The accuracy of AVA approximations in isotropic media assessed via synthetic numerical experiments: Implications for the determination of porosity

Analyses of seismic amplitude vs. angle are widely used to estimate hydrocarbon reservoir properties. This paper investigates the accuracy of existing approximations based on the Zoeppritz equation, using synthetic numerical experiments that correlate P-wave reflectivity in isotropic media with reservoir porosity. An effective medium non-interacting approach (NIA) in rock physics modelling was used to compute the properties of fluid-saturated (water + gas) reservoir, which were then used in seismic modelling. In parallel, a Bayesian approach was used to estimate reservoir porosities from angle-dependent reflection coefficients and seismic amplitudes. A Maximum a posteriori solution of the Bayesian approach was also utilised to obtain an inverted porosity distribution in the reservoir model. The results of our forward models are important as they suggest that most of the approximations deviate from the exact Zoeppritz solutions with increasing angles of incidence of seismic waves. The results from the Bayesian inversion show that the Rüger and Bortfeld approximations agree with the exact Zoeppritz solutions to accurately estimate reservoir porosity. All the other approximations, except for Smith's, underestimate reservoir porosity and should be used in pre-stack inversion with caution. Smith's and Fatti's approximations failed to estimate reservoir porosity because of associated uncertaintie

Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques

Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various computer-aided diagnosis (CAD) systems have previously been developed and are being used to aid radiologists in the diagnosis of cancer cells. However, due to intrinsic risks associated with the delayed and/or incorrect diagnosis, it is indispensable to improve the developed diagnostic systems. In this regard, machine learning has recently been playing a potential role in the early and precise detection of breast cancer. This paper presents a new machine learning-based framework that utilizes the Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and Multilayer Perception approaches to efficiently predict breast cancer from the patient data. For this purpose, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset has been utilized and classified using a hybrid Multilayer Perceptron Model (MLP) and 5-fold cross-validation framework as a working prototype. For the improved classification, a connection-based feature selection technique has been used that also eliminates the recursive features. The proposed framework has been validated on two separate datasets, i.e., the Wisconsin Prognostic dataset (WPBC) and Wisconsin Original Breast Cancer (WOBC) datasets. The results demonstrate improved accuracy of 99.12% due to efficient data preprocessing and feature selection applied to the input data

Sensor Fusion for Identification of Freezing of Gait Episodes Using Wi-Fi and Radar Imaging

Parkinson’s disease (PD) is a progressive and neurodegenerative condition causing motor impairments. One of the major motor related impairments that present biggest challenge is freezing of gait (FOG) in Parkinson’s patients. In FOG episode, the patient is unable to initiate, control or sustain a gait that consequently affects the Activities of Daily Livings (ADLs) and increases the occurrence of critical events such as falls. This paper presents continuous monitoring ADLs and classification freezing of gait episodes using Wi-Fi and radar imaging. The idea is to exploit the multi-resolution scalograms generated by channel state information (CSI) imprint and micro-Doppler signatures produced by reflected radar signal. A total of 120 volunteers took part in experimental campaign and were asked to perform different activities including walking fast, walking slow, voluntary stop, sitting down & stand up and freezing of gait. Two neural networks namely Autoencoder and a proposed enhanced Autoencoder were used classify ADLs and FOG episodes using data fusion process by combining the images acquired from both sensing techniques. The Autoencoder provided overall classification accuracy of ~87% for combined datasets. The proposed algorithm provided significantly better results by presenting an overall accuracy of ~98% using data fusion

L'intégration d’un retour sensoriel dans une interface cerveau-machine corticale en boucle fermée nécessite une somatotopie

Le développement des interfaces cerveau-machine (ICM) apporte une nouvelle perspective aux patients en perte d'autonomie motrice. En combinant des enregistrements en ligne de l'activité cérébrale avec un algorithme de décodage, les patients peuvent apprendre à contrôler un bras robotique afin d'effectuer des actions simples. Cependant, contrairement à la grande quantité d'informations somatosensorielles fournie par les membres physiologiques vers le cerveau, les ICM actuelles sont dépourvues de capteurs de toucher et de force. Les patients doivent donc se fier uniquement à la vision et à l'audition, qui sont inadaptées au contrôle d'une prothèse. Cela contraste avec le fait que dans le cas d’un membre sain, les entrées somatosensorielles seules peuvent guider efficacement la manipulation d'un objet fragile, ou assurer une trajectoire précise. Une caractéristique intéressante des entrées somatosensorielles est leur organisation topologique à la surface corticale. Cette carte corticale semble jouer un rôle déterminant dans la perception sensorielle. Par conséquent, l'intégration d'une rétroaction somatosensorielle artificielle alignée sur cette carte corticale pourrait aider grandement l’intégration avoir un impact déterminant dans le transporter un grand nombre d'informations. Pour vérifier cette hypothèse, nous avons développé un ICM chez la souris qui inclut une riche rétroaction corticale artificielle de type somatosensoriel. Notre installation comprend des enregistrements en ligne de l'activité de plusieurs neurones dans le cortex moteur primaire du whisker (wM1), et fournit une rétroaction simultanée via une photostimulation du cortex somatosensoriel primaire du whisker (wS1), à faible latence, haute fréquence et structure spatiale, basée sur une cartographie obtenue par imagerie intrinsèque. Nous démontrons le fonctionnement de la boucle et montrons que les souris peuvent détecter l’activité des neurons dans wS1 déclenchée par les photostimulations. Surtout, nous montrons qu'en utilisant l'ICM en boucle fermée, les souris peuvent avoir une meilleure performance dans une tâche comportementale lorsque la structure du feedback artificiel est respecté somatotopie connue de wS1.The development of brain-machine interfaces (BMIs) brings a new perspective to patients with a loss of motor autonomy. By combining online recordings of brain activity with a decoding algorithm, patients can learn to control a robotic arm in order to perform simple actions. However, in contrast to the vast amounts of somatosensory information channeled by limbs to the brain, current BMIs are devoid of touch and force sensors. Patients must therefore rely solely on vision and audition, which are maladapted to the control of a prosthesis. In contrast, in a healthy limb, somatosensory inputs alone can efficiently guide the handling of a brittle object, or ensure a smooth trajectory. One interesting feature of somatosensory inputs is its topological organization at the cortical surface. This cortical map plays a role in sensory perception. Therefore, integrating artificial somatosensory feedback aligned and consistent with this cortical map could potentially help the subject to decode the information conveyed by the feedback. To test this hypothesis, we have developed a BMI in the mouse model that includes a rich artificial somatosensory-like cortical feedback. Our setup includes online recordings of the activity of multiple neurons in the whisker primary motor cortex (wM1), and delivers feedback simultaneously via a low-latency, high-refresh rate photo-stimulation of the whisker primary somatosensory cortex (wS1) that is spatially structured at the mesoscopic scale, based on a mapping obtained by intrinsic imaging. We demonstrate the operation of the loop and show that mice can detect the wS1 neuronal spiking triggered by the photostimulations. Remarkably, we show that in the closed loop BMI, mice can have a significantly better performance in a behavioral task when the structure of the artificial feedback abides to the known wS1 somatotopy

A fast intracortical brain–machine interface with patterned optogenetic feedback

International audienceThe development of brain-machine interfaces (BMIs) brings new prospects to patients with a loss of autonomy. By combining online recordings of brain activity with a decoding algorithm, patients can learn to control a robotic arm in order to perform simple actions. However, in contrast to the vast amounts of somatosensory information channeled by limbs to the brain, current BMIs are devoid of touch and force sensors. Patients must therefore rely solely on vision and audition, which are maladapted to the control of a prosthesis. In contrast, in a healthy limb, somatosensory inputs alone can efficiently guide the handling of a fragile object, or ensure a smooth trajectory. We have developed a BMI in the mouse that includes a rich artificial somatosensory-like cortical feedback

Adsorptive defluoridation from aqueous solution using a novel blend of eggshell powder and chitosan nanofibers

Groundwater mostly contains many impurities thus can not be consumed as drinking water directly. The acceptable limit of fluoride in drinking water is 0.5–1.5 mg l ^−1 recommended by World Health Organization (WHO). In this research, a novel nanofiber hybrid; based on Chitosan (CTS) and Eggshell (EGG) was prepared via electrospinning technique and investigated for deflouridation from aqueous solution. SEM images reveal bead-free, smooth morphology and the FTIR confirmed the presence of chitosan and egg within the novel nanofiber blend. The defluoridation efficiency was assessed by varying the different parameters like pH, mass of nanofibers, contact time and initial concentration for adsorption. Studies revealed that CTS/EGG nanofibers hybrid shows incredible adsorption efficiency of 86%. Furthermore, isotherm studies show that the Langmuir isotherm model was well fitted for both CTS and CTS/EGG nanofibers

Enhancing Drought Tolerance in Wheat Cultivars through Nano-ZnO Priming by Improving Leaf Pigments and Antioxidant Activity

Climate change, global warming, stagnant productivity of wheat and food security concerns owing to frequent spells of drought stress (DS) have necessitated finding biologically viable drought-mitigation strategies. A trial was conducted to test two promising wheat cultivars (Ujala-16 and Zincol-16) that were subjected to pre-sowing priming treatments with different doses of ZnO nanoparticles (NPs = 40, 80, 120 and 160 ppm) under 50% and 100% field capacity (FC) conditions. The ZnO NPs were prepared with a co-precipitation method and characterized through X-ray diffraction (XRD) and with a scanning electron microscope (SEM). For comparison purposes, untreated seeds were sown as the control treatment. The response variables included botanical traits (lengths, fresh and dry wrights of root and shoot), chlorophyll (a, b and total) contents, antioxidant and proline contents and nutrients status of wheat cultivars. The results showed that DS significantly decreased all traits of wheat cultivars, while ZnO NPs, especially the 120 ppm dose, remained superior by increasing all botanical traits at 100% FC. In addition, ZnO NPs increased the chlorophyll a (1.73 mg/g FW in Ujala-16 and 1.75 mg/g FW in Zincole-16) b (0.70 mg/g FW in Ujala-16 and 0.71 mg/g FW in Zincole-16) and total chlorophyll content (2.43 mg/g FW in Ujala-16 and 2.46 mg/g FW in Zincole-16) by improving the activity of antioxidant and proline content. Moreover, plant nutrients such as Ca, Mg, Fe, N, P, K, and Zn contents were increased by ZnO NPs, especially in the Zincol-16 cultivar. To summarize, Zincol-16 remains superior to Ujala-16, while ZnO NPs (120 ppm dose under 100% FC) increases the growth and mineral contents of both wheat varieties. Thus, this combination might be recommended to wheat growers after testing further in-depth evaluation of more doses of ZnO NPs