Search results for: compressed stabilized earth block

Authors: Athina Puype, Lorenzo Malerba, Nico De Wispelaere, Roumen Petrov, Jilt Sietsma

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Reduced activation ferritic/martensitic (RAFM) steels like EUROFER97 are primary candidate structural materials for first wall application in the future demonstration (DEMO) fusion reactor. Existing steels of this type obtain their functional properties by a two-stage heat treatment, which consists of an annealing stage at 980°C for thirty minutes followed by quenching and an additional tempering stage at 750°C for two hours. This thermal quench and temper (Q&T) treatment creates a microstructure of tempered martensite with, as main precipitates, M23C6 carbides, with M = Fe, Cr and carbonitrides of MX type, e.g. TaC and VN. The resulting microstructure determines the mechanical properties of the steel. The ductility is largely determined by the tempered martensite matrix, while the resistance to mechanical degradation, determined by the spatial and size distribution of precipitates and the martensite crystals, plays a key role in the high temperature properties of the steel. Unfortunately, the high temperature response of EUROFER97 is currently insufficient for long term use in fusion reactors, due to instability of the matrix phase and coarsening of the precipitates at prolonged high temperature exposure. The objective of this study is to induce grain refinement by appropriate modifications of the processing route in order to increase the high temperature strength of a lab-cast EUROFER RAFM steel grade. The goal of the work is to obtain improved mechanical behavior at elevated temperatures with respect to conventionally heat treated EUROFER97. A dilatometric study was conducted to study the effect of the annealing temperature on the mechanical properties after a Q&T treatment. The microstructural features were investigated with scanning electron microscopy (SEM), electron back-scattered diffraction (EBSD) and transmission electron microscopy (TEM). Additionally, hardness measurements, tensile tests at elevated temperatures and Charpy V-notch impact testing of KLST-type MCVN specimens were performed to study the mechanical properties of the furnace-heated lab-cast EUROFER RAFM steel grade. A significant prior austenite grain (PAG) refinement was obtained by lowering the annealing temperature of the conventionally used Q&T treatment for EUROFER97. The reduction of the PAG results in finer martensitic constituents upon quenching, which offers more nucleation sites for carbide and carbonitride formation upon tempering. The ductile-to-brittle transition temperature (DBTT) was found to decrease with decreasing martensitic block size. Additionally, an increased resistance against high temperature degradation was accomplished in the fine grained martensitic materials with smallest precipitates obtained by tailoring the annealing temperature of the Q&T treatment. It is concluded that the microstructural refinement has a pronounced effect on the DBTT without significant loss of strength and ductility. Further investigation into the optimization of the processing route is recommended to improve the mechanical behavior of RAFM steels at elevated temperatures.

Keywords: ductile-to-brittle transition temperature (DBTT), EUROFER, reduced activation ferritic/martensitic (RAFM) steels, thermal treatments

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Authors: Juliana A. B. Silva, Caio H. T. L. Albuquerque, Leonardo L. dos Santos, Cristiane K. Oliveira, Ivani Malvestiti, Fernando Hallwass, Ricardo L. Longo

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Complexes with lanthanide ions have been extensively studied due to their applications as luminescent, magnetic and catalytic materials as molecular or extended crystals, thin films, glasses, polymeric matrices, ionic liquids, and in solution. NMR chemical shift data in solution have been reported and suggest fluxional structures in a wide range of coordination compounds with rare earth ions. However, the fluxional mechanisms for these compounds are still not established. This structural fluxionality may affect the photophysical, catalytic and magnetic properties in solution. Thus, understanding the structural interconversion mechanisms may aid the design of coordination compounds with, for instance, improved (electro)luminescence, catalytic and magnetic behaviors. The [Eu(btfa)₃bipy] complex, where btfa= 4,4,4-trifluoro-1-phenyl-1,3-butanedionate and bipy= 2,2’-bipiridyl, has a well-defined X-ray crystallographic structure and preliminary 1H NMR data suggested a structural fluxionality. Thus, we have investigated a series of coordination compounds with lanthanide ions [Ln(btfa)₃L], where Ln = La, Eu, Gd or Yb and L= bipy or phen (phen=1,10-phenanthroline) using a combined theoretical-experimental approach. These complexes were synthesized and fully characterized, and detailed NMR measurements were obtained. They were also studied by quantum chemical computational methods (DFT-PBE0). The aim was to determine the relevant factors in the structure of these compounds that favor or not the fluxional behavior. Measurements of the 1H NMR signals at variable temperature in CD₂Cl₂ of the [Eu(btfa)₃L] complexes suggest that these compounds have a fluxional structure, because the crystal structure has non-equivalent btfa ligands that should lead to non-equivalent hydrogen atoms and thus to more signals in the NMR spectra than those obtained at room temperature, where all hydrogen atoms of the btfa ligands are equivalent, and phen ligand has an effective vertical symmetry plane. For the [Eu(btfa)₃bipy] complex, the broadening of the signals at –70°C provides a lower bound for the coalescence temperature, which indicates the energy barriers involved in the structural interconversion mechanisms are quite small. These barriers and, consequently, the coalescence temperature are dependent upon the radii of the lanthanide ion as well as to their paramagnetic effects. The PBE0 calculated structures are in very good agreement with the crystallographic data and, for the [Eu(btfa)₃bipy] complex, this method provided several distinct structures with almost the same energy. However, the energy barrier for structural interconversion via dissociative pathways were found to be quite high and could not explain the experimental observations. Whereas the pseudo-rotation pathways, involving the btfa and bipy ligands, have very small activation barriers, in excellent agreement with the NMR data. The results also showed an increase in the activation barrier along the lanthanide series due to the decrease of the ionic radii and consequent increase of the steric effects. TD-DFT calculations showed a dependence of the ligand donor state energy with different structures of the complex [Eu(btfa)₃phen], which can affect the energy transfer rates and the luminescence. The energy required to promote the structural fluxionality may also enhance the luminescence quenching in solution. These results can aid in the design of more luminescent compounds and more efficient devices.

Keywords: computational chemistry, lanthanide-based compounds, NMR, structural fluxionality

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Authors: Bensaid A., Mostephaoui T., Nedjai R.

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Nowadays, wetlands are the subject of contradictory debates opposing scientific, political and administrative meanings. Indeed, given their multiple services (drinking water, irrigation, hydrological regulation, mineral, plant and animal resources...), wetlands concentrate many socio-economic and biodiversity issues. In some regions, they can cover vast areas (>100 thousand ha) of the landscape, such as the Camargue area in the south of France, inside the Rhone delta. The high biological productivity of wetlands, the strong natural selection pressures and the diversity of aquatic environments have produced many species of plants and animals that are found nowhere else. These environments are tremendous carbon sinks and biodiversity reserves depending on their age, composition and surrounding environmental conditions, wetlands play an important role in global climate projections. Covering more than 3% of the earth's surface, wetlands have experienced since the beginning of the 1990s a tremendous revival of interest, which has resulted in the multiplication of inventories, scientific studies and management experiments. The geographical and physical characteristics of the wetlands of the central region conceal a large number of natural habitats that harbour a great biological diversity. These wetlands, one of the natural habitats, are still influenced by human activities, especially agriculture, which affects its layout and functioning. In this perspective, decision-makers need to delimit spatial objects (natural habitats) in a certain way to be able to take action. Thus, wetlands are no exception to this rule even if it seems to be a difficult exercise to delimit a type of environment as whose main characteristic is often to occupy the transition between aquatic and terrestrial environment. However, it is possible to map wetlands with databases, derived from the interpretation of photos and satellite images, such as the European database Corine Land cover, which allows quantifying and characterizing for each place the characteristic wetland types. Scientific studies have shown limitations when using high spatial resolution images (SPOT, Landsat, ASTER) for the identification and characterization of small wetlands (1 hectare). To address this limitation, it is important to note that these wetlands generally represent spatially complex features. Indeed, the use of very high spatial resolution images (>3m) is necessary to map small and large areas. However, with the recent evolution of artificial intelligence (AI) and deep learning methods for satellite image processing have shown a much better performance compared to traditional processing based only on pixel structures. Our research work is also based on spectral and textural analysis on THR images (Spot and IRC orthoimage) using two object-oriented approaches, the nearest neighbour approach (k-NN) and the Super Vector Machine approach (SVM). The k-NN approach gave good results for the delineation of wetlands (wet marshes and moors, ponds, artificial wetlands water body edges, ponds, mountain wetlands, river edges and brackish marshes) with a kappa index higher than 85%.

Keywords: land development, GIS, sand dunes, segmentation, remote sensing

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Authors: K. C. Apil, Keshab Sharma, Bigul Pokharel

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Kathmandu Valley, capital city of Nepal houses numerous historical monuments as well as religious structures which are as old as from the 4th century A.D. The city alone is home to seven UNESCO’s world heritage sites including various public squares and religious sanctums which are often regarded as living heritages by various historians and archeological explorers. Recently on April 25, 2015, the capital city including other nearby locations was struck with Gorkha earthquake of moment magnitude (Mw) 7.8, followed by the strongest aftershock of moment magnitude (Mw) 7.3 on May 12. This study reports structural failures and collapse of heritage structures in Kathmandu Valley during the earthquake and presents preliminary findings as to the causes of failures and collapses. Field reconnaissance was carried immediately after the main shock and the aftershock, in major heritage sites: UNESCO world heritage sites, a number of temples and historic buildings in Kathmandu Durbar Square, Patan Durbar Square, and Bhaktapur Durbar Square. Despite such catastrophe, a significant number of heritage structures stood high, performing very well during the earthquake. Preliminary reports from archeological department suggest that 721 of such structures were severely affected, whereas numbers within the valley only were 444 including 76 structures which were completely collapsed. This study presents recorded accelerograms and geology of Kathmandu Valley. Structural typology and architecture of the heritage structures in Kathmandu Valley are briefly described. Case histories of damaged heritage structures, the patterns, and the failure mechanisms are also discussed in this paper. It was observed that performance of heritage structures was influenced by the multiple factors such as structural and architecture typology, configuration, and structural deficiency, local ground site effects and ground motion characteristics, age and maintenance level, material quality etc. Most of such heritage structures are of masonry type using bricks and earth-mortar as a bonding agent. The walls' resistance is mainly compressive, thus capable of withstanding vertical static gravitational load but not horizontal dynamic seismic load. There was no definitive pattern of damage to heritage structures as most of them behaved as a composite structure. Some structures were extensively damaged in some locations, while structures with similar configuration at nearby location had little or no damage. Out of major heritage structures, Dome, Pagoda (2, 3 or 5 tiered temples) and Shikhara structures were studied with similar variables. Studying varying degrees of damages in such structures, it was found that Shikhara structures were most vulnerable one where Dome structures were found to be the most stable one, followed by Pagoda structures. The seismic performance of the masonry-timber and stone masonry structures were slightly better than that of the masonry structures. Regular maintenance and periodic seismic retrofitting seems to have played pivotal role in strengthening seismic performance of the structure. The study also recommends some key functions to strengthen the seismic performance of such structures through study based on structural analysis, building material behavior and retrofitting details. The result also recognises the importance of documentation of traditional knowledge and its revised transformation in modern technology.

Keywords: Gorkha earthquake, field observation, heritage structure, seismic performance, masonry building

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Authors: Silvia Di Giacomo, Marcello Locatelli, Simone Carradori, Francesco Cacciagrano, Chiara Toniolo, Gabriela Mazzanti, Luisa Mannina, Stefania Cesa, Antonella Di Sotto

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Hyperglycemia represents the main pathogenic factor in the development of diabetes complications and has been found associated with mitochondrial dysfunction and oxidative stress, which in turn increase cell dysfunction. Therefore, counteract oxidative species appears to be a suitable strategy for preventing the hyperglycemia-induce cell damage and support the pharmacotherapy of diabetes and metabolic diseases. Antidiabetic potential of many food sources has been linked to the presence of polyphenolic metabolites, particularly flavonoids such as quercetin and its glycosylated form rutin. In line with this evidence, in the present study, we assayed the potential anti-hyperglycemic activity of an ethyl acetate extract from the peel of Punica granatum L. var. Dente di Cavallo (PGE), a fruit well known to traditional medicine for the beneficial properties of its edible juice. The effect of the extract on the glucidic metabolism has been evaluated by assessing its ability to inhibit α-amylase and α-glucosidase, two digestive enzymes responsible for the hydrolysis of dietary carbohydrates: their inhibition can delay the carbohydrate digestion and reduce glucose absorption, thus representing an important strategy for the management of hyperglycemia. Also, the PGE ability to block the release of advanced glycated end-products (AGEs), whose accumulation is known to be responsible for diabetic vascular complications, was studied. The iron-reducing and chelating activities, which are the primary mechanisms by which AGE inhibitors stop their metal-catalyzed formation, were evaluated as possible antioxidant mechanisms. At last, the phenolic content of PGE was characterized by chromatographic and spectrophotometric methods. Our results displayed the ability of PGE to inhibit α-amylase enzyme with a similar potency to the positive control: the IC₅₀ values were 52.2 (CL 27.7 - 101.2) µg/ml and 35.6 (CL 22.8 - 55.5) µg/ml for acarbose and PGE, respectively. PGE also inhibited the α-glucosidase enzyme with about a 25 higher potency than the positive controls of acarbose and quercetin. Furthermore, the extract exhibited ferrous and ferric ion chelating ability, with a maximum effect of 82.1% and 80.6% at a concentration of 250 µg/ml respectively, and reducing properties, reaching the maximum effect of 80.5% at a concentration of 10 µg/ml. At last, PGE was found able to inhibit the AGE production (maximum inhibition of 82.2% at the concentration of 1000 µg/ml), although with lower potency with respect to the positive control rutin. The phytochemical analysis of PGE displayed the presence of high levels of total polyphenols, tannins, and flavonoids, among which ellagic acid, gallic acid and catechin were identified. Altogether these data highlight the ability of PGE to control the carbohydrate metabolism at different levels, both by inhibiting the metabolic enzymes and by affecting the AGE formation likely by chelating mechanisms. It is also noteworthy that peel from pomegranate, although being a waste of juice production, can be reviewed as a nutraceutical source. In conclusion, present results suggest the possible role of PGE as a remedy for preventing hyperglycemia complications and encourage further in vivo studies.

Keywords: anti-hyperglycemic activity, antioxidant properties, nutraceuticals, polyphenols, pomegranate

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Authors: Pedro Llanos, Diego García

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Spaceflight is considered the last frontier in terms of science, technology, and engineering. But it is also the next frontier in terms of human physiology and performance. After more than 200,000 years humans have evolved under earth’s gravity and atmospheric conditions, spaceflight poses environmental stresses for which human physiology is not adapted. Hypoxia, accelerations, and radiation are among such stressors, our research involves suborbital flights aiming to develop effective countermeasures in order to assure sustainable human space presence. The physiologic baseline of spaceflight participants is subject to great variability driven by age, gender, fitness, and metabolic reserve. The objective of the present study is to characterize different physiologic variables in a population of STEM practitioners during an aerobatic flight. Cardiovascular and pulmonary responses were determined in Science Astronaut Candidates (SACs) during unusual attitude aerobatic flight indoctrination. Physiologic data recordings from 20 subjects participating in high-G flight training were analyzed. These recordings were registered by wearable sensor-vest that monitored electrocardiographic tracings (ECGs), signs of dysrhythmias or other electric disturbances during all the flight. The same cardiovascular parameters were also collected approximately 10 min pre-flight, during each high-G/unusual attitude maneuver and 10 min after the flights. The ratio (pre-flight/in-flight/post-flight) of the cardiovascular responses was calculated for comparison of inter-individual differences. The resulting tracings depicting the cardiovascular responses of the subjects were compared against the G-loads (Gs) during the aerobatic flights to analyze cardiovascular variability aspects and fluid/pressure shifts due to the high Gs. In-flight ECG revealed cardiac variability patterns associated with rapid Gs onset in terms of reduced heart rate (HR) and some scattered dysrhythmic patterns (15% premature ventricular contractions-type) that were considered as triggered physiological responses to high-G/unusual attitude training and some were considered as instrument artifact. Variation events were observed in subjects during the +Gz and –Gz maneuvers and these may be due to preload and afterload, sudden shift. Our data reveal that aerobatic flight influenced the breathing rate of the subject, due in part by the various levels of energy expenditure due to the increased use of muscle work during these aerobatic maneuvers. Noteworthy was the high heterogeneity in the different physiological responses among a relatively small group of SACs exposed to similar aerobatic flights with similar Gs exposures. The cardiovascular responses clearly demonstrated that SACs were subjected to significant flight stress. Routine ECG monitoring during high-G/unusual attitude flight training is recommended to capture pathology underlying dangerous dysrhythmias in suborbital flight safety. More research is currently being conducted to further facilitate the development of robust medical screening, medical risk assessment approaches, and suborbital flight training in the context of the evolving commercial human suborbital spaceflight industry. A more mature and integrative medical assessment method is required to understand the physiology state and response variability among highly diverse populations of prospective suborbital flight participants.

Keywords: g force, aerobatic maneuvers, suborbital flight, hypoxia, commercial astronauts

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Authors: Nafiah Solikhah Thoha

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One area that has spatial character as Heritage area is Kauman Villages. Kauman village in The City of Surakarta, Central Java, Indonesia was formed in 1757 by Paku Buwono III as the King of Kasunanan kingdom (Mataram Kingdom) for Kasunanan kingdom courtiers and scholars of Madrasa. Spatial character of Kauman village influenced by Islamic planning and socio-cultural rules of Kasunanan Kingdom. As traditional settlements influenced by Islamic planning, the Grand Mosque is a binding part of the whole area. Circulation pattern forming network (labyrinth) with narrow streets that ended at the Grand Mosque. The outdoor space can be used for circulation. Social activity is dominated by step movement from one place to a different place. Stalemate (the fina/cul de sac) generally only passable on foot, bicycles, and motorcycles. While the pass (main and branch) can be traversed by motor, vehicles. Kauman village has an area that can not be used as a public road that penetrates and serves as a liaison between the outside world to the other. Hierarchy of hall in Kauman village shows that the existence of a space is getting into more important. Firstly, woman in Kauman make the handmade batik for themself. In 2005 many people improving batik tradisional into commercial, and developed program named "Batik Tourism village of Kauman". That program affects the spatial transformations. This study aimed to explore the influence of tourism program towards spatial transformations. The factors that studied are the organization of space, circulation patterns, hierarchical space, and orientation through the descriptive-evaluation approach methods. Based on the study, tourism activity engenders transformations on the spatial scale (macro), residential block (mezo), homes (micro). First, the Grand Mosque and madrasa (religious school) as a binding zoning; tangle of roads as forming the structure of the area developed as a liaison with outside Kauman; organization of space in the residential of batik entrepreneurs firstly just a residential, then develop into residential, factory of batik including showroom. Second, the circulation pattern forming network (labyrinth) and ends at the Grand Mosque. Third, the hierarchy in the form of public space (the shari), semi-public, and private (the fina/culdesac) is no longer to provide protection to women, only as hierarchy of circulation path. Fourth, cluster building orientation does not follow the kiblat direction or axis oriented to cosmos, but influence by the new function as the showroom. It was need the direction of the main road. Kauman grow as an appropriate area for the community. During its development, the settlement function changes according to community activities, especially economic activities. The new function areas as tourism area affect spatial pattern of Kauman village. Spatial existence and activity as a local wisdom that has been done for generations have meaning of holistic, encompassing socio-cultural sustainability, economics, and the heritage area. By reviewing the local wisdom and the way of life of that society, we can learn how to apply the culture as education for sustainable of heritage area.

Keywords: impact of tourism, Kauman village, spatial transformation, sustainable of heritage area

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Authors: Kavitha Madhu, Karthik K. Srinivasan, R. Sivanandan

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Indian traffic can be considered as mixed and heterogeneous due to the presence of various types of vehicles that operate with weak lane discipline. Consequently, vehicles can position themselves anywhere in the traffic stream depending on availability of gaps. The choice of lateral positioning is an important component in representing and characterizing mixed traffic. The field data provides evidence that the trajectory of vehicles in Indian urban roads have significantly varying longitudinal and lateral components. Further, the notion of headway which is widely used for homogeneous traffic simulation is not well defined in conditions lacking lane discipline. From field data it is clear that following is not strict as in homogeneous and lane disciplined conditions and neighbouring vehicles ahead of a given vehicle and those adjacent to it could also influence the subject vehicles choice of position, speed and acceleration. Given these empirical features, the suitability of using headway distributions to characterize mixed traffic in Indian cities is questionable, and needs to be modified appropriately. To address these issues, this paper attempts to analyze the time gap distribution between consecutive vehicles (in a time-sense) crossing a section of roadway. More specifically, to characterize the complex interactions noted above, the influence of composition, manoeuvre types, and lateral placement characteristics on time gap distribution is quantified in this paper. The developed model is used for evaluating various performance measures such as link speed, midblock delay and intersection delay which further helps to characterise the vehicular fuel consumption and emission on urban roads of India. Identifying and analyzing exact interactions between various classes of vehicles in the traffic stream is essential for increasing the accuracy and realism of microscopic traffic flow modelling. In this regard, this study aims to develop and analyze time gap distribution models and quantify it by lead lag pair, manoeuvre type and lateral position characteristics in heterogeneous non-lane based traffic. Once the modelling scheme is developed, this can be used for estimating the vehicle kilometres travelled for the entire traffic system which helps to determine the vehicular fuel consumption and emission. The approach to this objective involves: data collection, statistical modelling and parameter estimation, simulation using calibrated time-gap distribution and its validation, empirical analysis of simulation result and associated traffic flow parameters, and application to analyze illustrative traffic policies. In particular, video graphic methods are used for data extraction from urban mid-block sections in Chennai, where the data comprises of vehicle type, vehicle position (both longitudinal and lateral), speed and time gap. Statistical tests are carried out to compare the simulated data with the actual data and the model performance is evaluated. The effect of integration of above mentioned factors in vehicle generation is studied by comparing the performance measures like density, speed, flow, capacity, area occupancy etc under various traffic conditions and policies. The implications of the quantified distributions and simulation model for estimating the PCU (Passenger Car Units), capacity and level of service of the system are also discussed.

Keywords: lateral movement, mixed traffic condition, simulation modeling, vehicle following models

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Authors: Marcial Blondet, Malena Serrano, Álvaro Rubiños, Elin Mattsson

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In Peru, there are more than two million houses made of adobe (sun dried mud bricks) or rammed earth (35% of the total houses), in which almost 9 million people live, mainly because they cannot afford to purchase industrialized construction materials. Although adobe houses are cheap to build and thermally comfortable, their seismic performance is very poor, and they usually suffer significant damage or collapse with tragic loss of life. Therefore, over the years, researchers at the Pontifical Catholic University of Peru and other institutions have developed many reinforcement techniques as an effort to improve the structural safety of earthen houses located in seismic areas. However, most rural communities live under unacceptable seismic risk conditions because these techniques have not been adopted massively, mainly due to high cost and lack of diffusion. The nylon rope mesh reinforcement technique is simple and low-cost, and two technological transference tools have been developed to diffuse it among rural communities: 1) Scale seismic simulations using a portable shaking table have been designed to prove its effectiveness to protect adobe houses; 2) A step-by-step illustrated construction manual has been developed to guide the complete building process of a nylon rope mesh reinforced adobe house. As a study case, it was selected the district of Pullo: a small rural community in the Peruvian Andes where more than 80% of its inhabitants live in adobe houses and more than 60% are considered to live in poverty or extreme poverty conditions. The research team carried out a one-day workshop in May 2015 and a two-day workshop in September 2015. Results were positive: First, the nylon rope mesh reinforcement procedure was proven simple enough to be replicated by adults, both young and seniors, and participants handled ropes and knots easily as they use them for daily livestock activity. In addition, nylon ropes were proven highly available in the study area as they were found at two local stores in variety of color and size.. Second, the portable shaking table demonstration successfully showed the effectiveness of the nylon rope mesh reinforcement and generated interest on learning about it. On the first workshop, more than 70% of the participants were willing to formally subscribe and sign up for practical training lessons. On the second workshop, more than 80% of the participants returned the second day to receive introductory practical training. Third, community members found illustrations on the construction manual simple and friendly but the roof system illustrations led to misinterpretation so they were improved. The technological transfer tools developed in this project can be used to train rural dwellers on earthquake-resistant self-construction with adobe, which is still very common in the Peruvian Andes. This approach would allow community members to develop skills and capacities to improve safety of their households on their own, thus, mitigating their high seismic risk and preventing tragic losses. Furthermore, proper training in earthquake-resistant self-construction with adobe would prevent rural dwellers from depending on external aid after an earthquake and become agents of their own development.

Keywords: adobe, Peruvian Andes, safe housing, technological transference

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Authors: Chenyi Ma

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Background: The second costliest hurricane in U.S. history, Sandy landed in southern New Jersey on October 29, 2012, and struck the entire state with high winds and torrential rains. The disaster killed more than 100 people, left more than 8.5 million households without power, and damaged or destroyed more than 200,000 homes across the state. Immediately after the disaster, public policy support was provided in nine coastal counties that constituted 98% of the major and severely damaged housing units in NJ overall. The programs include Individuals and Households Assistance Program, Small Business Loan Program, National Flood Insurance Program, and the Federal Emergency Management Administration (FEMA) Public Assistance Grant Program. In the most severely affected counties, additional funding was provided through Community Development Block Grant: Reconstruction, Rehabilitation, Elevation, and Mitigation Program, and Homeowner Resettlement Program. How these policies individually and as a whole impacted housing recovery across communities with different socioeconomic and demographic profiles has not yet been studied, particularly in relation to damage levels. The concept of community social vulnerability has been widely used to explain many aspects of natural disasters. Nevertheless, how communities are vulnerable has been less fully examined. Community resilience has been conceptualized as a protective factor against negative impacts from disasters, however, how community resilience buffers the effects of vulnerability is not yet known. Because housing recovery is a dynamic social and economic process that varies according to context, this study examined the path from community vulnerability and resilience to housing recovery looking at both community characteristics and policy interventions. Sample/Methods: This retrospective longitudinal case study compared a literature-identified set of pre-disaster community characteristics, the effects of multiple public policy programs, and a set of time-variant community resilience indicators to changes in housing stock (operationally defined by percent of building permits to total occupied housing units/households) between 2010 and 2014, two years before and after Hurricane Sandy. The sample consisted of 51 municipalities in the nine counties in which between 4% and 58% of housing units suffered either major or severe damage. Structural equation modeling (SEM) was used to determine the path from vulnerability to the housing recovery, via multiple public programs, separately and as a whole, and via the community resilience indicators. The spatial analytical tool ArcGIS 10.2 was used to show the spatial relations between housing recovery patterns and community vulnerability and resilience. Findings: Holding damage levels constant, communities with higher proportions of Hispanic households had significantly lower levels of housing recovery while communities with households with an adult >age 65 had significantly higher levels of the housing recovery. The contrast was partly due to the different levels of total public support the two types of the community received. Further, while the public policy programs individually mediated the negative associations between African American and female-headed households and housing recovery, communities with larger proportions of African American, female-headed and Hispanic households were “vulnerable” to lower levels of housing recovery because they lacked sufficient public program support. Even so, higher employment rates and incomes buffered vulnerability to lower housing recovery. Because housing is the "wobbly pillar" of the welfare state, the housing needs of these particular groups should be more fully addressed by disaster policy.

Keywords: community social vulnerability, community resilience, hurricane, public policy

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Authors: Deepika Joshi

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Operations in automotive industry rely greatly on information shared between Supply Chain (SC) partners. This leads to efficient and effective management of SC activity. Automotive sector in India is growing at 14.2 percent per annum and has huge economic importance. We find that no study has been carried out on the role of information sharing in SC management of Indian automotive manufacturers. Considering this research gap, the present study is planned to establish the significance of information sharing in Indian auto-component supply chain activity. An empirical research was conducted for large scale auto component manufacturers from India. Twenty four Supply Chain Performance Indicators (SCPIs) were collected from existing literature. These elements belong to eight diverse but internally related areas of SC management viz., demand management, cost, technology, delivery, quality, flexibility, buyer-supplier relationship, and operational factors. A pair-wise comparison and an open ended questionnaire were designed using these twenty four SCPIs. The questionnaire was then administered among managerial level employees of twenty-five auto-component manufacturing firms. Analytic Network Process (ANP) technique was used to analyze the response of pair-wise questionnaire. Finally, twenty-five priority indexes are developed, one for each respondent. These were averaged to generate an industry specific priority index. The open-ended questions depicted strategies related to information sharing between buyers and suppliers and their influence on supply chain performance. Results show that the impact of information sharing on certain performance indicators is relatively greater than their corresponding variables. For example, flexibility, delivery, demand and cost related elements have massive impact on information sharing. Technology is relatively less influenced by information sharing but it immensely influence the quality of information shared. Responses obtained from managers reveal that timely and accurate information sharing lowers the cost, increases flexibility and on-time delivery of auto parts, therefore, enhancing the competitiveness of Indian automotive industry. Any flaw in dissemination of information can disturb the cycle time of both the parties and thus increases the opportunity cost. Due to supplier’s involvement in decisions related to design of auto parts, quality conformance is found to improve, leading to reduction in rejection rate. Similarly, mutual commitment to share right information at right time between all levels of SC enhances trust level. SC partners share information to perform comprehensive quality planning to ingrain total quality management. This study contributes to operations management literature which faces scarcity of empirical examination on this subject. It views information sharing as a building block which firms can promote and evolve to leverage the operational capability of all SC members. It will provide insights for Indian managers and researchers as every market is unique and suppliers and buyers are driven by local laws, industry status and future vision. While major emphasis in this paper is given to SC operations happening between domestic partners, placing more focus on international SC can bring in distinguished results.

Keywords: Indian auto component industry, information sharing, operations management, supply chain performance indicators

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Authors: Abderrazak Bannari

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Acid Mine Drainage (AMD) from mine wastes and contaminations of soils and water with metals are considered as a major environmental problem in mining areas. It is produced by interactions of water, air, and sulphidic mine wastes. This environment problem results from a series of chemical and biochemical oxidation reactions of sulfide minerals e.g. pyrite and pyrrhotite. These reactions lead to acidity as well as the dissolution of toxic and heavy metals (Fe, Mn, Cu, etc.) from tailings waste rock piles, and open pits. Soil and aquatic ecosystems could be contaminated and, consequently, human health and wildlife will be affected. Furthermore, secondary minerals, typically formed during weathering of mine waste storage areas when the concentration of soluble constituents exceeds the corresponding solubility product, are also important. The most common secondary mineral compositions are hydrous iron oxide (goethite, etc.) and hydrated iron sulfate (jarosite, etc.). The objectives of this study focus on the detection and mapping of MIOP in the soil using Hyperion EO-1 (Earth Observing - 1) hyperspectral data and constrained linear spectral mixture analysis (CLSMA) algorithm. The abandoned Kettara mine, located approximately 35 km northwest of Marrakech city (Morocco) was chosen as study area. During 44 years (from 1938 to 1981) this mine was exploited for iron oxide and iron sulphide minerals. Previous studies have shown that Kettara surrounding soils are contaminated by heavy metals (Fe, Cu, etc.) as well as by secondary minerals. To achieve our objectives, several soil samples representing different MIOP classes have been resampled and located using accurate GPS ( ≤ ± 30 cm). Then, endmembers spectra were acquired over each sample using an Analytical Spectral Device (ASD) covering the spectral domain from 350 to 2500 nm. Considering each soil sample separately, the average of forty spectra was resampled and convolved using Gaussian response profiles to match the bandwidths and the band centers of the Hyperion sensor. Moreover, the MIOP content in each sample was estimated by geochemical analyses in the laboratory, and a ground truth map was generated using simple Kriging in GIS environment for validation purposes. The acquired and used Hyperion data were corrected for a spatial shift between the VNIR and SWIR detectors, striping, dead column, noise, and gain and offset errors. Then, atmospherically corrected using the MODTRAN 4.2 radiative transfer code, and transformed to surface reflectance, corrected for sensor smile (1-3 nm shift in VNIR and SWIR), and post-processed to remove residual errors. Finally, geometric distortions and relief displacement effects were corrected using a digital elevation model. The MIOP fraction map was extracted using CLSMA considering the entire spectral range (427-2355 nm), and validated by reference to the ground truth map generated by Kriging. The obtained results show the promising potential of the proposed methodology for the detection and mapping of mine iron oxide pollution in the soil.

Keywords: hyperion eo-1, hyperspectral, mine iron oxide pollution, environmental impact, unmixing

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Authors: Qianwei Cheng, A. K. M. Mahbubur Rahman, Anis Sarker, Abu Bakar Siddik Nayem, Ovi Paul, Amin Ahsan Ali, M. Ashraful Amin, Ryosuke Shibasaki, Moinul Zaber

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Thomas Friedman, in his famous book, argued that the world in this 21st century is flat and will continue to be flatter. This is attributed to rapid globalization and the interdependence of humanity that engendered tremendous in-flow of human migration towards the urban spaces. In order to keep the urban environment sustainable, policy makers need to plan based on extensive analysis of the urban environment. With the advent of high definition satellite images, high resolution data, computational methods such as deep neural network analysis, and hardware capable of high-speed analysis; urban planning is seeing a paradigm shift. Legacy data on urban environments are now being complemented with high-volume, high-frequency data. However, the first step of understanding urban space lies in useful categorization of the space that is usable for data collection, analysis, and visualization. In this paper, we propose a pragmatic categorization method that is readily usable for machine analysis and show applicability of the methodology on a developing world setting. Categorization to plan sustainable urban spaces should encompass the buildings and their surroundings. However, the state-of-the-art is mostly dominated by classification of building structures, building types, etc. and largely represents the developed world. Hence, these methods and models are not sufficient for developing countries such as Bangladesh, where the surrounding environment is crucial for the categorization. Moreover, these categorizations propose small-scale classifications, which give limited information, have poor scalability and are slow to compute in real time. Our proposed method is divided into two steps-categorization and automation. We categorize the urban area in terms of informal and formal spaces and take the surrounding environment into account. 50 km × 50 km Google Earth image of Dhaka, Bangladesh was visually annotated and categorized by an expert and consequently a map was drawn. The categorization is based broadly on two dimensions-the state of urbanization and the architectural form of urban environment. Consequently, the urban space is divided into four categories: 1) highly informal area; 2) moderately informal area; 3) moderately formal area; and 4) highly formal area. In total, sixteen sub-categories were identified. For semantic segmentation and automatic categorization, Google’s DeeplabV3+ model was used. The model uses Atrous convolution operation to analyze different layers of texture and shape. This allows us to enlarge the field of view of the filters to incorporate larger context. Image encompassing 70% of the urban space was used to train the model, and the remaining 30% was used for testing and validation. The model is able to segment with 75% accuracy and 60% Mean Intersection over Union (mIoU). In this paper, we propose a pragmatic categorization method that is readily applicable for automatic use in both developing and developed world context. The method can be augmented for real-time socio-economic comparative analysis among cities. It can be an essential tool for the policy makers to plan future sustainable urban spaces.

Keywords: semantic segmentation, urban environment, deep learning, urban building, classification

Procedia PDF Downloads 163

Authors: Mariia V. Efremova, Iana O. Tcareva, Anastasia D. Blokhina, Ivan S. Grebennikov, Anastasia S. Garanina, Maxim A. Abakumov, Yury I. Golovin, Alexander G. Savchenko, Alexander G. Majouga, Natalya L. Klyachko

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During last decades magnetite nanoparticles (NPs) attract a deep interest of scientists due to their potential application in therapy and diagnostics. However, magnetite nanoparticles are toxic and non-stable in physiological conditions. To solve these problems, we decided to create two types of hybrid systems based on magnetite and gold which is inert and biocompatible: gold as a shell material (first type) and gold as separate NPs interfacially bond to magnetite NPs (second type). The synthesis of the first type hybrid nanoparticles was carried out as follows: Magnetite nanoparticles with an average diameter of 9±2 nm were obtained by co-precipitation of iron (II, III) chlorides then they were covered with gold shell by iterative reduction of hydrogen tetrachloroaurate with hydroxylamine hydrochloride. According to the TEM, ICP MS and EDX data, final nanoparticles had an average diameter of 31±4 nm and contained iron even after hydrochloric acid treatment. However, iron signals (K-line, 7,1 keV) were not localized so we can’t speak about one single magnetic core. Described nanoparticles covered with mercapto-PEG acid were non-toxic for human prostate cancer PC-3/ LNCaP cell lines (more than 90% survived cells as compared to control) and had high R2-relaxivity rates (>190 mМ-1s-1) that exceed the transverse relaxation rate of commercial MRI-contrasting agents. These nanoparticles were also used for chymotrypsin enzyme immobilization. The effect of alternating magnetic field on catalytic properties of chymotrypsin immobilized on magnetite nanoparticles, notably the slowdown of catalyzed reaction at the level of 35-40 % was found. The synthesis of the second type hybrid nanoparticles also involved two steps. Firstly, spherical gold nanoparticles with an average diameter of 9±2 nm were synthesized by the reduction of hydrogen tetrachloroaurate with oleylamine; secondly, they were used as seeds during magnetite synthesis by thermal decomposition of iron pentacarbonyl in octadecene. As a result, so-called dumbbell-like structures were obtained where magnetite (cubes with 25±6 nm diagonal) and gold nanoparticles were connected together pairwise. By HRTEM method (first time for this type of structure) an epitaxial growth of magnetite nanoparticles on gold surface with co-orientation of (111) planes was discovered. These nanoparticles were transferred into water by means of block-copolymer Pluronic F127 then loaded with anti-cancer drug doxorubicin and also PSMA-vector specific for LNCaP cell line. Obtained nanoparticles were found to have moderate toxicity for human prostate cancer cells and got into the intracellular space after 45 minutes of incubation (according to fluorescence microscopy data). These materials are also perspective from MRI point of view (R2-relaxivity rates >70 mМ-1s-1). Thereby, in this work magnetite-gold hybrid nanoparticles, which have a strong potential for biomedical application, particularly in targeted drug delivery and magnetic resonance imaging, were synthesized and characterized. That paves the way to the development of special medicine types – theranostics. The authors knowledge financial support from Ministry of Education and Science of the Russian Federation (14.607.21.0132, RFMEFI60715X0132). This work was also supported by Grant of Ministry of Education and Science of the Russian Federation К1-2014-022, Grant of Russian Scientific Foundation 14-13-00731 and MSU development program 5.13.

Keywords: drug delivery, magnetite-gold, MRI contrast agents, nanoparticles, toxicity

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Authors: Stefan K. Behfar

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The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

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Authors: Damiano Omari, Topirian Kerempe, Dibo Sama, Walter Wafula, Sharon Chepkoech, Chrispine Juma, Gilbert Kirui, Simon Mburu, Susan Keino

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The One Health concept was officially adopted by the international organizations and scholarly bodies in 1984. It aims at combining human, animal and environmental components to address global health challenges. Using collaborative efforts optimal health to people, animals, and the environment can be achieved. One health approach plays a significant approach role in prevention and control of zoonosis diseases. It has also been noted that 75% of new emerging human infectious diseases are zoonotic. In Kenya, one health has been embraced and strongly advocated for by One Health East and Central Africa (OHCEA). It was inaugurated on 17th of October 2010 at a historic meeting facilitated by USAID with participants from 7 public health schools, seven faculties of veterinary medicine in Eastern Africa and 2 American universities (Tufts and University of Minnesota) in addition to respond project staff. The study was conducted in Loitoktok Sub County, specifically in the Amboseli Ecosystem. The Amboseli ecosystem covers an area of 5,700 square kilometers and stretches between Mt. Kilimanjaro, Chyulu Hills, Tsavo West National park and the Kenya/Tanzania border. The area is arid to semi-arid and is more suitable for pastoralism with a high potential for conservation of wildlife and tourism enterprises. The ecosystem consists of the Amboseli National Park, which is surrounded by six group ranches which include Kimana, Olgulului, Selengei, Mbirikani, Kuku and Rombo in Loitoktok District. The Manyatta of study was Osiram Cultural Manyatta in Mbirikani group ranch. Apart from visiting the Manyatta, we also visited the sub-county hospital, slaughter slab, forest service, Kimana market, and the Amboseli National Park. The aim of the study was to identify the one health issues facing the community. This was done by a conducting a community needs assessment and prioritization. Different methods were used in data collection for the qualitative and numerical data. They include among others; key informant interviews and focus group discussions. We also guided the community members in drawing their Resource Map this helped identify the major resources in their land and also help them identify some of the issues they were facing. Matrix piling, root cause analysis, and force field analysis tools were used to establish the one health related priority issues facing community members. Skits were also used to present to the community interventions to the major one health issues. Some of the prioritized needs among the community were water scarcity and inadequate markets for their beadwork. The group intervened on the various needs of the Manyatta. For water scarcity, we educated the community on water harvesting methods using gutters as well as proper storage by the use of tanks and earth dams. The community was also encouraged to recycle and conserve water. To improve markets; we educated the community to upload their products online, a page was opened for them and uploading the photos was demonstrated to them. They were also encouraged to be innovative to attract more clients.

Keywords: Amboseli ecosystem, community interventions, community needs assessment and prioritization, one health issues

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Authors: Dionyssia Lyra, Shoaib Ismail

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Salinization of natural resources constitutes a significant component of the degradation force that leads to depletion of productive lands and fresh water reserves. The global extent of salt-affected soils is approximately 7% of the earth’s land surface and is expanding. The problems of excessive salt accumulation are most widespread in coastal, arid and semi-arid regions, where agricultural production is substantially hindered. The use of crops that can withstand high saline conditions is extremely interesting in such a context. Salt-loving plants or else ‘halophytes’ thrive when grown in hostile saline conditions, where traditional crops cannot survive. Salicornia bigelovii, a halophytic crop with multiple uses (vegetable, forage, biofuel), has demonstrated remarkable adaptability to harsh climatic conditions prevailing in dry areas with great potential for its expansion. Since 2011, the International Center for Biosaline Agriculture (ICBA) with Masdar Institute (MI) and King Abdul Aziz University of Science & Technology (KAUST) to look into the potential for growing S. bigelovii under hot and dry conditions. Through the projects undertaken, 50 different S. bigelovii genotypes were assessed under high saline conditions. The overall goal was to select the best performing S. bigelovii populations in terms of seed and biomass production for future breeding. Specific objectives included: 1) evaluation of selected S. bigelovii genotypes for various agronomic and growth parameters under field conditions, 2) seed multiplication of S. bigelovii using saline groundwater and 3) acquisition of inbred lines for further breeding. Field trials were conducted for four consecutive years at ICBA headquarters. During the first year, one Salicornia population was evaluated for seed and biomass production at different salinity levels, fertilizer treatments and planting methods. All growth parameters and biomass productivity for the salicornia population showed better performance with optimal biomass production in terms of both salinity level and fertilizer application. During the second year, 46 Salicornia populations (obtained from KAUST and Masdar Institute) were evaluated for 24 growth parameters and treated with groundwater through drip irrigation. The plant material originated from wild collections. Six populations were also assessed for their growth performance under full-strength seawater. Salicornia populations were highly variable for all characteristics under study for both irrigation treatments, indicating that there is a large pool of genetic information available for breeding. Irrigation with the highest level of salinity had a negative impact on the agronomic performance. The maximum seed yield obtained was 2 t/ha at 20 dS/m (groundwater treatment) at 25 cm x 25 cm planting distance. The best performing Salicornia populations for fresh biomass and seed yield were selected for the following season. After continuous selection, the best performing salicornia will be adopted for scaling-up options. Taking into account the results of the production field trials, salicornia expansion will be targeted in coastal areas of the Arabian Peninsula. As a crop with high biofuel and forage potential, its cultivation can improve the livelihood of local farmers.

Keywords: biosaline agriculture, genotypes selection, halophytes, Salicornia bigelovii

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Authors: Eman Ghoneim

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The Arabian deserts include some of the driest areas on Earth. Yet, its landforms reserved a record of past wet climates. During humid phases, the desert was green and contained permanent rivers, inland deltas and lakes. Some of their water would have seeped and replenished the groundwater aquifers. When the wet periods came to an end, several thousand years ago, the entire region transformed into an extended band of desert and its original fluvial surface was totally covered by windblown sand. In this work, radar and thermal infrared images were used to reveal numerous hidden surface/subsurface features. Radar long wavelength has the unique ability to penetrate surface dry sands and uncover buried subsurface terrain. Thermal infrared also proven to be capable of spotting cooler moist areas particularly in hot dry surfaces. Integrating Radarsat images and GIS revealed several previously unknown paleoriver and lake basins in the region. One of these systems, known as the Kufrah, is the largest yet identified river basin in the Eastern Sahara. This river basin, which straddles the border between Egypt and Libya, flowed north parallel to the adjacent Nile River with an extensive drainage area of 235,500 km2 and massive valley width of 30 km in some parts. This river was most probably served as a spillway for an overflow from Megalake Chad to the Mediterranean Sea and, thus, may have acted as a natural water corridor used by human ancestors to migrate northward across the Sahara. The Gilf-Kebir is another large paleoriver system located just east of Kufrah and emanates from the Gilf Plateau in Egypt. Both river systems terminate with vast inland deltas at the southern margin of the Great Sand Sea. The trends of their distributary channels indicate that both rivers drained to a topographic depression that was periodically occupied by a massive lake. During dry climates, the lake dried up and roofed by sand deposits, which is today forming the Great Sand Sea. The enormity of the lake basin provides explanation as to why continuous extraction of groundwater in this area is possible. A similar lake basin, delimited by former shorelines, was detected by radar space data just across the border of Sudan. This lake, called the Northern Darfur Megalake, has a massive size of 30,750 km2. These former lakes and rivers could potentially hold vast reservoirs of groundwater, oil and natural gas at depth. Similar to radar data, thermal infrared images were proven to be useful in detecting potential locations of subsurface water accumulation in desert regions. Analysis of both Aster and daily MODIS thermal channels reveal several subsurface cool moist patches in the sandy desert of the Arabian Peninsula. Analysis indicated that such evaporative cooling anomalies were resulted from the subsurface transmission of the Monsoonal rainfall from the mountains to the adjacent plain. Drilling a number of wells in several locations proved the presence of productive water aquifers confirming the validity of the used data and the adopted approaches for water exploration in dry regions.

Keywords: radarsat, SRTM, MODIS, thermal infrared, near-surface water, ancient rivers, desert, Sahara, Arabian peninsula

Procedia PDF Downloads 235

Authors: Marilena Fatigante

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Contemporary psychoanalysis is increasingly acknowledging the existential demands of clients in psychotherapy. A significant aspect of the personal crises that patients face today is often rooted in the difficulty to find meaning in their own existence, even after working through or resolving traumatic memories and experiences. Tracing back to the correspondence between Freud and Romain Rolland (1927), psychoanalysis could not ignore that investigation of the psyche also encompasses the encounter with deep, psycho-sensory experiences, which involve a sense of "being one with the external world as a whole", the well-known “oceanic feeling”, as Rolland posed it. Despite the recognition of Non-ordinary States of Consciousness (NSC) as catalysts for transformation in clinical practice, highlighted by neuroscience and results from psychedelic-assisted therapies, there is few research on how psychoanalytic knowledge can integrate with other treatment traditions. These traditions, commonly rooted in non -Western, unconventional, and non-formal psychological knowledge, emphasize the individual’s innate tendency toward existential integrity and transcendence of self-boundaries. Inspired by an autobiographical account, this paper examines narratives of 12 individuals, who engaged in psychoanalytic therapy and also underwent treatment involving a non-formal helping relationship with an expert guide in consciousness, which included experience of this nature. The guide relies on 35 yrs of experience in Psychological, multidisciplinary studies in Human Sciences and Art, and demonstrates knowledge of many wisdom traditions, ranging from Eastern to Western philosophy, including Psychoanalysis and its development in cultural perspective (e.g, Ethnopsychiatry). Analyses focused primarily on two dimensions that research has identified as central in assessing the degree of treatment “success” in the patients’ narrative accounts of their therapies: agency and coherence, defined respectively as the increase, expressed in language, of the client’s perceived ability to manage his/her own challenges and the capacity, inherent in “narrative” itself as a resource for meaning making (Bruner, 1990), to provide the subject with a sense of unity, endowing his /her life experience with temporal and logical sequentiality. The present study reports that, in all narratives from the participants, agency and coherence are described differently than in “common” psychotherapy narratives. Although the participants consistently identified themselves as responsible agentic subject, the sense of agency derived from the non-conventional guidance pathway is never reduced to a personal, individual accomplishment. Rather, the more a new, fuller sense of “Life” (more than “Self”) develops out of the guidance pathway they engage with the expert guide, the more they “surrender” their own sense of autonomy and self-containment. Something, which Safran (2016) identified as well talking about the sense of surrender and “grace” in psychoanalytic sessions. Secondly, narratives of individuals engaging with the expert guide describe coherence not as repairing or enforcing continuity but as enhancing their ability to navigate dramatic discontinuities, falls, abrupt leaps and passages marked by feelings of loss and bereavement. The paper ultimately explores whether valid criteria can be established to analyze experiences of non-conventional paths of self-evolution. These paths are not opposed or alternative to conventional ones, and should not be simplistically dismissed as exotic or magical.

Keywords: oceanic feeling, non conventional guidance, consciousness, narratives, treatment outcomes

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Authors: Hoora Beheshti Haradasht, Abooali Golzary

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Environmental education plays a pivotal role in shaping the future stewards of our planet. Leveraging the power of artificial intelligence (AI) in this endeavor presents an innovative approach to captivate and educate elementary school children about environmental sustainability. This paper explores the application of AI technologies in designing interactive and personalized learning experiences that foster curiosity, critical thinking, and a deep connection to nature. By harnessing AI-driven tools, virtual simulations, and personalized content delivery, educators can create engaging platforms that empower children to comprehend complex environmental concepts while nurturing a lifelong commitment to protecting the Earth. With the pressing challenges of climate change and biodiversity loss, cultivating an environmentally conscious generation is imperative. Integrating AI in environmental education revolutionizes traditional teaching methods by tailoring content, adapting to individual learning styles, and immersing students in interactive scenarios. This paper delves into the potential of AI technologies to enhance engagement, comprehension, and pro-environmental behaviors among elementary school children. Modern AI technologies, including natural language processing, machine learning, and virtual reality, offer unique tools to craft immersive learning experiences. Adaptive platforms can analyze individual learning patterns and preferences, enabling real-time adjustments in content delivery. Virtual simulations, powered by AI, transport students into dynamic ecosystems, fostering experiential learning that goes beyond textbooks. AI-driven educational platforms provide tailored content, ensuring that environmental lessons resonate with each child's interests and cognitive level. By recognizing patterns in students' interactions, AI algorithms curate customized learning pathways, enhancing comprehension and knowledge retention. Utilizing AI, educators can develop virtual field trips and interactive nature explorations. Children can navigate virtual ecosystems, analyze real-time data, and make informed decisions, cultivating an understanding of the delicate balance between human actions and the environment. While AI offers promising educational opportunities, ethical concerns must be addressed. Safeguarding children's data privacy, ensuring content accuracy, and avoiding biases in AI algorithms are paramount to building a trustworthy learning environment. By merging AI with environmental education, educators can empower children not only with knowledge but also with the tools to become advocates for sustainable practices. As children engage in AI-enhanced learning, they develop a sense of agency and responsibility to address environmental challenges. The application of artificial intelligence in elementary environmental education presents a groundbreaking avenue to cultivate environmentally conscious citizens. By embracing AI-driven tools, educators can create transformative learning experiences that empower children to grasp intricate ecological concepts, forge an intimate connection with nature, and develop a strong commitment to safeguarding our planet for generations to come.

Keywords: artificial intelligence, environmental education, elementary children, personalized learning, sustainability

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Authors: Lassi Lehto, Jaakko Kahkonen, Juha Oksanen, Tapani Sarjakoski

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The use of raster-formatted data sets in geospatial analysis is increasing rapidly. At the same time, geographic data are being introduced into disciplines outside the traditional domain of geoinformatics, like climate change, intelligent transport, and immigration studies. These developments call for better methods to deliver raster geodata in an efficient and easy-to-use manner. Data cube technologies have traditionally been used in the geospatial domain for managing Earth Observation data sets that have strict requirements for effective handling of time series. The same approach and methodologies can also be applied in managing other types of geospatial data sets. A cloud service-based geodata cube, called GeoCubes Finland, has been developed to support online delivery and analysis of most important geospatial data sets with national coverage. The main target group of the service is the academic research institutes in the country. The most significant aspects of the GeoCubes data repository include the use of multiple resolution levels, cloud-optimized file structure, and a customized, flexible content access API. Input data sets are pre-processed while being ingested into the repository to bring them into a harmonized form in aspects like georeferencing, sampling resolutions, spatial subdivision, and value encoding. All the resolution levels are created using an appropriate generalization method, selected depending on the nature of the source data set. Multiple pre-processed resolutions enable new kinds of online analysis approaches to be introduced. Analysis processes based on interactive visual exploration can be effectively carried out, as the level of resolution most close to the visual scale can always be used. In the same way, statistical analysis can be carried out on resolution levels that best reflect the scale of the phenomenon being studied. Access times remain close to constant, independent of the scale applied in the application. The cloud service-based approach, applied in the GeoCubes Finland repository, enables analysis operations to be performed on the server platform, thus making high-performance computing facilities easily accessible. The developed GeoCubes API supports this kind of approach for online analysis. The use of cloud-optimized file structures in data storage enables the fast extraction of subareas. The access API allows for the use of vector-formatted administrative areas and user-defined polygons as definitions of subareas for data retrieval. Administrative areas of the country in four levels are available readily from the GeoCubes platform. In addition to direct delivery of raster data, the service also supports the so-called virtual file format, in which only a small text file is first downloaded. The text file contains links to the raster content on the service platform. The actual raster data is downloaded on demand, from the spatial area and resolution level required in each stage of the application. By the geodata cube approach, pre-harmonized geospatial data sets are made accessible to new categories of inexperienced users in an easy-to-use manner. At the same time, the multiresolution nature of the GeoCubes repository facilitates expert users to introduce new kinds of interactive online analysis operations.

Keywords: cloud service, geodata cube, multiresolution, raster geodata

Procedia PDF Downloads 118

Authors: Łukasz Mazur, Kamil Domaradzki, Bartosz Kamecki, Justyna Ignaczak, Sebastian Molin, Aleksander Gil, Tomasz Brylewski

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The rising global power consumption necessitates the development of new energy storage solutions. Prospective technologies include solid oxide electrolyzer cells (SOECs), which convert surplus electrical energy into hydrogen. An electrolyzer cell consists of a porous anode, and cathode, and a dense electrolyte. Power output is increased by connecting cells into stacks using interconnects. Interconnects are currently made from high-chromium ferritic steels – for example, Crofer 22 APU – which exhibit high oxidation resistance and a thermal expansion coefficient that is similar to that of electrode materials. These materials have one disadvantage – their area-specific resistance (ASR) gradually increases due to the formation of a Cr₂O₃ scale on their surface as a result of oxidation. The chromia in the scale also reacts with the water vapor present in the reaction media, forming volatile chromium oxyhydroxides, which in turn react with electrode materials and cause their deterioration. The electrochemical efficiency of SOECs thus decreases. To mitigate this, the interconnect surface can be modified with protective-conducting coatings of spinel or other materials. The high prices of SOEC components -especially the Crofer 22 APU- have prevented their widespread adoption. More inexpensive counterparts, therefore, need to be found, and their properties need to be enhanced to make them viable. Candidates include the Nirosta 4016/1,4016 low-chromium ferritic steel with a chromium content of just 16.3 wt%. This steel's resistance to high-temperature oxidation was improved by depositing Gd₂O₃ nanoparticles on its surface via either dip coating or electrolysis. Modification with CeO₂ or Ce₀.₉Y₀.₁O₂ nanoparticles deposited by means of spray pyrolysis was also tested. These methods were selected because of their low cost and simplicity of application. The aim of this study was to investigate the oxidation kinetics of Nirosta 4016/1,4016 modified using the afore-mentioned methods and to subsequently measure the obtained samples' ASR. The samples were oxidized for 100 h in the air as well as air/H₂O and Ar/H₂/H₂O mixtures at 1073 K. Such conditions reflect those found in the anode and cathode operating space during real-life use of SOECs. Phase and chemical composition and the microstructure of oxidation products were determined using XRD and SEM-EDS. ASR was measured over the range of 623-1073 K using a four-point, two-probe DC technique. The results indicate that the applied nanoparticles improve the oxidation resistance and electrical properties of the studied layered systems. The properties of individual systems varied significantly depending on the applied reaction medium. Gd₂O₃ nanoparticles improved oxidation resistance to a greater degree than either CeO₂ or Ce₀.₉Y₀.₁O₂ nanoparticles. On the other hand, the cerium-containing nanoparticles improved electrical properties regardless of the reaction medium. The ASR values of all surface-modified steel samples were below the 0.1 Ω.cm² threshold set for interconnect materials, which was exceeded in the case of the unmodified reference sample. It can be concluded that the applied modifications increased the oxidation resistance of Nirosta 4016/1.4016 to a level that allows its use as SOEC interconnect material. Acknowledgments: Funding of Research project supported by program "Excellence initiative – research university" for the AGH University of Krakow" is gratefully acknowledged (TB).

Keywords: cerium oxide, ferritic stainless steel, gadolinium oxide, interconnect, SOEC

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Authors: Sam Weber, Deepak Mishra, Susan Wilde, Elizabeth Kramer

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The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing over time, with point and non-point source eutrophication and shifting climate paradigms being blamed as the primary culprits. Excessive nutrients, warm temperatures, quiescent water, and heavy and less regular rainfall create more conducive environments for CyanoHABs. CyanoHABs have the potential to produce a spectrum of toxins that cause gastrointestinal stress, organ failure, and even death in humans and animals. To promote enhanced, proactive CyanoHAB management, risk modeling using geospatial tools can act as predictive mechanisms to supplement current CyanoHAB monitoring, management and mitigation efforts. The risk maps would empower water managers to focus their efforts on high risk water bodies in an attempt to prevent CyanoHABs before they occur, and/or more diligently observe those waterbodies. For this research, exploratory spatial data analysis techniques were used to identify the strongest predicators for CyanoHAB blooms based on remote sensing-derived cyanobacteria cell density values for 771 waterbodies in the Georgia Piedmont and landscape characteristics of their watersheds. In-situ datasets for cyanobacteria cell density, nutrients, temperature, and rainfall patterns are not widely available, so free gridded geospatial datasets were used as proxy variables for assessing CyanoHAB risk. For example, the percent of a watershed that is agriculture was used as a proxy for nutrient loading, and the summer precipitation within a watershed was used as a proxy for water quiescence. Cyanobacteria cell density values were calculated using atmospherically corrected images from the European Space Agency’s Sentinel-2A satellite and multispectral instrument sensor at a 10-meter ground resolution. Seventeen explanatory variables were calculated for each watershed utilizing the multi-petabyte geospatial catalogs available within the Google Earth Engine cloud computing interface. The seventeen variables were then used in a multiple linear regression model, and the strongest predictors of cyanobacteria cell density were selected for the final regression model. The seventeen explanatory variables included land cover composition, winter and summer temperature and precipitation data, topographic derivatives, vegetation index anomalies, and soil characteristics. Watershed maximum summer temperature, percent agriculture, percent forest, percent impervious, and waterbody area emerged as the strongest predictors of cyanobacteria cell density with an adjusted R-squared value of 0.31 and a p-value ~ 0. The final regression equation was used to make a normalized cyanobacteria cell density index, and a Jenks Natural Break classification was used to assign waterbodies designations of low, medium, or high risk. Of the 771 waterbodies, 24.38% were low risk, 37.35% were medium risk, and 38.26% were high risk. This study showed that there are significant relationships between free geospatial datasets representing summer maximum temperatures, nutrient loading associated with land use and land cover, and the area of a waterbody with cyanobacteria cell density. This data analytics approach to CyanoHAB risk assessment corroborated the literature-established environmental triggers for CyanoHABs, and presents a novel approach for CyanoHAB risk mapping in waterbodies across the greater southeastern United States.

Keywords: cyanobacteria, land use/land cover, remote sensing, risk mapping

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Authors: Lina Wu

Abstract:

The excessive discharge of nitrogen in sewage greatly intensifies the eutrophication of water bodies and threatens water quality. Nitrogen pollution control has become a global concern. The concentration of nitrogen in water is reduced by converting ammonia nitrogen, nitrate nitrogen and nitrite nitrogen into nitrogen-containing gas through biological treatment, physicochemical treatment and oxidation technology. However, some wastewater containing high ammonia nitrogen including landfill leachate, is difficult to be treated by traditional nitrification and denitrification because of its high COD content. The core process of denitrification is that denitrifying bacteria convert nitrous acid produced by nitrification into nitrite under anaerobic conditions. Still, its low-carbon nitrogen does not meet the conditions for denitrification. Many studies have shown that the natural autotrophic anammox bacteria can combine nitrous and ammonia nitrogen without a carbon source through functional genes to achieve total nitrogen removal, which is very suitable for removing nitrogen from leachate. In addition, the process also saves a lot of aeration energy consumption than the traditional nitrogen removal process. Therefore, anammox plays an important role in nitrogen conversion and energy saving. The short-range nitrification and denitrification coupled with anaerobic ammoX ensures total nitrogen removal. It improves the removal efficiency, meeting the needs of society for an ecologically friendly and cost-effective nutrient removal treatment technology. In recent years, research has found that the symbiotic system has more water treatment advantages because this process not only helps to improve the efficiency of wastewater treatment but also allows carbon dioxide reduction and resource recovery. Microalgae use carbon dioxide dissolved in water or released through bacterial respiration to produce oxygen for bacteria through photosynthesis under light, and bacteria, in turn, provide metabolites and inorganic carbon sources for the growth of microalgae, which may lead the algal bacteria symbiotic system save most or all of the aeration energy consumption. It has become a trend to make microalgae and light-avoiding anammox bacteria play synergistic roles by adjusting the light-to-dark ratio. Microalgae in the outer layer of light particles block most of the light and provide cofactors and amino acids to promote nitrogen removal. In particular, myxoccota MYX1 can degrade extracellular proteins produced by microalgae, providing amino acids for the entire bacterial community, which helps anammox bacteria save metabolic energy and adapt to light. As a result, initiating and maintaining the process of combining dominant algae and anaerobic denitrifying bacterial communities has great potential in treating landfill leachate. Chlorella has a brilliant removal effect and can withstand extreme environments in terms of high ammonia nitrogen, high salt and low temperature. It is urgent to study whether the algal mud mixture rich in denitrifying bacteria and chlorella can greatly improve the efficiency of landfill leachate treatment under an anaerobic environment where photosynthesis is stopped. The optimal dilution concentration of simulated landfill leachate can be found by determining the treatment effect of the same batch of bacteria and algae mixtures under different initial ammonia nitrogen concentrations and making a comparison. High-throughput sequencing technology was used to analyze the changes in microbial diversity, related functional genera and functional genes under optimal conditions, providing a theoretical and practical basis for the engineering application of novel bacteria-algae symbiosis system in biogas slurry treatment and resource utilization.

Keywords: nutrient removal and recovery, leachate, anammox, Partial nitrification, Algae-bacteria interaction

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Authors: Savvas Rogotis, Nikolaos Kalatzis, Stergios Dimou-Sakellariou, Nikolaos Marianos

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As precision holds the key for the introduction of distinct benefits in agriculture (e.g., energy savings, reduced labor costs, optimal application of inputs, improved products, and yields), it steadily becomes evident that new initiatives should focus on rendering Precision Agriculture (PA) more accessible to the average farmer. PA leverages on technologies such as the Internet of Things (IoT), earth observation, robotics and positioning systems (e.g., the Global Navigation Satellite System – GNSS - as well as individual positioning systems like GPS, Glonass, Galileo) that allow: from simple data georeferencing to optimal navigation of agricultural machinery to even more complex tasks like Variable Rate Applications. An identified customer pain point is that, from one hand, typical triangulation-based positioning systems are not accurate enough (with errors up to several meters), while on the other hand, high precision positioning systems reaching centimeter-level accuracy, are very costly (up to thousands of euros). Within this paper, a Ground-Based Augmentation System (GBAS) is introduced, that can be adapted to any existing IoT field-sensing station infrastructure. The latter should cover a minimum set of requirements, and in particular, each station should operate as a fixed, obstruction-free towards the sky, energy supplying unit. Station augmentation will allow them to function in pairs with GNSS rovers following the differential GNSS base-rover paradigm. This constitutes a key innovation element for the proposed solution that encompasses differential GNSS capabilities into an IoT field-sensing infrastructure. Integrating this kind of information supports the provision of several additional PA beneficial services such as spatial mapping, route planning, and automatic field navigation of unmanned vehicles (UVs). Right at the heart of the designed system, there is a high-end GNSS toolkit with base-rover variants and Real-Time Kinematic (RTK) capabilities. The GNSS toolkit had to tackle all availability, performance, interfacing, and energy-related challenges that are faced for a real-time, low-power, and reliable in the field operation. Specifically, in terms of performance, preliminary findings exhibit a high rover positioning precision that can even reach less than 10-centimeters. As this precision is propagated to the full dataset collection, it enables tractors, UVs, Android-powered devices, and measuring units to deal with challenging real-world scenarios. The system is validated with the help of Gaiatrons, a mature network of agro-climatic telemetry stations with presence all over Greece and beyond ( > 60.000ha of agricultural land covered) that constitutes part of “gaiasense” (www.gaiasense.gr) smart farming (SF) solution. Gaiatrons constantly monitor atmospheric and soil parameters, thus, providing exact fit to operational requirements asked from modern SF infrastructures. Gaiatrons are ultra-low-cost, compact, and energy-autonomous stations with a modular design that enables the integration of advanced GNSS base station capabilities on top of them. A set of demanding pilot demonstrations has been initiated in Stimagka, Greece, an area with a diverse geomorphological landscape where grape cultivation is particularly popular. Pilot demonstrations are in the course of validating the preliminary system findings in its intended environment, tackle all technical challenges, and effectively highlight the added-value offered by the system in action.

Keywords: GNSS, GBAS, precision agriculture, RTK, smart farming

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Authors: Hooshag Asadi Haroni, Maryam Veiskarami, Yongjun Lu

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The Dalli gold-rich porphyry deposit (17 Mt @ 0.5% Cu and 0.65 g/t Au) is located in the Urumieh-Dokhtar Magmatic Arc (UDMA), a small segment of the Tethyan metallogenic belt, hosting several porphyry Cu (Mo-Au) systems in Iran. This research characterizes the Dalli deposit to define exploration criteria in advanced exploration such as the drilling of possible blind porphyry centers. Geological map, trench/drill hole geochemical and ground magnetic data, and age dating and isotope trace element analyses, carried out at the John De Laeter Research Center of Curtin University, were used to characterize the Delli deposit. Mineralization at Dalli is hosted by NE-trending quartz-diorite porphyry stocks (~ 200m in diameter) intruded by a wall-rock andesite porphyry. Disseminated and stockwork Cu-Au mineralization is related to potassic alteration, comprising magnetite, late K-feldspar and biotite, and quartz-sericite-specularite overprint, surrounded by extensive barren argillic and propylitic alterations. In the peripheries of the porphyry centers, there are N-trending vuggy quartz veins, hosting epithermal Au-Ag-As-Sb mineralization. Geochemical analyses of drill core samples showed that the core of the porphyry stocks is low-grade, whereas the high-grade disseminated and stockwork mineralization (~ 1% Cu and ~ 1.2 g/t Au) occurred at the contact of the porphyry stocks and andesite porphyry. Geochemical studies of the drill hole and trench samples showed a strong correlation between Cu and Au and both show a second-order correlation with Fe and As. Magnetic survey revealed two significant magnetic anomalies, associated with intensive potassic alteration, in the reduced-to-the-pole magnetic map of the area. A relatively weaker magnetic anomaly, showing no surface porphyry expressions, is located on a lithocap, consisting of advanced argillic alteration, vuggy quartz veins, and surface expressions of epithermal geochemical signatures. The association of the lithocap and the weak magnetic anomaly could be indicative of a hidden mineralized porphyry center. Litho-geochemical analyses of the least altered Dalli intrusions and volcanic rocks indicated high Sr/Y (49-61) and Eu/Eu* (0.89-0.92), features typical of Cu porphyries. The U-Pb dating of zircons of the mineralized quartz diorite and andesite porphyry, carried out by laser ablation inductively coupled plasma mass spectrometry, yielded magmatic crystallization ages of 15.4-16.0 Ma (Middle Miocene). The zircon trace element concentrations of Dalli are characterized by high Eu/Eu* (0.3-0.8), (Ce/Nd)/Y (0.01-0.3), and 10000*(Eu/Eu*)/Y (2-15) ratios, similar to fertile porphyry suites such as the giant Sar-Cheshmeh and Qulong porphyry Cu deposits along the Tethyan belt. This suggests that the Middle Miocene Dalli intrusions are fertile and require extensive deep drillings to define their potential. Chondrite-normalized rare earth element (REE) patterns show no significant Eu anomalies, and are characterized by light-REE enrichments (La/Sm)n = 2.57–6.40). In normalized multi-element diagrams, analyzed rocks are characterized by enrichments in large ion lithophile elements (LILE) and depletions in high field strength elements (HFSE), and display typical features of subduction-related calc-alkaline magmas. The characteristics of the Dalli deposit provided several recognition criteria for detailed exploration of Cu-Au porphyry deposits and highlighted the importance of the UDMA as a potentially significant, economically important, but relatively underexplored porphyry province.

Keywords: porphyry, gold, geochronology, magnetic, exploration

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Authors: Sireetorn Kuharat, Anwar Beg

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Motivation- Solar energy constitutes the most promising renewable energy source on earth. Nanofluids are a very successful family of engineered fluids, which contain well-dispersed nanoparticles suspended in a stable base fluid. The presence of metallic nanoparticles (e.g. gold, silver, copper, aluminum etc) significantly improves the thermo-physical properties of the host fluid and generally results in a considerable boost in thermal conductivity, density, and viscosity of nanofluid compared with the original base (host) fluid. This modification in fundamental thermal properties has profound implications in influencing the convective heat transfer process in solar collectors. The potential for improving solar collector direct absorber efficiency is immense and to gain a deeper insight into the impact of different metallic nanoparticles on efficiency and temperature enhancement, in the present work, we describe recent computational fluid dynamics simulations of an annular solar collector system. The present work studies several different metallic nano-particles and compares their performance. Methodologies- A numerical study of convective heat transfer in an annular pipe solar collector system is conducted. The inner tube contains pure water and the annular region contains nanofluid. Three-dimensional steady-state incompressible laminar flow comprising water- (and other) based nanofluid containing a variety of metallic nanoparticles (copper oxide, aluminum oxide, and titanium oxide nanoparticles) is examined. The Tiwari-Das model is deployed for which thermal conductivity, specific heat capacity and viscosity of the nanofluid suspensions is evaluated as a function of solid nano-particle volume fraction. Radiative heat transfer is also incorporated using the ANSYS solar flux and Rosseland radiative models. The ANSYS FLUENT finite volume code (version 18.1) is employed to simulate the thermo-fluid characteristics via the SIMPLE algorithm. Mesh-independence tests are conducted. Validation of the simulations is also performed with a computational Harlow-Welch MAC (Marker and Cell) finite difference method and excellent correlation achieved. The influence of volume fraction on temperature, velocity, pressure contours is computed and visualized. Main findings- The best overall performance is achieved with copper oxide nanoparticles. Thermal enhancement is generally maximized when water is utilized as the base fluid, although in certain cases ethylene glycol also performs very efficiently. Increasing nanoparticle solid volume fraction elevates temperatures although the effects are less prominent in aluminum and titanium oxide nanofluids. Significant improvement in temperature distributions is achieved with copper oxide nanofluid and this is attributed to the superior thermal conductivity of copper compared to other metallic nano-particles studied. Important fluid dynamic characteristics are also visualized including circulation and temperature shoots near the upper region of the annulus. Radiative flux is observed to enhance temperatures significantly via energization of the nanofluid although again the best elevation in performance is attained consistently with copper oxide. Conclusions-The current study generalizes previous investigations by considering multiple metallic nano-particles and furthermore provides a good benchmark against which to calibrate experimental tests on a new solar collector configuration currently being designed at Salford University. Important insights into the thermal conductivity and viscosity with metallic nano-particles is also provided in detail. The analysis is also extendable to other metallic nano-particles including gold and zinc.

Keywords: heat transfer, annular nanofluid solar collector, ANSYS FLUENT, metallic nanoparticles

Procedia PDF Downloads 128