Miracle-dz/UniFAR-FTUs huggingface dataset:
Accuracy of time domain extension formulae of core losses in non-oriented electrical steel laminations under non-sinusoidal excitation

This study presents a comparative study on the accuracy of three iron loss prediction models. The models are based on the decomposition of core or iron losses into the hysteresis and the eddy current loss components. The time domain extensions of two frequency domain models have been used to predict the iron losses due to a number of non-sinusoidal waveforms with and without the presence of minor loops. A third model, by Boglietti, that has been proposed recently to predict core losses for non-sinusoidal and pulse-width modulated (PWM) waveforms has also been studied. The unknown coefficients of each model have been determined by data fitting iron losses obtained from Epstein frame experiments for induction levels and fundamental frequencies up to 1.6 T and 2 kHz, respectively. Core losses due to PWM waveforms have been measured at various fundamental and switching frequencies in unipolar and bipolar modes. The experimentally measured iron losses have been compared to those predicted using the three models and the accuracy and applicability of each model have been discussed.
Artificial intelligence and Internet of Things in a “smart home” context: A Distributed System Architecture

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Embedded Recording — Tiny, low-power audio solutions for wireless systems

The aim of this thesis was to explore and evaluate current methods for building tiny, low-power audio recording systems for wireless applications. Short audio clips should be recorded, transferred to a smartphone and played back. Optimization of the different parts should be explored and a prototype for usage in battery powered devices with limited space should be built. System requirements were defined in terms of audio quality, physical size and energy consumption. Additionally, the price should be kept low and the system should be reliable and stable, without notable delays. The system was divided into six focus areas; microphones, preamplifiers, signal processing, transmission, software design and energy consumption feedback. Each of those was explored theoretically and by practical prototyping, testing and tuning. Low energy consumption mainly proved to compromise audio quality, system speed and development time. Signal processing and transmission, especially range, speed and protocol overhead, played major roles. Size constraints did not affect performance very much, except for reduced battery capacity and microphone positioning options. Decent sound quality could be achieved with small size and low cost and signal processing could do much to keep energy consumption down and the perceived sound quality up.
Parietal Alpha Oscillatory Peak Frequency Mediates the Effect of Practice on Visuospatial Working Memory Performance

Visuospatial working memory (WM) requires the activity of a spread network, including right parietal regions, to sustain storage capacity, attentional deployment, and active manipulation of information. Notably, while the electrophysiological correlates of such regions have been explored using many different indices, evidence for a functional involvement of the individual frequency peaks in the alpha (IAF) and theta bands (ITF) is still poor despite their relevance in many influential theories regarding WM. Interestingly, there is also a parallel lack of literature about the effect of short-term practice on WM performance. Here, we aim to clarify whether the simple repetition of a change-detection task might be beneficial to WM performance and to which degree these effects could be predicted by IAF and ITF. For this purpose, 25 healthy participants performed a change-detection task at baseline and in a retest session, while IAF and ITF were also measured. Results show that task repetition improves WM performance. In addition, right parietal IAF, but not ITF, accounts for performance gain such that faster IAF predicts higher performance gain. Our findings align with recent literature suggesting that the faster the posterior alpha, the finer the perceptual sampling rate, and the higher the WM performance gain.
Performance evaluation of multihop wireless networks

In this thesis, we investigate the performance of protocols for multihop wireless networks, focusing on Wireless Sensor Networks (WSNs) and Wireless Mesh Networks (WMNs). For both type of networks, we design and implement a protocol development and evaluation environment, and propose a new protocol for each multihop network. For WSNs, we design and implement RaPTEX, a rapid prototyping tool for embedded communication systems, consisting of three major subsystems: a toolbox for protocol compositions, an analytical performance estimation for fast exploration of performance trade-offs between different protocols, and a hybrid emulation environment for better accuracy of performance evaluation. Using RaPTEX, we study the performance trade-offs between energy efficiency, end-to-end delay, and throughput for different protocols, configurations, and topologies. Based on the performance study, we propose a multichannel asynchronous scheduled MAC protocol for WSNs, which significantly improves packet delivery ratio and delay without compromising energy efficiency under a wide range of traffic loads. For WMNs, we design and develop a service oriented software platform for a WMN testbed to facilitate protocol development and experimental research. Using the testbed software, we study the spatial throughput unfairness in a saturated WMN. Based on the measurement study, we define a joint channel assignment and routing problem, find the optimum solution using a mixed-integer non-linear programming (MINLP) model, and propose a joint channel assignment and routing algorithm that heuristically balances the channel utilization of bottleneck links to maximize throughout in multi-radio, multi-channel, multi-rate WMNs, while maintaining fairness. Through simulations, we show that the proposed algorithm substantially outperforms the existing schemes.
Effect of extracorporeal shockwave therapy on medial tibial stress syndrome: a systematic review

Background: This systematic review evaluates the available evidence for extracorporeal shockwave therapy (ESWT) use in the treatment of medial tibial stress syndrome (MTSS). Method: PubMed, EMBASE, Scopus, ISI Web of Science, and Cochrane Central Register of Controlled Trials (Cochrane CENTRAL) database searches were performed without a time limit in August 2021. Two independent researchers performed the search, screening, and final eligibility of the articles. Data were extracted using a customized spreadsheet, which included detailed information on patient characteristics, interventions, and outcomes. The methodological quality of the included studies was independently assessed by two reviewers using the Physiotherapy Evidence Database scale (PEDro). Results: Three studies were identified that compared 23, 12, and 22 participants in the intervention group with 19, 12, and 20 participants in the control group, respectively. The mean age of participants in these studies was 26.51 yr, and the mean duration of symptoms in the two studies that reported this was 16.36 mo. All studies used focus shockwave therapy. Conclusions: Extracorporeal shockwaves reduced pain and time to recovery and increased patient satisfaction. No study reported adverse effects. Based on the limited studies, ESWT may reduce pain and shorten recovery duration in MTSS. Further randomized clinical trials with sham control may substantiate these findings in other patient populations. Level of Evidence: Level II.
Synthesis and Structure Determination of 2-Cyano-3-(1-phenyl-3-(thiophen-2-yl)-1H-pyrazol-4-yl)acrylamide

2-Cyano-3-(1-phenyl-3-(thiophen-2-yl)-1H-pyrazol-4-yl)acrylamide (3) was synthesized in 90% yield from condensation of equimolar equivalents of 1-phenyl-3-(thiophen-2-yl)-1H-pyrazole-4-carbaldehyde (1) and 2-cyanoacetamide (2) in boiling ethanol under basic condition for 45 min. The structure of 3 was determined using NMR spectroscopy and single crystal X-ray diffraction.
Integrating silicon detectors in a photonic chip

Quantum photonic integrated circuits (PICs) exploit the virtually null photon-photon interaction to realize systems that are robust to external disturbance. While this resilience is particularly interesting for a development towards room-temperature systems, many experiments rely on superconducting nanowires that need cryogenic temperatures to operate. For photons in the near infrared spectral region, single photon avalanche diodes (SPADs) could be used as a room temperature alternative. We show a novel method of PIC-detector coupling that allows for the monolithic fabrication of substrate-integrated photodiodes and a silicon nitride PIC on the same chip. With the use of an engineered wet-etching process, we shape the bottom cladding of the photonic layer into a basin with shallow wedge borders on top of the region of the detectors. In this way, the waveguides are gently laid on top of the detectors, allowing for a strong waveguide-detector optical coupling. We show experimental results of the first PIC-diode coupling with a total efficiency exceeding 40%, and the first promising results concerning the coupling with SPADs paving the way for on-chip, room-temperature, single photon detection.
Do partial glenohumeral degenerative changes in patients undergoing arthroscopic rotator cuff repair influence clinical outcomes?

Background The prognostic factors for patients with full-thickness rotator cuff tears (RCTs) include tear size, muscle atrophy and fatty infiltration. However, the influence of early coexisting degenerative changes on RCT outcomes is unappreciated. The purpose of this study was to calculate the impact that pre-existing partial glenohumeral cartilaginous changes have on patients undergoing arthroscopic RCT repair. Methods A study of 54 patients undergoing arthroscopic RCT repair was undertaken. The presence of co-existing patches of glenohumeral degenerative cartilaginous changes and RCT size was recorded at surgery. Pre- and postoperative outcomes were assessed using traditional (Oxford Shoulder Score [OSS], 5-level EuroQol-5D [EQ-5D-5L] questionnaire and EuroQol visual analog scale [EQ-VAS]) and patient-centric re-formatted prisms. Outcomes were assessed as an entire dataset, and sub-group analysis was performed according to the grade of co-existing arthritis and tear size. Results Significant improvements (p<0.05) in clinical outcomes were recognized when assessed using either the traditional or reformatted prisms (average % improvements in OSS, EQ-5D-5L and EQ-VAS were 47%, 33% and 43%, respectively; average improvements in pain, function, and psychological well-being were 48%, 33% and, 29%, respectively). Positive gain was noted in all sub-groups of arthritic grading and tear size. Conclusions Good clinical outcomes can be achieved following RCT repair even in the presence of local partial degenerative cartilage changes and advancing tear size. These benefits are patient-centered but require RCT repairability.
Uric Acid and Cortisol Levels in Plasma Correlate with Pre-Competition Anxiety in Novice Athletes of Combat Sports

Pre-competition anxiety is very prevalent in novice athletes, causing stress and drastic decreases in their performances. Cortisol plays a central role in the psychosomatic responses to stress and also in the physiology of strenuous exercise. Growing evidence links uric acid, an endogenous antioxidant, with oxidative stress and anxiety, as observed in many depressive-related disorders. We here compared anxiety inventory scores (BAI and CSAI-2), cortisol and biomarkers of oxidative stress in the plasma of novice combat athletes (white and blue belts) before and after their first official national competition, when levels of stress are presumably high. Although the novice fighters did not reveal high indexes of anxiety on questionnaires, significant correlations were confirmed between cortisol and cognitive anxiety (Pearson’s r = 0.766, p-value = 0.002, and a ‘strong’ Bayesian inference; BF10 = 22.17) and between pre-post changes of plasmatic uric acid and somatic anxiety (r = 0.804, p < 0.001, and ‘very strong’ inference; BF10 = 46.52). To our knowledge, this is the first study to report such strong correlations between uric acid and pre-competition anxiety in novice combat athletes. The cause-consequence association between these indexes cannot be directly inferred here, although the interplay between uric acid and anxiety deserves further investigation.
4-Chlorophenyl-N-furfuryl-1,2,4-triazole Methylacetamides as Significant 15-Lipoxygenase Inhibitors: an Efficient Approach for Finding Lead Anti-inflammatory Compounds

Lipoxygenases (LOXs) are a class of enzymes that catalyze the production of pro-inflammatory mediators, such as leukotrienes and lipoxins, via an arachidonic acid cascade as soon as they are released from the membrane phospholipids after tissue injury. In continuation of our efforts in search for new LOX inhibitors, a series of chlorophenyl-furfuryl-based 1,2,4-triazole derivatives were prepared and evaluated for their 15-LOX inhibitory activities. A simple precursor, 4-chlorobenzoic acid (a), was consecutively transformed into benzoate (1), hydrazide (2), semicarbazide (3), and N-furfuryl 5-(4-chlorobenzyl)-4H-1,2,4-triazole (4), which when further merged with electrophiles (6a–o) resulted in end products (7a–o). The structural elucidations of the newly synthesized compounds (7a–o) were carried out by Fourier transform infrared, 1H-, 13C NMR spectroscopy, EI-MS, and HR-EI-MS spectrometry. The inhibitive capability of compounds (7a–o) on soybean 15-LOX was performed in vitro using the chemiluminescence method. The compounds 7k, 7o, 7m, 7b, and 7i demonstrated potent activities (IC50 17.43 ± 0.38, 19.35 ± 0.71, 23.59 ± 0.68, 26.35 ± 0.62, and 27.53 ± 0.82 μM, respectively). These compounds revealed 79.5 to 98.8% cellular viability as measured by the MTT assay at 0.25 mM concentration. The structure-activity relationship (SAR) studies showed that the positions and the nature of substituents bonded to the phenyl ring are important in the determination of 15-LOX inhibitory activities. ADME, in silico, and density functional theory studies supported the evidence as yet another class of triazoles with potential lead properties in search for anti-LOX compounds with a safe gastrointestinal safety profile for various inflammatory diseases. Further work is in progress on the synthesis of more derivatives in search for anti-inflammatory agents.
Lithium-Ion Cell Sorting and Cell Performance Modeling for Spacecraft Battery

The Earth orbiting spacecrafts use lithium-ion batteries as an energy storage device. Each battery is made up of several series- and parallel-connected cells. The constraints concerning the cell parameters, obtained by testing, drive the positioning of the cells in the battery. Positioning by cell sorting to meet the required battery performance can be approached through the genetic algorithm (GA). A GA improved cell sorting algorithm (GICSA) is proposed in this work. It sorts the cell parameters randomly with GA to match them within a given set of tight constraints. It sorts different cell types and numbers of cells for different battery configurations. It gives the practically best possible number of matched cells for the battery. Even the closely matched cells in the battery exhibit slightly different performance and experience different temperatures during their use. Due to prohibitive costs, the spacecraft application does not have an option of repair or replacement once it is in space. Hence, there is a need to understand the behavior of cells mounted within a battery. A nonlinear regression model is developed to estimate cell performance in the battery (CPB) used for spacecraft applications. The CPB model is validated with experimental results and is within a root mean square error of 2% for the voltage.
GrAb: A Deep Learning-Based Data-Driven Analytics Scheme for Energy Theft Detection

Integrating information and communication technology (ICT) and energy grid infrastructures introduces smart grids (SG) to simplify energy generation, transmission, and distribution. The ICT is embedded in selected parts of the grid network, which partially deploys SG and raises various issues such as energy losses, either technical or non-technical (i.e., energy theft). Therefore, energy theft detection plays a crucial role in reducing the energy generation burden on the SG and meeting the consumer demand for energy. Motivated by these facts, in this paper, we propose a deep learning (DL)-based energy theft detection scheme, referred to as GrAb, which uses a data-driven analytics approach. GrAb uses a DL-based long short-term memory (LSTM) model to predict the energy consumption using smart meter data. Then, a threshold calculator is used to calculate the energy consumption. Both the predicted energy consumption and the threshold value are passed to the support vector machine (SVM)-based classifier to categorize the energy losses into technical, non-technical (energy theft), and normal consumption. The proposed data-driven theft detection scheme identifies various forms of energy theft (e.g., smart meter data manipulation or clandestine connections). Experimental results show that the proposed scheme (GrAb) identifies energy theft more accurately compared to the state-of-the-art approaches.
História Viva: A Sketch-Based Interactive Storytelling System

Storytelling is a powerful tool to convey values and enrich creativity, imagination, and critical thinking. However, digital native children are losing interest in traditional folktales as these stories now have to compete with more appealing and interactive forms of entertainment, such as videogames. New technologies allow stories to be told in different formats as digital interactions provide new experiences that can be more attractive for Generation Z children. In this context, sketch-based interaction stands out as a powerful tool to keep children interested and engaged in interactive folktales. This paper describes the design process that led to the development of a sketch-based interactive storytelling system, called História Viva, which was designed to transform traditional folktales into interactive narratives. The system explores Portuguese folktales and was designed for children within the age range of 6 to 11 years old. Preliminary results from a user study indicate that children are very receptive and entertained by the proposed interactive narrative.
System dynamics modelling and the use of evidence to inform policymaking

ABSTRACT In recent years there has been growing interest in the policy community to apply insights from system dynamics modelling to address the complexity of many policy issues. This, however, has occurred in parallel to recent developments in critical scholarship on the nature of evidence use within public policymaking. While system dynamics aims to assist in the analysis and solving of complex policy problems, in doing so it also serves to identify which pieces of data and evidence are considered policy-relevant, or how pieces of evidence fit within a complex policy space. In this paper, we combine insights from the fields of complex systems modelling and critical policy studies in relation to these issues. Scholars working on the use of evidence within policymaking have explored how policy problems, and their potential solutions, have a range of potential framings and constructions. They further identify how processes are undertaken to define problems, apply evidence, and choose solutions can themselves specify which constructions become realized. As system dynamics modelling is increasingly applied as a policy-informing tool, it is critical to reflect on how policy issues and their solutions are constructed or understood, as well as whose values and views are represented in doing so.
Critical Aspects of Various Techniques for Synthesizing Metal Oxides and Fabricating Their Composite-Based Supercapacitor Electrodes: A Review

Supercapacitors (SCs) have attracted attention as an important energy source for various applications owing to their high power outputs and outstanding energy densities. The electrochemical performance of an SC device is predominantly determined by electrode materials, and thus, the selection and synthesis of the materials are crucial. Metal oxides (MOs) and their composites are the most widely used pseudocapacitive SC electrode materials. The basic requirements for fabricating high-performance SC electrodes include synthesizing and/or chemically modifying unique conducting nanostructures, optimizing a heterostructure morphology, and generating large-surface-area electroactive sites, all of which predominantly rely on various techniques used for synthesizing MO materials and fabricating MO- and MO-composite-based SC electrodes. Therefore, an SC’s background and critical aspects, the challenges associated with the predominant synthesis techniques (including hydrothermal and microwave-assisted syntheses and chemical-bath and atomic-layer depositions), and resulting electrode electrochemical performances should be summarized in a convenient, accessible report to accelerate the development of materials for industrial SC applications. Therefore, we reviewed the most pertinent studies on these synthesis techniques to provide insight into the most recent advances in synthesizing MOs and fabricating their composite-based SC electrodes as well as to propose research directions for developing MO-based electrodes for applications to next-generation SCs.
Machine Learning-Based Structural Health Monitoring Using RFID for Harsh Environmental Conditions

Post Operation Clean Out (POCO) is the process to remove hazardous materials and decommission nuclear facilities at the end of a nuclear plant’s lifetime. The introduction of Internet of Things (IoT) technologies in the environment, especially radio frequency identification (RFID), would improve efficiency and safety by intelligently monitoring POCO activities. In this paper, we present a passive material identification and crack sensing method developed for the integration of sensing and communication using commercial off-the-shelf (COTS) RFID tags, which is a long-term solution to material property monitoring under insulation for harsh environmental conditions. To validate the effectiveness of material identification and crack monitoring, machine learning techniques have been applied, and the feasibility of the study has been outlined. The result shows that the material identification can be achieved with traditional features and obtain improved accuracy with three-layer multi-layer neural networks (MLNN). In crack characterization, the tree algorithm based on traditional features achieves a reasonable accuracy, while three-layer MLNN is the best solution, which supports the efficiency of traditional feature extraction methods in specific applications.