Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Coordination of Distinct Motor Learning Strategies: Latent Neurofunctional Mechanisms Elucidated via Computational Modeling

In this dissertation, a neurofunctional theory of learning is presented as an extension of functional analysis. This new theory clarifies the distinction— via applied quantitative analysis— between functionally intrinsic (essential) mechanistic structures and irrelevant structural details. This thesis is supported by a review of the relevant literature to provide historical context and sufficient scientific background. Further, the scope of this thesis is elucidated by two questions that are posed from a neurofunctional perspective— (1) how can specialized neuromorphology contribute to the functional dynamics of neural learning processes? (2) Can large-scale neurofunctional pathways emerge via inter-network communication between disparate neural circuits? These questions motivate the specific aims of this dissertation. Each aim is addressed by posing a relevant hypothesis, which is then tested via a neurocomputational experiment. In each experiment, computational techniques are leveraged to elucidate specific mechanisms that underlie neurofunctional learning processes. For instance, the role of specialized neuromorphology is investigated via the development of a computational model that replicates the neurophysiological mechanisms that underlie cholinergic interneurons’ regulation of dopamine in the striatum during reinforcement learning. Another research direction focuses on the emergence of large-scale neurofunctional pathways that connect the cerebellum and basal ganglia— this study also involves the construction of a neurocomputational model. The results of each study illustrate the capability of neurocomputational models to replicate functional learning dynamics of human subjects during a variety of motor adaptation tasks. Finally, the significance— and some potential applications— of neurofunctional theory are discussed. INDEX WORDS: Motor learning, Motor adaptation, Neurofunction, Basal ganglia, Cerebellum, Reinforcement learning, Model-free learning, Model-based learning, Striatal cholinergic interneurons, Tonically active neurons, Biomechanics, Neurophysics, Computational neuroscience NEURAL COORDINATION OF DISTINCT MOTOR LEARNING STRATEGIES: LATENT NEUROFUNCTIONAL MECHANISMS ELUCIDATED VIA COMPUTATIONAL MODELING
Explaining support for mandatory versus voluntary conservation actions among waterfowlers

ABSTRACT Personal conservation behavior and compliance with natural resource regulations are important to wildlife conservation. We examined how waterfowl-hunting involvement, motivations, satisfaction, and experience, along with institutional trust and demographics, correlated with support for waterfowl regulations and personal conservation actions. Regulations included zones, split seasons, and motorized decoys, while conservation behaviors addressed hunter recruitment, along with donations, volunteering, and voting in ways to support wildlife conservation. Results suggested that agency trust was positively related to support for regulations but negatively related to personal conservation behaviors. An increased orientation to harvest waterfowl was negatively related to both support for regulations and conservation behaviors. Education, income, Ducks Unlimited membership, and days hunting were positively related to personal conservation behavior. Results may help managers work cooperatively with hunters and conservation groups to support wildlife conservation.
A Compact High-Gain Wideband Lens Vivaldi Antenna for Wireless Communications and Through-the-Wall Imaging

Compact high-gain wideband antenna systems for through-the-wall imaging and wireless communications, featuring 160% fractional bandwidth, are presented. Each radiating system, consisting of two orthogonal Vivaldi antennas forming a cross-shaped configuration so as to excite linear (horizontal/vertical) and circular polarization (CP), is equipped with a top-mount spherical–axicon dielectric lens. The optimized shaping of the lens and of the outer edge Vivaldi arms allow achieving gain levels exceeding 15 dBi with good front-to-back-ratio. The proposed antenna features a broadside gain diagram with stable radiation pattern and wideband impedance matching in the frequency range between 650 MHz and 6 GHz. CST Microwave Studio, implementing a full-wave locally conformal finite integration technique (FIT), was employed to design and characterize the antenna and to guide its physical realization. Two-port equivalent circuits were developed to characterize parasitic coupling effects between the Vivaldi antennas ports, while a full-wave analysis allowed characterizing their frequency- and time-domain behaviors. The numerical results concerning the antennas parameters, illustrating the suitability of the proposed antennas to support narrow and ultra-wideband signal waveforms, are found to be in good agreement with the experimental measurements performed on an antenna prototype.
An Occurrence Description Logic

Description Logics (DLs) are a family of well-known terminological knowledge representation formalisms in modern semantics-based systems. This research focuses on analysing how our developed Occurrence Logic (OccL) can conceptually and logically support the development of a description logic. OccL is integrated into the alternative theory of natural language syntax in Deviational Syntactic Structures under the label ‘EFA(X)3’ (or the third version of Epi-Formal Analysis in Syntax, EFA(X), which is a radical linguistic theory). From the logical point of view, OccL is a formal logic that mainly deals with the occurrences of symbols as well as with their priorities within linguistic descriptions, i.e. natural language syntax, semantics and phonology. In this article—based on our OccL-based definitions of the concepts of strong implication and occurrence value as well as of the logical concept Identical Occurrence Constructor (IDOC) that is the most fundamental logical concept in our formalism—we will model Occurrence Description Logic (ODL). Accordingly, we will formally-logically analyse ‘occurrence(s) of symbol(s)’ within descriptions of the world in ODL. In addition, we will analyse and assess the logical concepts of occurrence and occurrence priority in ODL. This research can make a strong logical background for our future research in the development of a Modal Occurrence Description Logic.
Blockchain based End-to-end Tracking System for Distributed IoT Intelligence Application Security Enhancement

IoT devices provide a rich data source that is not available in the past, which is valuable for a wide range of intelligence applications, especially deep neural network (DNN) applications that are data-thirsty. An established DNN model provides useful analysis results that can improve the operation of IoT systems in turn. The progress in distributed/federated DNN training further unleashes the potential of integration of IoT and intelligence applications. When a large number of IoT devices are deployed in different physical locations, distributed training allows training modules to be deployed to multiple edge data centers that are close to the IoT devices to reduce the latency and movement of large amounts of data. In practice, these IoT devices and edge data centers are usually owned and managed by different parties, who do not fully trust each other or have conflicting interests. It is hard to coordinate them to provide end-to-end integrity protection of the DNN construction and application with classical security enhancement tools. For example, one party may share an incomplete data set with others, or contribute a modified sub DNN model to manipulate the aggregated model and affect the decision-making process. To mitigate this risk, we propose a novel blockchain based end-to-end integrity protection scheme for DNN applications integrated with an IoT system in the edge computing environment. The protection system leverages a set of cryptography primitives to build a blockchain adapted for edge computing that is scalable to handle a large number of IoT devices. The customized blockchain is integrated with a distributed/federated DNN to offer integrity and authenticity protection services.
Abrasion impact towards green turtle Chelonia mydas (Linnaeus, 1758) nesting areas in Sindangkerta, Tasikmalaya Regency, West Java, Indonesia

Green turtles (Chelonia mydas) are marine reptilians that have habitats in coastal areas to lay eggs. Abrasion is a phenomenon of beach erosion caused by waves and ocean currents which can cause damage to the coast. The purpose of this research is to analyze the impact of abrasion on green turtle’s nesting areas (Chelonia mydas). The research was conducted in the coasts of Sindangkerta, Tasikmalaya Regency, West Java, from December 2019 to January 2020. The method used in this research is observation and survey method, and the data are analyzed comparatively and descriptively. The data used consist of satellite imagery, tide, turtle’s landing, and the characteristics of turtle nesting areas in Sindangkerta coast in the year 1999, 2013, and 2019. The results showed that abrasion changes the condition of the Green Turtle (Chelonia mydas) nesting areas which led to a 40.09 m decrease in beach width, 2.04 ̊ decrease in beach slope, 15.51% increase in sand (fine-medium), and a loss of several coastal vegetation species.
Study of the Physiological Behavior of Both Varieties of Bread Wheat (Triticum aestivum L) Subjected to a Metallic Stress of the Cadmium

: The cadmium is one of the metallic element traces very toxic for vegetables, animals and human. Its presence in the soil is aggravated by the various cultural practices. This study is to test the effect of cadmium on the physiological parameters and its accumulation in two varieties of bread wheat ( Triticum aestivum L). The relative growth of seedlings, the rate of chlorophyll a and b, the proline, soluble sugars and rate of accumulation of Cadmium are measured under four concentrations of Cd staggered from 0 to 150mg/l and tested on the varieties Arz and Hiddab. Results show an important reduction of the relative growth of the seedlings of both varieties but in a different way. A disturbance of the various physiological processes is observed a decrease of the photosynthesis gradually with the increase of the dose in cadmium on the chlorophyll a and b compared with the control, increase of the rates of the proline, the soluble sugars and the rate of cadmium in seedlings following the increase in concentration of the metal study. The Arz variety is more accumulating than Hiddab. The presence of the cadmium in the environment causes a physiological disturbance of the processes and affects dramatically the production of the biomass.
Dynamic Service Function Chain Orchestration for NFV/MEC-Enabled IoT Networks: A Deep Reinforcement Learning Approach

Network function virtualization (NFV) and mobile-edge computing (MEC) have been introduced by Internet service providers (ISPs) to deal with various challenges, which hinder them from satisfying ambitious quality of experience demands of the Internet-of-Things (IoT) applications. In NFV/MEC-enabled IoT networks, any IoT service can be expressed as a service function chain (SFC) consisting of several strictly ordered virtual network functions (VNFs), which can be geographically placed onto edge clouds close to IoT terminals. However, regarding the large number of IoT terminals and constant dynamics of IoT networks, determining the placement of VNFs and routing service paths that optimize the end-to-end delays in the hybrid edge clouds is a challenging problem. This problem is also called SFC dynamic orchestration problem (SFC-DOP). To address the SFC-DOP, we are motivated to creatively present an SFC dynamic orchestration framework for IoT deep reinforcement learning (DRL). Also, a DRL-based algorithm for SFC-DOP with the actor–critic and the deterministic policy gradient scheme is provided, which can efficiently deal with the SFC-DOP in IoT networks. The obtained experimental results show the outstanding performance of our proposal compared with the current benchmarks.
Fault Analysis of Electrical Machine Drives Employing Novel Model Predictive Control

Electrical machine drives play a crucial role in various industrial applications. Therefore, its control system design became more significant in the last decades. Numerous high powers and high-efficiency machine drives require faultless continuous operation. Fault-tolerant control is a productive solution for the improvement of the reliability of the machine drives. Model Predictive Control (MPC) is an optimal control algorithm developed for constrained control of Multi-Input-Multi-Output (MIMO) systems. MPC can handle MIMO systems and can incorporate several constraints in the form of equalities and inequalities. A Novel Model Predictive Control(NMPC) method for a Synchronous Servo Motor Drive (SSMD) integrating a real-time fault diagnostic method for Insulated Gate Bipolar Transistor ((IGBT) faults in an inverter has been presented in this paper. NMPC ensures the system's better performance and minimal fault clearance time.
Deep Multiview Learning for Hyperspectral Image Classification

Recently, the field of hyperspectral image (HSI) classification is dominated by deep learning-based methods. However, training deep learning models usually needs a large number of labeled samples to optimize thousands of parameters. In this article, a deep multiview learning method is proposed to deal with the small sample problem of HSI. First, two views of an HSI scene are constructed by applying principal component analysis to different bands. Second, a deep residual network is designed to embed the different views of a sample to a latent space. The designed deep residual network is trained by maximizing agreement between differently augmented views of the same data sample via a contrastive loss in the latent space. Note that the training procedure of the designed deep residual network does not use labeled information. Therefore, the proposed method belongs to the category of unsupervised learning, which could alleviate the lack of labeled training samples. Finally, a conventional machine learning method (e.g., support vector machine) is used to complete the classification task in the learned latent space. To demonstrate the effectiveness of the proposed method, extensive experiments are carried on four widely used hyperspectral data sets. The experimental results demonstrate that the proposed method could improve the classification accuracy with small samples.
Explainable Machine Learning Prediction of PCB-138 Behavior Patterns in Edible Fish from Croatian Adriatic

Fish consumption, especially consumption of oily marine species, is globally increasing since it has been recommended by dieticians due to high content of polyunsaturated ω-3 and ω-6 (PUFAs) fatty acids in fish tissue. Health benefits of PUFA ingestion coincide with the risk of intake of hazardous lipophilic persistent pollutants including organochlorine pesticides (OCPs) and related polychlorinated biphenyls (PCBs). We examined the impacts of 18 fatty acids (FAs) and 36 toxic organic and inorganic contaminants on the behavior patterns of indicator congener PCB-138 in marine fish using eXtreme Gradient Boosting (XGBoost), SHapley Additive exPlanations (SHAP), and SHAP value fuzzy clustering. XGBoost indicated non-linear relationships between investigated variables that surpasses indications suggested by commonly applied correlation matrices. Ten extracted fuzzy clusters of SHAP values revealed that higher intake of saturated myristic-C14:0 and margaricC17:0 acids followed by intake of nutritionally beneficial eicosadienoic acid (C20:2n-6) mostly contributed to the PCB-138 bioaccumulation. Important impacts on PCB-138 behavior patterns were also registered for chemically allied indicator congeners (-153 and -180) and organochlorines’ metabolite p,p’-DDE. Less prominent were the associations between target congener and the most toxic dioxin-like PCBs.
Applications of functional fullerene materials in polymer solar cells

Bulk heterojunction (BHJ) polymer solar cells (PSCs) on the basis of polymer:fullerene blends have delivered numerous impressive results in the last decade and thus have drawn much attention of the scientific and industrial communities. However, BHJ PSCs often suffer from several intrinsic problems, such as the low open-circuit voltage (Voc), energetically-unfavorable and chemically-incompatible interfaces, and morphological phase separation of the polymer:fullerene blend driven by thermal treatment, hindering further advancement. In this review, we summarize the recent progress on applications of fullerene-based materials in the BHJ PSCs. Bis-adduct fullerenes possessing high-lying LUMO energy levels can substitute for mono-substituted fullerenes and higher Voc values can thus be achieved. Incorporation of an n-type fullerene interlayer between inorganic and organic active layers in either inverted or conventional PSCs can help in minimization of charge recombination losses at the interface and improvement of charge transfer from the active layer to the inorganic layer, thus resulting in superior PCEs. The optimal active-layer morphology can be maintained through several approaches, such as in situ polymerization of cross-linkable fullerenes, suppression of large-scale PC61BM aggregation by a polyethylene glycol-functionalized fullerene, supramolecular pentafluorophenyl-fullerene stabilization, and light-induced oligomerization of fullerenes. The morphological phase separation of the active-layer materials can thus be minimized. The research of employing fullerene materials will continue playing a critical role in BHJ PSCs. With further investigation and advancement in this field, it is expected that the ultimate goal of commercialization can be realized.
Laboratory Evaluation of the Marking Effect of Sudan Red 7B on Subterranean Termites (Reticulitermes speratus) in Republic of Korea

1) The dye marker Sudan Red 7B was tested as an effective and appropriate marking method for searching the territory of the termite, Reticulitermes speratus, in Korea. The sensitivity of termites to the dye marker was evaluated by measuring the feed consumption amount, survival rate, retention time, and transfer efficiency of R. speratus according to the concentration and feeding period of Sudan Red 7B. As the concentration conditions of dye marker increase, Sudan Red 7B remarkably reduced the feed consumption of the termites. The survival rate was not significantly different from that of the control group (more than 90%), except when feed containing 1% or 2% concentrations of the dye marker was given for five days. The duration of the marking effect persisted for seven days, even at a concentration of 2% dye marker in the feed. No dye transfer by trophallaxis occurred between termites marked with Sudan Red 7B and unmarked termites.
A State-based Refinement Technique for Event-B

Formal models can be used to describe and reason about the behavior and properties of a given system. In some cases, it is even possible to prove that the system satisfies the given properties. This allows detecting design errors and inconsistencies early and fixing them before starting development. Such models are usually created using stepwise refinement: starting with the simple, abstract model of the system, and then incrementally refining it adding more details at each subsequent level of refinement. Top levels of the model usually describe the high-level design or purpose of the system, while the lower levels are more directly comparable with the implementation code. In this paper, we present a new, alternative refinement technique for Event-B which can simplify the development of complicated models with a large gap between high-level design and implementation.
Entropy-Based Drowsiness Detection Using Adaptive Variational Mode Decomposition

Background: Drivers drowsiness is one of the prime reasons for road accidents. Electroencephalogram (EEG) signals provide crucial information regarding drowsy state due to neurological changes in the brain. But the complex nature of EEG signals makes it difficult to study these changes. A detailed analysis of the EEG signal can be done if it is decomposed into multi-modes. <italic>Method</italic>: In this paper, adaptive variational mode decomposition (AVMD) is used for accurate analysis and synthesis of EEG signals. The number of modes (<inline-formula> <tex-math notation="LaTeX">${J}$ </tex-math></inline-formula>) and quadratic penalty factor (<inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>) is selected adaptively to find out representative information from EEG signals. Selection of <inline-formula> <tex-math notation="LaTeX">${J}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> is done by minimizing the reconstruction error using the Jaya optimization algorithm. Features are extracted from the adaptively decomposed modes. Entropy-based features selected by statistical analysis are classified with different classification algorithms. Eight performance parameters are evaluated to test the system’s effectiveness. <italic>Results:</italic> The reconstruction error of <inline-formula> <tex-math notation="LaTeX">$4.035\times 10^{-09}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$1.564\times 10^{-09}$ </tex-math></inline-formula> for the alert and drowsy state shows that the proposed method gives a better synthesis of signals. An accuracy, sensitivity, specificity, F-1 score, Kappa, false-positive rate, error, and precision of 97.19%, 97.01%, 97.46%, 0.976, 94.23%, 2.54%, 2.81%, and 98.18% shows that the proposed method provides representative modes for analysis. <italic>Conclusion:</italic> The comparison shows that AVMD is superior over conventional and existing methods by about 7% and 1%, respectively. The solution provided in this paper takes a step ahead for efficient synthesis and analysis of EEG signals to detect the drowsy state of drivers.
DEPOSITION OF ULTRAFINE AEROSOLS IN HUMAN TRACHEOBRONCHIAL AIRWAYS

Theoretical modeling of ultrafine particle deposition in human airways can be employed as a valuable complement to difficult and expensive in vitro and in vivo experimental studies. If such modeling can be shown to be reasonably accurate (compared with laboratory data), versatile (simulate different conditions), and compact (easy to use), it would then be warranted to use it for the extrapolation of theoretical results in risk assessment applications. In this study, a closed-form solution proposed by Martonen et al. (in press), which quantitatively describes convective particle diffusion in a conduit including wall irregularities and entrance effects, has been validated by experimental data from the literature. Theoretical predictions of particle deposition efficiencies are not only in agreement with the best-fit empirical correlation presented by Cohen and Asgharian (1990) over a wide range of dimensionless diffusion parameters but also match individual experimental measurements with regard to effects of.
Branched-Chain amino acids intake is negatively related to body adiposity in individuals at cardiometabolic risk

Objective To assess the relationship between branched-chain amino acids intake in the current diet and the metabolic and body adiposity markers in a population at cardiovascular risk. Methods This is a cross-sectional study with 282 adults and elderly people from the Cardiovascular Health Care Program of the Universidade Federal de Viçosa. Sociodemographic, anthropometric and body composition data, as well as metabolic biomarkers were collected using standardized protocols. Dietary intake of branched amino acids was assessed using a 24-hour recall.