Search results for: forest fire detection

Authors: Maamar Ali Saud Al Tobi, Geraint Bevan, K. P. Ramachandran, Peter Wallace, David Harrison

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Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated.

Keywords: centrifugal pump setup, vibration analysis, artificial intelligence, genetic algorithm

Procedia PDF Downloads 411

Authors: Xiaohui Wang, Zhichuan Guan, Roman Shor, Chuanbin Xu

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Early monitoring and real-time quantitative description of gas intrusion under the premise of ensuring the integrity of the drilling fluid circulation system will greatly improve the accuracy and effectiveness of deepwater gas-kick monitoring. Therefore, in order to study the propagation characteristics of ultrasonic waves in the gas-liquid two-phase flow within the marine riser, in this paper, a numerical simulation method of ultrasonic propagation in the annulus of the riser was established, and the credibility of the numerical analysis was verified by the experimental results of the established gas intrusion monitoring simulation experimental device. The numerical simulation can solve the sound field in the gas-liquid two-phase flow according to different physical models, and it is easier to realize the single factor control. The influence of each parameter on the received signal can be quantitatively investigated, and the law with practical guiding significance can be obtained.

Keywords: gas-kick detection, ultrasonic, void fraction, coda wave velocity

Procedia PDF Downloads 157

Authors: Hailyie Tekleselase

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Deep learning is increasingly used as a building block of security systems. However, neural networks are hard to interpret and typically solid to the practitioner. This paper presents a detail survey of computing methods in cyber security, and analyzes the prospects of enhancing the cyber security capabilities by suggests that of accelerating the intelligence of the security systems. There are many AI-based applications used in industrial scenarios such as Internet of Things (IoT), smart grids, and edge computing. Machine learning technologies require a training process which introduces the protection problems in the training data and algorithms. We present machine learning techniques currently applied to the detection of intrusion, malware, and spam. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic. We conclude that problems can be solved successfully only when methods of artificial intelligence are being used besides human experts or operators.

Keywords: artificial intelligence, machine learning, deep learning, cyber security, big data

Procedia PDF Downloads 127

Authors: José Eugenio Ortiz, Pierre Plaza, Josefa Herrero, Iván Cabria, José Luis Blanco, Javier Gavilanes, José Ignacio Escavy, Ignacio López-Cilla, Virginia Yagüe, César Pérez, Silvia Rodríguez, Jorge Rico, Cecilia Serrano, Jesús Bernat

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The development of Artificial Intelligence services in mining processes, specifically in aggregate quarries, is facilitating automation and improving numerous aspects of operations. Ultimately, AI is transforming the mining industry by improving efficiency, safety and sustainability. With the ability to analyze large amounts of data and make autonomous decisions, AI offers great opportunities to optimize mining operations and maximize the economic and social benefits of this vital industry. Within the framework of the European DIGIECOQUARRY project, various services were developed for the identification of material quality, production estimation, detection of anomalies and prediction of consumption and production automatically with good results.

Keywords: aggregates, artificial intelligence, automatization, mining operations

Procedia PDF Downloads 89

Authors: Priya Tomar, Pallavi Mittal

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As industrialization is inevitable and progress with rapid acceleration, the need for innovative ways to get rid of waste has increased. Recent advancement in bioresource technology paves novel ideas for recycling of factory waste that has been polluting the agro-industry, soil and water bodies. Paper industries in India are in a considerable number, where molasses and impure alcohol are still being used as raw materials for manufacturing of paper. Paper mills based on nonconventional agro residues are being encouraged due to increased demand of paper and acute shortage of forest-based raw materials. The colouring body present in the wastewater from pulp and paper mill is organic in nature and is comprised of wood extractives, tannin, resins, synthetic dyes, lignin and its degradation products formed by the action of chlorine on lignin which imparts an offensive colour to the water. These mills use different chemical process for paper manufacturing due to which lignified chemicals are released into the environment. Therefore, the chemical oxygen demand (COD) of the emanating stream is quite high. This paper presents some new techniques that were developed for the efficiency of bioremediation on paper industry. A short introduction to paper industry and a variety of presently available methods of bioremediation on paper industry and different strategies are also discussed here. For solving the above problem, two bacterial strains (Pseudomonas aeruginosa and Bacillus subtilis) and their consortia (Pseudomonas aeruginosa and Bacillus subtilis) were utilized for the pulp and paper mill effluent. Pseudomonas aeruginosa and Bacillus subtilis named as T–1, T–2, T–3, T–4, T–5, T–6, for the decolourisation of paper industry effluent. The results indicated that a maximum colour reduction is (60.5%) achieved by Pseudomonas aeruginosa and COD reduction is (88.8%) achieved by Bacillus subtilis, maximum pH changes is (4.23) achieved by Pseudomonas aeruginosa, TSS reduction is (2.09 %) achieved by Bacillus subtilis, and TDS reduction is (0.95 %) achieved by Bacillus subtilis. When the wastewater was supplemented with carbon (glucose) and nitrogen (yeast extract) source and data revealed the efficiency of Bacillus subtilis, having more with glucose than Pseudomonas aeruginosa.

Keywords: bioremediation, paper and pulp mill effluent, treated effluent, lignin

Procedia PDF Downloads 252

Authors: Ripon Sarkar, Kabita Chaterjee, Ananya Barui

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Smoking is one of the leading causes of oral cancer. Cigarette smoke affects various cellular parameters and alters molecular metabolism of cells. Epithelial cells losses their cytoskeleton structure, membrane integrity, cellular polarity that subsequently initiates the process of epithelial cells to mesenchymal transition due to long exposure of cigarette smoking. It changes the normal cellular metabolic activity which induces oxidative stress and enhances the reactive oxygen spices (ROS) formation. Excessive ROS and associated oxidative stress are considered to be a driving force in alteration in cellular phenotypes, polarity distribution and mitochondrial metabolism. Noninvasive assessment of such parameters plays essential role in development of routine screening system for early diagnosis of oral cancer. Electrical cell-substrate impedance sensing (ECIS) is one of such method applied for detection of cellular membrane impedance which can be correlated to cell membrane integrity. Present study intends to explore the alteration in cellular impedance along with the expression of cellular polarity molecules and cytoskeleton distributions in oral epithelial cells of habitual smokers and to correlate the outcome to that of clinically diagnosed oral leukoplakia and oral squamous cell carcinoma patients. Total 80 subjects were categorized into four study groups: nonsmoker (NS), cigarette smoker (CS), oral leukoplakia (OLPK) and oral squamous cell carcinoma (OSCC). Cytoskeleton distribution was analyzed by staining of actin filament and generation of ROS was measured using assay kit using standard protocol. Cell impedance was measured through ECIS method at different frequencies. Expression of E-cadherin and protease-activated receptor (PAR) proteins were observed through immune-fluorescence method. Distribution of actin filament is well organized in NS group however; distribution pattern was grossly varied in CS, OLPK and OSCC. Generation of ROS was low in NS which subsequently increased towards OSCC. Expressions of E-cadherin and change in cellular electrical impedance in different study groups indicated the hallmark of cancer progression from NS to OSCC. Expressions of E-cadherin, PAR protein, and cell impedance were decreased from NS to CS and farther OSCC. Generally, the oral epithelial cells exhibit apico-basal polarity however with cancer progression these cells lose their characteristic polarity distribution. In this study expression of polarity molecule and ECIS observation indicates such altered pattern of polarity among smoker group. Overall the present study monitored the alterations in intracellular ROS generation and cell metabolic function, membrane integrity in oral epithelial cells in cigarette smokers. Present study thus has clinical significance, and it may help in developing a noninvasive technique for early diagnosis of oral cancer amongst susceptible individuals.

Keywords: cigarette smoking, early oral cancer detection, electric cell-substrate impedance sensing, noninvasive screening

Procedia PDF Downloads 178

Authors: Syed Mahbubuz Zaman, A. B. M. Abrar Haque, Mehedi Hassan Nayeem, Misbah Uddin Sagor

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Everyday e-mails are being used by millions of people as an effective form of communication over the Internet. Although e-mails allow high-speed communication, there is a constant threat known as spam. Spam e-mail is often called junk e-mails which are unsolicited and sent in bulk. These unsolicited emails cause security concerns among internet users because they are being exposed to inappropriate content. There is no guaranteed way to stop spammers who use static filters as they are bypassed very easily. In this paper, a smart system is proposed that will be using neural networks to approach spam in a different way, and meanwhile, this will also detect the most relevant features that will help to design the spam filter. Also, a comparison of different parameters for different neural network models has been shown to determine which model works best within suitable parameters.

Keywords: long short-term memory, bidirectional long short-term memory, gated recurrent unit, natural language processing, natural language processing

Procedia PDF Downloads 206

Authors: R. Gadea, J. M. Monzó, F. J. Puertas, M. Castro, A. Tebar, P. J. Fito, R. J. Colom

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There exists a wide array of ailments impacting the structural soundness of the esophageal walls, predominantly linked to digestive issues. Presently, the techniques employed for identifying esophageal tract complications are excessively invasive and discomforting, subjecting patients to prolonged discomfort in order to achieve an accurate diagnosis. This study proposes the creation of a sensor with profound measuring capabilities designed to detect fluids coursing through the esophageal tract. The multi-sensor detection system relies on radiofrequency photospectrometry. During experimentation, individuals representing diverse demographics in terms of gender and age were utilized, positioning the sensors amidst the trachea and diaphragm and assessing measurements in vacuum conditions, water, orange juice, and saline solutions. The findings garnered enabled the identification of various liquid mediums within the esophagus, segregating them based on their ionic composition.

Keywords: radiofrequency spectrophotometry, medical device, gastroesophageal disease, photonics

Procedia PDF Downloads 82

Authors: Balachandra Kumaraswamy, P. G. Poonacha

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Automatic Music Information Retrieval has been one of the challenging topics of research for a few decades now with several interesting approaches reported in the literature. In this paper we have developed a pitch extraction method based on a finite Fourier series approximation to the given window of samples. We then estimate pitch as the fundamental period of the finite Fourier series approximation to the given window of samples. This method uses analysis of the strength of harmonics present in the signal to reduce octave as well as harmonic errors. The performance of our method is compared with three best known methods for pitch extraction, namely, Yin, Windowed Special Normalization of the Auto-Correlation Function and Harmonic Product Spectrum methods of pitch extraction. Our study with artificially created signals as well as music files show that Fourier Approximation method gives much better estimate of pitch with less octave and harmonic errors.

Keywords: pitch, fourier series, yin, normalization of the auto- correlation function, harmonic product, mean square error

Procedia PDF Downloads 413

Authors: Bhekisipho Twala, Mantepu Masetshaba, Ramapulana Nkoana

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Multiple classifier systems combine several individual classifiers to deliver a final classification decision. However, an increasingly controversial question is whether such systems can outperform the single best classifier, and if so, what form of multiple classifiers system yields the most significant benefit. Also, multi-target tracking detection using multiple sensors is an important research field in mobile techniques and military applications. In this paper, several multiple classifiers systems are evaluated in terms of their ability to predict a system’s failure or success for multi-sensor target tracking tasks. The Bristol Eden project dataset is utilised for this task. Experimental and simulation results show that the human activity identification system can fulfill requirements of target tracking due to improved sensors classification performances with multiple classifier systems constructed using boosting achieving higher accuracy rates.

Keywords: single classifier, ensemble learning, multi-target tracking, multiple classifiers

Procedia PDF Downloads 271

Authors: Adnan Baig, Ishteeaq Naeem, Saad Mansoor

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The Internet of Things (IoT) gadgets have advanced quickly in recent years, and their use is steadily rising daily. However, cyber-attackers can target these gadgets due to their distributed nature. Additionally, many IoT devices have significant security flaws in their implementation and design, making them vulnerable to security threats. Hence, these threats can cause important data security and privacy loss from a single attack on network devices or systems. Botnets are a significant security risk that can harm the IoT network; hence, sophisticated techniques are required to mitigate the risk. This work uses a machine learning-based method to identify IoT orchestrated by botnets. The proposed technique identifies the net attack by distinguishing between legitimate and malicious traffic. This article proposes a hyperparameter tuning model to improvise the method to improve the accuracy of existing processes. The results demonstrated an improved and more accurate indication of botnet-based cyber-attacks.

Keywords: Internet of Things, Botnet, BoT-IoT dataset, ML techniques

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Authors: Y. Laib Dit Leksir, S. Bouhouche

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Analysis and processing of data bases resulting from infrared thermal measurements made on the electrical installation requires the development of new tools in order to obtain correct and additional information to the visual inspections. Consequently, the methods based on the capture of infrared digital images show a great potential and are employed increasingly in various fields. Although, there is an enormous need for the development of effective techniques to analyse these data base in order to extract relevant information relating to the state of the equipments. Our goal consists in introducing recent techniques of modeling based on new methods, image and signal processing to develop mathematical models in this field. The aim of this work is to capture the anomalies existing in electrical equipments during an inspection of some machines using A40 Flir camera. After, we use binarisation techniques in order to select the region of interest and we make comparison between these methods of thermal images obtained to choose the best one.

Keywords: infrared thermography, defect detection, troubleshooting, electrical equipment

Procedia PDF Downloads 477

Authors: Tian-Fang Kang, Chao-Nan Ge, Rui Li

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An electrochemical biosensor for the determination of organophosphorus pesticides was developed based on electrochemical co-deposition of Au and Pd nanoparticles on glassy carbon electrode (GCE). Energy disperse spectroscopy (EDS) analysis was used for characterization of the surface structure. Scanning electron micrograph (SEM) demonstrates that the films are uniform and the nanoclusters are homogeneously distributed on the GCE surface. Acetylcholinesterase (AChE) was immobilized on the Au and Pd nanoparticle modified electrode (Au-Pd/GCE) by cross-linking with glutaraldehyde. The electrochemical behavior of thiocholine at the biosensor (AChE/Au-Pd/GCE) was studied. The biosensors exhibited substantial electrocatalytic effect on the oxidation of thiocholine. The peak current of linear scan voltammetry (LSV) of thiocholine at the biosensor is proportional to the concentration of acetylthiocholine chloride (ATCl) over the range of 2.5 × 10-6 to 2.5 × 10-4 M in 0.1 M phosphate buffer solution (pH 7.0). The percent inhibition of acetylcholinesterase was proportional to the logarithm of parathion concentration in the range of 4.0 × 10-9 to 1.0 × 10-6 M. The detection limit of parathion was 2.6 × 10-9 M. The proposed method exhibited high sensitivity and good reproducibility.

Keywords: acetylcholinesterase, Au-Pd nanoparticles, electrochemical biosensors, parathion

Procedia PDF Downloads 407

Authors: Albert P. Ekanem, Austin I. Obiekezie, Elizabeth X. Ntia

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Introduction: Some African plants and bile from animals have shown efficacies in the treatment and control of diseases in farmed fish. The background of the study is based on the fact the African rain forest is blessed with the abundance of medicinal plants that should be investigated for their use in the treatment of diseases. The significance of the study is informed by the fact that chemical-based substances accumulate in the tissues of food fish, thereby reducing the food values of such products and moreover, the continuous use of chemotherapeutics in the aquatic environments tends to degrade the affected environment. Methodology: Plants and animal products were extracted, purified and applied under in vitro and in vivo conditions to the affected organisms. Effective plants and bills were analyzed for biologically active substances responsible for the activities by both qualitative and HPLC methods. Results: Extracts of Carica papaya and Mucuna pruriens were effective in the treatment of Ichthyophthiriasis in goldfish (Carassius auratus auratus) with high host tolerance. Similarly, ectoparasitic monogeneans were effectively dislodged from the gills and skin of goldfish by the application of extracts of Piper guineense at therapeutic concentrations. Artemesia annua with known antimalarial activities in human was also effective against fish monogenean parasites of Clarias gariepinus in a concentration-related manner without detriments to the host. Effective antibacterial activities against Aeromonas and Pseudomonas diseases of the African catfish (Heterobranchus longifilis) were demonstrated in some plants such as Phylanthus amarus, Allium sativum, A. annua, and Citrus lemon. Bile from some animals (fish, goat, chicken, cow, and pig) showed great antibacterial activities against some gastrointestinal bacterial pathogens of fish. Conclusions: African plants and some animal bile have shown potential promise in the treatment of diseases in fish and other aquatic animals. The use of chemical-based substances for control of diseases in the aquatic environments should be restricted.

Keywords: control, diseases, fish, treatment

Procedia PDF Downloads 449

Authors: Aravind Kumar

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The high terrains of Garhwal (Uttarakhand) Himalaya are the niches of a number of rare and endemic plant species of great therapeutic importance. However, the wild flora of the area is still under a constant threat due to rapid upsurge in human interferences, especially through multifarious tourism growth and peri-urban encroachments. After getting the status of a ‘Special State’ of the country since its inception in the year 2000, this newly borne State led to very rapid infrastructural growth and development. Consequently, its townships started expanding in an unmanaged way grabbing nearby agricultural lands and forest areas into peri-urban landscapes. Simultaneously, a boom in tourism and pilgrimage in the state and the infrastructural facilities raised by the government for tourists/pilgrims are destroying its biodiversity. Field survey revealed 242 plant species of therapeutic significance naturally growing in the area and being utilized by local inhabitants as traditional medicines. On conservation scale, 6 species (2.2%) were identified as critically endangered, 19 species (7.1%) as the endangered ones, 8 species (3.0%) under rare category, 17 species (6.4%) as threatened and 14 species (5.2%) as vulnerable. The Government of India has brought mega-biodiversity hot spots of the state under Biosphere Reserve, National Parks, etc. restricting all kinds of human interferences; however, the two most sacred shrines of Hindus and Sikhs viz. Shri Badrinath and Shri Hemkunt Sahib, and two great touristic attractions viz. Valley of Flowers and Auli-Joshimath Skiing Track oblige the government to maintain equilibrium between entries of visitors vis-à-vis biodiversity conservation in high terrains of Uttarakhand Himalaya.

Keywords: biodiversity conservation, ethno-botany, Garhwal (Uttarakhand) Himalaya, peri-urban encroachment, pilgrimage and tourism

Procedia PDF Downloads 228

Authors: Fatih Tarlak

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Predictive microbiology can be considered as an important field in food microbiology in which it uses predictive models to describe the microbial growth in different food products. Predictive models estimate the growth of microorganisms quickly, efficiently, and in a cost-effective way as compared to traditional methods of enumeration, which are long-lasting, expensive, and time-consuming. The mathematical models used in predictive microbiology are mainly categorised as primary and secondary models. The primary models are the mathematical equations that define the growth data as a function of time under a constant environmental condition. The secondary models describe the effects of environmental factors, such as temperature, pH, and water activity (aw) on the parameters of the primary models, including the maximum specific growth rate and lag phase duration, which are the most critical growth kinetic parameters. The combination of primary and secondary models provides valuable information to set limits for the quantitative detection of the microbial spoilage and assess product shelf-life.

Keywords: shelf-life, growth model, predictive microbiology, simulation

Procedia PDF Downloads 214

Authors: Degale Desta, Cheng Jian

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The most important factor in identifying a person is their face. Even identical twins have their own distinct faces. As a result, identification and face recognition are needed to tell one person from another. A face recognition system is a verification tool used to establish a person's identity using biometrics. Nowadays, face recognition is a common technique used in a variety of applications, including home security systems, criminal identification, and phone unlock systems. This system is more secure because it only requires a facial image instead of other dependencies like a key or card. Face detection and face identification are the two phases that typically make up a human recognition system.The idea behind designing and creating a face recognition system using deep learning with Azure ML Python's OpenCV is explained in this paper. Face recognition is a task that can be accomplished using deep learning, and given the accuracy of this method, it appears to be a suitable approach. To show how accurate the suggested face recognition system is, experimental results are given in 98.46% accuracy using Fast-RCNN Performance of algorithms under different training conditions.

Keywords: deep learning, face recognition, identification, fast-RCNN

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Authors: Nikolaos Georgoulopoulos, Alkis Hatzopoulos, Konstantinos Karamitsios, Konstantinos Kotrotsios, Alexandros I. Metsai

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In modern server systems, business critical applications run in different types of infrastructure, such as cloud systems, physical machines and virtualization. Often, due to high load and over time, various hardware faults occur in servers that translate to errors, resulting to malfunction or even server breakdown. CPU, RAM and hard drive (HDD) are the hardware parts that concern server administrators the most regarding errors. In this work, selected RAM, HDD and CPU errors, that have been observed or can be simulated in kernel ring buffer log files from two groups of Linux servers, are investigated. Moreover, a severity characterization is given for each error type. Better understanding of such errors can lead to more efficient analysis of kernel logs that are usually exploited for fault diagnosis and prediction. In addition, this work summarizes ways of simulating hardware errors in RAM and HDD, in order to test the error detection and correction mechanisms of a Linux server.

Keywords: hardware errors, Kernel logs, Linux servers, RAM, hard disk, CPU

Procedia PDF Downloads 158

Authors: Qi Xiu, Hiromu Hota, Yohsuke Ishii, Takuya Oda

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Recently, collectable manufacturing data are rapidly increasing. On the other hand, mega recall is getting serious as a social problem. Under such circumstances, there are increasing needs for preventing mega recalls by defect analysis such as root cause analysis and abnormal detection utilizing manufacturing data. However, the time to classify strings in manufacturing data by traditional method is too long to meet requirement of quick defect analysis. Therefore, we present String Length Distribution Classification method (SLDC) to correctly classify strings in a short time. This method learns character features, especially string length distribution from Product ID, Machine ID in BOM and asset list. By applying the proposal to strings in actual manufacturing data, we verified that the classification time of strings can be reduced by 80%. As a result, it can be estimated that the requirement of quick defect analysis can be fulfilled.

Keywords: string classification, data quality, feature selection, probability distribution, string length

Procedia PDF Downloads 319

Authors: Can Hu, Huixia Shi, Hongcheng Mei, Jun Zhu, Hongling Guo

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Textile fibers are important trace evidence and frequently encountered in criminal investigations. A significant aspect of fiber evidence examination is the determination of fiber dyes. Although several instrumental methods have been developed for dyes detection, the analysis speed is not fast enough yet. A rapid dye analysis method is still needed to further improve the efficiency of case handling. Capillary electrophoresis has the advantages of high separation speed and high separation efficiency and is an ideal method for the rapid analysis of fiber dyes. In this paper, acid dyes used for protein fiber dyeing were determined by a developed short-end injection capillary electrophoresis technique. Five acid red dyes with similar structures were successfully baseline separated within 5 min. The separation reproducibility is fairly good for the relative standard deviation of retention time is 0.51%. The established method is rapid and accurate which has great potential to be applied in forensic setting.

Keywords: acid dyes, capillary electrophoresis, fiber evidence, rapid determination

Procedia PDF Downloads 147

Authors: Alberto Alcalá-Alvarez, Pablo Padilla-Longoria

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The present paper describes a framework for constructing mathematical networks encoding relevant musical information from a music score for structural analysis. These graphs englobe statistical information about music elements such as notes, chords, rhythms, intervals, etc., and the relations among them, and so become helpful in visualizing and understanding important stylistic features of a music fragment. In order to build such networks, musical data is parsed out of a digital symbolic music file. This data undergoes different analytical procedures from Graph Theory, such as measuring the centrality of nodes, community detection, and entropy calculation. The resulting networks reflect important structural characteristics of the fragment in question: predominant elements, connectivity between them, and complexity of the information contained in it. Music pieces in different styles are analyzed, and the results are contrasted with the traditional analysis outcome in order to show the consistency and potential utility of this method for music analysis.

Keywords: computational musicology, mathematical music modelling, music analysis, style classification

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Authors: R. S. Remya, U. S. Sethulekshmi

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Detecting the authenticity of a video is an important issue in digital forensics as Video is used as a silent evidence in court such as in child pornography, movie piracy cases, insurance claims, cases involving scientific fraud, traffic monitoring etc. The biggest threat to video data is the availability of modern open video editing tools which enable easy editing of videos without leaving any trace of tampering. In this paper, we propose an efficient passive method for inter-frame video tampering detection, its type and location by estimating the optical flow of wavelet features of adjacent frames and thresholding the variation in the estimated feature. The performance of the algorithm is compared with the z-score thresholding and achieved an efficiency above 95% on all the tested databases. The proposed method works well for videos with dynamic (forensics) as well as static (surveillance) background.

Keywords: discrete wavelet transform, optical flow, optical flow variation, video tampering

Procedia PDF Downloads 360

Authors: Yusuf Seedat

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The necessity to understand global financial markets has increased following the unfortunate spread of the recent financial crisis around the world. Financial markets are considered to be complex systems consisting of highly volatile move-ments whose indexes fluctuate without any clear pattern. Analytic methods of stock prices have been proposed in which financial markets are modeled using common network analysis tools and methods. It has been found that two key components of social network analysis are relevant to modeling financial markets, allowing us to forecast accurate predictions of stock prices within the financial market. Financial markets have a number of interacting components, leading to complex behavioral patterns. This paper describes a social network approach to analyzing financial markets as a viable approach to studying the way complex stock markets function. We also look at how social network analysis techniques and metrics are used to gauge an understanding of the evolution of financial markets as well as how community detection can be used to qualify and quantify in-fluence within a network.

Keywords: network analysis, social networks, financial markets, stocks, nodes, edges, complex networks

Procedia PDF Downloads 192

Authors: Md Shohidul Islam, Jongmyon Kim, Ui-pil Chong

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Medical image is an integral part of e-health care and e-diagnosis system. Medical image watermarking is widely used to protect patients’ information from malicious alteration and manipulation. The watermarked medical images are transmitted over the internet among patients, primary and referred physicians. The images are highly prone to corruption in the wireless transmission medium due to various noises, deflection, and refractions. Distortion in the received images leads to faulty watermark detection and inappropriate disease diagnosis. To address the issue, this paper utilizes error correction code (ECC) with (8, 4) Hamming code in an existing watermarking system. In addition, we implement the high complex ECC on a graphics processing units (GPU) to accelerate and support real-time requirement. Experimental results show that GPU achieves considerable speedup over the sequential CPU implementation, while maintaining 100% ECC efficiency.

Keywords: medical image watermarking, e-health system, error correction, Hamming code, GPU

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Authors: Arul Ayyaswami, Vidhya Ramalingam

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Per- and polyfluoroalkyl substances (PFAS) have emerged as a significant environmental concern due to their ubiquity and persistence in the environment. Their chemical characteristics and adverse effects on human health demands more effective and sustainable solutions in remediation of the PFAS. The work presented here encompasses an overview of treatment technologies with two case studies that utilize effective approaches in addressing PFAS contaminated media. Currently the options for treatment of PFAS compounds include Activated carbon adsorption, Ion Exchange, Membrane Filtration, Advanced oxidation processes, Electrochemical treatment, and Precipitation and Coagulation. In the first case study, a pilot study application of colloidal activated carbon (CAC) was completed to address PFAS from aqueous film-forming foam (AFFF) used to extinguish a large fire. The pilot study was used to demonstrate the effectiveness of a CAC in situ permeable reactive barrier (PRB) in effectively stopping the migration of PFOS and PFOA, moving from the source area at high concentrations. Before the CAC PRB installation, an injection test using - fluorescein dye was conducted to determine the primary fracture-induced groundwater flow pathways. A straddle packer injection delivery system was used to isolate discrete intervals and gain resolution over the 70 feet saturated zone targeted for treatment. Flow rates were adjusted, and aquifer responses were recorded for each interval. The results from the injection test were used to design the pilot test injection plan using CAC PRB. Following the CAC PRB application, the combined initial concentration 91,400 ng/L of PFOS and PFOA were reduced to approximately 70 ng/L (99.9% reduction), after only one month following the injection event. The results demonstrate the remedy's effectiveness to quickly and safely contain high concentrations of PFAS in fractured bedrock, reducing the risk to downgradient receptors. The second study involves developing a reductive defluorination treatment process using UV and electron acceptor. This experiment indicates a significant potential in treatment of PFAS contaminated waste media such as landfill leachates. The technology also shows a promising way of tacking these contaminants without the need for secondary waste disposal or any additional pre-treatments.

Keywords: per- and polyfluoroalkyl substances (PFAS), colloidal activated carbon (CAC), destructive PFAS treatment technology, aqueous film-forming foam (AFFF)

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Authors: Sungroul Kim, Yeonjin Kim

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This study was conducted by comparing the concentrations of heavy metals analyzed from the same samples with three X-Ray fluorescence (XRF) spectrometer in three different global research institutions, including PAN (A Branch of Malvern Panalytical, Seoul, South Korea), RTI (Research Triangle Institute, NC, U.S.A), and aerosol laboratory in Harvard University, Boston, U.S.A. To achieve our research objectives, the indoor air filter samples were collected at homes (n=24) of adults or child asthmatics then analyzed in PAN followed by Harvard University and RTI consecutively. Descriptive statistics were conducted for data comparison as well as correlation and simple regression analysis using R version 4.0.3. As a result, detection rates of most heavy metals analyzed in three institutions were about 90%. Of the 25 elements commonly analyzed among those institutions, 16 elements showed an R² (coefficient of determination) of 0.7 or higher (10 components were 0.9 or higher). The findings of this study demonstrated that XRF was a useful device ensuring reproducibility and compatibility for measuring heavy metals in PM2.5 collected from indoor air of asthmatics’ home.

Keywords: heavy metals, indoor air quality, PM2.5, X-ray fluorescence

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Authors: Jude Inwumoh, Adam P. R. Taylor, Kosala Gunawardane

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Modular multilevel converters (MMC) exhibit a highly scalable and modular characteristic with good voltage/power expansion, fault tolerance capability, low output harmonic content, good redundancy, and a flexible front-end configuration. Fault detection, location, and isolation, as well as maintaining fault ride-through (FRT), are major challenges to MMC reliability and power supply sustainability. Different papers have been reviewed to seek the best MMC configuration with fault capability. DC faults are the most common fault, while the probability that AC fault occurs in a modular multilevel converter (MCC) is low; though, AC faults consequence are severe. This paper reviews several MMC topologies and modulation techniques in tackling faults. These fault control strategies are compared based on cost, complexity, controllability, and power loss. A meshed network of half-bridge (HB) MMC topology was optimal in rendering fault ride through than any other MMC topologies but only when combined with DC circuit breakers (CBS), AC CBS, and fault current limiters (FCL).

Keywords: MMC-HVDC, DC faults, fault current limiters, control scheme

Procedia PDF Downloads 140

Authors: Leo Nnamdi Ozurumba-Dwight

Abstract:

Messenger ribooxynucleic acid (mRNA) molecules are compositional, protein-based. These proteins, encoding mRNA molecules (which collectively connote the transcriptome), when analyzed by RNA sequencing (RNAseq), unveils the nature of gene expression in the RNA. The obtained gene expression provides clues of cellular traits and their dynamics in presentations. These can be studied in relation to function and responses. RNAseq is a practical concept in Genomics as it enables detection and quantitative analysis of mRNA molecules. Single cell and spatial transcriptomics both present varying avenues for expositions in genomic characteristics of single cells and pooled cells in disease conditions such as cancer, auto-immune diseases, hematopoietic based diseases, among others, from investigated biological tissue samples. Single cell transcriptomics helps conduct a direct assessment of each building unit of tissues (the cell) during diagnosis and molecular gene expressional studies. A typical technique to achieve this is through the use of a single-cell RNA sequencer (scRNAseq), which helps in conducting high throughput genomic expressional studies. However, this technique generates expressional gene data for several cells which lack presentations on the cells’ positional coordinates within the tissue. As science is developmental, the use of complimentary pre-established tissue reference maps using molecular and bioinformatics techniques has innovatively sprung-forth and is now used to resolve this set back to produce both levels of data in one shot of scRNAseq analysis. This is an emerging conceptual approach in methodology for integrative and progressively dependable transcriptomics analysis. This can support in-situ fashioned analysis for better understanding of tissue functional organization, unveil new biomarkers for early-stage detection of diseases, biomarkers for therapeutic targets in drug development, and exposit nature of cell-to-cell interactions. Also, these are vital genomic signatures and characterizations of clinical applications. Over the past decades, RNAseq has generated a wide array of information that is igniting bespoke breakthroughs and innovations in Biomedicine. On the other side, spatial transcriptomics is tissue level based and utilized to study biological specimens having heterogeneous features. It exposits the gross identity of investigated mammalian tissues, which can then be used to study cell differentiation, track cell line trajectory patterns and behavior, and regulatory homeostasis in disease states. Also, it requires referenced positional analysis to make up of genomic signatures that will be sassed from the single cells in the tissue sample. Given these two presented approaches to RNA transcriptomics study in varying quantities of cell lines, with avenues for appropriate resolutions, both approaches have made the study of gene expression from mRNA molecules interesting, progressive, developmental, and helping to tackle health challenges head-on.

Keywords: transcriptomics, RNA sequencing, single cell, spatial, gene expression.

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Authors: Kaleb Shiferaw, Berhanu Geberemedhin

Abstract:

Ethiopian farmers have a long tradition of beekeeping and the country has huge potential for honey production. However traditional mode of production still dominates the sub sector which negatively affect the total production and productivity. A number of studies have been conducted to better understand the working honey production, however, none of them systematically investigate the extent of technical efficiency of the sub-sector. This paper uses Stochastic Frontier production model to quantifying the extent of technical efficiency and identify exogenous determinant of inefficiency. The result showed that consistent with other studies traditional practice dominate small scale honey production in Ethiopia. The finding also revealed that use of purchased inputs such as bee forage and other supplement is very limited among honey producers indicating that natural bee forage is the primary source of bee forage. The immediate consequence of all these is low production and productivity. The number of hives the household owns, whether the household used improved apiculture technologies, availability of natural forest which is the primary sources of nectar for bees and amount of land owned by the households were found to have a significant influence on the amount of honey produced by beekeeper. Our result further showed that the mean technical efficiency of honey producers is 0.79 implying that, on average honey producer produce 80 percent of the maximum output. The implication is that 20 percent of the potential output is lost due to technical inefficiency. Number of hives owned by a honey produces, distance to district town-a proxy to market access, household wealth, and whether the household head has a leadership role in the PA affect the technical efficiency of honey producers. The finding suggest that policies that aim to expand the use of improved hives is expected to increase the honey production at household level. The result also suggest that investment on rural infrastructure would be instrumental in improving technical efficiency of honey producer.

Keywords: small-scale honey producer, Ethiopia, technical efficiency in apiculture, stochastic frontier analysis

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Authors: Albert P. Ekanem, Austin I. Obiekezie, Elizabeth X. Ntia

Abstract:

Introduction: Some African plants and bile from animals have shown efficacies in the treatment and control of diseases in farmed fish. The background of the study is based on the fact the African rain forest is blessed with abundance of medicinal plants that should be investigated for their use in the treatment of diseases. The significance of the study is informed by the fact that chemical-based substances accumulates in the tissues of food fish, thereby reducing the food values of such products and moreover, the continuous use of chemotherapeutants in the aquatic environments tends to degrades the affected environment. Methodology: Plants and animal products were extracted, purified and applied under in vitro and in vivo conditions to the affected organisms. Effective plants and biles were analyzed for active biological substances responsible for the activities by both qualitative and HPLC methods. Results: Extracts of Carica papaya and Mucuna pruriens were effective in the treatment of Ichthyophthiriasis in goldfish (Carassius auratus auratus) with high host tolerance. Similarly, ectoparasitic monogeneans were effectively dislodged from the gills and skin of goldfish by the application of extracts of Piper guineense at therapeutic concentrations. Artemesia annua with known antimalarial activities in human was also effective against fish monogenean parasites of Clarias gariepinus in a concentration related manner without detriments to the host. Effective antibacterial activities against Aeromonas and Pseudomonas diseases of the African catfish (Heterobranchus longifilis) were demonstrated in some plants such as Phylanthus amarus, Allium sativum, A. annua, and Citrus lemon. Bile from some animals (fish, goat, chicken, cow, and pig) showed great antibacterial activities against some gastrointestinal bacterial pathogens of fish. Conclusions: African plants and some animal bile have shown potential promise in the treatment of diseases in fish and other aquatic animals. The use of chemical-based substances for control of diseases in the aquatic environments should be restricted.

Keywords: control, diseases, fish, natural products, treatment

Procedia PDF Downloads 526