Abstract: Tool condition monitoring (TCM) is essential for ensuring good quality products, machining reliability, efficiency, and sustainability. Machine learning (ML), including deep learning (DL), has been extensively used in the literature to address different TCM tasks such as tool state recognition, tool wear prediction, and remaining useful life prediction. Nevertheless, the adoption of existing methods in real-world manufacturing is still hurdled by different practical challenges. This paper focuses on two key challenges facing ML-based TCM: 1) Variability of operating conditions; 2) Data scarcity. The first challenge arises from the variations in data distributions (domain shift) caused under cross operating conditions, which leads a trained ML model to generalize poorly on data from operating conditions unseen during training. The second challenge stems from the impracticality of collecting sufficient data on real-world shop floors, especially when labeled data is needed. Addressing simultaneously both challenges inherently leads to problem and evaluation settings different from those concerning only one single challenge without having the constraints imposed by the other (e.g., addressing the domain shift assuming the availability of sufficient data, or addressing the data scarcity under single-operating conditions). This paper presents a review focused on TCM considering both different operating conditions and data scarcity scenarios. The works reviewed are based on adaptation- and/or generalization oriented solutions leveraging prior knowledge across various related-yet-distinct learning settings, namely transfer learning, domain adaptation, domain generalization, meta-learning, and hybrid settings. Future research opportunities are also presented. This review can serve as a guide for both researchers and practitioners, presenting state-of-the-art practices and concrete insights to tackle and advance the challenging industry application of TCM.

Abstract: Water deficit is one of the main limiting factors for crop establishment and productivity, particularly affecting seed germination and early seedling growth. This study aimed to evaluate the biostimulant effect of Ascophyllum nodosum extract on maize (Zea mays L.) seeds subjected to osmotic stress induced by PEG-6000.Three independent bioassays were conducted under controlled conditions. First, osmotic potentials ranging from 0 to −0.8 MPa were tested to determine stress levels. In the second assay, seeds were treated with increasing doses (0 to 2 mL kg⁻¹) of a commercial seaweed extract and its isolated mineral fraction. In the third assay, selected doses were evaluated under no stress, moderate stress, and severe stress conditions. Germination percentage, normal and abnormal seedlings, radicle and epicotyl length, and vigor index were assessed. Osmotic stress significantly reduced germination and seedling growth, particularly at −0.6 and −0.8 MPa. Seed treatment with A. nodosum did not affect final germination but improved seedling growth and vigor, showing a dose-dependent response. Maximum efficiency was observed at intermediate doses (~0.45–0.66 mL kg⁻¹), which increased the percentage of normal seedlings and promoted root and shoot development. Under water stress conditions, the complete extract outperformed the mineral fraction, indicating that the beneficial effects are mainly associated with bioactive organic compounds. These findings demonstrate that A. nodosum extract is a promising strategy to mitigate water stress effects during maize seed germination, provided that optimal doses are used.

Abstract: Single-coin finds are increasingly valued as a source for understanding patterns of human activity in the Early Middle Ages. This pilot study examines whether the chronological evenness of single coins can serve as a quantitative proxy for persistent human occupation and landscape stability. Six sites in the Netherlands (450–1200 CE) are compared: three located near relatively stable Pleistocene topographic features (Noardeast-Fryslân/Dokkum, Nijmegen, and Maastricht) and three in dynamic coastal or near-coastal Holocene landscapes (Waadhoeke/Franeker, Katwijk, and Veere/Domburg). All single finds were assigned to five standardised 150-year periods. Chronological evenness was measured using three complementary indices: standard deviation of percentage shares, Shannon entropy, and cosine similarity to a uniform distribution. Sites were classified as “balanced” when at least two metrics met predefined thresholds. The results demonstrate a clear distinction between the two groups. Assemblages from stable landscape positions show relatively balanced chronological profiles, while those from dynamic coastal zones exhibit strongly peaked distributions dominated by the 600–750 CE period (χ² = 347.00, df = 4, N = 1,702, p < 0.001, Cramér’s V = 0.452). Greater chronological evenness appears linked to proximity to stable geomorphological settings. These findings suggest that single-coin evenness can function as a useful proxy for long-term landscape persistence when combined with geo-archaeological evidence. Limitations include recovery biases and variable sample sizes. The study advocates the development of standardised, open-access single-coin datasets to facilitate broader comparative research.

Abstract: A significant fraction of the HF waves is absorbed by the lowest ionospheric layer, the D-region. This region is perturbed by solar flares, which notably cause fast increases in the Sun’s X-ray flux. We present here a new chemistry model, the Lower Ionosphere Region — Absorption and Chemistry Modelling (LIR-ACheM), to study the D-region behaviour. It is based on the Mitra-Rowe [] scheme, and takes into account four distinct sources (EUV, Lyman-α, X-rays and cosmic rays) and seven species (electrons, NO+, O2+, O4+, positive cluster ions, O2− and other negative ions). It thus offers a compromise between accuracy and computing time. The D-region sluggishness and its recovery time after a flare are analysed, highlighting the importance of detachment at low altitudes and soft X-ray fluxes above 80 km.

Abstract: Anthropic’s April 2026 release of Claude Mythos Preview, and the subsequent emergence of “Mythos-class” as a descriptor for frontier autonomous offensive cyber capability, has prompted institutional response across financial regulation, but no blockchain-specific analytical or policy framework. This paper develops one. We define Mythos-class as a vendor-neutral capability profile comprising five primitives — autonomous discovery at codebase scale, multi-step exploit chaining, agentic execution with tool use, sub-day weaponization, and generality across target classes — and we engage the contested boundary between maximalist and distributional framings of the capability through analysis of independent evaluations by AISI and AISLE. The central thesis the paper defends is friction inversion: the patch primitives, segmentation, vendor-coordinated disclosure, and credential rotation that constrain Mythos-class capability in conventional IT environments are not reduced on-chain but structurally absent, making blockchain systemic exposure differently positioned in kind, not in degree, from enterprise IT exposure. We instantiate the thesis against Bitcoin and Ethereum/L2 architectures and four bridge case studies (Ronin, Wormhole, Nomad, Poly Network) totaling over $1.74 billion in losses. Vendor-neutral defensive and governance frameworks defined against the capability profile rather than against any specific model release are the correct unit of analysis. Recommendations follow for protocol governance, audit cadence, and regulatory posture.

Abstract: Background: Adverse event reporting is a critical component of patient safety systems; however, nurses’ engagement in reporting is influenced not only by reporting procedures but also by broader organizational characteristics of the nursing practice environment. Understanding how these organizational factors shape nurses’ perceptions of reporting systems remains insufficiently explored, particularly in post-Soviet healthcare contexts. Objective: This study aimed to examine how characteristics of the nursing professional practice environment are associated with nurses’ perceptions of the benefits of adverse event reporting in healthcare institutions in Kazakhstan. Methods: A cross-sectional survey was conducted among 468 nurses working in healthcare organizations across six major cities in Kazakhstan. The professional practice environment was assessed using the Revised Professional Practice Environment (RPPE) scale, while attitudes toward adverse event reporting were measured using the Reporting of Clinical Adverse Events Scale (ROCAES), specifically the “perceived benefits of reporting” dimension. Exploratory factor analysis, Spearman correlation analysis, and binary logistic regression were applied. Results: Exploratory factor analysis identified three key dimensions of the professional practice environment: professional motivation and teamwork, interprofessional conflict and communication, and staffing adequacy. Correlation analysis showed that several dimensions of the practice environment were negatively associated with perceived benefits of reporting. However, multivariable regression analysis revealed that cultural sensitivity, internal work motivation, and control over practice were positive predictors of perceived reporting benefits. This contrast between negative bivariate correlations and positive multivariable predictors highlights the complex organizational dynamics underlying nurses’ reporting attitudes. Conclusions: The findings indicate that nurses’ perceptions of adverse event reporting are embedded within a broader organizational ecology of nursing practice. Strengthening supportive professional environments—particularly those promoting motivation, autonomy, and culturally responsive care—may enhance nurses’ engagement in patient safety activities and improve the effectiveness of reporting systems.

Abstract: This paper presents an adaptive Threat Severity Assessment System (TSAS) designed to support real-time decision-making in defense environments. Existing systems primarily focus on detection and tracking, often lacking a dedicated layer for severity evaluation and prioritization. This limitation can lead to inefficient response strategies in time-critical scenarios.The proposed TSAS framework operates as an intermediate intelligence layer that processes heterogeneous sensor inputs and classifies threats into multiple severity levels using a feed-forward neural network. The system integrates entropy-based concept drift detection and incremental learning to maintain performance under evolving threat conditions. A composite severity score is generated by combining model predictions with proximity-based heuristics.The framework is evaluated using a high-fidelity synthetic dataset, achieving an overall classification accuracy of 96.4% and a millisecond-level end-to-end latency of approximately 4.5 ms under controlled hardware conditions. Experimental results demonstrate improved prioritization capability and robustness compared to traditional rule-based, fuzzy logic, and static machine learning approaches.These findings suggest that TSAS provides a computationally efficient and adaptable solution for real-time threat severity assessment, with potential applicability in defense systems, drone monitoring, and critical infrastructure protection.

Abstract: Multimodal sensors can collect multiple signals and have great potential in robotics and other technical fields. However, such sensors often encounter challenges of signal crosstalk and insufficient real-time performance, particularly in the detection of pressure and temperature, which significantly affect measurement accuracy. To address this issue, a multimodal PCSC sensor was developed. This sensor reduces signal crosstalk by separating force and temperature signals. It uses the pressure-resistance variation of carbon quantum dots (CQDs) to detect force and the thermochromic properties of spiropyran (SP) to detect temperature. When pressure and temperature act on the sensor simultaneously, the resistance increases with pressure and stabilizes when the pressure becomes constant. The response time is 0.4 s. As the temperature rises, the resistance decreases, and the color becomes deeper. Both resistance and color stabilize within 7.5 s. To improve temperature sensing accuracy, a lightweight ResNet-Transformer network (LRTNet) was proposed. This algorithm combines ResNet’s ability to extract features and Transformer’s ability to model sequences. It efficiently fuses color and resistance signals for temperature detection. Tests on a robotic manipulator for dual recognition of temperature and force showed that LRTNet achieved a runtime of 152.08 ms and a temperature sensing accuracy of 95%. LRTNet improved overall performance by at least 11% compared to traditional algorithms. The sensor and algorithm improved the performance and reliability of multimodal sensors.

Abstract: The role of IgE‑mediated allergy in atopic dermatitis (AD) has been progressively downplayed as contemporary models emphasize barrier dysfunction, type‑2 cytokine–driven inflammation, pruritus pathways, immune dysregulation, and microbial imbalance. This shift, however, has obscured a defining feature of extrinsic AD: a functional IgE‑dependent amplification loop operating across the epidermal and dermal immune network and extending into the draining lymph node. Emerging evidence shows that Langerhans cells, inflammatory dermal dendritic cells, inflammatory dendritic epidermal cells (IDECs), mast cells, and basophils can acquire environmental allergens through FcεRI‑bound IgE, enabling efficient antigen capture, processing, and T‑cell activation. Among these, IDECs appear central to IgE‑mediated delayed‑type hypersensitivity and the development of spongiosis following epicutaneous allergen exposure. Integrating these findings, we propose a mechanistic model in which IgE‑bearing antigen‑presenting cells initiate and sustain a positive feedback circuit that reinforces type‑2 inflammation and contributes to the chronicity of extrinsic AD. Re‑positioning this IgE‑dependent circuit within the broader pathophysiology of AD provides a revised framework that reconciles classical atopy with modern immunologic insights and highlights new therapeutic opportunities targeting IgE–FcεRI signaling, IDEC biology, and allergen‑driven epidermal immune activation.