This paper presents a reproducibly specified 128-bit Feistel-based encryption design with a 128-bit key and a key- and round-dependent Dynamic Cross-Swapping (DCS) operation. Correctness was verified by 200,000 random encryption–decryption tests across configurations with and without DCS. At seven rounds, all 128 one-bit modifications of each of 100 independent plaintexts produced a mean avalanche of 49.986
The dynamic topology and fluctuating loads of distribution networks render static fault diagnosis models inadequate for real-time operation. This study hypothesizes that an adaptively evolving graph structure, updated in response to real-time network conditions, can improve both fault detection accuracy and system stability. To test this hypothesis, the Adaptive Graph Learning-based Graph Convolutional Network is proposed. The methodology integrates wavelet transform for noise removal, K-nearest neighbor imputation for missing values, and min–max normalization for data standardization. The adaptive graph learning mechanism dynamically updates the adjacency matrix to reflect topological changes, while the graph convolutional network extracts spatiotemporal features for fault classification. The model is evaluated on simulated distribution network data with fault types including short circuit, overload, and ground fault. The AGL-GCN achieves 98.3
Education sector in any country has the ability to transform a country through development of human resources that is essential for overall transition to an advanced economy. Especially, in this era of education, students pursuing higher education plays a major role in the overall development of the nation. However, due to several factors such as demographical, financial, social and psychological, students feel discouraged to pursue and complete higher education. Though the gross enrolment ratio are high, low retention rates due to drop outs are one of the major worries in higher education. This necessitates that students at risk of dropout be identified early in their study programme and necessary and proactive remedial measures be taken to reduce such dropouts. This paper focuses on designing a classification based machine learning model to predict whether a newly enrolled student is likely to drop out after enrolling for higher education. The dataset consider for this study is the higher education data from the Polytechnic Institute of Portalegre, Portugal. Experimental results have shown that classification based machine learning models have been found to be effective in predicting students who are likely to dropout. Amongst them, the ensemble based classification models have performed the best. The use of these models would reduce student dropout thereby retaining them to pursue and complete higher education, which in turn would enable them to contribute to the overall economy of their country.
Microservices architecture is becoming popular because it is flexible and can easily grow with demand. But sometimes, resources are not used properly because of poor task scheduling. This research suggests a better task scheduling method to make resource use more efficient, reduce delays, and improve overall system performance. The proposed method uses dynamic task prioritization and load balancing to make sure resources are used in the best way. The results show a clear improvement in CPU and memory usage without affecting system performance. This approach can be used in many types of microservice systems to make them faster and more efficient. It also works well in environments where different systems have different capacities and performances. The scheduling algorithm introduced here focuses on working together to manage all resources effectively. It looks at both CPU use and network traffic, which helps achieve better resource allocation, improved service performance, and a more cost-effective and responsive microservices system.
The rapid proliferation of Renewable Energy Sources (RES), particularly Solar Photovoltaic (SPV) systems, into modern transmission networks has intensified concerns regarding voltage stability, power loss management, and grid reliability under increasing load demand. This paper presents a systematic technical framework for the optimal siting and sizing of SPV systems in large-scale transmission networks using the Voltage Stability Index (VSI) as a bus vulnerability indicator. Candidate buses prone to voltage collapse are identified through VSI-based ranking, and subsequently, an optimal SPV allocation strategy is formulated to maximise voltage profile improvement and minimise real power losses. Simulations are conducted in ETAP 19.1 employing the adaptive Newton-Raphson load flow algorithm on the standard IEEE 57-bus test system, encompassing ten case studies—four single SPV plant configurations and six dual SPV plant configurations. The proposed methodology consistently yielded significant enhancement in nodal voltage profiles and system-wide loss reduction across all tested scenarios. The maximum active power loss reduction achieved was 34.68
