Trust-aware caching-constrained tasks offloading in multi-access edge computing

Multi-access edge computing (MEC) networks face significant challenges in managing congestion and safeguarding personal privacy data on a massive scale. Integrating trust awareness into MEC networks presents an opportunity to enhance security and privacy by correlating human relationships with connected devices. Moreover, leveraging trust-aware task caching and offloading holds promise in mitigating latency and reducing energy consumption. Despite existing research efforts to address these challenges, they often overlook either trust awareness or caching optimization in task offloading, potentially compromising security or leading to task failures. To address this gap, this paper proposes a novel approach: a trust-aware task offloading strategy with cache constraints (TCTO) in MEC networks, which considers social relationships, task offloading, and caching. Drawing on the characteristics of bipartite graphs and bipartite perfect matching, we develop a trust-aware caching-constrained task offloading algorithm based on bipartite graphs. This algorithm aims to select task offloading strategies that minimize delay, energy consumption in task transmission and execution, while maximizing security among devices in MEC networks. Extensive simulations demonstrate that our proposed method has a better performance than other task offloading strategies for reducing delay and energy consumption in the process of task transmission and execution. Compared with the other baselines, the overhead of our proposed method is reduced 55 . 65 % ∼ 96 . 20 % compared with other baselines

A unified threshold-constrained optimization framework for consistent and interpretable cross-machine condition monitoring

Accurate detection of incipient faults during lifecycle degradation is crucial for continuous condition monitoring of industrial equipment. Condition indices (CIs) with pre-set thresholds are widely used in engineering practice due to their intuitiveness, simplicity, and convenience. However, uncertainties and variations in degradation patterns and fault initiation times across different industrial systems or even within the same system lead to inconsistent CI scales and thresholds, creating challenges for reliable and practical monitoring. To address this challenge, we propose a unified threshold-constrained optimization framework for consistent and interpretable cross-machine condition monitoring based on frequency-domain data fusion. Rather than directly using CIs, we introduce degradation rates of CIs, computed via first-order differences, which enable a consistent definition of normal operating levels across heterogeneous degradation patterns and multiple machines. Afterwards, a degradation rate and threshold constrained convex optimization model is formulated to automatically optimize weights in the frequency domain, ensuring sensitivity to incipient faults while preserving consistent thresholds across machines. Extensive experiments on multiple endurance datasets of rotating equipment demonstrate the consistency and superiority of the proposed approach over some famous and advanced CIs. Results show that a unified threshold can be established for the proposed CIs across diverse degradation patterns and multiple machines. Furthermore, the optimized frequency-domain weights highlight diagnostic frequency bands closely associated with system faults, thereby enhancing incipient fault sensitivity and offering interpretability compared with existing data-driven approaches

SceneLLM : Implicit language reasoning in LLM for dynamic scene graph generation

Dynamic scenes contain intricate spatio-temporal information, crucial for mobile robots, UAVs, and autonomous driving systems to make informed decisions. Parsing these scenes into semantic triplets Subject-Predicate-Object for accurate Scene Graph Generation (SGG) is highly challenging due to the fluctuating spatio-temporal complexity. Inspired by the reasoning capabilities of Large Language Models (LLMs), we propose SceneLLM, a novel framework that leverages LLMs as powerful scene analyzers for dynamic SGG. Our framework introduces a Video-to-Language (V2L) mapping module that transforms video frames into linguistic signals (scene tokens), making the input more comprehensible for LLMs. To better encode spatial information, we devise a Spatial Information Aggregation (SIA) scheme, inspired by the structure of Chinese characters, which encodes spatial data into tokens. Using Optimal Transport (OT), we generate an implicit language signal from the frame-level token sequence that captures the video's spatio-temporal information. To further improve the LLM's ability to process this implicit linguistic input, we apply Low-Rank Adaptation (LoRA) to fine-tune the model. Finally, we use a transformer-based SGG predictor to decode the LLM's reasoning and predict semantic triplets. Our method achieves state-of-the-art results on the Action Genome (AG) benchmark, and extensive experiments show the effectiveness of SceneLLM in understanding and generating accurate dynamic scene graphs

Micromechanical investigation of fracture behaviour in compacted graphite iron using the discrete element method

Compacted graphite iron (CGI) is widely used in engineering applications due to its excellent mechanical properties, thermal conductivity, and wear resistance. However, its heterogeneous microstructure, which consists of worm-like graphite embedded within a ferritic-pearlitic matrix, presents significant challenges in accurately predicting fracture behaviour and damage evolution. To address this gap, we develop a micromechanical simulation framework based on the discrete element method (DEM) for the first systematic investigation of tensile deformation and fracture in CGI. The model incorporates image-based microstructural reconstruction and is quantitatively validated against experimental results in terms of stress–strain response and crack path morphology. Based on this validated model, a comprehensive parametric study is performed to evaluate the influence of graphite morphology and spatial distribution. The results reveal that increasing graphite volume fraction and misaligned orientations are the dominant factors reducing strength, while larger aspect ratios and particle sizes promote damage localization. Furthermore, under microstructural randomness, particle size remains a key factor affecting failure modes. These findings provide mechanistic insights into microstructure-sensitive fracture of CGI that are difficult to obtain experimentally, and demonstrate the potential of DEM as a predictive tool for understanding microstructure-sensitive failure and guiding the design of high-performance cast iron components

Identification of new protein-coding potential in Leishmania braziliensis using a proteogenomics approach

American tegumentary leishmaniasis (ATL) is primarily caused by Leishmania (Viannia) species such as Leishmania braziliensis, Leishmania panamensis, and Leishmania guyanensis, which show complex genomic organisation and stage-specific adaptations underlying their pathogenicity. Despite the availability of its reference genome, limitations in gene annotation persist due to the presence of hypothetical proteins, pseudogenes, and unrecognised coding regions. In this study, we used a proteogenomic approach integrating publicly available high-resolution mass spectrometry data with a custom six-frame translated genome database to refine the genome annotation of L. braziliensis strain MHOM/BR/75/M2904. Utilising stringent database-dependent searches with a 1 % false discovery rate, we identified many unique peptides, of which 1034 were genome search-specific peptides (GSSPs) mapping exclusively to unannotated genomic regions. These GSSPs facilitated the discovery of 56 novel protein-coding genes and the correction of 228 existing gene models, including N- and C-terminal extensions. Notably, several novel genes encode proteins with conserved domains such as membrane attack complex/perforin (MACPF), kinesin K39, and peptidase S9/S15, suggesting functional relevance in parasite biology. Our findings demonstrate the power of proteogenomics to uncover cryptic protein-coding regions and improve genome annotations beyond conventional predictions. This refined annotation enhances our understanding of L. braziliensis biology, providing a more accurate proteomic landscape that can inform studies on parasite virulence, host interaction, and potential therapeutic targets. The study underscores the importance of integrating proteomic evidence with genomic data to capture the full coding potential of kinetoplastid parasites, paving the way for improved diagnostics and interventions against leishmaniasis

EO-ZT : Economically Informed Zero-Trust for Secure Spectrum Trading in Open Radio Access Networks (O-RAN)

The adoption of Zero-Trust Architecture (ZTA) in 6G O-RAN spectrum trading introduces additional energy overhead from continuous authentication and encryption, along with economic-security challenges such as moral hazard and reduced security investment. To address these challenges, we propose the EO-ZT framework, a novel ZTA tailored for the O-RAN. Our framework includes a novel deposit-refund system (DRS)-based trust evaluation component and adaptive policy components designed for O-RAN spectrum trading. The DRS-based trust evaluation component aims to suppress moral hazard, drawing inspiration from the sharing economy. Within the adaptive policy component, security, energy, zero-trust policy and spectrum trading policy are jointly formulated as coupled decision-making problems. Hence, we propose a game-theoretical spectrum trading scheme (GSTS) to enable fair spectrum trading between subscribers and cell sites, mitigating moral hazard and incentivizing dynamic security investment by RAN intelligent controllers (RICs). Building on this, the presented spectrum market competition scheme (SMCS), based on a winner-take-all model, employs cell zooming strategies to reduce energy consumption while promoting security investment among cell sites. Simulations demonstrate the superiority of EO-ZT over existing potential application algorithms for ZTA in O-RAN, effectively balancing energy efficiency, economic viability, and security requirements in spectrum trading markets

Language of Mental Health

This chapter starts by describing how our language has changed from talking about mental illness to talking about mental health. It then reviews linguistic research and shows that most studies have focused on mental illness in general or specific mental disorders. Next, it reflects on the key features of the language that different people use to talk about mental health - particularly, how people with mental health conditions use metaphors to discuss their experiences and how stigma is linguistically manifested in wider societal discussions. It concludes by considering how the language of mental health may vary by culture. Key Points • Describe a key change in the language that we use to talk about this issue - a movement away from illness and disorder to health and well-being. • Present some of the key features of the language that different people use to talk about mental health - particularly, how people with mental health conditions use metaphors to discuss their experiences and how stigma is linguistically manifested in wider societal discussions. • Consider how the language of mental health may vary by culture