Miracle-dz/UniFAR-FTUs huggingface dataset:
The effectiveness of guided mental practice in a computer-based single pilot resource management (srm) training program

The purpose of this work was to assess the feasibility of a new instructional design approach, guided mental practice, within an asynchronous computer-based single pilot resource management training program. 36 licensed pilots participated in the study and were randomly separated into one of three groups, (a) SRM training with hands-on practice of flight simulator scenarios facilitated through yoke and rudder pedals, (b) SRM training with guided mental practice facilitated through videos of flight simulator scenarios, and (c) a control group which did not receive any training. The pilot participants then completed a performance assessment within a high-fidelity flight simulator. Situation Awareness Global Assessment Technique (SAGAT) and secondary task metrics were utilized in the simulator to measure situation awareness and mental workload, respectively. Additional offline measures included the Situational Awareness Rating Technique (SART) and National Aeronautics and Space Administration's task load index (NASA-TLX). The results of the study determined that the experimental group that completed training demonstrated significantly higher performance in the flight simulator than the control group, as measured by the SAGAT. The other measures did not identify any significant differences between groups. Therefore, it was concluded that training incorporating guided mental practice was equally as effective as that with hands-on practice in improving pilot's situation awareness as measured by the SAGAT.
A Bayesian Allocation Model Based Approach to Mixed Membership Stochastic Blockmodels

ABSTRACT Although detecting communities in networks has attracted considerable recent attention, estimating the number of communities is still an open problem. In this paper, we propose a model, which replicates the generative process of the mixed-membership stochastic block model (MMSB) within the generic allocation framework of Bayesian allocation model (BAM) and BAM-MMSB. In contrast to traditional blockmodels, BAM-MMSB considers the observations as Poisson counts generated by a base Poisson process and marks according to the generative process of MMSB. Moreover, the optimal number of communities for BAM-MMSB is estimated by computing the variational approximations of the marginal likelihood for each model order. Experiments on synthetic and real data sets show that the proposed approach promises a generalized model selection solution that can choose not only the model size but also the most appropriate decomposition.
Multi-criteria decision-making in the selection of a renewable energy project in spain: The Vikor method

One of the characteristics of the Spanish energy system is its high degree of dependence on imports. In 2005, the Spanish government approved the new Renewable Energy Plan in the following sectors: Windpower, Hydroelectric, Solar Thermal, Solar Thermo-electric, Photovoltaic, Biomass, Biogas and Biofuels. The aim of the Plan is to make it possible to reach the target of 12% of primary energy being met from renewable sources by 2010. When selecting one from various Renewable Energy investment projects different groups of decision-makers become involved in the process. Decision-making has to take into consideration several conflicting objectives because of the increasingly complex social, economic, technological, and environmental factors that are present. Traditional single-criterion decision-making is no longer able to handle these problems. The Compromise Ranking method, also known as the VIKOR method, introduces the Multi-criteria ranking index based on the particular measure of “closeness” to the “ideal” solution. In this paper, we apply the method in the selection of a Renewable Energy project corresponding to the Renewable Energy Plan launched by the Spanish Government. The method is combined with the Analytical Hierarchy Process method for weighting the importance of the different criteria, which allows decision-makers to assign these values based on their preferences. The results show that the Biomass plant option (Co-combustion in a conventional power plant) is the best choice, followed by the Wind power and Solar Thermo-electric alternatives.
Risk Management and Agency Theory: Role of the Put Option in Corporate Bonds

This study sets out a new methodology to exemplify, through a set of risk metrics called the Greeks, impact of a bond’s structured provisions (e.g., call, put, and conversion options) on its risk characteristics and its propensity for agency conflicts. The methodology is assessed by applying it to a sample of 159 non-convertible bonds, with time-scheduled call and put provisions issued between 1977 and 2005. A structural contingent-claims valuation model is used to value the bonds and estimate the Greeks. The methodology is used to assess the impact of the call and put provisions on the bond’s credit risk and interest-rate risk, as well as the provisions’ ability to mitigate the agency conflict associated with over-investment, under-investment, asset-substitution, and information asymmetry about the firm’s true risk among stakeholders. The main findings of this study are that the put option plays a key role in reducing credit risk, mitigating agency conflict, and protecting against volatility shocks; conversely, the call option plays a key role in reducing interest-rate risk. The methodology is sufficiently general to apply to bonds and preferred stock with any set of structured provisions.
A survey of underwater wireless optical communication

Underwater wireless communication refers to transmitting data in unguided water environment through wireless carriers, i.e., radio-frequency (RF) wave, acoustic wave, and optical wave. We focus, in this review article, on the underwater wireless optical communication (UWOC) that employs optical wave as the transmission carriers. In comparison to RF and acoustic counterparts, UWOC has a much higher transmission bandwidth, thus providing much higher data rate. Due to this high-speed transmission advantage, UWOC has attracted considerable attention in recent years. Many potential applications of UWOC systems have been proposed for environmental monitoring, offshore exploration, disaster precaution, and military operations. However, UWOC systems also suffer from severe absorption and scattering introduced by underwater channel. In order to overcome these technical challenges, several new system design approaches, which are different from the conventional terrestrial free-space optical communication, have been explored in recent years. We provide a comprehensive survey of the state-ofthe-art of UWOC research in three aspects: channel characterization, modulation and coding techniques, as well as practical implementations of UWOC.
Heat Transfer in a Matrix With Embedded Particles

The transient heat transfer characteristics of heterogeneous materials consisting of a microstructure of relatively small particles embedded in a matrix is investigated. A constitutive model is developed where heat is initially transferred to the matrix and is then transferred to small, separated particles embedded throughout the matrix. The heat exchange between the matrix and particles is characterized by a contact conductance. This physical model leads to a system of coupled diffusion equations for the matrix and particles, similar to the parabolic two-step model used to describe the electron-lattice interactions in microscale heat transfer. In the current investigation, a one-dimensional model with an applied surface heat flux is used. A numerical solution to the coupled system of equations using the control volume based finite difference method is developed. This method readily allows for the inclusion of any combination of material properties, particle sizes and contact conductance. Case studies reveal that the property ratios and contact conductance can lead to a thermal lag where the temperature response to an applied heat flux is markedly different than that predicted with a homogeneous conduction model. An effective thermalization time can also be inferred from this modeling of the heterogeneous microstructure.
Bidirectional system on chip for intracorporeal pressure measurement

A multichannel pressure sensing integrated circuit utilizing wireless power and wireless data transmission for long term monitoring of patients following endovascular stent implantation has been developed in partial ful llment of the degree of Doctor of Engineering. Each of the 8 capacitance to digital converter signal conditioning chains is a fully di erential architecture from signal input to the digital output sampled at 4ksps with 3μW power consumption per channel. Power is supplied to the device using wireless radio frequency power transmission, and the signal conversion is performed using a single stage operational transconductance ampli er and a 10 bit fully differential charge redistribution ADC. The pressure sensing IC has integrated energy harvesting, power regulation, and wireless data interface electronics.
Effect of a Craniosacral Therapy Protocol in People with Migraine: A Randomized Controlled Trial

Background: Migraine is a common neurological disorder, and it is the second leading cause of disability worldwide. Manual techniques based on physical therapy have been proposed to improve migraine aspects; however, further research is needed on their effectiveness. The aim of this study was to evaluate the effectiveness of a craniosacral therapy protocol on different features in migraine patients. Methods: Fifty individuals with migraine were randomly divided into two groups (n = 25 per group): (i) craniosacral therapy group (CTG), following a craniosacral therapy protocol, and (ii) sham control group (SCG), with a sham treatment. The analyzed variables were pain, migraine severity and frequency of episodes, functional, emotional, and overall disability, medication intake, and self-reported perceived changes, at baseline, after a 4 week intervention, and at 8 week follow-up. Results: After the intervention, the CTG significantly reduced pain (p = 0.01), frequency of episodes (p = 0.001), functional (p = 0.001) and overall disability (p = 0.02), and medication intake (p = 0.01), as well as led to a significantly higher self-reported perception of change (p = 0.01), when compared to SCG. In addition, the results were maintained at follow-up evaluation in all variables. Conclusions: A protocol based on craniosacral therapy is effective in improving pain, frequency of episodes, functional and overall disability, and medication intake in migraineurs. This protocol may be considered as a therapeutic approach in migraine patients.
Enabling conditions and challenges to environmental assessment as a tool for knowledge brokerage: lessons from Nunavut

ABSTRACT Knowledge brokering is a process of communication and interaction aimed at knowledge exchange and learning between parties with different knowledge bases This paper examines knowledge brokering in Arctic environmental assessment (EA) as a mechanism to support learning, build capacity, and sharing power. The study assessed enabling conditions and challenges to knowledge brokering in the eastern Canadian Arctic under the Nunavut EA process, managed by the Nunavut Impact Review Board. Methods included focus groups and review of legislation, process documents, and EA guidance to examine how the regulatory environment and Board’s EA process support knowledge brokering. Results illustrate that if EA is to support knowledge brokering, it should focus not only on communities but also on EA institutions – those that directly enable and support knowledge brokering, and those that provide ancillary information and have important regulatory roles. Enabling EA as a platform for brokering knowledge also requires investment beyond that provided through project-by-project funding models. While many jurisdictions are catching up to EA systems in Canada’s Arctic, and learning how to incorporate best-practices for Indigenous engagement and knowledge sharing, Nunavut’s co-managed process offers a model and learning opportunities for exploring the implementation conditions necessary for effective knowledge brokering through EA.
Wall shear stress in open channel flow using different configurations of groynes

ABSTRACT The effects of groyne geometry and arrangement on wall shear stress in a straight channel are studied by means of Computational Fluid Dynamics (CFD) software. Two- and three-dimensional models including pressure-velocity coupling scheme SIMPLE and the equations are used to show that the 2D model can adequately predict flow in comparison to 3D cases, reducing computing time considerably. The numerical procedure is validated by comparing numerical results with experimental measurements obtained from a laboratory-scale glass open channel with no bed sediments. Operating parameters are calculated by the Froude similarity study of a real-scale channel situation. The validated scheme is used to analyze the wall shear stress behavior in terms of groyne geometry and, in the case of multiple configurations, of the optimal distance between them. In all the considered settings, the presence of groynes causes an increase in the wall stress on the opposite margin and a decrease in the margin of groyne location. The shorter the groyne length, the smaller the rise on the shear stress on the other margin. The most significant reduction of the wall shear stress on the groyne margin is obtained with three groyne configurations.
The Promotion of Reading Behavior in Early Childhood in a Densely Populated Area of Bangkok

This research aimed to study the factors associated with the promotion of reading behavior in early childhood, in a densely populated area of metropolitan Bangkok, Thailand. A self-administered questionnaire was used to collect data from 228 families with children aged 3-6 years old. It was analyzed by obtaining the correlations and variables influencing reading behavior promotion. The research findings revealed that factors associated with promotion of reading behavior in early childhood with statistical significance (p<0.05) were age, education, family income, and community environment. It is therefore suggested to promote reading behavior in early childhood by building a network or organization within community environments; supporting convenience, proximity, and opportunity for local families to access information on reading.
No Cell Left behind: Automated, Stochastic, Physics-Based Tracking of Every Cell in a Dense, Growing Colony

Motivation: Precise tracking of individual cells—especially tracking the family lineage, for example in a developing embryo—has widespread applications in biology and medicine. Due to significant noise in microscope images, existing methods have difficulty precisely tracking cell activities. These difficulties often require human intervention to resolve. Humans are helpful because our brain naturally and automatically builds a simulation “model” of any scene that we observe. Because we understand simple truths about the world—for example cells can move and divide, but they cannot instantaneously move vast distances—this model “in our heads” helps us to severely constrain the possible interpretations of what we see, allowing us to easily distinguish signal from noise, and track the motion of cells even in the presence of extreme levels of noise that would completely confound existing automated methods. Results: Here, we mimic the ability of the human brain by building an explicit computer simulation model of the scene. Our simulated cells are programmed to allow movement and cell division consistent with reality. At each video frame, we stochastically generate millions of nearby “Universes” and evolve them stochastically to the next frame. We then find and fit the best universes to reality by minimizing the residual between the real image frame and a synthetic image of the simulation. The rule-based simulation puts extremely stringent constraints on possible interpretations of the data, allowing our system to perform far better than existing methods even in the presense of extreme levels of image noise. We demonstrate the viability of this method by accurately tracking every cell in a colony that grows from 4 to over 300 individuals, doing about as well as a human can in the difficult task of tracking cell lineages.
Comparison of Neural Language Modeling Pipelines for Outcome Prediction From Unstructured Medical Text Notes

Machine learning techniques and algorithm-based approaches are becoming more and more vital to support clinical decision-making. In the medical area, natural language processing (NLP) techniques have shown the ability to extract useful information from electronic health records. On the one hand, statistic, semantic, and contextualized word embedding-based models and on the other hand preprocessing approaches are the keys to a better representation of a document. Using narratives from the Intensive Care Unit, we elaborated a comparison of the most used methods and preprocessing approaches to tackle an outcome prediction problem and guide researchers into NLP pipelines in the medical area. We used real data from Medical Information Mart for Intensive Care-III (MIMIC-III). We selected all notes related to patients with pneumonia. We conducted a deep analysis on text preprocessing tasks producing three datasets: raw data with minor preprocessing, meticulous preprocessing, and extreme preprocessing filtering only medical-related terminologies using Named Entity Recognition algorithms. We then used these three sets in five models, of which two are based on the traditional noncontextual word embedding techniques and three use contextualized word embedding based on a transformer. We demonstrated that transformer-based models outperform other word embedding models and a profound preprocessing yielded an accuracy of 98.2 F1-score. These results show the highly competitive ability of NLP predictive models against other models that use medical data. With an appropriate NLP pipeline, the information contained in medical narratives can be used to draw up a patient profile, and admission notes can help to ascertain a mortality risk of a patient admitted to the Intensive Care Unit.
Chemometric analysis of ketogenic diet formulated from low-cost dietary fibers

Background: Ketogenic diet (KD) is a beneficial nutritional plan consisting of low carbohydrate, high fat, and moderate protein levels and aids in amelioration of some metabolic disorders. The objective of this study is to develop a ketogenic diet model using cheap and readily available fiber sources. METHODS: Cabbage head and coconut fruits were obtained and processed into fiber and ketogenic diet chow. They were further analyzed using standard methods for proximate, mineral, and heavy metals, phytochemicals, and DPPH radical scavenging assay.RESULTS: Carbohydrate content of the samples were (3.35, 4.00, 3.16 and 2.08%) for cabbage feed, coconut feed, coconut fiber, and cabbage fibers, respectively. This conforms to the maximal 4% carbohydrate required for ketogenic diet daily allowable limit. Other nutrients such as lipids and proteins were in high and moderate amounts respectively. Phytochemicals were also present in varied proportions in the samples. CONCLUSION: The developed cabbage and coconut fiber is an appropriate fiber source for ketogenic diet preparation. They are rich in nutrients based on their mineral content. They may be positioned as a nutraceutical for therapeutic and disease prevention action due to their inherent bioactive chemicals and radical scavenging activity. They may pose negligible toxicity risks as the few detected heavy metals are within permissible limits. KEYWORDS: Fibers, ketogenic diet, cabbage, coconut, chemical analysis, and antioxidant
Network intrusion detection system: machine learning approach

The main goal of intrusion detection system (IDS) is to monitor the network performance and to investigate any signs of any abnormalities over the network. Recently, intrusion detection systems employ machine learning techniques, due to the fact that machine learning techniques proved to have the ability of learning and adapting in addition to allowing a prompt response. This work proposes a model for intrusion detection and classification using machine learning techniques. The model first acquires the data set and transforms it in the proper format, then performs feature selection to pick out a subset of attributes that worth being considered. After that, the refined data set was processed by the Konstanz information miner (KNIME). To gain better performance and a decent comparative analysis, three different classifiers were applied. The anticipated classifiers have been executed and assessed utilizing the KNIME analytics platform using (CICIDS2017) datasets. The experimental results showed an accuracy rate ranging between (98.6) as the highest obtained while the average was (90.59%), which was satisfying compared to other approaches. The gained statistics of this research inspires the researchers of this field to use machine learning in cyber security and data analysis and build intrusion detection systems with higher accuracy.
Distributed control for state‐of‐charge balance and load voltage regulation in DC microgrids with clustered generations

In this paper, the issue of state‐of‐charge balance and load voltage regulation of DC microgrids used in solar‐powered vehicles is investigated. The system consists of multiple distributed generation units, each of which is equipped with photovoltaic cells and energy storage systems. Based on the information shared among the neighbors, a single‐layered distributed control strategy is proposed to solve two aspects of the problem simultaneously. Rigorous analysis of the closed‐loop system ensures stability and the effectiveness of the control strategy. The power constraints of each distributed generation unit are taken into consideration so that safety and applicability are guaranteed. To avoid the cycling of the batteries, the state‐of‐charge balance is achieved by regulating the output power feeding a common load, promoting efficiency and battery life. The proposed controller operates without any knowledge about load, line impedance, and global information of communication network, and consequently possesses the capability of plug‐and‐play. Finally, the effectiveness and applicability are illustrated by an example from the Sky‐Sailor project.
Study of steel-aluminium joining under the influence of current waveforms using advanced CMT process variants

ABSTRACT In the conventional GMAW process, increasing weld deposition increases the heat input, which adversely affects the steel-aluminum joint performance due to the brittle IMC layer growth. In the present work, advanced CMT variants in AA6061-T6 and galvannealed steel joining were investigated. All Fe-Al joints are made at a constant joining speed and wire feed rate with CMT process variants, viz. CMT-Standard, CMT-Advanced, CMT-Pulsed, and CMT-Pulsed-Advanced. The influence of current and voltage characteristics of process variants on the thermal profile, joint profile, IMC layer morphology, and mechanical properties of the joint was studied. The welding arc and molten droplet transfer phenomena were explained using recorded welding current and voltage waveforms synchronized with welding arc images. The FE-SEM, EDS, and XRD analyses were performed to confirm the phases and morphology of the IMC layer. The results show that the localized heating due to positive current pulses in CMT-Pulsed and CMT-Pulsed-Advanced processes affects the IMC layer morphology and induces cracks at the Fe-Al joints’ root, leading to low shear tensile strength. The crack-free joints were observed with the CMT-Standard and CMT-Advanced processes. Also, with an increase in the positive CMT cycle in the CMT-Advanced process, heat input decreases and shear tensile strength increases.