Definition Of Variables Research Articles

Migrating a Well-Established Longitudinal Cohort Database From Oracle SQL to Research Electronic Data Entry (REDCap): Data Management Research and Design Study.

Providing user-friendly electronic data collection tools for large multicenter studies is key for obtaining high-quality research data. Research Electronic Data Capture (REDCap) is a software solution developed for setting up research databases with integrated graphical user interfaces for electronic data entry. The Swiss Mother and Child HIV Cohort Study (MoCHiV) is a longitudinal cohort study with around 2 million data entries dating back to the early 1980s. Until 2022, data collection in MoCHiV was paper-based. The objective of this study was to provide a user-friendly graphical interface for electronic data entry for physicians and study nurses reporting MoCHiV data. MoCHiV collects information on obstetric events among women living with HIV and children born to mothers living with HIV. Until 2022, MoCHiV data were stored in an Oracle SQL relational database. In this project, R and REDCap were used to develop an electronic data entry platform for MoCHiV with migration of already collected data. The key steps for providing an electronic data entry option for MoCHiV were (1) design, (2) data cleaning and formatting, (3) migration and compliance, and (4) add-on features. In the first step, the database structure was defined in REDCap, including the specification of primary and foreign keys, definition of study variables, and the hierarchy of questions (termed "branching logic"). In the second step, data stored in Oracle were cleaned and formatted to adhere to the defined database structure. Systematic data checks ensured compliance to all branching logic and levels of categorical variables. REDCap-specific variables and numbering of repeated events for enabling a relational data structure in REDCap were generated using R. In the third step, data were imported to REDCap and then systematically compared to the original data. In the last step, add-on features, such as data access groups, redirections, and summary reports, were integrated to facilitate data entry in the multicenter MoCHiV study. By combining different software tools-Oracle SQL, R, and REDCap-and building a systematic pipeline for data cleaning, formatting, and comparing, we were able to migrate a multicenter longitudinal cohort study from Oracle SQL to REDCap. REDCap offers a flexible way for developing customized study designs, even in the case of longitudinal studies with different study arms (ie, obstetric events, women, and mother-child pairs). However, REDCap does not offer built-in tools for preprocessing large data sets before data import. Additional software is needed (eg, R) for data formatting and cleaning to achieve the predefined REDCap data structure.

ANALISIS KEBIJAKAN DIVIDEN SERTA KINERJA KEUANGAN YANG BERDAMPAK PADA NILAI PERUSAHAAN PADA PERUSAHAAN MAKANAN DAN MINUMAN YANG TERDAFTAR DI BURSA EFEK INDONESIA

The purpose of this research is to prove the impact of dividend policy and financial performance on the value of food and beverage companies listed on the IDX. The operational variable definition in this study uses the dependent variable firm value, while the independent variables are dividend policy and financial performance. The research population is a food and beverage company listed on the IDX. Data collection technique using purposive sampling. The number of research samples is 10 companies with a total of 50 observations. Data analysis techniques with descriptive statistical analysis, classical assumption tests (multicollinearity, autocorrelation, heteroscedasticity, and normality), hypothesis testing and multiple linear regression analysis. The results of the analysis show that dividend policy (DPR) partially has a significant positive impact on firm value and financial performance (ROA) has an insignificant positive impact on firm value. The determination test shows an adjusted R2 value of 0.356, which means that the variable firm value can be explained by the two independent variables of dividend policy and financial performance of 35.6%, while the remaining 64.4% is explained by other factors outside this study.

Stratified split sampling of electronic health records

Although superficially similar to data from clinical research, data extracted from electronic health records may require fundamentally different approaches for model building and analysis. Because electronic health record data is designed for clinical, rather than scientific use, researchers must first provide clear definitions of outcome and predictor variables. Yet an iterative process of defining outcomes and predictors, assessing association, and then repeating the process may increase Type I error rates, and thus decrease the chance of replicability, defined by the National Academy of Sciences as the chance of “obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data.”[1] In addition, failure to account for subgroups may mask heterogeneous associations between predictor and outcome by subgroups, and decrease the generalizability of the findings. To increase chances of replicability and generalizability, we recommend using a stratified split sample approach for studies using electronic health records. A split sample approach divides the data randomly into an exploratory set for iterative variable definition, iterative analyses of association, and consideration of subgroups. The confirmatory set is used only to replicate results found in the first set. The addition of the word ‘stratified’ indicates that rare subgroups are oversampled randomly by including them in the exploratory sample at higher rates than appear in the population. The stratified sampling provides a sufficient sample size for assessing heterogeneity of association by testing for effect modification by group membership. An electronic health record study of the associations between socio-demographic factors and uptake of hepatic cancer screening, and potential heterogeneity of association in subgroups defined by gender, self-identified race and ethnicity, census-tract level poverty and insurance type illustrates the recommended approach.

What we talk about when we talk about COVID-19 vaccination campaign impact: a narrative review.

The lack of precise definitions and terminological consensus about the impact studies of COVID-19 vaccination leads to confusing statements from the scientific community about what a vaccination impact study is. The present work presents a narrative review, describing and discussing COVID-19 vaccination impact studies, mapping their relevant characteristics, such as study design, approaches and outcome variables, while analyzing their similarities, distinctions, and main insights. The articles screening, regarding title, abstract, and full-text reading, included papers addressing perspectives about the impact of vaccines on population outcomes. The screening process included articles published before June 10, 2022, based on the initial papers' relevance to this study's research topics. The main inclusion criteria were data analyses and study designs based on statistical modelling or comparison of pre- and post-vaccination population. The review included 18 studies evaluating the vaccine impact in a total of 48 countries, including 32 high-income countries (United States, Israel, and 30 Western European countries) and 16 low- and middle-income countries (Brazil, Colombia, and 14 Eastern European countries). We summarize the main characteristics of the vaccination impact studies analyzed in this narrative review. Although all studies claim to address the impact of a vaccination program, they differ significantly in their objectives since they adopt different definitions of impact, methodologies, and outcome variables. These and other differences are related to distinct data sources, designs, analysis methods, models, and approaches.

Use of Theory of Change (ToC) as methodological apprach to assess the Regional Development Program for Maradol papaya in the Mixteca Poblana region

Objective: To determine the extent to which the use of the Theory of Change (ToC) as a methodological approach is appropriate to assess the medium and long term effects generated by the Maradol Papaya Program in the Mixteca Poblana region (Puebla, Mexico). Design/methodology/approximation: A case study was conducted with qualitative approach with a phenomenological approximation directed at the validation of the ToC as methodological tool for the assessment of programs, through Documental Research methods, observation and interviews with producers and key informants for the study. Results: The design of the route or pathway of change allowed a punctual definition of the key variables for its operation and measurement as part of the assessment exercise. The use of qualitative methods (in-depth interviews) allowed an appropriate valuation of qualitative variables, such as learning, identificaiton of quantitative variables (production volumes, production costs, etc.) that can be made with conventional methods (questionnaires) without this representing any methodological conflict. Limitations on the study/implications: Since it is a case study, its generalization is not appropriate. However, there are elements that can be suggested as a general premise, such as the recommendation of the use of mixed methods in the assessment of programs and projects. Findings/conclusions: The Theory of Change (ToC) is a valuable tool in identifying change variables through the design of the route or pathway change; however, this exercise has great validity and wealth if it is constructed in a participative way. The use of mixed methods (quantitative and qualitative) is suggested for this type of assessment.

Thermoelastic instability on a frictional surface and its implication for size effect in friction experiments

Recent laboratory friction experiments on large rock samples revealed that dynamic weakening, a remarkable reduction in the friction coefficient at elevated slip rates, occurs at lower slip rates in larger samples. There is a large difference between the sizes of natural faults and those in laboratory experiments. Therefore, it is crucial to understand the effect of size on rock friction. In the field of tribology, the interaction between frictional heating and thermoelastic effect has long been investigated. It was shown that higher slip rates than the critical value {V}_{mathrm{cr}} causes growth of temperature and normal stress heterogeneity (thermoelastic instability), and {V}_{mathrm{cr}} is proportional to the wavenumber of the heterogeneity. Severely heterogeneous normal stress may cause concentration of frictional power, thus locally activating dynamic weakening and leading to macroscopic weakening. Because a larger sample hosts a perturbation of a smaller wavenumber, it is expected to weaken at a lower slip rate than a smaller sample. In this study, a new numerical method was developed for analysis of thermoelastic instability based on the definition of memory variables and numerical approximation to the integration kernel, for the 2-dimensional problem of a planar fault embedded in an infinite medium. This method was advantageous over the standard integral equation method in terms of numerical costs. Numerical solutions with the new method on sinusoidal perturbations in the normal stress were compared with previously derived steady-state solution and its stability for validation. The typical thermoelastic properties of gabbro yield {V}_{mathrm{cr}} in a range of experimentally adopted slip rates, indicating that the thermoelastic effect may play an important role in high-velocity friction experiments. Because the temperature rise and the resulting normal stress change smear out after the friction experiments, measurement of the temperature distribution in a sample during a friction experiment is important for further understanding the dynamic weakening and scale effect of rock friction.Graphical

ACVECC‐Veterinary Committee on Trauma registry report 2017–2019

To report summative data from the American College of Veterinary Emergency and Critical Care Veterinary Committee on Trauma (VetCOT) registry. Multi-institutional registry data report, April 1, 2017 to December 31, 2019. VetCOT identified and verified Veterinary Trauma Centers (VTCs). Dogs and cats with evidence of trauma. Data were input to a web-based data capture system (Research Electronic Data Capture) by data entry personnel trained in data software use and operational definitions of data variables. Data on demographics, trauma type, preadmission care, trauma severity assessment at presentation (modified Glasgow Coma Scale and Animal Trauma Triage score), key laboratory parameters, interventions, and case outcome were collected. Summary descriptive data for each species are reported. Thirty-one VTCs contributed data from 20,842 canine and 4003 feline trauma cases during the 33-month reporting period. Most cases presented directly to a VTC (82.1% dogs, 82.1% cats). Admission to hospital rates were slightly lower in dogs (27.8%) than cats (32.7%). Highest mortality rates by mechanism of injury in dogs were struck by vehicle (18.3%), ballistic injury (17.6%), injured inside vehicle (13.2%), nonpenetrating bite wound (10.2%), and choking/pulling injury (8.5%). Highest mortality rates by mechanism of injury in cats were struck by vehicle (43.3%), ejected from vehicle (33.3%), nonpenetrating bite wound (30.7%), ballistic injury (27.8%), and choking/pulling injury (25.0%). The proportion of animals surviving to discharge was 93.1% (dogs) and 82.5% (cats). The VetCOT registry is a powerful resource for collection of a large dataset on trauma in dogs and cats seen at VTCs. Overall survival to discharge was high indicating low injury severity for most recorded cases. Further evaluation of data on subsets of injury types, patient assessment parameters, interventions, and associated outcome are warranted. Data from the registry can be leveraged to inform clinical trial design and justification for naturally occurring trauma as a translational model to improve veterinary and human trauma patient outcome.

Identifying Lines and Interpreting Vertical Jumps in Eye Tracking Studies of Reading Text and Code

Eye tracking studies have shown that reading code, in contradistinction to reading text, includes many vertical jumps. As different lines of code may have quite different functions (e.g., variable definition, flow control, or computation), it is important to accurately identify the lines being read. We design experiments that require a specific line of text to be scrutinized. Using the distribution of gazes around this line, we then calculate how the precision with which we can identify the line being read depends on the font size and spacing. The results indicate that, even after correcting for systematic bias, unnaturally large fonts and spacing may be required for reliable line identification. Interestingly, during the experiments, the participants also repeatedly re-checked their task and if they were looking at the correct line, leading to vertical jumps similar to those observed when reading code. This suggests that observed reading patterns may be “inefficient,” in the sense that participants feel the need to repeat actions beyond the minimal number apparently required for the task. This may have implications regarding the interpretation of reading patterns. In particular, reading does not reflect only the extraction of information from the text or code. Rather, reading patterns may also reflect other types of activities, such as getting a general orientation, and searching for specific locations in the context of performing a particular task.

Generalizing progress variable definition in CFD simulation of combustion systems using tabulated chemistry models

In the Computational Fluid Dynamics (CFD) simulation of advanced combustion systems, the chemical kinetics must be examined in detail to predict the emissions and performance characteristics accurately. Nevertheless, the combustion simulation with detailed chemical kinetics is complicated because of the number of equations and a broad timescale spectrum. The Flamelet-Generated Manifold (FGM) is one of the examples of tabulation methods that has received much attention in recent years due to its fast and accurate prediction of combustion characteristics. The Progress Variable (PV) definition in FGM and other PV-based tabulated approaches is often selected randomly or depending on the user's experience. When complicated combustion systems are involved, such choices can become extremely difficult. In the current work, a generic approach for formulating a global PV is developed and tested in various operating conditions relevant to combustion engines. The method is based on a genetic algorithm optimization to maximize the monotonicity of PV, ensuring that for each value of PV, the dependent thermophysical properties have unique values. The FGM model's ability to reproduce the detailed kinetics evolution of the essential combustion and emission parameters of a non-premixed diffusion flame in Spray A configuration is evaluated in both one-dimensional counterflow and CFD simulation. It is concluded that with the use of the current approach, important combustion characteristics can be predicted much better compared to non-optimized PV while eliminating the manual selection of PV definition by the user. Since the algorithm needs to be executed before the chemistry tabulation in the pre-processing step, it does not increase the runtime of the FGM simulation. The algorithm only needs a few minutes to be finished on a standard desktop. The improvement in the results and the distribution of the values of important species in the computational domain is examined.

CORRIGENDUM: Predicting household water use behaviour for improved hygiene practices in internet of things environment via dynamic behaviour intervention model

IET NetworksEarly View CORRIGENDUMOpen Access CORRIGENDUM: Predicting household water use behaviour for improved hygiene practices in internet of things environment via dynamic behaviour intervention model First published: 23 March 2023 https://doi.org/10.1049/ntw2.12083AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Below content is reversed to correct an error of citation in our original publication in journal IET Networks [1]. On page 147, Figure 3 title is to be replaced as ‘Fig 3. ETPB model path analyses [2] and variable definition’. On page 146, Section 3.3, the sentence ‘As classical SEM model only represents a static system and cannot express behaviour change over time, to expand the ETPB SEM model with dynamic effects, we proposed a fluid analogy to postulate and parallel inventory management in supply chains’ has to be replaced as ‘As classical SEM model only represents a static system and cannot express behaviour change over time, to expand the ETPB SEM model with dynamic effects, we utilised a fluid analogy [2] to postulate and parallel inventory management in supply chains’. Open Research DATA AVAILABILITY STATEMENT Data sharing is not applicable to this article as no new data were created or analyzed in this study. REFERENCES 1Fu, Y., Wu, W.: Predicting household water use behaviour for improved hygiene practices in internet of things environment via dynamic behaviour intervention model. IET Netw. 5(5), 143– 151 (2016). https://doi.org/10.1048/iet-net.2015.0111 2Emeterio Navarro-Barrientos, J., Rivera, D.E., Collins, L.M.: A dynamical model for describing behavioural interventions for weight loss and body composition change. Math. Comput. Model. Dyn. Syst. 17(2), 183– 203 (2011). https://doi.org/10.1080/13873954.2010.520409 Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation

Preliminary Attainability Assessment of Real-World Data for Answering Major Clinical Research Questions in Breast Cancer Brain Metastasis: Framework Development and Validation Study.

In recent decades, real-world evidence (RWE) in oncology has rapidly gained traction for its potential to answer clinical questions that cannot be directly addressed by randomized clinical trials. Integrating real-world data (RWD) into clinical research promises to contribute to more sustainable research designs, including extension, augmentation, enrichment, and pragmatic designs. Nevertheless, clinical research using RWD is still limited because of concerns regarding the shortage of best practices for extracting, harmonizing, and analyzing RWD. In particular, pragmatic screening methods to determine whether the content of a data source is sufficient to answer the research questions before conducting the research with RWD have not yet been established. We examined the PAR (Preliminary Attainability Assessment of Real-World Data) framework and assessed its utility in breast cancer brain metastasis (BCBM), which has an unmet medical need for data attainability screening at the preliminary step of observational studies that use RWD. The PAR framework was proposed to assess data attainability from a particular data source during the early research process. The PAR framework has four sequential stages, starting with clinical question clarification: (1) operational definition of variables, (2) data matching (structural/semantic), (3) data screening and extraction, and (4) data attainability diagramming. We identified 5 clinical questions to be used for PAR framework evaluation through interviews and validated them with a survey of breast cancer experts. We used the Samsung Medical Center Breast Cancer Registry, a hospital-based real-time registry implemented in March 2021, leveraging the institution's anonymized and deidentified clinical data warehouse platform. The number of breast cancer patients in the registry was 45,129; it covered the period from June 1995 to December 2021. The registry consists of 24 base data marts that represent disease-specific breast cancer characteristics and care pathways. The outcomes included screening results of the clinical questions via the PAR framework and a procedural diagram of data attainability for each research question. Data attainability was tested for study feasibility according to the PAR framework with 5 clinical questions for BCBM. We obtained data sets that were sufficient to conduct studies with 4 of 5 clinical questions. The research questions stratified into 3 types when we developed data fields for clearly defined research variables. In the first, only 1 question could be answered using direct data variables. In the second, the other 3 questions required surrogate definitions that combined data variables. In the third, the question turned out to be not feasible for conducting further analysis. The adoption of the PAR framework was associated with more efficient preliminary clinical research using RWD from BCBM. Furthermore, this framework helped accelerate RWE generation through clinical research by enhancing transparency and reproducibility and lowering the entry barrier for clinical researchers.

Planning third generation minigrids: Multi-objective optimization and brownfield investment approaches in modelling village-scale on-grid and off-grid energy systems

Access to reliable and sustainable electricity is still precluded for a large share of global population living in rural areas of developing countries, especially in sub-Saharan Africa. Hybrid microgrids are considered a suitable solution for providing affordable and reliable access to electricity to isolated communities. Properly planning and sizing such systems is although an aspect that can greatly influence the sustainability of the intervention, and the arrival to the market of the third generation minigrids poses new challenges to the process. Three main challenges are identified as pivotal for the proper sizing of new generation microgrids: arrival of the main grid, inappropriateness of Net Present Cost as only objective function in the strategy selection process, and necessity to operate on already existing minigrids. Such aspects are addressed in this work by proposing a methodological advancement to an existing open-source microgrid sizing model: a grid outage model alongside the definition of new constraints and variables for the optimization problem with grid-connected microgrids, a multi-objective optimization option, and a brown-field optimization option. The new version of the model is tested on real life case studies in rural Rwanda (greenfield) and Mozambique (brownfield), proving the profitability of grid-connected and grid-extension solutions for sufficiently low connection distances. Sensitivity analyses are performed to assess variations in system size, cost and CO2 emissions with respect to microgrid and grid connection parameters.

Ethnicity and homophily effects in US M&As

AbstractWe show that ethnic diversity of CEOs of merging firms has been increasing and report evidence of ethnic homophily effect in M&As transactions. Specifically, M&As perform better when the CEOs of the merging firms share a common ethnic background. In a sample of 444 US mergers completed between 2000 and 2018, we find that ethnic homophily improves the probability of deal completion. Furthermore, we report mild variation of performance across ethnic groups. White CEOs exhibit better postmerger performance while Asian CEOs instigate a better market reaction (announcement return). Subsequent analyses show that White CEOs tend to have longer tenure and longer experience while Asian CEOs tend to be more transparent. We discuss our findings in light of a few extant theories and show that our findings are robust to several additional tests including instrumental variables, Heckman's selection bias correction, and several variations in the model specifications and definitions of key variables.

Forecasting Electricity Demand by Neural Networks and Definition of Inputs by Multi-Criteria Analysis

The planning of efficient policies based on forecasting electricity demand is essential to guarantee the continuity of energy supply for consumers. Some techniques for forecasting electricity demand have used specific procedures to define input variables, which can be particular to each case study. However, the definition of independent and casual variables is still an issue to be explored. There is a lack of models that could help the selection of independent variables, based on correlate criteria and level of importance integrated with artificial networks, which could directly impact the forecasting quality. This work presents a model that integrates a multi-criteria approach which provides the selection of relevant independent variables and artificial neural networks to forecast the electricity demand in countries. It provides to consider the particularities of each application. To demonstrate the applicability of the model a time series of electricity consumption from a southern region of Brazil was used. The dependent inputs used by the neural networks were selected using a traditional method called Wrapper. As a result of this application, with the multi-criteria ELECTRE I method was possible to recognize temperature and average evaporation as explanatory variables. When the variables selected by the multi-criteria approach were included in the predictive models, were observed more consistent results together with artificial neural networks, better than the traditional linear models. The Radial Basis Function Networks and Extreme Learning Machines stood out as potential techniques to be used integrated with a multi-criteria method to better perform the forecasting.

Artificial Intelligence in Continuous Kidney Replacement Therapy.

Artificial Intelligence in Continuous Kidney Replacement Therapy.

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

BackgroundIn electronic health records, patterns of missing laboratory test results could capture patients’ course of disease as well as ​​reflect clinician’s concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients. MethodsWe collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern. ResultsWith these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors. ConclusionIn this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient’s condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.

Flow distribution optimization of parallel pumps based on improved mayfly algorithm

An improved mayfly algorithm is proposed for the energy saving optimization of parallel chilled water pumps in central air conditioning system, with the minimum energy consumption of parallel pump units as the optimization objective and the speed ratio of each pump as the optimization variable for the solution. For the problem of uneven random initialization of mayflies, the variable definition method of Circle chaotic mapping is used to make the initial position of the population uniformly distributed in the solution space, and the mayfly fitness value and the optimal fitness value are incorporated into the calculation of the weight coefficient, which better balances the global exploration and local exploitation of the algorithm. For the problem that the algorithm is easy to fall into the local optimum at the later stage, a multi-subpopulation cooperative strategy is proposed to improve the global search ability of the algorithm. Finally, the performance of the improved mayfly algorithm is tested with two parallel pumping system cases, and the stability and time complexity of the algorithm are verified. The experiments show that the algorithm can get a better operation strategy in solving the parallel water pump energy saving optimization problem, and can achieve energy saving effect of 0.72% 8.68% compared with other optimization algorithms, and the convergence speed and stability of the algorithm have been significantly improved, which can be better applied to practical needs.

EFFECT OF GENETIC MATERIAL AND FERTILIZATION ON ESTIMATING THE DRYING POTENTIAL OF Tectona grandis Linn F.

Drying is a fundamental step in the lumber production process. The definition of variables that affect the quality of the process, such as initial and final temperatures as well as drying potential, depends on the known technological knowledge of the wood, as well as on the experience of the drying process developed for a particular species. With this in mind, we developed an experiment using wood from four genetic materials (three clones and one seminal) of the species Tectona grandis cultivated with and without fertilization. This was done to estimate and evaluate the parameters used in the explanation of drying programs. The values of the initial and final temperatures, and those of the drying potentials, were estimated using the drastic drying methodology, which evaluates the drying rate and defect score (cracks and collapse). In addition to the drying parameters, the physical properties that affect the drying performance were also evaluated. The results show that origin and fertilization did not affect the initial and final temperatures. However, the potential drying parameters were significantly affected by the combination of treatments. Furthermore, the wood drying potential of certain genetic materials was positively affected by fertilization. Finally, the material from the fertilized areas had the lowest basic densities, the highest initial moisture content, and the highest drying rates.

Strong instrumental variables biased propensity scores in comparative effectiveness research: A case study in oncology

Background and ObjectivesSome medications require specific medical procedures in the weeks before their start. Such procedures may meet the definition of instrumental variables (IVs). We examined how they may influence treatment effect estimation in propensity score (PS)-adjusted comparative studies, and how to remedy. Study Design and SettingDifferent covariate assessment periods (CAPs) did and did not include the month preceding treatment start were used to compute PS in the French claims database (Sytème National des Données de Santé-SNDS), and 1:1 match patients with metastatic castration resistant prostate cancer initiating abiraterone acetate or docetaxel. The 36-month survival was assessed. ResultsAmong 1, 213 docetaxel and 2, 442 abiraterone initiators, the PS distribution resulting from the CAP [-12; 0 months] distinctly separated populations (c = 0.93; 273 matched pairs). The CAPs [-12;-1 months] identified 765 pairs (c = 0.81). Strong docetaxel treatment predictors during the month before treatment start were implantable delivery systems (1% vs. 59%), which fulfilled IV conditions. The 36-month survival was not meaningfully different under the [-12; 0 months] CAP but differed by 10% points (38% vs. 28%) after excluding month −1. ConclusionIn the setting of highly predictive pretreatment procedures, excluding the immediate pre-exposure time from the CAP will reduce the risk of including potential IVs in PS models and may reduce bias.

Irreversible port-Hamiltonian modelling of 3D compressible fluids

Boundary controlled irreversible port-Hamiltonian systems (BC-IPHS) defined on 1, 2 and 3-dimensional spatial domains are defined by extending the formulation of reversible BC-PHS to irreversible thermodynamic systems controlled at the boundaries of their spatial domain. The structure of BC-IPHS has a clear physical interpretation, characterizing the coupling between energy storing and energy dissipating elements. By extending the definition of boundary port variables of BC-PHS to deal with the irreversible energy dissipation, a set of boundary port variables is defined so that BC-IPHS are passive with respect to a given set of conjugated inputs and outputs. As for finite-dimensional IPHS and 1-D infinite-dimensional IPHS recently defined in [Ramirez et al., Chem. Eng. Sci. (2022)], the first and second laws of Thermodynamics are satisfied as a structural property of the system. As a common thread, the 3D compressible fluid example is worked out to illustrate the proposed approach: both the reversible case of the isentropic fluid and the irreversible case of the non-isentropic fluid are presented.