Search results for: deep wells

Authors: Vyacheslav Furtak, Merja Roivainen, Olga Mirochnichenko, Majid Laassri, Bella Bidzhieva, Tatiana Zagorodnyaya, Vladimir Chizhikov, Konstantin Chumakov

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Molecular epidemiology and environmental surveillance are parts of a rational strategy to control infectious diseases. They have been widely used in the worldwide campaign to eradicate poliomyelitis, which otherwise would be complicated by the inability to rapidly respond to outbreaks and determine sources of the infection. The conventional scheme involves isolation of viruses from patients and the environment, followed by their identification by nucleotide sequences analysis to determine phylogenetic relationships. This is a tedious and time-consuming process that yields definitive results when it may be too late to implement countermeasures. Because of the difficulty of high-throughput full-genome sequencing, most such studies are conducted by sequencing only capsid genes or their parts. Therefore the important information about the contribution of other parts of the genome and inter- and intra-species recombination to viral evolution is not captured. Here we propose a new approach based on the rapid concentration of sewage samples with tangential flow filtration followed by deep sequencing and reconstruction of nucleotide sequences of viruses present in the samples. The entire nucleic acids content of each sample is sequenced, thus preserving in digital format the complete spectrum of viruses. A set of rapid algorithms was developed to separate deep sequence reads into discrete populations corresponding to each virus and assemble them into full-length consensus contigs, as well as to generate a complete profile of sequence heterogeneities in each of them. This provides an effective approach to study molecular epidemiology and evolution of natural viral populations.

Keywords: poliovirus, eradication, environmental surveillance, laboratory diagnosis

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Authors: Ishraq M. Anjum

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Distributed Bragg reflectors are used in vertical-cavity surface-emitting lasers (VCSELs) in order to achieve very high reflectivity. Use of metamaterial in place of distributed Bragg reflector can reduce the device size significantly. A silicon-based metamaterial near perfect reflector is designed to be used in place of distributed Bragg reflectors in VCSELs. Mie resonance in dielectric microparticles is exploited in order to design the metamaterial. A reflectivity of 98.31% is achieved using finite-difference time-domain method. An 808nm double intra-cavity contacted VCSEL structure with 1.5 λ cavity is proposed using this metamaterial near perfect reflector. The active region is designed to be composed of seven GaAs/AlGaAs quantum wells. Upon numerical investigation of the designed VCSEL structure, the threshold current is found to be 2.96 mA at an aperture of 40 square micrometers and the maximum output power is found to be 71 mW at a current of 141 mA. Miniaturization of conventional VCSELs is possible using this design.

Keywords: GaAs, LASER, metamaterial, VCSEL, vertical cavity surface emitting laser

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Authors: F. Ghazalnaz Sharifonnasabi, Iman Makhdoom

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Breast cancer is a serious health concern that affects many people around the world. According to a study published in the Breast journal, the global burden of breast cancer is expected to increase significantly over the next few decades. The number of deaths from breast cancer has been increasing over the years, but the age-standardized mortality rate has decreased in some countries. It’s important to be aware of the risk factors for breast cancer and to get regular check- ups to catch it early if it does occur. Machin learning techniques have been used to aid in the early detection and diagnosis of breast cancer. These techniques, that have been shown to be effective in predicting and diagnosing the disease, have become a research hotspot. In this study, we consider two deep learning approaches including: Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). We also considered the five-machine learning algorithm titled: Decision Tree (C4.5), Naïve Bayesian (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Algorithm and XGBoost (eXtreme Gradient Boosting) on the Breast Cancer Wisconsin Diagnostic dataset. We have carried out the process of evaluating and comparing classifiers involving selecting appropriate metrics to evaluate classifier performance and selecting an appropriate tool to quantify this performance. The main purpose of the study is predicting and diagnosis breast cancer, applying the mentioned algorithms and also discovering of the most effective with respect to confusion matrix, accuracy and precision. It is realized that CNN outperformed all other classifiers and achieved the highest accuracy (0.982456). The work is implemented in the Anaconda environment based on Python programing language.

Keywords: breast cancer, multi-layer perceptron, Naïve Bayesian, SVM, decision tree, convolutional neural network, XGBoost, KNN

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Authors: S. Sharma, Gaballa Aqeelah, Tawfig Alghbaili, Ali Elmessmari

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There were abrupt discontinuities in the Brute Stack in the northernmost locations during the acquisition of 2D (2007) and 3D (2021) seismic data in the northwest region of the Ghadames Basin, Libya. In both campaigns, complete fluid circulation loss was seen in these regions during up-hole drilling. Geophysics, sedimentology and shallow subsurface geology were all integrated to look into what was causing the seismic signal to disappear at shallow depths. The Upper Cretaceous Nalut Formation is the near-surface or surface formation in the studied area. It is distinguished by abnormally high resistivity in all the neighboring wells. The Nalut Formation in all the nearby wells from the present study and previous outcrop study suggests lithology of dolomite and chert/flint in nodular or layered forms. There are also reports of karstic caverns, vugs, and thick cracks, which all work together to produce the high resistivity. Four up-hole samples that were analyzed for microfacies revealed a near-coastal to tidal environment. Algal (Chara) infested deposits up to 30 feet thick and monotonous, very porous, are seen in two up-hole sediments; these deposits are interpreted to be scattered, continental algal travertine mounds. Chert/flint, dolomite, and calcite in varying amounts are confirmed by XRD analysis. Regional tracking of the high resistivity of the Nalut Formation, which is thought to be connected to the sea level drop that created the paleokarst layer, is possible. It is abruptly overlain by a blanket marine transgressive deposit caused by rapid sea level rise, which is a regional, relatively high radioactive layer of argillaceous limestone. The examined area's close proximity to the mountainous, E-W trending ridges of northern Libya made it easier for recent freshwater circulation, which later enhanced cavern development and mineralization in the paleokarst layer. Seismic signal loss at shallow depth is caused by extremely heterogeneous mineralogy of pore- filling or lack thereof. Scattering effect of shallow karstic layer on seismic signal has been well documented. Higher velocity inflection points at shallower depths in the northern part and deeper intervals in the southern part, in both cases at Nalut level, demonstrate the layer's influence on the seismic signal. During the Permian-Carboniferous, the Ghadames Basin underwent uplift and extensive erosion, which resulted in this karstic layer of the Nalut Formation uplifted to a shallow depth in the northern part of the studied area weakening the acoustic signal, whereas in the southern part of the 3D acquisition area the Nalut Formation remained at the deeper interval without affecting the seismic signal. Results from actions taken during seismic processing to deal with this signal loss are visible and have improved. This study recommends using denser spacing or dynamite to circumvent the karst layer in a comparable geographic area in order to prevent signal loss at lesser depths.

Keywords: well logging, seismic data acquisition, sesimic data processing, up-holes

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Authors: Aswan Aswan

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Ichnofossils were examined based on two wells cores of Jurassic sediment from Bintuni Basin, West Papua, Indonesia. The cores are the Jurassic interval and known as the potential reservoir interval in this area. Representative of 18 ichnogenera was recorded including forms assigned to Arenicolites, Asterosoma, Bergaueria, Chondrites, cryptic bioturbation, Glossifungites, Lockeia, Ophiomorpha, Palaeophycus, Phycosiphon, Planolites, Rhizocorallium, Rosselia, root structure, Skolithos, Teichicnus, Thalassinoides, and Zoophycos. The two cores represent a depositional system that is dominated by tidal flat, shallow marine shelf continuum possibly crossed by estuaries or tidal shoals channels. From the first core identified two deepening cycles. The shallow one is a shallow marine with tidal influence while the deeper one attached to the shelf. Shallow interval usually indicates by appearances of Ophiomorpha and Glossifungites while the deeper shallow marine interval signs by the abundance of Phycosiphon. The second core reveals eight deepening cycles.

Keywords: ichnofossil, Jurassic, sediment, reservoir, Bintuni, Indonesia, West Papua

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Authors: I. Filikhin, B. Vlahovic

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We focus on a novel type of detection based on electron tunneling properties of double nanoscale structures in semiconductor materials. Semiconductor heterostructures as quantum wells (QWs), quantum dots (QDs), and quantum rings (QRs) may have energy level structure of several hundred of electron confinement states. The single electron spectra of the double quantum objects (DQW, DQD, and DQR) were studied in our previous works with relation to the electron localization and tunneling between the objects. The wave function of electron may be localized in one of the QDs or be delocalized when it is spread over the whole system. The localizing-delocalizing tunneling occurs when an electron transition between both states is possible. The tunneling properties of spectra differ strongly for “regular” and “chaotic” systems. We have shown that a small violation of the geometry drastically affects localization of electron. In particular, such violations lead to the elimination of the delocalized states of the system. The same symmetry violation effect happens if electrical or magnetic fields are applied. These phenomena could be used to propose a new type of detection based on the high sensitivity of charge transport between double nanostructures and small violations of the shapes. It may have significant technological implications.

Keywords: double quantum dots, single electron levels, tunneling, electron localizations

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Authors: Dongkwon Han, Sangho Kim, Sunil Kwon

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Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that applies to advanced approaches for production forecasting due to uncertainty and complexity of fluid flow. In this study, artificial neural network (ANN) model which integrates machine learning and data-driven approach was developed to predict productivity in shale gas. The database of 129 wells of Eagle Ford shale basin used for testing and training of the ANN model. The Input data related to hydraulic fracturing, well completion and productivity of shale gas were selected and the output data is a cumulative production. The performance of the ANN using all data sets, clustering and variables importance (VI) models were compared in the mean absolute percentage error (MAPE). ANN model using all data sets, clustering, and VI were obtained as 44.22%, 10.08% (cluster 1), 5.26% (cluster 2), 6.35%(cluster 3), and 32.23% (ANN VI), 23.19% (SVM VI), respectively. The results showed that the pre-trained ANN model provides more accurate results than the ANN model using all data sets.

Keywords: unconventional gas, artificial neural network, machine learning, clustering, variables importance

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Authors: Yujiang Wu

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As electrical machine (e-machine) power density re-querulents become more stringent in vehicle electrification, mounting a temperature sensor for e-machine stator windings becomes increasingly difficult. This can lead to higher manufacturing costs, complicated harnesses, and reduced reliability. In this paper, we propose a deep-learning method for predicting electric machine winding temperature, which can either replace the sensor entirely or serve as a backup to the existing sensor. We compare the performance of our method, the stateful long short-term memory networks (LSTM) with truncated backpropagation through time (TBTT), with that of linear regression, as well as stateless LSTM with/without residual connection. Our results demonstrate the strength of combining stateful LSTM and TBTT in tackling nonlinear time series prediction problems with long sequence lengths. Additionally, in industrial applications, high-temperature region prediction accuracy is more important because winding temperature sensing is typically used for derating machine power when the temperature is high. To evaluate the performance of our algorithm, we developed a temperature-stratified MSE. We propose a simple but effective data preprocessing trick to improve the high-temperature region prediction accuracy. Our experimental results demonstrate the effectiveness of our proposed method in accurately predicting winding temperature, particularly in high-temperature regions, while also reducing manufacturing costs and improving reliability.

Keywords: deep learning, electrical machine, functional safety, long short-term memory networks (LSTM), thermal management, time series prediction

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Authors: Souradip Paul, Arijit Paramanick, M. Suheshkumar Singh

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Traumatic injury is the leading worldwide health problem. Annually, millions of surgical wounds are created for the sake of routine medical care. The healing of these unintended injuries is always monitored based on visual inspection. The maximal restoration of tissue functionality remains a significant concern of clinical care. Although minor injuries heal well with proper care and medical treatment, large injuries negatively influence various factors (vasculature insufficiency, tissue coagulation) and cause poor healing. Demographically, the number of people suffering from severe wounds and impaired healing conditions is burdensome for both human health and the economy. An incomplete understanding of the functional and molecular mechanism of tissue healing often leads to a lack of proper therapies and treatment. Hence, strong and promising medical guidance is necessary for monitoring the tissue regeneration processes. Photoacoustic imaging (PAI), is a non-invasive, hybrid imaging modality that can provide a suitable solution in this regard. Light combined with sound offers structural, functional and molecular information from the higher penetration depth. Therefore, molecular and structural mechanisms of tissue repair will be readily observable in PAI from the superficial layer and in the deep tissue region. Blood vessel formation and its growth is an essential tissue-repairing components. These vessels supply nutrition and oxygen to the cell in the wound region. Angiogenesis (formation of new capillaries from existing blood vessels) contributes to new blood vessel formation during tissue repair. The betterment of tissue healing directly depends on angiogenesis. Other optical microscopy techniques can visualize angiogenesis in micron-scale penetration depth but are unable to provide deep tissue information. PAI overcomes this barrier due to its unique capability. It is ideally suited for deep tissue imaging and provides the rich optical contrast generated by hemoglobin in blood vessels. Hence, an early angiogenesis detection method provided by PAI leads to monitoring the medical treatment of the wound. Along with functional property, mechanical property also plays a key role in tissue regeneration. The wound heals through a dynamic series of physiological events like coagulation, granulation tissue formation, and extracellular matrix (ECM) remodeling. Therefore tissue elasticity changes, can be identified using non-contact photoacoustic elastography (PAE). In a nutshell, angiogenesis and biomechanical properties are both critical parameters for tissue healing and these can be characterized in a single imaging modality (PAI).

Keywords: PAT, wound healing, tissue coagulation, angiogenesis

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Authors: Jennifer Chen, Pei Y. Hsu, Yu W. Chen

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Groundwater resources play a vital role in regional water supply because over 1/3 of total demand is satisfied by groundwater resources. Because over-pumpage might cause environmental impact such as land subsidence, a sustainable management of groundwater resource is required. In this study, a blueprint of smart management for groundwater resource is proposed and planned. The framework of the smart management can be divided into two major parts, hardware and software parts. First, an internet of groundwater (IoG) which is inspired by the internet of thing (IoT) is proposed to observe the migration of groundwater usage and the associated response, groundwater levels. Second, algorithms based on data mining and signal analysis are proposed to achieve the goal of providing highly efficient management of groundwater. The entire blueprint is a 4-year plan and this year is the first year. We have finished the installation of 50 flow meters and 17 observation wells. An underground hydrological model is proposed to determine the associated drawdown caused by the measured pumpages. Besides, an alternative to the flow meter is also proposed to decrease the installation cost of IoG. An accelerometer and 3G remote transmission are proposed to detect the on and off of groundwater pumpage.

Keywords: groundwater management, internet of groundwater, underground hydrological model, alternative of flow meter

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Authors: Faris Tarlochan, Siva Mahesh Tangutooru

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Lateral Geniculate Nucleus (LGN) is the relay center in the visual pathway as it receives most of the input information from retinal ganglion cells (RGC) and sends to visual cortex. Low threshold calcium currents (IT) at the membrane are the unique indicator to characterize this firing functionality of the LGN neurons gained by the RGC input. According to the LGN functional requirements such as functional mapping of RGC to LGN, the morphologies of the LGN neurons were developed. During the neurological disorders like glaucoma, the mapping between RGC and LGN is disconnected and hence stimulating LGN electrically using deep brain electrodes can restore the functionalities of LGN. A computational model was developed for simulating the LGN neurons with three predominant morphologies, each representing different functional mapping of RGC to LGN. The firings of action potentials at LGN neuron due to IT were characterized by varying the stimulation parameters, morphological parameters and orientation. A wide range of stimulation parameters (stimulus amplitude, duration and frequency) represents the various strengths of the electrical stimulation with different morphological parameters (soma size, dendrites size and structure). The orientation (0-1800) of LGN neuron with respect to the stimulating electrode represents the angle at which the extracellular deep brain stimulation towards LGN neuron is performed. A reduced dendrite structure was used in the model using Bush–Sejnowski algorithm to decrease the computational time while conserving its input resistance and total surface area. The major finding is that an input potential of 0.4 V is required to produce the action potential in the LGN neuron which is placed at 100 µm distance from the electrode. From this study, it can be concluded that the neuroprostheses under design would need to consider the capability of inducing at least 0.4V to produce action potentials in LGN.

Keywords: Lateral Geniculate Nucleus, visual cortex, finite element, glaucoma, neuroprostheses

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Authors: Lanchi Xie, Zhihui Li, Zhigang Li, Guiqiang Wang, Lei Xu, Yuwen Yan

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Image-based facial features can be classified into category recognition features and individual recognition features. Current automated face recognition systems extract a specific feature vector of different dimensions from a facial image according to their pre-trained neural network. However, to improve the efficiency of parameter calculation, an algorithm generally reduces the image details by pooling. The operation will overlook the details concerned much by forensic experts. In our experiment, we adopted a variety of face recognition algorithms based on deep learning, compared a large number of naturally collected face images with the known data of the same person's frontal ID photos. Downscaling and manual handling were performed on the testing images. The results supported that the facial recognition algorithms based on deep learning detected structural and morphological information and rarely focused on specific markers such as stains and moles. Overall performance, distribution of genuine scores and impostor scores, and likelihood ratios were tested to evaluate the accuracy of biometric systems and forensic experts. Experiments showed that the biometric systems were skilled in distinguishing category features, and forensic experts were better at discovering the individual features of human faces. In the proposed approach, a fusion was performed at the score level. At the specified false accept rate, the framework achieved a lower false reject rate. This paper contributes to improving the interpretability of the objective method of facial comparison and provides a novel method for human-machine collaboration in this field.

Keywords: likelihood ratio, automated facial recognition, facial comparison, biometrics

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Authors: Seyedeh Nasim Mirbahari, Taha Azad, Mehdi Totonchi

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Oncolytic viruses, which only replicate in tumor cells, are being extensively studied for their use in cancer therapy. A particular virus known as the vaccinia virus, a member of the poxvirus family, has demonstrated oncolytic abilities glioma. Treating Glioma with traditional methods such as chemotherapy and radiotherapy is quite challenging. Even though oncolytic viruses have shown immense potential in cancer treatment, their effectiveness in glioblastoma treatment is still low. Therefore, there is a need to improve and optimize immunotherapies for better results. In this study, we have designed oncoVV-TT, which can more effectively target tumor cells while minimizing replication in normal cells by replacing the thymidine kinase gene with a luc-p2a-GFP gene expression cassette. Human glioblastoma cell line U251 MG, rat glioblastoma cell line C6, and non-tumor cell line HFF were plated at 105 cells in a 12-well plates in 2 mL of DMEM-F2 medium with 10% FBS added to each well. Then incubated at 37°C. After 16 hours, the cells were treated with oncoVV-TT at an MOI of 0.01, 0.1 and left in the incubator for a further 24, 48, 72 and 96 hours. Viral replication assay, fluorescence imaging and viability tests, including trypan blue and crystal violet, were conducted to evaluate the cytotoxic effect of oncoVV-TT. The finding shows that oncoVV-TT had significantly higher cytotoxic activity and proliferation rates in tumor cells in a dose and time-dependent manner, with the strongest effect observed in U251 MG. To conclude, oncoVV-TT has the potential to be a promising oncolytic virus for cancer treatment, with a more cytotoxic effect in human glioblastoma cells versus rat glioma cells. To assess the effectiveness of vaccinia virus-mediated viral therapy, we have tested U251mg and C6 tumor cell lines taken from human and rat gliomas, respectively. The study evaluated oncoVV-TT's ability to replicate and lyse cells and analyzed the survival rates of the tested cell lines when treated with different doses of oncoVV-TT. Additionally, we compared the sensitivity of human and mouse glioma cell lines to the oncolytic vaccinia virus. All experiments regarding viruses were conducted under biosafety level 2. We engineered a Vaccinia-based oncolytic virus called oncoVV-TT to replicate specifically in tumor cells. To propagate the oncoVV-TT virus, HeLa cells (5 × 104/well) were plated in 24-well plates and incubated overnight to attach to the bottom of the wells. Subsequently, 10 MOI virus was added. After 48 h, cells were harvested by scraping, and viruses were collected by 3 sequential freezing and thawing cycles followed by removal of cell debris by centrifugation (1500 rpm, 5 min). The supernatant was stored at −80 ◦C for the following experiments. To measure the replication of the virus in Hela, cells (5 × 104/well) were plated in 24-well plates and incubated overnight to attach to the bottom of the wells. Subsequently, 5 MOI virus or equal dilution of PBS was added. At the treatment time of 0 h, 24 h, 48 h, 72 h and 96 h, the viral titers were determined under the fluorescence microscope (BZ-X700; Keyence, Osaka, Japan). Fluorescence intensity was quantified using the imagej software according to the manufacturer’s protocol. For the isolation of single-virus clones, HeLa cells seeded in six-well plates (5×105 cells/well). After 24 h (100% confluent), the cells were infected with a 10-fold dilution series of TianTan green fluorescent protein (GFP)virus and incubated for 4 h. To examine the cytotoxic effect of oncoVV-TT virus ofn U251mg and C6 cell, trypan blue and crystal violet assay was used.

Keywords: oncolytic virus, immune therapy, glioma, vaccinia virus

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Authors: Arun Prasad, P. B. Ramudu, Deep Shikha, Deep Jyoti Singh

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A number of studies have been conducted recently to investigate the influence of randomly oriented fibers on some engineering properties of cohesive soils.Freezing and thawing of soil affects the strength, durability and permeability of soil adversely. Experiments were carried out in order to investigate the effect of inclusion of randomly distributed polypropylene fibers on the strength, hydraulic conductivity and durability of local soil (CL) subjected to freeze–thaw cycles. For evaluating the change in strength of soil, a series of unconfined compression tests as well as tri-axial tests were carried out on reinforced and unreinforced soil samples. All the samples were subjected to seven cycles of freezing and thawing. Freezing was carried out at a temperature of - 15 to -18 °C; and thawing was carried out by keeping the samples at room temperature. The reinforcement of soil samples was done by mixing with polypropylene fibers, 12 mm long and with an aspect ratio of 240. The content of fibers was varied from 0.25 to 1% by dry weight of soil. The maximum strength of soil was found in samples having a fiber content of 0.75% for all the samples that were prepared at optimum moisture content (OMC), and if the OMC was increased (+2% OMC) or decreased (-2% OMC), the maximum strength observed at 0.5% fiber inclusion. The effect of fiber inclusion and freeze–thaw on the hydraulic conductivity was studied increased from around 25 times to 300 times that of the unreinforced soil, without subjected to any freeze-thaw cycles. For studying the increased durability of soil, mass loss after each freeze-thaw cycle was calculated and it was found that samples reinforced with polypropylene fibers show 50-60% less loss in weight than that of the unreinforced soil.

Keywords: fiber reinforcement, freezingand thawing, hydraulic conductivity, unconfined compressive strength

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Authors: Jaqueline Maria Ribeiro Vieira

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Different strategies and tools are available at the oil and gas industry for detecting and analyzing tension and possible fractures in borehole walls. Most of these techniques are based on manual observation of the captured borehole images. While this strategy may be possible and convenient with small images and few data, it may become difficult and suitable to errors when big databases of images must be treated. While the patterns may differ among the image area, depending on many characteristics (drilling strategy, rock components, rock strength, etc.). Previously we developed and proposed a novel strategy capable of detecting patterns at borehole images that may point to regions that have tension and breakout characteristics, based on segmented images. In this work we propose the inclusion of data-mining classification strategies in order to create a knowledge database of the segmented curves. These classifiers allow that, after some time using and manually pointing parts of borehole images that correspond to tension regions and breakout areas, the system will indicate and suggest automatically new candidate regions, with higher accuracy. We suggest the use of different classifiers methods, in order to achieve different knowledge data set configurations.

Keywords: image segmentation, oil well visualization, classifiers, data-mining, visual computer

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Authors: Raafat A. Mandour

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Background; Water may be regarded as polluted if it contains substances that render it unsafe for public use. The surface, subsoil waters and the shallow water-bearing geologic formation are more subjected to pollution due to its closeness to the human daily work. Aim of the work; determine the distribution of iron level in drinking water and its relation to iron level in blood patients suffering from liver diseases. Materials and Methods; For the present study, a total number of (71) drinking water samples (surface, wells and tap) have been collected and Blood samples were carried out on (71) selected inhabitants who attended in different hospitals, from different localities and suffering from liver diseases. Serum iron level in these patients was estimated by using IRON-B kit, Biocon company (Germany) and the 1, 10-phenanthroline method. Results; The water samples analyzed for iron are found suitable for drinking except two samples at Mit-Ghamr district showing values higher than the permissible limit of Egyptian Ministry of Health (EMH) and World Health Organization (WHO).The comparison between iron concentrations in drinking water and human blood samples shows a positive relationship. Conclusion; groundwater samples from the polluted areas should have special attention for treatment.

Keywords: water samples, blood samples, EMH, WHO

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Authors: C. Pearcy, C. Rees

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Cognitive behavioural therapy (CBT) and exposure and response prevention (ERP) are currently the most efficacious treatments for Obsessive-compulsive disorder (OCD). Many clients, however, remain symptomatic following treatment. As a result, refusal of treatment, withdrawal from treatment, and partial adherence to treatment are common amongst ERP. Such limitations have caused few professionals to actually engage in ERP therapy, which has warranted the exploration of alternative treatments. This study evaluated an online self-help treatment program for OCD (the OCD Doctor Online); a 4-week Metacognitive Therapy (MCT) program which has implemented strategies from Wells’ Metacognitive model of OCD. The aim of the present study was to investigate whether an online self-help treatment using MCT would reduce symptoms of OCD, reduce unhelpful metacognitions and improve quality of life. Treatment effectiveness was assessed using a case series methodology in 3 consecutively referred individuals. At post-treatment, all participants showed reductions in unhelpful metacognitive beliefs (MCQ-30) and improvements in quality of life (Q-LES-Q), which were maintained through to 4 week follow-up. Two of the three participants showed reductions in OCD symptomology (OCI-R), which were further reduced at 4-week follow-up. The present study suggests that internet-based self-help treatment may be an effective means of delivering MCT to adults with OCD.

Keywords: internet-based, metacognitive therapy, obsessive-compulsive disorder, self-help

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Authors: Pengfei Meng, Shuangcheng Jia, Qian Li

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We proposed a real-time high-precision semantic segmentation network based on a multi-resolution feature fusion module, the auxiliary feature extracting module, upsampling module, and atrous spatial pyramid pooling (ASPP) module. We designed a feature fusion structure, which is integrated with sufficient features of different resolutions. We also studied the effect of side-branch structure on the network and made discoveries. Based on the discoveries about the side-branch of the network structure, we used a side-branch auxiliary feature extraction layer in the network to improve the effectiveness of the network. We also designed upsampling module, which has better results than the original upsampling module. In addition, we also re-considered the locations and number of atrous spatial pyramid pooling (ASPP) modules and modified the network structure according to the experimental results to further improve the effectiveness of the network. The network presented in this paper takes the backbone network of Bisenetv2 as a basic network, based on which we constructed a network structure on which we made improvements. We named this network deep multiple-resolution bilateral networks for real-time, referred to as DMBR-Net. After experimental testing, our proposed DMBR-Net network achieved 81.2% mIoU at 119FPS on the Cityscapes validation dataset, 80.7% mIoU at 109FPS on the CamVid test dataset, 29.9% mIoU at 78FPS on the COCOStuff test dataset. Compared with all lightweight real-time semantic segmentation networks, our network achieves the highest accuracy at an appropriate speed.

Keywords: multi-resolution feature fusion, atrous convolutional, bilateral networks, pyramid pooling

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Authors: G. Venkata Rao, P. Kalpana, R. Srinivasa Rao

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Adequate and reliable estimates of aquifer parameters are of utmost importance for proper management of vital groundwater resources. At present scenario the ground water is polluted because of industrial waste disposed over the land and the contaminants are transported in the aquifer from one area to another area which is depending on the characteristics of the aquifer and contaminants. To know the contaminant transport, the accurate estimation of aquifer properties is highly needed. Conventionally, these properties are estimated through pumping tests carried out on water wells. The occurrence and movement of ground water in the aquifer are characteristically defined by the aquifer parameters. The pumping (aquifer) test is the standard technique for estimating various hydraulic properties of aquifer systems, viz, transmissivity (T), hydraulic conductivity (K), storage coefficient (S) etc., for which the graphical method is widely used. The study area for conducting pumping test is Pydibheemavaram Industrial area near the coastal belt of Srikulam, AP, India. The main objective of the present work is to estimate the aquifer properties for developing contaminant transport model for the study area.

Keywords: aquifer, contaminant transport, hydraulic conductivity, industrial waste, pumping test

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Authors: Nasrud Din, Fawad Saeed, Sajid Hussain, Rai Muhammad Dawood Sultan, Premkumar Sellan, Qasim Khan, Wei Lei

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The continuously increasing external quantum efficiencies of Perovskite light-emitting diodes (LEDs) have received significant interest in the scientific community. The need for monitoring and medical diagnostics has experienced a steady growth in recent years, primarily caused by older people and an increasing number of heart attacks, tumors, and cancer disorders among patients. The application of Perovskite near-infrared light-emitting diode (PeNIRLEDs) has exhibited considerable efficacy in bioimaging, particularly in the visualization and examination of blood arteries, blood clots, and tumors. PeNIRLEDs exhibit exciting potential in the field of blood vessel imaging because of their advantageous attributes, including improved depth penetration and less scattering in comparison to visible light. In this study, we synthesized FAPbI₃ Perovskite doped with different concentrations of 5-Aminovaleric acid (5-AVA) 1-6 mg. The incorporation of 5-AVA as a dopant during the FAPbI₃ Perovskite formation influences the FAPbI3 Perovskite’s structural and optical properties, improving its stability, photoluminescence efficiency, and charge transport characteristics. We found a resulting PL emission peak wavelength of 850 nm and bandwidth of 44 nm, along with a calculated quantum yield of 75%. The incorporation of 5-AVA-modified FAPbI₃ Perovskite into LEDs will show promising results, enhancing device efficiency, color purity, and stability. Making it suitable for various medical applications, including subcutaneous deep vein imaging, blood flow visualization, and tumor illumination.

Keywords: perovskite light-emitting diodes, deep vein imaging, blood flow visualization, tumor illumination

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Authors: A. Hadjadj, S. Maamir

Abstract:

Since the advent of the law 86/14 concerning the
exploitation of the national territory by foreign companies in
partnership with the Algerian oil and gas company, the problem of
hydrocarbons metering in the sharing production come out.
More generally, good management counting hydrocarbons can
provide data on the production wells, the field and the reservoir for
medium and long term planning, particularly in the context of the
management and field development.
In this work, we are interested in the transactional metering which
is a very delicate and crucial period in the current context of the new
hydrocarbon’s law characterized by assets system between the
various activities of Sonatrach and its foreign partners.
After a state of the art on hydrocarbons metering devices in
Algeria and elsewhere, we will decline the advantages and
disadvantages of each system, and then we describe the problem to
try to reach an optimal solution.

Keywords: transactional metering, flowmeter orifice, heat flow, Sonatrach

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Authors: Masibulele Fiko, Sanjay Balkara, Beauty Makiwane, Samson Asoba

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Several municipalities in the Eastern Cape Province of South Africa suffer from severe infrastructure dilapidation and a backlog in repairs and replacement. This scourge is most critical in black dominated areas, such as the rural communities and townships. Several critical service delivery activities have been impaired consequent to the deteriorating facilities and a lot of human endeavors impacted adversely. As such, this study investigated the water and sanitation challenges in King Sabatha Dalindyebo municipality, Eastern Cape Province of South Africa. Questionnaires were distributed to the communities and interviews were conducted with the communities’ leaders. The Participants mentioned that their main sources of water supply were a dam, streams, springs and wells; and the distances to the water sources were thought to be too long and women were often attacked and sometimes raped. South African local authorities are facing problems of insufficient funds to meet their daily operations. The municipality should provide street taps. The alternative way for government to supply financial aid to local authorities is to introduce the private sector in the service rendering process.

Keywords: communities, sanitation, managers, municipality

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Authors: Mahmoud S. Zaghloul, Mohammed M. Khalaf, Wael N. Thabet, Haidy N. Asham

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Background. Hypertrophic scarring is a difficult problem for burn patients, and scar management is an essential aspect of outpatient burn therapy. Post-burn pathologic scars involve functional and aesthetic limitations that have a dramatic influence on the patient’s quality of life. The aim was to investigate the use of extracorporeal shock wave therapy (ESWT), which targets the fibroblasts in scar tissue, as an effective modality for scar treatment in burn patients. Subjects and methods: forty patients with post-burn scars were assigned randomly into two equal groups; their ages ranged from 20-45 years. The study group received ESWT and traditional physical therapy program (deep friction massage, stretching exercises). The control group received traditional physical therapy program (deep friction massage, stretching exercises). All groups received two sessions per week for six successful weeks. The data were collected before and after the same period of treatment for both groups. Evaluation procedures were carried out to measure scar thickness using ultrasonography and Vancouver Scar Scale (VSS) was completed before and after treatment. Results: Post-treatment results showed that there was a significant improvement difference in scar thickness in both groups in favor of the study group. Percentage of improvement in scar thickness in the study group was 42.55%, while it was 12.15% in the control group. There was also a significant improvement difference between results obtained using VSS in both groups in favor of the study group. Conclusion: ESWT is effective in management of pathologic post burn scars.

Keywords: extracorporeal shock wave therapy, post-burn scars, ultrasonography, Vancouver scar scale

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Authors: Pedro Martinez-Santos, Víctor Gómez-Escalonilla, Silvia Díaz-Alcaide, Esperanza Montero, Miguel Martín-Loeches

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Groundwater monitoring networks are critical in evaluating the vulnerability of groundwater resources to depletion and contamination, both in space and time. Groundwater monitoring networks typically grow over decades, often in organic fashion, with relatively little overall planning. The groundwater monitoring networks in the Madrid area, Spain, were reviewed for the purpose of identifying gaps and opportunities for improvement. Spatial analysis reveals the presence of various monitoring networks belonging to different institutions, with several hundred observation wells in an area of approximately 4000 km2. This represents several thousand individual data entries, some going back to the early 1970s. Major issues included overlap between the networks, unknown screen depth/vertical distribution for many observation boreholes, uneven time series, uneven monitored species, and potentially suboptimal locations. Results also reveal there is sufficient information to carry out a spatial and temporal analysis of groundwater vulnerability based on machine learning applications. These can contribute to improve the overall planning of monitoring networks’ expansion into the future.

Keywords: groundwater monitoring, observation networks, machine learning, madrid

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Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Viviana Manzulli, Luigina Serrecchia, Adelia Donatiello, Valeria Rondinone, Sabine Zange, Alina Tscherne, Antonio Parisi, Antonio Fasanella

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Anthrax is a zoonotic disease that affects a wide range of animal species (primarily ruminant herbivores), and can be transmitted to humans through consumption or handling of contaminated animal products. The etiological agent B.anthracis is able to survive in unfavorable environmental conditions by forming endospore which remain viable in the soil for many decades. Furthermore, B.anthracis is considered as one of the most feared agents to be potentially misused as a biological weapon and the importance of the disease and its treatment in humans has been underscored before the bioterrorism events in the United States in 2001. Due to the often fatal outcome of human cases, antimicrobial susceptibility testing plays especially in the management of anthrax infections an important role. In Italy, animal anthrax is endemic (predominantly found in the southern regions and on islands) and is characterized by sporadic outbreaks occurring mainly during summer. Between 2012 and 2017 single human cases of cutaneous anthrax occurred. In this study, 90 diverse strains of B.anthracis, isolated in Italy from 2001 to 2017, were screened to their susceptibility to sixteen clinically relevant antimicrobial agents by using the broth microdilution method. B.anthracis strains selected for this study belong to the strain collection stored at the Anthrax Reference Institute of Italy located inside the Istituto Zooprofilattico Sperimentale of Puglia and Basilicata. The strains were isolated at different time points and places from various matrices (human, animal and environmental). All strains are a representative of over fifty distinct MLVA 31 genotypes. The following antibiotics were used for testing: gentamicin, ceftriaxone, streptomycin, penicillin G, clindamycin, chloramphenicol, vancomycin, linezolid, cefotaxime, tetracycline, erythromycin, rifampin, amoxicillin, ciprofloxacin, doxycycline and trimethoprim. A standard concentration of each antibiotic was prepared in a specific diluent, which were then twofold serial diluted. Therefore, each wells contained: bacterial suspension of 1–5x104 CFU/mL in Mueller-Hinton Broth (MHB), the antibiotic to be tested at known concentration and resazurin, an indicator of cell growth. After incubation overnight at 37°C, the wells were screened for color changes caused by the resazurin: a change from purple to pink/colorless indicated cell growth. The lowest concentration of antibiotic that prevented growth represented the minimal inhibitory concentration (MIC). This study suggests that B.anthracis remains susceptible in vitro to many antibiotics, in addition to doxycycline (MICs ≤ 0,03 µg/ml), ciprofloxacin (MICs ≤ 0,03 µg/ml) and penicillin G (MICs ≤ 0,06 µg/ml), recommend by CDC for the treatment of human cases and for prophylactic use after exposure to the spores. In fact, the good activity of gentamicin (MICs ≤ 0,25 µg/ml), streptomycin (MICs ≤ 1 µg/ml), clindamycin (MICs ≤ 0,125 µg/ml), chloramphenicol(MICs ≤ 4 µg/ml), vancomycin (MICs ≤ 2 µg/ml), linezolid (MICs ≤ 2 µg/ml), tetracycline (MICs ≤ 0,125 µg/ml), erythromycin (MICs ≤ 0,25 µg/ml), rifampin (MICs ≤ 0,25 µg/ml), amoxicillin (MICs ≤ 0,06 µg/ml), towards all tested B.anthracis strains demonstrates an appropriate alternative choice for prophylaxis and/or treatment. All tested B.anthracis strains showed intermediate susceptibility to the cephalosporins (MICs ≥ 16 µg/ml) and resistance to trimethoprim (MICs ≥ 128 µg/ml).

Keywords: Bacillus anthracis, antibiotic susceptibility, treatment, minimum inhibitory concentration

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Authors: Hermalina Sinay, Estri L. Arumingtyas

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The research objective was to determine the expression level of dehydration responsive element binding/DREB gene of local corn cultivars from Kisar Island Maluku. The study design was a randomized block design with single factor consist of six local corn cultivars obtained from farmers in Kisar Island and one reference varieties wich has been released by the government as a drought-tolerant varieties and obtained from Cereal Crops Research Institute (ICERI) Maros South Sulawesi. Leaf samples were taken is the second leaf after the flag leaf at the 65 days after planting. Isolation of total RNA from leaf samples was carried out according to the protocols of the R & A-BlueTM Total RNA Extraction Kit and was used as a template for cDNA synthesis. The making of cDNA from total RNA was carried out according to the protocol of One-Step Reverse Transcriptase PCR Premix Kit. Real Time-PCR was performed on cDNA from reverse transcription followed the procedures of Real MODTM Green Real-Time PCR Master Mix Kit. Data obtained from the real time-PCR results were analyzed using relative quantification method based on the critical point / Cycle Threshold (CP / CT). The results of gene expression analysis of DREB gene showed that the expression level of the gene was highest obtained at Deep Yellow local corn cultivar, and the lowest one was obtained at the Rubby Brown Cob cultivar. It can be concluded that the expression level of DREB gene of Deep Yellow local corn cultivar was highest than other local corn cultivars and Srikandi variety as a reference variety.

Keywords: expression, level, DREB gene, local corn cultivars, Kisar Island, Maluku

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Authors: Hussein Bizimana, Osman Sönmez

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Constructed wetlands are current the most widely recognized waste water treatment option, especially in developing countries where they have the potential for improving water quality and creating valuable wildlife habitat in ecosystem with treatment requirement relatively simple for operation and maintenance cost. Lack of grey waste water treatment facilities in Kigali İnstitute of Science and Technology in Rwanda, causes pollution in the surrounding localities of Rugunga sector, where already a problem of poor sanitation is found. In order to treat grey water produced at Kigali İnstitute of Science and Technology, with high BOD concentration, high nutrients concentration and high alkalinity; a Horizontal Sub-surface Flow pilot-scale constructed wetland was designed and can operate in Kigali İnstitute of Science and Technology. The study was carried out in a sedimentation tank of 5.5 m x 1.42 m x 1.2 m deep and a Horizontal Sub-surface constructed wetland of 4.5 m x 2.5 m x 1.42 m deep. The grey waste water flow rate of 2.5 m3/d flew through vegetated wetland and sandy pilot plant. The filter media consisted of 0.6 to 2 mm of coarse sand, 0.00003472 m/s of hydraulic conductivity and cattails (Typha latifolia spp) were used as plants species. The effluent flow rate of the plant is designed to be 1.5 m3/ day and the retention time will be 24 hrs. 72% to 79% of BOD, COD, and TSS removals are estimated to be achieved, while the nutrients (Nitrogen and Phosphate) removal is estimated to be in the range of 34% to 53%. Every effluent characteristic will meet exactly the Rwanda Utility Regulatory Agency guidelines primarily because the retention time allowed is enough to make the reduction of contaminants within effluent raw waste water. Treated water reuse system was developed where water will be used in the campus irrigation system again.

Keywords: constructed wetlands, hydraulic conductivity, grey waste water, cattails

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Authors: Chinedu Ejike

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The United States is now energy self-sufficient due to the production of shale oil reserves. With more than half of it being tapped daily in the United States, these unconventional reserves are massive and provide immense potential for future energy demands. Drilling horizontal wells and fracking are the primary methods for developing these reserves. Regrettably, recovery efficiency is rarely greater than 10%. As a result, optimizing recuperation offers a significant benefit. Huff and puff gas flooding and cyclic gas injection have all been demonstrated to be more successful than tapping the remaining oil in place. Methane, nitrogen, and carbon (IV) oxide, among other high-pressure gases, can be injected. Operators use Darcy's law to assess a reservoir's productive capacity, but they are unaware that the law may not apply to shale oil reserves. This is due to the fact that, unlike pressure differences alone, diffusion, concentration, and gas selection all play a role in the flow of gas injected into the wellbore. The reservoir drainage and oil sweep efficiency rates are determined by the transport method. This research assesses the parameters that influence the gas injection transport mechanism. Understanding the process causing these factors could accelerate recovery by two to three times, according to peer-reviewed studies and effective field testing.

Keywords: enhanced oil recovery, gas injection, shale oil, transport mechanism, unconventional reserve

Procedia PDF Downloads 173

Authors: G. Venkata Rao, R. Srinivasa Rao, B. Neelima Sri Priya

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The over extraction of groundwater from the coastal aquifers, result in reduction of groundwater resource and lowering of water level. In general, the depletion of groundwater level enhances the landward migration of saltwater wedge. Now a days the ground water extraction increases by year to year because increased population and industrialization. The ground water is the only source of irrigation, domestic and Industrial purposes at Pydibhimavaram industrial area, which is located in the coastal belt of Srikakulam district, India of Latitudes 18.145N 83.627E and Longitudes 18.099N 83.674E. The present study has been attempted to calculate amount of water getting recharged into this aquifer, status of rainfall pattern for the past two decades and the runoff is calculated by using Khosla’s formula with available rainfall and temperature in the study area. A decision support model has been developed on the basis of Monthly Extractions of the water from the ground through bore wells and the Net Recharge of the aquifer. It is concluded that the amount of extractions is exceeding the amount of recharge from May to October in a given year which will in turn damage the water balance in the subsurface layers.

Keywords: aquifer, decision support model, groundwater extraction, run off estimation and rainfall

Procedia PDF Downloads 303