While Very High-Resolution (VHR) imagery is favored for change detection due to its spatial detail, it presents challenges, notably intricate feature interactions and noise, complicating precise change identification. Addressing this, this paper introduces an unsupervised method for detecting building changes in Very High-Resolution (VHR) images, integrating the strengths of Principal Component Analysis (PCA) and K-Means clustering with a focus on building changes. Initially, PCA is employed to reduce data dimensionality, emphasizing the most significant variations across temporal datasets. The difference between the PCA-transformed images is computed, revealing areas of potential change. K-Means clustering then categorizes these regions based on their pixel values, labeling them as either changed or unchanged. A unique step in our approach is the building index extraction. This step refines the building detection by identifying contours in the segmented images based on their properties, such as area and perimeter emphasizing true building alterations and filtering out unrelated landscape changes. Experimental results on benchmark datasets, LEVIR-CD and CLCD, showcase the superior performance of the method, with an overall accuracy of 0. 97 and a Kappa coefficient of 0. 89. These results highlight the effectiveness of the proposed approach for building change detection in remote sensing and urban monitoring applications.

The Global Positioning System (GPS) is integral to the safety and efficiency of modern railway networks; however, its susceptibility to jamming and spoofing interference poses a significant threat to operational integrity. Conventional detection systems often rely on fully supervised models requiring extensive labeled data or specialized, costly hardware, limiting their scalability. This paper addresses this gap by proposing and evaluating an Enhanced Semi-Supervised K-Means (ESS-KMeans) Algorithm designed to operate effectively with minimal labeled data. We compare its performance against a standard unsupervised K-Means Algorithm using a challenging, synthetically generated dataset based on GPS signal characteristics such as latitude/longitude variation, altitude deviation, and Automatic Gain Control (AGC) levels. The proposed ESS-KMeans leverages a small labeled subset for robust centroid initialization and mutual information-based feature weighting, while also uniquely identifying and flagging ambiguous, low-confidence samples. Experimental results demonstrate that ESS-KMeans achieves perfect (1.000) accuracy on confidently classified samples, a significant improvement over standard K-Means (0.960), and improves cluster quality by over 45% (Silhouette Score). By delivering superior accuracy and providing a mechanism for uncertainty quantification with minimal supervision, this semi-supervised approach presents a scalable, cost-effective, and reliable solution for enhancing the resilience of railway systems against GPS interference.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic Algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic Algorithm and multi-objective optimization goals. The genetic Algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the Algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

Increasing population and food demand, disproportionate cultivation and annual production of various agricultural products with market needs and low productivity of the agricultural sector and the loss of water and soil resources have made it necessary to determine and implement the country's optimal cropping pattern. In this study, due to the limitations and problems of classical methods in order to reduce processing time and improve the quality of solutions, the Multi-Objective Chaotic Particle Swarm Optimization was used to determine the optimal cultivation pattern of Sistan plain in optimal conditions and deficit irrigation. The results of the Multi-Objective Chaotic Particle Swarm Optimization for the dominant cultures in the region showed that the current cropping pattern of the region is not optimal and with the implementation of the proposed model, the profit per unit area under cultivation will increase. The results of application of deficit irrigation during different growing periods of wheat, barley, alfalfa, sorghum, watermelon and grapes showed that applying deficit irrigation in this plain is not a good strategy and therefore only a full irrigation strategy is recommended. The results of sensitivity analysis of the model showed that at low prices, farmers reaction is less and at higher prices more reaction to price changes and with increasing prices, the program efficiency is lower.

One of the basic topics in hydrological and river engineering studies is flood routing.Flood flooding is common in multi-tributary rivers and rivers without intermediate basin statistics. Therefore, to achieve the determination of slopes and cross-sections in all sections of the river, the Muskingum hydrological model is a useful method that helps to save information on the depth and flow of the flood at any time by saving time and money. To specify. In this study, the nonlinear parameters of the new Muskingum model are optimized based on the fly Algorithm (MA). In this non-linear model of Muskingum, which has eight parameters, the recovery coefficient γ is used, which has more or less values ​​than the number of peaks discharged in the output hydrograph.To evaluate the performance of Muskingum's new nonlinear model with the new MA Algorithm, the Wilson and Weisman-Lewis case study has been used by many previous researchers for validation.The results of the MA Algorithm for Wilson and Weissman-Lewis rivers show the minimization of the residual squares (SSQ) as the objective function, which is 3.21 for the Wilson River and 68722 for the Weissman River. The results of this study showed that the proposed model has high accuracy in estimating the output discharge values.

Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means Algorithm, namely Persistent K-Means, which alters the convergence method of K-Means Algorithm to provide more accurate clustering results than the K-Means Algorithm and its variants by increasing the clusters’ coherence. Persistent K-Means uses an iterative approach to discover the best result for consecutive iterations of KMeans Algorithm.