Motivated by theoretically and experimentally observed structural phases with octagonal symmetry, we introduce families of octagonal tilings composed of three prototiles. These tilings are defined by substitution rules with inflation factors δ(m,n) = m + n(1 + √2), where m, n are nonnegative integers. We consider the cases (m, n) = (1, 1), (2, 2), (1, 2), (3, 2) and describe their basic properties. In addition, we discuss variants of the first three cases, and for (1, 1) we derive the relative frequencies of the vertex stars.

The Dutch government has expanded the status and scope of practice of bachelor's degree dental hygienists (DHs) compared to those with a diploma. The aim of this study is to investigate differences in the daily scope of practice of diploma and bachelor's degree DHs. A web-based survey was emailed to all 2972 members of the Dutch Association of Dental Hygienists and distributed via social media platforms. The survey had sections on participants' demographics, educational qualifications, work environment, and scope of practice. Absolute and relative frequencies for each question were reported and statistically compared between groups. In total, 473 DHs completed the survey, 288 in the 'Diploma' and 185 in the 'Bachelor' group. Altogether, Dutch DHs work on average 29 h per week; those with a bachelor's degree work significantly (p < 0.001) more hours. Bachelor DHs work significantly (p < 0.001) more often as paid employees in a general dental practice, a practice dedicated to periodontology, in the educational and research setting. Diploma DHs work significantly (p < 0.001) more often in their own private DH practice. Bachelor DHs are significantly more likely to perform the following activities: administer local anaesthesia, take intra-oral radiographs, and treat primary caries compared to Diploma DHs (p < 0.001). The overall majority in both groups does not treat caries (81.3%) but does administer local anaesthesia (85.1%). Bachelor DHs are more frequently engaged in three tasks in the extended scope of practice and are more likely to work in a team setting.

Microbial communities inhabiting natural and anthropogenically impacted environments are exposed to diverse abiotic stressors that can influence the distribution of functional traits. However, distinguishing the processes underlying phenotypic patterns remains challenging in microbial systems, where ecological and evolutionary dynamics often overlap. In this study, we experimentally assessed the distribution of biofilm formation and plastic degradation capacity in bacterial isolates across environments characterized by different stress regimes, to evaluate whether these traits are primarily associated with environmental context rather than phylogenetic relatedness, and may therefore reflect environment-dependent phenotypic modulation on a lineage-specific functional background. Taxonomic affiliation was assessed using 16S rRNA gene sequencing, while expressed biochemical profiles were characterized by Fourier-transform infrared (FTIR) spectroscopy. Multivariate ordination and Partial Least Squares analyses were used to explore relationships among taxonomy, biochemical profiles, functional phenotypes, and environment of isolation. Phylogenetic signal analysis confirmed that neither trait was strongly constrained by vertical inheritance, with Blomberg’s K ≈ 0 and Fritz &amp; Purvis’ D = 0.51, consistent with environment-driven rather than phylogenetically conserved trait distributions. Both biofilm production and plastic degradation capacity showed significant environment-dependent differences in their relative frequencies (Fisher’s exact test, biofilm: p = 5.5 × 10−5; PCL degradation: p = 2.5 × 10−4) and were not directly associated with each other (Wilcoxon rank-sum test, p = 0.45; linear model, p = 0.68). Overall, these results indicate that microbial functional traits are unevenly distributed across environments and weakly constrained by taxonomy, consistent with the contribution of multiple, non-mutually exclusive processes that remain difficult to disentangle empirically.

Introduction: Nipah virus is a fatal bat-borne pathogen that follows a seasonality from December to May in Bangladesh. Since 2001, Nipah outbreaks have been reported annually in Bangladesh. However, in early 2023, there has been an occurrence of a series of outbreaks here. This study aims to investigate the infodemiological aspect of this series of Nipah outbreaks by analyzing the Google search interest in Bangladesh. Methods: The “Explore” feature of Google Trends was utilized to analyze search behavior focusing on the topics "Nipah virus infection" and "Date Juice" in Bangladesh from January 2018 to July 2023. Data from Nipah outbreaks during the same period was obtained. Correlation analysis was done between Relative Search Volume (RSV) and outbreak frequency, and spatial analysis to compare heat maps showing RSV and outbreaks. Results: A line graph depicting the relative search volume (RSV) of Nipah virus infection reveals fluctuations in public interest, with spikes following outbreak events and during the Nipah season. Similarly, the RSV of "Date Juice" showcases changing patterns, occasionally aligning with Nipah outbreaks. Pearson correlation analysis indicates moderate positive correlations between Nipah-related RSVs and outbreaks, with p-values &lt; 0.01, underscoring the link between public interest and outbreak frequency. Heat maps depict regional variations, with higher RSV regions coinciding with reported outbreaks. Conclusion: The study found that RSV of both “Nipah Virus Infection” and “Date Juice” increased with the frequency of Nipah Outbreaks. We recommend continuous monitoring of health information regarding Nipah and other important public health issues.

Medicinal animals are integral to traditional healthcare worldwide, yet ethnozoological knowledge in many Ethiopian regions remains under-documented. This study documented the diversity of medicinal animals, associated cultural knowledge, preparation methods, and conservation implications among communities in Andracha District, Ethiopia. A cross-sectional ethnobiological survey was conducted from January to August 2025 using semi-structured interviews, focus group discussions (FGDs), and field observations. Sixty-five informants participated, including 25 key informants purposively selected and 40 general participants randomly chosen. FGDs refined data collection instruments and validated traditional knowledge. Quantitative indices Relative Frequency of Citation (RFC), Informant Consensus Factor (ICF), Fidelity Level (FL), and Zoological Ethnoknowledge Index (ZEI) summarized knowledge patterns, species importance, and cultural reliability. Jaccard Similarity Index (JSI) and Rahman's Similarity Index (RSI) assessed cross-community similarity. Statistical analyses, including t-tests, ANOVA, and linear regression, explored variations across gender, age, education, and experience using R software. A total of 52 medicinal animal species were documented in Andracha District. Bos taurus, Apis mellifera, and Halictus scabiosae exhibited the highest RFC values. ICF ranged from 0.60 to 0.97, with the strongest agreement observed for respiratory and gastrointestinal ailments. The highest FL was recorded for Capra aegagrus hircus (FL = 83.3%), followed by Apis mellifera (FL = 77.8%). Knowledge of medicinal animals varied significantly with age, gender, literacy, and healer experience (P < 0.05). Major threats to medicinal fauna included agricultural expansion, overgrazing, and overexploitation, while indigenous conservation practices such as sacred forest protection and community stewardship were actively maintained. Medicinal animals are crucial for healthcare, cultural identity, and livelihoods in Andracha District. Environmental pressures and generational gaps threaten both species and associated knowledge. Strengthening community-based conservation, sustainable harvesting, and intergenerational knowledge transmission is essential to safeguard this biocultural heritage. The study documents traditional practices but does not endorse ingestion or application of potentially hazardous animal products.

Wild edible plants (WEPs) play a crucial role in rural livelihoods, food security, and cultural heritage in Ethiopia. Despite their importance, comprehensive documentation of WEP diversity, utilization, and associated indigenous knowledge in Tegedie District remains limited. This study aimed to identify WEP species, evaluate their uses, assess knowledge distribution among community members, and identify threats to their sustainability. Ethnobotanical data were collected from 144 purposively selected informants across eight sites using semi-structured interviews, focus group discussions, and guided field observations. Quantitative indices, including the Relative Frequency of Citation (RFC) and Jaccard Similarity Index (JSI), were employed to assess species importance, cultural significance, and similarity with other Ethiopian districts. Preference ranking and direct matrix ranking were used to evaluate species preference, multifunctionality, and perceived threats. Data analysis was conducted using R software, and species identifications were cross-checked against the IUCN Red List. A total of 52 WEP species were documented, comprising trees, shrubs, herbs, and climbers. Fruit-bearing trees and shrubs were the most frequently cited, highly preferred, and culturally significant. The top-ranked WEPs included Cordia africana Lam., Syzygium guineense (Willd.) DC., Diospyros mespiliformis Hochst. ex A.DC., and Tamarindus indica L. Knowledge of WEPs was significantly higher among men, older individuals, illiterate participants, and key informants (P < 0.005), reflecting the cumulative and experiential nature of indigenous knowledge. Major threats to WEPs included agricultural expansion, overgrazing, firewood collection, charcoal production, and habitat degradation. WEPs were primarily harvested from forests, grazing lands, and uncultivated areas, with knowledge transferred orally and through hands-on participation in harvesting. WEPs in Tegedie District are vital for dietary diversity, food security, and cultural practices, but they face significant anthropogenic and environmental pressures. Conservation strategies, including habitat protection, sustainable harvesting, ex situ cultivation, and systematic documentation of indigenous knowledge, are urgently needed. Promoting intergenerational knowledge transfer is essential to ensure the continued availability and sustainable use of these valuable plant resources.

ABSTRACT Background Hidradenitis suppurativa (HS), chronic urticaria (CU), psoriasis (PsO) and atopic dermatitis (AD) are chronic inflammatory skin diseases associated with substantial morbidity and that compromise quality of life. Enhancing understanding of their prevalence, clinical characteristics, treatment patterns and outcomes is crucial for improving early diagnosis and effective care. Objectives To provide an overview of the proportion and characteristics of patients with HS, CU, PsO and AD by applying artificial intelligence (AI) to real‐world data. Methods DERMACLEAR was a multicenter, national, retrospective study. Electronic health records (EHRs) of patients with the four target diseases and at least one recorded visit at seven centers in Spain between 2016 and 2021 were processed using an AI‐based system incorporating machine learning and natural language processing (NLP). Disease relative frequency, patients' demographic and clinical characteristics and associated healthcare burden were assessed. Results Among 49,779 patients identified with only one of the studied diseases, 8.3% ( n = 4124) had HS, 13.4% ( n = 6660) CU, 58.8% ( n = 29,262) PsO and 19.6% ( n = 9733) AD. The mean age was 52.9 years (range: 18–105), and 56.5% were women. According to data retrieved by the AI system from EHRs of 434 patients, the mean body mass index was 29.0 kg/m 2 . Overall, a high prevalence of concomitant conditions was observed, with infections/infestations recorded in 68.7% of patients and respiratory disorders in 59.1% at any time during the study period. The annualized mean of dermatology visits was 26,843.2, 7,154.7, 5,063.1 and 3,272.9 in patients with PsO, AD, CU and HS, respectively. At the database closure, 5580 (11.2%) patients were recorded as deceased during the study period. Conclusions Applying AI and NLP to real‐world clinical data offers valuable insights into the characteristics and healthcare impact of HS, CU, PsO and AD. Our findings support the need for more personalized treatments and integrated follow‐up, addressing both the primary disease and associated comorbidities.

The importance of palliative care for Intensive Care Unit (ICU) patients/families is known. Little is known about implementing this care in practice, and how to support healthcare professionals in this implementation. This study uses survey methodology informed by Normalisation Process Theory to assess implementation processes for providing palliative care in the ICU. A descriptive cross-sectional survey was conducted with UK healthcare professionals involved in providing or organising palliative care in the ICU. Implementation processes were assessed using the validated 23-item Normalisation MeAsure Development (NoMAD) instrument. Absolute (n) and relative frequencies, median and interquartile ranges were reported. Mann-Whitney U Test assessed differences between specialist palliative care and ICU respondents. One open-ended item captured free-text responses, analysed using NPT-guided framework analysis. From 153 completed surveys, 69% of respondents were ICU professionals, 31% were specialist palliative care professionals. There was no statistically significant difference between responses from ICU and specialist palliative care professionals. Likert responses showed that respondents felt familiar with palliative care in the ICU and felt it was part of their normal work. Positive tendency was found toward implementation of palliative care in the ICU with coherence (sense-making work), cognitive participation (relational work) and reflexive monitoring (appraisal work). Rating of collective action (operational work) showed a more neutral tendency, highlighting this as a potential target for improvement. Free-text responses were categorised into themes within Normalisation Process Theory constructs: Coherence-recognising and stratifying need, and nuances within palliative care in the ICU; Cognitive participation-interdisciplinary interfaces and building capacity; Collective action-procedures for provision, pressures on provision, and perceived capability; Reflexive monitoring-perceived value. This novel study uses NPT to assess professional processes relating to implementation of palliative care in the ICU. Findings suggest important perceived implementation gaps may lie within operational work such as tailoring utilisation of existing resources, ensuring leadership support, and building skill sets. Dedicated qualitative research is needed to explain how these issues operate in context and to examine potential patient- and family-related influences.

Across the globe, traditional medicine, particularly herbal remedies, is a cornerstone of healthcare. While the medicinal uses of ferns are widely recognized globally, their therapeutic potential in Uganda remains significantly underexplored compared to higher plants. This limited research creates a critical gap in our understanding of the medicinal capacities of ferns, leading to their neglect in drug development. The main aim of this study, therefore, was to document the medicinal fern species and their uses in communities bordering Kalinzu Central Forest Reserve (KCFR). An ethnobotanical survey was conducted for 3 months (October, November, and December), 2024 in 20 villages bordering Kalinzu Central Forest Reserve (KCFR) in Kyamuhunga. The study utilized a semi-structured questionnaire and face-to-face interviews with 71 key informants to collect data on the use of ferns in herbal medicine. The questionnaire data was analyzed using various ethnobotanical indices, including Relative Frequency of Citation (RFC), Use Value (UV), Relative Importance (RI), Fidelity Level (FL), Informant Consensus Factor (ICF), and Combination Use Diversity Index (CUDI). Six fern species were documented for use in herbal medicine by communities around KCFR, and Pteridium capense was the most cited species. The use value (UV) index was highest for P. capense (0.72). The informant consensus factor (ICF) was highest for musculoskeletal illnesses (1.00), and the fidelity level (FL) was highest for Hypolepis sparsisora for treating burns (100%). Fronds were the most used part of the ferns in herbal medicine (76.06%), and dermal (smearing and rubbing) was the most common route of administration (57.75%). Medicinal ferns are an important component of traditional herbal medicine in communities bordering KCFR. The study documented different medicinal fern species for treating various ailments in communities bordering KCFR. Pteridium capense was the most frequently cited species, used to treat various illnesses. These findings highlight the significant ethnobotanical knowledge of the local communities and suggest that further research into the pharmacological properties of these fern species, especially P. capense, is warranted. Further research should characterize and elucidate bioactive compounds responsible for such therapeutic values.

Pulmonary diseases can be analyzed with computed tomography (CT) with regard to their severity, distribution, and morphology. Up to now, this has mainly been done descriptively by radiologists, resulting in a high variance of results. Standardized structured reporting has the great potential to systematically record pulmonary changes and make them available for machine-readable reuse. However, it is still unclear how these structured findings can be analyzed in a simple and communicable way. The aim of this study was therefore to develop a method for comprehensible visualization and to apply it to a quality-assured data set.For the retrospective evaluation, chest CTs of patients from a total of 15 different disease groups such as infectious diseases, structural and vascular lung diseases, malignant lung diseases, and pleural diseases were included monocentrically. Structured reporting was performed in the RACOON infrastructure using the RECO (Phase 1) template. The systematically reported changes were evaluated both for relative frequency and for location, severity, distribution, and specific additional criteria. The results were summarized into a "CT fingerprint" using a self-developed visual RGB schema and systematically evaluated regarding the differences across the disease groups.A total of 359 patients were included. The relative frequency of pathological CT changes shows some overlap between the disease groups. In particular, the differentiation of subgroups is therefore limited. The use of fingerprints allows for better differentiation, as here not only the frequency but also the severity, localization, and distribution of the CT changes and additional criteria are taken into account. For instance, pneumonias can be differentiated depending on the causative pathogen: Based on the frequency distribution of pathological CT changes alone, Covid-19 pneumonia can be quickly classified as a pneumonia, particularly as a subgroup of viral pneumonia, but it is not possible to clearly differentiate it from other viral pneumonias. In contrast, visual analysis of the CT fingerprint allows for a better distinction. Overall, each of the 15 diseases shows an individual fingerprint.CT fingerprints enable the visualization and differentiation of pulmonary diseases based on typical CT patterns, making them a potential contribution to pandemic preparedness. · CT fingerprints allow for a visually clear presentation of structured reports from chest CTs.. · Pulmonary disease groups can thus be compared and categorized based on CT characteristics.. · The retrospective analysis of Covid-19 pneumonia shows that even previously unknown lung diseases could be quickly assigned to known disease groups.. · Pneumonias show pathogen-specific patterns.. · Bachl M, Rüttinger T, Siegler L etal. CT Fingerprinting of Pulmonary Diseases: A Visual Analysis of Structured Reporting in RACOON-RECO. Rofo 2026; DOI 10.1055/a-2818-5917.

The aim of this exploratory study was to demonstrate how 7 T MP2RAGE T1 mapping can be used to evaluate spinal cord (SC) tissue damage and lesion characteristics in multiple sclerosis (MS) at both subregional and individual levels. Fifteen patients with relapsing-remitting MS (pwRRMS; mean disease duration = 32 ± 24.9 mo) and 15 age-matched healthy controls (HC) underwent 7 T cervical 3D MP2RAGE imaging with submillimetric spatial resolution. Automatic SC and lesion segmentations were obtained and manually corrected when necessary. Images were registered to the AMU7T template space to extract T1 values from specific regions of interest (ROIs), including white matter (WM) tracts: corticospinal (CST), lateral sensory (LST), posterior sensory (PST), ventral motor (VMT), and gray matter (GM) subregions: ventral, intermediate, and dorsal. Individual Z-score maps were computed and used to derive a global index of tissue impairment (patient-specific Z-score barplot) for lesion and normal appearing tissues (NAT). Finally, MS lesions were further characterized by their relative lesion load (RLL%), frequency maps, and topography across ROIs. Lesions were predominantly located in the posterior half of the cord, with GM showing the highest RLL. However, no lesions were observed exclusively in GM.An increasing gradient in T1 values was observed, with T1_HC <T1_NAT <T1_lesion (P < 0.01). Mixed GM-WM lesions exhibited higher T1 values and larger volumes than WM-only lesions. Elevated T1 values were also detected in NATs of SC lesion-free patients relative to HC, suggesting diffuse microstructural injury beyond visible lesions.The Z-score barplots provided individualized visualization of tissue damage, revealing more than 10% of abnormal SC voxels in 8/15 pwRRMS. This exploratory study demonstrates the potential and sensitivity of 7 T MP2RAGE T1 mapping for characterizing both focal and diffuse WM/GM SC tissue damage in MS. When combined with Z-score metrics, it provides a quantitative and individualized framework for assessing tissue integrity, grading the extent of microstructural impairment, and potentially monitoring neuroprotective treatment effects over time.

The convergence of Blockchain and Artificial Intelligence (AI) technologies have limitless possibilities in improving financial transparency in India. This paper examines the present situation of financial transparency within Indian institutions, the role of Blockchain in enhancing transaction security and accountability, the effect of AI on fraud detection and decision-making process and suggests a model of integrating these technologies. Using a multi-methodology, incorporating Latent Semantic Analysis (LSA), word frequency visualization, and the relative frequency trend analysis, the study determines major themes, issues, and opportunities of adopting Blockchain and AI in the Indian financial environment. The results demonstrate that transparency has become the key issue, but there are still some important challenges connected with fraud, privacy, compliance with regulations, and technological integration. Blockchain can be seen as a revolutionary approach to achieving immutable transaction history and increasing trust, whereas AI shows its potential in an increased fraud detection rate and streamlining the decision-making process. Nonetheless, the effective incorporation of these technologies is hampered by the factors which include the high cost of implementation, change resistance, and ethical issues. The paper suggests a theoretical model connecting latent constructs with measurable indicators, which can provide a practical value in addressing these issues. The study adds to the emerging field of knowledge on the integration of Blockchain and AI in financial services, and it has important implications to the managers, researchers, and the society in general. Future perspectives stress on the necessity of empirical verification, regulatory guidance and investigation of socio-economic implications to promote the use and confidence in these radical technologies. Keywords: Blockchain, Artificial Intelligence (AI) , Financial Transparency, Technological Innovation , Institutional Theory , Fintech

What are the variations in ovulation time and menstrual cycle characteristics among and within various individuals over the course of 12 menstrual cycles? There are considerable variations in both cycle length and ovulation time, with pronounced intra-individual variability over a 12-cycle observation period. Although it is commonly believed that healthy women have regular cycles with a predictable mid-cycle ovulation, more recent research shows a significant variation in cycle length and ovulation time. Previous studies have focused only on cycle length, often excluding cycles outside the 25-35-day range, thus limiting the understanding of natural variation; they have also lacked precise ovulation diagnostics or included small sample sizes, making it difficult to capture the full scope of cycle and ovulation variability. Similarly, a recent big data study, while valuable, was limited by a self-selected group and the absence of accurate ovulation diagnostics, reducing its generalizability. This study was designed as a prospective long-term observational study, which involved collecting data from 1923 women with a total of 43999 menstrual cycles from January 1985 to July 2019. After fulfilling the inclusion criteria, the main group consisted of 1051 women, all of whom contributed data for 12 cycles (12612 cycles), including 420 conception cycles. Participants in the study were between 18 and 44 years of age at study entry and did not take any reproductive hormones. Women who were postpartum, breastfeeding, amenorrheic, or within a 3-month period after stopping hormonal contraception were excluded. Participants agreed to keep cycle records according to the symptothermal method, 'Sensiplan'. Ovulation time was determined using an evidence-based algorithm based on evaluating cervical mucus patterns and basal body temperature shifts, with ovulation time defined as the day before the temperature rise. Data analysis was descriptive, using absolute and relative frequencies, standard deviation, percentiles, and ranges. Age dependency was assessed using unpaired sample t-tests and one-way ANOVA. Linear regression was used to assess long-term trends. In 62.4% of women, cycle lengths varied by 1week or more within 12 cycles. Accordingly, the time of ovulation varied by 1week or more within 12 cycles in 54.8% of women, with 96.5% experiencing fluctuations of 4days or more over the 12 months. The median spontaneous cycle length was 28 days, with a mean of 29.66 days (SD = 7.55). Only 52.7% of women consistently had cycle lengths between 23 and 35 days across all 12 cycles. Ovulation occurred most frequently between Days 12 and 16, with almost half of conceptions (45.7%) occurring after Day 16. A one-way analysis of variance revealed a significant reduction in mean cycle length with increasing age (P < 0.001), showing the shortest median cycle length of 27 days being in women aged 40-44 years. Age also impacted ovulation time, with women aged 35-39years showing more stable ovulation patterns compared to younger women. Over the 34-year study period, average cycle length increased slightly but significantly (β = 0.0161, P = 0.0306), corresponding to approximately half a day. Intra-individual variability also showed a slight, but non-significant, upward trend (β = 0.0262, P = 0.2173). Comorbidities such as hyperprolactinemia, obesity, and PCOS were not systematically excluded. However, by including only women with at least 12 cycles, the study largely avoided severe hormonal disorders. This study highlights the considerable individual variation of ovulation time and cycle length over 12 menstrual cycles. These findings contribute to a better understanding of fertility awareness, and highlight the implications for family planning and reproductive health management. The authors declare no conflicts of interest. No funding was provided. N/A.

Composite indices of neighborhood-level social environment are frequently used in cancer research, but relationships among these indices have been seldom studied, especially in race-stratified analyses. We considered four census tract-level social indices: Neighborhood Deprivation Index (NDI; continuous, quartiles, and clusters); Yost (continuous, quartiles, and clusters); racialized-income Index of Concentration at the Extremes (ICE; continuous, quartiles, and clusters); and a care access metric (categorical). These were linked to the Carolina Breast Cancer Study, a cohort of 2998 adults (50% Black, 50% aged < 50years) with invasive breast cancer, diagnosed 2008-2013 in North Carolina, USA. Age-adjusted associations of each index with late-stage breast cancer (stage III-IV) were assessed using generalized linear models and relative frequency differences (RFDs) and 95% confidence intervals (CIs). There were strong correlations (> 0.79 for pairwise comparisons) between the continuous NDI, Yost, and ICE, but pairwise agreement was moderate (55% to 70%) among indices as quartiles. Care access had low concordance with the other three indices. Among Black participants, late-stage breast cancer was associated with all indices, including the most disadvantaged quartile (vs least) of NDI (RFD = 7.0%, 95% CI: 1.6, 12.4), Yost (RFD = 8.8%, 95% CI: 3.3, 14.3), and ICE (RFD = 7.6%, 95% CI: 2.2, 13.0) and was strongly associated with care access (RFD = 9.4%, 95% CI: 3.8, 15.0). These associations were not significant among non-Black participants. Overall, multiple neighborhood-level social indices, including one based on healthcare data, are associated with late-stage breast cancer among Black participants, suggesting multiple dimensions of vulnerability to structural barriers.

Multistage centrifugal pumps (MCPs) are critical components in industrial systems, where early and reliable fault diagnosis remains challenging due to nonstationary operating conditions, noise contamination, and limited fault sensitive information in single domain representations. To address these issues, this paper proposes a physics guided fused (PGF) image learning framework with enhanced squeeze excitation (ESE) attention, for intelligent MCP fault diagnosis. First, a physics guided window selection strategy identifies the most informative signal segments by jointly considering energy concentration, impulsiveness, and fault related frequency band characteristics. From each selected segment, a PGF image is constructed by integrating a physics guided Mel spectrogram, a Gramian Angular Difference Field (GADF), and a Cross Interaction Map (CIM) that explicitly models their mutual dependency. This fused image captures complementary time frequency, nonlinear temporal, and interaction level fault characteristics in a unified representation. In addition, a low dimensional physics feature vector is extracted from each signal segment and injected into an ESE attention mechanism to adaptively recalibrate convolutional feature responses based on physical signal behavior. The proposed framework is validated on a real industrial MCP dataset under three operating pressures of 3bar, 3.5bar, and 4bar, covering multiple fault conditions. Experimental results demonstrate consistently high diagnostic performance across all pressure levels, achieving accuracy of greater than 99% across all pressure bars with macro average F1 scores exceeding 0.99. These results confirm the robustness and generalization capability of the proposed physics guided fused image and attention learning framework for real world MCP fault diagnosis.

BackgroundDespite the vast growth of vaccine studies during the SARS-CoV-2 pandemic, clinical trials failed to adequately represent diverse societal groups, resulting in the underrepresentation of specific populations. Understanding the factors hampering participation in vaccine clinical trials is essential to better identify structural, ethical, and communication barriers and to improve inclusive strategies for broader and more equitable participation in future vaccine research.ObjectiveThis study aimed to identify the perceived barriers to participation in vaccine trials among pregnant and lactating women, children aged younger than 18 years, and adults aged older than 65 years, as reported by professionals with expertise in vaccines or vaccine trials.MethodsAn online questionnaire was developed to gather personal information, group-specific barriers to vaccine trial participation, and suggestions to overcome these barriers. Data are presented as absolute (n/N) and relative frequencies (%).ResultsA total of 115 respondents, the majority (n=73, 63.5%) of whom were working in the scientific community, completed the online survey. Challenges in recruiting children were identified due to “safety or efficacy concerns,” “difficulties about ethics and regulatory issues,” and “lack of targeted information and communication.” Challenges in recruiting pregnant and lactating women were primarily “ethics and regulatory requirements,” “safety issues,” and “lack of prioritization or interest.” “Lack of information and communication channels adapted to the specific target group,” along with “lack of prioritization,” were the main challenges in recruiting older participants. Provision of health-related incentives, including but not limited to access to new treatments and receiving expert medical care, seems to be the top-rated motivation to participate in vaccine clinical trials.ConclusionsThe main challenges in recruiting pregnant and lactating women and children in vaccine trials involve safety and efficacy concerns, as well as lengthy ethical and regulatory processes. For older adults, key issues include poor communication channels tailored to their needs, limited information, lack of prioritization, funding, infrastructure, and industry interest. Across all underrepresented groups, low awareness of and poor communication about research opportunities were major barriers. Additionally, mobility issues affected older adults, while lack of motivation and incentives affected children, and low health literacy and provider uncertainty impacted pregnant and lactating women. Improving communication infrastructure and enhancing communication strategies with clear, tailored messages to build trust and motivate participation are essential to improve inclusion in vaccine research.

Abstract Geometric confinement is known to modify single-particle dynamics through effective potentials, yet its imprint on the interacting quantum vacuum remains largely unexplored. In this work, we investigate the Maxwell--Klein--Gordon system constrained to curved surfaces and demonstrate that the geometric potential $\Sigma_{\mathrm{geom}}(\mathbf{r})$ acts as a local renormalization environment. Going beyond standard approximations, we derive an exact analytical framework where extrinsic curvature modifies the scalar loop spectrum, entering the vacuum polarization as a position-dependent mass correction $M^2(\mathbf{r}) \to m^2 + \Sigma_{\mathrm{geom}}(\mathbf{r})$. This induces a finite, gauge-invariant ``geometry-induced running'' of the electromagnetic response. In the long-wavelength regime ($|{\bf Q}|R \ll 1$), we obtain a closed-form expression for the relative frequency shift $\Delta\omega/\omega$, governed by the overlap between the electric energy density and the geometric potential. Applying this formalism to Gaussian bumps, cylindrical shells, and tori, we identify distinct spectral signatures that distinguish these quantum loop corrections from classical geometric optics. Our results suggest that spatial curvature can serve as a tunable knob for ``vacuum engineering,'' offering measurable shifts in high-$Q$ cavities and plasmonic systems relevant to current nanophotonic experiments.

Abstract Minute added masses must be estimated with high accuracy while monitoring aerosol deposition, thin film growth, and molecular adsorption. Although the total added mass is reportedly estimated from shifts in the mechanical eigenfrequencies, single-mode measurements exhibit sensitivity degradation near the mode nodes and strongly depend on the attachment position. Therefore, in this study, we focus on “mode averaged” estimators that infer the total added mass ratio from relative frequency changes across multiple modes. Averaging over multiple modes reduces the influence of mode shape variations; however, doing so naively requires many readout channels, which increases the circuit complexity and load. As an alternative technique that preserves accuracy with only a few modes, we proposed a new estimator that linearly combined the fractional frequency shifts d_n with Fejér (triangular) weights, thereby smoothing the finite sum oscillations induced by position dependence via a Cesàro average. Finite element simulations of an Euler–Bernoulli cantilever validated the approach. Compared with equal weight averaging, the proposed estimator reduced position induced estimation errors by approximately an order of magnitude or more. Thus, Fejér weighted mode averaging provides an effective, low barrier means for improving robustness in applications that require stable monitoring of the total added mass.

As part of the framework of preparedness and response to influenza viruses with pandemic potential, the World Health Organization (WHO) has developed a protocol for the characterization of a series of first cases and contacts of an agent with pandemic potential (FFX). At the end of 2019, PAHO/WHO invited Colombia to be among the pilot countries for implementing this protocol. It was conducted by the National Institute of Health of Colombia. The WHO generic protocol was reviewed and adapted to Colombia. In its implementation, a series of 99 cases of COVID-19 and 360 total contacts (159 positive contacts) were evaluated between August 2020 and January 2021 in three municipalities of Colombia. Surveys were conducted, along with RT-PCR and antibody titer tests. Sociodemographic, clinical, and virological transmission conditions were analyzed to calculate the main epidemiological indicators of viral transmission using measures of central tendency and absolute and relative frequencies. A total of 258 cases were confirmed to be positive for COVID-19 for a secondary attack rate of 43%. The most common symptoms were cough, fever, headache, and odynophagia. The percentage of symptomatic contacts, clinical attack rate, incubation period, and serial interval were 34%, 16.2%, 4.6 days, and 4.7 days, respectively. The significant rate of infection and incubation periods among contacts was similar to those reported worldwide. The implementation of the WHO FFX protocol in a novel context allowed the country to test its capacity to implement global studies to determine early public health interventions. The study faced limitations due to the selection bias towards severe cases and challenges in case recruitment and contact tracing.

Diarrheal diseases are responsible for significant mortality worldwide. In many regions, local populations rely on traditional medicine, including medicinal plants, to manage these illnesses. This study aimed to identify the medicinal plants used by traditional healers in the Hauts-Bassins region of Burkina Faso for treating diarrheal diseases. An ethnobotanical survey was conducted from September 4 to 30, 2020, using a semi-structured questionnaire. Key indicators such as Family Importance Value (FIV), Relative Frequency of Citation (RFC), plant parts used, preparation methods, and routes of administration were assessed. A total of 51 traditional healers were interviewed, 90.20% of whom were male. Most participants were over 60 years of age, and 54.9% were from the village of Bala. Eighteen (18) plant species from eleven (11) botanical families were recorded, with the Annonaceae family being the most frequently cited (35.56%). The most commonly reported species included Annona senegalensis Pers. (35.56%), Euphorbia hirta L. (11.11%), and Erythrina senegalensis DC (8.89%). Roots (44.44%) and trunk bark (20.00%) were the most frequently used plant parts. Decoction (57.78%) and maceration (42.22%) were the primary preparation methods, with oral administration being the most common. Species including Opilia celtidifolia, Erythrina senegalensis, Entada africana, Acacia erythrocalyx, and Combretum nigricans were reported as rare. This study contributes to the preservation of indigenous knowledge and provides a foundation for future pharmacological investigations into antidiarrheal medicinal plants.