Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration

The demands for accurate positioning and navigation applications in complex indoor environments such as emergency call positioning, fire-fighting services, and rescue operations are increasing continuously. Indoor positioning approaches apply different types of sensors to increase the accuracy of the user’s position. Among these technologies, Bluetooth Low Energy (BLE) appeared as a popular alternative due to its low cost and energy efficiency. However, BLE faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations caused by human body shadowing. This work presents a method to compensate RSSI values by applying Artificial Neural Network (ANN) algorithms to RSSI measurements from three BLE advertising channels and a wearable camera as an additional source of information for the presence or absence of human obstacles. The resulting improved RSSI values are then converted into ranges using path loss models, and trilateration is applied to obtain indoor localization. The proposed artificial system provides significantly better localization solutions than fingerprinting or trilateration using uncorrected RSSI values.
Correlation of Y Balance with Clinical Scores and Functional Tests after Anterior Cruciate Ligament Reconstruction in Young and Middle-Aged Patients

Background Criteria for return to sports (RTS) following anterior cruciate ligament (ACL) reconstruction have been extensively studied. But there is no consensus among investigators regarding which factors are most important in determining a safe RTS. Methods Sixty-one patients who underwent ACL reconstruction were included. Subjective knee scoring systems (International Knee Documentation Committee [IKDC] score and Lysholm score), functional performance tests (carioca test and single-leg hop for distance [SLHD] test), and isokinetic knee strength test were used for assessment and analyzed for association with the limb symmetry index (LSI) of the Y-balance test for lower quarter (YBT-LQ). Results The LSI of the YBT-LQ was significantly correlated with Lysholm score, IKDC score, Carioca, LSI for the SLHD, and extensor strength deficit at 6 months after ACL reconstruction. At 12 months, Lysholm score, IKDC score, LSI for the SLHD, and extensor strength deficit were significantly correlated with the LSI of the YBT-LQ. Conclusions The YBT-LQ test could be used conveniently as an additional tool to assess the patient’s functional performance results after ACL reconstruction in outpatient clinics.
Relaxing Convergence Conditions To Improve The Convergence Rate

Standard global convergence proofs are examined to determine why some algorithms perform better than other algorithms. We show that relaxing the conditions required to prove global convergence can improve an algorithm's performance. Further analysis indicates that minimizing an estimate of the distance to the minimum relaxes the convergence conditions in such a way as to improve an algorithm's convergence rate. A new line-search algorithm based on these ideas is presented that does not force a reduction in the objective function at each iteration, yet it allows the objective function to increase during an iteration only if this will result in faster convergence. Unlike the nonmonotone algorithms in the literature , these new functions dynamically adjust to account for changes between the innuence of curvature and descent. The result is an optimal algorithm in the sense that an estimate of the distance to the minimum is minimized at each iteration. The algorithm is shown to be well deened and we prove global convergence of the algorithm. Performance of the algorithm on some standard test functions is presented to illustrate the features of the algorithm. iii This abstract accurately represents the content of the candidate's thesis. I recommend its publication. iv ACKNOWLEDGMENTS I would like to acknowledge Marathon Oil Company for its generous support throughout my pursuit of this degree. I would also like to express my sincerest gratitude to my wife Lois and daughter Michelle for their endless patience and support. Without them this thesis would not have been possible. I am indebted to Dr. H. J. Greenberg whose enthusiasm and knowledge of mathematics in general and optimization in particular have been truly inspiring. I would also like to thank
‘It’s Nothing Serious I Suppose’ Family Help-Seeking for Adolescent Social Anxiety

This article investigates how families seek professional supports for adolescent social anxiety. Many adolescents meet diagnostic criteria for social anxiety disorder but do not access professional mental health supports. Access to timely interventions is important because social anxiety has a range of developmental implications and typically persists into adulthood when untreated. Twelve adolescents (aged 14 to 18) who had attended mental health services for support with social anxiety and 13 mothers of such adolescents participated in semi-structured interviews which were analysed thematically. Findings indicated that mothers play an important role in noticing difficulties and initiating help-seeking for their children, although adolescents and their mothers can initially view anxiety as ‘just shyness’ and often seek help for a range of difficulties, including unhappiness, rather than anxiety specifically. Furthermore, many adolescents described experiencing help-seeking as embarrassing or shameful. Implications for facilitating families to access professional supports for adolescents are discussed.
AGE-TIME DISCONTINUOUS GALERKIN METHOD FOR THE LOTKA-MCKENDRICK EQUATION

The Lotka-McKendrick equation which describes the evolution of a single population under the phenomenological conditions is developed from the well-known Malthus’model. In this paper, we introduce the Lotka-McKendrick equation for the description of the dynamics of a population. We apply a discontinuous Galerkin finite element method in age-time domain to approximate the solution of the system. We provide some numerical results. It is experimentally shown that, when the mortality function is bounded, the scheme converges at the rate of in the case of piecewise linear polynomial space. It is also shown that the scheme converges at the rate of when the mortality function is unbounded.
Least Cost Tolerance Allocation for Mechanical Assemblies with Automated Process Selection

The allocation of tolerances among the components of a mechanical assembly can significantly affect the resulting manufacturing costs. If cost versus tolerance data are available for each dimension, the least cost tolerance allocation may be determined by optimization techniques. However, when alternate manufacturing processes are available for some of the components, a discrete optimization problem results. An exhaustive search of all possible combinations of processes will determine the global optimum, but the number of combinations increases geometrically, becoming very large for assemblies of only moderate complexity. Several methods were tested for systematically searching for the minimum cost process set for an assembly. Both discrete and continuous optimization schemes were compared to an exhaustive search, based on CPU times and the number of combinations required to find the global optimum.
Majority-Logic-Based Self-Checking Adder in Quantum-Dot Cellular Automata

<italic>Editor’s notes:</italic> Quantum-dot cellular automata (QCA), as one of the proposed improvements on conventional CMOS, has its own reliability challenges. In this work, a majority-logic-based self-checking feature is proposed as an addition to adders in QCA to increase fault tolerance. <italic>—Fei Su, Intel</italic>
Synthesis and characterization NS-reduced graphene oxide hydrogel and its electrochemical properties

Developing materials with good electrochemical performance is critical in energy storage applications. One of the promising materials for these applications is reduced graphene oxide (rGO) based materials. Utilizing thiourea as a nitrogen (N) and sulfur (S) source, we present a simple hydrothermal approach for simultaneous doping of nitrogen and sulfur into the rGO hydrogel structure. The visual photograph shows the hydrogel form of the sample. XRD and Raman analysis shows the carbon structural changes during the reduction process. The presence of N and S atoms which spread evenly on the hydrogel structure, was confirmed by energy-dispersive x-ray (EDX) mapping. A cyclic voltammetry measurement at a current density of 0.5 A / g reveals that the NS-rGOH sample has a high specific capacity of 750 C / g. Even at a current density of 10 A / g, it can maintain outstanding charge-discharge stability, with 83.3 % of the initial capacity preserved after 1000 charge-discharge cycles. Moreover, EIS analysis reveals that the low charge transfer resistance and high ionic diffusivity of the rGO hydrogel sample lead to good electrochemical performance. NS doping into the rGOH structure improves the sample's electrochemical performance compared to the undoped sample.
Optimized deep learning‐based intrusion detection for wireless sensor networks

The technological innovations and wide use of Wireless Sensor Network (WSN) applications need to handle diverse data. These huge data possess network security issues as intrusions that cannot be neglected or ignored. An effective strategy to counteract security issues in WSN can be achieved through the Intrusion Detection System (IDS). IDS ensures network integrity, availability, and confidentiality by detecting different attacks. Regardless of efforts by various researchers, the domain is still open to obtain an IDS with improved detection accuracy with minimum false alarms to detect intrusions. Machine learning models are deployed as IDS, but their potential solutions need to be improved in terms of detection accuracy. The neural network performance depends on feature selection, and hence, it is essential to bring an efficient feature selection model for better performance. An optimized deep learning model has been presented to detect different types of attacks in WSN. Instead of the conventional parameter selection procedure for Convolutional Neural Network (CNN) architecture, a nature‐inspired whale optimization algorithm is included to optimize the CNN parameters such as kernel size, feature map count, padding, and pooling type. These optimized features greatly improved the intrusion detection accuracy compared to Deep Neural network (DNN), Random Forest (RF), and Decision Tree (DT) models.
Distributional semantic phrases vs. semantic distributional nonsense: Adjective Modification in Compositional Distributional Semantics

In this thesis, I study the ability of compositional distributional semantics to model adjective modification. I present three studies that explore the degree to which semantic intuitions are grounded in the distributional representations of adjective-noun phrases, as well as provide insight into various linguistic phenomena by extracting unsupervised cues from these distributional representations. First, I investigate degrees of adjective modification. I contrast three types of adjectival modifiers – intersectively used color terms, subsectively used color terms, and intensional adjectives – and test the ability of different composition strategies to model their behavior. Next, I propose an approach to characterize semantic deviance of composite expressions using distributional semantic methods. I present a set of simple measures extracted from distributional representations of words and phrases, and show that they are more significant in determining the acceptability of novel adjective-noun phrases than measures classically employed in studies of compound processing. Finally, I use compositional distributional semantic methods to investigate restrictions in adjective ordering. Specifically, I focus on properties distinguishing adjective-adjective-noun phrases in which there is flexibility in the adjective ordering from those bound to a rigid order. I explore a number of measures extracted from the distributional representation of such phrases which may indicate a word order restriction. Overall, this work provides strong support for compositional distributional semantics, as it is able to generalize and capture the complex semantic intuition of natural language speakers for adjective-noun phrases, even without being able to rely on co-occurrence relations between the constituents.
Diversity and genetic structure of natural populations of the palm tree Euterpe precatoria (Arecaceae)

Introduction: The natural ecosystems of northern Mato Grosso, Brazil, are in process of fragmentation, mainly due to population growth and the expansion of agriculture. This endangers the palm Euterpe precatoria (locally known as açaí), used for construction, palm hearts, juices and ice cream. Objective: To evaluate the local diversity and genetic structure in native populations of E. precatoria. Methods: We collected leaves from 106 fruiting palms from five populations in Mato Grosso State, for analysis of microsatellite markers with Polymerase Chain Reaction. Results: The five SSR loci revealed a total of 30 alleles, ranging from 5 (EE23 and EE43) to 7 (EE2 and EE15), with an average of 6 alleles per locus. The mean PIC was 0.74 and confirmed low heterozygosity and inbreeding. The UPGMA dendrogram produced two groups and molecular variance revealed greater genetic differentiation within populations. The high levels of homozygous microsatellite loci indicate low genetic diversity. Conclusions: These populations have low gene diversity, high average number of alleles per locus, and rare and exclusive alleles. We recommend the establishment of permanent conservation units with corridors among them.
Diffusion and clustering on large graphs

This dissertation studies two important algorithmic problems on networks: graph diffusion and clustering. These problems are closely related: bottlenecks that diffusive processes find difficult to cross make good cluster boundaries and vice versa. First, we give an efficient global partitioning algorithm specifically tailored for a graph-theoretic setting. It uses sampling with PageRank vectors as probability distributions, optimizing new PageRank-distance metrics that depend only on the jumping constant α. Once k centers are found, we give a graph drawing algorithm that uses a force-based layout, with spring constants determined by PageRank vectors, that highlights clustered structure. We study graph clustering in two additional contexts. The first is with subsets of communication networks that have imposed boundaries when the topologies reach private subnetworks. We empirically show that spectral clustering using Dirichlet eigenvectors instead of the usual eigenvectors can be more suitable. Second, we study the extended planted partition model, a random-graph model that starts with a predetermined partitioning of the vertices with arbitrary expected degrees. We give a spectral clustering algorithm to recover the planted partition under certain conditions. Our algorithm uses a new matrix, the degree-corrected random-walk Laplacian, and unlike some prior work, does not assume knowledge of any graph generation parameters. Next, we propose and study Dirichlet PageRank, or PageRank vectors with arbitrary boundary conditions, to address vertex ranking problems resulting from the propagation of trust and distrust in networks. Using Dirichlet PageRank, we can compute vertex rankings in the presence of known spammers and negatively-weighted edges. We also study a diffusive network epidemic model: the contact process. We show that network epidemics can by stopped by finding a local cluster around the infection's starting points and inoculating the vertices in that cluster with antidote proportional to a personalized PageRank vector. This is more efficient than prior work which required distributing antidote widely to the entire graph. Finally, we propose a new voter model for the evolution of electoral opinions in social networks. Unlike the traditional voter model, our model operates on hypergraphs, allowing for more complex interactions, and it yields a wider range of outcomes.
Can propolis and their compounds be efficacy in the treatment of coronavirus disease 2019 (COVID-19)? A systematic review

Despite the advancement of vaccination and the reduction in the number of deaths, there is still the emergence of new variants, such as the omicron of SARS-CoV-2 (COVID-19). In this sense, new natural antiviral therapies are highly explored. One of these products, propolis, have shown promising results against COVID-19, including the inhibition of the binding between the coronavirus and ACE2. This systematic review aimed to gather a summary of scientific evidence existing on the effective of the therapeutic use of propolis and their components in the treatment of COVID-19. The protocol for the present systematic review was registered on the PROSPERO (CRD42021267016). In this study, we analyzed 185 articles, selecting 13 of them. Some phenolic compounds and flavonoids, such as artepillin C, hesperetin, CAPE and rutin, were widely cited, as they have great potential for binding with the molecular targets of SARS-CoV-2. Some clinical studies that evaluated the effects of propolis against COVID-19 were included, and confirmed the effectiveness of propolis and its components. The results of this review demonstrate the effectiveness of using propolis and their components in the treatment of COVID-19 due to its antiviral activities. Additionally, the anti-inflammatory and immunomodulatory properties can help to patients with COVID-19.
On GDM's: Geometrically Deformed Models for the Extraction of Closed Shapes from Volume Data

The advent of nondestructive sensing equipment (CT, MRI) created an entirely new eld of research for image engineers. The equipment generates a point sampling of a true three-dimensional object. Typically, this point sampling is presented as a series of slices through the 3D object. It has become apparent, however, that displaying individual slices does not lend itself to conveying the true three-dimensional structure of the scanned object. Research, therefore, has focused on alternative methods of presenting volume data. This thesis proposes an approach that will generate a topologically closed simple geometric model of an object within a scalar eld. A Geometrically Deformed Model, GDM, is created by placing an initial simple model in the data set which is then deformed by minimizing a set of constraints. The constraint functions evaluated at each vertex in the model control the local deformation, the interaction between the model and the data set, and maintain the shape and topology of the model. Once generated, a GDM can be used for visualization, shape recognition, geometric measurements, or subjected to a series geometric operations. viii Chapter
Hematological, Serum Biochemical and Parasitological investigation of calf diarrhea

This study aimed to isolate the most common pathogens causing diarrhea in calf in addition to studying the hematological, biochemical, and fecal changes associated with the disease. The study was conducted on 44 calves, 31 diarrheadiseased cases and 13 animals were apparently healthy and were served as control. Hematological, biochemical, and fecal changes were investigated through examining whole blood, serum and fecal samples collected from both healthy and diseased groups. The results implicated that Eimeria and Toxocara were the most common parasitic pathogens isolated from both healthy and diseased animals. Hematological parameters showed a significant increase in red cell parameters including red cell count, hemoglobin, and packed cell volume as well as total leukocytic counts in the diseased group compared to control. Significant increases were observed in the serum enzymatic activities of ALT, AST, ALP GGT, amylase and lipase. Serum concentrations of glucose, urea, BUN, creatinine were significantly increased, whereas serum levels of total protein, albumin, triglyceride, Na, K and Cl showed a significant decrease. No significant changes were observed in blood pH and bicarbonate levels in the diarrheic animals when compared to control group. We can conclude that Eimeria and Toxocara appeared to be the main parasitic causes of diarrhea in calf. The disease is associated with significant hematological, and biochemical alterations which upon understanding can provide a good knowledge of the pathogenesis and thus controlling the condition in calf.
Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review

As a practical and effective tool for quantitative in-plane deformation measurement of a planar object surface, two-dimensional digital image correlation (2D DIC) is now widely accepted and commonly used in the field of experimental mechanics. It directly provides full-field displacements to sub-pixel accuracy and full-field strains by comparing the digital images of a test object surface acquired before and after deformation. In this review, methodologies of the 2D DIC technique for displacement field measurement and strain field estimation are systematically reviewed and discussed. Detailed analyses of the measurement accuracy considering the influences of both experimental conditions and algorithm details are provided. Measures for achieving high accuracy deformation measurement using the 2D DIC technique are also recommended. Since microscale and nanoscale deformation measurement can easily be realized by combining the 2D DIC technique with high-spatial-resolution microscopes, the 2D DIC technique should find more applications in broad areas.