Miracle-dz/UniFAR-FTUs huggingface dataset:
Plasma Parameters Obtained with Planar Probe and Optical Emission Spectroscopy

A planar probe and optical emission spectroscopy were employed to analyze parameters in an inductively coupled plasma (ICP). The analyses were performed in Ar, Ar+SF6 and O2 plasmas at 40 mTorr. Typical probe results indicate an ion current of 10-3 A/cm2 and an electron temperature (high energy tail) between 1-2 eV in measurements at high powers. At low powers a distinct discharge regime is observed, typically with low density (low ion current) and high electron temperature. The optical emission studies also showed the presence of these two regimes. Moreover, the electron temperature determination using this diagnostic is also in good agreement with planar probe results.
A whole new ballgame: an analysis of the context and adoption of long-term athlete development in community sport

ABSTRACT Rationale/Purpose: The purpose of this study was to explore the context for innovation and how it ultimately affected sport club decisions to adopt Long-Term Athlete Development (LTAD). Design/methodology/approach: Interview and documentary data were gathered from Canadian sport clubs and provincial sport organizations implementing LTAD, an initiative intended to improve the quality of sport programs. Following a critical realist framework, a retroductive grounded theory approach was used to identify themes and structural mechanisms influencing club decisions to adopt. Findings: LTAD adoption generated internal conflict for clubs while creating opportunity for competition with rivals. Resource and institutional pressures both enabled and inhibited innovation. Clubs pursued innovation when resource controllers signaled that increased legitimacy would result. Clubs with capacity to adopt used LTAD as a means to maintain or improve their competitive position. Practical implications: Policies promoting community sport adoption of innovations to improve program quality may create thresholds allowing clubs with capacity to adopt the opportunity to out-perform or eliminate rivals. This may paradoxically result in increased costs and/or diminished access to sport for participants. Research Contribution: This provides new insight into how community sport clubs manage conflicting logics and compete through compliance with the policies of resource controllers. A contextual model of community sport club operation under conflicting logics is presented.
Multivariate curve resolution of multiway data using the multilinearity constraint

The extension of Multivariate Curve Resolution‐Alternating Least Squares (MCR‐ALS) to the analysis of multiway data using the multilinearity constraint is described in detail as one step forward of previous implementations of the trilinearity and quadrilinearity constraints for the analysis of three‐ and four‐way data sets, respectively. As in previous cases, the implementation of the multilinear model for multiway data sets is done algorithmically, within the frame of the alternating least squares (ALS) optimization in the MCR‐ALS method. This implementation is tested using multiway data sets of different complexity, and the obtained results have confirmed the adequacy of the proposed approach. Special advantages of the proposed methodology are that it allows for the implementation of the constraint separately for the different components in their different modes and that it also allows for the introduction of different levels of complexity of the multilinear model, including mixed multilinear models. These two features are especially relevant because they are not present in most of the most used multiway data analysis methods at present.
A Review on Thermoelectric Generators: Progress and Applications

A thermoelectric effect is a physical phenomenon consisting of the direct conversion of heat into electrical energy (Seebeck effect) or inversely from electrical current into heat (Peltier effect) without moving mechanical parts. The low efficiency of thermoelectric devices has limited their applications to certain areas, such as refrigeration, heat recovery, power generation and renewable energy. However, for specific applications like space probes, laboratory equipment and medical applications, where cost and efficiency are not as important as availability, reliability and predictability, thermoelectricity offers noteworthy potential. The challenge of making thermoelectricity a future leader in waste heat recovery and renewable energy is intensified by the integration of nanotechnology. In this review, state-of-the-art thermoelectric generators, applications and recent progress are reported. Fundamental knowledge of the thermoelectric effect, basic laws, and parameters affecting the efficiency of conventional and new thermoelectric materials are discussed. The applications of thermoelectricity are grouped into three main domains. The first group deals with the use of heat emitted from a radioisotope to supply electricity to various devices. In this group, space exploration was the only application for which thermoelectricity was successful. In the second group, a natural heat source could prove useful for producing electricity, but as thermoelectricity is still at an initial phase because of low conversion efficiency, applications are still at laboratory level. The third group is progressing at a high speed, mainly because the investigations are funded by governments and/or car manufacturers, with the final aim of reducing vehicle fuel consumption and ultimately mitigating the effect of greenhouse gas emissions.
MOTOR PROGRAMMING PREDICTS THE FUNCTIONAL RECOVERY GAIN IN PATIENTS WITH RIGHT HEMISPHERIC FRONTAL STROKE

Executive functioning significantly affects the functional rehabilitation progress in stroke patients. The current study are aimed to examine the impact of executive functions on functional recovery gain in a sample of right hemispheric (RH) frontal stroke patients. A total of 50 inpatients admitted to the Theseus Rehabilitation Center and diagnosed with RH frontal stroke were included in this study. The initial assessment of executive functioning was conducted using the Frontal Assessment Battery (FAB). Functional recovery of basic activities of daily living (ADLs) was computed by the difference between follow-up and baseline Barthel Index (BI) scores. Pearson’s analysis revealed that the FAB global scores significantly correlated with the BI gain scores. Additionally, multiple regression analysis showed a significant contribution of only motor programming to the basic ADLs recovery gain. The global status of executive function was found to be significantly associated with functional recovery gain, and motor programming was the strongest predictor in RH frontal stroke patients. These findings are discussed for the treatment rehabilitation prognosis.
A Review of the Moodle Gamification Plugin “Level Up”

This software review evaluates the Moodle plugin Level Up, which aims to add a gamification element to this commonly-used online education platform. The reviewers report their use of the plugin, describing its strengths, weakness and future potential. In doing so, they share their experiences of using it during a recent project that developed a Moodle-based activity for study of the Academic Word List (AWL) at a Sino-British university in China. This activity aimed to enhance student engagement and motivation to learn more academic vocabulary, and Level Up was selected for its claimed gamification potential. The activity's end product, and student responses to it are considered, and positive recommendations are given regarding Level Up's potential for enhancing Moodle.
Switching from face-to-face to electronic-mediated education caused by COVID-19 pandemic: An analysis of student perceptions

Mozambique, like several other countries in the region and all over the world, faces the dilemma of the need to guarantee the minimum learning conditions for students during the state of emergency due to COVID-19. It is in this sense, which the present study aimed to analyze the perceptions of students of the Pedagogical University of Maputo (UP-Maputo) in the use of electronic platforms as a resource to support classroom teaching during the State of Emergency. The research had a quanti-qualitative character, where the analysis and control of the different variables was foreseen. It consisted of collecting information on the objectives of operationalizing the teaching and learning process in the face of the COVID-19 pandemic, through the application of an electronically administered questionnaire. A sample of 1787 students was obtained, corresponding to 13.61% of the universe of students enrolled in the year 2020. The survey showed that most of the students, around 64% used some electronic platform to support their teaching and on-site learning process, after the declaration of a state of emergency the percentage of users of electronic platforms increased to 100%, highlighting the use of WhatsApp to the detriment of the others (SIGIUP, MOODLE® Institutional, Google Classroom and ZOOM®). The device they use, because 95% of the students access the platforms using their mobile phones, justifies the massive use of WhatsApp. Regarding the use of different electronic platforms, most of the students revealed a platform usage that varies from “Many Times” and “Always”. Students refer to the need for training in the use of the different platforms made available to them to support their teaching and learning process. The research made it possible to conclude that students are aware of the need to appropriate the new form of teaching they are subject to, despite having significant financial difficulties in the acquisition of internet packages and in the use of some of the electronic platforms made available for learning.
The chromosome‐level genome sequence and karyotypic evolution of Megadenia pygmaea (Brassicaceae)

Karyotypic changes in chromosome number and structure are drivers in the divergent evolution of diverse plant species and lineages. This study aimed to reveal the origins of the unique karyotype (2n = 12) and phylogenetic relationships of the genus Megadenia (Brassicaceae). A high‐quality chromosome‐scale genome was assembled for Megadenia pygmaea using Nanopore long reads and high‐throughput chromosome conformation capture (Hi‐C). The assembled genome is 215.2 Mb and is anchored on six pseudochromosomes. We annotated a total of 25,607 high‐confidence protein‐coding genes and corroborated the phylogenetic affinity of Megadenia with the Brassicaceae expanded lineage II, containing numerous agricultural crops. We dated the divergence of Megadenia from its closest relatives to 27.04 (19.11–36.60) million years ago. A reconstruction of the chromosomal composition of the species was performed based on the de novo assembled genome and comparative chromosome painting analysis. The karyotype structure of M. pygmaea is very similar to the previously inferred proto‐Calepineae karyotype (PCK; n = 7) of the lineage II. However, an end‐to‐end translocation between two ancestral chromosomes reduced the chromosome number from n = 7 to n = 6 in Megadenia. Our reference genome provides fundamental information for karyotypic evolution and evolutionary study of this genus.
Design of a Circular-Slot Multiband (UWB) Antenna with Non-Periodic DGS for WLAN/WiMAX Applications

The paper discusses bandwidth enhancement of a microstrip patch antenna. It explores the application of DGS with parasitic elements to obtain a compact UWB multiband antenna. The antenna is designed on a 14*25 mm2 RT-Duroid (5880) substrate. A comparison is made between four antenna designs with the same dielectric substrate. The antenna design with circular slot on the patch, facilitated with the shorting pins, shows bandwidth enhancement. It uses non-periodic Defective Ground Structure (DGS) for improvement in return loss characteristics, bandwidth enhancement and multiband operation. The slot on the patch provides compact size and DGS gives wide bandwidth (80.5%) with five resonant frequencies of 0.92 GHz, 2.36 GHz, 3.46 GHz, 4.98 GHz & 7.9 GHz making it applicable for GSM (890-966 MHz), WLAN/WiMAX (2.4/3.6/4.9), multi-band applications.
Parameter extraction for audio signal

Speech is a signal which consisting of various complex information, like the information contain in a signal which to be communicate, language, speaker, region, emotions etc. Speech Processing is one of the important parts of signal processing and finds applications in Audio mining, Human computer interfaces, Assistive technologies, Telecommunication, Security etc. For the speaker identification and emotion analysis Gender Identification is a pre requites. By analyzing the features of different voice signals of male & female it can be determined that which parameters are creating a difference in between the male voice & female voice. For this a comparison between the different parameters present in a voice signal like energy, mean, median and standard deviation is done. It observed that in the graph of mean and standard deviation one can differentiate between a male voice and female as the statistical results are shown. In this paper an input voice signal taken as . wav file and added some noise or unwanted signal; the unwanted noise signals are removed from the original signal by reducing the intensities of high frequencies by averaging and Gaussian window for the smoothing and conv function to removing the pseudo numbers that added as a noise to the original signal
Crystalline, membrane-embedded, and fibrillar proteins investigated by solid-state NMR spectroscopy

This thesis describes research on atomic structure and molecular dynamics of biomolecules in the solid phase using solid-state nuclear magnetic resonance spectroscopy. A method for detecting residue-specific mobility using double-quantum (13C,13C) spectroscopy was developed on a small model system and applied to the 76-residue protein ubiquitin. Together with a structure calculation employing an automated algorithm, results allowed to relate preparation-dependent chemical-shift changes in this protein to local differences in mobility or conformation. Work on the potassium channel KcsA-Kv1.3 in lipid bilayers led to resonance assignments for 59% of its residues. Secondary structure of its pore domain was found to closely resemble that of the parent KcsA channel, but differences became apparent with respect to KcsA preparations in micelles, highlighting the influence of the membrane environment. In close reference to functional experiments, activation and inactivation gating in KcsA-Kv1.3 and their dependence on pH and potassium concentration were investigated. At acidic pH, KcsA-Kv1.3 was found to reside in an open-inactivated conformation. It is characterized by bent inner transmembrane helices, corresponding to an opened activation gate, and by a nonconductive selectivity filter conformation, i.e. a closed inactivation gate. The open-inactivated state of KcsA-Kv1.3 was demonstrated to be correlated with protonation of specific glutamate residues. It is also strongly influenced by the potassium concentration. Potassium affects the conformation of both activation and inactivation gates via binding to the selectivity filter, providing a mechanism for gate coupling and coordinated gating transitions. Finally, fibrillar aggregates formed by disease-relevant polyglutamine peptides were investigated structurally. Experimental data argue against water-filled beta-helical models and are best explained by an antiparallel, superpleated cross-beta structure with a polar zipper arrangement of tightly interdigitated, hydrogen-bonded sidechains.
Modelling daily soil temperature at different depths via the classical and hybrid models

Soil temperature (ST) is one of the crucial variables of soil and it plays a fundamental role in different research scopes such as underground soil physical and agricultural applications. The study explores the modelling performance of a time series‐based model (i.e. bi‐linear, BL), and an artificial intelligence‐based approach including adaptive neuro‐fuzzy inference system (ANFIS), for modelling the daily ST of different soil depths (5, 10, 50 and 100 cm). The study also develops and proposes two diverse types of the hybrid models through coupling the ANFIS with the BL and wavelet analysis (W) to improve the accuracy of the ST modelling. Two stations in Iran (i.e. Isfahan and Urmia) were selected as the study locations. The results demonstrated that the ANFIS generally presented better results than the BL. Furthermore, the hybrid models (i.e. W‐ANFIS and ANFIS‐BL) gave superior performances than the classical ANFIS and BL for modelling the daily ST of the studied areas at various soil depths. In addition to the local evaluation of the ANFIS (i.e. modelling the ST at a specific depth by using the original ST data at that depth), an external analysis was also conducted. In doing so, the daily ST data at a 5 cm depth were modelled via the corresponding ST data at a 10 cm depth, and vice versa. The results denoted the applicability of the ST data at another depth for modelling the ST of each specific/target depth.
Feasibility of Machine Learning applied to Poincaré Plot Analysis on Patients with CHF

As an alternative to the traditional methods of analysis in the time and frequency domains regarding heart rate variability, new interest has been concentrated in using a non-linear analysis technique of the beat-beat time series, known as the Poincaré Plot Analysis. The parameters provided by the analysis can be used as input for machine learning algorithms in order to distinguish patients in three classes of congestive heart failure, according to the New York Heart Association. Tree-based algorithms for classification and synthetic minority oversampling technique (SMOTE) for balancing the dataset with artificial data were implemented in Knime analytics platform, reaching an overall accuracy between 75% and 80%, specificity and sensitivity greater than 90% in some classes and F-measures ranging from 68% to 92%. Further investigations could be pursued with bigger datasets and avoiding the use of artificial data to balance the classes.
A LoRa-Based Linear Sensor Network for Location Data in Underground Mining

In this paper, we describe a LoRa (from “Long Range”)-based, linear sensor network we have developed for transmitting location information of personnel and equipment in an underground mine. The system is intended to be used during emergencies when existing communications infrastructure has failed. Linear networks comprise a sequence of relays that forward data to a common destination, the headend. Relays forward location information transmitted from tags carried by personnel or equipment. Relays will usually be put in place as investigators or rescuers enter the mine. LoRa is used both by the tags to communicate to the relays and by the relays to forward messages to the headend. We have implemented and tested this system, and have carried out simulations and analyses to determine its scalability, reliability and fairness. The need for robustness and reliability has led us to use flooding rather than unicast communication. We also use message sequence numbers and time-to-live fields to prevent broadcast storms. Contention is managed using a simplified Carrier Sense Multiple Access (CSMA) scheme. We also address fairness. When the network is under load messages may be dropped by relays making messages generated more hops from the headend more likely to be dropped than messages nearer the headend. We explore the relationship between unfairness, traffic load and number of relays. We also observe that a network of larger numbers of lightly loaded relays performs more effectively than smaller numbers of heavily loaded relays.
Study on Self-Position Estimation and Control of Active Caster Type Omnidirectional Cart with Automatic / Manual Driving Modes

This paper presents the development of an active caster omnidirectional cart with manual/automatic modes. The proposed cart realizes holonomic and omnidirectional motions using dual-wheel active casters. Since the active caster has the same wheel structure as the passive caster, the active caster functions as a passive caster by disconnecting the drive shafts from the motors in the manual mode. In this mode, the cart can be moved by pushing and pulling by hand. Further, even in the manual mode, the movement of the cart by a human can be accurately recorded by measuring the rotation of the wheels and the steering shaft. In the proposed system, the operation of the cart manually moved is recorded, and playback of the motion by automatic driving is performed. This is a kind of teaching/playback system for carts. This function enables easy teaching of cart trajectories and accurate positions. This paper describes the mechanical design and control method of the omnidirectional cart, followed by the experimental results of the prototype omnidirectional cart.
Optimization of the Match‐Filtering Method for Robust Repeating Earthquake Detection: The Multisegment Cross‐Correlation Approach

Waveform match‐filtering (MF), based on cross‐correlation between an earthquake pair, is a powerful and widely used tool in seismology. However, its performance can be severely affected by several factors, including the length of the cross‐correlation window, the frequency band of the applied digital filter, and the presence of a large‐amplitude phase(s). To optimize the performance of MF, we first systematically examine the effects of different operational parameters and determine the generic rules for selecting the window length and the optimal frequency passband. To minimize the influence of a large‐amplitude phase(s), we then propose a new approach, namely, MF with multisegment cross‐correlation (MFMC). By equally incorporating the contributions from various segments of the waveforms, this new approach is much more sensitive to small separation between two sources compared to the conventional MF method using the entire waveform template. To compare the reliability and effectiveness of both methods in capturing interevent source separation and identifying repeating earthquakes, we systematically conduct experiments with both synthetic data and real observations. The results demonstrate that the conventional MF method can detect the existence of an event but sometimes lacks the resolution to tell whether the template and detected events are co‐located or not, whereas MFMC works in all cases. The far‐reaching implication from this study is that inferring source separation between an earthquake pair based on the conventional MF method, particularly with data from a single channel/station, may not be reliable.
Pedestrain Detection Based on a Gabor Weber Local Descriptor

: Pedestrian detection has attracted more and more attention in recent years. In this paper, a novel pedestrian detection method based on Gabor Weber Local Descriptor (GWLD) was proposed. According to the pedestrian's characteristics, the sliding window technique was adopted. Firstly, Gabor transforms were performed on the sliding window, and then the Weber Local Descriptor (WLD) was extracted over the average Gabor feature maps. Finally, the GWLD histogram was constructed to character the sliding window. Experimental results on INRIA pedestrian dataset and Daimler Chrysler (DC) pedestrian dataset validate that the effectiveness of the proposed GWLD detector. Comparing with the other pedestrian detection methods, the proposed GWLD method performs better.