Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetic Activity and Orbital Period Study for the Short-period RS CVn–type Eclipsing Binary DV Psc

Using 27 sets of new multiband photometry light curves acquired from our long-term photometric campaign carried out in the last 5 yr and high-resolution spectroscopic data from seven nights, we analyzed the physical mechanisms of period variation, starspot cycle, optical flares, and chromospheric activities of the eclipsing binary DV Psc. Our updated O − C diagram covering a period of approximately 20 yr shows an oscillation in its orbital period. This variations might be caused by a third body with an orbital period of 14.58 ± 0.28 yr. There are two active regions of starspots at longitude belts of about 90° and 270°. We obtained its starspot cycles with periods of 3.60 ± 0.03 yr and 3.42 ± 0.02 yr at about 90° and 270°, respectively. Moreover, the magnitude difference of Max. I–Max. II shows cyclic oscillation of 5.15 ± 0.01 yr. During our decade long photometric campaign, we observed DV Psc a total of 326.4 hr, detected 18 outbursts (12 of them have never been reported) with flare energies in the range of (6.62–1106.85) × 1024 J. The slope of the relationship between the phase of the max flare and spots is 0.842 ± 0.083, implying a correlation between spots and flares. We discovered evidence for a correlation between the rotation period and the activity cycle for the short-period eclipsing binaries. Our high-resolution spectroscopic observations of DV Psc show obvious emissions above continuum in the Hα line and small self-reversal emissions of the Ca ii IRT lines.
Study on Variation of Non-polar Metabolites in Gossypium hirsutum L. under Water Stress Condition Using Gas Chromatography-mass Spectroscopy Technique

Present work was aimed at studying the variation of non polar metabolites content in Gossypium hirsutum L. under water stress condition using a gas chromatography-mass spectrometry (GC-MS) technique. A total of 17 non-polar metabolites were detected in control and water stressed G. hirsutum leaf. The major metabolites were quinoline derivative (26.37±0.29%), 2- methylhexadecan-1-ol (7.47±0.07%), phytol (7.71±0.02%), myristic acid (5.94±0.04%), hexadecanol (14.30±0.94%), nonadecane (1.67±0.05%) and palmitic acid (3.20±1.39%). Fourteen metabolites were detected in control and water stressed G. hirsutum stem. The major metabolites were dodecene (1.67±0.11%), L-lysine (0.65±0.06%), dibutylphthalate (5.06±1.88%), linoleic acid (10.26±0.07%), campesterol (0.87±0.04%) and stigmasterol (1.13±0.55%). Statistical analysis of GC-MS data was carried out by Mann-Whitney U test without normal distribution using statistical software SYSTAT version 12.0. Significant variation in the content of the most of the metabolites were observed between control and water stressed leaf or stem (Mann-Whitney U test, P =0.05). It concludes that the major metabolites played an important role during water stress and can be consider as metabolites responsible for water stress tolerance in G. hirsutum.
Investigating the impact of ovarian carcinoma ascites on Toll-like receptor mediated dendritic cell activation

3 Acknowledgements . 4 Figure Index . 9 Table Index . 13 Abbreviations . 14 Chapter 1: Introduction . 18 1.1 Pattern recognition . 19 1.1.1 Toll-like receptors . 20 1.2 Innate immune activation . 23 1.2.1 Dendritic cells . 25 1.2.1.1 Myeloid DC . 26 1.2.1.2 Plasmacytoid DC . 30 1.3 Adaptive immune activation . 32 1.3. 1 B lymphocytes . 32 1.3. 2 T lymphocytes . 33 1.4 Cancer and the immune system . 37 1.4. 1 Cancer immunoediting . 38 1.4. 2 Ovarian carcinoma . 40 1.4. 3 Cancer immunotherapy . 44 1.5 Aims of this study . 48 Chapter 2: Material & Methods . 49 2.1 General laboratory equipment . 49
A Novel Wavelet-Based Algorithm for Detection of QRS Complex

Accurate QRS detection is an important first step for almost all automatic electrocardiogram (ECG) analyzing systems. However, QRS detection is difficult, not only because of the wide variety of ECG waveforms but also because of the interferences caused by various types of noise. This study proposes an improved QRS complex detection algorithm based on a four-level biorthogonal spline wavelet transform. A noise evaluation method is proposed to quantify the noise amount and to select a lower-noise wavelet detail signal instead of removing high-frequency components in the preprocessing stage. The QRS peaks can be detected by the extremum pairs in the selected wavelet detail signal and the proposed decision rules. The results show the high accuracy of the proposed algorithm, which achieves a 0.25% detection error rate, 99.84% sensitivity, and 99.92% positive prediction value, evaluated using the MIT-BIT arrhythmia database. The proposed algorithm improves the accuracy of QRS detection in comparison with several wavelet-based and non-wavelet-based approaches.
Application of artificial neural networks in the prediction of fruit damages and hand weight in Cavendish banana

The research study estimated the impact of season, maturity and temperature inside different material covers in predicting defect on banana peel from hand weight (HW), pulp per peel ratio (PPR), sunburn (SB) as well as thrips damages (TD) score of Cavendish banana in Chiang Rai. The banana bunch covers were conducted in summer (May to June 2017) and in rainy (August to October 2017). A one hidden layer feed forward backpropagation artificial neural network (ANN) was developed by varying the hidden node in a hidden layer for 2-20 nodes. Four separately ANN models were performed for Cavendish banana to predict qualities by using R and RStudio programs. Data input variables were rate of heat energy transmitted (Qx), hand location, and temperature profiles. The results showed that the 4-18-1, 6-16-1, 5-8-1, and 5-12-1 architectures were the most suitable model for HW, PPR, SB and TD score, respectively. The model performance presented the relatively high R 2 of 0.76, 0.96, 0.86, and 0.88 for HW, PPR, SB and TD score, respectively. Moreover, the selected model provided root mean square error (RMSE) of 1,157.62g, 0.252, 8.407, and 1.310 for HW, PPR, SB and TD score, respectively. The computational model ANN was used for the prediction of hand weight, pulp per peel ratio (maturity), sunburn, and thrips damages for Cavendish banana production in Thailand.
A framework for evaluating and distinguishing validity and generalization of prediction models

Introduction It is widely acknowledged that newly developed diagnostic or prognostic prediction models should be validated in samples with different (i.e. not included in the sample from which the model was developed) but related (i.e. similar characteristics or case mix) individuals . However, criteria for ’different but related’ are lacking, compromising structured model validation studies. Based on previous recommendations we describe a framework of methodological steps for analyzing and interpreting the results of prediction model validation studies, to enhance inferences about the model’s generalizability across populations, clinical practices and settings.
“Use what you have to secure what you have not”. On Design for and from Autonomy

This paper proposes a modality for engaging communities through design that is for, and from, autonomy. I will start by situating my experience using design in the social sector and introducing the concept of Autonomous Design, as proposed by Arturo Escobar. I will then unpack the three beliefs that Escobar says are historically embedded in the mainstream practices of design, to critic them and propose alternatives. I will then move to interrogate, by reflecting on an example from my own practice, how design could change, ontologically, when we move from a needs-based approach to one that focuses on strengths in groups and communities. I will then conclude by proposing a way to frame the design work with groups and communities, inspired by the work of Escobar.
Tandem dispersive liquid–liquid microextraction coupled with micro-sampling flame atomic absorption spectrometry for rapid determination of lead(II) and cadmium(II) ions in environmental water samples

ABSTRACT Tandem dispersive liquid liquid microextraction coupled with micro - sampling flame atomic absorption spectrometry for rapid determination of lead2 and cadmium2 ions in environmental water samples. A simple method termed as tandem dispersive liquid–liquid microextraction coupled with micro-sampling flame atomic absorption spectrometry is used for determination of the lead(II) and cadmium(II) ions in different environmental water samples. According to the proposed method, the target analytes are extracted from an aqueous sample solution (10 mL) into a micro-volume of an organic solvent, and then they are selectively back-extracted into an aqueous acceptor solution (150 μL) to increase the compatibility of the extractant phase with a final analyser system and provide a suitable enrichment factor. The developed method is very fast, implemented in just about 7 min, and provides a high sample clean-up. The factors influencing the extraction efficiency including the type and volume of the organic solvent, pH and volume of the acceptor solution, and number of extractions are thoroughly examined and optimised. Under the optimal experimental conditions, the developed method provides a good linearity (in the range of 0.4–300 ng mL−1 (R2 ≥ 0.994)), and low limits of detection (in the range of 0.07–0.31 ng mL−1). Finally, the method is successfully applied for the direct determination of the understudied analytes in the river, dam, and well water samples.
3-D-Printed Phase-Rectifying Transparent Superstrate for Resonant-Cavity Antenna

A three-dimensional (3-D)-printed nonplanar highly transmitting superstrate is presented to improve the directive radiation characteristics of a resonant-cavity antenna (RCA). Classical RCAs are reported with nonuniform aperture-field distribution that compromises their far-field directivity. The concept of near-field phase correction has been used here to design a phase-rectifying transparent superstrate (PRTS), which was fabricated using the 3-D printing technology. The PRTS is printed using easily accessible polylactic acid filament. It has a significantly lower cost and weight compared to its recently published counterparts, while its performance is comparable. The 3-D printing technology yielded the prototype in less than 4 h, which is considerably less compared to the traditional machining methods. Measurements of the prototype indicated close correspondence between the predicted and the measured results. Significant increase in the antenna performance has been achieved, due to the rectification of the aperture phase distribution. Notable aspects encompass 7.3 dB increase in the antenna peak directivity (from 13–20.3 dBi), significant sidelobe level suppression, and an improvement of aperture efficiency by 36.1%, with a PRTS that costs less than 2.5 USD.
Transfer of Training: The Contribution of “Coaching”

The literature on teacher training seems to indicate that mastery of teaching skills can be accomplished through the use of a combination of three training elements: the study of the theory underlying the skill, the opportunity to observe multiple demonstrations, and practice and feedback either under simulated conditions or in the classroom. However, transfer of the skills thus acquired into the teacher's active repertoire appears to involve new learning which requires the addition of a further important step in the training sequence: on-site coaching. The thesis is presented that transfer involves substantial new learning beyond the acquisition of the skills themselves, explaining the difficulty of curriculum implementation and the relatively low impact of most inservice education as presently conducted. A proposal extrapolated from the literature on transfer is presented for consideration as a model for inservice training.
Impact of Solute Properties and Water Matrix on Nanofiltration of Pesticides

The application of different nanofiltration membranes for the separation of pesticides, i.e., atrazine and dimethoate, from aqueous solutions is described. The nanoflitration membranes DK, NF270, NF200, and NF90 were tested for the pesticide retention performance in a stirred dead-end filtration system. NF90 demonstrated the best pesticide retention with over 95 % for atrazine and approximately 80 % for dimethoate. All membranes consistently showed better retention of atrazine than of dimethoate. Dissolving the pesticides in river or tap water amplified the overall pesticide retention performance, indicating that filtration in water treatment plants could render superior pesticide retention. However, a lower flux was obtained for the filtration of tap and river water.
Wind Speed Potential Analysis Based on Weibull Distribution

Weibull probability density function used to determine the wind energy potential commonly. In this paper, wind energy potential of Bilecik region is analyzed statistically by using the Turkish State Meteorological Service’s hourly wind speed data between 2010-2014 measured in Bilecik meteorological station. It is used the two- parameter Weibull to determine wind energy potential of the region. The parameters of the Weibull distributions used for finding the estimation of average speed, standard deviation, and power density is found by using the Maximum likelihood method. All analysis is carried out by Matrix Laboratory (MATLAB) programming language. Monthly and yearly wind speeds are modeled by Weibull distribution statistically. Accuracy of the modeling is evaluated in terms of Root Mean Square Error (RMSE).
Design of a low‐cost broadband loaded dipole antenna for VHF/UHF frequency range

This study describes the concept and design of a low-cost super broadband dipole antenna with a passive matching network. The proposed dipole antenna is designed in three steps. First, a simple planar dipole antenna with six different loads and a balanced to unbalanced (Balun) transformer is designed by the genetic algorithm optimisation. The position and value of the loads are optimised to achieve an acceptable radiation pattern and voltage standing wave ratio (VSWR) over the desired frequency bandwidth. In the next step, the shape of the printed strip dipole antenna is optimised to improve the frequency response of the designed antenna. Finally, an LC network is added to the antenna feed point to reduce the antenna VSWR at low frequencies. The final dipole antenna has a length of 1.7 m and can operate over 30-1200 MHz with VSWR -10 dBi. The proposed antenna can easily be fabricated at a low cost via the printed circuit board technology. An industrial prototype of the proposed antenna is fabricated and measured. A good agreement between measurement and simulation results is observed.
A new approach for modelling and identification of the pneumatic artificial muscle manipulator based on recurrent neural networks

Abstract Pneumatic artificial muscles (PAMs) are widely used in the various fields of medical robots and other industrial applications. As a powerful tool of intelligent control, neural networks nowadays are applied effectively to model, identify, and control highly non-linear systems, including the PAM manipulator. In the current paper a prototype PAM manipulator is modelled through recurrent neural networks (NN) modelling and identification based on experimental input-output training data. The proposed incremental back-propagation (INCBP) algorithm, which yields faster convergence than a conventional back-propagation (BP) algorithm, is applied to train the neural networks. The realization of the INCBP algorithm is given. An evaluation is carried out for different non-linear NN auto-regressive with exogenous input (NNARX) models of a PAM manipulator, using recurrent NN with various input nodes as well as various hidden layer nodes. For the first time, the non-linear NNARX model scheme of the prototype PAM manipulator has been investigated. The results show that the non-linear NNARX model trained by INCBP yields better performance and higher accuracy than the traditional linear ARX model. These results can be applied in modelling, identifying, and controlling not only the PAM manipulator, but also other highly non-linear systems.
Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.
Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms

Deep learning models, such as convolutional neural networks, utilize multiple specialized layers to encode spatial patterns at different scales. In this study, deep learning models are compared with standard machine learning approaches on the task of predicting the probability of severe hail based on upper-air dynamic and thermodynamic fields from a convection-allowing numerical weather prediction model. The data for this study come from patches surrounding storms identified in NCAR convection-allowing ensemble runs from 3 May to 3 June 2016. The machine learning models are trained to predict whether the simulated surface hail size from the Thompson hail size diagnostic exceeds 25 mm over the hour following storm detection. A convolutional neural network is compared with logistic regressions using input variables derived from either the spatial means of each field or principal component analysis. The convolutional neural network statistically significantly outperforms all other methods in terms of Brier skill score and area under the receiver operator characteristic curve. Interpretation of the convolutional neural network through feature importance and feature optimization reveals that the network synthesized information about the environment and storm morphology that is consistent with our understanding of hail growth, including large lapse rates and a wind shear profile that favors wide updrafts. Different neurons in the network also record different storm modes, and the magnitude of the output of those neurons is used to analyze the spatiotemporal distributions of different storm modes in the NCAR ensemble.
Low-complexity online correction and calibration of pedestrian dead reckoning using map matching and GPS

ABSTRACT Dead Reckoning is a relative positioning scheme that is used to infer the change of position relative to a point of origin by measuring the traveled distance and orientation change. Pedestrian Dead Reckoning (PDR) applies this concept to walking persons. The method can be used to track someone's movement in a building after a known landmark like the building's entrance is registered. Here, the movement of a foot and the corresponding direction change is measured and summed up, to infer the current position. Measuring and integrating the corresponding physical parameters, e.g. using inertial sensors, introduces small errors that accumulate quickly into large distance errors. Knowledge of a buildings geography may reduce these errors as it can be used to keep the estimated position from moving through walls and onto likely paths. In this paper, we use building maps to improve localization based on a single foot-mounted inertial sensor. We describe our localization method using zero velocity updates to accurately compute the length of individual steps and a Madgwick filter to determine the step orientation. Even though the computation of individual steps is quite accurate, small errors still accumulate in the long term. We show how correction algorithms using likely and unlikely paths can rectify errors intrinsic to pedestrian dead reckoning tasks, such as orientation and displacement drift, and discuss restrictions and disadvantages of these algorithms. We also present a method of deriving the initial position and orientation from GPS measurements. We verify our PDR correction methods analyzing the corrected and raw trajectories of six participants walking four routes of varying length and complexity through an office building, walking each route three times. Our quantitative results show an endpoint accuracy improvement of up to 60 when using likely paths and 23 when using unlikely paths. However, both approaches can also decrease accuracy in certain scenarios. We identify those scenarios and offer further ideas for improving Pedestrian Dead Reckoning methods.
Biodegradable Polymeric Solid Framework-Based Organic Phase-Change Materials for Thermal Energy Storage

Phase-change materials (PCMs) are utilized for thermal energy storage (TES) to bridge the gap between supply and demand of energy. Organic PCMs, similar to paraffins, fatty acids, and polyethylene glycol, are extensively explored, thanks to their high TES capacity (∼5–10 times more than the sensible heat storage of water/rock), wide temperature range (spanning from −5 °C to 190 °C), good thermal stability over heating–cooling cycles (∼100 cycles), etc. However, “leakage” of PCMs upon transformation from solid to liquid has limited their usage as TES materials; thus, PCMs are confined to a biopolymeric framework for circumventing seepages. However, extensive analysis has concluded that there is a deficiency in availability of consolidated data on biopolymeric-based framework for PCMs. Thus, this review covers various literatures on the application of assorted biopolymers as framework for PCM. Simultaneously, we have also discussed on importance of biopolymers for constructing a framework for PCMs. Finally,.
A Multistage Home Energy Management System With Residential Photovoltaic Penetration

Advances in bilateral communication technology foster the improvement and development of home energy management system (HEMS). This paper proposes a new HEMS to optimally schedule home energy resources (HERs) in a high rooftop photovoltaic penetrated environment. The proposed HEMS includes three stages: forecasting, day-ahead scheduling, and actual operation. In the forecasting stage, short-term forecasting is performed to generate day-ahead forecasted photovoltaic solar power and home load profiles; in the day-ahead scheduling stage, a peak-to-average ratio constrained coordinated HER scheduling model is proposed to minimize the one-day home operation cost; in the actual operation stage, a model predictive control based operational strategy is proposed to correct HER operations with the update of real-time information, so as to minimize the deviation of actual and day-ahead scheduled net-power consumption of the house. An adaptive thermal comfort model is applied in the proposed HEMS to provide decision support on the scheduling of the heating, ventilating, and air conditioning system of the house. The proposed approach is then validated based on Australian real datasets.