A Prediction of Future Land Use/Land Cover in Amman Area Using GIS-Based Markov Model and Remote Sensing

The paper aims to analyze land use/land cover (LULC) changes in western part and the populated area of Amman governorate and to identify the process of urbanization and urban expansion within the study area for the period of 1984-2014. It also aims to predict future LULC map for the year 2030 using Markov Model to provide city planners and decision makers with information about the past and current spatial dynamics of LULC change and strictly urban expansion towards successful management and better planning in the future. Images from Landsat 5-TM for the years 1984, 1999 and from Landsat 8-OLI for the year 2014 were used to investigate LULC within the study area during 1984-2014 and the resulted LULC maps in 1999 and 2014 were used to predict future LULC map based on Markov Model. The results indicated that the urban/built up area expanded by 147% during the period from 1984 to 2014 and predicted to expand by 43.9% from 2014 to 2030 based on Markov model predictions. The areas in the western, northwest and southwest parts of Amman as well as the areas of Marka and Uhud, the northeast of the study area, were predicted to witness the major urban expansion in 2030. And these are the areas where city planners and decision makers should take into consideration in future plans of Amman. The urban expansion was mainly attributed to the high population growth rate and large number of immigrants from neighboring countries and other socio-economic changes.

Impact of Rapid Urbanization and Changing Housing Patterns on Urban Open Public Spaces of Amman, Jordan: A GIS and RS Perspective

Rapid urbanization and population growth of the Amman Area were combined with land resource degradation when the city was heading for mounting urbanization from the early 1950s. The deterioration of natural resources and green areas, was coupled with the creation of different urban public open spaces in the city. The transformation from large single-family houses to dense residential apartments was associated with social and behavioral changes among the inhabitants living in the dense apartments. Consequently, a large private sector has been developed to provide public and social spaces. Photo-interpretation and satellite images were used to map and characterize land use/cover changes have been occurred in the Amman area between 1953 and 2017. Maximum Likelihood Classification method was employed to identify land use/cover changes between 1986 and 20017, and GIS was utilized also to map examples of the recently emerged socio-economic open spaces. Excessive urban development in the last two decades, and the adoption of neo-liberal privatization policies by the government, enhanced social stratification and residential segregation. So, instead of encouraging outdoor activity and social interaction among all groups of Amman residents, the freedom of accessibility to major open spaces has been restricted within the same urban fabric, thus, the “two Ammans” paradigm, a “tale of two cities”, has been recently acknowledged.

Groundwater Vulnerability and Hazard Mapping in an Arid Region: Case Study, Amman-Zarqa Basin (AZB)-Jordan

The importance of groundwater portability and the possible sources of anthropogenic contamination have led to the development of intrinsic groundwater vulnerability mapping. In this study, groundwater vulnerability map for Amman Zarqa Basin (AZB) has been generated based on information derived and calculated from processed remote sensing information and laboratory analysis. The database was prepared from soil hydro geological and hydrological data, Digital Elevation Model (DEM), and geological maps. For assessment of groundwater vulnerability, the method proposed by the state geological surveys of Germany (GLA-method) has been adapted and applied. The vulnerability map shows about 77% which is about 2919 Km2 of the AZB is classified as very low to low which could be corresponding to the pollution sources due to the absence of potential hazards and also due to low vulnerabilities. These areas could consequently be interesting for future development as they set preferable in view of ground water protection. In addition, about 14% (530 km2) is classified within the moderate vulnerability zone. About 5% (around 19 km2) of the study area lies under the area of high vulnerability zone. Only 4% can be classified as very high risk areas. Groundwater quality results revealed that water leach ate from point source is the main cause for groundwater contaminations in highly vulnerable karstic limestone aquifer (Amman Wadi Es Sir Aquifer-B2/A7). On the other hand, the Kurnub Sandstone aquifer (K) is generally well protected in the central and eastern part of the AZB due to its thick cover of partly marly sequences. However, the Kurnub aquifer might have a potential risk from the recharged infiltrating surface water from the Zarqa River, which is highly polluted due to industrial activities located along the river.

Application of Remote Sensing and GIS for Modeling and Assessment of Land Use/Cover Change in Amman/Jordan

Modeling and assessment of land use/cover and its impacts play a crucial role in land use planning and formulation of sustainable land use policies. In this study, remote sensing data were used within geographic information system (GIS) to map and predict land use/cover changes near Amman, where half of Jordan’s population is living. Images of Landsat TM, ETM+ and OLI were processed and visually interpreted to derive land use/cover for the years 1983, 1989, 1994, 1998, 2003 and 2013. The output maps were analyzed by using GIS and cross-tabulated to quantify land use/cover changes for the different periods. The main changes that altered the character of land use/cover in the area were the expansion of urban areas and the recession of forests, agricultural areas (after 1998) and rangelands. The Markov chain was used to predict future land use/cover, based on the historical changes during 1983-2013. Results showed that prediction of land use/cover would depend on the time interval of the multi-temporal satellite imagery from which the probability of change was derived. The error of prediction was in the range of 2%-5%, with more accurate prediction for urbanization and less accurate prediction for agricultural areas. The trends of land use/cover change showed that urban areas would expand at the expense of agricultural land and would form 33% of the study area (50 km×60 km) by year 2043. The impact of these land use/cover changes would be the increased water demand and wastewater generation in the future.

The Problematic Build-pollution and Its Architectural Impact beyond the Architectural Uprising in Jordan （Case Study： Housing ＆ Commercial Buildings of Amman City）

By the adoption of architecture as a means for communication and discourse between the architect and the recipient, yet current arguments took the conscious of local architect away from the uniqueness and the concerns of the society, as well. It also dominated its creative and educational capabilities through making him distracted in formal non-rational overestimated compositions without dealing with the concerns and needs of the society and sympathize with its affection that led to the appearance of an intellectual crisis resulted from the loss of design strategy in the current trends among some Jordanian architects. As such, not only substantial amount of the leading architectural intellect had deteriorated to formal practices but also it took another approach, becoming incomplete intellectual isolation practicing an overestimated architecture that satisfies free markets requirements which appeared along with the globalization economy. This study attempts to comparatively investigate the variation in some architectural practices as a methodology based on design readings of the previous issues which were characterized of architectural uniqueness and current issues that lack of the existing intellectual references.

Amman＇s Land Typology： The Importance of Site Characteristics on the Delivery of Sustainable Buildings

Amman＇s land typology is characterized by hilly slopes, and this presents challenges and opportunities for architects and designers aiming at delivering sustainable buildings. The research focuses on the importance of any site＇s given criteria, mainly its slope and topography on the delivery of sustainable buildings. Amman city consists broadly of two main types of buildings, apartment buildings and villas, by studying each type of building with regard to its environmental context on a given site in the city, the research seeks to identify the sustainable variables that site topography delimit or facilitate, using a set of attributes for each building type. The main objective of this research is to highlight the sustainable approach for building on sloped sites throughout the building project life-cycle in general, and to set a sustainability framework for designers during the initial design phase in particular. A number of case studies for both types of buildings are studied and analysed, and conclusions are given based on syntheses of available data from literature review or case analysis. At the end, the research provides a mechanism for the development of guidelines for sustainable and passive viability on preferred buildings orientation in hilly areas with regard to local climatic data.

Large Interactive Media Display and Its Influence on Transformation Urban Spaces from Neglecting to Action: The Case of Al-Thaqafa Street in Amman City

In 21st?century, media become the most important factor affecting the development of urban cities, including public places. As a result of the digital revolution, re-imaging and re-linkage public places by media are essential to create more interactions between public spaces and users, interaction media display, and urban screens, one of the most important defined media. This interaction can transform the urban space from being neglected to be more interactive space with users, specially the pedestrians. This paper aims to identify the effects of these new digital factors to transform public spaces, and the influences of large media display on the interaction between urban spaces and pedestrians. The paper focuses on Al-Thaqafa Street as one of the neglected spaces in Amman city, and attempts to analyse this street, explains its problems, and studies the influence of these new digital factors on its transformation, to be more active and vital by pedestrians.

Feasibility of Hybrid Wind and Solar Energy System for Heating Residential Buildings in Amman-Jordan

A design of a solar-wind electrical hybrid system to supply space heating requirements for a 1,200 m^2 residential building in Amman-Jordan was implemented. The building heating requirements were estimated from existing heating building data based on traditional heating design already adopted by engineering firms in Jordan. The traditional heating load was transferred into electrical load to be supplied by hybrid system. The hybrid system consists of a 75 kW vertical axis windmill and 140 solar modules. Because of the high cost of land in residential buildings, the hybrid system is to be installed on the building roof. The hybrid system and the conventional systems＇ cost were found to be compatible in four years period when oil prices reach $100 per barrel. As the international price of oil rises above $100 per barrel, the proposed hybrid system becomes more economical than the already existing hot water heating system.

Turning the tide:From cervical cancer's grip to complete response:A case report

BACKGROUND Cervical cancer is a formidable global health issue,particularly affecting women in lower-middle-income countries with little or no access to preventative vaccines,screening programs,and treatment modalities.The case report presents a unique case of a large cervical cancer achieving complete response(CR)with concurrent chemoradiotherapy(CCRT),highlighting the effectiveness of this treatment approach even in advanced stages and underscoring the importance of adaptive radiotherapy(RT)in optimizing patient outcomes.CASE SUMMARY We present the case of a 53-year-old woman who presented with four years of abnormal vaginal bleeding and was found to have p16-positive,moderately differentiated cervical squamous cell carcinoma.The tumor measured 14 cm×12 cm×8 cm,the largest size reported in the literature to achieve CR with CCRT.Despite this monumental feat,the patient remained disease-free and is currently on follow-up for 2 years;however,she continued to suffer from substantial morbidity caused by a vesicovaginal fistula and hydronephrosis,underscoring the continuing impact of cervical cancer on quality of life.CONCLUSION In this case report,we highlight the effectiveness of CCRT in achieving CR,even in cases of bulky cervical cancer,with adaptive RT offering a customized strategy to improve patient outcomes.We also emphasize the necessity for multidisciplinary team discussions and highlight the need for strategies to mitigate treatment-related toxicities and long-term complications.

Impact of the Syrian crisis on the socio-spatial transformation of Eastern Amman,Jordan

Amman,the capital of Jordan,has been subjected to incremental spatial transformation under the pressure of the emerging migration of various refugees since the beginning of the Syrian conflict(2011-present).Only 17%of the Syrian refugee influx to Jordan resides in camps.Amman hosts 28%of non-camp refugees(NCRs)who are living in urban areas and creating multiple forms of urban settlements.Diverse forms of NCR settlements are emerging extensively in Eastern Amman districts.These settlements are contributing to new morphological structures and leading to a gradual spatial change-socially and physically.This research intends to contribute to the increasing amount of available data by improving the understanding of the socio-spatial patterns of Syrian NCR settlements and providing insights,forecasts,and recommendations regarding this pressing issue on the basis of overall trends.Comprehensively contextualized areas in Amman were mapped,and after considering specific siteselection criteria,Jubilee Neighborhood was selected.Jubilee was built by the government in 1985 for low-income Jordanian families.This study aims to unravel one layer of the complex multilayered Amman City.It intends to explore and describe the urban forms of NCR settlements in Jubilee Neighborhood as a case study by interrelating spatial,social,and physical concepts.In addition,this study extensively uses rigorous qualitative and traditional methods.The outcome of this study is a comprehensive and descriptive spatial analysis that can provide socio-spatial interpretations and recommend urban response policies to cope with the future spatial transformation of urban forms.

Machine learning in solid organ transplantation:Charting the evolving landscape

BACKGROUND Machine learning(ML),a major branch of artificial intelligence,has not only demonstrated the potential to significantly improve numerous sectors of healthcare but has also made significant contributions to the field of solid organ transplantation.ML provides revolutionary opportunities in areas such as donorrecipient matching,post-transplant monitoring,and patient care by automatically analyzing large amounts of data,identifying patterns,and forecasting outcomes.AIM To conduct a comprehensive bibliometric analysis of publications on the use of ML in transplantation to understand current research trends and their implications.METHODS On July 18,a thorough search strategy was used with the Web of Science database.ML and transplantation-related keywords were utilized.With the aid of the VOS viewer application,the identified articles were subjected to bibliometric variable analysis in order to determine publication counts,citation counts,contributing countries,and institutions,among other factors.RESULTS Of the 529 articles that were first identified,427 were deemed relevant for bibliometric analysis.A surge in publications was observed over the last four years,especially after 2018,signifying growing interest in this area.With 209 publications,the United States emerged as the top contributor.Notably,the"Journal of Heart and Lung Transplantation"and the"American Journal of Transplantation"emerged as the leading journals,publishing the highest number of relevant articles.Frequent keyword searches revealed that patient survival,mortality,outcomes,allocation,and risk assessment were significant themes of focus.CONCLUSION The growing body of pertinent publications highlights ML's growing presence in the field of solid organ transplantation.This bibliometric analysis highlights the growing importance of ML in transplant research and highlights its exciting potential to change medical practices and enhance patient outcomes.Encouraging collaboration between significant contributors can potentially fast-track advancements in this interdisciplinary domain.

A Variationist Analysis of/ð/in Ammani Arabic

This study examines the realizations of variable/ð/sound in Ammani Arabic(AA)as well as the correlation between this variation and a number of sociolinguistic factors.Four phonetic variants([ð],[d],[z]and[ðˤ]),four social factors(sex,age,region and educational attainment)and two linguistic factors(the position of the variant in the word and the syntactic category of the word)were investigated.To achieve the objectives of the study,40 native speakers of AA were interviewed for approximately 30 min each.A multivariate analysis using GoldVarb X was carried out in order to discern the effects of the operationalized factors on the variant choice.The results confirmed that the social and linguistic factors condition the variant choice.Additionally,the study examined the possible social meanings of variation in pronouncing the variable/ð/in AA adopting Silverstein’s(Lang Commun,23(3–4),193–229,https://doi.org/10.1016/s0271-5309(03)00013-2,2003)concept of indexical order.The sociolinguistic investigation of the variable/ð/in AA appears to suggest that it is an object of stylistic variation.

Ground-level Ozone Prediction Using Machine Learning Techniques:A Case Study in Amman,Jordan

Air pollution is one of the most serious hazards to humans′health nowadays,it is an invisible killer that takes many human lives every year.There are many pollutants existing in the atmosphere today,ozone being one of the most threatening pollutants.It can cause serious health damage such as wheezing,asthma,inflammation,and early mortality rates.Although air pollution could be forecasted using chemical and physical models,machine learning techniques showed promising results in this area,especially artificial neural networks.Despite its importance,there has not been any research on predicting ground-level ozone in Jordan.In this paper,we build a model for predicting ozone concentration for the next day in Amman,Jordan using a mixture of meteorological and seasonal variables of the previous day.We compare a multi-layer perceptron neural network(MLP),support vector regression(SVR),decision tree regression(DTR),and extreme gradient boosting(XGBoost)algorithms.We also explore the effect of applying various smoothing filters on the time-series data such as moving average,Holt-Winters smoothing and Savitzky-Golay filters.We find that MLP outperformed the other algorithms and that using Savitzky-Golay improved the results by 50%for coefficient of determination(R2)and 80%for root mean square error(RMSE)and mean absolute error(MAE).Another point we focus on is the variables required to predict ozone concentration.In order to reduce the time required for prediction,we perform feature selection which greatly reduces the time by 91%as well as shrinking the number of features required for prediction to the previous day values of ozone,humidity,and temperature.The final model scored 98.653%for R^2,1.016 ppb for RMSE and 0.800 ppb for MAE.

Place attachment assessment of a heritage place： A case study of the Roman amphitheater in downtown Amman, Jordan

This research investigated the place attachment of a heritage place, namely, the Roman amphitheater, by using a valid model, the Kyle, Graefe, and Manning （2005） model. This model presents three factors to reach place attachment： place identification, place dependence, and social bonding. Although the validity of the used model was proved, statistical tests were used to verify the validity of the collected data because the model was used on a heritage site. In accordance with the mentioned model, the sample was interviewed using the model questionnaire to evaluate people＇s attachment to the heritage place during rush hours. Along with other statistical tests, the exploratory factor analysis of the sample elaborated that the Kyte, Graefe, and Manning model is not completely valid for this study, because the results added a new effective factor, namely, spiritual value. The place attachment estimation was then examined using the new model. The nature of the place was found to affect the model used to evaluate its place attachment.

基于高水压射流的盾构泥饼切割试验研究

为解决高水压射流清理泥饼布置方式无依据等问题,研制泥饼压制设备和高水压射流装置,并以强风化泥质粉砂岩作为试验材料,开展高水压射流切割泥饼试验,研究射流压力、流量、传输介质等参数对泥饼切割宽度和深度的影响。主要结论有:1)随着冲刷距离的增大,泥饼切割宽度先增大然后逐渐减小至0,而切割深度则逐渐减小;2)射流压力相同的情况下,当流量从49.1 L/min增大至110.0 L/min时,高水压射流对泥饼的切割宽度增大,当流量从110.0 L/min增加至202.8 L/min时,切割宽度反而减小,切割深度则随着流量的增加而增大;3)射流流量相同的情况下,水压力越大,则切割宽度和深度也越大;4)在水中进行泥饼切割时,高水压射流对泥饼的作用以冲刷为主,难以产生有效的切割。

Improving the Segmentation of Arabic Handwriting Using Ligature Detection Technique

Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.

Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections

The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection,which is then used as input to the CNN.The customized Convolutional Neural Network method is the date augmented-based CNN model to generate‘fake data’or‘fake images’.This study was carried out using Python and its libraries.We used 242 films from the dataset gathered by the Deep Fake Detection Challenge,of which 199 were made up and the remaining 53 were real.Ten seconds were allotted for each video.There were 318 videos used in all,199 of which were fake and 119 of which were real.Our proposedmethod achieved a testing accuracy of 91.47%,loss of 0.342,and AUC score of 0.92,outperforming two alternative approaches,CNN and MLP-CNN.Furthermore,our method succeeded in greater accuracy than contemporary models such as XceptionNet,Meso-4,EfficientNet-BO,MesoInception-4,VGG-16,and DST-Net.The novelty of this investigation is the development of a new Convolutional Neural Network(CNN)learning model that can accurately detect deep fake face photos.

Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization(FLO),which emulates the unique hunting behavior of frilled lizards in their natural habitat.FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards.The algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases:(i)an exploration phase,which mimics the lizard’s sudden attack on its prey,and(ii)an exploitation phase,which simulates the lizard’s retreat to the treetops after feeding.To assess FLO’s efficacy in addressing optimization problems,its performance is rigorously tested on fifty-two standard benchmark functions.These functions include unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions,as well as the challenging CEC 2017 test suite.FLO’s performance is benchmarked against twelve established metaheuristic algorithms,providing a comprehensive comparative analysis.The simulation results demonstrate that FLO excels in both exploration and exploitation,effectively balancing these two critical aspects throughout the search process.This balanced approach enables FLO to outperform several competing algorithms in numerous test cases.Additionally,FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems,further validating its robustness and versatility in solving real-world optimization challenges.Overall,the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

Robust Network Security:A Deep Learning Approach to Intrusion Detection in IoT

The proliferation of Internet of Things(IoT)technology has exponentially increased the number of devices interconnected over networks,thereby escalating the potential vectors for cybersecurity threats.In response,this study rigorously applies and evaluates deep learning models—namely Convolutional Neural Networks(CNN),Autoencoders,and Long Short-Term Memory(LSTM)networks—to engineer an advanced Intrusion Detection System(IDS)specifically designed for IoT environments.Utilizing the comprehensive UNSW-NB15 dataset,which encompasses 49 distinct features representing varied network traffic characteristics,our methodology focused on meticulous data preprocessing including cleaning,normalization,and strategic feature selection to enhance model performance.A robust comparative analysis highlights the CNN model’s outstanding performance,achieving an accuracy of 99.89%,precision of 99.90%,recall of 99.88%,and an F1 score of 99.89%in binary classification tasks,outperforming other evaluated models significantly.These results not only confirm the superior detection capabilities of CNNs in distinguishing between benign and malicious network activities but also illustrate the model’s effectiveness in multiclass classification tasks,addressing various attack vectors prevalent in IoT setups.The empirical findings from this research demonstrate deep learning’s transformative potential in fortifying network security infrastructures against sophisticated cyber threats,providing a scalable,high-performance solution that enhances security measures across increasingly complex IoT ecosystems.This study’s outcomes are critical for security practitioners and researchers focusing on the next generation of cyber defense mechanisms,offering a data-driven foundation for future advancements in IoT security strategies.

A Measurement Study of the Ethereum Underlying P2P Network

This work carried out a measurement study of the Ethereum Peer-to-Peer(P2P)network to gain a better understanding of the underlying nodes.Ethereum was applied because it pioneered distributed applications,smart contracts,and Web3.Moreover,its application layer language“Solidity”is widely used in smart contracts across different public and private blockchains.To this end,we wrote a new Ethereum client based on Geth to collect Ethereum node information.Moreover,various web scrapers have been written to collect nodes’historical data fromthe Internet Archive and the Wayback Machine project.The collected data has been compared with two other services that harvest the number of Ethereumnodes.Ourmethod has collectedmore than 30% more than the other services.The data trained a neural network model regarding time series to predict the number of online nodes in the future.Our findings show that there are less than 20% of the same nodes daily,indicating thatmost nodes in the network change frequently.It poses a question of the stability of the network.Furthermore,historical data shows that the top ten countries with Ethereum clients have not changed since 2016.The popular operating system of the underlying nodes has shifted from Windows to Linux over time,increasing node security.The results have also shown that the number of Middle East and North Africa(MENA)Ethereum nodes is neglected compared with nodes recorded from other regions.It opens the door for developing new mechanisms to encourage users from these regions to contribute to this technology.Finally,the model has been trained and demonstrated an accuracy of 92% in predicting the future number of nodes in the Ethereum network.