Investigation of spatiotemporal distribution and formation mechanisms of ozone pollution in eastern Chinese cities applying convolutional neural network.

Severe ground-level ozone (O3) pollution over major Chinese cities has become one of the most challenging problems, which have deleterious effects on human health and the sustainability of society. This study explored the spatiotemporal distribution characteristics of ground-level O3 and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021. Then, a high-performance convolutional neural network (CNN) model was established by expanding the moment and the concentration variations to general factors. Finally, the response mechanism of O3 to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables. The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern. When the wind direction (WD) ranges from east to southwest and the wind speed (WS) ranges between 2 and 3 m/sec, higher O3 concentration prone to occur. At different temperatures (T), the O3 concentration showed a trend of first increasing and subsequently decreasing with increasing NO2 concentration, peaks at the NO2 concentration around 0.02 mg/m3. The sensitivity of NO2 to O3 formation is not easily affected by temperature, barometric pressure and dew point temperature. Additionally, there is a minimum [Formula: see text] at each temperature when the NO2 concentration is 0.03 mg/m3, and this minimum [Formula: see text] decreases with increasing temperature. The study explores the response mechanism of O3 with the change of driving variables, which can provide a scientific foundation and methodological support for the targeted management of O3 pollution.

Improving I-ELM structure through optimal addition of hidden nodes: Compact I-ELM.

Incremental extreme learning machines (I-ELMs) can automatically determine the structure of neural networks and achieve high learning speeds. However, during the process of adding hidden nodes, unnecessary hidden nodes that have little relevance to the target may be added. Several studies have proposed methods to overcome this problem by measuring the relevance between hidden nodes and outputs and adding or removing hidden nodes accordingly. Random hidden nodes have the advantage of creating diverse patterns, but they encounter a problem in which hidden nodes that generate patterns with little or no relevance to the target can be added, thereby increasing the number of hidden nodes. Unlike in existing I-ELMs, which use random hidden nodes, we propose a compact I-ELM algorithm that initially adds linear regression nodes and subsequently applies a method to ensure that the hidden nodes have patterns differing from the existing ones. Based on benchmark data, we confirmed that the proposed method constructs a compact neural network structure with fewer hidden nodes compared to the existing I-ELM systems.

Precision estimation of winter wheat crop height and above-ground biomass using unmanned aerial vehicle imagery and oblique photoghraphy point cloud data.

Introduction: Crop height and above-ground biomass (AGB) serve as crucial indicators for monitoring crop growth and estimating grain yield. Timely and accurate acquisition of wheat crop height and AGB data is paramount for guiding agricultural production. However, traditional data acquisition methods suffer from drawbacks such as time-consuming, laborious and destructive sampling. Methods: The current approach to estimating AGB using unmanned aerial vehicles (UAVs) remote sensing relies solely on spectral data, resulting in low accuracy in estimation. This method fails to address the ill-posed inverse problem of mapping from two-dimensional to three-dimensional and issues related to spectral saturation. To overcome these challenges, RGB and multispectral sensors mounted on UAVs were employed to acquire spectral image data. The five-directional oblique photography technique was utilized to construct the three-dimensional point cloud for extracting crop height. Results and Discussion: This study comparatively analyzed the potential of the mean method and the Accumulated Incremental Height (AIH) method in crop height extraction. Utilizing Vegetation Indices (VIs), AIH and their feature combinations, models including Random Forest Regression (RFR), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRT), Support Vector Regression (SVR) and Ridge Regression (RR) were constructed to estimate winter wheat AGB. The research results indicated that the AIH method performed well in crop height extraction, with minimal differences between 95% AIH and measured crop height values were observed across various growth stages of wheat, yielding R2 ranging from 0.768 to 0.784. Compared to individual features, the combination of multiple features significantly improved the model's estimate accuracy. The incorporation of AIH features helps alleviate the effects of spectral saturation. Coupling VIs with AIH features, the model's R2 increases from 0.694-0.885 with only VIs features to 0.728-0.925. In comparing the performance of five machine learning algorithms, it was discovered that models constructed based on decision trees were superior to other machine learning algorithms. Among them, the RFR algorithm performed optimally, with R2 ranging from 0.9 to 0.93. Conclusion: In conclusion, leveraging multi-source remote sensing data from UAVs with machine learning algorithms overcomes the limitations of traditional crop monitoring methods, offering a technological reference for precision agriculture management and decision-making.

Increases in the incremental exercise mean response time across the steady state domain: Implications for exercise testing & prescription.

We hypothesized that slowed oxygen uptake ( V ˙ O 2 ) kinetics for exercise transitions to higher power outputs (PO) within the steady state (SS) domain would increase the mean response time (MRT) with increasing exercise intensity during incremental exercise. Fourteen highly trained cyclists (mean ±â€¯standard deviation [SD]; age (39 ±â€¯6) years [yr]; and V ˙ O 2 peak = (61 ±â€¯9) mL/kg/min performed a maximal, ramp incremental cycling test and on separate days, four 6-min bouts of cycling at 30%, 45%, 65% & 75% of their incremental peak PO (Wpeak). SS trial data were used to calculate the MRT and verified by mono-exponential and linear curve fitting. When the ramp protocol attained the value from SS, the PO, in Watts (W), was converted to time (min) based on the ramp function W to quantify the incremental MRT (iMRT). Slope analyses for the V ˙ O 2 responses of the SS versus incremental exercise data below the gas exchange threshold (GET) revealed a significant difference (p = 0.003; [0.437 ±â€¯0.08] vs. [0.382 ±â€¯0.05] L⋅min-1). There was a significant difference between the 45% Wpeak steady state V ˙ O 2 (ss V ˙ O 2 ) ([3.08 ±â€¯0.30] L⋅min-1, respectively), and 30% Wpeak ss V ˙ O 2 (2.26 ±â€¯0.24) (p < 0.0001; [3.61 ±â€¯0.80] vs. [2.20 ±â€¯0.39] L⋅min-1) and between the iMRT for 45% and 30% Wpeak ss V ˙ O 2 values ([50.58 ±â€¯36.85] s vs. [32.20 ±â€¯43.28] s). These data indicate there is no single iMRT, which is consistent with slowed V ˙ O 2 kinetics and an increasing V ˙ O 2 deficit for higher exercise intensities within the SS domain.

Cost-Effectiveness of a Pharmacist-Led Medication Therapy Management Clinic for Management of Type 2 Diabetes.

OBJECTIVES: This study aimed to evaluate the lifetime cost-effectiveness of a pharmacist-led medication therapy management clinic (MTMC) compared to usual care for individuals with type 2 diabetes mellitus (T2DM), from a United States payer perspective. METHODS: A cohort simulation Markov model was developed including the effects of diabetes and major complications from diabetes. Transition probabilities, MTMC treatment effects, health state costs, and utilities, were based on data from electronic health records, and published literature. Outcomes evaluated were lifetime incremental costs, quality adjusted life-years (QALYs), and ratios. Sensitivity analyses were conducted on all model inputs; scenario analyses assessed the impact of preventing additional diabetes complications on economic outcomes and of reduced MTMC visit frequency. RESULTS: Over a lifetime, MTMC resulted in $160,145 total costs and 6.73 QALYs; usual care resulted in $152,806 total costs and 6.65 QALYs. The incremental cost-effectiveness ratio (ICER) of the MTMC compared to usual care was $93,375 per QALY gained, indicating cost-effectiveness at a willingness-to-pay threshold of $100,000 per QALY gained. Scenario analyses showed that modeling additional complications or reduced visit frequency lowered the ICER. The results were most sensitive to MTMC costs, and hazard ratios for occurrence of stroke, myocardial infarction, and renal failure, with an improvement in glycosylated hemoglobin and systolic blood pressure. CONCLUSIONS: The study demonstrates the potential cost-effectiveness of integrating clinical pharmacy services into comprehensive care strategies. Findings support the broader coverage and reimbursement of such services to optimize clinical outcomes and reduce long-term healthcare costs.

Incremental net benefit of extending human papillomavirus vaccine to boys in oropharyngeal cancer burden: Meta-analysis of cost-effectiveness studies.

Background/purpose: The incidence of human papillomavirus (HPV)-related oropharyngeal cancer (OPC) is increasing worldwide. HPV vaccines have shown efficacy in preventing diseases in both males and females. Therefore, there is a need to develop cost-effective strategies for HPV vaccines to prevent HPV-related OPC. This meta-analysis aimed to evaluate cost-effectiveness using the global mean of incremental cost-effectiveness ratios compared to the willingness-to-pay threshold and incremental net benefits (INBs) of HPV vaccination strategies between boys' extension vaccine and girls only. These recommendations will be useful for countries that have not implemented universal HPV vaccines in national programs, such as Taiwan. Materials and methods: Studies evaluating the cost-effectiveness of HPV vaccination strategies in the prevention of OPC that included both sexes versus girls only were identified through the Cochrane Library, EMBASE, PubMed, ScienceDirect, and Web of Science databases on February 05, 2024, and a meta-analysis of pooled INBs was performed using a random-effects model. The outcome was an effective measurement of the OPC burden. The results are represented in USD (2024). Results: Fifteen model analyses were included. All the studies were conducted in high-income countries. The global mean of incremental cost-effectiveness ratio was $39,553 (95% CI, $27,008-66,641) per quality-adjusted life years gained, which was below the global mean of the willingness-to-pay threshold of $65,473 (95% CI, $52,138-83,755). Pooled INBs of $9370 (95% CI, $5046-13,695; P < 0.001) favored the extended HPV in boys. Conclusion: HPV vaccination strategies that include boys are cost-effective compared to those with girls only in preventing OPC burden. By implementing a universal HPV vaccination program, countries can receive $9370 in additional monetary benefits per patient. Given its relevance to high-income countries, this study offers key insights that can aid policymakers in Taiwan.

Continual learning in medical image analysis: A survey.

In the dynamic realm of practical clinical scenarios, Continual Learning (CL) has gained increasing interest in medical image analysis due to its potential to address major challenges associated with data privacy, model adaptability, memory inefficiency, prediction robustness and detection accuracy. In general, the primary challenge in adapting and advancing CL remains catastrophic forgetting. Beyond this challenge, recent years have witnessed a growing body of work that expands our comprehension and application of continual learning in the medical domain, highlighting its practical significance and intricacy. In this paper, we present an in-depth and up-to-date review of the application of CL in medical image analysis. Our discussion delves into the strategies employed to address specific tasks within the medical domain, categorizing existing CL methods into three settings: Task-Incremental Learning, Class-Incremental Learning, and Domain-Incremental Learning. These settings are further subdivided based on representative learning strategies, allowing us to assess their strengths and weaknesses in the context of various medical scenarios. By establishing a correlation between each medical challenge and the corresponding insights provided by CL, we provide a comprehensive understanding of the potential impact of these techniques. To enhance the utility of our review, we provide an overview of the commonly used benchmark medical datasets and evaluation metrics in the field. Through a comprehensive comparison, we discuss promising future directions for the application of CL in medical image analysis. A comprehensive list of studies is being continuously updated at https://github.com/xw1519/Continual-Learning-Medical-Adaptation.

Physical health mindsets and information avoidance.

Health mindsets refer to beliefs about the malleability (growth mindset) versus stability (fixed mindset) of physical health and have gained traction as a predictor of health beliefs and behaviors. Across two studies, we tested whether health mindsets were associated with avoiding personalized health risk information. In Study 2, we also tested whether conceptually-related constructs of internal and chance health locus of control, health self-efficacy, fatalism, and genetic determinism were associated with information avoidance. Health mindsets were manipulated in Study 1 (college students, n = 284; 79.58% female; Mage = 19.74) and measured in Study 2 (participants recruited through MTurk, n = 735; 42.04% female; Mage = 35.78). In both studies, participants viewed a prediabetes infographic and were informed they could learn their prediabetes risk by completing an online risk calculator. Behavioral obligation was also manipulated in both studies to test whether an additional behavioral requirement associated with learning one's risk would exacerbate any negative impact of health mindsets on avoidance rates. All participants then indicated their interest in learning their prediabetes risk (avoidance intentions) and decided whether to complete the online risk calculator (avoidance behavior). In Study 1, there was no impact of health mindsets, behavioral obligation, or their interaction on avoidance intentions or behavior. Study 2 similarly did not provide consistent evidence for an association of health mindsets, behavioral obligation, or their interaction with avoidance intentions or behavior. However, in Study 2, internal health locus of control was consistently associated with both intentions and behavior. Health information avoidance may be a barrier to prevention and early detection of disease. To encourage individuals to learn potentially important health information, public health interventions might seek to increase people's beliefs that their own actions play a role in their health outcomes. Interventions may also seek to increase people's knowledge about and skills regarding improving their health outcomes, which may influence health locus of control beliefs.

Integrated approach for seismic wave prediction and structural evaluation of Oya tuff quarry underground spaces: Field observations and numerical modeling.

This study evaluates the structural stability of large underground spaces in seismic conditions, represented by the Oya underground stone mining plant. By directly monitoring the seismic response of the underground mining site, significant earthquake activities at the plate boundaries of the Tokyo region and Ibaraki Prefecture offshore area were observed. Additionally, through an in-depth analysis of seismic records from different locations within the underground structure, the dynamic characteristics and motion patterns of the Oya underground stone mining plant were revealed, revealing its movement trajectory during earthquakes. Additionally, this study innovatively applied seismic waves measured at the original site as input parameters and artificially generated seismic waves based on their response spectra. A numerical analysis was performed after ensuring the model's high correlation with the original site was met. The findings demonstrate that the results of both parameter input methods are confirmable and valuable. Under severe seismic conditions, instability was observed in some regions of the underground mining site. The study also discusses the location and damage mechanisms of the mining site's structure under seismic effects, providing valuable insights for the safety assessment of similar large underground spaces and proposing new approaches for selecting input parameters in seismic analysis.

Microanatomy of incremental growth lines in dental tissues in reindeer Rangifer tarandus.

Counting growth layers in dentine and/or secondary cementum is widely used for age determination in wild mammals but the underlying seasonal changes in the structure and degree of mineralisation of dental tissue have not been well characterised. We embedded first (m1) and second (m2) mandibular permanent molar teeth from a 12-year-old female Svalbard reindeer (Rangifer tarandus platyrhynchus) in PolyMethylMethAcrylate (PMMA), prepared cut and polished surfaces coated with evaporated carbon and used 20 kV back-scattered electron imaging in a scanning electron microscope (BSE-SEM) to study aspects of dental tissue structure which depend on the degree of mineralisation at the micron and sub-micron scale. BSE-SEM revealed differences between the mineral content of growth layers (annulations) in the secondary cementum and the primary and secondary dentine, the latter, incidentally, still forming at death in m1. Wide bands of less well mineralised tissue formed in the cementum during active appositional phases. Thin, denser bands formed by maturation-mineralisation of existing tissue when growth slowed in winter. This maturation mimics the processes seen in lamellar bone and articular cartilage. Counter to previous suggestions, there was evidence of substantial resorption and repair of the secondary cementum and of formation of dentine throughout life. Secondary dentine is layered by mineral content like cementum. In the crown, this was mainly tubular dentine with well-marked interglobular dentine layers. In the lower pulp chamber and root, it was largely without tubules. Substantial non-mineralised spaces found at the cement-dentine junction in the root apical regions in m2 represent inclusions of the Hertwig's Epithelial Root Sheath (HERS) or the Epithelial Rests of Malassez (ERM) between the two tissues, a phenomenon which has previously only been identified in Muridae. The anatomical changes which result in the formation of the incremental lines (annulations) in dental tissues of reindeer, identified here for the first time at the micrometre level, are likely to be common across most if not all long-lived species of mammals living in seasonal environments.