We establish a general Nakano-Griffiths inequality with boundary conditions and apply it to derive holomorphic Morse inequalities for domains satisfying analytic convexity assumptions. The proof of these inequalities relies on analyzing the spectral spaces of the Laplace operator with partial derivative & strns;-Neumann boundary conditions. As an application, we obtain a criterion for Moishezon 1-concave manifolds. We further apply the holomorphic Morse inequalities on Levi q-concave domains in conjunction with the Kohn-Rossi extension theorem to obtain the following result. Let X be a compact complex manifold of dimension n equipped with a holomorphic line bundle that is semi-positive everywhere and positive at least at one point, and let D subset of X be a smooth qconcave domain (1 <= q <= n-1). We prove that every partial derivative & strns;b-closed (0,& ell;)-form on bD with values in a holomorphic vector bundle, admits a meromorphic extension to D for all q <= & ell; <= n - 1. (c) 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Db-closed
In the existing Scenario, threats are getting larger as a consistent cybersecurity problem, which attacks computer systems, handheld devices, and ubiquitous networks. The current generation of highly critical malware has also surpassed the traditional detection methods in various aspects such as accuracy, flexibility and resilience to the original attack methods. The given research is a systematic literature review of threat detection aiming to examine the literature published since 2015 to 2025 to assess the current status of the field of research and categorize the main issues or possible directions of further research that warrant further research. A new taxonomic system of discovery modes depending on deep learning and machine learning is proposed, which is viewed by datasets, feature extaction systems and algorithmic categorizers. Moreover, it gives some discussion of experimental bias that has a major influence on the performance of malware detection and defines critical measures of effectiveness to evaluate the effectiveness, and certain undefined research problems. This paper introduces possibilities of improving detection method and creates insights on plausible solutions and future research directions. Lastly, the current paper discusses the anomalies that may be experienced in digital twin and how to identify.
Ternary transition metal oxides (TTMOs) have recently gained attention as promising electrode materials due to their rich redox chemistry, high theoretical capacitance, and synergistic effects among constituent metals. In particular, nanocomposites incorporating ZnO:MnO:VO (ZMV) phases offer enhanced electrochemical performance, structural stability, and ion transport properties. In this study, we report the successful synthesis of ZMV nanocomposites via a controlled hydrothermal route followed by thermal treatment. X-ray diffraction (XRD) analysis revealed that the synthesized material predominantly features the face-centered cubic (FCC) phase of MnO, along with the Wurtzite phase of ZnO and Orthorhombic VO, confirming the multiphase composite structure. Cyclic voltammetry (CV) analysis demonstrated a specific capacitance of 232.11 Fg⁻1. Fabricated devices possess good energy density of 20.6 WhKg−1, excellent power density 800 WKg−1, and superior cyclic stability of 97.2
Federated learning (FL) has emerged as a promising paradigm for collaborative model training while preserving data privacy by keeping sensitive data localized at client devices. Instead of sharing raw data, participating clients exchange model updates, which can reduce communication overhead compared to centralized learning, depending on the training configuration and data distribution. However, this decentralized setting introduces significant security and privacy vulnerabilities. This review presents a structured and threat-based analysis of security risks in federated learning, focusing on backdoor attacks, Byzantine attacks (including Sybil-based mechanisms), and adversarial attacks. Following a systematic literature review of peer-reviewed and high-impact studies published between 2018 and 2025, we examine attack strategies, defense mechanisms, and their underlying assumptions. A comparative synthesis highlights critical trade-offs among robustness, privacy, computational cost, and convergence behavior, particularly under non-IID data distributions and resource-constrained environments. The paper proposes a taxonomy that clarifies the relationships between attack phases, threat models, and defensive strategies, and identifies open research challenges such as adaptive adversaries, privacy–robustness conflicts, and scalable defenses for real-world deployments in domains including healthcare, IoT, and edge intelligence. This work aims to provide both a consolidated reference and a roadmap for designing secure and trustworthy federated learning systems.
Vision-language-action (VLA) models have achieved strong performance in many robotic manipulation tasks, yet remain limited in contact-rich dexterous manipulation. To overcome this limitation, recent vision-tactile-language-action (VTLA) methods incorporate tactile sensing into VLA models to provide direct contact information. However, they typically treat tactile signals as passive auxiliary inputs, making it difficult to model tactile semantics and future physical interactions. To this end, we propose a unified tactile learning framework for contact-rich manipulation that models tactile signals as dynamic interaction cues for both contact understanding and prediction. Specifically, we construct a unified tactile latent space and jointly model current tactile states and future contact changes through tactile chain-of-thought reasoning and coarse-to-fine future tactile prediction, thereby forming a state-aware and dynamics-aware tactile prior. Based on this prior, we introduce a tactile-action mixed controller that combines real-time and predicted tactile feedback to refine low-frequency action chunks with high-frequency corrections. Real-world experiments on four categories of contact-rich tasks, including adjustment, insertion, wiping, and assembly, under both clean and externally perturbed settings, show that our method improves success rate, manipulation accuracy, and contact robustness over existing methods, demonstrating its effectiveness in dexterous physical interaction.
