Mapping Model Research Articles

A Systematic Literature Review and Bibliometric Analysis of Semantic Segmentation Models in Land Cover Mapping

Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, and ISPRS Journal Of Photogrammetry And Remote Sensing. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by the USA (11.76%), France (5.88%), Spain (4%), and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most used data sources. Benchmark datasets such as ISPRS Vaihingen and Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder–decoder and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions.

Climate-informed flood risk mapping using a GAN-based approach (ExGAN)

This study develops a class of robust models for flood risk mapping in highly vulnerable regions by focusing on accurately depicting extreme precipitation patterns aligned with regional climates. By implementing sophisticated hydrodynamics modeling and advanced probabilistic approaches, the present work underscores the efficacy of physical-based methodologies in the flood risk assessment. We propose a machine learning based ExGAN to address the challenge of synthesizing extreme precipitation scenarios which faithfully capture the nuances of local climatology. It is expected that through refined temporal disaggregation, the ExGAN approach exhibits exceptional proficiency in replicating a diverse spectrum of extreme precipitation patterns specific to the vulnerable region under scrutiny. Therefore, using these synthesized scenarios as inputs in a meticulously calibrated hydrological model would enable a comprehensive and detailed flood risk mapping exercise. To demonstrate the robustness of the developed mode, we perform a rigorous testing and validation within the highly susceptible Martil river basin, situated in the northern Mediterranean region of Morocco. The obtained results confirm that extending return periods would provide invaluable insights into the expanding geographical expanse of at-risk areas, clarifying the evolving landscape of vulnerability rather than merely amplifying inherent risk levels. Comparisons against the conventional Monte-Carlo sampling are also carried out in this study and the obtained results highlight significant overestimations within the latter, emphasizing the imperative need to account for diverse uncertainties beyond the basic sampling strategies within the realm of hydrodynamic modeling.

Stratigraphic cross-sections, geologic history, and provenance of material at the candidate landing sites of the Artemis missions

From the scientific perspective, Artemis lunar missions focus on the south circumpolar region (SCR) mainly to investigate the existence and abundance of volatiles and to explore and sample ancient lunar deposits. The volatile distribution is primarily related to the cold traps in permanently shadowed regions, while the availability of ancient material is due to the proximity to the early lunar ∼2300 km diameter South Pole-Aitken (SPA) impact basin. One of the critical factors for future missions will be determining the geological structure and provenance (sources) of material at each candidate landing site, which can be predicted utilizing three-dimensional stratigraphic reconstructions of geological map units and crater ejecta deposits. This type of reconstruction permits a better understanding of candidate material that can be collected and analyzed at each site, and a ranking of landing sites can be formulated on this basis. Here, we present reconstructed geological cross-sections at Artemis landing sites using our recent SCR geological map and numerical modelling of crater ejecta thicknesses and their sequence.

Research on deep-sea terrain refinement methods based on satellite remote sensing data

Bathymetric data is crucial for navigation safety and various applications. However, only a small portion of the seafloor has been systematically mapped through direct measurement. The remaining bathymetry is estimated from satellite altimeter data, providing only an approximate representation of the seafloor's shape. To address this issue, the convolutional neural network (CNN) is introduced for the analysis of seabed topography. To the best of our knowledge, this marks the first attempt to incorporate the CNN into the definitive map of the world ocean floor. By employing skip connections based on residual learning, we construct a deeper CNN layer that effectively reduces training complexity. The deeper convolutional neural network layer is used to create a more complex nonlinear mapping model, which is then employed to downscale low-resolution seabed topographic data in order to obtain high-precision seabed topographic information. This process enhances the representation of seabed topographic data to reflect the actual terrain more accurately. The results are compared with those obtained using the bicubic difference method. Conducting tests near the Gulf of Thailand, where Malaysia Airlines Flight MH370 was lost, the results demonstrate that the method proposed in this paper achieves higher accuracy in processing the downscaling of seabed topographic data.

Integrating Satellite Images and Machine Learning for Flood Prediction and Susceptibility Mapping for the Case of Amibara, Awash Basin, Ethiopia

Flood is one of the most destructive natural hazards affecting the environment and the socioeconomic system of the world. The effects are higher in the developing countries due to their higher vulnerability to disaster and limited coping capacity. The Awash basin is one of the flood-prone basins in Ethiopia where the frequency and severity of flooding has been increasing. Amibara district is one of the flood-affected areas in the Awash basin. To minimize the effects of flooding, reliable and up-to-date information on flooding is highly required. However, flood monitoring and forecasting systems are lacking in most basins of Ethiopia including the Awash basin. Therefore, this study aimed to (i) identify important flood causative factors, (ii) evaluate the performance of random forest (RF), linear regression, support vector machine (SVM), and long short-term memory (LSTM) machine learning models for flood prediction and susceptibility mapping in the Amibara area. For developing flood prediction and susceptibility modeling, nine causative factors were considered, namely elevation, slope, aspect, curvature, topographic wetness index, soil texture, rainfall, land use/land cover, and curve number. The Pearson correlation coefficient and information gain ratio (InGR) techniques were used to evaluate the relative importance of the factors. The machine learning models were trained and tested using 400 historic flood points collected from the 10 September 2020 Sentinel 2 image, during which a flood event occurred in the area. Multiple metrics, namely precession, recall, F1-score, accuracy, and receiver operating characteristics (area under curve), were used to evaluate the performance of the models. The results showed that all the factors considered in this study were important; elevation, rainfall, topographic wetness index, aspect, and slope were more important while land use/land cover, curve number, curvature, and soil texture were less important. Furthermore, the results showed that random forest outperformed in predicting and mapping flooding for the study area whereas the linear regression model showed the next best performance to RF. However, SVM performed poorly in flood prediction and susceptibility mapping. The integration of satellite and field datasets coupled with state-of-the-art-machine learning models are novel approaches and thus improved the accuracy of flood prediction and susceptibility mapping. Such methodology improves the state-of-the-art knowledge in this field and fills the gaps of traditional flood mapping techniques. Thus, the results of the study can provide crucial information for informed decision-making in the processes of designing flood control strategies and risk management.

Integrating deep learning, satellite image processing, and spatial-temporal analysis for urban flood prediction

Urban flooding is escalating worldwide due to the increasing impervious surfaces from urban developments and frequency of extreme rainfall events by climate change. Traditional flood extent prediction and mapping methods based on physical-based hydrological principles often face limitations due to model complexity and computational burden. In response to these challenges, there has been a notable shift toward satellite image processing and Artificial Intelligence (AI) based approaches, such as Deep Learning (DL) models, including architectures like Convolutional Neural Networks (CNN). The objective of this research is to predict near real-time (NRT) flood extents within urban areas. This research integrated CNN (U-Net) with Sentinel-1 satellite imagery, Digital Elevation Model (DEM), hydrologic soil group (HSG), imperviousness, and rainfall data to create a flood extent prediction model. To detect flooded areas, a binary raster map was created using calibrated backscatter values derived from the VV (vertical transmit and vertical receive) polarization mode of Sentinel-1 imagery, which was highlighted as having a significant impact on backscatter behavior and prediction results. Application of the model was demonstrated in urban areas of Miami-Dade County, Florida. The results demonstrated the capability of the model to provide rapid and accurate flood extent predictions at a spatial resolution of 10 m, with an overall accuracy of 97.05 %, F-1 Score of 92.49 %, and AUC of 93 % in the study area. The U-Net model’s flood predictions were compared with historical floodplain data and then using GIS overlay analysis, resulting in a Ground Truth Index of 84.05 % that shows the accuracy of the model in identifying flooded areas. The research incorporated crucial flood-influencing data (including rainfall) to the flood extent prediction models and expanded the focus models beyond major rainfall events only to encompass a wider range of flood events. The presented NRT flood extent mapping model has a broad range of applicability, including, but not limited to, the continuous monitoring of flood events and their potential impacts on civil infrastructure assets (e.g., construction, operation, and maintenance of roads and bridges), early warning systems for timely evacuation and preparedness measures, and insurance risk assessment.

Digital soil mapping of soil burn severity

AbstractFire alters soil hydrologic properties leading to increased risk of catastrophic debris flows and post‐fire flooding. As a result, US federal agencies map soil burn severity (SBS) via direct soil observation and adjustment of rasters of burned area reflectance. We developed a unique application of digital soil mapping (DSM) to map SBS in the Creek Fire which burned 154,000 ha in the Sierra Nevada. We utilized 169 ground‐based observations of SBS in combination with raster proxies of soil forming factors, pre‐fire fuel conditions, and fire effects to vegetation to build a digital soil mapping model of soil burn severity (DSMSBS) using a random forest algorithm and compared the DSMSBS map to the established SBS map. The DSMSBS model had a cross‐validation accuracy of 48%. The established technique had 46% agreement between field observations and pixels. However, since the established technique is manual, it could not be compared to the DSMSBS model via cross‐validation. We produced SBS class uncertainty maps, which showed high prediction probabilities around field observations, and low probabilities away from field observations. SBS prediction probabilities could aid post‐fire assessment teams with sample prioritization. We report 107 km2 more area classified as high and moderate SBS compared to the established technique. We conclude that blending soil forming factors based mapping and vegetation burn severity mapping can improve SBS mapping. This represents a shift in SBS mapping away from validating remotely sensed reflectance imagery and toward a quantitative soil landscape model, which incorporates both fire and soils information to directly predict SBS.

A biologically inspired computational model of human ventral temporal cortex

Our minds represent miscellaneous objects in the physical world metaphorically in an abstract and complex high-dimensional object space, which is implemented in a two-dimensional surface of the ventral temporal cortex (VTC) with topologically organized object selectivity. Here we investigated principles guiding the topographical organization of object selectivities in the VTC by constructing a hybrid Self-Organizing Map (SOM) model that harnesses a biologically inspired algorithm of wiring cost minimization and adheres to the constraints of the lateral wiring span of human VTC neurons. In a series of in silico experiments with functional brain neuroimaging and neurophysiological single-unit data from humans and non-human primates, the VTC-SOM predicted the topographical structure of fine-scale category-selective regions (face-, tool-, body-, and place-selective regions) and the boundary in large-scale abstract functional maps (animate vs. inanimate, real-word small-size vs. big-size, central vs. peripheral), with no significant loss in functionality (e.g., categorical selectivity and view-invariant representations). In addition, when the same principle was applied to V1 orientation preferences, a pinwheel-like topology emerged, suggesting the model's broad applicability. In summary, our study illustrates that the simple principle of wiring cost minimization, coupled with the appropriate biological constraint of lateral wiring span, is able to implement the high-dimensional object space in a two-dimensional cortical surface.

Construction of user-adaptive urban waterlogging emergency scenarios considering mapping concerns

Many current urban waterlogging emergency scenario constructions overlook the factors that influence users’ concerns, rendering the resulting scenarios less adaptable to various user types and hindering efficient comprehension of waterlogging risks. The objective of this study is to link user concerns with elements of waterlogging emergency scenarios and then proposes a model accommodating various user groups to enhance scenario applicability. Initially, we established a user classification system for urban waterlogging emergencies. Subsequently, we extracted relevant topic keywords from social media and emergency plans, aligning them with different user groups to address their specific concerns. Additionally, we expanded the geographical elements (time, location, people, things, events, and phenomena) of the scenario to encompass its constituent components. Utilising domain ontology, we developed a mapping model between user concerns and scenario elements. Finally, this study employed both prototype system development and questionnaire validation to assess the efficacy of the user model. The validation for mean values, include Accuracy 0.923, Completeness 0.945, Satisfaction 0.879, and model’s effectiveness is 91.7 %. The results indicate that the model has high reliability and could effectively expand the user universality of urban waterlogging emergency scenarios.

Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway)

The use of machine learning models for landslide susceptibility mapping is widespread but limited to spatial prediction. The potential of employing these techniques in spatiotemporal landslide forecasting remains largely unexplored. To address this gap, this study introduces an innovative dynamic (i.e., space–time-dependent) application of the random forest algorithm for evaluating landslide hazard (i.e., spatiotemporal probability of landslide occurrence). An area in Norway has been chosen as the case study because of the availability of a comprehensive, spatially, and temporally explicit rainfall-induced landslide inventory. The applied methodology is based on the inclusion of dynamic variables, such as cumulative rainfall, snowmelt, and their seasonal variability, as model inputs, together with traditional static parameters such as lithology and morphologic attributes. In this study, the variables’ importance was assessed and used to interpret the model decisions and to verify that they align with the physical mechanism responsible for landslide triggering. The algorithm, once trained and tested against landslide and non-landslide data sampled over space and time, produced a model predictor that was subsequently applied to the entire study area at different times: before, during, and after specific landslide events. For each selected day, a specific and space–time-dependent landslide hazard map was generated, then validated against field data. This study overcomes the traditional static applications of machine learning and demonstrates the applicability of a novel model aimed at spatiotemporal landslide probability assessment, with perspectives of applications to early warning systems.

Modeling and analysis of digital current‐mode controlled single‐inductor dual‐output DC–DC converter with different PWM modulation modes

SummaryCurrent‐mode control is extensively used in dc–dc converters as the merits of fast transient performance, simple control loop, and elimination of right‐half‐plane zeros for boost‐type dc–dc converters. In single‐inductor multiple‐output (SIMO) dc–dc converter, the inductor current would present different trends under different circuit parameters. Therefore, the control logic changes for different inductor current trends, and the corresponding modulations are absolutely different. In this paper, a digital current‐mode control technology for SIMO dc–dc converter is proposed to expend the convenience of current‐mode control with different inductor trends. Choosing continuous conduction mode (CCM) single‐inductor dual‐output (SIDO) dc–dc converter as the research object, the principles are analyzed under different pulse width modulation modes, and the corresponding control algorithms are derived. Moreover, the discrete iterative map model is established to analyze the stability of input and output voltages varying. The results show that not only the digital current‐mode controlled SIDO buck converter has fast load transient response and small cross‐regulation but also the input and output voltages will not affect the stability in the leading edge modulation, which is different from the corresponding analog current‐mode control technology. Finally, the experimental results verify the correctness of the theoretical analysis.

Ineffective Learning Behaviors and Their Psychological Mechanisms among Adolescents in Online Learning: A Narrative Review.

During the COVID-19 pandemic, many countries and regions experienced a surge in online learning, but the public complained about and questioned its effectiveness. One of the most important reasons for this was the inadequate metacognitive abilities of adolescents. Studies in learning sciences have identified various inefficient learning behaviors among students in online learning, including help abuse, help avoidance, and wheel spinning; all closely related to metacognition. Despite concerns about ecological validity, researchers in psychology have proposed the agenda-based regulation framework, the COPES model, and MAPS model, which may help explain the inefficient learning behaviors among adolescents in online learning. Future studies should aim to verify these theoretical frameworks within the context of online learning and elucidate the causes of inefficient learning behaviors; the design and optimization of online learning systems should be informed by theories in cognitive psychology.

Revealing the Correlation Between Lyapunov Exponent and Modulus of an n-Dimensional Nondegenerate Hyperchaotic Map

For their good randomness and long iteration periods, chaotic maps have been widely used in cryptography. Recently, we have revealed the correlation between Lyapunov exponent and sequence randomness of multidimensional chaotic maps based on modular operation. Since the modular operation can realize the boundedness of chaotic state points, it is important to further reveal the deterministic correlation between Lyapunov exponent and modulus. First, we constructed an [Formula: see text]-dimensional nondegenerate hyperchaotic map model with the desired Lyapunov exponents. Then, we gave the existence and uniqueness proof of quadrature rectangle decomposition theorem and revealed the correlation between Lyapunov exponent and modulus. The novelty lies in that (1) in order to realize the irreversibility of the iterative processes of chaotic maps, we constructed a chaotic map based on modular exponentiation, and its inverse function is the discrete logarithm problem; and (2) we reveal for the first time the correlation between Lyapunov exponent and modulus, and give the lower bound of the modulus of the nondegenerate chaotic map. In addition, to verify the effectiveness of the scheme, we constructed four-dimensional and five-dimensional chaotic maps, respectively, and analyzed their dynamical behaviors, and the results revealed that there exist linear or nonlinear correlation between Lyapunov exponent and modulus.

Two-dimensional Eclipse Mapping of the Hot-Jupiter WASP-43b with JWST MIRI/LRS

We present eclipse maps of the two-dimensional thermal emission from the dayside of the hot-Jupiter WASP-43b, derived from an observation of a phase curve with the JWST MIRI/LRS instrument. The observed eclipse shapes deviate significantly from those expected for a planet emitting uniformly over its surface. We fit a map to this deviation, constructed from spherical harmonics up to order ℓmax=2 , alongside the planetary, orbital, stellar, and systematic parameters. This yields a map with a meridionally averaged eastward hot-spot shift of (7.75 ± 0.36)°, with no significant degeneracy between the map and the additional parameters. We show the latitudinal and longitudinal contributions of the dayside emission structure to the eclipse shape, finding a latitudinal signal of ∼200 ppm and a longitudinal signal of ∼250 ppm. To investigate the sensitivity of the map to the method, we fix the parameters not used for mapping and derive an “eigenmap” fitted with an optimized number of orthogonal phase curves, which yields a similar map to the ℓmax=2 map. We also fit a map up to ℓmax=3 , which shows a smaller hot-spot shift, with a larger uncertainty. These maps are similar to those produced by atmospheric simulations. We conclude that there is a significant mapping signal which constrains the spherical harmonic components of our model up to ℓmax=2 . Alternative mapping models may derive different structures with smaller-scale features; we suggest that further observations of WASP-43b and other planets will drive the development of more robust methods and more accurate maps.

USV Path Planning in a Hybrid Map Using a Genetic Algorithm with a Feedback Mechanism

Unmanned surface vehicles (USVs) often operate in real-world environments with long voyage distances and complex routes. The use of a single-grid map model presents challenges, such as the high computational costs for high-resolution maps and loss of environmental information for low-resolution maps. This article proposes an environmental modeling method using a hybrid map that combines topology units and grids. The approach involves calibrating key nodes based on the watershed skeleton line, constructing a topology map using these nodes, decomposing the original map into unit maps, converting each unit map into a grid map, and creating a hybrid map environment model that comprises topology maps, unit map sets, and grid map sets. Then, the article introduces an improved genetic algorithm, called Genetic Algorithm with Feedback (FGA), to address path planning in hybrid maps. Experimental results demonstrate that FGA has better computational efficiency than other algorithms in similar experimental environments. In hybrid maps, path planning with FGA reduces the path lengths and time consumption, and the paths are more logical, smooth, and continuous. These findings contribute to enhancing the quality of path planning and the practical value of USVs.

Spatio-temporal pattern and associate meteorological factors of airborne diseases in Bangladesh using geospatial mapping and spatial regression model.

Airborne diseases due to climate change pose significant public health challenges in Bangladesh. Little was known about the spatio-temporal pattern of airborne diseases at the district level in the country. Therefore, this study aimed to investigate the spatio-temporal pattern and associated meteorological factors of airborne diseases in Bangladesh using exploratory analysis and spatial regression models. This study used district-level reported cases of airborne diseases (meningococcal, measles, mumps, influenza, tuberculosis, and encephalitis) and meteorological data (temperature, relative humidity, wind speed, and precipitation) from 2017 to 2020. Geospatial mapping and spatial error regression models were utilized to analyze the data. From 2017 to 2020, a total of 315 meningococcal, 5159 measles, 1341 mumps, 346 influenza, 4664 tuberculosis, and 229 encephalitis cases were reported in Bangladesh. Among airborne diseases, measles demonstrated the highest prevalence, featuring a higher incidence rate in the coastal Bangladeshi districts of Lakshmipur, Patuakhali, and Cox's Bazar, as well as in Maulvibazar and Bandarban districts from 2017 to 2020. In contrast, tuberculosis (TB) emerged as the second most prevalent disease, with a higher incidence rate observed in districts such as Khagrachhari, Rajshahi, Tangail, Bogra, and Sherpur. The spatial error regression model revealed that among climate variables, mean (β = 9.56, standard error [SE]: 3.48) and maximum temperature (β = 1.19, SE: 0.40) were significant risk factors for airborne diseases in Bangladesh. Maximum temperature positively influenced measles (β = 2.74, SE: 1.39), whereas mean temperature positively influenced both meningococcal (β = 5.57, SE: 2.50) and mumps (β = 11.99, SE: 3.13) diseases. The findings from the study provide insights for planning early warning, prevention, and control strategies to combat airborne diseases in Bangladesh and similar endemic countries. Preventive measures and enhanced monitoring should be taken in some high-risk districts for airborne diseases in the country.

Mapping models of care for children, adolescent and young adult cancer survivors: A scoping review.

e13793 Background: Young cancer survivors, with decades of life ahead, encounter substantial risks for treatment-related late effects. Achieving optimal outcomes requires a comprehensive multi-dimensional approach. This scoping review aimed to systematically map the evidence on models of survivorship care for Child (0-14) and Adolescent and Young Adult (AYA, 15-39) cancer survivors aligning with the Framework for Quality Cancer Survivorship. Methods: Conducted according to PRISMA extension for scoping reviews, this study systematically searched MEDLINE, CINAHL and Embase for English articles from Jan 2006 to Jan 2024. Inclusion criteria were studies describing patient and health service domain for Child and AYA cancer survivorship models of care. Dual processes were used for article screening, selection, and data extraction. A deductive analysis, using Content Analysis informed results, presented in a narrative synthesis. Results: Of the 5,298 records screened 38 described Child and 10 described AYA models of care. The majority were cohort and cross-sectional studies from the USA, Canada, and the UK representing 6,101 Child and 5,283 AYA cancer survivors. Across these diverse studies, a broad range of outcomes were identified with most domains of the Framework represented. No model of care described more domains than another. Across the 13 possible elements of care and outcomes, most (68%) reported on less than five of these. The domains most represented included elements of care rather than outcomes at the service level such as the clinic structure (52%), communication and decision making (52%) and patient/carer satisfaction (50%). At the patient level, quality of life outcomes and assessment and management of physical symptoms were represented in 40% of studies. There were few studies reporting on health promotion (23%), chronic conditions (13%), mortality and healthcare utilization (5% each) and none reported on costs. In terms of outcomes, oncologist long-term follow up care showed higher adherence to risk-based recommendations compared to primary care models in some studies. Other studies suggest a shared care model increases adherence and decrease lost to follow-up. At the service level, high satisfaction with care was common in all models. Overall, there was not enough to evidence to support one model over another in either Child or AYA survivorship care. Conclusions: Despite decades of research in survivorship care, there is not enough evidence about the effectiveness and efficiency of different models of care. No single model stands out as superior, and there are notable gaps in understanding the economic and value proposition of different models. Addressing these gaps is crucial for the future development, and provision of quality survivorship care tailored for this patient population.

Spherical Distortion Temporal Propagation and Spatial Mapping Model for Efficient Panoramic Video Coding

Spherical Distortion Temporal Propagation and Spatial Mapping Model for Efficient Panoramic Video Coding

Novel method for measuring surface residual stress using flat-ended cylindrical indentation

Instrumented indentation is a promising technique for estimating surface residual stresses and mechanical properties in engineering components. The relative difference between the indentation loads for unstressed and stressed specimens was selected as the key parameter for measuring surface residual stresses in flat-ended cylindrical indentations. Based on the equivalent material method and finite element simulations, a dimensionless mapping model with six constants was established between the relative load difference, constitutive model parameters, and normalized residual stress. A novel method for measuring the surface residual stress and constitutive model parameters of metallic material through flat-ended cylindrical indentations was proposed using this model and a mechanical properties determination method. Numerical simulations were conducted using numerous elastoplastic materials with different residual stresses to verify the proposed model; good agreements were observed between the predicted residual stresses and those previously applied in finite element analysis. Flat-ended cylindrical indentation tests were performed on four metallic materials using cruciform specimens subjected to various equibiaxial stresses. The results exhibited good conformance between the stress–strain curves obtained using the proposed method and those from traditional tensile tests, and the absolute differences between the predicted residual stresses and applied stresses were within 40 MPa in most cases.

Secure Stego Method to Protect SMS Transmission

A novel method of message steganography will be proposed. The method will use a digital speech file as a covering media. The 32 binary bits of each speech sample will be used for data hiding, using these bits will increase the hiding capacity, and changing them will not much affect the sample value, thus the quality of the stego file will be always high. The proposed method will use a private key to protect the hidden message, the protection will be applied by shuffling the rows of the speech binary matrix using the contents of the generated indices key, The induces key will be generated by running a chaotic logistic map model using the values of the chaotic parameters included in the private key. The proposed method will be tested and implemented using various messages, the quality, sensitivity and speed of the proposed method will be analyzed to prove the enhancements provided by the proposed method.