Gastrointestinal nematodes, especially Haemonchus contortus, threaten small ruminant health, welfare, and productivity through anemia, production losses, and anthelmintic resistance. This opinion article presents digital parasitology as a framework to strengthen targeted selective treatment and integrated parasite management. We synthesize advances in AI-based FAMACHA scoring, smartphone hematocrit imaging, bioelectrical impedance analysis, radiofrequency sensing, radiofrequency identification-supported behavior monitoring, WebGIS decision support, and nutraceutical forage planning. These tools should not replace classical parasitology or veterinary judgment, but they can improve consistency, scalability, and farm-level decision-making. Key challenges include validation, affordability, explainability, model drift, device standardization, bias, veterinary oversight, and adoption. Digital tools could shift parasite control from blanket and/or reactive deworming to predictive, AI-guided prevention.

Organizational culture is multidimensional, yet research rarely compares how the same cultural dimension relates to multiple employee judgments. This study defines resource scope as the design-contingent profile of a measured resource's signed and adjusted associations across a specified outcome set. It is a comparative description, not a new mechanism or stable organizational property. It analyzes overall satisfaction, career advancement, compensation and benefits, and work-life balance in 153,123 reviews from 388 South Korean firms (2018-2022). Text measures combined theory-guided dictionaries, word-embedding diagnostics, and leave-one-review-out firm aggregation. Independent two-rater context validation covered interpersonal dignity, fairness/inclusion, and innovation orientation only (Cohen's κ = 0.77). In the broad model, broad respect was positively associated with all four outcomes. After respect was decomposed and controls for generic evaluative language were added, interpersonal dignity retained a modest association with work-life balance (β = 0.105), and the positive association persisted with an ultra-strict dictionary; however, the block containing dignity and fairness/inclusion added only ΔR2 = 0.0036. The innovation-work-life-balance coefficient attenuated from β = -0.148 to -0.068 (54.4%). The targeted estimate survived false discovery rate correction (q = 0.041) but not Holm correction (Holm-adjusted p = .424). Within-firm and lagged estimates were weak, and adjacent-year correlations were 0.005-0.120. The measures are therefore interpreted as pooled, platform-based employee-perception signatures rather than temporally stable culture or causal change. Resource-scope conclusions depend on the outcome set, construct decomposition, and level of analysis; the innovation pattern is consistent with, but does not test, resource-demand bundling.

What does happiness mean to children? While it may seem obvious that fulfilling desires and preferences matters, they often conflict with personal goals. Three preregistered studies with U.S. 4- to 9-year-olds (N = 307; 54% female; 59% White; tested in 2023-2025) found that in conflict situations, children considered goals both when attributing happiness (Studies 1 and 2) and evaluating happiness beliefs (Study 3) (ds = 0.36-1.49). The effect of goals went beyond simpler forms of inner conflict (Study 2), and children's goal-oriented responses predicted healthier food choices (Studies 2 and 3). These findings suggest that even young children view goal fulfillment as a meaningful source of happiness, offering insights into how to foster a happy childhood.

Out-of-context (OOC) misinformation, where genuine images are paired with misleading text, poses substantial societal risks due to its deceptive narratives. Prior efforts primarily assess image-text consistency but often lack explainable judgments, limiting their usefulness for forensic validation. Recently, Multimodal Large Language Models (MLLMs) show promise with vast world knowledge and strong reasoning capabilities, yet their judgments in OOC detection remain unreliable. On one hand, their static internal knowledge quickly becomes outdated in fast-moving news domains, and compensating with externally retrieved evidence often introduces noise and conflicts with their internal priors. On the other hand, the absence of task-specific cognitive calibration leads to hallucinations, whereas instruction tuning introduces such specialization only at the cost of vast curated data, rendering it impractical for dynamic, real-world scenarios. In this paper, we propose IN3SIGHT, a cognitive framework that emulates expert forensic reasoning through three synergistic stages: Epistemic Inspection, which calibrates the epistemic state of an MLLM by task-aware prompt optimization and discrete confidence stratification, yielding a stable epistemic anchor without model tuning; Semantic Investigation, which audits multimodal evidence by enforcing event-level identity and deriving a relevance-aware external assessment under conditional visual anchoring, distilling retrieved evidence into claim-aligned snippets and summaries; and Dialectical Introspection, which conducts multi-fact reasoning by dynamically adjusting the scope of consulted evidence according to evidential certainty and produces the final consistency verdict. Experiments show that IN3SIGHT markedly improves zero-shot MLLM performance by 15.0 percentage points without parameter updates and achieves competitiveness with or even surpasses instruction-tuned methods. This indicates that imposing structured cognitive constraints and evidence-grounded reasoning on MLLMs offers a principled pathway toward reliable misinformation forensics.

The empirical aesthetics of dance is a relatively recent field of investigation, and most research has been focused on the aesthetic experience of observers when perceiving different Western dance styles. It is well-known from other domains that prior exposure and familiarity modulate value formation and aesthetic judgment. Dance cultures vary considerably with regard to emotional expressivity and types of movements used. Therefore, we hypothesised that culturally-dependent emotional expressivities and the choice of movements within a choreography would modulate observers' aesthetic experience. Across three experiments, with a total of 242 Iranian and English participants, we investigated cross-cultural aesthetic judgment to dance movements of an Iranian dance style. We asked how culture group, intended emotional expressivity, type of movement, motion energy and luminance interacted in aesthetic judgments of expressivity, beauty and liking. Taking advantage of a newly developed Iranian dance movement library, we found a general effect of culture and of emotion. Iranians generally rated the stimuli higher than English participants did, and joyful movements were preferred overall. Iranians found lower body and staccato movements more beautiful than English participants did - likely due to the subtle role of lower body movements, unfamiliar to English observers, in Iranian dance. While motion energy (but not luminance) had some influence on aesthetic judgment, intended emotion and movement type were stronger modulators of expressivity, beauty and liking judgments. This Iranian dance style is a largely unknown dance genre in many parts of the world and holds great promise as a subject for empirical aesthetics.

Smart TV recommenders operate under constraints that are less prominent on personal devices: viewing is often passive, one account may represent several viewers, direct feedback is scarce, and programmes are long and semantically rich. We mapped research published from 2015 to 2025 using a structured evidence-review protocol. The submitted bibliography comprised 122 DOI-bearing references, including two foundational sources and 120 records used in the evidence map. Crossref verified 116 records; six stable arXiv DOI records were retained. Searches across six sources yielded 2,526 database-level results. A combined deduplication and title/abstract screening stage removed 2,414 records, leaving 112 unique records for full-text assessment; all 112 full texts were retrieved, 87 records were excluded, and 25 met the eligibility criteria. These 25 studies were combined with the 95-study initial corpus to form the 120-study evidence map. Separately, we conducted an independent dual-reviewer eligibility audit of 126 records, comprising all 120 included studies and six representative boundary exclusions. Agreement was 97.6% (123/126; Cohen's κ = 0.788). Six records were excluded by both reviewers, and three discordant judgments were resolved by joint full-record review. For analysis, each study was assigned one primary technical category and one of six mutually exclusive primary functional goals; secondary technical labels captured hybrid methods. Evidence reporting was profiled separately for data transparency, reproducibility, evaluation design, external validation, and deployment validation. The final literature corpus encompasses intelligent recommendation systems based on deep learning, sequence analysis, graph theory, shared accounts, and multimodal approaches.

Multidisciplinary teams (MDTs) are central to colorectal cancer management, where treatment decisions increasingly depend on the integration of tumor stage, molecular characteristics, patient fitness, and multimodal treatment strategies. However, MDT workflows are time-consuming, subject to inter-team variability, and influenced by differences in expertise, local practices, and resource availability. Artificial intelligence (AI) has emerged as a potential support tool for data integration, standardization, and risk stratification. This review examines AI in colorectal cancer multidisciplinary decision-making, focusing on AI-MDT concordance, predictive models with potential relevance to MDT discussions, and clinical translation. Reported concordance between large language models and MDT decisions varies substantially and appears to be influenced by disease context, age, performance status, case complexity, input quality, and the inclusion of clinically important variables. Predictive models may provide additional prognostic information relevant to treatment planning. Prospective evidence of AI integrated into colorectal cancer MDT workflows remains very limited. Nevertheless, the literature remains limited by predominantly retrospective designs, small or selected cohorts, heterogeneous endpoints, and unresolved issues related to transparency, reproducibility, regulation, and accountability. Current evidence is insufficient to establish improvements in MDT decision quality or patient outcomes, and AI should therefore be regarded as a supervised support tool rather than a replacement for expert multidisciplinary judgment.

Commercial AI laboratories increasingly govern language models through documents that prescribe values, name dispositions, stage moral conflicts, and define acceptable forms of objection. In this article, we ask how such writing makes an AI assistant into a governable character and redistributes moral authority among the model, its producer, and its publics.