Statistical Modeling Methodology Research Articles

Electrocoagulation process for phosphate recovery of agricultural wastewater: effect of calcium adding, voltage, and time.

Recovery of valuable resources, such as phosphate recovery from wastewater, can help close the nutrient cycle and is interesting to investigate. This study aims to evaluate phosphate recovery and set aside TOC, OC, and IC in agricultural wastewater using electrocoagulation with a helix electrode configuration. This study employed the Response Surface Methodology (RSM) for statistical analysis and modeling, utilizing a central composite design (CCD). Variation of calcium concentration (2-7mg/L), voltage (15-45V), and electrocoagulation time (5-15min) was applied in an electrocoagulation reactor with a helix-shaped stainless steel cathode and a solid cylindrical Mg anode. Based on RSM analysis, electrocoagulation with a helical electrode configuration significantly affects phosphate recovery and the removal of TOC, OC, and IC when treating agricultural wastewater. Under operating conditions of 15V, 15min time, and 2mg/L calcium concentration, we achieved the lowest phosphate concentration of 0.003mg/L (99.74% reduction). The highest TOC allowance is > 100% of the initial concentration, the TC allowance is 55.79%, and the IC allowance is 30.91%. The formation of metal hydroxides affects the efficiency of TOC removal in the electrocoagulation process, and higher electrolysis times lead to higher TOC removal efficiency. Higher voltages also improve the coagulation and flotation processes in the reactor. Calcium concentration plays a role in enhancing the flocculation process and binding phosphonates from wastewater.

Experimental investigation of ultrasonic assisted coating on three-point bending behavior of 3D printed polymeric bone plates for biomedical applications

3D printed Poly Lactic Acid (PLA) bone plates exhibit limited three-point bending strength, restricting their viability in biomedical applications. The application of polydopamine (PDM) enhances the three-point bending strength by undergoing covalent interactions with PLA molecular structure. However, the heavy nature of PDM particles leads to settling at the container base at higher coating solution concentrations. This study investigates the impact of ultrasonic-assisted coating parameters on the three-point bending strength. Utilizing Response Surface Methodology (RSM) for statistical modeling, the study examines the influence of ultrasonic vibration power (UP), coating solution concentration (CC), and submersion time (TIME). RSM optimization recommended 100 % UP, 6 mg/ml CC, and 150 min TIME, resulting in maximum three-point bending strength of 83.295 MPa. Microscopic images from the comparative analysis revealed non-uniform coating deposition with mean thickness of 6.153 µm under normal coating. In contrast, ultrasonic-assisted coating promoted uniform deposition with mean thickness of 18.05 µm. The results demonstrate that ultrasonic-assisted coating induces PDM particle collision, preventing settling at the container base, and enhances three-point bending strength by 7.27 % to 23.24 % compared to the normal coating condition. This study emphasizes on the potential of ultrasonic-assisted coating to overcome the limitations of direct immersion coating technique.

Prediction of Factors for Patients with Hypertension and Dyslipidemia Using Multilayer Feedforward Neural Networks and Ordered Logistic Regression Analysis: A Robust Hybrid Methodology

Background: Hypertension is characterized by abnormally high arterial blood pressure and is a public health problem with a high prevalence of 20%–30% worldwide. This research combined multiple logistic regression (MLR) and multilayer feedforward neural networks to construct and validate a model for evaluating the factors linked with hypertension in patients with dyslipidemia. Methods: A total of 1000 data entries from Hospital Universiti Sains Malaysia and advanced computational statistical modeling methodologies were used to evaluate seven traits associated with hypertension. R-Studio software was utilized. Each sample's statistics were calculated using a hybrid model that included bootstrapping. Results: Variable validation was performed by using the well-established bootstrap-integrated MLR technique. All variables affected the hazard ratio as follows: total cholesterol (β1: −0.00664; p < 0.25), diabetes status (β2: 0.62332; p < 0.25), diastolic reading (β3: 0.08160; p < 0.25), height measurement (β4: −0.05411; p < 0.25), coronary heart disease incidence (β5: 1.42544; p < 0.25), triglyceride reading (β6: 0.00616; p < 0.25), and waist reading (β7: −0.00158; p < 0.25). Conclusions: A hybrid approach was developed and extensively tested. The hybrid technique is superior to other standalone techniques and allows an improved understanding of the influence of variables on outcomes.

Design and Performance Study of Improved Fuzzy System with Genetic Algorithm

Technical trading relies heavily on analysis, most of which is statistical in nature. When the data to be modeled is nonlinear, imprecise, or complicated, fuzzy inference systems (FISs) are used in conjunction with computational, mathematical, and statistical modeling methodologies to simulate technical trading. Fuzzy logic may be modeled using linear, nonlinear, geometric, dynamic, and integer programming. These techniques, when combined with fuzzy logic, help the decision-maker arrive at a better solution while still facing some degree of ambiguity or uncertainty. The moving average method is a useful metric that may give trade recommendations to aid investors further. While trading signals inform investors of when to purchase and sell, a simple moving average provides no such information. In this research, we suggest a fuzzy moving average approach in which the intensity of trading signals, measured in terms of trading volume, is determined by using the fuzzy logic rule. In this research, we propose using fuzzy logic technical trading rules, which are more resistant to decision-making mistakes, to mitigate the trading uncertainty inherent in the conventional technical indicators method.

A predictive hypertension model for patients with dyslipidemia and type 2 diabetes mellitus: a robust hybrid methodology

Background:Hypertension is a public health problem used to describe high blood pressure where the blood vessels are persistently increased in force. According to WHO, hypertension has been reported in one in four men and one in five women. Worldwide, hypertension is a common health problem that affects 20-30% of the adult population and more than 5-8% of pregnancies, and it is frequently curable when detected and treated early enough. Objective: This paper aims to validate the factor associatedwith hypertension status among patients with dyslipidemia and type 2 diabetes mellitus. This could help to improve the prediction of the probability of hypertension among studied patients. Material and Methods: 39 patients were recruited from the Hospital Universiti Sains Malaysia (USM). In this retrospective study, advanced computational statistical modeling methodologies were used to evaluate data descriptions of several variables such as hypertension, marital status, smoking status, systolic blood pressure, fasting blood glucose, total cholesterol, high-density lipoprotein, alanine transferase, alkaline phosphatase, and urea reading. The R-Studio software and syntax were used to implement and test the hazard ratio. The statistics for each sample were calculated using a combination model that included bootstrap and multiple logistic regression methods. Results: The statistical strategy showed R demonstrates that regression modeling outperforms an R-squared. It revealed that the hybrid model technique better predicts the outcome when data is partitioned into a training and testing dataset. The variable validation was determined using the well-established bootstrap-integrated MLRtechnique. In this case, eight variables are considered: marital status, systolic blood pressure, fasting blood glucose, total cholesterol, high-density lipoprotein, alanine transferase, alkaline phosphatase, and urea reading. It’s important to note that six things affect the hazard ratio: Marital status (β1: 1.183519; p< 0.25), systolic blood pressure ( :-0.144516; p< 0.25), total cholesterol (β2: 0.9585890; p< 0.25), high-density lipoprotein ( :-5.927411; p< 0.25), alkaline phosphatase ( :-0.008973; p> 0.25), and urea reading ( :0.064169; p< 0.25).There is a 0.003469102 MSE for the linear model in this scenario. Conclusion: In this study, a hybrid approach combining bootstrapping and multiple logistic regression will be developed and extensively tested. The R syntax for this methodology was designed to ensure that the researcher completely understood the illustration. In this case, a hybrid model demonstrates how this critical conclusion enables us to understand better the utility and relative contribution of the hybrid method to the outcome. The statistical technique used in this study, R, demonstrates that regression modelingoutperforms R-squared values of 0.9014 and 0.00882 for the Predicted Mean Squared Error, respectively. Thus, the study’s conclusion establishes the superiority of the hybrid model technique used in the study. Bangladesh Journal of Medical Science Vol. 22 No. 02 April’23 Page : 422-431

A robust hybrid methodology between applied linear regression model (alrm) and multilayer perceptron (mlp)

Background: The goal of this study is to illustrate an optimum variable selection method using established Multiple Linear Regression (MLR) models and to validate the variable using Multilayer Perceptron Neural Network (MLP) models. Initially, all selected variables will be passed through the bootstrap methodology, and they were screened for significant relationships. Objective: The goal of this work is to analyze and construct a model for the factor linked with total crime cases by combining an Applied Linear Regression Model (ALRM) and a Multilayer Perceptron (MLP). Material and Methods: Around 200 data was simulated to build the methodology. Advanced computational statistical modeling methodologies were used to evaluate data descriptions of several variables in this retrospective study, including the total victim, gender, age, marital status, social class, adult in the household, children in household, burglary’s victim, sexual’s victim, victim’s report, and household location. The case study was developed and implemented using the R-Studio program and syntax. Results: The statistical method demonstrated that regression modeling surpasses R-squared and mean square error test in most situations. Researchers observed that when data is divided into two datasets for training and testing, the hybrid model approach performs significantly better at predicting the experiment’s outcome. When it came time to determine variable validity, the well-established bootstrap-integrated MLR approach was applied. Ten characteristics are taken into consideration in this case: Gender (: -0.4369700; p< 0.25), age (: -0.0086757; p< 0.25), marital status (: 0.2646097; p< 0.25), social class ( : 0.0602540; p< 0.25), adult in household (: -0.0211293; p> 0.25), children in household (: -0.0025346; p> 0.25), burglary’s victim (: 1.3473593; p< 0.25), sexual’s victim (: 1.0382444; p< 0.25), victim’s report (: -0.3176104; p< 0.25), and location of household (: -0.1355046; p< 0.25).There is a 0.07745823 MSE for the linear model in this scenario. Conclusion: The neural network’s Predicted Mean Square Error (PMSE) was used to assess MLP’s performance (MSE-forecasts the Network). PMSE is used to determine how far our projections are from the actual data, and the lowest MSE from the MLP indicates the best achievement. The R syntax for MLR and MLP is also included in this research article.As a result, the study’s conclusion establishes the superiority of the hybrid model technique. Bangladesh Journal of Medical Science Vol. 22 No. 01 January’23 Page : 38-46

Group LARS-Based Iterative Reweighted Least Squares Methodology for Efficient Statistical Modeling of Memory Designs

Regularized logistic regression is a popular classification tool that can be employed to accurately model the binary nature of the memory cell fail mechanisms for purposes of the yield analysis of memory designs. The iterative reweighted least squares (IRLS) method has been employed along with the least angle regression (LARS) to efficiently solve the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> regularized logistic regression problem. In this brief, we propose an efficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> regularized logistic regression methodology. At the core lies a Group LARS-based approach that benefits from Group LARS inherent ability to handle groups of variables and exploits the natural evolution of the solution to speed up the search for the critical features of the classifier. Thus, it tracks Newton’s step direction from one round of the solution to the next and employs weighted directions to efficiently solve for the underlying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> constrained iterative least squares problem. We apply the methodology in the context of an importance sampling-based yield analysis framework targeting rare fail probability estimation. We study the yield of 14-nm FinFET SRAM designs with programmable and resonant boosting. Our results demonstrate up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$14\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20\times $ </tex-math></inline-formula> speedup for the Group LARS compared to the pure LARS-based approach, and we report 98.7% accuracy and 0.12 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> average error compared to pure circuit-simulations approach for the resulting classifier.

Descriptive and inferential study of hardness, fatigue life, and crack propagation on PLA 3D-printed parts

This research work deals with the development of a methodology for the preliminary assessment of hardness, fatigue, and crack propagation performance of plastic parts manufactured employing polylactic acid (PLA), deposited layer by layer using Fused Filament Fabrication (FFF) technology. A commercial polylactic acid filament was used to print different sample geometries to assess shore D hardness and its correlation with raster direction angles of 0°, 0°/90°, and 0°/45°/− 45°, relative to a reference point, and with three different heat treatment temperatures namely 60 °C, 100 °C, and 140 °C. Additionally, fatigue life and crack propagation were assessed as a function of raster direction angle. Finally, a statistical modeling methodology was employed to obtain both linear equations and response surface plots to correlate performance with layer orientation and heat treatments. The results showed that samples produced at 0°/90° and 0°/45°/− 45° raster direction angles and heat-treated at 100 °C showed the highest hardness performance. Also, the samples produced at 0°/45°/− 45° raster direction angle showed the lowest crack propagation rate until a 200 µm length was achieved, after that the crack rate increased abruptly, reaching 1500 µm at around 4500 fatigue cycles; the 0°/90° raster direction angle reached a crack length of approximately 800 µm at the same number of cycles. The fatigue results showed that samples printed at 0°/45°/− 45° raster direction angles showed a fatigue life of 18,505 cycles, which is above the fatigue life found in samples produced employing the 0° and 0°/90° raster direction angles. The statistical analysis derived from the obtained results shows that the raster direction angle is key to defining the bulk properties of the 3D-printed PLA parts. It was also demonstrated that the use of statistical modeling is a design alternative in contrast to the theory of laminates for composites, which is worth studying and developing for the manufacturing of mechanical parts by 3D printing.

Isolating the Effect of Arch Architecture on Aortic Hemodynamics Late After Coarctation Repair: A Computational Study.

ObjectivesEffective management of aortic coarctation (CoA) affects long-term cardiovascular outcomes. Full appreciation of CoA hemodynamics is important. This study aimed to analyze the relationship between aortic shape and hemodynamic parameters by means of computational simulations, purposely isolating the morphological variable.MethodsComputational simulations were run in three aortic models. MRI-derived aortic geometries were generated using a statistical shape modeling methodology. Starting from n = 108 patients, the mean aortic configuration was derived in patients without CoA (n = 37, “no-CoA”), with surgically repaired CoA (n = 58, “r-CoA”) and with unrepaired CoA (n = 13, “CoA”). As such, the aortic models represented average configurations for each scenario. Key hemodynamic parameters (i.e., pressure drop, aortic velocity, vorticity, wall shear stress WSS, and length and number of strong flow separations in the descending aorta) were measured in the three models at three time points (peak systole, end systole, end diastole).ResultsComparing no-CoA and CoA revealed substantial differences in all hemodynamic parameters. However, simulations revealed significant increases in vorticity at the site of CoA repair, higher WSS in the descending aorta and a 12% increase in power loss, in r-CoA compared to no-CoA, despite no clinically significant narrowing (CoA index >0.8) in the r-CoA model.ConclusionsSmall alterations in aortic morphology impact on key hemodynamic indices. This may contribute to explaining phenomena such as persistent hypertension in the absence of any clinically significant narrowing. Whilst cardiovascular events in these patients may be related to hypertension, the role of arch geometry may be a contributory factor.

Hybrid statistical modelling of the frequency response function of industrial robots

• A hybrid model to predict the pose-dependent FRF of industrial robots was developed • Experimental Modal Analysis was conducted to calibrate a data-driven model • Operational Modal Analysis was performed from milling data to update the model • The hybrid model combining EMA and OMA was shown to improve calibration efficiency Models that predict the Frequency Response Function (FRF) of six degree-of-freedom (6-dof) industrial robots used for machining operations such as milling are usually built using Experimental Modal Analysis (EMA) of vibration data obtained from modal impact hammer tests performed at a finite number of points in the robot's workspace corresponding to specific arm configurations. While modal impact hammer tests are not constrained by the operating conditions of the robot, such as specific arm configurations allowed by part fixturing, they are limited by the number of workspace points that can be practically sampled and the associated robot downtime. Alternatively, the process of determining robot FRFs from on-line machining process data (e.g., forces and vibration) through Operational Modal Analysis (OMA) enables a denser sampling of the robot's workspace without requiring robot downtime. However, OMA may require several long tool paths and one or more complex part setups to enable sampling of a sufficiently large number of locations/arm configurations. This paper presents an efficient hybrid statistical modelling methodology that combines the two approaches, thus enabling possible optimization of sampling density and robot downtime, to efficiently determine the robot FRFs as a function arm configuration. The approach consists of first calibrating a Gaussian Process Regression (GPR) model with FRF data derived from EMA conducted at a small number of discrete locations in the robot's workspace. Then, FRFs calculated from OMA of milling forces and tool tip vibration data derived from robotic milling tests are used to update the initial GPR model using Bayesian inference and efficient hyperparameter updating. The proposed hybrid robot FRF modelling method is experimentally validated and shown to yield accurate predictions of the robot FRF while being computationally efficient.

A Human Impacts-Driven Framework for Quantifying Disaster-Dependent Social Vulnerability: A Case Study of Hurricane Maria in Puerto Rico

We propose a supervised learning approach to statistically quantify the magnitude of extreme events on vulnerable communities using publicly available panel data directly reflective of the different dimensions and manifestations of social suffering. The manifestations along these dimensions include suicides, substance abuse, excess mortality, unemployment, and others. Our modified treatment-effect model allows counterfactual baseline conditions to be posited from which a quantitative multi-faceted measure of social suffering can be determined. This capability is greatly beneficial to policymakers who have to allocate scarce resources to strengthen a geographic region consisting of several administrative units. Our work represents a distinct deviation from the established approach of assessing social vulnerabilities of communities subject to extraordinary events that rely on composite indices (such as SoVI) based on published census data. Recent academic research points out that such approaches are ad hoc, do not consider the dynamics of actual events, and lack formal validation of the variables and models. Further, there is an ongoing challenge for indices to be validated with dependent variables that proxy realizations of vulnerability. We describe the supervised treatment-effect approach in detail and illustrate its applicability using panel data encompassing the 2017 Hurricane Maria event across various municipalities in Puerto Rico. Our statistical modeling methodology stands apart since it explicitly and more realistically captures the social hardships of the actual event as manifested by different published panel data indicators. Our methodology is flexible enough to accommodate individual preferences of different stakeholders in how they assign importance to different manifestations of social hardship.

Extracellular neutral protease from Arthrospira platensis: Production, optimization and partial characterization

Proteases are industrially important catalysts. They belong to a complex family of enzymes that perform highly focused proteolysis functions. Given their potential use, there has been renewed interest in the discovery of proteases with novel properties and a constant thrust to optimize the enzyme production. In the present study, a novel extracellular neutral protease produced from Arthrospira platensis was detected and characterized. Its proteolytic activity was strongly activated by β-mercaptoethanol, 5,5-dithio-bis-(2-nitrobenzoic acid) and highly inhibited by Hg2+ and Zn2+ metal ions which support the fact that the studied protease belongs to the cysteine protease family. Using statistical modelling methodology, the logistic model has been selected to predict A. platensis growth-kinetic values. The optimal culture conditions for neutral protease production were found using Box-Behnken Design. The maximum experimental protease activities (159.79 U/mL) was achieved after 13 days of culture in an optimized Zarrouk medium containing 0.625 g/L NaCl, 0.625 g/L K2HPO4 and set on 9.5 initial pH.The extracellular protease of A. platensis can easily be used in the food industry for its important activity at neutral pH and its low production cost since it is a valuation of the residual culture medium after biomass recovery.

TCAD‐based statistical modeling methodology for nanoscale FinFET variability

AbstractWe demonstrated in this article a TCAD‐based (TCAD is defined as Technology Computer Aided Design) statistical modeling methodology for FinFET devices, which included TCAD Monte Carlo simulation, SPICE model library buildup, and model verification in circuit level. As a demonstrative example, a set of 14 nm FinFET devices are investigated for TCAD variability simulation and statistical model development. The stability and variability of different SRAM cells are further evaluated to verify the applicability of proposed statistical modeling methodology. The demonstrative statistical modeling method in this article is fully compatible with industrial standard methodology of CMOS device model, which can be easily immigrated into advanced technologies.

A Statistical Modeling Methodology for Long-Term Wind Generation and Power Ramp Simulations in New Generation Locations

In future power systems, a large share of the energy will be generated with wind power plants (WPPs) and other renewable energy sources. With the increasing wind power penetration, the variability of the net generation in the system increases. Consequently, it is imperative to be able to assess and model the behavior of the WPP generation in detail. This paper presents an improved methodology for the detailed statistical modeling of wind power generation from multiple new WPPs without measurement data. A vector autoregressive based methodology, which can be applied to long-term Monte Carlo simulations of existing and new WPPs, is proposed. The proposed model improves the performance of the existing methodology and can more accurately analyze the temporal correlation structure of aggregated wind generation at the system level. This enables the model to assess the impact of new WPPs on the wind power ramp rates in a power system. To evaluate the performance of the proposed methodology, it is verified against hourly wind speed measurements from six locations in Finland and the aggregated wind power generation from Finland in 2015. Furthermore, a case study analyzing the impact of the geographical distribution of WPPs on wind power ramps is included.

Predicting occupancy counts using physical and statistical Co2-based modeling methodologies

Energy consumption and indoor environment quality (IEQ) of buildings have been linked to human occupants. Predicting the number of occupants in a space is essential for the effective management of various building operation functions as well as improve energy efficiency. This study is the first to compare the performance of physical and statistical models in predicting occupant counts in a high volume lecture theatre (Occ = 200) using CO2 sensors. CO2 measurements and actual occupant numbers were obtained for 4 months to provide robust data comparison of the methodologies. It was found that that the dynamic physical models and Support Vector Machines (SVM) and Artificial Neural Networks (ANN) models utilizing a combination of average and first order differential CO2 concentrations performed the best in terms of predicting occupancy counts with the ANN and SVM models showing higher predictive performance. RMSE values for the corresponding models were 12.8, 12.6 and 12.1 respectively and correlation coefficients were all greater than 0.95. The relatively good agreement between dynamic physical model predictions and ground truth shows that the dynamic mass balanced model is adequate for predicting occupancy counts provided that the air exchange rates measured are accurate. Model average accuracies across all tolerance was between 70 and 76% demonstrating good performance for a large number of occupants. A discussion on the merits and limitations of each model types was presented to provide guidance on the adoption of various models.

Application of response surface methodology for statistical analysis, modeling, and optimization of malachite green removal from aqueous solutions by manganese-modified pumice adsorbent

Application of response surface methodology for statistical analysis, modeling, and optimization of malachite green removal from aqueous solutions by manganese-modified pumice adsorbent

유동가속부식에 대한 통계적 모델링 해석방법 개발: 유속, 온도, pH 및 Cr 함량의 효과

유동가속부식에 대한 통계적 모델링 해석방법 개발: 유속, 온도, pH 및 Cr 함량의 효과

WE‐A‐201‐00: Anne and Donald Herbert Distinguished Lectureship On Modern Statistical Modeling

Chris Marshall: Memorial IntroductionDonald Edmonds Herbert Jr., or Don to his colleagues and friends, exemplified the “big tent” vision of medical physics, specializing in Applied Statistics and Dynamical Systems theory. He saw, more clearly than most, that “Making models is the difference between doing science and just fooling around [ref Woodworth, 2004]”.Don developed an interest in chemistry at school by “reading a book” ‐ a recurring theme in his story. He was awarded a Westinghouse Science scholarship and attended the Carnegie Institute of Technology (later Carnegie Mellon University) where his interest turned to physics and led to a BS in Physics after transfer to Northwestern University. After (voluntary) service in the Navy he earned his MS in Physics from the University of Oklahoma, which led him to Johns Hopkins University in Baltimore to pursue a PhD. The early death of his wife led him to take a salaried position in the Physics Department of Colorado College in Colorado Springs so as to better care for their young daughter. There, a chance invitation from Dr. Juan del Regato to teach physics to residents at the Penrose Cancer Hospital introduced him to Medical Physics, and he decided to enter the field. He received his PhD from the University of London (UK) under Prof. Joseph Rotblat, where I first met him, and where he taught himself statistics. He returned to Penrose as a clinical medical physicist, also largely self‐taught. In 1975 he formalized an evolving interest in statistical analysis as Professor of Radiology and Head of the Division of Physics and Statistics at the College of Medicine of the University of South Alabama in Mobile, AL where he remained for the rest of his career. He also served as the first Director of their Bio‐Statistics and Epidemiology Core Unit working in part on a sickle‐cell disease. After retirement he remained active as Professor Emeritus.Don served for several years as a consultant to the Nuclear Regulatory Commission and may be remembered for his critique of the National Academy of Sciences BEIR III report (stating that their methodology “imposes a Delphic quality to the .. risk estimates”.) This led to his appointment as a member of the BEIR V committee. Don presented refresher courses at the AAPM, ASTRO and RSNA meetings and was active in the AAPM as a member or chair of several committees. He was the principal author of AAPM Report 43, which is essentially a critique of established clinical studies prior to 1992. He was co‐editor of the Proceedings of many symposia on Time, Dose and Fractionation held in Madison, Wisconsin. He received the AAPM lifetime Achievement award in 2004.Don's second wife of 46 years, Ann, predeceased him and he is survived by daughters Hillary and Emily, son John and two grandsons. Don was a true gentleman with a unique and erudite writing style illuminated by pithy quotations. If he had a fault it was, perhaps, that he did not realize how much smarter he was than the rest of us.This presentation draws heavily on a biography and video interview in the History and Heritage section of the AAPM website. The quote is his own.Andrzej Niemierko:Statistical modeling plays an essential role in modern medicine for quantitative evaluation of the effect of treatment. This session will feature an overview of statistical modeling techniques used for analyzing the many types of research data and an exploration of recent advances in new statistical modeling methodologies. Learning Objectives: To learn basics of statistical modeling methodology. To discuss statistical models that are frequently used in radiation oncology To discuss advanced modern statistical modeling methods and applications.

WE‐A‐201‐02: Modern Statistical Modeling

Chris Marshall: Memorial IntroductionDonald Edmonds Herbert Jr., or Don to his colleagues and friends, exemplified the “big tent” vision of medical physics, specializing in Applied Statistics and Dynamical Systems theory. He saw, more clearly than most, that “Making models is the difference between doing science and just fooling around [ref Woodworth, 2004]”.Don developed an interest in chemistry at school by “reading a book” ‐ a recurring theme in his story. He was awarded a Westinghouse Science scholarship and attended the Carnegie Institute of Technology (later Carnegie Mellon University) where his interest turned to physics and led to a BS in Physics after transfer to Northwestern University. After (voluntary) service in the Navy he earned his MS in Physics from the University of Oklahoma, which led him to Johns Hopkins University in Baltimore to pursue a PhD. The early death of his wife led him to take a salaried position in the Physics Department of Colorado College in Colorado Springs so as to better care for their young daughter. There, a chance invitation from Dr. Juan del Regato to teach physics to residents at the Penrose Cancer Hospital introduced him to Medical Physics, and he decided to enter the field. He received his PhD from the University of London (UK) under Prof. Joseph Rotblat, where I first met him, and where he taught himself statistics. He returned to Penrose as a clinical medical physicist, also largely self‐taught. In 1975 he formalized an evolving interest in statistical analysis as Professor of Radiology and Head of the Division of Physics and Statistics at the College of Medicine of the University of South Alabama in Mobile, AL where he remained for the rest of his career. He also served as the first Director of their Bio‐Statistics and Epidemiology Core Unit working in part on a sickle‐cell disease. After retirement he remained active as Professor Emeritus.Don served for several years as a consultant to the Nuclear Regulatory Commission and may be remembered for his critique of the National Academy of Sciences BEIR III report (stating that their methodology “imposes a Delphic quality to the .. risk estimates”.) This led to his appointment as a member of the BEIR V committee. Don presented refresher courses at the AAPM, ASTRO and RSNA meetings and was active in the AAPM as a member or chair of several committees. He was the principal author of AAPM Report 43, which is essentially a critique of established clinical studies prior to 1992. He was co‐editor of the Proceedings of many symposia on Time, Dose and Fractionation held in Madison, Wisconsin. He received the AAPM lifetime Achievement award in 2004.Don's second wife of 46 years, Ann, predeceased him and he is survived by daughters Hillary and Emily, son John and two grandsons. Don was a true gentleman with a unique and erudite writing style illuminated by pithy quotations. If he had a fault it was, perhaps, that he did not realize how much smarter he was than the rest of us.This presentation draws heavily on a biography and video interview in the History and Heritage section of the AAPM website. The quote is his own.Andrzej Niemierko:Statistical modeling plays an essential role in modern medicine for quantitative evaluation of the effect of treatment. This session will feature an overview of statistical modeling techniques used for analyzing the many types of research data and an exploration of recent advances in new statistical modeling methodologies. Learning Objectives: To learn basics of statistical modeling methodology. To discuss statistical models that are frequently used in radiation oncology To discuss advanced modern statistical modeling methods and applications.

WE‐A‐201‐01: Memorial Introduction

Chris Marshall: Memorial IntroductionDonald Edmonds Herbert Jr., or Don to his colleagues and friends, exemplified the “big tent” vision of medical physics, specializing in Applied Statistics and Dynamical Systems theory. He saw, more clearly than most, that “Making models is the difference between doing science and just fooling around [ref Woodworth, 2004]”.Don developed an interest in chemistry at school by “reading a book” ‐ a recurring theme in his story. He was awarded a Westinghouse Science scholarship and attended the Carnegie Institute of Technology (later Carnegie Mellon University) where his interest turned to physics and led to a BS in Physics after transfer to Northwestern University. After (voluntary) service in the Navy he earned his MS in Physics from the University of Oklahoma, which led him to Johns Hopkins University in Baltimore to pursue a PhD. The early death of his wife led him to take a salaried position in the Physics Department of Colorado College in Colorado Springs so as to better care for their young daughter. There, a chance invitation from Dr. Juan del Regato to teach physics to residents at the Penrose Cancer Hospital introduced him to Medical Physics, and he decided to enter the field. He received his PhD from the University of London (UK) under Prof. Joseph Rotblat, where I first met him, and where he taught himself statistics. He returned to Penrose as a clinical medical physicist, also largely self‐taught. In 1975 he formalized an evolving interest in statistical analysis as Professor of Radiology and Head of the Division of Physics and Statistics at the College of Medicine of the University of South Alabama in Mobile, AL where he remained for the rest of his career. He also served as the first Director of their Bio‐Statistics and Epidemiology Core Unit working in part on a sickle‐cell disease. After retirement he remained active as Professor Emeritus.Don served for several years as a consultant to the Nuclear Regulatory Commission and may be remembered for his critique of the National Academy of Sciences BEIR III report (stating that their methodology “imposes a Delphic quality to the .. risk estimates”.) This led to his appointment as a member of the BEIR V committee. Don presented refresher courses at the AAPM, ASTRO and RSNA meetings and was active in the AAPM as a member or chair of several committees. He was the principal author of AAPM Report 43, which is essentially a critique of established clinical studies prior to 1992. He was co‐editor of the Proceedings of many symposia on Time, Dose and Fractionation held in Madison, Wisconsin. He received the AAPM lifetime Achievement award in 2004.Don's second wife of 46 years, Ann, predeceased him and he is survived by daughters Hillary and Emily, son John and two grandsons. Don was a true gentleman with a unique and erudite writing style illuminated by pithy quotations. If he had a fault it was, perhaps, that he did not realize how much smarter he was than the rest of us.This presentation draws heavily on a biography and video interview in the History and Heritage section of the AAPM website. The quote is his own.Andrzej Niemierko:Statistical modeling plays an essential role in modern medicine for quantitative evaluation of the effect of treatment. This session will feature an overview of statistical modeling techniques used for analyzing the many types of research data and an exploration of recent advances in new statistical modeling methodologies. Learning Objectives: To learn basics of statistical modeling methodology. To discuss statistical models that are frequently used in radiation oncology To discuss advanced modern statistical modeling methods and applications.