Correction of the radon exhalation rate using the Kalman filtering to reduce measurement errors

The RAD7 detector is widely used for measuring the radon exhalation rate from the surfaces of media such as soil, rocks, and building materials. However, during the measurement process, the accuracy of the results is prone to interference due to the instrument's inherent statistical errors and environmental noise. To reduce these measurement errors, the Kalman filtering was introduced in this study to correct the radon exhalation rate, which was obtained through data fitting of radon concentration measured by the RAD7 detector. Ten verified experiments were performed with a radon exhalation standard device. The experimental result shows that 80 % of the radon exhalation rate, corrected by Kalman filtering, significantly approached the theoretical value of the standard device, compared to the uncorrected experimental results. It confirms the effectiveness of the Kalman filtering in correcting RAD7 measurements, thereby enhancing the accuracy of radon exhalation rate measurements. The proposed method provides a reference technical pathway for improving the measurement accuracy of similar radon measurement instruments

Assessing resilience potentials in management of occupational safety and health in hospitals: Development and validation of a tool

A resilient Occupational Safety and Health (OSH) management system is crucial for effectively addressing potential future public emergencies, ensuring the continuous protection of workers' safety and health. Therefore, it is essential for organizations, particularly hospitals, to assess their resilient performance and employ tools that are appropriate and tailored to their specific context. This study aims to enhance the understanding of resilience potentials in OSH management within hospital settings. To this end, an assessment tool was developed based on the Resilience Assessment Grid (RAG). A Delphi study involving subject matter experts was conducted to refine the tailored RAG tool. Following this, a pilot test was administered to 404 healthcare professionals across three public hospitals, with subsequent psychometric analysis. Exploratory Factor Analysis (EFA) identified a four-dimensional structure. Goodness-of-fit indices demonstrated acceptable values, confirming the adequacy of the measurement model. Reliability testing indicated that the 29 item assessment tool is both valid and reliable. The tailored RAG tool was successfully validated, enabling the identification of strengths and weaknesses in OSH management

Comparative analysis of multi-nuclide spectrum recognition methods based on neural network

To address the limitations of traditional peak-finding approaches in complex radiation environments, this paper conducts a systematic comparative analysis of multi-nuclide energy spectrum recognition technology based on neural network methods. Eight nuclides from the industrial nuclide library are selected for the study. The energy spectrum dataset is constructed through the Monte Carlo simulation method, and the performance of four typical neural network models (BP, CNN, ResNet and LSTM) in nuclide identification is compared. The experimental results show that the ResNet model exhibits optimal performance under both expected label formats (1 × 8 and 1 × 1024). Its nuclide recognition accuracy rate is as high as 87.6 %, the average error of relative activity prediction is the lowest at 0.14, and it significantly outperforms other models in weak peak recovery and anti-interference ability. CNN and BP models perform second best in complex tasks, while LSTM models have relatively limited performance due to the indistinct characteristics of energy spectrum sequences. In addition, ResNet demonstrated excellent stability in both high and low activity ranges, verifying its practical application potential in complex radiation fields. This study provides a reference for model selection in the field of nuclide identification and promotes the optimization of neural networks in energy spectrum analysis

Paleoproterozoic rift-related volcanism and associated ore mineralization in the Alwar Basin, India

Alwar Basin, located within the North Delhi Fold Belt (NDFB) of India, is a key area to study the processes of Precambrian geological history and related mineralization. In this study, we document two discrete pulses of volcanic activity in the Alwar Basin, dated at 1.865 ± 0.021 Ga and 1.635 ± 0.017 Ga, recorded by weighted‐mean zircon U–Pb ages. These episodic eruptions generated a compositional spectrum from basalt through rhyolite, emplacing volcanic sequences in a shallow‐marine, within‐plate rift environment. Subsequent percolation of metal‐rich hydrothermal fluids – driven by sustained heat flux and extensional fracturing – focused along stratigraphic contacts and pre‐existing weak planes, precipitating Cu–Pb–Zn sulfide mineralization within quartz–carbonate veins. Geochemical signatures, including peraluminous major‐ and trace‐element patterns and characteristic REE anomalies, further attest to syn-rift magma evolution and crustal assimilation. This pulsatory volcanism and linked hydrothermal activity were pivotal in concentrating base metals, revealing the interplay between Precambrian rifting and ore genesis in the North Delhi Fold Belt

Balancing energy efficiency and avian conservation: divergent nest-site selection responses of Barn Swallows and Red-rumped Swallows to attached sunspaces in cold rural landscapes

Human-modified landscapes serve as ecological filters, determining species distributions and persistence. Energy-efficient technologies, while crucial for climate change mitigation, represent novel filters whose impacts on synanthropic biodiversity are poorly understood. We investigated how attached sunspaces, a widely adopted energy-saving technology in rural China, filter the distribution of two ecologically important aerial insectivores, the Barn Swallow (Hirundo rustica) and Red-rumped Swallow (Cecropis daurica). We surveyed 106 villages during the 2024 and 2025 breeding seasons and recorded a total of 2323 nests (612 Barn Swallow, 1711 Red-rumped Swallow). Using Generalized Linear Models, we assessed their responses to building characteristics, landscape composition and the prevalence of sunspaces. Barn Swallow nests preferred perches at the base and single attachment faces, while Red-rumped Swallow nests favored multiple attachment faces and avoided long shelters. The proportion of buildings with sunspaces acted as a strong positive filter for Barn Swallow nest abundance (+24%) but as a significant negative filter for Red-rumped Swallow (−51%). Other landscape variables (e.g., human population density, NDVI, Human Footprint Index) were not significant. This study demonstrates that specific architectural innovations can act as powerful ecological filters, leading to divergent distributional outcomes for sympatric species reliant on anthropogenic structures. Our findings reveal a critical trade-off in sustainable development: energy efficiency gains may inadvertently reduce habitat suitability for certain species. To reconcile climate and biodiversity goals in rural landscapes, we advocate integrating species-specific habitat requirements into building design. We propose actionable modifications to sunspaces to support swallows without compromising energy savings. These principles provide a template for mitigating the distributional impacts of green infrastructure globally

Evidence for dysbiosis in the gut microbiome of patients with systemic mastocytosis

Background: Limited research studies have investigated the role of the gut microbiome in systemic mastocytosis (SM), which is characterized by an aberrant expansion of clonal mast cells in specific tissues including the skin, marrow, liver, and the gastrointestinal tract. Objectives: We sought to investigate the relationship between the intestinal microbiome and clinical manifestations of SM. Methods: The V4 region of the 16S rRNA gene was sequenced from stool samples of 22 patients with SM and 9 healthy controls. Microbial community composition, diversity, and functional genes inferred from 16S rRNA gene sequences were analyzed. ClinicalTrials.gov Identifier NCT00044122. Results: Changes in microbial community composition were associated with SM, KIT D816V, and tryptase (PERMANOVA, P = .004, P = .05, P = .005, respectively). The differences with SM were driven by the composition of Firmicutes (P = .04) and an increase in Bacteroidetes abundance (P = .04). Predicted functions of the gut microbiome suggested that there were differences in metabolite profiles, including short-chain fatty acids, increased virulence factors, and decreased bacterial defense mechanisms in patients with SM. Dietary components were associated with symptoms, quality of life, and markers of mast cell activation and inflammation, as well as changes in microbial composition and predicted function in patients with SM. Conclusions: Dysbiosis of the gut microbiome is evident in patients with SM and is seemingly associated with mast cell activation. In addition, diet may further alter microbial composition and metabolism in the gut of patients with SM

Advancing sustainable energy transitions: Insights on finance, policy, infrastructure, and demand-side integration

Achieving the 1.5 °C global temperature target and reaching net-zero emissions by 2050 require a fundamental transformation of energy systems, driven by the rapid deployment of renewable energy technologies and underpinned by systemic policy, financial, and infrastructural reform. The manuscript adopts a literature-driven approach, synthesizing findings from existing scholarly sources that shape the transition to sustainable energy systems. It begins by outlining global progress toward climate targets, emphasizing the critical role of renewable energy in decarbonizing electricity, industry, and transport sectors. The manuscript explores recent technological advancements and trends in solar, wind, hydrogen, and emerging clean technologies, highlighting their impact on global energy supply chains and production models. Particular attention is given to the complexities of integrating renewable energy into existing infrastructure, including grid modernization, digitaliation, and storage technologies. On the demand side, the article examines changing consumption patterns, electrification, and the role of distributed generation in shaping future energy landscapes. Investment and finance emerge as central challenges, with the paper analyzing the disparities in capital costs between developed and developing economies, and the need for innovative green finance instruments to de-risk investment. The manuscript further identifies structural barriers, including policy uncertainty, supply chain constraints, and permitting delays, as key impediments to progress. Nonetheless, the article outlines significant opportunities for scaling up renewable deployment through international cooperation, targeted subsidies, and public-private partnerships. The manuscript concludes by emphasizing the necessity of coherent and enforceable policy frameworks to align national commitments with global climate goals. It calls for an integrated, multi-stakeholder approach to ensure that finance, infrastructure, demand, and governance evolve in tandem, thereby enabling a just, inclusive, and resilient global energy transition

AI as a cognitive collaborator: Assimilation and accommodation in human–machine teaming for innovation

This study investigates how generative artificial intelligence (GenAI) transforms collaborative innovation by serving not only as a productivity tool but also as a cognitive partner in workplace problem-solving. Drawing on Piaget’s theory of assimilation and accommodation, we propose that the impact of GenAI depends on how users cognitively engage with it, either by fitting it into existing schemas (assimilation) or using it to restructure mental models and workflows (accommodation). To address our hypotheses, we conducted a randomized 2 × 2 factorial field experiment involving 371 professionals in South Korea to compare individuals and teams with or without access to GenAI. Participants completed an open-ended innovation challenge, and their cognitive strategies, emotional responses, and solution outcomes were measured using surveys, behavioral data, and expert evaluations. The results show that GenAI significantly improves innovation quality and emotional engagement, especially when users adopt accommodative strategies. Furthermore, accommodation mediates the integration of cross-functional knowledge, suggesting that cognitive adaptation is a critical mechanism for unlocking GenAI’s collaborative potential. These findings provide new theoretical insights into human–AI teaming, highlighting the importance of organizational support for cognitive flexibility in AI adoption. We conclude that GenAI’s value is maximized not through passive use, but through reflective collaboration and schema-level transformation