IoT-enabled Cyber–Physical System(CPS) integrates sensing, communication, and control into coupled services, where localized disturbances easily escalate into cross-layer cascading failures. However, existing studies often rely on single-layer or strongly coupled models, which are insufficient to capture spatial embedding, heterogeneous inter-layer propagation mechanisms, and engineering-oriented resilience-cost trade-offs. To address this, we develop a spatially embedded two-layer CPS modeling framework based on interdependent networks. We propose a spatial cascading failure model hybridizing load-capacity dynamics with threshold-based rules to describe cross-layer failure propagation triggered by targeted cyber attacks and physical disruptions. Building on this, we design and compare practical resilience enhancement strategies focusing on redundant capacity allocation, metaheuristic-assisted cyber-layer control node placement, and inter-layer coupling strength. We introduce a system performance metric accounting for load-carrying capability and spatial service coverage, constructing resilience measures from performance evolution curves. Simulations confirm the framework reproduces cascading behaviors in spatial multilayer CPSs, enabling a systematic evaluation of resilience enhancement strategies. Analyses indicate that properly configured redundancy, metaheuristic-assisted control-node placement, and coupling strength can suppress cross-layer propagation and improve service continuity under practical cost-related constraints. Overall, this work provides a practical analytical framework and engineering-oriented insights for resilience-aware planning of IoT-enabled smart-city infrastructure CPSs.
Computer vision is becoming a core technology in PLF, enabling non-contact monitoring, phenotyping, and decision support at both animal and group levels. This umbrella review synthesised review-level evidence from 112 reviews on cattle, pig, and poultry systems, covering visual applications for identification, phenotyping, health, behaviour, locomotion, body condition, growth, reproduction, mortality, and resource use. To preserve the review as the bibliographic unit while capturing multi-topic evidence, multi-label coding generated 328 review-domain assignments across six PLF computer vision domains. Health/stress and posture/activity were the most frequently reviewed areas; however, these patterns reflect review coverage rather than evidence strength. Benchmarking reporting averaged 11.09 out of 14 criteria, corresponding to 79.2%, but validation-critical items, including class balance, annotation protocols, validation splits, and farm or site reporting, remained less complete. Methodological confidence was limited: AMSTAR 2 classified 95 reviews as critically low and 17 as moderate, while ROBIS classified 78 as high, 17 as unclear, and 17 as low risk of bias. The corpus-level candidate-reference overlap estimate was 0.122% and is retained only as a sensitivity estimate, not as formal primary-study CCA or evidence of independence. Review-derived performance and readiness tiers identified a mean descriptive performance-to-deployment gap of 0.63 across 328 assignments. Overall, reported model performance continues to exceed documented farm-deployment evidence, highlighting the need for stronger external validation, transparent benchmarking, workflow integration, economic assessment, and user-readiness evidence before routine deployment claims can be supported.
Laser-assisted Atom Probe Tomography (LA-APT) has demonstrated a unique potential for the study of the 3D distribution of atomic species in semiconductor materials and devices, and in a growing list of inorganic non-metallic solids. A crucial and often underestimated issue with APT is its accuracy in compositional measurements of non-metallic systems. This work introduces the principles of APT as an experimental technique, recalling the aspects potentially leading to compositional biases and underlining in particular the role of the surface electric field in governing the different physical–chemical phenomena that enable the measurement. It reviews the possible mechanisms of specific losses, as well as the methods for assessing a compositional bias and proposing possible correction methods. Finally, it establishes a state of the art on compositional biases in APT of non-metallic materials, on the basis of which it will be possible to conclude on specific recommendations for best practices, and the perspective of application of APT to new materials.
Agentic artificial intelligence is emerging as an extension of Precision Livestock Farming by linking perception, reasoning, planning, and bounded action within human-supervised livestock-housing workflows. This review synthesizes 90 publications on agentic AI, multi-agent systems, retrieval-augmented generation, large language models, foundation models, robotics, digital twins, simulation, computer vision, cyber-physical control, and related enabling technologies for livestock-housing management. We propose a Perception–Reasoning–Action–Safety (PRAS) loop and an Agentic Livestock Housing Readiness Scale to classify systems from passive monitoring and advisory decision support to supervised, safety-constrained closed-loop operation. A staged benchmarking perspective is also used to integrate algorithmic performance, biological relevance, safety, auditability, economic feasibility, and human–AI interaction. Current evidence is strongest for perception, advisory reasoning, natural-language data access, welfare-risk interpretation, and simulation-supported decision support, whereas robust barn-wide autonomous control remains largely unvalidated. Technology categories were coded non-exclusively; therefore, publication frequencies indicate representation within the selected corpus rather than effectiveness, evidence strength, or deployment readiness. Across species, dairy cattle provide the most developed evidence base, poultry studies mainly address environmental comfort and nutrition support, swine systems emphasize simulation-based precision feeding, and small-ruminant evidence remains concentrated in advisory tools and contextual embodied monitoring. Overall, agentic AI in livestock housing is currently more mature as an orchestration, explanation, and decision-support layer than as an autonomous control technology. By distinguishing direct housing applications, semi-agentic prototypes, and enabling technologies, this review clarifies the gap between current evidence and deployable autonomy. Progress towards higher readiness will require cross-farm validation, biological plausibility, source-grounding audits, safety assurance, interoperability, economic assessment, transparent benchmarking, and explicit human oversight.
The 16th Acromegaly Consensus Conference in September 2024 updated recommendations on diagnosis and treatment of acromegaly comorbidities. Since the 2020 acromegaly comorbidity management guideline was published, new evidence has emerged on novel and known comorbidities and new treatment approaches. Forty-three experts in the management of acromegaly reviewed the current literature and assessed changes in clinical practice standards and management. Current outcome goals were considered and updated, with a focus on the impact of current and emerging treatments of these comorbidities. Participants assessed factors that determine pharmacological choices, as well as use of specific agents in the management of the most relevant acromegaly comorbidities. We present consensus recommendations highlighting optimization of evidence-based acromegaly comorbidities management.
