Search results for: encrypted traffic classification

Authors: Abdulmalik Altuwayjiri, Milad Pirhadi, Mohammed Kalafy, Badr Alharbi, Constantinos Sioutas

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In this study, we investigated the chemical and toxicological characteristics of PM10 in the metropolitan area of Riyadh, Saudi Arabia. PM10 samples were collected on quartz and teflon filters during cold (December 2019–April 2020) and warm (May 2020–August 2020) seasons, including dust and non-dust events. The PM10 constituents were chemically analyzed for their metal, inorganic ions, and elemental and organic carbon (EC/OC) contents. Additionally, the PM10 oxidative potential was measured by means of the dithiothreitol (DTT) assay. Our findings revealed that the oxidative potential of the collected ambient PM10 samples was significantly higher than those measured in many urban areas worldwide. The oxidative potential of the collected ambient PM¹⁰⁻ samples was also higher during dust episodes compared to non-dust events, mainly due to higher concentrations of metals during these events. We performed Pearson correlation analysis, principal component analysis (PCA), and multi-linear regression (MLR) to identify the most significant sources contributing to the toxicity of PM¹⁰⁻ The results of the MLR analyses indicated that the major pollution sources contributing to the oxidative potential of ambient PM10 were soil and resuspended dust emissions (identified by Al, K, Fe, and Li) (31%), followed by secondary organic aerosol (SOA) formation (traced by SO₄-² and NH+₄) (20%), and industrial activities (identified by Se and La) (19%), and traffic emissions (characterized by EC, Zn, and Cu) (17%). Results from this study underscore the impact of transported dust emissions on the oxidative potential of ambient PM10 in Riyadh and can be helpful in adopting appropriate public health policies regarding detrimental outcomes of exposure to PM₁₀-

Keywords: ambient PM10, oxidative potential, source apportionment, Riyadh, dust episodes

Procedia PDF Downloads 177

Authors: Rumesh Liyanage, S. B. Nawaratne, K. K. D. S. Ranaweera, Indira Wickramasinghe, K. G. S. C. Katukurunda

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Study it was attempted to identify an effect of food habits and publics’ attitudes on micro-sleepiness and preventive measures to develop a food product to combat. Statistical data pertaining to road accidents were collected from, Sri Lanka Police Traffic Division and a pre-tested questionnaire was used to collect data from 250 respondents. They were selected representing drivers (especially highway drivers), private and public sector workers (shift based) and cramming students (university and school). Questionnaires were directed to fill independently and personally and collected data were analyzed statistically. Results revealed that 76.84, 96.39 and 80.93% out of total respondents consumed rice for all three meals which lead to ingesting higher glycemic meals. Taking two hyper glycemic meals before 14.00h was identified as a cause of micro-sleepiness within these respondents. Peak level of road accidents were observed at 14.00 - 20.00h (38.2%)and intensity of micro-sleepiness falls at the same time period (37.36%) while 14.00 to 16.00h was the peak time, 16.00 to 18.00h was the least; again 18.00 to 20.00h it reappears slightly. Even though respondents of the survey expressed that peak hours of micro- sleepiness is 14.00-16.00h, according to police reports, peak hours fall in between 18.00-20.00h. Out of the interviewees, 69.27% strongly wanted to avoid micro-sleepiness and intend to spend LKR 10-20 on a commercial product to combat micro sleepiness. As age-old practices to suppress micro-sleepiness are time taken, modern day respondents (51.64%) like to have a quick solution through a drink. Therefore, food habits of morning and noon may cause for micro- sleepiness while dinner may cause for both, natural and micro-sleepiness due to the heavy glycemic load of food. According to the study micro-sleepiness, can be categorized into three zones such as low-risk zone (08.00-10.00h and 18.00-20.00h), manageable zone (10.00-12.00h), and high- risk zone (14.00-16.00h).

Keywords: food habits, glycemic load, micro-sleepiness, road accidents

Procedia PDF Downloads 547

Authors: Hachaichi Mohamed Nour El-Islem, Baouni Tahar

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Environmental sustainability rather than a trans-disciplinary and a scientific issue, is the main problem that characterizes all modern cities nowadays. In developing countries, this concern is expressed in a plethora of critical urban ills: traffic congestion, air pollution, noise, urban decay, increase in energy consumption and CO₂ emissions which blemish cities’ landscape and might threaten citizens’ health and welfare. As in the same manner as developing world cities, the rapid growth of Algiers’ human population and increasing in city scale phenomena lead eventually to increase in daily trips, energy consumption and CO₂ emissions. In addition, the lack of proper and sustainable planning of the city’s infrastructure is one of the most relevant issues from which Algiers suffers. The aim of this contribution is to estimate the carbon deficit of the City of Algiers, Algeria, using the Ecological Footprint Model (carbon footprint). In order to achieve this goal, the amount of CO₂ from fuel combustion has been calculated and aggregated into five sectors (agriculture, industry, residential, tertiary and transportation); as well, Algiers’ biocapacity (CO₂ uptake land) has been calculated to determine the ecological overshoot. This study shows that Algiers’ transport system is not sustainable and is generating more than 50% of Algiers total carbon footprint which cannot be sequestered by the local forest land. The aim of this research is to show that the Carbon Footprint Assessment might be a relevant indicator to design sustainable strategies/policies striving to reduce CO₂ by setting in motion the energy consumption in the transportation sector and reducing the use of fossil fuels as the main energy input.

Keywords: biocapacity, carbon footprint, ecological footprint assessment, energy consumption

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Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

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Authors: Benedict O. Muyale, Emmanuel S. Murunga

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Cities have always been the loci for nationals as well as growth of cultural fusion and innovation. Over 50%of global population dwells in cities and urban centers. This means that cities are prolific users of natural resources and generators of waste; hence they produce most of the greenhouse gases which are causing global climate change. The root cause of increase in the transport sector carbon curve is mainly the greater numbers of individually owned cars. Development in these cities is geared towards economic progress while environmental sustainability is ignored. Infrastructure projects focus on road expansion, electrification, and more parking spaces. These lead to more carbon emissions, traffic congestion, and air pollution. Recent development plans for Nairobi city are now on road expansion with little priority for electric train solutions. The Vision 2030, Kenya’s development guide, has shed some light on the city with numerous road expansion projects. This chapter seeks to realize the following objectives; (1) to assess the current transport situation of Nairobi; (2) to review green transport solutions being undertaken in the city; (3) to give an overview of alternative green transportation solutions, and (4) to provide a green transportation framework matrix. This preliminary study will utilize primary and secondary data through mainly desktop research and analysis, literature, books, magazines and on-line information. This forms the basis for formulation of approaches for incorporation into the green transportation framework matrix of the main study report.The main goal is the achievement of a practical green transportation system for implementation by the City County of Nairobi to reduce carbon emissions and congestion and promote environmental sustainability.

Keywords: cities, transport, Nairobi, green technologies

Procedia PDF Downloads 323

Authors: Dhiraj Dolai, Rupayan Bhattacharya

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In this study, a comparison of Range Of Motion (ROM) of middle and long-distance runners and swimmers has been made. The mobility of the various joints is essential for the quick movement of any sportsman. Knowledge of a ROM helps in preventing injuries, in repeating the movement, and in generating speed and power. ROM varies among individuals, and it is influenced by factors such as gender, age, and whether the motion is performed actively or passively. ROM for running and swimming, both performed with due consideration on speed, plays an important role. The time of generation of speed and mobility of the particular joints are very important for both kinds of athletes. The difficulties that happen during running and swimming in the direction of motion is changed. In this study, data were collected for a total of 102 subjects divided into three groups: control group (22), middle and long-distance runners (40), and swimmers (40), and their ages are between 12 to 18 years. The swimmers have higher ROM in shoulder joint flexion, extension, abduction, and adduction movement. Middle and long-distance runners have significantly greater ROM from Control Group in the left shoulder joint flexion with a 5.82 mean difference. Swimmers have significantly higher ROM from the Control Group in the left shoulder joint flexion with 24.84 mean difference and swimmers have significantly higher ROM from the Middle and Long distance runners in left shoulder flexion with 19.02 mean difference. The picture will be clear after a more detailed investigation.

Keywords: range of motion, runners, swimmers, significance

Procedia PDF Downloads 131

Authors: Durata Haciu, Elif Sena Tekin, Gokce Ozturk, Patricia Ramey Balcı

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Coastal zones are under increasing threat due to anthropogenic activities that introduce considerable pollutants such as heavy metals into marine ecosystems. As part of a larger experimental study examining species responses to contaminated marine sediments, surface sediments (top 5cm) were analysed for major trace elements at three locations in Istanbul Straight. Samples were randomly collected by divers (May 2018) using hand-corers from Istinye (n=4), Garipce (n=10) and Poyrazköy (n=6), at water depths of 4-8m. Twelve metals were examined: As, arsenic; Pb, lead; Cd, cadmium; Cr, chromium; Cu, Copper; Fe, Iron; Ni, Nickel; Zn, Zinc; V, vanadium; Mn, Manganese; Ba, Barium; and Ag, silver by wavelength-dispersive X-ray fluorescence spectrometry (WDXRF) and Inductively Coupled Plasma/Mass Spectroscopy (ICP/MS). Preliminary results indicate that the average concentrations of metals (mg kg⁻¹) varied considerably among locations. In general, concentrations were relatively lower at Garipce compared to either Istinye or Poyrazköy. For most metals mean concentrations were highest at Poyrazköy and Ag and Cd were below detection limits (exception= Ag in a few samples). While Cd and As were undetected in all stations, the concentrations of Fe and Ni fall in the criteria of moderately polluted range and the rest of the metals in the range of low polluted range as compared to Effects Range Low (ERL) and Effects Range median (ERM) values determined by US Environmental Protection Agency (EPA).

Keywords: effect-range classification, ICP/MS, marine sediments, XRF

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Authors: Taghreed Alghamdi, Wendy Hall, David Millard

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Massive Open Online Courses (MOOCs) have recently gained widespread interest in the academic world, starting a wide range of discussion of a number of issues. One of these issues, using MOOCs in teaching and learning in the higher education by integrating MOOCs’ contents with traditional face-to-face activities in blended learning format, is called blended MOOCs (bMOOCs) and is intended not to replace traditional learning but to enhance students learning. Most research on MOOCs has focused on students’ perception and institutional threats whereas there is a lack of published research on academics’ experiences and practices. Thus, the first aim of the study is to develop a classification of blended MOOCs models by conducting a systematic literature review, classifying 19 different case studies, and identifying the broad types of bMOOCs models namely: Supplementary Model and Integrated Model. Thus, the analyses phase will emphasize on these different types of bMOOCs models in terms of adopting MOOCs by lecturers. The second aim of the study is to improve the understanding of lecturers’ acceptance of bMOOCs by investigate the factors that influence academics’ acceptance of using MOOCs in traditional learning by distributing an online survey to lecturers who participate in MOOCs platforms. These factors can help institutions to encourage their lecturers to integrate MOOCs with their traditional courses in universities.

Keywords: acceptance, blended learning, blended MOOCs, higher education, lecturers, MOOCs, professors

Procedia PDF Downloads 132

Authors: Ali Zazi, Ouiza Cherifi

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The Catalysts materials Ni-SiO₂, Ni-Co-SiO₂ and Ni-Ce-SiO₂ were synthetized by classical method impregnation and supported by silica. This involves combing the silica with an adequate rate of the solution of nickel nitrates, or nickel nitrate and cobalt nitrate, or nickel nitrate and cerium nitrate, mixed, dried and calcined at 700 ° c. These catalysts have been characterized by different physicochemical analysis techniques. The atomic absorption spectrometry indicates that the real contents of nickel, cerium and cobalt are close to the theoretical contents previously assumed, which let's say that the nitrate solutions have impregnated well the silica support. The BET results show that the surface area of the specific surfaces decreases slightly after impregnation with nickel nitrates or Co and Ce metals and a further slight decrease after the reaction. This is likely due to coke deposition. X-ray diffraction shows the presence of the different SiO₂ and NiO phases for all catalysts—theCoO phase for that promoted by Co and the Ce₂O₂ phase for that promoted by Ce. The methane steam reforming reaction was carried out on a quartz reactor in a fixed bed. Reactants and products of the reaction were analyzed by a gas chromatograph. This study shows that the metal addition of Cerium or Cobalt improves the majority of the catalytic performance of Ni for the steam reforming reaction of methane. And we conclude the classification of our Catalysts in order of decreasing activity and catalytic performances as follows: Ni-Ce / SiO₂ >Ni-Co / SiO₂> Ni / SiO₂ .

Keywords: cerium, cobalt, heterogeneous catalysis, hydrogen, methane, steam reforming, synthesis gas

Procedia PDF Downloads 195

Authors: Seongjun Kim, Jinwooung Kim, Sung-Ah Kim

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The development of information and communication technology also affects human cognition and thinking, especially in the field of design, new techniques are being tried. In architecture, new design methodologies such as machine learning or data-driven design are being applied. In particular, these methodologies are used in analyzing the factors related to the value of real estate or analyzing the feasibility in the early planning stage of the apartment housing. However, since the value of apartment buildings is often determined by external factors such as location and traffic conditions, rather than the interior elements of buildings, data is rarely used in the design process. Therefore, although the technical conditions are provided, the internal elements of the apartment are difficult to apply the data-driven design in the design process of the apartment. As a result, the designers of apartment housing were forced to rely on designer experience or modular design alternatives rather than data-driven design at the design stage, resulting in a uniform arrangement of space in the apartment house. The purpose of this study is to propose a methodology to support the designers to design the apartment unit plan with high consumer preference by deriving the correlation and importance of the floor plan elements of the apartment preferred by the consumers through the machine learning and reflecting this information from the early design process. The data on the pre-sale competition rate and the elements of the floor plan are collected as data, and the correlation between pre-sale competition rate and independent variables is analyzed through machine learning. This analytical model can be used to review the apartment unit plan produced by the designer and to assist the designer. Therefore, it is possible to make a floor plan of apartment housing with high preference because it is possible to feedback apartment unit plan by using trained model when it is used in floor plan design of apartment housing.

Keywords: apartment unit plan, data-driven design, design methodology, machine learning

Procedia PDF Downloads 269

Authors: Hang Yang, Jichao Li, Kewei Yang, Tianyang Lei

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Multivariate time series anomaly detection is a significant research topic in the field of data mining, encompassing a wide range of applications across various industrial sectors such as traffic roads, financial logistics, and corporate production. The inherent spatial dependencies and temporal characteristics present in multivariate time series introduce challenges to the anomaly detection task. Previous studies have typically been based on the assumption that all variables belong to the same spatial hierarchy, neglecting the multi-level spatial relationships. To address this challenge, this paper proposes a multi-scale spatial and unified temporal feature fusion network, denoted as MSUT-Net, for multivariate time series anomaly detection. The proposed model employs a multi-level modeling approach, incorporating both temporal and spatial modules. The spatial module is designed to capture the spatial characteristics of multivariate time series data, utilizing an adaptive graph structure learning model to identify the multi-level spatial relationships between data variables and their attributes. The temporal module consists of a unified temporal processing module, which is tasked with capturing the temporal features of multivariate time series. This module is capable of simultaneously identifying temporal dependencies among different variables. Extensive testing on multiple publicly available datasets confirms that MSUT-Net achieves superior performance on the majority of datasets. Our method is able to model and accurately detect systems data with multi-level spatial relationships from a spatial-temporal perspective, providing a novel perspective for anomaly detection analysis.

Keywords: data mining, industrial system, multivariate time series, anomaly detection

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Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia Makedon, Glenn Wylie

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Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.

Keywords: fMRI, brain imaging, deep learning, self-supervised learning, contrastive learning, cognitive fatigue

Procedia PDF Downloads 192

Authors: Jeevan Jyoti Mohindroo, Umesh Kumar Garg

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The district of Tarntaran is located25 km south of Amritsar city in Punjab State of Northwestern India. It is 5059 Sq. Km in area. It is surrounded by Amritsar in the North, Kapurthala in the East, and Ferozepur in the South and Pakistan in the West. Patti Town is a municipal council of the Tarntaran district of the Indian state of Punjab, located 45 km from Amritsar its geographical coordinates are 310 16' 51" north to 740 51' 25" East Longitude. The town spreads over an area of 50sq. Km. Moisture content is very less in the air, falling within the semiarid region and frequently facing water scarcity as well as water quality problems. The major sources of employment are agriculture, horticulture and animal husbandry engaging almost 80% of the workforce. Water samples are collected from 400 locations in 20 villages on the Patti –Khem Karan highway with 20 samples from each village, and were subjected to analysis of chemical characteristics. The type of water that predominates in the study area is Ca-Mg-HCO3 type, based on hydro-chemical analysis. Besides, suitability of water for irrigation is evaluated based on the sodium adsorption ratio (SAR), residual sodium carbonate, sodium percent and salinity hazard. Other Physico-chemical parameters such as pH, TDS, conductance, etc. were also determined using a water analysis kit. Analysis of water samples for heavy metal analysis was also carried out in the present study.

Keywords: groundwater, chemical classification, SAR, RSC, USSL diagram

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Authors: Awad Khireldin, Colin Law

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Runways are the major infrastructure of airports around the world. Efficient operations of runways are key to ensure that airports are running smoothly with minimal delays. There are many factors that affect the efficiency of runway operations, such as the aircraft wake separation, runways system configuration, the fleet mix, and the runways separation distance. This paper aims to address how to maximize runway operations using a Discrete Event Simulation model. A case study of Cairo International Airport (CIA) is developed to maximize the utilizing of three parallel runways using a simulation model. Different scenarios have been designed where every runway could be assigned for arrival, departure, or mixed operations. A benchmarking study was also included to compare the actual to the proposed results to spot the potential improvements. The simulation model shows that there is a significant difference in utilization and delays between the actual and the proposed ones, there are several recommendations that can be provided to airport management, in the short and long term, to increase the efficiency and to reduce the delays. By including the recommendation with different operations scenarios, such as upgrading the airport slot Coordination from Level 1 to Level 2 in the short term. In the long run, discuss the possibilities to increase the International Air Transport association (IATA) slot coordination to Level 3 as more flights are expected to be handled by the airport. Technological advancements such as radar in the approach full airside simulation model could improve the airport performance where the airport is recommended to review the standard operations procedures with the appropriate authorities. Also, the airport can adopt a future operational plan to accommodate the forecasted additional traffic density in case of adding a fourth terminal building to increase the airport capacity.

Keywords: airport performance, runway, discrete event simulation, capacity, airside

Procedia PDF Downloads 140

Authors: Hui Wang

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Traditional collection building approaches are limited in breadth and scope and are not necessarily suitable for multidisciplinary fields development in the institutes of the Chinese Academy of Sciences. The increasing of multidisciplinary fields researches require a viable approach to collection development in these libraries. This study uses qualitative and quantitative analysis to assess collection. The quantitative analysis consists of three levels of evaluation, which including realistic demand, potential demand and trend demand analysis. For one institute, three samples were separately selected from the object institute, more than one international top institutes in highly relative research fields and future research hotspots. Each sample contains an appropriate number of papers published in recent five years. Several keywords and the organization names were reasonably combined to search in commercial databases and the institutional repositories. The publishing information and citations in the bibliographies of these papers were selected to build the dataset. One weighted evaluation model and citation analysis were used to calculate the demand intensity index of every journal and book. Principal Investigator selector and database traffic provide a qualitative evidence to describe the demand frequency. The demand intensity, demand frequency and academic committee recommendations were comprehensively considered to recommend collection development. The collection gaps or weaknesses were ascertained by comparing the current collection and the recommend collection. This approach was applied in more than 80 institutes’ libraries in Chinese Academy of Sciences in the past three years. The evaluation results provided an important evidence for collections building in the second year. The latest user survey results showed that the updated collection’s capacity to support research in a multidisciplinary subject area have increased significantly.

Keywords: citation analysis, collection assessment, collection development, quantitative analysis

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Authors: Yu-Hsiu Lin, Wen-Chun Lin, Yen-Chang Cheng, Chia-Ju Yeh, Yu-Chuan Chen, Tai-You Li

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Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example.

Keywords: demand-side management, edge intelligence, energy management system, fault detection and classification

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Authors: S. Rasti, M. A. Rajabzadeh

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Nickel-bearing laterites occur as two parallel belts along Sedimentary Zagros Orogenic (SZO) and Metamorphic Sanandaj-Sirjan (MSS) petrostructural zones, Fars Province, south Iran. An undisturbed vertical profile of these laterites includes protolith, saprolite, clay, and oxide horizons from base to top. Highly serpentinized harzburgite with relicts of olivine and orthopyroxene is regarded as the source rock. The laterites are unusual in lacking a significant saprolite zone with little development of Ni-silicates. Hematite, saponite, dolomite, smectite and clinochlore increase, while calcite, olivine, lizardite and chrysotile decrease from saprolite to oxide zones. Smectite and clinochlore with minor calcite are the major minerals in clay zone. Contacts of different horizons in laterite profiles are gradual and characterized by a decrease in Mg concentration ranging from 18.1 to 9.3 wt.% in oxide and saprolite, respectively. The maximum Ni concentration is 0.34 wt.% (NiO) in the base of the oxide zone, and goethite is the major Ni-bearing phase. From saprolite to oxide horizons, Al₂O₃, K₂O, TiO₂, and CaO decrease, while SiO₂, MnO, NiO, and Fe₂O₃ increase. Silica content reaches up to 45 wt.% in the upper part of the soil profile. There is a decrease in pH (8.44-8.17) and an increase in organic matter (0.28-0.59 wt.%) from base to top of the soils. The studied laterites are classified in the oxide clans which were derived from ophiolite ultramafic rocks under Mediterranean climate conditions.

Keywords: Iran, laterite, mineralogy, ophiolite

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Authors: Mohiuddin Sakib, Fatin Nihal, Rabeya Akter, Anisul Haque, Munsur Rahman, Wasif-E-Elahi

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Surge generating cyclones are one of the deadliest natural disasters that threaten the life of coastal environment and communities worldwide. Due to the geographic location, ‘low lying alluvial plain, geomorphologic characteristics and 710 kilometers exposed coastline, Bangladesh is considered as one of the greatest vulnerable country for storm surge flooding. Bay of Bengal is possessing the highest potential of creating storm surge inundation to the coastal areas. Bangladesh is the most exposed country to tropical cyclone with an average of four cyclone striking every years. Frequent cyclone landfall made the country one of the worst sufferer within the world for cyclone induced storm surge flooding and casualties. During the years from 1797 to 2009 Bangladesh has been hit by 63 severe cyclones with strengths of different magnitudes. Though detailed studies were done focusing on the specific cyclone like Sidr or Aila, no study was conducted where vulnerable areas of exposed coast were identified based on the strength of cyclones. This study classifies the vulnerable areas of the exposed coast based on storm surge inundation depth and area due to cyclones of varying strengths. Classification of the exposed coast based on hazard induced cyclonic vulnerability will help the decision makers to take appropriate policies for reducing damage and loss.

Keywords: cyclone, landfall, storm surge, exposed coastline, vulnerability

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Authors: Luljeta Gashi, Naser Ramadani, Zana Deva, Dafina Gexha-Bunjaku

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The official HIV/AIDS data in Kosovo are based on HIV case reporting from health-care services, the blood transfusion system and Voluntary Counselling and Testing centres. Between 1986 and 2014, are reported 95 HIV and AIDS cases, of which 49 were AIDS, 46 HIV and 40 deaths. The majority (69%) of cases were men, age group 25 to 34 (37%) and route of transmission is: heterosexual (90%), MSM (7%), vertical transmission (2%) and IDU (1%). Based on existing data and the UNAIDS classification system, Kosovo is currently still categorised as having a low-level HIV epidemic. Even though with a low HIV prevalence, Kosovo faces a number of threatening factors, including increased number of drug users, a stigmatized and discriminated MSM community, high percentage of youth among general population (57% of the population under the age of 25), with changing social norms and especially the sexual ones. Methods: Data collection was done using self administered structured questionnaires amongst 249 high school students. Data were analysed using the Statistical Package for Social Sciences (SPSS). Results: The findings revealed that 68% of students know that HIV transmission can be reduced by having sex with only one uninfected partner who has no other partners, 94% know that the risk of getting HIV can be reduced by using a condom every time they have sex, 68% know that a person cannot get HIV from mosquito bites, 81% know that they cannot get HIV by sharing food with someone who is infected and 46% know that a healthy looking person can have HIV. Conclusions: Seventy one percent of high school students correctly identify ways of preventing the sexual transmission of HIV and who reject the major misconceptions about HIV transmission. The findings of the study indicate a need for more health education and promotion.

Keywords: Kosovo, KPAR, HIV, high school

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

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

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

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

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

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

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Authors: Ki-Yeo Kim

Abstract:

Trends in genetics are transforming in order to identify differential coexpressions of correlated gene expression rather than the significant individual gene. Moreover, it is known that a combined biomarker pattern improves the discrimination of a specific cancer. The identification of the combined biomarker is also necessary for the early detection of invasive oral squamous cell carcinoma (OSCC). To identify the combined biomarker that could improve the discrimination of OSCC, we explored an appropriate number of genes in a combined gene set in order to attain the highest level of accuracy. After detecting a significant gene set, including the pre-defined number of genes, a combined expression was identified using the weights of genes in a gene set. We used the Principal Component Analysis (PCA) for the weight calculation. In this process, we used three public microarray datasets. One dataset was used for identifying the combined biomarker, and the other two datasets were used for validation. The discrimination accuracy was measured by the out-of-bag (OOB) error. There was no relation between the significance and the discrimination accuracy in each individual gene. The identified gene set included both significant and insignificant genes. One of the most significant gene sets in the classification of normal and OSCC included MMP1, SOCS3 and ACOX1. Furthermore, in the case of oral dysplasia and OSCC discrimination, two combined biomarkers were identified. The combined genomic expression achieved better performance in the discrimination of different conditions than in a single significant gene. Therefore, it could be expected that accurate diagnosis for cancer could be possible with a combined biomarker.

Keywords: oral squamous cell carcinoma, combined biomarker, microarray dataset, correlated genes

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

Abstract:

The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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Authors: Somayeh Abedian, Pirhossein Kolivand, Hamid Reza Lornejad, Amin Karampour, Ebrahim Keshavarz Safari

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For emergency and relief service providers such as pre-hospital emergencies, quick arrival at the scene of an accident or any EMS mission is one of the most important requirements of effective service delivery. Response time (the interval between the time of the call and the time of arrival on scene) is a critical factor in determining the quality of pre-hospital Emergency Medical Services (EMS). This is especially important for heart attack, stroke, or accident patients. Location-based e-services can be broadly defined as any service that provides information pertinent to the current location of an active mobile handset or precise address of landline phone call at a specific time window, regardless of the underlying delivery technology used to convey the information. According to research, one of the effective methods of meeting this goal is determining the location of the caller via the cooperation of landline and mobile phone operators in the country. The follow-up of the Communications Regulatory Authority (CRA) organization has resulted in the receipt of two separate secured electronic web services. Thus, to ensure human privacy, a secure technical architecture was required for launching the services in the pre-hospital EMS information management system. In addition, to quicken medics’ arrival at the patient's bedside, rescue vehicles should make use of an intelligent transportation system to estimate road traffic using a GPS-based mobile navigation system independent of the Internet. This paper seeks to illustrate the architecture of the practical national model used by the Iranian EMS organization.

Keywords: response time, geographic location inquiry service (GLIS), location-based service (LBS), emergency medical services information system (EMSIS)

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Authors: Ayman Hegab

Abstract:

Increasing evidence supports the use of platelet-rich plasma (PRP) combined with hyaluronic acid (HA) for the treatment of knee osteoarthritis, which effectively promotes cartilage repair. This study aimed to determine whether injection of PRP+HA following arthrocentesis reduces pain and improves maximum incisal opening. This was a single-blind, prospective, randomized control study. The patients were selected based on the Hegab classification: Group I: patients treated with arthrocentesis followed by a single PRP injection; Group II (Control): patients treated with arthrocentesis followed by a single HA injection; and Group III: patients treated with arthrocentesis followed by a single PRP+HA combination injection. The primary predictor variable was the medication used for injection. The primary outcome variables were the maximum voluntary mouth opening and pain index scores. The secondary outcome variable was joint sounds. All outcome variables were assessed and compared among the three groups at baseline and at 1-, 3-, 6-, and 12-month intervals. Other variables, including patients’ age and sex, were evaluated in relation to the patient outcomes. Injecting PRP+HA showed statistically significant improvement in the primary and secondary treatment outcomes over PRP or HA injection throughout the study period (P<0.005). Injection of PRP+HA following arthrocentesis had significant long-term clinical efficacy regarding pain relief that was considered the main concern of both the patient and clinician.

Keywords: TMJ, HA, PRP, osteoarthritis

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Authors: Kun Xu, Yuan Xu, Jia Qiao

Abstract:

The document detection plays an important role in optical character recognition and text analysis. Because the traditional detection methods have weak generalization ability, and deep neural network has complex structure and large number of parameters, which cannot be well applied in mobile devices, this paper proposes a lightweight Document Corner Detection Network (DCDNet). DCDNet is a two-stage architecture. The first stage with Encoder-Decoder structure adopts depthwise separable convolution to greatly reduce the network parameters. After introducing the Feature Attention Union (FAU) module, the second stage enhances the feature information of spatial and channel dim and adaptively adjusts the size of receptive field to enhance the feature expression ability of the model. Aiming at solving the problem of the large difference in the number of pixel distribution between corner and non-corner, Weighted Binary Cross Entropy Loss (WBCE Loss) is proposed to define corner detection problem as a classification problem to make the training process more efficient. In order to make up for the lack of Dataset of document corner detection, a Dataset containing 6620 images named Document Corner Detection Dataset (DCDD) is made. Experimental results show that the proposed method can obtain fast, stable and accurate detection results on DCDD.

Keywords: document detection, corner detection, attention mechanism, lightweight

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Authors: Xianping Du, Runlu Miao, Guanjun Zhang, Libo Cao, Feng Zhu

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Current vehicle safety standards and human body injury criteria were established based on the biomechanical response of Euro-American human body, without considering the difference in the body anthropometry and injury characteristics among different races, particularly the East Asian people with smaller body size. Absence of such race specific design considerations will negatively influence the protective performance of safety products for these populations, and weaken the accuracy of injury thresholds derived. To resolve these issues, in this study, we aim to develop a race specific finite element model to simulate the impact response of the lower extremity of a 50th percentile East Asian (Chinese) male. The model was built based on medical images for the leg of an average size Chinese male and slightly adjusted based on the statistical data. The model includes detailed anatomic features and is able to simulate the muscle active force. Thirteen biomechanical tests available in the literature were used to validate its biofidelity. Using the validated model, a pedestrian-car impact accident taking place in China was re-constructed computationally. The results show that the newly developed lower leg model has a good performance in predicting dynamic response and tibia fracture pattern. An additional comparison on the fracture tolerance of the East Asian and Euro-American lower limb suggests that the current injury criterion underestimates the degree of injury of East Asian human body.

Keywords: lower limb, East Asian body characteristics, traffic accident reconstruction, finite element analysis, injury tolerance

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Authors: Getaneh Berie Tarekegn

Abstract:

A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Bingying Liu, Dandong Ge, Xinlan Zhang, Haoyang Liang

Abstract:

Over the past two years, a new generation of No-Pile Bike sharing, represented by the Ofo, Mobike and HelloBike, has sprung up in various cities in China, and spread rapidly in countries such as Britain, Japan, the United States and Singapore. As a new green public transportation mode, bike sharing can bring a series of benefits to urban space. At first, this paper analyzes the specific impact of bike sharing on urban space in China. Based on the market research and data analyzing, it is found that bike sharing can improve the quality of urban space in three aspects: expanding the radius of public transportation service, filling service blind spots, alleviating urban traffic congestion, and enhancing the vitality of urban space. On the other hand, due to the immature market and the imperfect system, bike sharing has gradually revealed some difficulties, such as parking chaos, malicious damage, safety problems, imbalance between supply and demand, and so on. Then the paper investigates the characteristics of shared bikes, business model, operating mechanism on Chinese market currently. Finally, in order to make bike sharing serve urban construction better, this paper puts forward some specific countermeasures from four aspects. In terms of market operations, it is necessary to establish a public-private partnership model and set up a unified bike-sharing integrated management platform. From technical methods level, the paper proposes to develop an intelligent parking system for regulating parking. From policy formulation level, establishing a bike-sharing assessment mechanism would strengthen supervision. As to urban planning, sharing data and redesigning slow roadway is beneficial for transportation and spatial planning.

Keywords: bike sharing, impact analysis, improvement strategy, urban space

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Authors: Emmanuel Okechukwu Nwafor, Folake Olubunmi Akintayo, Denis Alcides Rezende

Abstract:

Decision tree models are versatile and interpretable machine learning algorithms widely used for both classification and regression tasks, which can be related to cities, whether physical or digital. The aim of this research is to assess how well decision tree algorithms can predict trip purposes in Makurdi, Nigeria, while also exploring their connection to the strategic digital city initiative. The research methodology involves formalizing household demographic and trips information datasets obtained from extensive survey process. Modelling and Prediction were achieved using Python Programming Language and the evaluation metrics like R-squared and mean absolute error were used to assess the decision tree algorithm's performance. The results indicate that the model performed well, with accuracies of 84% and 68%, and low MAE values of 0.188 and 0.314, on training and validation data, respectively. This suggests the model can be relied upon for future prediction. The conclusion reiterates that This model will assist decision-makers, including urban planners, transportation engineers, government officials, and commuters, in making informed decisions on transportation planning and management within the framework of a strategic digital city. Its application will enhance the efficiency, sustainability, and overall quality of transportation services in Makurdi, Nigeria.

Keywords: decision tree algorithm, trip purpose, intelligent transport, strategic digital city, travel pattern, sustainable transport

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