Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Investigation of Electron Small Polarons in α-MoO3

The properties of electron small polarons in α-MoO3 are investigated computationally employing density-functional-theory with Hubbard-U corrections (DFT+U) and hybrid functionals (HSE06). These methods are used to compute the electronic and atomic structures of polarons localized on Mo ions, the barrier for adiabatic polaron hopping, and the magnitude of the binding energy with intercalated Li ions. The calculations establish a pronounced anisotropy in polaron mobilities, both within the bilayer sheets and across the van der Waals (vdW) gaps characteristic of the α-MoO3 structure. The lowest and highest energy barriers are found for hopping within the same bilayer plane and across the vdW gap, respectively. The binding energies between polarons and intercalated Li ions are calculated in supercells with composition Li0.028MoO3, yielding values of approximately 0.3 eV when Li ions are located in the one-dimensional channels within the bilayer sheets, and values that are approximately 0.1 eV lower in magnitu.
Modeling the behavior of elastic pouch motors

Pouch motors are one of the recently developed soft actuators, which are known particularly for their low-weight, ease of fabrication and large stroke. To date, several studies have been performed to develop and model new pouch motors designs to improve their functionality. All models assume that the material is behaving inextensibly, i.e. not stretchable. Here, we propose an analytical model for pouch motors where we consider the materials to be stretchable, and show that stretchability of pouch motors sets a limit for the maximum contraction and force, and therefore cannot be neglected even when using nearly inextensible materials. We evaluate our model qualitatively by conducting ‘blocked-displacement’ experiments on single pouches made of various materials with different elasticity.
Microfacet Based BRDF Solar Cell Model Modification Using Experimental Data

Light curve analysis is often used to understand satellite activity in geosynchronous orbits which are too far away for resolved imagery from ground-based optical systems. Glinting features can provide identifiable information, but large sources of error have been documented. The bidirectional reflectance distribution function (BRDF) describes the spatial distribution of a material's reflectance by relating incident irradiance to scattered radiance, and BRDFs play an integral role in light curve observation, simulation, and interpretation. Microfacet models are a popular class of BRDF, which assume geometric optics and typically trade accuracy for computing speed in both scene generation and computer graphics. Another popular class of BRDF is comprised of physical optics models which account for wave optics effects, but their main drawbacks are complexity and computing power. For materials that exhibit wave optics effects, if a known wave optics solution can be combined with a mi-crofacet model, perhaps computing speed can be retained while improving accuracy of the model. In this work, new models are suggested as inputs into light curve analysis to reduce glinting error by accounting for distinctive solar cell diffraction features. Previous work extended out-of-plane measurements to identify and then model a clear diffraction effect from a solar cell, and this work conducts validation of the model using alternative laser sources and additional solar cell samples. While prior work focused on the specular region, in this work, the theoretical modification is analyzed using high-fidelity, low-density, out-of-plane measurements gained from a modified Complete Angle Scatter Instrument (CASI) capturing BRDF data at scattered angles not just on the specular direction. This data informs a model used to show out-of-plane behavior resulting in otherwise unexpected reflection patterns. The results suggest that for remote sensing scenarios involving materials with diffractive properties, such as solar cells, incorporating a fundamental known solution for wave optics phenomena with a microfacet model foundation can improve the radiometric accuracy as well as retain computing speed.
Direct georeferencing for Hyperspectral Imaging of ocean surface

This paper presents a low-cost, low-weight setup for aerial imaging spectroscopy and an effective methodology for direct georeferencing onto the sea surface. The algorithm and the miniaturized payload described in this work promise great potential to increase the flexibility and versatility of small airborne image spectroscopy devices. This new method is a simplification when compared to existing methods.
THE IMPACT OF PHYSICAL ACTIVITIES ON THE ACADEMIC PERFORMANCE, AND BURNOUT OF FEMALE MEDICAL STUDENTS IN INDIA

Background: Lack of physical activities leads to many consequences such as obesity, anxiety, stress, weight gain, depression and burnout. Due to busy schedule of medical students majority of the students overlooked the participation in exercise and physical activities and prefer studies over health. This pattern has brought many issues not only in students but in the societies as well. Aim: Purpose of this paper was to determine the impact of physical activities on academic performance and burnout of medical female students in India. Material and Methods: Survey approach using self-administered closed ended scale was distributed among 250 female students. Non probability convenience sampling technique was used to select the sample size. SPSS and PLS-SEM were used for statistical analysis. It was found that there is significant effect on burnout and academic performance of students due to academic year, physical activities, time played, regularity maintained, no of hours played per week, family restrictions, non-availability of space and laziness. Findings/Results: Measurement model was also found reliable and valid. All factor loadings, AVE and CR values met threshold criteria. It is concluded that taking part in games could reduce burnout, emotional exhaustion, and depersonalization and enhance personal achievement and academic performance. Conclusions: From the findings it is revealed that taking part in any game such as football, baseball, hockey, cricket could bring positive outcomes such as good health, fitness, students become active and come up with new ideas, It is concluded that taking part in healthy activities help the students to develop skills, effectively handle stress, depression and anxiety.
Understanding the Role of Leisure in Portuguese Adolescent Wellbeing Experience

Because adolescent leisure is important to development, we consider its role in Portuguese adolescent wellbeing. Data for this study came from 303 adolescents in grades 10, 11, and 12 living in a large urban area in northern Portugal. Self-report data were collected in classrooms using a cross-sectional design in two urban high schools. Hypothesis testing used seven hierarchical linear regression models. Except for subjective happiness, experiencing boredom in leisure and/or the ability to make a boring situation more interesting were strong predictors of each wellbeing experience in the predicted direction. Perceptions of healthy leisure were associated with higher levels of life satisfaction, subjective happiness, self-esteem, and positive affect. Active leisure was important to adolescent self efficacy and positive affect. Those who could restructure a boring situation into something more interesting exhibited higher levels of wellbeing experience. Adolescents who perceived parental autonomy control were more likely to experience boredom in leisure.
Sensing and Control Strategies for a Synergy-Based, Cable-Driven Exosuit via a Modular Test Bench

Ageing results in the eventual loss of muscle mass and strength, joint problems, and overall slowing of movements, with a greater risk of suffering falls or other such accidents. The use of gait assistance exoskeletons can help in the active aging of this segment of the population. Given the user specificity of the mechanics and control these devices need, the facility used to test different design parameters is indispensable. This work deals with the modeling and construction of a modular test bench and prototype exosuit to test different mounting and control schemes for a cable-driven exoskeleton or exosuit. The test bench allows the experimental implementation of postural or kinematic synergies to assist multiple joints by using only one actuator and the optimization of the control scheme to better adapt to the characteristics of the specific patient. The design is open to the research community and it is expected to improve the design of cable-driven systems for exosuits.
Forecasting Traffic Speed during Daytime from Google Street View Images using Deep Learning

Traffic forecasting plays an important role in urban planning. Deep learning methods outperform traditional traffic flow forecasting models because of their ability to capture spatiotemporal characteristics of traffic conditions. However, these methods require high-quality historical traffic data, which can be both difficult to acquire and non-comprehensive, making it hard to predict traffic flows at the city scale. To resolve this problem, we implemented a deep learning method, SceneGCN, to forecast traffic speed at the city scale. The model involves two steps: firstly, scene features are extracted from Google Street View (GSV) images for each road segment using pretrained Resnet18 models. Then, the extracted features are entered into a graph convolutional neural network to predict traffic speed at different hours of the day. Our results show that the accuracy of the model can reach up to 86.5% and the Resnet18 model pretrained by Places365 is the best choice to extract scene features for traffic forecasting tasks. Finally, we conclude that the proposed model can predict traffic speed efficiently at the city scale and GSV images have the potential to capture information about human activities.
ReRAM CiM Fluctuation Pattern Classification by CNN Trained on Artificially Created Dataset

A CNN-based Fluctuation Pattern Classifier (FPC) is proposed. FPC is fully trained on the artificially created dataset with assumed fluctuation patterns such as random telegraph noise (RTN) and Oxygen Vacancy movement. FPC is applied to the measured ReRAM signals under different write conditions before read cycles and physical models are established based on the classification results. Proposed fluctuation reduction write (FRW) reduces ReRAM fluctuation rate by 35.1% to improve the inference accuracy of neural network.
Dynamic model of an online programmable textile soft actuator

Soft actuators exhibiting versatile behaviors have potential applications in robotics. This paper proposes kinematics, kinetics, and dynamic models of an online-programmable soft actuator. The actuator is composed of four strings and an inflatable textile tube folded inside a housing structure. Each string is controlled by a single DC motor which has an optical encoder. Pulling a string produces bending in one direction, while pulling the four strings in a coordinated manner produces additional motions. With the proposed forward and inverse kinematic model, the actuator was able to follow a desired end-effector trajectory in the Cartesian space. Furthermore, due to the dynamic model, our simulation study shows that the soft actuator can handle external force changes at the end-effector, such as mass changes and friction forces.
DETECTION OF FLAXSEED OIL ADULTERATION BASED ON TWO-DIMENSIONAL CORRELATION NEAR-INFRARED SPECTRA

Flaxseed oil is rich in α-linolenic acid and other nutrients, and the adulteration happens frequently because of its high price. To detect the adulteration of flaxseed oil quickly and accurately, a method was proposed based on weighted reconstructed two-dimensional correlation near-infrared(NIR) spectra. The near-infrared spectra of 79 adulterated flaxseed oil samples (adulterated by rapeseed oil with the doping volume ratio 1%-40%) were measured, and the traditional two-dimensional correlation synchronous spectra were calculated. The two-dimensional correlation synchronous spectra of all samples were decomposed into multiple components of different scales by the bi-dimensional empirical mode decomposition algorithm (BEMD). According to the root mean square error(RMSE) values of the adulteration detection sub-models established by each component, the weights of the corresponding components were calculated, and then the two-dimensional correlation spectra of all samples were reconstructed by accumulating the weighted components. A quantitative analysis model of flaxseed oil adulteration was established based on the weighted reconstructed two-dimensional correlation spectra combined with the N-way partial least square(N-PLS)algorithm. Compared with the traditional two-dimensional correlation spectroscopy, the model built by the weighted reconstructed two-dimensional correlation spectra had better performance with the calibration determination coefficient increased by 6.05%, and the prediction determination coefficient increased by 7.5%. The proposed method could effectively enhance the spectral feature information, reduce the spectral noise interference, and hence provide a new idea for the detection of edible oil adulteration.
Geometry and Topology of 3-manifolds

Low-dimensional topology is an extremely rich eld of study, with many dierent and interesting aspects. The aim of this project was to expand upon work previously done by I. R. Aitchison and J.H. Rubinstein on 3-manifolds admitting a polyhedral metric of non-positive curvature. The primary focus was on manifolds with cubings of non-positive curvature, and in particular the open question of whether or not every 3manifold admitting such a cubing bounds a 4-manifold admitting such a cubing. Investigation was done using combinatorial techniques, ideas from cobordism theory and surgery theory, hyperbolic geometry and the theory of Coxeter groups.
Consumer-based Approach for Smart Baggage with RFID and Smart Locks

Security with the handling of baggage is always a concern within many airports in SE Asia. This paper aims to propose a system design to improve the handling of baggage through utilizing one-time use baggage RFID tags provided by the airlines which can be tracked using a mobile phone which is associated with the tag from when the baggage was first checked in to ensure that it has been safely received by the owner only, not others. This will be complemented with a biometric scan at the exit to ensure the user has received the baggage. As part of this, current TSA locks that most baggage use at present and future possible methods to also check if it has been opened using an encrypted digital system lock to open the luggage as to see if it was an airport officer who opened it will be discussed. This system within the closed ecosystem of the airport will provide much more secure baggage handling, hence increasing the integrity of airports. An analysis of how such a system would perform in Soekarno-Hatta International airport, an international airport in Indonesia, will be discussed. The paper will be concluded with a vision of how smart airports will function with these new proposed methods in place.
Augmentation of Sensitivity of FBG Sensor Using Microwave Photonics and Nonlinear Effect

A simple and unique scheme of high-sensitive fiber Bragg grating (FBG) interrogation system based on microwave signal measurement utilizing the concept of microwave photonics and nonlinear four-wave mixing (FWM) is proposed. The concept of FWM process is combined with microwave photonics to enhance the sensitivity of FBG sensor. The strain-induced wavelength shift of the sensing FBG is translated to a higher wavelength shift of the corresponding higher order FWM signals on either side of FBG sensor wavelength in opposite directions. Thus, the wavelength separation between the FWM signals increases with a small increase in strain. Utilizing the FWM effect and microwave photonics technique, the microwave phase difference is altered, which is ultimately converted to a significant change in RF intensity. A novel mathematical relationship relating FWM, applied strain, and the microwave power of the proposed scheme is established. A high strain sensitivity of <inline-formula> <tex-math notation="LaTeX">$36.52 ~\mu \text{V}/\mu \varepsilon $ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$26.67 ~\mu \text{W}/\mu \varepsilon {)}$ </tex-math></inline-formula> is attained using the proposed FWM and microwave photonics technique against the initial sensitivity of <inline-formula> <tex-math notation="LaTeX">$4.9 ~\mu \text{V}/\mu \varepsilon $ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$0.48 ~\mu \text{W}/\mu \varepsilon {)}$ </tex-math></inline-formula> using only microwave photonics technique. The proposed scheme automatically compensates the temperature variations and eliminates the requirement of high dispersive component to enhance the sensitivity.
Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review

Diagnosis of lung diseases like asthma, chronic obstructive pulmonary disease, tuberculosis, cancer, etc., by clinicians rely on images taken through various means like X-ray and MRI. Deep Learning (DL) paradigm has magnified growth in the medical image field in current years. With the advancement of DL, lung diseases in medical images can be efficiently identified and classified. For example, DL can detect lung cancer with an accuracy of 99.49% in supervised models and 95.3% in unsupervised models. The deep learning models can extract unattended features that can be effortlessly combined into the DL network architecture for better medical image examination of one or two lung diseases. In this review article, effective techniques are reviewed under the elementary DL models, viz. supervised, semi-supervised, and unsupervised Learning to represent the growth of DL in lung disease detection with lesser human intervention. Recent techniques are added to understand the paradigm shift and future research prospects. All three techniques used Computed Tomography (C.T.) images datasets till 2019, but after the pandemic period, chest radiographs (X-rays) datasets are more commonly used. X-rays help in the economically early detection of lung diseases that will save lives by providing early treatment. Each DL model focuses on identifying a few features of lung diseases. Researchers can explore the DL to automate the detection of more lung diseases through a standard system using datasets of X-ray images. Unsupervised DL has been extended from detection to prediction of lung diseases, which is a critical milestone to seek out the odds of lung sickness before it happens. Researchers can work on more prediction models identifying the severity stages of multiple lung diseases to reduce mortality rates and the associated cost. The review article aims to help researchers explore Deep Learning systems that can efficiently identify and predict lung diseases at enhanced accuracy.
Determining sentiment views of verbal multiword expressions using linguistic features

We examine the binary classification of sentiment views for verbal multiword expressions (MWEs). Sentiment views denote the perspective of the holder of some opinion. We distinguish between MWEs conveying the view of the speaker of the utterance (e.g., in “The company reinvented the wheel” the holder is the implicit speaker who criticizes the company for creating something already existing) and MWEs conveying the view of explicit entities participating in an opinion event (e.g., in “Peter threw in the towel” the holder is Peter having given up something). The task has so far been examined on unigram opinion words. Since many features found effective for unigrams are not usable for MWEs, we propose novel ones taking into account the internal structure of MWEs, a unigram sentiment-view lexicon and various information from Wiktionary. We also examine distributional methods and show that the corpus on which a representation is induced has a notable impact on the classification. We perform an extrinsic evaluation in the task of opinion holder extraction and show that the learnt knowledge also improves a state-of-the-art classifier trained on BERT. Sentiment-view classification is typically framed as a task in which only little labeled training data are available. As in the case of unigrams, we show that for MWEs a feature-based approach beats state-of-the-art generic methods.
On $(a,b)$-balancing numbers

Year: 2010 Vol.: 77 Fasc. : 3-4 Title: On (a; b)-balancing numbers Author(s): TAunde Kov¶acs , K¶alm¶an Liptai and P¶eter Olajos A positive integer n is called a balancing number if 1+: : : +(n i 1) = (n + 1) + ¢ ¢ ¢ + (n + r) for some positive integer r. Balancing numbers and their generaliza- tions have been investigated by several authors, from many aspects. In this paper we introduce the concept of balancing numbers in arithmetic progressions, and prove several e®ective ¯niteness and explicit results about them. In the proofs of our results, among others, we combine Baker's method, the modular method developed by Wiles and others, a result of Bennett about the diophantine equation jaxn i bynj = 1, the Chabauty method and the theory of elliptic curves. Address: TAunde Kov¶acs Institute of Mathematics University of Debrecen H-4010 Debrecen, P.O. Box 12 Hungary Address: K¶alm¶an Liptai Institute of Mathematics and Informatics Eszterh¶azy K¶aroly College H-3300 Eger, Eszterh¶azy t¶er 1 Hungary Address: P¶eter Olajos Department of Applied Mathematics University of Miskolc H-3515 Miskolc-Egyetemv¶aros Hungary
The Chemical Space of Marine Antibacterials: Diphenyl Ethers, Benzophenones, Xanthones, and Anthraquinones

The emergence of multiresistant bacteria and the shortage of antibacterials in the drug pipeline creates the need to search for novel agents. Evolution drives the optimization of the structure of marine natural products to act as antibacterial agents. Polyketides are a vast and structurally diverse family of compounds that have been isolated from different marine microorganisms. Within the different polyketides, benzophenones, diphenyl ethers, anthraquinones, and xanthones have shown promising antibacterial activity. In this work, a dataset of 246 marine polyketides has been identified. In order to characterize the chemical space occupied by these marine polyketides, molecular descriptors and fingerprints were calculated. Molecular descriptors were analyzed according to the scaffold, and principal component analysis was performed to identify the relationships among the different descriptors. Generally, the identified marine polyketides are unsaturated, water-insoluble compounds. Among the different polyketides, diphenyl ethers tend to be more lipophilic and non-polar than the remaining classes. Molecular fingerprints were used to group the polyketides according to their molecular similarity into clusters. A total of 76 clusters were obtained, with a loose threshold for the Butina clustering algorithm, highlighting the large structural diversity of the marine polyketides. The large structural diversity was also evidenced by the visualization trees map assembled using the tree map (TMAP) unsupervised machine-learning method. The available antibacterial activity data were examined in terms of bacterial strains, and the activity data were used to rank the compounds according to their antibacterial potential. This potential ranking was used to identify the most promising compounds (four compounds) which can inspire the development of new structural analogs with better potency and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties.