A consistency regularization training method for automatic modulation classification under incomplete information

The Frailty Index (FI) is a well-established predictor of accelerated biological aging and a reliable tool for estimating all-cause and cardiovascular disease (CVD) mortality in older adults in the United States. However, its predictive value remains unclear in other U.S. population subgroups. This study aimed to examine the association between FI levels and both all-cause and CVD mortality among patients diagnosed with Cardiovascular-Kidney-Metabolic Syndrome (CKM syndrome). This study utilized the data from the National Health and Nutrition Examination Survey (NHANES 2011-2018), which included 7049 participants with complete information for CKM staging (stages 0-4). We employed multivariate Cox proportional hazards models in conjunction with restricted cubic splines (RCS) to account for potential non-linear relationships in the data. Additionally, segmented Cox proportional hazards models were used to examine the association between FI levels and both all-cause and CVD mortality in the CKM syndrome population. Subgroup analyses stratified by demographic and clinical factors, along with interaction tests, were performed to evaluate the consistency of these associations. After adjusting for potential confounding variables, a nonlinear association was observed between the FI and CKM syndrome. Multivariable Cox regression analysis based on nationally representative data demonstrated that higher FI levels were significantly associated with increased risks of both all-cause and CVD mortality among patients with CKM syndrome. Multivariable analysis indicated a robust association between higher FI levels and the presence of CKM syndrome. Among patients diagnosed with CKM syndrome, each 10-unit increase in the FI was associated with a 54% higher risk of CVD mortality (HR = 1.54, 95% CI: 1.24-1.91; P < 0.001) and a 55% higher risk of all-cause mortality (HR = 1.55; 95% CI: 1.38-1.73, P < 0.0001). Stratified analyses revealed no significant interaction effects between the FI and demographic or clinical factors on mortality outcomes. The results highlight a robust and statistically significant association between FI and increased risk of both all-cause and CVD mortality among individuals with KM syndrome. Notably, FI may serve as a valuable marker for CKM stage stratification and for identifying high-risk patients.

Concerns about the efficacy of high-dose methotrexate (HD-MTX) in preventing CNS recurrence in large B-cell lymphomas (LBCL) are based on studies with interpretation biases and incomplete information about HD-MTX dosing schedule and CNS events. We evaluated a pharmacokinetic-informed, CNS-directed HD-MTX protocol (3 g/m2 over 3 h, preceded by a bolus) in 336 LBCL patients achieving CMR after RCHOP or derivatives. HD-MTX use was based on institutional risk scores. In this study, CNS risk was reassessed using updated criteria: CNS-IPI ≥ 4, ≥ 3 extranodal sites, or involvement of testis, kidney, adrenal gland, uterus, or breast. According to these criteria, risk was low in 228 (68%) patients and high in 108 (32%); HD-MTX was given to 20 (9%) and 49 (45%), respectively. HD-MTX was well tolerated: 96% completed therapy. After a median follow-up of 77 months, 13 (4%) patients experienced CNS relapse, always as isolated events. Among high-risk patients, CNS relapse occurred in 19% (11/59) without HD-MTX versus 0% (0/49) with HD-MTX (p = 0.0009); significant reductions were seen in patients with high-risk organ involvement (32% to 0%, p = 0.002) or ≥ 3 extranodal sites (18% to 0%, p = 0.04). HD-MTX was independently associated with improved PFS and OS in high-risk patients, likely due to reduced CNS relapses (0% vs. 19%; p = 0.0009), whereas rates of unrelated deaths (8% vs. 15%; p = 0.26) and systemic relapses (18% vs. 22%; p = 0.81) were similar. In conclusion, HD-MTX, administered via a pharmacokinetic-informed, CNS-directed schedule, with or without intrathecal chemotherapy, significantly reduces CNS relapses and improves outcomes in high-risk LBCL patients in CMR. Trial Registration: ClinicalTrials.gov Identifier: NCT07181785.

ABSTRACT This paper develops a dynamic game with incomplete information to examine how digital technology and industrial policy drive nonlinear growth and internationalization for China’s specialized, refined, distinctive, and innovative (SRDI) firms. Three findings emerge: (1) Digital empowerment enables high-ability firms to escape linear trajectories through a convex learning function. (2) Optimal policy constitutes a three-stage subsidy menu: screening → reinforcement → commitment. This structure satisfies incentive compatibility and separates firm types to minimize resource misallocation. (3) Standard-setting power is governed by an endogenous tipping point. Once domestic market share crosses a critical mass, policy support amplifies market gains into durable rule-making advantage via network externalities. The framework complements heterogeneous-firm trade theory by endogenizing the interaction between digital learning speed and strategic technical standards.

Public funding for pharmaceuticals often follows repeated negotiation between manufacturers and a public agency, yet little empirical work examines how the timing of agreement reflects the economic structure of these interactions. Using a duration model of negotiations in Australia from 2005 to 2018, we assess whether observed patterns of delay and agreement align with dynamic bargaining theory under incomplete information. Agreements from 634 submissions for 400 therapies required a median of 16months, and 71% of negotiation rounds ended without agreement. Therapies with lower expected value to the agency-reflected in higher incremental cost per QALY, greater budget impact, or evidence uncertainty-experienced longer delays and lower agreement rates, while those with strong clinical importance, perceived need, or elevated public interest were listed more quickly. Delays also varied across therapeutic classes and with a therapy's position in the sequencing of available treatments. The observed patterns point to a systematic listing rule in which therapies are funded when expected health gains justify their opportunity costs. They also support the view that the timing of agreement reflects strategic negotiation under uncertainty, not simply procedural delay.

ABSTRACT Although rarely explicitly acknowledged within teacher education programmes, fictions play an integral role in supporting student learning. Fictitious, ‘as if’ philosophies led the way for the development of constructivist learning theories which often, despite being antithetical to the ‘truth’, hold significant utility in supporting our comprehension of the complex world around us. Fictions allow for the advancement of thought and enquiry in the absence of complete information. Fictions form the basis for many of the teaching and learning heuristics employed in classrooms, as well as supporting the delivery of content knowledge appropriate to the age and experience of respective students. This article seeks to elevate the place of fictions within teacher education through the explicit acknowledgement of their utility across most areas of study. The tenacious challenges that militate against embracing fictions are noted, while spaces for recognising the important role of fictions within teacher education are also explored.

Indirect reciprocity promotes cooperation by allowing individuals to help others based on reputation rather than direct reciprocation. Because it relies on accurate reputation information, its effectiveness can be undermined by information gaps. We examine two forms of incomplete information: incomplete observation, in which donor actions are observed only probabilistically, and reputation fading, in which recipient reputations are sometimes classified as "Unknown". Using analytical frameworks for public assessment, we show that these models with incomplete reputation information yield qualitatively different outcomes. Under incomplete observation, the conditions for cooperation are unchanged, because less frequent updates are exactly offset by higher reputational stakes. In contrast, reputation fading hinders cooperation, requiring higher benefit-to-cost ratios as the identification probability decreases. We then evaluate costly punishment as a third action alongside cooperation and defection. Norms incorporating punishment can sustain cooperation across broader parameter ranges without reducing efficiency in the reputation fading model. This contrasts with previous work, which found punishment ineffective under a different type of information limitation, and highlights the importance of distinguishing between types of information constraints. Finally, we review past studies to identify when punishment is effective and when it is not in indirect reciprocity.

The relationship between sleep and gallstones (GS) has rarely been reported. We aimed to investigate whether sleep traits are associated with the prevalence of GS. A cross-sectional study was conducted using data from the National Health and Nutrition Examination Survey 2017-2020. Participants aged ≥ 20years with complete information on sleep and GS questionnaires were enrolled. Binary logistic regression analyses were performed to investigate the relationship between sleep traits and GS while adjusting for confounding factors. Stratified and interaction analyses were conducted to evaluate whether factors such as age, gender, race, education, marital status, body mass index, smoking, and comorbidities modified the association. A total of 7329 participants were included in this study, and 736 had a self-reported history of GS. In the fully adjusted model, we found that each hour of delay in workday bedtime was associated with an 8.60% increase in the odds of GS (OR = 1.09; 95% CI: 1.02-1.15, P = 0.026). Compared to individuals with a workday bedtime between 18:00-≦20:00, those sleeping later, between 22:00-≦24:00 faced higher odds of GS (OR = 5.82; 95% CI: 1.63-20.83, P = 0.035), while the odds were even greater for those sleeping between 24:00-≦06:00 (OR = 6.68; 95% CI: 1.76-25.36, P = 0.032). Subgroup analyses revealed that age acted as an effect modifier in the relationship between workday bedtime and GS. In participants aged > 40years, the odds of GS increased significantly with delayed workday bedtime (OR = 1.14; 95% CI: 1.07-1.22, P = 0.009). Furthermore, the fully adjusted model found no significant association between wake-up time, sleep duration, or sleep disorders and the prevalence of GS. Delayed workday bedtime is associated with a higher prevalence of GS. Age modifies the association between workday bedtime and the prevalence of GS. Notably, for individuals aged > 40years, the odds of GS increased significantly with delayed bedtime on workdays.

Abstract In recent years, models of a fortiori argumentation from the field of artificial intelligence and law, developed to describe legal case-based reasoning based on precedent, have been successfully applied to improve interpretability of data-driven decision systems. To aid with these applications, we further develop the theory of a fortiori case-based reasoning by extending the knowledge representations on which these models operate. More specifically, we modify the representations to accommodate incomplete information, as well as to incorporate both dimensional (as opposed to binary) and hierarchical (as opposed to unstructured) information. This results in four models—one for each combination of accommodating dimensional or hierarchical information. We investigate their formal properties, and find they are monotonic with respect to the addition of new precedents and of new facts, and that some are conservative extensions of other models. In addition, we exemplify each through a running example from the penitentiary law domain.

To evaluate the actual situation of patient handover based on the nurse’s situation-background-assessment-recommendation (SBAR) model and identify some related factors at Ho Chi Minh City International Hospital, Vietnam, 2020. Application of a cross-sectional study. There were 167 SBAR nurse handovers observed. The study was carried out from January to November 2020. Verbal and written information on patient satisfaction based on SBAR components at handover by nurses on both sides accounted for 71.3% and 81.4%, respectively. There was a relationship between the type of case and the completion of the complete information on the checklist between nurses of the two handover parties ( p = .033 &lt; .05), in which acute cases had a higher completion rate than chronic illness. There was no significant relationship between case type, receiving place/department and compliance with verbal communication between the two handing parties. Nurses communicated in writing better than in oral communication when handing over patients. There was a statistically significant relationship between the results of written information and the type of disease, better-written information with acute disease. There was no statistically significant relationship between the type of disease, the location/department of handover, and the results of the verbal handover between the two parties.

In modern industrial systems, the fault diagnosis of rotating machinery is crucial for ensuring safe equipment operation. However, practical fault data are often contaminated by noise, and the scarcity of samples across fault conditions makes effective feature extraction challenging. Moreover, single-sensor measurements provide limited and incomplete information, further degrading the accuracy and reliability of diagnostic models. To address these challenges, this paper proposes an explainable intelligent fault diagnosis for rotating machinery via multi-source information fusion under noisy environments and small sample conditions. Firstly, a multi-sensor data intelligent fusion module (MSDIFM) is developed. It converts multi-sensor vibration signals into time-frequency maps via continuous wavelet transform (CWT). Pixel-level cross-channel fusion is then performed using a variance-driven dynamic weighting strategy to generate a unified fusion map, adaptively highlighting high information channels. Secondly, a multi-dimensional adaptive asymmetric soft-threshold residual shrinkage block (MASRSB) is proposed to implement differentiated and dynamic threshold control for positive and negative features, enhancing representation and discrimination capabilities. Thirdly, the multi-scale Swin Transformer (MSSwin-T) is designed. This module significantly enhances the model's feature extraction capability by expanding multi-level receptive fields, strengthening key channel representations, and reinforcing cross-window feature interactions. Finally, to validate the effectiveness of the proposed method, experiments are conducted on both the Case Western Reserve University (CWRU) dataset and the self-created PT890 dataset. Results demonstrate that the proposed method exhibits outstanding diagnostic performance and robustness under noisy conditions and with small sample sizes.

DNA as a data storage medium.

Active Defense Guidance for Spacecraft in Multi-Strategy Engagement with Incomplete Information

BackgroundMedication use during pregnancy is common but safety data remain limited. The FDA previously categorized drugs from A-X, later replaced by the Pregnancy and Lactation Labeling Rule (PLLR), which provides narrative risk summaries, clinical considerations, and supporting data.ObjectiveTo assess the safety of medications prescribed to pregnant women in Midwestern Brazil using both FDA categories and the PLLR.MethodsThis cross-sectional study reviewed medical records of women receiving prenatal care at a university outpatient clinic in Goiânia, Brazil. Patient characteristics, prescribed medications, and trimester of use were recorded. Each drug was classified by FDA categories and evaluated for PLLR structure.ResultsOf 93 women, 76 (81.7%) received at least one prescription (41 drugs; 241 prescriptions), most during the first trimester (61.8%). Supplements were the most frequent (38.6%), followed by analgesics/anti-inflammatory (26.6%) and gastrointestinal agents (17.0%). FDA categories were: 14.6% A, 41.5% B, 29.3% C, and 7.3% D. Under PLLR, 17 drugs (41.5%) had complete information; among 24 incomplete labels, the Data section was most often missing (46.3%), followed by Clinical Considerations (14.6%).ConclusionsMost women received medications, mainly supplements and analgesics. Although FDA categories suggested low apparent risk, PLLR assessment revealed information gaps that limit evidence-based prescribing.

This research assessed management strategies for overtourism in Zadar County. Overtourism has become apparent in both city and seaside destinations, affecting residents’ quality of life. This study defines overtourism as a challenge for urban management, emphasizing that exploring strategies to address overtourism also influences the management of sustainability and quality of life in urban areas. Here, a methodological framework was created with five strategies, each evaluated against seven criteria. The evaluation was carried out by the directors of the county’s tourist boards. Since these strategies have not yet been implemented, the directors had to rate them with some uncertainty, as they lacked complete information about the criteria and potential effects. To handle this uncertainty, the intuitionistic fuzzy set (IFS) approach was used. Additionally, the SiWeC method determined the importance of the criteria, and the TOPSIS method ranked the strategies. Results, based on ratings from 12 directors, indicated that Digital Support and Environmental Sustainability are the most important criteria. Strategy C, which aims to redirect tourists to lesser-known locations within the county, performed best, maintaining visitor numbers while helping preserve the region’s natural resources. This research has shown that strategies for managing overtourism help reduce the pressure tourists place on urban environments, thereby improving the quality of life and sustainable development of these environments.

This study aims to analyze the effectiveness of Rengginang MSME registration on Google Maps as a strategy to strengthen the economy in Pahang Village, Babata Village. The background of the research shows that most MSMEs still rely on conventional marketing so that business visibility is relatively low. With a qualitative case study approach, this study explores the experience of the owner of Rengginang Azam MSMEs, Mrs. Siti, who registered her business location on Google Maps. The results of the study show that digitalization through Google Maps has a positive impact in the form of increasing accessibility and reach for customers, especially from outside the village. Location registration allows customers to find more complete information about products and locations, increasing the number of visits to the store. The real impact felt is an increase in sales and profits without additional promotional costs. This study concludes that the use of Google Maps as a digital promotional medium is an effective strategy in strengthening the competitiveness of MSMEs in the digital era. In addition, this finding confirms the importance of digital literacy for MSME actors to utilize technology as a means of business development and strengthening the local economy.

Loop Closure Detection (LCD) is a key component of Simultaneous Localization and Mapping (SLAM) systems, responsible for correcting odometric drift and maintaining global consistency in localization and mapping. However, single-modality LCD methods suffer from inherent limitations: LiDAR-based approaches are affected by point cloud sparsity, limiting feature representation in unstructured environments, while vision-based methods are sensitive to illumination and weather variations, reducing robustness. To address these issues, this paper presents a LiDAR–vision multimodal fusion LCD algorithm. Spatiotemporal alignment between LiDAR point clouds and images is achieved through extrinsic calibration and timestamp interpolation to ensure cross-modal consistency. Harris corner detection and BRIEF descriptors are employed to extract visual features, and a LiDAR-projected sparse depth map is used to complete depth information, mapping 2D features into 3D space. A hybrid feature representation is then constructed by fusing LiDAR geometric triangle descriptors with visual BRIEF descriptors, enabling efficient loop candidate retrieval via hash indexing. Finally, an improved RANSAC algorithm performs geometric verification to enhance the robustness of relative pose estimation. Experiments on the KITTI and NCLT datasets show that the proposed method achieves average F1 scores of 85.28% and 77.63%, respectively, outperforming both unimodal and existing multimodal approaches. When integrated into a SLAM framework, it reduces the Absolute Error (ATE) RMSE by 11.2–16.4% compared with LiDAR-only methods, demonstrating improved loop detection accuracy and overall system robustness in complex environments.

Due to the complexity of data collection in the real world, Multi-view Representation Learning (MvRL) always encounters the incomplete information challenge, typically manifested as the Sample-missing Problem (SP) and the View-unaligned Problem (VP). Although several methods have been proposed, they fail to find a good trade-off among sample restoration, view alignment, and data diversity preservation. To address this issue, we take and mathematically formulate two sociological concepts for MvRL, i.e., community commonality and community versatility, where the former refers to the identical custom shared within the same community, and the latter refers to the similar but non-identical custom within communities of the same minority. One could find that the community commonality can enhance the compactness of view-specific clusters, and the community versatility can preserve the view diversity. Moreover, combining both of them could facilitate achieving robust MvRL with incomplete information. With the formulations, we propose a novel method dubbed Community-Aware Multi-viEw RepresentAtion learning with incomplete information (CAMERA). In brief, CAMERA employs a novel dual-stream network and an elaborate objective function that theoretically and empirically embraces community commonality and versatility. Extensive experimental results on seven datasets demonstrate that CAMERA remarkably outperforms 24 competitive multi-view learning methods on clustering, classification, and human action recognition tasks.

Leveraging preference disaggregation for context-dependent adaptive multi-criteria sorting with incomplete information

Random allocations of multiple objects with incomplete information