Search results for: algorithm techniques

Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Takuro Kida, Yuichi Kida

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We show a base of the new trend of algorithm mathematically that treats a historical reason of continuous discrimination in the world as well as its solution by introducing new concepts of parallel world that includes an invisible set of errors as its companion. With respect to a matrix operator-filter bank that the matrix operator-analysis-filter bank H and the matrix operator-sampling-filter bank S are given, firstly, we introduce the detailed algorithm to derive the optimum matrix operator-synthesis-filter bank Z that minimizes all the worst-case measures of the matrix operator-error-signals E(ω) = F(ω) − Y(ω) between the matrix operator-input-signals F(ω) and the matrix operator-output signals Y(ω) of the matrix operator-filter bank at the same time. Further, feedback is introduced to the above approximation theory and it is indicated that introducing conversations with feedback does not superior automatically to the accumulation of existing knowledge of signal prediction. Secondly, the concept of category in the field of mathematics is applied to the above optimum signal approximation and is indicated that the category-based approximation theory is applied to the set-theoretic consideration of the recognition of humans. Based on this discussion, it is shown naturally why the narrow perception that tends to create isolation shows an apparent advantage in the short term and, often, why such narrow thinking becomes intimate with discriminatory action in a human group. Throughout these considerations, it is presented that, in order to abolish easy and intimate discriminatory behavior, it is important to create a parallel world of conception where we share the set of invisible error signals, including the words and the consciousness of both worlds.

Keywords: signal prediction, pseudo inverse matrix, artificial intelligence, conditional optimization

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Authors: Bhim Kumar Dahal

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Transportation network development in the developing country is in rapid pace. The majority of the network belongs to railway and expressway which passes through diverse topography, landform and geological conditions despite the avoidance principle during route selection. Construction of such networks demand many low to high embankment which required improvement in the foundation soil. This paper is mainly focused on the various advanced ground improvement techniques used to improve the soft soil, modelling approach and its predictability for embankments construction. The ground improvement techniques can be broadly classified in to three groups i.e. densification group, drainage and consolidation group and reinforcement group which are discussed with some case studies.  Various methods were used in modelling of the embankments from simple 1-dimensional to complex 3-dimensional model using variety of constitutive models. However, the reliability of the predictions is not found systematically improved with the level of sophistication.  And sometimes the predictions are deviated more than 60% to the monitored value besides using same level of erudition. This deviation is found mainly due to the selection of constitutive model, assumptions made during different stages, deviation in the selection of model parameters and simplification during physical modelling of the ground condition. This deviation can be reduced by using optimization process, optimization tools and sensitivity analysis of the model parameters which will guide to select the appropriate model parameters.

Keywords: cement, improvement, physical properties, strength

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Authors: Zeyad Abdelmageid, Xianbin Wang

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Choosing the operational channel for a WLAN access point (AP) in WLAN networks has been a static channel assignment process initiated by the user during the deployment process of the AP, which fails to cope with the dynamic conditions of the assigned channel at the station side afterward. However, the dramatically growing number of Wi-Fi APs and stations operating in the unlicensed band has led to dynamic, distributed, and often severe interference. This highlights the urgent need for the AP to dynamically select the best overall channel of operation for the basic service set (BSS) by considering the distributed and changing channel conditions at all stations. Consequently, dynamic channel selection algorithms which consider feedback from the station side have been developed. Despite the significant performance improvement, existing channel selection algorithms suffer from very high feedback overhead. Feedback latency from the STAs, due to the high overhead, can cause the eventually selected channel to no longer be optimal for operation due to the dynamic sharing nature of the unlicensed band. This has inspired us to develop our own dynamic channel selection algorithm with reduced overhead through the proposed low-overhead, cluster-based station reporting mechanism. The main idea behind the cluster-based station reporting is the observation that STAs which are very close to each other tend to have very similar channel conditions. Instead of requesting each STA to report on every candidate channel while causing high overhead, the AP divides STAs into clusters then assigns each STA in each cluster one channel to report feedback on. With the proper design of the cluster based reporting, the AP does not lose any information about the channel conditions at the station side while reducing feedback overhead. The simulation results show equal performance and, at times, better performance with a fraction of the overhead. We believe that this algorithm has great potential in designing future dynamic channel selection algorithms with low overhead.

Keywords: channel assignment, Wi-Fi networks, clustering, DBSCAN, overhead

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Authors: Nihal Atif Salim

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To improve the quality of life in cities, a variety of interventions are used. Noise is a substantial and important sort of pollution that has a negative impact on the urban environment and human health. According to the complaint survey, it ranks second among environmental contamination complaints (conducted by EEAA in 2019). The most significant source of noise in the city is traffic noise. In order to improve the sound urban environment, many physical techniques are applied. In the local area, noise barriers are considered as one of the most appropriate physical techniques along existing traffic routes. Alexandria is Egypt's second-largest city after Cairo. It is located along the Mediterranean Sea, and El- Gish Road is one of the city's main arteries. It impacts the waterfront promenade that extends along with the city by a high level of traffic noise. The purpose of this paper is to clarify the design considerations for the most appropriate noise barrier type along with the promenade, with the goal of improving the Quality of Life (QOL) and the sonic urban environment specifically. The proposed methodology focuses on how noise affects human perception and the environment. Then it delves into the various physical noise control approaches. After that, the paper discusses sustainable design decisions making. Finally, look into the importance of incorporating sustainability into design decisions making. Three stages will be followed in the case study. The first stage involves doing a site inspection and using specific sound measurement equipment (a noise level meter) to measure the noise level along the promenade at many sites, and the findings will be shown on a noise map. The second step is to inquire about the site's user experience. The third step is to investigate the various types of noise barriers and their effects on QOL along existing routes in order to select the most appropriate type. The goal of this research is to evaluate the suitable design of noise barriers that fulfill environmental and social perceptions while maintaining a balanced approach to the noise issue in order to improve QOL along existing roadways in the local area.

Keywords: noise pollution, sonic urban environment, traffic noise, noise barrier, acoustic sustainability, noise reduction techniques

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Authors: Magdy Roman, Mostafa Shoeib, Mostafa Rostom

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Global vision, whether provided by overhead fixed cameras, on-board aerial vehicle cameras, or satellite images can always provide detailed information on the environment around mobile robots. In this paper, an intelligent vision-based method of path planning and obstacle avoidance for mobile robots is presented. The method integrates visual perception with a new proposed field-based path-planning method to overcome common path-planning problems such as local minima, unreachable destination and unnecessary lengthy paths around obstacles. The method proposes an exponential angle deviation field around each obstacle that affects the orientation of a close robot. As the robot directs toward, the goal point obstacles are classified into right and left groups, and a deviation angle is exponentially added or subtracted to the orientation of the robot. Exponential field parameters are chosen based on Lyapunov stability criterion to guarantee robot convergence to the destination. The proposed method uses obstacles' shape and location, extracted from global vision system, through a collision prediction mechanism to decide whether to activate or deactivate obstacles field. In addition, a search mechanism is developed in case of robot or goal point is trapped among obstacles to find suitable exit or entrance. The proposed algorithm is validated both in simulation and through experiments. The algorithm shows effectiveness in obstacles' avoidance and destination convergence, overcoming common path planning problems found in classical methods.

Keywords: path planning, collision avoidance, convergence, computer vision, mobile robots

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Authors: Mochamad Nuruz Zaman, Mangatur Rudolf Nababan, M. A. Djatmika

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Both society and education areas teach to have good communication for building the interpersonal skills up. Everyone has the capacity to understand something new, either well comprehension or worst understanding. Worst understanding makes the language errors when the interactions are done by someone in the first meeting, and they do not know before it because of distance area. “The Space between Us” movie delivers the love-adventure story between Mars Boy and Earth Girl. They are so many missing conversations because of the different climate and environment. As the moviegoer also must be focused on the subtitle in order to enjoy well the movie. Furthermore, Indonesia subtitle and English conversation on the movie still have overlapping understanding in the translation. Translation hereby consists of source language -SL- (English conversation) and target language -TL- (Indonesia subtitle). These research gap above is formulated in research question by how the language errors happened in that movie and their effects on translation quality which is deepest analyzed by translation study toward discourse analysis approach. The research goal is to expand the language errors and their translation qualities in order to create a good atmosphere in movie media. The research is studied by embedded research in qualitative design. The research locations consist of setting, participant, and event as focused determined boundary. Sources of datum are “The Space between Us” movie and informant (translation quality rater). The sampling is criterion-based sampling (purposive sampling). Data collection techniques use content analysis and questioner. Data validation applies data source and method triangulation. Data analysis delivers domain, taxonomy, componential, and cultural theme analysis. Data findings on the language errors happened in the movie are referential, register, society, textual, receptive, expressive, individual, group, analogical, transfer, local, and global errors. Data discussions on their effects to translation quality are concentrated by translation techniques on their data findings; they are amplification, borrowing, description, discursive creation, established equivalent, generalization, literal, modulation, particularization, reduction, substitution, and transposition.

Keywords: discourse analysis, language errors, The Space between Us movie, translation techniques, translation quality instruments

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Authors: Mariangela Viviani, Daniele Germano, Simone Colace, Agostino Forestiero, Giuseppe Papuzzo, Sara Laurita

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Nowadays, people spend most of their time in closed environments, in offices, or at home. Therefore, secure and highly livable environmental conditions are needed to reduce the probability of aerial viruses spreading. Also, to lower the human impact on the planet, it is important to reduce energy consumption. Heating, Ventilation, and Air Conditioning (HVAC) systems account for the major part of energy consumption in buildings [1]. Devising systems to control and regulate the airflow is, therefore, essential for energy efficiency. Moreover, an optimal setting for thermal comfort and air quality is essential for people’s well-being, at home or in offices, and increases productivity. Thanks to the features of Artificial Intelligence (AI) tools and techniques, it is possible to design innovative systems with: (i) Improved monitoring and prediction accuracy; (ii) Enhanced decision-making and mitigation strategies; (iii) Real-time air quality information; (iv) Increased efficiency in data analysis and processing; (v) Advanced early warning systems for air pollution events; (vi) Automated and cost-effective m onitoring network; and (vii) A better understanding of air quality patterns and trends. We propose AIR SAFE, an IoT-based infrastructure designed to optimize air quality and thermal comfort in indoor environments leveraging AI tools. AIR SAFE employs a network of smart sensors collecting indoor and outdoor data to be analyzed in order to take any corrective measures to ensure the occupants’ wellness. The data are analyzed through AI algorithms able to predict the future levels of temperature, relative humidity, and CO₂ concentration [2]. Based on these predictions, AIR SAFE takes actions, such as opening/closing the window or the air conditioner, to guarantee a high level of thermal comfort and air quality in the environment. In this contribution, we present the results from the AI algorithm we have implemented on the first s et o f d ata c ollected i n a real environment. The results were compared with other models from the literature to validate our approach.

Keywords: air quality, internet of things, artificial intelligence, smart home

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Authors: Drago Diaz Aleman, Jose Luis Saorin Perez, Cecile Meier, Itahisa Perez Conesa, Jorge De La Torre Cantero

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Traditional manufacturing techniques used in artistic contexts compete with highly productive and efficient industrial procedures. The craft techniques and associated business models tend to disappear under the pressure of the appearance of mass-produced products that compete in all niche markets, including those traditionally reserved for the work of art. The surplus value derived from the prestige of the author, the exclusivity of the product or the mastery of the artist, do not seem to be sufficient reasons to preserve this productive model. In the last years, the adoption of open source digital manufacturing technologies in small art workshops can favor their permanence by assuming great advantages such as easy accessibility, low cost, and free modification, adapting to specific needs of each workshop. It is possible to use pieces modeled by computer and made with FDM (Fused Deposition Modeling) 3D printers that use PLA (polylactic acid) in the procedures of artistic casting. Models printed by PLA are limited to approximate minimum sizes of 3 cm, and optimal layer height resolution is 0.1 mm. Due to these limitations, it is not the most suitable technology for artistic casting processes of smaller pieces. An alternative to solve size limitation, are printers from the type (SLS) "selective sintering by laser". And other possibility is a laser hardens, by layers, metal powder and called DMLS (Direct Metal Laser Sintering). However, due to its high cost, it is a technology that is difficult to introduce in small artistic foundries. The low-cost DLP (Digital Light Processing) type printers can offer high resolutions for a reasonable cost (around 0.02 mm on the Z axis and 0.04 mm on the X and Y axes), and can print models with castable resins that allow the subsequent direct artistic casting in precious metals or their adaptation to processes such as electroforming. In this work, the design of a DLP 3D printer is detailed, using backlit LCD screens with ultraviolet light. Its development is totally "open source" and is proposed as a kit made up of electronic components, based on Arduino and easy to access mechanical components in the market. The CAD files of its components can be manufactured in low-cost FDM 3D printers. The result is less than 500 Euros, high resolution and open-design with free access that allows not only its manufacture but also its improvement. In future works, we intend to carry out different comparative analyzes, which allow us to accurately estimate the print quality, as well as the real cost of the artistic works made with it.

Keywords: traditional artistic techniques, DLP 3D printer, artistic casting, electroforming

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Authors: M. Amrita, R. R. Srikant, A. V. Sita Rama Raju

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Water miscible cutting fluids are conventionally used to lubricate and cool the machining zone. But issues related to health hazards, maintenance and disposal costs have limited their usage, leading to application of Minimum Quantity Lubrication (MQL). To increase the effectiveness of MQL, nanocutting fluids are proposed. In the present work, water miscible nanographite cutting fluids of varying concentration are applied at cutting zone by two systems A and B. System A utilizes high pressure air and supplies cutting fluid at a flow rate of 1ml/min. System B uses low pressure air and supplies cutting fluid at a flow rate of 5ml/min. Their performance in machining is evaluated by measuring cutting temperatures, tool wear, cutting forces and surface roughness and compared with dry machining and flood machining. Application of nano cutting fluid using both systems showed better performance than dry machining. Cutting temperatures and cutting forces obtained by both techniques are more than flood machining. But tool wear and surface roughness showed improvement compared to flood machining. Economic analysis has been carried out in all the cases to decide the applicability of the techniques.

Keywords: economic analysis, machining, minimum quantity lubrication, nanofluid

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Authors: Mrinmoy Dhara, Vivek K. Sengar, Shovan L. Chattoraj, Soumiya Bhattacharjee

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Remote sensing techniques have emerged as an asset for various geological studies. Satellite images obtained by different sensors contain plenty of information related to the terrain. Digital image processing further helps in customized ways for the prospecting of minerals. In this study, an attempt has been made to map the hydrothermally altered zones using multispectral and hyperspectral datasets of South East Rajasthan. Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and Hyperion (Level1R) dataset have been processed to generate different Band Ratio Composites (BRCs). For this study, ASTER derived BRCs were generated to delineate the alteration zones, gossans, abundant clays and host rocks. ASTER and Hyperion images were further processed to extract mineral end members and classified mineral maps have been produced using Spectral Angle Mapper (SAM) method. Results were validated with the geological map of the area which shows positive agreement with the image processing outputs. Thus, this study concludes that the band ratios and image processing in combination play significant role in demarcation of alteration zones which may provide pathfinders for mineral prospecting studies.

Keywords: ASTER, hyperion, band ratios, alteration zones, SAM

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Authors: Joanna Estkowska

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The Sensory Integration Method is aimed primarily at children with learning disabilities. It can also be used as a complementary method in treatment of children with cerebral palsy, autistic, mentally handicapped, blind and deaf. Autism is holistic development disorder that manifests itself in the specific functioning of a child. The most characteristic are: disorders in communication, difficulties in social relations, rigid patterns of behavior and impairment in sensory processing. In addition to these disorders may occur abnormal intellectual development, attention deficit disorders, perceptual disorders and others. This study was focused on the application sensory integration techniques in science education of autistic students. The lack of proper sensory integration causes problems with complicated processes such as motor coordination, movement planning, visual or auditory perception, speech, writing, reading or counting. Good functioning and cooperation of proprioceptive, tactile and vestibular sense affect the child’s mastery of skills that require coordination of both sides of the body and synchronization of the cerebral hemispheres. These include, for example, all sports activities, precise manual skills such writing, as well as, reading and counting skills. All this takes place in stages. Achieving skills from the first stage determines the development of fitness from the next level. Any deficit in the scope of the first three stages can affect the development of new skills. This ultimately reflects on the achievements at school and in further professional and personal life. After careful analysis symptoms from the emotional and social spheres appear to be secondary to deficits of sensory integration. During our research, the students gained knowledge and skills in the classroom of experience by learning biology, chemistry and physics with application sensory integration techniques. Sensory integration therapy aims to teach the child an adequate response to stimuli coming to him from both the outside world and the body. Thanks to properly selected exercises, a child can improve perception and interpretation skills, motor skills, coordination of movements, attention and concentration or self-awareness, as well as social and emotional functioning.

Keywords: autism spectrum disorder, science education, sensory integration, special educational needs

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Authors: Ł. Morawiński, C. Jasiński, M. Jurkiewicz, S. Bou Habib, M. Bondyra

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With the development of optics, electronics, and computers, vision systems are increasingly used in various areas of life, science, and industry. Vision systems have a huge number of applications. They can be used in quality control, object detection, data reading, e.g., QR-code, etc. A large part of them is used for measurement purposes. Some of them make it possible to obtain a 3D reconstruction of the tested objects or measurement areas. 3D reconstruction algorithms are mostly based on creating depth maps from data that can be acquired from active or passive methods. Due to the specific appliance in airfield technology, only passive methods are applicable because of other existing systems working on the site, which can be blinded on most spectral levels. Furthermore, reconstruction is required to work long distances ranging from hundreds of meters to tens of kilometers with low loss of accuracy even with harsh conditions such as fog, rain, or snow. In response to those requirements, HRESS (Heliport REmote Safeguard System) was developed; which main part is a rotational head with a two-camera stereovision rig gathering images around the head in 360 degrees along with stereovision 3D reconstruction and point cloud combination. The sub-pixel analysis introduced in the HRESS system makes it possible to obtain an increased distance measurement resolution and accuracy of about 3% for distances over one kilometer. Ultimately, this leads to more accurate and reliable measurement data in the form of a point cloud. Moreover, the program algorithm introduces operations enabling the filtering of erroneously collected data in the point cloud. All activities from the programming, mechanical and optical side are aimed at obtaining the most accurate 3D reconstruction of the environment in the measurement area.

Keywords: airfield monitoring, artificial intelligence, stereovision, 3D reconstruction

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Authors: Johann Van Niekerk

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The disciplines of conducting (instrumental or choral) and of singing presume a willingness toward an open posture and, in many cases, demand it for effective communication and technique. Yet, this very openness, with the "spread-eagle" gesture as an extreme, is oftentimes counterintuitive for musicians and within the trajectory of human evolution. Conversely, it is in this very gesture of "taking up space" that confidence-gaining techniques such as the popular "power pose" are based. This paper consists primarily of a literature review, exploring the topics of physical openness and vulnerability, considering the semiotics of the "spread-eagle" and its accompanying letter X. A major finding of this research is the discrepancy between evolutionary instinct towards physical self-protection and “folding in” and the demands of the discipline of physical and gestural openness, expansiveness and vulnerability. A secondary finding is ways in which encouragement of confidence-gaining techniques may be more effective in obtaining the required results than insistence on vulnerability, which is influenced by various cultural contexts and socialization. Choral conductors and music educators are constantly seeking ways to promote engagement and healthy singing. Much of the information and direction toward this goal is gleaned by students from conducting gestures and other pedagogies employed in the rehearsal. The findings of this research provide yet another avenue toward reaching the goals required for sufficient and effective teaching and artistry on the part of instructors and students alike.

Keywords: conducting, gesture, music, pedagogy, posture, vulnerability

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Authors: Annalakshmi G., Sakthivel Murugan S.

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This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.

Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization

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Authors: Rashid S. Mohammad, Shicheng Zhang, Sun Lu, Syed Jamal-Ud-Din, Xinzhe Zhao

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A compositional reservoir simulation model (CMG-GEM) was used for cyclic CO₂ injection process in unconventional tight reservoir. Cyclic CO₂ injection is an enhanced oil recovery process consisting of injection, shut-in, and production. The study of cyclic CO₂ injection and hydrocarbon recovery in ultra-low permeability reservoirs is mainly a function of rock, fluid, and operational parameters. CMG-GEM was used to study several design parameters of cyclic CO₂ injection process to distinguish the parameters with maximum effect on the oil recovery and to comprehend the behavior of cyclic CO₂ injection in tight reservoir. On the other hand, permeability reduction induced by asphaltene precipitation is one of the major issues in the oil industry due to its plugging onto the porous media which reduces the oil productivity. In addition to asphaltene deposition, solubility of CO₂ in the aquifer is one of the safest and permanent trapping techniques when considering CO₂ storage mechanisms in geological formations. However, the effects of the above uncertain parameters on the process of CO₂ enhanced oil recovery have not been understood systematically. Hence, it is absolutely necessary to study the most significant parameters which dominate the process. The main objective of this study is to improve techniques for designing cyclic CO₂ injection process while considering the effects of asphaltene deposition and solubility of CO₂ in the brine in order to prevent asphaltene precipitation, minimize CO₂ emission, optimize cyclic CO₂ injection, and maximize oil production.

Keywords: tight reservoirs, cyclic O₂ injection, asphaltene, solubility, reservoir simulation

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Authors: Manel Langar, Riad Bourguiba, Jaouhar Mouine

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In this paper, we propose the design and implementation of an enhanced wormhole virtual channel on chip router. It is a heart of a mesh NoC using the XY deterministic routing algorithm. It is characterized by its simple virtual channel allocation strategy which allows reducing area and complexity of connections without affecting the performance. We implemented our router on a Tezzaron process to validate its performances. This router is a basic element that will be used later to design a 3D mesh NoC.

Keywords: NoC, mesh, router, 3D NoC

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Authors: Adam Majczak, Michał Bialy, Zbigniew Czyż, Zdzislaw Kaminski

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Increasingly stringent emission standards for passenger cars require us to find alternative drives. The share of electric vehicles in the sale of new cars increases every year. However, their performance and, above all, range cannot be today successfully compared to those of cars with a traditional internal combustion engine. Battery recharging lasts hours, which can be hardly accepted due to the time needed to refill a fuel tank. Therefore, the ways to reduce the adverse features of cars equipped with electric motors only are searched for. One of the methods is a combination of an electric engine as a main source of power and a small internal combustion engine as an electricity generator. This type of drive enables an electric vehicle to achieve a radically increased range and low emissions of toxic substances. For several years, the leading automotive manufacturers like the Mazda and the Audi together with the best companies in the automotive industry, e.g., AVL have developed some electric drive systems capable of recharging themselves while driving, known as a range extender. An electricity generator is powered by a Wankel engine that has seemed to pass into history. This low weight and small engine with a rotating piston and a very low vibration level turned out to be an excellent source in such applications. Its operation as an energy source for a generator almost entirely eliminates its disadvantages like high fuel consumption, high emission of toxic substances, or short lifetime typical of its traditional application. The operation of the engine at a constant rotational speed enables a significant increase in its lifetime, and its small external dimensions enable us to make compact modules to drive even small urban cars like the Audi A1 or the Mazda 2. The algorithm to maintain a constant speed was investigated on the engine dynamometer with an eddy current brake and the necessary measuring apparatus. The research object was the Aixro XR50 rotary engine with the electronic power supply developed at the Lublin University of Technology. The load torque of the engine was altered during the research by means of the eddy current brake capable of giving any number of load cycles. The parameters recorded included speed and torque as well as a position of a throttle in an inlet system. Increasing and decreasing load did not significantly change engine speed, which means that control algorithm parameters are correctly selected. This work has been financed by the Polish Ministry of Science and Higher Education.

Keywords: electric vehicle, power generator, range extender, Wankel engine

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Authors: Jamie E. Banker

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For many families, pregnancy and the postpartum time are filled with both anticipation and change. For some pregnant or postpartum women, this time is marked by the onset of a mood or anxiety disorder. Experiencing a mood or anxiety disorders during this time of life differs from depression or anxiety at other times of life. Not only because of the physical changes occurring in the mother’s body but also the mental and physical preparation necessary to redefine family roles, responsibilities, and develop new identities in the life transition. The presence of a mood or anxiety disorder can influence the way in which a mother defines herself and can complicate her understanding of her abilities and competencies as a mother. The complexity of experiencing a mood or anxiety disorder in the midst of these changes necessitates specific treatment interventions to match both the symptomatology and psychological adjustments. This study explores the use of narrative family therapy techniques when treating a mother who is experiencing postpartum depression. Externalization is a common technique used in narrative family therapy and can help client’s separate their identity from the problems they are experiencing. This is crucial to a new mom who is in the middle of defining her identity during her transition to parenthood. The goal of this study is to examine how the use of externalization techniques help postpartum women separate their mood and anxiety symptoms from their identity as a mother. An exploratory case study design was conducted in a single setting, private practice therapy office, and explored how a narrative family therapy approach can be used to treat perinatal mood and anxiety disorders. The therapy sessions were audio recorded and transcribed. Constructivism and narrative theory are used as theoretical frameworks and data from the therapy sessions, and a follow-up survey was triangulated and analyzed. During the course of the treatment, the participant reports using the new externalizing labels for her symptoms. Within one month of treatment, the participant reports that she could stop herself from thinking the harmful thoughts faster, and within three months, the harmful thoughts went away. The main themes in this study were building courage and less self-blame. This case highlights the role narrative family therapy can play in the treatment of perinatal mood and anxiety disorders and the importance of separating a women’s mood from her identity as a mother. This conceptual framework was beneficial to the postpartum mother when treating perinatal mood and anxiety disorder symptoms.

Keywords: externalizing techniques, narrative family therapy, perinatal mood and anxiety disorders, postpartum depression

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: Jaehong Yu, Seoung Bum Kim

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Clustering analysis can facilitate the extraction of intrinsic patterns in a dataset and reveal its natural groupings without requiring class information. For effective clustering analysis in high dimensional datasets, unsupervised dimensionality reduction is an important task. Unsupervised dimensionality reduction can generally be achieved by feature extraction or feature selection. In many situations, feature selection methods are more appropriate than feature extraction methods because of their clear interpretation with respect to the original features. The unsupervised feature selection can be categorized as feature subset selection and feature ranking method, and we focused on unsupervised feature ranking methods which evaluate the features based on their importance scores. Recently, several unsupervised feature ranking methods were developed based on ensemble approaches to achieve their higher accuracy and stability. However, most of the ensemble-based feature ranking methods require the true number of clusters. Furthermore, these algorithms evaluate the feature importance depending on the ensemble clustering solution, and they produce undesirable evaluation results if the clustering solutions are inaccurate. To address these limitations, we proposed an ensemble-based feature ranking method with random subspace and multiple-k ensemble (FRRM). The proposed FRRM algorithm evaluates the importance of each feature with the random subspace ensemble, and all evaluation results are combined with the ensemble importance scores. Moreover, FRRM does not require the determination of the true number of clusters in advance through the use of the multiple-k ensemble idea. Experiments on various benchmark datasets were conducted to examine the properties of the proposed FRRM algorithm and to compare its performance with that of existing feature ranking methods. The experimental results demonstrated that the proposed FRRM outperformed the competitors.

Keywords: clustering analysis, multiple-k ensemble, random subspace-based feature evaluation, unsupervised feature ranking

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Authors: Małgorzata Jakubowska, Monika Michałowska

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Since the beginning of the cinematographic industry, there has been a visible interest in two leading themes: death and sexuality. One of the reasons of the unfading popularity of these motives was the fact that death or sex employed as leitmotivs attracted great attention of the viewers, and this guaranteed a financial success. What seems interesting is the fact that the themes of death and sexuality/eroticism seem to be mutually exclusive in the mainstream movies to such extent that they almost never appear together on the screen. As leitmotivs they describe opposite experiences of human life, one refers to affirmation of life, the other points to atrophy and decay. This film paradigm is rarely challenged. Thus, a relatively less attention has been devoted so far to entwining dying and sexuality/eroticism in one movie. In our paper, we wish to have a closer look at the visualizations of dying with focus on the aspect of sexuality/eroticism. Our analysis will concentrate on the contemporary European and American cinema, and especially the recent productions that contribute to the cultural phenomenon of entwining the two realms of human life. We will investigate the main clichés, plot and visual schemes, motives and narrative techniques on the examples of Sweet November (2001), A Little Bit of Heaven (2011) and Now is good (2012). We will also shed some light on the recent film productions that seem to provide a shift in portraying the realms of dying and sexuality concentrating on The Garden of Earthly Delights (2003) as the most paradigmatic example.

Keywords: contemporary cinema, dying and sexuality, narrative techniques, plot and visual schemes

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Authors: Niroshan K. Walgama Wellalage, Tieling Zhang, Richard Dwight

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State-based Markov deterioration models (SMDM) sometimes fail to find accurate transition probability matrix (TPM) values, and hence lead to invalid future condition prediction or incorrect average deterioration rates mainly due to drawbacks of existing nonlinear optimization-based algorithms and/or subjective function types used for regression analysis. Furthermore, a set of separate functions for each condition state with age cannot be directly derived by using Markov model for a given bridge element group, which however is of interest to industrial partners. This paper presents a new approach for generating Homogeneous SMDM model output, namely, the Modified Weibull approach, which consists of a set of appropriate functions to describe the percentage condition prediction of bridge elements in each state. These functions are combined with Bayesian approach and Metropolis Hasting Algorithm (MHA) based Markov Chain Monte Carlo (MCMC) simulation technique for quantifying the uncertainty in model parameter estimates. In this study, factors contributing to rail bridge deterioration were identified. The inspection data for 1,000 Australian railway bridges over 15 years were reviewed and filtered accordingly based on the real operational experience. Network level deterioration model for a typical bridge element group was developed using the proposed Modified Weibull approach. The condition state predictions obtained from this method were validated using statistical hypothesis tests with a test data set. Results show that the proposed model is able to not only predict the conditions in network-level accurately but also capture the model uncertainties with given confidence interval.

Keywords: bridge deterioration modelling, modified weibull approach, MCMC, metropolis-hasting algorithm, bayesian approach, Markov deterioration models

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Authors: Koray Altintas, Ozalp Vayvay

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Increased global issues such as depletion of sources, environmental problems and social inequality triggered public awareness towards finding sustainable solutions in order to ensure the well-being of the current as well as future generations. Since energy plays a significant role in improved social and economic well-being and is imperative on both industrial and commercial wealth creation, it is a must to develop a standardized set of metrics which makes it possible to indicate the present condition relative to conditions in the past and to develop any perspective which is required to frame actions for the future. This is not an easy task by considering the complexity of the issue which requires integrating economic, environmental and social aspects of sustainable energy. Multi-criteria decision making (MCDM) can be considered as a form of integrated sustainability evaluation and a decision support approach that can be used to solve complex problems featuring; conflicting objectives, different forms of data and information, multi-interests and perspectives. On that matter, MCDM methods are useful for providing solutions to complex energy management problems. The aim of this study is to review MCDM approaches that can be used for examining sustainable energy management. This study presents an insight into MCDM techniques and methods that can be useful for engineers, researchers and policy makers working in the energy sector.

Keywords: sustainable energy, sustainability criteria, multi-criteria decision making, sustainability dimensions

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Authors: Maryam Haghi, Ivan Contreras, Nadia Bhuiyan

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Chemotherapy is one of the most popular and effective cancer treatments offered to patients in outpatient oncology centers. In such clinics, patients first consult with an oncologist and the oncologist may prescribe a chemotherapy treatment plan for the patient based on the blood test results and the examination of the health status. Then, when the plan is determined, a set of chemotherapy and consultation appointments should be scheduled for the patient. In this work, a comprehensive mathematical formulation for planning and scheduling different types of chemotherapy patients over a planning horizon considering blood test, consultation, pharmacy and treatment stages has been proposed. To be more realistic and to provide an applicable model, this study is focused on a case study related to a major outpatient cancer treatment clinic in Montreal, Canada. Comparing the results of the proposed model with the current practice of the clinic under study shows significant improvements regarding different performance measures. These major improvements in the patients’ schedules reveal that using optimization techniques in planning and scheduling of patients in such highly demanded cancer treatment clinics is an essential step to provide a good coordination between different involved stages which ultimately increases the efficiency of the entire system and promotes the staff and patients' satisfaction.

Keywords: chemotherapy patients scheduling, integer programming, integrated scheduling, staff balancing

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Authors: Oludare Olupemi Ezekiel

Abstract:

This research examines the rhythmic structure of Sakara music by tracing its roots and analyzing the various rhythmic patterns of this neo-traditional genre, as well as the contributions of the major exponents and contemporary practitioners, using these as a model for understanding and establishing African rhythms. Biography of the major exponents and contemporary practitioners, interviews and participant observational methods were used to elicit information. Samples of the genre which were chosen at random were transcribed, notated and analyzed for academic use and documentation. The research affirmed that rhythms such as the Hemiola, Cross-rhythm, Clave or Bell rhythm, Percussive, Speech and Melodic rhythm and other relevant rhythmic theories were prevalent and applicable to Sakara music, while making important contributions to musical scholarship through its analysis of the music. The analysis and discussions carried out in the research pointed towards a conclusion that the Yoruba musicians are guided by some preconceptions and sound musical considerations in making their rhythmic patterns, used as compositional techniques and not mere incidental occurrence. These rhythmic patterns, with its consequential socio-cultural connotations, enhance musical values and national identity in Nigeria. The study concludes by recommending that musicologists need to carry out more research into this and other neo-traditional genres in order to advance the globalisation of African music.

Keywords: compositional techniques, globalisation, identity, neo-traditional, rhythmic theory, Sakara music

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Authors: Min Wang, Sergey Utev

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The lessons from the 2007-09 global financial crisis have driven scientific research, which considers the design of new methodologies and financial models in the global market. The quantum mechanics approach was introduced in the unpredictable stock market modeling. One famous quantum tool is Feynman path integral method, which was used to model insurance risk by Tamturk and Utev and adapted to formalize the path-dependent option pricing by Hao and Utev. The research is based on the path-dependent calculation method, which is motivated by the Feynman path integral method. The path calculation can be studied in two ways, one way is to label, and the other is computational. Labeling is a part of the representation of objects, and generating functions can provide many different ways of representing share price paths. In this paper, the recent works on graphical theoretical construction of individual share price path via matroid is presented. Firstly, a study is done on the knowledge of matroid, relationship between lattice path matroid and Tutte polynomials and ways to connect points in the lattice path matroid and Tutte polynomials is suggested. Secondly, It is found that a general binary tree can be validly constructed from a connected lattice path matroid rather than general lattice path matroid. Lastly, it is suggested that there is a way to represent share price paths via a general binary tree, and an algorithm is developed to construct share price paths from general binary trees. A relationship is also provided between lattice integer points and Tutte polynomials of a transversal matroid. Use this way of connection together with the algorithm, a share price path can be constructed from a given connected lattice path matroid.

Keywords: combinatorial construction, graphical representation, matroid, path calculation, share price, Tutte polynomial

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Authors: Islam Sherif, Ahmed Ashour, Ahmed Hassan, Hatem Osman

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Anterior Cruciate Ligament (ACL) injuries are common among athletes and individuals participating in sports with sudden stops, pivots, and changes in direction. Arthroscopic surgery is the gold standard for ACL reconstruction, aiming to restore knee stability and function. Recent years have witnessed significant advancements in arthroscopic surgery techniques, graft materials, and technological innovations, revolutionizing the field of ACL reconstruction. This presentation delves into the latest advancements in arthroscopic surgery techniques for ACL reconstruction and their potential impact on patient outcomes. Traditionally, autografts from the patellar tendon, hamstring tendon, or quadriceps tendon have been commonly used for ACL reconstruction. However, recent studies have explored the use of allografts, synthetic scaffolds, and tissue-engineered grafts as viable alternatives. This abstract evaluates the benefits and potential drawbacks of each graft type, considering factors such as graft incorporation, strength, and risk of graft failure. Moreover, the application of augmented reality (AR) and virtual reality (VR) technologies in surgical planning and intraoperative navigation has gained traction. AR and VR platforms provide surgeons with detailed 3D anatomical reconstructions of the knee joint, enhancing preoperative visualization and aiding in graft tunnel placement during surgery. We discuss the integration of AR and VR in arthroscopic ACL reconstruction procedures, evaluating their accuracy, cost-effectiveness, and overall impact on surgical outcomes. Beyond graft selection and surgical navigation, patient-specific planning has gained attention in recent research. Advanced imaging techniques, such as MRI-based personalized planning, enable surgeons to tailor ACL reconstruction procedures to each patient's unique anatomy. By accounting for individual variations in the femoral and tibial insertion sites, this personalized approach aims to optimize graft placement and potentially improve postoperative knee kinematics and stability. Furthermore, rehabilitation and postoperative care play a crucial role in the success of ACL reconstruction. This abstract explores novel rehabilitation protocols, emphasizing early mobilization, neuromuscular training, and accelerated recovery strategies. Integrating technology, such as wearable sensors and mobile applications, into postoperative care can facilitate remote monitoring and timely intervention, contributing to enhanced rehabilitation outcomes. In conclusion, this presentation provides an overview of the cutting-edge advancements in arthroscopic surgery techniques for ACL reconstruction. By embracing innovative graft materials, augmented reality, patient-specific planning, and technology-driven rehabilitation, orthopedic surgeons and sports medicine specialists can achieve superior outcomes in ACL injury management. These developments hold great promise for improving the functional outcomes and long-term success rates of ACL reconstruction, benefitting athletes and patients alike.

Keywords: arthroscopic surgery, ACL, autograft, allograft, graft materials, ACL reconstruction, synthetic scaffolds, tissue-engineered graft, virtual reality, augmented reality, surgical planning, intra-operative navigation

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Authors: Ramzi Farra, Detre Teschner, Marc Willinger, Robert Schlögl

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Introduction: The conventional approach of characterizing solid catalysts under static conditions, i.e., before and after reaction, does not provide sufficient knowledge on the physicochemical processes occurring under dynamic conditions at the molecular level. Hence, the necessity of improving new in situ characterizing techniques with the potential of being used under real catalytic reaction conditions is highly desirable. In situ Prompt Gamma Activation Analysis (PGAA) is a rapidly developing chemical analytical technique that enables us experimentally to assess the coverage of surface species under catalytic turnover and correlate these with the reactivity. The catalytic HCl oxidation (Deacon reaction) over bulk ceria will serve as our example. Furthermore, the in situ Transmission Electron Microscopy is a powerful technique that can contribute to the study of atmosphere and temperature induced morphological or compositional changes of a catalyst at atomic resolution. The application of such techniques (PGAA and TEM) will pave the way to a greater and deeper understanding of the dynamic nature of active catalysts. Experimental/Methodology: In situ Prompt Gamma Activation Analysis (PGAA) experiments were carried out to determine the Cl uptake and the degree of surface chlorination under reaction conditions by varying p(O2), p(HCl), p(Cl2), and the reaction temperature. The abundance and dynamic evolution of OH groups on working catalyst under various steady-state conditions were studied by means of in situ FTIR with a specially designed homemade transmission cell. For real in situ TEM we use a commercial in situ holder with a home built gas feeding system and gas analytics. Conclusions: Two complimentary in situ techniques, namely in situ PGAA and in situ FTIR were utilities to investigate the surface coverage of the two most abundant species (Cl and OH). The OH density and Cl uptake were followed under multiple steady-state conditions as a function of p(O2), p(HCl), p(Cl2), and temperature. These experiments have shown that, the OH density positively correlates with the reactivity whereas Cl negatively. The p(HCl) experiments give rise to increased activity accompanied by Cl-coverage increase (opposite trend to p(O2) and T). Cl2 strongly inhibits the reaction, but no measurable increase of the Cl uptake was found. After considering all previous observations we conclude that only a minority of the available adsorption sites contribute to the reactivity. In addition, the mechanism of the catalysed reaction was proposed. The chlorine-oxygen competition for the available active sites renders re-oxidation as the rate-determining step of the catalysed reaction. Further investigations using in situ TEM are planned and will be conducted in the near future. Such experiments allow us to monitor active catalysts at the atomic scale under the most realistic conditions of temperature and pressure. The talk will shed a light on the potential and limitations of in situ PGAA and in situ TEM in the study of catalyst dynamics.

Keywords: CeO2, deacon process, in situ PGAA, in situ TEM, in situ FTIR

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Authors: H. N. S. Silva, S. N. Silva

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Introduction: The latest WHO data published in 2014 states that Sri Lanka has reached 2,773 of total deaths and over 14000 individuals’ sustained injuries due to RTAs each year. It was noticed in previous studies that policemen, three wheel drivers and also pedestrians were the first to respond to RTAs but the victim’s condition was aggravated due to unskilled attempts made by the responders while management of the victim’s wounds, moving and positioning of the victims and also mainly while transportation of the victims. Objective: To observe the practices of the urban public in Sri Lanka who are encountered with RTAs. Methods: A qualitative study was done to analyze public behavior seen on video recordings of scenes of accidents purposefully selected from social media, news websites, YouTube and Google. Results: The results showed that all individuals who tried to help during the RTA were middle aged men, who were mainly pedestrians, motorcyclists and policemen during that moment. Vast majority were very keen to actively help the victims to get to hospital as soon as possible and actively participated in providing 'aid'. But main problem was the first aid attempts were disorganized and uncoordinated. Even though all individuals knew how to control external bleeding, none of them was aware of spinal prevention techniques or management of limb injuries. Most of the transportation methods and transfer techniques used were inappropriate and more injury prone. Conclusions: The public actively engages in providing aid despite their inappropriate practices in giving first aid.

Keywords: encountered, pedestrians, road traffic accidents, urban public

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