Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Petrophysical Properties of Shale Rock Using Conventional Neural Networks CNN

Digital Rock Physics (DRP) serves as a powerful computational tool for analyzing the petrophysical properties of rock. Obtaining the properties of a fine-grained sample, such as shale is very challenging due to its highly variable and complex nature. Capturing the micro-features of this structure requires advanced microscopy techniques such as SEM (scanning electron microscopy) and FIB-SEM (Focused ion beam- scanning electron microscopy). However, performing advanced microscopy techniques to capture the heterogeneity of the sample is quite difficult; the slow speeds of data collection and analysis are two critical problems that limit more extensive use of this technology. In this study, an alternative approach is proposed to quantify the physical properties of the rock sample. This study aims to accelerate the process of SEM image analysis and reduce the computational cost by using machine learning. A deep learning-based method, Convolutional Neural Networks (CNN), is utilized to predict the properties from the 2D grayscale SEM images of Marcellus shale. The image data set is segmented by applying watershed segmentation to extract the pore network of the sample. Porosity and average pore size are the two properties computed for this study. The SEM images are down sampled to low-resolution images which are fed as an input, and the computed properties are used for training and validation of the CNN network. A detailed description of the image segmentation process, CNN architecture and the predicted results are discussed in this work.
Evaluation of Low-Cost Sensors for Weather and Carbon Dioxide Monitoring in Internet of Things Context

In a context of increased environmental awareness, the Internet of Things has allowed individuals or entities to build their own connected devices to share data about the environment. These data are often obtained from widely available low-cost sensors. Some companies are also selling low-cost sensing kits for in-house or outdoor use. The work described in this paper evaluated, in the short term, the performance of a set of low-cost sensors for temperature, relative humidity, atmospheric pressure and carbon dioxide, commonly used in these platforms. The research challenge addressed with this work was assessing how trustable the raw data obtained from these sensors are. The experiments made use of 18 climatic sensors from six different models, and they were evaluated in a controlled climatic chamber that reproduced controlled situations for temperature and humidity. Four CO2 sensors from two different models were analysed through exposure to different gas concentrations in an indoor environment. Our results revealed temperature sensors with a very high positive coefficient of determination (r2 ≥ 0.99), as well as the presence of bias and almost zero random error; the humidity sensors demonstrated a very high positive correlation (r2 ≥ 0.98), significant bias and small-yet-relevant random error; the atmospheric pressure sensors presented good reproducibility, but further studies are required to evaluate their accuracy and precision. For carbon dioxide, the non-dispersive infra-red sensors demonstrated very satisfactory results (r2 ≥ 0.97, with a minimum root mean squared error (RMSE) value of 26 ppm); the metal oxide sensors, despite their moderate results (minimum RMSE equal to 40 ppm and r2 of 0.8–0.96), presented hysteresis, environmental dependence and even positioning interference. The results suggest that most of the evaluated low-cost sensors can provide a good sense of reality at a very good cost–benefit ratio in certain situations.
CANDOCK: Chemical atomic network based hierarchical flexible docking algorithm using generalized statistical potentials

Small molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations, such as, ignoring interactions with essential components in the chemical environment of the binding pocket (e.g. cofactors, metal-ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and they are unable to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample chemical relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions and cofactors interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind and Astex proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions, such that, the statistical score of best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best docked pose with biological activity.
Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor

Photoplethysmography (PPG) biometric recognition has recently received considerable attention and is considered to be a promising biometric trait. Although some promising results on PPG biometric recognition have been reported, challenges in noise sensitivity and poor robustness remain. To address these issues, a PPG biometric recognition framework is presented in this article, that is, a PPG biometric recognition model based on a sparse softmax vector and k-nearest neighbor. First, raw PPG data are rerepresented by sliding window scanning. Second, three-layer features are extracted, and the features of each layer are represented by a sparse softmax vector. In the first layer, the features are extracted by PPG data as a whole. In the second layer, all the PPG data are divided into four subregions, then four subfeatures are generated by extracting features from the four subregions, and finally, the four subfeatures are averaged as the second layer features. In the third layer, all the PPG data are divided into 16 subregions, then 16 subfeatures are generated by extracting features from the 16 subregions, and finally, the 16 subfeatures are averaged as the third layer features. Finally, the features with first, second, and third layers are combined into three-layer features. Extensive experiments were conducted on three PPG datasets, and it was found that the proposed method can achieve a recognition rate of 99.95%, 97.21%, and 99.92% on the respective sets. The results demonstrate that the proposed method can outperform current state-of-the-art methods in terms of accuracy.
Sentiment analysis of tweets using a unified convolutional neural network‐long short‐term memory network model

Sentiment analysis focuses on identifying and classifying the sentiments expressed in text messages and reviews. Social networks like Twitter, Facebook, and Instagram generate heaps of data filled with sentiments, and the analysis of such data is very fruitful when trying to improve the quality of both products and services alike. Classic machine learning techniques have a limited capability to efficiently analyze such large amounts of data and produce precise results; they are thus supported by deep learning models to achieve higher accuracy. This study proposes a combination of convolutional neural network and long short‐term memory (CNN‐LSTM) deep network for performing sentiment analysis on Twitter datasets. The performance of the proposed model is analyzed with machine learning classifiers, including the support vector classifier, random forest (RF), stochastic gradient descent (SGD), logistic regression, a voting classifier (VC) of RF and SGD, and state‐of‐the‐art classifier models. Furthermore, two feature extraction methods (term frequency‐inverse document frequency and word2vec) are also investigated to determine their impact on prediction accuracy. Three datasets (US airline sentiments, women's e‐commerce clothing reviews, and hate speech) are utilized to evaluate the performance of the proposed model. Experiment results demonstrate that the CNN‐LSTM achieves higher accuracy than those of other classifiers.
Influence of terpenes and terpenoids on the phase behavior of micro- and macro-emulsions

Comprendre les interactions du parfum avec les formulations cosmetiques represente un defi pour les parfumeurs. Predire l'influence des parfums sur le comportement de phase des microemulsions et des emulsions est un besoin crucial pour le formulateur. Dans ce contexte, notre travail est consacre a la determination du comportement des phases, en utilisant le concept du HLD, de systemes ternaires bases sur les CiE4 purs et une serie de terpenes comme huiles. Chaque systeme est etudie en localisant le point X (T*, C*) dans le diagramme Fish et les terpenes ont ete caracterises par leur EACN permettant une classification des differents parfums etudies. L’evolution des valeurs d'EACN a ete expliquee en considerant les changements structurels des terpenes. Un modele QSPR pour la prevision de T* en fonction de la structure chimique des huiles a ete etabli. Les resultats sont discutes en termes d’evolution du parametre d’empilement effectif du tensioactif selon le degre de penetration de l'huile dans le film interfacial et la temperature. La presence d’une huile polaire peut avoir des consequences significatives non seulement sur la PIT du systeme mais egalement sur la stabilite des emulsions formulees avec des alcanes ou des silicones. L’influence des derives terpeniques sur la PIT d'une emulsion formulee par le systeme Brij30/octane/eau en fonction de leurs concentrations est rapportee et discutee. L'equation du HLD rationnalise l'effet du parfum sur la PIT en comparant leurs parametres a.f(A) avec une serie d'alcools lineaires. L'effet de la structure de la chaine hydrophobe et la nature de la fonction polaire sur leurs proprietes cotensioactives sont discutes. Finalement, la stabilite de l'emulsion Brij30/octane/eau est etudiee et discutee de meme qu’une emulsion cosmetique est preparee et sert de modele pour evaluer l'influence des alcools terpeniques sur sa stabilite.
Memory recovery is related to default mode network impairment and neurite density during brain tumours treatment

The aim of this study is to test brain tumour interactions with brain networks thereby identifying protective features and risk factors for memory recovery after surgical resection.Seventeen patients with diffuse non-enhancing glioma (aged 22-56 years) were longitudinally MRI-scanned before and after surgery, and during a 12-months recovery period (47 MRI in total after exclusion). After each scanning session, a battery of memory tests was performed using a tablet-based screening tool, including free verbal memory, overall verbal memory, episodic memory, orientation, forward digit span and backwards digit span. Using structural MRI and Neurite Orientation Dispersion and Density Imaging (NODDI) derived from diffusion-weighted images, we respectively estimated lesion overlap and Neurite Density with brain networks derived from normative data in healthy participants (somato-motor, dorsal attention, ventral attention, fronto-parietal and Default Mode Network -DMN-). Linear Mixed Models (LMMs) that regressed out the effect of age, gender, tumour grade, type of treatment, total lesion volume and total neurite density were used to test the potential longitudinal associations between imaging markers and memory recovery.Memory recovery was not significantly associated with tumour location based on traditional lobe classification nor with the type of treatment received by patients (i.e. surgery alone or surgery with adjuvant chemoradiotherapy). Non-local effects of tumours were evident on Neurite Density, which was reduced not only within the tumour, but also beyond the tumour boundary. In contrast, high preoperative Neurite Density outside the tumour, but within the DMN, was associated with better memory recovery (LMM, Pfdr<10−3). Furthermore, postoperative and follow-up Neurite Density within the DMN and fronto-parietal network were also associated with memory recovery (LMM, Pfdr=0.014 and Pfdr=0.001, respectively). Preoperative tumour, and post-operative lesion, overlap with the DMN showed a significant negative association with memory recovery (LMM, Pfdr=0.002 and Pfdr<10−4, respectively). Imaging biomarkers of cognitive recovery and decline can be identified using NODDI and resting-state networks. Brain tumours and their corresponding treatment affecting brain networks that are fundamental for memory functioning such as the DMN can have a major impact on patient’s memory recovery.
Analysis of the Electronic Structure of Modified CuGaS2 with Selected Substitutional Impurities: Prospects for Intermediate-Band Thin-Film Solar Cells Based on Cu-Containing Chalcopyrites

The electronic structure of modified CuGaS2, which belongs to the family of Cu-containing chalcopyrites, has been analyzed from first principles within the density functional theory. The chalcopyrite matrix has been modified by introducing a high concentration of atomic impurities that included transition metals and elements of group IVa at substitutional sites of the lattice host. For selected cases, an intermediate band has been found that potentially fulfills the requirements as stated for intermediate-band solar cell materials. Preliminary thermochemical estimations of the stability of the compounds proposed against eventual secondary phases in the form of binary chalcogenides have greatly simplified the general screening. Elements of groups VIIIb and IVa have been identified as interesting impurity candidates to obtain intermediate bands within the main gap of the modified ternary host. Additionally, modified chalcopyrite compounds with potential applications as magnetic semiconductors or spintronic .
The Temperature Prediction of Permanent Magnet Synchronous Machines Based on Proximal Policy Optimization

Accurate temperature prediction plays an important role in the thermal protection of permanent magnet synchronous motors. A temperature prediction method of permanent magnet synchronous machines (PMSMs) based on proximal policy optimization is proposed. In the proposed method, the actor-critic framework of reinforcement learning is introduced to model the effective temperature prediction mechanism, and the correlations between the input features are then analyzed to select the appropriate input features. Finally, the simplified proximal policy optimization algorithm is introduced to optimize the value of the prediction temperature of PMSMs. Experimental results reveal the high accuracy and reliability of the proposed method compared with an exponential weighted moving average method (EWMA), a recurrent neural network (RNN), and long short-term memory (LSTM).
Interval optimization based operational strategy of integrated energy system under renewable energy resources and loads uncertainties

Uncertainties from renewable energy resources (RESs) and energy demands have brought enormous challenges to the optimal operation of integrated energy system (IES). An interval optimization based operational strategy for IES is proposed to overcome uncertainties. Firstly, embarking from a deterministic IES operation model, an interval method is presented to quantify the uncertainties instead of possibility distribution so as to better characterize the impact of RESs and loads on the operation of the IES. Secondly, the interval optimization model under multiple uncertainties is presented. In the proposed model, the total daily cost is optimized and system operation constraints are fully considered. Thirdly, the order interval relation and possibility degree are adopted to transform the interval model to deterministic model, which is solved by CPLEX optimizer. Finally, case studies considering influence of different uncertainty objects and uncertainty possibility degree levels are performed and analyzed extensively. The simulation results show that the optimized interval numbers will be increased gradually as uncertainty fluctuation degree increased from ±5% to ±25%. Comparing with automatic robust convex optimization method, the robust optimized values are in accordance with the upper values of optimized interval number optimization method, and the midpoints of interval results optimized by interval method are 4.1%, 8.7%, 11.7%, 16.5%, and 8.0% less than robust optimization results, respectively.
Left and right G-outer inverses

The main contribution of this paper is to present new generalized inverses as weaker versions of a G-outer inverse. In particular, we define and characterize left and right G-outer inverses of rectangular matrices. Solvability of matrix equation systems as AXA = AEA and BAEAX = B; or AXA = AEA and XAEAD = D, where , , and , is studied by means of left and right G-outer inverses. The general solution forms of these systems give descriptions of the sets of all left and right G-outer inverses. Using left and right G-outer inverses, we introduce new partial orders and establish their relations with minus partial order and space pre-order. We apply these results to present and investigate left and right G-Drazin inverses of square matrices and corresponding partial orders.
Management of Soil-Borne Fungi and Root-Knot Nematodes in Cucurbits through Breeding for Resistance and Grafting

Soil-borne pathogenic fungi (SBPF) and root-knot nematodes (RKN) co-exist in the rhizosphere and are major pathogens causing root diseases in cucurbits. Current knowledge on soil-borne pathogens of cucurbit crops grown under protected cultivation, their host-pathogen interactions, and mechanisms of resistance has been reviewed. Plant resistance is an effective and sustainable method to control soil-borne diseases and the available resistant cultivars and rootstocks to key soil-borne pathogens are reported. The importance of proper pathogen diagnosis in the right choice of cultivar or rootstock is highlighted because of the specificity in the response of the cucurbit crops to fungal and nematode species and races. Plants protect themselves through common mechanisms of resistance against SBPF and RKN including hardening of their cell walls, pathogenesis-related (PR) proteins, and production of antimicrobial molecules. The activity of some enzymes, such as peroxidases and phenylalanine lyase, is increased after pathogen infection and is higher on SBPF and RKN resistant than susceptible cucurbits. Plant hormones such as salicylic acid, jasmonic acid, and ethylene are involved in the response of cucurbits to SBPF. Most mechanisms of resistance to RKN affect post-infection development of the nematode, which results in a delay or disruption of the life cycle. Traditional and biotechnological tools used for breeding for resistance in cucurbits are described. Grafting is an effective non-host resistance method to control primarily Fusarium wilt but not to control RKN. However, new rootstocks with resistance to both pathogens have been developed recently and their effects on fruit quality and yield stability need additional studies. The impact of grafting on yield in pathogen-infested soils is discussed.
Effects of substrates on the performance of optoelectronic devices: A review

Abstract This review discusses the effects of substrates on devices fabricated for optoelectronic applications. It includes the types and characteristics of substrates, synthesis and fabrication of substrates, and the influence of substrates on the optical properties, surface morphology and current-voltage behaviour of optoelectronic devices. The study showed that two main types of substrates: planar and textured are commonly used in the industry. Flexibility, semi-rigidity and rigidity are characteristics of the substrates and they vary in modulus, transparency and texture. Whereas glass and metal substrates can be produced via melt casting, polydimethylsiloxane (PDMS), polyethylene terephthalate (PET), etc are produced by crosslinking polymer base materials with curing agents. The mechanical and current-voltage characteristics are also shown for planar and textured substrate-based devices. The textured substrates showed ridges, wrinkles, buckled surface morphology whereas the planar showed uniform and largely flat morphology. Textured substrates also recorded higher optical absorbance and improved device efficiencies compared with planar substrates. The molecular configuration of the polymer chains are edged-on for planar substrates and both edge-on and face-on for textured substrates. The findings and their implications have been discussed to highlight the importance of substrates in the fabrication and performance of optoelectronic devices.
Unravelling hagfish slime

Hagfish slime is a unique predator defence material containing a network of long fibrous threads each ∼10 cm in length. Hagfish release the threads in a condensed coiled state known as skeins (∼100 µm), which must unravel within a fraction of a second to thwart a predator attack. Here we consider the hypothesis that viscous hydrodynamics can be responsible for this rapid unravelling, as opposed to chemical reaction kinetics alone. Our main conclusion is that, under reasonable physiological conditions, unravelling due to viscous drag can occur within a few hundred milliseconds, and is accelerated if the skein is pinned at a surface such as the mouth of a predator. We model a single skein unspooling as the fibre peels away due to viscous drag. We capture essential features by considering simplified cases of physiologically relevant flows and one-dimensional scenarios where the fibre is aligned with streamlines in either uniform or uniaxial extensional flow. The peeling resistance is modelled with a power-law dependence on peeling velocity. A dimensionless ratio of viscous drag to peeling resistance appears in the dynamical equations and determines the unraveling time scale. Our modelling approach is general and can be refined with future experimental measurements of peel strength for skein unravelling. It provides key insights into the unravelling process, offers potential answers to lingering questions about slime formation from threads and mucous vesicles, and will aid the growing interest in engineering similar bioinspired material systems.