Search Results (21)

We investigate Metropolis–Hastings (MH) algorithms to approximate the distribution of independent binomial random variables conditioned on the sum. Let $X_i\sim \mathrm{BIN}(n_i,p_i)$. We want the distribution of $[X_1,\dots ,X_k]$ conditioned on $X_1+\cdots +X_k=n$. We propose both a random walk MH algorithm and an independence sampling MH algorithm for simulating from this conditional distribution. The acceptance probability in the MH algorithm always involves the probability mass function of the proposal distribution. For the random walk MH algorithm, we take this distribution to be uniform across all possible proposals. There is an inherent asymmetry; the number of moves from one state to another is not in general equal to the number of moves from the other state to the one. This requires a careful counting of the number of possible moves out of each possible state. The independence sampler proposes a move based on the Poisson approximation to the binomial. While in general, random walk MH algorithms tend to outperform independence samplers, we find that in this case the independence sampler is more efficient. Full article

A desirable property of any theory of causal reasoning is to explain not only why people make causal reasoning errors but also when they make them. The mutation sampler is a rational process model of human causal reasoning that yields normatively correct inferences when sufficient cognitive resources are available but introduces systematic errors when they are not. The mutation sampler has been shown to account for a number of causal reasoning errors, including Markov violations, the phenomenon in which human reasoners treat causally related variables as statistically dependent when they are normatively independent. A Markov violation arises, for example, when an individual reasoning about a causal chain $X\to Y\to Z$ treats X as informative about the state of Z even when the state of Y is known. Recently, the mutation sampler was used to predict the existence of previously untested experimental conditions in which the sign of Markov violations would switch from positive to negative. Here, it was used to predict the existence of conditions in which Markov violations should disappear entirely. In fact, asking subjects to reason about a novel causal structure with nothing but generative causal relations (a cause makes its effect more likely) resulted in Markov violations in the usual positive direction. But simply describing one of four causal relations as inhibitory (the cause makes its effect less likely) resulted in the elimination of those violations. Theoretical model fitting confirmed how this novel result is predicted by the mutation sampler. Full article

One of the most significant challenges in human health risk assessment is to evaluate hazards from exposure to environmental chemical mixtures. Polycyclic aromatic hydrocarbons (PAHs) are a class of ubiquitous contaminants typically found as mixtures in gaseous and particulate phases in ambient air pollution associated with petrochemicals from Superfund sites and the burning of fossil fuels. However, little is understood about how PAHs in mixtures contribute to toxicity in lung cells. To investigate mixture interactions and component additivity from environmentally relevant PAHs, two synthetic mixtures were created from PAHs identified in passive air samplers at a legacy creosote site impacted by wildfires. The primary human bronchial epithelial cells differentiated at the air–liquid interface were treated with PAH mixtures at environmentally relevant proportions and evaluated for the differential expression of transcriptional biomarkers related to xenobiotic metabolism, oxidative stress response, barrier integrity, and DNA damage response. Component additivity was evaluated across all endpoints using two independent action (IA) models with and without the scaling of components by toxic equivalence factors. Both IA models exhibited trends that were unlike the observed mixture response and generally underestimated the toxicity across dose suggesting the potential for non-additive interactions of components. Overall, this study provides an example of the usefulness of mixture toxicity assessment with the currently available methods while demonstrating the need for more complex yet interpretable mixture response evaluation methods for environmental samples. Full article

In this paper, we explore a nonlinear interactive network system comprising nodalized flapping-wing micro air vehicles (FMAVs) to address the distributed $H_{\infty }$ state estimation problem associated with FMAVs. We enhance the model by introducing an information fusion function, leading to an information-fusionized estimator model. This model ensures both estimation accuracy and the completeness of FMAV topological information within a unified framework. To facilitate the analysis, each FMAV’s received signal is individually sampled using independent and time-varying samplers. Transforming the received signals into equivalent bounded time-varying delays through the input delay method yields a more manageable and analyzable time-varying nonlinear network error system. Subsequently, we construct a Lyapunov–Krasovskii functional (LKF) and integrate it with the refined Wirtinger and relaxed integral inequalities to derive design conditions for the FMAVs’ distributed $H_{\infty }$ state estimator, minimizing conservatism. Finally, we validate the effectiveness and superiority of the designed estimator through simulations. Full article

Polycyclic aromatic hydrocarbon (PAH) derivatives are widely present in the environment, and some are more hazardous than their parent PAHs. However, compared to PAHs, PAH derivatives are less studied due to challenges in monitoring as a result of their low concentrations in environmental matrixes. Here, we developed a new passive sampler based on diffusive gradients in thin films (DGT) to monitor PAH derivatives and PAHs in waters. In the laboratory study, the XAD18-DGT device exhibited high adsorption rates and was demonstrated to be suitable for deployment in environmental waters on the timescale of months. The diffusion coefficients, D, were 5.30 × 10⁻⁶ cm² s⁻¹, 4.51 × 10⁻⁶ cm² s⁻¹, 4.03 × 10⁻⁶ cm² s⁻¹ and 3.34 × 10⁻⁶ cm² s⁻¹ for 9-fluorenone (9-FL), 1-chloroanthraquinone (1-CLAQ), 9-nitroanthracene (9-NA) and phenanthrene (Phe), respectively, at 25 °C. The DGT device’s performance was independent of pH, ionic strength, deployment time and storage time, indicating it can be widely used in natural waters. In the field study, the target pollutant concentrations measured by the DGT are in good accordance with those determined via grab sampling. Then, the DGT devices were utilized to quantify PAH derivatives and PAHs in several rivers in Hefei, China. This work demonstrates the feasibility of using the DGT technique to detect trace PAH derivatives and PAHs in waters. Full article

This clinical study presents a comprehensive investigation into the utility of breath analysis as a non-invasive method for the early detection of lung cancer. The study enrolled 14 lung cancer patients, 14 non-lung cancer controls with diverse medical conditions, and 3 tuberculosis (TB) patients for biomarker discovery. Matching criteria including age, gender, smoking history, and comorbidities were strictly followed to ensure reliable comparisons. A systematic breath sampling protocol utilizing a BIO-VOC sampler was employed, followed by VOC analysis using Thermal Desorption–Gas Chromatography–Mass Spectrometry (TD-GC/MS). The resulting VOC profiles were subjected to stringent statistical analysis, including Orthogonal Projections to Latent Structures—Discriminant Analysis (OPLS-DA), Kruskal–Wallis test, and Receiver Operating Characteristic (ROC) analysis. Notably, 13 VOCs exhibited statistically significant differences between lung cancer patients and controls. The combination of eight VOCs (hexanal, heptanal, octanal, benzaldehyde, undecane, phenylacetaldehyde, decanal, and benzoic acid) demonstrated substantial discriminatory power with an area under the curve (AUC) of 0.85, a sensitivity of 82%, and a specificity of 76% in the discovery set. Validation in an independent cohort yielded an AUC of 0.78, a sensitivity of 78%, and a specificity of 64%. Further analysis revealed that elevated aldehyde levels in lung cancer patients’ breath could be attributed to overactivated Alcohol Dehydrogenase (ADH) pathways in cancerous tissues. Addressing methodological challenges, this study employed a matching of physiological and pathological confounders, controlled room air samples, and standardized breath sampling techniques. Despite the limitations, this study’s findings emphasize the potential of breath analysis as a diagnostic tool for lung cancer and suggest its utility in differentiating tuberculosis from lung cancer. However, further research and validation are warranted for the translation of these findings into clinical practice. Full article

Analytical methods based on the mass balance approach were developed for the purity evaluation of tetracycline hydrochloride, a representative salt compound used in pure veterinary drug analysis. The purity assignment method was used to quantify individual classes of impurities via independent analytical techniques. The mass fraction of the free base or salt form contained in a high-purity organic compound with a hydrochloride salt can be determined. The chloride content by ion chromatography-conductivity detector (IC-CD) and general classes of impurities, including structurally related impurities by liquid chromatography–ultraviolet (LC-UV) detector, water by Karl Fischer (KF) coulometric titration, residual solvents by headspace sampler gas chromatography/mass spectrometry (HS-GC/MS), and non-volatiles by thermogravimetric analyzer (TGA), were considered to calculate the purity of the mass fraction. The chloride content of the salt compound can be considered the main impurity in the mass fraction of the free base in the salt compound. A purity assay using quantitative nuclear magnetic resonance (q-NMR) as a direct determination method was performed to confirm the results of the mass balance method. The assigned purities of the tetracycline free form and its salt form in mass fraction were (898.80 ± 1.60) mg/g and (972.65 ± 1.58) mg/g, respectively, which are traceable to the international system of units (SI). Thus, the procedure for evaluating the purity of the free base and salt forms in the salt compound is newly demonstrated in this study. Full article

A new two-parameter weighted-exponential (WE) distribution, as a beneficial competitor model to other lifetime distributions, namely: generalized exponential, gamma, and Weibull distributions, is studied in the presence of adaptive progressive Type-II hybrid data. Thus, based on different frequentist and Bayesian estimation methods, we study the inferential problem of the WE parameters as well as related reliability indices, including survival and failure functions. In frequentist setups, besides the standard likelihood-based estimation, the product of spacing (PS) approach is also taken into account for estimating all unknown parameters of life. Making use of the delta method and the observed Fisher information of the frequentist estimators, approximated asymptotic confidence intervals for all unknown parameters are acquired. In Bayes methodology, from the squared-error loss with independent gamma density priors, the point and interval estimates of the unknown parameters are offered using both joint likelihood and the product of spacings functions. Because a closed solution to the Bayes estimators is not accessible, the Metropolis–Hastings sampler is presented to approximate the Bayes estimates and also to create their associated highest interval posterior density estimates. To figure out the effectiveness of the developed approaches, extensive Monte Carlo experiments are implemented. To highlight the applicability of the offered methodologies in practice, one real-life data set consisting of 30 failure times of repairable mechanical equipment is analyzed. This application demonstrated that the offered WE model provides a better fit compared to the other eight lifetime models. Full article

A new two-parameter statistical model, obtained by compounding the generalized-exponential and exponential distributions, called the PRC lifetime model, is explored in this paper. This model can be easily linked to other well-known six-lifetime models; namely the exponential, log-logistic, Burr, Pareto and generalized Pareto models. Adaptive progressively hybrid Type-II censored strategy, used to increase the efficiency of statistical inferential results and save the total duration of a test, has become widely used in various sectors such as medicine, biology, engineering, etc. Via maximum likelihood and Bayes inferential methodologies, given the presence of such censored data, the challenge of estimating the unknown parameters and some reliability time features, such as reliability and failure rate functions, of the PRC model is examined. The Markov-Chain Monte Carlo sampler, when the model parameters are assumed to have independent gamma density priors, is utilized to produce the Bayes’ infer under the symmetric (squared-error) loss of all unknown subjects. Asymptotic confidence intervals as well as the highest posterior density intervals of the unknown parameters and the unknown reliability indices are also created. An extensive Monte Carlo simulation is implemented to investigate the accuracy of the acquired point and interval estimators. Four various optimality criteria, to select the best progressive censored design, are used. To demonstrate the applicability and feasibility of the proposed model in a real-world scenario, two data sets from the engineering sector; one based on industrial devices and the other on aircraft windshield, are analyzed. Numerical evaluations showed that the PRC model furnishes a superior fit compared to seven other models in the literature, including: alpha-power exponential, log-logistic, Nadarajah–Haghighi, generalized-exponential, Weibull, gamma and exponential lifetime distributions. The findings demonstrate that, in order to obtain the necessary estimators, the Bayes’ paradigm via Metropolis–Hastings sampler is recommended compared to its competitive likelihood approach. Full article

A new weighted Nadarajah–Haghighi (WNH) distribution, as an alternative competitor model to gamma, standard half-logistic, generalized-exponential, Weibull, and other distributions, is considered. This paper explores both maximum likelihood and Bayesian estimation approaches for estimating the parameters, reliability, and hazard rate functions of the WNH distribution when the sample type is Type-II progressive censored order statistics. In the classical interval setup, both asymptotic and bootstrap intervals of each unknown parameter are constructed. Using independent gamma priors and symmetric squared-error loss, the Bayes estimators cannot be obtained theoretically. Thus, two approximation techniques, namely: Lindley and Markov-Chain Monte Carlo (MCMC) methods, are used. From MCMC variates, the Bayes credible and highest posterior density intervals of all unknown parameters are also created. Extensive Monte Carlo simulations are implemented to compare the performance of the proposed methodologies. Numerical evaluations showed that the estimates developed by the MCMC sampler performed better than the Lindley estimates, and both behaved significantly better than the frequentist estimates. To choose the optimal censoring scheme, several optimality criteria are considered. Three engineering applications, including vehicle fatalities, electronic devices, and electronic components data sets, are provided. These applications demonstrated how the proposed methodologies could be applied in real practice and showed that the proposed model provides a satisfactory fit compared to three new weighted models, namely: weighted exponential, weighted Gompertz, and new weighted Lindley distributions. Full article

A key challenge in the use of drones for an aerial manipulation task such as cutting tree branches is the control problem, especially in the presence of an unpredictable and nonlinear environment. While prior work focused on simplifying the problem by modeling a simple interaction with branches and controlling the system with nonlinear and non-robust control schemes, the current work deals with the problem by designing novel robust nonlinear controllers for aerial manipulation systems that are appropriate for vegetation sampling. In this regard, two different potential control schemes are proposed: nonlinear disturbance observer-based control (NDOBC) and adaptive sliding mode control (ASMC). Each considers the external disturbances and unknown parameters in controller design. The proposed control scheme in both methods employs a decoupled architecture that treats the unmanned aerial vehicle and the manipulator arm of the sampler payload as separate units. In the proposed control structures, controllers are designed after comprehensively investigating the dynamics of both the aerial vehicle and the robotic arm. Each system is then controlled independently in the presence of external disturbances, unknown parameter changes, and the nonlinear coupling between the aerial vehicle and robotic arm. In addition, fully actuated and underactuated aerial platforms are examined, and their stability and controllability are compared so as to choose the most practical framework. Finally, the simulation findings verify and compare the performance and effectiveness of the proposed control strategies for a custom aerial manipulation system that has been designed and developed for field trials. Full article

In this article, we propose the discrete version of the binomial exponential II distribution for modelling count data. Some of its statistical properties including hazard rate function, mode, moments, skewness, kurtosis, and index of dispersion are derived. The shape of the failure rate function is increasing. Moreover, the proposed model is appropriate for modelling equi-, over- and under-dispersed data. The parameter estimation through the classical point of view has been done using the method of maximum likelihood, whereas, in the Bayesian framework, assuming independent beta priors of model parameters, the Metropolis–Hastings algorithm within Gibbs sampler is used to obtain sample-based Bayes estimates of the unknown parameters of the proposed model. A detailed simulation study is carried out to examine the outcomes of maximum likelihood and Bayesian estimators. Finally, two distinctive real data sets are analyzed using the proposed model. These applications showed the flexibility of the new distribution. Full article

Nowadays, there is a wide range of different methods available for the monitoring of pollen and allergens, but their relative efficiency is sometimes unclear, as conventional pollen monitoring does not thoroughly describe pollen allergenicity. This study aims to evaluate airborne pollen, allergen, and protein levels, associating them with meteorological and chemical parameters. The sampling was performed in Bologna (Italy) during the grass flowering period, with three different devices: a Cyclone sampler (CS), a Dicothomous sampler (DS), and a Berner impactor (BI). Total proteins were extracted from aerosol samples, and grass allergens Phl p 1 and Phl p 5 were quantified by ELISA. Airborne Poaceae pollen concentrations were also evaluated, using a Hirst-type trap. Proteins and allergens collected by CS resulted about ten times higher than those collected by the other two instruments, possibly due to their different cut-offs, while DS and BI results appeared consistent only for the total proteins collected in the fine fraction (1.3 vs. 1.6 μg/m³). Airborne proteins correlated neither with Poaceae pollen nor with its aeroallergens, while aeroallergens correlated with pollen only in the coarse particulate, indicating the presence of pollen-independent aeroallergens in the fine particulate, promoted by high wind speed. Full article

Airborne pollen surveys provide information on various aspects of biodiversity and human health monitoring. Such surveys are typically conducted using the Burkard Multi-Vial Cyclone Sampler, but have to be technically optimized for eDNA barcoding. We here developed and tested a new airborne pollen trap, especially suitable for autonomous eDNA-metabarcoding analyses, called the A1 volumetric air sampler. The trap can sample pollen in 24 different tubes with flexible intervals, allowing it to operate independently in the field for a certain amount of time. We compared the efficiency of the new A1 volumetric air sampler with another automated volumetric spore trap, the Burkard Multi-Vial Cyclone Sampler, which features shorter and fewer sampling intervals to evaluate the comparability of ambient pollen concentrations. In a sterile laboratory environment, we compared trap performances between the automated volumetric air samplers by using pure dry pollen of three species—Fagus sylvatica, Helianthus annuus and Zea mays—which differ both by exine ornamentation and pollen size. The traps had a standard suction flow rate of 16.5 L/min, and we counted the inhaled pollen microscopically after a predefined time interval. Our results showed that though we put three different pollen types in the same container, both the traps inhaled all the pollens in a statistically significant manner irrespective of their size. We found that, on average, both traps inhaled equal an number of pollens for each species. We did not detect any cross-contamination between tubes. We concluded that the A1 volumetric air sampler has the potential to be used for longer and more flexible sampling intervals in the wild, suitable for autonomous monitoring of eDNA pollen diversity. Full article

Clinical trials are conducted to evaluate the efficacy of new treatments. Clinical trials involving multiple treatments utilize the randomization of treatment assignments to enable the evaluation of treatment efficacies in an unbiased manner. Such evaluation is performed in post hoc studies that usually use supervised-learning methods that rely on large amounts of data collected in a randomized fashion. That approach often proves to be suboptimal in that some participants may suffer and even die as a result of having not received the most appropriate treatments during the trial. Reinforcement-learning methods improve the situation by making it possible to learn the treatment efficacies dynamically during the course of the trial, and to adapt treatment assignments accordingly. Recent efforts using multi-arm bandits, a type of reinforcement-learning method, have focused on maximizing clinical outcomes for a population that was assumed to be homogeneous. However, those approaches have failed to account for the variability among participants that is becoming increasingly evident as a result of recent clinical-trial-based studies. We present a contextual-bandit-based online treatment optimization algorithm that, in choosing treatments for new participants in the study, takes into account not only the maximization of the clinical outcomes as well as the patient characteristics. We evaluated our algorithm using a real clinical trial dataset from the International Stroke Trial. We simulated the online setting by sequentially going through the data of each participant admitted to the trial. Two bandits (one for each context) were created, with four choices of treatments. For a new participant in the trial, depending on the context, one of the bandits was selected. Then, we took three different approaches to choose a treatment: (a) a random choice (i.e., the strategy currently used in clinical trial settings), (b) a Thompson sampling-based approach, and (c) a UCB-based approach. Success probabilities of each context were calculated separately by considering the participants with the same context. Those estimated outcomes were used to update the prior distributions within the bandit corresponding to the context of each participant. We repeated that process through the end of the trial and recorded the outcomes and the chosen treatments for each approach. We also evaluated a context-free multi-arm-bandit-based approach, using the same dataset, to showcase the benefits of our approach. In the context-free case, we calculated the success probabilities for the Bernoulli sampler using the whole clinical trial dataset in a context-independent manner. The results of our retrospective analysis indicate that the proposed approach performs significantly better than either a random assignment of treatments (the current gold standard) or a multi-arm-bandit-based approach, providing substantial gains in the percentage of participants who are assigned the most suitable treatments. The contextual-bandit and multi-arm bandit approaches provide 72.63% and 64.34% gains, respectively, compared to a random assignment. Full article