Search results for: distributed algorithms

Authors: Abdullah Malek, Muzn Al Khaldi, Lian Odeh, Atheer Tariq, Mohammad Al Fardan, Hiba Barqawi

Abstract:

H. pylori bacterium is a known underlying agent for gastritis, peptic ulcer disease, and gastric cancer and is believed to infect half of the world’s population. Even with the ubiquity of H. pylori bacterium, there is lack of knowledge regarding its modes of transmission, associated diseases, carcinogenic effect and means of prevention; especially in the UAE. A cross sectional study of 500 participants, of which 58% (n= 289) of the respondents were female, and 42% (n=210) were male, was conducted in Sharjah to assess the knowledge, and explore the attitudes and practices among UAE residents towards Helicobacter Pylori and its associated PUD as well as its carcinogenic nature. A structured self-administered questionnaire was distributed to the target population to establish their demographic background and selected aspects of their lifestyle. General knowledge about H. Pylori was poor, only 24.6% stated they have heard of H. pylori. Attitudes towards prevention and practices were relatively poor as well. Subjects who suffered from severe symptoms (ALARM symptoms) had significantly lower habit scores than those with mild and moderate symptoms (p=0.0078**). To the authours’ knowledge, no previous studies were conducted in the United Arab Emirates regarding the epidemiology of the infection to detect the extent of H. Pylori’s impact on the public health. The results of this study can be used to draw conclusions about the average knowledge of the UAE population regarding H. pylori. It can also be a starting point to devise new education programs and campaigns that raise awareness of this health issue which could be easily avoided with early diagnosis and antibiotic treatment.

Keywords: chronic gastritis, community health, gastric cancer, Helicobacter pylori, peptic ulcers

Procedia PDF Downloads 234

Authors: Karima Benfadel, Lamia Talbi, Sabiha Anas Boussaa, Afaf Brik, Assia Boukezzata, Yahia Ouadah, Samira Kaci

Abstract:

In order to prevent global warming, which is mainly caused by the increase in carbon dioxide levels in the atmosphere, it is interesting to produce renewable energy in the form of chemical energy by converting carbon dioxide into alternative fuels and other energy-dense products. Photoelectrochemical reduction of carbon dioxide to value-added products and fuels is a promising and current method. The objective of our study is to develop Cu₂S-based photoélectrodes, in which Cu₂S is used as a CO₂ photoelectrocatalyst deposited on nanostructured silicon substrates. Cu₂S thin layers were deposited using the chemical bath deposition (CBD) technique. Silicon nanowires and nanopyramids were obtained by alkaline etching. SEM and UV-visible spectroscopy was used to analyse the morphology and optical characteristics. By using a potentiostat station, we characterized the photoelectrochemical properties. We performed cyclic voltammetry in the presence and without CO₂ purging as well as linear voltammetry (LSV) in the dark and under white light irradiation. We perform chronoamperometry to study the stability of our photocathodes. The quality of the nanowires and nanopyramids was visible in the SEM images, and after Cu₂S deposition, we could see how the deposition was distributed over the textured surfaces. The inclusion of the Cu₂S layer applied on textured substrates significantly reduces the reflectance (R%). The catalytic performance towards CO₂ conversion of Cu₂S/Si-based photocathodes revealed that the texturing of the silicon surface with nanowires and pyramids has a better photoelectrochemical behavior than those without surface modifications.

Keywords: CO₂ conversion, Cu₂S photocathode, silicone nanostructured, electrochemistry

Procedia PDF Downloads 56

Authors: Johannes Dregger, Michael Schmidt, Christian Prasse, Michael ten Hompel

Abstract:

Most of the today’s companies face increasing need to operate efficiently. Driven by global trends like shorter product cycles, mass customization and the rising speed of delivery, manufacturing value chains are becoming more and more distributed. Manufacturing processes are becoming highly integrated, e.g. 3D printing. All these changes are affecting companies´ organization. They are leading towards individual, small scale, and ad-hoc logistics processes and structures, and finally, towards a significant increase in the importance of logistics itself since traditional value chains transform into agile value networks. In the past logistics has been following manufacturing but in the future industry, this role allocation might change. With this increase in the logistics practice of companies and businesses, the relevance of logistics research as the methodological foundation of logistics networks and processes is gaining importance. Logistics research is evolving into a central and highly interdisciplinary science for the future industry. Using the example of Germany, this paper discusses ways to establish logistics as a central scientific discipline of the future industry. About three million people work in the logistics sector in Germany. Only automotive and retail industry have more employees. Even though there is a bunch of logistics degree programs at more than 100 institutions of higher education, a common understanding of logistics as a research discipline is missing. In this paper an innovative approach will be presented, including; identified perspectives on logistics, such as process orientation, IT orientation or employees orientation, relevant scientific disciplines for logistics science, a concept for interdisciplinary research approaches to unify the perspectives of the different scientific disciplines on logistics and the methodological base of logistics science.

Keywords: logistics, logistics science, logistics management, future challenges

Procedia PDF Downloads 291

Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

Abstract:

Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

Procedia PDF Downloads 123

Authors: Alaa Allakany, Koji Okamura

Abstract:

Multicast technology is an efficient and scalable technology for data distribution in order to optimize network resources. However, in the IP network, the responsibility for management of multicast groups is distributed among network routers, which causes some limitations such as delays in processing group events, high bandwidth consumption and redundant tree calculation. Software Defined Networking (SDN) represented by OpenFlow presented as a solution for many problems, in SDN the control plane and data plane are separated by shifting the control and management to a remote centralized controller, and the routers are used as a forwarder only. In this paper we will proposed fast switching mechanism for solving the problem of link failure in multicast tree based on Tabu Search heuristic algorithm and modifying the functions of OpenFlow switch to fasts switch to the pack up sub tree rather than sending to the controller. In this work we will implement multicasting OpenFlow controller, this centralized controller is a core part in our multicasting approach, which is responsible for 1- constructing the multicast tree, 2- handling the multicast group events and multicast state maintenance. And finally modifying OpenFlow switch functions for fasts switch to pack up paths. Forwarders, forward the multicast packet based on multicast routing entries which were generated by the centralized controller. Tabu search will be used as heuristic algorithm for construction near optimum multicast tree and maintain multicast tree to still near optimum in case of join or leave any members from multicast group (group events).

Keywords: multicast tree, software define networks, tabu search, OpenFlow

Procedia PDF Downloads 239

Authors: Zhu Weimin, Allegorico Carmine, Ruggiero Gionata

Abstract:

During decades of Baker Hughes (BH) iCenter experience, it is demonstrated that in addition to conventional insights on equipment steady operation conditions, insights on transient conditions can add significant and exclusive value for anomaly detection, downtime saving, and predictive maintenance. Our work shows examples from the BH iCenter experience to introduce the advantages and features of using transient condition analytics: (i) Operation under critical engine conditions: e.g., high level or high change rate of temperature, pressure, flow, vibration, etc., that would not be reachable in normal operation, (ii) Management of dedicated sub-systems or components, many of which are often bottlenecks for reliability and maintenance, (iii) Indirect detection of anomalies in the absence of instrumentation, (iv) Repetitive sequences: if data is properly processed, the engineering features of transients provide not only anomaly detection but also problem characterization and prognostic indicators for predictive maintenance, (v) Engine variables accounting for fatigue analysis. iCenter has been developing and deploying a series of analytics based on transient conditions. They are contributing to exclusive value adding in the following areas: (i) Reliability improvement, (ii) Startup reliability improvement, (iii) Predictive maintenance, (iv) Repair/overhaul cost down. Illustrative examples for each of the above areas are presented in our study, focusing on challenges and adopted techniques ranging from purely statistical approaches to the implementation of machine learning algorithms. The obtained results demonstrate how the value is obtained using transient condition analytics in the BH iCenter experience.

Keywords: analytics, diagnostics, monitoring, turbomachinery

Procedia PDF Downloads 52

Authors: Robin Molinier

Abstract:

Industrial symbiosis (I.S) deals with both substitution synergies (exchange of waste materials, fatal energy and utilities as resources for production) and infrastructure/service sharing synergies. The latter is based on the intensification of use of an asset and thus requires to balance capital costs increments with snowball effects (network externalities) for its implementation. Initial investors must specify ex-ante arrangements (cost sharing and pricing schedule) to commit toward investments in capacities and transactions. Our model investigate the decision of 2 actors trying to choose cooperatively a level of infrastructure capacity oversizing to set a plug-and-play offer to a potential entrant whose capacity requirement is randomly distributed while satisficing their own requirements. Capacity cost exhibits sub-additive property so that there is room for profitable overcapacity setting in the first period. The entrant’s willingness-to-pay for the access to the infrastructure is dependent upon its standalone cost and the capacity gap that it must complete in case the available capacity is insufficient ex-post (the complement cost). Since initial capacity choices are driven by ex-ante (expected) yield extractible from the entrant we derive the expected complement cost function which helps us defining the investors’ objective function. We first show that this curve is decreasing and convex in the capacity increments and that it is shaped by the distribution function of the potential entrant’s requirements. We then derive the general form of solutions and solve the model for uniform and triangular distributions. Depending on requirements volumes and cost assumptions different equilibria occurs. We finally analyze the effect of a per-unit subsidy a public actor would apply to foster such sharing synergies.

Keywords: capacity, cooperation, industrial symbiosis, pricing

Procedia PDF Downloads 186

Authors: Anastasios Papathanasiou, George Liontos, Athanasios Katsouras, Vasiliki Liagkou, Euripides Glavas

Abstract:

Email remains a crucial communication tool due to its efficiency, accessibility and cost-effectiveness, enabling rapid information exchange across global networks. However, the global adoption of email has also made it a prime target for cyber threats, including phishing, malware and Business Email Compromise (BEC) attacks, which exploit its integral role in personal and professional realms in order to perform fraud and data breaches. To combat these threats, this research advocates for a multi-layered defense strategy incorporating advanced technological tools such as anti-spam and anti-malware software, machine learning algorithms and authentication protocols. Moreover, we developed an artificial intelligence model specifically designed to analyze email headers and assess their security status. This AI-driven model examines various components of email headers, such as "From" addresses, ‘Received’ paths and the integrity of SPF, DKIM and DMARC records. Upon analysis, it generates comprehensive reports that indicate whether an email is likely to be malicious or benign. This capability empowers users to identify potentially dangerous emails promptly, enhancing their ability to avoid phishing attacks, malware infections and other cyber threats.

Keywords: email security, artificial intelligence, header analysis, threat detection, phishing, DMARC, DKIM, SPF, ai model

Procedia PDF Downloads 20

Authors: Hasan Basaran, Emre Unal

Abstract:

Formation flying of satellites is a mission that involves a relative position keeping of different satellites in the constellation. In this study, different control algorithms are compared with one another in terms of ΔV, velocity increment, and tracking error. Various control methods, covering continuous and impulsive approaches are implemented and tested for satellites flying in low Earth orbit. Feedback linearization, sliding mode control, and model predictive control are designed and compared with an impulsive feedback law, which is based on mean orbital elements. Feedback linearization and sliding mode control approaches have identical mathematical models that include second order Earth oblateness effects. The model predictive control, on the other hand, does not include any perturbations and assumes circular chief orbit. The comparison is done with 4 different initial errors and achieved with velocity increment, root mean square error, maximum steady state error, and settling time. It was observed that impulsive law consumed the least ΔV, while produced the highest maximum error in the steady state. The continuous control laws, however, consumed higher velocity increments and produced lower amounts of tracking errors. Finally, the inversely proportional relationship between tracking error and velocity increment was established.

Keywords: chief-deputy satellites, feedback linearization, follower-leader satellites, formation flight, fuel consumption, model predictive control, rendezvous, sliding mode

Procedia PDF Downloads 79

Authors: Eunice Oluwayemisi Adeparusi, Mary Adedolapo Ijadeyila

Abstract:

The study was carried out at the Federal University of Technology, Akure, Nigeria, West Africa. Seven set of diets were prepared comprising of two sets. The first set consisted of a combination of three diets from a combination of two different citrus peels from Orange (Citrus sinesis), Tangerine (Citrus tangerina / Citrus reticulata) and Tangelo (Citrus tangelo a hybrid of Citrus reticulata and Citrus maxima) at 50:50 while the other three consisted f50:50. Diet with 100% vitamin premix served as the control. Air-dried citrus peels were added in a 40% crude protein diet for the juveniles (4.49±0.05g) Clarias gariepinus. The experiment was carried out for a period of 56 days in triplicate trials. Fish were randomly distributed into twenty-one tanks at ten fish per tanks. The feed was extruded and fed to satiation twice daily. The result shows that fish fed Tangelo and Tangerine (TGL-TGR) had the best growth response in terms of final weight, specific growth rate, feed conversion ratio and feed utilization efficiency when compared with other diets. The FCR of fish in the diet ranges from 0.93-1.62. Fish fed the mixture of Orange peel and Vitamin-mineral premix (ORG-VIT) and those on Tangelo and Vitamin-mineral premix (TGL-VIT) had higher survival rate. There were significant differences (P<0.05) in the mean final weight, weight gain and specific growth rate. The result shows that citrus peels enhance the growth performance and feed utilization of the juvenile of African mud catfish, thereby reducing the cost of fish production.

Keywords: African mud catfish, growth, citrus peels, vitamin-mineral premix, nutrient utilization, additives

Procedia PDF Downloads 55

Authors: Hossein Jafari, Xiangfang Li, Lijun Qian, Alexander Aved, Timothy Kroecker

Abstract:

Traditionally in sensor networks and recently in the Internet of Things, numerous heterogeneous sensors are deployed in distributed manner to monitor a phenomenon that often can be model by an underlying stochastic process. The big time-series data collected by the sensors must be analyzed to detect change in the stochastic process as quickly as possible with tolerable false alarm rate. However, sensors may have different accuracy and sensitivity range, and they decay along time. As a result, the big time-series data collected by the sensors will contain uncertainties and sometimes they are conflicting. In this study, we present a framework to take advantage of Evidence Theory (a.k.a. Dempster-Shafer and Dezert-Smarandache Theories) capabilities of representing and managing uncertainty and conflict to fast change detection and effectively deal with complementary hypotheses. Specifically, Kullback-Leibler divergence is used as the similarity metric to calculate the distances between the estimated current distribution with the pre- and post-change distributions. Then mass functions are calculated and related combination rules are applied to combine the mass values among all sensors. Furthermore, we applied the method to estimate the minimum number of sensors needed to combine, so computational efficiency could be improved. Cumulative sum test is then applied on the ratio of pignistic probability to detect and declare the change for decision making purpose. Simulation results using both synthetic data and real data from experimental setup demonstrate the effectiveness of the presented schemes.

Keywords: CUSUM, evidence theory, kl divergence, quickest change detection, time series data

Procedia PDF Downloads 307

Authors: Sophia Anastasiou, Cosmas Nathanailides, Fotini Kakali, Kostas Karipoglou

Abstract:

Wild fish are considered as testier and in fish restaurants are offered at twice the price of farmed fish. Several chemical and structural differences can affect the consumer's attitudes for farmed fish. The structure and chemical composition of fish muscle is also important for the performance of farmed fish during handling, storage and processing. In the present work we present the chemical and sensory parameters which are used as indicators of fish flesh quality and we investigated the perceptions of consumers for farmed sea bass and the organoleptic differences between samples of wild and farmed sea bass. A questionnaire was distributed to a group of various ages that were regular consumers of sea bass. The questionnaire included a survey on the perceptions on taste and appearance differences between wild and farmed sea bass. A significant percentage (>40%) of the participants stated their perception of superior taste of wild sea bass versus the farmed fish. The participants took part in an organoleptic assessment of wild and farmed sea bass prepared and cooked by a local fish restaurant. Portions were evaluated for intensity of sensorial attributes from 1 (low intensity) to 5 (high intensity). The results indicate that contrary to the assessor's perception, farmed sea bass scored better in al organoleptic parameters assessed with marked superiority in texture and taste over the wild sea bass. This research has been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: ARCHIMEDES III. Investing in knowledge society through the European Social Fund.

Keywords: fish marketing, farmed fish, seafood quality, wild fish

Procedia PDF Downloads 381

Authors: Badr Alharbi

Abstract:

There is significant debate on the evolution of retail marketing as an economy matures. In penetrating new markets, global brands are efficient in establishing a presence and replacing less effective competitors by engaging in superior advertising, pricing and sometimes quality. However, national brands adapt over time and may either partner with global brands in distribution and services or directly compete more efficiently in the new, open market. This explorative study investigates brand preferences in Saudi Arabia. As a conservative society, which is nevertheless highly commercialised, Saudi Arabia markets could be fragmenting with consumer preferences and rejections based on country of origin, globalisation, or perhaps regionalisation. To investigate this, an online survey was distributed to Saudis in Jeddah to gather data on their preferences for travel, technology, clothes and accessories, eating out, vehicles, and influential brands. The results from 710 valid responses were that there are distinct regional and national brand preferences among the young Saudi men who contributed to the survey. Apart from a preference for Saudi food providers, airline preferences were the United Emirates, holiday preferences were Europe, study and work preferences were the United States, hotel preferences were United States-based, car preferences were Japanese, and clothing preferences were United States-based. The results were broadly in line with international research findings; however, the study participants varied from Arab research findings by describing themselves as innovative in their purchase selections, rarely loyal (exception of Apple products) and continually seeking new brand experiences. This survey contributes to an understanding of evolving Saudi consumer preferences.

Keywords: Saudi marketing, globalisation, country of origin, brand preferences

Procedia PDF Downloads 250

Authors: Mercy Chepkirui, Paul Orina, Priscilla Boera, Judith Achoki

Abstract:

Oreochromis jipe is a tropical freshwater bentho-pelagic fish belonging to the Cichlid family that is endemic to the Pangani River basin and Lake Jipe in Kenya and northern Tanzania, while Oreochromis niloticus inhabits the Lake Victoria basin with reported cases in Lake jipe too. Unlike O. jipe, Oreochromis niloticus is spreading across the globe due to its cultural potential. This, however, could cause genetic purity concerns in the event of cross-breeding among the tilapiines, which is already taking place in the wild. The study envisaged establishing the possibility of hybridization among the two species under aquaculture conditions and phenotypically informing the difference between pure and cross lines. Two hundred sixteen mature brooders weighing 100-120g were selected randomly, 108 of Oreochromis Jipe and 108 of Oreochromis niloticus; for each trial, 72 males and 144 females were distributed into 3 crosses, each grouped in triplicates (Oreochromis niloticus (♀) X Oreochromis niloticus(♂);Oreochromis niloticus (♂) X Oreochromis jipe ( ♀); Oreochromis jipe (♂) X Oreochromis niloticus (♀); Oreochromis jipe (♂) X Oreochromis jipe (♀). All trials had the F1 generation, which is currently undergoing growth trials and assessing its viability for the 2nd generation. The results indicated that Oreochromis niloticus has better growth, followed by crosses (Oreochromis niloticus X Oreochromis jipe) and, finally, pure line Oreochromis jipe. Further, pure Oreochromis jipe F1 demonstrated potential for aquaculture adoption despite its recent introduction into aquaculture; thus, this will help towards the conservation of indigenous fish species of Lake Jipe fishery, which is currently under the Internationa Union for Conservation of Nature Red List of endangered fish species. However, there is a need to inform the purity of existing Oreochromis jipe wild stocks to inform genetic material conservation.

Keywords: biodiversity, climate change, fisheries, oreochromis jipe, conservation

Procedia PDF Downloads 94

Authors: Ekin Nurbaş

Abstract:

One of the essential challenges in array signal processing, which has drawn enormous research interest over the past several decades, is estimating the direction of arrival (DOA) of plane waves impinging on an array of sensors. In recent years, the Compressive Sensing based DoA estimation methods have been proposed by researchers, and it has been discovered that the Compressive Sensing (CS)-based algorithms achieved significant performances for DoA estimation even in scenarios where there are multiple coherent sources. On the other hand, the Genetic Algorithm, which is a method that provides a solution strategy inspired by natural selection, has been used in sparse representation problems in recent years and provides significant improvements in performance. With all of those in consideration, in this paper, a method that combines the Genetic Algorithm (GA) and the Multi-Criteria Decision Making (MCDM) approaches for Direction of Arrival (DoA) estimation in the Compressive Sensing (CS) framework is proposed. In this method, we generate a multi-objective optimization problem by splitting the norm minimization and reconstruction loss minimization parts of the Compressive Sensing algorithm. With the help of the Genetic Algorithm, multiple non-dominated solutions are achieved for the defined multi-objective optimization problem. Among the pareto-frontier solutions, the final solution is obtained with the multiple MCDM methods. Moreover, the performance of the proposed method is compared with the CS-based methods in the literature.

Keywords: genetic algorithm, direction of arrival esitmation, multi criteria decision making, compressive sensing

Procedia PDF Downloads 122

Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

Abstract:

Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

Procedia PDF Downloads 70

Authors: Guillaume Caulier, Lola Brasseur, Patrick Flammang, Pascal Gerbaux, Igor Eeckhaut

Abstract:

Saponins and quinones are two major groups of secondary metabolites widely distributed in the biosphere. More specifically, triterpenoid saponins and anthraquinones are mainly found in a wide variety of plants, bacteria and fungi. In the animal kingdom, these natural organic compounds are rare and only found in small quantities in arthropods, marine sponges and echinoderms. In this last group, triterpenoid saponins are specific to holothuroids (sea cucumbers) while anthraquinones are the chemical signature of crinoids (feather stars). Depending on the species, they present different molecular cocktails. Despite presenting different chemical properties, these molecules share numerous similarities. This study compares the biological distribution, the pharmacological effects and the ecological roles of holothuroid saponins and crinoid anthraquinones. Both of them have been defined as allomones repelling predators and parasites (i.e. chemical defense) and have interesting pharmacological properties (e.g. anti-bacterial, anti-fungal, anti-cancer). Our study investigates the chemical ecology of two symbiotic associations models; between the snapping shrimp Synalpheus stimpsonii associated with crinoids and the Harlequin crab Lissocarcinus orbicularis associated with holothuroids. Using behavioral experiments in olfactometers, chemical extractions and mass spectrometry analyses, we discovered that saponins and anthraquinones present a second ecological role: the attraction of obligatory symbionts towards their hosts. They can, therefore, be defined as kairomones. This highlights a new paradigm in marine chemical ecology: Chemical repellents are attractants to obligatory symbionts because they constitute host specific chemical signatures.

Keywords: anthraquinones, kairomones, marine symbiosis, saponins, attractant

Procedia PDF Downloads 171

Authors: Lee Ping Ang, Salvatore Tomasello, Jun Wen, Marc S. Appelhans

Abstract:

With about 225 species, Zanthoxylum L. is the second most species rich genus in Rutaceae. It is the only genus with a pantropical distribution. Economically, it is used in several Asian countries as traditional medicine and spice. In the past Zanthoxylum was divided into two genera, the temperate Zanthoxylum sensu strictu (s.s.) and the (sub)tropical Fagara, due to the large differences in flower morphology: heterochlamydeous in Fagara and homochlamydeous in Zanthoxylum s.s.. This genus is much under studied and previous phylogenetic studies using Sanger sequencing did not resolve the relationships sufficiently. In this study, we use Hybrid Capture with a specially designed bait set for Zanthoxylum to sequence 347 putatively single-copy genes. The taxon sampling has been largely improved as compared to previous studies and the preliminary results will be based on 371 specimens representing 133 species from all continents and major island groups. Our preliminary results reveal similar tree topology as the previous studies while providing more details to the backbone of the phylogeny. The phylogenetic tree consists of four main clades: A) African/Malagasy clade, B) Z. asiaticum clade - a clade consisting widespread species occurring in (sub)tropical Asia and Africa as well as Madagascar, C) Asian/Pacific clade and D) American clade, which also includes the temperate Asian species. The merging of Fagara and Zanthoxylum is supported by our results and the homochlamydeous flowers of Zanthoxylum s.s. are likely derived from heterochlamydeous flowers. Several of the morphologically defined sections within Zanthoxylum are not monophyletic. The study dissemination will (1) introduce the framework of this project; (2) present preliminary results and (3) the ongoing progress of the study.

Keywords: Zanthoxylum, phylogenomic, hybrid capture, pantropical

Procedia PDF Downloads 41

Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

Abstract:

Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

Procedia PDF Downloads 386

Authors: Abdelkader Elhanaoui, Mhamed Hadji, Rachid Skouri, Said Agounad

Abstract:

The purpose of this paper is to study the phenomenon of acoustic scattering by using a new method. The signal processing (Fast Fourier Transform FFT Inverse Fast Fourier Transform iFFT and BESSEL functions) is widely applied to obtain information with high precision accuracy. Signal processing has a wider implementation in general-purpose pro-cessors. Our interest was focused on the use of FPGAs (Field-Programmable Gate Ar-rays) in order to minimize the computational complexity in single processor architecture, then be accelerated on FPGA and meet real-time and energy efficiency requirements. Gen-eral-purpose processors are not efficient for signal processing. We implemented the acous-tic backscattered signal processing model on the Altera DE-SOC board and compared it to Odroid xu4. By comparison, the computing latency of Odroid xu4 and FPGA is 60 sec-onds and 3 seconds, respectively. The detailed SoC FPGA-based system has shown that acoustic spectra are performed up to 20 times faster than the Odroid xu4 implementation. FPGA-based system of processing algorithms is realized with an absolute error of about 10⁻³. This study underlines the increasing importance of embedded systems in underwater acoustics, especially in non-destructive testing. It is possible to obtain information related to the detection and characterization of submerged cells. So we have achieved good exper-imental results in real-time and energy efficiency.

Keywords: DE1 FPGA, acoustic scattering, form function, signal processing, non-destructive testing

Procedia PDF Downloads 55

Authors: Ashique Mahmud, MD. Ataul Gani Osmani, Shoria Sharmin

Abstract:

In the first quarter of the twenty-first century, the most substantial global concern is environmental contamination, and it has gained the prioritization of both the national and international community. Keeping in mind this crucial fact, this study conducted different statistical and econometrical methods to identify whether the gross national income of the country has a significant impact on electricity production from nonrenewable sources and different air pollutants like carbon dioxide, nitrous oxide, and methane emissions. Besides, the primary objective of this research was to analyze whether the environmental Kuznets curve hypothesis holds for the examined variables. After analyzing different statistical properties of the variables, this study came to the conclusion that the environmental Kuznets curve hypothesis holds for gross national income and carbon dioxide emission in Bangladesh in the short run as well as the long run. This study comes to this conclusion based on the findings of ordinary least square estimations, ARDL bound tests, short-run causality analysis, the Error Correction Model, and other pre-diagnostic and post-diagnostic tests that have been employed in the structural model. Moreover, this study wants to demonstrate that the outline of gross national income and carbon dioxide emissions is in its initial stage of development and will increase up to the optimal peak. The compositional effect will then force the emission to decrease, and the environmental quality will be restored in the long run.

Keywords: environmental Kuznets curve hypothesis, carbon dioxide emission in Bangladesh, gross national income in Bangladesh, autoregressive distributed lag model, granger causality, error correction model

Procedia PDF Downloads 122

Authors: Nada Mohammad Eljeshi

Abstract:

In the United Arab Emirates (UAE), preschoolers can enroll in kindergarten after completing four years old by August 31 of their admission year. This study aims to better understand how Sharjah-based parents’ engagement with preschoolers contributes to their phonological awareness, literacy development, and print knowledge before their kindergarten admission considering cognitive development is addressed in the UAE national child care standards. More specifically, it will discuss the importance of cognitive development activities to preschoolers, the rationale behind defining the admission age to kindergarten and compare and benchmark the policy to other countries. To achieve this study's objectives, an online survey was conducted and distributed. Respondents were asked 13 dichotomous questions related to activities that promote the preschooler’s linguistics literacy and cognitive development. The results suggested parents’ emphasis on phonological awareness, followed by developing their print knowledge. However, the majority of the surveyed parents did not engage in literacy development with their preschoolers. On this basis, it is clear parents’ awareness should occur by introducing various activities such as book reading, that there is a need to introduce and encourage parents to various activities such as reading a printed book and drawings to keep up with their children's cognitive development. The survey results suggested an emphasis on phonological awareness, followed by developing their print knowledge. However, the majority of the surveyed parents did not engage in literacy development with their preschoolers. On this basis, parental awareness of the importance of preschoolers' cognitive development should be developed and engage the parents in understanding their preschooler’s cognitive development before entering kindergarten.

Keywords: preschoolers, cognitive development, parental engagement, Sharjah-based case study

Procedia PDF Downloads 219

Authors: Tae-Heon Moon, Sun-Young Heo, Sang-Ho Lee, Youn-Taik Leem, Kwang-Woo Nam

Abstract:

This study aims to investigate the possibility of crime prevention through CCTV by analyzing the appropriateness of the CCTV location, whether it is installed in the hotspot of crime-prone areas, and exploring the crime prevention effect and transition effect. The real crime and CCTV locations of case city were converted into the spatial data by using GIS. The data was analyzed by hotspot analysis and weighted displacement quotient(WDQ). As study methods, it analyzed existing relevant studies for identifying the trends of CCTV and crime studies based on big data from 1800 to 2014 and understanding the relation between CCTV and crime. Second, it investigated the current situation of nationwide CCTVs and analyzed the guidelines of CCTV installation and operation to draw attention to the problems and indicating points of domestic CCTV use. Third, it investigated the crime occurrence in case areas and the current situation of CCTV installation in the spatial aspects, and analyzed the appropriateness and effectiveness of CCTV installation to suggest a rational installation of CCTV and the strategic direction of crime prevention. The results demonstrate that there was no significant effect in the installation of CCTV on crime prevention. This indicates that CCTV should be installed and managed in a more scientific way reflecting local crime situations. In terms of CCTV, the methods of spatial analysis such as GIS, which can evaluate the installation effect, and the methods of economic analysis like cost-benefit analysis should be developed. In addition, these methods should be distributed to local governments across the nation for the appropriate installation of CCTV and operation. This study intended to find a design guideline of the optimum CCTV installation. In this regard, this study is meaningful in that it will contribute to the creation of a safe city.

Keywords: CCTV, safe city, crime prevention, spatial analysis

Procedia PDF Downloads 414

Authors: Soumya Sajjan

Abstract:

Liver cancer is the most common cancer disease worldwide in men and women, and is one of the few cancers still on the rise. Liver disease is the 4th leading cause of death. According to new NHS (National Health Service) figures, deaths from liver diseases have reached record levels, rising by 25% in less than a decade; heavy drinking, obesity, and hepatitis are believed to be behind the rise. In this study, we focus on Development of Diagnostic Classifier for Ultrasound liver lesion. Ultrasound (US) Sonography is an easy-to-use and widely popular imaging modality because of its ability to visualize many human soft tissues/organs without any harmful effect. This paper will provide an overview of underlying concepts, along with algorithms for processing of liver ultrasound images Naturaly, Ultrasound liver lesion images are having more spackle noise. Developing classifier for ultrasound liver lesion image is a challenging task. We approach fully automatic machine learning system for developing this classifier. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. For classification, Support Vector Machine is used based on the risk bounds of statistical learning theory. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver lesion image is given to the SVM classifier system, the features are calculated, classified, as normal and diseased liver lesion. We hope the result will be helpful to the physician to identify the liver cancer in non-invasive method.

Keywords: segmentation, Support Vector Machine, ultrasound liver lesion, co-occurance Matrix

Procedia PDF Downloads 387

Authors: Gerardo Arias, Richard Wall, Jamie Stevens

Abstract:

Lucilia bufonivora Moniez, is an obligate parasite of toads and frogs widely distributed in Europe. Its sister taxon Lucilia silvarum Meigen behaves mainly as a carrion breeder in Europe, however it has been reported as a facultative parasite of amphibians. These two closely related species are morphologically almost identical, which has led to misidentification, and in fact, it has been suggested that the amphibian myiasis cases by L. silvarum reported in Europe should be attributed to L. bufonivora. Both species remain poorly studied and their taxonomic relationships are still unclear. The identification of the larval specimens involved in amphibian myiasis with molecular tools and phylogenetic analysis of these two closely related species may resolve this problem. In this work seventeen unidentified larval specimens extracted from toad myiasis cases of the UK, the Netherlands and Switzerland were obtained, their COX1 (mtDNA) and EF1-α (Nuclear DNA) gene regions were amplified and then sequenced. The 17 larval samples were identified with both molecular markers as L. bufonivora. Phylogenetic analysis was carried out with 10 other blowfly species, including L. silvarum samples from the UK and USA. Bayesian Inference trees of COX1 and a combined-gene dataset suggested that L. silvarum and L. bufonivora are separate sister species. However, the nuclear gene EF1-α does not appear to resolve their relationships, suggesting that the rates of evolution of the mtDNA are much faster than those of the nuclear DNA. This work provides the molecular evidence for successful identification of L. bufonivora and a molecular analysis of the populations of this obligate parasite from different locations across Europe. The relationships with L. silvarum are discussed.

Keywords: calliphoridae, molecular evolution, myiasis, obligate parasitism

Procedia PDF Downloads 210

Authors: Rakesh Kumar Meena, Tanushree Chatterjee

Abstract:

Okra (Abelmoschus esculentas L. Moench) or lady’s finger is an important vegetable crop belonging to the Malvaceae family. Unfortunately, production and productivity of Okra are majorly affected by Yellow Vein mosaic virus (YVMV). The AO: 189 (resistant parent) X AO: 191(susceptible parent) used for the development of mapping population. The mapping population has 143 individuals (F₂:F₃). Population was characterized by physiological and pathological observations. Screening of 360 DNA markers was performed to survey for parental polymorphism between the contrasting parents’, i.e., AO: 189 and AO: 191. Out of 360; 84 polymorphic markers were used for genotyping of the mapping population. Total markers were distributed into four linkage groups (LG1, LG2, LG3, and LG4). LG3 covered the longest span (106.8cM) with maximum number of markers (27) while LG1 represented the smallest linkage group in terms of length (71.2cM). QTL identification using the composite interval mapping approach detected two prominent QTLs, QTL1 and QTL2 for resistance against YVMV disease. These QTLs were placed between the marker intervals of NBS-LRR72-Path02 and NBS-LRR06- NBS-LRR65 on linkage group 02 and linkage group 04 respectively. The LOD values of QTL1 and QTL2 were 5.7 and 6.8 which accounted for 19% and 27% of the total phenotypic variation, respectively. The findings of this study provide two linked markers which can be used as efficient diagnostic tools to distinguish between YVMV resistant and susceptible Okra cultivars/genotypes. Lines identified as highly resistant against YVMV infection can be used as donor lines for this trait. This will be instrumental in accelerating the trait improvement program in Okra and will substantially reduce the yield losses due to this viral disease.

Keywords: Okra, yellow vein mosaic virus, resistant, linkage map, QTLs

Procedia PDF Downloads 193

Authors: Chengcao Sun, Cuili Yang, Shujun Li, Ruilin Xue, Yongyong Xi, Liang Wang, Dejia Li

Abstract:

Backgrounds: Inflammation is widely distributed in patients with Duchenne muscular dystrophy (DMD), and ultimately leads to progressive deterioration of muscle function with the co-effects of chronic muscle damage, oxidative stress, and reduced oxidative capacity. NF-E2-related factor 2 (Nrf2) plays a critical role in defending against inflammation in different tissues via activation of phase II enzymes, heme oxygenase-1 (HO-1). However, whether Nrf2/HO-1 pathway can attenuate muscle inflammation on DMD remains unknown. The purpose of this study was to determine the anti-inflammatory effects of Sulforaphane (SFN) on DMD. Methods: 4-week-old male mdx mice were treated with SFN by gavage (2 mg/kg body weight per day) for 4 weeks. Gastrocnemius, tibial anterior and triceps brachii muscles were collected for related analysis. Immune cell infiltration in skeletal muscles was analyzed by H&E staining and immuno-histochemistry. Moreover, the expressions of inflammatory cytokines,pro-inflammatory cytokines and Nrf2/HO-1 pathway were detected by western blot, qRT-PCR, immunohistochemistry and immunofluorescence assays. Results: Our results demonstrated that SFN treatment increased the expression of muscle phase II enzymes HO-1 in Nrf2 dependent manner. Inflammation in mdx skeletal muscles was reduced by SFN treatment as indicated by decreased immune cell infiltration and lower expressions of the inflammatory cytokines CD45, pro-inflammatory cytokines tumour necrosis factor-α and interleukin-6 in the skeletal muscles of mdx mice. Conclusions: Collectively, these results show that SFN can ameliorate muscle inflammation in mdx mice by Nrf2/HO-1 pathway, which indicates Nrf2/HO-1 pathway may represent a new therapeutic target for DMD.

Keywords: sulforaphane, Nrf2, HO-1, inflammation

Procedia PDF Downloads 310

Authors: Hichem Sassi

Abstract:

Pneumonia, being a respiratory infection, has garnered global attention due to its rapid transmission and relatively high mortality rates. Timely detection and treatment play a crucial role in significantly reducing mortality associated with pneumonia. Presently, X-ray diagnosis stands out as a reasonably effective method. However, the manual scrutiny of a patient's X-ray chest radiograph by a proficient practitioner usually requires 5 to 15 minutes. In situations where cases are concentrated, this places immense pressure on clinicians for timely diagnosis. Relying solely on the visual acumen of imaging doctors proves to be inefficient, particularly given the low speed of manual analysis. Therefore, the integration of artificial intelligence into the clinical image diagnosis of pneumonia becomes imperative. Additionally, AI recognition is notably rapid, with convolutional neural networks (CNNs) demonstrating superior performance compared to human counterparts in image identification tasks. To conduct our study, we utilized a dataset comprising chest X-ray images obtained from Kaggle, encompassing a total of 5216 training images and 624 test images, categorized into two classes: normal and pneumonia. Employing five mainstream network algorithms, we undertook a comprehensive analysis to classify these diseases within the dataset, subsequently comparing the results. The integration of artificial intelligence, particularly through improved network architectures, stands as a transformative step towards more efficient and accurate clinical diagnoses across various medical domains.

Keywords: deep learning, computer vision, pneumonia, models, comparative study

Procedia PDF Downloads 35

Authors: Ojetunde Ojewola, Mark Morgan, Sonja Wilhelm-Stanis

Abstract:

State parks and historic sites are vulnerable to extreme weather events which can affect visitor experiences, management priorities, and legislative requests for disaster relief funds. Recently, global attention has been focused on the perceptions of global warming and how the presence of extreme weather events might impact protected areas, both now and in the future. The effects of climate change are not equally distributed across the United States, leading to varied perceptions based on personal experience with extreme weather events. This study describes employee perceptions of vulnerability and resilience in Missouri State Parks & Historic Sites due to extreme weather events that occur across the state but grouped according to physiographic provinces. Using a four-point rating scale, perceptions of vulnerability and resilience were divided into high and low sub-groups, thus allowing researchers to construct a two by two typology of employee responses. Subsequently, this data was used to develop a three-point continuum of environmental concern (higher scores meant more concern). Employee scores were then compared against a statewide assessment which combined social, economic, infrastructural and environmental indicators of vulnerability and resilience. State park employees thought the system was less vulnerable and more resilient to climate change than data found in statewide assessment This result was also consistent in three out of five physiographic regions across Missouri. Implications suggest that Missouri state park should develop a climate change adaptation strategy for emergency preparedness.

Keywords: extreme weather events, resilience, state parks, vulnerability

Procedia PDF Downloads 104

Authors: Mirco Tarozzi, Giacomo Pignagnoli, Andrea Benedetti

Abstract:

Structural systems are often subjected to degradation processes due to different kind of phenomena like unexpected loadings, ageing of the materials and fatigue cycles. This is true especially for bridges, in which their safety evaluation is crucial for the purpose of a design of planning maintenance. This paper discusses the experimental evaluation of the stiffness reduction from frequency changes due to uniform damage scenario. For this purpose, a 1:4 scaled bridge has been built in the laboratory of the University of Bologna. It is made of concrete and its cross section is composed by a slab linked to four beams. This concrete deck is 6 m long and 3 m wide, and its natural frequencies have been identified dynamically by exciting it with an impact hammer, a dropping weight, or by walking on it randomly. After that, a set of loading cycles has been applied to this bridge in order to produce a uniformly distributed crack pattern. During the loading phase, either cracking moment and yielding moment has been reached. In order to define the relationship between frequency variation and loss in stiffness, the identification of the natural frequencies of the bridge has been performed, before and after the occurrence of the damage, corresponding to each load step. The behavior of breathing cracks and its effect on the natural frequencies has been taken into account in the analytical calculations. By using a sort of exponential function given from the study of lot of experimental tests in the literature, it has been possible to predict the stiffness reduction through the frequency variation measurements. During the load test also crack opening and middle span vertical displacement has been monitored.

Keywords: concrete bridge, damage detection, dynamic test, frequency shifts, operational modal analysis

Procedia PDF Downloads 162