Orthopedic Research Online Journal Early Bioengineering Research in Orthopedics-A Historical Report of Personal Experience John N Hatzopoulos1,2* 1Professor Emeritus at the University of the Aegean, Greece 2Former Professor at California State University, USA *Corresponding author:John N Hatzopoulos, PhD, Professor emeritus at the University of the Aegean, Greece Submission: May 05, 2021;Published: June 01, 2021 DOI: 10.31031/OPROJ.2021.08.000684 ISSN: 2576-8875 Volume8 Issue2

This work was developed to train graduate students as part of the Delphi4Delphi project dealing with the digital reconstruction of the archaeological site of Delphi. In this part of the project, various technologies were used for 3-d digital mapping cultural heritage structures for maintenance and restoration purposes. The use of various surveying technologies such as UAS, Total station, digital camera, Lidar scanner and GPS to map in 3d the remaining of the monument Tholos and the surrounding area in Delphi Greece and based on such mapping to restore the entire structure is covered in detail. The remains of such monuments are a few columns standing up joined with original elements on top. In this part of the project GPS was used to establish the reference system, total station was used to measure a number of control points for UAS, close range photogrammetry and Lidar scanner, UAS was used to map precisely the surrounding area together with the structure, close range photogrammetry and Lidar scanner were used to map the vertical surfaces of the structure. Processing of above data from all surveying technologies created enough point cloud to map precisely the remains of the structure and expand their architectural design to precisely restore the entire Tholos monument. Also all digital data are used by software for the construction of 3D terrain and 3D models which when inserted into Game Engines software aim at the creation of educational scenarios.

Mytilene is the capital of Lesvos, the eighth largest island in the Mediterranean Sea and the largest in the North Aegean. The region of North Aegean is a geotectonically complex area, because its geodynamic status is directly affected by the North Anatolian Fault Zone. In the present paper, microtremor data have been analyzedfor the city of Mytilene using Nakamura technique of Horizontal to Vertical Spectral Ratio (HVSR) to ascertain the structure in terms of the predominant frequency. 100 microtremor measurements have been performed in the city of Mytilene. At each point of microtremor measurement, the natural frequency and amplification factor have been determined. The predominant frequency varies from 0.4 Hz to 6.6 Hz. The amplification factor in 0.4-8.07 range has been obtained from the HVSR analysis. The results are presented in terms of maps, including the spatial variability of the predominant frequency and developed GIS database. The results of this study make it clear that the characteristics of microtremors depend on the type of soil deposits.

In this study, remote sensing and spatial modeling techniques have been applied for the analysis and modeling of urban uses, observed during the time frame from 1984 to 2009, in the Kapodistrian Municipality of Mytilene, in Lesvos Island, Greece. Our scope was to capture and analyze the spatial pattern of urban uses in the area, in order to model the observed urban environment and predict its future expansion.Undoubtedly, satellite data allow for mapping and monitoring of land cover changes, which occur due to anthropogenic interventions in the natural environment. In this study, Landsat satellite imagery was used, in order for past urban expansion to be mapped, after the interpretation of images for the years 1984, 1990, 2001 and 2009.Based on the classified images, maps of the past were created, showing the changes that occurred to the built environment of the study area. These maps were used to calibrate a spatial predictive model, with the use of which we produced maps of expected future change to the year 2020, in the context of different policy scenarios.In order to assess the spatial accuracy of the model, a probability map of urban change for 2009 was produced. Initially, inspection was made for the total of the modeled urban uses in relation to the total actual urban uses (map of historical data for 2009). The accuracy of the model was further asserted by comparing the areas of predicted changes to the areas of actual changes, for the reference year 2009.In conclusion, we suggest that the combined approach of applying remote sensing and spatial modeling methods can be particularly useful for a better representation, understanding and modeling of the spatiotemporal changes occurring due to the process of urbanization. We believe that the proposed approach leads to a better understanding and promotion of urban dynamics and contributes to the development of alternative approaches to urban planning. By providing the ability to derive results through the application of various scenarios of urban growth in a region, such a model serves as a decision support tool as it aids in the understanding of the effects that would result from possible actions. Specifically, the simulation results under the two scenarios for the urban growth in the municipality of Mytilene shows great potential for use by managers in the region.

Beach erosion, which is already significant along the global coastline, is likely to be exacerbated due to mean sea level rise (SLR) and changes in the wind/wave regimes and coastal sediment supply. Erosion is already alarming at the Aegean Archipelago beaches that form the pillar of the Greek 'sun and sea' tourist industry. The objectives of the ISLA project (2012-2015) are to: (i) create an inventory of the spatial characteristics of the Aegean Archipelago beaches, using readily available remote sensing information and web-GIS tools; (ii) assess the range of their potential retreat under sea level rise scenarios, through morphodynamic model ensembles; (iii) develop/evaluate low cost, optical systems to monitor beach morphology; (iv) assess the accuracy/sensitivity of satellite coastal images using appropriate ground truthing; (v) study the river sediment supply dynamics at representative island beaches; and (vi) assess erosion-driven socioeconomic impacts. Preliminary results show that the Aegean Archipelago island beaches are generally small (64% of all beaches show maximum widths < 20 m) and mostly composed of sand (similar to 49%) and coarse-grained (similar to 25%) sediments. Comparison between their maximum widths and the projected beach retreats (from morphodynamic modeling) shows that sea level rise will have devastating impacts: almost 20% of all beaches will be inundated to about 50% of their maximum width under 0.6 m storm surges, whereas in the case of a 1 m mean SLR, similar to 90% of all beaches may retreat/lose more than 50% of their maximum width and similar to 68% will be entirely lost. Regarding the development of the beach monitoring system, automation of the shoreline detection from SIGMA images has advanced considerably, with the accuracy of the method being also significantly improved by the employment of RBF neural networks.

Reclaimed wastewater and biosludge reuse is a multifactor problem directly related to environmental planning and management. The rational and effective disposal in the agroecological environment depends on the balanced interaction of spatial, technological, environmental, social-political and economical parameters. Consequently, an optimum solution could be achieved by the application of a Multicriteria Decision Analysis (MCDA), which could take into account all the above parameters. This type of model taking into consideration the local constraints, is currently considered as offering the most compatible solution for such complex problems of conflicting and opposing interests. A MCDA was applied to the management of the Sparti's Wastewater Treatment Plant (WWTP) output with the view to find an optimum solution of the wastewater and biosludge disposal. Three scenarios were formulated based on the above parameters. It was found that the best (optimum or compatible) scenario was the 2(nd) one, according to which the outputs of the Sparti's WWTP could be applied successfully to an agricultural area of Laconia's prefecture cultivated with horticultural citrus and olive trees.

Education beyond training has to develop a healthy mind and to do so needs philosophical bases supported with strong scientific evidence which are obtained only if philosophy is reunited with science. This work is an effort to put together all elements which compose the education puzzle and identify the sources of human error anatomically and conceptually and how these affect education. Modelling human error to define the boundaries of right and wrong has a vital impact on education because otherwise education has no reason to exist and it is suppressed to a strict skill training which is the tendency today. The analysis followed in this research is based on the healthy mind model developed by Plato as it was improved by the model of human error variance developed by Aristotle as a midway of virtue. The aim of this work is to identify the real reasons of today’s crises in human values which lead to economic crisis and to recommend solutions based on the education which creates a healthy balanced human mind.

This work deals with the management of landscape at water territories and water areas. The work is focused on a case study in the prefecture of Corinthia, Greece. Algorithms and remote sensing / GIS technology are used to develop a model of comparative temporal approach to the landscapes of principal urban area which is located at coastal zone to provide information for flood protection. Algorithms using remote sensing / GIS technology of best practices are also developed for the coordination of public policies in the field of integrated interventions at modern urban water landscapes compatible to the methods for flood protection.

Satellite remote sensing is nowadays used for aerosol monitoring on an operational basis via specially designed algorithms which are based on multidimensional data. The development of sensors suitable for aerosol monitoring, has given way to the implementation of algorithms for multispectral (e.g. MODIS, MERIS and SEVIRI sensors), hyper-spectral (e.g. CHRIS sensor), multi-angle (e.g. MISR and CHRIS sensors) and multi-polarization observations (e.g. POLDER sensor) both over ocean and land. These sensors have been providing data on a continuous basis for less than two decades (e.g. MODIS archived aerosol data are available since 2001), a period which cannot be considered adequate for studies related to global climate change. On the other hand, archived data from the first generation meteorological sensors such as AVHRR and MVIRI (aboard the NOAA and METEOSAT series satellites respectively) span a period of almost thirty years a fact that is challenging as regards re-processing of such data. In the past, single channel algorithms developed for operational AOD retrievals over oceans have been successfully applied with METEOSAT data (Moulin et al. 1997) and are still used on an operational basis in several cases for AVHRR (Ignatov et al. 2004), SEVIRI (Bridley & Ignatov 2006) and MODIS (Ignatov et al. 2006).One of the main limitations of such algorithms affecting the accuracy of the AOD retrievals is the need for a universal aerosol model. Such an approach although have led to accurate results in open oceanic areas it can be problematic in more complex environments such as the Mediterranean where multiple types of aerosol particles (i.e. desert dust, pollution aerosol and oceanic particles) are encountered (Myhre et al. 2005). In the present paper the expected accuracy of a single channel algorithm developed for the visible MVIRI band is assessed as a function of the aerosol model and the geometry of observation of the geostationary METEOSAT satellite. Two different aerosol models are used as candidate models corresponding to desert dust and water soluble particles encountered in the Mediterranean region. The theoretical simulations were based on radiative transfer computations performed with the 6S code. Results showed that that optimum geometries can be defined where the AOD error is minimized. The results are confirmed using Meteosat-6 data along with concurrent AERONET measurements from the Mediterranean.

Insular environment is characterized by a series of particularities which have been given a special attention in the past years at international level. This work investigates and describes the most important elements of these particularities in an effort to develop models of sustainable management. Such elements affect: (a) The natural environment and the landscape due to insular vulnerability on natural hazards, climate change, etc. (b) The cultural environment due to population problems such as: small size of permanent inhabitants, fluctuations of seasonal population size and population aging. (c) The economic development due to problems such as: small size economy, limited public works infrastructure, small size of private investments for development, weakness of local private and public administration ability, transportation costs due to large distances and dependence on other markets, limited variety in producing and exporting goods. Sustainable management models are proposed by adapting to the insular environment GIS and remote sensing technologies.

The scope of the present study is the spraying pattern testing, GIS modelling, validation and mapping of the application spray rate of field crop sprayer machinery in order to develop a method for the application of more accurate weed control with improved adjustments (pressure and volume of the chemical fluid, spraying boom height, spraying nozzles angle, calculation and selection of the appropriate forward speed of the tractor) and aiming to reduce environmental and economic costs associated with weed control. The spraying pattern testing, GIS modelling and mapping results provide an opportunity for the application of more accurate weed control technology in order to reduce environmental and agroeconomic costs associated with weed control.

Groundwater has been considered as an important source of water supply due to its relatively low susceptibility to pollution in comparison to surface water, and its large storage capacity. For most people in the European Union (EU), access to clean water in abundant quantities is taken for granted. It is not realized, however, that many human activities (diffuse pollution from agricultural sources) put a burden on water quality and quantity. The objectives of this paper are to document and evaluate regional trends and occurrences of nitrate in the groundwater of agricultural watersheds in Central Greece. A land use survey and water quality survey was performed in 2004 and ground water samples from various wells were collected in the time interval April June and analysed in laboratory. The pH of the samples was measured by a pH electronic meter with a sensor probe. For the analyses in the laboratory in nitrate (NO3) and in NO3-N, was used method 8039 (Procedure Code N5) or Cadmium Reduction Method that is suitable for water, wastewater and marine water (USEPA 1980), by the use of a laboratory instrument (Spectrophotometer) with sensors controlled by a microprocessor. Finally, a general research on cultivators’ practises, agricultural wastes disposal and fertilizers use was conducted. Also with the use of GIS and Remote Sensing tools, a hydrological analysis was conducted and it was studied from satellite images the correlated vegetation, (types and extend of agricultural plantations). Also, a model calculation was performed on wastes and fertilizers loads of the study area in conjunction with remote sensing acquired satellite images and finally the nitrates pollution of an agricultural ecosystem watershed was studied and assessed, based on the above. Results showed that GIS and Remote Sensing are significant tools that help to depict the groundwater nitrate pollution and to identify contaminated areas. Also, GIS based maps of nitrogen pollution from agricultural sources (wastes, fertilizers, etc) are characterised by remarkable spatial variability, and in many cases, results of the nitrate concentrations were found above the EU suggestive limit of 25 mg/l and more important, in other cases they were found above the EU maximum limit of 50 mg/l. Based on the results of research, Remote Sensing analysis and GIS modelling and mapping with the nitrates contamination of the groundwater, constitutes an important tool of research that is offered for environmental policy measures advisement, proposal and consideration of environmental management practices, aiming at the protection and sustainable management of water resources and at farm economy (fertilizers’ reduction).

Artificial neural networks (ANNs) show a significant ability to discover patterns in data that are too obscure to go through standard statistical methods. Data of natural phenomena usually exhibit significantly unpredictable non-linearity, but the robust behavior of a neural network makes it perfectly adaptable to environmental models such as a wildland fire danger rating system. These systems have been adopted by many developed countries that have invested in wildland fire prevention, and thus civil protection agencies are able to identify areas with high probabilities of fire ignition and resort to necessary actions. Since one of the drawbacks of ANNs is the interpretation of the final model in terms of the importance of variables, this article presents the results of sensitivity analysis performed in a back-propagation neural network (BPN) to distinguish the influence of each variable in a fire ignition risk scheme developed for Lesvos Island in Greece. Four different methods were utilized to evaluate the three fire danger indices developed within the above scheme; three of the methods are based on network’s weights after the training procedure (i.e., the percentage of influence—PI, the weight product—WP, and the partial derivatives—PD methods), and one is based on the logistic regression (LR) model between BPN inputs and observed outputs. Results showed that the occurrence of rainfall, the 10-h fuel moisture content, and the month of the year parameter are the most significant variables of the Fire Weather, Fire Hazard, and Fire Risk Indices, respectively. Relative humidity, elevation, and day of the week have a small contribution to fire ignitions in the study area. The PD method showed the best performance in ranking variables’ importance, while performance of the rest of the methods was influenced by the number of input parameters and the magnitude of their importance. The results can be used by local forest managers and other decision makers dealing with wildland fires to take the appropriate preventive measures by emphasizing on the important factors of fire occurrence.

Results are reported of an effort to develop a spatial decision-making methodology to support collective decisions on integrated urban planning. The focus is on managing the spatial conflicts -interest, use and value related- which are unavoidably created by land use change planning options so that cooperative final decisions can be reached supportive of sustainable development. The area of application is the urban coastal region of Perama in Athens, Greece. The proposed methodology, referred to as Spatial-AGORA integrates elements of the participatory conflict management algorithm AGORA (Assessment of Group Options with Reasonable Accord), GIS (Geographic Information Systems) and CA (Cellular Automata). The former utilizes Multi-Criteria Evaluation Methods, Core theory and Game theory. The GIS is used to gather, analyze and manage all necessary data from the study area as a whole as well as from predetermined sub-areas serving as decision units. Cellular Automata serve as the logic of the applied land use change (LUC) simulation model. Stakeholders from the decision units are the main participants in the application of the LUC model. Conflict and cooperation dynamics regarding the study area revealed by the application of the proposed socio-spatial methodology are also reported.

Scope of the present study is the modelling and mapping of corn (Zea mays L.) biomass yield for biofuell use, in correlation with irrigation water management effects in an experimental field with combinational use of GIS, GPS, Geostatistic modelling and on situ measurements. Also the investigation of drip irrigation frequency effect in yield and in the proportion of biomass in the various plant parts of corn and in the distribution of soil moisture (measured with the TDR method) and availiable soil moisture depletion were studied, in an experimental parcel of 3 interventions and 4 repetitions in the T.E.I. farm in Larissa, central Greece, at the farming period of year 2003. The amount of water used in each irrigation session was equal to the cumulative Evapotranspiration between two successive irrigation sessions as measured using Evaporation Pan type A. Corn biomass, plants fractions biomass (grain, stalk (including tassel and leaf sheaths), leaves (leaf blades only), cobs and husks) and their moisture were measured in the field and in the laboratory and it was found the distribution of the above ground corn biomass and the distribution of corn biomass in stover. Corn biomass productivity was modelled and mapped using Precision Agriculture and GIS techniques and methods, GPS systems, geostatistical methods, geostatistical and statistical analysis. Finally, results showed that corn biomass yield differences between treatments were not noted statistically significantly difference. However the spatial evaluation and geostatistical analysis at field level indicated significant (significance level 0.05) spatial autocorrelation of the measured biomass areas among the 3 treatments.

A study on nitrates concentration GIS mapping and the effect of drip irrigation interval in the movement and concentration of total N, NO3-N and NH4-N in the soil and concretely in active rhizosphere of maize cultivation showed serious infield variability in an experimental field in Technological Educational Institute of Larissa, in Greece at the farming period of year 2001. Spatial evaluation, analysis and classification at field(treatments) level derived nitrogen management zones. Results showed that nitrogenous fertilizers and irrigation water require both, careful management in order to be minimised the dangers of NO3-N leaching under the root zone in irrigated cultivations of maize. The present study correlates irrigation frequency, soil nitrogen depletion with nitrates concentration GIS maps and also an attempt was made to formulate a more precise and environmental friendly management scheme with VRT farm machinery and precision agriculture.

Prevention is one of the most important stages in wildfire and other natural hazard management regimes. Fire danger rating systems have been adopted by many developed countries dealing with wildfire prevention and pre-suppression planning, so that civil protection agencies are able to define areas with high probabilities of fire ignition and resort to necessary actions. This present paper presents a fire ignition risk scheme, developed in the study area of Lesvos Island, Greece, that can be an integral component of a quantitative Fire Danger Rating System. The proposed methodology estimates the geo-spatial fire risk regardless of fire causes or expected burned area, and it has the ability of forecasting based on meteorological data. The main output of the proposed scheme is the Fire Ignition Index, which is based on three other indices: Fire Weather Index, Fire Hazard Index, and Fire Risk Index. These indices are not just a relative probability for fire occurrence, but a rather quantitative assessment of fire danger in a systematic way. Remote sensing data from the high-resolution QuickBird and the Landsat ETM satellite sensors were utilised in order to provide part of the input parameters to the scheme, while Remote Automatic Weather Stations and the SKIRON/Eta weather forecasting system provided real-time and forecasted meteorological data, respectively. Geographic Information Systems were used for management and spatial analyses of the input parameters. The relationship between wildfire occurrence and the input parameters was investigated by neural networks whose training was based on historical data.