Household air pollution and attributable burden of disease in rural China: A literature review and a modelling study.

Household air pollution prevails in rural residences across China, yet a comprehensive nationwide comprehending of pollution levels and the attributable disease burdens remains lacking. This study conducted a systematic review focusing on elucidating the indoor concentrations of prevalent household air pollutants-specifically, PM2.5, PAHs, CO, SO2, and formaldehyde-in rural Chinese households. Subsequently, the premature deaths and economic losses attributable to household air pollution among the rural population of China were quantified through dose-response relationships and the value of statistical life. The findings reveal that rural indoor air pollution levels frequently exceed China's national standards, exhibiting notable spatial disparities. The estimated annual premature mortality attributable to household air pollution in rural China amounts to 966 thousand (95% CI: 714-1226) deaths between 2000 and 2022, representing approximately 22.2% (95% CI: 16.4%-28.1%) of total mortality among rural Chinese residents. Furthermore, the economic toll associated with these premature deaths is estimated at 486 billion CNY (95% CI: 358-616) per annum, constituting 0.92% (95% CI: 0.68%-1.16%) of China's GDP. The findings quantitatively demonstrate the substantial disease burden attributable to household air pollution in rural China, which highlights the pressing imperative for targeted, region-specific interventions to ameliorate this pressing public health concern.

Theory-Guided Care of Intimate Partner Violence Trauma Patients in the Acute Care Setting Using the Vulnerable Populations Conceptual Model.

Background and Purpose: Vulnerable populations are social groups at increased risk for poor health outcomes. According to the vulnerable populations conceptual model (VPCM) nursing theory, vulnerable groups such as survivors of intimate partner violence (IPV) are at risk for disease, morbidity, and mortality due to limited resources. The purpose of this article is to propose the VPCM as an organizing theoretical framework in the acute care setting of trauma patients suffering from IPV by outlining the factors affecting the care of this vulnerable population. Results: This synthesis of the literature outlines the decreased resource availability and increased relative risk encountered by IPV survivors, which results in poor health, which supports the application of the VPCM as a guiding theory. The VPCM provides a structure for understanding IPV patients and equips nursing with a framework for taking action through engagement, assessment, intervention, and evaluation of practice when caring for this vulnerable trauma population in the acute care setting. Implications for Practice: Using a theory-based model provides a framework for clinical practice interventions. Further research in the application of the VPCM as a theoretical basis for caring for trauma patients who are survivors of IPV is needed.

Study on agricultural drought disaster risk assessment in Heilongjiang reclamation area based on SSAPSO optimization projection pursuit model.

Heilongjiang reclamation area serves as a crucial hub for commodity grain production and strategic reserves in China, playing a vital role in maintaining national food security. Investigating the assessment of agricultural drought risk in this region can yield valuable insights into spatial and temporal variations in drought risk. Such insights can aid in formulating effective strategies for disaster prevention and mitigation, thereby minimizing food losses caused by drought disasters. This study employs a comprehensive indicator system comprising 17 indicators categorized into hazard, exposure, vulnerability, and resistance capacity. The projection pursuit model is applied to evaluate regional drought risk, while the PSO algorithm, optimized by the SSA algorithm, addresses the limitations of low local search ability and search accuracy during the large-scale search process of the PSO optimization algorithm. This study examines and compares the optimization and convergence capabilities of three algorithms: real number encoding-based genetic algorithm (RAGA), particle swarm optimization algorithm (PSO), and sparrow algorithm-based improved particle swarm optimization algorithm (SSAPSO). The analysis demonstrates that SSAPSO exhibits superior optimization performance and convergence properties, establishing it as a highly effective algorithm for optimization tasks. The findings reveal the following trends: over time, agricultural drought risk in Heilongjiang reclamation area has generally declined, with fluctuations observed in hazard and vulnerability, an increase in exposure, and a continuous enhancement of resistance capacity. Spatially, the western region exhibits significantly higher agricultural drought risk compared to the eastern region, primarily due to elevated hazard and vulnerability, coupled with lower resistance capacity. As the agricultural economy grows and agricultural expertise accumulates, the risk of agricultural drought decreases. However, variations in economic growth among different regions lead to diverse spatial distributions of risk.

Ecological inferences on invasive carp survival using hydrodynamics and egg drift models.

Bighead carp (Hypophthalmichthys nobilis), silver carp (H. molitrix), black carp (Mylopharyngodon piceus), and grass carp (Ctenopharyngodon idella), are invasive species in North America. However, they hold significant economic importance as food sources in China. The drifting stage of carp eggs has received great attention because egg survival rate is strongly affected by river hydrodynamics. In this study, we explored egg-drift dynamics using computational fluid dynamics (CFD) models to infer potential egg settling zones based on mechanistic criteria from simulated turbulence in the Lower Missouri River. Using an 8-km reach, we simulated flow characteristics with four different discharges, representing 45-3% daily flow exceedance. The CFD results elucidate the highly heterogeneous spatial distribution of flow velocity, flow depth, turbulence kinetic energy (TKE), and the dissipation rate of TKE. The river hydrodynamics were used to determine potential egg settling zones using criteria based on shear velocity, vertical turbulence intensity, and Rouse number. Importantly, we examined the difference between hydrodynamic-inferred settling zones and settling zones predicted using an egg-drift transport model. The results indicate that hydrodynamic inference is useful in determining the 'potential' of egg settling, however, egg drifting paths should be taken into account to improve prediction. Our simulation results also indicate that the river turbulence does not surpass the laboratory-identified threshold to pose a threat to carp eggs.

Edge computing based real-time Nephrops (Nephrops norvegicus) catch estimation in demersal trawls using object detection models.

In demersal trawl fisheries, the unavailability of the catch information until the end of the catching process is a drawback, leading to seabed impacts, bycatches and reducing the economic performance of the fisheries. The emergence of in-trawl cameras to observe catches in real-time can provide such information. This data needs to be processed in real-time to determine the catch compositions and rates, eventually improving sustainability and economic performance of the fisheries. In this study, a real-time underwater video processing system counting the Nephrops individuals entering the trawl has been developed using object detection and tracking methods on an edge device (NVIDIA Jetson AGX Orin). Seven state-of-the-art YOLO models were tested to discover the appropriate training settings and YOLO model. To achieve real-time processing and accurate counting simultaneously, four frame skipping ideas were evaluated. It has been shown that adaptive frame skipping approach, together with YOLOv8s model, can increase the processing speed up to 97.47 FPS while achieving correct count rate and F-score of 82.57% and 0.86, respectively. In conclusion, this system can improve the sustainability of the Nephrops directed trawl fishery by providing catch information in real-time.

Understanding HIV/AIDS dynamics: insights from CD4+T cells, antiretroviral treatment, and country-specific analysis.

In this article, we present a mathematical model for human immunodeficiency virus (HIV)/Acquired immune deficiency syndrome (AIDS), taking into account the number of CD4+T cells and antiretroviral treatment. This model is developed based on the susceptible, infected, treated, AIDS (SITA) framework, wherein the infected and treated compartments are divided based on the number of CD4+T cells. Additionally, we consider the possibility of treatment failure, which can exacerbate the condition of the treated individual. Initially, we analyze a simplified HIV/AIDS model without differentiation between the infected and treated classes. Our findings reveal that the global stability of the HIV/AIDS-free equilibrium point is contingent upon the basic reproduction number being less than one. Furthermore, a bifurcation analysis demonstrates that our simplified model consistently exhibits a transcritical bifurcation at a reproduction number equal to one. In the complete model, we elucidate how the control reproduction number determines the stability of the HIV/AIDS-free equilibrium point. To align our model with the empirical data, we estimate its parameters using prevalence data from the top four countries affected by HIV/AIDS, namely, Eswatini, Lesotho, Botswana, and South Africa. We employ numerical simulations and conduct elasticity and sensitivity analyses to examine how our model parameters influence the control reproduction number and the dynamics of each model compartment. Our findings reveal that each country displays distinct sensitivities to the model parameters, implying the need for tailored strategies depending on the target country. Autonomous simulations highlight the potential of case detection and condom use in reducing HIV/AIDS prevalence. Furthermore, we identify that the quality of condoms plays a crucial role: with higher quality condoms, a smaller proportion of infected individuals need to use them for the potential eradication of HIV/AIDS from the population. In our optimal control simulations, we assess population behavior when control interventions are treated as time-dependent variables. Our analysis demonstrates that a combination of condom use and case detection, as time-dependent variables, can significantly curtail the spread of HIV while maintaining an optimal cost of intervention. Moreover, our cost-effectiveness analysis indicates that the condom use intervention alone emerges as the most cost-effective strategy, followed by a combination of case detection and condom use, and finally, case detection as a standalone strategy.

Design of a novel intelligent computing framework for predictive solutions of malaria propagation model.

The paper presents an innovative computational framework for predictive solutions for simulating the spread of malaria. The structure incorporates sophisticated computing methods to improve the reliability of predicting malaria outbreaks. The study strives to provide a strong and effective tool for forecasting the propagation of malaria via the use of an AI-based recurrent neural network (RNN). The model is classified into two groups, consisting of humans and mosquitoes. To develop the model, the traditional Ross-Macdonald model is expanded upon, allowing for a more comprehensive analysis of the intricate dynamics at play. To gain a deeper understanding of the extended Ross model, we employ RNN, treating it as an initial value problem involving a system of first-order ordinary differential equations, each representing one of the seven profiles. This method enables us to obtain valuable insights and elucidate the complexities inherent in the propagation of malaria. Mosquitoes and humans constitute the two cohorts encompassed within the exposition of the mathematical dynamical model. Human dynamics are comprised of individuals who are susceptible, exposed, infectious, and in recovery. The mosquito population, on the other hand, is divided into three categories: susceptible, exposed, and infected. For RNN, we used the input of 0 to 300 days with an interval length of 3 days. The evaluation of the precision and accuracy of the methodology is conducted by superimposing the estimated solution onto the numerical solution. In addition, the outcomes obtained from the RNN are examined, including regression analysis, assessment of error autocorrelation, examination of time series response plots, mean square error, error histogram, and absolute error. A reduced mean square error signifies that the model's estimates are more accurate. The result is consistent with acquiring an approximate absolute error close to zero, revealing the efficacy of the suggested strategy. This research presents a novel approach to solving the malaria propagation model using recurrent neural networks. Additionally, it examines the behavior of various profiles under varying initial conditions of the malaria propagation model, which consists of a system of ordinary differential equations.

Mathematical modeling and dynamics of immunological exhaustion caused by measles transmissibility interaction with HIV host.

This paper mainly addressed the study of the transmission dynamics of infectious diseases and analysed the effect of two different types of viruses simultaneously that cause immunodeficiency in the host. The two infectious diseases that often spread in the populace are HIV and measles. The interaction between measles and HIV can cause severe illness and even fatal patient cases. The effects of the measles virus on the host with HIV infection are studied using a mathematical model and their dynamics. Analysing the dynamics of infectious diseases in communities requires the use of mathematical models. Decisions about public health policy are influenced by mathematical modeling, which sheds light on the efficacy of various control measures, immunization plans, and interventions. We build a mathematical model for disease spread through vertical and horizontal human population transmission, including six coupled nonlinear differential equations with logistic growth. The fundamental reproduction number is examined, which serves as a cutoff point for determining the degree to which a disease will persist or die. We look at the various disease equilibrium points and investigate the regional stability of the disease-free and endemic equilibrium points in the feasible region of the epidemic model. Concurrently, the global stability of the equilibrium points is investigated using the Lyapunov functional approach. Finally, the Runge-Kutta method is utilised for numerical simulation, and graphic illustrations are used to evaluate the impact of different factors on the spread of the illness. Critical factors that effect the dynamics of disease transmission and greatly affect the rate and range of the disease's spread in the population have been determined through a thorough analysis. These factors are crucial in determining the expansion of the disease.

Improving device design in insulator-based electrokinetic tertiary separations.

This study presents a methodology for designing effective insulator-based electrokinetic (iEK) systems for separating tertiary microparticle samples, which can be extended to more complex samples. First, 144 distinct iEK microchannel designs were built considering different shapes and arrangements of the insulating posts. Second, a mathematical model was developed with COMSOL software to predict the retention time of each particle type in the microchannel, this allowed identifying the best channel designs for two distinct types of separations: charge-based and sized-based. Third, the experimental charge-based and size-based separations of the tertiary microparticle mixtures were performed employing the improved designs identified with COMSOL modeling. The experimental results demonstrated successful separation in terms of separation resolution and good agreement with COMSOL predictions. The findings from this study show that the proposed method for device design, which combines mathematical modeling with varying post shape and post arrangement is an effective approach for identifying iEK systems capable of separating complex microparticle samples.

Numerical optimization of longitudinal collimator geometry for novel x-ray field.

Objective. A novel x-ray field produced by an ultrathin conical target is described in the literature. However, the optimal design for an associated collimator remains ambiguous. Current optimization methods using Monte Carlo calculations restrict the efficiency and robustness of the design process. A more generic optimization method that reduces parameter constraints while minimizing computational load is necessary. A numerical method for optimizing the longitudinal collimator hole geometry for a cylindrically-symmetrical x-ray tube is demonstrated and compared to Monte Carlo calculations.Approach. The x-ray phase space was modelled as a four-dimensional histogram differential in photon initial position, final position, and photon energy. The collimator was modeled as a stack of thin washers with varying inner radii. Simulated annealing was employed to optimize this set of inner radii according to various objective functions calculated on the photon flux at a specified plane.Main results. The analytical transport model used for optimization was validated against Monte Carlo calculations using Geant4 via its wrapper, TOPAS. Optimized collimators and the resulting photon flux profiles are presented for three focal spot sizes and five positions of the source. Optimizations were performed with multiple objective functions based on various weightings of precision, intensity, and field flatness metrics. Finally, a select set of these optimized collimators, plus a parallel-hole collimator for comparison, were modeled in TOPAS. The evolution of the radiation field profiles are presented for various positions of the source for each collimator.Significance. This novel optimization strategy proved consistent and robust across the range of x-ray tube settings regardless of the optimization starting point. Common collimator geometries were re-derived using this algorithm while simultaneously optimizing geometry-specific parameters. The advantages of this strategy over iterative Monte Carlo-based techniques, including computational efficiency, radiation source-specificity, and solution flexibility, make it a desirable optimization method for complex irradiation geometries.

Mid-ocean ridge unfaulting revealed by magmatic intrusions.

Mid-ocean ridges (MORs) are quintessential sites of tectonic extension1-4, at which divergence between lithospheric plates shapes abyssal hills that cover about two-thirds of the Earth's surface5,6. Here we show that tectonic extension at the ridge axis can be partially undone by tectonic shortening across the ridge flanks. This process is evidenced by recent sequences of reverse-faulting earthquakes about 15 km off-axis at the Mid-Atlantic Ridge and Carlsberg Ridge. Using mechanical models, we show that shallow compression of the ridge flanks up to the brittle failure point is a natural consequence of lithosphere unbending away from the axial relief. Intrusion of magma-filled fractures, which manifests as migrating swarms of extensional seismicity along the ridge axis, can provide the small increment of compressive stress that triggers reverse-faulting earthquakes. Through bathymetric analyses, we further find that reverse reactivation of MOR normal faults is a widely occurring process that can reduce the amplitude of abyssal hills by as much as 50%, shortly after they form at the ridge axis. This 'unfaulting' mechanism exerts a first-order influence on the fabric of the global ocean floor and provides a physical explanation for reverse-faulting earthquakes in an extensional environment.

Theoretical and experimental study on the effect of scanning speed on FAIMS peaks.

High-Field Asymmetric Ion Mobility Spectrometry (FAIMS) is a technique for ion separation and detection based on ion mobility variation under high electronic field. While compensation voltage scanning speed is a fundamental parameter in FAIMS, its impact on spectra remains unclear. In this work, a function referred to as F-EMG is introduced to describe the impact of compensation voltage scanning speed on FAIMS spectra, and the properties of the function are studied. Theoretical analysis emphasizes the impact of the scanning speed on peak height, position, and symmetry, as well as the capability of the F-EMG function to progressively approach Gaussian function at lower scanning speeds. Furthermore, the function indicates that spectra obtained in positive and negative scanning modes exhibits symmetry. An experimental validation, conducted with a custom FAIMS setup and analyzing hydrogen sulfide, ethylbenzene, toluene, styrene, benzene and ammonia, confirms the model's influence on peak features, fitting accuracy, and exhibits a closer alignment with the Gaussian function at lower scanning speeds. Additionally, the experimental data indicate that the spectra show symmetry in positive and negative scanning models. This work not only improves understanding of FAIMS spectral analysis but also introduces a robust method for enhancing data accuracy across varying scanning speeds.

Stripped-envelope supernova light curves argue for central engine activity.

The luminosity of stripped-envelope supernovae, a common type of stellar explosion, is believed to be mainly driven by the radioactive decay of the nickel synthesized in the explosion and carried in its ejecta. Additional possible energy sources have been previously suggested1-5, in which the two most observationally based results have been from a comparison of the observed time-weighted luminosity with the inferred radioactive power1 and from a comparison of the light curves with particular theoretical models3. However, the former result1 was not statistically significant, and the latter3 is highly dependent on the specific models assumed. Here we analyse the energy budget of a sample of 54 well-observed stripped-envelope supernovae of all sub-types and present statistically significant, largely model-independent, observational evidence for a non-radioactive power source in most of them (and possibly in all). We consider various energy sources, or alternatively, plausible systematic errors, that could drive this result, and conclude that the most likely option is the existence of a long-lived central engine, operating over ≈103-106 s after the explosion. We infer, from the observations, constraints on the engine properties. If, for example, the central engine is a magnetized neutron star, then the initial magnetic field is ≈1015 G and the initial rotation period is 1-100 ms, suggesting that stripped-envelope supernovae may constitute the formation events of the objects known as magnetars.

A robust optimization model for multi-objective blood supply chain network considering scenario analysis under uncertainty: a multi-objective approach.

Annually, different regions of the world are affected by natural disasters such as floods and earthquakes, resulting in significant loss of lives and financial resources. These events necessitate rescue operations, including the provision and distribution of relief items like food and clothing. One of the most critical challenges in such crises is meeting the blood requirement, as an efficient and reliable blood supply chain is indispensable. The perishable nature of blood precludes the establishment of a reserve stock, making it essential to minimize shortages through effective approaches and designs. In this study, we develop a mathematical programming model to optimize supply chains in post-crisis scenarios using multiple objectives. Presented model allocates blood to various demand facilities based on their quantity and location, considering potential situations. We employ real data from a case study in Iran and a robust optimization approach to address the issue. The study identifies blood donation centers and medical facilities, as well as the number and locations of new facilities needed. We also conduct scenario analysis to enhance the realism of presented approach. Presented research demonstrates that with proper management, crises of this nature can be handled with minimal expense and deficiency.

The Imperial College Storm Model (IRIS) Dataset.

Assessing tropical cyclone risk on a global scale given the infrequency of landfalling tropical cyclones (TC) and the short period of reliable observations remains a challenge. Synthetic tropical cyclone datasets can help overcome these problems. Here we present a new global dataset created by IRIS, the ImpeRIal college Storm model. IRIS is novel because, unlike other synthetic TC models, it only simulates the decay from the point of lifetime maximum intensity. This minimises the bias in the dataset. It takes input from 42 years of observed tropical cyclones and creates a 10,000 year synthetic dataset of wind speed which is then validated against the observations. IRIS captures important statistical characteristics of the observed data. The return periods of the landfall maximum wind speed are realistic globally.

Enhancing Content Validity Assessment With Item Response Theory Modeling.

BACKGROUND: Ensuring the validity of assessments requires a thorough examination of the test content. Subject matter experts (SMEs) are commonly employed to evaluate the relevance, representativeness, and appropriateness of the items. This article proposes incorporating item response theory (IRT) into model assessments conducted by SMEs. Using IRT allows for the estimation of discrimination and threshold parameters for each SME, providing evidence of their performance in differentiating relevant from irrelevant items, thus facilitating the detection of suboptimal SME performance while improving item relevance scores. METHOD: Use of IRT was compared to traditional validity indices (content validity index and Aiken's V) in the evaluation of items. The aim was to assess the SMEs' accuracy in identifying whether items were designed to measure conscientiousness or not, and predicting their factor loadings. RESULTS: The IRT-based scores effectively identified conscientiousness items (R2 = 0.57) and accurately predicted their factor loadings (R2 = 0.45). These scores demonstrated incremental validity, explaining 11% more variance than Aiken's V and up to 17% more than the content validity index. CONCLUSIONS: Modeling SME assessments with IRT improves item alignment and provides better predictions of factor loadings, enabling improvement of the content validity of measurement instruments.

Interconnecting global threats: climate change, biodiversity loss, and infectious diseases.

The concurrent pressures of rising global temperatures, rates and incidence of species decline, and emergence of infectious diseases represent an unprecedented planetary crisis. Intergovernmental reports have drawn focus to the escalating climate and biodiversity crises and the connections between them, but interactions among all three pressures have been largely overlooked. Non-linearities and dampening and reinforcing interactions among pressures make considering interconnections essential to anticipating planetary challenges. In this Review, we define and exemplify the causal pathways that link the three global pressures of climate change, biodiversity loss, and infectious disease. A literature assessment and case studies show that the mechanisms between certain pairs of pressures are better understood than others and that the full triad of interactions is rarely considered. Although challenges to evaluating these interactions-including a mismatch in scales, data availability, and methods-are substantial, current approaches would benefit from expanding scientific cultures to embrace interdisciplinarity and from integrating animal, human, and environmental perspectives. Considering the full suite of connections would be transformative for planetary health by identifying potential for co-benefits and mutually beneficial scenarios, and highlighting where a narrow focus on solutions to one pressure might aggravate another.

The research of river basin ecological compensation based on water emissions trading mechanism.

By integrating the successful case of the European Union emissions trading system, this study proposes a water emissions trading system, a novel method of reducing water pollution. Assuming that upstream governments allocate initial quotas to upstream businesses as the compensation standard, this approach defines the foundational principles of market trading mechanisms and establishes a robust watershed ecological compensation model to address challenges in water pollution prevention. To be specific, the government establishes a reasonable initial quota for upstream enterprises, which can be used to limit the emissions of upstream pollution. When enterprises exceed their allocated emissions quota, they face financial penalties. Conversely, these emissions rights can be transformed into profitable assets by participating in the trading market as a form of ecological compensation. Numerical simulations demonstrate that various pollutant emissions from upstream businesses will have various effects on the profits of other businesses. Businesses in the upstream region received reimbursement from the assigned emission rights through the market mechanism, demonstrating that ecological compensation for the watershed can be achieved through the market mechanism. This novel market trading system aims at controlling emissions management from the perspectives of individual enterprises and ultimately optimizing the aquatic environment.

Modeling endosymbioses: Insights and hypotheses from theoretical approaches.

Endosymbiotic relationships are pervasive across diverse taxa of life, offering key avenues for eco-evolutionary dynamics. Although a variety of experimental and empirical frameworks have shed light on critical aspects of endosymbiosis, theoretical frameworks (mathematical models) are especially well-suited for certain tasks. Mathematical models can integrate multiple factors to determine the net outcome of endosymbiotic relationships, identify broad patterns that connect endosymbioses with other systems, simplify biological complexity, generate hypotheses for underlying mechanisms, evaluate different hypotheses, identify constraints that limit certain biological interactions, and open new lines of inquiry. This Essay highlights the utility of mathematical models in endosymbiosis research, particularly in generating relevant hypotheses. Despite their limitations, mathematical models can be used to address known unknowns and discover unknown unknowns.