Goodness Of Fit Parameters Research Articles

Multi-component Adsorption Isotherms: Review and Modeling Studies

Adsorption is an important phenomenon widely used for the removal of contaminants. Several drinking water contaminants such as arsenic and fluoride, vanadium and chromium, nickel, cadmium and cobalt are found to coexist in nature as multi-component mixtures in water. Hence, the modeling of multi-component adsorption isotherms for designing water treatment systems has gained importance recently. However, review studies of multi-component adsorption and competitive adsorption modeling are limited. The current review paper summarizes twenty-six multi-component adsorption isotherm models. Also, case studies of several common multi-component adsorption systems and the mechanisms of multi-component adsorption are discussed. Furthermore, a modeling analysis of four multi-component isotherms models for three commonly found two-component adsorption systems, i.e., cadmium-nickel, nickel–cobalt, and cadmium-cobalt, is reported. The Extended Langmuir isotherm, Competitive Langmuir isotherm, Extended Langmuir–Freundlich isotherm and Extended Freundlich isotherm models were applied in the modeling study for the competitive adsorption of Cd, Ni, and Co. The goodness of fit parameters and adsorption isotherm constants were estimated for these models. The factors influencing competitive adsorption, mechanisms of adsorption, various single and multi-component isotherm models, their significance, and limitations are also discussed in this review article.Highlights• Twenty-six multi-component and ten single-component isotherm models are compiled• Factors affecting multi-component competitive adsorption isotherms are discussed in this review paper• The applications of four multi-component isotherm models for three binary contaminant systems are presented.Graphical

Bridging the Gap between the X-ray Absorption Spectroscopy and the Computational Catalysis Communities in Heterogeneous Catalysis: A Perspective on the Current and Future Research Directions

X-ray absorption spectroscopy (XAS) [extended X-ray absorption fine structure (EXAFS) and X-ray absorption near-edge structure (XANES)] is a key technique within the heterogeneous catalysis community to probe the structure and properties of the active site(s) for a diverse range of catalytic materials. However, the interpretation of the raw experimental data to derive an atomistic picture of the catalyst requires modeling and analysis; the EXAFS data are compared to a model, and a goodness of fit parameter is used to judge the best fit. This EXAFS modeling can often be nontrivial and time-consuming; overcoming or improving these limitations remains a central challenge for the community. Considering these limitations, this Perspective highlights how recent developments in analysis software, increased availability of reliable computational models, and application of data science tools can be used to improve the speed, accuracy, and reliability of EXAFS interpretation. In particular, we emphasize the advantages of combining theory and EXAFS as a unified technique that should be treated as a standard (when applicable) to identify catalytic sites and not two separate complementary methods. Building on the recent trends in the computational catalysis community, we also present a community-driven approach to adopt FAIR Guiding Principles for the collection, analysis, dissemination, and storage of XAS data. Written with both the experimental and theory audience in mind, we provide a unified roadmap to foster collaborations between the two communities.

Gallium Anomaly: critical view from the global picture of νe and {overline{nu}}_e disappearance

The significance of the Gallium Anomaly, from the BEST, GALLEX, and SAGE radioactive source experiments, is quantified using different theoretical calculations of the neutrino detection cross section, and its explanation due to neutrino oscillations is compared with the bounds from the analyses of reactor rate and spectral ratio data, β-decay data, and solar neutrino data. In the 3+1 active-sterile neutrino mixing scheme, the Gallium Anomaly is in strong tension with the individual and combined bounds of these data sets. In the combined scenario with all available data, the parameter goodness of fit is below 0.042%, corresponding to a severe tension of 4–5σ, or stronger. Therefore, we conclude that one should pursue other possible solutions beyond short-baseline oscillations for the Gallium Anomaly. We also present a new global fit of νe and {overline{nu}}_e disappearance data, showing that there is a 2.6–3.3σ preference in favor of short-baseline oscillations, which is driven by an updated analysis of reactor spectral ratio data.

Study on equilibrium moisture sorption characteristics and modeling of Karaya (Sterculia urens Roxb.) gum

BackgroundKaraya gum is heavily acetylated polysaccharide and used for medicinal purposes as a bulk-forming laxative. It is also used in the paper and textile industries as well as food stabilizer. So its moisture sorption behavior study is important. The moisture sorption isotherms of karaya gum were investigated at four experimental temperatures (30, 40, 50 and 60 °C) under the water activities range from 0.10 to 0.95 using static gravimetric method.ResultsMaximum value of equilibrium moisture content (EMC) of karaya gum (39.7%, db) was noted with the set of highest water activity (0.92) at lowest temperature (30 °C) and minimum value of EMC (2.5%, db) with lowest water activity (0.11) at highest temperature (60 °C). The sorption data were fitted with GAB, Iglesias and Chirife, Caurie, Halsey and BET models. The adequacy of the sorption isotherm models were observed statistically using goodness of fit parameters. The well-known GAB model best interpreted the equilibrium data in a satisfactory manner in the test gum. The monolayer moisture content, estimated using GAB model, was found to be 12.66 g/100 g and 7.4 g/100 g gum at 30 °C and 60 °C, respectively. The isosteric heat of sorption was found to be maximum (36.24 kJ/mol) at 5% moisture content (db) of karaya gum.ConclusionMoisture sorption behavior of karaya gum was found to be temperature dependent. EMC decreases with the increase in temperature at particular water activity. The sorption behavior of the test gum expressed the typical type-II sigmoid-shaped curve. Isosteric heat of sorption of karaya gum was decreased with the increase in moisture content.

Evaluation of the effectiveness of bioaugmentation and biostimulation in atrazine removal in a polluted matrix using degradation kinetics

ABSTRACT The objective of the present study was the restoration of sterilized soil contaminated with atrazine (5.52 mg kg−1) using atrazine-degrading bacteria, biochar, and poultry manure amendments. The atrazine-degrading bacterium was purified from a mixed culture isolated from a long-history atrazine soil, and identified as Alcaligenes sp. strain AZUHUB-A31-CH52 ND4 based on the 16S rRNA sequence. After 35-day bioremediation, the sterile soil inoculated with Alcaligenes sp. and amended with biochar or poultry manure improved atrazine removal efficiency (98–99%) while the soil containing only atrazine degraders resulted in the lowest atrazine removal (78%). The atrazine degradation kinetics in the amended and unamended soils adequately fit the double first-order in parallel (DFOP) model given the goodness of fit parameters (high r2 (0.9703–0.9997) and low AICc (−106.30 to −28.92) values. The DFOP model also predicted that DT50 (half-life) in all treatments was < 1 day (0.03–0.13 day) at a fast dissipation rate k1 (5.422–20). The DT90 (90% atrazine removal) estimated by DFOP ranged between 23–94 days. The results demonstrated that low-cost bio-waste could offer a sustainable and eco-friendly approach for the reclamation of soils contaminated with atrazine.

Investigation of structural, mechanical, magnetic properties and hysteresis modelling of Dawasir meteorite

The experimental reports of structural modification, mechanical and magnetic properties of the Saudi meteorite were studied and discussed. The meteorite specimen was studied using the following different characterization techniques like X-ray powder diffraction, scanning electron microscopy, backscattered electron imaging, Energy Dispersive X-Ray Analysis, magnetic property measurements and hardness testing (Rockwell, Vicker, Brinell). The compositional analysis of the meteorite revealed that the sample was mainly composed of an iron-nickel alloy role. The hardness testing showed that the meteorite consisted of a soft material with 22.5 HRC Rockwell hardness. The magnetic measurements of the meteorite specimen indicated that it is a soft ferromagnetic material. The magnetic saturation (Ms) of 0.701 emu/g was observed for the saturation field of (Hs) = 5025 Oe. A sensitive GM counter showed no traces of radioactivity. Mathematical modelling of hysteresis data is also performed using already available models. Few modified models are also proposed and evaluated for the goodness of fit. Finally, most suitable model is proposed as bi-exponential model based on the goodness of fit parameters.

Study of pollution transport through the rivers using aggregated dead zone and hybrid cells in series models

This paper aims at studying the data-based mechanistic pollution transport models of the aggregated dead zone (ADZ) and hybrid cells in series (HCIS) models. For a better understanding of them, it was tried to connect their parameters to the physical parameters of the classical advection–dispersion (ADE) model. A series of experiments were conducted using the laboratory model of the gravel bed river, and the sodium chloride solution was chosen as a conservative tracer. A new analytical solution of HCIS model, including triple dispersion cells, was developed. Also, the theoretical temporal moment equations were derived and attempted to develop explicit relationships for both ADZ and HCIS parameters based on hydraulically and geometrically variables. Detailed descriptions of the Pe number and the skewness coefficients (CSK) were presented, and the theoretical limit of CSK was extracted. It was concluded that the range of (0.93–1.24) for CSK is acceptable, which confirms the finding of previous investigators. A sensitivity analysis was operated to find the importance of different variables in estimating model parameters. Finally, the theoretical breakthrough curves of ADZ and HCIS models were drawn and confirmed with experimental BTCs. Statistical goodness of fit parameters of RMSE, DC, and MAE was calculated as triple sets of (0.0158, 0.99, 21.58 ppm), (0.0188, 0.97, 27.16 ppm), and (0.105, 0.83, 78.84 ppm) for ADZ, HCIS, and ADE models, respectively, which showed the superiority of data-based models to the classical ADE model.

Regression between EEG and Speech Signals for Spoken Vowels.

One of the primary difference of mankind from other species is his ability to communicate verbally. Our brain upon framing a sentence, coordinates with the oro-pharyngeal-laryngeal muscle groups to produce the speech with the help of vocal cord and mouth aperture. However, some individuals due to congenital or illness, may loose their ability to speak in spite of their brain framing speech. Research on speech restoration through brain computer interface (BCI) is still at an early stage. Through this study, we have explored the regression between the chaos parameters of acoustic signal and electroencephalography (EEG) signal for different vowels chosen from International Phonetic Alphabets (IPA). The vowels were categorised into two categories, namely, soft vowels and diphthongs. We have selected the EEG channels based on their higher contribution towards the first principal component. Goodness of fit parameters were evaluated for the regression analysis to explore the most suitable chaos parameter.

Validation of the Maslach Burnout Inventory – Human Services Survey to assess burnout of prison officers working in Sri Lankan Prisons

Introduction: Prison officers undergo a high amount of work-stress and consequently suffer from burnout, which is detrimental to their health, the organization and the inmates. There are no studies conducted on burnout of prison officers in Sri Lanka, partly due to the non-availability of a validated scale. Objectives: To validate the Maslach Burnout Inventory – Human Services Survey (MBI-HSS) for assessing burnout among prison officers working in Sri Lankan prisons. Methods: A cross-sectional validation study was carried out among a representative sample of 267 correctional and rehabilitation officers working in the Colombo Remand Prison and Colombo New Magazine Prison, selected through probability-proportionate-to-sample size and random sampling methods. The Sinhala-translated MBIHSS was judgmentally validated and pre-tested prior to administration. Confirmatory factor analysis (CFA) was performed using LISREL 9.3 software to assess the construct validity of MBI-HSS. The presence of job-related neurasthenia in participants, diagnosed by a psychiatrist was taken as the gold standard to assess the criterion validity. Internal consistency and test-retest methods were used to assess the reliability. Results: A three-factor model with items 6, 13, 16 and 22 of the instrument deleted, was identified as the model best fitting the data during CFA, achieving the best goodness of fit parameters (RMSEA=0.0748; χ2=185.2, p=0.001; χ2/df=1.4; CFI=0.974; NNFI=0.97; GFI=0.985; SRMR=0.056). Clinical burnout was seen in 32.6% of the sample. According to the cut-off values achieved through receiver operating characteristics (ROC) curves for burnout and its three sub-scales of emotional exhaustion, depersonalization and diminished personal accomplishment, the sensitivity and specificity were 94.3% & 87.8%; 82.8% & 80.6%; 72.4% & 73.9%; and 77% & 77.2%, respectively. The reliability of the tool was satisfactory. Conclusions: Translated and validated MBI-HSS was found to be a valid and reliable tool for assessing burnout among Sri Lankan prison officers. Using it during their medical examinations is recommended.

Kinetics of neptunium sorption and desorption in the presence of aluminum (hydr)oxide minerals: Evidence for multi-step desorption at low pH

Kinetics analyses of sorption and desorption provide important insight into reaction mechanisms occurring at the mineral-water interface. They are also needed to determine when equilibrium is achieved, identify intermediate chemical species, and inform models describing neptunium mobility. Neptunium sorption to and desorption from four different aluminum (hydr)oxides - bayerite (α-Al(OH)3), gibbsite (γ-Al(OH)3), corundum (α-Al2O3), and γ-alumina (γ-Al2O3) - were investigated as a function of mineral concentration (5 – 170 m2 L−1), neptunium concentration (10−9 – 10−7 M), and pH (5.5 – 10.5). Neptunium sorption was characterized by a two-step reaction with an initial fast sorption step occurring within minutes followed by a slower equilibrium process, which was attributed to initial sorption of neptunium to a small number of strong sorption sites followed by sorption of neptunium to a larger number of weak sorption sites. The kinetics data were modeled using the linear and non-linear forms of the pseudo-first and pseudo-second order rate equations and the goodness of fit parameters were compared. Non-linear pseudo-second order rate constants described neptunium sorption to aluminum (hydr)oxides most accurately and were used to determine the reaction orders with respect to mineral concentration and [H+]. Neptunium desorption experiments demonstrated that the desorption mechanism changed as a function of pH and that the forward and reverse reactions were not equivalent. At pH ≥ 7.5, desorption reached steady-state within an hour and was accurately described by the non-linear pseudo-second order rate equations. A desorption plateau was observed at pH 5.5 that could not be described by either pseudo-first or –second order kinetics, suggesting the possibility of a multi-step desorption reaction. The comparatively slow desorption kinetics observed here suggests that sorbed neptunium could be slowly released back into the aqueous phase and act as a continuous source of contamination to the environment.

Modelling of expansion ratio and half-life of foamed bitumen using gene expression programming

ABSTRACT A typical foamed bituminous mixture design process involves production and evaluation of foamed bitumen over a range of foaming temperatures and water contents. Such a process of elaborate testing (over a range of temperatures and water contents) is a time- and resource-consuming exercise. This work presents a novel gene expression programming-based approach to predict the expansion ratio and half-life of foamed bitumen. To develop these predictive models, a database that consists of foamed bitumen characteristics (i.e. expansion ratio and half-life) and physico-chemical properties of bitumen was developed using six distinct binders (of different viscosity grades). Initially, all binders were tested for their physical properties and chemical composition. Furthermore, foamed bitumen characteristics of these binders were measured over the range of test temperatures (110°C to 200°C) and water contents (2% to 11%). The final database consisted of 166 observations (arising out of different combinations of 6 binder types, 6 water contents, and 8 test temperatures and an average of 3 replicates for each condition). Expressions to predict foamed bitumen characteristics have been developed based on temperature, water content, viscosity, and chemical composition using this database through the gene expression programming technique. The numerical values of goodness of fit parameters indicated that models based on physical properties are able to predict expansion ratio and half-life with reasonable accuracy. Furthermore, the use of information regarding chemical composition along with physical properties improved the accuracy of predictive models significantly. These expressions can be used to identify the feasible range of test conditions for the production of foamed bitumen before actual testing is commenced.

Characterizing and Monitoring Drought over Upper Blue Nile of Ethiopia with the Aid of Copula Analysis

The main aim of this study is to characterize and monitor drought distribution and expansion over Upper Blue Nile of Ethiopia by using univariate standard precipitation index (SPI) and standardized soil moisture index (SMI) whose joint distribution leads to multi standardized drought index (MSDI). The soil moisture and CHIRPS precipitation data from first January 1980 to 2016 are modeled. The indices of SPI, SMI and the joint MSDI value over the Upper Blue Nile are analyzed. The SPI for different time scales is implemented. The correlation between severity, duration and intensity including wetness and drought strengths is computed and analyzed. It is found that the correlation between duration and severity is 0.96 and normal conditions for SPI 3, 6, 12 month time scales are frequently observed rather than moderate, severe and extreme severe drought or wetness. Building on soil moisture and precipitation data of the summer season, the Clayton copula model is selected based on goodness of fit parameters. After setting the best copula family for the Upper Blue Nile then we applied the joint distribution method is applied for characterizing and monitoring drought. It is found that the MSDI more clearly showed that the severity of drought across the time series of each time scales, than SPI and SMI. As the time scale increases there is decline of fluctuation or frequency of drought and the rising of drought duration is shown by SPI, SMI and MSDI. By using SPI6, SMI6 and MSDI6 the spatial distribution of drought is determined from June to August in the years 1984 and 2015 indicate the drought expansions in the eastern and western parts of Upper Blue Nile during the respective years.

Combining different diagnostic studies of lymphatic filariasis for risk mapping in Papua New Guinea: a predictive model from microfilaraemia and antigenaemia prevalence surveys

BackgroundThe Global Programme to Eliminate Lymphatic Filariasis has encouraged countries to follow a set of guidelines to help them assess the need for mass drug administration and evaluate its progress. Papua New Guinea (PNG) is one of the highest priority countries in the Western Pacific for lymphatic filariasis and the site of extensive research on lymphatic filariasis and surveys of its prevalence. However, different diagnostic tests have been used and thresholds for each test are unclear.MethodsWe reviewed the prevalence of lymphatic filariasis reported in 295 surveys conducted in PNG between 1990 and 2014, of which 65 used more than one test. Results from different diagnostics were standardised using a set of criteria that included a model to predict antigen prevalence from microfilariae prevalence. We mapped the point location of each of these surveys and categorised their standardised prevalence estimates.ResultsSeveral predictive models were produced and investigated, including the effect of any mass drug administration and number of rounds prior to the surveys. One model was chosen based on goodness of fit parameters and used to predict antigen prevalence for surveys that tested only for microfilariae. Standardised prevalence values show that 72% of all surveys reported a prevalence above 0.05. High prevalence was situated on the coastal north, south and island regions, while the central highland area of Papua New Guinea shows low levels of prevalence.ConclusionsOur study is the first to provide an explicit predictive relationship between the prevalence values based on empirical results from antigen and microfilaria tests, taking into account the occurrence of mass drug administration. This is a crucial step to combine studies to develop risk maps of lymphatic filariasis for programme planning and evaluation, as shown in the case of Papua New Guinea.

Biodegradation of metformin and guanylurea by aerobic cultures enriched from sludge

Sewage sludge from a municipal wastewater treatment facility employing activated sludge process was pre-incubated with varying substrates and mixtures of substrates including metformin (MET), guanylurea (GUA) and glucose. The biomass from enriched cultures separately utilising MET and glucose/GUA was then used to investigate the kinetics of aerobic biodegradation of MET and GUA, respectively, as individual substrates in batch reactors. The results showed that GUA can be completely degraded as a nitrogen source when glucose is provided as a carbon and energy source. On the contrary, MET can be biodegraded as a sole carbon and energy source. However, formation of by-product GUA in solution, which acts as a nitrogen source, rapidly increased the degradation rate of MET resembling autocatalytic behaviour. At low starting concentration of 5 mg/L, the specific substrate utilisation rates of MET and GUA were 0.0033 day−1 and 0.0013 day−1, respectively, which is reported first time in this study. Out of the five biodegradation kinetic models used to describe substrate utilisation, the Quiroga-Sales-Romero (QSR) model was found to predict the measured MET and GUA degradation profile well supported by the goodness of fit parameters. Furthermore, the QSR model was able to describe the autocatalytic degradation of MET and the incomplete biodegradation of GUA in solution.

Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies.

The current era demands high quality software in a limited time period to achieve new goals and heights. To meet user requirements, the source codes undergo frequent modifications which can generate the bad smells in software that deteriorate the quality and reliability of software. Source code of the open source software is easily accessible by any developer, thus frequently modifiable. In this paper, we have proposed a mathematical model to predict the bad smells using the concept of entropy as defined by the Information Theory. Open-source software Apache Abdera is taken into consideration for calculating the bad smells. Bad smells are collected using a detection tool from sub components of the Apache Abdera project, and different measures of entropy (Shannon, Rényi and Tsallis entropy). By applying non-linear regression techniques, the bad smells that can arise in the future versions of software are predicted based on the observed bad smells and entropy measures. The proposed model has been validated using goodness of fit parameters (prediction error, bias, variation, and Root Mean Squared Prediction Error (RMSPE)). The values of model performance statistics (, adjusted , Mean Square Error (MSE) and standard error) also justify the proposed model. We have compared the results of the prediction model with the observed results on real data. The results of the model might be helpful for software development industries and future researchers.

Positive Matrix Factorization dynamics in fingerprinting: A comparative study of PMF2 and EPA-PMF3 for source apportionment of sediment polychlorinated biphenyls

Receptor models were typically used in air pollution studies and few publications are available for Positive Matrix Factorization (PMF) that consider the details of parameters and procedures in evaluating the trace organic pollutants in sediments. In this study, environmental fate and source composition of Lake Eymir sediments contaminated by polychlorinated biphenyls (PCBs) were explored by applying two PMF models, Paatero's PMF2 and United States Environmental Protection Agency's (US EPA) EPA-PMF3. PMF2 and EPA-PMF3 rely on different algorithms; Paatero's algorithm and multilinear engine algorithm, respectively. Here, the approaches of two PMF models were compared for the identification of PCB patterns taking into consideration the effects of various uncertainty matrices, residual matrices and goodness-of fit parameters. As a result of the study, it was understood that both models resolved five factors and indicated Clophen A60 as the source of PCBs. These results were consistent with the results resolved by Chemical Mass Balance model applied to the same data set in a previous study. However, source contributions identified by two models differed in quantity, but with similar patterns. This study indicates a way in understanding behavior, fate and global source of persistent organic pollutants in sediment by applying and comparing with a special data including high percentage of below detected value (38.2%) to understand the dynamics of PMF model parameters.

A Statistical Comparison of Mathematical Models for Heavy Metal Leaching Phenomena from Solidified Landfill Waste Mortar

Abstract In this research, landfill solid waste was solidified as cement-waste matrix to protect the environment from excessive intrusive contaminants like Fe, Cu and Ni and minimize the waste load. Within this context, ingredients of cement-waste mortar were characterized to determine their physical properties. Long-term feasibility study was conducted to examine the metal contents stabilization by employing the standard mass transfer-leaching test. The cumulative leaching concentration of Fe, Cu and Ni were found to be 1.29 mg/l, 0.18 mg/l and 0.63 mg/l respectively up to 180 days static leaching test period that satisfied the surface water quality standard. Mechanical strength test was also conducted to characterize the solidification technique. Five well-established non-linear mathematical Models were conducted to evaluate the mechanisms of Fe, Cu and Ni migration. Goodness of fit statistical parameter analysis and visual examination indicated that polynomial equation Model is better for explaining the experimentally generated data. Moreover, parameter of polynomial equation was extended from five to nine for examining the best calibration profile to the observations. In context of slope-intercept and visual observation analysis resulted that polynomial equation based Model bearing five parameters with 0.5 power interval of each parameter describes the leaching phenomena quite similar with the experimental observations whereas goodness of fit parameters and information criterion shows reverse. It was found that the studied immobilized landfill waste mortar have acceptable mechanical performance that confirms to be used as construction material.

Log logistic distribution to model water demand data

There had been insufficient studies previously to conclude the suitability of the appropriate probability distribution functions in modelling water demand. The purpose of this study is to find an appropriate probability density function to apply in simulating water demand using real water consumption data. Daily water consumption data for four years obtained from a water company in UK and analysed using normal, log normal, log logistic and Weibull distributions and a comparison on the applicability of each distribution was assessed. Statistical modelling was performed using MINITAB. The Anderson Darling (AD) statistic was used as the goodness of fit parameter in the analysis.

Functionalization and Structural Characterization of a Novel Nacrite-LiCl Nanohybrid Material

The nacrite-LiCl hybrid composite material was prepared at room temperature by indirect inter-calation of lithium chloride between the planar layers of nacrite, a clay mineral, using acetone as a solvent. The structural identification of the hybrid clay material was determined by means of X-ray diffraction (XRD), thermogravimetric analysis (TGA) and infrared spectroscopy (IR). The qualitative XRD analysis showed that the basal spacing value increased from 0.72 nm to 1.14 nm and revealed that the alkali halide intercalated successfully in the interlayer space of the nacrite framework. The quantitative XRD analysis allowed us to determine the optimum structural parameters related to the position and number of keyed ions and water molecules per half unit cell calculated along the c* axis and the goodness of fit parameter (Rp). The thermal properties of the elaborated hybrid were in great accordance with the XRD study and confirm the intercalation of the hydrated salt in the interlamellar space of nacrite. Moreover, IR spectroscopy enabled the study of the interactions between the silicate ‘‘networks’’ and the alkali halide.

SLDMOL: A tool for the structural characterization of thermally disordered membrane proteins

SLDMOL is a program for modeling the 1-D scattering length density (SLD) profile of proteins at the lipid membrane–solution interface or adsorbed to other surfaces. The program reads experimental SLD data from neutron or X-ray reflectivity measurements and compares the results to a trajectory of protein structures, finding the conformation and orientation that best fits the experimental data. SLDMOL is a freely distributed open source program written in python that can be run independently using command lines or a GUI. SLDMOL has also been integrated into the larger SASSIE package extending molecular modeling capabilities. Sample environment conditions can be replicated including H2O/D2O solvent contrasts, specific amino acid deuteration and complex molecular assemblies. Ensembles of protein conformations can be generated independently (e.g. molecular dynamics simulations) or with SASSIE. For each individual structure a best-fit SLD profile is outputted along with a goodness of fit parameter, protein depth penetration and surface coverage. In addition to individual comparisons SLD profiles can be calculated over ensemble averages of protein structures. As a result, SLDMOL provides a detailed molecular interpretation of reflectivity data or conversely can be used to predict experimental outcomes for different protein conformation and specific deuteration schemes prior to measurements. Program summaryProgram title: SLDMOLCatalogue identifier: AETX_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AETX_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: GNU General Public License, version 3No. of lines in distributed program, including test data, etc.: 3302107No. of bytes in distributed program, including test data, etc.: 125715760Distribution format: tar.gzProgramming language: Python.Computer: PC/Mac.Operating system: 32- and 64-bit Linux (Ubuntu 10.04, Centos 5.6) and Mac OS X (10.7–10.9).RAM: 1 GBClassification: 3.External routines: Python 2.6.5, numpy 1.4.0, scipy 0.8.0, Tcl 8.5, Tk 8.5, Mac installation requires Xcode 3 development tools.Subprograms used:CatIdTitleReferenceAEKL_v1_0SASSIECPC 183(2012)382Nature of problem:Reflectivity is a powerful technique to study the conformation of biological molecules at surface and interfaces. Open source software that facilitates computational modeling and interpretation of experimental results in terms of detailed molecular structure is currently lacking.Solution method:SLDMOL takes one or an ensemble of atomistic configurations of proteins and compares them to 1-D scattering length density (SLD) profiles determined from reflectivity experiments. Protein structures can be generated independently (e.g. molecular dynamics simulations) or through the SASSIE software package. SLDMOL performs an optimization to determine which structure best fits the SLD profile. Structures can also be averaged either directly or through a Monte-Carlo weighting algorithm. Finally SLDMOL allows the modeling of reflectivity experiments through the in silico selective deuteration of amino acids and the calculation of SLD profiles in a number of different aqueous solvent contrasts.Additional comments:!!!!Due to the large file size, SLDMOL is not delivered directly when download or Email is requested. Instead an html file giving details of how the program can be obtained is sent.!!!!Running time:Varies depending on application. Typically 10 min to 24 h depending on the number of structures to be evaluated.