Search results for: state machine

Authors: Ashwini Tiwari

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This qualitative case study examines students' perceptions of accommodations in higher education institutions. The data were collected from focus groups and one-to-one interviews with 15 students enrolled in a 4-year state university in the southern United States. The data were analyzed using a thematic analysis process. The findings suggest that students perceived that their instructors were willing to accommodate their educational needs. However, the participants expressed concerns about the lack of a formal labeling process in higher education settings, creating a barrier to receiving adequate services to gain meaningful educational experiences.

Keywords: disability, accomodation, services, higher educaiton

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Authors: Nayla El-Kork, Farah Korjieh, Ahmed Bentiba, Mahmoud Korek

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Ab-initio calculations have been performed to investigate the spectroscopic constants for the diatomic compound BeF. Values of the internuclear distance Re, the harmonic frequency ωe, the rotational constants Be, the electronic transition energy with respect to the ground state Te, the eignvalues Ev, the abscissas of the turning points Rmin, Rmax, the rotational constants Bv and the centrifugal distortion constants Dv have been calculated for the molecule’s ground and excited electronic states. Results are in agreement with experimental data.

Keywords: spectroscopic constant, potential energy curve, diatomic molecule, spectral analysis

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Authors: M. Shadmand

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The curing process of insulation system for electrical machines plays a determinative role for its durability and reliability. Polar structure of insulating resin molecules and used filler of insulation system can be taken as an occasion to leverage it to enhance overall characteristics of insulation system, mechanically and electrically. The curing process regime for insulating system plays an important role for its mechanical and electrical characteristics by arranging the polymerization of chain structure for resin. In this research, the effect of electrical field application on in-curing insulating system for Global Vacuum Pressurized Impregnation (GVPI) system for traction motor was considered by performing the dissipation factor, polarization and de-polarization current (PDC) and voltage endurance (aging) measurements on sample test objects. Outcome results depicted obvious improvement in mechanical strength of the insulation system as well as higher electrical characteristics with routing and long-time (aging) electrical tests. Coming together, polarization of insulation system during curing process would enhance the machine life time. 

Keywords: insulation system, GVPI, PDC, aging

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Authors: Evren Isen

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In grid-connected renewable energy systems, input power is controlled by AC/DC converter or/and DC/DC converter depending on output voltage of input source. The power is injected to DC-link, and DC-link voltage is regulated by inverter controlling the grid current. Inverter performance is considerable in grid-connected renewable energy systems to meet the utility standards. In this paper, modelling and simulation of hysteresis current controlled single-phase grid-connected inverter that is utilized in renewable energy systems, such as wind and solar systems, are presented. 2 kW single-phase grid-connected inverter is simulated in Simulink and modeled in Matlab-m-file. The grid current synchronization is obtained by phase locked loop (PLL) technique in dq synchronous rotating frame. Although dq-PLL can be easily implemented in three-phase systems, there is difficulty to generate β component of grid voltage in single-phase system because single-phase grid voltage exists. Inverse-Park PLL with low-pass filter is used to generate β component for grid angle determination. As grid current is controlled by constant bandwidth hysteresis current control (HCC) technique, average switching frequency and variation of switching frequency in a fundamental period are considered. 3.56% total harmonic distortion value of grid current is achieved with 0.5 A bandwidth. Average value of switching frequency and total harmonic distortion curves for different hysteresis bandwidth are obtained from model in m-file. Average switching frequency is 25.6 kHz while switching frequency varies between 14 kHz-38 kHz in a fundamental period. The average and maximum frequency difference should be considered for selection of solid state switching device, and designing driver circuit. Steady-state and dynamic response performances of the inverter depending on the input power are presented with waveforms. The control algorithm regulates the DC-link voltage by adjusting the output power.

Keywords: grid-connected inverter, hysteresis current control, inverter modelling, single-phase inverter

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Authors: Ebere Ellison Obisike, Justina N. Adalikwu-Obisike

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Artificial intelligence (AI) is progressively transforming health and social care. With the rapid invention of various electronic devices, machine learning, and computing systems, the use of AI istraversing many health and social care practices. In this systematic review of journal and grey literature, this study explores how the applications of AI might promote the prevention of micro and macrovascular complications in type 1 diabetic patients. This review focuses on the use of a digitized blood glucose meter and the application of insulin pumps for the effective management of type 1 diabetes in low and middle-income countries. It is projected that the applications of AI may assist individuals with type 1 diabetes to monitor and control their blood glucose level and prevent the early onset of micro and macrovascular complications.

Keywords: artificial intelligence, blood glucose meter, insulin pump, low and middle-income countries, micro and macrovascular complications, type 1 diabetes

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Authors: S. Pesina, T. Solonchak

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Phenomenological analysis is not based on natural language, but ideal language which is able to be a carrier of ideal meanings – eidos representing typical structures or essences. For this purpose, it’s necessary to release from the spatio-temporal definiteness of a subject and then state its noetic essence (eidos) by means of free fantasy generation. Herewith, as if a totally new objectness is created - the universal, confirming the thesis that thinking process takes place in generalizations passing by numerous means through the specific to the general and from the general through the specific to the singular.

Keywords: lexical eidos, phenomenology, noema, polysemantic word, semantic core

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Syed Sibtain Hussain Shah

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The Afghan Taliban returned to power corridors in Afghanistan in August 2021 after waging a twenty-year insurgency in the country. U.S.-led forces completed their withdrawal from Afghanistan on August 30, 2021, but the Taliban took control of the whole country till August 15, 2021. At the same time, some of the militant groups such as Tehrik-e-Taliban Pakistan (TTP) and Islamic State Khurasan (IS-K) reappeared in Pakistan’s borders and other areas and by increasing attacks on the armed forces of Pakistan and minorities communities. These groups once again created a crucial challenge to the internal security of the country. Since mid of 2021, many of the terrorist incidents in the countries specified in the areas of Pakistan bordering Afghanistan were committed by TTP and IS-K. The aim of this paper is to investigate the reappearance of TTP and IS-K in 2021 and 2022 as a crucial threat to the internal security of Pakistan. The author will particularly probe threats to the security of military personnel and their installations and threats to human security, including danger to religious minority communities in the different areas of the country, including border areas such as Waziristan, which was once a hub of TTP and other militant groups in the 2000s. The author will employ the relevant method and appropriate theories of security studies, such as religious extremism and terrorism, in this study. TTP, inspired by the Afghan Taliban, initially emerged in Pakistan in 2007 and this group has so far targeted various religious and ethnic communities and government installations in Pakistan. The group is not only against Pakistan’s government policies, but it also committed terrorist attacks on the communities of the other Muslim sects and as well as non-Muslim communities. Most of the prominent figures of this violent group disappeared or escaped to Afghanistan after military actions, such as the larger “Zarb-e-Azb” operation in Pakistan in 2015. IS-K, which established its branch of Khurasan covering Pakistan and Afghanistan in 2015, with its main formation in Iraq and Syria in 2015, by targeting religious minorities such as Shia Muslims, has so far created a vital security challenge for the security of the country.

Keywords: Pakistan, Afghanistan, Afghan Taliban, Pakistani Taliban, Islamic state Khorasan, security threat

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Authors: Stefano Barone

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Seismic retrofit of buildings through base isolation represents a consolidated protection strategy against earthquakes. It consists in decoupling the ground motion from that of the structure and introducing anti-seismic devices at the base of the building, characterized by high horizontal flexibility and medium/high dissipative capacity. This allows to protect structural elements and to limit damages to non-structural ones. For these reasons, full functionality is guaranteed after an earthquake event. Base isolation is applied extensively to both new and existing buildings. For the latter, it usually does not require any interruption of the structure use and occupants evacuation, a special advantage for strategic buildings such as schools, hospitals, and military buildings. This paper describes the application of seismic isolation to three existing masonry buildings in Italy: Villa “La Maddalena” in Macerata (Marche region), “Giacomo Matteotti” and “Plinio Il Giovane” school buildings in Perugia (Umbria region). The seismic hazard of the sites is characterized by a Peak Ground Acceleration (PGA) of 0.213g-0.287g for the Life Safety Limit State and between 0.271g-0.359g for the Collapse Limit State. All the buildings are isolated with a combination of free sliders type TETRON® CD with confined elastomeric disk and anti-seismic rubber isolators type ISOSISM® HDRB to reduce the eccentricity between the center of mass and stiffness, thus limiting torsional effects during a seismic event. The isolation systems are designed to lengthen the original period of vibration (i.e., without isolators) by at least three times and to guarantee medium/high levels of energy dissipation capacity (equivalent viscous damping between 12.5% and 16%). This allows the structures to resist 100% of the seismic design action. This article shows the performances of the supplied anti-seismic devices with particular attention to the experimental dynamic response. Finally, a special focus is given to the main site activities required to isolate a masonry building.

Keywords: retrofit, masonry buildings, seismic isolation, energy dissipation, anti-seismic devices

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Authors: Abdelghani Choucha, Lakhdar Chaib, Salem Arif

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This paper treated the problem of power system stability with the aid of Static Synchronous Series Compensator (SSSC) installed in the transmission line of single machine infinite bus (SMIB) power system. A fractional order PID (FOPID) controller has been applied as a robust controller for optimal SSSC design to control the power system characteristics. Additionally, the SSSC based FOPID parameters are smoothly tuned using Invasive Weed Optimization algorithm (IWO). To verify the strength of the proposed controller, SSSC based FOPID controller is validated in a wide range of operating condition and compared with the conventional scheme SSSC-POD controller. The main purpose of the proposed process is greatly enhanced the dynamic states of the tested system. Simulation results clearly prove the superiority and performance of the proposed controller design.

Keywords: SSSC-FOPID, SSSC-POD, SMIB power system, invasive weed optimization algorithm

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Authors: Telma Fontao de Castro, Kylee Miller, Maria Xavier Araújo, Isabel Brandao, Sandra Torres

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Objective: There is a dearth of research investigating the long-term emotional functioning of individuals recovered from anorexia nervosa (AN). This 15-year longitudinal study aimed to examine whether difficulties in cognitive processing of emotions persisted after long-term AN recovery and its link to anxiety and depression. Method: Twenty-four females, who were tested longitudinally during their acute and recovered AN phases, and 24 healthy control (HC) women, were screened for anxiety, depression, alexithymia, and emotion regulation difficulties (ER; only assessed in recovery phase). Results: Anxiety, depression, and alexithymia levels decreased significantly with AN recovery. However, scores on anxiety and difficulty in identifying feelings (alexithymia factor) remained high when compared to the HC group. Scores on emotion regulation difficulties were also lower in HC group. The abovementioned differences between AN recovered group and HC group in difficulties in identifying and accepting feelings and lack of emotional clarity were no longer present when the effect of anxiety and depression was controlled. Conclusions: Findings suggest that emotional dysfunction tends to decrease in AN recovered phase. However, using an HC group as a reference, we conclude that several emotional difficulties are still increased after long-term AN recovery, in particular, limited access to emotion regulation strategies, and difficulty controlling impulses and engaging in goal-directed behavior, thus suggesting to be a trait vulnerability. In turn, competencies related to emotional clarity and acceptance of emotional responses seem to be state-dependent phenomena linked to anxiety and depression. In sum, managing emotions remains a challenge for individuals recovered from AN. Under this circumstance, maladaptive eating behavior can serve as an affect regulatory function, increasing the risk of relapse. Emotional education and stabilization of depressive and anxious symptomatology after recovery emerge as an important avenue to protect from long-term AN relapse.

Keywords: alexithymia, anorexia nervosa, emotion recognition, emotion regulation

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Authors: Yasser F. Hassan

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In this paper, a proposed model of cellular automata is studied by means of fractional integral function. A cellular automaton is a decentralized computing model providing an excellent platform for performing complex computation with the help of only local information. The paper discusses how using fractional integral function for representing cellular automata memory or state. The architecture of computing and learning model will be given and the results of calibrating of approach are also given.

Keywords: fractional integral, cellular automata, memory, learning

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Authors: Deepak

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WEDM is an amazingly potential electro-wire process for machining of hard metal compounds and metal grid composites without making contact. Wire electrical machining is a developing noncustomary machining process for machining hard to machine materials that are electrically conductive. It is an exceptionally exact, precise, and one of the most famous machining forms in nontraditional machining. WEDM has turned into the fundamental piece of many assembling process ventures, which require precision, variety, and accuracy. In the present examination, AA-6063 is utilized as a workpiece, and execution investigation is done to discover the critical control factors. Impact of different parameters like a pulse on time, pulse off time, servo voltage, peak current, water pressure, wire tension, wire feed upon surface hardness has been researched while machining on AA-6063. RSM has been utilized to advance the yield variable. A variety of execution measures with input factors was demonstrated by utilizing the response surface methodology.

Keywords: AA-6063, response surface methodology, WEDM, surface roughness

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Authors: Jozef Jurcik, Miroslav Gutten, Milan Sebok, Daniel Korenciak, Jerzy Roj

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The paper presents the research results of electronic fuel injection system, which can be used for diagnostics of automotive systems. In the paper is described the construction and operation of a typical fuel injection system and analyzed its electronic part. It has also been proposed method for the detection of the injector malfunction, based on the analysis of differential current or voltage characteristics. In order to detect the fault state, it is needed to use self-learning process, by the use of an appropriate self-learning algorithm.

Keywords: electronic fuel injector, diagnostics, measurement, testing device

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Authors: Renu Satish Nair

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Indian higher education system is the third largest in the world next to United States and China. India is experiencing a rapid growth in higher education in terms of student enrollment as well as establishment of new universities, colleges and institutes of national importance. Presently about 22 million students are being enrolled in higher education and more than 46 thousand institutions’ are functioning as centers of higher education. Indian government plays a 'command and control' role in higher education. The main governing body is University Grants Commission, which enforces its standards, advises the government, and helps coordinate between the centre and the state. Accreditation of higher learning is over seen by 12 autonomous institutions established by the University Grants Commission. The present paper is an effort to analyze the strength, weakness, opportunities and threat (SWOT Analysis) of Indian Higher education system. The higher education in India is progressing ahead by virtue of its strength which is being recognized at global level. Several institutions of India, such as Indian Institutes of Technology (IITs), Indian Institutes of Management (IIMs) and National Institutes of Technology (NITs) have been globally acclaimed for their standard of education. Three Indian universities were listed in the Times Higher Education list of the world’s top 200 universities i.e. Indian Institutes of Technology, Indian Institute of Management and Jawahar Lal Nehru University in 2005 and 2006. Six Indian Institutes of Technology and the Birla Institute of Technology and Science - Pilani were listed among the top 20 science and technology schools in Asia by the Asia Week. The school of Business situated in Hyderabad was ranked number 12 in Globe MBA ranking by the Financial Times of London in 2010 while the All India Institute of Medical Sciences has been recognized as a global leader in medical research and treatment. But at the same time, because of vast expansion, the system bears several weaknesses. The Indian higher education system in many parts of the country is in the state of disrepair. In almost half the districts in the country higher education enrollment are very low. Almost two third of total universities and 90% of colleges are rated below average on quality parameters. This can be attributed to the under prepared faculty, unwieldy governance and other obstacles to innovation and improvement that could prohibit India from meeting its national education goals. The opportunities in Indian higher education system are widely ranged. The national institutions are training their products to compete at global level and make them capable to grab opportunities worldwide. The state universities and colleges with their limited resources are giving the products that are capable enough to secure career opportunities and hold responsible positions in various government and private sectors with in the country. This is further creating opportunities for the weaker section of the society to join the main stream. There are several factors which can be defined as threats to Indian higher education system. It is a matter of great concern and needs proper attention. Some important factors are -Conservative society, particularly for women education; -Lack of transparency, -Taking higher education as a means of business

Keywords: Indian higher education system, SWOT analysis, university grants commission, Indian institutes of technology

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Authors: Jennifer Buz, Alvin Spivey

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The most common greenhouse gas emitted through human activities, carbon dioxide (CO2), is naturally consumed by plants during photosynthesis. This process is actively being monetized by companies wishing to offset their carbon dioxide emissions. For example, companies are now able to purchase protections for vegetated land due-to-be clear cut or purchase barren land for reforestation. Therefore, by actively preventing the destruction/decay of plant matter or by introducing more plant matter (reforestation), a company can theoretically offset some of their emissions. One of the biggest issues in the carbon credit market is validating and verifying carbon offsets. There is a need for a system that can accurately and frequently ensure that the areas sold for carbon credits have the vegetation mass (and therefore for carbon offset capability) they claim. Traditional techniques for measuring vegetation mass and determining health are costly and require many person-hours. Orbital Sidekick offers an alternative approach that accurately quantifies carbon mass and assesses vegetation health through satellite hyperspectral imagery, a technique which enables us to remotely identify material composition (including plant species) and condition (e.g., health and growth stage). How much carbon a plant is capable of storing ultimately is tied to many factors, including material density (primarily species-dependent), plant size, and health (trees that are actively decaying are not effectively storing carbon). All of these factors are capable of being observed through satellite hyperspectral imagery. This abstract focuses on speciation. To build a species classification model, we matched pixels in our remote sensing imagery to plants on the ground for which we know the species. To accomplish this, we collaborated with the researchers at the Teakettle Experimental Forest. Our remote sensing data comes from our airborne “Kato” sensor, which flew over the study area and acquired hyperspectral imagery (400-2500 nm, 472 bands) at ~0.5 m/pixel resolution. Coverage of the entire teakettle experimental forest required capturing dozens of individual hyperspectral images. In order to combine these images into a mosaic, we accounted for potential variations of atmospheric conditions throughout the data collection. To do this, we ran an open source atmospheric correction routine called ISOFIT1 (Imaging Spectrometer Optiman FITting), which converted all of our remote sensing data from radiance to reflectance. A database of reflectance spectra for each of the tree species within the study area was acquired using the Teakettle stem map and the geo-referenced hyperspectral images. We found that a wide variety of machine learning classifiers were able to identify the species within our images with high (>95%) accuracy. For the most robust quantification of carbon mass and the best assessment of the health of a vegetated area, speciation is critical. Through the use of high resolution hyperspectral data, ground-truth databases, and complex analytical techniques, we are able to determine the species present within a pixel to a high degree of accuracy. These species identifications will feed directly into our carbon mass model.

Keywords: hyperspectral, satellite, carbon, imagery, python, machine learning, speciation

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Authors: Payal Patel

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Textile machinery is one of the fastest evolving areas which has an application of mechanical engineering. The modular approach towards the processing right from the stage of cotton to the fabric, allows us to observe the result of each process on its input. Cost and space being the major constraints. The flyer is a component of roving machine, which is used as a part of spinning process. In the present work using the application of Hyper Works, the flyer arm has been modified which saves the material used for manufacturing the flyer. The size optimization of the flyer is carried out with the objective of reduction in weight under the constraints of standard operating conditions. The new design of the flyer is proposed and validated using the module of HyperWorks which is equally strong, but light weighted compared to the existing design. Dynamic balancing of the optimized model is carried out to align a principal inertia axis with the geometric axis of rotation. For the balanced geometry of flyer, air resistance is obtained theoretically and with Gambit and Fluent. Static analysis of the balanced geometry has been done to verify the constraint of operating condition. Comparison of weight, deflection, and factor of safety has been made for different aluminum alloys.

Keywords: flyer, size optimization, textile, weight

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Authors: Hui Li, Xueying Zhao, Ke Ma, Yu Cao, Fan Yang, Qingwen Xu, Wenbin Liu

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Soil can often link a person or item to a crime scene, which makes it a valuable evidence in forensic casework. Several techniques have been utilized in forensic soil discrimination in previous studies. Because soil contains a vast number of microbiomes, the analyse of soil microbiomes is expected to be a potential way to characterise soil evidence. In this study, we applied massively parallel sequencing (MPS) to soil bacterial profiling on the Ion Torrent Personal Genome Machine (PGM). Soils from different regions were collected repeatedly. V-region 3 and 4 of Bacterial 16S rRNA gene were detected by MPS. Operational taxonomic units (OTU, 97%) were used to analyse soil bacteria. Several bioinformatics methods (PCoA, NMDS, Metastats, LEfse, and Heatmap) were applied in bacterial profiles. Our results demonstrate that MPS can provide a more detailed picture of the soil microbiomes and the composition of soil bacterial components from different region was individualistic. In conclusion, the utility of soil bacterial profiling via MPS of the 16S rRNA gene has potential value in characterising soil evidences and associating them with their place of origin, which can play an important role in forensic science in the future.

Keywords: bacterial profiling, forensic, massively parallel sequencing, soil evidence

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Authors: Umar Albalawi, Sang C. Suh, Jinoh Kim

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As internet continues to expand its usage with an enormous number of applications, cyber-threats have significantly increased accordingly. Thus, accurate detection of malicious traffic in a timely manner is a critical concern in today’s Internet for security. One approach for intrusion detection is to use Machine Learning (ML) techniques. Several methods based on ML algorithms have been introduced over the past years, but they are largely limited in terms of detection accuracy and/or time and space complexity to run. In this work, we present a novel method for intrusion detection that incorporates a set of supervised learning algorithms. The proposed technique provides high accuracy and outperforms existing techniques that simply utilizes a single learning method. In addition, our technique relies on partial flow information (rather than full information) for detection, and thus, it is light-weight and desirable for online operations with the property of early identification. With the mid-Atlantic CCDC intrusion dataset publicly available, we show that our proposed technique yields a high degree of detection rate over 99% with a very low false alarm rate (0.4%).

Keywords: intrusion detection, supervised learning, traffic classification, computer networks

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Authors: Aweda Francis Olatunbosun, Falaiye Oluwasesan Adeniran

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The suspended harmattan dust was collected at seven different stations in Nigeria: Iwo (7º 63'N, 4º 19'E), Oyo (8º 12'N, 3º 42'E), Ilorin (8º36'N, 4º 35'E), Minna (9º36'N, 06º35'E), Abuja (09º 09'N, 07º 11'E), Lafia (08º 49'N, 07º50'E), and Jos (9º55'N, 8º55'E), which were analyzed to determine elements and minerals present in the sample using X-Ray Fluorescence (XRF), and Particle Induced X-Ray Emission (PIXE). The collected sample results show the elemental concentration of the sample in various forms across each station. Cr, Ce, Mo, Zr, Sr, V, Ti, K, As, Ni, Mn, Ca, Pb, Fe, Zn, and Cu were found in the sample using an XRF machine. The minerals discovered in the sample include, but are not limited to, Corundum [Al₂O₃], Periclase [MgO], Rutile [TiO₂], and Quartz [SiO₂] in various proportions. Furthermore, the results revealed the enrichment factor for Iwo (1.3998 μg/m³), Oyo (1.3998 μg/m³), Ilorin (1.79765 μg/m³), Minna (1.737325 μg/m³), Abuja (1.635425 μg/m³), Lafia (1.409695 μg/m³), and Jos (1.787075 μg/m³). The study concluded that the sample contains sixteen (16) elements and minerals in varying percentages and concentrations. It is therefore recommended that appropriate safety procedures be put in place to raise community awareness of the presence of elements in harmattan dust.

Keywords: elements, minerals, harmattan dust, XRF, PIXE

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Authors: S. Kreitlein, N. Eder, J. Franke

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The importance of energy efficiency within the production process increases steadily. Unfortunately, so far no tools for a comprehensive assessment of energy efficiency within the production process exist. Therefore the Institute for Factory Automation and Production Systems of the Friedrich-Alexander-University Erlangen-Nuremberg has developed two methods with the goal of achieving transparency and a quantitative assessment of energy efficiency: EEV (Energy Efficiency Value) and EPE (Energetic Process Efficiency). This paper describes the basics and state of the art as well as the developed approaches.

Keywords: energy efficiency, energy efficiency value, energetic process efficiency, production

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Authors: Imran Badshah

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Pedestrian service time reflects the performance of the facility, and it’s a key parameter to analyze the capability of facilities provided to serve pedestrians. The level of service of pedestrians (LOS) mainly depends on pedestrian time and safety. The pedestrian time utilized by taking a service is mainly influenced by the number of available services and the time utilized by each pedestrian in receiving a service; that is called a delay time. In this paper, we analyzed the simulated pedestrian service time with different delay times. A simulation is performed in AnyLogic by developing a model that reflects the real scenario of pedestrian services such as ticket machine gates at rail stations, airports, shopping malls, and cinema halls. The simulated pedestrian time is determined for various delay values. The simulated result shows how pedestrian time changes with the delay pattern. The histogram and time plot graph of a model gives the mean, maximum and minimum values of the pedestrian time. This study helps us to check the behavior of pedestrian time at various services such as subway stations, airports, shopping malls, and cinema halls.

Keywords: agent-based simulation, anylogic model, pedestrian behavior, time delay

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Authors: Avrokomi Zavitsanou, Spiros Papadopoulos, Theofanis Alexandridis

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This paper presents the research project ‘Colour & Travel’, which is co-funded by the European Union and national resources through the Operational Programme “Competitiveness, Entrepreneurship and Innovation” 2014-2020, under the Single RTDI State Aid Action "RESEARCH - CREATE - INNOVATE". The research project proposes the design of an innovative, playful framework for exploring a variety of travel destinations and creating personalised travel narratives, aiming to entertain, educate, and promote culture and tourism. Gamification of the cultural and touristic environment can enhance its experiential, multi-sensory aspects and broaden the perception of the traveler. The latter's involvement in creating and shaping his personal travel narrations and the possibility of sharing it with others can offer him an alternative, more binding way of getting acquainted with a place. In particular, the paper presents the design of an infrastructure: (a) for the development of interactive travel guides for mobile devices, where sites with specific points of interest will be recommended, with which the user can interact in playful ways and then create his personal travel narratives, (b) for the development of innovative games within virtual reality environment, where the interaction will be offered while the user is moving within the virtual environment; and (c) for an online application where the content will be offered through the browser and the modern 3D imaging technologies (WebGL). The technological products that will be developed within the proposed project can strengthen important sectors of economic and social life, such as trade, tourism, exploitation and promotion of the cultural environment, creative industries, etc. The final applications delivered at the end of the project will guarantee an improved level of service for visitors and will be a useful tool for content creators with increased adaptability, expansibility, and applicability in many regions of Greece and abroad. This paper aims to present the research project by referencing the state of the art and the methodological scheme, ending with a brief reflection on the expected outcome in terms of results.

Keywords: gamification, culture, tourism, AR, VR, applications

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Authors: Yagya Raj Pandeya, Joonwhoan Lee

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The dynamic behavior of music and video makes it difficult to evaluate musical instrument playing in a video by computer system. Any television or film video clip with music information are rich sources for analyzing musical instruments using modern machine learning technologies. In this research, we integrate the audio and video information sources using convolutional neural network (CNN) and pass network learned features through recurrent neural network (RNN) to preserve the dynamic behaviors of audio and video. We use different pre-trained CNN for music and video feature extraction and then fine tune each model. The music network use 2D convolutional network and video network use 3D convolution (C3D). Finally, we concatenate each music and video feature by preserving the time varying features. The long short term memory (LSTM) network is used for long-term dynamic feature characterization and then use late fusion with generalized mean. The proposed network performs better performance to recognize the musical instrument using audio-video multimodal neural network.

Keywords: multimodal, 3D convolution, music-video feature extraction, generalized mean

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Authors: Laura Butabayeva, Svetlana Ismagulova, Gulbarshin Nogaibayeva, Maiya Temirbayeva, Aidana Zhussip

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The article presents the main results of the study conducted within the grant project «Organizational and methodological foundations for ensuring the inclusiveness of school students’ career guidance» (2022-2024). The main aim of the project is to study the issue of the absence of developed mechanisms, coordinating the activities of all stakeholders in preparing school students for conscious career choice, taking into account their individual opportunities and special educational needs. To achieve the aim of the project, according to the implementation plan, the analysis of foreign and national literature on the studied problem, as well as the study of the state of school students’ career guidance and their socialization in the context of inclusive education were conducted, the international experience on this issue was explored. The analysis of the national literature conducted by the authors has shown the State’s annual increase in the number of students with special educational needs as well as the rapid demand of labour market, influencing their professional self-determination in modern society. The participants from 5 State’s regions, including students, their parents, general secondary schools administration and educators, as well as employers, took part in the study, taking into account the geographical location: south, north, west, centre, and the cities of republican significance. To ensure the validity of the study’s results, the triangulation method was utilised, including both qualitative and quantitative methods. The data were analysed independently and compared with each other. Ethical principles were considered during all stages of the study. The characteristics of the system of career guidance in the modern school, the role and the involvement of stakeholders in the system of career guidance, the opinions of educators on school students’ preparedness for career choice, and the factors impeding the effectiveness of career guidance in schools were examined. The problem of stakeholders’ disunity and inconsistency, causing the systemic labor market distortions, the growth of low-skilled labor, and the unemployed, including people with special educational needs, were revealed. The other issue identified by the researchers was educators’ insufficient readiness for students’ career choice preparation in the context of inclusive education. To study cutting-edge experience in organizing a system of career guidance for young people and develop mechanisms coordinating the actions of all stakeholders in preparing students for career choice, the institutions of career guidance in France, Japan, and Germany were explored by the researchers. To achieve the aim of the project, the systemic contemporary model of school students’ professional self-determination, considering their individual opportunities and special educational needs, has been developed based on the study results and international experience. The main principles of this model are consistency, accessibility, inclusiveness, openness, coherence, continuity. The perspectives of students’ career guidance development in the context of inclusive education have been suggested.

Keywords: career guidance, inclusive education, model of school students’ professional self-determination, psychological and pedagogical support, special educational needs

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Authors: Scott N. Gerard, Aliza Heching, Susann M. Keohane, Samuel S. Adams

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The world-wide population of people over 60 years of age is growing rapidly. The explosion is placing increasingly onerous demands on individual families, multiple industries and entire countries. Current, human-intensive approaches to eldercare are not sustainable, but IoT and AI technologies can help. The Knowledge Reactor (KR) is a contextual, data fusion engine built to address this and other similar problems. It fuses and centralizes IoT and System of Record/Engagement data into a reactive knowledge graph. Cognitive applications and services are constructed with its multiagent architecture. The KR can scale-up and scaledown, because it exploits container-based, horizontally scalable services for graph store (JanusGraph) and pub-sub (Kafka) technologies. While the KR can be applied to many domains that require IoT and AI technologies, this paper describes how the KR specifically supports the challenging domain of cognitive eldercare. Rule- and machine learning-based analytics infer activities of daily living from IoT sensor readings. KR scalability, adaptability, flexibility and usability are demonstrated.

Keywords: ambient sensing, AI, artificial intelligence, eldercare, IoT, internet of things, knowledge graph

Procedia PDF Downloads 156

Authors: Fatma Abdedayem

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We propose an extension to the famous method called Bag of words (BOW) which proved a successful role in the field of image categorization. Practically, this method based on representing image with visual words. In this work, firstly, we extract features from images using Spatial Pyramid Representation (SPR) and two dissimilar color descriptors which are opponent-SIFT and transformed-color-SIFT. Secondly, we fuse color local features by joining the two histograms coming from these descriptors. Thirdly, after collecting of all features, we generate multi-dictionaries coming from n random feature subsets that obtained by dividing all features into n random groups. Then, by using these dictionaries separately each image can be represented by n histograms which are lately concatenated horizontally and form the final histogram, that allows to combine Multiple Dictionaries (MDBoW). In the final step, in order to classify image we have applied Support Vector Machine (SVM) on the generated histograms. Experimentally, we have used two dissimilar image datasets in order to test our proposition: Caltech 256 and PASCAL VOC 2007.

Keywords: bag of words (BOW), color descriptors, multi-dictionaries, MDBoW

Procedia PDF Downloads 287

Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

Procedia PDF Downloads 48

Authors: Bin Ji, Kai Xu, Shunyu Yao, Jingjing Liu, Ye Pan

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Human pose estimation is a popular research area in computer vision for its important application in human-machine interface. In recent years, 2D human pose estimation based on convolution neural network has got great progress and development. However, in more and more practical applications, people often need to deal with tasks based on video. It’s not far-fetched for us to consider how to combine the spatial and temporal information together to achieve a balance between computing cost and accuracy. To address this issue, this study proposes a new spatiotemporal model, namely Spatiotemporal Net (STNet) to combine both temporal and spatial information more rationally. As a result, the predicted keypoints heatmap is potentially more accurate and spatially more precise. Under the condition of ensuring the recognition accuracy, the algorithm deal with spatiotemporal series in a decoupled way, which greatly reduces the computation of the model, thus reducing the resource consumption. This study demonstrate the effectiveness of our network over the Penn Action Dataset, and the results indicate superior performance of our network over the existing methods.

Keywords: convolutional long short-term memory, deep learning, human pose estimation, spatiotemporal series

Procedia PDF Downloads 132

Authors: Arojojoye Oluwatosin Adetola, Nwaechefu Olajumoke Olufunlayo, Ademola Adetokunbo Oyagbemi, Jeremiah Moyinoluwalogo Afolabi, Asaolu Racheal Oluwabukola

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The effects of man’s activities on the environment include depletion of natural resources alongside pollution of water bodies. Petroleum exploration in the Niger Delta region of Nigeria has compromised the aquatic environment with grave consequences on the entire ecosystem. In this study, we assessed the environmental safety of Araromi River, located in an oil-producing area in Ondo State, in the Niger Delta region of Nigeria by determining the levels of heavy metals (copper, cadmium, chromium, nickel, lead) and some biomarkers of oxidative stress (malondialdehyde, glutathione-S-transferase, glutathione peroxidase, catalase, superoxide dismutase, myeloperoxidase and reduced glutathione) in Clarias gariepinus (350-400g) from the river using standard methods. Clarias gariepinus from a clean fish farm in the same geographical location as the reference site (Ilesannmi fishery) was used as a control. Water samples from both sites were also analysed for some physicochemical parameters, heavy metals, and bacterial contamination. Our findings show a significant increase in malondialdehyde level (index of lipid peroxidation) as well as alterations in antioxidant status in the organs of Clarias gariepinus from Araromi River compared with control. A significant increase in bacterial contaminants, heavy metal pollutants, and particulate matter deposits were also observed in the water sample from Araromi River compared with control. In conclusion, high levels of indicators of environmental pollution observed in the water sample from Araromi River coupled with induction of oxidative stress in Clarias gariepinus from the river show that Araromi River is polluted; therefore, consumption of fishes and other aquatic organisms from the river may be unsafe for the people in that community.

Keywords: Araromi River, Clarias gariepinus, environmental pollution, heavy metals, oxidative stress

Procedia PDF Downloads 147