Search results for: quantification accuracy

Authors: Andrey V. Timofeev, Dmitry V. Egorov

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This paper introduces an original method of parametric optimization of the structure for multimodal decision-level fusion scheme which combines the results of the partial solution of the classification task obtained from assembly of the mono-modal classifiers. As a result, a multimodal fusion classifier which has the minimum value of the total error rate has been obtained.

Keywords: classification accuracy, fusion solution, total error rate, multimodal fusion classifier

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Authors: Hyeon-Kyo Lim, Hyunjung Kim, Kune-Woo Lee

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Decommissioning process of nuclear facilities can be said to consist of a sequence of problem solving activities, partly because there may exist working environments contaminated by radiological exposure, and partly because there may also exist industrial hazards such as fire, explosions, toxic materials, and electrical and physical hazards. As for an individual hazard factor, risk assessment techniques are getting known to industrial workers with advance of safety technology, but the way how to integrate those results is not. Furthermore, there are few workers who experienced decommissioning operations a lot in the past. Therefore, not a few countries in the world have been trying to develop appropriate counter techniques in order to guarantee safety and efficiency of the process. In spite of that, there still exists neither domestic nor international standard since nuclear facilities are too diverse and unique. In the consequence, it is quite inevitable to imagine and assess the whole risk in the situation anticipated one by one. This paper aimed to find out an appropriate technique to integrate individual risk assessment results from the viewpoint of experts. Thus, on one hand the whole risk assessment activity for decommissioning operations was modeled as a sequence of individual risk assessment steps, and on the other, a hierarchical risk structure was developed. Then, risk assessment procedure that can elicit individual hazard factors one by one were introduced with reference to the standard operation procedure (SOP) and hierarchical task analysis (HTA). With an assumption of quantification and normalization of individual risks, a technique to estimate relative weight factors was tried by using the conventional Analytic Hierarchical Process (AHP) and its result was reviewed with reference to judgment of experts. Besides, taking the ambiguity of human judgment into consideration, debates based upon fuzzy inference was added with a mathematical case study.

Keywords: decommissioning, risk assessment, analytic hierarchical process (AHP), fuzzy inference

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Authors: Mohammed Al Sulaimani, Hamad Al Manhi

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With the development of Remote Sensing technology, the resolution of optical Remote Sensing images has greatly improved, and images have become largely available. Numerous detectors have been developed for detecting different types of objects. In the past few years, Remote Sensing has benefited a lot from deep learning, particularly Deep Convolution Neural Networks (CNNs). Deep learning holds great promise to fulfill the challenging needs of Remote Sensing and solving various problems within different fields and applications. The use of Unmanned Aerial Systems in acquiring Aerial Photos has become highly used and preferred by most organizations to support their activities because of their high resolution and accuracy, which make the identification and detection of very small features much easier than Satellite Images. And this has opened an extreme era of Deep Learning in different applications not only in feature extraction and prediction but also in analysis. This work addresses the capacity of Machine Learning and Deep Learning in detecting and extracting Oil Leaks from Flowlines (Onshore) using High-Resolution Aerial Photos which have been acquired by UAS fixed with RGB Sensor to support early detection of these leaks and prevent the company from the leak’s losses and the most important thing environmental damage. Here, there are two different approaches and different methods of DL have been demonstrated. The first approach focuses on detecting the Oil Leaks from the RAW Aerial Photos (not processed) using a Deep Learning called Single Shoot Detector (SSD). The model draws bounding boxes around the leaks, and the results were extremely good. The second approach focuses on detecting the Oil Leaks from the Ortho-mosaiced Images (Georeferenced Images) by developing three Deep Learning Models using (MaskRCNN, U-Net and PSP-Net Classifier). Then, post-processing is performed to combine the results of these three Deep Learning Models to achieve a better detection result and improved accuracy. Although there is a relatively small amount of datasets available for training purposes, the Trained DL Models have shown good results in extracting the extent of the Oil Leaks and obtaining excellent and accurate detection.

Keywords: GIS, remote sensing, oil leak detection, machine learning, aerial photos, unmanned aerial systems

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Authors: Dina Atmani-Kilani, Farah Yous, Djebbar Atmani

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The etiology of peptic ulcer is closely related to stress, excessive consumption of nonsteroidal anti-inflammatory drugs, or ethanol. Clematis flammula (Ranunculaceae) is a medicinal plant widely used by rural populations to treat inflammatory disorders. This study was designed to assess the gastroprotective potential of C. flammula extracts. Gastric ulcer was induced by stress, indomethacin, HCl / ethanol, and absolute ethanol on NMRI-type mice. The antioxidant potency of the ethanolic extract of Clematis flammula (EECF) was evaluated on catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GPx) activities. Glutathione (GSH) and malonaldehyde (MDA) levels were also quantified. The anti-inflammatory potential was evaluated through the effect of EECF on myeloperoxidase activity (MPO) and vascular permeability. Complementary tests concerning the quantification of mucus levels, gastric motility, inhibition of ATPase H+/K+activity, as well as a histopathological study were also undertaken to explore the mechanism of action of the EECF. The EECF exhibited a significant (p <0.001) and optimal (100 mg/kg) gastroprotective effect by elevating SOD, CAT, and GSH levels, thereby minimizing the production of MDA and lowering the activity of MPO and vascular permeability. EECF also increased the rate of mucus production, decreased gastric motility, and completely suppressed the H+/K+ ATPase activity. Histopathological study confirmed the effectiveness of the extract in the prevention of peptic ulcer. The results obtained in this study demonstrated the gastro-protective effect of EECF via acidic antioxidant, anti-inflammatory, cytoprotective and anti-secretory mechanisms, which may justify its use as a substitute in peptic ulcer treatment.

Keywords: clematis flammula, superoxide dismutase, myeloperoxidase, ATPase, pump

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Authors: Z. Gharaee

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Recognizing actions performed by others is important in our daily lives since it is necessary for communicating with others in a proper way. We perceive an action by observing the kinematics of motions involved in the performance. We use our experience and concepts to make a correct recognition of the actions. Although building the action concepts is a life-long process, which is repeated throughout life, we are very efficient in applying our learned concepts in analyzing motions and recognizing actions. Experiments on the subjects observing the actions performed by an actor show that an action is recognized after only about two hundred milliseconds of observation. In this study, hierarchical action recognition architecture is proposed by using growing grid layers. The first-layer growing grid receives the pre-processed data of consecutive 3D postures of joint positions and applies some heuristics during the growth phase to allocate areas of the map by inserting new neurons. As a result of training the first-layer growing grid, action pattern vectors are generated by connecting the elicited activations of the learned map. The ordered vector representation layer receives action pattern vectors to create time-invariant vectors of key elicited activations. Time-invariant vectors are sent to second-layer growing grid for categorization. This grid creates the clusters representing the actions. Finally, one-layer neural network developed by a delta rule labels the action categories in the last layer. System performance has been evaluated in an experiment with the publicly available MSR-Action3D dataset. There are actions performed by using different parts of human body: Hand Clap, Two Hands Wave, Side Boxing, Bend, Forward Kick, Side Kick, Jogging, Tennis Serve, Golf Swing, Pick Up and Throw. The growing grid architecture was trained by applying several random selections of generalization test data fed to the system during on average 100 epochs for each training of the first-layer growing grid and around 75 epochs for each training of the second-layer growing grid. The average generalization test accuracy is 92.6%. A comparison analysis between the performance of growing grid architecture and self-organizing map (SOM) architecture in terms of accuracy and learning speed show that the growing grid architecture is superior to the SOM architecture in action recognition task. The SOM architecture completes learning the same dataset of actions in around 150 epochs for each training of the first-layer SOM while it takes 1200 epochs for each training of the second-layer SOM and it achieves the average recognition accuracy of 90% for generalization test data. In summary, using the growing grid network preserves the fundamental features of SOMs, such as topographic organization of neurons, lateral interactions, the abilities of unsupervised learning and representing high dimensional input space in the lower dimensional maps. The architecture also benefits from an automatic size setting mechanism resulting in higher flexibility and robustness. Moreover, by utilizing growing grids the system automatically obtains a prior knowledge of input space during the growth phase and applies this information to expand the map by inserting new neurons wherever there is high representational demand.

Keywords: action recognition, growing grid, hierarchical architecture, neural networks, system performance

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Authors: Jing Jiang, Wenhuan XU, Lei Zhang, Shiyi Zhang, Tongli Wang

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Climate data quality significantly affects the reliability of ecological modeling. In the Asia Pacific (AP) region, low-quality climate data hinders ecological modeling. ClimateAP, a software developed in 2017, generates high-quality climate data for the AP region, benefiting researchers in forestry and agriculture. However, its adoption remains limited. This study aims to confirm the validity of biologically relevant variable data generated by ClimateAP during the normal climate period through comparison with the currently available gridded data. Climate data from 2,366 weather stations were used to evaluate the prediction accuracy of ClimateAP in comparison with the commonly used gridded data from WorldClim1.4. Univariate regressions were applied to 48 monthly biologically relevant variables, and the relationship between the observational data and the predictions made by ClimateAP and WorldClim was evaluated using Adjusted R-Squared and Root Mean Squared Error (RMSE). Locations were categorized into mountainous and flat landforms, considering elevation, slope, ruggedness, and Topographic Position Index. Univariate regressions were then applied to all biologically relevant variables for each landform category. Random Forest (RF) models were implemented for the climatic niche modeling of Cunninghamia lanceolata. A comparative analysis of the prediction accuracies of RF models constructed with distinct climate data sources was conducted to evaluate their relative effectiveness. Biologically relevant variables were obtained from three unpublished Chinese meteorological datasets. ClimateAPv3.0 and WorldClim predictions were obtained from weather station coordinates and WorldClim1.4 rasters, respectively, for the normal climate period of 1961-1990. Occurrence data for Cunninghamia lanceolata came from integrated biodiversity databases with 3,745 unique points. ClimateAP explains a minimum of 94.74%, 97.77%, 96.89%, and 94.40% of monthly maximum, minimum, average temperature, and precipitation variances, respectively. It outperforms WorldClim in 37 biologically relevant variables with lower RMSE values. ClimateAP achieves higher R-squared values for the 12 monthly minimum temperature variables and consistently higher Adjusted R-squared values across all landforms for precipitation. ClimateAP's temperature data yields lower Adjusted R-squared values than gridded data in high-elevation, rugged, and mountainous areas but achieves higher values in mid-slope drainages, plains, open slopes, and upper slopes. Using ClimateAP improves the prediction accuracy of tree occurrence from 77.90% to 82.77%. The biologically relevant climate data produced by ClimateAP is validated based on evaluations using observations from weather stations. The use of ClimateAP leads to an improvement in data quality, especially in non-mountainous regions. The results also suggest that using biologically relevant variables generated by ClimateAP can slightly enhance climatic niche modeling for tree species, offering a better understanding of tree species adaptation and resilience compared to using gridded data.

Keywords: climate data validation, data quality, Asia pacific climate, climatic niche modeling, random forest models, tree species

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Authors: Jianrong Wu, Xuan Fu, Akihiro Higashi, Zhiming Tan

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CMOS image sensors with a rolling shutter are used broadly in the digital cameras embedded in mobile devices. The rolling shutter suffers the flicker artifacts from the fluorescent lamp, and it could be observed easily. In this paper, the characteristics of illumination flicker in motion case were analyzed, and two efficient detection methods based on matching fragment selection were proposed. According to the experimental results, our methods could achieve as high as 100% accuracy in static scene, and at least 97% in motion scene.

Keywords: illumination flicker, embedded camera, rolling shutter, detection

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Authors: Yun Chen, Qihang Liu, Catherine Ticehurst, Chandrama Sarker, Fazlul Karim, Dave Penton, Ashmita Sengupta

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The application of ICESAT-2 altimetry data in river hydrology critically depends on the accuracy of the mean water surface elevation (WSE) at a virtual station (VS) where satellite observations intersect with water. The ICESAT-2 track generates multiple VSs as it crosses the different water bodies. The difficulties are particularly pronounced in large river basins where there are many tributaries and meanders often adjacent to each other. One challenge is to split photon segments along a beam to accurately partition them to extract only the true representative water height for individual elements. As far as we can establish, there is no automated procedure to make this distinction. Earlier studies have relied on human intervention or river masks. Both approaches are unsatisfactory solutions where the number of intersections is large, and river width/extent changes over time. We describe here an automated approach called “auto-segmentation”. The accuracy of our method was assessed by comparison with river water level observations at 10 different stations on 37 different dates along the Lower Murray River, Australia. The congruence is very high and without detectable bias. In addition, we compared different outlier removal methods on the mean WSE calculation at VSs post the auto-segmentation process. All four outlier removal methods perform almost equally well with the same R2 value (0.998) and only subtle variations in RMSE (0.181–0.189m) and MAE (0.130–0.142m). Overall, the auto-segmentation method developed here is an effective and efficient approach to deriving accurate mean WSE at river VSs. It provides a much better way of facilitating the application of ICESAT-2 ATL13 altimetry to rivers compared to previously reported studies. Therefore, the findings of our study will make a significant contribution towards the retrieval of hydraulic parameters, such as water surface slope along the river, water depth at cross sections, and river channel bathymetry for calculating flow velocity and discharge from remotely sensed imagery at large spatial scales.

Keywords: lidar sensor, virtual station, cross section, mean water surface elevation, beam/track segmentation

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Authors: Wajad Nazeer, Saghir Ahmad, Khalid Mahmood, Altaf Hussain, Abid Mahmood, Baoliang Zhou

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MNH-886(Bt.) is a upland cotton cultivar (Gossypium hirsutum L.) developed through hybridization of three parents [(FH-207×MNH-770)×Bollgard-1] at Cotton Research Station Multan, Pakistan. It is resistant to CLCuVD with 16.25 % disease incidence (60 DAS, March sowing) whereas moderately susceptible to CLCuVD when planted in June with disease incidence 34 % (60 DAS). This disease reaction was lowest among 25 cotton advanced lines/varieties tested at hot spots of CLCuVD. Its performance was tested during 2009 to 2012 in various indigenous, provincial, and national varietal trials in comparison with the commercial variety IR-3701 and AA-802 & CIM-496. In PCCT trial during 2009-10; 2011-12, MNH-886 surpassed all the existing Bt. strains along with commercial varieties across the Punjab province with seed cotton yield production 2658 kg ha-1 and 2848 kg ha-1 which was 81.31 and 13% higher than checks, respectively. In National Coordinated Bt. Trial, MNH-886(Bt.) produced 3347 kg ha-1 seed cotton at CCRI, Multan; the hot spot of CLCuVD, in comparison to IR-3701 which gave 2556 kg ha-1. It possesses higher lint percentage (41.01%), along with the most desirable fibre traits (staple length 28.210mm, micronaire value 4.95 µg inch-1 and fibre strength 99.5 tppsi, and uniformity ratio 82.0%). The quantification of toxicity level of crystal protein was found positive for Cry1Ab/Ac protein with toxicity level 2.76µg g-1 and Mon 531 event was confirmed. Having tremendous yield potential, good fibre traits, and great tolerance to CLCuVD we can recommended this variety for cultivation in CLCuVD hotspots of Pakistan.

Keywords: cotton, cultivar, cotton leaf curl virus, CLCuVD hit districts

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Authors: Hung-Yuan Chang, Wen-Lung Chiang, Kuo-Liang Wu, Chen-Tsung Lin

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National Space Organization (NSPO) has completed in 2014 the development of a space-borne GPS receiver, including design, manufacture, comprehensive functional test, environmental qualification test and so on. The main performance of this receiver include 8-meter positioning accuracy, 0.05 m/sec speed-accuracy, the longest 90 seconds of cold start time, and up to 15g high dynamic scenario. The receiver will be integrated in the autonomous FORMOSAT-7 NSPO-Built satellite scheduled to be launched in 2019 to execute pre-defined scientific missions. The flight model of this receiver manufactured in early 2015 will pass comprehensive functional tests and environmental acceptance tests, etc., which are expected to be completed by the end of 2015. The space-borne GPS receiver is a pure software design in which all GPS baseband signal processing are executed by a digital signal processor (DSP), currently only 50% of its throughput being used. In response to the booming global navigation satellite systems, NSPO will gradually expand this receiver to become a multi-mode, multi-band, high-precision navigation receiver, and even a science payload, such as the reflectometry receiver of a global navigation satellite system. The fundamental purpose of this extension study is to port some software algorithms such as signal acquisition and correlation, reused code and large amount of computation load to the FPGA whose processor is responsible for operational control, navigation solution, and orbit propagation and so on. Due to the development and evolution of the FPGA is pretty fast, the new system architecture upgraded via an FPGA should be able to achieve the goal of being a multi-mode, multi-band high-precision navigation receiver, or scientific receiver. Finally, the results of tests show that the new system architecture not only retains the original overall performance, but also sets aside more resources available for future expansion possibility. This paper will explain the detailed DSP/FPGA architecture, development, test results, and the goals of next development stage of this receiver.

Keywords: space-borne, GPS receiver, DSP, FPGA, multi-mode multi-band

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Authors: Li Xiaoge, Wang Chaodong

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We introduce the first Chinese Coreference Resolution Model based on BERT (CCRM-BERT) and show that it significantly outperforms all previous work. The key idea is to consider the features of the mention, such as part of speech, width of spans, distance between spans, etc. And the influence of each features on the model is analyzed. The model computes mention embeddings that combine BERT with features. Compared to the existing state-of-the-art span-ranking approach, our model significantly improves accuracy on the Chinese OntoNotes benchmark.

Keywords: BERT, coreference resolution, deep learning, nature language processing

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Authors: A. M. Tahsini, S. A. Hosseini

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In the present study, the surface temperature history of the adaptor part in a two-stage supersonic launch vehicle is accurately predicted. The full Navier-Stokes equations are used to estimate the aerodynamic heat flux. The one-dimensional heat conduction in solid phase is used to compute the temperature history. The instantaneous surface temperature is used to improve the applied heat flux, to improve the accuracy of the results.

Keywords: aerodynamic heating, heat conduction, numerical simulation, supersonic flight, launch vehicle

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Authors: Ahmedou Moulaye Idriss, Tfeil Yahya, Tamas Ungi, Gabor Fichtinger

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Image-guided therapy (IGT) plays a crucial role in minimally invasive procedures for liver interventions. Accurate segmentation of the liver and precise needle placement is essential for successful interventions such as needle aspiration. In this study, we propose a deep learning-based liver 3D slicer designed to enhance segmentation accuracy and facilitate needle aspiration procedures. The developed 3D slicer leverages state-of-the-art convolutional neural networks (CNNs) for automatic liver segmentation in medical images. The CNN model is trained on a diverse dataset of liver images obtained from various imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI). The trained model demonstrates robust performance in accurately delineating liver boundaries, even in cases with anatomical variations and pathological conditions. Furthermore, the 3D slicer integrates advanced image registration techniques to ensure accurate alignment of preoperative images with real-time interventional imaging. This alignment enhances the precision of needle placement during aspiration procedures, minimizing the risk of complications and improving overall intervention outcomes. To validate the efficacy of the proposed deep learning-based 3D slicer, a comprehensive evaluation is conducted using a dataset of clinical cases. Quantitative metrics, including the Dice similarity coefficient and Hausdorff distance, are employed to assess the accuracy of liver segmentation. Additionally, the performance of the 3D slicer in guiding needle aspiration procedures is evaluated through simulated and clinical interventions. Preliminary results demonstrate the effectiveness of the developed 3D slicer in achieving accurate liver segmentation and guiding needle aspiration procedures with high precision. The integration of deep learning techniques into the IGT workflow shows great promise for enhancing the efficiency and safety of liver interventions, ultimately contributing to improved patient outcomes.

Keywords: deep learning, liver segmentation, 3D slicer, image guided therapy, needle aspiration

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Authors: Djeddi Khaled, Houssou Hind, Miloudi Abdellatif, Rabah Siham

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In the field of veterinary medicine, there is a growing application of artificial intelligence (AI) for diagnosing bovine mastitis, a prevalent inflammatory disease in dairy cattle. AI technologies, such as automated milking systems, have streamlined the assessment of key metrics crucial for managing cow health during milking and identifying prevalent diseases, including mastitis. These automated milking systems empower farmers to implement automatic mastitis detection by analyzing indicators like milk yield, electrical conductivity, fat, protein, lactose, blood content in the milk, and milk flow rate. Furthermore, reports highlight the integration of somatic cell count (SCC), thermal infrared thermography, and diverse systems utilizing statistical models and machine learning techniques, including artificial neural networks, to enhance the overall efficiency and accuracy of mastitis detection. According to a review of 15 publications, machine learning technology can predict the risk and detect mastitis in cattle with an accuracy ranging from 87.62% to 98.10% and sensitivity and specificity ranging from 84.62% to 99.4% and 81.25% to 98.8%, respectively. Additionally, machine learning algorithms and microarray meta-analysis are utilized to identify mastitis genes in dairy cattle, providing insights into the underlying functional modules of mastitis disease. Moreover, AI applications can assist in developing predictive models that anticipate the likelihood of mastitis outbreaks based on factors such as environmental conditions, herd management practices, and animal health history. This proactive approach supports farmers in implementing preventive measures and optimizing herd health. By harnessing the power of artificial intelligence, the diagnosis of bovine mastitis can be significantly improved, enabling more effective management strategies and ultimately enhancing the health and productivity of dairy cattle. The integration of artificial intelligence presents valuable opportunities for the precise and early detection of mastitis, providing substantial benefits to the dairy industry.

Keywords: artificial insemination, automatic milking system, cattle, machine learning, mastitis

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Authors: Amiya Kumar Pati, Spandan Sahu, Kishanjit Kumar Khatua

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River is our main source of water which is a form of open channel flow and the flow in the open channel provides with many complex phenomena of sciences that needs to be tackled such as the critical flow conditions, boundary shear stress, and depth-averaged velocity. The development of society, more or less solely depends upon the flow of rivers. The rivers are major sources of many sediments and specific ingredients which are much essential for human beings. A river flow consisting of small and shallow channels sometimes divide and recombine numerous times because of the slow water flow or the built up sediments. The pattern formed during this process resembles the strands of a braid. Braided streams form where the sediment load is so heavy that some of the sediments are deposited as shifting islands. Braided rivers often exist near the mountainous regions and typically carry coarse-grained and heterogeneous sediments down a fairly steep gradient. In this paper, the apparent shear stress formulae were suitably modified, and the Energy Concept Method (ECM) was applied for the prediction of discharges at the junction of a two-flow braided compound channel. The Energy Concept Method has not been applied for estimating the discharges in the braided channels. The energy loss in the channels is analyzed based on mechanical analysis. The cross-section of channel is divided into two sub-areas, namely the main-channel below the bank-full level and region above the bank-full level for estimating the total discharge. The experimental data are compared with a wide range of theoretical data available in the published literature to verify this model. The accuracy of this approach is also compared with Divided Channel Method (DCM). From error analysis of this method, it is observed that the relative error is less for the data-sets having smooth floodplains when compared to rough floodplains. Comparisons with other models indicate that the present method has reasonable accuracy for engineering purposes.

Keywords: critical flow, energy concept, open channel flow, sediment, two-flow braided compound channel

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Authors: Ying Li, Yan Li

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A new algorithm for single hidden layer feedforward neural networks (SLFN), Orthogonal Basis Extreme Learning (OBEL) algorithm, is proposed and the algorithm derivation is given in the paper. The algorithm can decide both the NNs parameters and the neuron number of hidden layer(s) during training while providing extreme fast learning speed. It will provide a practical way to develop NNs. The simulation results of function approximation showed that the algorithm is effective and feasible with good accuracy and adaptability.

Keywords: neural network, orthogonal basis extreme learning, function approximation

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Authors: Alhaji Modu Bukar, Faez Firdaus Abdullah Jesse, Che Azurahanim Che Abdullah, Mustapha M. Noordin, Mohd-Lila Mohd Azmia

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Orf virus (ORFV) is the causative agent of a proliferative skin lesion known as contagious ecthyma in sheep and goats. Currently used live attenuated vaccines against ORFV infection have been reported to cause severe outbreaks in vaccinated animals. In this study, we investigated the immunogenicity of the B2L and F1L proteins of the virus, which are thought to elicit a protective immune response The 6-week-old 50 female mice were divided into 8 groups: seven experimental groups and one control group. Each animal in the experimental group received an initial immunisation with the nanoparticles or proteins coated with the nanoparticles, followed by two booster immunizations with the same products 14 days apart. Ten days after the last booster inoculation, the mice were either humanely killed or lethally challenged with UPM /HSN-2-ORFV at a dose of 106 TCID50/mL in a volume of 50 μl. The spleen was examined for histopathological changes and quantification of T cells by flow cytometry. On the other hand, the degree of protection of mice from the lethal virus was evaluated by lesion size, weight loss, and histopathological examination of skin and liver. The results showed that mice immunised with rB2L alone, rB2L-Al₂O₃-NPs, rB2L/rF1L, and rB2L/rF1L-Al₂O₃-NPs elicited statistically higher levels of anti-rB2L and/or rF1L-specific IgA/IgG and CD4/CD8 cell immune responses than mice in the control groups (p < 0.01). The vaccine candidate did not exhibit severe skin damage after monitoring histopathology, morbidity, and mortality. Overall, the results suggest that recombinant rB2L and rF1L antigens may be useful universal vaccine candidates against ORFV infections.

Keywords: orf virus, antigen nanoparticles, virus, nanoparticles

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Authors: Giovanna Borges Bottino, Luis Felipe Freitas do Nascimento Alves Teixeira

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This article aims to present a case study of the National Civil Aviation Agency (ANAC) in Brazil, ANAC-id. ANAC-id is the artificial intelligence algorithm developed for image analysis that recognizes standard images of unobstructed and uprighted face without sunglasses, allowing to identify potential inconsistencies. It combines YOLO architecture and 3 libraries in python - face recognition, face comparison, and deep face, providing robust analysis with high level of accuracy.

Keywords: artificial intelligence, deepface, face compare, face recognition, YOLO, computer vision

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Authors: N. Fuad, M. N. Taib, R. Jailani, M. E. Marwan

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In this paper, the comparison between k-Nearest Neighbor (kNN) algorithms for classifying the 3D EEG model in brain balancing is presented. The EEG signal recording was conducted on 51 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, maximum PSD values were extracted as features from the model. There are three indexes for the balanced brain; index 3, index 4 and index 5. There are significant different of the EEG signals due to the brain balancing index (BBI). Alpha-α (8–13 Hz) and beta-β (13–30 Hz) were used as input signals for the classification model. The k-NN classification result is 88.46% accuracy. These results proved that k-NN can be used in order to predict the brain balancing application.

Keywords: power spectral density, 3D EEG model, brain balancing, kNN

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Authors: Eduardo Kanagushiku Pereira, Frank Gregory Cavalcante da Silva, Barbara Soares Gonçalves, Ana Lúcia Bergamasco Galastri, Ronei Luciano Mamoni

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The inflammatory response initiates after the recognition of pathogens by receptors expressed by innate immune cells. Among these receptors, the NLRP3 was associated with the recognition of pathogenic fungi in experimental models. NLRP3 operates forming a multiproteic complex called inflammasome, which actives caspase-1, responsible for the production of the inflammatory cytokines IL-1beta and IL-18. In this study, we aimed to investigate the involvement of NLRP3 in the inflammatory response elicited in macrophages against Paracoccidioides brasiliensis (Pb), the etiologic agent of PCM. Macrophages were differentiated from THP-1 cells by treatment with phorbol-myristate-acetate. Following differentiation, macrophages were stimulated by Pb yeast cells for 24 hours, after previous treatment with specific NLRP3 (3,4-methylenedioxy-beta-nitrostyrene) and/or caspase-1 (VX-765) inhibitors, or specific inhibitors of pathways involved in NLRP3 activation such as: Reactive Oxigen Species (ROS) production (N-Acetyl-L-cysteine), K+ efflux (Glibenclamide) or phagossome acidification (Bafilomycin). Quantification of IL-1beta and IL-18 in supernatants was performed by ELISA. Our results showed that the production of IL-1beta and IL-18 by THP-1-derived-macrophages stimulated with Pb yeast cells was dependent on NLRP3 and caspase-1 activation, once the presence of their specific inhibitors diminished the production of these cytokines. Furthermore, we found that the major pathways involved in NLRP3 activation, after Pb recognition, were dependent on ROS production and K+ efflux. In conclusion, our results showed that NLRP3 participates in the recognition of Pb yeast cells by macrophages, leading to the activation of the NLRP3-inflammasome and production of IL-1beta and IL-18. Together, these cytokines can induce an inflammatory response against P. brasiliensis, essential for the establishment of the initial inflammatory response and for the development of the subsequent acquired immune response.

Keywords: inflammation, IL-1beta, IL-18, NLRP3, Paracoccidioidomycosis

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Authors: Ahmad Alwosheel, Ahmed Alqaraawi

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This work proposes a new speaker segmentation approach for two speakers. It is an online approach that does not require a prior information about speaker models. It has two phases, a conventional approach such as unsupervised BIC-based is utilized in the first phase to detect speaker changes and train a Neural Network, while in the second phase, the output trained parameters from the Neural Network are used to predict next incoming audio stream. Using this approach, a comparable accuracy to similar BIC-based approaches is achieved with a significant improvement in terms of computation time.

Keywords: artificial neural network, diarization, speaker indexing, speaker segmentation

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Authors: S. Delgado, C. Cerrada, R. S. Gómez

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This research introduces an approach to voxelizing the surfaces of triangular meshes with efficiency and accuracy. Our method leverages parallel equidistant scan-lines and introduces a Gap Detection technique to address the limitations of existing approaches. We present a comprehensive study showcasing the method's effectiveness, scalability, and versatility in different scenarios. Voxelization is a fundamental process in computer graphics and simulations, playing a pivotal role in applications ranging from scientific visualization to virtual reality. Our algorithm focuses on enhancing the voxelization process, especially for complex models and high resolutions. One of the major challenges in voxelization in the Graphics Processing Unit (GPU) is the high cost of discovering the same voxels multiple times. These repeated voxels incur in costly memory operations with no useful information. Our scan-line-based method ensures that each voxel is detected exactly once when processing the triangle, enhancing performance without compromising the quality of the voxelization. The heart of our approach lies in the use of parallel, equidistant scan-lines to traverse the interiors of triangles. This minimizes redundant memory operations and avoids revisiting the same voxels, resulting in a significant performance boost. Moreover, our method's computational efficiency is complemented by its simplicity and portability. Written as a single compute shader in Graphics Library Shader Language (GLSL), it is highly adaptable to various rendering pipelines and hardware configurations. To validate our method, we conducted extensive experiments on a diverse set of models from the Stanford repository. Our results demonstrate not only the algorithm's efficiency, but also its ability to produce 26 tunnel free accurate voxelizations. The Gap Detection technique successfully identifies and addresses gaps, ensuring consistent and visually pleasing voxelized surfaces. Furthermore, we introduce the Slope Consistency Value metric, quantifying the alignment of each triangle with its primary axis. This metric provides insights into the impact of triangle orientation on scan-line based voxelization methods. It also aids in understanding how the Gap Detection technique effectively improves results by targeting specific areas where simple scan-line-based methods might fail. Our research contributes to the field of voxelization by offering a robust and efficient approach that overcomes the limitations of existing methods. The Gap Detection technique fills a critical gap in the voxelization process. By addressing these gaps, our algorithm enhances the visual quality and accuracy of voxelized models, making it valuable for a wide range of applications. In conclusion, "Closing the Gap: Efficient Voxelization with Equidistant Scan-lines and Gap Detection" presents an effective solution to the challenges of voxelization. Our research combines computational efficiency, accuracy, and innovative techniques to elevate the quality of voxelized surfaces. With its adaptable nature and valuable innovations, this technique could have a positive influence on computer graphics and visualization.

Keywords: voxelization, GPU acceleration, computer graphics, compute shaders

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Authors: Abdullah Al-Mamun, Md. Nuruzzaman, Md. Noor Salleh, Muhammad Abu Eusuf, Ahmad Jalal Khan Chowdhury, Mohd. Zaki M. Amin, Norlida Mohd. Dom

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Identification of pollution sources and determination of pollution loads from all areas are very important for sustainable rehabilitation of any contaminated river. Pusu is a small river which, flows through the main campus of International Islamic University Malaysia (IIUM) at Gombak. Poor aesthetics of the river, which is flowing through the entrance of the campus, gives negative impression to the local and international visitors. As such, this study is being conducted to find ways to rehabilitate the river in a sustainable manner. The point and non-point pollution sources of the river basin are identified. Upper part of the 12.6 km2 river basin is covered with secondary forest. However, it is the lower-middle reaches of the river basin which is being cleared for residential development and source of high sediment load. Flow and concentrations of the common pollutants, important for a healthy river, such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Suspended Solids (SS), Turbidity, pH, Ammoniacal Nitrogen (AN), Total Nitrogen (TN) and Total Phosphorus (TP) are determined. Annual pollution loading to the river was calculated based on the primary and secondary data. Concentrations of SS were high during the rainy day due to contribution from the non-point sources. There are 7 ponds along the river system within the campus, which are severely affected by high sediment load from the land clearing activities. On the other hand, concentrations of other pollutants were high during the non-rainy days. The main sources of point pollution are the hostels, cafeterias, sewage treatment plants located in the campus. Therefore, both pollution sources need to be controlled in order to rehabilitate the river in a sustainable manner.

Keywords: river pollution, rehabilitation, point pollution source, non-point pollution sources, pollution loading

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Authors: Mazyar Yosofi, Olivier Kerbrat, Pascal Mognol

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Life cycle assessment of additive manufacturing processes has evolved significantly since these past years. A lot of existing studies mainly focused on energy consumption. Nowadays, new methodologies of life cycle inventory acquisition came through the literature and help manufacturers to take into account all the input and output flows during the manufacturing step of the life cycle of products. Indeed, the environmental analysis of the phenomena that occur during the manufacturing step of additive manufacturing processes is going to be well known. Now it becomes possible to count and measure accurately all the inventory data during the manufacturing step. Optimization of the environmental performances of processes can now be considered. Environmental performance improvement can be made by varying process parameters. However, a lot of these parameters (such as manufacturing speed, the power of the energy source, quantity of support materials) affect directly the mechanical properties, surface finish and the dimensional accuracy of a functional part. This study aims to improve the environmental performance of an additive manufacturing process without deterioration of the part quality. For that purpose, the authors have developed a generic method that has been applied on multiple parts made by additive manufacturing processes. First, a complete analysis of the process parameters is made in order to identify which parameters affect only the environmental performances of the process. Then, multiple parts are manufactured by varying the identified parameters. The aim of the second step is to find the optimum value of the parameters that decrease significantly the environmental impact of the process and keep the part quality as desired. Finally, a comparison between the part made by initials parameters and changed parameters is made. In this study, the major finding claims by authors is to reduce the environmental impact of an additive manufacturing process while respecting the three quality criterion of parts, mechanical properties, dimensional accuracy and surface roughness. Now that additive manufacturing processes can be seen as mature from a technical point of view, environmental improvement of these processes can be considered while respecting the part properties. The first part of this study presents the methodology applied to multiple academic parts. Then, the validity of the methodology is demonstrated on functional parts.

Keywords: additive manufacturing, environmental impact, environmental improvement, mechanical properties

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Authors: Mario M. J. Musonda

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Access to health care is a human right, which includes having timely access to affordable and quality essential medicines at the right place and in sufficient quantity. However, inefficient public sector supply chain management contributes to constant shortages of essential medicines at health facilities. Literature review involved a desktop study of published research studies and reports on risk management, supply chain management of essential medicines and their integration to increase the efficiency of the latter. The research was conducted on a sample population of offices under Ministry of Health Headquarters, Lusaka Provincial and District Offices, selected health facilities in Lusaka, Medical Stores Limited, Zambia Medicines Regulatory Authority and Cooperating Partners. Individuals involved in study were selected judgmentally by their functions under selection and quantification, regulation, procurement, storage, distribution, quality assurance, and dispensing of essential medicines. Structured interviews and discussions were held with selected experts and self-administered questionnaires were distributed. Collected and analysed data of 35 returned and usable questionnaires from the 50 distributed. The highest prioritised risks were; inadequate and inconsistent fund disbursements, weak information management systems, weak quality management systems and insufficient resources (HR and infrastructure) among others. The results for this research can be used to increase the efficiency of the public sector supply chain of essential medicines and other pharmaceuticals. The results of the study showed that there is need to implement effective risk management systems by participating institutions and organisations to increase the efficiency of the entire supply chain in order to avoid and/or reduce shortages of essential medicines at health facilities.

Keywords: essential medicine, risk assessment, risk management, supply chain, supply chain risk management

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Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations of previous approaches, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with attention mechanism. In a previous work on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: transformers, generative ai, gene expression design, classification

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Authors: Nashrawan Naser Taha, Andrew M. Cox

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In a multi-cultural learning context, where ties are weak and dynamic, combining qualitative with quantitative research methods may be more effective. Such a combination may also allow us to answer different types of question, such as about people’s perception of the network. In this study the use of observation, interviews and photos were explored as ways of enhancing data from social network questionnaires. Integrating all of these methods was found to enhance the quality of data collected and its accuracy, also providing a richer story of the network dynamics and the factors that shaped these changes over time.

Keywords: mixed methods, social network analysis, multi-cultural learning, social network dynamics

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Authors: Sebastian Töpfer, Marta Gilaberte Basset, Jorge Fuenzalida, Fabian Steinlechner, Juan P. Torres, Markus Gräfe

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This work introduces a combination of digital phase-shifting holography with a non-linear interferometer using undetected photons. Non-linear interferometers can be used in combination with a measurement scheme called quantum imaging with undetected photons, which allows for the separation of the wavelengths used for sampling an object and detecting it in the imaging sensor. This method recently faced increasing attention, as it allows to use of exotic wavelengths (e.g., mid-infrared, ultraviolet) for object interaction while at the same time keeping the detection in spectral areas with highly developed, comparable low-cost imaging sensors. The object information, including its transmission and phase influence, is recorded in the form of an interferometric pattern. To collect these, this work combines the method of quantum imaging with undetected photons with digital phase-shifting holography with a minimal sampling of the interference. With this, the quantum imaging scheme gets extended in its measurement capabilities and brings it one step closer to application. Quantum imaging with undetected photons uses correlated photons generated by spontaneous parametric down-conversion in a non-linear interferometer to create indistinguishable photon pairs, which leads to an effect called induced coherence without induced emission. Placing an object inside changes the interferometric pattern depending on the object’s properties. Digital phase-shifting holography records multiple images of the interference with determined phase shifts to reconstruct the complete interference shape, which can afterward be used to analyze the changes introduced by the object and conclude its properties. An extensive characterization of this method was done using a proof-of-principle setup. The measured spatial resolution, phase accuracy, and transmission accuracy are compared for different combinations of camera exposure times and the number of interference sampling steps. The current limits of this method are shown to allow further improvements. To summarize, this work presents an alternative holographic measurement method using non-linear interferometers in combination with quantum imaging to enable new ways of measuring and motivating continuing research.

Keywords: digital holography, quantum imaging, quantum holography, quantum metrology

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Authors: Jaehoon Ha, Byungmo Kang, Kilho Hong, Jungsoo Park

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Electro-optical (EO) stabilized platforms have been widely used for surveillance and reconnaissance on various types of vehicles, from surface ships to unmanned air vehicles (UAVs). EO stabilized platforms usually consist of an assembly of structure, bearings, and motors called gimbals in which a gyroscope is installed. EO elements such as a CCD camera and IR camera, are mounted to a gimbal, which has a range of motion in elevation and azimuth and can designate and track a target. In addition, a laser range finder (LRF) can be added to the gimbal in order to acquire the precise slant range from the platform to the target. Recently, a versatile functionality of target localization is needed in order to cooperate with the weapon systems that are mounted on the same platform. The target information, such as its location or velocity, needed to be more accurate. The accuracy of the target information depends on diverse component errors and alignment errors of each component. Specially, the type of moving platform can affect the accuracy of the target information. In the case of flying platforms, or UAVs, the target location error can be increased with altitude so it is important to measure altitude as precisely as possible. In the case of surface ships, target location error can be increased with obliqueness of the elevation angle of the gimbal since the altitude of the EO stabilized platform is supposed to be relatively low. The farther the slant ranges from the surface ship to the target, the more extreme the obliqueness of the elevation angle. This can hamper the precise acquisition of the target information. So far, there have been many studies on EO stabilized platforms of flying vehicles. However, few researchers have focused on ship-borne EO stabilized platforms of the surface ship. In this paper, we deal with a target localization method when an EO stabilized platform is located on the mast of a surface ship. Especially, we need to overcome the limitation caused by the obliqueness of the elevation angle of the gimbal. We introduce a well-known approach for target localization using Unscented Kalman Filter (UKF) and present the problem definition showing the above-mentioned limitation. Finally, we want to show the effectiveness of the approach that will be demonstrated through computer simulations.

Keywords: target localization, ship-borne electro-optical stabilized platform, unscented kalman filter

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Authors: Syeda Fahria Hoque Mimmi

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Many new and promising treatments for reducing or diminishing the adverse effects of microorganisms are being discovered day by day. On the other hand, the dairy industry is accelerating the economic wheel of Bangladesh. Considering all these facts, new thoughts were developed to isolate milk proteins by the present experiment for opening up a new era of developing natural antibiotics from milk. Lactoferrin, an iron-binding glycoprotein with multifunctional properties, is crucial to strengthening the immune system and also useful for commercial applications. The protein’s iron-binding capacity makes it undoubtedly advantageous to immune system modulation and different bacterial strains. For fulfilling the purpose, 4 of raw and 17 of commercially available milk samples were collected from different farms and stores in Bangladesh (Dhaka, Chittagong, and Cox’s Bazar). Protein quantification by nanodrop technology has confirmed that raw milk samples have better quantities of protein than the commercial ones. All the samples were tested for their antimicrobial activity against 18 pathogens, where raw milk samples showed a higher percentage of antibacterial activity. In addition to this, SDS-PAGE (Sodium Dodecyl Sulfate–Polyacrylamide Gel Electrophoresis) was performed to identify lactoferrin in the milk samples. Lactoferrin was detected in 9 samples from which 4 were raw milk samples. Interestingly, Streptococcus pyogenes, Klebsiella pneumoniae, Bacillus cereus, Pseudomonas aeruginosa, Vibrio cholera, Staphylococcus aureus, and enterotoxigenic E. coli significantly displayed sensitivity against lactoferrin collected from raw milk. Only Bacillus cereus, Pseudomonas aeruginosa, Streptococcus pneumonia, Enterococcus faecalis, and ETEC (Enterotoxigenic Escherichia coli) were susceptible to lactoferrin obtained from a commercial one. This study suggested that lactoferrin might be used as the potential alternative of antibiotics for many diseases and also can be used to reduce microbial deterioration in the food and feed industry.

Keywords: alternative of antibiotics, commercially available milk, lactoferrin, nanodrop technology, pathogens, raw milk

Procedia PDF Downloads 169