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We introduce an approach for the interactive visual analysis of weighted, dynamic networks. These networks arise in areas such as computational neuroscience, sociology, and biology. Network analysis remains challenging due to complex time-varying network behavior. For example, edges disappear/reappear, communities grow/vanish, or overall network topology changes. Our technique, TimeSum, detects the important topological changes in graph data to abstract the dynamic network and visualize one summary representation for each temporal phase, a state. We define a network state as a graph with similar topology over a specific time interval. To enable a holistic comparison of networks, we use a difference network to depict edge and community changes. We present case studies to demonstrate that our methods are effective and useful for extracting and exploring complex dynamic behavior of networks.

In the era of big data, ensuring privacy while processing vast amounts of sensitive information poses a significant challenge. Traditional encryption methods often fall short in maintaining both privacy and data utility during computation. This paper introduces Two-Trapdoor Homomorphic Encryption (TTHE), a novel approach designed to enhance privacy-preserving capabilities in big data and information security. TTHE combines the strengths of trapdoor functions with homomorphic encryption to enable secure data processing without compromising privacy. With the exponential growth of data, safeguarding sensitive information has become a critical concern. Existing encryption schemes often struggle to balance between privacy preservation and computational efficiency. Homomorphic encryption offers a potential solution by allowing computations on encrypted data, but current methods are limited by performance and scalability issues. The key challenge addressed is the inefficiency and performance bottlenecks in current homomorphic encryption schemes, which hinder their practical application in big data environments. Traditional methods often face limitations in processing large datasets efficiently while maintaining robust security. TTHE is proposed as an enhancement over traditional homomorphic encryption. It integrates two distinct trapdoor functions to provide a dual-layer security approach, enabling efficient and scalable computation on encrypted data. The method involves a novel encryption scheme where operations on ciphertexts are performed without decryption, preserving data privacy throughout the process. Extensive experiments demonstrate that TTHE significantly improves both computational efficiency and security. The proposed method achieved a processing speed increase of 45% compared to conventional homomorphic encryption schemes. Additionally, TTHE maintained a privacy level with a security strength of 128-bit encryption, providing robust protection against potential attacks. [ABSTRACT FROM AUTHOR]

Ferrite-based photocatalysts have raised as potent agents for breaking down organic pollutants in wastewater treatment. However, the challenge of developing an environmentally sustainable and economically viable photocatalyst with heightened efficacy persists. In this study, we prepared a green biochar-supported ZnFe2O4 (BC-ZnFe2O4) composite catalyst through a simple calcination method using waste banana peel (Macho plantain). The physicochemical properties of the prepared catalysts were thoroughly characterized, encompassing phase structure, surface morphology, purity, size, optical features, and charge separation efficiency. The characterization results confirmed that the ZnFe2O4 had a specific crystal structure in the composite. Microscopic analysis showed that the BC-ZnFe2O4 composite had a consistent cubic-like shape, with grains sized between 60–80 nm. Importantly, these BC-ZnFe2O4 composite had a direct band gap of about 2.29 eV, making them suitable for photocatalytic reactions. The photocatalytic methylene blue (MB) degradation experiments revealed that BC-ZnFe2O4 (91.08%; k = 0.3938 min−1) exhibits a superior photocatalytic activity compared to its pure BC-ZnO (75.26; k = 0.2129 min−1) counterparts within 100 min visible light irradiation. The excellent photocatalytic performance of the BC-ZnFe2O4 composite was due to the combined effect of the biochar and ZnFe2O4, which generated reactive oxygen species during the degradation process. In addition, the as-prepared BC-ZnFe2O4 composite demonstrated remarkable catalytic stability throughout multiple reaction cycles, making it suitable for sustained degradation of dye pollutants. Considering the outstanding performance of BC-ZnFe2O4 composite for the photocatalytic reaction, we believe that BC-ZnFe2O4 holds significant potential for purifying pollutants in various applications. [ABSTRACT FROM AUTHOR]

Summary: Non‐orthogonal multiple access (NOMA) is an efficient technology for future wireless communication systems. The resource management on NOMA networks is flattering for improving energy efficiency (EE) of systems. Therefore, self‐attention convolutional neural network (SACNN) optimized with Remora Optimization Algorithm (ROA) for energy aware resource management in NOMA network (SACNN‐ROA‐EE) is proposed in this paper for solving user allocation, channel allocation, and power allocation problems. The Matching‐Coalition approach is considered to face the user allocation issue, and the SACNN is considered to face the channel allocation and power allocation problems. SACNN does not adopt any optimization methods to determine the optimal parameters in channel allocation and power allocation. Therefore, the ROA is applied to optimize the SACNN weight parameters. The proposed technique is activated in MATLAB, and its efficacy is analyzed with under performances metrics, such as minimum user rate, sum rate, energy, and spectral performance. The proposed SACNN‐ROA‐EE attains 24.35%, 27.84%, and 38.58% better sum rate and 27.45%, 43.28%, 30.56% better EE compared with existing methods, such as EE utilizing communication deep neural network optimized with power allocation optimization (PAO‐CDNN‐EE), EE under deep transfer deterministic policy gradient along deep deterministic policy gradient approach (DPPGA‐DTDPPG‐EE), and EE utilizing deep reinforcement learning with baseline algorithms (BA‐DRL‐EE), respectively. [ABSTRACT FROM AUTHOR]

A continuous energy supply to the load side is required by modern power systems. This calls for a sound understanding of how to forecast load demand in the present and the future with the least degree of inaccuracy. Typically, a sequential method with two steps—forecasting and optimization—is used to derive judgments from data. For achieving this goal, optimized power flow is focused in this paper through load forecasting, mode selection, and optimization of power forecasting. Firstly, load forecasting is implemented using time series, and economic and weather-related information for the different consumer's load. Then mode selection is implemented using Hidden Markov Model that determines the requested load for grid-connected or RES mode. When composite RES is developed, the percentage of serviced load rises as more renewable energy sources are added. Following the implementation of the consumer load and mode selection, optimization is used to improve the power flow. The empirical findings show enhanced prescriptive performance when compared to answers found in single- and multi-household contexts. Also, we offer insightful information on how explaining performance is described. [ABSTRACT FROM AUTHOR]

We present ECoG ClusterFlow, a novel interactive visual analysis tool for the exploration of high-resolution Electrocorticography (ECoG) data. Our system detects and visualizes dynamic high-level structures, such as communities, using the time-varying spatial connectivity network derived from the high-resolution ECoG data. ECoG ClusterFlow provides a multi-scale visualization of the spatio-temporal patterns underlying the time-varying communities using two views: 1) an overview summarizing the evolution of clusters over time and 2) a hierarchical glyph-based technique that uses data aggregation and small multiples techniques to visualize the propagation of clusters in their spatial domain. ECoG ClusterFlow makes it possible 1) to compare the spatio-temporal evolution patterns across various time intervals, 2) to compare the temporal information at varying levels of granularity, and 3) to investigate the evolution of spatial patterns without occluding the spatial context information. We present case studies done in collaboration with neuroscientists on our team for both simulated and real epileptic seizure data aimed at evaluating the effectiveness of our approach.