Search results for: weather forecasting

Authors: Mohammed Dabboor

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Spaceborne Synthetic Aperture Radar (SAR) systems are active remote sensing systems independent of weather and sun illumination, two factors which usually inhibit the use of optical satellite imagery. A SAR system could acquire single, dual, compact or fully polarized SAR imagery. Each SAR imagery type has its advantages and disadvantages. The sensitivity of SAR images is a function of the: 1) band, polarization, and incidence angle of the transmitted electromagnetic signal, and 2) geometric and dielectric properties of the radar target. The RADARSAT-1 (launched on November 4, 1995), RADARSAT-2 ((launched on December 14, 2007) and RADARSAT Constellation Mission (to be launched in July 2018) are three past, current, and future Canadian SAR space missions. Canada is developing the RADARSAT Constellation Mission (RCM) using small satellites to further maximize the capability to carry out round-the-clock surveillance from space. The Canadian Space Agency, in collaboration with other government-of-Canada departments, is leading the design, development and operation of the RADARSAT Constellation Mission to help addressing key priorities. The purpose of our presentation is to give an overview of the future Canadian RCM SAR mission with its satellites. Also, the RCM SAR imaging modes along with the expected SAR products will be described. An emphasis will be given to the mission unique capabilities and characteristics, such as the new compact polarimetry SAR configuration. In this presentation, we will summarize the RCM advancement from previous RADARSAT satellite missions. Furthermore, the potential of the RCM mission for different Earth observation applications will be outlined.

Keywords: compact polarimetry, RADARSAT, SAR mission, SAR applications

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Authors: H. Fraga, J. A. Santos

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Vitiviniculture in Portugal is a key socio-economic sector, with a strong connection to local traditions and culture. Despite being a relatively small country, with prevailing Mediterranean environments, Portugal comprises an exceptionally large diversity of growth conditions (Terroirs). The vineyard area in Portugal is over 190 thousand hectares, being the eleventh wine producer and ninth wine exporter worldwide. Owing to the strong impact of weather and climate conditions on grapevine physiological development, grape berry quantity and quality show important inter-annual variability. Grapevines are also susceptible to climate change, as their responses will be unavoidably different under future climates. These impacts may change wine typicity of a given region or even its viticultural suitability. The current study reveals that the projected warming and drying trends for Portugal under the Representative Concentration Pathway (RCP) 4.5 and 8.5, are projected to 1) significantly shift current grapevine growing thermal conditions (e.g., heat and chill accumulation), 2) enhance water stress, 3) anticipate phenological timings and 4) modify yields. Moreover, the present study provides some hints regarding the effectiveness of mulching and irrigation as climate change adaptation measures. Our results show that the effectiveness of these adaptation measures will strongly rest on the strength of the climate change signal at a local scale, thus emphasizing the need for local-to-regional climate change assessments.

Keywords: viticulture, climate change, adaptation measures, Portugal

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Authors: Edilene Machado, Carolina Karoly, Amanda Britz, Luciane Salvi, Claudineia Brazil

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Climate teleconnections refer to the climatic relationships between geographically distant regions, where changes in one location can influence weather patterns in another. These connections can occur through atmospheric and oceanic processes, leading to variations in temperature, precipitation, and other climatic elements. Studying teleconnections is crucial for better understanding the mechanisms that govern global climate and the potential consequences of climate change. A notable example of a teleconnection is the El Niño-Southern Oscillation (ENSO), which involves the interaction between the Equatorial Pacific Ocean and the atmosphere. During El Niño episodes, there is anomalous warming of the surface waters in the Equatorial Pacific, resulting in significant changes in global climate patterns. These changes can affect rainfall distribution, wind patterns, and temperatures in different parts of the world. The cold phase of ENSO, known as La Niña, is often associated with reduced precipitation and below-average temperatures in the state of Rio Grande do Sul, Brazil. Therefore, the objective of this research is to identify patterns between El Niño-Southern Oscillation (ENSO) events in their different phases and dengue transmission. Meteorological data and dengue case records for the city of Porto Alegre, in the southern region of Brazil, were used for the development of this research. The study highlighted that the highest incidence of dengue cases occurred during the cold phase of the El Niño-Southern Oscillation (ENSO).

Keywords: climate patterns, climate teleconnections, climate variability, dengue, El Niño-Southern oscillation

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Authors: G. Sridevi, Amalendu Jyotishi, Sushanta Mahapatra, G. Jagadeesh, Satyasiba Bedamatta

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Climate change is a main challenge for agriculture, food security and rural livelihoods for millions of people in India. Agriculture is the sector most vulnerable to climate change due to its high dependence on climate and weather conditions. Among India’s population of more than one billion people, about 68% are directly or indirectly involved in the agricultural sector. This sector is particularly vulnerable to present-day climate variability. In this contest this paper examines the Socio-economic and climate analytical study of the vulnerability index in Indian states of Andhra Pradesh and Karnataka. Using secondary data; it examines the vulnerability through five different sub-indicator of socio-demographic, agriculture, occupational, common property resource (CPR), and climate in respective states among different districts. Data used in this paper has taken from different sources, like census in India 2011, Directorate of Economics and Statistics of respective states governments. Rainfall data was collected from the India Meteorological Department (IMD). In order to capture the vulnerability from two different states the composite vulnerability index (CVI) was developed and used. This indicates the vulnerability situation of different districts under two states. The study finds that Adilabad district in Andhra Pradesh and Chamarajanagar in Karnataka had highest level of vulnerability while Hyderabad and Bangalore in respective states have least level of vulnerability.

Keywords: vulnerability, agriculture, climate change, global warming

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Authors: Muhammad Fanseem E. M., Rama Satya Satish Kumar, Indrajeet Bhausaheb Bhavar, Deepak M.

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Keeping the right balance of electricity between the supply and demand sides of the grid is one of the most important objectives of electrical grid operation. Power generation and demand forecasting are the core of power management and generation scheduling. Large, centralized producing units were used in the construction of conventional power systems in the past. A certain level of balance was possible since the generation kept up with the power demand. However, integrating renewable energy sources into power networks has proven to be a difficult challenge due to its intermittent nature. The power imbalance caused by rising demands and peak loads is negatively affecting power quality and dependability. Demand side management and demand response were one of the solutions, keeping generation the same but altering or rescheduling or shedding completely the load or demand. However, shedding the load or rescheduling is not an efficient way. There comes the significance of virtual power plants. The virtual power plant integrates distributed generation, dispatchable load, and distributed energy storage organically by using complementing control approaches and communication technologies. This would eventually increase the utilization rate and financial advantages of distributed energy resources. Most of the writing on virtual power plant models ignored technical limitations, and modeling was done in favor of a financial or commercial viewpoint. Therefore, this paper aims to address the modeling intricacies of VPPs and their technical limitations, shedding light on a holistic understanding of this innovative power management approach.

Keywords: cost optimization, distributed energy resources, dynamic modeling, model quality tests, power system modeling

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Authors: Areej Salaymeh, Loren Schwiebert, Stephen Remias, Jonathan Waddell

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Extracting information from new data sources has emerged as a crucial task in many traffic planning processes, such as identifying traffic patterns, route planning, traffic forecasting, and locating infrastructure improvements. Given the advanced technologies used to collect Global Positioning System (GPS) data from dedicated GPS devices, GPS equipped phones, and navigation tools, intelligent data analysis methodologies are necessary to mine this raw data. In this research, an automatic detection framework is proposed to help identify and classify the locations of stopped GPS waypoints into two main categories: signalized intersections or highway congestion. The Delaunay triangulation is used to perform this assessment in the clustering phase. While most of the existing clustering algorithms need assumptions about the data distribution, the effectiveness of the Delaunay triangulation relies on triangulating geographical data points without such assumptions. Our proposed method starts by cleaning noise from the data and normalizing it. Next, the framework will identify stoppage points by calculating the traveled distance. The last step is to use clustering to form groups of waypoints for signalized traffic and highway congestion. Next, a binary classifier was applied to find distinguish highway congestion from signalized stop points. The binary classifier uses the length of the cluster to find congestion. The proposed framework shows high accuracy for identifying the stop positions and congestion points in around 99.2% of trials. We show that it is possible, using limited GPS data, to distinguish with high accuracy.

Keywords: Delaunay triangulation, clustering, intelligent transportation systems, GPS data

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Authors: Bin Mu, Site Li, Shijin Yuan

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Under the circumstance of environment deterioration, people are increasingly concerned about the quality of the environment, especially air quality. As a result, it is of great value to give accurate and timely forecast of AQI (air quality index). In order to simplify influencing factors of air quality in a city, and forecast the city’s AQI tomorrow, this study used MATLAB software and adopted the method of constructing a mathematic model of PCA-GABP to provide a solution. To be specific, this study firstly made principal component analysis (PCA) of influencing factors of AQI tomorrow including aspects of weather, industry waste gas and IAQI data today. Then, we used the back propagation neural network model (BP), which is optimized by genetic algorithm (GA), to give forecast of AQI tomorrow. In order to verify validity and accuracy of PCA-GABP model’s forecast capability. The study uses two statistical indices to evaluate AQI forecast results (normalized mean square error and fractional bias). Eventually, this study reduces mean square error by optimizing individual gene structure in genetic algorithm and adjusting the parameters of back propagation model. To conclude, the performance of the model to forecast AQI is comparatively convincing and the model is expected to take positive effect in AQI forecast in the future.

Keywords: AQI forecast, principal component analysis, genetic algorithm, back propagation neural network model

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Authors: Eman Mayah, Raid Hanna

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This study presents the use of daylight in the case study of the Reid building at the Glasgow School of Art in the city of Glasgow, UK. In Nordic countries, daylight is one of the main considerations within building design, especially in the face of long, lightless winters. A shortage of daylight, contributing to dark and gloomy conditions, necessitates that designs incorporate strong daylight performance. As such, the building in question is designed to capture natural light for varying needs, where studios are located on the North and South façades. The study’s approach presents an analysis of different façade scenarios, where daylight from the North is observed, analyzed and compared with the daylight from the South façade for various design studios in the building. The findings then are correlated with the results of daylight levels from the daylight simulation program (Autodesk Ecotect Analysis) for the investigated studios. The study finds there to be a dramatic difference in daylight nature and levels between the North and South façades, where orientation, obstructions and designed façade fenestrations have major effects on the findings. The study concludes that some of the studios positioned on the North façade do not have a desirable quality of diffused northern light, due to the outside building’s obstructions, area and volume of the studio and the shadow effect of the designed mezzanine floor in the studios.

Keywords: daylight levels, educational building, Façade fenestration, overcast weather

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Authors: Rajkumar Ghosh

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The study presents an overview of the many uses and approaches for estimating out-of-sequence thrust movement in earthquake mitigation. The study investigates how knowing and forecasting thrust movement during seismic occurrences might assist to effective earthquake mitigation measures. The review begins by discussing out-of-sequence thrust movement and its importance in earthquake mitigation strategies. It explores how typical techniques of estimating thrust movement may not capture the full complexity of seismic occurrences and emphasizes the benefits of include out-of-sequence data in the analysis. A thorough review of existing research and studies on out-of-sequence thrust movement estimates for earthquake mitigation. The study demonstrates how to estimate out-of-sequence thrust movement using multiple data sources such as GPS measurements, satellite imagery, and seismic recordings. The study also examines the use of out-of-sequence thrust movement estimates in earthquake mitigation measures. It investigates how precise calculation of thrust movement may help improve structural design, analyse infrastructure risk, and develop early warning systems. The potential advantages of using out-of-sequence data in these applications to improve the efficiency of earthquake mitigation techniques. The difficulties and limits of estimating out-of-sequence thrust movement for earthquake mitigation. It addresses data quality difficulties, modelling uncertainties, and computational complications. To address these obstacles and increase the accuracy and reliability of out-of-sequence thrust movement estimates, the authors recommend topics for additional study and improvement. The study is a helpful resource for seismic monitoring and earthquake risk assessment researchers, engineers, and policymakers, supporting innovations in earthquake mitigation measures based on a better knowledge of thrust movement dynamics.

Keywords: earthquake mitigation, out-of-sequence thrust, satellite imagery, seismic recordings, GPS measurements

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Authors: Hossam E. Emam

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The functional textiles development history in the military field could be ascribed as a uniquely interesting research topic. Soldiers are like a high-performance athletes, where monitoring their physical and physiological capabilities is a vital requirement. Functional clothes represent a “second skin” that has a close, “intimate” relationship with the human body. For the application of textiles in military purposes, which is normally required in difficult weather and environmental conditions, several functions are required. The requirements for designing functional military textiles for soldier's protection can be categorized into three categories; i) battle field (protection from chemical warfare agents, flames, and thermal radiation), ii) environmental (water proof, air permeable, UV-protection, antibacterial), iii) physiological (minimize heat stress, low weight, insulative, durability). All of these requirements are important, but the means to fulfill these requirements are not simple and straight forward. Additionally, the combination of more than one function is reported to be very expensive and requires many complicated steps, and the final product is found to be low durability. Not only do all of these requirements are overlapping, but they are also contradicting each other at various levels. Thus, we plan to produce multi-functional textiles (e.g., anti-microbial, UV-protection, fire retardant, photoluminescent) to be applied in military clothes. The current project aims to use quite a simple and applicable technique through the modification of textiles with different coordination polymers and functionalized coordination polymers.

Keywords: functional textiles, military clothes, coordination polymers, antimicrobial, fire retardant, photolumenscent

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Authors: Haidy S. Ghali, Engy Serag, A. Samer Ezeldin

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Copper is an essential raw material used in the construction industry. During the year 2021 and the first half of 2022, the global market suffered from a significant fluctuation in copper raw material prices due to the aftermath of both the COVID-19 pandemic and the Russia-Ukraine war, which exposed its consumers to an unexpected financial risk. Thereto, this paper aims to develop two ANN-LSTM price prediction models, using Python, that can forecast the average monthly copper prices traded in the London Metal Exchange; the first model is a multivariate model that forecasts the copper price of the next 1-month and the second is a univariate model that predicts the copper prices of the upcoming three months. Historical data of average monthly London Metal Exchange copper prices are collected from January 2009 till July 2022, and potential external factors are identified and employed in the multivariate model. These factors lie under three main categories: energy prices and economic indicators of the three major exporting countries of copper, depending on the data availability. Before developing the LSTM models, the collected external parameters are analyzed with respect to the copper prices using correlation and multicollinearity tests in R software; then, the parameters are further screened to select the parameters that influence the copper prices. Then, the two LSTM models are developed, and the dataset is divided into training, validation, and testing sets. The results show that the performance of the 3-Month prediction model is better than the 1-Month prediction model, but still, both models can act as predicting tools for diverse economic situations.

Keywords: copper prices, prediction model, neural network, time series forecasting

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Authors: Lubna Eljabu, Mohammad Etemad, Stan Matwin

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Port authorities have many challenges in congested ports to allocate their resources to provide a safe and secure loading/ unloading procedure for cargo vessels. Selecting a destination port is the decision of a vessel master based on many factors such as weather, wavelength and changes of priorities. Having access to a tool which leverages AIS messages to monitor vessel’s movements and accurately predict their next destination port promotes an effective resource allocation process for port authorities. In this research, we propose a method, namely, Reference Route of Trajectory (RRoT) to assist port authorities in predicting inflow and outflow traffic in their local environment by monitoring Automatic Identification System (AIS) messages. Our RRoT method creates a reference route based on historical AIS messages. It utilizes some of the best trajectory similarity measure to identify the destination of a vessel using their recent movement. We evaluated five different similarity measures such as Discrete Fr´echet Distance (DFD), Dynamic Time Warping (DTW), Partial Curve Mapping (PCM), Area between two curves (Area) and Curve length (CL). Our experiments show that our method identifies the destination port with an accuracy of 98.97% and an fmeasure of 99.08% using Dynamic Time Warping (DTW) similarity measure.

Keywords: spatial temporal data mining, trajectory mining, trajectory similarity, resource optimization

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Authors: Hassan Gholibeigian, Kazem Gholibeigian

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In this paper, our goal is determination of loading versus time in crust. For this goal, we present a computational procedure to propose a cumulative strain energy time profile which can be used to predict the approximate location and time of the next major earthquake (M > 4.5) along a specific fault, which we believe, is more accurate than many of the methods presently in use. In the coming pages, after a short review of the research works presently going on in the area of earthquake analysis and prediction, earthquake mechanisms in both the jerk and sequence earthquake direction is discussed, then our computational procedure is presented using differential equations of equilibrium which govern the nonlinear dynamic response of a system of finite elements, modified with an extra term to account for the jerk produced during the quake. We then employ Von Mises developed model for the stress strain relationship in our calculations, modified with the addition of an extra term to account for thermal effects. For calculation of the strain energy the idea of Pulsating Mantle Hypothesis (PMH) is used. This hypothesis, in brief, states that the mantle is under diurnal cyclic pulsating loads due to unbalanced gravitational attraction of the sun and the moon. A brief discussion is done on the Denali fault as a case study. The cumulative strain energy is then graphically represented versus time. At the end, based on some hypothetic earthquake data, the final results are verified.

Keywords: pulsating mantle hypothesis, inner core’s dislocation, outer core’s bulge, constitutive model, transient hydro-magneto-thermo-mechanical load, diurnal stress, jerk, fault behaviour

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Authors: Arshad A. Narejo, M. Javed Sheikh, G. Mujtaba Khushk, Naeem A Qureshi, M. Ali Sheikh

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Climate change not only influences on agriculture activities but also has certain effects on daily human activities, as well as on overall human health. Keeping in view the significance and huge research gap on the issues, the researchers have found an opportunity to conduct a study in Sindh province of Pakistan, in which the issue of climate shifting/change regarding temperature and precipitation were discussed with the local farmers of district Hyderabad. The quantified perception was gathered on a reliable and valid scale from 200 respondents and was analyzed through SPSS and AMOS software. The result of this study revealed that the significant changes are being occurred in summer (r²=0.96; M=6.78) and winter seasons (r²=0.71; M=6.57), therefore it is leaving bad effects on human health (r²=0.96) and behavior of the local population (r²=0.70). In addition, the change in the cropping calendar, i.e., timing of sowing (r²=0.69; M=8.42) and harvesting (r²=0.79; M=8.27) of different crops have been altered due to changes in local weather patterns. Since the local farmers are also facing seed germination (r²=0.57; M=7.98) problems, it is therefore recommended that concerned authorities/departments should revise the agricultural calendar. Besides this, respondents were in opinion that actual summer starts even before the vacation and cold season starts when winter vacations ended. Thus, the government and other concerned departments should reconsider or reschedule the vacation regulation policy (r²=0.70) at least at the provincial level.

Keywords: climate, climate shifting/change, impact on daily life, impact on agricultural activities

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Authors: K. G. Sorokozherdyev, E. A. Efimov

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The socio-economic development of regions and countries is an important research issue. Today, in the face of many negative events in the global and regional economies, it is especially important to identify those areas that can serve as sources of economic growth and the basis for the well-being of the population. This study aims to identify the most important sectors of the economy of the Arkhangelsk region that can contribute to the socio-economic development of the region as a whole. For research, the Arkhangelsk region was taken as one of the typical Russian regions that do not have significant reserves of hydrocarbons nor there are located any large industrial complexes. In this regard, the question of possible origins of economic growth seems especially relevant. The basis of this study constitutes the distributed lag regression model (ADL model) developed by the authors, which is based on quarterly data on the socio-economic development of the Arkhangelsk region for the period 2004-2016. As a result, we obtained three equations reflecting the dynamics of three indicators of the socio-economic development of the region -the average wage, the regional GRP, and the birth rate. The influencing factors are the shares in GRP of such sectors as agriculture, mining, manufacturing, construction, wholesale and retail trade, hotels and restaurants, as well as the financial sector. The study showed that the greatest influence on the socio-economic development of the region is exerted by such industries as wholesale and retail trade, construction, and industrial sectors. The study can be the basis for forecasting and modeling the socio-economic development of the Arkhangelsk region in the short and medium term. It also can be helpful while analyzing the effectiveness of measures aimed at stimulating those or other industries of the region. The model can be used in developing a regional development strategy.

Keywords: regional economic development, regional sectoral structure, ADL model, Arkhangelsk region

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Authors: Mamta Rana, K. K. Singh, Nisha Kumari

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The simulation models with its soil-weather-plant atmosphere interacting system are important tools for assessing the crops in changing climate conditions. The CERES-Wheat & Rice vs. 4.6 DSSAT was calibrated and evaluated for one of the major producers of wheat and rice state- Haryana, India. The simulation runs were made under irrigated conditions and three fertilizer applications dose of N-P-K to estimate crop yield and other growth parameters along with the phenological development of the crop. The genetic coefficients derived by iteratively manipulating the relevant coefficients that characterize the phenological process of wheat and rice crop to the best fit match between the simulated and observed anthesis, physological maturity and final grain yield. The model validated by plotting the simulated and remote sensing derived LAI. LAI product from remote sensing provides the edge of spatial, timely and accurate assessment of crop. For validating the yield and yield components, the error percentage between the observed and simulated data was calculated. The analysis shows that the model can be used to simulate crop yield and yield components for wheat and rice cultivar under different management practices. During the validation, the error percentage was less than 10%, indicating the utility of the calibrated model for climate risk assessment in the selected region.

Keywords: simulation model, CERES-wheat and rice model, crop yield, genetic coefficient

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Authors: Selin Öztürk Demirkiran

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Approaches towards sustainability and energy efficiency are significant topics of our era. These approaches need to be addressed across various fields and are relevant to multiple disciplines. Building facades, as the first surface encountering external weather conditions, should be considered and analyzed within this context. Current seasonal changes due to global warming and the influence on climates have highlighted the necessity for building systems to adapt to these changes, emphasizing the need for long-lasting solutions. Therefore, this study aims to examine adaptive system applications using examples from similar climatic regions and buildings of different functions, classifying them according to adaptive system criteria. It also aims to explore and evaluate the current stage of such systems in Turkey and the potential for their implementation. In this study, six building examples with different functions, including two examples for each adaptive type, were analyzed from regions with climates similar to those in Turkey, with detailed examination sheets prepared. The purpose of this study is to contribute to ongoing developments by presenting findings on current concepts and analyses and proposing a distinct approach for the characterization of these elements at the scale of Turkey. From this perspective, there is a considerable amount of literature on adaptive facade designs, and while application examples exist, adaptive approaches have been developed and partially implemented. It is expected that innovative solutions in this field will find a place in Turkey in the near future, following the increasing number of examples globally.

Keywords: adaptive facade, smart building facades, facade innovation, sustainability.

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Authors: Szu-Hua Wang

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Urban ecosystems, as complex coupled human-environment systems, contain abundant natural resources for breeding natural assets and, at the same time, attract urban assets and consume natural resources, triggered by urban development. Land use change illustrates the interaction between human activities and environments factually. However, IPCC (2014) announces that land use change and urbanization due to human activities are the major cause of climate change, leading to serious impacts on urban ecosystem resilience and risk. For this reason, risk assessment and resilience analysis are the keys for responding to climate change on urban ecosystems. Urban spatial planning can guide urban development by land use planning, transportation planning, and environmental planning and affect land use allocation and human activities by building major constructions and protecting important national land resources simultaneously. Urban spatial planning can aggravate climate change and, on the other hand, mitigate and adapt climate change. Research on effects of spatial planning on land use change and climate change is one of intense issues currently. Therefore, this research focuses on developing frameworks for risk assessment and resilience analysis from the aspect of ecosystem based on typhoon precipitation in Taipei area. The integrated method of risk assessment and resilience analysis will be also addressed for applying spatial planning practice and sustainable development.

Keywords: ecosystem, land use change, risk analysis, resilience

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Authors: Surendra Choudhary, Sapan Tiwari

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The driver has two fundamental duties i) controlling the position of the vehicle along the longitudinal and lateral direction of movement ii) roadway width. Both of these duties are interdependent and are concurrently referred to as two-dimensional driver behavior. One of the main problems facing driver behavior modeling is to identify the parameters for describing the exemplary driving conduct and car maneuver under distinct traffic circumstances. Still, to date, there is no well-accepted theory that can comprehensively model the 2-D driver conduct (longitudinal and lateral). The primary objective of this research is to explore the vehicle's lateral longitudinal behavior in the heterogeneous condition of traffic on horizontal curves as well as the effect of road geometry on dynamic traffic parameters, i.e., car velocity and lateral placement. In this research, with their interrelationship, a thorough assessment of dynamic car parameters, i.e., speed, lateral acceleration, and turn radius. Also, horizontal curve road parameters, i.e., curvature radius, pavement friction, are performed. The dynamic parameters of the various types of car drivers are gathered using a VBOX GPS-based tool with high precision. The connection between dynamic car parameters and curve geometry is created after the removal of noise from the GPS trajectories. The major findings of the research are that car maneuvers with higher than the design limits of speed, acceleration, and lateral deviation on the studied curves of the highway. It can become lethal if the weather changes from dry to wet.

Keywords: geometry, maneuverability, terrain, trajectory, VBOX

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Authors: Mohsin Javed

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School Education department is usually criticized for utilizing quite low or fewer academic days due to many reasons like extreme weather conditions, sudden holidays, summer vocations, pandemics and, terrorism etc. The purpose of the experimental study was to determine the efficacy of online cooperative learning (OCL) integrated in the rotation model of blended learning. The effects on academic achievement of students and students' attitude about OCL embedded learning were assessed. By using a posttest only control group design, sixty-two first-year students were randomly allocated to either the experimental (30) or control (32) group. The control group received face to face classes for six sessions per week, while the experimental group had three OCL and three formal sessions per week under rotation model. Students' perceptions of OCL were evaluated using a survey questionnaire. Data was analyzed by independent sample t test and one sample t test. According to findings, the intervention greatly improved the state of the dependent variables. The results demonstrate that OCL can be successfully implemented in formal education using a blended learning rotation approach. Higher secondary institutions are advised to use this model in situations like Covid 19, smog, unexpected holidays, instructor absence from class due to increased responsibilities, and summer vacations.

Keywords: blended learning, online cooperative learning, rotation model of blended learning, supplementing

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Authors: Loubna Hamami, Bouchaib Nassereddine

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Due to the evolution of technologies, the need to observe and manage hostile environments, and reduction in size, wireless sensor networks (WSNs) are becoming essential and implicated in the most fields of life. WSNs enable us to change the style of living, working and interacting with the physical environment. The agricultural sector is one of such sectors where WSNs are successfully used to get various benefits. For successful agricultural production, the irrigation system is one of the most important factors, and it plays a tactical role in the process of agriculture domain. However, it is considered as the largest consumer of freshwater. Besides, the scarcity of water, the drought, the waste of the limited available water resources are among the critical issues that touch the almost sectors, notably agricultural services. These facts are leading all governments around the world to rethink about saving water and reducing the volume of water used; this requires the development of irrigation practices in order to have a complete and independent system that is more efficient in the management of irrigation. Consequently, the selection of WSNs in irrigation system has been a benefit for developing the agriculture sector. In this work, we propose a prototype for a complete and intelligent irrigation system based on wireless sensor networks and we present and discuss the design of this prototype. This latter aims at saving water, energy and time. The proposed prototype controls water system for irrigation by monitoring the soil temperature, soil moisture and weather conditions for estimation of water requirements of each plant.

Keywords: precision irrigation, sensor, wireless sensor networks, water resources

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Authors: Jeana Cadby, Kae Miyazawa

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With rapidly expanding worldwide adoption of mobile technologies, Information and Communication Technology (ITC) is a major energy user and a contributor to global carbon emissions, due to infrastructure and operational energy consumption. The agricultural sector is also significantly responsible for contributing to global carbon emissions. However, ICT cloud computing using mobile technology can directly reduce environmental impacts in the agricultural sector through applications and mobile connectivity, such as precision fertilizer and pesticide applications, or access to weather data, for example. While direct impacts are easily calculated, indirect environmental impacts from ICT cloud computing usage have not been thoroughly investigated. For example, while women may be more poorly equipped for adaptation to environmentally sustainable agricultural practices due to resource constraints, this research concludes that indirect environmental benefits can be achieved by improving rural access to mobile technology for women. Women in advanced roles and secure land tenure are more likely to invest in long-term agricultural conservation strategies, which protect against environmental degradation. This study examines how ICT using mobile technology advances the role of women in rural agricultural systems and indirectly reduces environmental impacts from agricultural production, through literature examination from secondary sources. Increasing access for women to ICT mobile technology provides indirect environmental and social benefits in the rural agricultural sector.

Keywords: cloud computing, environmental benefits, mobile technology, women

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Authors: M. Hussaina, K. Mahboobb, S. Z. Ilyasa, S. Shaheena

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Plume dispersion modeling is a computational procedure to establish a relationship between emissions, meteorology, atmospheric concentrations, deposition and other factors. The emission characteristics (stack height, stack diameter, release velocity, heat contents, chemical and physical properties of the gases/particle released etc.), terrain (surface roughness, local topography, nearby buildings) and meteorology (wind speed, stability, mixing height, etc.) are required for the modeling of the plume dispersion and estimation of ground and air concentration. During the early phase of Fukushima accident, plume dispersion modeling and decisions were taken for the implementation of protective measures. A difference in estimated results and decisions made by different countries for taking protective actions created a concern in local and international community regarding the exact identification of the safe zone. The current study is focused to highlight the importance of accurate and exact weather data availability, scientific approach for decision making for taking urgent protective actions, compatible and harmonized approach for plume dispersion modeling during a nuclear emergency. As a case study, the influence of meteorological data on plume dispersion modeling and decision-making process has been performed.

Keywords: decision making process, radiation doses, nuclear emergency, meteorological implications

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Authors: Farid Eltom A. E., Mustafa Abdul-Halim, Abdalla Markaz, Sami Atta, Mohamed Azhari, Ahmed Rashed

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Smart environment sensors integrated with ‘Internet of Things’ (IoT) technology can provide a new concept in tracking, sensing, and monitoring objects in the environment. The aim of the study is to evaluate the farm environmental monitoring system based on (IoT) and to realize the automated management of agriculture and the implementation of precision production. Until now, irrigation monitoring operations in Sudan have been carried out using traditional methods, which is a very costly and unreliable mechanism. However, by utilizing soil moisture sensors, irrigation can be conducted only when needed without fear of plant water stress. The result showed that software application allows farmers to display current and historical data on soil moisture and nutrients in the form of line charts. Design measurements of the soil factors: moisture, electrical, humidity, conductivity, temperature, pH, phosphorus, and potassium; these factors, together with a timestamp, are sent to the data server using the Lora WAN interface. It is considered scientifically agreed upon in the modern era that artificial intelligence works to arrange the necessary procedures to take care of the terrain, predict the quality and quantity of production through deep analysis of the various operations in agricultural fields, and also support monitoring of weather conditions.

Keywords: smart environment, monitoring systems, IoT, LoRa Gateway, center pivot

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Authors: Adam Musa Adam Eissa

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Environmental and weather changes especially rainfall affects the distribution and densities of mosquitoes. This work was carried out to study the distribution and densities of mosquitoes adults and larvae in a total of five selected stations in El Obied Town. A cross-sectional survey of Anopheline mosquito larval habitats was conducted. The survey was conducted during the dry season (January 2013). Larvae were collected by using the standard dipping technique, while adult stages were collected by rearing larvae in cage, because the density of adults Anopheles mosquito per room was zero by using spray sheet method by using Permethrin pesticide 25%E.C, during the study period. The results revealed that (2347) Anopheline mosquito larvae were found and collected from only one station. All of which (2347) larvae (100%) were classified as probably Anopheles Squamosus. The study also showed that, a number of 81 adults (100%) Anopheline mosquito were classified as probably Anopheles Squamosus. Anopheles Squamosus were found only in the shallow pond water habitat in Alrahma west area of El Obied, the mean Anopheline density in the study area for larvae was 0.313 per dip while the mean density of adult was 0 per room. The high mosquito larval density in Alrahma west area indicated that, this part of El Obied Town is at risk of mosquito-borne diseases including malaria. This study recommended to apply the control program against mosquito at this part of the Town.

Keywords: anopheles, squamosus, Alrahma, distribution

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Authors: R. M. Srivastava, Subbanna A.R.N.S, Md Abbas Ahmad, S. P.More, Shivashankar, B. Kalyanbabu

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The similar morphology associated with high genetic variability poses problems in phylogenetic studies of Helicoverpa armigera (Hubner). To identify genetic variation of North Western Himalayan population’s, partial (Mid to terminal region) cytochrome c oxidase subunit I (COX-1) gene was amplified and sequenced for three populations collected from Pantnagar, Almora, and Chinyalisaur. The alignment of sequences with other two populations, Nagpur representing central India population and Anhui, China representing complete COX-1 sequence revealed unanimity in middle region with eleven single nucleotide polymorphisms (SNPs) in Nagpur populations. However, the consensus is missing when approaching towards terminal region, which is associated with 15 each SNPs and pair base substitutions in Chinyalisaur populations. In minimum evolution tree, all the five populations were majorly separated into two clades, one comprising of only Nagpur population and the other with rest. Amongst, North Western populations, Chinyalisaur one is promising by farming a separate clade. The pairwise genetic distance ranges from 0.025 to 0.192 with the maximum between H. armigera populations of Nagpur and Chinyalisaur. This genetic isolation of populations can be attributed to a key role of topological barriers of weather and mountain ranges and temporal barriers due to cropping patterns.

Keywords: cytochrome c oxidase subunit I, northwestern Himalayan population, Helicoverpa armigera (Noctuidae: Lepidoptera), phylogenetic relationship, genetic variation

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Authors: R. V. Suresh Reddi, Vishnu S. Das, Mathew Leslie

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The weather systems within the Arabian Sea is very dynamic in terms of monsoon and cyclone events. The storms generated in the Arabian Sea are more likely to progress in the north-west or west direction towards Oman. From the database of Joint Typhoon Warning Center (JTWC), the number of cyclones that hit the Oman coast or pass within close vicinity is noteworthy and therefore they must be considered when looking at coastal/port engineering design and development projects. This paper provides a case study of two cyclones, i.e., Nanauk (2014) and Ashobaa (2015) to assess the impact on storm surge off the Oman coast. These two cyclones have been selected since they are comparable in terms of maximum wind, cyclone duration, central pressure and month of occurrence. They are of similar strength but differ in track, allowing the impact of proximity to the coast to be considered. Of the two selected cyclones, Ashobaa is the 'extreme' case with close proximity while Nanauk remains further offshore and is considered as a more typical case. The available 'best-track' data from JTWC is obtained for the 2 selected cyclones, and the cyclone winds are generated using a 'Cyclone Wind Generation Tool' from MIKE (modelling software) from DHI (Danish Hydraulic Institute). Using MIKE 21 Hydrodynamic model powered by DHI the storm surge is estimated at selected offshore locations along the Oman coast.

Keywords: costal engineering, cyclone, storm surge, modelling

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Authors: Meng Su

Abstract:

High-dimensional data analysis often presents challenges in capturing the complex, nonlinear relationships and manifold structures inherent to the data. This article presents a novel approach that leverages the strengths of two powerful techniques, Diffusion Maps and Diffusion Probabilistic Models (DPMs), to address these challenges. By integrating the dimensionality reduction capability of Diffusion Maps with the data modeling ability of DPMs, the proposed method aims to provide a comprehensive solution for analyzing and generating high-dimensional data. The Diffusion Map technique preserves the nonlinear relationships and manifold structure of the data by mapping it to a lower-dimensional space using the eigenvectors of the graph Laplacian matrix. Meanwhile, DPMs capture the dependencies within the data, enabling effective modeling and generation of new data points in the low-dimensional space. The generated data points can then be mapped back to the original high-dimensional space, ensuring consistency with the underlying manifold structure. Through a detailed example implementation, the article demonstrates the potential of the proposed hybrid approach to achieve more accurate and effective modeling and generation of complex, high-dimensional data. Furthermore, it discusses possible applications in various domains, such as image synthesis, time-series forecasting, and anomaly detection, and outlines future research directions for enhancing the scalability, performance, and integration with other machine learning techniques. By combining the strengths of Diffusion Maps and DPMs, this work paves the way for more advanced and robust data analysis methods.

Keywords: diffusion maps, diffusion probabilistic models (DPMs), manifold learning, high-dimensional data analysis

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Authors: Ahmed. A. Hossam Eldin, Karim H. Youssef, Kareem M. AboRas

Abstract:

Recent current rapid industrial development and energy shortage are essential problems, which face most of the developing countries. Moreover, increased prices of fossil fuel and advanced energy conversion technology lead to the need for renewable energy resources. A study, modelling and simulation of an outdoor micro scale stand alone wind turbine was carried out. For model validation an experimental study was applied. In this research the aim was to clarify effects of real outdoor operating conditions and the instantaneous fluctuations of both wind direction and wind speed on the actual produced power. The results were compared with manufacturer’s data. The experiments were carried out in Borg Al-Arab, Alexandria. This location is on the north Western Coast of Alexandria. The results showed a real max output power for outdoor micro scale wind turbine, which is different from manufacturer’s value. This is due to the fact that the direction of wind speed is not the same as that of the manufacturer’s data. The measured wind speed and direction by the portable metrological weather station anemometer varied with time. The blade tail response could not change the blade direction at the same instant of the wind direction variation. Therefore, designers and users of micro scale wind turbine stand alone system cannot rely on the maker’s name plate data to reach the required power.

Keywords: micro-turbine, wind turbine, inverters, renewable energy, hybrid system

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Authors: Houda Najeh, Mahendra Pratap Singh, Stéphane Ploix, Antoine Caucheteux, Karim Chabir, Mohamed Naceur Abdelkrim

Abstract:

Building system is highly vulnerable to different kinds of faults and human misbehaviors. Energy efficiency and user comfort are directly targeted due to abnormalities in building operation. The available fault diagnosis tools and methodologies particularly rely on rules or pure model-based approaches. It is assumed that model or rule-based test could be applied to any situation without taking into account actual testing contexts. Contextual tests with validity domain could reduce a lot of the design of detection tests. The main objective of this paper is to consider fault validity when validate the test model considering the non-modeled events such as occupancy, weather conditions, door and window openings and the integration of the knowledge of the expert on the state of the system. The concept of heterogeneous tests is combined with test validity to generate fault diagnoses. A combination of rules, range and model-based tests known as heterogeneous tests are proposed to reduce the modeling complexity. Calculation of logical diagnoses coming from artificial intelligence provides a global explanation consistent with the test result. An application example shows the efficiency of the proposed technique: an office setting at Grenoble Institute of Technology.

Keywords: heterogeneous tests, validity, building system, sensor grids, sensor fault, diagnosis, fault detection and isolation

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