bluuebunny/crossref_metadata_2025 huggingface dataset:
Real estate assessment of agricultural lands outside the zoning plan with artificial neural networks and multiple regression analysis methods: The case of Aksaray, Bahçesaray and Kırımlı rural districts
Authors: KARATAŞ Kamil , KARADUMAN Hakan
Published: January 2023

This study aims to determine the market value of the agricultural lands in the rural Bahçesaray and Kirimli districts, which are outside the zoning plan, where agricultural production continues in Aksaray Province, Central District, in Turkey, by mass valuation methods. It is also aims to provide value estimation and value map production with the help of geographic information systems (GIS). Using the sales data from 125 parcels in the study area, the market value of the real estates for which the value is unknown in the region, was estimated. The most frequently used criteria in the assessment of agricultural lands were determined, and the valuation was carried out with Multiple Regression Analysis (MRA) and Artificial Neural Networks (ANN). By means of the assessment and the valuation study, the performance of the valuation methods was compared, and it was determined that the best result according to the test data was the valuation with ANN. In the performance analysis conducted with ANN, the Coefficient of Determination (R²)=0.87, Mean Absolute Percentage Error (MAPE)=0.192, Mean Absolute Error (MAE)=0.047 and Root Mean Square Error (RMSE)=0.059 was found. Moreover, according to the proportional standards guide determined by the International Association of Assessing Officers (IAAO), the performance measurement, the values derived for the Coefficient of Dispersion as (COD)=19.58 and Price-Related Differential as (PRD)=1.02 were also found to be within acceptable limits. Since the valuation of agricultural lands is a less studied subject, there are few articles in the literature. For this reason, it will be useful to increase such as article and evaluate the results applying it region by region. In this study, estimates were found with MRA and ANN methods and value maps were created.
The nature of working in the hospitality industry: Evidence from 3S tourism in Turkiye
Authors: ÇIVAK Bariş
Published: January 2023

The main purpose of the study is to reveal the nature of working in the field of hospitality by examining employment forms and working conditions. For this purpose, the phenomenology approach was used and interviews were conducted with 32 hotel employees. According to the findings, it has been seen that temporary and precarious forms of employment are common in tourism. It is seen that the employees are kept under strict control for efficiency and productivity and the managerial hegemony is at a significant level. In addition, it has been determined that due to standardization and simplification in hotel businesses with an all-inclusive system, employees are deprived of their qualifications and become unqualified.
Solomon-Greenwell as the most accurate nomogram for female bladder outlet obstruction
Authors: Castro-Nuñez Patricia , Noyola-Ávila Immer , Maldonado-Alcaraz Efraín , Moreno-Palacios Jorge , Terán Amaya Frida Sofia
Published: January 2023

Objective: Compare the different definitions for bladder outlet obstruction (BOO) in women with dysfunctional voiding (DV) and find which one is most specific and sensitive to achieve a more accurate diagnosis.
Materials and methods: a cross-sectional study of urodynamic investigation studies from women diagnosed with DV in a period from 2017-2020 were classified with obstruction or not, based on 6 urodynamic definitions. The control group were women who had been categorized with obstruction in at least 3 definitions of BOO. Likelihood positive ratio, sensitivity and specificity were calculated. Statistical analysis was carried out by IBM SPSS Statistics for Windows, Version 23.0 (IBM Corp., Armonk, N.Y., USA). A p <0.5 was considered statistically significant.
Results: A total of 146 urodynamic studies were analyzed, Solomon-Greenwell nomogram has shown to be the more specific and sensitive, obtaining the highest likelihood positive ratio >100. The comparison between our the control group and every definition of BOO has shown statistically significant difference.
Conclusions: The diagnosis of BOO requires expertise and individualization, it is made through physical examination and complete urodynamic study, Solomon-Greenwell has shown to be specific and sensitive for diagnosis in the group of study.
Leiomiomas del tracto urinario: neoplasias infrecuentes con un relevante diagnóstico diferencial
Authors: Sarabia-Ochoa Rosalía , García De La Torre Juan Pablo , Armas-Álvarez Azucena Lirio
Published: January 2023

Caso clínico: Los leiomiomas son tumores mesenquimatosos benignos que infrecuentemente afectan el tracto urinario. El uso generalizado de estudios de imagen ha aumentado su detección en pacientes asintomáticos.
 
Materiales y métodos: Análisis descriptivo de pacientes con diagnóstico histopatológico de leiomioma del tracto urinario en el Hospital General de Albacete entre los años 2012 a 2020.
 
Resultados: Identificamos 7 casos (2 leiomiomas renales, 4 leiomiomas vesicales y 1 caso con leiomioma vesical y renal). En el leiomioma renal la edad media fue de 79 años y en el vesical de 39 años. La mayor parte de los leiomiomas renales se presentaron como un hallazgo incidental en el estudio intraoperatorio de trasplante renal y los leiomiomas vesicales como un hallazgo radiológico. Hubo predominio del sexo femenino en ambos tipos de leiomiomas. El tamaño medio tumoral renal fue de 0.73 cm y en el vesical de 4.75 cm. El diagnóstico definitivo se realizó mediante biopsia renal y biopsia-RTU vesical. Histológicamente estaban constituidos por células musculares lisas dispuestas en fascículos entrecruzados sin mitosis, atipia nuclear ni necrosis tumoral. La nefrectomía y la cistectomía parcial constituyeron el principal tratamiento quirúrgico. Durante el seguimiento no se detectaron recidivas.
 
Conclusiones: Los estudios de imagen nos orientan al diagnóstico, pero el diagnóstico definitivo precisa estudio histopatológico e inmunohistoquímico. El tipo de cirugía depende del tamaño y localización del leiomioma. Su pronóstico es bueno.
The Role and underlying mechanism of dental pulp stem cell-derived exosomal miR-31 in the treatment of osteoarthritis by targeting mTOR to enhance chondrocyte autophagy levels
Authors: Zhao Guanglei , Lyu Jinyang , Huang Xin , Huang Gangyong , Chen Feiyan , Wei Yibing , Wang Siqun , Xia Jun , Shi Jingsheng , Chen Jie
Published: January 2023

Introduction

Osteoarthritis is the most prevalent musculoskeletal progressive disease that leads to functional impairment and decreased quality of life. However, the current treatments remain unsatisfactory. Recent studies reveal that exosomes derived from mesenchymal stem cells offer a promising approach to improve the pathological changes of osteoarthritis, cartilage tissue, and chondrocyte homeostasis.

Material and methods

A randomized, controlled animal experiment was conducted on nine-week-old male C57BL/6 mice that were obtained from Charles River Laboratories. In this in vitro and in vivo study, we studied the role and mechanisms of dental pulp stem cell-derived exosomes (DPSCs-exosomes) on osteoarthritis in the mouse model.

Results

The study findings showed that a dental pulp stem cell could generate typical-characteristic exosomes. The injection of DPSCs-exosomes ameliorated destruction of cartilage, promoted matrix synthesis, inhibited cell apoptosis, and decreased the catabolic factors expression. However, this effect was shown to be almost eliminated when miR-31 antagomir was injected.

Conclusions

Furthermore, DPSCs-exosomes show an ability to promote autophagy in chondrocytes through mTOR inhibition, in addition to reducing the mTOR luciferase activity. The ability of DPSCs-exosomes to partially regulate autophagy was blocked upon inhibition of miR-31.
In brief, DPSCs-exosomes have a chondroprotective role in the mice osteoarthritis model. The underlying mechanism is possibly related to miR-31-mediated suppression of the mTOR-autophagy pathway.
Implantación de un modelo de control de gestión en «Pink Company»
Authors: Santamaría Ulloa Montserrat
Published: January 2023

Todos los que hemos estudiado en escuelas de negocio o somos lectores asiduos de artículos de gestión hemos desarrollado una particular, llamémosle «fe», en las técnicas y herramientas que se tratan en ambos foros, que habitualmente son las «best practices» más avanzadas y que instintivamente utilizamos como mantas de seguridad en nuestras empresas. En el ámbito de control de gestión, el área staff por excelencia más cerca de la Alta Dirección, esto es especialmente patente en la construcción de modelos y sistemas.
En este artículo se presenta el proceso real de construcción de un modelo de control de gestión en la filial española de una compañía multinacional de consultoría especializada en marketing. Por supuesto, se encontrarán muchos ingredientes y «matrices» que se asemejan a las best practices, sin embargo no pretende ser un ejemplo paradigmático y ortodoxo de aplicación de las mismas. Su objetivo es hacer reflexionar al lector, y especialmente al Controller, sobre qué modelo construir en función de los objetivos a lograr y qué pasos dar, partiendo desde el contexto general y concreto de la compañía, las etapas en función del nivel de madurez de la organización desde el punto de vista de control, así como los factores claves de éxito a tener en cuenta.
Embedding Undergraduate Research through Industry-Based Projects: Student Experiences
Authors: Naicker Ashika , Singh Evonne
Published: January 2023

Traditionally, formal training in research methodology begins in postgraduate programmes; however, potential exists to embed high-impact research skills during the undergraduate programme to develop learning by inquiry, promote critical thinking and engaged scholarship. Identifying factors that affect students’ research engagement can provide important practical implications to effectively encourage and facilitate undergraduate research opportunities. This exploratory qualitative study investigates the undergraduate student experience of the research process embedded in collaborative industry-based projects, through focus-group discussions. Using predefined domains from the interview guide, key themes emerging from the focus group discussions included a process-orientated approach to research, knowledge synthesis through data collection and handling, data collection experience, and students’ experience of a collaborative and deep approach to learning. Key enablers of the research engagement included funding for undergraduate research and transfer of research skills into higher levels of study whilst key barriers were limited foundational research knowledge and the impact of emergency situations. Other emergent themes included knowledge transfer through early initiation of research in the undergraduate programme. Institutional and programmatic engagement is required to support undergraduate students with the rigours of becoming knowledge co-constructors for their graduate destination or continuation of postgraduate studies.
Assessing the Impact of Undergraduate Research on Graduate Attributes Development: A Case Study of DUT Civil Engineering Students
Authors: Ngubane Zesizwe , Hay Shanley , Adedeji Jacob
Published: January 2023

Engineering graduates are expected to demonstrate competence after their engineering programmes in the form of graduate attributes (GAs) prescribed by the Engineering Council of South Africa (ECSA). It has, however, been challenging to develop and assess these attributes, especially using conventional assessment or examination methods, just as on the global scale. Nevertheless, studies have demonstrated that undergraduate research enables students to develop independent critical skills, as they do in graduate studies, by identifying a problem that needs to be solved. Undergraduate research has not been widely explored as a tool in developing and accessing GAs in engineering students. This study examined the impact of undergraduate research in engineering student ECSA GAs development and assessment using a case study of civil engineering diploma students. Therefore, using purposeful quantitative sampling methods, first-year and second-year diploma students were interviewed on their experience with newly introduced undergraduate research. Observations of the students' responses indicated that students' understanding, and views of GAs do improve from the first year to the second year due to continuous exposure to research. A conceptual model for assessing and developing GAs among engineering students is proposed in this study. This conceptual framework can assist in the further development of strategies in the implementation of undergraduate research at universities of technology.
Model Alternatif Konstruksi Pengetahuan Bencana di Masyarakat
Authors: Larobi Aulian Milki Toha , Lily Handayani Baiq
Published: January 2023

Indonesia is one of the countries prone to natural disasters. This raises public anxiety about the coming disaster which sometimes cannot be predicted with certainty. In some communities, the construction of knowledge about disasters is still considered as a destiny that is felt to be unavoidable. Such belief in destiny reduces efforts to take preventive measures against the impact of disasters. Furthermore, there is a building on the importance of disaster knowledge through various alternatives. This study aims to explain various alternative methods of disaster knowledge construction. The method used in this study is library research, which is a technique carried out by reviewing various related literatures that are relevant to the discussion. The results of the study show that currently there are several alternative models that can construct public knowledge about disasters, including local wisdom, disaster education, disaster socialization, and the use of social media. The disaster knowledge construction base is explained with different conditions in each area to be compared. These various sources of knowledge can still be developed and can be used as a framework for developing transformative steps, as an effort to improve the character of community preparedness for disasters.

Indonesia merupakan salah satu negara yang rawan mengalami bencana alam. Hal ini menimbulkan kecemasan masyarakat akan datangnya bencana yang terkadang tidak bisa terprediksi secara pasti. Pada sebagian masyarakat, konstruksi pengetahuan mengenai bencana masih dalam anggapan sebagai suatu takdir yang dirasa tidak dapat dihindari. Kepercayaan mengenai takdir yang seperti demikian membuat berkurangnya usaha untuk melakukan langkah preventif dampak bencana. Selanjutnya, mulai terdapat bangunan atas pentingnya pengetahuan kebencanaan melalui berbagai alternatif. Kajian ini bertujuan untuk menjelaskan berbagai alternatif metode konstruksi pengetahuan bencana. Metode yang digunakan dalam kajian ini adalah studi kepustakaan atau library research, yaitu teknik yang dilakukan dengan mengkaji berbagai literatur terkait yang relevan dengan pembahasan. Hasil penelitian menunjukkan bahwa saat ini terdapat beberapa model alternatif yang dapat mengkonstruksi pengetahuan masyarakat terhadap bencana, antara lain kearifan lokal, pendidikan kebencanaan, sosialisasi kebencanaan, serta pemanfaatan media sosial. Basis konstruksi pengetahuan bencana dijelaskan dengan kondisi yang berbeda pada setiap daerah yang akan dikomparasikan. Ragam sumber pengetahuan tersebut masih bisa berkembang dan dapat menjadi kerangka dalam menyusun langkah-langkah transformatif, sebagai upaya meningkatkan karakter kesiapsiagaan masyarakat terhadap bencana.
Universal Health Coverage and Utilization of BPJS’s Health Insurance Service in Ponorogo Regency
Authors: Utomo Eko Priyo , Suryantoro Agustinus , Mafruhah Izza
Published: January 2023

Universal Health Coverage (UHC) is the primary goal of the Indonesian government, that needs to be realized by health service providers in every district and city in Indonesia, including Ponorogo Regency. This study aims to identify UHC and the Utilization of BPJS Healthcare in Ponorogo Regency. This study employs a qualitative methodology based on in-depth interviews with informants serving as research respondents. Data analysis employs content analysis by collecting data, reducing data, and displaying data. The results of the study indicate that Ponorogo Regency is still not included in the UHC category, as only 76.9% of the total population participates in the BPJS Healthcare program. In addition, the government's lack of support for universal health care. This is evident from the fact that the budget has remained unchanged for three fiscal years and there are no local regulations governing health insurance in the region.
The Effect of Financial Literacy, Financial Inclusion, Peers and Pocket Money on Student Saving Behaviour at Universities in Yogyakarta
Authors: Kinayung Dwike Anggun Wendar , Sutrisno
Published: January 2023

One of the good qualities that students should have is to get used to being frugal by saving. This study aims to analyze the factors that influence saving behavior. The factors that are thought to influence the saving behavior of students to be studied are financial literacy, financial inclusion, peers and pocket money. The population in this study were students of private universities in Yogyakarta. Samples were taken by purposive sampling of 100 students from various private universities. Collecting data using a questionnaire that is distributed online using a google form. The analytical technique used to test the hypothesis is the multiple regression method which is processed with the SPSS version 25 data processing program. The results show that (1) financial literacy has a significant effect on the saving behavior of private university students in Yogyakarta, (2) financial inclusion has a significant effect on significant effect on the saving behavior of private university students in Yogyakarta, (3) peers significantly influence the saving behavior of private university students in Yogyakarta, and (4) pocket money significantly affects the saving behavior of private university students in Yogyakarta
Inequality, Labour Markets, Economic Development and the Impact of Migration on Poverty, Patterns and Employment Dynamics in India - A Macroeconomic Development Viewpoint
Authors: Dr. G.YOGANANDHAM
Published: January 2023

The Indian economy has experienced a transformation from a mixed-plan economy to an emergent middle-income social market economy with strong state intervention in major industries. India has experienced very low average growth rates and significant annual swings since gaining its independence in 1947. The growth rate of India was marginally greater in the 1990s than in earlier decades, and it has since increased. Together with China, its expansion is the key engine powering the continuous expansion of the world economy. Its previous enormous boom contrasts sharply with its current modest growth of 8%. Growth rates and per capita income disparities are much higher in developing countries than in the global average. Low employment growth and the subcontracting of labour to unorganised industries are the causes of the significant decline in the share of workers in the organised sector. This type of employment growth has a small but comparatively significant impact on lowering rural poverty. This research is descriptive and has theoretical foundations. The analysis of theories and points of view in this study is organized. The compilation of secondary data involves using a variety of reports, both published and unpublished. This research article's overall goal is to assess the theoretical aspects of inequality, labour markets, economic development, and the impact of migration on poverty, patterns, and employment dynamics in India from the perspective of macroeconomic development in order to foster human growth in the context of a globalised economy. The research article raises a significant, urgent, and contemporary subject that is relevant from a social, political, and economic point of view.
The General Election Commission Strategy Through Election Education in Increasing the Political Participation of Beginners Voters Based on Democracy Principles
Authors: Sari Soleha Rully , Widodo Urip , Sophia Erna , Rahimia Nisa , Akbar Hayat Muzahid
Published: January 2023

This study aims to examine the General Election Commission’s strategy through election education in increasing the political participation of new voters based on democratic principles. This research is a qualitative research. The results of this study indicate that novice voters basically need political education regarding elections held by the General Elections Commission. The condition of beginner voters who are still vulnerable to negative politics is certainly an obstacle to the progress of democracy in Indonesia. With political education for novice voters, of course the expected results at the upcoming democratic party in 2024 are increasing awareness of young voters.
Analisis Praktik Trading Saham Syariah Dalam Perspektif Hukum Ekonomi Syariah Studi Kasus IDX Kalimantan Selatan
Authors: Pajrianor Muhammad , Komarudin Parman , Hani Umi
Published: January 2023

This study aims to analyze the practice of Islamic stock trading in the perspective of Islamic economic law, the case study of the Indonesia Stock Exchange (IDX) in South Kalimantan. Using a qualitative research method with a field research approach, the author goes directly to the field to conduct observations and interviews related to the Islamic stock trading system at Indonesia Stock Exchange (IDX) South Kalimantan. The object of this research is Sharia Stock Trading Practice. The results of this study are that the mechanism for trading in Indonesian sharia shares uses Sharia Online Trading System (SOTS), namely the omission of the Margin Trading and Short Selling features and the shares traded may only be shares included in the Sharia Securities flat. Even though using the Sharia Online Trading System (SOTS) system, it turns out that several transaction activities that are prohibited by Fatwa of the National Sharia Council of the Indonesian Ulema Council include fake demand/supply, pump and dump, cornering, heading behavior and fear of missing out. In the perspective of sharia economic law, the practice of trading sharia shares in a trading mechanism has been declared to be in accordance with the findings of several prohibited practices, which can make the practice of trading sharia shares inconsistent with the principles of sharia economic law.
TOWARD A MODEL FOR THE POLICY REQUIREMENTS OF TECHNOLOGICAL ENTREPRENEURSHIP IN THE URBAN ECOSYSTEM
Authors: Ghorashi S. S. , Hejazi S. R.
Published: January 2023

Abstract. Cities are creating an environment that encourages digital growth. Cities' capacity to facilitate and drive the development of a digital core system emphasizes the importance of cities in creating the necessary circumstances for a successful ecosystem - access to talent, access to funding, access to spaces and locations, and access to markets. Cities, on the other hand, can contribute to the creation of mechanisms such as co-investment funds, in which the public and private sectors share the risk of supporting new creative businesses. All facets of manufacturing, consumption, and regulatory services could be altered by digital technologies in our everyday routines. By introducing new capabilities and business models, as well as by influencing their environment and the policy systems that govern them, they will have a significant impact on entrepreneurial ecosystems. In this article, we are looking for a model that may express policy requirements of technological entrepreneurship in the urban ecosystem, based on knowledge about the importance and requirements of entrepreneurial ecosystems, the smart city approach, and the knowledge-based development strategy.A comparative analytical method was used to conduct this research. We review the literature on ecosystems, urban ecosystems, smart cities, and knowledge-based urban development. Then we classified the parameters of each and presented how these categories are related in a model. In the research literature and in an executive position, dealing with a model for the policy requirements of technological entrepreneurship in the urban ecosystem can explain how the issue is operationalized, examine the city as an ecosystem where entrepreneurship occurs through technology and requires its own policies.
AN OBJECT-BASED CLASSIFICATION FRAMEWORK FOR ALS POINT CLOUD IN URBAN AREAS
Authors: Hasanpour Zaryabi E. , Saadatseresht M. , Ghanbari Parmehr E.
Published: January 2023

Abstract. This article presents an automated and effective framework for segmentation and classification of airborne laser scanning (ALS) point clouds obtained from LiDAR-UAV sensors in urban areas. Segmentation and classification are among the main processes of the point cloud. They are used to transform 3D point coordinates into a semantic representation. The proposed framework has three main parts, including the development of a supervoxel data structure, point cloud segmentation based on local graphs, and using three methods for object-based classification. The results of the point cloud segmentation with an average segmentation error of 0.15 show that the supervoxel structure with an optimal parameter for the number of neighbors can reduce the computational cost and the segmentation error. Moreover, weighted local graphs that connect neighboring supervoxels and examine their similarities play a significant role in improving and optimizing the segmentation process. Finally, three classification methods including Random Forest, Gradient Boosted Trees, and Bagging Decision Trees were evaluated. As a result, the extracted segments were classified with an average precision of higher than 83%.
INTERFEROMETRIC PROCESSING OF A DEVELOPED MIMO GBSAR FOR DISPLACEMENT MONITORING
Authors: Hosseiny B. , Amini J. , Aghababaei H.
Published: January 2023

Abstract. This study demonstrates the interferometric processing experiments of our developed multiple-input multiple-output ground-based synthetic aperture radar (MIMO GBSAR) system. GBSAR systems are known as precise noncontact instruments for monitoring earth dynamics. In recent years W band MIMO radars have shown interesting potential in this field due to their low cost, compact size, and high phase sensitivity. MIMO capability enables the angular discrimination of multiple targets in the same range as the radar sensor. In our previous works, we developed a high-resolution MIMO GBSAR system based on the combination of MIMO radar and mechanical rail. Accordingly, this study investigates the developed system’s displacement monitoring capability by presenting a controlled experiment, using fixed and moving corner reflectors and gathering 36 time series of data. We compare and discuss the results obtained from MIMO GBSAR and MIMO radar configurations. The results show that our developed system highly agrees with MIMO radar’s interferometric measurements while providing a better target discrimination capability and higher signal noise ratio.
GNSS-DERIVED PRECIPITABLE WATER VAPOR MODELING USING MACHINE LEARNING METHODS
Authors: Izanlou S. , Amerian Y. , Seyed Mousavi S. M.
Published: January 2023

Abstract. Atmospheric water vapor plays a vital role in phenomena related to the global hydrological cycle and climate changes, and its Spatio-temporal modeling and prediction help to identify and predict climatic phenomena. Accordingly, in this study, hourly precipitable water vapor (PWV) data sets for 27 stations receiving Global Navigation Satellite Systems (GNSS) observations in one month and machine learning methods were used to estimate PWV. Machine learning methods used in this study 1. Random Forest Regression (RFR) method 2. Extreme Gradient Boosting Regression (XGBR). The root mean square error (RMSE) in PWV estimation with the RFR method (RFR PWV) is 2.42 mm, and in PWV estimation with the XGBR method (XGBR PWV) is 2.75 mm, and the R-squared (R2) of the RFR method is 0.74, and for the XGBR method, these values are equal to 0.71. The obtained results show the efficiency and accuracy of both models in estimating PWV, which shows that machine learning methods have been able to recognize the behavior and changes of precipitable vapor in a small spatial and temporal interval. Although both ways had high accuracies, the RFR model performed slightly better and had better accuracy than the XGBR model.
PREDICTION OF FLOOD IN KARKHEH BASIN USING DATA-DRIVEN METHODS
Authors: Kamali S. , Saedi F. , Asghari K.
Published: January 2023

Abstract. Flood causes several threats with outcomes which include peril to human and animal life, damage to property, and adversity to agricultural fields. Hence, flood prediction is highly significant for the mitigating municipal and environmental damage. The aim of this study was assessing the performance of different machine learning methods in predicting flood in Karkheh basin. To aim this, we used Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM), Feed Forward Back Propagation Neural Network (FFBPNN), and Radial Basis Function Neural Network (RBFNN) to simulate monthly streamflow in the study area. Furthermore, the performance of models was compared in predicting flood. All four models indicated good performance in simulating streamflow. However, LSSVM model had the highest accuracy compared with other models with R2 and RMSE of 85.89% and 30.02 m3/s during testing periods, respectively. Similarly, LSSVM model performed better in predicting annual maximum streamflow in comparison with other machine learning models.
A SPATIAL PLANNING SUPPORT SYSTEM FOR WIND FARM CONSTRUCTION WITH MACRO AND MICRO PERSPECTIVES
Authors: Kamkar K. , Motieyan H.
Published: January 2023

Abstract. Wind energy is well-known Renewable energy that contributes to countries achieving their sustainable development goals, so governments have planned to increase electricity generation from wind energy in recent years. Selecting suitable places for installing wind farms is one of the most challenging parts of wind energy projects because of the necessity of identifying various parameters, which is considered a site selection problem and needs spatial planning to solve. Furthermore, in constructing a wind farm, finding the optimum placement of wind turbines, known as the wind farm layout optimization (WFLO) problem, is crucial for obtaining maximum total power capacity while minimizing the number of turbines installed. In this research, the analysis is divided into two macro and micro levels to find an efficient and comprehensive approach. In the first level, after removing the restricted area, the MCDM method is used to find the most suitable sites, considering economic, social, and environmental criteria. After selecting one of the most suitable areas located in the middle of Alborz province, the GA algorithm as a meta-heuristic method is employed to solve the WFLO problem at the micro level. The essential criterion in this level is the wake effect among turbines which is simulated according to the Jensen model. As a test case, the selected area was subdivided into 144 cells that, after micro siting, 28 V47-660-45 turbines were placed, which could have a total power of 8732.283 kW capacity.
EVALUATION OF RGB VEGETATION INDICES DERIVED FROM UAV IMAGES FOR RICE CROP GROWTH MONITORING
Authors: Kazemi F. , Ghanbari Parmehr E.
Published: January 2023

Abstract. The unmanned aerial vehicles (UAVs) are widely used for agricultural monitoring due to reduce the cost and time of crop monitoring via the acquisition of images with high spatial-temporal resolution. The normalized difference vegetation index (NDVI) is the most widely studied and used for mapping crop growth. A relatively expensive multispectral sensor is required to produce an NDVI map. The visible vegetation indices (VIs) derived from UAV images showed potential capabilities for predicting crop growth. The purpose of this paper is to evaluate the RGB indices to monitor the growth of the rice crop. The images were obtained from the study area by DJI PM4 multispectral UAV. The multispectral images were used to calculate NDVI as a reference vegetation index and different RGB indices were implemented and compared with the reference index. The results showed that RGB indices can be used as the vegetation index in the case of unavailable multispectral images.
MEASURING THE SPATIAL SIMILARITIES IN VOLUNTEERED GEOGRAPHIC INFORMATION
Authors: Mahmoody-Vanolya N. , Jelokhani-Niaraki M. R.
Published: January 2023

Abstract. Volunteered citizens have the potential to be used as social and distributed sensors, monitoring their surroundings and producing and sharing massive amounts of geographic data. The degree of spatial similarities in Volunteered Geographic Information (VGI) somehow indicates an index for the accuracy and precision of user-generated spatial data. In other words, the spatial similarities in VGI refer to how close a citizen-generated spatial feature is to the true (or accepted one) or how close the citizen-generated spatial features of the same geographic phenomenon are to each other. The present study aims at developing a Web-based GIS tool to collect VGI and extract the spatial similarity indexes. To this end, a case study involving the identification of optimal areas for restaurants in Babolsar, Mazandaran province was used. The degree of similarity between the areas (polygons) proposed by citizens was then investigated using spatial indicators of intersection and minimum central distance. The results show that with the increase in the frequency of citizen-generated polygons, the geometric dispersion of the polygons decreases, and the similarity of citizens’ polygons to establish a restaurant increases. With the increasing agreement, the amount of standard deviation in the area, perimeter, and minimum central distance of intersection areas reduces from 4645 to 15.4, 134.5 to 21.6, and 42.4 to 4.2, respectively.
A METHOD FOR DETECTION OF DOORS IN BUILDING INDOOR POINT CLOUD THROUGH MULTI-LAYER THRESHOLDING AND HISTOGRAM ANALYSIS
Authors: Akhoundi Khezrabad M. , Valadan Zoej M. J. , Safdarinezhad A.
Published: January 2023

Abstract. Indoor navigation is a critical service providing safe paths for humans in an emergency. Since doors connect different parts of a building, door detection is essential in creating a navigation map and walkable spaces. Considering the Manhattan World Assumption (MWA), this paper proposes a method for detecting doors that connect two scanned rooms. In contrast with most existing approaches, the proposed method requires neither trajectory nor scanning position. This method consists of two main steps. At first, with the help of multi-layer thresholding, a raster will be created from the point cloud that its Digital Numbers (DNs) correspond to the ceiling elevation. Then, this raster's pixels will be segmented based on their DNs, and those segments whose elevations are local minimums are chosen as door candidates. The second step extracts the part of the point cloud corresponding to each door candidate and analyses its coordinates components' histograms to decide whether there is a door or not. The proposed scheme has been tested on two different datasets and could accurately detect 91% of the inner doors. Although this method is designed to detect inner doors, it also detected 65% of marginal doors.
SEMANTIC SEGMENTATION OF UAV IMAGES BASED ON U-NET IN URBAN AREA
Authors: Majidizadeh A. , Hasani H. , Jafari M.
Published: January 2023

Abstract. Semantic segmentation of aerial data has been one of the leading researches in the field of photogrammetry, remote sensing, and computer vision in recent years. Many applications, including airborne mapping of urban scenes, object positioning in aerial images, automatic extraction of buildings from remote sensing or high-resolution aerial images, etc., require accurate and efficient segmentation algorithms. According to the high potential of deep learning algorithms in the classification of complex scenes, this paper aims to train a deep learning model to evaluate the semantic segmentation accuracy of UAV-based images in urban areas. The proposed method implements a deep learning framework based on the U-Net encoder-decoder architecture, which extracts and classifies features through layers of convolution, max pooling, activation, and concatenation in an end-to-end process. The obtained results compare with two traditional machine learning models, Random Forest (RF) and Multi-Layer Perceptron (MLP). They rely on two steps that involve extracting features and classifying images. In this study, the experiments are performed on the UAVid2020 semantic segmentation dataset from the ISPRS database. Results show the effectiveness of the proposed deep learning framework, so that the U-Net architecture achieved the best results with 75.15% overall accuracy, compared to RF and MLP algorithms with 52.51% and 54.65% overall accuracy, respectively.
THE LARGE-SCALE WILDFIRE SPREAD PREDICTION USING A MULTI-KERNEL CONVOLUTIONAL NEURAL NETWORK
Authors: Marjani M. , Mesgari M. S.
Published: January 2023

Abstract. In the last twenty years, destructive wildfires have affected the environment to the tune of billions of dollars. An accurate model is crucial for predicting the spreading of wildfires in a variety of conditions. In this study, a multi-kernel convolution neural network (CNN) deep learning model was proposed based on elevation, wind direction, and speed, minimum and maximum temperatures, humidity, precipitation, drought index, normalized difference vegetation index (NDVI), and energy release component to predict wildfire spread across the United States. Using multi-kernel CNN, it is possible to predict whether a pixel will be on fire at a future time. Compared to the model presented by other authors, the multi-kernel CNN model achieved high accuracy and F1 score. In comparison with CNNs without a multi-kernel mechanism and fixed kernel size, the proposed model predicted more accurate results based on the test data set. The multi-kernel CNN model reached an overall accuracy of 98.6 and F1 score of 70.97 on test data.
EFFICIENCY OF MACHINE LEARNING ALGORITHMS IN SOIL SALINITY DETECTION USING LANDSAT-8 OLI IMAGERY
Authors: Alamdar S. , Ghazban F. , Zarei A.
Published: January 2023

Abstract. Climate change is one of the biggest problems facing today’s world. Rising temperatures and declining rainfall have had a profound effect on the planet, one of which is the destructive effects of soil salinity. Soil salinity phenomena commonly occur in arid and semi-arid regions. Maharloo Salt Lake, southeast of Shiraz, Iran, with an arid and semi-arid climate, has faced severe droughts in the past and is dealing with the soil salinity problem. One useful way to manage land and soil in such areas is regular monitoring of the soils and lands and keeping abreast of changes to prevent land degradation and erosion. With the advancement of technology, remote sensing techniques to monitor natural factors have become very popular. Landsat sensor images were used in this research, and several environmental indicators were extracted by combining satellite bands. Three machine learning algorithms, RF, GBM, and MLP, were used to evaluate methods for monitoring and mapping saline soils. The models were trained and then tested to compare the accuracy and performance of each model in predicting soil salinity. GBM algorithm showed the best performance with R2 = 0.89 and RMSE = 0.63 for testing the dataset after that RF model with R2 = 0.85 and RMSE = 0.71 and the worst performance was for MLP model with R2 = 0.75 and RMSE = 0.88. The figures mapped from the results of these algorithms for salinity distribution in this region showed that by choosing the appropriate algorithm and suitable in-situ data, it could be possible to estimate soil salinity to an excellent extent by satellite data.
IDENTIFICATION AND SEPARATION OF RICE FIELDS USING REMOTE SENSING
Authors: Mirzapour S. , Alemi Safaval P. , Karimi S. , Behzadi S. , Kheirkhah Zarkesh M. , Zavar H. , Hashemi Ashka S. H.
Published: January 2023

Abstract. Rice is one of the main crops that plays an important role in world food security. Due to the suitable climate in the north of Iran, rice is the dominant crop of farmers in this region. In this study, rice paddies were identified and separated by using the relationship between NDVI and LSWI2105 from the OLI sensor in the year 2014. To identify suitable fields, an algorithm was presented to identify the paddy fields of waterlogged soils during the stages of transplanting. According to the climate of the region, the factors of evergreen covers, water bodies, and altitude classes were identified. Finally, a spatial distribution map of rice paddies was created. 56 control points were used in rice and non-rice fields to evaluate the work. The results showed that the spatial distribution map of rice paddy fields for Amol city has an overall accuracy of 83.6364% and a kappa coefficient of 0.8108.
MONITORING GROUND DEFORMATION OF MUD VOLCANOES USING RADAR INTERFEROMETRIC METHOD (SBAS) AND THERMAL DATA CASE STUDY: THE SOUTH-EASTERN PART OF THE CASPIAN SEA
Authors: Amini L. , Fathallahzadeh M. , Kakroodi A. A.
Published: January 2023

Abstract. Mud volcano, as the most attractive geomorphological natural phenomena, includes the internal resources such as water sources or hydrocarbon reservoirs such as oil and gas fields. Therefore, monitoring their deformation and temperature anomaly has some important information of subsurface activity and mud-volcano structure. Interferometric synthetic aperture radar (InSAR) has shown to be an effective instrument for calculating mud volcano deformation. The purpose of this study is to monitor the deformation of the mud volcanoes and also its characteristics to thermal anomaly using SBAS method by ENVISAT (C-band) radar satellite images from 2003 to 2011. Therefore, the Landsat-8 image was used to retrieve the land surface temperature (LST). The results show the most surface deformation belongs to the mud volcanoes located in the northern part of Golestan Plain, and decreased towards the southern mud volcanoes. Moreover, the highest temperature belongs to Ounegh Yilghay and Sofikam mud volcanoes, but they indicate up-lifting (207 mm) and down-lifting (−14.64 mm) respectively.
OPTIMIZATION OF THE SPARSE REPRESENTATION PARAMETERS FOR THE FUSION OF REMOTELY SENSED SATELLITE IMAGES
Authors: Asefpour Vakilian A. , Saradjian M. R.
Published: January 2023

Abstract. Image fusion methods are widely used in remote sensing applications to obtain more information about the features in the study area. One of the recent satellite image fusion techniques that can deal with noise and reduce computational cost and deal with geometric misregistration is sparse representation model. The important part of creating a generalized sparse representation model for satellite image fusion problems is defining initial constraints and adjusting the corresponding regularization coefficients. Regularization coefficients play an essential role in the performance of the sparse representation model and convergence of the optimization solution. Also, the number and size of sub-images extracted from the dictionary matrix in the sparse representation model, and the number of iterations of the optimization step are important in building a sparse representation model. Therefore, in this research, the four parameters that affect the performance of the sparse representation model were investigated: the number of sub-images, the size of sub-images, regularization coefficients, and the number of iterations. Results obtained from pan-sharpening of OLI-8 images showed that optimal values for the number and size of sub-images, regularization coefficients, and the number of iterations were equal to 150, 9×9 pixels, 10-4, and 4 respectively. Results from this study can be generalized to other satellite image fusion problems using sparse representation models.
A COMPARISON OF SIMULATED RUNOFF BASED ON GROUND RAIN GAUGES AND PERSIANN-CDR SATELLITE PRECIPITATION RECORDS USING SWAT MODEL
Authors: Barezaei A. , Jalali J.
Published: January 2023

Abstract. Easy access to valid climatic data has always played a fundamental role in progressing hydrological studies. That is why numerous satellite-based precipitation products (SPPs) have been generated in the contemporary era. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) is considered one of the most popular climatic databases which started to produce daily rainfall data with 0.25° × 0.25° temporal and spatial resolutions in 1983. The aim of this research is to evaluate how well PERSIANN-CDR has performed in a rainfall-runoff modeling application over the period of 1994 to 2015. In this regard, using Soil & Water Assessment Tool (SWAT), two rainfall-runoff models based on Ground-based Rain Gauge stations (GRGs) and PERSIANN-CDR precipitation records were developed for the Chelgerd sub-basin, which is the main branch of the Zayandeh-Roud Basin in central Iran, in order to analyze how accurate the simulated runoff by PERSIANN-CDR database is. Comparing the developed SWAT model calibration results using the satellite database precipitation (NS = 0.78, P-Factor = 0.52, and R-Factor = 0.41) to calibration results of the developed model based on GRGs (NS = 0.81, P-Factor = 0.54, and R-Factor = 0.42) showed that although PERSIANN-CDR precipitation magnitudes were typically less than GRGs records, accuracy indicators of simulated runoffs to Ghale-Shahrokh were almost the same.
Reflexões Sobre Gêmeos Digitais (GD) No Ambiente Construído
Authors: Marques Sarah Yasmin Pereira , Gonçalves Pedro Henrique , Schaitl Souza Pedro
Published: January 2023

O diálogo entre os avanços tecnológicos e a Arquitetura e o Urbanismo conduziu ao aprimoramento e à criação de novas ferramentas de difusão e aquisição de informações em diversas escalas. Estas informações passaram a agregar aos processos de projeto e às análises que auxiliam nas tomadas de decisões. O conceito de Gêmeos Digitais (GD) apresenta uma conexão entre os objetos físicos e suas representações virtuais, ganhando cada vez mais espaço em diferentes áreas. Com o cenário crescente de dispositivos conectados à internet, o paradigma da Internet das Coisas passou a ter grande relevância dentro deste conceito, alimentando os GD com dados gerados por dispositivos instalados nas edificações, o que permite o acompanhamento das alterações de modo quase simultâneo. O objetivo deste trabalho foi observar a perspectiva de uso dos Gêmeos Digitais com a Internet das Coisas no contexto do ambiente construído na busca da identificação do nível de maturidade da aplicação ao uso do conceito de maneira mais ampla na acelerada evolução tecnológica.