Search results for: delay tolerant networks

Authors: Teja Sai Puligadda

Abstract:

Satellite images in detail can serve an important role in the geographic study. Quantitative and qualitative information provided by the satellite and remote sensing images minimizes the complexity of work and time. Data/images are captured at regular intervals by satellite remote sensing systems, and the amount of data collected is often enormous, and it expands rapidly as technology develops. Interpreting remote sensing images, geographic data mining, and researching distinct vegetation types such as agricultural and forests are all part of satellite image categorization. One of the biggest challenge data scientists faces while classifying satellite images is finding the best suitable classification algorithms based on the available that could able to classify images with utmost accuracy. In order to categorize satellite images, which is difficult due to the sheer volume of data, many academics are turning to deep learning machine algorithms. As, the CNN algorithm gives high accuracy in image recognition problems and automatically detects the important features without any human supervision and the ANN algorithm stores information on the entire network (Abhishek Gupta., 2020), these two deep learning algorithms have been used for satellite image classification. This project focuses on remote sensing through Deep Neural Networks i.e., ANN and CNN with Deep Sat (SAT-4) Airborne dataset for classifying images. Thus, in this project of classifying satellite images, the algorithms ANN and CNN are implemented, evaluated & compared and the performance is analyzed through evaluation metrics such as Accuracy and Loss. Additionally, the Neural Network algorithm which gives the lowest bias and lowest variance in solving multi-class satellite image classification is analyzed.

Keywords: artificial neural network, convolutional neural network, remote sensing, accuracy, loss

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Authors: Kamal KC, Zhendong Yin, Dasen Li, Zhilu Wu

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Intra-plant disease classification based on leaf images is a challenging computer vision task due to similarities in texture, color, and shape of leaves with a slight variation of leaf spot; and external environmental changes such as lighting and background noises. Deep convolutional neural network (DCNN) has proven to be an effective tool for binary classification. In this paper, two methods for binary classification of diseased plant leaves using DCNN are presented; model created from scratch and transfer learning. Our main contribution is a thorough evaluation of 4 networks created from scratch and transfer learning of 5 pre-trained models. Training and testing of these models were performed on a plant leaf images dataset belonging to 16 distinct classes, containing a total of 22,265 images from 8 different plants, consisting of a pair of healthy and diseased leaves. We introduce a deep CNN model, Optimized MobileNet. This model with depthwise separable CNN as a building block attained an average test accuracy of 99.77%. We also present a fine-tuning method by introducing the concept of a convolutional block, which is a collection of different deep neural layers. Fine-tuned models proved to be efficient in terms of accuracy and computational cost. Fine-tuned MobileNet achieved an average test accuracy of 99.89% on 8 pairs of [healthy, diseased] leaf ImageSet.

Keywords: deep convolution neural network, depthwise separable convolution, fine-grained classification, MobileNet, plant disease, transfer learning

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Authors: Adenike Adesanya, Nonthaphat Wong, Xiang-Yun Lan, Shea Ping Yip, Chien-Ling Huang

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Background: The most challenging mutation of the oncokinase BCR-ABL protein T315I, which is commonly known as the “gatekeeper” mutation and is notorious for its strong resistance to almost all tyrosine kinase inhibitors (TKIs), especially imatinib. Therefore, this study aims to identify T315I-dependent downstream microRNA (miRNA) pathways associated with drug resistance in chronic myeloid leukemia (CML) for prognostic and therapeutic purposes. Methods: T315I-carrying K562 cell clones (K562-T315I) were generated by the CRISPR-Cas9 system. Imatinib-treated K562-T315I cells were subjected to small RNA library preparation and next-generation sequencing. Putative lncRNA-miRNA-mRNA networks were analyzed with (i) DESeq2 to extract differentially expressed miRNAs, using Padj value of 0.05 as cut-off, (ii) STarMir to obtain potential miRNA response element (MRE) binding sites of selected miRNAs on lncRNA H19, (iii) miRDB, miRTarbase, and TargetScan to predict mRNA targets of selected miRNAs, (iv) IntaRNA to obtain putative interactions between H19 and the predicted mRNAs, (v) Cytoscape to visualize putative networks, and (vi) several pathway analysis platforms – Enrichr, PANTHER and ShinyGO for pathway enrichment analysis. Moreover, mitochondria isolation and transcript quantification were adopted to determine the new mechanism involved in T315I-mediated resistance of CML treatment. Results: Verification of the CRISPR-mediated mutagenesis with digital droplet PCR detected the mutation abundance of ≥80%. Further validation showed the viability of ≥90% by cell viability assay, and intense phosphorylated CRKL protein band being detected with no observable change for BCR-ABL and c-ABL protein expressions by Western blot. As reported by several investigations into hematological malignancies, we determined a 7-fold increase of H19 expression in K562-T315I cells. After imatinib treatment, a 9-fold increment was observed. DESeq2 revealed 171 miRNAs were differentially expressed K562-T315I, 112 out of these miRNAs were identified to have MRE binding regions on H19, and 26 out of the 112 miRNAs were significantly downregulated. Adopting the seed-sequence analysis of these identified miRNAs, we obtained 167 mRNAs. 6 hub miRNAs (hsa-let-7b-5p, hsa-let-7e-5p, hsa-miR-125a-5p, hsa-miR-129-5p, and hsa-miR-372-3p) and 25 predicted genes were identified after constructing hub miRNA-target gene network. These targets demonstrated putative interactions with H19 lncRNA and were mostly enriched in pathways related to cell proliferation, senescence, gene silencing, and pluripotency of stem cells. Further experimental findings have also shown the up-regulation of mitochondrial transcript and lncRNA MALAT1 contributing to the lncRNA-miRNA-mRNA networks induced by BCR-ABL T315I mutation. Conclusions: Our results have indicated that lncRNA-miRNA regulators play a crucial role not only in leukemogenesis but also in drug resistance, considering the significant dysregulation and interactions in the K562-T315I cell model generated by CRISPR-Cas9. In silico analysis has further shown that lncRNAs H19 and MALAT1 bear several complementary miRNA sites. This implies that they could serve as a sponge, hence sequestering the activity of the target miRNAs.

Keywords: chronic myeloid leukemia, imatinib resistance, lncRNA-miRNA-mRNA, T315I mutation

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Authors: Usama Abdulwahab, Mohammed Wahab

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This paper presents the application of uncapacitated facility location problems (UFLP) and 1-median problems to support decision making in blood supply chain networks. A plethora of factors make blood supply-chain networks a complex, yet vital problem for the regional blood bank. These factors are rapidly increasing demand; criticality of the product; strict storage and handling requirements; and the vastness of the theater of operations. As in the UFLP, facilities can be opened at any of $m$ predefined locations with given fixed costs. Clients have to be allocated to the open facilities. In classical location models, the allocation cost is the distance between a client and an open facility. In this model, the costs are the allocation cost, transportation costs, and inventory costs. In order to address this problem the median algorithm is used to analyze inventory, evaluate supply chain status, monitor performance metrics at different levels of granularity, and detect potential problems and opportunities for improvement. The Euclidean distance data for some Ontario cities (demand nodes) are used to test the developed algorithm. Sitation software, lagrangian relaxation algorithm, and branch and bound heuristics are used to solve this model. Computational experiments confirm the efficiency of the proposed approach. Compared to the existing modeling and solution methods, the median algorithm approach not only provides a more general modeling framework but also leads to efficient solution times in general.

Keywords: approximate dynamic programming, facility location, perishable product, inventory model, blood platelet, P-median problem

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Authors: He Li Wen, Meng Qing Xiang, Li De Yong, Zhang Ya Wei, Ren Li Ping

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Stress always exerts some extent adverse effects on the animal production, food safety and quality concerns. Stress is commonly-seen in livestock industry, but there is rare literature focusing on the effects of nutrition stress. What’s more, the research always concentrates on the effect of single stress additionally, there is scarce information about the stress effect on waterfowl like goose as they are commonly thought to be tolerant to stress. To our knowledge, it is not always true. The object of this study was to evaluate the response of growing Greylag geese offered different fiber sources to the comprehensive stress, primarily involving the procedures of fasting, transport, capture, etc. The birds were randomly selected to rear with the diets differing in fiber source, being corn straw silage (CSS), steam-exploded corn straw (SECS), steam-exploded wheat straw (SEWS), and steam-exploded rice straw (SERS), respectively. Blood samples designated for the determination of stress status were collected before (pre-stress ) and after (post-stress ) the stressors carried out. No difference (P>0.05) was found on the pre-stress blood parameters of growing Greylags fed alternative fiber sources. Irrespective of the dietary differences, the comprehensive stress decreased (P<0.01) the concentration of SOD and increased (P<0.01) that of CK. Between the dietary treatments, the birds fed CSS had a higher (P<0.05)post-stress concentration of MDA than those offered SECS, along with a similarity to those fed the other two fiber sources. There was no difference (P>0.05) found on the stress response of the birds fed different fiber sources. In conclusion, SOD and CK concentration in blood may be more sensitive in indicating stress status and dietary fiber source exerted no effect on the stress response of growing Greylags. There is little chance to improve the stress status by ingesting different fiber sources.

Keywords: blood parameter, fiber source, Greylag goose, stress

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Authors: Mohamed H. Khalifa, Fikry I. El-Shahawi, Nabil A. Mansour

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The cotton leafworm, Spodoptera littoralis (Boisduval) is a major insect pest of vegetables and cotton crops in Egypt, and exhibits different levels of tolerance to certain insecticides. Chlorantraniliprole has been registered recently in Egypt for control this insect. The susceptibilities of three S. littoralis populations collected from El Behaira governorate, north Egypt to chlorantraniliprole were determined by leaf-dipping technique on 4^th instar larvae. Obvious variation of toxicity was observed among the laboratory susceptible, and three field populations with LC₅₀ values ranged between 1.53 µg/ml and 6.22 µg/ml. However, all the three field populations were less susceptible to chlorantraniliprole than a laboratory susceptible population. The most tolerant populations were sampled from El Delengat (ED) Province where S. littoralis had been frequently challenged by insecticides. Certain enzyme activity assays were carried out to be correlated with the mechanism of the observed field population tolerance. All field populations showed significantly enhanced activities of detoxification enzymes compared with the susceptible strain. The regression analysis between chlorantraniliprole toxicities and enzyme activities revealed that the highest correlation is between α-esterase or β-esterase (α-β-EST) activity and collected field strains susceptibility, otherwise this correlation is not significant (P > 0.05). Synergism assays showed the ED and susceptible strains could be synergized by known detoxification inhibitors such as piperonyl butoxide (PBO), triphenyl phosphate (TPP) and diethyl-maleate (DEM) at different levels (1.01-8.76-fold and 1.09-2.94 fold, respectively), TPP showed the maximum synergism in both strains. The results show that there is a correlation between the enzyme activity and tolerance, and carboxylic-esterase (Car-EST) is likely the main detoxification mechanism responsible for tolerance of S. littoralis to chlorantraniliprole.

Keywords: chlorantraniliprole, detoxification enzymes, Egypt, Spodoptera littoralis

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Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

Procedia PDF Downloads 158

Authors: Aminah Salim, Idrees Ahmed Nasir, Abdul Qayyum Rao, Muhammad Ali, Muhammad Sohail Anjum, Ayesha Hameed, Bushra Tabassum, Anwar Khan, Arfan Ali, Mariyam Zameer, Tayyab Husnain

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Risk assessment of transgenic herbicide tolerant sugarcane having CEMB codon optimized cp4EPSPS gene was done in present study. Fifteen days old chicks taken from K&Ns Company were randomly assorted into four groups with eight chicks in each group namely control chicken group fed with commercial diet, non-transgenic group fed with non-experimental sugarcane and transgenic group fed with transgenic sugarcane with minimum and maximum level. Body weights, biochemical analysis for Urea, alkaline phosphatase, alanine transferase, aspartate transferase, creatinine and bilirubin determination and histological examination of chicks fed with four types of feed was taken at fifteen days interval and no significant difference was observed in body weight biochemical and histological studies of all four groups. Protein isolated from the serum sample was analyzed through dipstick and SDS-PAGE, showing the absence of transgene protein in the serum sample of control and experimental groups. Moreover the amplification of cp4EPSPS gene with gene specific primers of DNA isolated from chicks blood and also from commercial diet was done to determine the presence and mobility of any nucleotide fragment of the transgene in/from feed and no amplification was obtained in feed as well as in blood extracted DNA of any group. Also no mRNA expression of cp4EPSPS gene was obtained in any tissue of four groups of chicks. From the results it is clear that there is no deleterious or harmful effect of the CEMB codon optimized transgenic cp4EPSPS sugarcane on the chicks health.

Keywords: chicks, cp4EPSPS, glyphosate, sugarcane

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Authors: Flavio Cannavo

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Continuous evaluation of the status of potentially hazardous volcanos plays a key role for civil protection purposes. The importance of monitoring volcanic activity, especially for energetic paroxysms that usually come with tephra emissions, is crucial not only for exposures to the local population but also for airline traffic. Presently, real-time surveillance of most volcanoes worldwide is essentially delegated to one or more human experts in volcanology, who interpret data coming from different kind of monitoring networks. Unfavorably, the high nonlinearity of the complex and coupled volcanic dynamics leads to a large variety of different volcanic behaviors. Moreover, continuously measured parameters (e.g. seismic, deformation, infrasonic and geochemical signals) are often not able to fully explain the ongoing phenomenon, thus making the fast volcano state assessment a very puzzling task for the personnel on duty at the control rooms. With the aim of aiding the personnel on duty in volcano surveillance, here we introduce a system based on an ensemble of data-driven classifiers to infer automatically the ongoing volcano status from all the available different kind of measurements. The system consists of a heterogeneous set of independent classifiers, each one built with its own data and algorithm. Each classifier gives an output about the volcanic status. The ensemble technique allows weighting the single classifier output to combine all the classifications into a single status that maximizes the performance. We tested the model on the Mt. Etna (Italy) case study by considering a long record of multivariate data from 2011 to 2015 and cross-validated it. Results indicate that the proposed model is effective and of great power for decision-making purposes.

Keywords: Bayesian networks, expert system, mount Etna, volcano monitoring

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Authors: S. Ahmed, P. Dlask, B. Hasan

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There are several ways to estimate the cost of the construction project: simple and detailed. The process of estimating cost is usually done during the design stage, which should take long-time and the designer must give attention to all details. This paper explain the causes of the deviations occurring in the cost of the construction project, and determines the reasons of these differences between contractual cost and final cost of the construction project, through the study of literature review related to this field, and benefiting from the experience of workers in the field of building (owners, contractors) through designing a questionnaire, and finding the most ten important reasons and explain the relation between the contractual cost and the final cost according to these reasons. The difference between those values will be showed through diagrams drawn using the statistical program. In addition to studying the effects of overrun costs on the advancing of the project, and identify the most five important effects. According to the results, we can propose the right direction for the final cost evaluation and propose some measures that would help to control and adjust the deviation in the costs.

Keywords: construction projects, building, cost, estimating costs, delay, overrun

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Authors: Adedolapo Ayoade, John the Beloved Dada

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This study identified the phytoplankton assemblage of the Dandaru Lake (that received effluents from a zoological garden and hospital) as bioindicators of water quality. Physicochemical parameters including Dissolved Oxygen (DO), biochemical oxygen demand, nitrate, phosphate and heavy metals were also determined. Samples of water and plankton were collected once monthly from April to September, 2015 at five stations (I – V). The mean physicochemical parameters were within the limits of National Environmental Standards and Regulations Enforcement Agency (NESREA) and USEPA except Lead, 0.02 ± 0.08 mg/ L; Manganese, 0.46 ± 1.00 mg/ L and Zinc, 0.05 ± 0.17 mg/ L. Means of DO, alkalinity, and phosphate were significantly different between the stations at p < 0.05. While highest mean DO (6.88 ± 1.34 mg/L) was recorded in station I with less anthropogenic activities, highest phosphate concentration (0.28 ± 0.28 mg/L) occurred in station II, the entry point of wastewater from hospital and zoological garden. The 147 phytoplankton species found in the lake belonged to six classes: Chlorophyceae (50), Euglenophyceae (40), Bacillariophyceae (37), Cyanophyceae (17), Xanthophyceae and Chrysophyceae (3). The order of abundance for phytoplankton was Euglenophyceae (49.77%) > Bacillariophyceae (18.00%) > Cyanophyceae (17.39%) > Chlorophyceae (13.7%) > Xanthophyceae (1.06%) > Chrysophyceae (0.02%). The stations impacted with effluents were dominated by members of Euglenophyceae (Station III, 77.09%; IV, 50.55%) and Cyanophyceae (Station II, 27.7%; V, 32.57%). While station I was dominated by diatoms (57.98%). The species richness recorded was 0.32 – 4.49. Evenness index was highest in station I and least in station III. Generally, pollution tolerant species (Microcystis, Oscillatoria, Scenedesmus, Anabaena, and Euglena) showed greater density in areas impacted by human activities. The phytoplankton assemblage and comparatively low biotic diversity in Dandaru Lake could be attributed to perturbations in the water column that exerted selective effects on the biological assemblage.

Keywords: manmade lake, Nigeria, phytoplankton, water quality

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Authors: Zhongmin Wang, Wudong Fan, Hengshan Zhang, Yimin Zhou

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In data-driven prognostic methods, the prediction accuracy of the estimation for remaining useful life of bearings mainly depends on the performance of health indicators, which are usually fused some statistical features extracted from vibrating signals. However, the existing health indicators have the following two drawbacks: (1) The differnet ranges of the statistical features have the different contributions to construct the health indicators, the expert knowledge is required to extract the features. (2) When convolutional neural networks are utilized to tackle time-frequency features of signals, the time-series of signals are not considered. To overcome these drawbacks, in this study, the method combining convolutional neural network with gated recurrent unit is proposed to extract the time-frequency image features. The extracted features are utilized to construct health indicator and predict remaining useful life of bearings. First, original signals are converted into time-frequency images by using continuous wavelet transform so as to form the original feature sets. Second, with convolutional and pooling layers of convolutional neural networks, the most sensitive features of time-frequency images are selected from the original feature sets. Finally, these selected features are fed into the gated recurrent unit to construct the health indicator. The results state that the proposed method shows the enhance performance than the related studies which have used the same bearing dataset provided by PRONOSTIA.

Keywords: continuous wavelet transform, convolution neural net-work, gated recurrent unit, health indicators, remaining useful life

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Authors: M. Almutairi, S. Hadjiloucas

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The harmonic distortion of voltage is important in relation to power quality due to the interaction between the large diffusion of non-linear and time-varying single-phase and three-phase loads with power supply systems. However, harmonic distortion levels can be reduced by improving the design of polluting loads or by applying arrangements and adding filters. The application of passive filters is an effective solution that can be used to achieve harmonic mitigation mainly because filters offer high efficiency, simplicity, and are economical. Additionally, possible different frequency response characteristics can work to achieve certain required harmonic filtering targets. With these ideas in mind, the objective of this paper is to determine what size single tuned passive filters work in distribution networks best, in order to economically limit violations caused at a given point of common coupling (PCC). This article suggests that a single tuned passive filter could be employed in typical industrial power systems. Furthermore, constrained optimization can be used to find the optimal sizing of the passive filter in order to reduce both harmonic voltage and harmonic currents in the power system to an acceptable level, and, thus, improve the load power factor. The optimization technique works to minimize voltage total harmonic distortions (VTHD) and current total harmonic distortions (ITHD), where maintaining a given power factor at a specified range is desired. According to the IEEE Standard 519, both indices are viewed as constraints for the optimal passive filter design problem. The performance of this technique will be discussed using numerical examples taken from previous publications.

Keywords: harmonics, passive filter, power factor, power quality

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Authors: Thavananthan Sivananthawerl

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Coconut palm is existing for more than 2000 years in Sri Lanka. However, cultivation on a large scale (plantation) began only in the 19th century. Due to different light perceptions during the growth stages of palm, there is a huge potential to grow crops in-between rows of coconut plants which are grown with wider, fixed spacing. Intercropping under coconut will have multiple benefits such as increasing soil fertility, increasing sunlight utilization, increasing total crop productivity, increasing income & profit, maximum use of resources, reducing the risk, and increasing food security. Growing potential annual, agricultural intercrops could be classified as ‘agri-silvicultural’ system. This is the best agri-silvicultural system that can be named under any perennial crop system in Sri Lanka. In the late 1970’s cassava, pepper and cacao are the major intercrops under the coconut plantations. At the early ages of the palm (<5 years) light-loving crops such as pineapple, passion, papaya, and cassava are recommended and preferred by the cultivators. In between 5-20 years of age, the availability of light is very low, and therefore shade tolerant/loving crops (pasture, yam, ginger) could be used as the intercrops. However, after 20 years of age (>20 years) canopy is getting small, and the light availability on the ground increases. So, light demanding crops such as pepper, banana, pineapple, betel, cassava, and seasonal crops could be grown successfully. Even though this is a sustainable system in several aspects, there are potential challenges ahead to the system. The major ones are land fragmentation and infrastructure development. The other factors are drought, lack of financial support, price instability of the intercrops, availability of improved planting materials, and development of dwarf varieties which reduces the light.

Keywords: coconut cultivation, agri-silviculture, intercrop, sunlight, annuals, sustainability

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Authors: Animut Meseret Simachew

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Parallel Code Phase Search Acquisition (PCSA) Algorithm has been considered as a promising method in GPS software receivers for detection and estimation of the accurate correlation peak between the received Global Positioning System (GPS) signal and locally generated replicas. GPS signal acquisition in highly dense multipath environments is the main research challenge. In this work, we proposed a robust variable step-size (RVSS) PCSA algorithm based on fast frequency transform (FFT) filtering technique to mitigate short time delay multipath signals. Simulation results reveal the effectiveness of the proposed algorithm over the conventional PCSA algorithm. The proposed RVSS-PCSA algorithm equalizes the received carrier wiped-off signal with locally generated C/A code.

Keywords: adaptive PCSA, detection and estimation, GPS signal acquisition, GPS software receiver

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Authors: Milad Mirzaei Aminiyan, Abdolreza Akhgar, Farzad Mirzaei Aminiyan

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Water quality is the critical factor that influence on human health and quantity and quality of grain production in semi-humid and semi-arid area. Pistachio is a main crop that accounts for a considerable portion of Iranian agricultural exports. Give that pistachio tree is a tolerant type of tree to saline and alkaline soil and water conditions, but groundwater and irrigation water quality play important roles in main production this crop. For this purpose, 94 well water samples were taken from 25 wells and samples were analyzed. The results showed give that region’s geological, climatic characteristics, statistical analysis, and based on dominant cations and anions in well water samples (piper diagram); four main types of water were found: Na-Cl, K-Cl, Na-SO4, and K-SO4. It seems that most wells in terms of water quality (salinity and alkalinity) and based on Wilcox diagram have critical status. The analysis suggested that more than eighty-seven percentage of the well water samples have high values of EC that these values are higher than into critical limit EC value for irrigation water, which may be due to the sandy soils in this area. Most groundwater were relatively unsuitable for irrigation but it could be used by application of correct management such as removing and reducing the ion concentrations of Cl‾, SO42‾, Na+ and total hardness in groundwater and also the concentrated deep groundwater was required treatment to reduce the salinity and sodium hazard. Given that irrigation water quality in this area was relatively unsuitable for most agriculture production but pistachio tree was adapted to this area conditions. The integrated management of groundwater for irrigation is the way to solve water quality issues not only in Rafsanjan area, but also in other arid and semi-arid areas.

Keywords: groundwater quality, irrigation water quality, salinity, alkalinity, Rafsanjan plain, pistachio

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Authors: Monica Sharma, Harjit Pal Singh, Jasbinder Singh, Manju Bala

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In Voice over Internet Protocol (VoIP) system, the speech signal is degraded when passed through the network layers. The speech signal is processed through the best effort policy based IP network, which leads to the network degradations including delay, packet loss and jitter. The packet loss is the major issue of the degradation in the VoIP signal quality; even a single lost packet may generate audible distortion in the decoded speech signal. In addition to these network degradations, the quality of the speech signal is also affected by the environmental noises and coder distortions. The signal quality of the VoIP system is improved through the interleaving technique. The performance of the system is evaluated for various types of noises at different network conditions. The performance of the enhanced VoIP signal is evaluated using perceptual evaluation of speech quality (PESQ) measurement for narrow band signal.

Keywords: VoIP, interleaving, packet loss, packet size, background noise

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Authors: Mai A. Aljaberi

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The anchorage of fibre-reinforced polymer (FRP) sheets is the most effective solution to prevent or delay debonding failure; this system has proven to get better levels of FRP utilization. Unfortunately, the related design information is still unclear. This shortcoming limits the widespread use of the anchorage system. In order to minimize the knowledge gap about the design of U-wrap anchors, this paper reports the results of tested beams which were strengthened with carbon fiber-reinforced polymer (CFRP) sheets at their tension sides and secured with U-wrap anchors at each end of the longitudinal CFRP. The beams were tested under four-point loading until failure. The parameters examined include the compressive strength of the concrete and the number of longitudinal CFRP. It is concluded that these parameters have a considerable effect on the debonding of the strain. The greatest improvement in the strain was 55.8% over the control beam.

Keywords: CFRP, concrete strengthening, debonding failure, debonding strain, U-wrap anchor

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Authors: Nilam S. Octaviani, Semin

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The diesel engine is an internal combustion engine that uses compressed air to combust. The diesel engines are widely used in the world because it has the most excellent combustion efficiency than other types of internal combustion engine.  However, the exhaust emissions of it produce pollutants that are harmful to human health and the environment. Therefore, natural gas used as an alternative fuel using on compression ignition engine to respond those environment issues. This paper aims to discuss the comparison of the technical characteristics and exhaust gases emission from conventional diesel engine and dual fuel diesel engine. According to the study, the dual fuel engine applications have a lower compression pressure and has longer ignition delay compared with normal diesel mode. The engine power is decreased at dual fuel mode. However, the exhaust gases emission on dual fuel engine significantly reduce the nitrogen oxide (NOx), carbon dioxide (CO₂) and particular metter (PM) emissions.

Keywords: diesel engine, dual fuel diesel engine, emission reduction, technical characteristics

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Authors: Adane Kassa Shikur

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Today’s supply chains (SC) have become vulnerable to unexpected and ever-intensifying disruptions from myriad sources. Consequently, the concept of supply chain resilience (SCRes) has become crucial to complement the conventional risk management paradigm, which has failed to cope with unexpected SC disruptions, resulting in severe consequences affecting SC performances and making business continuity questionable. Advancements in cutting-edge technologies like artificial intelligence (AI) and their potential to enhance SCRes by improving critical antecedents in the different phases have attracted the attention of scholars and practitioners. The research from academia and the practical interest of the industry have yielded significant publications at the nexus of AI and SCRes during the last two decades. However, the applications and examinations have been primarily conducted independently, and the extant literature is dispersed into research streams despite the complex nature of SCRes. To close this research gap, this study conducts a systematic literature review of 106 peer-reviewed articles by curating, synthesizing, and consolidating up-to-date literature and presents the state-of-the-art development from 2010 to 2022. Bayesian networks are the most topical ones among the 13 AI techniques evaluated. Concerning the critical antecedents, visibility is the first ranking to be realized by the techniques. The study revealed that AI techniques support only the first 3 phases of SCRes (readiness, response, and recovery), and readiness is the most popular one, while no evidence has been found for the growth phase. The study proposed an AI-SCRes framework to inform research and practice to approach SCRes holistically. It also provided implications for practice, policy, and theory as well as gaps for impactful future research.

Keywords: ANNs, risk, Bauesian networks, vulnerability, resilience

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Authors: Jianwei Qian, Rob Law

Abstract:

With the expansion of globalization and the increment of market competition, the festival, especially the traditional one, has demonstrated its vitality under the new context. As a new tourist attraction, festivals play a critically important role in promoting the tourism economy, because the organization of a festival can engage more tourists, generate more revenues and win a wider media concern. However, in the current stage of China, traditional festivals as a way to disseminate national culture are undergoing the challenge of foreign festivals and the related culture. Different from those special events created solely for developing economy, traditional festivals have their own culture and connotation. Therefore, it is necessary to conduct a study on not only protecting the tradition, but promoting its development as well. This study conducts a comparative study of the development of China’s Valentine’s Day and Western Valentine’s Day under the Chinese context and centers on newspaper reports in China from 2000 to 2016. Based on the literature, two main research focuses can be established: one is concerned about the festival’s impact and the other is about tourists’ motivation to engage in a festival. Newspaper reports serve as the research discourse and can help cover the two focal points. With the assistance of content mining techniques, semantic networks for both Days are constructed separately to help depict the status quo of these two festivals in China. Based on the networks, two models are established to show the key component system of traditional festivals in the hope of perfecting the positive role festival tourism plays in the promotion of economy and culture. According to the semantic networks, newspaper reports on both festivals have similarities and differences. The difference is mainly reflected in its cultural connotation, because westerners and Chinese may show their love in different ways. Nevertheless, they share more common points in terms of economy, tourism, and society. They also have a similar living environment and stakeholders. Thus, they can be promoted together to revitalize some traditions in China. Three strategies are proposed to realize the aforementioned aim. Firstly, localize international festivals to suit the Chinese context to make it function better. Secondly, facilitate the internationalization process of traditional Chinese festivals to receive more recognition worldwide. Finally, allow traditional festivals to compete with foreign ones to help them learn from each other and elucidate the development of other festivals. It is believed that if all these can be realized, not only the traditional Chinese festivals can obtain a more promising future, but foreign ones are the same as well. Accordingly, the paper can contribute to the theoretical construction of festival images by the presentation of the semantic network. Meanwhile, the identified features and issues of festivals from two different cultures can enlighten the organization and marketing of festivals as a vital tourism activity. In the long run, the study can enhance the festival as a key attraction to keep the sustainable development of both the economy and the society.

Keywords: Chinese context, comparative study, festival tourism, semantic network analysis, valentine’s day

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Authors: Aitor Barbero-López, Soumaya Chibily, Gerhard Scheepers, Thomas Grahn, Martti Venäläinen, Antti Haapala

Abstract:

Wood decay is addressed continuously within the wood industry through use and development of wood preservatives. The increasing awareness on the negative effects of many chemicals towards the environment is causing political restrictions in their use and creating more urgent need for research on green alternatives. This paper discusses some of the possible natural extracts for wood preserving applications and compares the analytical methods available for testing their behavior and efficiency against decay fungi. The results indicate that natural extracts have interesting chemical constituents that delay fungal growth but vary in efficiency depending on the chemical concentration and substrate used. Results also suggest that presence and redistribution of preservatives in wood during exposure trials can be assessed by spectral imaging methods although standardized methods are not available. This study concludes that, in addition to the many standard methods available, there is a need to develop new faster methods for screening potential preservative formulation while maintaining the comparability and relevance of results.

Keywords: analytics, methods, preservatives, wood decay

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Authors: Cheng Zeng, George Michailidis, Hitoshi Iyatomi, Leo L. Duan

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The conditional density characterizes the distribution of a response variable y given other predictor x and plays a key role in many statistical tasks, including classification and outlier detection. Although there has been abundant work on the problem of Conditional Density Estimation (CDE) for a low-dimensional response in the presence of a high-dimensional predictor, little work has been done for a high-dimensional response such as images. The promising performance of normalizing flow (NF) neural networks in unconditional density estimation acts as a motivating starting point. In this work, the authors extend NF neural networks when external x is present. Specifically, they use the NF to parameterize a one-to-one transform between a high-dimensional y and a latent z that comprises two components [zₚ, zₙ]. The zₚ component is a low-dimensional subvector obtained from the posterior distribution of an elementary predictive model for x, such as logistic/linear regression. The zₙ component is a high-dimensional independent Gaussian vector, which explains the variations in y not or less related to x. Unlike existing CDE methods, the proposed approach coined Augmented Posterior CDE (AP-CDE) only requires a simple modification of the common normalizing flow framework while significantly improving the interpretation of the latent component since zₚ represents a supervised dimension reduction. In image analytics applications, AP-CDE shows good separation of 𝑥-related variations due to factors such as lighting condition and subject id from the other random variations. Further, the experiments show that an unconditional NF neural network based on an unsupervised model of z, such as a Gaussian mixture, fails to generate interpretable results.

Keywords: conditional density estimation, image generation, normalizing flow, supervised dimension reduction

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Authors: Yupaporn Areepong

Abstract:

The main goal of this paper is to study Statistical Process Control (SPC) with Exponentially Weighted Moving Average (EWMA) control chart when observations are serially-correlated. The characteristic of control chart is Average Run Length (ARL) which is the average number of samples taken before an action signal is given. Ideally, an acceptable ARL of in-control process should be enough large, so-called (ARL0). Otherwise it should be small when the process is out-of-control, so-called Average of Delay Time (ARL1) or a mean of true alarm. We find explicit formulas of ARL for EWMA control chart for Seasonal Autoregressive and Moving Average processes (SARMA) with Exponential white noise. The results of ARL obtained from explicit formula and Integral equation are in good agreement. In particular, this formulas for evaluating (ARL0) and (ARL1) be able to get a set of optimal parameters which depend on smoothing parameter (λ) and width of control limit (H) for designing EWMA chart with minimum of (ARL1).

Keywords: average run length, optimal parameters, exponentially weighted moving average (EWMA), control chart

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Authors: Ghazwan Al-Haji

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Most accidents occur in urban areas, and the most related casualties are vulnerable road users (pedestrians and cyclists). The traffic calming measures (TCMs) are widely used and considered to be successful in reducing speed and traffic volume. However, TCMs create unwanted effects include: noise, emissions, energy consumption, vehicle delays and emergency response time (ERT). Different vertical and horizontal TCMs have been already applied nationally (Sweden) and internationally with different impacts. It is a big challenge among traffic engineers, planners, and policy-makers to choose and priorities the best TCMs to be implemented. This study will assess the existing guidelines for TCMs in relation to safety and ERT with focus on data from Norrkoping city in Sweden. The expected results will save lives, time, and money on particularly Swedish Roads. The study will also review newly technologies and how they can improve safety and reduce ERT.

Keywords: traffic calming measures, traffic safety, delay time, vulnerable road users

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Authors: Pius Marthin, Nihal Ata Tutkun

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Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional and machine learning models and our model demonstrates better performance across all datasets.

Keywords: cumulative incidence function (CIF), risk information weight (RIW), autoencoders (AE), survival analysis, recurrent events with competing risks, recurrent neural networks (RNN), long short-term memory (LSTM), self-attention, multilayers perceptrons (MLPs)

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Authors: Moustafa Ahmed, Ahmed Bakry, Fumio Koyama

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We report on the use of strong external optical feedback to enhance the modulation response of semiconductor lasers over a frequency passband around modulation frequencies higher than 60 GHz. We show that this modulation enhancement is a type of photon-photon resonance (PPR) of oscillating modes in the external cavity formed between the laser and the external reflector. The study is based on a time-delay rate equation model that takes into account both the strong feedback and multiple reflections in the external cavity. We examine the harmonic and intermodulation distortions associated with single and two-tone modulations in the mm-wave band of the resonant modulation. We show that compared with solitary lasers modulated around the carrier-photon resonance frequency, the present mm-wave modulated signal has lower distortions.

Keywords: semiconductor laser, optical feedback, modulation, harmonic distortion

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Authors: Maurizio E. Picarella, Ludovica Fumelli, Francesca Siligato, Andrea Mazzucato

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Studies on reproductive dynamics in crops subjected to heat stress are crucial to breed more tolerant cultivars. In tomato, cultivars, breeding lines, and wild species have been thoroughly evaluated for the response to heat stress in several studies. Here, we address the reaction to temperature stress in a panel of selected landraces representing genotypes cultivated before the advent of professional varieties that usually show high adaptation to local environments. We adopted an experimental design with two open field trials, where transplanting was spaced by one month. In the second field, plants were thus subjected to mild stress with natural temperature fluctuations. The genotypes showed wide variation for both vegetative (plant height) and reproductive (stigma exsertion, pollen viability, number of flowers per inflorescence, and fruit set) traits. On average, all traits were affected by heat conditions; except for the number of flowers per inflorescence, the “G*E” interaction was always significant. In agreement with studies based on different materials, estimated broad sense heritability was high for plant height, stigma exsertion, and pollen viability and low for the number of flowers per inflorescence and fruit set. Despite the interaction, traits recorded in control and in heat conditions were positively correlated. The first two principal components estimated by multivariate analysis explained more than 50% of the total variability. The study indicated that landraces present a wide variability for the response of reproductive traits to temperature stress and that such variability could be very informative to dissect the traits with higher heritability and identify new QTL useful for breeding more resilient varieties.

Keywords: fruit set, heat stress, solanum lycopersicum L., style exsertion, tomato

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Authors: Kanpop Saion, Sakreya Chitwong

Abstract:

Control of superheated steam temperature in the steam generation is essential for the efficiency safety and increment age of the boiler. Conventional cascade PID temperature control in the super heater is known to be efficient to compensate disturbance. However, the complex of thermal power plant due to nonlinearity, load disturbance and time delay of steam of superheater system is bigger than other control systems. The cascade loop with feed forward steam temperature control with energy balance compensator using thermodynamic model has been used for the compensation the complex structure of superheater. In order to improve the performance of steam temperature control. The experiment is implemented for 100% load steady and load changing state. The cascade with feed forward with energy balance steam temperature control has stabilized the system as well.

Keywords: cascade with feed forward, boiler, superheated steam temperature control, enthalpy balance

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Authors: Behrooz Daneshmand

Abstract:

Software Defined Networking (SDN) is designed to meet the future needs of 5G mobile networks. The SDN architecture offers a new solution that involves separating the control plane from the data plane, which is usually paired together. Network functions traditionally performed on specific hardware can now be abstracted and virtualized on any device, and a centralized software-based administration approach is based on a central controller, facilitating the development of modern applications and services. These plan standards clear the way for a more adaptable, speedier, and more energetic network beneath computer program control compared with a conventional network. We accept SDN gives modern inquire about openings to security, and it can significantly affect network security research in numerous diverse ways. Subsequently, the SDN architecture engages systems to effectively screen activity and analyze threats to facilitate security approach modification and security benefit insertion. The segregation of the data planes and control and, be that as it may, opens security challenges, such as man-in-the-middle attacks (MIMA), denial of service (DoS) attacks, and immersion attacks. In this paper, we analyze security threats to each layer of SDN - application layer - southbound interfaces/northbound interfaces - controller layer and data layer. From a security point of see, the components that make up the SDN architecture have a few vulnerabilities, which may be abused by aggressors to perform noxious activities and hence influence the network and its administrations. Software-defined network assaults are shockingly a reality these days. In a nutshell, this paper highlights architectural weaknesses and develops attack vectors at each layer, which leads to conclusions about further progress in identifying the consequences of attacks and proposing mitigation strategies.

Keywords: software-defined networking, security, SDN, 5G/IMT-2020

Procedia PDF Downloads 80