The findings suggest that the certification acts as a substitute for more accurate, but complex energy information. Using the structural model, I find that the opportunity cost of having imperfectly informed consumers in the refrigerator market ranges from $12 to $17 per refrigerator sold.
Application of a hybrid process software requirements management

Software requirements management is essential to achieve expected levels of productivity and quality in software development. This paper proposes to apply this process a governamental institution, by proposing a standardized methodology of requirements management, adapted to the needs of the institution, to improve productivity and quality. Therefore we analysed the traditional method of software-development, with emphasis on the Rational Unified Process (RUP) and agile methods such as Scaled Agile Framework process (SAFe), in order to propose a hybrid method for requirements management. Interviews, technical applications to establish the context, literature searches on documents, standards and best practices were held in order to validate the hybrid approach of the proposed methodology that was applied to a case study.
Precision measurements, extra quark-lepton generations and 50 GeV neutrinos

The existence of extra chiral generations with all fermions heavier than $m_Z$ is strongly disfavored by the precision electroweak data. However the data still allow a few extra generations if the neutral leptons have masses close to 50 GeV. Such heavy neutrino can be searched in the reaction $e^+ e^- \to N\bar{N}\gamma$. Existence of 50 GeV neutrinos makes standard model Higgs boson invisible.
Wireless Mesh Networking for In-Network Processing of Low-Cost GPS Devices

In this paper, we explore the use of wireless mesh networks (WMN) for the purpose of exchanging GPS raw data to support in-network processing of low-cost GPS devices. A proof of concept deployment with a small number of GPS stations is studied. This experiment validates the feasibility of using WMN to enable in-network processing in a dense GPS network. Experimental results demonstrate a significant reduction in station location errors when epoch-by- epoch processing method is used.
Robust object tracking via incremental subspace dynamic sparse model

Sparse representation has been widely applied to some generative tracking methods. However, these methods do not consider the correlation between sparse representation coefficients in the time domain. In this paper, we propose a novel incremental subspace dynamic sparse tracking (ISDST) model with the error term of Gaussian-Laplacian distribution, which fully considers the correlation of object representations between consecutive frames by compressive sensing, and can effectively handle the occlusion in scenes. Next, the outlier entries, especially caused by the occlusion, have some group effect, so we adopt the spatial structured sparse via l1, 2 mixed norms instead of the original l1 sparse items. In addition, since the occlusion changes is very little between consecutive frames, we maintain an occlusion mask and eliminate the influence of occlusion pixels in the process of calculating the likelihood probability. Extensive experiments on challenging sequences demonstrate that our method consistently outperforms existing state-of-the-art methods.
Boundedness properties of nonlinear quasi-dissipative systems

Boundedness results for feedback interconnections of quasi-dissipative systems are presented. A classical result due to Hill and Moylan is generalized to the case of finite power gain stability of feedback interconnection of quasi-dissipative systems. It is also shown that under the assumption of strong finite-time detectability, such an interconnection has uniformly ultimately bounded trajectories for any uniformly bounded input.
Molecular Characterization of Circulating Plasma Cells in Patients with Active Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a generalized autoimmune disease characterized by abnormal B cell activation and the occurrence of increased frequencies of circulating plasma cells (PC). The molecular characteristics and nature of circulating PC and B cells in SLE have not been completely characterized. Microarray analysis of gene expression was used to characterize circulating PC in subjects with active SLE. Flow cytometry was used to sort PC and comparator B cell populations from active SLE blood, normal blood and normal tonsil. The gene expression profiles of the sorted B cell populations were then compared. SLE PC exhibited a similar gene expression signature as tonsil PC. The differences in gene expression between SLE PC and normal tonsil PC and tonsil plasmablasts (PB) suggest a mature Ig secreting cell phenotype in the former population. Despite this, SLE PC differed in expression of about half the genes from previously published gene expression profiles of normal bone marrow PC, indicating that these cells had not achieved a fully mature status. Abnormal expression of several genes, including CXCR4 and S1P1, suggests a mechanism for the persistence of SLE PC in the circulation. All SLE B cell populations revealed an interferon (IFN) gene signature previously only reported in unseparated SLE peripheral blood mononuclear cells. These data indicate that SLE PC are a unique population of Ig secreting cells with a gene expression profile indicative of a mature, but not fully differentiated phenotype.