Hasil untuk "Maps"

Yicong Zhou, Long Bao, C. L. P. Chen

This paper introduces a simple and effective chaotic system using a combination of two existing one-dimension (1D) chaotic maps (seed maps). Simulations and performance evaluations show that the proposed system is able to produce many 1D chaotic maps with larger chaotic ranges and better chaotic behaviors compared with their seed maps. To investigate its applications in multimedia security, a novel image encryption algorithm is proposed. Using a same set of security keys, this algorithm is able to generate a completely different encrypted image each time when it is applied to the same original image. Experiments and security analysis demonstrate the algorithm's excellent performance in image encryption and various attacks.

Zhongyun Hua, Yicong Zhou, Chi-Man Pun et al.

John Y. A. Wang, E. Adelson

D. Hall, G. Riggs, V. Salomonson

W. Trochim

J. Robinson

R. Kaplan, D. Norton

A. Siegel, S. White

Africa Couto, M. Alenius, B. Dickson

Zhongyun Hua, Fan Jin, Binxuan Xu et al.

Abstract Image encryption is a straightforward strategy to protect digital images by transforming images into unrecognized ones. The chaos theory is a widely used technology for image encryption as it has many significant properties such as ergodicity and initial state sensitivity. When chaotic systems are used in image encryption, their chaos performance highly determines the security level. This paper presents a two-dimensional (2D) Logistic-Sine-coupling map (LSCM). Performance estimations demonstrate that it has better ergodicity, more complex behavior and larger chaotic range than several newly developed 2D chaotic maps. Utilizing the proposed 2D-LSCM, we further propose a 2D-LSCM-based image encryption algorithm (LSCM-IEA), which adopts the classical confusion-diffusion structure. A permutation algorithm is designed to permutate image pixels to different rows and columns while a diffusion algorithm is developed to spread few changes of plain-image to the whole encrypted result. We compare the efficiency of LSCM-IEA with several advanced algorithms and the results show that it has higher encryption efficiency. To show the superiority of LSCM-IEA, we also analyze the security of LSCM-IEA in terms of key security, ability of defending differential attack, local Shannon entropy and contrast analysis. The analysis results demonstrate that LSCM-IEA has better security performance than several existing algorithms.

Yajun Yu, Steve Jiang, Robert Timmerman et al.

Personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is a novel treatment that delivers radiation in pulses of protracted intervals. Accurate prediction of gross tumor volume (GTV) changes through regression models has substantial prognostic value. This study aims to develop a multi-omics based support vector regression (SVR) model for predicting GTV change. A retrospective cohort of 39 patients with 69 brain metastases was analyzed, based on radiomics (magnetic resonance image images) and dosiomics (dose maps) features. Delta features were computed to capture relative changes between two time points. A feature selection pipeline using least absolute shrinkage and selection operator (Lasso) algorithm with weight- or frequency-based ranking criterion was implemented. SVR models with various kernels were evaluated using the coefficient of determination ( R ^2 ) and relative root mean square error (RRMSE). Five-fold cross-validation with 10 repeats was employed to mitigate the limitation of small data size. Multi-omics models that integrate radiomics, dosiomics, and their delta counterparts outperform individual-omics models. Delta-radiomic features play a critical role in enhancing prediction accuracy relative to features at single time points. The top-performing model achieves an R ^2 of 0.743 and an RRMSE of 0.022. The proposed multi-omics SVR model shows promising performance in predicting continuous change of GTV. It provides a more quantitative and personalized approach to assist patient selection and treatment adjustment in PULSAR.

Titi Sari Nurul Rachmawati, Mustika Sari, Daniel Darma Widjaja et al.

Green building incentives constitute a policy instrument for mitigating economic, technical, and behavioral barriers to the adoption of green buildings, yet existing studies remain fragmented across incentive types, stakeholders, and building life cycle stage. A coherent synthesis that explains how incentive strategies evolve and interact across these dimensions is still missing. This study addresses that gap through a systematic literature review guided by the PRISMA 2020 protocol. A total of 69 peer-reviewed journal articles published between 2016 and 2025 were identified from Scopus and analyzed using thematic synthesis. The review maps temporal trends, incentive typologies, stakeholder roles, and implementation challenges across different regional and market contexts. The findings indicate that incentive effectiveness depends on alignment between life cycle stage, market maturity, and stakeholder capacity, rather than on any single policy instrument. Financial incentives remain critical in early market phases, while non-financial and regulatory instruments gain prominence as markets mature. The synthesis also demonstrates how evolutionary game theory has been increasingly applied to analyse dynamic incentive and penalty strategies under bounded rationality, offering a structured lens for adaptive policy design. By integrating life cycle perspectives, stakeholder interactions, and game theoretical insights, this study advances current understanding of these incentive designs. The results provide a foundation for more adaptive and context-sensitive incentive frameworks and identify clear directions for future empirical and comparative policy research.

Zixiang Ye, Xuewen Chen, Lvxin Qian et al.

Traditional water quality monitoring methods are limited in providing timely chlorophyll-<i>a</i> (Chl-<i>a</i>) assessments in small inland reservoirs. This study presents a rapid Chl-<i>a</i> retrieval approach based on a cooperative unmanned aerial vehicle–uncrewed surface vessel (UAV–USV) framework that integrates UAV hyperspectral imaging, machine learning algorithms, and synchronized USV in situ sampling. We carried out a three-day cooperative monitoring campaign in the Longhu Reservoir of Fujian Province, during which high-frequency hyperspectral imagery and water samples were collected. An innovative median-based correction method was developed to suppress striping noise in UAV hyperspectral data, and a two-step band selection strategy combining correlation analysis and variance inflation factor screening was used to determine the input features for the subsequent inversion models. Four commonly used machine-learning-based inversion models were constructed and evaluated, with the random forest model achieving the highest accuracy and stability across both training and testing datasets. The generated Chl-<i>a</i> maps revealed overall good water quality, with localized higher concentrations in weakly hydrodynamic zones. Overall, the cooperative UAV–USV framework enables synchronized data acquisition, rapid processing, and fine-scale mapping, demonstrating strong potential for fast-response and emergency water-quality monitoring in small inland drinking-water reservoirs.

M. R. Çevikalp, B. Mutlu, M. Yanalak et al.

Road networks are crucial to urban infrastructure and significantly affect transportation, traffic management, and emergency response. Besides, accurate mapping is essential for detecting road networks effectively, but traditional methods like manual digitization and field surveys often struggle in fast-changing urban environments. Remote sensing and deep learning techniques have emerged as effective alternatives, although initial road segmentation faced challenges such as limited image resolution. Recent advances in satellite technology have alleviated these issues by providing ultra-high-resolution (sub-meter) imagery, which is vital for accurately representing road networks. Deep learning models like U-Net have enhanced road segmentation by accurately capturing complex features. This research examines the effectiveness of multi-resolution satellite imagery for road segmentation. This study aims to analyze the accuracy assessment of road segmentation using Sentinel-2 imagery (10 m resolution) and ultra-high-resolution Pl&eacute;iades Neo imagery (sub-meter resolution). Ground truth data from the Google Maps API were used for validation. Among the tested resolutions, Pl&eacute;iades Neo at 30 cm achieved the highest accuracy, with an F-score of 0.87. Pl&eacute;iades Neo at 15 cm resolution followed closely with an F-score of about 0.85. Pl&eacute;iades Neo at 1 m resolution (upscaled data) showed a moderate decline (F-score of 0.82), while Sentinel-2 had the lowest performance (F-score of 0.78). Overall, Pl&eacute;iades Neo at 30 cm resolution offers the best balance of accuracy and data efficiency for road segmentation.

Snehal M. Shekatkar

Many models of population dynamics are formulated as deterministic iterated maps although real populations are stochastic. This is justifiable in the limit of large population sizes, as the stochastic fluctuations are negligible then. However, this also makes it challenging to use the same models for small populations where finite size effects like demographic noise and extinction cannot be ignored. Moreover, adding noise to the equations does not solve this problem as it can only represent the environmental stochasticity. An approach, sometimes used in ecological literature, but surprisingly uncommon in dynamical systems community, is \emph{Binomial maps}, which allow stochastic evolution of deterministic iterated map models of population. Here we present their formulation in a way so as to make their connection to the agent-based models explicit, and demonstrate it for the Logistic and Ricker maps. We also show that the Binomial maps are not completely equivalent to their deterministic counterparts, and derive sufficient conditions under which the equivalence holds. This approach enables rigorous finite-population analysis within familiar map-based models, bridging the deterministic map models and stochastic agent-based models.

Max Weinreich

Skew pentagram maps act on polygons by intersecting diagonals of different lengths. They were introduced by Khesin-Soloviev in 2015 as conjecturally non-integrable generalizations of the pentagram map, a well-known integrable system. In this paper, we show that certain skew pentagram maps have exponential degree growth and no preserved fibration. To formalize this, we introduce a general notion of first dynamical degree for lattice maps, or shift-invariant self-maps of $(\mathbb{P}^N)^\mathbb{Z}$. We show that the dynamical degree of any equal-length pentagram map is 1, but that there are infinitely many skew pentagram maps with dynamical degree 4.

Atia Bano Memon, Atia Bano Memon, Dileep Kumar Sootahar et al.

With the rapid accumulation of large amounts of user-generated content through social media, social data reuse and integration have gained increasing attention recently. This has made it almost obsolete for software applications to collect, store, and work with their own data stored on local servers. While, with the provision of Application Programming Interfaces from the leading social networking sites, data acquisition and integration has become possible, the meaningful usage of such unstructured, non-uniform, and incoherent data collections needs special procedures of data summarization, understanding, and visualization. One particular aspect in this regard that needs special attention is the procedures for data (text snippets in the form of social media posts) categorization and concept tagging to filter out the relevant and most suitable data for the particular audience and for the particular purpose. In this regard, we propose a corpus-based approach for searching and successively categorizing and tagging the social data with relevant concepts in real time. The proposed approach is capable of addressing the semantical and morphological similarities, as well as domain-specific vocabularies of query strings and tagged concepts. We demonstrate the feasibility and application of our proposed approach in a web-based tool that allows searching Facebook posts and provides search results together with a concept map for further navigation, filtering, and refining of search results. The tool has been evaluated by performing multiple search queries, and resultant concept maps and annotated texts are analyzed in terms of their precision. The approach is thereby found effective in achieving its stated goal of classifying text snippets in real time.

Hatem M. El-Desoky, Imane Bachri, Ahmed M. El Mezayen et al.

Abstract The main objective of this study was to use deep learning, and convolutional neural networks (CNN), integrated with field geology to identify distinct lithological units. The Samadia-Tunduba region of the South Eastern Desert of Egypt was mapped geologically for the first time thanks to the use of processed developed CNN algorithms using Landsat 9 OLI-2, which were further enhanced by geological fieldwork, spectral measurements of field samples, and petrographic examination. According to previously published papers, a significant difference was observed in the distribution of rocks and their boundaries, as well as the previously published geological maps that were not accurately compatible with the nature of the area. The many lithologic units in the region are refined using principal component analysis, color ratio composites, and false-color composites. These techniques demonstrated the ability to distinguish between various igneous and metamorphic rock types, especially metavolcanics, metasediments, granodiorite, and biotite monzogranite. The Key structural trends, lithological units, and wadis affecting the area under study are improved by the principal component analysis approach (PC 3, 2, 1), (PC 2, 3, 4), (PC 4, 3, 2), (PC 5, 4, 3), and (PC 6, 5, 4) in RGB, respectively. The best band ratios recorded in the area are recorded the good discrimination (6/5, 4/3, and 2/1), (4/2, 6/7, and 5/6), and (3/2, 5/6, and 4/6) for RGB. The classification map achieved an overall accuracy of 95.27%, and these results from Landsat-9 data were validated by field geology and petrographical studies. The results of this survey can make a significant difference to detailed geological studies. A detailed map of the new district has been prepared through a combination of deep learning and fieldwork.

Arthur Sard

of a region R of euclidean m-space into part of euclidean w-space. Suppose that each f unction ƒ' 0' = 1, • • • , n) is of class C in R (q^l). A critical point of the map (1.1) is a point in R at which the matrix of first derivatives 2)? = ||/*|| (i = ly • • • , m;j = l, • • • , n) is of less than maximum rank. The rank of a critical point # is the rank of 5DÎ at x. A critical value is the image under (1.1) of a critical point. If » = 1, these definitions are the usual definitions of critical point and value of a continuously differentiable function. We prove the following result: If m^n, the set of critical values of the map (1.1) is of m-dimensional measure zero without further hypothesis on q; if m>n, the set of critical values of the map (1.1) is of n-dimensional measure zero providing that q^m — n + 1. Using an example due to Hassler Whitney we show that the hypothesis on q cannot be weakened. We prove also that the critical values of (1.1) corresponding to critical points of rank zero constitute a set of (m/q)dimensional measure zero. The idea of considering the measure of the set of critical values of one function or of several functions is due to Marston Morse. The first result stated above reduces, if » = 1, to the known theorem : The critical values of a function of m variables of class C constitute a set of linear measure zero. A. P. Morse has given a proof of this theorem for all m. In the present paper we make use of one of A. P. Morse's results.

Yopi Ilhamsyah, Yustya Rahmy, Marwan Marwan et al.

The objective is to analyse temperature changes and their future projection in Aceh. The activities consist of collecting past and future temperature data, preparing materials for processing, and analyzing past and future temperature data (climate change projections). The data used are monthly average temperature data from data global climate model, e.g., csiromk3.6-hist-1986-2005-tas, csiromk3.6-rcp45- 2016-2035-tas, csiromk3.6-rcp45-2046-2065-tas, csiromk3.6-rcp45-2081-2100- tas, csiromk3.6-rcp85-2016-2035-tas, csiromk3.6-rcp85-2046-2065-tas, and csiromk3.6-rcp85-2081-2100-tas. The study began with reading climate data in NetCDF format using GRADS software, data processing using CDO software, providing a coordinate system using QGIS software, making climate change projection maps using ArcGIS software, and making climate change graphs using spreadsheet programs. Two scenarios, i.e., RCP4.5 and RCP8.5 are used to analyse the projected temperature changes in the short-term (2016 – 2035), medium-term (2046-2065), and long-term (2081-2100). The results show that the RCP4.5 projection shows a lower change in temperature rise than the RCP 8.5. A change in a temperature rise of up to 5°C was found in the RCP8.5 scenario.