Search results for: vector map

Authors: Fantu Lombamo Untiso, Sylvia Murphy, Emily Pieracci

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Background: Rabies is a highly fatal viral disease of all warm-blooded animals including human globally. However, effective rabies control program still remains to be a reality and needs to be strengthened. Objective: Reviewing of recorded data and analyzing it to generate information on the status of rabies in Addis Ababa in the year 2012/13. Methods: A retrospective data were used from the Ethiopian Public Health Institute rabies case record book registered in the year 2012/13. Results: Among 1357 suspected rabid animals clinically examined; only 8.84% were positive for rabies. Out of 216 animal brains investigated in the laboratory with Fluorescent Antibody Technique, 55.5% were confirmed rabies positive. Among the laboratory confirmed positive rabies cases, high percentage of the animals came from Yeka (20%) and lower number from Kirkos subcity (3.3%). Out of 1149 humans who came to the institute seeking anti-rabies post-exposure prophylaxis, 85.65% and 7.87% of them were exposed to suspected dogs and cats respectively. 3 human deaths due to rabies were reported in the year after exposure to dog bite of unknown vaccination status. Conclusion: The principal vector of rabies in Addis Ababa is dog. Effective rabies management and control based on confirmed cases and mass-immunization and control of stray dog populations is recommended.

Keywords: Addis Ababa, exposure, rabies, surveillance

Procedia PDF Downloads 179

Authors: Siphat Lim

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This study used Autoregressive Distributed Lag (ARDL) approach to cointegration. In the long-run the general price level and exchange rate have a positively significant effect on domestic output. The estimated result further revealed that fiscal stimulus help stimulate domestic output in the long-run, but not in the short-run, while monetary expansion help to stimulate output in both short-run and long-run. The result is complied with the theory which is the macroeconomic policies, fiscal and monetary policy; help to stimulate domestic output in the long-run. The estimated result of the Vector Error Correction Model (VECM) has indicated more clearly that the consumer price index has a positive effect on output with highly statistically significant. Increasing in the general price level would increase the competitiveness among producers than increase in the output. However, the exchange rate also has a positive effect and highly significant on the gross domestic product. The exchange rate depreciation might increase export since the purchasing power of foreigners has increased. More importantly, fiscal stimulus would help stimulate the domestic output in the long-run since the coefficient of government expenditure is positive. In addition, monetary expansion would also help stimulate the output and the result is highly significant. Thus, fiscal stimulus and monetary expansionary would help stimulate the domestic output in the long-run in Cambodia.

Keywords: fiscal policy, monetary policy, ARDL, VECM

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Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

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Authors: Umair Ashraf, Kjetil Uhlen, Sverre Eriksen, Nadeem Jelani

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Although advancement in technology has increased the reliability and ease of work in ship power system, but these advancements are also adding complexities. Ever increasing non linear loads, like power electronics (PE) devices effect the stability of the system. Frequent load variations and complex load dynamics are due to the frequency converters and motor drives, these problem are more prominent when system is connected with the weak grid. In the ship power system major consumers are thruster motors for the propulsion. For the control operation of these motors variable frequency drives (VFD) are used, mostly VFDs operate on nominal voltage of the system. Some of the consumers in ship operate on lower voltage than nominal, these consumers got supply through step down transformers. In this paper the vector control scheme is used for the control of both rectifier and inverter, parallel operation of the synchronous generators is also demonstrated. The simulation have been performed with induction motor as load on VFD and parallel RLC load. Fault analysis has been performed first for the system which do not have VFD and then for the system with VFD. Three phase to the ground, single phase to the ground fault were implemented and behavior of the system in both the cases was observed.

Keywords: non-linear load, power electronics, parallel operating generators, pulse width modulation, variable frequency drives, voltage source converters, weak grid

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Authors: Francis Mugabi, Kevin Duffy, Joseph J. Y. T Mugisha, Obiora Collins

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Bluetongue virus (BTV) has 27 serotypes, with some of them coexisting in patchy (different) environments, which make its control difficult. Wind-aided midge movement is a known mechanism in the spread of BTV. However, its effects on the probability of coexistence of multiple BTV serotypes are not clear. Deterministic and stochastic models for r BTV serotypes in n discrete patches connected by midge and/or cattle movement are formulated and analyzed. For the deterministic model without midge and cattle movement, using the comparison principle, it is shown that if the patch reproduction number R0 < 1, i=1,2,...,n, j=1,2,...,r, all serotypes go extinct. If R^j_i0>1, competitive exclusion takes place. Using numerical simulations, it is shown that when the n patches are connected by midge movement, coexistence takes place. To account for demographic and movement variability, the deterministic model is transformed into a continuous-time Markov chain stochastic model. Utilizing a multitype branching process, it is shown that the midge movement can have a large effect on the probability of coexistence of multiple BTV serotypes. The probability of coexistence can be brought to zero when the control interventions that directly kill the adult midges are applied. These results indicate the significance of wind-aided midge movement and vector control interventions on the coexistence and control of multiple BTV serotypes in patchy environments.

Keywords: bluetongue virus, coexistence, multiple serotypes, midge movement, branching process

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Authors: Shaira L. Kee, Michael Aaron G. Sy, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

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Skin diseases are considered the fourth most common disease, with melanoma and non-melanoma skin cancer as the most common type of cancer in Caucasians. The alarming increase in Skin Cancer cases shows an urgent need for further research to improve diagnostic methods, as early diagnosis can significantly improve the 5-year survival rate. Machine Learning algorithms for image pattern analysis in diagnosing skin lesions can dramatically increase the accuracy rate of detection and decrease possible human errors. Several studies have shown the diagnostic performance of computer algorithms outperformed dermatologists. However, existing methods still need improvements to reduce diagnostic errors and generate efficient and accurate results. Our paper proposes an ensemble method to classify dermoscopic images into benign and malignant skin lesions. The experiments were conducted using the International Skin Imaging Collaboration (ISIC) image samples. The dataset contains 3,297 dermoscopic images with benign and malignant categories. The results show improvement in performance with an accuracy of 88% and an F1 score of 87%, outperforming other existing models such as support vector machine (SVM), Residual network (ResNet50), EfficientNetB0, EfficientNetB4, and VGG16.

Keywords: deep learning - VGG16 - efficientNet - CNN – ensemble – dermoscopic images - melanoma

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Authors: Edilene Mercedes Mauer Machado, Carolina Hadassa Marques Karoly, Amanda Britz, Claudineia Brazil

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The El Niño phenomenon, characterized by the anomalous warming of surface waters in the Equatorial Pacific Ocean, can influence weather patterns in various parts of the world, including the occurrence of extreme events such as droughts or heavy rainfall. Studies have suggested a relationship between El Niño and an increase in the incidence of dengue in certain areas. During El Niño periods, there can be changes in climatic conditions, such as increased temperatures and reduced rainfall in certain tropical and subtropical regions. These conditions can favor the reproduction of the Aedes aegypti mosquito, the vector for dengue transmission. Research aims to investigate how climate events like El Niño and La Niña can influence the incidence and transmission of dengue. The results have shown that, on average, there was a significant increase in dengue cases during La Niña years compared to years of climatic neutrality, contradicting the findings of Hopp et al. (2015). The study also highlighted that regions affected by El Niño exhibited greater variability in dengue incidence. However, it is important to emphasize that the effects of El Niño on dengue transmission can vary depending on the region and local factors, such as socioeconomic context and implemented control measures, as described by Johansson et al. (2009). Not all areas affected by El Niño will necessarily experience an increase in dengue incidence, and the interaction between climate and disease transmission is complex.

Keywords: anomalous warming, climatic patterns, dengue incidence, extreme events

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Authors: Umm-E-Hani, Faiza Babar, Hanif Durad

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Malware-detecting approaches suffer many limitations, due to which all anti-malware solutions have failed to be reliable enough for detecting zero-day malware. Signature-based solutions depend upon the signatures that can be generated only when malware surfaces at least once in the cyber world. Another approach that works by detecting the anomalies caused in the environment can easily be defeated by diligently and intelligently written malware. Solutions that have been trained to observe the behavior for detecting malicious files have failed to cater to the malware capable of detecting the sandboxed or protected environment. Machine learning and deep learning-based approaches greatly suffer in training their models with either an imbalanced dataset or an inadequate number of samples. AI-based anti-malware solutions that have been trained with enough samples targeted a selected feature vector, thus ignoring the input of leftover features in the maliciousness of malware just to cope with the lack of underlying hardware processing power. Our research focuses on producing an anti-malware solution for detecting malicious PE files by circumventing the earlier-mentioned shortcomings. Our proposed framework, which is based on automated feature engineering through autoencoders, trains the model over a fairly large dataset. It focuses on the visual patterns of malware samples to automatically extract the meaningful part of the visual pattern. Our experiment has successfully produced a state-of-the-art accuracy of 99.54 % over test data.

Keywords: malware, auto encoders, automated feature engineering, classification

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Authors: Natália Maria Rego

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ALeibnizn-algebraGis aK-vector space endowed whit a n-linearbracket operation [-,…-] : GG … G→ Gsatisfying the fundamental identity, which can be expressed saying that the right multiplication map Ry2, …, ᵧₙ: Gn→ G, Rᵧ₂, …, ᵧₙn(ˣ¹, …, ₓₙ) = [[ˣ¹, …, ₓₙ], ᵧ₂, …, ᵧₙ], is a derivation. This structure, together with its skew-symmetric version, named as Lie n-algebra or Filippov algebra, arose in the setting of Nambumechanics, an n-ary generalization of the Hamiltonian mechanics. Thefirst goal of this work is to provide a characterization of various classes of central extensions of Leibniz n-algebras in terms of homological properties. Namely, Commutator extension, Quasi-commutator extension, Stem extension, and Stem cover. These kind of central extensions are characterized by means of the character of the map *(E): nHL1(G) → M provided by the five-term exact sequence in homology with trivial coefficients of Leibniz n-algebras associated to an extension E : 0 → M → K → G → 0. For a free presentation 0 →R→ F →G→ 0of a Leibniz n-algebra G,the term M(G) = (R[F,…n.., F])/[R, F,..n-1..,F] is called the Schur multiplier of G, which is a Baer invariant, i.e., it does not depend on the chosen free presentation, and it is isomorphic to the first Leibniz n-algebras homology with trivial coefficients of G. A central extension of Leibniz n-algebras is a short exact sequenceE : 0 →M→K→G→ 0such that [M, K,.. ⁿ⁻¹.., K]=0. It is said to be a stem extension if M⊆[G, .. n.., G]. Additionally, if the induced map M(K) → M(G) is the zero map, then the stem extension Eis said to be a stem cover. The second aim of this work is to analyze the interplay between stem covers of Leibniz n-algebras and the Schur multiplier. Concretely, in the case of finite-dimensional Leibniz n-algebras, we show the existence of coverings, and we prove that all stem covers with finite-dimensional Schur multiplier are isoclinic. Additionally, we characterize stem covers of perfect Leibniz n-algebras.

Keywords: leibniz n-algebras, central extensions, Schur multiplier, stem cover

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Authors: Manop Aorpimai, Ponthep Navakitkanok

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In this paper, the development of the ephemeris generation module used for the Thaichote satellite operations is presented. It is a vital part of the flight dynamics system, which comprises, the orbit determination, orbit propagation, event prediction and station-keeping maneuver modules. In the generation of the spacecraft ephemeris data, the estimated orbital state vector from the orbit determination module is used as an initial condition. The equations of motion are then integrated forward in time to predict the satellite states. The higher geopotential harmonics, as well as other disturbing forces, are taken into account to resemble the environment in low-earth orbit. Using a highly accurate numerical integrator based on the Burlish-Stoer algorithm the ephemeris data can be generated for long-term predictions, by using a relatively small computation burden and short calculation time. Some events occurring during the prediction course that are related to the mission operations, such as the satellite’s rise/set viewed from the ground station, Earth and Moon eclipses, the drift in ground track as well as the drift in the local solar time of the orbital plane are all detected and reported. When combined with other modules to form a flight dynamics system, this application is aimed to be applied for the Thaichote satellite and successive Thailand’s Earth-observation missions.

Keywords: flight dynamics system, orbit propagation, satellite ephemeris, Thailand’s Earth Observation Satellite

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Authors: Majid Mathlouthi, Fethi Lebdi

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Throughout the world, the extent and magnitude of droughts have economic, social and environmental consequences. Today climate change has become more and more felt; most likely they increase the frequency and duration of droughts. An analysis by event of dry event, from series of observations of the daily rainfall is carried out. A daily precipitation threshold value has been set. A catchment localized in Northern Tunisia where the average rainfall is about 600 mm has been studied. Rainfall events are defined as an uninterrupted series of rainfall days understanding at least a day having received a precipitation superior or equal to a fixed threshold. The dry events are constituted of a series of dry days framed by two successive rainfall events. A rainfall event is a vector of coordinates the duration, the rainfall depth per event and the duration of the dry event. The depth and duration are found to be correlated. So we use conditional probabilities to analyse the depth per event. The negative binomial distribution fits well the dry event. The duration of the rainfall event follows a geometric distribution. The length of the climatically cycle adjusts to the Incomplete Gamma. Results of this analysis was used to study of the effects of climate change on water resources and crops and to calibrate precipitation models with little rainfall records. In response to long droughts in the basin, the drought management system is based on three phases during each of the three phases; different measurements are applied and executed. The first is before drought, preparedness and early warning; the second is drought management, mitigation in the event of drought; and the last subsequent drought, when the drought is over.

Keywords: dry spell, precipitation threshold, climate vulnerability, adaptation measures

Procedia PDF Downloads 84

Authors: Atefeh Heydari, Mohammadali Tavakoli, Naomie Salim

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In recent years, we have witnessed that online reviews are the most important source of customers’ opinion. They are progressively more used by individuals and organisations to make purchase and business decisions. Unfortunately, for the reason of profit or fame, frauds produce deceptive reviews to hoodwink potential customers. Their activities mislead not only potential customers to make appropriate purchasing decisions and organisations to reshape their business, but also opinion mining techniques by preventing them from reaching accurate results. Spam reviews could be divided into two main groups, i.e. multiple and singleton spam reviews. Detecting a singleton spam review that is the only review written by a user ID is extremely challenging due to lack of clue for detection purposes. Singleton spam reviews are very harmful and various features and proofs used in multiple spam reviews detection are not applicable in this case. Current research aims to propose a novel supervised technique to detect singleton spam reviews. To achieve this, various features are proposed in this study and are to be combined with the most appropriate features extracted from literature and employed in a classifier. In order to compare the performance of different classifiers, SVM and naive Bayes classification algorithms were used for model building. The results revealed that SVM was more accurate than naive Bayes and our proposed technique is capable to detect singleton spam reviews effectively.

Keywords: classification algorithms, Naïve Bayes, opinion review spam detection, singleton review spam detection, support vector machine

Procedia PDF Downloads 309

Authors: Jalila Jbilou, Salaheddine El Adlouni, Pierre Gosselin

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Background: West Nile virus is increasingly an important public health issue in North America. In Canada, WVN was officially reported in Toronto and Montréal for the first time in 2001. During the last decade, several WNV events have been reported in several Canadian provinces. The main objective of the present study is to update the frequency of the climate conditions favorable to WNV outbreaks in Canada. Method: Statistical frequency analysis has been used to estimate the return period for climate conditions associated with WNV outbreaks for the 1961–2050 period. The best fit is selected through the Akaike Information Criterion, and the parameters are estimated using the maximum likelihood approach. Results: Results show that the climate conditions related to the 2002 event, for Montreal and Toronto, are becoming more frequent. For Saskatoon, the highest DD20 events recorded for the last few decades were observed in 2003 and 2007. The estimated return periods are 30 years and 70 years, respectively. Conclusion: The emergence of WNV was related to extremely high DD values in the summer. However, some exceptions may be related to several factors such as virus persistence, vector migration, and also improved diagnosis and reporting levels. It is clear that such climate conditions have become much more common in the last decade and will likely continue to do so over future decades.

Keywords: West Nile virus, climate, North America, statistical frequency analysis, risk estimation, public health, modeling, scenario, temperature, precipitation

Procedia PDF Downloads 346

Authors: Rajvir Kaur, Jeewani Anupama Ginige

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With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.

Keywords: artificial neural networks, breast cancer, classifiers, cervical cancer, f-score, machine learning, precision, recall

Procedia PDF Downloads 276

Authors: Jyoti Pandey, Rajiv Kumar Bhatt

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In the absence of proper sanitation, people suffered from high levels of infectious diseases leading to high incidences of morbidity and mortality. This directly affected the ability of a country to maintain an efficient economy and implied great personal suffering among infected individuals and their families. This paper aims to estimate the catastrophic expenditure of households in terms of direct and indirect losses which a person has to face due to the illness of WASH diseases; the severity of the scenario is answered by finding out the impoverishment effect. We used the primary data survey for the objective outlined. Descriptive and analytical research types are used. The survey is done with the questionnaire formulated precisely, taking care of the inclusion of all the variables and probable outcomes. A total of 300 households is covered under this study. In order to pursue the objectives outlined, multistage random sampling of households is used. In this study, the cost of illness approach is followed for accessing economic impact. The study brought out the attention that a significant portion of the total consumption expenditure is going lost for the treatment of water and sanitation related diseases. The infectious and water vector-borne disease can be checked by providing sufficient required sanitation facility, and that 2.02% loss in income can be gained if the mechanisms of the pathogen is checked.

Keywords: water, sanitation, impoverishment, catastrophic expenditure

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Authors: Tomas Trainys, Algimantas Venckauskas

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Application of biometric features to the cryptography for human identification and authentication is widely studied and promising area of the development of high-reliability cryptosystems. Biometric cryptosystems typically are designed for patterns recognition, which allows biometric data acquisition from an individual, extracts feature sets, compares the feature set against the set stored in the vault and gives a result of the comparison. Preprocessing and fusion of biometric data are the most important phases in generating a feature vector for key generation or authentication. Fusion of biometric features is critical for achieving a higher level of security and prevents from possible spoofing attacks. The paper focuses on the tasks of initial processing and fusion of multiple representations of finger vein modality patterns. These tasks are solved by applying conventional image preprocessing methods and machine learning techniques, Convolutional Neural Network (SVM) method for image segmentation and feature extraction. An article presents a method for generating sets of biometric features from a finger vein network using several instances of the same modality. Extracted features sets were fused at the feature level. The proposed method was tested and compared with the performance and accuracy results of other authors.

Keywords: bio-cryptography, biometrics, cryptographic key generation, data fusion, information security, SVM, pattern recognition, finger vein method.

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Authors: Isaac Laryea

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Underwater remote-operated vehicles (ROVs) are highly useful in aquatic research and underwater operations. Unfortunately, unsteady and unpredictable conditions underwater make it difficult for underwater vehicles to maintain a steady attitude during motion. Existing underwater vehicles make use of multiple thrusters positioned at specific positions on their frame to maintain a certain pose. This study proposes an alternate way of maintaining a steady attitude during horizontal motion at low speeds by making use of a thrust vector-controlled propulsion system. The study began by carrying out some preliminary calculations to get an idea of a suitable shape and form factor. Flow simulations were carried out to ensure that enough thrust could be generated to move the system. Using the Lagrangian approach, a mathematical system was developed for the ROV, and this model was used to design a control system. A PID controller was selected for the control system. However, after tuning, it was realized that a PD controller satisfied the design specifications. The designed control system produced an overshoot of 6.72%, with a settling time of 0.192s. To achieve the effect of thrust vectoring, an inverse kinematics synthesis was carried out to determine what angle the actuators need to move to. After building the system, intermittent angular displacements of 10°, 15°, and 20° were given during bench testing, and the response of the control system as well as the servo motor angle was plotted. The final design was able to move in water but was not able to handle large angular displacements as a result of the small angle approximation used in the mathematical model.

Keywords: PID control, thrust vectoring, parallel manipulators, ROV, underwater, attitude control

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Authors: James Jr. Mashiyane, Risuna Nkolele, Stephanie J. Müller, Gciniwe S. Dlamini, Rebone L. Meraba, Darlington S. Mapiye

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When performing language tasks in natural language processing (NLP), the dimensionality of word embeddings is chosen either ad-hoc or is calculated by optimizing the Pairwise Inner Product (PIP) loss. The PIP loss is a metric that measures the dissimilarity between word embeddings, and it is obtained through matrix perturbation theory by utilizing the unitary invariance of word embeddings. Unlike in natural language, in genomics, especially in genome sequence processing, unlike in natural language processing, there is no notion of a “word,” but rather, there are sequence substrings of length k called k-mers. K-mers sizes matter, and they vary depending on the goal of the task at hand. The dimensionality of word embeddings in NLP has been studied using the matrix perturbation theory and the PIP loss. In this paper, the sufficiency and reliability of applying word-embedding algorithms to various genomic sequence datasets are investigated to understand the relationship between the k-mer size and their embedding dimension. This is completed by studying the scaling capability of three embedding algorithms, namely Latent Semantic analysis (LSA), Word2Vec, and Global Vectors (GloVe), with respect to the k-mer size. Utilising the PIP loss as a metric to train embeddings on different datasets, we also show that Word2Vec outperforms LSA and GloVe in accurate computing embeddings as both the k-mer size and vocabulary increase. Finally, the shortcomings of natural language processing embedding algorithms in performing genomic tasks are discussed.

Keywords: word embeddings, k-mer embedding, dimensionality reduction

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

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Authors: Hajar Hosseini Khorami

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Porphyrins are organic, aromatic compounds found in heme, cytochrome, cobalamin, chlorophyll , and many other natural products with essential roles in biological processes that their cationic forms have been used as groups of favorable non-viral vectors recently. Cationic porphyrins are self-chromogenic reagents with a high capacity for modifications, great interaction with DNA and protection of DNA from nuclease during delivery of it into a cell with low toxicity. In order to have high efficient gene transfection into the cell while causing low toxicity, genetically manipulations of the non-viral vector, cationic porphyrin, would be useful. In this study newly modified cationic porphyrin derivative, 5, 10-dihexyl-15, 20bis (N-methyl-4-pyridyl) porphyrin was applied. Cytotoxicity of synthesized cationic porphyrin on Chinese Hamster Ovarian (CHO) cells was evaluated by using MTT assay. This cationic derivative is dose-dependent, with low cytotoxicity at the ranges from 100 μM to 0.01μM. It was uptake by cells at high concentration. Using direct non-viral gene transfection method and different concentration of cationic porphyrin were tested on transfection of CHO cells by applying derived transfection reagent with X-tremeGENE HP DNA as a positive control. However, no transfection observed by porphyrin derivative and the parameters tested except for positive control. Results of this study suggested that applying different protocol, and also trying other concentration of cationic porphyrins and DNA for forming a strong complex would increase the possibility of efficient gene transfection by using cationic porphyrins.

Keywords: cationic porphyrins, gene delivery, non-viral vectors, transfection reagents

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Authors: Saqib Ali, Man-Qun Wang

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The Japanese pine sawyer, Monochamus alternatus Hope (Coleoptera: Cerambycidae), is a major pest of pines and it is also the key vector of the exotic pinewood nematode in China. In the present study, we cloned, expressed, and purified a chemosensory protein (CSP) in M. alternatus. We surveyed its expression in various developmental stages of male and female adult tissues and determined its binding affinities for different pine volatiles using a competitive binding fluorescence assay. A CSP known as CSP5 in M. alternatus was obtained from an antennal cDNA library and expressed in Escherichia coli. Quantitative reverse transcription polymerase chain reaction results indicated that the CSP5 gene was mainly expressed in male and female antennae. Competitive binding assays were performed to test the binding affinity of recombinant CSP5 to 13 odour molecules of pine volatiles. The results showed that CSP5 showed very strong binding abilities to myrcene, (+)-β-pinene, and (−)-isolongifolene, whereas the volatiles 2-methoxy-4-vinylphenol, p-cymene, and (+)-limonene oxide have relatively weak binding affinity at pH 5.0. Three volatiles myrcene, (+)-β-pinene, and (−)-isolongifolene may play crucial roles in CSP5 binding with ligands, but this needs further study for confirmation. The sensitivity of insect to host plant volatiles can effectively be used to control and monitor the population through mass trapping as part of integrated pest management programs.

Keywords: olfactory-specific protein, volatiles, competitive binding assay, expression characteristics, qPCR

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Authors: Arabi Abderrazak, Bourouba Nacerdine, Ayad Mouloud, Belaout Abdeslam

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In this paper, a new test technique of analog circuits using time mode simulation is proposed for the single catastrophic faults detection in analog circuits. This test process is performed to overcome the problem of catastrophic faults being escaped in a DC mode test applied to the inverter amplifier in previous research works. The circuit under test is a second-order low pass filter constructed around this type of amplifier but performing a function that differs from that of the previous test. The test approach performed in this work is based on two key- elements where the first one concerns the unique square pulse signal selected as an input vector test signal to stimulate the fault effect at the circuit output response. The second element is the filter response conversion to a square pulses sequence obtained from an analog comparator. This signal conversion is achieved through a fixed reference threshold voltage of this comparison circuit. The measurement of the three first response signal pulses durations is regarded as fault effect detection parameter on one hand, and as a fault signature helping to hence fully establish an analog circuit fault diagnosis on another hand. The results obtained so far are very promising since the approach has lifted up the fault coverage ratio in both modes to over 90% and has revealed the harmful side of faults that has been masked in a DC mode test.

Keywords: analog circuits, analog faults diagnosis, catastrophic faults, fault detection

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Authors: Tae-Uk Yun, Pullip Chung, Kee-Young Kwahk, Hyunchul Ahn

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Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection using machine learning techniques over the past years. But, there have been no prior studies proposed an automated fake news detection method for Korean news to our best knowledge. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (topic modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as logistic regression, backpropagation network, support vector machine, and deep neural network can be applied. To validate the effectiveness of the proposed method, we collected about 200 short Korean news from Seoul National University’s FactCheck. which provides with detailed analysis reports from 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

Keywords: fake news detection, Korean news, machine learning, text mining

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Authors: Florin Leon, Silvia Curteanu

Abstract:

Developing complete mechanistic models for polymerization reactors is not easy, because complex reactions occur simultaneously; there is a large number of kinetic parameters involved and sometimes the chemical and physical phenomena for mixtures involving polymers are poorly understood. To overcome these difficulties, empirical models based on sampled data can be used instead, namely regression methods typical of machine learning field. They have the ability to learn the trends of a process without any knowledge about its particular physical and chemical laws. Therefore, they are useful for modeling complex processes, such as the free radical polymerization of methyl methacrylate achieved in a batch bulk process. The goal is to generate accurate predictions of monomer conversion, numerical average molecular weight and gravimetrical average molecular weight. This process is associated with non-linear gel and glass effects. For this purpose, an adaptive sampling technique is presented, which can select more samples around the regions where the values have a higher variation. Several machine learning methods are used for the modeling and their performance is compared: support vector machines, k-nearest neighbor, k-nearest neighbor and random forest, as well as an original algorithm, large margin nearest neighbor regression. The suggested method provides very good results compared to the other well-known regression algorithms.

Keywords: batch bulk methyl methacrylate polymerization, adaptive sampling, machine learning, large margin nearest neighbor regression

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Authors: Ravi Vishwanath, Saumya Kumaar, S. N. Omkar

Abstract:

Brain-computer interfacing (BCI) is a technology that is almost four decades old, and it was developed solely for the purpose of developing and enhancing the impact of neuroprosthetics. However, in the recent times, with the commercialization of non-invasive electroencephalogram (EEG) headsets, the technology has seen a wide variety of applications like home automation, wheelchair control, vehicle steering, etc. One of the latest developed applications is the mind-controlled quadrotor unmanned aerial vehicle. These applications, however, do not require a very high-speed response and give satisfactory results when standard classification methods like Support Vector Machine (SVM) and Multi-Layer Perceptron (MLPC). Issues are faced when there is a requirement for high-speed control in the case of fixed-wing unmanned aerial vehicles where such methods are rendered unreliable due to the low speed of classification. Such an application requires the system to classify data at high speeds in order to retain the controllability of the vehicle. This paper proposes a novel method of classification which uses a combination of Common Spatial Paradigm and Linear Discriminant Analysis that provides an improved classification accuracy in real time. A non-linear SVM based classification technique has also been discussed. Further, this paper discusses the implementation of the proposed method on a fixed-wing and VTOL unmanned aerial vehicles.

Keywords: brain-computer interface, classification, machine learning, unmanned aerial vehicles

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Authors: Samina Jam Nazeer Ahmad, Samia Yasin, Ijaz Ahmad, Muhammad Tahir, Jam Nazeer Ahmad

Abstract:

Phytoplasmas are phloem-inhibited plant pathogenic bacteria that are transferred by insect vectors. Among biotic factors, Phytoplasma infection induces abnormality influencing the physiology as well as morphology of plants. In 16Sr-IX group phytoplasma-infected brassica compestris, flower abnormalities have been associated with changes in the expression of floral development genes. To determine whether methylation was involved in down-regulation of flower development, the process of DNA methylation and Demethylation was investigated as a possible mechanism for regulation of floral gene expression in phytoplasma infected Brassica transmitted by Orosious orientalis vector by using RT-PCR, MSRE-PCR, Southern blotting, Bisulfite Sequencing, etc. Transcriptional expression of methylated genes was found to be globally down-regulated in plants infected with phytoplasma, but not severely in those infested by insect vectors and variation in expression was found in genes involved in methylation. These results also showed that genes particularly orthologous to Arabidopsis APETALA3 involved in petal formation and flower development was down-regulated severely in phytoplasma-infected brassica and with the fact that phytoplasma and insect induce variation in developmental gene expression. The DNA methylation status of flower developmental gene in phytoplasma infected plants with 5-azacytidine restored gene expression strongly suggesting that DNA methylation was involved in down-regulation of floral development genes in phytoplasma infected brassica.

Keywords: genes expression, phytoplasma, DNA methylation, flower development

Procedia PDF Downloads 374

Authors: Rui Liu, Klaus Greve

Abstract:

The navigation information is a kind of dynamic geographic information, and the navigation information system is a kind of special geographic information system. At present, there are many researches on the application of centralized management and cross-integration application of basic geographic information. However, the idea of information integration and sharing is not deeply applied into the research of navigation information service. And the imperfection of navigation target coordination and navigation information sharing mechanism under certain navigation tasks has greatly affected the reliability and scientificity of navigation service such as path planning. Considering this, the project intends to study the multi-source information fusion and multi-objective united navigation information interaction mechanism: first of all, investigate the actual needs of navigation users in different areas, and establish the preliminary navigation information classification and importance level model; and then analyze the characteristics of the remote sensing and GIS vector data, and design the fusion algorithm from the aspect of improving the positioning accuracy and extracting the navigation environment data. At last, the project intends to analyze the feature of navigation information of the land, sea and air navigation targets, and design the united navigation data standard and navigation information sharing model under certain navigation tasks, and establish a test navigation system for united navigation simulation experiment. The aim of this study is to explore the theory of united navigation service and optimize the navigation information service model, which will lay the theory and technology foundation for the united navigation of land, sea and air targets.

Keywords: information fusion, united navigation, dynamic path planning, navigation information visualization

Procedia PDF Downloads 288

Authors: O. Sunantha, S. Zhenfeng, S. Phattraporn, A. Zeeshan

Abstract:

The decline of soil organic carbon (SOC) in global agriculture is a critical issue requiring rapid and accurate estimation for informed policymaking. While it is recognized that SOC predictors vary significantly when derived from remote sensing data and environmental variables, identifying the specific parameters most suitable for accurately estimating SOC in diverse agricultural areas remains a challenge. This study utilizes remote sensing data to precisely estimate SOC and identify influential factors in diverse agricultural areas, such as paddy, corn, sugarcane, cassava, and perennial crops. Extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR) models are employed to analyze these factors' impact on SOC estimation. The results show key factors influencing SOC estimation include slope, vegetation indices (EVI), spectral reflectance indices (red index, red edge2), temperature, land use, and surface soil moisture, as indicated by their averaged importance scores across XGBoost, RF, and SVR models. Therefore, using different machine learning algorithms for SOC estimation reveals varying influential factors from remote sensing data and environmental variables. This approach emphasizes feature selection, as different machine learning algorithms identify various key factors from remote sensing data and environmental variables for accurate SOC estimation.

Keywords: factors influencing SOC estimation, remote sensing data, environmental variables, machine learning

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Authors: D. J. Kalita

Abstract:

Cancer is one of the prime causes of early death worldwide. Mutation of the gene involve in DNA repair and damage, like BRCA2 (Breast cancer gene two) genes, can be detected efficiently by PCR-RFLP to early breast cancer diagnosis and adopt the suitable method of treatment. Host Defense Peptide can be used as blueprint for the design and synthesis of novel anticancer drugs to avoid the side effect of conventional chemotherapy and chemo resistance. The change at nucleotide position 392 of a -› c in the cancer sample of dog mammary tumour at BRCA2 (exon 7) gene lead the creation of a new restriction site for SsiI restriction enzyme. This SNP may be a marker for detection of canine mammary tumour. Support vector machine (SVM) algorithm was used to design and predict the anticancer peptide from the mature functional peptide. MTT assay of MCF-7 cell line after 48 hours of post treatment showed an increase in the number of rounded cells when compared with untreated control cells. The ability of the synthesized peptide to induce apoptosis in MCF-7 cells was further investigated by staining the cells with the fluorescent dye Hoechst stain solution, which allows the evaluation of the nuclear morphology. Numerous cells with dense, pyknotic nuclei (the brighter fluorescence) were observed in treated but not in control MCF-7 cells when viewed using an inverted phase-contrast microscope. Thus, PCR-RFLP is one of the attractive approach for early diagnosis, and synthetic cationic peptide can be used for the treatment of canine mammary tumour.

Keywords: cancer, cationic peptide, host defense peptides, Breast cancer genes

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Authors: Stavros Palavouzis, Alexandra Triantafyllopoulou, Aliki Tzima, Epaminondas Paplomatas

Abstract:

Mating-type genes are present in most filamentous fungi, even though teleomorphs for all species have not been recorded. Our study tries to explore the effect of different growth conditions on the expression of MAT genes in Neofusicoccum mediterraneum. As such, selected isolates were grown in potato dextrose broth or in water agar supplemented with pine needles under a 12 h photoperiod, as well as in constant darkness. Mycelia and spores were collected at different time points, and RNA extraction was performed, with the extracted product being used for cDNA synthesis. New primers for MAT gene expression were designed while qPCR results are underway. The second part of the study involved the isolation and cloning in a selected pGEM-T vector of the Botryosphaeria dothidea MAT1 1 1 and MAT1 2 1 mating genes, including flanking regions. As a next step, the genes were amplified using newly designed primers with engineered restriction sites. Amplicons were excised and subsequently sub-cloned in appropriate binary vectors. The constructs were afterward inserted into Agrobacterium tumefaciens and utilized for Agrobacterium-mediated transformation (ATMT) of Neofusicoccum mediterraneum. At the same time, the transformation of a Verticillium dahliae tomato race 1 strain (70V) was performed as a control. While the procedure was successful in regards to V. dahliae, transformed strains of N. mediterraneum could not be obtained. At present, a new transformation protocol, which utilizes a combination of protoplast and Agro transformation, is being evaluated.

Keywords: anamorph, heterothallism, perithecia, pycnidia, sexual stage

Procedia PDF Downloads 180