Search results for: inflammation detection

Authors: Abdelaziz Fellah, Allaoua Maamir

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We propose a domain-independent merging-cluster filter approach complemented with a set of algorithms for identifying approximate duplicate entities efficiently and accurately within a single and across multiple data sources. The near-optimal merging-cluster filter (MCF) approach is based on the Monge-Elkan well-tuned algorithm and extended with an affine variant of the Smith-Waterman similarity measure. Then we present constant, variable, and function threshold algorithms that work conceptually in a divide-merge filtering fashion for detecting near duplicates as hierarchical clusters along with their corresponding representatives. The algorithms take recursive refinement approaches in the spirit of filtering, merging, and updating, cluster representatives to detect approximate duplicates at each level of the cluster tree. Experiments show a high effectiveness and accuracy of the MCF approach in detecting approximate duplicates by outperforming the seminal Monge-Elkan’s algorithm on several real-world benchmarks and generated datasets.

Keywords: data mining, data cleaning, approximate duplicates, near-duplicates detection, data mining applications and discovery

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Authors: Aisulu Tileukulova, Zhexebay Dauren

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Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

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Authors: S. D. N. K. Bathige, G. I. Godahewa, Navaneethaiyer Umasuthan, Jehee Lee

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Kunitz-type serine protease inhibitors (KTIs) are identified in various organisms including animals, plants and microbes. These proteins shared single or multiple Kunitz inhibitory domains link together or associated with other types of domains. Characteristic Kunitz type domain composed of around 60 amino acid residues with six conserved cysteine residues to stabilize by three disulfide bridges. KTIs are involved in various physiological processes, such as ion channel blocking, blood coagulation, fibrinolysis and inflammation. In this study, two Kunitz-type domain containing protein was identified from rock bream database and designated as RbKunitz. The coding sequence of RbKunitz encoded for 507 amino acids with 56.2 kDa theoretical molecular mass and 5.7 isoelectric point (pI). There are several functional domains including MANEC superfamily domain, PKD superfamily domain, and LDLa domain were predicted in addition to the two characteristic Kunitz domain. Moreover, trypsin interaction sites were also identified in Kunitz domain. Homology analysis revealed that RbKunitz shared highest identity (77.6%) with Takifugu rubripes. Completely conserved 28 cysteine residues were recognized, when comparison of RbKunitz with other orthologs from different taxonomical groups. These structural evidences indicate the rigidity of RbKunitz folding structure to achieve the proper function. The phylogenetic tree was constructed using neighbor-joining method and exhibited that the KTIs from fish and non-fish has been evolved in separately. Rock bream was clustered with Takifugu rubripes. The SYBR Green qPCR was performed to quantify the RbKunitz transcripts in different tissues and challenged tissues. The mRNA transcripts of RbKunitz were detected in all tissues (muscle, spleen, head kidney, blood, heart, skin, liver, intestine, kidney and gills) analyzed and highest transcripts level was detected in gill tissues. Temporal transcription profile of RbKunitz in rock bream blood tissues was analyzed upon LPS (lipopolysaccharide), Poly I:C (Polyinosinic:polycytidylic acid) and Edwardsiella tarda challenge to understand the immune responses of this gene. Compare to the unchallenged control RbKunitz exhibited strong up-regulation at 24 h post injection (p.i.) after LPS and E. tarda injection. Comparatively robust expression of RbKunits was observed at 3 h p.i. upon Poly I:C challenge. Taken together all these data indicate that RbKunitz may involve into to immune responses upon pathogenic stress, in order to protect the rock bream.

Keywords: Kunitz-type, rock bream, immune response, serine protease inhibitor

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Authors: Gerardette Darkwah, Gloria Ankar Brewoo, John Barimah, Gilbert Owiah Sampson, Vincent Abe-Inge

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Soft drinks which are widely consumed in Ghana have been reported in other countries to contain toxic heavy metals beyond the acceptable limits in other countries. Therefore, the objective of this study was to assess the quality characteristics of selected locally produced and marketed soft drinks. Three (3) different batches of 23 soft drinks were sampled from the Takoradi markets. The samples were prescreened for the presence of reducing sugars, phosphates, alcohol and carbon dioxide. The heavy metal contents and physicochemical properties were also determined with AOAC methods. The results indicated the presence of reducing sugars, carbon dioxide and the absence of alcohol in all the selected soft drink samples. The pH, total sugars, moisture, total soluble solids (TSS) and titratable acidity ranged from 2.42 – 3.44, 3.30 – 10.44%, 85.63 – 94.85%, 5.00 – 13.33°Brix, and 0.21 – 1.99% respectively. The concentration of heavy metals were also below detection limits in all samples. The quality of the selected were within specifications prescribed by regulatory bodies.

Keywords: heavy metal contamination, locally manufactured, quality, soft drinks

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Authors: M. Naeimi, H. Aghazadeh, E. Afjei, A. Siadatan

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In this paper, a novel view of air gap magnetic field analysis of synchronous reluctance motor and permanent magnet assisted synchronous reluctance motor under static eccentricity to provide the precise fault diagnosis based on three-dimensional finite element method is presented. Analytical nature of this method makes it possible to simulate reliable and precise model by considering the end effects and axial fringing effects. The results of the three-dimensional finite element analysis of synchronous reluctance motor and permanent magnet synchronous reluctance motor such as flux linkage, flux density, and compression both of SynRM and PM-SynRM for various eccentric motor conditions are obtained and analyzed. These results present useful information regarding to the detection of static eccentricity.

Keywords: synchronous reluctance motor (SynRM), permanent magnet assisted synchronous reluctance motor (PMaSynRM), finite element method, static eccentricity, fault analysis

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Authors: Jasmeet Kaur, Anup Singh, Shashikant Iyengar, Arun Kumar, Ira Sahay

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Latent autoimmune diabetes in adults (LADA) is an irreversible autoimmune disease that affects insulin production. LADA is characterized by the production of Glutamic acid decarboxylase (GAD) antibodies, which is similar to type 1 diabetes. Individuals with LADA may eventually develop overt diabetes and require insulin. In this condition, the pancreas produces little or no insulin, which is a hormone used by the body to allow glucose to enter cells and produce energy. While type 1 diabetes was traditionally associated with children and teenagers, its prevalence has increased in adults as well. LADA is frequently misdiagnosed as type 2 diabetes, especially in adulthood when type 2 diabetes is more common. LADA develops in adulthood, usually after age 30. Managing LADA involves metabolic control with exogenous insulin and prolonging the life of surviving beta cells, thereby slowing the disease's progression. This case study examines the impact of approximately 3 months of low-carbohydrate dietary intervention in a 42-year-old woman with LADA who was initially misdiagnosed as having type 2 diabetes. Her c-peptide was 0.13 and her HbA1c was 9.3% when this trial began. Low-carbohydrate interventions have been shown to improve blood sugar levels, including fasting, post-meal, and random blood sugar levels, as well as haemoglobin levels, blood pressure, energy levels, sleep quality, and satiety levels. The use of low-carbohydrate dietary intervention significantly reduces both hypo- and hyperglycaemia events. During the 3 months of the study, there were 2 to 3 hyperglycaemic events owing to physical stress and a single hypoglycaemic event. Low-carbohydrate dietary therapies lessen insulin dose inaccuracy, which explains why there were fewer hyperglycaemic and hypoglycaemic events. In three months, the glycated haemoglobin (HbA1c) level was reduced from 9.3% to 6.3%. These improvements occur without the need for caloric restriction or physical activity. Stress management was crucial aspect of the treatment plan as stress-induced neuroendocrine hormones can cause immunological dysregulation. Additionally, supplements that support immune system and reduce inflammation were used as part of the treatment during the trial. Long-term studies are needed to track disease development and corroborate the claim that such dietary treatments can prolong the honeymoon phase in LADA. Various factors can contribute to additional autoimmune attacks, so measuring c-peptide is crucial on a regular basis to determine whether insulin levels need to be adjusted.

Keywords: autoimmune, diabetes, LADA, low_carb, nutrition

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Authors: Kaustubh Chakradeo, Michael Delves, Sofya Titarenko

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Malaria is a serious disease which affects hundreds of millions of people around the world, each year. If not treated in time, it can be fatal. Despite recent developments in malaria diagnostics, the microscopy method to detect malaria remains the most common. Unfortunately, the accuracy of microscopic diagnostics is dependent on the skill of the microscopist and limits the throughput of malaria diagnosis. With the development of Artificial Intelligence tools and Deep Learning techniques in particular, it is possible to lower the cost, while achieving an overall higher accuracy. In this paper, we present a VGG-based model and compare it with previously developed models for identifying infected cells. Our model surpasses most previously developed models in a range of the accuracy metrics. The model has an advantage of being constructed from a relatively small number of layers. This reduces the computer resources and computational time. Moreover, we test our model on two types of datasets and argue that the currently developed deep-learning-based methods cannot efficiently distinguish between infected and contaminated cells. A more precise study of suspicious regions is required.

Keywords: convolution neural network, deep learning, malaria, thin blood smears

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Authors: E. Kam, G. Karahan, H. Aslıyuksek, A. Bozkurt

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This study aims to determine the natural radioactivity levels in some foodstuffs produced in Turkey. For this purpose, 48 different foods samples were collected from different land parcels throughout the country. All samples were analyzed to designate both gross alpha and gross beta radioactivities and the radionuclides’ concentrations. The gross alpha radioactivities were measured as below 1 Bq kg^-1 in most of the samples, some of them being due to the detection limit of the counting system. The gross beta radioactivity levels ranged from 1.8 Bq kg^-1 to 453 Bq kg^-1, larger levels being observed in leguminous seeds while the highest level being in haricot bean. The concentrations of natural radionuclides in the foodstuffs were investigated by the method of gamma spectroscopy. High levels of ⁴⁰K were measured in all the samples, the highest activities being again in leguminous seeds. Low concentrations of ²³⁸U and ²²⁶Ra were found in some of the samples, which are comparable to the reported results in the literature. Based on the activity concentrations obtained in this study, average annual effective dose equivalents for the radionuclides ²²⁶Ra, ²³⁸U, and ⁴⁰K were calculated as 77.416 µSv y^-1, 0.978 µSv y^-1, and 140.55 µSv y^-1, respectively.

Keywords: foods, radioactivity, gross alpha, gross beta, annual equivalent dose, Turkey

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Authors: Avijit Dey, Antony Gomes, Subir Chandra Dasgupta

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Tea (Camellia sinensis) is the most popular beverages in the world and is ranked second after the water. Tea has been considered as a health promoting beverage since ancient times due to its health-promoting activity. Recently, immunomodulatory, anti-arthritic, antioxidant, anticancer and cardioprotective activity of tea has been established. Very few studies have demonstrated the effect of high dose of black tea on health. The aim of the present study was to evaluate the role of low & high dose of Black Tea Extract (BTE) on the different physiological parameters of mother and pups during prenatal and postnatal developmental period in the experimental rodent. BTE was orally administered in LD (50mg BTE/kg/day) and HD (100mg BTE/kg/day) except control groups of rats (n=6/group) throughout the prenatal (day 0-21) and postnatal (day 21-42) periods. During prenatal period (0, 7th, 14th, 20th days) body weight, urinary calcium, magnesium, urea and creatinine was measured. In postnatal period physical (0, 10th, 21th days) parameters of pups like body weight, cranial length, cranial diameter, neck width, tail length, craniosacral length of pups were analyzed. Liver and lungs from pups and kidney spleen, etc. from mothers were collected on day 42 for histopathological studies. The comparative urine strip and morphology of RBC was also analyzed by SEM from mothers of different groups on day 42. The level of cytokines like IL-1alpha, IL-1beta, IL-6, IL-10, TNF-alpha were analysed by enzyme-linked immunosorbent assay (ELISA) on day 0, day 20 and day 42. The body weight of LD and HD mothers were also significantly (P<0.05) less than control mothers at 20th day of pregnancy and there was also significant changes in urinary calcium, urea, creatinine. The bio morphometric analysis of pups showed significant alteration (P<0.05) in HD groups relative to control. Some histological alterations were also observed in pups and mothers. Comparative urine strip analysis and morphology of RBC showed significant changes in treated groups. LD and HD treated mothers showed an increase in proinflammatory cytokines like IL-1beta, TNF-alpha and decrease in anti-inflammatory cytokine-like IL-10 on day 20 compared to PC mothers. This study clearly indicated that high dose of BTE possesses detrimental effect on pregnant mother and the pup. Further studies are in progress to elucidate the molecular mechanism of actions. This project work has been sponsored by National Tea Research Foundation vide Project Sanction No.: 17 (305)/2013/4423 dated 11th March, 2014. All experimental protocols described in the study were approved by animal ethics committee.

Keywords: black tea extract, pregnancy, prenatal and postnatal development, inflammation

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Authors: E. M. Condori-Peñaloza, S. S. Costa

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Eleusine indica L, known as goose-grass, is considered a troublesome weed that can cause important economic losses in the agriculture worldwide. However, this grass is used as a medicinal plant in some regions of Brazil to treat influenza and pneumonia. In Africa and Asia, it is used to treat malaria and as diuretic, anti-helminthic, among other uses. Despite its therapeutic potential, little is known about the chemical composition and bioactive compounds of E. indica. Hitherto, two major flavonoids, schaftoside and vitexin, were isolated from aerial part of the species and showed inhibitory activity on lung neutrophil influxes in mice, suggesting a beneficial effect on airway inflammation. Therefore, the aim of this study was to analyze the chemical profile of aqueous extracts from aerial parts of Eleusine indica specimens using high performance liquid chromatography (HPLC-DAD) and 1H nuclear magnetic resonance spectroscopy (NMR), with emphasis on phenolic compounds. Specimens of E. indica were collected in Minas Gerais state, Brazil. Aerial parts of fresh plants were extracted by decoction (10% p/v). After spontaneous precipitation of the aqueous extract at 10-12°C for 24 hours, the supernatant obtained was frozen and lyophilized. After that, 1 g of the extract was dissolved into 25 mL of water and fractionated on a reverse phase chromatography column (RP-2), eluted with a gradient of H2O/EtOH. Five fractions were obtained. The extract and fractions had their chemical profile analyzed by using HPLC-DAD (C-18 column: 20 μL, 256 -365 nm; gradient water 0.01% phosphoric acid/ acetonitrile. The extract was also analyzed by NMR (1H, 500 MHz, D2O) in order to access its global chemical composition. HPLC-DAD analyses of crude extract allowed the identification of ten phenolic compounds. Fraction 1, eluted with 100% water, was poor in phenolic compounds and no major peak was detected. In fraction 2, eluted with 100% water, it was possible to observe one major peak at retention time (RT) of 23.75 minutes compatible with flavonoid; fraction 3, also eluted with 100% water, showed four peaks at RT= 21.47, 23.52, 24.33 and 25.84 minutes, all of them compatible with flavonoid. In fraction 4, eluted with 50%/ethanol/50% water, it was possible to observe 3 peaks compatible with flavonoids at RT=24.65, 26.81, 27.49 minutes, and one peak (28.83 min) compatible with a phenolic acid derivative. Finally, in fraction 5, eluted with 100% ethanol, no phenolic substance was detected. The UV spectra of all flavonoids detected were compatible with the flavone subclass (λ= 320-345 nm). The 1H NMR spectra of aerial parts extract showed signals in three regions: δ 0.8-3.0 ppm (aliphatic compounds), δ 3.0-5.5 ppm corresponding to carbohydrates (signals most abundant and overlapped), and δ 6.0-8.5 ppm (aromatic compounds). Signals compatible with flavonoids (rings A and B) could also be detected in the crude extract spectra. These results suggest the presence of several flavonoids in E. indica, which reinforces their therapeutic potential. The pharmacological activities of Eleusine indica extracts and fractions will be further evaluated.

Keywords: flavonoids, HPLC, NMR, phenolic compounds

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Authors: Walid Mousa, Mohamed Nayel, Ahmed Zaghawa, Akram Salama, Ahmed El-Sify, Hesham Rashad, Dina El-Shafey

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Mycoplasmosis in small ruminants constitutes a serious contagious problem in smallholders causing severe economic losses worldwide. This study was conducted to determine the clinical, Minimum Inhibitory Concentration (MIC) and molecular characterization of Mycoplasma species associated in sheep breeding herds in Menoufiya governorate, Egypt. Out of the examination of 400 sheep, 104 (26%) showed respiratory manifestations, nasal discharges, cough and conjunctivitis with systemic body reaction. Meanwhile, out of these examined sheep, only 56 (14%) were positive for mycoplasma isolation onto PPLO(Pleuropneumonia-like organisms) specific medium. The MIC for evaluating the efficacy of sensitivity of Mycoplasma isolates against different antibiotics groups revealed that both the Linospectin and Tylosin with 2ug, 0.25ug/ml concentration were the most effective antibiotics for Mycoplasma isolates. The application of PCR was the rapid, specific and sensitive molecular approach for detection of M. ovipneumoniae, and M. arginine at 390 and 326 bp, respectively, in all tested isolates. In conclusion, the diagnosis of Mycoplsamosis in sheep is important to achieve effective control measures and minimizing the disease dissemination among sheep herds.

Keywords: MIC, mycoplasmosis, PCR, sheep

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Authors: Sanjay Adhikesaven

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Workplace accidents are a critical problem that causes many deaths, injuries, and financial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively find unsafe environments where injuries could occur by detecting the use of personal protective equipment (PPE) and identifying unsafe activities. Thus, we propose an industrial workplace alerting and monitoring platform to detect PPE use and classify unsafe activity in group settings involving multiple humans and objects over a long period of time. Our proposed method is the first to analyze prolonged actions involving multiple people or objects. It benefits from combining pose estimation with PPE detection in one platform. Additionally, we propose the first open-source annotated data set with video data from industrial workplaces annotated with the action classifications and detected PPE. The proposed system can be implemented within the surveillance cameras already present in industrial settings, making it a practical and effective solution.

Keywords: computer vision, deep learning, workplace safety, automation

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Authors: Tiffanie-Marie Borg, Yasmin Zeinolabediny, Nima Heidari, Ali Noorani, Mark Slevin, Angel Cullen, Stefano Olgiati, Alberto Zerbi, Alessandro Danovi, Adrian Wilson

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Knee Osteoarthritis (OA) is a critical cause of disability globally. In recent years, there has been growing interest in non-invasive treatments, such as intra-articular injection of micro-fragmented fat (MFAT), showing great potential in treating OA. Mesenchymal stem cells (MSCs), originating from pericytes of micro-vessels in MFAT, can differentiate into mesenchymal lineage cells such as cartilage, osteocytes, adipocytes, and osteoblasts. Secretion of growth factor and cytokines from MSCs have the capability to inhibit T cell growth, reduced pain and inflammation, and create a micro-environment that through paracrine signaling, can promote joint repair and cartilage regeneration. Here we have shown, for the first time, data supporting the hypothesis that women respond better in terms of improvements in pain and function to MFAT injection compared to men. Historically, women have been underrepresented in studies, and studies with both sexes regularly fail to analyse the results by sex. To mitigate this bias and quantify it, we describe a technique using reproducible statistical analysis and replicable results with Open Access statistical software R to calculate the magnitude of this difference. Genetic, hormonal, environmental, and age factors play a role in our observed difference between the sexes. This observational, intention-to-treat study included the complete sample of 456 patients who agreed to be scored for pain (visual analogue scale (VAS)) and function (Oxford knee score (OKS)) at baseline regardless of subsequent changes to adherence or status during follow-up. We report that a significantly larger number of women responded to treatment than men: [90% vs. 60% change in VAS scores with 87% vs. 65% change in OKS scores, respectively]. Women overall had a stronger positive response to treatment with reduced pain and improved mobility and function. Pre-injection, our cohort of women were in more pain with worse joint function which is quite common to see in orthopaedics. However, during the 2-year follow-up, they consistently maintained a lower incidence of discomfort with superior joint function. This data clearly identifies a clear need for further studies to identify the cell and molecular biological and other basis for these differences and be able to utilize this information for stratification in order to improve outcome for both women and men.

Keywords: gender differences, micro-fragmented adipose tissue, knee osteoarthritis, stem cells

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Authors: Stephen Tullis, M. David DiDonato, Hong Sung Park

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Rowing boats (shells) are often steered by a small rudder operated by one of the backward-facing rowers; the attention required of that athlete then slightly decreases the power that that athlete can provide. Reducing the steering distraction would then increase the overall boat speed. Races are straight 2000 m courses with each boat in a 13.5 m wide lane marked by small (~15 cm) widely-spaced (~10 m) buoys, and the boat trajectory is affected by both cross-currents and winds. An optical buoy recognition and tracking system has been developed that provides the boat’s location and orientation with respect to the lane edges. This information is provided to the steering athlete as either: a simple overlay on a video display, or fed to a simplified autopilot system giving steering directions to the athlete or directly controlling the rudder. The system is then effectively a “lane-assist” device but with small, widely-spaced lane markers viewed from a very shallow angle due to constraints on camera height. The image is captured with a lightweight 1080p webcam, and most of the image analysis is done in OpenCV. The colour RGB-image is converted to a grayscale using the difference of the red and blue channels, which provides good contrast between the red/yellow buoys and the water, sky, land background and white reflections and noise. Buoy detection is done with thresholding within a tight mask applied to the image. Robust linear regression using Tukey’s biweight estimator of the previously detected buoy locations is used to develop the mask; this avoids the false detection of noise such as waves (reflections) and, in particular, buoys in other lanes. The robust regression also provides the current lane edges in the camera frame that are used to calculate the displacement of the boat from the lane centre (lane location), and its yaw angle. The interception of the detected lane edges provides a lane vanishing point, and yaw angle can be calculated simply based on the displacement of this vanishing point from the camera axis and the image plane distance. Lane location is simply based on the lateral displacement of the vanishing point from any horizontal cut through the lane edges. The boat lane position and yaw are currently fed what is essentially a stripped down marine auto-pilot system. Currently, only the lane location is used in a PID controller of a rudder actuator with integrator anti-windup to deal with saturation of the rudder angle. Low Kp and Kd values decrease unnecessarily fast return to lane centrelines and response to noise, and limiters can be used to avoid lane departure and disqualification. Yaw is not used as a control input, as cross-winds and currents can cause a straight course with considerable yaw or crab angle. Mapping of the controller with rudder angle “overall effectiveness” has not been finalized - very large rudder angles stall and have decreased turning moments, but at less extreme angles the increased rudder drag slows the boat and upsets boat balance. The full system has many features similar to automotive lane-assist systems, but with the added constraints of the lane markers, camera positioning, control response and noise increasing the challenge.

Keywords: auto-pilot, lane-assist, marine, optical, rowing

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Authors: Wei Ji

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This study was conducted in the Kansas City metropolitan area of the United States, which has experienced significant urban sprawling in recent decades. The remote sensing of land cover changes in this area spanned over four decades from 1972 through 2010. The project was implemented in two stages: the first stage focused on detection of long-term trends of urban land cover change, while the second one examined how to detect the coupled effects of human impact and climate change on urban landscapes. For the first-stage study, six Landsat images were used with a time interval of about five years for the period from 1972 through 2001. Four major land cover types, built-up land, forestland, non-forest vegetation land, and surface water, were mapped using supervised image classification techniques. The study found that over the three decades the built-up lands in the study area were more than doubled, which was mainly at the expense of non-forest vegetation lands. Surprisingly and interestingly, the area also saw a significant gain in surface water coverage. This observation raised questions: How have human activities and precipitation variation jointly impacted surface water cover during recent decades? How can we detect such coupled impacts through remote sensing analysis? These questions led to the second stage of the study, in which we designed and developed approaches to detecting fine-scale surface waters and analyzing coupled effects of human impact and precipitation variation on the waters. To effectively detect urban landscape changes that might be jointly shaped by precipitation variation, our study proposed “urban wetscapes” (loosely-defined urban wetlands) as a new indicator for remote sensing detection. The study examined whether urban wetscape dynamics was a sensitive indicator of the coupled effects of the two driving forces. To better detect this indicator, a rule-based classification algorithm was developed to identify fine-scale, hidden wetlands that could not be appropriately detected based on their spectral differentiability by a traditional image classification. Three SPOT images for years 1992, 2008, and 2010, respectively were classified with this technique to generate the four types of land cover as described above. The spatial analyses of remotely-sensed wetscape changes were implemented at the scales of metropolitan, watershed, and sub-watershed, as well as based on the size of surface water bodies in order to accurately reveal urban wetscape change trends in relation to the driving forces. The study identified that urban wetscape dynamics varied in trend and magnitude from the metropolitan, watersheds, to sub-watersheds in response to human impacts at different scales. The study also found that increased precipitation in the region in the past decades swelled larger wetlands in particular while generally smaller wetlands decreased mainly due to human development activities. These results confirm that wetscape dynamics can effectively reveal the coupled effects of human impact and climate change on urban landscapes. As such, remote sensing of this indicator provides new insights into the relationships between urban land cover changes and driving forces.

Keywords: urban land cover, human impact, climate change, rule-based classification, across-scale analysis

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Authors: Li Minghui, Min Shaorong, Zhang Jun

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This paper aims for studying the operational efficiency of surface warship’s motorized evasion to acoustic homing torpedo. It orderly developed trajectory model, self-guide detection model, vessel evasion model, as well as anti-torpedo error model in three-dimensional space to make up for the deficiency of precious researches analyzing two-dimensionally confrontational models. Then, making use of the Monte Carlo method, it carried out the simulation for the confrontation process of evasion in the environment of MATLAB. At last, it quantitatively analyzed the main factors which determine vessel’s survival probability. The results show that evasion relative bearing and speed will affect vessel’s survival probability significantly. Thus, choosing appropriate evasion relative bearing and speed according to alarming range and alarming relative bearing for torpedo, improving alarming range and positioning accuracy and reducing the response time against torpedo will improve the vessel’s survival probability significantly.

Keywords: acoustic homing torpedo, vessel evasion, monte carlo method, torpedo defense, vessel's survival probability

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Authors: Montaceur Zaghdoud, Mohamed Moussaoui, Jalel Akaichi

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Frequent subgraph mining refers usually to graph matching and it is widely used in when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structuralsemantic graph matching to discover a set of frequent subgraphs. It uses simultaneously an approximate structural similarity function based on graph edit distance function and a possibilistic vertices similarity function based on affinity function. Both structural and semantic filters contribute together to prune extracted frequent set. Indeed, new hybrid structural-semantic frequent subgraph mining approach searches will be suitable to be applied to several application such as community detection in social networks.

Keywords: approximate graph matching, hybrid frequent subgraph mining, graph mining, possibility theory

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Authors: Amit Kumar, Himanshu Singal, Arnav Bhavsar

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In this modern era of automation, most of the academic exams and competitive exams are Multiple Choice Questions (MCQ). The responses of these MCQ based exams are recorded in the Optical Mark Reader (OMR) sheet. Evaluation of the OMR sheet requires separate specialized machines for scanning and marking. The sheets used by these machines are special and costs more than a normal sheet. Available process is non-economical and dependent on paper thickness, scanning quality, paper orientation, special hardware and customized software. This study tries to tackle the problem of evaluating the OMR sheet without any special hardware and making the whole process economical. We propose an image processing based algorithm which can be used to read and evaluate the scanned OMR sheets with no special hardware required. It will eliminate the use of special OMR sheet. Responses recorded in normal sheet is enough for evaluation. The proposed system takes care of color, brightness, rotation, little imperfections in the OMR sheet images.

Keywords: OMR, image processing, hough circle trans-form, interpolation, detection, binary thresholding

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Authors: Rahul Agarwal, Ashutosh Singh

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Colorectal cancer (CRC) also refers as bowel cancer or colon cancer. It involves the development of abnormal growth of cells in colon or rectum part of the body. This work leads to the development of a miRNA database in colorectal cancer. We named this database- miCoRe. This database comprises of all validated colon-rectal cancer miRNAs information from various published literature with an effectual knowledge based information retrieval system. miRNAs have been collected from various published literature reports. MySQL is used for main-framework of miCoRe while the front-end was developed in PHP script. The aim of developing miCoRe is to create a comprehensive central repository of colorectal carcinoma miRNAs with all germane information of miRNAs and their target genes. The current version of miCoRe consists of 238 miRNAs which are known to be implicated in malignancy of CRC. Alongside with miRNA information, miCoRe also contains the information related to the target genes of these miRNA. miCoRe furnishes the information about the mechanism of incidence and progression of the disease, which would further help the researchers to look for colorectal specific miRNAs therapies and CRC specific targeted drug designing. Moreover, it will also help in development of biomarkers for the better and early detection of CRC and will help in better clinical management of the disease.

Keywords: colorectal cancer, database, miCoRe, miRNAs

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights

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Authors: Muthukumarasamy Govindarajan

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Detection of unwanted, unsolicited mails called spam from email is an interesting area of research. It is necessary to evaluate the performance of any new spam classifier using standard data sets. Recently, ensemble-based classifiers have gained popularity in this domain. In this research work, an efficient email filtering approach based on ensemble methods is addressed for developing an accurate and sensitive spam classifier. The proposed approach employs Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA) as base classifiers along with different ensemble methods. The experimental results show that the ensemble classifier was performing with accuracy greater than individual classifiers, and also hybrid model results are found to be better than the combined models for the e-mail dataset. The proposed ensemble-based classifiers turn out to be good in terms of classification accuracy, which is considered to be an important criterion for building a robust spam classifier.

Keywords: accuracy, arcing, bagging, genetic algorithm, Naive Bayes, sentiment mining, support vector machine

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Authors: Shu-Min Tsao, Chung-Ming Lo, Shao-Chun Chen

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Most diabetes patients tend to suffer from its complication of retina diseases. Therefore, early detection and early treatment are important. In clinical examinations, using color fundus image was the most convenient and available examination method. According to the exudates appeared in the retinal image, the status of retina can be confirmed. However, the routine screening of diabetic retinopathy by color fundus images would bring time-consuming tasks to physicians. This study thus proposed a computer-aided exudate diagnosis for the screening of diabetic retinopathy. After removing vessels and optic disc in the retinal image, six quantitative features including region number, region area, and gray-scale values etc… were extracted from the remaining regions for classification. As results, all six features were evaluated to be statistically significant (p-value < 0.001). The accuracy of classifying the retinal images into normal and diabetic retinopathy achieved 82%. Based on this system, the clinical workload could be reduced. The examination procedure may also be improved to be more efficient.

Keywords: computer-aided diagnosis, diabetic retinopathy, exudate, image processing

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Authors: Marcela De Oliveira, Marina P. Da Silva, Fernando C. G. Da Rocha, Jorge M. Santos, Jaime S. Cardoso, Paulo N. Lisboa-Filho

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Multiple Sclerosis (MS) is an immune-mediated disease of the central nervous system characterized by neurodegeneration, inflammation, demyelination, and axonal loss. Magnetic resonance imaging (MRI), due to the richness in the information details provided, is the gold standard exam for diagnosis and follow-up of neurodegenerative diseases, such as MS. Brain atrophy, the gradual loss of brain volume, is quite extensive in multiple sclerosis, nearly 0.5-1.35% per year, far off the limits of normal aging. Thus, the brain volume quantification becomes an essential task for future analysis of the occurrence atrophy. The analysis of MRI has become a tedious and complex task for clinicians, who have to manually extract important information. This manual analysis is prone to errors and is time consuming due to various intra- and inter-operator variability. Nowadays, computerized methods for MRI segmentation have been extensively used to assist doctors in quantitative analyzes for disease diagnosis and monitoring. Thus, the purpose of this work was to evaluate the brain volume in MRI of MS patients. We used MRI scans with 30 slices of the five patients diagnosed with multiple sclerosis according to the McDonald criteria. The computational methods for the analysis of images were carried out in two steps: segmentation of the brain and brain volume quantification. The first image processing step was to perform brain extraction by skull stripping from the original image. In the skull stripper for MRI images of the brain, the algorithm registers a grayscale atlas image to the grayscale patient image. The associated brain mask is propagated using the registration transformation. Then this mask is eroded and used for a refined brain extraction based on level-sets (edge of the brain-skull border with dedicated expansion, curvature, and advection terms). In the second step, the brain volume quantification was performed by counting the voxels belonging to the segmentation mask and converted in cc. We observed an average brain volume of 1469.5 cc. We concluded that the automatic method applied in this work can be used for the brain extraction process and brain volume quantification in MRI. The development and use of computer programs can contribute to assist health professionals in the diagnosis and monitoring of patients with neurodegenerative diseases. In future works, we expect to implement more automated methods for the assessment of cerebral atrophy and brain lesions quantification, including machine-learning approaches. Acknowledgements: This work was supported by a grant from Brazilian agency Fundação de Amparo à Pesquisa do Estado de São Paulo (number 2019/16362-5).

Keywords: brain volume, magnetic resonance imaging, multiple sclerosis, skull stripper

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Authors: Mamta M. Barapatre, V. N. Sahare

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Since from long time two wheeled vehicle self-balancing has always been a back-breaking task for both human and robots. Leaning a bicycle driving is long time process and goes through building knowledge base for parameter decision making while balancing robots. In order to create this machine learning phase with embedded system the proposed system is designed. The system proposed aims to construct a bicycle automaton, power-driven by an electric motor, which could balance by itself and move along a specific path. This path could be wavy with bumps and varying widths. The key aim was to construct a cycle which self-balances itself by controlling its handle. In order to take a turn, the mass was transferred to the center. In order to maintain the stability, the bicycle bot automatically turned the handle and a turn. Some problems were faced by the team which were Speed, Steering mechanism through mass- distribution (leaning), Center of mass location and gyroscopic effect of its wheel. The idea proposed have potential applications in automation of transportation system and is most efficient.

Keywords: gyroscope-flywheel, accelerometer, servomotor-controller, self stability concept

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Authors: N. Garrós, P. Bustos, N. Beirampour, R. Mohammadi, M. Mallandrich, A.C. Calpena, H. Colom

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Atopic dermatitis (AD) is a persistent skin condition characterized by chronic inflammation caused by an autoimmune response. It is a prevalent clinical issue that requires continual treatment to enhance the patient's quality of life. Systemic therapy often involves the use of glucocorticoids or immunosuppressants to manage symptoms. Our objective was to create and assess topical liposomal formulations containing Baricitinib (BNB), a reversible inhibitor of Janus-associated kinase (JAK), which is involved in various immune responses. These formulations were intended to address flare-ups and improve treatment outcomes for AD. We created three distinct liposomal formulations by combining different amounts of 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine (POPC), cholesterol (CHOL), and ceramide (CER): (i) pure POPC, (ii) POPC mixed with CHOL (at a ratio of 8:2, mol/mol), and (iii) POPC mixed with CHOL and CER (at a ratio of 3.6:2.4:4.0 mol/mol/mol). We conducted various tests to determine the formulations' skin tolerance, irritancy capacity, and their ability to cause erythema and edema on altered skin. We also assessed the transepidermal water loss (TEWL) and skin hydration of rabbits to evaluate the efficacy of the formulations. Histological analysis, the HET-CAM test, and the modified Draize test were all used in the evaluation process. The histological analysis revealed that liposome POPC and POPC:CHOL avoided any damage to the tissues structures. The HET-CAM test showed no irritation effect caused by any of the three liposomes, and the modified Draize test showed a good Draize score for erythema and edema. Liposome POPC effectively counteracted the impact of xylol on the skin, and no erythema or edema was observed during the study. TEWL values were constant for all the liposomes with similar values to the negative control (within the range 8 - 15 g/h·m2, which means a healthy value for rabbits), whereas the positive control showed a significant increase. The skin hydration values were constant and followed the trend of the negative control, while the positive control showed a steady increase during the tolerance study. In conclusion, the developed formulations containing BNB exhibited no harmful or irritating effects, they did not demonstrate any irritant potential in the HET-CAM test and liposomes POPC and POPC:CHOL did not cause any structural alteration according to the histological analysis. These positive findings suggest that additional research is necessary to evaluate the efficacy of these liposomal formulations in animal models of the disease, including mutant animals. Furthermore, before proceeding to clinical trials, biochemical investigations should be conducted to better understand the mechanisms of action involved in these formulations.

Keywords: baricitinib, HET-CAM test, histological study, JAK inhibitor, liposomes, modified draize test

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Authors: J. Hidalgo de Quintana, I. Stoner, M. Tackett, G. Doran, C. Rafferty, A. Windemuth, J. Tytell, D. Pregibon

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We have developed the Multiplexed Circulating microRNA assay that allows the detection of up to 68 microRNA targets per sample. The assay combines particle­based multiplexing, using patented Firefly hydrogel particles, with single­ step RT-PCR signal. Thus, the Circulating microRNA assay leverages PCR sensitivity while eliminating the need for separate reverse transcription reactions and mitigating amplification biases introduced by target­-specific qPCR. Furthermore, the ability to multiplex targets in each well eliminates the need to split valuable samples into multiple reactions. Results from the Circulating microRNA assay are interpreted using Firefly Analysis Workbench, which allows visualization, normalization, and export of experimental data. To aid discovery and validation of biomarkers, we have generated fixed panels for Oncology, Cardiology, Neurology, Immunology, and Liver Toxicology. Here we present the data from several studies investigating circulating and tumor microRNA, showcasing the ability of the technology to sensitively and specifically detect microRNA biomarker signatures from fluid specimens.

Keywords: biomarkers, biofluids, miRNA, photolithography, flowcytometry

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Authors: Md. Mazharul Anwar, Md. Samim Hossain Molla, Md. Akkas Ali, Mian Sayeed Hassan

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After 68 years, the agreement between Bangladesh and India was ratified on 6 June 2015 and Bangladesh received 111 Indian enclaves. Millions of farm household lived in these previous enclaves, being detached from the mainland of the country, they were socially, economically and educationally deprived people in the world. This study was undertaken to compare of the Stochastic Frontier Analysis (SFA) and the constant returns to scale (CRS) and variable returns to scale (VRS) output-oriented DEA models, based on a sample of 300 farms from the three largest enclaves of Bangladesh in 2017. However, the aim of the study was not only to compare estimates of technical efficiency obtained from the two approaches, but also to examine the determinants of inefficiency. The results from both the approaches indicated that there is a potential for increasing farm production through efficiency improvement and that farmers' age, educational level, new technology dissemination and training on crop production technology have a significant effect on efficiency. The detection and measurement of technical inefficiency and its determinants can be used as a basis of policy recommendations.

Keywords: DEA approach, previous enclaves, SFA approach, technical inefficiency

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Authors: Bangoura Issa, Ji-Young Kang, M. T. H. Chowdhury, Ji-Eun Lee, Yong-Ki Hong

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Investigation was carried out for the production of value-added abalone Haliotis discus hannai containing bioactive phlorotannin by feeding phlorotannin-rich seaweed Eisenia bicyclis 2 weeks prior to harvesting. Accumulation of phlorotannins was proceded by feeding with E. bicyclis after 4 days of starvation. HPLC purification afforded two major phlorotannins. Mass spectrometry and 1H-nuclear magnetic resonance analysis clarified their structures to be as 7-phloroeckol and eckol. Throughout the feeding period of 20 days, 7-phloroeckolol was accumulated in the muscle (foot muscle tissue) up to 0.18±0.12 mg g-1 dry weight of tissue after 12 days. Eckol reached 0.21±0.03 mg g-1 dry weight of tissue after 18 days. By feeding Laminaria japonica as reference, abalone showed no detection of phlorotannins in the muscle tissue. Seaweed consumption and growth rate of abalone revealed almost similar when feed with E. bicyclis or L. japonicain 20 days. Phlorotannins reduction to half-maximal accumulation values took 1.0 day and 2.7 days for 7-phloroeckol and eckol respectively, after replacing the feed to L. japonica.

Keywords: abalone, accumulation, eisenia bicyclis, phlorotannins

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Authors: Yu-Lin Shen, Ming-Kuen Chang

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In addition to a considerable amount of machinery and equipment, intricacies of the transmission pipeline exist in Petrochemical plants. Long term corrosion may lead to pipeline thinning and rupture, causing serious safety concerns. With the advances in non-destructive testing technology, more rapid and long-range ultrasonic detection techniques are often used for pipeline inspection, EMAT without coupling to detect, it is a non-contact ultrasonic, suitable for detecting elevated temperature or roughened e surface of line. In this study, we prepared artificial defects in pipeline for Electromagnetic Acoustic Transducer testing (EMAT) to survey the relationship between the defect location, sizing and the EMAT signal. It was found that the signal amplitude of EMAT exhibited greater signal attenuation with larger defect depth and length. In addition, with bigger flat hole diameter, greater amplitude attenuation was obtained. In summary, signal amplitude attenuation of EMAT was affected by the defect depth, defect length and the hole diameter and size.

Keywords: EMAT, artificial defect, NDT, ultrasonic testing

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Authors: Spoorthi Sripad

Abstract:

Rapid advancements in LiDAR (Light Detection and Ranging) technology, coupled with the synergy of remote sensing, have revolutionized Earth observation methodologies. This paper delves into the transformative impact of integrated LiDAR and remote sensing systems. Focusing on miniaturization, cost reduction, and improved resolution, the study explores the evolving landscape of terrestrial and aquatic environmental monitoring. The integration of multi-wavelength and dual-mode LiDAR systems, alongside collaborative efforts with other remote sensing technologies, presents a comprehensive approach. The paper highlights the pivotal role of LiDAR in environmental assessment, urban planning, and infrastructure development. As the amalgamation of LiDAR and remote sensing reshapes Earth observation, this research anticipates a paradigm shift in our understanding of dynamic planetary processes.

Keywords: LiDAR, remote sensing, earth observation, advancements, integration, environmental monitoring, multi-wavelength, dual-mode, technology, urban planning, infrastructure, resolution, miniaturization

Procedia PDF Downloads 57