Search

Sarcopenia, sarcopenic obesity, malnutrition, and cachexia clinical guidelines were created by expert consensus over the past decade. These pathological states all share in common deficits in skeletal muscle mass, and in some cases muscle function, which adversely impact patient outcomes. Early identification is key as some detrimental outcomes are potentially preventable with available treatments. The four guidelines share common design features: patients suspected of having the condition are first screened with a focused clinical history; if positive, the next step is evaluation with either a measure of body "form" (e.g., mass, shape, and composition) or function (e.g., mechanical, endurance, and metabolic); combined form and functional criteria are also recognized. The form and functional "gateway" nodes establish whether or not to proceed with further evaluations and treatments. Intensive discussions among experts focus on selection of these gateway nodes and the final choice is made when consensus is reached. Form and functional measures are often treated as equivalent alternatives when framed in the context of "outcomes" for which they are intended to predict. Here we adapt a classic biological concept stating that "function follows form" to show that pathophysiological links are present between these two different muscle qualities and clinical outcomes. We argue that a hierarchy exists such that outcomes closely follow functions that, in turn, follow form...the OFF rule. The OFF rule explains why functional measures often show stronger associations with outcomes than those quantifying form, helps to frame debates on how to structure the gateway nodes used to identify patients for further evaluation and treatment, and sets out a pathophysiological structure for developing future outcome prediction models. [ABSTRACT FROM AUTHOR]

A series of numerical simulations was performed to study the discharge capacity of symmetrical hump weirs in free flow condition. A wide range of ramp slopes was selected from 1 V:1 H to 1 V:5 H and the effects of flow discharge and ramp slope on variations of discharge coefficient were investigated. The variations of upstream water head with discharge indicated the existence of two distinct flow regimes on free flow over symmetrical hump weirs. It was found that the discharge coefficient increased with increasing the upstream water head until the maximum discharge coefficient was achieved. Further increase of water head caused a reduction in discharge coefficient and the flow regime became inefficient. The proposed models for prediction of discharge characteristics of sharp-crested weirs with an upstream and (or) downstream ramp(s) from the literature were also compared with the discharge capacity of sharp-crested weirs to study the effects of ramp slope. It was found that the available head–discharge models are acceptable for hump weirs with small ramp length and more accurate prediction models are required for symmetrical hump weirs with larger ramps. The boundary curve to determine the optimum performance in symmetrical hump weirs was introduced, and the head–discharge models for both efficient and inefficient discharge conditions were proposed. [ABSTRACT FROM AUTHOR]

This study compiled a database called CYCU/RockTip/51 consisting of 51 rock-socketed drilled shafts installed at different sites worldwide, covering a wide variety of rock properties and shaft geometries. The tip resistances from seven representative prediction models were compared with the measured values. These measured values were obtained from the load–displacement curves of field load tests using three interpretation criteria. It was found that the prediction models of Teng, Coates, Rowe and Armitage, and ARGEMA overpredicted the measured capacity, while Zhang and Einstein, Vipulanandan et al. and Zhang are less biased. These tip prediction models also could be classified according to displacement requirements. The proposed tip prediction models of this study are presented based on different interpretation methods and the displacement ranges that they are mobilized. Finally, the normalized load–displacement curve was fitted to the hyperbolic curve with two model parameters (a and b). The parameters a and b define the reciprocal of the initial slope and the asymptotic resistance, respectively. The statistics of (b, a) for rock-socketed drilled shafts in CYCU/RockTip/51 are mean = (0.76, 1.09), COV = (0.13, 0.59), and correlation coefficient = −0.79. These statistics are useful for reliability-based design. [ABSTRACT FROM AUTHOR]

The behaviour of partially grouted (PG) masonry shear walls is complex due to masonry's anisotropic nature and the nonlinear interactions among its constituents. Currently available code- and research-based shear strength equations provide highly variable results when predicting the in-plane shear strength of PG masonry walls. It is crucial to develop a greater understanding in this area, as sudden shear failures of masonry walls can lead to catastrophic losses of human life and property. This study presents the development of several new in-plane shear strength models for PG masonry walls using stepwise regression and model trees with data compiled from 292 experimentally tested walls. The models are found to significantly outperform existing code- and research-based shear strength equations. It was found that, of the variables studied, the most significant ones are the axial load, wall geometry, compressive strength of mortar, and area of interior vertical reinforcement. [ABSTRACT FROM AUTHOR]

Environmental recruitment indices may improve the precision of stock assessments, allow hindcasting, and aid in near-term forecasting. We used Bayesian dynamic factor analysis (DFA) to find common trends in sea level from 16 tide gauges spanning the US West Coast. We then used these dynamic factors as predictors of sablefish Anoplopoma fimbria recruitment deviations from the 2021 assessment. We evaluated the ability of the resulting northern sea-level index (north of Cape Mendocino, ∼40°N) to inform recruitment estimates and its impacts on assessment model predictions by running two hindcast stock assessment models: (1) a catch-only model, which assumed average recruitment from the stock–recruit relationship, and (2) a catch plus sea-level model. In both cases, survey data were removed from 2011 forward. The model including sea-level index captured the observed increase in stock biomass from 2016 onwards, while the catch-only model did not, predicting a continued biomass decline. This work provides evidence of the potential to improve forward-looking stock projections by better capturing stock trends, providing an advance over average recruitment assumptions. [ABSTRACT FROM AUTHOR]

Airtankers are commonly used for initial attack (IA) to reduce the likelihood of wildland fires escaping containment efforts. We examined IA airtanker dispatch decisions for forest fires in Ontario, Canada, through an analysis of historical fire records from 2001 to 2019. A hurdle modelling approach that predicts the probability of airtanker(s) being dispatched and then the number of airtankers sent was used. Two different hurdle models were considered depending on the timing of the information available to the decision maker: a "fire report model" based upon the information available when a fire is first reported, and an "initial attack model" using information available at the time IA action began. Both models indicated that the most influential covariates for airtanker dispatch are the fire weather index, fuel volatility, observed fire rate of spread, fire size at IA, and cause of ignition. When evaluating the predictive ability of the models on a validation data set, the "initial attack model" performed better than the "fire report model". Our models generally perform well when predicting none, one, or two airtankers on IA, but they generally underpredict when more than two airtankers are dispatched, which suggests risk-averse decision-making in fire management resource dispatching. [ABSTRACT FROM AUTHOR]

Buffer materials that are used to isolate heat-emitting waste canisters must bear a strong thermal load and have good thermal conductivity and stability. This study investigated the strengthening of internal thermal conduction via bentonite sealing. An admixture of quartz and graphite accelerated the heat transfer into the host rock. Samples containing different bentonite–sand–graphite (BSG) mixtures were prepared. The influence of the volumetric water content, degree of saturation, dry density, porosity, sand content, and particle size on the thermal conductivity of the BSG mixtures was analyzed by conducting a series of thermal needle tests. Electrical resistivity tests were conducted to examine the electrical resistivity of the BSG mixtures, and the dependency of soil thermal conductivity on the volumetric water content based on electrical resistivity data. The results indicated that the dependency of thermal conductivity on the volumetric water content was closely related to electrical resistivity. Based on the thermal conductivity inflection point as determined by the volumetric water content corresponding to the electrical resistivity inflection point, an improved series–parallel thermal conductivity prediction model and the method to determine model parameters for the unsaturated BSG mixtures were proposed. The precision of the proposed prediction model was verified based on laboratory data. [ABSTRACT FROM AUTHOR]

Given that the prevalence of smartwatches has allowed them to become a hallmark in health monitoring, they are primed to provide accessible body composition estimations. The purpose of this study was to evaluate the precision and agreement of smartwatch-based bioimpedance analysis (SW-BIA) and multifrequency bioimpedance analysis (MFBIA) against a 4-compartment (4C) model criterion. A total of 186 participants (114 females) underwent body composition assessments necessary for a 4C model and SW-BIA and MFBIA. Values of total body water (TBW) from each device were compared with those obtained from bioimpedance spectroscopy. Precision was adequate though slightly lower for the smartwatch compared with other methods. No device demonstrated equivalence with the 4C model. Specifically, the SW-BIA overestimated and MFBIA underestimated body fat. MFBIA, but not SW-BIA, demonstrated equivalence for TBW. Overall error was higher for males using the smartwatch compared with females. While these findings do not invalidate the use of smartwatch-based estimates, clinicians should consider that there may be large errors relative to clinical measures. If this wearable device is intended to be used to monitor body composition change over time, these findings demonstrate the need for future research to evaluate its accuracy during follow-up testing. [ABSTRACT FROM AUTHOR]

Large-area growth estimates can be obtained by coupling growth model predictions with wall-to-wall remotely sensed auxiliary variables through a generalized hierarchical model-based (GHMB) inferential framework. So far, most GHMB variance estimators do not account for the residual errors of the submodels and their spatial correlations. This likely induces an underestimation of the true variance of the point estimator. In this study, we provide an example of large-area growth estimation obtained through the GHMB framework. To do this, we developed a new variance estimator that accounts for residual errors as well as potential spatial correlations among them. We tested this variance estimator through a simulation study and then used it to estimate the annual volume increment for a forest management unit in Quebec, Canada. Our results show that, contrary to our expectation, neglecting the residual errors of the different submodels leads to overestimating the true variance of the point estimator. We observed increases in the overestimation with small populations and spatially correlated residual errors. Our developed variance estimator corrected this overestimation and made it possible to derive reliable confidence intervals for annual volume increments at the population level. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Construction labour productivity (CLP) is affected by numerous variables made up of subjective and objective factors. Thus, CLP modelling and prediction are a complex task, leading to high computational cost and the risk of overfitting of data. This paper proposes a predictive model for CLP by integrating hybrid feature selection (HFS), as a combination of filter and wrapper methods, with principal component analysis (PCA). This developed HFS-PCA method reduces the dimensionality and complexity of CLP data and obtains better prediction performance by identifying the most predictive factors. Identified factors are utilized as inputs for various classification methods to predict CLP. Finally, prediction errors of the classification methods with and without using the proposed HFS-PCA method are compared, and the most accurate classification method is selected to develop the CLP predictive model. Experimental results show that using HFS-PCA for CLP prediction leads to better performances compared with past studies. [ABSTRACT FROM AUTHOR]

Freeway control has been increasingly used as an innovative approach to ease traffic congestion, improve traffic safety and reduce exhaust emissions. As an important predictive model involved in freeway control, the predictive performance of METANET greatly influences the effect of freeway control. This paper focuses on modifying the METANET model by modeling the critical density. First, the critical density model is deduced based on the catastrophe theory. Then, the perturbation wave and traveling wave that are obtained using the macro and micro data, respectively, have been developed to modify the above proposed critical density model. Finally, the numerical simulation is established to evaluate the effectiveness of the modified METANET model based on the field data from the realistic motorway network. The results show that overall, the predicted data from the modified METANET model are closer to the field data than those obtained from the original model. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Machine learning may improve use of observational data to understand the nutritional epidemiology of cardiovascular disease (CVD) through better modelling of non-linearity, non-additivity, and dietary complexity. Our objective was to develop machine learning prediction models for exploring how nutrients are related to CVD risk and to evaluate their predictive performance. We established a population-based cohort from the Canadian Community Health Survey and measured CVD incidence and mortality from 2004 to 2018 using administrative databases of national hospital discharges and deaths. Predictors included 61 nutrition variables and fourteen socioeconomic, demographic, psychological, and behavioural variables. Conditional inference forest models were interpreted and evaluated by permutation feature importance, accumulated local effects, and predictive discrimination and calibration. A total of 12 130 individuals were included in the study. Use of supplements, caffeine, and alcohol were the most important nutrition variables for prediction of CVD. Supplement use was associated with decreased risk, caffeine was associated with increasing risk, and alcohol had a u-shaped association with risk. The model had an out-of-sample c-statistic of 0.821 (95% confidence interval = 0.801–0.842). Exploratory findings included both known and novel associations and predictive performance was competitive, suggesting that further application of machine learning to nutritional epidemiology may help elucidate risks and improve predictive models. Novelty: Machine learning prediction models were developed for CVD using dietary data. Models were interpreted with interpretable machine learning techniques, revealing diverse associations between diet and CVD. Models achieved comparable or superior predictive performance to existing CVD risk prediction models. [ABSTRACT FROM AUTHOR]

Sustainability is central to forest management. To determine the sustainable annual harvest, practitioners rely on a simulation framework that combines inventory data, growth models, and optimization software. Because this standard simulation framework is based on model predictions aggregated into yield tables, it may not properly capture natural dynamics. In this paper, we designed an alternative simulation framework that does not require aggregated model predictions. However, the growth model must implement a harvest submodel and produce stochastic predictions. To showcase this alternative simulation framework, we used a forest management unit in southwestern Quebec, Canada, and compared our simulation results with those of the standard simulation framework. Our alternative simulation framework showed that the standing volume of most coniferous species would decrease, whereas that of maple species would increase over the 21st century. The annual harvest of one species as determined through the standard simulation framework was found to be unsustainable in the alternative simulation framework. Being much lighter in terms of computation, this alternative simulation framework can be used as a complement to the standard simulation framework, notably for checking if the optimization-based annual harvest is sustainable. [ABSTRACT FROM AUTHOR]

Yield prediction has been determined to be vital in sustainable forest management. Recently, research trends have shifted from stand-level to individual-level yield prediction. In this study, we examined the effectiveness of yield prediction models based on a distance-independent approach for Japanese cedar (Cryptomeria japonica (L.f.) D.Don) trees in western Japan. We further examined the accuracy of the models with reference to existing data collected over the long-term. First, we constructed distance-independent height, diameter growth, and survival models. Then, we simulated for approximately 50 years individual tree height, diameter at breast height (DBH), and volume growth using the test data. We then compared the predicted and observed values and calculated root-mean-square error (RMSE) and bias to evaluate the model accuracy. The models were noted to perform well when predicting mean height, DBH, and volume for Japanese cedar trees; in fact, they adequately predicted the diameter distribution. Our results suggest that distance-independent models could adequately predict long-term mean values and diameter distribution. However, RMSE and bias indicated that error propagation occurred over longer time spans. Thus, it is effective to conduct actual measurements at some point in the forest development phase and use the measurements as initial values for short- or medium-term predictions. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Owing to its rapidity and accuracy, near-infrared (NIR) spectroscopy is a powerful tool to establish appropriate prediction models with an innovative method to evaluate wood properties. To reveal the mechanical qualities of clonal Chinese fir woods and to determine sound prediction models of mechanical properties, four main mechanical properties of six Chinese fir clones (Yang 020, Yang 061, Kaihua 3, Kaihua 13, Daba 8, Kailin 24) were evaluated by NIR spectroscopy. The clones Kaihua 13, Kailin 24, and Yang 020 showed good mechanical properties. To estimate mechanical properties with NIR spectroscopy, different methods should be adopted for different properties. The average spectra of radial and tangential sections combined with multiple scattering correction (MSC) and Savitzky–Golay (S–G) smoothing methods were used to predict the modulus of rupture and modulus of elasticity. By adopting cross section spectra and taking MSC and S–G smoothing methods for pretreatment, the models of compressive strength parallel to grain could deliver the best results. For wood hardness, the models established with average spectra of three sections and first-derivative method were preferred. The correlation coefficients of the prediction models were between 0.84 and 0.90, and those of the calibration models were between 0.75 and 0.96. [ABSTRACT FROM AUTHOR]

This paper aims to develop asphalt binder fatigue models by time sweep tests under constant strain. By investigating the fatigue life in neat and modified asphalts under linear and nonlinear viscoelastic responses, results were analyzed in terms of strain levels, asphalt type, and additive content. The dissipated energy and dissipated energy ratio concepts were utilized to develop the phenomenological models for estimating fatigue lives of asphalt binders. Results showed that the dissipated energy ratio-based equations give relatively identical fatigue models irrespective of the strain level or asphalt binder type. Moreover, statistical analyses were performed to investigate the contribution of each parameter in the fatigue model prediction. A comparison was made between fatigue life predictions by means of traditional and energy-based methods. Results proved that the fatigue life prediction models based on the concept of dissipated energy ratio follow a similar trend. [ABSTRACT FROM AUTHOR]

The article discusses a 2021 study on tapering in training of middle-distance runners. It states that running faster than race pace late in a low-volume taper is recommended to improve 1500-meter track performance, and that a large reduction in volume during tapering and an increase in final interval session intensity improves running performance. Also noted is the sound adoption of a negative pacing strategy before tapering and a positive-pacing strategy after tapering.

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)