This study presents the development of a comprehensive three-dimensional groundwater flow model for the Erbil Basin utilizing the Groundwater Modeling System(GMS).The Erbil Basin,situated in the Kurdistan Region of Ir...This study presents the development of a comprehensive three-dimensional groundwater flow model for the Erbil Basin utilizing the Groundwater Modeling System(GMS).The Erbil Basin,situated in the Kurdistan Region of Iraq,is a vital water resource area facing increasing water demands and environ-mental challenges.The three-dimensional nature of the groundwater flow system is crucial for accurately understanding and managing water resources in the basin.The modeling process involved data collection,geological and hydrogeological characterization,conceptual model development,and numerical simulation using GMS software MODFLOW 2000 package.Various parameters such as hydraulic conductivity,recharge rates,and boundary conditions were integrated into the model to represent the complex hydrogeo-logical conditions of the basin.Model calibration was performed by comparing simulated groundwater levels with observed data from monitoring wells across the basin,using the automatic calibration method of automated Parameter Estimation(PEST).Pilot points were applied to adjust the hydraulic conductivity in the model area spatially.Sensitivity analysis was conducted to assess the influence of key parameters on model predictions and to identify areas of uncertainty.The developed three-dimensional groundwater flow model provides valuable insights into the dynamics of groundwater flow,recharge-discharge mechanisms,and potential impacts of future scenarios such as climate change and water resource management strategies.It serves as a useful tool for decision-makers,water resource managers,and researchers to evaluate differ-ent management scenarios and formulate sustainable groundwater management policies for the Erbil Basin.In conclusion,this study demonstrates the effectiveness of using GMS for developing three-dimensional groundwater flow models in complex hydrogeological settings like the Erbil Basin,contributing to improved understanding and management of groundwater resources in the region.展开更多
Erbil city is constructed in a flat plain with moderate gradient increasing northwards, the plain is dissected by tens of ephemeral wadis. The maximum elevation in the high lands NE of Erbil city is 1062 m (a.s.l.), w...Erbil city is constructed in a flat plain with moderate gradient increasing northwards, the plain is dissected by tens of ephemeral wadis. The maximum elevation in the high lands NE of Erbil city is 1062 m (a.s.l.), whereas the elevation of the center of Erbil city is 420 m (a.s.l.). The average gradient from the highest part to the center of the city is 2.15%, increasing towards northeast to reach 4.79%. The mean annual rainfall is 365 mm, while the average monthly rainfall is about 24.42 mm. The site of the city is mainly covered by alluvial fan sediments. Towards northeast, however, the exposures of the Bia Hassan Formation, which consists of alternation of conglomerate and claystone cover considerable areas (82 km<sup>2</sup>) and form the high lands NE of Erbil city. The exposed rocks are thick claystone alternating with coarse conglomerate. Two very heavy torrential rain events have caused flash floods in Erbil city on 30th October and 17th December 2021. The rainfall intensities were 52 mm/day and 60 mm/day, respectively, causing destructive floods. The most affected areas were Zirin (north of Erbil) and Dara Too (northeast of Erbil), respectively, with very heavy property damages and tens of fatalities. The main reasons for the floods were the partially constructed embankments of the 150 m ring-road, and the urban development within large and wide drainage basins. Different climate data were used for the purpose of this article, with field checks.展开更多
Groundwater vulnerability assessment is a crucial step in the efficient management of groundwater resources,especially in areas with intensive anthropogenic activities and groundwater pollution.In the present study,th...Groundwater vulnerability assessment is a crucial step in the efficient management of groundwater resources,especially in areas with intensive anthropogenic activities and groundwater pollution.In the present study,the DRASTIC method was applied using Geographic Information System(GIS)to delineate groundwater vulnerability zones in the Erbil Dumpsite area,Central Erbil Basin,North Iraq.Results showed that the area was classified into four vulnerability classes:Very low(16.97%),low(27.67%),moderate(36.55%)and high(18.81%).The southern,south-eastern and northern parts of the study area exhibited the highest vulnerability potential,while the central-northern,northern and north-western regions displayed the lowest vulnerability potential.Moreover,results of the single-parameter sensitivity analysis indicated that amongst the seven DRASTIC parameters,the unsaturated zone and the aquifer media were the most influencing parameters.In conclustion,the correlation of 25 nitrate concentration values with the final vulnerability map,assessed using the Pearson correlation coefficient,yielded a satisfactory result of R=0.72.展开更多
Increasing population growth and water demand for various purposes such as irrigation, domestic and industrial production in many parts of the Kurdistan Region is causing deficit in fresh water and rising groundwater ...Increasing population growth and water demand for various purposes such as irrigation, domestic and industrial production in many parts of the Kurdistan Region is causing deficit in fresh water and rising groundwater dependence. Drilling many deep wells in the area unsystematically and continuously increased pumping water from groundwater reservoirs results in lowering of water table. Therefore, it is essential to assess the management of water resources. The study focuses on the groundwater modeling for the Qushtapa District plain area in particular under steady state flow conditions. The aquifer was simulated under unconfined condition and is represented by a single layer of 100 m thickness. MODPATH was used to measure contamination track lines and travel times. This approach involved the introduction of particles at sources of contaminants in the wells and the recharge area, then the identification of the path lines and the determination of the special distribution of contaminants through steady state flow conditions. The simulation of the groundwater head shows that the groundwater head starts from the northeastern part of the plain and decreases towards Lesser Zab River in the south of the plain from 420 m to 140 m above sea level. The modeled layer was calibrated under steady state conditions using hydraulic parameters obtained from observation and pumping wells. The calibrated model is effective in producing steady-state groundwater head distribution and good compliance with observed data. The standard error was estimated as 4.88 m, the normalized root mean square error is 8.3% and the residual mean is 15.79 m. The results of the forward tracking show the source of potential pollutants from the recharge area after different travel time, the particles released at the northern boundary travels to the center and the western part toward the pollution sources. The results of the backward tracking show that the particles located in the extraction wells moved toward the recharge area in the north and northeastern part of the study area.展开更多
Objective: To estimate the prevalence of polycystic ovarian syndrome (PCOS), and to compare the clinical, hormonal and ultrasonography features between infertile women with or without PCOS. Design: A descriptive, comp...Objective: To estimate the prevalence of polycystic ovarian syndrome (PCOS), and to compare the clinical, hormonal and ultrasonography features between infertile women with or without PCOS. Design: A descriptive, comparative study. Materials and Methods: This study was conducted from May 1, 2007 to August 1, 2008, in the Infertility Care and IVF center in Maternity Teaching Hospital, Erbil city, Kurdistan region, North ofIraq. A total of 320 infertile women aged 18 - 45 years, were evaluated for clinical features (oligo-/amenorrhea, hirsutism), body mass index, waist-hip ratio (WHR) and hormonal measures. Transvaginal ultrasonography was used to assess the ovarian morphology. The Rotterdam 2003 criteria adopted by the European Society for Human reproduction and Embryology and the American Society for Reproductive Medicine were used to diagnose cases of PCOS. Data analysis was performed using the SPSS version 15. Results: The prevalence of PCOS was 33%. There were a significant differences between the two groups in terms of oligo-/amenorrhea, hirsutism, WHR, and ovarian ultrasound features. There were no significant differences between the two groups in correlations between the level of obesity with the incidences of anovulation, hyperandrogenemia and hirsutism or with hormonal features. Conclusions: A high prevalence rate of PCOS was observed among infertile women attending IVF center using the Rotterdam 2003 criteria for diagnosis.展开更多
Urban expansion of cities has caused changes in land use and land cover(LULC)in addition to transformations in the spatial characteristics of landscape structure.These alterations have generated heat islands and rise ...Urban expansion of cities has caused changes in land use and land cover(LULC)in addition to transformations in the spatial characteristics of landscape structure.These alterations have generated heat islands and rise of land surface temperature(LST),which consequently have caused a variety of environmental issues and threated the sustainable development of urban areas.Greenbelts are employed as an urban planning containment policy to regulate urban expansion,safeguard natural open spaces,and serve adaptation and mitigation functions.And they are regarded as a powerful measure for enhancing urban environmental sustainability.Despite the fact that,the relation between landscape structure change and variation of LST has been examined thoroughly in many studies,but there is a limitation concerning this relation in semi-arid climate and in greenbelts as well,with the lacking of comprehensive research combing both aspects.Accordingly,this study investigated the spatiotemporal changes of landscape pattern of LULC and their relationship with variation of LST within an inner greenbelt in the semi-arid Erbil City of northern Iraq.The study utilized remote sensing data to retrieve LST,classified LULC,and calculated landscape metrics for analyzing spatial changes during the study period.The results indicated that both composition and configuration of LULC had an impact on the variation of LST in the study area.The Pearson's correlation showed the significant effect of Vegetation 1 type(VH),cultivated land(CU),and bare soil(BS)on LST,as increase of LST was related to the decrease of VH and the increases of CU and BS,while,neither Vegetation 2 type(VL)nor built-up(BU)had any effects.Additionally,the spatial distribution of LULC also exhibited significant effects on LST,as LST was strongly correlated with landscape indices for VH,CU,and BS.However,for BU,only aggregation index metric affected LST,while none of VL metrics had a relation.The study provides insights for landscape planners and policymakers to not only develop more green spaces in greenbelt but also optimize the spatial landscape patterns to reduce the influence of LST on the urban environment,and further promote sustainable development and enhance well-being in the cities with semi-arid climate.展开更多
AIM:To determine the prevalence of tropia,phoria,and abnormality of near point of convergence(NPC),along with associated ocular symptoms,in high school students.METHODS:This cross-sectional study was conducted in Erbi...AIM:To determine the prevalence of tropia,phoria,and abnormality of near point of convergence(NPC),along with associated ocular symptoms,in high school students.METHODS:This cross-sectional study was conducted in Erbil,Iraq.The target population consisted of high school students selected through a multi-stage cluster sampling method.Comprehensive visual examinations were performed for all students,including measurement of uncorrected and corrected visual acuity,objective and subjective refraction,and distance and near cover tests.NPC was evaluated using a single 6/12 visual target mounted on a centrally positioned Gulden fixation stick.Ocular symptoms were investigated through interviews.RESULTS:Of the 996 selected students,921 participated in the study.Of them,543(58.96%)were female,and their ages ranged from 13 to 22y.The prevalence of tropia was 3.58%[95%confidence interval(CI):2.38%-4.78%],observed in 3.44%of males and 3.68%of females.Exotropia(1.95%,95%CI:1.06%-2.85%)was more common than esotropia(1.52%,95%CI:0.73%-2.31%).The 15.42%(95%CI:13.09%-17.75%)of students had phoria.Exophoria(13.79%,95%CI:11.56%-16.02%)was significantly more prevalent than esophoria(1.63%,95%CI:0.81%-2.45%).The prevalence of NPC abnormality in the total study population was 24.97%(95%CI:22.18%-27.77%).It was 26.72%(95%CI:22.26%-31.18%)in males and 23.76%(95%CI:20.18%-27.34%)in females(P=0.307).The most common symptom in phoria was headache(86.62%,95%CI:81.02%-92.22%),followed by tired or sore eyes(61.97%,95%CI:53.99%-69.96%).The most common symptoms in tropia were blurry vision(93.94%,95%CI:79.77%-99.26%)and difficulty concentrating(87.88%,95%CI:76.74%-99.01%).CONCLUSION:Among Erbil’s high school students,the prevalence of strabismus,particularly the exodeviation type,is relatively high,and a significant percentage of students have NPC abnormalities.Addressing and correcting these binocular vision problems,due to their associated visual symptoms,can lead to an improvement in students’quality of life and academic performance.展开更多
Coiled tube heat exchangers are widely preferred in shell structures due to their superior heat transfer performance,driven by favorable flow characteristics.This study investigates the effect of modifying coil and sh...Coiled tube heat exchangers are widely preferred in shell structures due to their superior heat transfer performance,driven by favorable flow characteristics.This study investigates the effect of modifying coil and shell configurations on heat transfer efficiency.Two key enhancements were examined:adding fins to the outer coil surface and integrating longitudinal slots within a hollowed shell.These modifications promote turbulence and extend heat transfer duration,thereby improving performance.However,they also introduce challenges,including increased pressure loss andmanufacturing complexity.Numerical simulationswere conducted usingANSYS Fluent 2024R1 under identical boundary conditions.With a fixed cold-side flow rate of 3 L/min,the input temperatures for the hot and cold fluids were 333.15 and 291.65 K,respectively.The hot-side flow rate varied between 2 and 6 L/min.Simulation outcomes were reported for the objectives of the study that included the improvement in heat exchangers’heat transfer enhancement.As it was indicated in the study outcomes,the average heat transfer rate increased by 15.56%,the overall heat transfer coefficient enhanced by about 29.51%,and the convective heat transfer coefficient improved by about 75.96%compared to the conventional shell-and-coil tube heat exchanger model.However,the modified technique resulted in a significant pressure drop.展开更多
The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT n...The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.展开更多
The hybrid photovoltaic solar-assisted heat pump are primarily used to generate electricity and provide thermal energy for heating applications.This study investigates the performance enhancement of a hybrid Photovolt...The hybrid photovoltaic solar-assisted heat pump are primarily used to generate electricity and provide thermal energy for heating applications.This study investigates the performance enhancement of a hybrid Photovoltaic Thermal Solar-Assisted Heat Pump(PV/T-SAHP)system integrated with a solar tracking mechanism.The system was simulated using TRNSYS to evaluate its monthly electrical output and coefficient of performance(COP)of the heat pump system over a year.The results showed a significant improvement in energy generation and efficiency compared to a conventional PV/T system without SAHP system.Overall,the solar tracking configuration of the PV/T-SAHP generated 10%–40%more electricity than the fixed system.The system for the tracking mode achieved a maximum monthly average electrical energy output of 634.349 kWh in June.Throughout the year,the tracking mode consistently outperformed the fixed mode.During the winter months of January and December,the tracking system produced 328.7 and 323.6 kWh,respectively,compared to 297.8 and 299.7 kWh for the fixed mode.The highest COP of 5.65 occurred in July,indicating a strong seasonal correlation with solar irradiance.In contrast,the minimum COP of 4.55 was observed in the months,February and March,reflecting reduced solar availability.The solar tracking feature consistently maintained an optimal panel angle,increasing energy gains and improving system efficiency.Overall,the integration of a heat pump and tracking control significantly improved system performance,making the hybrid PV/T-SAHP configuration a promising solution for year-round renewable energy generation.展开更多
This study delves into both experimental and analytical examinations of heat exchange in a straight channel, where Al_(2)O_(3)-water nanofluids are utilized, spanning the Reynolds number spectrum from 100 to 1800. Div...This study delves into both experimental and analytical examinations of heat exchange in a straight channel, where Al_(2)O_(3)-water nanofluids are utilized, spanning the Reynolds number spectrum from 100 to 1800. Diverse volume fractions(1%, 2%, and 3%) of Al_(2)O_(3)-water nanofluids are meticulously prepared and analyzed. The essential physical properties of these nanofluids, critical for evaluating their thermal and flow characteristics, have been comprehensively assessed. From a quantitative perspective, numerical simulations are employed to predict the Nusselt number(Nu) and friction factor(f). The empirical findings reveal intriguing trends: the friction factor experiences an upward trend with diminishing velocity, attributed to heightened molecular cohesion. Conversely, the friction factor demonstrates a decline with diminishing volume fractions, a consequence of reduced particle size. Both the nanofluid's viscosity and heat transfer coefficient exhibit a rise in tandem with augmented volume flow rate and concentration gradient. Notably, the simulation results harmonize remarkably well with experimental data. Rigorous validation against prior studies underscores the robust consistency of these outcomes. In the pursuit of augmenting heat transfer, a volume fraction of 3% emerges as particularly influential, yielding an impressive 53.8% enhancement. Minor increments in the friction factor, while present, prove negligible and can be safely overlooked.展开更多
This work presents a study on the use of cathodic protection as a measure against corrosion in pipelines.The cathodic protection,compliant with the API 5L standard,is implemented here by applying an impressed current,...This work presents a study on the use of cathodic protection as a measure against corrosion in pipelines.The cathodic protection,compliant with the API 5L standard,is implemented here by applying an impressed current,while carefully considering several essential variables,such as soil characteristics,the type and color of the pipeline material,as well as the placement and size of the anode.Therefore,it is crucial to optimize the location and values of anodic overflows or ground resistances to ensure a uniform distribution of potential across the entire structure.In this method,impressed current protection uses an auxiliary anode and an external direct current source to induce a current through the electrolyte and the pipeline,thus countering the resistance of the steel.This approach is advantageous as it allows for the adjustment of electrical characteristics,particularly current levels,to meet specific needs.The factors essential to the effectiveness of cathodic protection systems,which optimize the distribution of protection potential across the structure,largely depend on the precise management of ground resistances during anodic discharge,particularly the attenuation coefficient(α).These factors were studied,and the results obtained were presented and discussed based on their influence.展开更多
A comprehensive numerical investigation into mixed⁃mode delamination is presented in this study.It aims to assess the impact of thermal and friction effects through mixed⁃mode flexure crack propagation testing.Finite ...A comprehensive numerical investigation into mixed⁃mode delamination is presented in this study.It aims to assess the impact of thermal and friction effects through mixed⁃mode flexure crack propagation testing.Finite element analysis was employed to model the delamination process,incorporating a contact cohesive zone model.This model couples the traction⁃separation law,the contact law,and the Coulomb friction law simultaneously.The thermomechanical analysis in this study is performed using a sequentially coupled approach,implemented with the finite element software ABAQUS.The findings underscore the importance of this study.展开更多
The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health.This study aims to enhance risk prediction in pregnancywith a novel deep learningmodel based on a Long Short...The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health.This study aims to enhance risk prediction in pregnancywith a novel deep learningmodel based on a Long Short-Term Memory(LSTM)generator,designed to capture temporal relationships in cardiotocography(CTG)data.This methodology integrates CTG signals with demographic characteristics and utilizes preprocessing techniques such as noise reduction,normalization,and segmentation to create high-quality input for themodel.It uses convolutional layers to extract spatial information,followed by LSTM layers to model sequences for superior predictive performance.The overall results show that themodel is robust,with an accuracy of 91.5%,precision of 89.8%,recall of 90.4%,and F1-score of 90.1%that outperformed the corresponding baselinemodels,CNN(Convolutional Neural Network)and traditional RNN(Recurrent Neural Network),by 2.3%and 6.1%,respectively.Rather,the ability to detect pregnancy-related abnormalities has considerable therapeutic potential,with the possibility for focused treatments and individualized maternal healthcare approaches,the research team concluded.展开更多
A detailed comparative examination of the Weibull and Gaussian statistical methods is offered to analyze the mechanical properties of natural date palm fibers. Tensile tests were conducted on 35 fiber samples using a ...A detailed comparative examination of the Weibull and Gaussian statistical methods is offered to analyze the mechanical properties of natural date palm fibers. Tensile tests were conducted on 35 fiber samples using a universal testing machine to gather data on stress, strain, and Young's modulus. This data was then analyzed through both statistical approaches to evaluate their ability to model important mechanical characteristics, including tensile strength, strain at break, and Young's modulus. The study identifies the strengths and weaknesses of each statistical method when it is applied to natural fibers, emphasizing their suitability for modeling different mechanical properties. The results of this analysis provide important insights that can guide the selection of the most appropriate statistical method, depending on the type of mechanical property being studied and the specific characteristics of the data. This research makes significant contributions to advancing the understanding of natural fiber mechanics and improving the methods used for their characterization.展开更多
Artifcial light at night(ALAN)is a common anthropogenic disturbance,which alters animal behavior.However,little is known about the impact of the spectral composition of ALAN and co-occurring predation risk on the beha...Artifcial light at night(ALAN)is a common anthropogenic disturbance,which alters animal behavior.However,little is known about the impact of the spectral composition of ALAN and co-occurring predation risk on the behavior of aquatic organisms.We experimentally investigated how ALAN of different spectra(cool white LED and HPS light)affects the behavior and foraging of Gammarus jazdzewskii(Amphipoda)on chironomid prey,both as a single stressor and in combination with an olfactory predation cue.Gammarids exposed to ALAN in the absence of predation cues consumed less,compared with darkness,mainly due to their lower activity.Moreover,gammarids showed a stronger response to LED light,spending more time in the shelter and increasing prey handling time in this treatment.The addition of predation cues did not enhance the negative impact of ALAN on the foraging success.Gammarids maintained similar consumption levels as in the ALAN treatment without predation cues and in darkness with predation cues.However,gammarids in LED light altered their behavior in response to predation threat:they decreased prey handling time and consumed prey faster,which may have compensated for the higher food demand in stressful conditions.They also tended to exhibit risky behavior,leaving the shelter and moving towards the lit area,presumably to escape and avoid the combined effects of light and predation cues.Therefore,when assessing the effects of ALAN on organisms,light quality and co-occurring biotic factors should be considered,as predator pressure is common in natural environments.展开更多
The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering applications.Structural optimization approaches seek to determine the optimal de...The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering applications.Structural optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural safety.The design approaches aim to reduce the built environment’s energy use and carbon emissions.This comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these methodologies.The trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were identified.The review also discusses emerging technologies,such as machine learning applications with different optimization techniques.Optimization of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and discussed.Optimization techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure types.Linear and non-linear structures,including geometric and material nonlinearity,are distinguished.The role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.展开更多
Soilcrete is a composite material of soil and cement that is highly valued in the construction industry.Accurate measurement of its mechanical properties is essential,but laboratory testing methods are expensive,timec...Soilcrete is a composite material of soil and cement that is highly valued in the construction industry.Accurate measurement of its mechanical properties is essential,but laboratory testing methods are expensive,timeconsuming,and include inaccuracies.Machine learning(ML)algorithms provide a more efficient alternative for this purpose,so after assessment with a statistical extraction method,ML algorithms including back-propagation neural network(BPNN),K-nearest neighbor(KNN),radial basis function(RBF),feed-forward neural networks(FFNN),and support vector regression(SVR)for predicting the uniaxial compressive strength(UCS)of soilcrete,were proposed in this study.The developed models in this study were optimized using an optimization technique,gradient descent(GD),throughout the analysis(direct optimization for neural networks and indirect optimization for other models corresponding to their hyperparameters).After doing laboratory analysis,data pre-preprocessing,and data-processing analysis,a database including 600 soilcrete specimens was gathered,which includes two different soil types(clay and limestone)and metakaolin as a mineral additive.80%of the database was used for the training set and 20%for testing,considering eight input parameters,including metakaolin content,soil type,superplasticizer content,water-to-binder ratio,shrinkage,binder,density,and ultrasonic velocity.The analysis showed that most algorithms performed well in the prediction,with BPNN,KNN,and RBF having higher accuracy compared to others(R^(2)=0.95,0.95,0.92,respectively).Based on this evaluation,it was observed that all models show an acceptable accuracy rate in prediction(RMSE:BPNN=0.11,FFNN=0.24,KNN=0.05,SVR=0.06,RBF=0.05,MAD:BPNN=0.006,FFNN=0.012,KNN=0.008,SVR=0.006,RBF=0.009).The ML importance ranking-sensitivity analysis indicated that all input parameters influence theUCS of soilcrete,especially the water-to-binder ratio and density,which have themost impact.展开更多
This study introduces an innovative hybrid approach that integrates deep learning with blockchain technology to improve cybersecurity,focusing on network intrusion detection systems(NIDS).The main goal is to overcome ...This study introduces an innovative hybrid approach that integrates deep learning with blockchain technology to improve cybersecurity,focusing on network intrusion detection systems(NIDS).The main goal is to overcome the shortcomings of conventional intrusion detection techniques by developing amore flexible and robust security architecture.We use seven unique machine learning models to improve detection skills,emphasizing data quality,traceability,and transparency,facilitated by a blockchain layer that safeguards against datamodification and ensures auditability.Our technique employs the Synthetic Minority Oversampling Technique(SMOTE)to equilibrate the dataset,therefore mitigating prevalent class imbalance difficulties in intrusion detection.The model selection procedure determined that Random Forest was the most successful model,with a notable detection accuracy of 97%.This substantially surpasses conventional methods and enhances the system’s capacity to identify both established and novel threats with exceptional accuracy.To optimize feature selection and maximize performance,we use Extreme Gradient Boosting(XGBoost),which improves the significance of chosen features while reducing the danger of overfitting.Our study indicates that the integrated use of machine learning for pattern identification,multi-factor authentication(MFA)for access security,and blockchain for data validation constitutes a thorough and sustainable cybersecurity solution.This architecture not only increases security but also lowers the need for regular human monitoring,significantly cutting energy consumption connected with cybersecurity infrastructure.The research finds that this integrated strategy provides a realistic road for increasing network security,addressing real-world cyber threats,and promoting eco-friendly practices in IT security.展开更多
In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning technique.Vital applications like military operations...In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning technique.Vital applications like military operations,healthcare monitoring,and environmental surveillance increasingly deploy WSNs,recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational integrity.The proposed method innovatively partitions the network into logical segments or virtual barriers,allowing for targeted monitoring and data collection that aligns with specific traffic patterns.This approach not only improves the diversit.There are more types of data in the training set,and this method uses more advanced machine learning models,like Convolutional Neural Networks(CNNs)and Long Short-Term Memory(LSTM)networks together,to see coIn our work,we used five different types of machine learning models.These are the forward artificial neural network(ANN),the CNN-LSTM hybrid models,the LR meta-model for linear regression,the Extreme Gradient Boosting(XGB)regression,and the ensemble model.We implemented Random Forest(RF),Gradient Boosting,and XGBoost as baseline models.To train and evaluate the five models,we used four possible features:the size of the circular area,the sensing range,the communication range,and the number of sensors for both Gaussian and uniform sensor distributions.We used Monte Carlo simulations to extract these traits.Based on the comparison,the CNN-LSTM model with Gaussian distribution performs best,with an R-squared value of 99%and Root mean square error(RMSE)of 6.36%,outperforming all the other models.展开更多
文摘This study presents the development of a comprehensive three-dimensional groundwater flow model for the Erbil Basin utilizing the Groundwater Modeling System(GMS).The Erbil Basin,situated in the Kurdistan Region of Iraq,is a vital water resource area facing increasing water demands and environ-mental challenges.The three-dimensional nature of the groundwater flow system is crucial for accurately understanding and managing water resources in the basin.The modeling process involved data collection,geological and hydrogeological characterization,conceptual model development,and numerical simulation using GMS software MODFLOW 2000 package.Various parameters such as hydraulic conductivity,recharge rates,and boundary conditions were integrated into the model to represent the complex hydrogeo-logical conditions of the basin.Model calibration was performed by comparing simulated groundwater levels with observed data from monitoring wells across the basin,using the automatic calibration method of automated Parameter Estimation(PEST).Pilot points were applied to adjust the hydraulic conductivity in the model area spatially.Sensitivity analysis was conducted to assess the influence of key parameters on model predictions and to identify areas of uncertainty.The developed three-dimensional groundwater flow model provides valuable insights into the dynamics of groundwater flow,recharge-discharge mechanisms,and potential impacts of future scenarios such as climate change and water resource management strategies.It serves as a useful tool for decision-makers,water resource managers,and researchers to evaluate differ-ent management scenarios and formulate sustainable groundwater management policies for the Erbil Basin.In conclusion,this study demonstrates the effectiveness of using GMS for developing three-dimensional groundwater flow models in complex hydrogeological settings like the Erbil Basin,contributing to improved understanding and management of groundwater resources in the region.
文摘Erbil city is constructed in a flat plain with moderate gradient increasing northwards, the plain is dissected by tens of ephemeral wadis. The maximum elevation in the high lands NE of Erbil city is 1062 m (a.s.l.), whereas the elevation of the center of Erbil city is 420 m (a.s.l.). The average gradient from the highest part to the center of the city is 2.15%, increasing towards northeast to reach 4.79%. The mean annual rainfall is 365 mm, while the average monthly rainfall is about 24.42 mm. The site of the city is mainly covered by alluvial fan sediments. Towards northeast, however, the exposures of the Bia Hassan Formation, which consists of alternation of conglomerate and claystone cover considerable areas (82 km<sup>2</sup>) and form the high lands NE of Erbil city. The exposed rocks are thick claystone alternating with coarse conglomerate. Two very heavy torrential rain events have caused flash floods in Erbil city on 30th October and 17th December 2021. The rainfall intensities were 52 mm/day and 60 mm/day, respectively, causing destructive floods. The most affected areas were Zirin (north of Erbil) and Dara Too (northeast of Erbil), respectively, with very heavy property damages and tens of fatalities. The main reasons for the floods were the partially constructed embankments of the 150 m ring-road, and the urban development within large and wide drainage basins. Different climate data were used for the purpose of this article, with field checks.
文摘Groundwater vulnerability assessment is a crucial step in the efficient management of groundwater resources,especially in areas with intensive anthropogenic activities and groundwater pollution.In the present study,the DRASTIC method was applied using Geographic Information System(GIS)to delineate groundwater vulnerability zones in the Erbil Dumpsite area,Central Erbil Basin,North Iraq.Results showed that the area was classified into four vulnerability classes:Very low(16.97%),low(27.67%),moderate(36.55%)and high(18.81%).The southern,south-eastern and northern parts of the study area exhibited the highest vulnerability potential,while the central-northern,northern and north-western regions displayed the lowest vulnerability potential.Moreover,results of the single-parameter sensitivity analysis indicated that amongst the seven DRASTIC parameters,the unsaturated zone and the aquifer media were the most influencing parameters.In conclustion,the correlation of 25 nitrate concentration values with the final vulnerability map,assessed using the Pearson correlation coefficient,yielded a satisfactory result of R=0.72.
文摘Increasing population growth and water demand for various purposes such as irrigation, domestic and industrial production in many parts of the Kurdistan Region is causing deficit in fresh water and rising groundwater dependence. Drilling many deep wells in the area unsystematically and continuously increased pumping water from groundwater reservoirs results in lowering of water table. Therefore, it is essential to assess the management of water resources. The study focuses on the groundwater modeling for the Qushtapa District plain area in particular under steady state flow conditions. The aquifer was simulated under unconfined condition and is represented by a single layer of 100 m thickness. MODPATH was used to measure contamination track lines and travel times. This approach involved the introduction of particles at sources of contaminants in the wells and the recharge area, then the identification of the path lines and the determination of the special distribution of contaminants through steady state flow conditions. The simulation of the groundwater head shows that the groundwater head starts from the northeastern part of the plain and decreases towards Lesser Zab River in the south of the plain from 420 m to 140 m above sea level. The modeled layer was calibrated under steady state conditions using hydraulic parameters obtained from observation and pumping wells. The calibrated model is effective in producing steady-state groundwater head distribution and good compliance with observed data. The standard error was estimated as 4.88 m, the normalized root mean square error is 8.3% and the residual mean is 15.79 m. The results of the forward tracking show the source of potential pollutants from the recharge area after different travel time, the particles released at the northern boundary travels to the center and the western part toward the pollution sources. The results of the backward tracking show that the particles located in the extraction wells moved toward the recharge area in the north and northeastern part of the study area.
文摘Objective: To estimate the prevalence of polycystic ovarian syndrome (PCOS), and to compare the clinical, hormonal and ultrasonography features between infertile women with or without PCOS. Design: A descriptive, comparative study. Materials and Methods: This study was conducted from May 1, 2007 to August 1, 2008, in the Infertility Care and IVF center in Maternity Teaching Hospital, Erbil city, Kurdistan region, North ofIraq. A total of 320 infertile women aged 18 - 45 years, were evaluated for clinical features (oligo-/amenorrhea, hirsutism), body mass index, waist-hip ratio (WHR) and hormonal measures. Transvaginal ultrasonography was used to assess the ovarian morphology. The Rotterdam 2003 criteria adopted by the European Society for Human reproduction and Embryology and the American Society for Reproductive Medicine were used to diagnose cases of PCOS. Data analysis was performed using the SPSS version 15. Results: The prevalence of PCOS was 33%. There were a significant differences between the two groups in terms of oligo-/amenorrhea, hirsutism, WHR, and ovarian ultrasound features. There were no significant differences between the two groups in correlations between the level of obesity with the incidences of anovulation, hyperandrogenemia and hirsutism or with hormonal features. Conclusions: A high prevalence rate of PCOS was observed among infertile women attending IVF center using the Rotterdam 2003 criteria for diagnosis.
文摘Urban expansion of cities has caused changes in land use and land cover(LULC)in addition to transformations in the spatial characteristics of landscape structure.These alterations have generated heat islands and rise of land surface temperature(LST),which consequently have caused a variety of environmental issues and threated the sustainable development of urban areas.Greenbelts are employed as an urban planning containment policy to regulate urban expansion,safeguard natural open spaces,and serve adaptation and mitigation functions.And they are regarded as a powerful measure for enhancing urban environmental sustainability.Despite the fact that,the relation between landscape structure change and variation of LST has been examined thoroughly in many studies,but there is a limitation concerning this relation in semi-arid climate and in greenbelts as well,with the lacking of comprehensive research combing both aspects.Accordingly,this study investigated the spatiotemporal changes of landscape pattern of LULC and their relationship with variation of LST within an inner greenbelt in the semi-arid Erbil City of northern Iraq.The study utilized remote sensing data to retrieve LST,classified LULC,and calculated landscape metrics for analyzing spatial changes during the study period.The results indicated that both composition and configuration of LULC had an impact on the variation of LST in the study area.The Pearson's correlation showed the significant effect of Vegetation 1 type(VH),cultivated land(CU),and bare soil(BS)on LST,as increase of LST was related to the decrease of VH and the increases of CU and BS,while,neither Vegetation 2 type(VL)nor built-up(BU)had any effects.Additionally,the spatial distribution of LULC also exhibited significant effects on LST,as LST was strongly correlated with landscape indices for VH,CU,and BS.However,for BU,only aggregation index metric affected LST,while none of VL metrics had a relation.The study provides insights for landscape planners and policymakers to not only develop more green spaces in greenbelt but also optimize the spatial landscape patterns to reduce the influence of LST on the urban environment,and further promote sustainable development and enhance well-being in the cities with semi-arid climate.
文摘AIM:To determine the prevalence of tropia,phoria,and abnormality of near point of convergence(NPC),along with associated ocular symptoms,in high school students.METHODS:This cross-sectional study was conducted in Erbil,Iraq.The target population consisted of high school students selected through a multi-stage cluster sampling method.Comprehensive visual examinations were performed for all students,including measurement of uncorrected and corrected visual acuity,objective and subjective refraction,and distance and near cover tests.NPC was evaluated using a single 6/12 visual target mounted on a centrally positioned Gulden fixation stick.Ocular symptoms were investigated through interviews.RESULTS:Of the 996 selected students,921 participated in the study.Of them,543(58.96%)were female,and their ages ranged from 13 to 22y.The prevalence of tropia was 3.58%[95%confidence interval(CI):2.38%-4.78%],observed in 3.44%of males and 3.68%of females.Exotropia(1.95%,95%CI:1.06%-2.85%)was more common than esotropia(1.52%,95%CI:0.73%-2.31%).The 15.42%(95%CI:13.09%-17.75%)of students had phoria.Exophoria(13.79%,95%CI:11.56%-16.02%)was significantly more prevalent than esophoria(1.63%,95%CI:0.81%-2.45%).The prevalence of NPC abnormality in the total study population was 24.97%(95%CI:22.18%-27.77%).It was 26.72%(95%CI:22.26%-31.18%)in males and 23.76%(95%CI:20.18%-27.34%)in females(P=0.307).The most common symptom in phoria was headache(86.62%,95%CI:81.02%-92.22%),followed by tired or sore eyes(61.97%,95%CI:53.99%-69.96%).The most common symptoms in tropia were blurry vision(93.94%,95%CI:79.77%-99.26%)and difficulty concentrating(87.88%,95%CI:76.74%-99.01%).CONCLUSION:Among Erbil’s high school students,the prevalence of strabismus,particularly the exodeviation type,is relatively high,and a significant percentage of students have NPC abnormalities.Addressing and correcting these binocular vision problems,due to their associated visual symptoms,can lead to an improvement in students’quality of life and academic performance.
文摘Coiled tube heat exchangers are widely preferred in shell structures due to their superior heat transfer performance,driven by favorable flow characteristics.This study investigates the effect of modifying coil and shell configurations on heat transfer efficiency.Two key enhancements were examined:adding fins to the outer coil surface and integrating longitudinal slots within a hollowed shell.These modifications promote turbulence and extend heat transfer duration,thereby improving performance.However,they also introduce challenges,including increased pressure loss andmanufacturing complexity.Numerical simulationswere conducted usingANSYS Fluent 2024R1 under identical boundary conditions.With a fixed cold-side flow rate of 3 L/min,the input temperatures for the hot and cold fluids were 333.15 and 291.65 K,respectively.The hot-side flow rate varied between 2 and 6 L/min.Simulation outcomes were reported for the objectives of the study that included the improvement in heat exchangers’heat transfer enhancement.As it was indicated in the study outcomes,the average heat transfer rate increased by 15.56%,the overall heat transfer coefficient enhanced by about 29.51%,and the convective heat transfer coefficient improved by about 75.96%compared to the conventional shell-and-coil tube heat exchanger model.However,the modified technique resulted in a significant pressure drop.
文摘The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.
文摘The hybrid photovoltaic solar-assisted heat pump are primarily used to generate electricity and provide thermal energy for heating applications.This study investigates the performance enhancement of a hybrid Photovoltaic Thermal Solar-Assisted Heat Pump(PV/T-SAHP)system integrated with a solar tracking mechanism.The system was simulated using TRNSYS to evaluate its monthly electrical output and coefficient of performance(COP)of the heat pump system over a year.The results showed a significant improvement in energy generation and efficiency compared to a conventional PV/T system without SAHP system.Overall,the solar tracking configuration of the PV/T-SAHP generated 10%–40%more electricity than the fixed system.The system for the tracking mode achieved a maximum monthly average electrical energy output of 634.349 kWh in June.Throughout the year,the tracking mode consistently outperformed the fixed mode.During the winter months of January and December,the tracking system produced 328.7 and 323.6 kWh,respectively,compared to 297.8 and 299.7 kWh for the fixed mode.The highest COP of 5.65 occurred in July,indicating a strong seasonal correlation with solar irradiance.In contrast,the minimum COP of 4.55 was observed in the months,February and March,reflecting reduced solar availability.The solar tracking feature consistently maintained an optimal panel angle,increasing energy gains and improving system efficiency.Overall,the integration of a heat pump and tracking control significantly improved system performance,making the hybrid PV/T-SAHP configuration a promising solution for year-round renewable energy generation.
文摘This study delves into both experimental and analytical examinations of heat exchange in a straight channel, where Al_(2)O_(3)-water nanofluids are utilized, spanning the Reynolds number spectrum from 100 to 1800. Diverse volume fractions(1%, 2%, and 3%) of Al_(2)O_(3)-water nanofluids are meticulously prepared and analyzed. The essential physical properties of these nanofluids, critical for evaluating their thermal and flow characteristics, have been comprehensively assessed. From a quantitative perspective, numerical simulations are employed to predict the Nusselt number(Nu) and friction factor(f). The empirical findings reveal intriguing trends: the friction factor experiences an upward trend with diminishing velocity, attributed to heightened molecular cohesion. Conversely, the friction factor demonstrates a decline with diminishing volume fractions, a consequence of reduced particle size. Both the nanofluid's viscosity and heat transfer coefficient exhibit a rise in tandem with augmented volume flow rate and concentration gradient. Notably, the simulation results harmonize remarkably well with experimental data. Rigorous validation against prior studies underscores the robust consistency of these outcomes. In the pursuit of augmenting heat transfer, a volume fraction of 3% emerges as particularly influential, yielding an impressive 53.8% enhancement. Minor increments in the friction factor, while present, prove negligible and can be safely overlooked.
文摘This work presents a study on the use of cathodic protection as a measure against corrosion in pipelines.The cathodic protection,compliant with the API 5L standard,is implemented here by applying an impressed current,while carefully considering several essential variables,such as soil characteristics,the type and color of the pipeline material,as well as the placement and size of the anode.Therefore,it is crucial to optimize the location and values of anodic overflows or ground resistances to ensure a uniform distribution of potential across the entire structure.In this method,impressed current protection uses an auxiliary anode and an external direct current source to induce a current through the electrolyte and the pipeline,thus countering the resistance of the steel.This approach is advantageous as it allows for the adjustment of electrical characteristics,particularly current levels,to meet specific needs.The factors essential to the effectiveness of cathodic protection systems,which optimize the distribution of protection potential across the structure,largely depend on the precise management of ground resistances during anodic discharge,particularly the attenuation coefficient(α).These factors were studied,and the results obtained were presented and discussed based on their influence.
文摘A comprehensive numerical investigation into mixed⁃mode delamination is presented in this study.It aims to assess the impact of thermal and friction effects through mixed⁃mode flexure crack propagation testing.Finite element analysis was employed to model the delamination process,incorporating a contact cohesive zone model.This model couples the traction⁃separation law,the contact law,and the Coulomb friction law simultaneously.The thermomechanical analysis in this study is performed using a sequentially coupled approach,implemented with the finite element software ABAQUS.The findings underscore the importance of this study.
文摘The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health.This study aims to enhance risk prediction in pregnancywith a novel deep learningmodel based on a Long Short-Term Memory(LSTM)generator,designed to capture temporal relationships in cardiotocography(CTG)data.This methodology integrates CTG signals with demographic characteristics and utilizes preprocessing techniques such as noise reduction,normalization,and segmentation to create high-quality input for themodel.It uses convolutional layers to extract spatial information,followed by LSTM layers to model sequences for superior predictive performance.The overall results show that themodel is robust,with an accuracy of 91.5%,precision of 89.8%,recall of 90.4%,and F1-score of 90.1%that outperformed the corresponding baselinemodels,CNN(Convolutional Neural Network)and traditional RNN(Recurrent Neural Network),by 2.3%and 6.1%,respectively.Rather,the ability to detect pregnancy-related abnormalities has considerable therapeutic potential,with the possibility for focused treatments and individualized maternal healthcare approaches,the research team concluded.
文摘A detailed comparative examination of the Weibull and Gaussian statistical methods is offered to analyze the mechanical properties of natural date palm fibers. Tensile tests were conducted on 35 fiber samples using a universal testing machine to gather data on stress, strain, and Young's modulus. This data was then analyzed through both statistical approaches to evaluate their ability to model important mechanical characteristics, including tensile strength, strain at break, and Young's modulus. The study identifies the strengths and weaknesses of each statistical method when it is applied to natural fibers, emphasizing their suitability for modeling different mechanical properties. The results of this analysis provide important insights that can guide the selection of the most appropriate statistical method, depending on the type of mechanical property being studied and the specific characteristics of the data. This research makes significant contributions to advancing the understanding of natural fiber mechanics and improving the methods used for their characterization.
文摘Artifcial light at night(ALAN)is a common anthropogenic disturbance,which alters animal behavior.However,little is known about the impact of the spectral composition of ALAN and co-occurring predation risk on the behavior of aquatic organisms.We experimentally investigated how ALAN of different spectra(cool white LED and HPS light)affects the behavior and foraging of Gammarus jazdzewskii(Amphipoda)on chironomid prey,both as a single stressor and in combination with an olfactory predation cue.Gammarids exposed to ALAN in the absence of predation cues consumed less,compared with darkness,mainly due to their lower activity.Moreover,gammarids showed a stronger response to LED light,spending more time in the shelter and increasing prey handling time in this treatment.The addition of predation cues did not enhance the negative impact of ALAN on the foraging success.Gammarids maintained similar consumption levels as in the ALAN treatment without predation cues and in darkness with predation cues.However,gammarids in LED light altered their behavior in response to predation threat:they decreased prey handling time and consumed prey faster,which may have compensated for the higher food demand in stressful conditions.They also tended to exhibit risky behavior,leaving the shelter and moving towards the lit area,presumably to escape and avoid the combined effects of light and predation cues.Therefore,when assessing the effects of ALAN on organisms,light quality and co-occurring biotic factors should be considered,as predator pressure is common in natural environments.
文摘The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering applications.Structural optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural safety.The design approaches aim to reduce the built environment’s energy use and carbon emissions.This comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these methodologies.The trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were identified.The review also discusses emerging technologies,such as machine learning applications with different optimization techniques.Optimization of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and discussed.Optimization techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure types.Linear and non-linear structures,including geometric and material nonlinearity,are distinguished.The role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.
基金The support of Prince Sultan University for paying the Article Processing Charge(APC)of this publication and their support.Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R300).
文摘Soilcrete is a composite material of soil and cement that is highly valued in the construction industry.Accurate measurement of its mechanical properties is essential,but laboratory testing methods are expensive,timeconsuming,and include inaccuracies.Machine learning(ML)algorithms provide a more efficient alternative for this purpose,so after assessment with a statistical extraction method,ML algorithms including back-propagation neural network(BPNN),K-nearest neighbor(KNN),radial basis function(RBF),feed-forward neural networks(FFNN),and support vector regression(SVR)for predicting the uniaxial compressive strength(UCS)of soilcrete,were proposed in this study.The developed models in this study were optimized using an optimization technique,gradient descent(GD),throughout the analysis(direct optimization for neural networks and indirect optimization for other models corresponding to their hyperparameters).After doing laboratory analysis,data pre-preprocessing,and data-processing analysis,a database including 600 soilcrete specimens was gathered,which includes two different soil types(clay and limestone)and metakaolin as a mineral additive.80%of the database was used for the training set and 20%for testing,considering eight input parameters,including metakaolin content,soil type,superplasticizer content,water-to-binder ratio,shrinkage,binder,density,and ultrasonic velocity.The analysis showed that most algorithms performed well in the prediction,with BPNN,KNN,and RBF having higher accuracy compared to others(R^(2)=0.95,0.95,0.92,respectively).Based on this evaluation,it was observed that all models show an acceptable accuracy rate in prediction(RMSE:BPNN=0.11,FFNN=0.24,KNN=0.05,SVR=0.06,RBF=0.05,MAD:BPNN=0.006,FFNN=0.012,KNN=0.008,SVR=0.006,RBF=0.009).The ML importance ranking-sensitivity analysis indicated that all input parameters influence theUCS of soilcrete,especially the water-to-binder ratio and density,which have themost impact.
文摘This study introduces an innovative hybrid approach that integrates deep learning with blockchain technology to improve cybersecurity,focusing on network intrusion detection systems(NIDS).The main goal is to overcome the shortcomings of conventional intrusion detection techniques by developing amore flexible and robust security architecture.We use seven unique machine learning models to improve detection skills,emphasizing data quality,traceability,and transparency,facilitated by a blockchain layer that safeguards against datamodification and ensures auditability.Our technique employs the Synthetic Minority Oversampling Technique(SMOTE)to equilibrate the dataset,therefore mitigating prevalent class imbalance difficulties in intrusion detection.The model selection procedure determined that Random Forest was the most successful model,with a notable detection accuracy of 97%.This substantially surpasses conventional methods and enhances the system’s capacity to identify both established and novel threats with exceptional accuracy.To optimize feature selection and maximize performance,we use Extreme Gradient Boosting(XGBoost),which improves the significance of chosen features while reducing the danger of overfitting.Our study indicates that the integrated use of machine learning for pattern identification,multi-factor authentication(MFA)for access security,and blockchain for data validation constitutes a thorough and sustainable cybersecurity solution.This architecture not only increases security but also lowers the need for regular human monitoring,significantly cutting energy consumption connected with cybersecurity infrastructure.The research finds that this integrated strategy provides a realistic road for increasing network security,addressing real-world cyber threats,and promoting eco-friendly practices in IT security.
文摘In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning technique.Vital applications like military operations,healthcare monitoring,and environmental surveillance increasingly deploy WSNs,recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational integrity.The proposed method innovatively partitions the network into logical segments or virtual barriers,allowing for targeted monitoring and data collection that aligns with specific traffic patterns.This approach not only improves the diversit.There are more types of data in the training set,and this method uses more advanced machine learning models,like Convolutional Neural Networks(CNNs)and Long Short-Term Memory(LSTM)networks together,to see coIn our work,we used five different types of machine learning models.These are the forward artificial neural network(ANN),the CNN-LSTM hybrid models,the LR meta-model for linear regression,the Extreme Gradient Boosting(XGB)regression,and the ensemble model.We implemented Random Forest(RF),Gradient Boosting,and XGBoost as baseline models.To train and evaluate the five models,we used four possible features:the size of the circular area,the sensing range,the communication range,and the number of sensors for both Gaussian and uniform sensor distributions.We used Monte Carlo simulations to extract these traits.Based on the comparison,the CNN-LSTM model with Gaussian distribution performs best,with an R-squared value of 99%and Root mean square error(RMSE)of 6.36%,outperforming all the other models.
