Search results for: spatial metrics

Authors: W. Riahi, M. L. Mansour

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Using tools of data processing such as geographic information system (GIS) for the contribution of the space management becomes more and more frequent. It allows collecting and analyzing diverse natural information relative to the same territory. Space technologies play crucial role in agricultural phenomenon analysis. For this, satellite images treatment were used to classify vegetation density and particularly agricultural areas in Setif province by making recourse to the Normalized Difference Vegetation Index (NDVI). This step was completed by mapping agricultural activities of the province by using ArcGIS.10 software in order to display an overall view and to realize spatial analysis of various themes combined between them which are chosen according to their strategic importance in different thematic maps. The synthesis map elaborately showed that geographic information system can contribute significantly to agricultural management by describing potentialities and development opportunities of production systems and agricultural sectors.

Keywords: GIS, satellite image, agriculture, NDVI, thematic map

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Authors: Simone Galati, Adriano Troia

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Ultrasound (US) is widely used in medical field for a variety diagnostic techniques but, in recent years, it has also been creating great interest for therapeutic aims. Regarding drug delivery, the use of US as an activation source provides better spatial delivery confinement and limits the undesired side effects. However, at present there is no complete characterization at a fundamental level of the different signals produced by sono-activated nanocarriers. Therefore, the aim of this study is to obtain a metrological characterization of the cavitation phenomena induced by US through three parallel investigation approaches. US was focused into a channel of a customized phantom in which a solution with oxygen-loaded nanodroplets (OLNDs) was led to flow and the cavitation activity was monitored. Both quantitative and qualitative real-time analysis were performed giving information about the dynamics of bubble formation, oscillation and final implosion with respect to the working acoustic pressure and the type of nanodroplets, compared with pure water. From this analysis a possible interpretation of the observed results is proposed.

Keywords: cavitation, drug delivery, nanodroplets, ultra-sound

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: Sanghamitra Sadhu

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A paradigm shift from capitalism to information technology is discerned in the era globalisation. The idea of public sphere, which was theorized in terms of its decline in the wake of the rise of commercial mass media has now emerged as a transnational or global sphere with the discourse being dominated by the ‘network society’. In other words, the dynamic of globalisation has brought about ‘a spatial turn’ in the social and political sciences which is also manifested in the public sphere, Especially the global public sphere. The paper revisits the Habermasian concept of the public sphere and focuses on the various social networking sites with their plausibility to create a virtual global public sphere. Situating Habermas’s notion of the bourgeois public sphere in the present context of global public sphere, it considers the changing dimensions of the public sphere across time and examines the concept of the ‘public’ with its shifting transformation from the concrete collective to the fluid ‘imagined’ category. The paper addresses the problematic of multimodal self-portraiture in the social networking sites as well as various online diaries/journals with an attempt to explore the nuances of the networked self.

Keywords: globalisation, network society, public sphere, self-fashioning, identity, autonomy

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Authors: Bhargavi G., Arathi R. Shankar

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Transmitter and receiver closer to every other, which creates the twin benefits of better-nice links and more spatial reuse. In a network with nomadic customers, this inevitably includes deploying greater infrastructure, normally in the form of microcells, hot spots, disbursed antennas, or relays. A less pricey alternative is the recent concept of femtocells, additionally known as domestic base stations that are facts get admission to points installed by means of domestic users to get higher indoor voice and records insurance. Femtocells have the potential to offer excessive exceptional community get entry to indoor customers at low cost, even as concurrently reducing the load. gift femtocells that perform in 4G can also be extended for 5G sub-6 GHz band. Designing the femtocell in mmWave band of 5G may have many blessings in terms of bandwidth availability and coverage. Multiband microstrip patch antennas can be considered as a low value and prominent antennas in designing the femtocells because the single antenna helps multiple frequency.

Keywords: 5G, mmWave, antennas, wireless communications, femtocell

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Authors: Krzysztof Malczewski

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Magnetic Resonance Imaging (MRI) reconstruction algorithm using compressed sensing is presented in this paper. It is exhibited that the offered approach improves MR images spatial resolution in circumstances when highly undersampled k-space trajectories are applied. Compressed Sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were conventionally assumed necessary. Magnetic Resonance Imaging (MRI) is a fundamental medical imaging method struggles with an inherently slow data acquisition process. The use of CS to MRI has the potential for significant scan time reductions, with visible benefits for patients and health care economics. In this study the objective is to combine super-resolution image enhancement algorithm with CS framework benefits to achieve high resolution MR output image. Both methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity. The presented algorithm considers the cardiac and respiratory movements.

Keywords: super-resolution, MRI, compressed sensing, sparse-sense, image enhancement

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Authors: Andreas Aagaard Nohr

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International Relations is an academic discipline which is rarely in direct contact with its field. However, there has in recent years been a growing interest in the different agents within and beyond the state and their associated practices; yet some of the research tools with which to study them are not widely used. This paper introduces video-observation as a method for the study of IR and argues that it offers a unique way of studying the complexity of the everyday context of actors. The paper is divided into two main parts: First, the philosophical and methodological underpinnings of the kind of data that video-observation produces are discussed; primarily through a discussion of the phenomenology of Husserl, Heidegger, and Merleau-Ponty. Second, taking simulation of a WTO negotiation round as an example, the paper discusses how the data created can be analysed: in particular with regard to the structure of events, the temporal and spatial organization of activities, rhythm and periodicity, and the concrete role of artefacts and documents. The paper concludes with a discussion of the ontological, epistemological, and practical challenges and limitations that ought to be considered if video-observation is chosen as a method within the field of IR.

Keywords: video-observation, phenomenology, international relations

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Authors: Cecilia Sosa, Fernanda Figueroa, Leonardo Calzada

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Biosphere reserves (BR) are considered as the main strategy for biodiversity and ecosystems conservation. Mexican BR are mainly inhabited by rural communities who strongly depend on forests and their resources. Even though the dual objective of conservation and development has been sought in BR, land cover change is a common process in these areas, while most rural communities are highly marginalized, partly as a result of restrictions imposed by conservation to the access and use of resources. Achieving ecosystems conservation and social development face serious challenges. Factors such as financial support for development projects (public/private), environmental conditions, infrastructure and regional economic conditions might influence both land use change and wellbeing. Examining the temporal trends of conservation and development in BR is central for the evaluation of outcomes for these conservation strategies. In this study, we analyzed changes in primary vegetation cover (as a proxy for conservation) and the index of marginalization (as a proxy for development) in Mexican BR (2000-2015); we also explore the influence of various factors affecting these trends, such as conservation-development projects financial support (public or private), geographical distribution in ecoregions (as a proxy for shared environmental conditions) and in economic zones (as a proxy for regional economic conditions). We developed a spatial analysis at the municipal scale (2,458 municipalities nationwide) in ArcGIS, to obtain road densities, geographical distribution in ecoregions and economic zones, the financial support received, and the percent of municipality area under protection by protected areas and, particularly, by BR. Those municipalities with less than 25% of area under protection were regarded as part of the protected area. We obtained marginalization indexes for all municipalities and, using MODIS in Google Earth Engine, the number of pixels covered by primary vegetation. We used a linear mixed model in RStudio for the analysis. We found a positive correlation between the marginalization index and the percent of primary vegetation cover per year (r=0.49-0.5); i.e., municipalities with higher marginalization also show higher percent of primary vegetation cover. Also, those municipalities with higher area under protection have more development projects (r=0.46) and some environmental conditions were relevant for percent of vegetation cover. Time, economic zones and marginalization index were all important. Time was particularly, in 2005, when both marginalization and deforestation decreased. Road densities and financial support for conservation-development projects were irrelevant as factors in the general correlation. Marginalization is still being affected by the conservation strategies applied in BR, even though that this management category considers both conservation and development of local communities as its objectives. Our results suggest that roads densities and support for conservation-development projects have not been a factor of poverty alleviation. As better conservation is being attained in the most impoverished areas, we face the dilemma of how to improve wellbeing in rural communities under conservation, since current strategies have not been able to leave behind the conservation-development contraposition.

Keywords: deforestation, local development, marginalization, protected areas

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Authors: Devika Tanna

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'Biometrics' means 'life measurement' but the term is usually associated with the use of unique physiological characteristics to identify an individual. It is important to secure the privacy of digital face image that is stored in central database. To impart privacy to such biometric face images, first, the digital face image is split into two host face images such that, each of it gives no idea of existence of the original face image and, then each cover image is stored in two different databases geographically apart. When both the cover images are simultaneously available then only we can access that original image. This can be achieved by using the XM2VTS and IMM face database, an adaptive algorithm for spatial greyscale. The algorithm helps to select the appropriate host images which are most likely to be compatible with the secret image stored in the central database based on its geometry and appearance. The encryption is done using GEVCS which results in a reconstructed image identical to the original private image.

Keywords: adaptive algorithm, database, host images, privacy, visual cryptography

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Authors: Mustaffa Kamal Bashar Mohd Fauzi, Puteri Shireen Jahn Kassim, Nurul Syala Abdul Latip

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It is increasingly realized that sustainability includes both a response to the climatic and cultural context of a place. To assess the cultural context, a morphological analysis of urban patterns from heritage legacies is necessary. While the climatic form is derived from an analysis of meteorological data, cultural patterns and forms must be abstracted from a typological and morphological study. This current study aims to analyzes morphological and formal elements of water-based architectural and urban design of past Islamic vernacular complexes in the hot arid regions and how a vast utilization of water was shaped and sited to act as cooling devices for an entire complex. Apart from its pleasant coolness, water can be used in an aesthetically way such as emphasizing visual axes, vividly enhancing the visual of the surrounding environment and symbolically portraying the act of purity in the design. By comparing 2 case studies based on the analysis of interactions of water features into the form, planning and morphology of 2 Islamic heritage complexes, Fatehpur Sikri (India) and Lahore Fort (Pakistan) with a focus on Shish Mahal of Lahore Fort in terms of their mass, architecture and urban planning, it is agreeable that water plays an integral role in their climatic amelioration via different methods of water conveyance system. Both sites are known for their substantial historical values and prominent for their sustainable vernacular buildings for example; the courtyard of Shish Mahal in Lahore fort are designed to provide continuous coolness by constructing various miniatures water channels that run underneath the paved courtyard. One of the most remarkable features of this system that all water is made dregs-free before it was inducted into these underneath channels. In Fatehpur Sikri, the method of conveyance seems differed from Lahore Fort as the need to supply water to the ridge where Fatehpur Sikri situated is become the major challenges. Thus, the achievement of supplying water to the palatial complexes is solved by placing inhabitable water buildings within the two supply system for raising water. The process of raising the water can be either mechanical or laborious inside the enclosed well and water rising houses. The studies analyzes and abstract the water supply forms, patterns and flows in 3-dimensional shapes through the actions of evaporative cooling and wind-induced ventilation under arid climates. Through the abstraction analytical and descriptive relational morphology of the spatial configurations, the studies can suggest the idealized spatial system that can be used in urban design and complexes which later became a methodological and abstraction tool of sustainability to suit the modern contemporary world.

Keywords: heritage site, Islamic vernacular architecture, water features, morphology, urban design

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Authors: Huda Algharib, Amal Algharib, Hanan Algharib, Ali Mohammad Alqudah

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Image registration is an important topic for many imaging systems and computer vision applications. The standard image registration techniques such as Mutual information/ Normalized mutual information -based methods have a limited performance because they do not consider the spatial information or the relationships between the neighbouring pixels or voxels. In addition, the amount of image noise may significantly affect the registration accuracy. Therefore, this paper proposes an efficient method that explicitly considers the relationships between the adjacent pixels, where the gradient information of the reference and scene images is extracted first, and then the cosine similarity of the extracted gradient information is computed and used to improve the accuracy of the standard normalized mutual information measure. Our experimental results on different data types (i.e. CT, MRI and thermal images) show that the proposed method outperforms a number of image registration techniques in terms of the accuracy.

Keywords: image registration, mutual information, image gradients, image transformations

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Authors: Majid Karimabadi, Ahmad Farzaneh Kore, Behnam Azadegan

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The evolution of magnetized quark gluon plasma (QGP) in the framework of magneto- hydrodynamics is the focus of our study. We are investigating the temporal and spatial evolution of QGP using a second order viscous hydrodynamic framework. The fluid is considered to be magnetized and subjected to the influence of a magnetic field that is generated during the early stages of relativistic heavy ion collisions. We assume boost invariance along the beam line, which is represented by the z coordinate, and fluid expansion in the x direction. Additionally, we assume that the magnetic field is perpendicular to the reaction plane, which corresponds to the y direction. The fluid is considered to have infinite electrical conductivity. To analyze this system, we solve the coupled Maxwell and conservation equations. By doing so, we are able to determine the time and space dependence of the energy density, velocity, and magnetic field in the transverse plane of the viscous magnetized hot plasma. Furthermore, we obtain the spectrum of hadrons and compare it with experimental data.

Keywords: QGP, magnetohydrodynamics, hadrons, conversation

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Authors: Osama Hidayat, Yoshitaka Kajiat

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Kabul is a city that is highly representative of Afghanistan’s rapid urbanization process. As the city rapidly expands, there are enormous challenges to the sustainable use of land resources. This paper evaluates land use change and urban spatial expansion, from 1950 to 2016, in Kabul the capital of Afghanistan, using satellite images, field observation, and socio-economic data. The discussion covers the reduction in rural-to-urban land conversion, the delineation of urban growth boundaries, arable land reclamation and the establishment of farmland protection areas, urban upgrading, and the investigation and prosecution of illegal construction. This paper considers the aspects of urbanization and land management systems in Afghanistan. Efficient frames are outlined in Kabul for the following elements: governmental self-restraint and policy modification. The paper concludes that Kabul’s sustainable land use practices can provide a reference for other cities in Afghanistan.

Keywords: urban land expansion, urbanization, land use policy, sustainable development

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Authors: Ma Cecilia Siva

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This study introduces a Bidirectional Encoder Representations from Transformers (BERT)-based machine learning model aimed at improving educational counseling by automating the process of recommending academic strands for students. The framework is designed to streamline and enhance the strand selection process by analyzing students' profiles and suggesting suitable academic paths based on their interests, strengths, and goals. Data was gathered from a sample of 200 grade 10 students, which included personal essays and survey responses relevant to strand alignment. After thorough preprocessing, the text data was tokenized, label-encoded, and input into a fine-tuned BERT model set up for multi-label classification. The model was optimized for balanced accuracy and computational efficiency, featuring a multi-category classification layer with sigmoid activation for independent strand predictions. Performance metrics showed an F1 score of 88%, indicating a well-balanced model with precision at 80% and recall at 100%, demonstrating its effectiveness in providing reliable recommendations while reducing irrelevant strand suggestions. To facilitate practical use, the final deployment phase created a recommendation framework that processes new student data through the trained model and generates personalized academic strand suggestions. This automated recommendation system presents a scalable solution for academic guidance, potentially enhancing student satisfaction and alignment with educational objectives. The study's findings indicate that expanding the data set, integrating additional features, and refining the model iteratively could improve the framework's accuracy and broaden its applicability in various educational contexts.

Keywords: tokenized, sigmoid activation, transformer, multi category classification

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

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Authors: Dong Wu, Michael Grenn

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Managing software projects effectively is crucial for meeting deadlines, ensuring quality, and managing resources well. Traditional methods often struggle with predicting project timelines accurately due to uncertain schedules and complex data. This study addresses these challenges by combining time prediction algorithms with Large Language Models (LLMs). It makes use of real-world software project data to construct and validate a model. The model takes detailed project progress data such as task completion dynamic, team Interaction and development metrics as its input and outputs predictions of project timelines. To evaluate the effectiveness of this model, a comprehensive methodology is employed, involving simulations and practical applications in a variety of real-world software project scenarios. This multifaceted evaluation strategy is designed to validate the model's significant role in enhancing forecast accuracy and elevating overall management efficiency, particularly in complex software project environments. The results indicate that the integration of time prediction algorithms with LLMs has the potential to optimize software project progress management. These quantitative results suggest the effectiveness of the method in practical applications. In conclusion, this study demonstrates that integrating time prediction algorithms with LLMs can significantly improve the predictive accuracy and efficiency of software project management. This offers an advanced project management tool for the industry, with the potential to improve operational efficiency, optimize resource allocation, and ensure timely project completion.

Keywords: software project management, time prediction algorithms, large language models (LLMS), forecast accuracy, project progress prediction

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Authors: Manikandan Sathianarayanan, Pernaidu Pasala

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This paper presents the results of our studies concerning the implementation and exploitation of a Geographical Information System (GIS) dedicated to the support and assistance of decisions requested by drought management. In this study on diverting of surplus water through canals, pond sand check dams in the study area was carried out. The remote sensing data and GIS data was used to identify the drought prone villages in sivaganga taluk and to generate present land use, drainage pattern as well as slope and contour. This analysis was carried out for diverting surplus water through proposed canal and pond. The results of the study indicate that if the surplus water from the ponds and streams are diverted to the drought villages in Sivaganga taluk, it will definitely improve the agricultural production due to availability of water in the ponds. The improvements in agricultural production will help to improve the economical condition of the farmers in the region.

Keywords: interlinking, spatial analysis, remote sensing, GIS

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Authors: Kehabtimer Shiferaw Kotiso, Getachew Hailemariam, Abiy Seifu Estifanos

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Introduction: Despite the highest share of low birth weight (LBW) for neonatal mortality and morbidity, predicting births with LBW for better intervention preparation is challenging. This study aims to predict LBW using a dataset encompassing 53,637 birth cohorts collected from 36 primary hospitals across seven regions in Ethiopia from February 2022 to June 2024. Methods: We identified ten explanatory variables related to maternal and neonatal characteristics, including maternal education, age, residence, history of miscarriage or abortion, history of preterm birth, type of pregnancy, number of livebirths, number of stillbirths, antenatal care frequency, and sex of the fetus to predict LBW. Using WEKA 3.8.2, we developed and compared seven machine learning algorithms. Data preprocessing included handling missing values, outlier detection, and ensuring data integrity in birth weight records. Model performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the Receiver Operating Characteristic curve (ROC AUC) using 10-fold cross-validation. Results: The results demonstrated that the decision tree, J48, logistic regression, and gradient boosted trees model achieved the highest accuracy (94.5% to 94.6%) with a precision of 93.1% to 93.3%, F1-score of 92.7% to 93.1%, and ROC AUC of 71.8% to 76.6%. Conclusion: This study demonstrates the effectiveness of machine learning models in predicting LBW. The high accuracy and recall rates achieved indicate that these models can serve as valuable tools for healthcare policymakers and providers in identifying at-risk newborns and implementing timely interventions to achieve the sustainable developmental goal (SDG) related to neonatal mortality.

Keywords: low birth weight, machine learning, classification, neonatal mortality, Ethiopia

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Authors: Probuddha Mukhopadhyay

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The infiltration of key elements to the civil structure is foraying its way to reclaim, what is its own. The reclamation of lives and spaces, once challenged, becomes a consistent process of ingress, disguised as parallels to the moving city, disperses into discourses often unheard of and conveniently forgotten. In this age of 'hyper'-urbanization, there are solutions suggested to a plethora of issues faced by citizens, in improving their standards of living. Problems are ancillary to proposals that emerge out of the underlying disorders of the townscape. These interventions result in the formulation of urban policies, to consolidate and optimize, to regularize and to streamline resources. Policy and practice are processes where the politics in policies define the way in which urban solutions are prescribed. Social constraints, that formulate the various cycles of order and disorders within the urban realm, are the stigmas for such interventions. There is often a direct relation of policy to place, no matter how people-centric it may seem to be projected. How we live our lives depends on where we live our lives - a relative statement for urban problems, varies from city to city. Communal compositions, welfare, crisis, socio-economic balance, need for management are the generic roots for urban policy formulation. However, in reality, the gentry administering its environmentalism is the criterion, that shapes and defines the values and expanse of such policies. In relation to the psycho-spatial characteristic of urban spheres with respect to the other side of this game, there have been instances, where the associational values have been reshaped by interests. The public domain reclaimed for exclusivity, thus creating fortified neighborhoods. Here, the citizen cumulative is often drifted by proposals that would over time deplete such landscapes of the city. It is the organized rebellion that in turn formulates further inward looking enclaves of latent aggression. In recent times, it has been observed that the unbalanced division of power and the implied processes of regulating the weak, stem the rebellion who respond in kits and parts. This is a phenomenon that mimics the guerilla warfare tactics, in order to have systems straightened out, either by manipulations or by force. This is the form of the city determined by the various forms insinuated by the state of city wide decisions. This study is an attempt at understanding the way in which development is interpreted by the state and the civil society and the role that community driven processes undertake to reinstate their claims to the city. This is a charter of consolidated patterns of negotiations that tend to counter policies. The research encompasses a study of various contested settlements in two cities of India- Mumbai and Kolkata, tackling dissent through spatial order. The study has been carried out to identify systems - formal and informal, catering to the most challenged interests of the people with respect to their habitat, a model to counter the top-down authoritative framework challenging the legitimacy of such settlements.

Keywords: urban design, insurgence, tactical urbanism, urban governance, civil society, state

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Authors: Changshan Zhang, Zhijin Yu, Shixing Fan

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A comprehensive hazard evaluation for coal self-heating in composite long-wall gobs is heavily dependent on computational simulation. In this study, the spatial distributions of cracks which caused significant air leakage were simulated by universal distinct element code (UDEC) simulation. Based on the main routes of air leakage and characteristics of coal self-heating, a computational fluid dynamics (CFD) modeling was conducted to model the coal spontaneous combustion dangerous area in composite long-wall gobs. The results included the oxygen concentration distributions and temperature profiles showed that the numerical approach is validated by comparison with the test data. Furthermore, under the conditions of specific engineering, the major locations where some techniques for extinguishing and preventing long-wall gob fires need to be put into practice were also examined.

Keywords: computational simulation, UDEC simulation, coal self-heating, CFD modeling, long-wall gobs

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Authors: Samuel Hyellamada Jerry

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This study focused on assessing conservation priorities within the Karlahi Forest Reserve of Fufore Local Government in Adamawa State. The main objective was to identify specific areas within the forest reserve that require immediate conservation attention. The research employed remote sensing and GIS techniques to achieve this goal. By overlaying the IDRIS Silva module results, a spatial distribution map was generated, highlighting the cumulative priority areas within and outside the forest. Among the total vegetated area of 26.38 km² in the Karlahi Forest Reserve, the analysis revealed that 16.16 km² were classified as high-priority conservation zones. Additionally, 4.59 km² and 5.63 km² were identified as medium and low-priority areas, respectively. In light of these findings, it is recommended that conservation efforts incorporate detailed land cover information and regular assessments of species diversity. Furthermore, strict adherence to national and state policies regarding forest reserves and parks is crucial for effective conservation management.

Keywords: priority, Karlahi, forest, reserve, IDRISI Silva, species diversity

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Authors: Menghzheng Yao

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Generalized social trust among Chinese has been declining in the past few decades, making the search for its causes necessary. Drawing on the symbolic interaction theory and the 2012 Chinese General Social Survey data, this research investigated the impact of people’s socialization preferences and frequencies on their perceptions of generalized social trust in China. This research also took a preliminary step towards understanding the spatial differences of the generalized social trust using the ArcGIS software. The results show that respondents who interacted with their neighbors more frequently were more likely to have higher levels of perceptions of generalized social trust. Several demographics were also significantly related to perception of generalized social trust. Elderly and better educated Chinese and people with higher self-perceived social status were associated with greater levels of generalized social trust perception, while urban dwellers and religious respondents expressed lower levels of such perception. Implications for future research and policy are discussed.

Keywords: China, generalized social trust, symbolic interaction, ArcGIS

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Authors: Ahmed Abdelghany, Bernard Uguen, Christophe Moy, Dominique Lemur

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The Internet of Things (IoT) is developed to ensure monitoring and connectivity within different applications. Thus, it is critical to study the channel propagation characteristics in Low Power Wide Area Network (LPWAN), especially Long Range Wide Area Network (LoRaWAN). In this paper, an in-depth investigation of the reciprocity between the uplink and downlink Channel State Information (CSI) is done by performing an outdoor measurement campaign in the area of Campus Beaulieu in Rennes. At each different location, the CSI reciprocity is quantified using the Pearson Correlation Coefficient (PCC) which shows a very high linear correlation between the uplink and downlink CSI. This reciprocity feature could be utilized for the physical layer security between the node and the gateway. On the other hand, most of the CSI shapes from different locations are highly uncorrelated from each other. Hence, it can be anticipated that this could achieve significant localization gain by utilizing the frequency hopping in the LoRa systems by getting access to a wider band.

Keywords: IoT, LPWAN, LoRa, effective signal power, onsite measurement

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Authors: C. Trapp, A. Vijay, M. Khorasani

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Decentralised micro-grids with sector coupling can combat the spatial and temporal intermittence of renewable energy by combining power, transportation and infrastructure sectors. Intelligent energy conversion concepts such as electrolysers, hydrogen engines and fuel cells combined with energy storage using intelligent batteries and hydrogen storage form the back-bone of such a system. This paper describes a micro-grid based on Photo-Voltaic cells, battery storage, innovative modular and scalable Anion Exchange Membrane (AEM) electrolyzer with an efficiency of up to 73%, high-pressure hydrogen storage as well as cutting-edge combustion-engine based Combined Heat and Power (CHP) plant with more than 85% efficiency at the university campus to address the challenges of decarbonization whilst eliminating the necessity for expensive high-voltage infrastructure.

Keywords: sector coupling, micro-grids, energy self-sufficiency, decarbonization, AEM electrolysis, hydrogen CHP

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Authors: Gulijianati Abake, Ryutaro Tateishi

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Snow is one kind of special underlying surface, has high reflectivity, low thermal conductivity, and snow broth hydrological effect. Every year, frequent snow disaster in Xinjiang causing considerable economic loss and serious damage to towns and farms, such as livestock casualties, traffic jams and other disaster, therefore monitoring SWE (snow volume) in Xinjiang has a great significance. The problems of how this disaster distributes and what disaster-forming environments are important to its occurrence are the most pressing problems in disaster risk assessment and salvage material arrangement. The present study aims 1) to monitor accurate SWE using MODIS, AMSRE, and CMC data, 2) to establish the regularity of snow disaster outbreaks and the important disaster-forming environmental factors. And a spatial autocorrelation analysis method and a canonical correlation analysis method are used to answer these two questions separately, 3) to prepare the way to salvage material arrangements for snow disasters.

Keywords: snow water equivalent (snow volume), AMSR-E, CMC snow depth, snow disaster

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Authors: Ashwani Kumar

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Use of NIR light for imaging the biological tissue and to quantify its optical properties is a good choice over other invasive methods. Optical tomography involves two steps. One is the forward problem and the other is the reconstruction problem. The forward problem consists of finding the measurements of transmitted light through the tissue from source to detector, given the spatial distribution of absorption and scattering properties. The second step is the reconstruction problem. In X-ray tomography, there is standard method for reconstruction called filtered back projection method or the algebraic reconstruction methods. But this method cannot be applied as such, in optical tomography due to highly scattering nature of biological tissue. A hybrid algorithm for reconstruction has been implemented in this work which takes into account the highly scattered path taken by photons while back projecting the forward data obtained during Monte Carlo simulation. The reconstructed image suffers from blurring due to point spread function.

Keywords: NIR light, tissue, blurring, Monte Carlo simulation

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Authors: Srinivas Vangari

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In the era of big data, ensuring the accuracy, consistency, and reliability of critical data assets is crucial for data-driven enterprises. Master Data Management (MDM) plays a crucial role in this endeavor. This paper investigates the role of Artificial Intelligence (AI) in enhancing MDM, focusing on how AI-driven solutions can automate and optimize various stages of the master data lifecycle. By integrating AI (Quantitative and Qualitative Analysis) into processes such as data creation, maintenance, enrichment, and usage, organizations can achieve significant improvements in data quality and operational efficiency. Quantitative analysis is employed to measure the impact of AI on key metrics, including data accuracy, processing speed, and error reduction. For instance, our study demonstrates an 18% improvement in data accuracy and a 75% reduction in duplicate records across multiple systems post-AI implementation. Furthermore, AI’s predictive maintenance capabilities reduced data obsolescence by 22%, as indicated by statistical analyses of data usage patterns over a 12-month period. Complementing this, a qualitative analysis delves into the specific AI-driven strategies that enhance MDM practices, such as automating data entry and validation, which resulted in a 28% decrease in manual errors. Insights from case studies highlight how AI-driven data cleansing processes reduced inconsistencies by 25% and how AI-powered enrichment strategies improved data relevance by 24%, thus boosting decision-making accuracy. The findings demonstrate that AI significantly enhances data quality and integrity, leading to improved enterprise performance through cost reduction, increased compliance, and more accurate, real-time decision-making. These insights underscore the value of AI as a critical tool in modern data management strategies, offering a competitive edge to organizations that leverage its capabilities.

Keywords: artificial intelligence, master data management, data governance, data quality

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Authors: Rajkumar Ghosh, Bhabani Prasad Mukhopadhay

Abstract:

Groundwater contamination with fluoride has emerged as a global concern affecting millions of people, leading to the widespread occurrence of fluorosis. It affects bones and teeth, leading to dental and skeletal fluorosis. This study presents a comprehensive analysis of the relationship between groundwater contamination and fluorosis. It delves into the causes of fluoride contamination in groundwater, its spatial distribution, and adverse health impacts of fluorosis on affected communities. Fluoride contamination in groundwater can be attributed to both natural and anthropogenic sources. Geogenic sources involve the dissolution of fluoride-rich minerals present in the aquifer materials. On the other hand, anthropogenic activities such as industrial discharges, agricultural practices, and improper disposal of fluoride-containing waste contribute to the contamination of groundwater. The spatial distribution of fluoride contamination varies widely across different regions and geological formations. High fluoride levels are commonly observed in areas with fluorine-rich geological deposits. Additionally, agricultural and industrial centres often exhibit elevated fluoride concentrations due to anthropogenic contributions. Excessive fluoride ingestion during tooth development leads to dental fluorosis, characterized by enamel defects, discoloration, and dental caries. The severity of dental fluorosis varies based on fluoride exposure levels during tooth development. Long-term consumption of fluoride-contaminated water causes skeletal fluorosis, resulting in bone and joint pain, decreased joint mobility, and skeletal deformities. In severe cases, skeletal fluorosis can lead to disability and reduced quality of life. Various defluoridation techniques such as activated alumina, bone char, and reverse osmosis have been employed to reduce fluoride concentrations in drinking water. These methods effectively remove fluoride, but their implementation requires careful consideration of cost, maintenance, and sustainability. Diversifying water sources, such as rainwater harvesting and surface water supply, can reduce the reliance on fluoride-contaminated groundwater, especially in regions with high fluoride concentrations. Groundwater contamination with fluoride remains a significant public health challenge, leading to the widespread occurrence of fluorosis globally. This scientific report emphasizes the importance of understanding the relationship between groundwater contamination and fluorosis. Implementing effective mitigation strategies and preventive measures is crucial to combat fluorosis and ensure sustainable access to safe drinking water for communities worldwide. Collaborative efforts between government agencies, local communities, and scientific researchers are essential to address this issue and safeguard the health of vulnerable populations. Additionally, the report explores various mitigation strategies and preventive measures to address the issue and offers recommendations for sustainable management of groundwater resources to combat fluorosis effectively.

Keywords: fluorosis, fluoride contamination, groundwater contamination, groundwater resources

Procedia PDF Downloads 92

Authors: Isa Muhammad Zumo

Abstract:

Malaria is one of the worst illnesses that may affect humans. It is typically transmitted by the bite of a female Anopheles mosquito and is caused by parasitic protozoans from the Plasmodium parasite. Government and non-governmental organizations made numerous initiatives to combat the threat of malaria in communities. Nevertheless, the necessary attention was not paid to accurate and current information regarding the size and location of these favourable locations for mosquito development. Because mosquitoes can only reproduce in specific habitats with surface water, this study will locate and map those favourable sites that act as mosquito breeding grounds. Spatial and attribute data relating to favourable mosquito breeding places will be collected and analysed using Geographic Information Systems (GIS). The major findings will be in five classes, showing the vulnerable and risky areas for malaria cases. These risk categories are very high, high, moderate, low, and extremely low vulnerable areas. The maps produced by this study will be of great use to the health department in combating the malaria pandemic.

Keywords: Malaria, vulnerability, mapping, space, Damaturu

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Authors: Gaurav Kumar Sinha

Abstract:

In an era marked by increasing concerns about energy sustainability, this research endeavors to address the pressing challenge of energy consumption in buildings and industries. This study delves into the transformative potential of AWS services in optimizing energy efficiency. The research is founded on the recognition that effective management of energy consumption is imperative for both environmental conservation and economic viability. Buildings and industries account for a substantial portion of global energy use, making it crucial to develop advanced techniques for analysis and reduction. This study sets out to explore the integration of AWS services with big data analytics to provide innovative solutions for energy consumption analysis. Leveraging AWS's cloud computing capabilities, scalable infrastructure, and data analytics tools, the research aims to develop efficient methods for collecting, processing, and analyzing energy data from diverse sources. The core focus is on creating predictive models and real-time monitoring systems that enable proactive energy management. By harnessing AWS's machine learning and data analytics capabilities, the research seeks to identify patterns, anomalies, and optimization opportunities within energy consumption data. Furthermore, this study aims to propose actionable recommendations for reducing energy consumption in buildings and industries. By combining AWS services with metrics-driven insights, the research strives to facilitate the implementation of energy-efficient practices, ultimately leading to reduced carbon emissions and cost savings. The integration of AWS services not only enhances the analytical capabilities but also offers scalable solutions that can be customized for different building and industrial contexts. The research also recognizes the potential for AWS-powered solutions to promote sustainable practices and support environmental stewardship.

Keywords: energy consumption analysis, big data analytics, AWS services, energy efficiency

Procedia PDF Downloads 63