Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximation, reformulation and convex techniques for cardinality optimization problems

The cardinality minimization problem (CMP) is finding a vector with minimum cardinality, which satisfies certain linear (or non-linear) constraints. This problem is closely related to the so-called compressive sensing problems. In this paper we survey and study different approximation, reformulation and convex relaxations for both cardinality constraint problems and cardinality minimization problems, and discuss how to add a penalty function to the objective in order to get a reformulation/approximation model of the original problems, instead of simply dropping the rank constraint. By reformulation techniques, under some mild condition we may either transform the problem to a mixed integer linear program (MILP) or the so-called bilevel SDP problems. We also point out that a continuous approximation of cardinality functions can enable us to apply majorization method to extract proper weights for the (re)weighted l1 algorithms.
The Prototype of Microwave-Induced Thermo-Acoustic Tomography Imaging by Time Reversal Mirror

Microwave-Induced Thermo-Acoustic Tomography (MITAT) is a promising technique in biologic tissue imaging. It has predominant advantages in both contrast and resolution compared with conventional microwave or ultrasound imaging system for malignant tumors. In this paper, an integrated prototype MITAT system is briefly introduced. And Time Reversal Mirror (TRM) imaging technique based on Pseudo-Spectrum Time Domain (PSTD) is also first time applied to MITAT system. Two tissue strips of porcine muscle are used as targets in the MITAT experiment to demonstrate the imaging potential of the system. TRM and Filtered Back-Projection (FBP) imaging results are both presented to compare the performance of the TRM and other imaging techniques in contrast and resolution for MITAT.
Domain-wall-motion cell with perpendicular anisotropy wire and in-plane magnetic tunneling junctions

A wire with perpendicular magnetic anisotropy (PA) is suitable for domain-wall-motion (DWM) cells because its critical current is small. However, it is not easy to design a cell consisting of a high magnetoresistance (MR) ratio magnetic tunneling junction (MTJ) and a DWM using PA material alone. We propose a combination of a PA–DWM element and in-plane (IP) MTJ for detection. The structure can be designed in a way that reduces the write current by the use of a perpendicular layer, yet maintains a high MR ratio by the independent use of in-plane material stacks. We fabricated a cell and ran tests to determine its read and write properties. A critical write current of 700 μA and a MR ratio of 50% were achieved. These properties are almost the same as when a DWM wire and in-plane MTJ are fabricated separately, which means it is possible to design the two elements independently.
Litter quality modifies soil organic carbon mineralization in an ecological restoration area

Soil organic carbon (SOC) mineralization impacts the loss and accumulation of carbon (C) in crucial ways. However, it is still unclear how exactly litter quality regulates the SOC mineralization process and SOC stock. We conducted a laboratory incubation experiment under different temperatures, that is 10°C, 20°C, 30°C, and 40°C, and moisture, that is 5%, 10%, and 20% regimes, as well as a control (CK), and different litter qualities, that is, Artemisia ordosica, Caragana korshinskii, and Eragrostis minor. This was conducted in soil from a revegetated area in the Tengger Desert, northern China. We investigated the impacts of litter addition on SOC mineralization by considering the chemical properties of litter, temperature, and soil moisture, as well as the cumulative CO2‐C emissions (CCE), maximum rate (MR), potential mineralizable C (C0), priming effect (PE), temperature sensitivity (Q10), moisture sensitivity (MS), and SOC stock. Litter addition increased the CCE, MR, C0, and MS but decreased the Q10, and the effects of different litter qualities on CCE, MR, C0, MS, and Q10 differed significantly. The SOC mineralization rate increased with increasing temperature and moisture and was also accelerated by the interactions between temperature and moisture. The total C and lignin contents of litter were the key factors that governed SOC mineralization. Litter addition had a positive PE on SOC mineralization, and high‐quality litter with low C:N ratio (A. ordosica and C. korshinskii) increased the PE compared with low‐quality litter with high C:N ratio (E. minor). The PE also decreased with increasing temperature and moisture. Litter addition resulted in an acceleration of the loss of SOC mineralization, and an increase in the SOC mineralization response toward moisture but reduced the SOC mineralization response toward temperature, whereas litter quality was inversely correlated with the SOC stock. Therefore, the input of low‐quality litter will decrease the loss of mineralized SOC caused by PE and diminish the accumulation of SOC in the revegetated regions. These findings may provide a reasonable litter management model to promote the stability of soil C pool in arid revegetated areas.
The Pathfinder Atmospheres–Extended AVHRR Climate Dataset

The Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres–Extended (PATMOS-x) dataset offers over three decades of global observations from the NOAA Polar-orbiting Operational Environmental Satellite (POES) project and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) [Meteorological Operational (MetOp)] satellite series. The AVHRR has flown since 1978 and continues to provide radiometrically consistent observations with a spatial resolution of roughly 4 km and a temporal resolution of an ascending and descending node per satellite per day, achieving global coverage. The AVHRR PATMOS-x data provide calibrated AVHRR observations in addition to properties about tropospheric clouds and aerosols, Earth's surface, Earth's radiation budget, and relevant ancillary data. To provide three decades of data in a convenient format, PATMOS-x generates mapped and sampled results with a spatial resolution of 0.1° on a global latitude–longitude grid. This format avoid.
Optimisation of electric vehicle with the in-wheel motor as a dynamic vibration absorber considering ride comfort and motor vibration based on particle swarm algorithm

Compared with other electric vehicles, the ride comfort of the electric vehicle with the in-wheel motor as a dynamic vibration absorber has significantly improved. However, the in-wheel motor is used as a dynamic vibration absorber, aggravating the motor vibration, thus affecting motor performance and life. In addition, the space inside the wheel is limited, so the vibration displacement of the motor relative to the hub needs to be constrained. In fact, the literature on ride comfort of the electric vehicle with the in-wheel motor as a dynamic vibration absorber has hardly considered motor vibration. So, this article comprehensively investigates the ride comfort and motor vibration of this electric vehicle. Firstly, the vibration model of the electric vehicle with the in-wheel motor as a dynamic vibration absorber is established. Then, the optimisation model of this electric vehicle is founded. Next, the multi-objective particle swarm optimisation algorithms based on adaptive grid and crowding distance are used to optimise the model, and these two algorithms are compared. The optimal solutions in three typical cases are obtained. Finally, the vibration responses of this electric vehicle model before and after optimisation, the traditional electric vehicle model, and the in-wheel motor drive electric vehicle model are compared in case 1. The results show that all vibration responses are improved to different degrees after optimisation; the algorithm based on crowding distance can seek out the optimal solutions faster, but it takes longer to complete the whole optimisation process.
Analysis of fast‐ion velocity distributions in laser plasmas with a truncated Maxwellian velocity distribution of hot electrons

Fast‐ion production in laser plasmas with a truncated Maxwellian velocity distribution of hot electrons is investigated by using a new numerical method, namely the systematic Newton’s iteration method which has been applied to nonlinear equations. The analysis of the temporal evolution of coronal plasma expansion discloses that the ion‐front velocity approaches a constant value within thirty ion plasma oscillation periods, when the high‐energy tail is truncated. In such a case, the maximum ion velocity is found to be proportional to the maximum energy of hot electrons. Consequently, the ion velocity distributions are steepened and truncated. Furthermore, it is found that the energy partition of the absorbed laser energy to fast ions is appreciably reduced when the maximum electron energy is below a few times the hot‐electron temperature.
Copper alumina reinforced poly(aniline-co-indole) nanocomposites for optoelectronic devices

ABSTRACT Copper alumina (Cu-Al2O3) reinforced poly(aniline-co-indole) (PANI-co-PIN) nanocomposites were prepared and characterised by UV-vis spectroscopy, FE-SEM and DSC. The bandgap energy decreased with the addition of Cu-Al2O3 to PANI-co-PIN. The FE-SEM confirms the uniform dispersion of nanoparticles in the copolymer. The DSC showed a shift in glass transition temperatures to higher regions with Cu-Al2O3 concentrations. The frequency-temperature-dependent dielectric parameters and electrical conductivity of nanocomposites were investigated. Activation energy calculated from Arrhenius equation decreases with increasing frequency. The real and imaginary part of electric modulus implies the thermally activated dielectric relaxations. The decreasing radius of semicircles in Nyquist plot with increasing temperatures indicates the existence of thermally activated conduction mechanisms. Different theoretical models were used to predict the DC conductivity mechanism in copolymer nanocomposites.
Vortex noise from nonrotating cylinders and airfoils

An experimental study of vortex-shedding noise was conducted in an acoustic research tunnel over a Reynolds-number range applicable to full-scale helicopter tail-rotor blades. Two-dimensional tapered-chord nonrotating models were tested to simulate the effect of spanwise frequency variation on the vortex-shedding mechanism. Both a tapered circular cylinder and tapered airfoils were investigated. The results were compared with data for constant-diameter cylinder and constant-chord airfoil models also tested during this study. Far-field noise, surface pressure fluctuations, and spanwise correlation lengths were measured for each configuration. Vortex-shedding noise for tapered cylinders and airfoils was found to contain many narrowband-random peaks which occurred within a range of frequencies corresponding to a predictable Strouhal number referenced to the maximum and minimum chord. The noise was observed to depend on surface roughness and Reynolds number.
A Deep Neural Network as a Strategy for Optimal Sizing and Location of Reactive Compensation Considering Power Consumption Uncertainties

This research proposes a methodology for the optimal location and sizing of reactive compensation in an electrical transmission system through a deep neural network (DNN) by considering the smallest cost for compensation. An electrical power system (EPS) is subjected to unexpected increases in loads which are physically translated as an increment of users in the EPS. This phenomenon decreases voltage profiles in the whole system which also decreases the EPS’s reliability. One strategy to face this problem is reactive compensation; however, finding the optimal location and sizing of this compensation is not an easy task. Different algorithms and techniques such as genetic algorithms and non-linear programming have been used to find an optimal solution for this problem; however, these techniques generally need big processing power and the processing time is usually considerable. That being stated, this paper’s methodology aims to improve the voltage profile in the whole transmission system under scenarios in which a PQ load is randomly connected to any busbar of the system. The optimal location of sizing of reactive compensation will be found through a DNN which is capable of a relatively small processing time. The methodology is tested in three case studies, IEEE 14, 30 and 118 busbar transmission systems. In each of these systems, a brute force algorithm (BFA) is implemented by connecting a PQ load composed of 80% active power and 20% reactive power (which varies from 1 MW to 100 MW) to every busbar, for each scenario, reactive compensation (which varies from 10 Mvar to 300 Mvar) is connected to every busbar. Then power flows are generated for each case and by selecting the scenario which is closest to 90% of the original voltage profiles, the optimal scenario is selected and overcompensation (which would increase cost) is avoided. Through the BFA, the DNN is trained by selecting 70% of the generated data as training data and the other 30% is used as test data. Finally, the DNN is capable of achieving a 100% accuracy for location (in all three case studies when compared with BFA) and objective deviation has a difference of 3.18%, 7.43% and 0% for the IEEE 14, 30 and 118 busbar systems, respectively (when compared with the BFA). With this methodology, it is possible to find the optimal location and sizing of reactive compensation for any transmission system under any PQ load increment, with almost no processing time (with the DNN trained, the algorithm takes seconds to find the optimal solution).
Estimation of Sentiment Analysis Base Stock Market Crisis

Stock market trends generally represent the market situation and flow of investment. These trends generally vary according to real life scenarios like disasters, industrial revolutions, political elections etc. Daily news headlines generally include all this information and can be used without the further context of the news article. Most of the research done by the researchers is done using only daily stock data or using standard machine learning algorithms. In this project, we find the importance of using the sentiment data extracted from news headlines and daily stock data combined for the prediction of trends and stock prices. To carry out market trends prediction we propose use of ensemble techniques like XGBoost and for price prediction time series models like LSTM (Long Short Term Memory) cells. Our results indicate that combining both data as input using ensemble techniques and time series models gave much better and more accurate output than the standard methods.
Associations of Physical Activity and Handgrip Strength with Health-Related Quality of Life in Older Korean Cancer Survivors

Simple Summary Along with a complete cure for cancer, health-related quality of life (HRQoL) is equally important to many cancer patients and survivors. Uncertainty exists regarding the associations of physical activity (PA) and relative handgrip strength (RHGS) with health-related quality of life (HRQoL) among elderly Korean cancer survivors. The current findings of the study support the prognostic roles of PA and RHGS in determining the HRQoL of older Korean cancer survivors. This study is unique to report that the negative influence of physical inactivity on HRQoL was observed in the cancer survivors of weak RHGS, but not in the cancer survivors of normal RHGS. Abstract Background: Uncertainty exists regarding the associations between physical activity (PA), relative handgrip strength (RHGS), and health-related quality of life (HRQoL) among elderly Korean cancer survivors. This study investigated the moderating effect of RHGS on the relationship between PA and HRQoL in 308 cancer survivors aged ≥65 years using the data from the Korean National Health and Nutrition Examination Survey in 2014–2019. Methods: HRQoL was evaluated with the EuroQol-5-dimension instrument. PA and handgrip strength were assessed with the Global Physical Activity Questionnaire and a hand dynamometer, respectively. Age, sex, body mass index, income, marital status, educational background, heavy alcohol consumption, smoking status, multimorbidity, and hemoglobin were included as covariates. Results: Bivariate logistic regression showed that insufficient PA and no PA were associated with higher odds of a low HRQoL (odds ratio, OR = 2.6, 95% confidence interval, CI = 1.3~5.1, p = 0.005; OR = 2.4, 95% CI = 1.1~5.0, p = 0.024, respectively), compared with sufficient PA (OR = 1). Weak RHGS was associated with high odds of a low HRQoL (OR = 2.6, 95%= 1.530~4.6, p < 0.001) compared with normal RHGS (OR = 1). Particularly, RHGS (β = −0.0573, 95% CI = −0.1033~−0.0112) had a significant moderating effect on the relationship between PA and HRQoL even after adjustments for all the covariates. The negative influence of physical inactivity on HRQoL was observed in cancer survivors with weak RHGS but not in cancer survivors with normal RHGS. Conclusions: The current findings suggest that maintaining or promoting muscular strength through regular exercise is critical for the HRQoL of elderly Korean cancer survivors.
Does father’s class or mother’s education matter more? Decomposing and contextualising the influence of social origin on adult learning participation

ABSTRACT This article argues for the need to regard social origin as a complex phenomenon in order to better understand the determinants of individuals’ participation in adult learning. It: 1) discusses the conceptualisation of social origin; 2) analyses how two of its components – parental (both father’s and mother’s) class and education – are interrelated with participation in non-formal job-related adult learning; 3) explores whether this relationship is embedded in different welfare regimes. We argue that social origin includes different interrelated but not interchangeable components that refer to both fathers and mothers. Each one of them has its own influence and that is why all of them should be considered at once in exploring adults’ involvement in learning. Empirically, the analyses are based on the latest data from the ninth round of the European Social Survey, carried out in 2018, for 29 countries. The data were analysed with logistic regression modelling. The results show that parental (mother’s and father’s) class and education have an independent influence on individuals’ participation in adult learning and that this influence is embedded in different welfare regimes creating considerable differences between welfare regimes in terms of whether father’s or mother’s characteristics and which ones are more important.
Graph Neural Networks based Model for Aroma Prediction using Molecular Structures

In scientific and forensic research investigations, predicting sensory systems stimulus as evidence is inevitable. It is a decades-old challenge in the field of cheminformatics, which can be solved using various Learning techniques. These techniques have made a good impact in Computer Vision (CV), Nature Language Processing (NLP) and Speech Recognition (SR). Among sensory systems, the Olfaction Mechanism is moderately under-explored due to its high complexity in odorant molecular structures. Advanced computers facilitate the machine learning techniques to classify into clusters and predict the aroma. An attempt is made to embed Graph Neural Networks (GNNs) for olfactory senses prediction, significantly encompasses the traditional methods. In this investigation, the dataset curated by the olfactory experts is used for transfer learning which is based on the spatial odor representations (pattern recognition) of the molecular structures of the substance. Aroma prediction plays a key role in various research domains including synthetic-fragrance manufacturing, human dietetics, sensory neurosciences, and forensic crime investigations.
Suppression of antiferromagnetism by Kondo effect and quantum critical behavior in CeCoGe3-xSix (0≤ x< 3)

Several pseudoternary compounds of the CeCoGe 3- x Si x system were studied by magnetic-susceptibility, electrical-resistivity and specific-heat measurements. Substitution of silicon for germanium generates the normal chemical pressure and enhances the exchange interaction. The antiferromagnetism in CeCoGe 3 is ultimately suppressed around x =1.2 by the enhanced Kondo effect. Non-Fermi liquid-like behavior has been observed around the critical concentration and an intermediate-valence regime with a very high Kondo temperature of the order of 900 K is followed farther on in the silicon rich region.
Random forest for the detection of unauthorized consumption in water supply systems: a case study in Southern Brazil

ABSTRACT Clandestine connections represent a significant portion of the apparent water losses. The objective of this research work is to propose a systematic approach to inspect sites with possible illegal water consumption more precisely. A case study was conducted in a District Metered Area (DMA) located in the city of Joinville, Southern Brazil, using technical information and consumption history from 1400 consumer units located in this area. The proposed methodology uses the bagging technique called Random Forest, through a machine learning algorithm. With success rates of 88.10% when classifying frauds in the model training phase and 88.20% in the test phase, the obtained model shows great ability to properly classify frauds in the water supply system in the studied DMA. The use of the random forest classification model, combined with the SMOTE technique for data balancing, proves to be a viable technical alternative for the detection of unauthorized water consumption.
An alternative solution algorithm for certain transportation problems

This paper presents an algorithm consisting of two pre-processing techniques that go part way in solving certain transportation problems. The proposed algorithm approaches the problem in a novel way and consists of Procedures A and B. Procedure A searches for absolute cells, loads those with the maximum feasible amount, and reduces the TP matrix. In some cases, this preprocessing, Procedure A, might solve a transportation problem completely and yield the optimal solution. Procedure B searches for inverse absolute cells and eliminates those from further considerations. If enough of these cells are eliminated, the optimal solution is squeezed into the remaining cells and found by an analysis in the last step. Unlike other methods, the first solution is the optimal solution. The algorithm provides the user/student an insight into the problem and the ability to critically analyze the data. It helps to better understand the phenomena of alternative solutions. It is easy to apply and can serve as an effective p.