Search results for: hybrid working models

Authors: Lisdey Espinoza Pedraza

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Organised crime in Mexico evolves faster that our capacity to understand and explain it. Organised gangs have become successful entrepreneurs in many ways ad they have somehow mimicked the working ways of the authorities and in many cases, they have successfully infiltrated the governmental spheres. This business model is only possible under a clear scheme of rampant impunity. Impunity, however, is not exclusive to the PRI. Nor the PRI, PAN, or PRD can claim the monopoly of corruption, but what is worse is that none can claim full honesty in their acts either. The current security crisis in Mexico shows a crisis in the Mexican political party system. Corruption today is not only a problem of dishonesty and the correct use of public resources. It is the principal threat to Mexican democracy, governance, and national security.

Keywords: security, war on drugs, drug trafficking, Mexico, Latin America, United States

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Authors: Alice Robba, Renaud Bouchet, Celine Barchasz, Jean-Francois Colin, Erik Elkaim, Kristina Kvashnina, Gavin Vaughan, Matjaz Kavcic, Fannie Alloin

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With their high theoretical energy density (~2600 Wh.kg-1), lithium/sulfur (Li/S) batteries are highly promising, but these systems are still poorly understood due to the complex mechanisms/equilibria involved. Replacing S8 by Li2S as the active material allows the use of safer negative electrodes, like silicon, instead of lithium metal. S8 and Li2S have different conductivity and solubility properties, resulting in a profoundly changed activation process during the first cycle. Particularly, during the first charge a high polarization and a lack of reproducibility between tests are observed. Differences observed between raw Li2S material (micron-sized) and that electrochemically produced in a battery (nano-sized) may indicate that the electrochemical process depends on the particle size. Then the major focus of the presented work is to deepen the understanding of the Li2S material charge mechanism, and more precisely to characterize the effect of the initial Li2S particle size both on the mechanism and the electrode preparation process. To do so, Li2S nanoparticles were synthetized according to two ways: a liquid path synthesis and a dissolution in ethanol, allowing Li2S nanoparticles/carbon composites to be made. Preliminary chemical and electrochemical tests show that starting with Li2S nanoparticles could effectively suppress the high initial polarization but also influence the electrode slurry preparation. Indeed, it has been shown that classical formulation process - a slurry composed of Polyvinylidone Fluoride polymer dissolved in N-methyle-2-pyrrolidone - cannot be used with Li2S nanoparticles. This reveals a complete different Li2S material behavior regarding polymers and organic solvents when going at the nanometric scale. Then the coupling between two operando characterizations such as X-Ray Diffraction (XRD) and X-Ray Absorption and Emission Spectroscopy (XAS/XES) have been carried out in order to interpret the poorly understood first charge. This study discloses that initial particle size of the active material has a great impact on the working mechanism and particularly on the different equilibria involved during the first charge of the Li2S based Li-ion batteries. These results explain the electrochemical differences and particularly the polarization differences observed during the first charge between micrometric and nanometric Li2S-based electrodes. Finally, this work could lead to a better active material design and so to more efficient Li2S-based batteries.

Keywords: Li-ion/Sulfur batteries, Li2S nanoparticles effect, Operando characterizations, working mechanism

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Authors: Peter Sauer, Thomas Hinze, Petra Hofstedt

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To avoid obstacles in the surrounding environment and to navigate to a given target belong to the most important tasks for mobile robots. According to these tasks different data structures are suitable. To avoid near obstacles, occupancy grid maps are an ideal representation of the surroundings. For less fine grained tasks, such as navigating from one room to another in an apartment, pure grid maps are inappropriate. Grid maps are very detailed, calculating paths to navigate between rooms based on grid maps would take too long. Instead, graph-based data structures, so-called topologies, turn out to be a proper choice for such tasks. In this paper we present two methods to dynamically create topologies from grid maps. Both methods are based on non-uniform grid maps. The topologies are generated on-the-fly and can easily be modified to represent changes in the environment. This allows a hybrid approach to control mobile robots, where, depending on the situation and the current task, either the grid map or the generated topology may be used.

Keywords: robot navigation, occupancy grids, topological maps, dynamic map creation

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Authors: Milica Karadžić, Lidija Jevrić, Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Anamarija Mandić, Aleksandar Oklješa, Andrea Nikolić, Marija Sakač, Katarina Penov Gaši

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Using chemometric approach, the relationships between the chromatographic lipophilicity and in silico molecular descriptors for twenty-nine selected steroid derivatives were studied. The chromatographic lipophilicity was predicted using artificial neural networks (ANNs) method. The most important in silico molecular descriptors were selected applying stepwise selection (SS) paired with partial least squares (PLS) method. Molecular descriptors with satisfactory variable importance in projection (VIP) values were selected for ANN modeling. The usefulness of generated models was confirmed by detailed statistical validation. High agreement between experimental and predicted values indicated that obtained models have good quality and high predictive ability. Global sensitivity analysis (GSA) confirmed the importance of each molecular descriptor used as an input variable. High-quality networks indicate a strong non-linear relationship between chromatographic lipophilicity and used in silico molecular descriptors. Applying selected molecular descriptors and generated ANNs the good prediction of chromatographic lipophilicity of the studied steroid derivatives can be obtained. This article is based upon work from COST Actions (CM1306 and CA15222), supported by COST (European Cooperation and Science and Technology).

Keywords: artificial neural networks, chemometrics, global sensitivity analysis, liquid chromatography, steroids

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Authors: Piotr A. Kowalski, Kasper Sapala, Wiktor Warchalowski

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PM10 is a suspended dust that primarily has a negative effect on the respiratory system. PM10 is responsible for attacks of coughing and wheezing, asthma or acute, violent bronchitis. Indirectly, PM10 also negatively affects the rest of the body, including increasing the risk of heart attack and stroke. Unfortunately, Poland is a country that cannot boast of good air quality, in particular, due to large PM concentration levels. Therefore, based on the dense network of Airly sensors, it was decided to deal with the problem of prediction of suspended particulate matter concentration. Due to the very complicated nature of this issue, the Machine Learning approach was used. For this purpose, Convolution Neural Network (CNN) neural networks have been adopted, these currently being the leading information processing methods in the field of computational intelligence. The aim of this research is to show the influence of particular CNN network parameters on the quality of the obtained forecast. The forecast itself is made on the basis of parameters measured by Airly sensors and is carried out for the subsequent day, hour after hour. The evaluation of learning process for the investigated models was mostly based upon the mean square error criterion; however, during the model validation, a number of other methods of quantitative evaluation were taken into account. The presented model of pollution prediction has been verified by way of real weather and air pollution data taken from the Airly sensor network. The dense and distributed network of Airly measurement devices enables access to current and archival data on air pollution, temperature, suspended particulate matter PM1.0, PM2.5, and PM10, CAQI levels, as well as atmospheric pressure and air humidity. In this investigation, PM2.5, and PM10, temperature and wind information, as well as external forecasts of temperature and wind for next 24h served as inputted data. Due to the specificity of the CNN type network, this data is transformed into tensors and then processed. This network consists of an input layer, an output layer, and many hidden layers. In the hidden layers, convolutional and pooling operations are performed. The output of this system is a vector containing 24 elements that contain prediction of PM10 concentration for the upcoming 24 hour period. Over 1000 models based on CNN methodology were tested during the study. During the research, several were selected out that give the best results, and then a comparison was made with the other models based on linear regression. The numerical tests carried out fully confirmed the positive properties of the presented method. These were carried out using real ‘big’ data. Models based on the CNN technique allow prediction of PM10 dust concentration with a much smaller mean square error than currently used methods based on linear regression. What's more, the use of neural networks increased Pearson's correlation coefficient (R²) by about 5 percent compared to the linear model. During the simulation, the R² coefficient was 0.92, 0.76, 0.75, 0.73, and 0.73 for 1st, 6th, 12th, 18th, and 24th hour of prediction respectively.

Keywords: air pollution prediction (forecasting), machine learning, regression task, convolution neural networks

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Authors: Josip Arneric, Antonio Sostaric

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Crikvenica is one of the favourite tourist destinations at the Adriatic Sea in Croatia which attracts guests with its natural beauties and is one of the top destinations with the highest number of overnights stays. The main purpose of this research is to determine which elements of tourist offer should be improved to achieve sustainable development of the town of Crikvenica. Namely, the survey results from a random sample have shown that the most common reason of the visits is relaxation and vacation, and that more attention should be given to the following categories: restaurants and the working hours of stores and banks. We have also examined whether or not there is a correlation between an average daily expenditure and the overnight stay. The paper ends with the conclusion and the recommendations on how to improve the tourist offer of Crikvenica in order to increase guest satisfaction and to keep its reputation at a high level.

Keywords: tourism development, survey, Crikvenica, management sciences

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Authors: Norfiza Ibrahim, Norshuhada Shiratuddin, Siti Mahfuzah Sarif

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Decision-making system is used to facilitate people in making the right choice for their important daily activities. For the youth, proper guidance in making important decisions is needed. Their skills in decision-making aid decisions will indirectly affect their future. For that reason, this study focuses on the intelligent aspects in the development of intelligent decision support application. The aid apparently integrates Personality Traits (PT) and Multiple Intelligence (MI) data in development of a computerized personal decision aid for youth named as Youth Personal Decision Aid (Youth PDA). This study is concerned with the aid’s helpfulness based on the hybrid intelligent process. There are four main items involved which are reliability, decision making effort, confidence, as well as decision process awareness. Survey method was applied to the actual user of this system, namely the school and the Institute of Higher Education (IPT)’s students. An establish instrument was used to evaluate the study. The results of the analysis and findings in the assessment indicates a high mean value of the four dimensions in helping Youth PDA to be accepted as a useful tool for the youth in decision-making.

Keywords: decision support, multiple intelligent, personality traits, youth personal decision aid

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Authors: Orsolya Horvath, Laszlo Deres, Krisztian Eros, Katalin Ordog, Tamas Habon, Balazs Sumegi, Kalman Toth, Robert Halmosi

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Introduction: Oxidative stress induces an imbalance in mitochondrial fusion and fission processes, finally leading to cell death. The two antioxidant molecules, BGP-15 and L2286 have beneficial effects on mitochondrial functions and on cellular oxidative stress response. In this work, we studied the effects of these compounds on the processes of mitochondrial quality control. Methods: We used H9c2 cardiomyoblast and isolated neonatal rat cardiomyocytes (NRCM) for the experiments. The concentration of stressors and antioxidants was beforehand determined with MTT test. We applied 1-Methyl-3-nitro-1-nitrosoguanidine (MNNG) in 125 µM, 400 µM and 800 µM concentrations for 4 and 8 hours on H9c2 cells. H₂O₂ was applied in 150 µM and 300 µM concentration for 0.5 and 4 hours on both models. L2286 was administered in 10 µM, while BGP-15 in 50 µM doses. Cellular levels of the key proteins playing role in mitochondrial dynamics were measured in Western blot samples. For the analysis of mitochondrial network dynamics, we applied electron microscopy and immunocytochemistry. Results: Due to MNNG treatment the level of fusion proteins (OPA1, MFN2) decreased, while the level of fission protein DRP1 elevated markedly. The levels of fusion proteins OPA1 and MNF2 increased in the L2286 and BGP-15 treated groups. During the 8 hour treatment period, the level of DRP1 also increased in the treated cells (p < 0.05). In the H₂O₂ stressed cells, administration of L2286 increased the level of OPA1 in both H9c2 and NRCM models. MFN2 levels in isolated neonatal rat cardiomyocytes raised considerably due to BGP-15 treatment (p < 0.05). L2286 administration decreased the DRP1 level in H9c2 cells (p < 0.05). We observed that the H₂O₂-induced mitochondrial fragmentation could be decreased by L2286 treatment. Conclusion: Our results indicated that the PARP-inhibitor L2286 has beneficial effect on mitochondrial dynamics during oxidative stress scenario, and also in the case of directly induced DNA damage. We could make the similar conclusions in case of BGP-15 administration, which, via reducing ROS accumulation, propagates fusion processes, this way aids preserving cellular viability. Funding: GINOP-2.3.2-15-2016-00049; GINOP-2.3.2-15-2016-00048; GINOP-2.3.3-15-2016-00025; EFOP-3.6.1-16-2016-00004; ÚNKP-17-4-I-PTE-209

Keywords: H9c2, mitochondrial dynamics, neonatal rat cardiomyocytes, oxidative stress

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Authors: Moustafa Osman Mohammed

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This paper will explore formation of HCl aerosol at atmospheric boundary layers and encourages the uptake of environmental modeling systems (EMSs) as a practice evaluation of gaseous emissions (“framework measures”) from small and medium-sized enterprises (SMEs). The conceptual model predicts greenhouse gas emissions to ecological points beyond landfill site operations. It focuses on incorporation traditional knowledge into baseline information for both measurement data and the mathematical results, regarding parameters influence model variable inputs. The paper has simplified parameters of aerosol processes based on the more complex aerosol process computations. The simple model can be implemented to both Gaussian and Eulerian rural dispersion models. Aerosol processes considered in this study were (i) the coagulation of particles, (ii) the condensation and evaporation of organic vapors, and (iii) dry deposition. The chemical transformation of gas-phase compounds is taken into account photochemical formulation with exposure effects according to HCl concentrations as starting point of risk assessment. The discussion set out distinctly aspect of sustainability in reflection inputs, outputs, and modes of impact on the environment. Thereby, models incorporate abiotic and biotic species to broaden the scope of integration for both quantification impact and assessment risks. The later environmental obligations suggest either a recommendation or a decision of what is a legislative should be achieved for mitigation measures of landfill gas (LFG) ultimately.

Keywords: air pollution, landfill emission, environmental management, monitoring/methods and impact assessment

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Authors: Ahmed Al Ansari, Khalid Al Khalifa

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Objective: Multisource feedback (MSF), also called the 360-Degree evaluation is an evaluation process by which questionnaires are distributed amongst medical peers and colleagues to assess physician performance from different sources other than the attending or the supervising physicians. The aim of this study was to design, implement, and evaluate a 360-Degree process in assessing emergency physicians trainee in the Kingdom of Bahrain. Method: The study was undertaken in Bahrain Defense Force Hospital which is a military teaching hospital in the Kingdom of Bahrain. Thirty emergency physicians (who represent the total population of the emergency physicians in our hospital) were assessed in this study. We developed an instrument modified from the Physician achievement review instrument PAR which was used to assess Physician in Alberta. We focused in our instrument to assess professionalism, communication skills and collaboration only. To achieve face and content validity, table of specification was constructed and a working group was involved in constructing the instrument. Expert opinion was considered as well. The instrument consisted of 39 items; were 15 items to assess professionalism, 13 items to assess communication skills, and 11 items to assess collaboration. Each emergency physicians was evaluated with 3 groups of raters, 4 Medical colleague emergency physicians, 4 medical colleague who are considered referral physicians from different departments, and 4 Coworkers from the emergency department. Independent administrative team was formed to carry on the responsibility of distributing the instruments and collecting them in closed envelopes. Each envelope was consisted of that instrument and a guide for the implementation of the MSF and the purpose of the study. Results: A total of 30 emergency physicians 16 males and 14 females who represent the total number of the emergency physicians in our hospital were assessed. The total collected forms is 269, were 105 surveys from coworkers working in emergency department, 93 surveys from medical colleague emergency physicians, and 116 surveys from referral physicians from different departments. The total mean response rates were 71.2%. The whole instrument was found to be suitable for factor analysis (KMO = 0.967; Bartlett test significant, p<0.00). Factor analysis showed that the data on the questionnaire decomposed into three factors which counted for 72.6% of the total variance: professionalism, collaboration, and communication. Reliability analysis indicated that the instrument full scale had high internal consistency (Cronbach’s α 0.98). The generalizability coefficients (Ep2) were 0.71 for the surveys. Conclusions: Based on the present results, the current instruments and procedures have high reliability, validity, and feasibility in assessing emergency physicians trainee in the emergency room.

Keywords: MSF system, emergency, validity, generalizability

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Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

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Authors: Abdelhadi Adel, Kadri Ouahab

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This paper proposes an improved approach based on MAS Architecture and Heuristic Algorithm for systematic maintenance to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.

Keywords: multi-agent systems, emergence, genetic algorithm, makespan, systematic maintenance, scheduling, hybrid flow shop scheduling

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Authors: Maymona Al-Husari, Craig Murdoch, Steven Webb

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Some tumors are known to exhibit an extracellular pH that is more acidic than the intracellular, creating a 'reversed pH gradient' across the cell membrane and this has been shown to affect their invasive and metastatic potential. Tumour hypoxia also plays an important role in tumour development and has been directly linked to both tumour morphology and aggressiveness. In this paper, we present a hybrid mathematical model of intracellular pH regulation that examines the effect of oxygen and pH on tumour growth and morphology. In particular, we investigate the impact of pH regulatory mechanisms on the cellular pH gradient and tumour morphology. Analysis of the model shows that: low activity of the Na+/H+ exchanger or a high rate of anaerobic glycolysis can give rise to a 'fingering' tumour morphology; and a high activity of the lactate/H+ symporter can result in a reversed transmembrane pH gradient across a large portion of the tumour mass. Also, the reversed pH gradient is spatially heterogenous within the tumour, with a normal pH gradient observed within an intermediate growth layer, that is the layer between the proliferative inner and outermost layer of the tumour.

Keywords: acidic pH, cellular automaton, ebola, tumour growth

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Authors: M. J. Eichner, Y. S. Sarhan

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Overall study purpose has been the evaluation of ‘Vertical Villages’ as a new sustainable building typology, reducing significantly negative impacts of rapid urbanization processes in third world capital cities. Commonly in fast-growing cities, housing and job supply, educational and recreational opportunities, as well as public transportation infrastructure, are not accommodating rapid population growth, exposing people to high noise and emission polluted living environments with low-quality neighborhoods and a lack of recreational areas. Like many others, Egypt’s capital city Cairo, according to the UN facing annual population growth rates of up to 428.000 people, is struggling to address the general deterioration of urban living conditions. New settlements typologies and urban reconstruction approach hardly follow sustainable urbanization principles or socio-ecologic urbanization models with severe effects not only for inhabitants but also for the local environment and global climate. The authors prove that ‘Vertical Village’ buildings can offer a sustainable solution for increasing urban density with at the same time improving the living quality and urban environment significantly. Inserting them within high-density urban fabrics the ecologic and socio-cultural conditions of low-quality neighborhoods can be transformed towards districts, considering all needs of sustainable and social urban life. This study analyzes existing building typologies in Cairo’s «low quality - high density» districts Ard el Lewa, Dokki and Mohandesen according to benchmarks for sustainable residential buildings, identifying major problems and deficits. In 3 case study design projects, the sustainable transformation potential through ‘Vertical Village’ buildings are laid out and comparative studies show the improvement of the urban microclimate, safety, social diversity, sense of community, aesthetics, privacy, efficiency, healthiness and accessibility. The main result of the paper is that the disadvantages of density and overpopulation in developing countries can be converted with ‘Vertical Village’ buildings into advantages, achieving attractive and environmentally friendly living environments with multiple synergies. The paper is documenting based on scientific criteria that mixed-use vertical building structures, designed according to sustainable principles of low rise housing, can serve as an alternative to convert «low quality - high density» districts in megacities, opening a pathway for governments to achieve sustainable urban transformation goals. Neglected informal urban districts, home to millions of the poorer population groups, can be converted into healthier living and working environments.

Keywords: sustainable, architecture, urbanization, urban transformation, vertical village

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Authors: Souhir Ben Amor, Heni Boubaker, Lotfi Belkacem

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This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts.

Keywords: electricity price, k-factor GARMA, LLWNN, G-GARCH, forecasting

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Authors: Gabriel Montague, Max Vilgalys, Catherine H. Crawford, Jorge Ortiz, Dava Newman

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There has never been so much publicly accessible atmospheric and environmental data. The possibilities of these data are exciting, but the sheer volume of available datasets represents a new challenge for researchers. The task of identifying and working with a new dataset has become more difficult with the amount and variety of available data. Datasets are often documented in ways that differ substantially from the common English used to describe the same topics. This presents a barrier not only for new scientists, but for researchers looking to find comparisons across multiple datasets or specialists from other disciplines hoping to collaborate. This paper proposes a method for addressing this obstacle: creating a knowledge graph to bridge the gap between everyday English language and the technical language surrounding these datasets. Knowledge graph generation is already a well-established field, although there are some unique challenges posed by working with Earth data. One is the sheer size of the databases – it would be infeasible to replicate or analyze all the data stored by an organization like The National Aeronautics and Space Administration (NASA) or the European Space Agency. Instead, this approach identifies topics from metadata available for datasets in NASA’s Earthdata database, which can then be used to directly request and access the raw data from NASA. By starting with a single metadata standard, this paper establishes an approach that can be generalized to different databases, but leaves the challenge of metadata harmonization for future work. Topics generated from the metadata are then linked to topics from a collection of English queries through a variety of standard and custom natural language processing (NLP) methods. The results from this method are then compared to a baseline of elastic search applied to the metadata. This comparison shows the benefits of the proposed knowledge graph system over existing methods, particularly in interpreting natural language queries and interpreting topics in metadata. For the research community, this work introduces an application of NLP to the ecological and environmental sciences, expanding the possibilities of how machine learning can be applied in this discipline. But perhaps more importantly, it establishes the foundation for a platform that can enable common English to access knowledge that previously required considerable effort and experience. By making this public data accessible to the full public, this work has the potential to transform environmental understanding, engagement, and action.

Keywords: earth metadata, knowledge graphs, natural language processing, question-answer systems

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Authors: Yuexi Xiong, Jingwu He

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The structural components in an energy facility such as steam turbine machines are operated under high stress and elevated temperature in an endured time period and thus the creep deformation and creep rupture failure are important issues that need to be addressed in the design of such components. There are numerous creep models being used for creep analysis that have both advantages and disadvantages in terms of accuracy and efficiency. The Isochronous Creep Analysis is one of the simplified approaches in which a full-time dependent creep analysis is avoided and instead an elastic-plastic analysis is conducted at each time point. This approach has been established based on the rupture dependent creep equations using the well-known Larson-Miller parameter. In this paper, some fundamental aspects of creep deformation and the rupture dependent creep models are reviewed and the analysis procedures using isochronous creep curves are discussed. Four rupture failure criteria are examined from creep fundamental perspectives including criteria of Stress Damage, Strain Damage, Strain Rate Damage, and Strain Capability. The accuracy of these criteria in predicting creep life is discussed and applications of the creep analysis procedures and failure predictions of simple models will be presented. In addition, a new failure criterion is proposed to improve the accuracy and effectiveness of the existing criteria. Comparisons are made between the existing criteria and the new one using several examples materials. Both strain increase and stress relaxation form a full picture of the creep behaviour of a material under high temperature in an endured time period. It is important to bear this in mind when dealing with creep problems. Accordingly there are two sets of rupture dependent creep equations. While the rupture strength vs LMP equation shows how the rupture time depends on the stress level under load controlled condition, the strain rate vs rupture time equation reflects how the rupture time behaves under strain-controlled condition. Among the four existing failure criteria for rupture life predictions, the Stress Damage and Strain Damage Criteria provide the most conservative and non-conservative predictions, respectively. The Strain Rate and Strain Capability Criteria provide predictions in between that are believed to be more accurate because the strain rate and strain capability are more determined quantities than stress to reflect the creep rupture behaviour. A modified Strain Capability Criterion is proposed making use of the two sets of creep equations and therefore is considered to be more accurate than the original Strain Capability Criterion.

Keywords: creep analysis, high temperature mateials, rapture evalution, steam turbine machines

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Authors: Ziad Abdeldayem, Jakub Markiewicz, Kunal Kansara, Laura Edwards

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Airborne Laser Scanning (ALS) is one of the main technologies for generating high-resolution digital terrain models (DTMs). DTMs are crucial to several applications, such as topographic mapping, flood zone delineation, geographic information systems (GIS), hydrological modelling, spatial analysis, etc. Laser scanning system generates irregularly spaced three-dimensional cloud of points. Raw ALS data are mainly ground points (that represent the bare earth) and non-ground points (that represent buildings, trees, cars, etc.). Removing all the non-ground points from the raw data is referred to as filtering. Filtering heavily forested areas is considered a difficult and challenging task as the canopy stops laser pulses from reaching the terrain surface. This research presents an approach for removing non-ground points from raw ALS data in densely forested areas. Smoothing splines are exploited to interpolate and fit the noisy ALS data. The presented filter utilizes a weight function to allocate weights for each point of the data. Furthermore, unlike most of the methods, the presented filtering algorithm is designed to be automatic. Three different forested areas in the United Kingdom are used to assess the performance of the algorithm. The results show that the generated DTMs from the filtered data are accurate (when compared against reference terrain data) and the performance of the method is stable for all the heavily forested data samples. The average root mean square error (RMSE) value is 0.35 m.

Keywords: airborne laser scanning, digital terrain models, filtering, forested areas

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Authors: M. Yilmaz, I. Yilmaz, M. Uysal

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The Earth system generates different phenomena that are observable at the surface of the Earth such as mass deformations and displacements leading to plate tectonics, earthquakes, and volcanism. The dynamic processes associated with the interior, surface, and atmosphere of the Earth affect the three pillars of geodesy: shape of the Earth, its gravity field, and its rotation. Geodesy establishes a characteristic structure in order to define, monitor, and predict of the whole Earth system. The traditional and new instruments, observables, and techniques in geodesy are related to the gravity field. Therefore, the geodesy monitors the gravity field and its temporal variability in order to transform the geodetic observations made on the physical surface of the Earth into the geometrical surface in which positions are mathematically defined. In this paper, the main components of the gravity field modeling, (Free-air and Bouguer) gravity anomalies are calculated via recent global models (EGM2008, EIGEN6C4, and GECO) over a selected study area. The model-based gravity anomalies are compared with the corresponding terrestrial gravity data in terms of standard deviation (SD) and root mean square error (RMSE) for determining the best fit global model in the study area at a regional scale in Turkey. The least SD (13.63 mGal) and RMSE (15.71 mGal) were obtained by EGM2008 for the Free-air gravity anomaly residuals. For the Bouguer gravity anomaly residuals, EIGEN6C4 provides the least SD (8.05 mGal) and RMSE (8.12 mGal). The results indicated that EIGEN6C4 can be a useful tool for modeling the gravity field of the Earth over the study area.

Keywords: free-air gravity anomaly, Bouguer gravity anomaly, global model, land gravity

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Authors: Irina Peterburgsky

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We have been working on finding internship positions for our math and computer science majors. Among many other positive outcomes of internship for students majoring in mathematics, there are: students see new applications of mathematics to real life and see new scientific problems; they learn new methods, tools, etc. that they have not seen in their classes; they appreciate the power of mathematics that increases their interest in learning mathematics; they make decisions to take more advanced math courses; students understand better what their potentials, strong points, and limitations are; learn what work ethic is; learn how to work as a member of a team at a workplace; understand better how to offer their help and how to ask for help; start building their professional relationship; build self-confidence as young professionals, and what is the most important - they get a better understanding of their goals in their future professional careers.

Keywords: internship, mathematics, positive outcoms for students, workplace

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Authors: Thanakrit Taweesoontorn, Sarucha Yanyong, Poom Konghuayrob

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Robotic arms have gained popularity in various industries due to their accuracy and efficiency. This research proposes a method for bin-picking tasks using the Cobot, combining the YOLOv5 CNNs model for object detection and pose estimation with traditional feature detection (FAST), feature description (BRISK), and matching algorithms. By integrating these algorithms and utilizing a small-scale depth sensor camera for capturing depth and color images, the system achieves real-time object detection and accurate pose estimation, enabling the robotic arm to pick objects correctly in both position and orientation. Furthermore, the proposed method is implemented within the ROS framework to provide a seamless platform for robotic control and integration. This integration of robotics, cameras, and AI technology contributes to the development of industrial robotics, opening up new possibilities for automating challenging tasks and improving overall operational efficiency.

Keywords: robotic vision, image processing, applications of robotics, artificial intelligent

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Authors: Charmaine Lamiel, Van Hoa Nguyen, Deivasigamani Ranjith Kumar, Jae-Jin Shim

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The quest for alternative sources of energy storage had led to the exploration on supercapacitors. Hybrid supercapacitors, a combination of carbon-based material and transition metals, had yielded long and improved cycle life as well as high energy and power densities. In this study, microwave irradiation was used for the facile and rapid synthesis of mesoporous RGO@(Co,Mn)3O4 nanosheets as an active electrode material. The advantages of this method include the non-use of reducing agents and acidic medium, and no further post-heat treatment. Additionally, it offers shorter reaction time at low temperature and low power requirement, which allows low fabrication and energy cost. The as-prepared electrode material demonstrated a high capacitance of 953 F•g−1 at 1 A•g−1 in a 6 M KOH electrolyte. Furthermore, the electrode exhibited a high energy density of 76.2 Wh•kg−1 (power density of 720 W•kg−1) and a high power density of 7200 W•kg−1 (energy density of 38 Wh•kg−1). The successful synthesis was considered to be efficient and cost-effective, with very promising electrochemical performance that can be used as an active material in supercapacitors.

Keywords: cobalt manganese oxide, electrochemical, graphene, microwave synthesis, supercapacitor

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Authors: Kassahun Birhanu Tadesse, Megersa Olumana Dinka

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Reliable future river flow information is basic for planning and management of any river systems. For data scarce river system having only a river flow records like the Waterval River, a univariate time series models are appropriate for river flow forecasting. In this study, a univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied for forecasting Waterval River flow using GRETL statistical software. Mean monthly river flows from 1960 to 2016 were used for modeling. Different unit root tests and Mann-Kendall trend analysis were performed to test the stationarity of the observed flow time series. The time series was differenced to remove the seasonality. Using the correlogram of seasonally differenced time series, different SARIMA models were identified, their parameters were estimated, and diagnostic check-up of model forecasts was performed using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AIc) and Hannan-Quinn (HQc) criteria, SARIMA (3, 0, 2) x (3, 1, 3)12 was selected as the best model for Waterval River flow forecasting. Therefore, this model can be used to generate future river information for water resources development and management in Waterval River system. SARIMA model can also be used for forecasting other similar univariate time series with seasonality characteristics.

Keywords: heteroscedasticity, stationarity test, trend analysis, validation, white noise

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Authors: Patricia Huedo, Maria José Ruá, Raquel Agost-Felip

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Ensuring sustainable development of urban environments means guaranteeing adequate environmental conditions, being resilient and meeting conditions of safety and inclusion for all people, regardless of their condition. All existing buildings should meet basic safety conditions and be equipped with safe and accessible routes, along with visual, acoustic and tactile signals to protect their users or potential visitors, and regardless of whether they undergo rehabilitation or change of use processes. Moreover, from a social perspective, we consider the need to prioritize buildings occupied by the most vulnerable groups of people that currently do not have specific regulations tailored to their needs. Some residential models in operation are not only outside the scope of application of the regulations in force; they also lack a project or technical data that would allow knowing the fire behavior of the construction materials. However, the difficulty and cost involved in adapting the entire building stock to current regulations can never justify the lack of safety for people. Hence, this work develops a simplified model to assess compliance with the basic safety conditions in case of fire and its compatibility with the specific accessibility needs of each user. The purpose is to support the designer in decision making, as well as to contribute to the development of a basic fire safety certification tool to be applied in inclusive residential models. This work has developed a methodology to support designers in adapting Social Services Centers, usually intended to vulnerable people. It incorporates a checklist of 9 items and information from sources or standards that designers can use to justify compliance or propose solutions. For each item, the verification system is justified, and possible sources of consultation are provided, considering the possibility of lacking technical documentation of construction systems or building materials. The procedure is based on diagnosing the degree of compliance with fire conditions of residential models used by vulnerable groups, considering the special accessibility conditions required by each user group. Through visual inspection and site surveying, the verification model can serve as a support tool, significantly streamlining the diagnostic phase and reducing the number of tests to be requested by over 75%. This speeds up and simplifies the diagnostic phase. To illustrate the methodology, two different buildings in the Valencian Region (Spain) have been selected. One case study is a mental health facility for residential purposes, located in a rural area, on the outskirts of a small town; the other one, is a day care facility for individuals with intellectual disabilities, located in a medium-sized city. The comparison between the case studies allow to validate the model in distinct conditions. Verifying compliance with a basic security level can allow a quality seal and a public register of buildings adapted to fire regulations to be established, similarly to what is being done with other types of attributes such as energy performance.

Keywords: fire safety, inclusive housing, universal accessibility, vulnerable people

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Authors: Jamaiah H. Yahaya, Aziz Deraman, Abdul Razak Hamdan, Yusmadi Yah Jusoh

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One of the most essential issues in software products is to maintain it relevancy to the dynamics of the user’s requirements and expectation. Many studies have been carried out in quality aspect of software products to overcome these problems. Previous software quality assessment models and metrics have been introduced with strengths and limitations. In order to enhance the assurance and buoyancy of the software products, certification models have been introduced and developed. From our previous experiences in certification exercises and case studies collaborating with several agencies in Malaysia, the requirements for user based software certification approach is identified and demanded. The emergence of social network applications, the new development approach such as agile method and other varieties of software in the market have led to the domination of users over the software. As software become more accessible to the public through internet applications, users are becoming more critical in the quality of the services provided by the software. There are several categories of users in web-based systems with different interests and perspectives. The classifications and metrics are identified through brain storming approach with includes researchers, users and experts in this area. The new paradigm in software quality assessment is the main focus in our research. This paper discusses the classifications of users in web-based software system assessment and their associated factors and metrics for quality measurement. The quality model is derived based on IEEE structure and FCM model. The developments are beneficial and valuable to overcome the constraints and improve the application of software certification model in future.

Keywords: software certification model, user centric approach, software quality factors, metrics and measurements, web-based system

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Authors: Mohammad Fereidoonnezhad, Seán Leen, Stephen Nash, Patrick McGarry

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From the perspective of research innovation, the tidal energy industry is still in its early stages. While a very small number of turbines have progressed to utility-scale deployment, blade breakage is commonly reported due to the enormous hydrodynamic loading applied to devices. The aim of this study is the development of computer simulation technologies for the design of next-generation fibre-reinforced composite tidal turbines. This will require significant technical advances in the areas of tidal turbine testing and multi-scale computational modelling. The complex turbine blade profiles are designed to incorporate non-linear distributions of airfoil sections to optimize power output and self-starting capability while reducing power fluctuations. A number of candidate blade geometries are investigated, ranging from spiral geometries to parabolic geometries, with blades arranged in both cylindrical and spherical configurations on a vertical axis turbine. A combined blade element theory (BET-start-up model) is developed in MATLAB to perform computationally efficient parametric design optimisation for a range of turbine blade geometries. Finite element models are developed to identify optimal fibre-reinforced composite designs to increase blade strength and fatigue life. Advanced fluid-structure-interaction models are also carried out to compute blade deflections following design optimisation.

Keywords: tidal turbine, composite materials, fluid-structure-interaction, start-up capability

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Authors: Mary Booth

Abstract:

Biophotovoltaics is a method of green energy generation derived from exposing plants to lights. Its vast potential is hampered by the public’s relative ignorance of its existence. This work aims to formalize the principles of the physical processes of biophotovoltaics into a comprehensible visual software model, thus amplifying the human thought process. The methods used involve initially crafting a scale model of a working biophotovoltaic system from household materials inspired by the work of Paolo Bombelli. The scale model is then programmed into a system-level simulation, wherein a 3D animation dissects the system and its general energy generation process. The completed 3D system-level simulation ultimately creates a simplified visual understanding of the complex principles of the biophotovoltaic system.

Keywords: 3D, biophotovoltaics, render

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Authors: Boumediene Hamzi, Turky N. AlOtaiby, Saleh AlShebeili, Arwa AlAnqary

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We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that are computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection.

Keywords: kernel methods, maximum mean discrepancy, seizure detection, machine learning

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Authors: Kwak, Hyo-Gyung, Gang, Han Gul

Abstract:

In the context of an increasing need for reliability and safety in concrete structures under blast and impact loading condition, the behavior of concrete under high strain rate condition has been an important issue. Since concrete subjected to impact loading associated with high strain rate shows quite different material behavior from that in the static state, several material models are proposed and used to describe the high strain rate behavior under blast and impact loading. In the process of modelling, in advance, mesh dependency in the used finite element (FE) is the key problem because simulation results under high strain-rate condition are quite sensitive to applied FE mesh size. It means that the accuracy of simulation results may deeply be dependent on FE mesh size in simulations. This paper introduces an improved criterion which can minimize the mesh-dependency of simulation results on the basis of the fracture energy concept, and HJC (Holmquist Johnson Cook), CSC (Continuous Surface Cap) and K&C (Karagozian & Case) models are examined to trace their relative sensitivity to the used FE mesh size. To coincide with the purpose of the penetration test with a concrete plate under a projectile (bullet), the residual velocities of projectile after penetration are compared. The correlation studies between analytical results and the parametric studies associated with them show that the variation of residual velocity with the used FE mesh size is quite reduced by applying a unique failure strain value determined according to the proposed criterion.

Keywords: high strain rate concrete, penetration simulation, failure strain, mesh-dependency, fracture energy

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Authors: Ola Hall, Ibrahim Wahab, Thorsteinn Rognvaldsson, Mattias Ohlsson

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The subfield of poverty and welfare estimation that applies machine learning tools and methods on satellite imagery is a nascent but rapidly growing one. This is in part driven by the sustainable development goal, whose overarching principle is that no region is left behind. Among other things, this requires that welfare levels can be accurately and rapidly estimated at different spatial scales and resolutions. Conventional tools of household surveys and interviews do not suffice in this regard. While they are useful for gaining a longitudinal understanding of the welfare levels of populations, they do not offer adequate spatial coverage for the accuracy that is needed, nor are their implementation sufficiently swift to gain an accurate insight into people and places. It is this void that satellite imagery fills. Previously, this was near-impossible to implement due to the sheer volume of data that needed processing. Recent advances in machine learning, especially the deep learning subtype, such as deep neural networks, have made this a rapidly growing area of scholarship. Despite their unprecedented levels of performance, such models lack transparency and explainability and thus have seen limited downstream applications as humans generally are apprehensive of techniques that are not inherently interpretable and trustworthy. While several studies have demonstrated the superhuman performance of AI models, none has directly compared the performance of such models and human readers in the domain of poverty studies. In the present study, we directly compare the performance of human readers and a DL model using different resolutions of satellite imagery to estimate the welfare levels of demographic and health survey clusters in Tanzania, using the wealth quintile ratings from the same survey as the ground truth data. The cluster-level imagery covers all 608 cluster locations, of which 428 were classified as rural. The imagery for the human readers was sourced from the Google Maps Platform at an ultra-high resolution of 0.6m per pixel at zoom level 18, while that of the machine learning model was sourced from the comparatively lower resolution Sentinel-2 10m per pixel data for the same cluster locations. Rank correlation coefficients of between 0.31 and 0.32 achieved by the human readers were much lower when compared to those attained by the machine learning model – 0.69-0.79. This superhuman performance by the model is even more significant given that it was trained on the relatively lower 10-meter resolution satellite data while the human readers estimated welfare levels from the higher 0.6m spatial resolution data from which key markers of poverty and slums – roofing and road quality – are discernible. It is important to note, however, that the human readers did not receive any training before ratings, and had this been done, their performance might have improved. The stellar performance of the model also comes with the inevitable shortfall relating to limited transparency and explainability. The findings have significant implications for attaining the objective of the current frontier of deep learning models in this domain of scholarship – eXplainable Artificial Intelligence through a collaborative rather than a comparative framework.

Keywords: poverty prediction, satellite imagery, human readers, machine learning, Tanzania

Procedia PDF Downloads 106