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Phase-lagged tACS between executive and default mode networks modulates working memory
Does education sculpt healthcare choices? Exploring factors influencing healthcare utilization among female youths in eight low and lower-middle-income countries
Introduction Emphasizing the global commitment to universal health coverage, this research addresses geographical variations and challenges faced by young females across low and lower-middle-income countries. Therefore, the objective of this study is to determine the factors that influence the decision-making of young females when it comes to seeking healthcare services in low or lower-middle-income countries. Methodology We examined healthcare usage among female youth across eight countries. We used data from the DHS and employed regression and Chi 2 tests for our analysis. Our focus was on females aged 15 to 24 and their visits to healthcare facilities. To ensure the validity of our findings, we used weighted sampling to represent the population. Results We had a total sample of 51,298 female youth groups between the ages of 15 and 24 who participated in our study. When it comes to the demographics of the participants, most of those in the 15-19 age group were from Burkina Faso (54.26%), while most of those in the 20-24 age group were from Ghana (50.19%). The impact of education varied across countries; primary education led to increased healthcare utilization in Kenya, Tanzania, and Cambodia, but unexpected trends were observed in Burkina Faso. Conclusions Education significantly influences healthcare utilization, positively impacting both primary and secondary education. Rural residents face challenges accessing timely healthcare. Geographical challenges, like diseases and limited accessibility, contribute to varied healthcare usage in Kenya and the Philippines. Addressing infrastructure issues is critical, emphasizing education and promoting transparency to enhance healthcare equity.
Creep behavior of clayey soil and its model prediction in the Cangzhou land subsidence area
Abstract In the Cangzhou area of China, groundwater over-exploitation has led to serious land subsidence, and the creep deformation of aquitards has been monitored and found to be closely related to the development of land subsidence. The objective of this paper is to develop a computational model to reflect the creep deformation of aquitards in this area. Firstly, creep tests were conducted on clayey soils with burial depths ranging from 65.7 to 121.7 m. The results show that the total strain consists of three parts: instantaneous strain, primary consolidation strain and creep strain. Creep-time curves and isochronous creep stress–strain curves under stepwise loading were obtained by using the Boltzmann superposition principle, and both types of curves were characterized by nonlinearity, and the creep curves as a whole showed a trend of stable development. Secondly, on the basis of analyzing the advantages and disadvantages of the classical rheological models for clayey soils, a nonlinear creep model of NCE_CS that can take into account the influence of primary consolidation is proposed. The model contains five parameters, which can be solved by using genetic algorithm, and then a simple determination method of the parameters is proposed. Finally, by comparing with the test data and the calculation results of four classical creep models, it is confirmed that the NCE_CS model can fit the creep curves better. The NCE_CS model was also successfully used to estimate the creep behavior in another subsidence area located in Renqiu City in northwest of Cangzhou. This study will provide a basis for quantitative calculation of creep of clayey soils in the Cangzhou area.
A protocol of a randomized control trial to test the feasibility and efficacy of the EMPOWER social-emotional learning curriculum for youth aged 11–14 years in after-school settings
Introduction Promoting youth mental health and well-being is a global concern. Administering social-emotional learning programs in contexts that are familiar to youth have the potential to increase mental well-being by helping youth develop fundamental coping skills that may contribute to their resilience. Implementing social-emotional learning programs in after-school settings is a unique opportunity to improve mental well-being skills in communities that face inequities. Methods The study is a partnership between investigators at an academic mental health hospital and an after-school program embedded within economically and socially vulnerable neighborhoods in a large metropolitan city in Ontario, Canada. This 20-week covariate-constrained randomized controlled trial will test the feasibility and preliminary efficacy of the EMPOWER social-emotional learning curriculum for youth aged 11-14 years in an after-school program. Twenty sites will be randomized to an intervention group or no-intervention control. Program staff in the intervention arm will receive training on the manualized curriculum and weekly coaching sessions to build capacity and support implementation over the 16-week program. Program staff and youth across both intervention and no-intervention control groups will be asked to participate in baseline and post-intervention data collection where they may complete questionnaires about youth’s social-emotional learning skills, global quality of functioning, and resilience skills. The no-intervention control group will carry on with their regular programming while the intervention group implements the 16-week social-emotional learning curriculum, after the collection of baseline data. Program staff in the intervention group will be asked to complete weekly fidelity measures and monthly feasibility, acceptability, and appropriateness of implementation scales. Parents/caregivers of youth in the intervention group will be asked to participate in a brief interview to report their observations of their children’s social-emotional learning skills. Dissemination Results from this pilot study will be disseminated in a peer-reviewed journal and at community and academic conferences.
Research on indoor thermal comfort of orthopedic patients in hospital
Correction: A navigational risk evaluation of ferry transport: Continuous risk management matrix based on fuzzy Best-Worst Method
Resource allocation Of 5G mmWave communication under random interference
Design of S/C/X/Ku frequency band five notch broadband filter based on novel coupled signal path
This article present the design of a broadband filter structure spanning the S, C, X, and Ku uplink bands based on a novel coupling signal path generation. The core design features a centrally symmetric serial via hole array, microstrip line, and curved coplanar waveguide. This structure offers advantages such as low loss band pass, low frequency stopband in the S band, and stable transmission zeros while providing a broadband passband. Additionally, to address the signal suppression requirements in commercial communication frequencies, such as 5G base stations, 6G IMT, satellite TV, and Starlink, across the S, C, X, and Ku uplink bands, a dual-band notch structure for the C band and three improved signal suppression structures are proposed. Finally, the design achieves miniaturization and integration while ensuring excellent performance across all five independent stopbands. Experimental results closely match simulation findings: the filter exhibits a –3 dB bandwidth of 2.4–14.3 GHz with a relative bandwidth of up to 141%. Each of the five suppression depths reaches –15 dB, occupying a compact footprint of only 14×10.8mm2,with a relative wave guide area of 0.38 × 0.29λ2.
Evolutionary trend of the broad-snouted crocodile from the Eocene, Early Miocene and recent ones from Egypt
Abstract Skulls are a critical part of the crocodile through which we can distinguish between the different genera and species. Most of the crocodiles which previously studied from the Eocene–Oligocene to the Miocene times in Egypt were concerned with the identification of the genus and sometimes on the species without a detailed focusing on the evolution, comparing between them and trying to determine the ancestor or the closest species of them to the living crocodile in Egypt. The only known living species of Crocodylus in Egypt is Crocodylus niloticus which inhabits Lake Nasser in Aswan, southern of Egypt. From the Cenozoic era, broad snouted crocodiles diversity had been reported in Egypt. About 35 million years ago, through the Eocene epoch, the crocodilian fossils from Fayum provided evidence of the diversity of crocodile species including Crocodylus articeps and Crocodylus megarhinus. In addition to that, throughout the Early Miocene epoch, from about 18 million years ago, in Wadi Moghra Egypt crocodilian fossils demonstrate another diversity, extended to the first appearance of Rimasuchus lloydi which placed inside the Osteolaeminae later. By various measurements and carefully morphological examination of the different species recorded from Egypt, it was found that there are high levels of variation in morphology of the skulls including their dimensions, and the sutures shapes especially between premaxilla and maxilla ventrally and also between maxilla and palatine, as well as the extension of the maxillary ramus of the ectopterygoid. Using cluster analysis, it is proven that Eocene Crocodylus is the ancestor to all known broad snouted species recorded from Egypt since the Eocene time. The closest species to the Eocene specimen is the living Crocodylus niloticus. That in fact make that most of the broad snouted crocodiles in Egypt are endemic.
RRMSE-enhanced weighted voting regressor for improved ensemble regression
Ensemble regression methods are widely used to improve prediction accuracy by combining multiple regression models, especially when dealing with continuous numerical targets. However, most ensemble voting regressors use equal weights for each base model’s predictions, which can limit their effectiveness, particularly when there is no specific domain knowledge to guide the weighting. This uniform weighting approach doesn’t consider that some models may perform better than others on different datasets, leaving room for improvement in optimizing ensemble performance. To overcome this limitation, we propose the RRMSE (Relative Root Mean Square Error) Voting Regressor, a new ensemble regression technique that assigns weights to each base model based on their relative error rates. By using an RRMSE-based weighting function, our method gives more importance to models that demonstrate higher accuracy, thereby enhancing the overall prediction quality. We tested the RRMSE Voting Regressor on six popular regression datasets and compared its performance with several state-of-the-art ensemble regression algorithms. The results show that the RRMSE Voting Regressor consistently achieves lower prediction errors than existing methods across all tested datasets. This improvement highlights the effectiveness of using relative error metrics for weighting in ensemble models. Our approach not only fills a gap in current ensemble regression techniques but also provides a reliable and adaptable method for boosting prediction performance in various machine learning tasks. By leveraging the strengths of individual models through smart weighting, the RRMSE Voting Regressor offers a significant advancement in the field of ensemble learning.
Determination of health status during aging using bending and pumping rates at various survival rates in Caenorhabditis elegans
Optimization of load-bearing parameters for anisotropic nodes and prefabricated wall panels in prefabricated shear wall structures
As the global construction industry develops, prefabricated buildings are gradually emerging and widely used. However, the bearing capacity of anisotropic nodes and prefabricated wall panels in prefabricated shear wall structures remains a technical challenge that restricts their widespread application. Therefore, the study improves the quality information model for prefabricated nodes and the dragonfly algorithm by introducing principal component dimensionality reduction methods and multiple strategies through data modeling. Finally, a quality control model for anisotropic nodes and an optimization model for the load-bearing parameters of prefabricated wall panels are proposed. The experimental results showed that the control error of the nodal quality control model was as low as 0.9 mm. The displacement angle was as low as 0.037 rad. The maximum shear strength was 7.6 MPa. The minimum number of iterations of the parametric optimization model was 160 and the number of optimal solution strategies generated was 4500. The ratio of anti-overturning moment under wind and earthquake loads decreased by 0.45 and 0.13 compared with before optimization, respectively. Therefore, the proposed model can improve the load-bearing capacity and energy consumption capacity of nodes, and reduce control errors. The optimization of anisotropic nodes significantly enhances the seismic performance of shear walls. This offers a scientific foundation for designing and constructing prefabricated shear walls.
Evolutionary game of dynamic adjustment mechanisms in tripartite cooperation for ESG performance
Hidden diversity of Trypanosomatidae (Protozoa: Kinetoplastea) in bats from an urban park in Brazil
Trypanosomatids are obligate flagellated parasites, with the genera Leishmania and Trypanosoma acting as etiological agents of significant diseases such as leishmaniasis and Chagas disease. Although ecological studies have increasingly highlighted the role of bats as potential reservoirs of these parasites, the diversity of trypanosomatids in urban bat populations remains poorly understood. This study investigates the occurrence and diversity of Trypanosomatidae in bats from Mangabeiras Municipal Park (MMP), an urban park in Belo Horizonte, Minas Gerais, Brazil, a region of ecological interest due to the prior detection of Leishmania in sand flies. A total of 56 bats representing seven species were captured, and 149 biological samples (blood, tissues, and organs) were analyzed using NNN/LIT culture medium. Contamination was reported in 32.2% of the samples, while 67.8% yielded negative results with no growth of trypanosomatids. Detection of trypanosomatids was achieved using the V7V8 Nested-PCR technique, revealing positive results in nine bats: Artibeus lituratus (Leishmania infantum, Trypanosoma sp. Neobat 3), Anoura caudifer (Trypanosoma sp. Neobat 4), Carollia perspicillata and Glossophaga soricina (Leishmania infantum), Sturnira lilium (Trypanosoma sp. Neobat 3), and Platyrrhinus lineatus (mixed infection with Leishmania infantum and Leishmania braziliensis). The integrity of the extracted DNA was confirmed through the amplification of cytb and gamma-actin genes. By expanding knowledge of trypanosomatid diversity in urban bats, this study highlights the ecological and epidemiological relevance of bats as hosts and underscores the need for targeted surveillance to assess their role in pathogen transmission dynamics.
Author Correction: Genomic epidemiology and phenotypic characterization of Staphylococcus aureus isolated from atopic dermatitis patients in South China
Information criterion for approximation of unnormalized densities
This paper considers the problem of approximating an unknown density when it can be evaluated up to a normalizing constant at a finite number of points. This density approximation problem is ubiquitous in statistics, such as approximating a posterior density for Bayesian inference and estimating an optimal density for importance sampling. We consider a parametric approximation approach and cast it as a model selection problem to find the best model in pre-specified distribution families (e.g., select the best number of Gaussian mixture components and their parameters). This problem cannot be addressed with traditional approaches that maximize the (marginal) likelihood of a model, for example, using the Akaike information criterion (AIC) or Bayesian information criterion (BIC). We instead aim to minimize the cross-entropy that gauges the deviation of a parametric model from the target density. We propose a novel information criterion called the cross-entropy information criterion (CIC) and prove that the CIC is an asymptotically unbiased estimator of the cross-entropy (up to a multiplicative constant) under some regularity conditions. We propose an iterative method to approximate the target density by minimizing the CIC. We demonstrate how the proposed method selects a parametric model that well approximates the target density through multiple numerical studies in the Supporting Information.
Modeling the impact of antiretroviral therapy on HIV and related kidney diseases in Tanzania
Abstract This work presents a mathematical model for the dynamics of HIV-related kidney diseases. The study examines two cases, considering the effects of the absence of treatment and the effects of Highly Active Antiretroviral Therapy (HAART) on different infection groups. Studying these cases is important because many developing countries implement HAART late, and not all HIV-infected individuals receive this therapy. Kidney diseases in HIV individuals are modeled as arising from both HIV infection itself and the use of nephrotoxic drugs. In the analysis of the mathematical model, it is shown that the state variables of the model are non-negative and bounded. Furthermore, we derived a formula for control reproduction number $$R_c$$ which was used to compare the cases considered. The sensitivity analysis of the model reveals that the effect of HAART in reducing the progression from HIV to HIV-related kidney diseases is more significant compared to other effects of HAART on disease dynamics, which is also confirmed through numerical simulations. The Markov Chain Monte Carlo (MCMC) method was used to estimate parameters and evaluate the model using real data of the HIV population from Tanzania from 1990 to 2022. Numerical simulations demonstrated that while HAART reduces HIV progression to the AIDS stage, the population of individuals with HIV-related kidney diseases is increasing and is projected to continue increasing. Additionally, the results show that improving the effectiveness of HAART by 90% in preventing the progression from HIV to HIV-related kidney diseases can significantly decrease the prevalence of HIV-related kidney diseases. This study addresses a gap in understanding how population-level HAART availability influences the dynamics of HIV-related kidney disease, particularly in settings with delayed or inconsistent treatment access. By analyzing disease progression under these conditions, our findings provide insights that can inform public health strategies for improving HIV care in resource-limited settings and other contexts where access disparities persist. In addition, the work investigated scenarios related to data quality in which the model parameters can be well identified, which can serve as a guide for obtaining informative real data.
Centralized allocation of decision rights and enterprise transformation and upgrading: Based on human capital level and capital allocation efficiency
Under the pressure of transformation and upgrading caused by complex environments at home and abroad, it is significant to explore how the centralized allocation of decision rights affects enterprise transformation and upgrading. Employing the data of Chinese A-share listed companies from 2010 to 2021, we demonstrate that the centralized allocation of decision rights significantly promotes enterprise transformation and upgrading. Further analysis indicates that enhanced human capital level and capital allocation efficiency are potential mechanisms through which centralized allocation of decision rights affects enterprise transformation and upgrading. Moreover, we document that the above positive correlation is more evident for groups with a high matching between cash flow rights and control rights, groups with strong supervision of major shareholders, and groups with a strong willingness of subsidiaries to cooperate. Collectively, these findings confirm the governance advantages of the centralized allocation of decision rights and provide important implications for enterprises’ transformation and upgrading in emerging market countries.