Heliyon最新文献

Cardiovascular disease (CVD) is the most important cause of morbidity and mortality worldwide. Early detection, prevention or even prediction is of pivotal importance to reduce the burden of cardiovascular disease and its associated costs. Low cost, consumer-grade smartwatches have the potential to revolutionize cardiovascular medicine by enabling continuous monitoring of heart rate and activity. When combined with machine learning(ML), the resulting large amounts of time series data hold the potential of detection, or exclusion of CVD. However, analyzing such large datasets is challenging due to the sparse presence of informative segments. Efficient selection of these segments is essential for developing predictive models for clinical deployment. The objective of this paper was to investigate the potential of an acceleration-deceleration curvebased ML model as a novel clinical indicator for the detection of cardiovascular diseases. We used data from the ME-TIME study; 42 participants from which 21 have a cardiovascular disease and 21 are health controls. Data from each subject was normalized to decrease inter-subject variability. A neural network model aggregated predictions per week. We showed that per-subject normalization by the peak value of curves during inactivity, aggregation of model predictions over a week, and using a contrastive loss, resulted in a predictive model with 99 % ± 3 % specificity and 40 % ± 49 % sensitivity on the development set, and 100 % specificity with 67 % ± 47 % sensitivity on the test set. Acceleration-deceleration curves are effective patterns for ruling out the presence of cardiovascular disease, but caution must be taken to properly pre-process the curves and carefully choosing a model that reduces the variability in the extracted curves.

Identification of photovoltaic (PV) module characteristics in solar systems is a vital task, nowadays, for optimal PV power estimation. In this paper, this challenge task has been studied using a novel advanced Kepler optimization algorithm (KOA). The standard version of KOA is adopted and assessed for getting the nine parameters of the PV triple diode model (3DM) considering three different practical PV modules. Kepler's principles of planetary motion are used by KOA to forecast the location and velocity of planets at any particular moment. However, the success rate of the KOA is not compatible, and its efficiency needs to be enhanced. As a result, an Improved KOA (IKOA) is created by incorporating an advanced mechanism of Local Escaping Operator (LEO), resulting in improved process of searching with evading local optima. This mechanism means that the exploitation approach will activate with around half of the solutions for every iteration starting at the initial phase of the iteration journey. The suggested IKOA besides the standard KOA are developed for predicting PV parameters for three distinct PV modules which are Photowatt PWP201, R.T.C France and STM6-40/36. The results corresponding to the latest algorithms are also compared with the proposed IKOA about different published works. The simulation findings reveal that the suggested IKOA exhibits notable average improvement rates for the three modules of 62.27 %, 55.1 %, and 32.12 %, respectively. Furthermore, the suggested IKOA asserts significant superiority and robustness over previously reported results.

Purpose: To compare comfort outcomes between a novel daily disposable contact lens - designed to maximize comfort - and an established control. The hypothesis was that the test lens would be superior to the control for four key comfort questionnaire items: end-of-day comfort, all-day comfort, visual comfort while driving at night, and reduction of ocular fatigue from digital device use.

Methods: This randomized, controlled, subject-masked, parallel-arm study enrolled young (18-39 years), healthy, myopic, contact lens wearers with an up-to-date prescription at 19 investigational sites in the United States. Subjects wore either the test (ACUVUE® OASYS MAX 1-Day, senofilcon A) or control (Dailies Total1®, delefilcon A) lens for 2 weeks of bilateral, daily disposable wear before completing comfort questionnaire items, each of which had 5 or 6 response options. For each item, the odds ratio for positive (top-two-box) responses was estimated from a binomial generalized linear mixed model. A gatekeeping approach combined with the truncated Hochberg procedure was used for multiplicity adjustment.

Results: Of 344 enrolled subjects, 342 subjects were randomized and dispensed lenses, with 171 subjects per lens group. Among the 342 subjects, 68.4% were female, 83.6% were White, and the average age was 29.7 (±5.53) years. The test lens was statistically superior to the control for all four comfort questions: odds ratios (test vs. control) were 2.01 (95% CI: 1.25, 3.22) for end-of-day comfort, 2.17 (alpha-adjusted CI: 1.30, 3.64) for all-day comfort, 2.00 (alpha-adjusted CI: 1.18, 3.41) for reducing ocular fatigue from digital device use, and 1.77 (95% CI: 1.04, 3.02) for comfortable vision while driving at night.

Conclusion: The test lens demonstrated statistically superior physical and visual comfort, as measured by the four comfort endpoints, compared to the control. The test lens had significantly greater odds of favorable responses for all comfort items compared to the control.

In recent years, online platforms have made educational, medical, and other professional content easily accessible, but research evaluating such platforms is still scarce. The purpose of the current study is to evaluate LONDI, a German learning disorders platform. The platform offers scientifically based information for different user groups, and an algorithm-based help system that professionals can use to facilitate diagnosing and planning interventions. The evaluation is focused on the user group of learning therapists using the platform and its help system. It is theoretically grounded on the RE-AIM framework and assesses four of its dimensions: Reach, Adoption, Implementation and Maintenance. Results from an online questionnaire (N = 496) showed that the platform reaches a large proportion of learning therapists. Another online questionnaire (N = 150) revealed that most users say they would adopt the help system, and this is predicted by its pragmatic qualities. Data from the Matomo web analytics software (N = 8,459 online visits) displayed diverse patterns in the platform's implementation. Future research is needed to further examine their meaning in the context of health-related education. Web analytics also revealed that usage patterns are not maintained. Rather, there is an increase in the number of users and in smartphone usage over time, coinciding with a decrease in the average time spent on the platform. Consequently, future efforts will be dedicated to optimizing smartphone compatibility. This study is the first to utilize the RE-AIM framework with web analytics, paving the way for further theory-grounded platform evaluations.

Maximal voluntary force is known to be enhanced by shouting during sustained maximal voluntary contraction (MVC) via the enhancement of motor cortical excitability. However, whether excitatory input to the primary motor cortex from areas other than the motor-related cortical area induces muscular force-enhancing effects on the exertion of sustained maximal force remains unclear. Therefore, by examining motor evoked potentials to transcranial magnetic stimulation during sustained MVC and assessing handgrip force, the present study aimed to investigate the effects of subliminal goal-priming with motivational rewards on the state of the motor system. The findings revealed that when combined with rewards in the form of a consciously visible positive stimulus, barely visible priming of an action concept increased the maximal voluntary force and reduced the silent period (i.e., reduced motor cortical inhibition). To our knowledge, this is the first study to report a link between the muscular force of subliminal reward-goal priming during MVC and the enhancement of motor system activity through subliminal reward-goal priming operating on the motor system, possibly through the potentiation of activity of the reward-linked dopaminergic system.

Halloysite nanotubes (HNTs) are naturally occurring aluminosilicate minerals, known for their unique tubular structure, which have garnered significant interest for a wide range of applications. This study explores the morphological changes of HNTs when subjected to thermal treatment ranging from 25 °C to 1100 °C using a combination of experimental characterization techniques and molecular dynamics simulations. Techniques such as solid-state NMR (SSNMR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Brunauer-Emmett-Teller (BET) surface area measurements, and Fourier Transform Infrared Spectroscopy (FT-IR) were employed to analyse the structural evolution. The results reveal two major transitions: the first occurring between 400 and 500 °C, corresponding to the release of intercalated water and partial distortion of the HNT structure, and the second occurring between 900 and 1000 °C, marked by the collapse of the tubular structure and the exposure of alumina on the surface. These findings provide significant insights into the thermal stability of HNTs, informing future applications, especially in high-temperature environments.

Exploring the intrinsic link between the intensity of intellectual property protection and the optimization of regional technological innovation structure from the perspective of economic complexity theory. Using panel data and employing the dual fixed effects estimation method, we empirically examine the impact of intellectual property protection strength on innovation factors, the innovation environment, and innovation output. Our study finds that intellectual property protection has a positive incentive effect on knowledge acquisition, knowledge creation, the innovation environment, and substantive innovation, while it inhibits non-substantive innovation. Strong intellectual property protection effectively promotes the optimization of innovation factors, the innovation environment, and innovation output structures, thereby driving the optimization of the regional technological innovation structure. In terms of impact magnitude, the order from high to low is: knowledge creation, substantive innovation, innovation environment, knowledge acquisition, and non-substantive innovation. The "optimal intensity" of intellectual property protection significantly promotes the optimization of the regional technological innovation structure, whereas excessively strong or weak intellectual property protection hinders this optimization. Implementing a strategy of innovation-driven development requires further reform and improvement of the national intellectual property protection system to establish the optimal intensity of protection and promote the optimization of the regional technological innovation structure.

Land-based livelihoods in Pakistan's high mountain regions are highly vulnerable to climate change due to the complex interactions between people and their natural environment. This study uses a mixed-method approach to explore how climate change affects land-based livelihoods in the high mountain Nagar District, Pakistan. Data were collected using a structured household survey of 430 randomly selected farmers, supplemented by focus group discussions and key informant interviews. The findings reveal that 87.7 % of farmers have observed negative impacts of climate change, such as increased crop diseases, reduced water for irrigation, and lower crop yields. Bivariate results indicate that factors related to farming practices, such as farming experience and cropping zones, significantly influence farmers' perceptions of impacts. The study emphasizes the urgent need for targeted government intervention and agricultural planning to boost the resilience of farmers in Nagar District. It calls for improved irrigation, crop disease management, and support tailored to high-mountain farming practices. The research highlights the importance of developing adaptation strategies to protect vulnerable farming communities from climate change impacts and supports the need for effective autonomous adaptation measures. This research contributes to a better understanding of climate change impacts on high-mountain agriculture and emphasizes the need to safeguard vulnerable farming communities.

This study develops a generalized $H_2$ observer to estimate the road profile in the semi-active automotive suspension system. The dynamics of the quarter-car model are represented in the descriptor nonlinear parameter-varying system framework, with the road profile as a system's state that is of arbitrary dynamics. Then, a road profile estimation method is designed by using a generalized $H_2$ observer, which utilizes the onboard accelerometers as the observer's input. The adverse impact of measurement noises on the accuracy of estimation results is alleviated via the generalized $H_2$ framework. The proposed observer is designed by solving an LMI-based constrained optimization problem. Frequency domain analysis and time domain simulations illustrate the efficiency of the proposed strategy.

This study investigates the impact of thermal decomposition on the ion mobility spectrum of L-alanine using ion mobility spectrometry (IMS) and computational methods. By employing a post-injection delay system, we examined the evolution of ion peaks corresponding to thermal decomposition products and their interaction with protonated alanine. Experimental results revealed that the observed ion mobility spectra predominantly feature protonated isomers and adduct ions. Computational analysis using Density Functional Theory (DFT) predicted the thermodynamically favored structures and stabilities of these products. Findings indicate that protonation at the nitrogen site in alanine is more stable than at the oxygen site, and observed peaks correspond to protonated isomers and adducts formed with ammonium ions. Further investigations showed that thermal decomposition of alanine generates ammonia, contributing to the formation of new adduct ions. This research provides new insights into the behavior of amino acids under thermal conditions with implications for analytical chemistry and biochemistry.