Spreadsheet Implementation Research Articles

Contact restriction time after common nuclear medicine therapies: spreadsheet implementation based on conservative retention function and individual measurements

The increasing use of new radiopharmaceuticals invites us to reconsider some radiation protection issues, such as the contact restriction time that limits public exposure by nuclear medicine patients. Contact restriction time should be patient specific and conservative, and its assessment made easy for clinicians. Here a method is proposed based on conservative estimation of the whole-body retention function and at least one measurement of the patient’s dose rate. Recommended values of the retention function are given for eight therapies: 131I (Graves’ disease, remnant ablation, patient follow-up, meta-iodobenzylguanidine), 177Lu-prostate-specific membrane antigen and 177Lu-DOTATATE therapies, and 90Y and 166Ho microsphere injection of the liver. The patient line source model for scaling dose rate from one distance to another is included in the restriction time calculation. The method is benchmarked against published values and the influence of the dose rate scaling and whole-body retention function illustrated. A spreadsheet is provided, along with the source code, with recommended values for the eight therapies. The recommended values can be changed as well as the dose rate scaling function, and other radiopharmaceuticals can be included in the spreadsheet provided retention functions are defined.

In-vitro evaluation of the effectiveness of polyphenols based strawberry extracts for dental bleaching

To formulate a dental bleaching agent with strawberry extract that has potent bleaching properties and antimicrobial efficacy. Enamel specimens (3 × 3 × 2 mm3) were prepared. Quaternary Ammonium Silane (CaC2 enriched) was homogenized with fresh strawberries: Group 1: supernatant strawberry (10 g) extract < Group 2: supernatant strawberry (10 g) extract + 15%HA (Hydroxyapatite) < Group 3: supernatant strawberry (10 g) extract + 15% (HA-2%k21) < Group 4: supernatant strawberry (20 g) extract only (20 g strawberries) < Group 5: supernatant strawberry (20 g) extract + 15% HA < Group 6: supernatant strawberry (20 g) extract + 15% (HA-2%K21) < Group 7: In-office Opalescence Boost 35%. Single-colony lactobacillus was examined using confocal microscopy identifying bacterial growth and inhibition in presence of bleaching agents using 300 µL aliquot of each bacterial culture. Images were analysed by illuminating with a 488 nm argon/helium laser beam. Colour difference (∆E00) was calculated using an Excel spreadsheet implementation of the CIEDE2000 colour difference formula and colour change measured between after staining and after bleaching. Scanning electron microscope was used to image specimens. Raman spectra were collected, and enamel slices were used for STEM/TEM analysis. HPLC was used for strawberry extract analysis. Nano-indentation was performed and X-ray photoelectron spectroscopy. Antioxidant activity was determined along with molecular simulation. hDPSCs were expanded for Alamar Blue Analysis and SEM. Mean colour change was significantly reduced in group 1 compared to other groups (p < 0.05). CLSM showed detrimental effects of different strawberry extracts on bioflms, especially with antimicrobial (p < 0.05). Groups 1, 2 and 3 showed flatter/irregular surfaces with condensation of anti-microbial in group 3. In strawberry specimens, bands predominate at 960 cm−1. HPLC determined the strawberry extracts content. Molecular simulation verified interaction between calcium and polyphenol components. XPS peak-fitted high-resolution corresponding results of Ca2p3/2 and Ca2p1/2 for all k21 groups. Combination of 10 g strawberry extract supernatant and 15% (hydroxyapatite 2%k21) improved the whiteness and provided additional antimicrobial potential. The novel strawberry extract and antimicrobial based dental formulation had immediate bleaching effect without promoting significant changes in enamel morphology.

Model for calculating ammonia emission from stored animal liquid manure

Ammonia (NH3) emissions from agriculture have increased by 90% from 1970 to 2005, and agriculture is now the largest source of NH3 to the atmosphere. Calculated national NH3 emissions from agriculture using static emission factors do not reflect regional conditions. We propose, parameterize and test a simple model to calculate emission rates which incorporates effects of temperature, pH, total ammoniacal nitrogen (TAN) concentration, exposed storage area, and storage cover. This is the first time that several known algorithms were combined in this semi-dynamic user-friendly model concept and that model parameters (uploaded on the internet) were estimated from a unique database comprising 44 studies. The calculator is designed to be used correctly even if there exists only little knowledge about manure chemistry or micrometeorology, and can calculate emissions with a low demand for input data Calculations using the new model are as accurate as the standard method. The proposed approach has two advantages compared to the standard alternative: it does not require an estimate of TAN flow through the store, and calculated values reflect management (e.g., storage area or covers), TAN concentration, pH and temperature based on well-established principles. The simple and process-related approach has the potential to deliver more accurate estimates after a more precise parameterization from dedicated studies where the focus is on emission measurements, slurry composition characterization, air and slurry temperature and turbulence. To facilitate this approach, data need to be collected over relatively short time intervals (less than twice per day) to ensure that they cover cardinal diurnal conditions at the same time and right place. A spreadsheet implementation of the model is publicly available from https://github.com/sashahafner/AMOSTO.

Quantification of hard X‐ray photoelectron spectroscopy: Calculating relative sensitivity factors for 1.5‐ to 10‐keV photons in any instrument geometry

A method for the rapid determination of theoretical relative sensitivity factors (RSFs) for hard X‐ray photoelectron spectroscopy (HAXPES) instruments of any type and photon energy has been developed. We develop empirical functions to describe discrete theoretically calculated values for photoemission cross sections and asymmetry parameters across the photon energy range from 1.5 to 10 keV for all elements from lithium to californium. The formulae describing these parameters, in conjunction with similar practical estimates for inelastic mean free paths, allow the calculation of a full set of theoretical sensitivity factors for a given X‐ray photon energy, X‐ray polarisation and instrument geometry. We show that the anticipated errors on these RSFs are less than the typical errors generated by extracting X‐ray photoelectron spectroscopy (XPS) intensities from the spectra and thus enable adequate quantification for any XPS/HAXPES experiment up to 10 keV. A spreadsheet implementation of this method is provided in the supporting information, along with example RSFs for existing commercial instruments.

On the semantics for spreadsheets with sheet-defined functions

Abstract We give an operational semantics for the evaluation of spreadsheets, including sheet-defined and built-in numeric functions in the Funcalc spreadsheet platform. The semantics allows for different implementations and we discuss sheet-defined functions implemented using both interpretation and run-time code generation. The semantics specifies the expected result of a computation, also considering non-deterministic functions, independently of an evaluation mechanism. It can be extended to include the cost of formula evaluation for a cost analysis e.g. for use in parallelization of computations. An interesting future direction is to investigate experimentally how close our semantics is to that of major spreadsheet implementations.

Simplified algorithm for reliability sensitivity analysis of structures: A spreadsheet implementation.

An important segment of the reliability-based optimization problems is to get access to the sensitivity derivatives. However, since the failure probability is not a closed-form function of the input variables, the derivatives are not explicitly computable and rather require a full reliability analysis which is computationally expensive. In this paper, a step-by-step algorithm has been presented to calculate the derivatives of the probability of failure and safety index with respect to the input parameters based on the advanced first-order second-moment (AFOSM) reliability method. The proposed algorithm is then implemented in a spreadsheet using Visual Basic for Application (VBA) programming language. Two geotechnical and structural examples are then presented to examine the program and describe the modeling procedure. The robustness of the proposed method is examined using a Gaussian random perturbation. The capability of the proposed method in the calculation of the sensitivity derivatives of the model uncertainty is explained in a separate section. Finally, the proposed model has been compared to the forward finite difference (FFD) method and the results are validated.

Rank-permutation tests for behavior analysis, and a test for trend allowing unequal data numbers for each subject.

We advocate for rank-permutation tests as the best choice for null-hypothesis significance testing of behavioral data, because these tests require neither distributional assumptions about the populations from which our data were drawn nor the measurement assumption that our data are measured on an interval scale. We provide an algorithm that enables exact-probability versions of such tests without recourse to either large-sample approximation or resampling approaches. We particularly consider a rank-permutation test for monotonic trend, and provide an extension of this test that allows unequal number of data points, or observations, for each subject. We provide an extended table of critical values of the test statistic for this test, and both a spreadsheet implementation and an Oracle® Java Web Start application to generate other critical values at https://sites.google.com/a/eastbayspecialists.co.nz/rank-permutation/.

Interactive Excel-Based Procedure for Line Balancing

The assignment of tasks to workstations that results in the assembly of an item or provision of a service is a common topic of discussion in operations management (OM) courses. In this paper, we present an interactive Excel spreadsheet assisted procedure for assigning the required tasks. The procedure actively engages the student in the process and serves as a tool for learning how to effectively and efficiently assign the tasks to a series (line) of connected workstations. The Excel implementation provides more flexibility in selecting tasks than textbook or other Excel based methods that rely on certain priority rules, and is considerably easier to use than math programming models. The procedure and its spreadsheet implementation are illustrated and discussed, with an example.

Diagnosing risk factors alongside mass drug administration using serial diagnostic tests-which test first?

BackgroundWhen tests are used in series to determine individual risk factors and infection status in a mass drug administration (MDA), the diagnostics, test order and subsequent treatment decisions (the testing algorithm) affect population-level treatment coverage and cost, but there is no existing framework for evaluating which algorithm optimizes any given outcome.MethodsWe present a mathematical tool (with spreadsheet implementation) to analyse the effect of test ordering, illustrated using treatment for onchocerciasis in an area where high-burden Loa loa co-infections present a known risk factor.ResultsThe prevalence of the infection and risk factor have a non-linear impact on the optimal ordering of tests. Testing for the MDA infection first always leaves more infected people untreated but fewer people with the risk factor being misclassified. The cost of the treatment given to infected individuals with the risk factor does not affect which algorithm is more cost effective.ConclusionsFor a given test and treat algorithm and its costs, the correct strategy depends on the expected prevalence. In most cases, when the apparent prevalence of the target infection is greater than the apparent prevalence of the risk factor, it is cheaper to do the risk factor test first, and vice versa.

Retention Modeling at Scholastic Travel Company (A)

This case, along with its B case (UVA-QA-0865), is an effective vehicle for introducing students to the use of machine-learning techniques for classification. The specific context is predicting customer retention based on a wide range of customer attributes/features. The specific techniques could include (but are not limited to): regressions (linear and logistic), variable selection (forward/backward and stepwise), regularizations (e.g., LASSO), classification and regression trees (CART), random forests, graduate boosted trees (xgboost), neural networks, and support vector machines (SVM).The case is suitable for an advanced data analysis (data science, machine learning, and artificial intelligence) class at all levels: upper-level business undergraduate, MBA, EMBA, as well as specialized graduate or undergraduate programs in analytics (e.g., masters of science in business analytics [MSBA] and masters of management analytics [MMA]) and/or in management (e.g., masters of science in management [MScM] and masters in management [MiM, MM]).The teaching note for the case contains the pedagogy and the analyses, alongside the detailed explanations of the various techniques and their implementations in R (code provided in Exhibits and supplementary files). Python code, as well as the spreadsheet implementation in XLMiner, are available upon request.

The risk assessment of potentially hazardous carbon nanomaterials for small scale operations

Carbon nanomaterial applications are expected for consumer use within next decade. Risk management methods are reviewed by National Institute for Occupational Safety and Health, Safe Work Australia, Health and Safety Executive, and additionally Dupont's and Environmental Defense Fund's Nano Risk Framework. A new risk management method for handling carbon nanoparticles is presented, directed especially to universities and to research institutes. Small scale operations are different than those conducted in enterprises. Typically work started from the scratch, and researchers with limited experience of working with hazardous materials are distinctions to enterprise work flows. The promoter in this study has also been the life-cycle perspective for handling hazardous materials already in early stages of the research. Two risk evaluation cases are introduced in the use of multiwalled carbon nanotubes. The method behind inference logics in risk evaluation is demonstrated, and it was successfully implemented in the real carbon nanotube research project. The straight-forward spreadsheet implementation is an additional advantage due to short set-up time, due to easy system maintenance and due to the easy use of different evaluation parameter weights.

Retention Modeling at Scholastic Travel Company (a)

This case, along with its B case (UVA-QA-0865), is an effective vehicle for introducing students to the use of machine-learning techniques for classification. The specific context is predicting customer retention based on a wide range of customer attributes/features. The specific techniques could include (but are not limited to): regressions (linear and logistic), variable selection (forward/backward and stepwise), regularizations (e.g., LASSO), classification and regression trees (CART), random forests, graduate boosted trees (xgboost), neural networks, and support vector machines (SVM). The case is suitable for an advanced data analysis (data science, machine learning, and artificial intelligence) class at all levels: upper-level business undergraduate, MBA, EMBA, as well as specialized graduate or undergraduate programs in analytics (e.g., masters of science in business analytics [MSBA] and masters of management analytics [MMA]) and/or in management (e.g., masters of science in management [MScM] and masters in management [MiM, MM]).The teaching note for the case contains the pedagogy and the analyses, alongside the detailed explanations of the various techniques and their implementations in R (code provided in Exhibits and supplementary files). Python code, as well as the spreadsheet implementation in XLMiner, are available upon request. Excerpt UVA-QA-0864 Rev. Aug. 23, 2018 Retention Modeling at Scholastic Travel Company (A) On a sunny Monday afternoon in early spring 2013, David Powell entered his new office and took a deep breath. He pondered his first few days as the new data analyst for Scholastic Travel Company (STC), an educational tourism firm. Powell had filled his first week of employment meeting the firm's departmental leadership and attending a company-wide new-employee-orientation program, and he was eager to get started on his first project. Just a few hours earlier, at the weekly marketing strategy meeting, Powell's new supervisor, Stephen Blackford, stressed the urgency of a new data initiative centered on customer retention. As Blackford outlined, in less than two weeks, contract renewal opportunities would begin for customers who had gone on an STC trip in 2012. During the meeting, he presented a dataset with all of the known information about the previous year's client base (see Exhibits 1 and 2). From his past experience, Blackford was confident that models could be constructed to predict whether or not a customer would book again in 2013. With such a model, he hoped to design a more nuanced marketing strategy that would target certain subsets of the client population to save cost and improve yield. With multiple plausible methodologies in mind, Powell knew he needed to get to work immediately so he could give Blackford an accurate prediction model before the end of the week. Company Background . . .

Vigilância sanitária: aliada na construção de um espaço de trabalho seguro

Introdução: o Projeto Vigilância Sanitária (Projeto VISA) iniciou-se em maio/2006 após vistorias realizadas pela VISA-Região Norte nas áreas do HC, apontando não conformidades. Desde aquele momento até os dias atuais a equipe tem atuado para corrigir distorções nos processos relacionados à assistência, higiene e estrutura predial reconhecendo-se o papel da VISA de oferecer um olhar externo para as condições de assistência à saúde e contribuir com apontamentos de melhorias(1). Objetivo geral: manter o HD dentro de princípios sanitários definidos pela VISA e pela Comissão de Controle de Infecção Hospitalar (CCIH)/HC. Método: o trabalho foi realizado no HD/HC/UNICAMP que é uma unidade de desospitalização dos pacientes. Os executores foram: enfermeiras, técnicos de enfermagem e administrativo e a assessora do Departamento de Enfermagem/HC (DENF). Foram realizadas 13 vistorias entre 2011-2016, desde a implantação da planilha VISA. Os eventos de não conformidade foram categorizados: "Enfermagem/Processos", "Estrutura" e "Limpeza". Resultados: foram encaminhadas ordens de serviço à Divisão de Engenharia/HC para correções na estrutura e treinamento para as equipes de limpeza e enfermagem. As medianas dos percentuais de aprovação foram: 88,2% para "Enfermagem/Processo", 91,6% para "Estrutura" e 79,7% para "Limpeza". Discussão: a literatura propõe que se conceitue como "Ótimo" para aprovação entre 90-100% dos itens; "Bom" entre 70-89%; "Regular" entre 50-69% e "Ruim" menor que 49%(2). Usou-se a ferramenta gerencial "PDSA"(3). Conclusões: os dados mostram que o processo de vistoria tem garantido uma avaliação permanente da área e dos processos, mantendo-os com conceito "Bom" e "Ótimo". O respeito entre os serviços e os profissionais foi fundamental, e o DENF foi um facilitador para a elaboração dos indicadores. A Divisão de Serviços Gerais/HC (responsável pelo contrato de limpeza terceirizada) e a Divisão de Engenharia/HC tem sido parceiros na execução correta da limpeza e na preservação do patrimônio público.

An alternative method to estimate parameters in modelling the behaviour of commodity prices

Measuring the risk of investment projects involving commodities and modelling its price dynamics behaviour is usually implemented with Kalman filtering techniques. However, because the use of these techniques has high implementation requirements, recent literature has employed approximate models. This paper proposes a new and simpler spreadsheet implementation procedure which presents lower implementation requirements than the widely used Kalman filtering estimation procedure. The proposal needs to estimate fewer parameters than usual and does not directly estimate sequences but considers the relationship between the states implicitly when defining the regression matrices. This translates into a significant reduction in processing time. We apply the proposal to estimate the parameters of a 4-factor model for four commercial commodities: crude oil, heating oil, unleaded gasoline and natural gas; we then compare the accuracy with results using the Kalman filter method. Results indicate that error measurements are approximately equal for the actual model and the approximation proposed in this paper, for both the in- and out-of-sample data-sets.

Screening tool to evaluate the vulnerability of down-gradient receptors to groundwater contaminants from uncapped landfills

A screening tool for quantifying levels of concern for contaminants detected in monitoring wells on or near landfills to down-gradient receptors (streams, wetlands and residential lots) was developed and evaluated. The tool uses Quick Domenico Multi-scenario (QDM), a spreadsheet implementation of Domenico-based solute transport, to estimate concentrations of contaminants reaching receptors under steady-state conditions from a constant-strength source. Unlike most other available Domenico-based model applications, QDM calculates the time for down-gradient contaminant concentrations to approach steady state and appropriate dispersivity values, and allows for up to fifty simulations on a single spreadsheet. Sensitivity of QDM solutions to critical model parameters was quantified. The screening tool uses QDM results to categorize landfills as having high, moderate and low levels of concern, based on contaminant concentrations reaching receptors relative to regulatory concentrations.The application of this tool was demonstrated by assessing levels of concern (as defined by the New Jersey Pinelands Commission) for thirty closed, uncapped landfills in the New Jersey Pinelands National Reserve, using historic water-quality data from monitoring wells on and near landfills and hydraulic parameters from regional flow models. Twelve of these landfills are categorized as having high levels of concern, indicating a need for further assessment. This tool is not a replacement for conventional numerically-based transport model or other available Domenico-based applications, but is suitable for quickly assessing the level of concern posed by a landfill or other contaminant point source before expensive and lengthy monitoring or remediation measures are taken. In addition to quantifying the level of concern using historic groundwater-monitoring data, the tool allows for archiving model scenarios and adding refinements as new data become available.

Optimal design of reinforced concrete beams: A review

This paper summarizes available literature on the optimization of reinforced concrete (RC) beams. The objective of optimization (e.g. minimum cost or weight), the design variables and the constraints considered by different studies vary widely and therefore, different optimization methods have been employed to provide the optimal design of RC beams, whether as isolated structural components or as part of a structural frame. The review of literature suggests that nonlinear deterministic approaches can be efficiently employed to provide optimal design of RC beams, given the small number of variables. This paper also presents spreadsheet implementation of cost optimization of RC beams in the familiar MS Excel environment to illustrate the efficiency of the exhaustive enumeration method for such small discrete search spaces and to promote its use by engineers and researchers. Furthermore, a sensitivity analysis is performed on the contribution of various design parameters to the variability of the overall cost of RC beams.

An analytical model for calculating internal dose conversion coefficients for non-human biota

To assess the radiation burden of non-human living organisms, dose coefficients are available in the literature, precalculated by assuming an ellipsoidal shape of each organism. A previously developed analytical method was applied for the determination of absorbed fractions inside ellipsoidal volumes from alpha, beta, and gamma radiations to the calculation of dose conversion coefficients (DCCs) for 15 reference organisms, animals and plants, either terrestrial, amphibian, or aquatic, and six radionuclides ((14)C, (90)Sr, (60)Co, (137)Cs, (238)U, and (241)Am). The results were compared with the reference values reported in Publication 108 of the International Commission on Radiological Protection, in which a different calculation approach for DCCs was employed. The results demonstrate that the present analytical method, originally intended for applications in internal dosimetry of nuclear medicine therapy, gives consistent results for all the beta-, beta-gamma-, and alpha-emitting radionuclides tested in a wide range of organism masses, between 8 mg and 1.3 kg. The applicability of the method proposed can take advantage from its ease of implementation in an ordinary electronic spreadsheet, allowing to calculate, for virtually all possible radionuclide emission spectra, the DCCs for ellipsoidal models of non-human living organisms in the environment.

An explicit algebraic reduced order algorithm for lithium ion cell voltage prediction

The detailed isothermal electrochemical model for a lithium ion cell has ten coupled partial differential equations to describe the cell behavior. In an earlier publication [Journal of Power Sources, 222, 426 (2013)], a reduced order model (ROM) was developed by reducing the detailed model to a set of five linear ordinary differential equations and nonlinear algebraic expressions, using uniform reaction rate, volume averaging and profile based approximations. An arbitrary current profile, involving charge, rest and discharge, is broken down into constant current and linearly varying current periods. The linearly varying current period results are generic, since it includes the constant current period results as well. Hence, the linear ordinary differential equations in ROM are solved for a linearly varying current period and an explicit algebraic algorithm is developed for lithium ion cell voltage prediction. While the existing battery management system (BMS) algorithms are equivalent circuit based and ordinary differential equations, the proposed algorithm is an explicit algebraic algorithm. These results are useful to develop a BMS algorithm for on-board applications in electric or hybrid vehicles, smart phones etc. This algorithm is simple enough for a spread-sheet implementation and is useful for rapid analysis of laboratory data.

Online partial evaluation of sheet-defined functions

We present a spreadsheet implementation, extended with sheet-defined functions, that allows users to define functions using only standard spreadsheet concepts such as cells, formulas and references, requiring no new syntax. This implements an idea proposed by Peyton-Jones and others. As the main contribution of this paper, we then show how to add an online partial evaluator for such sheet-defined functions. The result is a higher-order functional language that is dynamically typed, in keeping with spreadsheet traditions, and an interactive platform for function definition and function specialization. We describe an implementation of these ideas, present some performance data from microbenchmarks, and outline desirable improvements and extensions.

Volume averaged reduced order Donnan Steric Pore Model for nanofiltration membranes

A reduced order model (ROM) is derived for the Donnan Steric Pore Model (DSPM) [Bowen and Welfoot, Chem. Engng. Sci. 57 (2002) 1121], using volume averaging principles. DSPM has coupled ordinary differential equations and requires an iterative solution procedure. The proposed ROM reduces the original differential equations to set of algebraic equations. It has a direct solution, leading to elegant parameter extraction and spreadsheet implementation. When applied to desalination data at nominal salt concentrations, charge density and salt rejections predicted by ROM compare well with detailed DSPM results. The water flux across desalination membranes reduces as feed salinity increases. This experimental data can be used to back-calculate the concentration polarization mass transfer coefficient using the method developed by Sutzkover et al. Desalination 131 (2000) 117. Once concentration polarization effects are accounted for using their method, the proposed ROM describes the water flux and salt rejection data accurately for a wide range of feed concentrations without any additional parameters.