Search results for: Honeycomb network

Authors: Sanchita Baitalik, Nijhuma Kayal, Omprakash Chakrabarti

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Recently SiC ceramics have been a focus of interest in the field of porous materials due to their unique combination of properties and hence they are considered as an ideal candidate for catalyst supports, thermal insulators, high-temperature structural materials, hot gas particulate separation systems etc. in different industrial processes. Several processing methods are followed for fabrication of porous SiC at low temperatures but all these methods are associated with several disadvantages. Therefore processing of porous SiC ceramics at low temperatures is still challenging. Concerning that of incorporation of secondary bond phase additives by an infiltration technique should result in a homogenous distribution of bond phase in the final ceramics. Present work is aimed to synthesis cordierite (2MgO.2Al2O3.5SiO2) bonded porous SiC ceramics following incorporation of sol-gel bond phase precursor into powder compacts of SiC and heat treating the infiltrated body at 1400 °C. In this paper the primary aim was to study the effect of infiltration of a precursor sol of cordierite into a porous SiC powder compact prepared with pore former of different particle sizes on the porosity, pore size, microstructure and the mechanical properties of the porous SiC ceramics. Cordierite sol was prepared by mixing a solution of magnesium nitrate hexahydrate and aluminium nitrate nonahydrate in 2:4 molar ratio in ethanol another solution containing tetra-ethyl orthosilicate and ethanol in 1:3 molar ratio followed by stirring for several hours. Powders of SiC (α-SiC; d50 =22.5 μm) and 10 wt. % polymer microbead of two sizes 8 and 50µm as the pore former were mixed in a suitable liquid medium, dried and pressed in the form of bars (50×20×16 mm3) at 23 MPa pressure. The well-dried bars were heat treated at 1100° C for 4 h with a hold at 750 °C for 2 h to remove the pore former. Bars were evacuated for 2 hr upto 0.3 mm Hg pressure into a vacuum chamber and infiltrated with cordierite precursor sol. The infiltrated samples were dried and the infiltration process was repeated until the weight gain became constant. Finally the infiltrated samples were sintered at 1400 °C to prepare cordierite bonded porous SiC ceramics. Porous ceramics prepared with 8 and 50 µm sized microbead exhibited lower oxidation degrees of respectively 7.8 and 4.8 % than the sample (23 %) prepared with no microbead. Depending on the size of pore former, the porosity of the final ceramic varied in the range of 36 to 40 vol. % with a variation of flexural strength from 33.7 to 24.6 MPa. XRD analysis showed major crystalline phases of the ceramics as SiC, SiO2 and cordierite. Two forms of cordierite, α-(hexagonal) and µ-(cubic), were detected by the XRD analysis. The SiC particles were observed to be bonded both by cristobalite with fish scale morphology and cordierite with rod shape morphology and thereby formed a porous network. The material and mechanical properties of cordierite bonded porous SiC ceramics are good in agreement to carry out further studies like thermal shock, corrosion resistance etc.

Keywords: cordierite, infiltration technique, porous ceramics, sol-gel

Procedia PDF Downloads 271

Authors: Mustafa İlhan Akbaş, Özlem Garibay, Ivan Garibay

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The economic entities in innovation ecosystems form various industry clusters, in which they compete and cooperate to survive and grow. Within a successful and stable industry cluster, the entities acquire different roles that complement each other in the system. The universities and research centers have been accepted to have a critical role in these systems for the creation and development of innovations. However, the real effect of research institutions on regional economic growth is difficult to assess. In this paper, we present our approach for the identification of the impact of research activities on the regional entrepreneurship for a specific high-tech industry: optics and photonics. The optics and photonics has been defined as an enabling industry, which combines the high-tech photonics technology with the developing optics industry. The recent literature suggests that the growth of optics and photonics firms depends on three important factors: the embedded regional specializations in the labor market, the research and development infrastructure, and a dynamic small firm network capable of absorbing new technologies, products and processes. Therefore, the role of each factor and the dynamics among them must be understood to identify the requirements of the entrepreneurship activities in optics and photonics industry. There are three main contributions of our approach. The recent studies show that the innovation in optics and photonics industry is mostly located around metropolitan areas. There are also studies mentioning the importance of research center locations and universities in the regional development of optics and photonics industry. These studies are mostly limited with the number of patents received within a short period of time or some limited survey results. Therefore the first contribution of our approach is conducting a comprehensive analysis for the state and recent history of the photonics and optics research in the US. For this purpose, both the research centers specialized in optics and photonics and the related research groups in various departments of institutions (e.g. Electrical Engineering, Materials Science) are identified and a geographical study of their locations is presented. The second contribution of the paper is the analysis of regional entrepreneurship activities in optics and photonics in recent years. We use the membership data of the International Society for Optics and Photonics (SPIE) and the regional photonics clusters to identify the optics and photonics companies in the US. Then the profiles and activities of these companies are gathered by extracting and integrating the related data from the National Establishment Time Series (NETS) database, ES-202 database and the data sets from the regional photonics clusters. The number of start-ups, their employee numbers and sales are some examples of the extracted data for the industry. Our third contribution is the utilization of collected data to investigate the impact of research institutions on the regional optics and photonics industry growth and entrepreneurship. In this analysis, the regional and periodical conditions of the overall market are taken into consideration while discovering and quantifying the statistical correlations.

Keywords: entrepreneurship, industrial clusters, optics, photonics, emerging industries, research centers

Procedia PDF Downloads 406

Authors: D. Varadaradjou, H. Kebir, J. Mespoulet, D. Tingaud, S. Bouvier, P. Deconick, K. Ameyama, G. Dirras

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The development of new architecture metallic alloys with controlled microstructures is one of the strategic ways for designing materials with high innovation potential and, particularly, with improved mechanical properties as required for structural materials. Indeed, unlike conventional counterparts, metallic materials having so-called harmonic structure displays strength and ductility synergy. The latter occurs due to a unique microstructure design: a coarse grain structure surrounded by a 3D continuous network of ultra-fine grain known as “core” and “shell,” respectively. In the present study, pure harmonic-structured (HS) Nickel samples were processed via controlled mechanical milling and followed by spark plasma sintering (SPS). The present work aims at characterizing the mechanical properties of HS pure Nickel under room temperature dynamic loading through a Split Hopkinson Pressure Bar (SHPB) test and the underlying microstructure evolution. A stopper ring was used to maintain the strain at a fixed value of about 20%. Five samples (named B1 to B5) were impacted using different striker bar velocities from 14 m/s to 28 m/s, yielding strain rate in the range 4000-7000 s-1. Results were considered until a 10% deformation value, which is the deformation threshold for the constant strain rate assumption. The non-deformed (INIT – post-SPS process) and post-SHPB microstructure (B1 to B5) were investigated by EBSD. It was observed that while the strain rate is increased, the average grain size within the core decreases. An in-depth analysis of grains and grain boundaries was made to highlight the thermal (such as dynamic recrystallization) or mechanical (such as grains fragmentation by dislocation) contribution within the “core” and “shell.” One of the most widely used methods for determining the dynamic behavior of materials is the SHPB technique developed by Kolsky. A 3D simulation of the SHPB test was created through ABAQUS in dynamic explicit. This 3D simulation allows taking into account all modes of vibration. An inverse approach was used to identify the material parameters from the equation of Johnson-Cook (JC) by minimizing the difference between the numerical and experimental data. The JC’s parameters were identified using B1 and B5 samples configurations. Predictively, identified parameters of JC’s equation shows good result for the other sample configuration. Furthermore, mean rise of temperature within the harmonic Nickel sample can be obtained through ABAQUS and show an elevation of about 35°C for all fives samples. At this temperature, a thermal mechanism cannot be activated. Therefore, grains fragmentation within the core is mainly due to mechanical phenomena for a fixed final strain of 20%.

Keywords: 3D simulation, fragmentation, harmonic structure, high strain rate, Johnson-cook model, microstructure

Procedia PDF Downloads 231

Authors: Maturos Chongchaikit, Sitthikorn Sumalee, Nopphawan Chimroylarp, Nongluck Manowaluilou, Thapanee Thammetha

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The 2015 Kasetsart University Research Plan, which was funded by the National Research Institutes: TRF – NRCT, comprises four sub-research projects on the development of three OER websites and on their usage study by students in international programs. The goals were to develop the open educational resources (OER) in the form of websites that will promote three key skills of quality learning and achievement: Academic English, Educational Research, and ICT Literacy, to graduate students in international programs of Thailand. The statistics from the Office of Higher Education showed that the number of foreign students who come to study in international higher education of Thailand has increased respectively by 25 percent per year, proving that the international education system and institutes of Thailand have been already recognized regionally and globally as meeting the standards. The output of the plan: the OER websites and their materials, and the outcome: students’ learning improvement due to lecturers’ readiness for open educational media, will ultimately lead the country to higher business capabilities for international education services in ASEAN Community in the future. The OER innovation is aimed at sharing quality knowledge to the world, with the adoption of Creative Commons Licenses that makes sharing be able to do freely (5Rs openness), without charge and leading to self and life-long learning. The research has brought the problems on the low usage of existing OER in the English language to develop the OER on three specific skills and try them out with the sample of 100 students randomly selected from the international graduate programs of top 10 Thai universities, according to QS Asia University Rankings 2014. The R&D process was used for product evaluation in 2 stages: the development stage and the usage study stage. The research tools were the questionnaires for content and OER experts, the questionnaires for the sample group and the open-ended interviews for the focus group discussions. The data were analyzed using frequency, percentage, mean and SD. The findings revealed that the developed websites were fully qualified as OERs by the experts. The students’ opinions and satisfaction were at the highest levels for both the content and the technology used for presentation. The usage manual and self-assessment guide were finalized during the focus group discussions. The direct participation according to the concept of 5Rs Openness Activities through the provided tools of OER models like MERLOT and OER COMMONS, as well as the development of usage manual and self-assessment guide, were revealed as a key approach to further extend the output widely and sustainably to the network of users in various higher education institutions.

Keywords: open educational resources, international education services business, academic English, educational research, ICT literacy, international graduate program, OER

Procedia PDF Downloads 222

Authors: Ian Kingsbury

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The rapid expansion of charter schools (public schools that receive government but do not face the same regulations as traditional public schools) over the preceding two decades has raised concerns over the potential for graft and fraud. These concerns are largely justified: Incidents of financial crime and mismanagement are not unheard of, and the charter sector has become a darling of hedge fund managers. In response, several states have strengthened their charter school regulatory regimes. Imposing regulations and attempting to increase accountability seem like sensible measures, and perhaps they are necessary. However, increased regulation may come at the cost of imposing barriers to entry. Specifically, increased regulation often entails evidence for a high likelihood of fiscal solvency. That should theoretically entail access to capital in the short-term, which may systematically preclude Black or Hispanic applicants from opening charter schools. Moreover, increased regulation necessarily entails more red tape. The institutional wherewithal and the number of hours required to complete an application to open a charter school might favor those who have partnered with an education service provider, specifically a charter management organization (CMO) or education management organization (EMO). These potential barriers to entry pose a significant policy concern. Just as policymakers hope to increase the share of minority teachers and principals, they should sensibly care whether individuals who open charter schools look like the students in that school. Moreover, they might be concerned if successful applications in states with stringent regulations are overwhelmingly affiliated with education service providers. One of the original missions of charter schools was to serve as a laboratory of innovation. Approving only those applications affiliated with education service providers (and in effect establishing a parallel network of schools rather than a diverse marketplace of schools) undermines that mission. Data and methods: The analysis examines more than 2,000 charter school applications from 15 states. It compares the outcomes of applications from states with a strong regulatory environment (those with high scores) from NACSA-the National Association of Charter School Authorizers- to applications from states with a weak regulatory environment (those with a low NACSA score). If the hypothesis is correct, applicants not affiliated with an ESP are more likely to be rejected in high-regulation states compared to those affiliated with an ESP, and minority candidates not affiliated with an education service provider (ESP) are particularly likely to be rejected. Initial returns indicate that the hypothesis holds. More applications in low NASCA-scoring Arizona come from individuals not associated with an ESP, and those individuals are as likely to be accepted as those affiliated with an ESP. On the other hand, applicants in high-NACSA scoring Indiana and Ohio are more than 20 percentage points more likely to be accepted if they are affiliated with an ESP, and the effect is particularly pronounced for minority candidates. These findings should spur policymakers to consider the drawbacks of charter school accountability and consider accountability regimes that do not impose barriers to entry.

Keywords: accountability, barriers to entry, charter schools, choice

Procedia PDF Downloads 159

Authors: Aarthi Mariappan, Janani Selvaraj, P. B. Harathi, M. Prashanthi Devi

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Anthropogenic modification of the urban environment has largely increased in the recent years in order to sustain the growing human population. Intense industrial activity, permanent and high traffic on the roads, a developed subterranean infrastructure network, land use patterns are just some specific characteristics. Every day, the urban environment is polluted by more or less toxic emissions, organic or metals wastes discharged from specific activities such as industrial, commercial, municipal. When these eventually deposit into the soil, the physical and chemical properties of the surrounding soil is changed, transforming it into a human exposure indicator. Metals are non-degradable and occur cumulative in soil due to regular deposits are a result of permanent human activity. Due to this, metals are a contaminant factor for soil when persistent over a long period of time and a possible danger for inhabitant’s health on prolonged exposure. Metals accumulated in contaminated soil may be transferred to humans directly, by inhaling the dust raised from top soil, or by ingesting, or by dermal contact and indirectly, through plants and animals grown on contaminated soil and used for food. Some metals, like Cu, Mn, Zn, are beneficial for human’s health and represent a danger only if their concentration is above permissible levels, but other metals, like Pb, As, Cd, Hg, are toxic even at trace level causing gastrointestinal and lung cancers. In urban areas, metals can be emitted from a wide variety of sources like industrial, residential, commercial activities. Our study interrogates the spatial distribution of heavy metals in soil in relation to their permissible levels and their association with the health risk to the urban population in Coimbatore, India. Coimbatore region is a high cancer risk zone and case records of gastro intestinal and respiratory cancer patients were collected from hospitals and geocoded in ArcGIS10.1. The data of patients pertaining to the urban limits were retained and checked for their diseases history based on their diagnosis and treatment. A disease map of cancer was prepared to show the disease distribution. It has been observed that in our study area Cr, Pb, As, Fe and Mg exceeded their permissible levels in the soil. Using spatial overlay analysis a relationship between environmental exposure to these potentially toxic elements in soil and cancer distribution in Coimbatore district was established to show areas of cancer risk. Through this, our study throws light on the impact of prolonged exposure to soil contamination in soil in the urban zones, thereby exploring the possibility to detect cancer risk zones and to create awareness among the exposed groups on cancer risk.

Keywords: soil contamination, cancer risk, spatial analysis, India

Procedia PDF Downloads 403

Authors: Seyed Mehdi Seyed Alizadeh, Christoph Arns, Shane Latham

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Geomechanical characterization of rocks in detail and its possible implications on flow properties is an important aspect of reservoir characterization workflow. In order to gain more understanding of the microstructure evolution of reservoir rocks under stress a series of axisymmetric triaxial tests were performed on two different analogue rock samples. In-situ compression tests were coupled with high resolution micro-Computed Tomography to elucidate the changes in the pore/grain network of the rocks under pressurized conditions. Two outcrop sandstones were chosen in the current study representing a various cementation status of well-consolidated and weakly-consolidated granular system respectively. High resolution images were acquired while the rocks deformed in a purpose-built compression cell. A detailed analysis of the 3D images in each series of step-wise compression tests (up to the failure point) was conducted which includes the registration of the deformed specimen images with the reference pristine dry rock image. Digital Image Correlation (DIC) technique based on the intensity of the registered 3D subsets and particle tracking are utilized to map the displacement fields in each sample. The results suggest the complex architecture of the localized shear zone in well-cemented Bentheimer sandstone whereas for the weakly-consolidated Castlegate sandstone no discernible shear band could be observed even after macroscopic failure. Post-mortem imaging a sister plug from the friable rock upon undergoing continuous compression reveals signs of a shear band pattern. This suggests that for friable sandstones at small scales loading mode may affect the pattern of deformation. Prior to mechanical failure, the continuum digital image correlation approach can reasonably capture the kinematics of deformation. As failure occurs, however, discrete image correlation (i.e. particle tracking) reveals superiority in both tracking the grains as well as quantifying their kinematics (in terms of translations/rotations) with respect to any stage of compaction. An attempt was made to quantify the displacement field in compression using continuum Digital Image Correlation which is based on the reference and secondary image intensity correlation. Such approach has only been previously applied to unconsolidated granular systems under pressure. We are applying this technique to sandstones with various degrees of consolidation. Such element of novelty will set the results of this study apart from previous attempts to characterize the deformation pattern in consolidated sands.

Keywords: deformation mechanism, displacement field, shear behavior, triaxial compression, X-ray micro-CT

Procedia PDF Downloads 189

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

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

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

Procedia PDF Downloads 119

Authors: Hemendra Singh Rathod

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Frequency is, among other factors, one of the governing parameters for maintaining electrical grid stability. The quality of an electrical transmission and supply system is mainly described by the stability of the grid frequency. Over the past few decades, energy generation by intermittent sustainable sources like wind and solar has seen a significant increase globally. Consequently, controlling the associated deviations in grid frequency within safe limits has been gaining momentum so that the balance between demand and supply can be maintained. Lithium-ion battery energy storage system (Li-Ion BESS) has been a promising technology to tackle the challenges associated with grid instability. BESS is, therefore, an effective response to the ongoing debate whether it is feasible to have an electrical grid constantly functioning on a hundred percent renewable power in the near future. In recent years, large-scale manufacturing and capital investment into battery production processes have made the Li-ion battery systems cost-effective and increasingly efficient. The Li-ion systems require very low maintenance and are also independent of geographical constraints while being easily scalable. The paper highlights the use of stationary and moving BESS for balancing electrical energy, thereby maintaining grid frequency at a rapid rate. Moving BESS technology, as implemented in the selected railway network in Germany, is here considered as an exemplary concept for demonstrating the same functionality in the electrical grid system. Further, using certain applications of Li-ion batteries, such as self-consumption of wind and solar parks or their ancillary services, wind and solar energy storage during low demand, black start, island operation, residential home storage, etc. offers a solution to effectively integrate the renewables and support Europe’s future smart grid. EMT software tool DIgSILENT PowerFactory has been utilised to model an electrical transmission system with 100% renewable energy penetration. The stability of such a transmission system has been evaluated together with BESS within a defined frequency band. The transmission system operators (TSO) have the superordinate responsibility for system stability and must also coordinate with the other European transmission system operators. Frequency control is implemented by TSO by maintaining a balance between electricity generation and consumption. Li-ion battery systems are here seen as flexible, controllable loads and flexible, controllable generation for balancing energy pools. Thus using Li-ion battery storage solution, frequency-dependent load shedding, i.e., automatic gradual disconnection of loads from the grid, and frequency-dependent electricity generation, i.e., automatic gradual connection of BESS to the grid, is used as a perfect security measure to maintain grid stability in any case scenario. The paper emphasizes the use of stationary and moving Li-ion battery storage for meeting the demands of maintaining grid frequency and stability for near future operations.

Keywords: frequency control, grid stability, li-ion battery storage, smart grid

Procedia PDF Downloads 150

Authors: Ran Goldblatt, Nicholas Jones, Jennifer Mannix, Brad Bottoms

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Accurate, complete, and up-to-date geospatial information is the foundation of successful disaster management. When the 2010 Haiti Earthquake struck, accurate and timely information on the distribution of critical infrastructure was essential for the disaster response community for effective search and rescue operations. Existing geospatial datasets such as Google Maps did not have comprehensive coverage of these features. In the days following the earthquake, many organizations released high-resolution satellite imagery, catalyzing a worldwide effort to map Haiti and support the recovery operations. Of these organizations, OpenStreetMap (OSM), a collaborative project to create a free editable map of the world, used the imagery to support volunteers to digitize roads, buildings, and other features, creating the most detailed map of Haiti in existence in just a few weeks. However, large portions of the island are still not fully covered by OSM. There is an increasing need for a tool to automatically identify which areas in Haiti, as well as in other countries vulnerable to disasters, that are not fully mapped. The objective of this project is to leverage different types of remote sensing measurements, together with machine learning approaches, in order to identify geographical areas where OSM coverage of building footprints is incomplete. Several remote sensing measures and derived products were assessed as potential predictors of OSM building footprints coverage, including: intensity of light emitted at night (based on VIIRS measurements), spectral indices derived from Sentinel-2 satellite (normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), soil-adjusted vegetation index (SAVI), urban index (UI)), surface texture (based on Sentinel-1 SAR measurements)), elevation and slope. Additional remote sensing derived products, such as Hansen Global Forest Change, DLR`s Global Urban Footprint (GUF), and World Settlement Footprint (WSF), were also evaluated as predictors, as well as OSM street and road network (including junctions). Using a supervised classification with a random forest classifier resulted in the prediction of 89% of the variation of OSM building footprint area in a given cell. These predictions allowed for the identification of cells that are predicted to be covered but are actually not mapped yet. With these results, this methodology could be adapted to any location to assist with preparing for future disastrous events and assure that essential geospatial information is available to support the response and recovery efforts during and following major disasters.

Keywords: disaster management, Haiti, machine learning, OpenStreetMap, remote sensing

Procedia PDF Downloads 125

Authors: Elaine Lima Da Fonseca, Eliomar Pereira Da Silva Filho

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The modeling of areas susceptible to soil loss by hydro erosive processes consists of a simplified instrument of reality with the purpose of predicting future behaviors from the observation and interaction of a set of geoenvironmental factors. The models of potential areas for soil loss will be obtained through binary logistic regression, artificial neural networks, and support vector machines. The choice of the municipality of Colorado do Oeste in the south of the western Amazon is due to soil degradation due to anthropogenic activities, such as agriculture, road construction, overgrazing, deforestation, and environmental and socioeconomic configurations. Initially, a soil erosion inventory map constructed through various field investigations will be designed, including the use of remotely piloted aircraft, orbital imagery, and the PLANAFLORO/RO database. 100 sampling units with the presence of erosion will be selected based on the assumptions indicated in the literature, and, to complement the dichotomous analysis, 100 units with no erosion will be randomly designated. The next step will be the selection of the predictive parameters that exert, jointly, directly, or indirectly, some influence on the mechanism of occurrence of soil erosion events. The chosen predictors are altitude, declivity, aspect or orientation of the slope, curvature of the slope, composite topographic index, flow power index, lineament density, normalized difference vegetation index, drainage density, lithology, soil type, erosivity, and ground surface temperature. After evaluating the relative contribution of each predictor variable, the erosion susceptibility model will be applied to the municipality of Colorado do Oeste - Rondônia through the SPSS Statistic 26 software. Evaluation of the model will occur through the determination of the values of the R² of Cox & Snell and the R² of Nagelkerke, Hosmer and Lemeshow Test, Log Likelihood Value, and Wald Test, in addition to analysis of the Confounding Matrix, ROC Curve and Accumulated Gain according to the model specification. The validation of the synthesis map resulting from both models of the potential risk of soil erosion will occur by means of Kappa indices, accuracy, and sensitivity, as well as by field verification of the classes of susceptibility to erosion using drone photogrammetry. Thus, it is expected to obtain the mapping of the following classes of susceptibility to erosion very low, low, moderate, very high, and high, which may constitute a screening tool to identify areas where more detailed investigations need to be carried out, applying more efficient social resources.

Keywords: modeling, susceptibility to erosion, artificial intelligence, Amazon

Procedia PDF Downloads 66

Authors: Mao Zhaofang, Xu Yida, Fang Kan, Fu Enyuan, Zhao Zhao

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Nowadays, last-mile distribution plays an increasingly important role in the whole industrial chain delivery link and accounts for a large proportion of the whole distribution process cost. Promoting the upgrading of logistics networks and improving the layout of final distribution points has become one of the trends in the development of modern logistics. Due to the discrete and heterogeneous needs and spatial distribution of customer demand, which will lead to a higher delivery failure rate and lower vehicle utilization, last-mile delivery has become a time-consuming and uncertain process. As a result, courier companies have introduced a range of innovative parcel storage facilities, including pick-up points and lockers. The introduction of pick-up points and lockers has not only improved the users’ experience but has also helped logistics and courier companies achieve large-scale economy. Against the backdrop of the COVID-19 of the previous period, contactless delivery has become a new hotspot, which has also created new opportunities for the development of collection services. Therefore, a key issue for logistics companies is how to design/redesign their last-mile distribution network systems to create integrated logistics and distribution networks that consider pick-up points and lockers. This paper focuses on the introduction of self-pickup facilities in new logistics and distribution scenarios and the heterogeneous demands of customers. In this paper, we consider two types of demand, including ordinary products and refrigerated products, as well as corresponding transportation vehicles. We consider the constraints associated with self-pickup points and lockers and then address the location-routing problem with self-pickup facilities and heterogeneous demands (LRP-PFHD). To solve this challenging problem, we propose a mixed integer linear programming (MILP) model that aims to minimize the total cost, which includes the facility opening cost, the variable transport cost, and the fixed transport cost. Due to the NP-hardness of the problem, we propose a hybrid adaptive large-neighbourhood search algorithm to solve LRP-PFHD. We evaluate the effectiveness and efficiency of the proposed algorithm by using instances generated based on benchmark instances. The results demonstrate that the hybrid adaptive large neighbourhood search algorithm is more efficient than MILP solvers such as Gurobi for LRP-PFHD, especially for large-scale instances. In addition, we made a comprehensive analysis of some important parameters (e.g., facility opening cost and transportation cost) to explore their impacts on the results and suggested helpful managerial insights for courier companies.

Keywords: city logistics, last-mile delivery, location-routing, adaptive large neighborhood search

Procedia PDF Downloads 78

Authors: Kalaya Sribuddhachart, Mayuree Pumipaiboon, Mayuva Youngsabanant-Areekijseree

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The ultrastructure of 20 species of pheasant eggshells in Thailand, (Simese Fireback, Lophura diardi), (Silver Pheasant, Lophura nycthemera), (Kalij Pheasant, Lophura leucomelanos crawfurdii), (Kalij Pheasant, Lophura leucomelanos lineata), (Red Junglefowl, Gallus gallus spadiceus), (Crested Fireback, Lophura ignita rufa), (Green Peafowl, Pavo muticus), (Indian Peafowl, Pavo cristatus), (Grey Peacock Pheasant, Polyplectron bicalcaratum bicalcaratum), (Lesser Bornean Fireback, Lophura ignita ignita), (Green Junglefowl, Gallus varius), (Hume's Pheasant, Syrmaticus humiae humiae), (Himalayan Monal, Lophophorus impejanus), Golden Pheasant, Chrysolophus pictus, (Ring-Neck Pheasant, Phasianus sp.), (Reeves’s Pheasant, Syrmaticus reevesi), (Polish Chicken, Gallus sp.), (Brahma Chicken, Gallus sp.), (Yellow Golden Pheasant, Chrysolophus pictus luteus), and (Lady Amhersts Pheasant, Chrysolophus amherstiae) were studied by Secondary electron imaging (SEI) and Energy dispersive X-ray analysis (EDX) detectors of scanning electron microscope. Generally, all pheasant eggshells showed 3 layers of cuticle, palisade, and mammillary. The total thickness was ranging from 190.28±5.94-838.96±16.31µm. The palisade layer is the most thickness layer following by mammillary and cuticle layers. The palisade layer in all pheasant eggshells consisted of numerous vesicle holes that were firmly forming as network thorough the layer. The vesicle holes in all pheasant eggshells had difference porosity ranging from 0.44±0.11-0.23±0.05 µm. While the mammillary layer was the most compact layer with a variable shape (broad-base V and U-shape) connect to shell membrane. Elemental analysis by of 20 specie eggshells showed 9 apparent elements including carbon (C), oxygen (O), calcium (Ca), phosphorous (P), sulfur (S), magnesium (Mg), silicon (Si), aluminum (Al), and copper (Cu) at the percentage of 28.90- 8.33%, 60.64-27.61%, 55.30-14.49%, 1.97-0.03%, 0.08-0.03%, 0.50-0.16%, 0.30-0.04%, 0.06-0.02%, and 2.67-1.73%, respectively. It was found that Ca, C, and O showed highest elemental compositions, which essential for pheasant embryonic development, mainly presented as composited structure of calcium carbonate (CaCO3) more than 97%. Meanwhile, Mg, S, Si, Al, and P were major inorganic constituents of the eggshells which directly related to an increase of the shell hardness. Finally, the percentage of heavy metal copper (Cu) has been observed in 4 eggshell species. There are Golden Pheasant (2.67±0.16%), Indian Peafowl (2.61±0.13%), Green Peafowl (1.97±0.74%), and Silver Pheasant (1.73±0.11%), respectively. A non-significant difference was found in the percentages of 9 elements in all pheasant eggshells. This study is useful to provide the information of biology and taxonomic of pheasant study in Thailand for conservation.

Keywords: pheasants eggshells, secondary electron imaging (SEI) and energy dispersive X-ray analysis (EDX), morphology, Thailand

Procedia PDF Downloads 235

Authors: Riddhi Chopra

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Alfred Thayer Mahan described the sea as a ‘great common,’ which would serve as a medium for communication, trade, and transport. The seas of Asia are witnessing an intriguing historical anomaly – rise of an indigenous maritime power against the backdrop of US domination over the region. As China transforms from an inward leaning economy to an outward-leaning economy, it has become increasingly dependent on the global sea; as a result, we witness an evolution in its maritime strategy from near seas defense to far seas deployment strategies. It is not only patrolling the international waters but has also built a network of civilian and military infrastructure across the disputed oceanic expanse. The paper analyses the reorientation of China from a naval power to a blue water navy in an era of extensive globalisation. The actions of the Chinese have created a zone of high alert amongst its neighbors such as Japan, Philippines, Vietnam and North Korea. These nations are trying to align themselves so as to counter China’s growing brinkmanship, but China has been pursuing claims through a carefully calibrated strategy in the region shunning any coercive measures taken by other forces. If China continues to expand its maritime boundaries, its neighbors – all smaller and weaker Asian nations would be limited to a narrow band of the sea along its coastlines. Hence it is essential for the US to intervene and support its allies to offset Chinese supremacy. The paper intends to provide a profound analysis over the disputes in South China Sea and East China Sea focusing on Philippines and Japan respectively. Moreover, the paper attempts to give an account of US involvement in the region and its alignment with its South Asian allies. The geographic dynamics is said the breed a national coalition dominating the strategic ambitions of China as well as the weak littoral states. China has conducted behind the scenes diplomacy trying to persuade its neighbors to support its position on the territorial disputes. These efforts have been successful in creating fault lines in ASEAN thereby undermining regional integrity to reach a consensus on the issue. Chinese diplomatic efforts have also forced the US to revisit its foreign policy and engage with players like Cambodia and Laos. The current scenario in the SCS points to a strong Chinese hold trying to outspace all others with no regards to International law. Chinese activities are in contrast with US principles like Freedom of Navigation thereby signaling US to take bold actions to prevent Chinese hegemony in the region. The paper ultimately seeks to explore the changing power dynamics among various claimants where a rival superpower like US can pursue the traditional policy of alliance formation play a decisive role in changing the status quo in the arena, consequently determining the future trajectory.

Keywords: China, East China Sea, South China Sea, USA

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Authors: Roberto M. G. Velásquez, Jonas R. Gazoli, Nelson Ponce Jr, Valério L. Borges, Alessandro Sete, Fernanda M. C. Tomé, Julian D. Hunt, Heitor C. Lira, Cristiano L. de Souza, Fabio T. Bindemann, Wilmar Wounnsoscky

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Nowadays, society is working toward reducing energy losses and greenhouse gas emissions, as well as seeking clean energy sources, as a result of the constant increase in energy demand and emissions. Energy loss occurs in the gas pressure reduction stations at the delivery points in natural gas distribution systems (city gates). Installing pressure reduction turbines (PRT) parallel to the static reduction valves at the city gates enhances the energy efficiency of the system by recovering the enthalpy of the pressurized natural gas, obtaining in the pressure-lowering process shaft work and generating electrical power. Currently, the Brazilian natural gas transportation network has 9,409 km in extension, while the system has 16 national and 3 international natural gas processing plants, including more than 143 delivery points to final consumers. Thus, the potential of installing PRT in Brazil is 66 MW of power, which could yearly avoid the emission of 235,800 tons of CO2 and generate 333 GWh/year of electricity. On the other hand, an economic viability analysis of these energy efficiency projects is commonly carried out based on estimates of the project's cash flow obtained from several variables forecast. Usually, the cash flow analysis is performed using representative values of these variables, obtaining a deterministic set of financial indicators associated with the project. However, in most cases, these variables cannot be predicted with sufficient accuracy, resulting in the need to consider, to a greater or lesser degree, the risk associated with the calculated financial return. This paper presents an approach applied to the technical-economic viability analysis of PRTs projects that explicitly considers the uncertainties associated with the input parameters for the financial model, such as gas pressure at the delivery point, amount of energy generated by TRP, the future price of energy, among others, using sensitivity analysis techniques, scenario analysis, and Monte Carlo methods. In the latter case, estimates of several financial risk indicators, as well as their empirical probability distributions, can be obtained. This is a methodology for the financial risk analysis of PRT projects. The results of this paper allow a more accurate assessment of the potential PRT project's financial feasibility in Brazil. This methodology will be tested at the Cuiabá thermoelectric plant, located in the state of Mato Grosso, Brazil, and can be applied to study the potential in other countries.

Keywords: pressure reduction turbine, natural gas pressure drop station, energy efficiency, electricity generation, monte carlo methods

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Authors: George R. Puno, Eric N. Bruno

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The natural landscape of the Philippine archipelago plus the current realities of climate change make the country vulnerable to flood hazards. Flooding becomes the recurring natural disaster in the country resulting to lose of lives and properties. Musimusi is among the rivers which exhibited inundation particularly at the inhabited floodplain portion of its watershed. During the event, rescue operations and distribution of relief goods become a problem due to lack of high resolution flood maps to aid local government unit identify the most affected areas. In the attempt of minimizing impact of flooding, hydrologic modelling with high resolution mapping is becoming more challenging and important. This study focused on the analysis of flood extent as a function of different geomorphologic characteristics of Musimusi watershed. The methods include the delineation of morphometric parameters in the Musimusi watershed using Geographic Information System (GIS) and geometric calculations tools. Digital Terrain Model (DTM) as one of the derivatives of Light Detection and Ranging (LiDAR) technology was used to determine the extent of river inundation involving the application of Hydrologic Engineering Center-River Analysis System (HEC-RAS) and Hydrology Modelling System (HEC-HMS) models. The digital elevation model (DEM) from synthetic Aperture Radar (SAR) was used to delineate watershed boundary and river network. Datasets like mean sea level, river cross section, river stage, discharge and rainfall were also used as input parameters. Curve number (CN), vegetation, and soil properties were calibrated based on the existing condition of the site. Results showed that the drainage density value of the watershed is low which indicates that the basin is highly permeable subsoil and thick vegetative cover. The watershed’s elongation ratio value of 0.9 implies that the floodplain portion of the watershed is susceptible to flooding. The bifurcation ratio value of 2.1 indicates higher risk of flooding in localized areas of the watershed. The circularity ratio value (1.20) indicates that the basin is circular in shape, high discharge of runoff and low permeability of the subsoil condition. The heavy rainfall of 167 mm brought by Typhoon Seniang last December 29, 2014 was characterized as high intensity and long duration, with a return period of 100 years produced 316 m3s-1 outflows. Portion of the floodplain zone (1.52%) suffered inundation with 2.76 m depth at the maximum. The information generated in this study is helpful to the local disaster risk reduction management council in monitoring the affected sites for more appropriate decisions so that cost of rescue operations and relief goods distribution is minimized.

Keywords: flooding, geomorphology, mapping, watershed

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Authors: Christine Böckmann, Julia Rosemann

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We present an inversion algorithm that is used in the European Aerosol Lidar Network for the inversion of data collected with multi-wavelength Raman lidar. These instruments measure backscatter coefficients at 355, 532, and 1064 nm, and extinction coefficients at 355 and 532 nm. The algorithm is based on manually controlled inversion of optical data which allows for detailed sensitivity studies and thus provides us with comparably high quality of the derived data products. The algorithm allows us to derive particle effective radius, volume, surface-area concentration with comparably high confidence. The retrieval of the real and imaginary parts of the complex refractive index still is a challenge in view of the accuracy required for these parameters in climate change studies in which light-absorption needs to be known with high accuracy. Single-scattering albedo (SSA) can be computed from the retrieve microphysical parameters and allows us to categorize aerosols into high and low absorbing aerosols. From mathematical point of view the algorithm is based on the concept of using truncated singular value decomposition as regularization method. This method was adapted to work for the retrieval of the particle size distribution function (PSD) and is called hybrid regularization technique since it is using a triple of regularization parameters. The inversion of an ill-posed problem, such as the retrieval of the PSD, is always a challenging task because very small measurement errors will be amplified most often hugely during the solution process unless an appropriate regularization method is used. Even using a regularization method is difficult since appropriate regularization parameters have to be determined. Therefore, in a next stage of our work we decided to use two regularization techniques in parallel for comparison purpose. The second method is an iterative regularization method based on Pade iteration. Here, the number of iteration steps serves as the regularization parameter. We successfully developed a semi-automated software for spherical particles which is able to run even on a parallel processor machine. From a mathematical point of view, it is also very important (as selection criteria for an appropriate regularization method) to investigate the degree of ill-posedness of the problem which we found is a moderate ill-posedness. We computed the optical data from mono-modal logarithmic PSD and investigated particles of spherical shape in our simulations. We considered particle radii as large as 6 nm which does not only cover the size range of particles in the fine-mode fraction of naturally occurring PSD but also covers a part of the coarse-mode fraction of PSD. We considered errors of 15% in the simulation studies. For the SSA, 100% of all cases achieve relative errors below 12%. In more detail, 87% of all cases for 355 nm and 88% of all cases for 532 nm are well below 6%. With respect to the absolute error for non- and weak-absorbing particles with real parts 1.5 and 1.6 in all modes the accuracy limit +/- 0.03 is achieved. In sum, 70% of all cases stay below +/-0.03 which is sufficient for climate change studies.

Keywords: aerosol particles, inverse problem, microphysical particle properties, regularization

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Authors: Andrea Cretu, Calin Cadar, Maria Miclaus, Lucian Barbu-Tudoran, Siegfried Stapf, Ioan Ardelean

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Additives and mineral admixtures have become an integral part of cement-based materials. It is common practice to add silica fume to cement based mixes in order to produce high-performance concrete. There is still a lack of scientific understanding regarding the effects that silica fume has on the microstructure of hydrated cement paste. The aim of the current study is to develop high-performance materials with low permeability and high resistance to flexural stress using silica fume and an organosilane. Organosilane bonds with cement grains and silica fume, influencing both the workability and the final properties of the mix, especially the pore size distributions and pore connectivity. Silica fume is a known pozzolanic agent which reacts with the calcium hydroxide in hydrated cement paste, producing more C-S-H and improving the mechanical properties of the mix. It is believed that particles of silica fume act as capillary pore fillers and nucleation centers for C-S-H and other hydration products. In order to be able to design cement-based materials with added silica fume and organosilane, it is necessary first to understand the formation of the porous network during hydration and to observe the distribution of pores and their connectivity. Nuclear magnetic resonance (NMR) methods in low-fields are non-destructive and allow the study of cement-based materials from the standpoint of their porous structure. Other methods, such as XRD and SEM-EDS, help create a comprehensive picture of the samples, along with the classic mechanical tests (compressive and flexural strength measurements). The transverse relaxation time (T₂) was measured during the hydration of 16 samples prepared with two water/cement ratios (0.3 and 0.4) and different concentrations or organosilane (APTES, up to 2% by mass of cement) and silica fume (up to 6%). After their hydration, the pore size distribution was assessed using the same NMR approach on the samples filled with cyclohexane. The SEM-EDS and XRD measurements were applied on pieces and powders prepared from the samples that were used in mechanical testing, which were kept under water for 28 days. Adding silica fume does not influence the hydration dynamics of cement paste, while the addition of organosilane extends the dormancy stage up to 10 hours. The size distribution of the capillary pores is not influenced by the addition of silica fume or organosilane, while the connectivity of capillary pores is decreased only when there is organosilane in the mix. No filling effect is observed even at the highest concentration of silica fume. There is an apparent increase in flexural strength of samples prepared only with silica fume and a decrease for those prepared with organosilane, with a few exceptions. XRD reveals that the pozzolanic reactivity of silica fume can only be observed when there is no organosilane present and the SEM-EDS method reveals the pore distribution, as well as hydration products and the presence or absence of calcium hydroxide. The current work was funded by the Romanian National Authority for Scientific Research, CNCS – UEFISCDI, through project PN-III-P2-2.1-PED-2016-0719.

Keywords: cement hydration, concrete admixtures, NMR, organosilane, porosity, silica fume

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Authors: Yasmine Salehy, Yann Leroy, Francois Cluzel, Hong-Minh Hoang, Laurence Fournaison, Anthony Delahaye, Bernard Yannou

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Taking into consideration all the life cycle of a product is now an important step in the eco-design of a product or a technology. Life cycle assessment (LCA) is a standard tool to evaluate the environmental impacts of a system or a process. Despite the improvement in refrigerant regulation through protocols, the environmental damage of refrigeration systems remains important and needs to be improved. In this paper, the environmental impacts of refrigeration systems in a typical supermarket are compared using the LCA methodology under different conditions. The system is used to provide cold at two levels of temperature: medium and low temperature during a life period of 15 years. The most commonly used architectures of supermarket cold production systems are investigated: centralized direct expansion systems and indirect systems using a secondary loop to transport the cold. The variation of power needed during seasonal changes and during the daily opening/closure periods of the supermarket are considered. R134a as the primary refrigerant fluid and two types of secondary fluids are considered. The composition of each system and the leakage rate of the refrigerant through its life cycle are taken from the literature and industrial data. Twelve scenarios are examined. They are based on the variation of three parameters, 1. location: France (Paris), Spain (Toledo) and Sweden (Stockholm), 2. different sources of electric consumption: photovoltaic panels and low voltage electric network and 3. architecture: direct and indirect refrigeration systems. OpenLCA, SimaPro softwares, and different impact assessment methods were compared; CML method is used to evaluate the midpoint environmental indicators. This study highlights the significant contribution of electric consumption in environmental damages compared to the impacts of refrigerant leakage. The secondary loop allows lowering the refrigerant amount in the primary loop which results in a decrease in the climate change indicators compared to the centralized direct systems. However, an exhaustive cost evaluation (CAPEX and OPEX) of both systems shows more important costs related to the indirect systems. A significant difference between the countries has been noticed, mostly due to the difference in electric production. In Spain, using photovoltaic panels helps to reduce efficiently the environmental impacts and the related costs. This scenario is the best alternative compared to the other scenarios. Sweden is a country with less environmental impacts. For both France and Sweden, the use of photovoltaic panels does not bring a significant difference, due to a less sunlight exposition than in Spain. Alternative solutions exist to reduce the impact of refrigerating systems, and a brief introduction is presented.

Keywords: eco-design, industrial engineering, LCA, refrigeration system

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Authors: Omwoyo Bosire Onyancha

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The Journal Citation Reports (JCR) of the Thomson Reuters has, for decades, provided the data for bibliometrically assessing the impact of journals. In their criticism of the journal impact factor (JIF), a number of scholars such as Priem, Taraborelli, Groth and Neylon (2010) observe that the “JIF is often incorrectly used to assess the impact of individual articles. It is troubling that the exact details of the JIF are a trade secret, and that significant gaming is relatively easy”. The emergence of alternative metrics (Altmetrics) has introduced another dimension of re-assessing how the impact of journals (and other units such as articles and even individual researchers) can be measured. Altmetrics is premised upon the fact that research is increasingly being disseminated through social network sites such as ResearchGate, Mendeley, Twitter, Facebook, LinkedIn, and ImpactStory, among others. This paper adopts informetrics (including altmetrics) techniques to report on the findings of a study conducted to investigate and compare the social media impact of 274 South Africa Post Secondary Education (SAPSE)-accredited journals, which are recognized and accredited by the Department of Higher Education and Training (DHET) of South Africa (SA). We used multiple sources to extract data for the study, namely Altmetric.com and the Thomson Reuters’ Journal Citation Reports. Data was analyzed in order to determine South African journals’ presence and impact on social media as well as contrast the social media impact with Thomson Reuters’ citation impact. The Spearman correlation test was performed to compare the journals’ social media impact and JCR citation impact. Preliminary findings reveal that a total of 6360 articles published in 96 South African journals have received some attention in social media; the most commonly used social media platform was Twitter, followed by Mendeley, Facebook, News outlets, and CiteULike; there were 29 SA journals covered in the JCR in 2008 and this number has grown to 53 journals in 2014; the journals indexed in the Thomson Reuters performed much better, in terms of their altmetrics, than those journals that are not indexed in Thomson Reuters databases; nevertheless, there was high correlation among journals that featured in both datasets; the journals with the highest scores in Altmetric.com included the South African Medical Journal, African Journal of Marine Science, and Transactions of the Royal Society of South Africa while the journals with high impact factors in JCR were South African Medical Journal, Onderstepoort: Journal of Veterinary Research, and Sahara: Journal of Social Aspects of HIV-AIDS; and that Twitter has emerged as a strong avenue of sharing and communicating research published in the South African journals. Implications of the results of the study for the dissemination of research conducted in South Africa are offered. Discussions based on the research findings as well as conclusions and recommendations are offered in the full text paper.

Keywords: altmetrics, citation impact, journal citation reports, journal impact factor, journals, research, scholarly publishing, social media impact, South Africa

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Authors: Ilana Singer, Anastasia Lučić, Julie Leclerc

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Introduction: Tics, characterized by repetitive, sudden, non-voluntary motor movements or vocalizations, are prevalent in chronic tic disorder (CT) and Tourette Syndrome (TS). These neurodevelopmental disorders often coexist with various psychiatric conditions, leading to challenges and reduced quality of life. While medication in conjunction with behavioral interventions, such as Habit Reversal Training (HRT), Exposure Response Prevention (ERP), and Comprehensive Behavioral Intervention for Tics (CBIT), has shown efficacy, a significant proportion of patients experience persistent tics. Thus, innovative treatment approaches are necessary to improve therapeutic outcomes, such as mindfulness-based approaches. Nonetheless, the effectiveness of mindfulness-based interventions in the context of CT and TS remains understudied. Objective: The objective of this scoping review is to provide an overview of the current state of research on mindfulness-based interventions for CT and TS, identify knowledge and evidence gaps, discuss the effectiveness of mindfulness-based interventions with other treatment options, and discuss implications for clinical practice and policy development. Method: Using guidelines from Peters (2020) and the PRISMA-ScR, a scoping review was conducted. Multiple electronic databases were searched from inception until June 2023, including MEDLINE, EMBASE, PsychInfo, Global Health, PubMed, Web of Science, and Érudit. Inclusion criteria were applied to select relevant studies, and data extraction was independently performed by two reviewers. Results: Five papers were included in the study. Firstly, we found that mindfulness interventions were found to be effective in reducing anxiety and depression while enhancing overall well-being in individuals with tics. Furthermore, the review highlighted the potential role of mindfulness in enhancing functional connectivity within the Default Mode Network (DMN) as a compensatory function in TS patients. This suggests that mindfulness interventions may complement and support traditional therapeutic approaches, particularly HRT, by positively influencing brain networks associated with tic regulation and control. Conclusion: This scoping review contributes to the understanding of the effectiveness of mindfulness-based interventions in managing CT and TS. By identifying research gaps, this review can guide future investigations and interventions to improve outcomes for individuals with CT or TS. Overall, these findings emphasize the potential benefits of incorporating mindfulness-based interventions as a smaller subset within comprehensive treatment strategies. However, it is essential to acknowledge the limitations of this scoping review, such as the exclusion of a pre-established protocol and the limited number of studies available for inclusion. Further research and clinical exploration are necessary to better understand the specific mechanisms and optimal integration of mindfulness-based interventions with existing behavioral interventions for this population.

Keywords: scoping reviews, Tourette Syndrome, tics, mindfulness-based, therapy, intervention

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Authors: Afsheen Aman, Zainab Bibi, Shah Ali Ul Qader

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Introduction: Xylan is a heteropolysaccharide composed of xylose monomers linked together through 1,4 linkages within a complex xylan network. Owing to wide applications of xylan hydrolytic products (xylose, xylobiose and xylooligosaccharide) the researchers are focusing towards the development of various strategies for efficient xylan degradation. One of the most important strategies focused is the use of heat tolerant biocatalysts which acts as strong and specific cleaving agents. Therefore, the exploration of microbial pool from extremely diversified ecosystem is considerably vital. Microbial populations from extreme habitats are keenly explored for the isolation of thermophilic entities. These thermozymes usually demonstrate fast hydrolytic rate, can produce high yields of product and are less prone to microbial contamination. Another possibility of degrading xylan continuously is the use of immobilization technique. The current work is an effort to merge both the positive aspects of thermozyme and immobilization technique. Methodology: Geobacillus stearothermophilus was isolated from soil sample collected near the blast furnace site. This thermophile is capable of producing thermostable endo-β-1,4-xylanase which cleaves xylan effectively. In the current study, this thermozyme was immobilized within a synthetic and a non-synthetic matrice for continuous production of metabolites using entrapment technique. The kinetic parameters of the free and immobilized enzyme were studied. For this purpose calcium alginate and polyacrylamide beads were prepared. Results: For the synthesis of immobilized beads, sodium alginate (40.0 gL-1) and calcium chloride (0.4 M) was used amalgamated. The temperature (50°C) and pH (7.0) optima of immobilized enzyme remained same for xylan hydrolysis however, the enzyme-substrate catalytic reaction time raised from 5.0 to 30.0 minutes as compared to free counterpart. Diffusion limit of high molecular weight xylan (corncob) caused a decline in Vmax of immobilized enzyme from 4773 to 203.7 U min-1 whereas, Km value increased from 0.5074 to 0.5722 mg ml-1 with reference to free enzyme. Immobilized endo-β-1,4-xylanase showed its stability at high temperatures as compared to free enzyme. It retained 18% and 9% residual activity at 70°C and 80°C, respectively whereas; free enzyme completely lost its activity at both temperatures. The Immobilized thermozyme displayed sufficient recycling efficiency and can be reused up to five reaction cycles, indicating that this enzyme can be a plausible candidate in paper processing industry. Conclusion: This thermozyme showed better immobilization yield and operational stability with the purpose of hydrolyzing the high molecular weight xylan. However, the enzyme immobilization properties can be improved further by immobilizing it on different supports for industrial purpose.

Keywords: immobilization, reusability, thermozymes, xylanase

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Authors: Moanaro Ao, Nzanthung Ngullie

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Municipal solid waste management (MSWM) has unique challenges in hilly urban settlements. Efforts have been taken by municipalities, private players, non-governmental organizations, etc. for managing solid waste by preventing its generation, reusing, and recovering them into useful products to the extent possible, thereby minimizing its impact on the environment and human health. However, there are many constraints that lead to inadequate management of solid waste. Kohima is an unplanned hill station city in the North Eastern Region of India. The city is facing numerous issues due to the mismanagement of the MSW generated. Kohima Municipal Council (KMC) is the Urban Local Body (ULB) responsible for providing municipal services. The present MSWM system in Kohima comprises of collection, transportation, and disposal of waste without any treatment. Several efforts and experimental projects on waste management have been implemented without any success. Waste management in Kohima city is challenging due to its remote location, difficult topography, dispersed settlements within the city, sensitive ecosystem, etc. Furthermore, the narrow road network in Kohima with limited scope for expansion, inadequate infrastructure facilities, and financial constraints of the ULB add up to the problems faced in managing solid waste. This hill station also has a unique system of traditional local self-governance. Thus, shifting from a traditional system to a modern system in implementing systematic and scientific waste management is also a challenge in itself. This study aims to analyse the existing situation of waste generation, evaluate the effectiveness of the existing management system of MSW, and evolve a strategic approach to achieve a sustainable and resilient MSWM system. The results from the study show that a holistic approach, including social aspects, technical aspects, environmental aspects, and financial aspects, is needed to reform the MSWM system. Stringent adherence to source segregation is required by encouraging public participation through awareness programs. Active involvement of community-based organizations (CBOs) has brought a positive change in sensitizing the public. A waste management model was designed to be adopted at a micro-level such as composting household biodegradable waste and incinerator plants at the community level for non-biodegradable waste. Suitable locations for small waste stations were identified using geographical information system (GIS) tools for waste recovery and recycling. Inculcating the sense of responsibility in every waste generator towards waste management by implementing incentive-based strategies at the Ward level was explored. Initiatives based on the ‘polluters pay principle’ were also explored to make the solid waste management model “self-sustaining”.

Keywords: municipal solid waste management, public participation, source segregation, sustainable

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Authors: C. A. Barros, Ana P. Barroso

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Any industrial company needs to determine the amount of variation that exists within its measurement process and guarantee the reliability of their data, studying the performance of their measurement system, in terms of linearity, bias, repeatability and reproducibility and stability. This issue is critical for automotive industry suppliers, who are required to be certified by the 16949:2016 standard (replaces the ISO/TS 16949) of International Automotive Task Force, defining the requirements of a quality management system for companies in the automotive industry. Measurement System Analysis (MSA) is one of the mandatory tools. Frequently, the measurement system in companies is not connected to the equipment and do not incorporate the methods proposed by the Automotive Industry Action Group (AIAG). To address these constraints, an R&D project is in progress, whose objective is to develop a web and cloud-based MSA tool. This MSA tool incorporates Industry 4.0 concepts, such as, Internet of Things (IoT) protocols to assure the connection with the measuring equipment, cloud computing, artificial intelligence, statistical tools, and advanced mathematical algorithms. This paper presents the preliminary findings of the project. The web and cloud-based MSA tool is innovative because it implements all statistical tests proposed in the MSA-4 reference manual from AIAG as well as other emerging methods and techniques. As it is integrated with the measuring devices, it reduces the manual input of data and therefore the errors. The tool ensures traceability of all performed tests and can be used in quality laboratories and in the production lines. Besides, it monitors MSAs over time, allowing both the analysis of deviations from the variation of the measurements performed and the management of measurement equipment and calibrations. To develop the MSA tool a ten-step approach was implemented. Firstly, it was performed a benchmarking analysis of the current competitors and commercial solutions linked to MSA, concerning Industry 4.0 paradigm. Next, an analysis of the size of the target market for the MSA tool was done. Afterwards, data flow and traceability requirements were analysed in order to implement an IoT data network that interconnects with the equipment, preferably via wireless. The MSA web solution was designed under UI/UX principles and an API in python language was developed to perform the algorithms and the statistical analysis. Continuous validation of the tool by companies is being performed to assure real time management of the ‘big data’. The main results of this R&D project are: MSA Tool, web and cloud-based; Python API; New Algorithms to the market; and Style Guide of UI/UX of the tool. The MSA tool proposed adds value to the state of the art as it ensures an effective response to the new challenges of measurement systems, which are increasingly critical in production processes. Although the automotive industry has triggered the development of this innovative MSA tool, other industries would also benefit from it. Currently, companies from molds and plastics, chemical and food industry are already validating it.

Keywords: automotive Industry, industry 4.0, Internet of Things, IATF 16949:2016, measurement system analysis

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Authors: Julio Jesus Salazar, Julio Jesus De Lama

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the fundamental objective of hydrological studies applied to the engineering field is to determine the statistically consistent volumes or water flows that, in each case, allow us to size or design a series of elements or structures to effectively manage and develop a river basin. To determine these values, there are several ways of working within the framework of traditional hydrology: (1) Study each of the factors that influence the hydrological cycle, (2) Study the historical behavior of the hydrology of the area, (3) Study the historical behavior of hydrologically similar zones, and (4) Other studies (rain simulators or experimental basins). Of course, this range of studies in a certain basin is very varied and complex and presents the difficulty of collecting the data in real time. In this complex space, the study of variables can only be overcome by collecting and transmitting data to decision centers through the Internet of things and artificial intelligence. Thus, this research work implemented the learning project of the sub-basin of the Shullcas river in the Andean basin of the Mantaro river in Peru. The sensor firmware to collect and communicate hydrological parameter data was programmed and tested in similar basins of the European Union. The Machine Learning applications was programmed to choose the algorithms that direct the best solution to the determination of the rainfall-runoff relationship captured in the different polygons of the sub-basin. Tests were carried out in the mountains of Europe, and in the sub-basins of the Shullcas river (Huancayo) and the Yauli river (Jauja) with heights close to 5000 m.a.s.l., giving the following conclusions: to guarantee a correct communication, the distance between devices should not pass the 15 km. It is advisable to minimize the energy consumption of the devices and avoid collisions between packages, the distances oscillate between 5 and 10 km, in this way the transmission power can be reduced and a higher bitrate can be used. In case the communication elements of the devices of the network (internet of things) installed in the basin do not have good visibility between them, the distance should be reduced to the range of 1-3 km. The energy efficiency of the Atmel microcontrollers present in Arduino is not adequate to meet the requirements of system autonomy. To increase the autonomy of the system, it is recommended to use low consumption systems, such as the Ashton Raggatt McDougall or ARM Cortex L (Ultra Low Power) microcontrollers or even the Cortex M; and high-performance direct current (DC) to direct current (DC) converters. The Machine Learning System has initiated the learning of the Shullcas system to generate the best hydrology of the sub-basin. This will improve as machine learning and the data entered in the big data coincide every second. This will provide services to each of the applications of the complex system to return the best data of determined flows.

Keywords: hydrology, internet of things, machine learning, river basin

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Authors: Lucie Speyer, Vincent Lecocq, Séverine Humbert, Antoine Hugon

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Mesoporous alumina is commonly used as a catalyst support for the hydrotreating of heavy petroleum cuts. The process of fabrication usually involves: the synthesis of the boehmite AlOOH precursor, a kneading-extrusion step, and a calcination in order to obtain the final alumina extrudates. Alumina is described as a complex porous medium, generally agglomerates constituted of aggregated nanocrystallites. Its porous texture directly influences the active phase deposition and mass transfer, and the catalytic properties. Then, it is easy to figure out that each step of the fabrication of the supports has a role on the building of their porous network, and has to be well understood to optimize the process. The synthesis of boehmite by precipitation of aluminum salts was extensively studied in the literature and the effect of various parameters, such as temperature or pH, are known to influence the size and shape of the crystallites and the specific surface area of the support. The calcination step, through the topotactic transition from boehmite to alumina, determines the final properties of the support and can tune the surface area, pore volume and pore diameters from those of boehmite. However, the kneading extrusion step has been subject to a very few studies. It generally consists in two steps: an acid, then a basic kneading, where the boehmite powder is introduced in a mixer and successively added with an acid and a base solution to form an extrudable paste. During the acid kneading, the induced positive charges on the hydroxyl surface groups of boehmite create an electrostatic repulsion which tends to separate the aggregates and even, following the conditions, the crystallites. The basic kneading, by reducing the surface charges, leads to a flocculation phenomenon and can control the reforming of the overall structure. The separation and reassembling of the particles constituting the boehmite paste have a quite obvious influence on the textural properties of the material. In this work, we are focused on the influence of the kneading step on the alumina catalysts supports. Starting from an industrial boehmite, extrudates are prepared using various kneading conditions. The samples are studied by nitrogen physisorption in order to analyze the evolution of the textural properties, and by synchrotron small-angle X-ray scattering (SAXS), a more original method which brings information about agglomeration and aggregation of the samples. The coupling of physisorption and SAXS enables a precise description of the samples, as same as an accurate monitoring of their evolution as a function of the kneading conditions. These ones are found to have a strong influence of the pore volume and pore size distribution of the supports. A mechanism of evolution of the texture during the kneading step is proposed and could be attractive in order to optimize the texture of the supports and then, their catalytic performances.

Keywords: alumina catalyst support, kneading, nitrogen physisorption, small-angle X-ray scattering

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Authors: O. A. Adeyemi, M. O. Idris, W. A. Oke, O. T. Olorode, S. O. Alayande, A. E. Adeoye

Abstract:

The purpose of this paper is to investigate the process of capability building at subsidiary level and the challenges to such process. The relevance of external factors for capability development, have not been explicitly addressed in empirical studies. Though, internal factors, acting as enablers, have been more extensively studied. With reference to external factors, subsidiaries are actively influenced by specific characteristics of the host country, implying a need to become fully immersed in local culture and practices. Specifically, in MNCs, there has been a widespread trend in management practice to increase subsidiary autonomy,  with subsidiary managers being encouraged to act entrepreneurially, and to take advantage of host country specificity. As such, it could be proposed that: P1: The degree at which subsidiary management is connected to the host country, will positively influence the capability development process. Dynamic capabilities reside to a large measure with the subsidiary management team, but are impacted by the organizational processes, systems and structures that the MNC headquarter has designed to manage its business. At the subsidiary level, the weight of the subsidiary in the network, its initiative-taking and its profile building increase the supportive attention of the HQs and are relevant to the success of the process of capability building. Therefore, our second proposition is that: P2: Subsidiary role and HQ support are relevant elements in capability development at the subsidiary level. Design/Methodology/Approach: This present study will adopt the multiple case studies approach. That is because a case study research is relevant when addressing issues without known empirical evidences or with little developed prior theory. The key definitions and literature sources directly connected with operations of western subsidiaries in emerging markets, such as China, are well established. A qualitative approach, i.e., case studies of three western subsidiaries, will be adopted. The companies have similar products, they have operations in China, and both of them are mature in their internationalization process. Interviews with key informants, annual reports, press releases, media materials, presentation material to customers and stakeholders, and other company documents will be used as data sources. Findings: Western Subsidiaries in Emerging Market operate in a way substantially different from those in the West. What are the conditions initiating the outsourcing of operations? The paper will discuss and present two relevant propositions guiding that process. Practical Implications: MNCs headquarter should be aware of the potential for capability development at the subsidiary level. This increased awareness could induce consideration in headquarter about the possible ways of encouraging such known capability development and how to leverage these capabilities for better MNC headquarter and/or subsidiary performance. Originality/Value: The paper is expected to contribute on the theme: drivers of subsidiary performance with focus on emerging market. In particular, it will show how some external conditions could promote a capability-building process within subsidiaries.

Keywords: case studies, dynamic capability, emerging market, subsidiary

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Authors: Ismene Ithai Bras-Ruiz

Abstract:

Learning Analytics (LA) is employed by universities not only as a tool but as a specialized ground to enhance students and professors. However, not all the academic programs apply LA with the same goal and use the same tools. In fact, LA is formed by five main fields of study (academic analytics, action research, educational data mining, recommender systems, and personalized systems). These fields can help not just to inform academic authorities about the situation of the program, but also can detect risk students, professors with needs, or general problems. The highest level applies Artificial Intelligence techniques to support learning practices. LA has adopted different techniques: statistics, ethnography, data visualization, machine learning, natural language process, and data mining. Is expected that any academic program decided what field wants to utilize on the basis of his academic interest but also his capacities related to professors, administrators, systems, logistics, data analyst, and the academic goals. The Open and Distance University System (SUAYED in Spanish) of the University National Autonomous of Mexico (UNAM), has been working for forty years as an alternative to traditional programs; one of their main supports has been the employ of new information and communications technologies (ICT). Today, UNAM has one of the largest network higher education programs, twenty-six academic programs in different faculties. This situation means that every faculty works with heterogeneous populations and academic problems. In this sense, every program has developed its own Learning Analytic techniques to improve academic issues. In this context, an investigation was carried out to know the situation of the application of LA in all the academic programs in the different faculties. The premise of the study it was that not all the faculties have utilized advanced LA techniques and it is probable that they do not know what field of study is closer to their program goals. In consequence, not all the programs know about LA but, this does not mean they do not work with LA in a veiled or, less clear sense. It is very important to know the grade of knowledge about LA for two reasons: 1) This allows to appreciate the work of the administration to improve the quality of the teaching and, 2) if it is possible to improve others LA techniques. For this purpose, it was designed three instruments to determinate the experience and knowledge in LA. These were applied to ten faculty coordinators and his personnel; thirty members were consulted (academic secretary, systems manager, or data analyst, and coordinator of the program). The final report allowed to understand that almost all the programs work with basic statistics tools and techniques, this helps the administration only to know what is happening inside de academic program, but they are not ready to move up to the next level, this means applying Artificial Intelligence or Recommender Systems to reach a personalized learning system. This situation is not related to the knowledge of LA, but the clarity of the long-term goals.

Keywords: academic improvements, analytical techniques, learning analytics, personnel expertise

Procedia PDF Downloads 128

Authors: Solanki Ravirajsinh

Abstract:

In the software testing life cycle, failing to conduct thorough performance testing can result in significant losses for an organization due to application crashes and improper behavior under high user loads in production. Simulating large volumes of requests, such as 5 million within 5-10 minutes, is challenging without a scalable performance testing framework. Leveraging cloud services to implement a performance testing framework makes it feasible to handle 5-10 million requests in just 5-10 minutes, helping organizations ensure their applications perform reliably under peak conditions. Implementing a scalable performance testing framework using cloud services and tools like JMeter, EC2 instances (Virtual machine), cloud logs (Monitor errors and logs), EFS (File storage system), and security groups offers several key benefits for organizations. Creating performance test framework using this approach helps optimize resource utilization, effective benchmarking, increased reliability, cost savings by resolving performance issues before the application is released. In performance testing, a master-slave framework facilitates distributed testing across multiple EC2 instances to emulate many concurrent users and efficiently handle high loads. The master node orchestrates the test execution by coordinating with multiple slave nodes to distribute the workload. Slave nodes execute the test scripts provided by the master node, with each node handling a portion of the overall user load and generating requests to the target application or service. By leveraging JMeter's master-slave framework in conjunction with cloud services like EC2 instances, EFS, CloudWatch logs, security groups, and command-line tools, organizations can achieve superior scalability and flexibility in their performance testing efforts. In this master-slave framework, JMeter must be installed on both the master and each slave EC2 instance. The master EC2 instance functions as the "brain," while the slave instances operate as the "body parts." The master directs each slave to execute a specified number of requests. Upon completion of the execution, the slave instances transmit their results back to the master. The master then consolidates these results into a comprehensive report detailing metrics such as the number of requests sent, encountered errors, network latency, response times, server capacity, throughput, and bandwidth. Leveraging cloud services, the framework benefits from automatic scaling based on the volume of requests. Notably, integrating cloud services allows organizations to handle more than 5-10 million requests within 5 minutes, depending on the server capacity of the hosted website or application.

Keywords: identify crashes of application under heavy load, JMeter with cloud Services, Scalable performance testing, JMeter master and slave using cloud Services

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Authors: Robert Caulk

Abstract:

A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

Procedia PDF Downloads 88