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Effective energy management and optimal utilization of assets are crucial for a nation’s progress and prosperity. However, as electricity travels from power plants to end users, it incurs two types of losses: Technical Losses (TL) and Non-Technical Losses (NTL). TLs arise from outdated or inefficient technologies, whereas NTLs result from abnormal electricity usage, including electricity theft, which is often conducted to reduce expenses. These losses pose significant challenges to maintaining grid reliability and result in reduced profits for utility operators. Although the implementation of Automatic Metering Infrastructure (AMI) has improved grid predictability, it has also created new vulnerabilities for NTLs through Cyber-Physical Theft Attacks (CPTA). Machine learning techniques have been applied to identify and mitigate CPTA; however, they often fail to capture comprehensive Energy Consumption Patterns (ECPs), limiting their effectiveness in detecting malicious activities. Generative Adversarial Networks (GANs) have shown remarkable success in various anomaly detection applications. In the context of electricity anomaly identification and diagnosis, GANs are particularly effective in modeling typical energy usage patterns and detecting deviations. Building on this foundation, this study introduces an Adaptive GAN (AGAN) model to detect and diagnose electricity anomalies. The AGAN dynamically optimizes key parameters, such as the number of hidden neurons, epochs and steps per epoch, using the Improved Honey Badger Algorithm (IHBA). This optimization enhances the model’s performance, particularly in terms of Detection Accuracy, Critical Success Index (CSI) and False Omission Rate (FOR). The proposed model demonstrates its capability to accurately identify electricity anomalies and has been rigorously evaluated against existing anomaly detection methods. Results highlight AGAN’s superior effectiveness and robustness, establishing it as a reliable solution for improving the reliability and efficiency of electricity grid systems.

This paper proposes diagnostic and evolutionary models for the spatial equilibrium allocation of transboundary water resources, aiming to improve the match between riparian countries’ water allocations, and their natural, social and economic factors. The paper develops a two-stage diagnostic model to diagnose the spatial equilibrium for allocation schemes of transboundary water resources. The first stage is the matching diagnosis, and the second stage is the suitability diagnosis, which diagnoses the correctness of the size relationship or size degree of the allocated water volumes in the pair of riparian countries. For allocation schemes that have not passed the diagnosis, this study improves the scheme based on an evolutionary model by establishing rules for adjusting the amount of water. The model is applied to the Lancang–Mekong River basin. The results show that the spatial equilibrium allocation scheme in the Lancang–Mekong River Basin has improved the overall equilibrium of the water quantity allocation scheme of the river basin compared to the existing one, making it more acceptable to riparian countries. Policy recommendations are made on aspects such as improving information exchange platforms and establishing benefit compensation mechanisms. This paper provides a new quantitative analysis method for the allocation of transboundary water resources.

In Lung adenocarcinoma (ADC), Qi-Yin deficiency syndrome (QY) is the most common Traditional Chinese medicine (TCM) syndrome. This study aimed to investigate the diversity and composition of gut microbiota in ADC patients with QY syndrome. 90 stool samples, including 30 healthy individuals (H), 30 ADC patients with QY syndrome, and 30 ADC patients with another syndrome (O) were collected. Then, 16s-RNA sequencing was used to analyze stool samples to clarify the structure of gut microbiota, and linear discriminant analysis (LDA) effect size (LEfSe) was applied to identify biomarkers for ADC with QY syndrome. Logistic regression analysis was performed to establish a diagnostic model for the diagnosis of QY syndrome in ADC patients, which was assessed with the AUC. Finally, 20 fecal samples (QY: 10; O: 10) were analyzed with Metagenomics to validate the diagnostic model. The $\alpha$ diversity and $\beta$ diversity demonstrated that the structure of gut microbiota in the QY group was different from that of the H group and O group. In the QY group, the top 3 taxonomies at phylum level were Firmicutes, Bacteroidetes, and Proteobacteria, and at genus level were Faecalibacterium, Prevotella_9, and Bifidobacterium. LEfSe identified Prevotella_9 and Streptococcus might be the biomarkers for QY syndrome. A diagnostic model was constructed using those 2 genera with the AUC = 0.801, similar to the AUC based on Metagenomics (0.842). The structure of gut microbiota in ADC patients with QY syndrome was investigated, and a diagnostic model was developed for the diagnosis of QY syndrome in ADC patients, which provides a novel idea for the understanding and diagnosis of TCM syndrome.