Current Models Research Articles

Autonomy in Eating Disorder Treatment and Recovery (Emerald Full Text).

Abstract Purpose: This study has two aims: the first is to develop a rich and nuanced understanding of autonomy informed by lived experience data from peer-reviewed studies in the Eating Disorder literature; the second is to examine how experiences of autonomy influence treatment and recovery. Background: Eating Disorders are of significant concern, affecting approximately 9% of the global population and have the highest mortality rate of any mental health disorder (Arcelus, Mitchell, Wales and Nielsen 2011). Even the most effective interventions fail to benefit a substantial number of people, with a projected trajectory of enduring disability in 20-30% of Eating Disorder cases internationally (Strand 2021: 19). On average, recovery from an Eating Disorder lasts from one to six years (Butterfly Foundation 2015: 16). Despite these concerning statistics, current understandings of autonomy are individualistic, with treatment practices exerting external control and influence, rather than supporting autonomy. Furthermore, the absence of trauma acknowledgment likely affects how people with lived experience of an Eating Disorder, and their treatment providers, negotiate treatment and recovery goals. Research Design: A rapid evidence assessment was conducted to answer the research question: How do we understand the autonomy of people with lived experience of an Eating Disorder, and what does this mean for their treatment and recovery?. A systematic search of five electronic databases, the International Journal of Eating Disorders, and topic-specific grey literature from 2000-2021 was undertaken. Twenty articles were deemed eligible for inclusion in the study. Interpretive phenomenological analysis was undertaken to inform the key findings and discussion. Findings and Discussion: The researcher identified four central themes: Self versus Eating Disorder, Self in Treatment, Self in Recovery, and Others in Relation to Self. These themes enabled rich re-conceptualisation of autonomy, identifying a recursive Lack of Autonomy Loop affecting many clients currently being treated for Eating Disorders, and under-researched aspects of autonomy including relational factors and the absence of trauma-informed approaches for (re) establishing autonomy. Conclusion: Research findings illuminate the shortcomings of current treatment models, provide options for alternate models, generate implications for social work practice and offer direction for future research and necessary policy change.

Theoretical and kinetic study of the hydrogen abstraction reactions of ethyl propionate

Ethyl propionate shows great promise as a biodiesel molecule model. However, the current oxidation kinetic model falls short in accurately predicting its combustion behavior. Therefore, this study employed ab initio quantum chemistry methods to analyze the hydrogen abstraction reactions of ethyl propionate with various reactive radicals, including OH, O, H, CH3, C2H3, C2H5, HO2, CH3O and CH3O2, which play a critical role in refining the oxidation kinetics. Theoretical calculations were conducted to determine the rate constants and branching ratios for these reactions at temperatures ranging from 500 K to 2500 K using transition state theory, quasi-rigid rotor harmonic oscillator model, and tunneling correction. After updating the kinetics of hydrogen abstraction reactions in the oxidation mechanism proposed by Metcalfe et al., the modified model was validated by comparing the simulated mole fraction evolutions of ethyl propionate and major products in a jet-stirred reactor, ignition delay in a closed homogeneous batch reactor, and laminar flame speed with experimental data. The findings indicate that the modified model reliably reproduces the experimental data and significantly improves the prediction accuracy for low-temperature combustion of ethyl propionate when compared to the original model.

Hierarchical medical image report adversarial generation with hybrid discriminator

Background and objectivesGenerating coherent reports from medical images is an important task for reducing doctors' workload. Unlike traditional image captioning tasks, the task of medical image report generation faces more challenges. Current models for generating reports from medical images often fail to characterize some abnormal findings, and some models generate reports with low quality. In this study, we propose a model to generate high-quality reports from medical images. MethodsIn this paper, we propose a model called Hybrid Discriminator Generative Adversarial Network (HDGAN), which combines Generative Adversarial Network (GAN) with Reinforcement Learning (RL). The HDGAN model consists of a generator, a one-sentence discriminator, and a one-word discriminator. Specifically, the RL reward signals are judged on the one-sentence discriminator and one-word discriminator separately. The one-sentence discriminator can better learn sentence-level structural information, while the one-word discriminator can learn word diversity information effectively. ResultsOur approach performs better on the IU-X-ray and COV-CTR datasets than the baseline models. For the ROUGE metric, our method outperforms the state-of-the-art model by 0.36 on the IU-X-ray, 0.06 on the MIMIC-CXR and 0.156 on the COV-CTR. ConclusionsThe compositional framework we proposed can generate more accurate medical image reports at different levels.

Entropy Model of Rosin Autonomous Boolean Network Digital True Random Number Generator

A True Random Number Generator (TRNG) is an important component in cryptographic algorithms and protocols. The Rosin Autonomous Boolean Network (ABN) digital TRNG has been widely studied due to its nice properties, such as low energy consumption, high speed, strong platform portability, and strong randomness. However, there is still a lack of suitable entropy models to deduce the requirement of design parameters to ensure true randomness. The current model to evaluate the entropy of oscillator-based TRNGs is not applicable for Rosin ABN TRNGs due to low-frequency noise. This work presents a new, suitable stochastic model to evaluate the entropy of Rosin ABN TRNGs. Theoretical analysis and simulation experiments verify the correctness and the effectiveness of the model, and, finally, the appropriate sampling parameters for Rosin ABN TRNGs are given for sufficient entropy per random bit to ensure true randomness.

Cost-Effectiveness of Parathyroid Cryopreservation and Autotransplantation

Delayed autotransplantation of cryopreserved parathyroid tissue (DACP) is the only surgical treatment for permanent postoperative hypoparathyroidism. Studies suggest that only a small minority of cryopreserved samples are ultimately autotransplanted with highly variable outcomes. For these reasons, many have questioned the economic utility of the process, although, to the authors' knowledge, this has never been formally studied. To report the clinical outcomes of parathyroid cryopreservation and DACP at a large academic institution and to determine the cost-effectiveness of this treatment. An institutional review board-approved, retrospective review of patients at a single institution who underwent DACP over a 17-year period was conducted with a median follow-up of 48.2 months. A forward-looking cost-utility analysis was then performed to determine the economic utility of cryopreservation/DACP vs usual care (monitoring and supplementation). Patients who had parathyroid tissue in cryopreserved storage between August 2005 to September 2022 at a single-center, academic, quaternary care center were identified. Parathyroid cryopreservation and DACP. Graft functionality, clinical outcomes, and cost utility using a willingness-to-pay threshold of $100 000 per quality-adjusted life-year (QALY). A total of 591 patients underwent cryopreservation. Of these, 10 patients (1.7%; mean [SD] age, 45.6 [17.9] years; 6 male [60%]) underwent DACP. A minority of autografts (2 [20%]) were subsequently fully functional, one-half (5 [50%]) were partially functional, and 3 (30%) were not functional. The cost-utility model estimated that at a large academic center over 10 years, the additional cost of 591 patients undergoing cryopreservation and 10 patients undergoing autotransplantation would be $618 791.64 (2022 dollars) and would add 8.75 QALYs, resulting in a cost per marginal QALY of $70 719.04, which is less than the common willingness-to-pay threshold of $100 000/QALY. The reimplantation rate of cryopreserved tissue was low (<2%), but when implanted, autografts were at least partially functional 70% of the time. In the first-ever, to the authors' knowledge, formal cost analysis for this treatment, results of the current model suggest that cryopreservation and autotransplantation were cost-effective compared with the usual care for hypoparathyroidism at a large, academic institution. It is recommended that each surgical center consider whether the economic and logistical commitments necessary for cryopreservation are worthwhile for their individual needs.

Application of coastal acoustic tomography: calibration of open boundary conditions on a numerical ocean model for tidal currents

Coastal acoustic tomography (CAT), which measures path-averaged currents from reciprocal acoustic transmission experiments and reconstructs velocity fields from the multiple path-averaged current data, is useful for monitoring tidal currents in coastal shallow water, especially if data assimilation is employed. Previous CAT data assimilation studies have focused on state estimation problems, i.e., the reconstruction of tidal currents and following dynamical discussion. In this study, we investigate the use of path-averaged currents in a boundary control problem. Specifically, we aim to use the observed path-averaged currents to determine the parameters of a numerical ocean model, which were tidal amplitudes and phases as the open boundary conditions in this study. We investigate two methods: using the ensemble Kalman filter (EnKF) results and a linearization approach called model Green’s function method. Both calibration methods decreased the amplitudes of tidal constituents at the open boundaries. We compare the model performance between the model predictions with and without the calibration of the open boundary conditions. The model predictions with the calibrated open boundary conditions improved the agreement with the observed path-averaged current. We also implemented the sequential updates of EnKF with the two calibrated open boundary conditions. The EnKF results with the independently calibrated two open boundary conditions improved the agreement with the comparison data obtained by acoustic Doppler current profiler measurement compared with the original EnKF result with the initial open boundary conditions.

Small data-driven semantic segmentation of wear debris in ferrography images

Abstract The segmentation of wear debris images is a prerequisite for ferrographic analysis, and uncertainties and errors in wear debris segmentation will inevitably affect the subsequent analysis. In this work, a small-data semantic segmentation model of wear debris images is constructed based on HRNetv2 for ferrography images acquired by using an online visual ferrography. A major advantage of the current model is that fewer ferrography images are required for training, and fewer iterations are needed. The current work is performed for ferrography images with both clean oil and contaminated oil, and good segmentation results can be found. Specially, the experimental results show that the current model can achieve accurate segmentation of wear debris images with mean intersection over union values of 91.47% and mean pixel accuracy values of 96.48%.

Understanding accommodative control in the clinic: Modeling latency and amplitude for uncorrected refractive error, presbyopia and cycloplegia.

Accommodation is the process of adjusting the eye's optical power so as to focus at different distances. Uncorrected refractive error and/or functional presbyopia mean that sharp focus may not be achievable for some distances, so observers experience sustained defocus. Here, we identify a problem with current models of accommodative control: They predict excessive internal responses to stimuli outside accommodative range, leading to unrealistic adaptation effects. Specifically, after prolonged exposure to stimuli outside range, current models predict long latencies in the accommodative response to stimuli within range, as well as unrealistic dynamics and amplitudes of accommodative vergence innervation driven by the accommodative neural controller. These behaviors are not observed empirically. To solve this issue, we propose that the input to blur-driven accommodation is not retinal defocus, but correctable defocus. Predictive models of accommodative control already estimate demand from sensed defocus, using a realistic "virtual plant" to estimate accommodation. Correctable defocus can be obtained by restricting this demand to values physically attainable by the eye. If we further postulate that correctable defocus is computed using an idealized virtual plant that retains a young accommodative range, we can explain why accommodative-convergence responses are observed for stimuli that are too near-but not too far-to focus on. We model cycloplegia as a change in gain, and postulate a form of neural myopia to explain the additional relaxation of accommodation often seen with cycloplegia. This model produces plausible predictions for the accommodative response and accommodative convergence signal in a wide range of clinically relevant situations.

Crucial role of obliquely propagating gravity waves in the quasi-biennial oscillation dynamics

Abstract. ​​​​​​​In climate modelling, the reality of simulated flows in the middle atmosphere is largely affected by the model's representation of gravity wave processes that are unresolved, while these processes are usually simplified to facilitate computations. The simplification commonly applied in existing climate models is to neglect wave propagation in horizontal direction and time. Here we use a model that fully represents the propagation of unresolved waves in all directions, thereby elucidating its dynamical effect upon the most important climate mode in the tropical stratosphere, i.e. the quasi-biennial oscillation. Our simulation shows that the waves in the equatorial stratosphere, which are known to drive this climate mode, can originate far away from the Equator in the troposphere. The waves propagating obliquely toward the Equator are found to play a huge role in the phase progression of the quasi-biennial oscillation as well as in its penetration into the lower stratosphere. Such waves will require further attention, given that current climate models are struggling to simulate the quasi-biennial oscillation down to the lower stratosphere, which may be needed to reproduce its observed impacts on the surface climate.

Editorial: Advances in Nuclear Fusion Energy and Cross-Cutting Technologies

The current energy model, which is based largely on the use of fossil fuels and has a direct influence on global warming and climate change, presents serious problems of unsustainability in the long term [...]

Triage of the Gaia DR3 astrometric orbits. II. A census of white dwarfs

ABSTRACT The third data release of Gaia was the first to include orbital solutions assuming non-single stars. Here, we apply the astrometric triage technique of Shahaf et al. to identify binary star systems with companions that are not single main-sequence stars. Gaia’s synthetic photometry of these binaries is used to distinguish between systems likely to have white-dwarf companions and those that may be hierarchical triples. The study uncovered a population of nearly $3\, 200$ binaries, characterized by orbital separations on the order of an astronomical unit, in which the faint astrometric companion is probably a white dwarf. This sample increases the number of orbitally solved binary systems of this type by about two orders of magnitude. Remarkably, over 110 of these systems exhibit significant ultraviolet excess flux, confirming this classification and, in some cases, indicating their relatively young cooling ages. We show that the sample is not currently represented in synthetic binary populations, and is not easily reproduced by available binary population synthesis codes. Therefore, it challenges current binary evolution models, offering a unique opportunity to gain insights into the processes governing white-dwarf formation, binary evolution, and mass transfer.

Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables.

Objective Contrast agents used for radiological examinations are an important cause of acute kidney injury (AKI). We developed and validated a machine learning and clinical scoring prediction model to stratify the risk of contrast-induced nephropathy, considering the limitations of current classical and machine learning models. Methods This retrospective study included 38,481 percutaneous coronary intervention cases from 23,703 patients in a tertiary hospital. We divided the cases into development and internal test sets (8:2). Using the development set, we trained a gradient boosting machine prediction model (complex model). We then developed a simple model using seven variables based on variable importance. We validated the performance of the models using an internal test set and tested them externally in two other hospitals. Results The complex model had the best area under the receiver operating characteristic (AUROC) curve at 0.885 [95% confidence interval (CI) 0.876-0.894] in the internal test set and 0.837 (95% CI 0.819-0.854) and 0.850 (95% CI 0.781-0.918) in two different external validation sets. The simple model showed an AUROC of 0.795 (95% CI 0.781-0.808) in the internal test set and 0.766 (95% CI 0.744-0.789) and 0.782 (95% CI 0.687-0.877) in the two different external validation sets. This was higher than the value in the well-known scoring system (Mehran criteria, AUROC=0.67). The seven precatheterization variables selected for the simple model were age, known chronic kidney disease, hematocrit, troponin I, blood urea nitrogen, base excess, and N-terminal pro-brain natriuretic peptide. The simple model is available at http://52.78.230.235:8081/Conclusions We developed an AKI prediction machine learning model with reliable performance. This can aid in bedside clinical decision making.

AI-enhanced forecasting of Indian primary energy demand: Fuzzy auto-regressive distributed lag models

As the demand for energy in India continues to surge, accurate forecasting becomes paramount for efficient resource allocation and sustainable development. This study proposes an innovative approach to forecasting Indian primary energy demand by integrating Artificial Intelligence (AI) techniques with Fuzzy Auto-regressive Distributed Lag (FADL) models. FADL models, incorporating fuzzy logic, allow for a nuanced representation of uncertainties and complexities within the energy demand dynamics. In this research, historical energy consumption data is analysed using FADL models with both symmetric and non-symmetric triangular coefficients, enhancing the model’s adaptability to the inherent uncertainties associated with energy forecasting. This study addresses the urgent need for enhanced energy planning models in the context of sustainable development. Our research aims to provide a comprehensive framework for predicting future Total Final Consumption (TFC) in alignment with the Indian National Energy Plan’s net-zero emissions target by 2035. Recognizing the limitations of current models, our research introduces a novel approach that integrates advanced algorithms and methodologies, offering a more flexible and realistic assessment of TFC trends. The primary objective of this study is to develop an improved energy planning model that surpasses existing projections by incorporating sophisticated algorithms. We aim to refine

Design of corporate human capital value measurement model based on binary tree

The measurement process of corporate human capital value ignores the in-depth analysis of the influencing factors of value measurement, which leads to the inaccuracy of the measurement result of corporate human capital value. Therefore, taking an enterprise in Heilongjiang Province as the research object, a measurement model of enterprise human capital value based on binary tree is designed. Establish the corporate human capital value measurement index system, and initially fix the human capital value. Calculate the floating value of human capital under the complete information, and determine the influencing factors of human capital value measurement. The human capital value of enterprise under incomplete information is calculated to complete the construction of the measurement model of human capital value of enterprise based on binary tree. Taking the actual human resource performance score of an enterprise from 2019 to 2022 as an example, the experiment proved that the design model and the traditional measurement model evaluated the enterprise human resource performance, and proved that the measurement model based on binary tree was more accurate than the traditional model, and the final measurement accuracy of the design model reached 93.5%. Compared with the traditional measurement model ahead of 18.5%–10.3%, compared with the current advanced measurement model ahead of 5.2%–5.7%. The evaluation Angle in the research is slightly lacking of practical significance, and further research can carry out further evaluation on income to maximize the practical significance of the research. However, the model is evaluated from the perspective of pricing, but in real life, the value of enterprise human capital is reflected in income distribution. Therefore, if the measurement of human capital value is combined with income distribution in the next step, the research results will be closer to the actual situation and will have more practical significance.

Development and validation of 3-dimensional simulators for penile prosthesis surgery.

The acquisition of skills in penile prosthesis surgery has many limitations mainly due to the absence of simulators and models for training. Three-dimensional (3D) printed models can be utilized for surgical simulations, as they provide an opportunity to practice before entering the operating room and provide better understanding of the surgical approach. This study aimed to evaluate and validate a 3D model of human male genitalia for penile prosthesis surgery. This study included 3 evaluation and validation stages. The first stage involved verification of the 3D prototype model for anatomic landmarks compared with a cadaveric pelvis. The second stage involved validation of the improved model for anatomic accuracy and teaching purposes with the Rochester evaluation score. The third stage comprised validation of the suitability of the 3D prototype model as a surgical simulator and for skill acquisition. The third stage was performed at 3 centers using a modified version of a pre-existing, validated questionnaire and correlated with the Rochester evaluation score. We sought to determine the suitability of 3D model for training in penile prosthesis surgery in comparison with the available cadaveric model. The evaluation revealed a high Pearson correlation coefficient (0.86) between questions of the Rochester evaluation score and modified validated questionnaire. The 3D model scored 4.33 ± 0.57 (on a Likert scale from 1 to 5) regarding replication of the relevant human anatomy for the penile prosthesis surgery procedure. The 3D model scored 4.33 ± 0.57 (on a Likert scale from 1 to 5) regarding its ability to improve technical skills, teach and practice the procedure, and assess a surgeon's ability. Furthermore, the experts stated that compared with the cadaver, the 3D model presented greater ethical suitability, reduced costs, and easier accessibility. A validated 3D model is a suitable alternative for penile prosthesis surgery training. This is the first validated 3D hydrogel model for penile prosthesis surgery teaching and training that experts consider suitable for skill acquisition. Because specific validated guidelines and questionnaires for the validation and verifications of 3D simulators for penile surgery are not available, a modified questionnaire was used. The current 3D model for penile prosthesis surgery shows promising results regarding anatomic properties and suitability to train surgeons to perform penile implant surgery. The possibility of having an ethical, easy-to-use model with lower costs and limited consequences for the environment is encouraging for further development of the models.

The Philippine National Public Key Infrastructure in Pamantasan ng Lungsod ng Muntinlupa Towards Digital Services Security Model for Public Universities

The integration of the Philippine National Public Key Infrastructure (PNPKI) into Pamantasan ng Lungsod ng Muntinlupa’s (PLMun) digital services framework is the main topic of the study. The research tackles the urgent need for a strong security model to protect sensitive data and strengthen public institutions’ overall security posture as digitalization becomes more widespread in academic settings. Its goals are to determine if integrating PNPKI into PLMun’s digital services is feasible, examine how PNPKI improves data security and integrity, and develop a complete security model for public universities based on PNPKI. The research study uses a methodical approach, looking closely at PNPKI features and how well they fit with PLMun’s digital infrastructure. Comparison studies of current security models for public universities based on PNPKI. The research study uses a methodical approach, looking closely at PNPKI features and how well they fit with PLMun’s digital infrastructure. We’ll compare the security approaches that are currently in use in educational institutions, and we might use surveys or interviews to get feedback from pertinent parties. The study expects to demonstrate the possible advantages of PNPKI implementation in PLMun, including increased data security, better authentication procedures, and improved digital resilience in general. Analyses of comparisons will shed light on the benefits of PNPKI over other security methods. In summary, it offers PLMun and other public universities a workable implementation roadmap for PNPKI. Policymakers, university administrators, and IT experts will find great value in the expected outcomes, which highlight the importance of a uniform security architecture. Keywords: Information Technology, Public Key Infrastructure, Digital Services, Security Model, Data Integrity

Decision making in complex land systems: outline of a holistic theory of agency

ContextModels of human agency within research on land systems and landscapes do not fully account for social and cultural factors in decision making. Conversely, within social theory, parallel concepts of agency do not fully take biophysical and spatial factors into account. This calls for a synthesis of conceptual models addressing human decision making in land systems.ObjectivesThe review identifies parallels between social and ecological perspectives on humans as co-constituent parts of complex land systems. On this basis selected models of agency combining insights from social theory and land systems research are outlined and compared, and improved concepts are outlined.MethodsElements of agency in modern agricultural land systems are reviewed. A case study illustrating the application of agency concepts in an analysis of decision making among farmers on the Canterbury Plains (New Zealand) is presented. On this basis it is discussed how to improve understandings of human agency in land systems.ResultsThe review identifies and compares parallel conceptions of agency, practice and holism in landscape ecology and social theory. Taking the agency of farmers in contemporary agricultural landscapes as an example, theories currently used to characterise and interpret the agency of farmers are discussed and improvements considered. Potentials for improvement of current conceptual models are indicated and discussed, and an improved model of agency is suggested.ConclusionsBased on the review, the article presents an improved conceptual model of agency in land systems emphasizing the position of agents in social-ecological contexts of action.

Smart logistics nodes: concept and classification

ABSTRACT This paper presents the Smart Logistics Node concept, which combines the physical infrastructure of logistics nodes with digital systems to enhance collaboration. The Smart Logistics Node benefits from data sharing, supporting infrastructure, and Connected and Automated Transport (CAT) technologies. Based on a literature review on logistics nodes and CAT, we propose a general classification of Smart Logistics Nodes distinguishing upon the node function, degree of organisational (de-)centralisation, digital integration, and infrastructure support for automated driving. Then, we classify sixteen logistics nodes and find that high digital integration is common while automation is lacking. Further automation entails mixed traffic on public roads and requires organisational changes that do not always align with current business models. Our work supports the adoption of emerging technology at logistics nodes and the comparability of business cases. Ultimately, node authorities can use our concept and classification to draw a roadmap to develop CAT capabilities.

Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty.

Estimating the risk of revision after arthroplasty could inform patient and surgeon decision-making. However, there is a lack of well-performing prediction models assisting in this task, which may be due to current conventional modeling approaches such as traditional survivorship estimators (such as Kaplan-Meier) or competing risk estimators. Recent advances in machine learning survival analysis might improve decision support tools in this setting. Therefore, this study aimed to assess the performance of machine learning compared with that of conventional modeling to predict revision after arthroplasty. Does machine learning perform better than traditional regression models for estimating the risk of revision for patients undergoing hip or knee arthroplasty? Eleven datasets from published studies from the Dutch Arthroplasty Register reporting on factors associated with revision or survival after partial or total knee and hip arthroplasty between 2018 and 2022 were included in our study. The 11 datasets were observational registry studies, with a sample size ranging from 3038 to 218,214 procedures. We developed a set of time-to-event models for each dataset, leading to 11 comparisons. A set of predictors (factors associated with revision surgery) was identified based on the variables that were selected in the included studies. We assessed the predictive performance of two state-of-the-art statistical time-to-event models for 1-, 2-, and 3-year follow-up: a Fine and Gray model (which models the cumulative incidence of revision) and a cause-specific Cox model (which models the hazard of revision). These were compared with a machine-learning approach (a random survival forest model, which is a decision tree-based machine-learning algorithm for time-to-event analysis). Performance was assessed according to discriminative ability (time-dependent area under the receiver operating curve), calibration (slope and intercept), and overall prediction error (scaled Brier score). Discrimination, known as the area under the receiver operating characteristic curve, measures the model's ability to distinguish patients who achieved the outcomes from those who did not and ranges from 0.5 to 1.0, with 1.0 indicating the highest discrimination score and 0.50 the lowest. Calibration plots the predicted versus the observed probabilities; a perfect plot has an intercept of 0 and a slope of 1. The Brier score calculates a composite of discrimination and calibration, with 0 indicating perfect prediction and 1 the poorest. A scaled version of the Brier score, 1 - (model Brier score/null model Brier score), can be interpreted as the amount of overall prediction error. Using machine learning survivorship analysis, we found no differences between the competing risks estimator and traditional regression models for patients undergoing arthroplasty in terms of discriminative ability (patients who received a revision compared with those who did not). We found no consistent differences between the validated performance (time-dependent area under the receiver operating characteristic curve) of different modeling approaches because these values ranged between -0.04 and 0.03 across the 11 datasets (the time-dependent area under the receiver operating characteristic curve of the models across 11 datasets ranged between 0.52 to 0.68). In addition, the calibration metrics and scaled Brier scores produced comparable estimates, showing no advantage of machine learning over traditional regression models. Machine learning did not outperform traditional regression models. Neither machine learning modeling nor traditional regression methods were sufficiently accurate in order to offer prognostic information when predicting revision arthroplasty. The benefit of these modeling approaches may be limited in this context.

Full-field solution from an oblique shock to estimate ground motion from blasting

The full-field solution (FFS) of the seismic radiation from a blasthole is refined with the use of a source function consistent with the shock physics on the detonation shock/rock interface. This source function accounts for the directionality of the flow in the detonation front, that is curved under the influence of the confining rock, inducing on it an oblique shock mostly on axial direction, contrary to current models that consider a radial source. The interaction of the explosive/confiner system is solved with the oblique shock local polar theory. From this analysis, pressure (p) versus deflection angle (Θ) polar curves are constructed and plotted in the p-Θ plane. The intersection of the explosive and confiner polar curves corresponds to the possible shock matching solution across the explosive/rock interface, which leads to a shock incident angle. A code has been written in MATLAB to perform the shock polar analysis. The resulting detonation front angle of the explosive with the confiner is considered to build a combined radial/axial source. For this, the displacement potentials for the axial component of the source are formulated and incorporated to the solution. Experimental data from quarry blasting are used to illustrate the application of the FFS model and the calibration of results in terms of attenuation coefficients. The effect of introducing the radial/axial combined source is also discussed.