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 pe...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.展开更多
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 ...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.展开更多
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 ma...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.展开更多
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 o...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.展开更多
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 infor...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.展开更多
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...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.展开更多
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 o...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.展开更多
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 e...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.展开更多
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 existi...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.展开更多
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...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.展开更多
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[&#...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.展开更多
The advent of 5G technology has significantly enhanced the transmission of images over networks,expanding data accessibility and exposure across various applications in digital technology and social media.Consequently...The advent of 5G technology has significantly enhanced the transmission of images over networks,expanding data accessibility and exposure across various applications in digital technology and social media.Consequently,the protection of sensitive data has become increasingly critical.Regardless of the complexity of the encryption algorithm used,a robust and highly secure encryption key is essential,with randomness and key space being crucial factors.This paper proposes a new Robust Deoxyribonucleic Acid(RDNA)nucleotide-based encryption method.The RDNA encryption method leverages the unique properties of DNA nucleotides,including their inherent randomness and extensive key space,to generate a highly secure encryption key.By employing transposition and substitution operations,the RDNA method ensures significant diffusion and confusion in the encrypted images.Additionally,it utilises a pseudorandom generation technique based on the random sequence of nucleotides in the DNA secret key.The performance of the RDNA encryption method is evaluated through various statistical and visual tests,and compared against established encryption methods such as 3DES,AES,and a DNA-based method.Experimental results demonstrate that the RDNA encryption method outperforms its rivals in the literature,and achieves superior performance in terms of information entropy,avalanche effect,encryption execution time,and correlation reduction,while maintaining competitive values for NMAE,PSNR,NPCR,and UACI.The high degree of randomness and sensitivity to key changes inherent in the RDNA method offers enhanced security,making it highly resistant to brute force and differential attacks.展开更多
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threa...The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threats is both necessary and complex,yet these interconnected healthcare settings generate enormous amounts of heterogeneous data.Traditional Intrusion Detection Systems(IDS),which are generally centralized and machine learning-based,often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy.Moreover,traditional AI-driven IDS usually face challenges in handling large-scale,heterogeneous healthcare data while ensuring data privacy and operational efficiency.To address these issues,emerging technologies such as Big Data Analytics(BDA)and Federated Learning(FL)provide a hybrid framework for scalable,adaptive intrusion detection in IoT-driven healthcare systems.Big data techniques enable processing large-scale,highdimensional healthcare data,and FL can be used to train a model in a decentralized manner without transferring raw data,thereby maintaining privacy between institutions.This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing,privacy,and compliance with ethical regulations.To strengthen the reliability of the reported findings,the resultswere validated using cross-dataset testing and 95%confidence intervals derived frombootstrap analysis,confirming consistent performance across heterogeneous healthcare data distributions.This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability,privacy,adaptability,and earlydetection capabilities.The proposed global model achieves a test accuracy of 99.93%±0.03(95%CI)and amiss-rate of only 0.07%±0.02,representing state-of-the-art performance in privacy-preserving intrusion detection.The proposed FL-driven IDS framework offers an efficient,privacy-preserving,and scalable solution for securing next-generation healthcare infrastructures by combining adaptability,early detection,and ethical data management.展开更多
There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reac...There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reactive coating processes,but existing work is not uncharacteristically remiss regarding viscoelasticity,radiative heating,viscous dissipation,and homogeneous–heterogeneous reactions within a single scheme that is calibrated.This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation,thermal radiation,and homogeneous-heterogeneous reactions.The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary differential equations using similarity transformations.RK4 is the technique for gaining numerical solutions,but with the addition of ANNs,there is an improvement in prediction accuracy and computational efficiency.The study investigates the influence of wedge angle parameter,along with Weissenberg number,thermal radiation parameter and Brownian motion parameter,and Schmidt number,on velocity distribution,temperature distribution,and concentra-tion distribution.Enhanced Weissenberg numbers enhance viscoelastic responses that modify velocity patterns,but radiation parameters and thermophoresis have key impacts on thermal transfer phenomena.This research develops findings that are of enormous application in aerospace,biomedical(artificial hearts and drug delivery),and industrial cooling technology applications.New findings on non-Newtonian nanofluids under full flow systems are included in this work to enhance heat transfer methods in novel fluid-based systems.展开更多
Adversarial Reinforcement Learning(ARL)models for intelligent devices and Network Intrusion Detection Systems(NIDS)improve systemresilience against sophisticated cyber-attacks.As a core component of ARL,Adversarial Tr...Adversarial Reinforcement Learning(ARL)models for intelligent devices and Network Intrusion Detection Systems(NIDS)improve systemresilience against sophisticated cyber-attacks.As a core component of ARL,Adversarial Training(AT)enables NIDS agents to discover and prevent newattack paths by exposing them to competing examples,thereby increasing detection accuracy,reducing False Positives(FPs),and enhancing network security.To develop robust decision-making capabilities for real-world network disruptions and hostile activity,NIDS agents are trained in adversarial scenarios to monitor the current state and notify management of any abnormal or malicious activity.The accuracy and timeliness of the IDS were crucial to the network’s availability and reliability at this time.This paper analyzes ARL applications in NIDS,revealing State-of-The-Art(SoTA)methodology,issues,and future research prospects.This includes Reinforcement Machine Learning(RML)-based NIDS,which enables an agent to interact with the environment to achieve a goal,andDeep Reinforcement Learning(DRL)-based NIDS,which can solve complex decision-making problems.Additionally,this survey study addresses cybersecurity adversarial circumstances and their importance for ARL and NIDS.Architectural design,RL algorithms,feature representation,and training methodologies are examined in the ARL-NIDS study.This comprehensive study evaluates ARL for intelligent NIDS research,benefiting cybersecurity researchers,practitioners,and policymakers.The report promotes cybersecurity defense research and innovation.展开更多
Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these netw...Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.展开更多
In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task schedul...In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.展开更多
With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contex...With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.展开更多
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘The advent of 5G technology has significantly enhanced the transmission of images over networks,expanding data accessibility and exposure across various applications in digital technology and social media.Consequently,the protection of sensitive data has become increasingly critical.Regardless of the complexity of the encryption algorithm used,a robust and highly secure encryption key is essential,with randomness and key space being crucial factors.This paper proposes a new Robust Deoxyribonucleic Acid(RDNA)nucleotide-based encryption method.The RDNA encryption method leverages the unique properties of DNA nucleotides,including their inherent randomness and extensive key space,to generate a highly secure encryption key.By employing transposition and substitution operations,the RDNA method ensures significant diffusion and confusion in the encrypted images.Additionally,it utilises a pseudorandom generation technique based on the random sequence of nucleotides in the DNA secret key.The performance of the RDNA encryption method is evaluated through various statistical and visual tests,and compared against established encryption methods such as 3DES,AES,and a DNA-based method.Experimental results demonstrate that the RDNA encryption method outperforms its rivals in the literature,and achieves superior performance in terms of information entropy,avalanche effect,encryption execution time,and correlation reduction,while maintaining competitive values for NMAE,PSNR,NPCR,and UACI.The high degree of randomness and sensitivity to key changes inherent in the RDNA method offers enhanced security,making it highly resistant to brute force and differential attacks.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
文摘The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threats is both necessary and complex,yet these interconnected healthcare settings generate enormous amounts of heterogeneous data.Traditional Intrusion Detection Systems(IDS),which are generally centralized and machine learning-based,often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy.Moreover,traditional AI-driven IDS usually face challenges in handling large-scale,heterogeneous healthcare data while ensuring data privacy and operational efficiency.To address these issues,emerging technologies such as Big Data Analytics(BDA)and Federated Learning(FL)provide a hybrid framework for scalable,adaptive intrusion detection in IoT-driven healthcare systems.Big data techniques enable processing large-scale,highdimensional healthcare data,and FL can be used to train a model in a decentralized manner without transferring raw data,thereby maintaining privacy between institutions.This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing,privacy,and compliance with ethical regulations.To strengthen the reliability of the reported findings,the resultswere validated using cross-dataset testing and 95%confidence intervals derived frombootstrap analysis,confirming consistent performance across heterogeneous healthcare data distributions.This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability,privacy,adaptability,and earlydetection capabilities.The proposed global model achieves a test accuracy of 99.93%±0.03(95%CI)and amiss-rate of only 0.07%±0.02,representing state-of-the-art performance in privacy-preserving intrusion detection.The proposed FL-driven IDS framework offers an efficient,privacy-preserving,and scalable solution for securing next-generation healthcare infrastructures by combining adaptability,early detection,and ethical data management.
基金supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and the Ministry of Trade,Industry&Energy(MOTIE)of the Republic of Korea(No.RS-2025-02315209).
文摘There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reactive coating processes,but existing work is not uncharacteristically remiss regarding viscoelasticity,radiative heating,viscous dissipation,and homogeneous–heterogeneous reactions within a single scheme that is calibrated.This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation,thermal radiation,and homogeneous-heterogeneous reactions.The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary differential equations using similarity transformations.RK4 is the technique for gaining numerical solutions,but with the addition of ANNs,there is an improvement in prediction accuracy and computational efficiency.The study investigates the influence of wedge angle parameter,along with Weissenberg number,thermal radiation parameter and Brownian motion parameter,and Schmidt number,on velocity distribution,temperature distribution,and concentra-tion distribution.Enhanced Weissenberg numbers enhance viscoelastic responses that modify velocity patterns,but radiation parameters and thermophoresis have key impacts on thermal transfer phenomena.This research develops findings that are of enormous application in aerospace,biomedical(artificial hearts and drug delivery),and industrial cooling technology applications.New findings on non-Newtonian nanofluids under full flow systems are included in this work to enhance heat transfer methods in novel fluid-based systems.
文摘Adversarial Reinforcement Learning(ARL)models for intelligent devices and Network Intrusion Detection Systems(NIDS)improve systemresilience against sophisticated cyber-attacks.As a core component of ARL,Adversarial Training(AT)enables NIDS agents to discover and prevent newattack paths by exposing them to competing examples,thereby increasing detection accuracy,reducing False Positives(FPs),and enhancing network security.To develop robust decision-making capabilities for real-world network disruptions and hostile activity,NIDS agents are trained in adversarial scenarios to monitor the current state and notify management of any abnormal or malicious activity.The accuracy and timeliness of the IDS were crucial to the network’s availability and reliability at this time.This paper analyzes ARL applications in NIDS,revealing State-of-The-Art(SoTA)methodology,issues,and future research prospects.This includes Reinforcement Machine Learning(RML)-based NIDS,which enables an agent to interact with the environment to achieve a goal,andDeep Reinforcement Learning(DRL)-based NIDS,which can solve complex decision-making problems.Additionally,this survey study addresses cybersecurity adversarial circumstances and their importance for ARL and NIDS.Architectural design,RL algorithms,feature representation,and training methodologies are examined in the ARL-NIDS study.This comprehensive study evaluates ARL for intelligent NIDS research,benefiting cybersecurity researchers,practitioners,and policymakers.The report promotes cybersecurity defense research and innovation.
文摘Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R195)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.
