Search results for: Artificial Intelligence

Authors: P. Krachodnok

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In this paper, the design of a multiple U-slotted microstrip patch antenna with frequency selective surface (FSS) as a superstrate for WLAN and WiMAX applications is presented. The proposed antenna is designed by using substrate FR4 having permittivity of 4.4 and air substrate. The characteristics of the antenna are designed and evaluated the performance of modelled antenna using CST Microwave studio. The proposed antenna dual resonant frequency has been achieved in the band of 2.37-2.55 GHz and 3.4-3.6 GHz. Because of the impact of FSS superstrate, it is found that the bandwidths have been improved from 6.12% to 7.35 % and 3.7% to 5.7% at resonant frequencies 2.45 GHz and 3.5 GHz, respectively. The maximum gain at the resonant frequency of 2.45 and 3.5 GHz are 9.3 and 11.33 dBi, respectively.

Keywords: multi-slotted antenna, microstrip patch antenna, frequency selective surface, artificial magnetic conduction

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Authors: Ugo Galvanetto, Pirto G. Pavan, Mirco Zaccariotto

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The discipline of Computer-integrated surgery (CIS) will provide equipment able to improve the efficiency of healthcare systems and, which is more important, clinical results. Surgeons and machines will cooperate in new ways that will extend surgeons’ ability to train, plan and carry out surgery. Patient specific CIS of the brain requires several steps: 1 - Fast generation of brain models. Based on image recognition of MR images and equipped with artificial intelligence, image recognition techniques should differentiate among all brain tissues and segment them. After that, automatic mesh generation should create the mathematical model of the brain in which the various tissues (white matter, grey matter, cerebrospinal fluid …) are clearly located in the correct positions. 2 – Reliable and fast simulation of the surgical process. Computational mechanics will be the crucial aspect of the entire procedure. New algorithms will be used to simulate the mechanical behaviour of cutting through cerebral tissues. 3 – Real time provision of visual and haptic feedback A sophisticated human-machine interface based on ergonomics and psychology will provide the feedback to the surgeon. The present work will address in particular point 2. Modelling the cutting of soft tissue in a structure as complex as the human brain is an extremely challenging problem in computational mechanics. The finite element method (FEM), that accurately represents complex geometries and accounts for material and geometrical nonlinearities, is the most used computational tool to simulate the mechanical response of soft tissues. However, the main drawback of FEM lies in the mechanics theory on which it is based, classical continuum Mechanics, which assumes matter is a continuum with no discontinuity. FEM must resort to complex tools such as pre-defined cohesive zones, external phase-field variables, and demanding remeshing techniques to include discontinuities. However, all approaches to equip FEM computational methods with the capability to describe material separation, such as interface elements with cohesive zone models, X-FEM, element erosion, phase-field, have some drawbacks that make them unsuitable for surgery simulation. Interface elements require a-priori knowledge of crack paths. The use of XFEM in 3D is cumbersome. Element erosion does not conserve mass. The Phase Field approach adopts a diffusive crack model instead of describing true tissue separation typical of surgical procedures. Modelling discontinuities, so difficult when using computational approaches based on classical continuum Mechanics, is instead easy for novel computational methods based on Peridynamics (PD). PD is a non-local theory of mechanics formulated with no use of spatial derivatives. Its governing equations are valid at points or surfaces of discontinuity, and it is, therefore especially suited to describe crack propagation and fragmentation problems. Moreover, PD does not require any criterium to decide the direction of crack propagation or the conditions for crack branching or coalescence; in the PD-based computational methods, cracks develop spontaneously in the way which is the most convenient from an energy point of view. Therefore, in PD computational methods, crack propagation in 3D is as easy as it is in 2D, with a remarkable advantage with respect to all other computational techniques.

Keywords: computational mechanics, peridynamics, finite element, biomechanics

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Authors: Sabina Żołędowska, Tadeusz Ossowski, Robert Bogdanowicz, Jacek Ryl, Paweł Rostkowski, Michał Kruczkowski, Michał Sobaszek, Zofia Cebula, Grzegorz Skowierzak, Paweł Jakóbczyk, Lilit Hovhannisyan, Paweł Ślepski, Iwona Kaczmarczyk, Mattia Pierpaoli, Bartłomiej Dec, Dawid Nidzworski

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The circular economy of water presents a pressing environmental challenge in our society. Water contains various harmful substances, such as drugs, antibiotics, hormones, and dioxides, which can pose silent threats. Water pollution has severe consequences for aquatic ecosystems. It disrupts the balance of ecosystems by harming aquatic plants, animals, and microorganisms. Water pollution poses significant risks to human health. Exposure to toxic chemicals through contaminated water can have long-term health effects, such as cancer, developmental disorders, and hormonal imbalances. However, effective remediation systems can be implemented to remove these contaminants using electrocatalytic processes, which offer an environmentally friendly alternative to other treatment methods, and one of them is the innovative iCLARE system. The project's primary focus revolves around a few main topics: Reactor design and construction, selection of a specific type of reticulated vitreous carbon foams (RVC), analytical studies of harmful contaminants parameters and AI implementation. This high-performance electrochemical reactor will be build based on a novel type of electrode material. The proposed approach utilizes the application of reticulated vitreous carbon foams (RVC) with deposited modified metal oxides (MMO) and diamond thin films. The following setup is characterized by high surface area development and satisfactory mechanical and electrochemical properties, designed for high electrocatalytic process efficiency. The consortium validated electrode modification methods that are the base of the iCLARE product and established the procedures for the detection of chemicals detection: - deposition of metal oxides WO3 and V2O5-deposition of boron-doped diamond/nanowalls structures by CVD process. The chosen electrodes (porous Ferroterm electrodes) were stress tested for various parameters that might occur inside the iCLARE machine–corosis, the long-term structure of the electrode surface during electrochemical processes, and energetic efficacy using cyclic polarization and electrochemical impedance spectroscopy (before and after electrolysis) and dynamic electrochemical impedance spectroscopy (DEIS). This tool allows real-time monitoring of the changes at the electrode/electrolyte interphase. On the other hand, the toxicity of iCLARE chemicals and products of electrolysis are evaluated before and after the treatment using MARA examination (IBMM) and HPLC-MS-MS (NILU), giving us information about the harmfulness of using electrode material and the efficiency of iClare system in the disposal of pollutants. Implementation of data into the system that uses artificial intelligence and the possibility of practical application is in progress (SensDx).

Keywords: waste water treatement, RVC, electrocatalysis, paracetamol

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Authors: J. Jessa

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Background: Autism is a pervasive developmental disorder, the incidence of which has significantly increased in recent years. Children with autism have impairments in social skills, communication, and imagination. Children with autism has more common than healthy children feeding problems: food selectivity, problems with gastrointestinal tract: diarrhea, constipations, abdominal pain, reflux and others. Many parents of autistic children report that after implementation of gluten-, casein- and sugar free diet those symptoms disappear and even cognitive functions become better. Some children begin to understand speech and to communicate with parents, regain eye contact, become more calm, sleep better and has better concentration. Probably at the root of this phenomenon lies elimination from the diet peptides construction of which is similar to opiates. Enhanced permeability of gut causes absorption of not fully digested opioid-like peptides from food, like gluten and casein and probably others (proteins from soy and corn) which impact on brain of autistic children. Aim of the study: The aim of the study is to assess the safety of gluten-free diet in children with autism, aged 2,5-7. Methods: Participants of the study (n=70) – children aged 2,5-7 with autism are divided into 3 groups. The first group (research group) are patients whose parents want to implement a gluten-free diet. The second group are patients who have been recommended to eliminate from the diet artificial substances, such as preservatives, artificial colors and flavors, and others (control group 1). The third group (control group 2) are children whose parents did not agree for implementation of the diet. Caregivers of children on the diet are educated about the specifics of the diet and how to avoid malnutrition. At the start of the study we exclude celiac disease. Before the implementation of the diet we performe a blood test for patients (morphology, ferritin, total cholesterol, dry peripheral blood drops to detect some genetic metabolic diseases), plasma aminogram) and urine tests (excretion of ions: Mg, Na, Ca, the profile of organic acids in urine), which assess nutritional status as well as the psychological test assessing the degree of the child's psychological functioning (PEP-R). All of these tests will be repeated after one year from the implementation of the diet. Results: To the present moment we examined 42 children with autism. 12 of children are on gluten- free diet. Our preliminary results are promising. Parents of 9 of them report that, there is a big improvement in child behavior, concentration, less aggression incidents, better eye contact and better verbal skills. Conclusion: Our preliminary results suggest that dietary intervention may positively affect developmental outcome for some children diagnosed with ASD.

Keywords: gluten free diet, autism spectrum disorder, autism, blood test

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Authors: Tusar Kanti Dash, Ganapati Panda

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The evaluation of speech quality and intelligence is critical to the overall effectiveness of the Speech Enhancement Algorithms. Several intrusive and non-intrusive measures are employed to calculate these parameters. Non-Intrusive Evaluation is most challenging as, very often, the reference clean speech data is not available. In this paper, a novel non-intrusive speech evaluation measure is proposed using audio features derived from the Stockwell transform. These features are used with the Light Gradient Boosting Machine for the effective prediction of speech quality and intelligibility. The proposed model is analyzed using noisy and reverberant speech from four databases, and the results are compared with the standard Intrusive Evaluation Measures. It is observed from the comparative analysis that the proposed model is performing better than the standard Non-Intrusive models.

Keywords: non-Intrusive speech evaluation, S-transform, light GBM, speech quality, and intelligibility

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Authors: Timothy Garvey, Don Driscoll

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Within Tasmania, the growling grass frog (Litoria raniformis) has experienced a rapid contraction in distribution. This decline is primarily attributed to habitat loss through landscape modification and improved land drainage. Reductions in seasonal water-sources have placed increasing importance on permanent water bodies for reproduction and foraging. Tasmanian agricultural and commercial forestry landscapes often feature small artificial ponds, utilized for watering livestock and fighting wildfires. Improved knowledge of how L. raniformis may be exploiting anthropogenic ponds is required for improved conservation management. We implemented telemetric tracking in order to evaluate the spatial ecology of L. raniformis (n = 20) within agricultural and managed forestry sites, with tracking conducted periodically over the breeding season (November/December, January/February, March/April). We investigated (1) potential differences in habitat utilization between agricultural and plantation sites, and (2) the post-breeding dispersal of individual frogs. Frogs were found to remain in close proximity to ponds throughout November/December, with individuals occupying vegetative depauperate water bodies beginning to disperse by January/February. Dispersing individuals traversed exposed plantation understory and agricultural pasture land in order to enter patches of native scrubland. By March/April all individuals captured at minimally vegetated ponds had retreated to adjacent scrub corridors. Animals found in ponds featuring dense riparian vegetation were not recorded to disperse. No difference in behavior was recorded between sexes. Rising temperatures coincided with increased movement by individuals towards native scrub refugia. The patterns of movement reported in this investigation emphasize the significant contribution of manmade water-bodies towards the conservation of L. raniformis within modified landscapes. The use of natural scrubland as cyclical retreats between breeding seasons also highlights the importance of the continued preservation of remnant vegetation corridors. Loss of artificial dams or buffering scrubland in heavily altered landscapes could see the breakdown of the greater L. raniformis meta-population further threatening their regional persistence.

Keywords: habitat loss, modified landscapes, spatial ecology, telemetry

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Authors: Neerja Meena, Paresh Sukhani

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Dandy walker malformation is a generalised disorder of mesenchymal development that affect both the cerebellum and overlying meninges. Classically dandy-walker malformation consists of a triad of- 1:vermian and hemispheric cerebellar hypoplasia 2:cystic dilatation of 4th ventricle 3: enlarged posterior fossa with the upward migration of tentorium(lambdoid- torcular inversion). Clinical presentation: four months old female child with hydrocephalus and neurological symptoms. Generally- early death is common in classic dandy walker malformation. However, if it is relatively mild and uncomplicated by other CNS anomalies, intelligence can be normal and neurologic deficits minimal. Usually, VP shunting is the treatment of choice for this hydrocephalus. Conclusion: MRI is the modality of choice to diagnose posterior fossa malformation. However, it can be ruled out through using during the antenatal check as the prognosis of this malformation is not good; it's better to diagnose it inutero.

Keywords: Dandy Walker, Mri, Earlydaignosis, Treatment

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Authors: Oluwaponmile David Alao

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Misdiagnosis has been the major problem in health sector. Heart attack has been one of diseases that have high level of misdiagnosis recorded on the part of physicians. In this paper, an intelligent system has been developed for diagnosis of heart attack in the health sector. Dataset of heart attack obtained from UCI repository has been used. This dataset is made up of thirteen attributes which are very vital in diagnosis of heart disease. The system is developed on the multilayer perceptron trained with back propagation neural network then simulated with feed forward neural network and a recognition rate of 87% was obtained which is a good result for diagnosis of heart attack in medical field.

Keywords: heart attack, artificial neural network, diagnosis, intelligent system

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Authors: İkbal Eski

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This investigation is researched on design of robust neural network predictors for analyzing vibration effects on moving parts of a drilling machine. Moreover, the research is divided two parts; first part is experimental investigation, second part is simulation analysis with neural networks. Therefore, a real time the drilling machine is used to vibrations during working conditions. The measured real vibration parameters are analyzed with proposed neural network. As results: Simulation approaches show that Radial Basis Neural Network has good performance to adapt real time parameters of the drilling machine.

Keywords: artificial neural network, vibration analyses, drilling machine, robust

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Authors: Jing Jin

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The incorporation of artificial intelligence (AI) and machine learning (ML) in aircraft manufacturing and aerospace supply chains leads to the generation of a substantial amount of data among various tiers of suppliers and OEMs. Identifying the high-quality information challenges decision-makers. The application of AI/ML models necessitates access to 'high-quality' information to yield desired outputs. However, the process of information sharing introduces complexities, including distortion through various communication channels and biases introduced by both human and AI entities. This phenomenon significantly influences the quality of information, impacting decision-makers engaged in configuring supply chain systems. Traditionally, distorted information is categorized as 'low-quality'; however, this study challenges this perception, positing that distorted information, contributing to stakeholder goals, can be deemed high-quality within supply chains. The main aim of this study is to identify and evaluate the dimensions of information quality crucial to the UK aerospace supply chain. Guided by a central research question, "What information quality dimensions are considered when defining information quality in the UK aerospace supply chain?" the study delves into the intricate dynamics of information quality in the aerospace industry. Additionally, the research explores the nuanced impact of information distortion on stakeholders' decision-making processes, addressing the question, "How does the information distortion phenomenon influence stakeholders’ decisions regarding information quality in the UK aerospace supply chain system?" This study employs deductive methodologies rooted in positivism, utilizing a cross-sectional approach and a mono-quantitative method -a questionnaire survey. Data is systematically collected from diverse tiers of supply chain stakeholders, encompassing end-customers, OEMs, Tier 0.5, Tier 1, and Tier 2 suppliers. Employing robust statistical data analysis methods, including mean values, mode values, standard deviation, one-way analysis of variance (ANOVA), and Pearson’s correlation analysis, the study interprets and extracts meaningful insights from the gathered data. Initial analyses challenge conventional notions, revealing that information distortion positively influences the definition of information quality, disrupting the established perception of distorted information as inherently low-quality. Further exploration through correlation analysis unveils the varied perspectives of different stakeholder tiers on the impact of information distortion on specific information quality dimensions. For instance, Tier 2 suppliers demonstrate strong positive correlations between information distortion and dimensions like access security, accuracy, interpretability, and timeliness. Conversely, Tier 1 suppliers emphasise strong negative influences on the security of accessing information and negligible impact on information timeliness. Tier 0.5 suppliers showcase very strong positive correlations with dimensions like conciseness and completeness, while OEMs exhibit limited interest in considering information distortion within the supply chain. Introducing social network analysis (SNA) provides a structural understanding of the relationships between information distortion and quality dimensions. The moderately high density of ‘information distortion-by-information quality’ underscores the interconnected nature of these factors. In conclusion, this study offers a nuanced exploration of information quality dimensions in the UK aerospace supply chain, highlighting the significance of individual perspectives across different tiers. The positive influence of information distortion challenges prevailing assumptions, fostering a more nuanced understanding of information's role in the Industry 4.0 landscape.

Keywords: information distortion, information quality, supply chain configuration, UK aerospace industry

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Authors: Andrea Giacomelli, Luciano Massetti, Elena Maggi, Antonio Raschi

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The presentation will report the activities managed during the Summer of 2017 by a team composed by staff from a University Department, a National Research Council Institute, and an outreach NGO, collecting measurements of night sky brightness and other information on artificial lighting, in order to characterize light pollution issues on portions of the Tuscan coast, in Central Italy. These activities combine measurements collected by the principal scientists, citizen science observations led by students, and outreach events targeting a broad audience. This campaign aggregates the efforts of three actors: the BuioMetria Partecipativa project, which started collecting light pollution data on a national scale in 2008 with an environmental engineering and free/open source GIS core team; the Institute of Biometeorology from the National Research Council, with ongoing studies on light and urban vegetation and a consolidated track record in environmental education and citizen science; the Department of Biology from the University of Pisa, which started experiments to assess the impact of light pollution in coastal environments in 2015. While the core of the activities concerns in situ data, the campaign will account also for remote sensing data, thus considering heterogeneous data sources. The aim of the campaign is twofold: (1) To test actions of citizen and student engagement in monitoring sky brightness (2) To collect night sky brightness data and test a protocol for applications to studies on the ecological impact of light pollution, with a special focus on marine coastal ecosystems. The collaboration of an interdisciplinary team in the study of artificial lighting issues is not a common case in Italy, and the possibility of undertaking the campaign in Tuscany has the added value of operating in one of the territories where it is possible to observe both sites with extremely high lighting levels, and areas with extremely low light pollution, especially in the Southern part of the region. Combining environmental monitoring and communication actions in the context of the campaign, this effort will contribute to the promotion of night skies with a good quality as an important asset for the sustainability of coastal ecosystems, as well as to increase citizen awareness through star gazing, night photography and actively participating in field campaign measurements.

Keywords: citizen science, light pollution, marine coastal biodiversity, environmental education

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Authors: Maria David, Tunde A. Tasko, Katalin Hejja-Nagy, Laszlo Dorner

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The authors have been concerned with research on learning since 1998. Recently, the focus of our interest is how prevalent use of information and communication technology (ICT) influences students' learning abilities, skills of self-regulated learning and learning efficiency. Nowadays, there are three dominant theories about the psychic effects of ICT use: According to social optimists, modern ICT devices have a positive effect on thinking. As to social pessimists, this effect is rather negative. And, regarding the views of biological optimists, the change is obvious, but these changes can fit into the mankind's evolved neurological system as did writing long ago. Mentality of 'digital natives' differ from that of elder people. They process information coming from the outside world in an other way, and different experiences result in different cerebral conformation. In this regard, researchers report about both positive and negative effects of ICT use. According to several studies, it has a positive effect on cognitive skills, intelligence, school efficiency, development of self-regulated learning, and self-esteem regarding learning. It is also proven, that computers improve skills of visual intelligence such as spacial orientation, iconic skills and visual attention. Among negative effects of frequent ICT use, researchers mention the decrease of critical thinking, as permanent flow of information does not give scope for deeper cognitive processing. Aims of our present study were to uncover developmental characteristics of self-regulated learning in different age groups and to study correlations of learning efficiency, the level of self-regulated learning and frequency of use of computers. Our subjects (N=1600) were primary and secondary school students and university students. We studied four age groups (age 10, 14, 18, 22), 400 subjects of each. We used the following methods: the research team developed a questionnaire for measuring level of self-regulated learning and a questionnaire for measuring ICT use, and we used documentary analysis to gain information about grade point average (GPA) and results of competence-measures. Finally, we used computer tasks to measure cognitive abilities. Data is currently under analysis, but as to our preliminary results, frequent use of computers results in shorter response time regarding every age groups. Our results show that an ordinary extent of ICT use tend to increase reading competence, and had a positive effect on students' abilities, though it didn't show relationship with school marks (GPA). As time passes, GPA gets worse along with the learning material getting more and more difficult. This phenomenon draws attention to the fact that students are unable to switch from guided to independent learning, so it is important to consciously develop skills of self-regulated learning.

Keywords: digital natives, ICT, learning efficiency, reading competence, self-regulated learning

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Authors: Kumar Satyam Das, Murali Raghunathan

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This note discusses the feasibility of field development of a challenging shallow offshore field in South East Asia and how its learnings can be applied to marginal field development across the world especially developing marginal fields in this low oil price world. The field was found to be economically challenging even during high oil prices and the project was put on hold. Shell started development study with the aim to significantly reduce cost through competitively scoping and revive stranded projects. The proposed strategy to achieve this involved Improve Per platform recovery and Reduction in CAPEX. Methodology: Based on various Benchmarking Tool such as Woodmac for similar projects in the region and economic affordability, a challenging target of 50% reduction in unit development cost (UDC) was set for the project. Technical scope was defined to the minimum as to be a wellhead platform with minimum functionality to ensure production. The evaluation of key project decisions like Well location and number, well design, Artificial lift methods and wellhead platform type under different development concept was carried out through integrated multi-discipline approach. Key elements influencing per platform recovery were Wellhead Platform (WHP) location, Well count, well reach and well productivity. Major Findings: Reservoir being shallow posed challenges in well design (dog-leg severity, casing size and the achievable step-out), choice of artificial lift and sand-control method. Integrated approach amongst relevant disciplines with challenging mind-set enabled to achieve optimized set of development decisions. This led to significant improvement in per platform recovery. It was concluded that platform recovery largely depended on the reach of the well. Choice of slim well design enabled designing of high inclination and better productivity wells. However, there is trade-off between high inclination Gas Lift (GL) wells and low inclination wells in terms of long term value, operational complexity, well reach, recovery and uptime. Well design element like casing size, well completion, artificial lift and sand control were added successively over the minimum technical scope design leading to a value and risk staircase. Logical combinations of options (slim well, GL) were competitively screened to achieve 25% reduction in well cost. Facility cost reduction was achieved through sourcing standardized Low Cost Facilities platform in combination with portfolio execution to maximizing execution efficiency; this approach is expected to reduce facilities cost by ~23% with respect to the development costs. Further cost reductions were achieved by maximizing use of existing facilities nearby; changing reliance on existing water injection wells and utilizing existing water injector (W.I.) platform for new injectors. Conclusion: The study provides a spectrum of technically feasible options. It also made clear that different drivers lead to different development concepts and the cost value trade off staircase made this very visible. Scoping of the project through competitive way has proven to be valuable for decision makers by creating a transparent view of value and associated risks/uncertainty/trade-offs for difficult choices: elements of the projects can be competitive, whilst other parts will struggle, even though contributing to significant volumes. Reduction in UDC through proper scoping of present projects and its benchmarking paves as a learning for the development of marginal fields across the world, especially in this low oil price scenario. This way of developing a field has on average a reduction of 40% of cost for the Shell projects.

Keywords: benchmarking, full field development, CAPEX, feasibility

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Authors: Majid Rezayi, Sh. Shahaboddin, HNM E. Mahmud, A. Yadollah, A. Saeid, A. Yatimah

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In this study, the adaptive neuro-fuzzy inference system (ANFIS) was applied to obtain the membrane composition model affecting the potential response of our reported polymeric PVC sensor for determining the titanium (III) ions. The performance statistics of the artificial neural network (ANN) and linear regression models for potential slope prediction of membrane composition of titanium (III) ion selective electrode were compared with ANFIS technique. The results show that the ANFIS model can be used as a practical tool for obtaining the Nerntian slope of the proposed sensor in this study.

Keywords: adaptive neuro fuzzy inference, PVC sensor, titanium (III) ions, Nerntian slope

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Authors: Nuwan Kuruwitaarachchi, M. K. M. Peiris, C. N. Madawala, K. M. A. R. Perera, V. U. N Perera

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Dealing with businesses with the foreign market always took a special place in a country’s economy. Political and social factors came into play making currency rate changes fluctuate rapidly. Currency rate prediction has become an important factor for larger international businesses since large amounts of money exchanged between countries. This research focuses on comparing the accuracy of mainly three models; Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks(ANN) and Support Vector Machines(SVM). series of data import, export, USD currency exchange rate respect to LKR has been selected for training using above mentioned algorithms. After training the data set and comparing each algorithm, it was able to see that prediction in SVM performed better than other models. It was improved more by combining SVM and SVR models together.

Keywords: ARIMA, ANN, FFNN, RMSE, SVM, SVR

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Authors: M. Khouil, N. Saber, M. Mestari

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This study aimed to propose, a different architecture of a Path Planning using the NECMOP. where several nonlinear objective functions must be optimized in a conflicting situation. The ability to detect and avoid collision is very important for mobile intelligent machines. However, many artificial vision systems are not yet able to quickly and cheaply extract the wealth information. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons linear and threshold logic, which simplified the actual implementation of all the networks proposed. This article represents a comprehensive algorithm that determine through the AMAXNET network a measure (a mini-maximum point) in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: path planning, collision detection, convex polyhedron, neural network

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Authors: Mohammad Nadeem, Haider Banka, R. Venugopal

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Granulation or agglomeration is a size enlargement process to transform the fine particulates into larger aggregates since the fine size of available materials and minerals poses difficulty in their utilization. Though a long list of methods is available in the literature for the modeling of granulation process to facilitate the in-depth understanding and interpretation of the system, there is still scope of improvements using novel tools and techniques. Intelligent techniques, such as artificial neural network, fuzzy logic, self-organizing map, support vector machine and others, have emerged as compelling alternatives for dealing with imprecision and complex non-linearity of the systems. The present study tries to review the applications of intelligent techniques in the modeling of size enlargement process for fine materials.

Keywords: fine material, granulation, intelligent technique, modelling

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Authors: Saahith M. S., Sivakami R.

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In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

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Authors: Maria Ostroukhova, Zhanneta Zalutskaya, Elena Ermilova

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The alternative oxidase (AOX) mediates cyanide-resistant respiration, which bypasses proton-pumping complexes III and IV of the cytochrome pathway to directly transfer electrons from reduced ubiquinone to molecular oxygen. In Chlamydomonas reinhardtii, AOX is a monomeric protein that is encoded by two genes of discrete subfamilies, AOX1 and AOX2. Although AOX has been proposed to play essential roles in stress tolerance of organisms, the role of subfamily AOX2 is largely unknown. In C. reinhardtii, AOX2 was initially identified as one of constitutively low expressed genes. Like other photosynthetic organisms C. reinhardtii cells frequently experience periods of hypoxia. To examine AOX2 transcriptional regulation and role of AOX2 in hypoxia adaptation, real-time PCR analysis and artificial microRNA method were employed. Two experimental approaches have been used to induce the anoxic conditions: dark-anaerobic and light-anaerobic conditions. C. reinhardtii cells exposed to the oxygen deprivation have shown increased AOX2 mRNA levels. By contrast, AOX1 was not an anoxia-responsive gene. In C. reinhardtii, a subset of genes is regulated by transcription factor CRR1 in anaerobic conditions. Notable, the AOX2 promoter region contains the potential motif for CRR1 binding. Therefore, the role of CRR1 in the control of AOX2 transcription was tested. The CRR1-underexpressing strains, that were generated and characterized in this work, exhibited low levels of AOX2 transcripts under anoxic conditions. However, the transformants still slightly induced AOX2 gene expression in the darkness. These confirmed our suggestions that darkness is a regulatory stimulus for AOX genes in C. reinhardtii. Thus, other factors must contribute to AOX2 promoter activity under dark-anoxic conditions. Moreover, knock-down of CRR1 caused a complete reduction of AOX2 expression under light-anoxic conditions. These results indicate that (1) CRR1 is required for AOX2 expression during hypoxia, and (2) AOX2 gene is regulated by CRR1 together with yet-unknown regulatory factor(s). In addition, the AOX2-underexpressing strains were generated. The analysis of amiRNA-AOX2 strains suggested a role of this alternative oxidase in hypoxia adaptation of the alga. In conclusion, the results reported here show that C. reinhardtii AOX2 gene is stress inducible. CRR1 transcriptional factor is involved in the regulation of the AOX2 gene expression in the absence of oxygen. Moreover, AOX2 but not AOX1 functions under oxygen deprivation. This work was supported by Russian Science Foundation (research grant № 16-14-10004).

Keywords: alternative oxidase 2, artificial microRNA approach, chlamydomonas reinhardtii, hypoxia

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Authors: Yosra M. Badiei, Evelyn Ortiz, Marisa Portenti, David Szalda

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Water-oxidation half-reaction is a critical reaction that can be driven by a sustainable energy source (e.g., solar or wind) and be coupled with a chemical fuel making reaction which stores the released electrons and protons from water (e.g., H₂ or methanol). The use of molecular water-oxidation catalysts (WOC) allow the rationale design of redox active metal centers and provides a better understanding of their structure-activity-relationship. Herein, the structure of a Ru(III) complex bearing a doubly deprotonated N,N'-bis(aryl)pyridine-2,6-dicarboxamide ligand which contains a water molecule in its primary coordination sphere was elucidated by single-crystal X-ray diffraction. Further spectroscopic experimental data and pH-dependent electrochemical studies reveal its water-oxidation reactivity. Emphasis on mechanistic details for O₂ formation of this complex will be addressed.

Keywords: water-oxidation, catalysis, ruthenium, artificial photosynthesis

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Authors: Farnaz Farhangi

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Many people in New Zealand believe that the access to water is inexhaustible, and it comes from a history of virtually unrestricted access to it. For the region like Hamilton which is one of New Zealand’s fastest growing cities, it is crucial for policy makers to know about the future water consumption and implementation of rules and regulation such as universal water metering. Hamilton residents use water freely and they do not have any idea about how much water they use. Hence, one of proposed objectives of this research is focusing on forecasting water consumption using different methods. Residential water consumption time series exhibits seasonal and trend variations. Seasonality is the pattern caused by repeating events such as weather conditions in summer and winter, public holidays, etc. The problem with this seasonal fluctuation is that, it dominates other time series components and makes difficulties in determining other variations (such as educational campaign’s effect, regulation, etc.) in time series. Apart from seasonality, a stochastic trend is also combined with seasonality and makes different effects on results of forecasting. According to the forecasting literature, preprocessing (de-trending and de-seasonalization) is essential to have more performed forecasting results, while some other researchers mention that seasonally non-adjusted data should be used. Hence, I answer the question that is pre-processing essential? A wide range of forecasting methods exists with different pros and cons. In this research, I apply double seasonal ARIMA and Artificial Neural Network (ANN), considering diverse elements such as seasonality and calendar effects (public and school holidays) and combine their results to find the best predicted values. My hypothesis is the examination the results of combined method (hybrid model) and individual methods and comparing the accuracy and robustness. In order to use ARIMA, the data should be stationary. Also, ANN has successful forecasting applications in terms of forecasting seasonal and trend time series. Using a hybrid model is a way to improve the accuracy of the methods. Due to the fact that water demand is dominated by different seasonality, in order to find their sensitivity to weather conditions or calendar effects or other seasonal patterns, I combine different methods. The advantage of this combination is reduction of errors by averaging of each individual model. It is also useful when we are not sure about the accuracy of each forecasting model and it can ease the problem of model selection. Using daily residential water consumption data from January 2000 to July 2015 in Hamilton, I indicate how prediction by different methods varies. ANN has more accurate forecasting results than other method and preprocessing is essential when we use seasonal time series. Using hybrid model reduces forecasting average errors and increases the performance.

Keywords: artificial neural network (ANN), double seasonal ARIMA, forecasting, hybrid model

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Authors: Henrry Vicente Rojas Asuero, Holger Manuel Benavides Muñoz

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Approximately 80% of the energy consumed worldwide is generated from fossil sources, which are responsible for the emission of a large volume of greenhouse gases. For this reason, the global trend, at present, is the widespread use of energy produced from renewable sources. This seeks safety and diversification of energy supply, based on social cohesion, economic feasibility and environmental protection. In this scenario, small hydropower systems (P ≤ 10MW) stand out due to their high efficiency, economic competitiveness and low environmental impact. Small hydropower systems, along with wind and solar energy, are expected to represent a significant percentage of the world's energy matrix in the near term. Among the various technologies present in the state of the art, relating to small hydropower systems, is the Gravitational Water Vortex Power Plant, a recent technology that excels because of its versatility of operation, since it can operate with jumps in the range of 0.70 m-2.00 m and flow rates from 1 m3/s to 20 m3/s. Its operating system is based on the utilization of the energy of rotation contained within a large water vortex artificially induced. This paper presents the study and experimental design of an optimal hydraulic structure with the capacity to induce the artificial formation of a gravitational water vortex trough a system of easy application and high efficiency, able to operate in conditions of very low head and minimum flow. The proposed structure consists of a channel, with variable base, vortex inductor, tangential flow generator, coupled to a circular tank with a conical transition bottom hole. In the laboratory test, the angular velocity of the water vortex was related to the geometric characteristics of the inductor channel, as well as the influence of the conical transition bottom hole on the physical characteristics of the water vortex. The results show angular velocity values of greater magnitude as a function of depth, in addition the presence of the conical transition in the bottom hole of the circular tank improves the water vortex formation conditions while increasing the angular velocity values. Thus, the proposed system is a sustainable solution for the energy supply of rural areas near to watercourses.

Keywords: experimental model, gravitational water vortex power plant, renewable energy, small hydropower

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Authors: Melby Chacko, Jagannath Nayak

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The aging behaviour of 6061 Al-15 vol% SiC composite was investigated using Rockwell B hardness measurement. The composite was solutionized at 350°C and quenched in water. The composite was aged at room temperature (T4 treatment) and also at 140°C, 160°C, 180°C and 200°C (T6 treatment). The natural and artificial aging behaviour of composite was studied using aging curves determined at different temperatures. The aging period for peak aging for different temperatures was identified. The time required for attaining peak aging decreased with increase in the aging temperature. The peak hardness was found to increase with increase with aging temperature and the highest peak hardness was observed at 180ºC. Beyond 180ºC the peak hardness was found to be decreasing.

Keywords: 6061 Al-SiC composite, aging curve, Rockwell B hardness, T4, T6 treatments

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Authors: Lorena Blasco-Arcas, Francesca Pucciarelli

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Business contexts have become globalised and digitalised, which requires that managers develop a strong sense of cross-cultural intelligence while working in geographically distant teams by means of digital technologies. How to better equip future managers on these kinds of skills has been put forward as a critical issue in Business Schools. In pursuing these goals, higher education is shifting from a passive lecture approach, to more active and experiential learning approaches that are more suitable to learn skills. For example, through the use of case studies, proposing plausible business problem to be solved by students (or teams of students), these institutions have focused for long in fostering learning by doing. Though, case studies are no longer enough as a tool to promote active teamwork and experiential learning. Moreover, digital advancements applied to educational settings have enabled augmented classrooms, expanding the learning experience beyond the class, which increase students’ engagement and experiential learning. Different authors have highlighted the benefits of digital engagement in order to achieve a deeper and longer-lasting learning and comprehension of core marketing concepts. Clickers, computer-based simulations and business games have become fairly popular between instructors, but still are limited by the fact that are fictional experiences. Further exploration of real digital platforms to implement real, live projects in the classroom seem relevant for marketing and business education. Building on this, this paper describes the development of an experiential learning activity in class, in which students developed a communication campaign in teams using the BuzzFeed platform, and subsequently implementing the campaign by using other social media platforms (e.g. Facebook, Instagram, Twitter…). The article details the procedure of using the project for a marketing module in a Bachelor program with students located in France, Italy and Spain campuses working on multi-campus groups. Further, this paper describes the project outcomes in terms of students’ engagement and analytics (i.e. visits achieved). the project included a survey in order to analyze and identify main aspects related to how the learning experience is influenced by the cultural competence developed through working in geographically distant and culturally diverse teamwork. Finally, some recommendations to use project-based social media tools while working with virtual teamwork in the classroom are provided.

Keywords: cultural competences, experiential learning, social media, teamwork, virtual group work

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Authors: Angga S. Fajar, A. Aminullah, J. Kiyono, R. A. Safitri

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This paper discusses about the application of ANN for optimizing of bridge structure design. ANN has been applied in various field of science concerning prediction and optimization. The structural optimization has several benefit including accelerate structural design process, saving the structural material, and minimize self-weight and mass of structure. In this paper, there are three types of bridge structure that being optimized including PSC I-girder superstructure, composite steel-concrete girder superstructure, and RC bridge pier. The different optimization strategy on each bridge structure implement back propagation method of ANN is conducted in this research. The optimal weight and easier design process of bridge structure with satisfied error are achieved.

Keywords: bridge structures, ANN, optimization, back propagation

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Authors: Caner Koc, Dilara Gerdan Koc, Mustafa Vatandas

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The fundamental components of autonomous agricultural robot design, such as having a working understanding of coordinates, correctly constructing the desired route, and sensing environmental elements, are the most important. A variety of sensors, hardware, and software are employed by agricultural robots to find these systems.These enable the fully automated driving system of an autonomous vehicle to simulate how a human-driven vehicle would respond to changing environmental conditions. To calculate the vehicle's motion trajectory using data from the sensors, this automation system typically consists of a sophisticated software architecture based on object detection and driving decisions. In this study, the software architecture of an autonomous agricultural vehicle is compared to the trajectory planning techniques.

Keywords: agriculture 5.0, computational intelligence, motion planning, trajectory planning

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Authors: A. Majidian

Abstract:

The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.

Keywords: life prediction, condenser tube, neural network, fuzzy logic

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Authors: Yeliz Ekinci, Melis Koyuncu

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Successful planning of solid waste management systems requires successful prediction of the amount of solid waste generated in an area. Waste management planning can protect the environment and human health, hence it is tremendously important for countries. The lack of information in waste generation can cause many environmental and health problems. Turkey is a country that plans to join European Union, hence, solid waste management is one of the most significant criteria that should be handled in order to be a part of this community. Solid waste management system requires a good forecast of solid waste generation. Thus, this study aims to forecast solid waste generation in Turkey. Artificial Neural Network and Linear Regression models will be used for this aim. Many models will be run and the best one will be selected based on some predetermined performance measures.

Keywords: forecast, solid waste generation, solid waste management, Turkey

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Authors: Aldila Dyas Nurfitri

Abstract:

Nowadays, leadership has became the one of main issues in forming organization groups even countries. The concept of a social contract between the leaders and subordinates become one of the explanations for the leadership process. The interests of the two parties are not always the same, but they must work together to achieve both goals. Based on the concept at the previous it comes “The Leader Member Exchange Theory”—well known as LMX Theory, which assumes that leadership is a process of social interaction interplay between the leaders and their subordinates. High-quality LMX relationships characterized by a high carrying capacity, informal supervision, confidence, and power negotiation enabled, whereas low-quality LMX relationships are described by low support, large formal supervision, less or no participation of subordinates in decision-making, and less confidence as well as the attention of the leader Application of formal supervision system in a low LMX behavior was in line with strict controls on toxic leadership model. Leaders must be able to feel toxic control all aspects of the organization every time. Leaders with this leadership model does not give autonomy to the staff. This behavior causes stagnation and make a resistant organizational culture in an organization. In Indonesia, the pattern of toxic leadership later evolved into a dysfunctional system that is growing rapidly. One consequence is the emergence of corrupt behavior. According to Kellerman, corruption is defined as a pattern and some subordinates behave lie, cheat or steal to a degree that goes beyond the norm, they put self-interest than the common good.According to the corruption data in Indonesia based on the results of ICW research on 2012 showed that the local government sector ranked first with 177 cases. Followed by state or local enterprises as much as 41 cases. LMX is defined as the quality of the relationship between superiors and subordinates are implications for the effectiveness and progress of the organization. The assumption of this theory that leadership as a process of social interaction interplay between the leaders and his followers are characterized by a number of dimensions, such as affection, loyalty, contribution, and professional respect. Meanwhile, the toxic leadership is dysfunctional leadership in organization that is led by someone with the traits are not able to adjust, do not have integrity, malevolent, evil, and full of discontent marked by a number of characteristics, such as self-centeredness, exploiting others, controlling behavior, disrespecting others, suppress innovation and creativity of employees, and inadequate emotional intelligence. The leaders with some characteristics, such as high self-centeredness, exploiting others, controlling behavior, and disrespecting others, tends to describe a low LMX relationships directly with subordinates compared with low self-centeredness, exploiting others, controlling behavior, and disrespecting others. While suppress innovation and creativity of employees aspect and inadequate emotional intelligence, tend not to give direct effect to the low quality of LMX.

Keywords: leader-member exchange, toxic leadership, leadership

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Authors: Z. H. Safari, M. Anbia, G. H. Kouzegari, R. Amirkhani

Abstract:

Toluene and Nitro derivatives are widely used in industry particularly in various defense applications. Tri-nitro-toluene derivative is a powerful basic explosive material that is a basis upon which to compare equivalent explosive power of similar materials. The aim of this paper is to measure the explosive power of these hazardous substances in fuels having different shelf-life and therefore optimizing their storage and maintenance. The methodology involves measuring the amounts of di- nitro- toluene and tri-nitro-toluene in the aged samples at 90 ° C by gas chromatography. Results show no significant difference in the concentration of the TNT compound over a given time while there was a significant difference in DNT compound over the same period. The underlying reason is attributed to the simultaneous production of the material with destruction of stabilizer.

Keywords: dinitrotoluene, trinitrotoluene, double-base solid propellants, artificial aging

Procedia PDF Downloads 383