Search results for: remote regions

Authors: Emily Saurman, David Lyle

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In Australia, a well serviced rural town might have a psychiatrist visit once-a-month with more frequent visits from a psychiatric nurse, but many have no resident access to mental health specialists. Access to specialist care, would not only reduce patient distress and benefit outcomes, but facilitate the effective use of limited resources. The Mental Health Emergency Care-Rural Access Program (MHEC-RAP) was developed to improve access to specialist emergency mental health care in rural and remote communities using telehealth technologies. However, there has been no current benchmark to gauge program efficiency or capacity; to determine whether the program activity is justifiably sufficient. The evaluation of MHEC-RAP used multiple methods and applied a modified theory of access to assess the program and its aim of improved access to emergency mental health care. This was the first evaluation of a telepsychiatry service to include a time and motion study design examining program time expenditure, efficiency, and capacity. The time and motion study analysis was combined with an observational study of the program structure and function to assess the balance between program responsiveness and efficiency. Previous program studies have demonstrated that MHEC-RAP has improved access and is used and effective. The findings from the time and motion study suggest that MHEC-RAP has the capacity to manage increased activity within the current model structure without loss to responsiveness or efficiency in the provision of care. Enhancing program responsiveness and efficiency will also support a claim of the program’s value for money. MHEC-RAP is a practical telehealth solution for improving access to specialist emergency mental health care. The findings from this evaluation have already attracted the attention of other regions in Australia interested in implementing emergency telepsychiatry programs and are now informing the progressive establishment of mental health resource centres in rural New South Wales. Like MHEC-RAP, these centres will provide rapid, safe, and contextually relevant assessments and advice to support local health professionals to manage mental health emergencies in the smaller rural emergency departments. Sharing the application of this methodology and research activity may help to improve access to and future evaluations of telehealth and telepsychiatry services for others around the globe.

Keywords: access, emergency, mental health, rural, time and motion

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Authors: Frederik Schulte, Stefan Voß

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The adverse effects of air pollution on public health are an increasingly vital problem in planning for urban regions in many parts of the world. The issue is addressed from various angles and by distinct disciplines in research. Epidemiological studies model the relative increase of numerous diseases in response to an increment of different forms of air pollution. A significant share of air pollution in urban regions is related to transport emissions that are often measured and stored in emission inventories. Though, most approaches in transport planning, engineering, and operational design of transport activities are restricted to general emission limits for specific air pollutants and do not consider more nuanced exposure models. We conduct an extensive literature review on exposure models and emission inventories used to study the health impact of transport emissions. Furthermore, we review methods applied in both domains and use emission inventory data of transportation hubs such as ports, airports, and urban traffic for an in-depth analysis of public health impacts deploying medical exposure models. The results reveal specific urban health risks related to transport emissions that may improve urban planning for environmental health by providing insights in actual health effects instead of only referring to general emission limits.

Keywords: emission inventories, exposure models, transport emissions, urban health

Procedia PDF Downloads 385

Authors: Favour Makuochukwu Orji, Yingkui Zhao

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A myriad of environmental issues face the Nigerian industrial region, resulting from; oil and gas production, mining, manufacturing and domestic wastes. Amidst these, much effort has been directed by stakeholders in the Nigerian oil producing regions, because of the impacts of the region on the wider Nigerian economy. Research to date has suggested that collaborative environmental management could be an effective approach in managing environmental issues; but little attention has been given to the roles and practices of stakeholders in effecting a collaborative environmental management framework for the Nigerian oil-producing region. This paper produces a framework to expand and deepen knowledge relating to stakeholders aspects of collaborative roles in managing environmental issues in the Nigeria oil-producing region. The knowledge is derived from analysis of stakeholders’ practices – studied through multiple case studies using document analysis. Selected documents of key stakeholders – Nigerian government agencies, multi-national oil companies and host communities, were analyzed. Open and selective coding was employed manually during document analysis of data collected from the offices and websites of the stakeholders. The findings showed that the stakeholders have a range of roles, practices, interests, drivers and barriers regarding their collaborative roles in managing environmental issues. While they have interests for efficient resource use, compliance to standards, sharing of responsibilities, generating of new solutions, and shared objectives; there is evidence of major barriers which includes resource allocation, disjointed policy and regulation, ineffective monitoring, diverse socio- economic interests, lack of stakeholders’ commitment and limited knowledge sharing. However, host communities hold deep concerns over the collaborative roles of stakeholders for economic interests, particularly, where government agencies and multi-national oil companies are involved. With these barriers and concerns, a genuine stakeholders’ collaboration is found to be limited, and as a result, optimal environmental management practices and policies have not been successfully implemented in the Nigeria oil-producing region. A framework is produced that describes practices that characterize collaborative environmental management might be employed to satisfy the stakeholders’ interests. The framework recommends critical factors, based on the findings, which may guide a collaborative environmental management in the oil producing regions. The recommendations are designed to re-define the practices of stakeholders in managing environmental issues in the oil producing regions, not as something wholly new, but as an approach essential for implementing a sustainable environmental policy. This research outcome may clarify areas for future research as well as to contribute to industry guidance in the area of collaborative environmental management.

Keywords: collaborative environmental management framework, case studies, document analysis, multinational oil companies, Nigerian oil producing regions, Nigerian government agencies, stakeholders analysis

Procedia PDF Downloads 172

Authors: Molakala Mourya Vardhan Reddy, Kandimala Revanth, Koduru Sumanth, Beena B. M.

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Emergency response and recovery operations are severely hampered by natural catastrophes, especially floods. Understanding post-flood scenarios is essential to disaster management because it facilitates quick evaluation and decision-making. To this end, we introduce FloodNet, a brand-new high-resolution aerial picture collection created especially for comprehending post-flood scenes. A varied collection of excellent aerial photos taken during and after flood occurrences make up FloodNet, which offers comprehensive representations of flooded landscapes, damaged infrastructure, and changed topographies. The dataset provides a thorough resource for training and assessing computer vision models designed to handle the complexity of post-flood scenarios, including a variety of environmental conditions and geographic regions. Pixel-level semantic segmentation masks are used to label the pictures in FloodNet, allowing for a more detailed examination of flood-related characteristics, including debris, water bodies, and damaged structures. Furthermore, temporal and positional metadata improve the dataset's usefulness for longitudinal research and spatiotemporal analysis. For activities like flood extent mapping, damage assessment, and infrastructure recovery projection, we provide baseline standards and evaluation metrics to promote research and development in the field of post-flood scene comprehension. By integrating FloodNet into machine learning pipelines, it will be easier to create reliable algorithms that will help politicians, urban planners, and first responders make choices both before and after floods. The goal of the FloodNet dataset is to support advances in computer vision, remote sensing, and disaster response technologies by providing a useful resource for researchers. FloodNet helps to create creative solutions for boosting communities' resilience in the face of natural catastrophes by tackling the particular problems presented by post-flood situations.

Keywords: image classification, segmentation, computer vision, nature disaster, unmanned arial vehicle(UAV), machine learning.

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Authors: Olumide A. Fafore, Khondlo Mtshali

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The beginning of the 21st century saw the emergence of new and global threats to national and transnational security in West Africa and the Asia Pacific regions as a result of the spread of jihadist terrorism across borders, a manifestation of the rise of radical Islam. Extremist and armed Islamic movements influenced by Salafism, the Jihad in Afghanistan and the Muslim Brotherhood are prevalent in Northern Nigeria, Niger, Cameroon, Mali, Chad, Pakistan, Afghanistan, and India. Carrying out attacks across borders, including assassinations, murders, armed robberies, and kidnapping, assisted by open and porous borders and large flow of illegal immigrants across borders. This paper examines the effect of Radical Islam on Transnational security through a review of past literature and the social and security consequences on the people of the regions. Our findings indicate that the activities of armed Islamic movements such as Boko Haram, Ansaru and Al-Qaeda are having a negative impact on the economy, development, and security of the states and people of West Africa and the Asia Pacific. It stresses the importance of regional, transnational and international cooperation, as these threats to national and transnational security can no longer be solved in a national or regional framework.

Keywords: Islamic movements, jihadist terrorism, radical Islam, transnational security

Procedia PDF Downloads 159

Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

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Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: artificial neural networks, digital image processing, pattern recognition, phytosanitary

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Authors: Mohamed Aboelghar, Ayman Abou Hadid, Usama Albehairy, Asmaa Khater

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Sustainable agricultural development of the desert areas of Egypt under the pressure of irrigation water scarcity is a significant national challenge. Existing water harvesting techniques on the northwest coast of Egypt do not ensure the optimal use of rainfall for agricultural purposes. Basin-scale hydrology potentialities were studied to investigate how available annual rainfall could be used to increase agricultural production. All data related to agricultural production included in the form of geospatial layers. Thematic classification of Sentinal-2 imagery was carried out to produce the land cover and crop maps following the (FAO) system of land cover classification. Contour lines and spot height points were used to create a digital elevation model (DEM). Then, DEM was used to delineate basins, sub-basins, and water outlet points using the Soil and Water Assessment Tool (Arc SWAT). Main soil units of the study area identified from Land Master Plan maps. Climatic data collected from existing official sources. The amount of precipitation, surface water runoff, potential, and actual evapotranspiration for the years (2004 to 2017) shown as results of (Arc SWAT). The land cover map showed that the two tree crops (olive and fig) cover 195.8 km2 when herbaceous crops (barley and wheat) cover 154 km2. The maximum elevation was 250 meters above sea level when the lowest one was 3 meters below sea level. The study area receives a massive variable amount of precipitation; however, water harvesting methods are inappropriate to store water for purposes.

Keywords: water catchements, remote sensing, GIS, sustainable agricultural development

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Authors: Andrea Gion Saluz, Tal Hertig, Axel Heinrich, Stefan Stevanovic

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Higher temperatures, seasonal changes in precipitation, and extreme weather events are increasingly affecting trees. To counteract the increasing challenges of urban trees, strategies are increasingly being sought to preserve existing tree populations on the one hand and to prepare for the coming years on the other. One such strategy lies in strategic climate tree species selection. The search is on for species or varieties that can cope with the new climatic conditions. Many efforts in German-speaking countries deal with this in detail, such as the tree lists of the German Conference of Garden Authorities (GALK), the project Stadtgrün 2021, or the instruments of the Climate Species Matrix by Prof. Dr. Roloff. In this context, different methods for a correct species selection are offered. One possibility is to select certain physiological attributes that indicate the climate resilience of a species. To calculate the dissimilarity of the present climate of different geographic regions in relation to the future climate of any city, a weighted (standardized) Euclidean distance (SED) for seasonal climate values is calculated for each region of the Earth. The calculation was performed in the QGIS geographic information system, using global raster datasets on monthly climate values in the 1981-2010 standard period. Data from a European forest inventory were used to identify tree species growing in the calculated analogue climate regions. The inventory used is the compilation of georeferenced point data at a 1 km grid resolution on the occurrence of tree species in 21 European countries. In this project, the results of the methodological application are shown for the city of Zurich for the year 2060. In the first step, analog climate regions based on projected climate values for the measuring station Kirche Fluntern (ZH) were searched for. In a further step, the methods mentioned above were applied to generate tree species lists for the city of Zurich. These lists were then qualitatively evaluated with respect to the suitability of the different tree species for the Zurich area to generate a cleaned and thus usable list of possible future tree species.

Keywords: climate change, climate region, climate tree, urban tree

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Authors: Bin Wang, Yong Xu, Honggang Qu, Rongmei Liu, Zhenji Gao

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Based on the geodynamic environment, basic geological characteristics of minerals and so on, gold deposits in China are divided into 11 categories, of which tectonic fracture altered rock, mid-intrudes and contact zone, micro-fine disseminated and continental volcanic types are the main prospecting kinds. The metallogenic age of gold deposits in China is dominated by the Mesozoic and Cenozoic. According to the geotectonic units, geological evolution, geological conditions, spatial distribution, gold deposits types, metallogenic factors etc., 42 gold concentration areas are initially determined and have a concentrated distribution feature. On the basis of the gold exploration density, gold concentration areas are divided into high, medium and low level areas. High ones are mainly distributed in the central and eastern regions. 93.04% of the gold exploration drillings are within 500 meters, but there are some problems, such as less and shallower of drilling verification etc.. The paper discusses the resource potentials of gold deposits and proposes the future prospecting directions and suggestions. The deep and periphery of old mines in the central and eastern regions and western area, especially in Xinjiang and Qinghai, will be the future key prospecting one and have huge potential gold reserves. If the exploration depth is extended to 2,000 meters shallow, the gold resources will double.

Keywords: gold deposits, gold deposits types, gold concentration areas, prospecting, resource potentiality

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Authors: Harini Aiyer, Kalyani Premkumar

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Health professionals are the key players who can help achieve the goals of population health equity. Social accountability (SA) of health professionals emphasizes their role in addressing issues of equity in the population they serve. Therefore, health professional education must focus on instilling SA in health professionals. There is limited literature offering a longitudinal perspective of how students sustain the practice of SA in later life. This project aims to identify the drivers of social accountability among physicians. This study employed an exploratory mixed methods design (QUAL-> Quant) to explore alumni perceptions and experiences. The qualitative data, collected via 20 in-depth, semi-structured interviews, provided an understanding of the perceptions of the alumni regarding the influence of their undergraduate learning environment on their SA. This was followed by a quantitative portion -a questionnaire designed from the themes identified from the qualitative data. Emerging themes from the study highlighted community-centered education and a focus on social and preventative medicine in both curricular and non-curricular facilitators of SA among physicians. Curricular components included opportunities to engage with the community, such as roadside clinics, community-orientation programs, and postings at a secondary hospital. Other facilitators that emerged were the faculty leading by example, a subsidized fee structure, and a system that prepared students for practice in rural and remote areas. The study offers a fresh perspective and dimension on how SA is addressed by medical schools. The findings may be adapted by medical schools to understand how their own SA initiatives have been sustained among physicians over the long run.

Keywords: community-based work, global health, health education, medical education, providing health in remote areas, social accountability

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Authors: Issam Aouari, Abdelmalek Abdelhamid

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For seismologists, the characterization of seismic demand should include the amplitude and duration of strong shaking in the system. The duration of ground shaking is one of the key parameters in earthquake resistant design of structures. This paper proposes a nonlinear statistical model to estimate earthquake ground motion duration in soft soils using multiple seismicity indicators. Three definitions of ground motion duration proposed by literature have been applied. With a comparative study, we select the most significant definition to use for predict the duration. A stochastic model is presented for the McCann and Shah Method using nonlinear regression analysis based on a data set for moment magnitude, source to site distance and site conditions. The data set applied is taken from PEER strong motion databank and contains shallow earthquakes from different regions in the world; America, Turkey, London, China, Italy, Chili, Mexico...etc. Main emphasis is placed on soft site condition. The predictive relationship has been developed based on 600 records and three input indicators. Results have been compared with others published models. It has been found that the proposed model can predict earthquake ground motion duration in soft soils for different regions and sites conditions.

Keywords: duration, earthquake, prediction, regression, soft soil

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Authors: Hadush Kidane Meresa

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The hydrological extremes frequency analysis is the foundation for the hydraulic engineering design, flood protection, drought management and water resources management and planning to utilize the available water resource to meet the desired objectives of different organizations and sectors in a country. This spatial variation of the statistical characteristics of the extreme flood and drought events are key practice for regional flood and drought analysis and mitigation management. For different hydro-climate of the regions, where the data set is short, scarcity, poor quality and insufficient, the regionalization methods are applied to transfer at-site data to a region. This study aims in regional high and low flow frequency analysis for Poland River Basins. Due to high frequent occurring of hydrological extremes in the region and rapid water resources development in this basin have caused serious concerns over the flood and drought magnitude and frequencies of the river in Poland. The magnitude and frequency result of high and low flows in the basin is needed for flood and drought planning, management and protection at present and future. Hydrological homogeneous high and low flow regions are formed by the cluster analysis of site characteristics, using the hierarchical and C- mean clustering and PCA method. Statistical tests for regional homogeneity are utilized, by Discordancy and Heterogeneity measure tests. In compliance with results of the tests, the region river basin has been divided into ten homogeneous regions. In this study, frequency analysis of high and low flows using AM for high flow and 7-day minimum low flow series is conducted using six statistical distributions. The use of L-moment and LL-moment method showed a homogeneous region over entire province with Generalized logistic (GLOG), Generalized extreme value (GEV), Pearson type III (P-III), Generalized Pareto (GPAR), Weibull (WEI) and Power (PR) distributions as the regional drought and flood frequency distributions. The 95% percentile and Flow duration curves of 1, 7, 10, 30 days have been plotted for 10 stations. However, the cluster analysis performed two regions in west and east of the province where L-moment and LL-moment method demonstrated the homogeneity of the regions and GLOG and Pearson Type III (PIII) distributions as regional frequency distributions for each region, respectively. The spatial variation and regional frequency distribution of flood and drought characteristics for 10 best catchment from the whole region was selected and beside the main variable (streamflow: high and low) we used variables which are more related to physiographic and drainage characteristics for identify and delineate homogeneous pools and to derive best regression models for ungauged sites. Those are mean annual rainfall, seasonal flow, average slope, NDVI, aspect, flow length, flow direction, maximum soil moisture, elevation, and drainage order. The regional high-flow or low-flow relationship among one streamflow characteristics with (AM or 7-day mean annual low flows) some basin characteristics is developed using Generalized Linear Mixed Model (GLMM) and Generalized Least Square (GLS) regression model, providing a simple and effective method for estimation of flood and drought of desired return periods for ungauged catchments.

Keywords: flood , drought, frequency, magnitude, regionalization, stochastic, ungauged, Poland

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Authors: Baratali Monfaredraz

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The concept of justice has been able to occupy a lot of people’s minds and speeches for a long time. Justice has various dimensions such as economic justice, judicial justice, political justice, educational justice, ethnical justice and etc. Educational justice as one of the most basic dimensions of justice can alter our education in every field and it can flourish the talents and capabilities on macro level. One of the most efficient ways for social justice realization is to provide equal opportunities for all people in the society to be able to access equally to education as their human rights since today how progress occurs in education is regarded as the index of social development. On this basis, especially developing countries try to provide equal opportunities for all people in terms of access to education, specifically in higher education. At present, private education system violates the principles of conducting effort, meeting the needs and in part realizing the capabilities and so it cannot be justified to be a fair conductance. It seems that providing higher quality education in public schools and lowering role of teacher and educational facilities in educational achievement can be considered as a proper way to remove the discrimination in terms of unequal distribution of educational facilities. In addition, higher education development in deprived regions can initialize social activities among the inhabitants of these regions. Justice in educational field can result in access of all people to economic and social situations and job opportunities in future.

Keywords: educational justice, deprivation, private schools, higher education, job opportunities

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: Utpal Das, Sona Das

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InAs/GaSb type-II superlattice (T2SL) Mid-wave infrared (MWIR) focal plane arrays (FPAs) have recently seen rapid development. However, in small pixel size large format FPAs, the occurrence of high mesa sidewall surface leakage current is a major constraint necessitating proper surface passivation. A simple pixel isolation technique in InAs/GaSb T2SL detector arrays without the conventional mesa etching has been proposed to isolate the pixels by forming a more resistive higher band gap material from the SL, in the inter-pixel region. Here, a single step femtosecond (fs) laser anneal of the T2SL structure of the inter-pixel T2SL regions, have been used to increase the band gap between the pixels by QW-intermixing and hence increase isolation between the pixels. The p-i-n photodiode structure used here consists of a 506nm, (10 monolayer {ML}) InAs:Si (1x10¹⁸cm⁻³)/(10ML) GaSb SL as the bottom n-contact layer grown on an n-type GaSb substrate. The undoped absorber layer consists of 1.3µm, (10ML)InAs/(10ML)GaSb SL. The top p-contact layer is a 63nm, (10ML)InAs:Be(1x10¹⁸cm⁻³)/(10ML)GaSb T2SL. In order to improve the carrier transport, a 126nm of graded doped (10ML)InAs/(10ML)GaSb SL layer was added between the absorber and each contact layers. A 775nm 150fs-laser at a fluence of ~6mJ/cm² is used to expose the array where the pixel regions are masked by a Ti(200nm)-Au(300nm) cap. Here, in the inter-pixel regions, the p+ layer have been reactive ion etched (RIE) using CH₄+H₂ chemistry and removed before fs-laser exposure. The fs-laser anneal isolation improvement in 200-400μm pixels due to spatially selective quantum well intermixing for a blue shift of ~70meV in the inter-pixel regions is confirmed by FTIR measurements. Dark currents are measured between two adjacent pixels with the Ti(200nm)-Au(300nm) caps used as contacts. The T2SL quality in the active photodiode regions masked by the Ti-Au cap is hardly affected and retains the original quality of the detector. Although, fs-laser anneal of p+ only etched p-i-n T2SL diodes show a reduction in the reverse dark current, no significant improvement in the full RIE-etched mesa structures is noticeable. Hence for a 128x128 array fabrication of 8μm square pixels and 10µm pitch, SU8 polymer isolation after RIE pixel delineation has been used. X-n+ row contacts and Y-p+ column contacts have been used to measure the optical response of the individual pixels. The photo-response of these 8μm and other 200μm pixels under a 2ns optical pulse excitation from an Optical-Parametric-Oscillator (OPO), shows a peak responsivity of ~0.03A/W and 0.2mA/W, respectively, at λ~3.7μm. Temporal response of this detector array is seen to have a fast response ~10ns followed typical slow decay with ringing, attributed to impedance mismatch of the connecting co-axial cables. In conclusion, response times of a few ns have been measured in 8µm pixels of a 128x128 array. Although fs-laser anneal has been found to be useful in increasing the inter-pixel isolation in InAs/GaSb T2SL arrays by QW inter-mixing, it has not been found to be suitable for passivation of full RIE etched mesa structures with vertical walls on InAs/GaSb T2SL.

Keywords: band-gap blue-shift, fs-laser-anneal, InAs/GaSb T2SL, Inter-pixel isolation, ns-Response, photodiode array

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Authors: Moses Obenade, Henry U. Okeke, Francis I. Okpiliya, Eugene J. Aniah

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Population growth in Eleme has been rapid over the past 75 years with its attendant pressure on the natural resources of the area. Between 1937 and 2006 the population of Eleme grew from 2,528 to 190,194 and is projected to be above 265,707 in 2016 based on an annual growth rate of 3.4%. Using the combined technologies of Geographic Information Systems (GIS), remote sensing (RS) and Demography techniques as its methodology, this paper examines the environmental implications of rapid population growth on land use dynamics in Eleme between 1986 and 2015. The study reveals that between 1986 and 2006, Built-up area and Farmland increased by 72.67 and 12.77% respectively, while light and thick vegetation recorded a decrease of -6.92 and -61.64% respectively. Water body remains fairly constant with minimal changes. Also, between 2006 and 2015 covering a period of 9 years, Built-up area further increased by 53% with an annual growth rate of 2.32 km2 gaining more land area on the detriment of other land uses. Built-up area has an annual growth rate of 2.32km2 and is expected to increase from 18.67km2 in 2006 to 41.87km2 in 2016.The observed Land used/Land cover dynamics is derived by the demographic characteristics of the Study area. Eleme has a total area of 138km2 out of which the Federal Government of Nigeria compulsorily acquired an estimated area of 59.34km2 for industrial purposes excluding acquisitions by the Rivers State Government. It is evident from the findings of this study that the carrying capacity of Eleme ecosystem is under threat due to the current population growth and land consumption rates. Therefore, measures such as use of appropriate technologies in farming techniques, waste management; investment in family planning and female empowerment, maternal health and education, afforestation programs; and amendment of Land Use Act of 1978 are recommended.

Keywords: population growth, Eleme, land use, GIS and remote sensing

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Authors: Aicha Majda, Abdelhamid El Hassani

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Lung CT image segmentation is a prerequisite in lung CT image analysis. Most of the conventional methods need a post-processing to deal with the abnormal lung CT scans such as lung nodules or other lesions. The simplest similarity measure in the standard Graph Cuts Algorithm consists of directly comparing the pixel values of the two neighboring regions, which is not accurate because this kind of metrics is extremely sensitive to minor transformations such as noise or other artifacts problems. In this work, we propose an improved version of the standard graph cuts algorithm based on the Patch-Based similarity metric. The boundary penalty term in the graph cut algorithm is defined Based on Patch-Based similarity measurement instead of the simple intensity measurement in the standard method. The weights between each pixel and its neighboring pixels are Based on the obtained new term. The graph is then created using theses weights between its nodes. Finally, the segmentation is completed with the minimum cut/Max-Flow algorithm. Experimental results show that the proposed method is very accurate and efficient, and can directly provide explicit lung regions without any post-processing operations compared to the standard method.

Keywords: graph cuts, lung CT scan, lung parenchyma segmentation, patch-based similarity metric

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Authors: Nuseiba M. Altarawneh, Suhuai Luo, Brian Regan, Guijin Tang

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Liver segmentation from medical images poses more challenges than analogous segmentations of other organs. This contribution introduces a liver segmentation method from a series of computer tomography images. Overall, we present a novel method for segmenting liver by coupling density matching with shape priors. Density matching signifies a tracking method which operates via maximizing the Bhattacharyya similarity measure between the photometric distribution from an estimated image region and a model photometric distribution. Density matching controls the direction of the evolution process and slows down the evolving contour in regions with weak edges. The shape prior improves the robustness of density matching and discourages the evolving contour from exceeding liver’s boundaries at regions with weak boundaries. The model is implemented using a modified distance regularized level set (DRLS) model. The experimental results show that the method achieves a satisfactory result. By comparing with the original DRLS model, it is evident that the proposed model herein is more effective in addressing the over segmentation problem. Finally, we gauge our performance of our model against matrices comprising of accuracy, sensitivity and specificity.

Keywords: Bhattacharyya distance, distance regularized level set (DRLS) model, liver segmentation, level set method

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Authors: D. Courault, L. Hossard, V. Demarez, E. Ndikumana, D. Ho Tong Minh, N. Baghdadi, F. Ruget

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In the global change context, efficient management of the available resources has become one of the most important topics, particularly for sustainable crop development. Timely assessment with high precision is crucial for water resource and pest management. Rice cultivated in Southern France in the Camargue region must face a challenge, reduction of the soil salinity by flooding and at the same time reduce the number of herbicides impacting negatively the environment. This context has lead farmers to diversify crop rotation and their agricultural practices. The objective of this study was to evaluate this crop diversity both in crop systems and in agricultural practices applied to rice paddy in order to quantify the impact on the environment and on the crop production. The proposed method is based on the combined use of crop models and multispectral data acquired from the recent Sentinel 2 satellite sensors launched by the European Space Agency (ESA) within the homework of the Copernicus program. More than 40 images at fine spatial resolution (10m in the optical range) were processed for 2016 and 2017 (with a revisit time of 5 days) to map crop types using random forest method and to estimate biophysical variables (LAI) retrieved by inversion of the PROSAIL canopy radiative transfer model. Thanks to the high revisit time of Sentinel 2 data, it was possible to monitor the soil labor before flooding and the second sowing made by some farmers to better control weeds. The temporal trajectories of remote sensing data were analyzed for various rice cultivars for defining the main parameters describing the phenological stages useful to calibrate two crop models (STICS and SAFY). Results were compared to surveys conducted with 10 farms. A large variability of LAI has been observed at farm scale (up to 2-3m²/m²) which induced a significant variability in the yields simulated (up to 2 ton/ha). Observations on more than 300 fields have also been collected on land use. Various maps were elaborated, land use, LAI, flooding and sowing, and harvest dates. All these maps allow proposing a new typology to classify these paddy crop systems. Key phenological dates can be estimated from inverse procedures and were validated against ground surveys. The proposed approach allowed to compare the years and to detect anomalies. The methods proposed here can be applied at different crops in various contexts and confirm the potential of remote sensing acquired at fine resolution such as the Sentinel2 system for agriculture applications and environment monitoring. This study was supported by the French national center of spatial studies (CNES, funded by the TOSCA).

Keywords: agricultural practices, remote sensing, rice, yield

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Authors: Gauri Sreekumar, Sabuj Kumar Mandal

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Contributing about 18% to India’s Gross Domestic Product, the agricultural sector plays a significant role in the Indian rural economy. Despite being the primary source of livelihood for more than half of India’s population, most of them are marginal and small farmers facing several challenges due to agro-climatic shocks. Climate change is expected to increase the risk in the regions that are highly agriculture dependent. With systematic and scientific evidence of changes in rainfall, temperature and other extreme climate events, migration started to emerge as a survival strategy for the farm households. In this backdrop, our present study aims to combine the two strands of literature and attempts to explore whether migration is the only adaptation strategy for the farmers once they experience crop failures due adverse climatic condition. Combining the temperature and rainfall information from the weather data provided by the Indian Meteorological Department with the household level panel data on Indian states belonging to the Eastern and Semi-Arid Tropics regions from the Village Dynamics in South Asia (VDSA) collected by the International Crop Research Institute for the Semi-arid Tropics, we form a rich panel data for the years 2010-2014. A Recursive Econometric Model is used to establish the three-way nexus between climate change-yield-migration while addressing the role of irrigation and local non-farm income diversification. Using Three Stage Least Squares Estimation method, we find that climate change induced yield loss is a major driver of farmers’ migration. However, irrigation and local level non-farm income diversification are found to mitigate the adverse impact of climate change on migration. Based on our empirical results, we suggest for enhancing irrigation facilities and making local non-farm income diversification opportunities available to increase farm productivity and thereby reduce farmers’ migration.

Keywords: climate change, migration, adaptation, mitigation

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Authors: Mirza Waseem Abbas, Syed Danish Raza

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For many countries agriculture is still the major force driving the economy and a critically important socioeconomic sector, despite exceptional industrial development across the globe. In countries like Pakistan, this sector is considered the backbone of the economy, and most of the economic decision making revolves around agricultural outputs and data. Timely and accurate facts and figures about this vital sector hold immense significance and have serious implications for the long-term development of the economy. Therefore, any significant improvements in the statistics and other forms of data regarding agriculture sector are considered important by all policymakers. This is especially true for decision making for the betterment of crops and the agriculture sector in general. Provincial and federal agricultural departments collect data for all cash and non-cash crops and the sector, in general, every year. Traditional data collection for such a large sector i.e. agriculture, being time-consuming, prone to human error and labor-intensive, is slowly but gradually being replaced by remote sensing techniques. For this study, remotely sensed data were used for change detection (machine learning, supervised & unsupervised classification) to assess the increase or decrease in area under agriculture over the last fifteen years due to urbanization. Detailed Landsat Images for the selected agricultural districts were acquired for the year 2000 and compared to images of the same area acquired for the year 2016. Observed differences validated through detailed analysis of the areas show that there was a considerable decrease in vegetation during the last fifteen years in four major agricultural districts of the Punjab province due to urbanization (housing societies).

Keywords: change detection, area estimation, machine learning, urbanization, remote sensing

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Authors: Armand Okende, Benjamin Beaumont

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Introduction: Deforestation is a change in the structure and composition of flora and fauna, which leads to a loss of biodiversity, production of goods and services and an increase in fires. It concerns vast territories in tropical zones particularly; this is the case of the territory of Bolobo in the current province of Maï- Ndombe in the Democratic Republic of Congo. Indeed, through this study between 2001 and 2018, we believe that it was important to show and analyze quantitatively the important forests changes and analyze quantitatively. It’s the overall objective of this study because, in this area, we are witnessing significant deforestation. Methodology: Mapping and quantification are the methodological approaches that we have put forward to assess the deforestation or forest changes through satellite images or raster layers. These satellites data from Global Forest Watch are integrated into the GIS software (GRASS GIS and Quantum GIS) to represent the loss of forest cover that has occurred and the various changes recorded (e.g., forest gain) in the territory of Bolobo. Results: The results obtained show, in terms of quantifying deforestation for the periods 2001-2006, 2007-2012 and 2013-2018, the loss of forest area in hectares each year. The different change maps produced during different study periods mentioned above show that the loss of forest areas is gradually increasing. Conclusion: With this study, knowledge of forest management and protection is a challenge to ensure good management of forest resources. To do this, it is wise to carry out more studies that would optimize the monitoring of forests to guarantee the ecological and economic functions they provide in the Congo Basin, particularly in the Democratic Republic of Congo. In addition, the cartographic approach, coupled with the geographic information system and remote sensing proposed by Global Forest Watch using raster layers, provides interesting information to explain the loss of forest areas.

Keywords: deforestation, loss year, forest change, remote sensing, drivers of deforestation

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Authors: Fei Gao, Xinyang Dong, Yisu Ge, Shufang Lu, Libo Weng

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Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition.

Keywords: feature extraction, convolutional neural networks, intelligent transportation, vehicle color recognition

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Authors: Gulseren Kirbas, Mushap Kuru, Buket Bakir, Ebru Karadag Sari

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Capra (mountain goat) is a genus comprising nine species. The domestic goat (C. aegagrus hircus) is a subspecies of the wild goat that is domesticated. This study aimed to determine the anatomical structure of the salpinx and ovary of the Anatolian wild goat (C. aegagrus aegagrus). Animals that were taken to the Kafkas University Wildlife Rescue and Rehabilitation Center, Kars, Turkey, because of various reasons, such as traffic accidents and firearm injuries, were used in this study. The salpinges and ovaries of four wild goats of similar ages, which could not be rescued by the Center despite all interventions, were dissected. Measurements were taken from the right-left salpinx and ovary using digital calipers. The weights of each ovary and salpinx were measured using a precision scale (min: 0.0001 g − max: 220 g, code: XB220A; Precisa, Swiss). The histological structure of the tissues was examined after weighing the organs. The tissue samples were fixed in 10% formaldehyde for 24 h. Then a routine procedure was applied, and the tissues were embedded in paraffin. Mallory’s modified triple staining was used to demonstrate the general structure of the salpinx. The salpinx was found to consist of three different regions (infundibulum, ampulla, and isthmus). These regions consisted of tunica mucosa, tunica muscularis, and tunica serosa. The prismatic epithelial cells were observed in the lamina epithelialis of tunica mucosa in every region, but the prismatic fimbrae cells occurred most in the infundibulum. The ampulla was distinguished by its many mucosal folds. It was the longest region of the salpinx and was joined to the isthmus via the ampullary–isthmus junction. Isthmus was the caudal end of the salpinx joined to the uterus and had the thickest tunica muscularis compared with the other regions. The mean length of the ovary was 13.22 ± 1.27 mm, width was 8.46 ± 0.88 mm, the thickness was 5.67 ± 0.79 mm, and weight was 0.59 ± 0.17 g. The average length of the salpinx was 58.11 ± 14.02 mm, width was 0.80 ± 0.22 mm, the thickness was 0.41 ± 0.01 mm, and weight was 0.30 ± 0.08 g. In conclusion, the Anatolian wild goat, which is included in wildlife diversity in Turkey, has been disappearing due to illegal and uncontrolled hunting as well as traffic accidents in recent years. These findings are believed to contribute to the literature.

Keywords: Anatolian wild goat, anatomy, ovary, salpinx

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Authors: Tina Melkoshvili

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Drastic development of information and communication technologies in Georgia has led to the necessity of launching conceptually new, effective, flexible, transparent and society oriented form of government that is e-government. Through applying information technologies, the electronic system enables to raise the efficacy of state governance and increase citizens’ participation in the process. Focusing on the topic of e-government allows us to analyze success stories, attributed benefits and, at the same time, observes challenges hampering the government development process. There are number of methodologies elaborated to study the conditions in the field of electronic governance. They enable us to find out if the government is ready to apply broad opportunities of information and communication technologies and if the government is apt to improve the accessibility and quality of delivering mainly social services. This article seeks to provide comparative analysis of widely spread methodologies used for Electronic government projects’ assessment. It has been concluded that applying current methods of assessment in Georgia is related to difficulties due to inaccessible data and the necessity of involving number of experts. The article presents new indicators for e-government development assessment that reflect efficacy of e-government conception realization in the regions of Georgia and enables to provide quantitative evaluation of regional e-government projects including all significant aspects of development.

Keywords: development methodology, e-government in Georgia, information and communication technologies, regional government

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Authors: Aayushi Pandey, Manoj Kumar Pandey, Ashutosh Tiwari, Kireet Kumar

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Glaciers play an important role in climate change and are sensitive phenomena of global climate change scenario. Glaciers in Himalayas are unique as they are predominantly valley type and are located in tropical, high altitude regions. These glaciers are often covered with debris which greatly affects ablation rate of glaciers and work as a sensitive indicator of glacier health. The aim of this study is to map high altitude Glacier surface with a focus on glacial lake and debris estimation using different techniques in Nagling glacier of dhauliganga basin in Himalayan region. Different Image Classification techniques i.e. thresholding on different band ratios and supervised classification using maximum likelihood classifier (MLC) have been used on high resolution sentinel 2A level 1c satellite imagery of 14 October 2017.Here Near Infrared (NIR)/Shortwave Infrared (SWIR) ratio image was used to extract the glaciated classes (Snow, Ice, Ice Mixed Debris) from other non-glaciated terrain classes. SWIR/BLUE Ratio Image was used to map valley rock and Debris while Green/NIR ratio image was found most suitable for mapping Glacial Lake. Accuracy assessment was performed using high resolution (3 meters) Planetscope Imagery using 60 stratified random points. The overall accuracy of MLC was 85 % while the accuracy of Band Ratios was 96.66 %. According to Band Ratio technique total areal extent of glaciated classes (Snow, Ice ,IMD) in Nagling glacier was 10.70 km2 nearly 38.07% of study area comprising of 30.87 % Snow covered area, 3.93% Ice and 3.27 % IMD covered area. Non-glaciated classes (vegetation, glacial lake, debris and valley rock) covered 61.93 % of the total area out of which valley rock is dominant with 33.83% coverage followed by debris covering 27.7 % of the area in nagling glacier. Glacial lake and Debris were accurately mapped using Band ratio technique Hence, Band Ratio approach appears to be useful for the mapping of debris covered glacier in Himalayan Region.

Keywords: band ratio, Dhauliganga basin, glacier mapping, Himalayan region, maximum likelihood classifier (MLC), Sentinel-2 satellite image

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Authors: Abdelkader T. Ahmed, Ayed E. Alluqmani

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The population growth in the KSA beside the increase in the urbanization level and standard of living improvement have resulted in the rapid growth of the country’s Municipal Solid Waste (MSW) generation. Municipalities are managing the MSW system in the KSA by collecting and getting rid of it by dumping it in nearest open landfill sites. Solid waste management is one of the main critical issues considered worldwide due to its significant impact on the environment and the public health. In this study, municipal solid waste (MSW) generation, composition and collection of Madinah city, as one of largest cities in KSA, were examined to provide an overview of current state of MSW management, an analysis of existing problem in MSW management, and recommendations for improving the waste treatment and management system in this area. These recommendations would be not specific to Madinah region, but also would be applied to other cities in KSA or any other regions with similar features. The trend of waste generation showed that current waste generation would be increased as much as two to three folds in 2030. Approximately 25% of total generated waste is disposed to a sanitary landfill, while 75% is sent to normal dumpsites. This study also investigated the environmental impacts of MSW through the Life Cycle Assessment (LCA) of waste generations and related processes. LCA results revealed that among the seven scenarios, recycling and composting are the best scenario for the solid waste management in Madinah and similar regions.

Keywords: municipal solid waste, waste recycling and land-filling, waste management, life cycle assessment

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Authors: Yasir M. Alyazichi, Brian G. Jones, Errol McLean, Hamd N. Altalyan, Ali K. M. Al-Nasrawi

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The main purpose of this study is to assess the sediment quality and potential ecological risk in marine sediments in Gymea Bay located in south Sydney, Australia. A total of 32 surface sediment samples were collected from the bay. Current track trajectories and velocities have also been measured in the bay. The resultant trace elements were compared with the adverse biological effect values Effect Range Low (ERL) and Effect Range Median (ERM) classifications. The results indicate that the average values of chromium, arsenic, copper, zinc, and lead in surface sediments all reveal low pollution levels and are below ERL and ERM values. The highest concentrations of trace elements were found close to discharge points and in the inner bay, and were linked with high percentages of clay minerals, pyrite and organic matter, which can play a significant role in trapping and accumulating these elements. The lowest concentrations of trace elements were found to be on the shoreline of the bay, which contained high percentages of sand fractions. It is postulated that the fine particles and trace elements are disturbed by currents and tides, then transported and deposited in deeper areas. The current track velocities recorded in Gymea Bay had the capability to transport fine particles and trace element pollution within the bay. As a result, hydrodynamic measurements were able to provide useful information and to help explain the distribution of sedimentary particles and geochemical properties. This may lead to knowledge transfer to other bay systems, including those in remote areas. These activities can be conducted at a low cost, and are therefore also transferrable to developing countries. The advent of portable instruments to measure trace elements in the field has also contributed to the development of these lower cost and easily applied methodologies available for use in remote locations and low-cost economies.

Keywords: current track velocities, gymea bay, surface sediments, trace elements

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Authors: Preetham Shankapal, Jill King, Kori Murray, Corby Martin, Paula Giselman, Jason Hicks, Owen Carmicheal

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Activation of reward and satiety networks in the brain while processing palatable food cues, as well as functional connectivity during rest has been studied using functional Magnetic Resonance Imaging of the brain in various obesity phenotypes. However, functional connectivity within the reward and satiety network during food cue processing is understudied. 14 obese individuals underwent two fMRI scans during viewing of Macronutrient Picture System images. Each scan included two blocks of images of High Sugar/High Fat (HSHF), High Carbohydrate/High Fat (HCHF), Low Sugar/Low Fat (LSLF) and also non-food images. Seed voxels within seven food reward relevant ROIs: Insula, putamen and cingulate, precentral, parahippocampal, medial frontal and superior temporal gyri were isolated based on a prior meta-analysis. Beta series correlation for task-related functional connectivity between these seed voxels and the rest of the brain was computed. Voxel-level differences in functional connectivity were calculated between: first and the second scan; individuals who saw novel (N=7) vs. Repeated (N=7) images in the second scan; and between the HC/HF, HSHF blocks vs LSLF and non-food blocks. Computations and analysis showed that during food image viewing, reward network ROIs showed significant functional connectivity with each other and with other regions responsible for attentional and motor control, including inferior parietal lobe and precentral gyrus. These functional connectivity values were heightened among individuals who viewed novel HS/HF images in the second scan. In the second scan session, functional connectivity was reduced within the reward network but increased within attention, memory and recognition regions, suggesting habituation to reward properties and increased recollection of previously viewed images. In conclusion it can be inferred that Functional Connectivity within reward network and between reward and other brain regions, varies by important experimental conditions during food photography viewing, including habituation to shown foods.

Keywords: fMRI, functional connectivity, task-based, beta series correlation

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Authors: Elif Unveren, Ozlem Bozkurt

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Emotion processing happens when someone has a negative, stressful experience and gets over it in time, and it is a different experience for every person. As to look into emotion processing can be categorised by intensity, awareness, coordination, speed, accuracy and response. It may vary depending on people’s age, sex and conditions. Each emotion processing shows different activation patterns in different brain regions. Activation is significantly higher in the right frontal areas. The highest activation happens in extended frontotemporal areas during the processing of happiness, sadness and disgust. Those emotions also show widely disturbed differences and get produced earlier than anger and fear. For different occasions, listed variables may have less or more importance. A borderline personality disorder is a condition that creates an unstable personality, sudden mood swings and unpredictability of actions. According to a study that was made with healthy people and people who had BPD, there were significant differences in some categories of emotion processing, such as intensity, awareness and accuracy. According to another study that was made to show the emotional processing differences between puberty and was made for only females who were between the ages of 11 and 17, it was perceived that for different ages and hormone levels, different parts of the brain are used to understand the given task. Also, in the different study that was made for kids that were between the age of 4 and 15, it was observed that the older kids were processing emotion more intensely and expressing it to a greater extent. There was a significant increase in fear and disgust in those matters. To sum up, we can say that the activity of undertaking negative experiences is a unique thing for everybody for many different reasons.

Keywords: age, sex, conditions, brain regions, emotion processing

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