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AIP: A Named Entity Recognition Method Combining Glyphs and Sounds
(Association for Computing Machinery, 2022-11-12) Liu B; Su Z; Qu G
In recent years, a large number of Chinese electronic texts have been produced in the process of information construction in various fields. Identifying specific entities in these electronic texts has become a major research focus. Most existing research methods use radicals to extract the glyph features of Chinese characters but have seen its limitation. This paper extracts the features of Chinese characters from three aspects: glyph features, phonetic features, and character features, and improves conventional feature extraction methods for each kind of feature. A new named entity recognition method (AIP) is proposed by transforming Chinese characters into corresponding images for glyph feature extraction, dividing pinyin into initials, vowels, and tones for phonetic feature extraction, and fine-tuning the A Lite Bert model for character feature extraction to improve the performance of the model. This paper compares the performance of the AIP model and mainstream neural network models on Chinese named entity recognition tasks on commonly used data sets and the data sets in specific domains. The results showed that AIP achieved better results than the related work. The F1 values on the two data sets are 94.4% and 80.5%, respectively, which validates the model's versatility.
Assessment of Heavy Metals in Organic and Non-Organic Vegetables Post Severe Tropical Cyclone Gabrielle: A cross-sectional comparative analysis
(F1000 Research Ltd, 2025-12-26) Dearing C; Ye Z; Robertshaw G
Background Heavy metals such as cadmium, lead, and mercury are ubiquitous in the environment, accumulating in plants, animals, water and human food. Human exposure through the consumption of vegetable crops is a global concern because such metals may be toxic even in trace amounts. There are many factors influencing heavy metal concentrations in vegetables including, soil properties, growing practices and flooding events. This study aimed to investigate heavy metal concentrations in vegetable samples in Hawke’s Bay, New Zealand, one year post Severe Tropical Cyclone Gabrielle, which caused widespread flooding on the 14th of February 2023. Methods This cross-sectional study included organic and non-organic vegetables and sites impacted and not-impacted by cyclone flooding. In total, 736 vegetable samples were combined to form 153 representative samples collected from 14 markets grown at 10 growing sites. Samples were analysed by ICP-MS in an ISO-17025 accredited laboratory. Results Cadmium (p = 0.003) and nickel (p < 0.001) contamination were higher in non-organic vegetables. Growing vegetables on flood-affected land was independently associated with reduced cadmium (p = 0.030) and nickel (p = 0.024) contamination. Three samples exceeded Codex Alimentarius lead permissible levels (0.1 mg kg−1 fresh weight), and one sample exceeded cadmium permissible levels (0.05 mg kg−1 fresh weight in Brassica). Conclusions This study suggests that Hawke’s Bay vegetables by global standards, are generally low risk, for heavy metal toxicity and organic vegetables, carry the lowest risk. However, some vegetables do exceed maximum limits for lead and cadmium. We speculate that recent Severe Tropical Cyclone Gabrielle did not increase risk and may have reduced the risk of heavy metal toxicity from vegetable consumption.
Seek and You Shall SOC: Blending Human Expertise with Multimodal Generative AI for Scalable Threat Prevention
(Association for Computing Machinery (ACM), 2026-02-26) Xu D; Gondal I; Yi X; Susnjak T; McIntosh T
Large language models (LLMs) are increasingly employed within Security Operations Centres (SOCs), including SOC for Digital Risk Protection (DRP), yet their outputs often exhibit partial coverage, hallucinations, verbosity, and lack of localized insights. This article proposes a hybrid reasoning pipeline that combines multimodal LLMs with stable human-curated references to mitigate these issues, and is distinct from standard retrieval-augmented generation because offline, human-curated references are applied as an explicit decision-time override rather than used solely as supportive retrieved context. We introduce a step-by-step process that incorporates multi-vantage crawling for evasive content, deterministic prompts to manage inconsistency, and a structured approach to override or refine the model’s classifications when local brand knowledge contradicts global assumptions, together with an analyst-governed escalation loop that records when and why overrides occur in external-SOC DRP settings. Empirical evaluations with multiple commercial and open-source model providers show that this method significantly boosts scam detection accuracy, lowers token costs through caching, and reduces misleading outputs by adopting curated domain data, including comparisons against a RAG-only configuration and classical non-LLM baselines. Results underline how offline reference injection fosters a reliable collaboration pattern that harmonizes automated tasks with human expertise, thereby enhancing scalability and trust in real-world SOC environments.
The role rural mothers play in supporting agricultural students during their first year at university
(Taylor and Francis Group, 2026-03-11) Stanley-Clarke N; Hay A; Maris R; Andrews C; Winder L; Knook J
First year university is a time of transition and identity development for young people. Faced with new challenges, they navigate study, social relationships and financial independence. Young people from rural communities are especially vulnerable, with increased mental health and suicide risk. This article presents qualitative findings from exploratory research focused on understanding the mental health and self-care knowledge and behaviours of first-year students in agriculture programmes at two universities in New Zealand. The research involved semi-structured interviews with 15 first-year students. Findings showed that mothers delivered key messages about self-care and mental well-being that supported young people from rural communities during their first year of university study. This research acknowledges the role rural mothers play as key supports and mental health educators for young people. It highlights the importance of ensuring that educators and researchers consider the role rural mothers play as protective factors for young people as they transition to early adulthood.
Symbolic Action Motivates Further Collective Action by Increasing Identification With the Common Cause
(John Wiley and Sons Ltd., 2026-03-01) Bingley WJ; Mirnajafi Z; Rhee JJ; Robinson K; Stanley SK; Wilson MS; Haslam SA; Subašić E
Publicly broadcasting one's support for a social cause is often maligned as ‘slacktivism’. We investigate whether such symbolic action by advantaged group members in support of the cause of a disadvantaged group can motivate more concrete collective action when it solidifies a sense of belonging to a common cause. Across four preregistered studies (total N = 1204), voluntary (Studies 2 and 3), but not involuntary (Studies 1 and 4) symbolic action by advantaged group members increased opinion-based identification as part of a cause. This change in identification predicted increased collective action intentions and collective action in the form of donating to relevant charities. Moreover, in Study 3 we also found a significant indirect effect of symbolic action on collective action via increased identification. A competing hypothesis, that symbolic action might demotivate further action by reducing collective guilt, was not supported.