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Observational longitudinal data on treatments and covariates are increasingly used to investigate treatment effects, but are often subject to time‐dependent confounding. Marginal structural models (MSMs), estimated using inverse probability of treatment weighting or the g‐formula, are popular for handling this problem. With increasing development of advanced causal inference methods, it is important to be able to assess their performance in different scenarios to guide their application. Simulation studies are a key tool for this, but their use to evaluate causal inference methods has been limited. This paper focuses on the use of simulations for evaluations involving MSMs in studies with a time‐to‐event outcome. In a simulation, it is important to be able to generate the data in such a way that the correct forms of any models to be fitted to those data are known. However, this is not straightforward in the longitudinal setting because it is natural for data to be generated in a sequential conditional manner, whereas MSMs involve fitting marginal rather than conditional hazard models. We provide general results that enable the form of the correctly specified MSM to be derived based on a conditional data generating procedure, and show how the results can be applied when the conditional hazard model is an Aalen additive hazard or Cox model. Using conditional additive hazard models is advantageous because they imply additive MSMs that can be fitted using standard software. We describe and illustrate a simulation algorithm. Our results will help researchers to effectively evaluate causal inference methods via simulation.

COVID-19 pandemic has affected all aspects of people's lives and disrupted the economy. Forecasting the number of cases infected with this virus can help authorities make accurate decisions on the interventions that must be implemented to control the pandemic. Investigation of the studies on COVID-19 forecasting indicates that various techniques such as statistical, mathematical, and machine and deep learning have been utilized. Although deep learning models have shown promising results in this context, their performance can be improved using auxiliary features. Therefore, in this study, we propose two hybrid deep learning methods that utilize the statistical features as auxiliary inputs and associate them with their main input. Specifically, we design a hybrid method of the multihead attention mechanism and the statistical features (ATT_FE) and a combined method of convolutional neural network and the statistical features (CNN_FE) and apply them to COVID-19 data of 10 countries with the highest number of confirmed cases. The results of experiments indicate that the hybrid models outperform their conventional counterparts in terms of performance measures. The experiments also demonstrate the superiority of the hybrid ATT_FE method over the long short-term memory model.

Funder: National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre, A standard two-arm randomised controlled trial usually compares an intervention to a control treatment with equal numbers of patients randomised to each treatment arm and only data from within the current trial are used to assess the treatment effect. Historical data are used when designing new trials and have recently been considered for use in the analysis when the required number of patients under a standard trial design cannot be achieved. Incorporating historical control data could lead to more efficient trials, reducing the number of controls required in the current study when the historical and current control data agree. However, when the data are inconsistent, there is potential for biased treatment effect estimates, inflated type I error and reduced power. We introduce two novel approaches for binary data which discount historical data based on the agreement with the current trial controls, an equivalence approach and an approach based on tail area probabilities. An adaptive design is used where the allocation ratio is adapted at the interim analysis, randomising fewer patients to control when there is agreement. The historical data are down-weighted in the analysis using the power prior approach with a fixed power. We compare operating characteristics of the proposed design to historical data methods in the literature: the modified power prior; commensurate prior; and robust mixture prior. The equivalence probability weight approach is intuitive and the operating characteristics can be calculated exactly. Furthermore, the equivalence bounds can be chosen to control the maximum possible inflation in type I error.

[EN] This position paper follows from a previous proposal to integrate a time-triggered scheduler in a prioritybased, preemptive scheduler such as that supported by Ada¿s task dispatching policy FIFO Within Priorities . The resulting combined scheduling carries the advantages of both time-triggered and priority-based scheduling, and helps mitigating their drawbacks. The paper presents a system model for the time-triggered subsystem that extends the original proposal, and describes a Ravenscar implementation of the scheduler at the run-time system level, in the form of a new package Ada.Dispatching.TTS. Multiple programming patterns can be implemented on top of this scheduler. With respect to the previously proposed full-Ada implementation, only patterns that implied the use of asynchronous transfer of control have been excluded. On the other hand, the extension of the original model enables new patterns, not supported in our previous proposal, using the new types of continuation and optional slots. We hold that bringing the time-triggered paradigm to Ravenscar is both feasible and convenient for the High-Integrity and Embedded application domains., This work has been partly supported by the Spanish Government’s project M2C2 (TIN2014-56158-C4-1-P-AR) and the European Commission’s projects ENABLE-S3 and AQUAS (ECSEL-JU, Contracts 692455 and 737475)

In this paper we analyze two stochastic versions of one of the simplest classes of contagion models, namely so-called SIS models. Several formulations of such models, based on stochastic differential equations, have been recently discussed in literature, mainly with a focus on the existence and uniqueness of stationary distributions. With applicability in view, the present paper uses the Fokker-Planck equations related to SIS stochastic differential equations, not only in order to derive basic facts, but also to derive explicit expressions for stationary densities and further characteristics related to the asymptotic behaviour. Two types of models are analyzed here: The first one is a version of the SIS model with external parameter noise and saturated incidence. The second one is based on the Kramers-Moyal approximation of the simple SIS Markov chain model, which leads to a model with scaled additive noise. In both cases we analyze the asymptotic behaviour, which leads to limiting stationary distributions in the first case and limiting quasistationary distributions in the second case. Finally, we use the derived properties for analyzing the decision problem of choosing the cost-optimal level of treatment intensity.

For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.

For continuous variables of randomized controlled trials, recently, longitudinal analysis of pre‐ and posttreatment measurements as bivariate responses is one of analytical methods to compare two treatment groups. Under random allocation, means and variances of pretreatment measurements are expected to be equal between groups, but covariances and posttreatment variances are not. Under random allocation with unequal covariances and posttreatment variances, we compared asymptotic variances of the treatment effect estimators in three longitudinal models. The data‐generating model has equal baseline means and variances, and unequal covariances and posttreatment variances. The model with equal baseline means and unequal variance–covariance matrices has a redundant parameter. In large sample sizes, these two models keep a nominal type I error rate and have high efficiency. The model with equal baseline means and equal variance–covariance matrices wrongly assumes equal covariances and posttreatment variances. Only under equal sample sizes, this model keeps a nominal type I error rate. This model has the same high efficiency with the data‐generating model under equal sample sizes. In conclusion, longitudinal analysis with equal baseline means performed well in large sample sizes. We also compared asymptotic properties of longitudinal models with those of the analysis of covariance (ANCOVA) and t‐test.

In the era of precision medicine, novel designs are developed to deal with flexible clinical trials that incorporate many treatment strategies for multiple diseases in one trial setting. This situation often leads to small sample sizes in disease‐treatment combinations and has fostered the discussion about the benefits of borrowing of external or historical information for decision‐making in these trials. Several methods have been proposed that dynamically discount the amount of information borrowed from historical data based on the conformity between historical and current data. Specifically, Bayesian methods have been recommended and numerous investigations have been performed to characterize the properties of the various borrowing mechanisms with respect to the gain to be expected in the trials. However, there is common understanding that the risk of type I error inflation exists when information is borrowed and many simulation studies are carried out to quantify this effect. To add transparency to the debate, we show that if prior information is conditioned upon and a uniformly most powerful test exists, strict control of type I error implies that no power gain is possible under any mechanism of incorporation of prior information, including dynamic borrowing. The basis of the argument is to consider the test decision function as a function of the current data even when external information is included. We exemplify this finding in the case of a pediatric arm appended to an adult trial and dichotomous outcome for various methods of dynamic borrowing from adult information to the pediatric arm. In conclusion, if use of relevant external data is desired, the requirement of strict type I error control has to be replaced by more appropriate metrics.

In confirmatory clinical trials, the pre-specification of the primary analysis model is a universally accepted scientific principle to allow strict control of the type-I-error. Accordingly both the ICH E9 guideline and the EMA guideline on missing data in confirmatory clinical trials require the explicit[for full text, please go to the a.m. URL], 64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)

The main goal of this short paper is to prove the existence of rank 2 simple and special Ulrich bundles on a wide class of elliptic surfaces: namely, on regular Weierstrass fibrations \(\pi : S\rightarrow \mathbb {P}^1\). Alongside we also show the existence of rank 2 weakly Ulrich sheaves on arbitrary Weierstrass fibrations \(S\rightarrow C_0\) and we deal with the (non-)existence of rank one Ulrich bundles on them.

Background: Pertussis is a highly contagious respiratory disease caused by the bacterium Bordetella pertussis (B. pertussis). The infection is difficult to diagnose especially in underserved or resource-limited areas. We developed a low-cost and instrument-free diagnostic method for rapid and accurate detection of B. pertussis on a point-of-care (POC) testing device. Methods: We developed a paper/polymer hybrid microfluidic biochip integrated with loop-mediated isothermal amplification (LAMP) method for the rapid and accurate detection of B. pertussis. This microfluidic approach was validated by testing 100 de-identified remnant clinical nasopharyngeal swabs and aspirates, which were confirmed to be either positive or negative for B. pertussis by a validated real-time PCR assay at the Children's Hospital Los Angeles. Findings: The instrument-free detection results could be successfully read by the naked eye within 45 min with a limit of detection (LOD) of 5 DNA copies per well. Our optimized bacterial lysis protocol allowed the direct testing of clinical samples without any complicated sample processing/preparation (i.e. DNA extraction) or the use of any equipment (e.g. centrifuges). The validation of the microfluidic approach was accomplished by testing 100 clinical samples. High sensitivity (100%) and specificity (96%) with respect to real-time PCR were achieved. Interpretation: This microfluidic biochip shows great potential for point-of-care disease diagnosis in various venues including schools and physician's offices, especially in low-resource settings in developing nations. Funding: NIH/NIAID under award number R21AI107415, NIH RCMI Pilot Grant, the Philadelphia Foundation, the Medical Center of the Americas Foundation. Keywords: Pertussis diagnosis, Point-of-care detection, Microfluidic biochip, Whooping cough, Loop-mediated isothermal amplification (LAMP)

The standardized mean difference is a well‐known effect size measure for continuous, normally distributed data. In this paper we present a general basis for important other distribution families. As a general concept, usable for every distribution family, we introduce the relative effect, also called Mann–Whitney effect size measure of stochastic superiority. This measure is a truly robust measure, needing no assumptions about a distribution family. It is thus the preferred tool for assumption‐free, confirmatory studies. For normal distribution shift, proportional odds, and proportional hazards, we show how to derive many global values such as risk difference average, risk difference extremum, and odds ratio extremum. We demonstrate that the well‐known benchmark values of Cohen with respect to group differences—small, medium, large—can be translated easily into corresponding Mann–Whitney values. From these, we get benchmarks for parameters of other distribution families. Furthermore, it is shown that local measures based on binary data (2 × 2 tables) can be associated with the Mann–Whitney measure: The concept of stochastic superiority can always be used. It is a general statistical value in every distribution family. It therefore yields a procedure for standardizing the assessment of effect size measures. We look at the aspect of relevance of an effect size and—introducing confidence intervals—present some examples for use in statistical practice.

Systems of illative logic are logical calculi formulated in the untyped λ-calculus supplemented with certain logical constants.1 In this short paper, I consider a paradox that arises in illative logic. I note two prima facie attractive ways of resolving the paradox. The first is well known to be consistent, and I briefly outline a now standard construction used by Scott and Aczel that establishes this. The second, however, has been thought to be inconsistent. I show that this isn’t so, by providing a nonempty class of models that establishes its consistency. I then provide an illative logic which is sound and complete for this class of models. I close by briefly noting some attractive features of the second resolution of this paradox.

In this short paper, we extend the result of Shinbrot (1974) to an incompressible fluid with shear dependent viscosity. It is shown that a weak solution to the equations of non-Newtonian fluids lying in a space L q ( 0 , T ; L p ) satisfies an energy equality, where 2 r r − 1 ≤ p ≤ 2 r r − 2 and 1 p + 1 q ≤ r − 1 r , if r > 2 ; p ≥ 2 r r − 1 and r − 1 p + 1 q = r − 1 2 , if 2 ( n + 1 ) n + 2 r ≤ 2 . In particular, our result implies that the weak solution must satisfy the energy equality when r ≥ 3 n + 2 n + 2 , which is consistent with the known fact.

In this short paper we study the existence and uniqueness of solutions of free boundary problems coming from ecology in heterogeneous environments.

© 2019, Pleiades Publishing, Ltd. It is well known that the classical and Sobolev wave fronts were extended to nonequivalent global versions by the use of the short-time Fourier transform. In this very short paper, we give complete characterizations of the former wave front sets in terms of the short-time Fourier transform.

In studies with recurrent event endpoints, misspecified assumptions of event rates or dispersion can lead to underpowered trials or overexposure of patients. Specification of overdispersion is often a particular problem as it is usually not reported in clinical trial publications. Changing event rates over the years have been described for some diseases, adding to the uncertainty in planning. To mitigate the risks of inadequate sample sizes, internal pilot study designs have been proposed with a preference for blinded sample size reestimation procedures, as they generally do not affect the type I error rate and maintain trial integrity. Blinded sample size reestimation procedures are available for trials with recurrent events as endpoints. However, the variance in the reestimated sample size can be considerable in particular with early sample size reviews. Motivated by a randomized controlled trial in paediatric multiple sclerosis, a rare neurological condition in children, we apply the concept of blinded continuous monitoring of information, which is known to reduce the variance in the resulting sample size. Assuming negative binomial distributions for the counts of recurrent relapses, we derive information criteria and propose blinded continuous monitoring procedures. The operating characteristics of these are assessed in Monte Carlo trial simulations demonstrating favourable properties with regard to type I error rate, power, and stopping time, ie, sample size.

Hospitalists provide a significant amount of direct clinical care in both academic and community hospitals. Peer feedback is a potentially underutilized and low resource method for improving clinical performance, which lends itself well to the frequent patient care handoffs that occur in the practice of hospital medicine. We review current literature on peer feedback to provide an overview of this performance improvement tool, briefly describe its incorporation into multi-source clinical performance appraisals across disciplines, highlight how peer feedback is currently used in hospital medicine, and present practical steps for hospital medicine programs to implement peer feedback to foster clinical excellence among their clinicians.

BACKGROUND: In the current opioid epidemic, opioid addiction and overdose deaths are a public health crisis. Researchers have uncovered other concerning findings related to opioid use, such as the association between prescribed opioids and respiratory infection, including pneumonias. Potential mechanisms include the immunosuppressive effects of certain opioids, respiratory depression, and cough suppression. We conducted a systematic review assessing whether prescribed opioid receipt is a risk factor for community-acquired pneumonia (CAP). METHODS: A systematic literature search of published studies was conducted using Ovid MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), Embase, Web of Science, AMED, and CINAHL from database inception through March 11, 2020. We included any clinical trial, cohort, or case–control study that reported an association between prescribed opioid receipt and CAP in adults. Two reviewers independently performed data extraction and quality assessment using the Newcastle-Ottawa Quality Assessment Scale. The risk of CAP from prescribed opioid receipt was studied by pooling studies using random effects meta-analysis. RESULTS: We identified 3229 studies after removing duplicates. After detailed selection, 33 articles were reviewed in full and eight studies (representing 567,472 patients) met inclusion criteria. The pooled effect for the four case–control studies and three cohort studies showed a significant increase in the risk of CAP requiring hospitalization among those with prescribed opioid receipt compared with those without opioid prescribed receipt (OR 1.57 [95% CI (1.34, 1.84)]; HR 1.18 [95% CI (1.00, 1.40)]). CONCLUSION: The findings suggest prescribed opioid receipt is a risk factor for CAP. The included studies examined post-operative patients and patients with chronic medical conditions. Further research is needed to examine the impact of opioids on the incidence of CAP in an otherwise healthy population. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11606-020-06155-9) contains supplementary material, which is available to authorized users.

Mobility data at EU scale can help understand the dynamics of the pandemic and possibly limit the impact of future waves. Still, since a reliable and consistent method to measure the evolution of contagion at international level is missing, a systematic analysis of the relationship between human mobility and virus spread has never been conducted. A notable exceptions are France and Italy, for which data on excess deaths, an indirect indicator which is generally considered to be less affected by national and regional assumptions, are available at department and municipality level, respectively. Using this information together with anonymised and aggregated mobile data, this study shows that mobility alone can explain up to 92% of the initial spread in these two EU countries, while it has a slow decay effect after lockdown measures, meaning that mobility restrictions seem to have effectively contribute to save lives. It also emerges that internal mobility is more important than mobility across provinces and that the typical lagged positive effect of reduced human mobility on reducing excess deaths is around 14-20 days. An analogous analysis relative to Spain, for which an IgG SARS-Cov-2 antibody screening study at province level is used instead of excess deaths statistics, confirms the findings. The same approach adopted in this study can be easily extended to other European countries, as soon as reliable data on the spreading of the virus at a suitable level of granularity will be available. Looking at past data, relative to the initial phase of the outbreak in EU Member States, this study shows in which extent the spreading of the virus and human mobility are connected., This is the final published version of the article appeared in Nonlinear Dynamics under open access license CC 4.0

We review and discuss results obtained through an application of tools of nonlinear optimal control to biomedical problems. We discuss various aspects of the modeling of the dynamics (such as growth and interaction terms), modeling of treatment (including pharmacometrics of the drugs), and give special attention to the choice of the objective functional to be minimized. Indeed, many properties of optimal solutions are predestined by this choice which often is only made casually using some simple ad hoc heuristics. We discuss means to improve this choice by taking into account the underlying biology of the problem.

Lignin is the second most abundant biopolymer, exceeded only by cellulose, and comprises 15-25% of the dry weight of woody plants, with around 285,000 tons/year of production capacity globally. This study aims to utilize the lignin obtained from the waste of bioethanol production from oil palm empty fruit bunches (OPEFB) as a mortar additive. The use of mortar as a material for road construction is increasing, but its long time hardening is causing problems such as traffic jams. Lignin can be used as an additive to shorten the hardening time of mortar. Lignin was isolated at various NaOH concentrations and temperatures of OPEFB pretreatment for bioethanol production. The workability of the slump and compressive strength of mortars generated were further tested. Lignin from OPEFB can be used as a water reducer in the mortar with improved workability as much as 24.4% compared to controls. The addition of lignin could also increase the compressive strength at the mortar age of 7 and 28 days compared to the commercial lignosulfonate and control on the various water-cement ratios. The setting time of mortar with the lignin addition increased rapidly, reaching up to 80% at the 7 days, indicating that curing time is getting shorter. The most remarkable improvement of compressive strength with suitable workability and high-quality concrete was reached by 1% lignin addition and 0.45 water-cement ratio with compressive strength 38.81 N/mm2 at 28 days.Keywords: compressive strength, lignin, mortar, OPEFB, water reducer

Efforts to improve cancer care primarily come from two fields: improvement science and implementation science. The two fields have developed independently, yet they have potential for synergy. Leveraging that synergy to enhance alignment could both reduce duplication and, more importantly, enhance the potential of both fields to improve care. To better understand potential for alignment, we examined 20 highly cited cancer-related improvement science and implementation science studies published in the past 5 years, characterizing and comparing their objectives, methods, and approaches to practice change. We categorized studies as improvement science or implementation science based on authors’ descriptions when possible; otherwise, we categorized studies as improvement science if they evaluated efforts to improve the quality, value, or safety of care, or implementation science if they evaluated efforts to promote the implementation of evidence-based interventions into practice. All implementation studies (10/10) and most improvement science studies (6/10) sought to improve uptake of evidence-based interventions. Improvement science and implementation science studies employed similar approaches to change practice. For example, training was employed in 8/10 implementation science studies and 4/10 improvement science studies. However, improvement science and implementation science studies used different terminology to describe similar concepts and emphasized different methodological aspects in reporting. Only 4/20 studies (2 from each category) described using a formal theory or conceptual framework to guide program development. Most studies were multi-site (10/10 implementation science and 6/10 improvement science) and a minority (2 from each category) used a randomized design. Based on our review, cancer-related improvement science and implementation science studies use different terminology and emphasize different methodological aspects in reporting but share similarities in purpose, scope, and methods, and are at similar levels of scientific development. The fields are well-positioned for alignment. We propose that next steps include harmonizing language and cross-fertilizing methods of program development and evaluation.

A key strategy to prevent a local outbreak during the COVID-19 pandemic is to restrict incoming travel. Once a region has successfully contained the disease, it becomes critical to decide when and how to reopen the borders. Here we explore the impact of border reopening for the example of Newfoundland and Labrador, a Canadian province that has enjoyed no new cases since late April, 2020. We combine a network epidemiology model with machine learning to infer parameters and predict the COVID-19 dynamics upon partial and total airport reopening, with perfect and imperfect quarantine conditions. Our study suggests that upon full reopening, every other day, a new COVID-19 case would enter the province. Under the current conditions, banning air travel from outside Canada is more efficient in managing the pandemic than fully reopening and quarantining 95% of the incoming population. Our study provides quantitative insights of the efficacy of travel restrictions and can inform political decision making in the controversy of reopening.“There is one and only one way to absolutely prevent it and that is by establishing absolute isolation. It is necessary to shut off those who are capable of giving off the virus from those who are capable of being infected, or vice versa.” The Lessons Of The Pandemic, Science 1919.

Operating a bioreactor with a low contact area greatly reduces the efficiency of methanogenic bacteria to produce biogas using an anaerobic digestion process. In this work, a packed-bed bioreactor under a flow-mixing technique was used to enhance the contact area to produce biogas from sawdust. The bioreactor was designed with a diameter of 10 cm and a height of 60 cm and was packed with 1.5-cm spherical glass beads. The effect of the digestion temperature on the cumulative biogas production was investigated at different temperatures (i.e., 30, 35, 40, 45, and 50 °C). Also, the influence of the flow-mixing technique on the biogas yield was evaluated at different substrate flow rates of 0.5. 1, 1.5, 2, and 2.5 m3/h. It was observed that the best operating conditions for the methanogenic bioconversion to achieve high biomethane production (> 71.2%) were 40 °C and 1.5 m3/h for the temperature and substrate flow rate, respectively. At these conditions, the biogas and biomethane yields were 850 mL/g VS and 605.2 mL CH4/g VS, respectively. The results show that at a mild flow rate, a biothin film of a high surface area formed over the packing, with a low resistance to transport processes. Moreover, at a high flow rate, the thin film was destroyed, achieving a low biogas yield. Finally, this technique produced a high efficiency, high homogeneity mixture, simple operation, and low mixing time in the packed-bed bioreactor.

A summary is given of the mechanical characteristics of virus contaminants and the transmission via droplets and aerosols. The ordinary and partial differential equations describing the physics of these processes with high fidelity are presented, as well as appropriate numerical schemes to solve them. Several examples taken from recent evaluations of the built environment are shown, as well as the optimal placement of sensors.

The equivalence between pure discrete spectrum and regular model sets in d-dimensional unimodular substitution tilings is discussed., Primitive substitution tilings on whose expansion maps are unimodular are considered. It is assumed that all the eigenvalues of the expansion maps are algebraic conjugates with the same multiplicity. In this case, a cut-and-project scheme can be constructed with a Euclidean internal space. Under some additional condition, it is shown that if the substitution tiling has pure discrete spectrum, then the corresponding representative point sets are regular model sets in that cut-and-project scheme.

We propose methods to estimate sufficient reductions in matrix-valued predictors for regression or classification. We assume that the first moment of the predictor matrix given the response can be decomposed into a row and column component via a Kronecker product structure. We obtain least squares and maximum likelihood estimates of the sufficient reductions in the matrix predictors, derive statistical properties of the resulting estimates and present fast computational algorithms with assured convergence. The performance of the proposed approaches in regression and classification is compared in simulations.We illustrate the methods on two examples, using longitudinally measured serum biomarker and neuroimaging data.

Hydrocarbons originated from automobile oils are significant polluting agents of the soil. Lipase-mediated catalytic degradation of hydrocarbons from this used automobile oil is attempted in the present approach. Lipase was produced from peanut oil industry waste biomass using thermophilic alkaline-tolerant Pseudomonas sp. S1 obtained from sediment of Taptapani hot spring, Odisha. The experiments were designed as per central composite design, which is response surface methodology, to study the effect of the parameters and their interaction on the activity. A quadratic regression was found to be best with the experimental data, R2 = 0.899741 and the maximum lipase production of 51.3 IU/g was predicted from peanut oil seed waste biomass at an optimum pH of 8.31, temperature of 50.58 °C, moisture content of 50.8%, and biosurfactant content of 1.051 mg. The significant interaction of pH and moisture content with biosurfactant during lipase production was observed through the contour plots and 3D surface responses. Pseudomonas sp. S1 could degrade 92.6% of the used automobile oil after incubation for 24 h under optimum pH of 8.31 and temperature of 50.58 °C. Degradation of petroleum hydrocarbons by this thermophilic isolate could be effective due to the ability of lipase to utilize the used automobile oils as sole source of carbon and energy.

In this work, we propose a parameter estimation framework for fracture propagation problems. The fracture problem is described by a phase-field method. Parameter estimation is realized with a Bayesian approach. Here, the focus is on uncertainties arising in the solid material parameters and the critical energy release rate. A reference value (obtained on a sufficiently refined mesh) as the replacement of measurement data will be chosen, and their posterior distribution is obtained. Due to time- and mesh dependencies of the problem, the computational costs can be high. Using Bayesian inversion, we solve the problem on a relatively coarse mesh and fit the parameters. In several numerical examples our proposed framework is substantiated and the obtained load-displacement curves, that are usually the target functions, are matched with the reference values.

Purpose Our objectives were to (a) identify oral and pharyngeal physiologic swallowing impairment severity classes based on latent class analyses (LCAs) of the Modified Barium Swallow Impairment Profile (MBSImP) swallow task scores and (b) quantify the probability of severity class membership given composite MBSImP oral total (OT) and pharyngeal total (PT) scores. Method MBSImP scores were collected from a patient database of 319 consecutive modified barium swallow studies. Because of missing swallow task scores, LCA was performed using 25 multiply imputed data sets. Results LCA revealed a three-class structure for both oral and pharyngeal models. We identified OT and PT score intervals to assign subjects to oral and pharyngeal impairment latent severity classes, respectively, with high probability (probability of class membership ≥ 0.9 given OT or PT scores within specified ranges) and high confidence (95% credible interval [CI] widths ≤ 0.24 for all total scores within specified ranges). OT scores ranging from 0 to 10 and from 14 to 18 yielded assignments in Oral Latent Classes 1 and 2, respectively, while OT = 22 was assigned to Oral Latent Class 3. PT scores ranging from 0 to 13 and from 18 to 24 yielded assignments in Pharyngeal Latent Classes 1 and 2, respectively, while PT = 26 was assigned to Pharyngeal Latent Class 3. Conclusions LCA of MBSImP task-level data revealed significant underlying oral and pharyngeal ordinal class structures representing increasingly severe gradations of physiologic swallow impairment. Clinically meaningful OT and PT score ranges were derived facilitating latent class assignment. Supplemental Material https://doi.org/10.23641/asha.12315677

Throughout the past six months, no number has dominated the public media more persistently than the reproduction number of COVID-19. This powerful but simple concept is widely used by the public media, scientists, and political decision makers to explain and justify political strategies to control the COVID-19 pandemic. Here we explore the effectiveness of political interventions using the reproduction number of COVID-19 across Europe. We propose a dynamic SEIR epidemiology model with a time-varying reproduction number, which we identify using machine learning. During the early outbreak, the basic reproduction number was 4.22±1.69, with maximum values of 6.33 and 5.88 in Germany and the Netherlands. By May 10, 2020, it dropped to 0.67±0.18, with minimum values of 0.37 and 0.28 in Hungary and Slovakia. We found a strong correlation between passenger air travel, driving, walking, and transit mobility and the effective reproduction number with a time delay of 17.24±2.00 days. Our new dynamic SEIR model provides the flexibility to simulate various outbreak control and exit strategies to inform political decision making and identify safe solutions in the benefit of global health.

BackgroundThe 2016 European Tobacco Products Directive (TPD) required Member States (MS) to implement new regulations for electronic cigarettes (ECs). We conducted a longitudinal study to assess changes over 2 years in smokers’ support for EC policies and identify predictors of support in seven European countries after TPD implementation.MethodsProspective cohort surveys were conducted among adult smokers in Germany, Greece, Hungary, Poland, Romania, Spain and England in 2016 (n = 9547; just after TPD) and 2018 (n = 10 287; 2 years after TPD). Multivariable logistic regression models employing generalized estimating equations assessed changes in support for four EC policies, and tested for country differences and strength of key predictors of support.ResultsBanning EC use in smoke-free places was supported by 53.1% in 2016 and 54.6% in 2018 with a significant increase in Greece (51.7–66.0%) and a decrease in Spain (60.1–48.6%). Restricting EC/e-liquid nicotine content was supported by 52.2 and 47.4% in 2016 and 2018, respectively, with a significant decrease in England (54.2–46.5%) and Romania (52.5–41.0%). An EC promotion ban was supported by 41.1 and 40.2%. A flavour ban was supported by 33.3% and 32.3% with a significant increase in Hungary (34.3–43.3%). Support was generally higher in Poland, Hungary and Greece vs. England. Support was lower among dual and EC-only users, and low-income smokers.ConclusionsSmokers in all countries strongly supported banning EC use in smoke-free places and restricting nicotine content after TPD implementation, with no clear trends for changes in policy support.

The inclusion of covariates in population models during drug development is a key step to understanding drug variability and support dosage regimen proposal, but high correlation among covariates often complicates the identification of the true covariate. We compared three covariate selection methods balancing data information and prior knowledge: (1) full fixed effect modelling (FFEM), with covariate selection prior to data analysis, (2) simplified stepwise covariate modelling (sSCM), data driven selection only, and (3) Prior-Adjusted Covariate Selection (PACS) mixing both. PACS penalizes the a priori less likely covariate model by adding to its objective function value (OFV) a prior probability-derived constant: $$2*{\kern 1pt} \,{\ln}\left( {{\Pr}\left( X \right)/\left( {1 - {\Pr}\left( X \right)} \right)} \right)$$ 2 ∗ ln Pr X / 1 - Pr X , Pr(X) being the probability of the more likely covariate. Simulations were performed to compare their external performance (average OFV in a validation dataset of 10,000 subjects) in selecting the true covariate between two highly correlated covariates: 0.5, 0.7, or 0.9, after a training step on datasets of 12, 25 or 100 subjects (increasing power). With low power data no method was superior, except FFEM when associated with highly correlated covariates ($$r=0.9$$ r = 0.9 ), sSCM and PACS suffering both from selection bias. For high power data, PACS and sSCM performed similarly, both superior to FFEM. PACS is an alternative for covariate selection considering both the expected power to identify an anticipated covariate relation and the probability of prior information being correct. A proposed strategy is to use FFEM whenever the expected power to distinguish between contending models is 80% but

Objective:To characterise dietary habits, their temporal and spatial patterns and associations with BMI in the 23andMe study population.Design:We present a large-scale cross-sectional analysis of self-reported dietary intake data derived from the web-based National Health and Nutrition Examination Survey 2009–2010 dietary screener. Survey-weighted estimates for each food item were characterised by age, sex, race/ethnicity, education and BMI. Temporal patterns were plotted over a 2-year time period, and average consumption for select food items was mapped by state. Finally, dietary intake variables were tested for association with BMI.Setting:US-based adults 20–85 years of age participating in the 23andMe research programme.Participants:Participants were 23andMe customers who consented to participate in research (n 526 774) and completed web-based surveys on demographic and dietary habits.Results:Survey-weighted estimates show very few participants met federal recommendations for fruit: 2·6 %, vegetables: 5·9 % and dairy intake: 2·8 %. Between 2017 and 2019, fruit, vegetables and milk intake frequency declined, while total dairy remained stable and added sugars increased. Seasonal patterns in reporting were most pronounced for ice cream, chocolate, fruits and vegetables. Dietary habits varied across the USA, with higher intake of sugar and energy dense foods characterising areas with higher average BMI. In multivariate-adjusted models, BMI was directly associated with the intake of processed meat, red meat, dairy and inversely associated with consumption of fruit, vegetables and whole grains.Conclusions:23andMe research participants have created an opportunity for rapid, large-scale, real-time nutritional data collection, informing demographic, seasonal and spatial patterns with broad geographical coverage across the USA.

Background Infection with coronavirus SARS-CoV-2, causing COVID-19 disease, leads to inflammation and a prothrombotic state. Objective This rapid systematic review aims to synthesize evidence on thromboembolism incidence and outcomes with antithrombotic therapies in COVID-19. Data Sources We searched MEDLINE (Ovid), Cochrane Rapid Reviews, PROSPERO, and the WHO COVID-19 Database from January 1, 2003, to April 22, 2020, for studies meeting pre-specified inclusion criteria. Study Selection, Data Extraction, and Synthesis One investigator identified articles for inclusion, abstracted data, and performed quality assessment, with second reviewer checking. Results Incidence of thromboembolism among hospitalized patients with COVID-19 ranged from 25 to 53% in 4 retrospective series. We identified 3 studies (1 retrospective cohort study, 1 prospective uncontrolled observational study, and 1 case series) examining outcomes among COVID-19 patients who received antithrombotic therapies. These studies all included different interventions (thromboprophylaxis with unfractionated heparin (UFH) or low molecular-weight heparin (LMWH); an intensive thromboprophylaxis protocol with LMWH, antithrombin, and clopidogrel; and salvage therapy with tissue plasminogen activator and heparin). These studies are overall poor quality due to methodological limitations including unclear patient selection protocols, lack of reporting or adjustment for patient baseline characteristics, inadequate duration of follow-up, and partial reporting of outcomes. Conclusions New evidence on thromboembolism in COVID-19 does not warrant a change in current guidance on thromboprophylaxis among hospitalized patients. Prospective trials of antithrombotic treatment strategies among patients with COVID-19 are urgently needed.

In recent years, the use of geopolymer materials for ground improvement has increased with the growth in the construction sector. The lignite coal used in the thermal power plants in Turkey has low-calorie content. Thus, fly ash from these thermal power plants has high-lime content, making it unsuitable for cement production or as a cement additive material. Therefore, it is necessary to find different areas where fly ash can be utilised. In this study, the fly ashes of six power plants were tested to improve the properties of cohesive soil. The effects of fly ash along with the soil properties on improving the physicomechanical properties of the soil were examined with different mix designs. The effects of the curing period on the soil strength values were examined, and the unconfined compressive strength values of all mixtures were compared with those of the controlled specimens prepared with optimum water content. In the literature, seventeen various case studies on fly ash–soil remediation have been investigated. The data of this study and other studies were evaluated together, and multiple regression and artificial neural network analyses were performed to estimate the improved soil strength. Additionally, ternary contour diagrams were designed for assessing the fly ash–mixed soil strength using the physical and mechanical properties of the soil and fly ash.

Carbonation of concrete is a process which absorbs carbon dioxide (CO2). Recycled aggregate concrete (RAC) may own greater potential in CO2 uptake due to the faster carbonation rate than natural aggregate concrete (NAC). A quantitative model was employed to predict the CO2 uptake of RAC in this study. The carbonation of RAC and the specific surface area of recycled coarse aggregates (RCAs) were tested to verify accuracy of the quantitative model. Based on the verified model, results show that the CO2 uptake capacity increases with the increase of RCA replacement percentage. The CO2 uptake amount of 1 m3 C30 RAC within 50 years is 10.6, 13.8, 17.2, and 22.4 kg when the RCA replacement percentage is 30%, 50%, 70%, and 100%, respectively. The CO2 uptake by RCAs is remarkable and reaches 35.8%–64.3% of the total CO2 uptake by RAC when the RCA storage time being 30 days. Considering the fact that the amount of old hardened cement paste in RCAs is limited, there is an upper limit for the CO2 uptake of RCAs.

Amidst the opioid overdose crisis, there are increased efforts to expand access to medications for opioid use disorder (MOUD). Hospitalization for the complications of substance use in the United States (US) provides an opportunity to initiate methadone, buprenorphine, and extended release naltrexone and link high-risk, not otherwise engaged, patients into outpatient care. However, treatment options for patients are quickly exhausted when these medications are not desired, tolerated, or beneficial. As an example, we discuss the case of a man who was hospitalized 27 times over 2 years for complications related to his opioid use disorder (OUD), including recurring methicillin-resistant Staphylococcus aureus vertebral osteomyelitis, increasing antimicrobial resistance, new infections, and multiple overdoses in and out of the hospital. The patient suffered these complications despite efforts to treat his OUD with methadone and buprenorphine while hospitalized, and repeated attempts to link him to outpatient care. We use this case to review evidence-based treatments for refractory OUD, which are not approved in the US, but are available in Canada. If hospitalized in Vancouver, Canada, this patient could have been offered slow-release oral morphine and injectable opioid agonist therapy, as well as access to sterile syringes and injection equipment at an in-hospital supervised injection facility. Each of these approaches is supported by evidence and has been implemented successfully in Canada, yet none are available in the US. In order to combat the multiple harms from opioids, it is critical that we consider every evidence-based tool.

Owing to the increased population in developing country like India, the demand of sustainable building material is likewise increased to meet its excessive consumption. In the present study, non-autoclaved aerated geopolymer (NAAG) blocks were developed using industrial waste [fly ash, granulated blast furnace slag (GGBS)], foam and aerating agents with less concentration of alkaline solution (i.e. 1.5–4 M) and tested for their physio-mechanical properties. These NAAG blocks achieved up to 13.5 MPa compressive strength. Optimized samples were tested for thermal and durability related properties. Performance evaluation of NAAG blocks exhibited low thermal conductivity and durable nature as compared to the clay bricks. This developed NAAG block is proven to be a cost-effective and energy effective solution and can be used as building material.

The purpose of this study is to evaluate the feasibility of reusing filter backwash water (FBWW) from the water treatment plant as a substitute for conventional coagulants (primary coagulant) and coagulant aid with aluminum sulfate (Al2(SO4)3) in the coagulation–sedimentation process as tertiary treatment urban effluents for reuse. The impact of reuse filter backwash water has been studied on the water quality in coagulation process. The analysis Fourier Transform Infrared showed the presence of Al(OH)3 in the filter backwash water. The results showed a very good performance of turbidity and organic matter removal by FBWW in both cases. For a dose of 22.5 mg Al/L of FBWW as primary coagulant, the removal of turbidity and UV254 absorbance was significant, with percentages of 65.8% and 31.7%, respectively, during the treatment of the secondary effluent. The best results were obtained for a neutral pH. With FBWW as coagulant aid, the optimal dose of Al2(SO4)3 decreased from 9.5 to 6.3 mg Al/L with better percentages of turbidity reduction and UV254 of 92.9 and 71.4%. This study permits to reduce the coagulant concentration.

Motivation Recent developments in technology have enabled researchers to collect multiple OMICS datasets for the same individuals. The conventional approach for understanding the relationships between the collected datasets and the complex trait of interest would be through the analysis of each OMIC dataset separately from the rest, or to test for associations between the OMICS datasets. In this work we show that integrating multiple OMICS datasets together, instead of analysing them separately, improves our understanding of their in-between relationships as well as the predictive accuracy for the tested trait. Several approaches have been proposed for the integration of heterogeneous and high-dimensional (p≫n) data, such as OMICS. The sparse variant of canonical correlation analysis (CCA) approach is a promising one that seeks to penalize the canonical variables for producing sparse latent variables while achieving maximal correlation between the datasets. Over the last years, a number of approaches for implementing sparse CCA (sCCA) have been proposed, where they differ on their objective functions, iterative algorithm for obtaining the sparse latent variables and make different assumptions about the original datasets. Results Through a comparative study we have explored the performance of the conventional CCA proposed by Parkhomenko et al., penalized matrix decomposition CCA proposed by Witten and Tibshirani and its extension proposed by Suo et al. The aforementioned methods were modified to allow for different penalty functions. Although sCCA is an unsupervised learning approach for understanding of the in-between relationships, we have twisted the problem as a supervised learning one and investigated how the computed latent variables can be used for predicting complex traits. The approaches were extended to allow for multiple (more than two) datasets where the trait was included as one of the input datasets. Both ways have shown improvement over conventional predictive models that include one or multiple datasets. Availability and implementation https://github.com/theorod93/sCCA. Supplementary information Supplementary data are available at Bioinformatics online.

There has been a growing interest in using agricultural waste in concrete construction lately, because of environmental friendliness, resource conservation and economic considerations. Laboratory experiments were conducted to examine the possibility of using palm frond waste ash, as an additive in cement-based composites. The parameters investigated included the effect of palm frond waste ash at 10%, 20%, and 30% admixing levels by weight of cement, on normal consistency and setting times of cement paste, flow, compressive and flexural strength of mortar at 7, 28, 56, and 90 days, and chloride ion permeability of mortar at 28 and 90 days. Similar experiments were also performed on commercially available mineral admixture such as F type fly ash for comparison purposes. Palm frond waste ash additions to portland cement (PC) mortars somewhat increased the water required for normal consistency, prolonged the setting times, marginally lowered the workability, and improved considerably the compressive strength and chloride ion permeability at 28 and 90 days compared to control mixes without ash. Test results indicated that, up to 30% of palm frond waste ash can be added to PC cement mortar without compromising the strength and durability properties and compare fairly well with similar test results reported on a commercial admixture for concrete such as F type fly ash.

To describe multiple Bragg reflection from a thick, ideally imperfect crystal, the transport equations are reformulated in three-dimensional phase space and solved by spectral collocation in the depth coordinate. Example solutions illustrate the orientational spread of multiply reflected rays and the distortion of rocking curves, especially for finite detectors., The generalized Darwin–Hamilton equations [Wuttke (2014 ▸). Acta Cryst. A70, 429–440] describe multiple Bragg reflection from a thick, ideally imperfect crystal. These equations are simplified by making full use of energy conservation, and it is demonstrated that the conventional two-ray Darwin–Hamilton equations are obtained as a first-order approximation. Then an efficient numeric solution method is presented, based on a transfer matrix for discretized directional distribution functions and on spectral collocation in the depth coordinate. Example solutions illustrate the orientational spread of multiply reflected rays and the distortion of rocking curves, especially if the detector only covers a finite solid angle.

MotivationImproved DNA technology has made it practical to estimate single-nucleotide polymorphism (SNP)-heritability among distantly related individuals with unknown relationships. For growth- and development-related traits, it is meaningful to base SNP-heritability estimation on longitudinal data due to the time-dependency of the process. However, only few statistical methods have been developed so far for estimating dynamic SNP-heritability and quantifying its full uncertainty.ResultsWe introduce a completely tuning-free Bayesian Gaussian process (GP)-based approach for estimating dynamic variance components and heritability as their function. For parameter estimation, we use a modern Markov Chain Monte Carlo method which allows full uncertainty quantification. Several datasets are analysed and our results clearly illustrate that the 95% credible intervals of the proposed joint estimation method (which ‘borrows strength’ from adjacent time points) are significantly narrower than of a two-stage baseline method that first estimates the variance components at each time point independently and then performs smoothing. We compare the method with a random regression model using MTG2 and BLUPF90 software and quantitative measures indicate superior performance of our method. Results are presented for simulated and real data with up to 1000 time points. Finally, we demonstrate scalability of the proposed method for simulated data with tens of thousands of individuals.Availability and implementationThe C++ implementation dynBGP and simulated data are available in GitHub: https://github.com/aarjas/dynBGP. The programmes can be run in R. Real datasets are available in QTL archive: https://phenome.jax.org/centers/QTLA.Supplementary informationSupplementary data are available at Bioinformatics online.

BACKGROUND: Prior meta-analyses measuring thiazide-induced glycemic change have demonstrated an increased risk of incident diabetes; however, this measure’s definition has changed over time. AIM: To determine the magnitude of change in fasting plasma glucose (FPG) for thiazide diuretics. DATA SOURCES: A research librarian designed and conducted searches in Medline®, EMBASE, and EBM Reviews-Cochrane Central Register of Controlled Trials (inception through July 2018) and International Pharmaceutical Abstracts (inception to December 2014). STUDY SELECTION: Randomized, controlled trials comparing a thiazide or thiazide-like diuretic to any comparator reporting FPG were identified. Trials enrolling

The effects of different electron donors on sulfate reduction at high COD/SO42− and haloalkaline conditions were investigated. For this purpose, the anaerobic submerged membrane reactor, fed with acetate and ethanol as an electron donor, was operated at a salinity of 10 g NaCl/L and a high pH (about 9.5) for 210 days. The bioreactor was inoculated with a mixed haloalkaliphilic culture and was enriched with saline soda from Lake Van, Turkey. The bioreactor’s performance was mainly evaluated with the removal of chemical oxygen demand (COD) and sulfate and changes in microbial community. A significant increase for both COD and sulfate removal was simultaneously achieved in the ethanol-fed bioreactor. The highest removal efficiency of COD and sulfate was about 70% and 80%, respectively, once the ratio of COD/SO42− was 6. The effect of hydraulic retention time on treatment performance of the bioreactor was inconsiderable as a result of the low flux during the whole operation time. There was no biogas production throughout the experiment due to the haloalkaline condition in reactor medium. From DGGE analysis, it was postulated that ethanol usage can cause bacterial abundance and diversity in bioreactor medium with no detection of sulfate-reducing bacteria.

Saw Dust Ash (SDA) is an industrial waste that has been used by many researchers in concrete to achieve economic and environmental sustainability. In this study, 5% of sand was replaced with SDA to produce concrete with different mix ratios. Scheffe’s simplex theory was used for five mix ratios in a {5,2} experimental design which resulted in additional ten mix ratios. Additional fifteen mix ratios were generated from the initial fifteen, for verification and testing. Concrete cubes of 150mmX150mmX150mm were formed using the thirty concrete mix ratios generated, and soaked in water for 24hours. The water absorptions of cubes from each mix ratio were determined with the standard procedure. The results of the first fifteen water absorption values were used for the calibration of the model constant coefficients, while those from the second fifteen were used for the model verification using Scheffe’s simplex lattice design. A mathematical regression model was formulated from the results, with which the water absorptions were predicted. The model was then subjected to a two-tailed t-test with 5% significance, which ascertained the model to be adequate and fit with an R2 value of 0.8244. The study also revealed that SDA can replace 5% of sand and promote environmental sustainability without significantly changing the water absorption.

BACKGROUND: Despite evidence-based guidelines, high-quality diabetes care is not always achieved. Identifying factors associated with the quality of management in primary care may inform service improvements, facilitating the tailoring of quality improvement interventions to practice needs and resources. METHODS: We searched MEDLINE, EMBASE, CINAHL and Web of Science from January 1990 to March 2019. Eligible studies were cohort studies, cross-sectional studies and randomised controlled trials (baseline data) conducted among adults with diabetes, which examined the relationship between any physician and/or practice factors and any objective measure(s) of quality. Studies which examined patient factors only were ineligible. Where possible, data were pooled using random-effects meta-analysis. RESULTS: In total, 82 studies were included. The range of individual quality measures and the construction of composite measures varied considerably. Female physicians compared with males ((odds ratio (OR) = 1.07, 95% CI: 1.04, 1.10), 8 studies), physicians with higher diabetes volume compared with lower volume (OR = 1.24, 95% CI: 1.05–1.47, 4 studies) and practices with Electronic Health Records (EHR) versus practices without (OR = 1.43, 95% CI: 1.11–1.84, 4 studies) were associated with a higher quality of care. There was no association between physician experience, practice location and type of practice and quality. Based on the narrative synthesis, increasing physician age and higher practice socio-economic deprivation may be associated with lower quality of care. DISCUSSION: Identification of physician- and practice-level factors associated with the quality of care (female gender, younger age, physician-level diabetes volume, practice deprivation and EHR use) may explain differences across practices and physicians, provide potential targets for quality improvement interventions and indicate which practices need specific supports to deliver improvements in diabetes care. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11606-020-05676-7) contains supplementary material, which is available to authorized users.

Biodiesel and ethanol are known as the typical alternative and renewable fuels that can replace fossil and mineral fuels; however, they can affect combustion due to their undesirable innate traits. The investigation is a numerical attempt to compare the synthesized blended fuels of diesel–ethanol (DE) and diesel–biodiesel (DB) and pure diesel (D100) when combusted in a high-speed direct injection (HSDI) diesel engine. The blends are B10D90, B50D50, E05D95, E10D90, and E85D15. In the initial condition of the code, the fuel is defined as multi-component and the share of each component is determined. The results are indicative of lower ethanol-blended fuel’s mean spray droplet (SMD), thus lower CO2 and soot emissions although it is afflicted with low engine output in terms of power and efficiency. The interaction of SMDE85D15 = 2.5 µm and LHVE85D15 = 31,045.3 kJ/kg with the SMDB50D50 = 2.664 µm and LHVB50D50 = 40335 kJ/kg, droplet diameter is more efficient in emission reduction in better spraying, and on the other hand fuel’s intrinsic heat value is more significant for power generation.