Content and Face Validation of the Lifestyle Medicine Assessment.

Introduction: There are no validated global lifestyle medicine brief screening tools that measure health behaviors in all six lifestyle domains. The Lifestyle Medicine Assessment (LMA) tool was initially developed and revised based on feedback elicited from colleagues, experts, and patients. During the developmental process, every item underwent language changes. Three of the original 24 items were removed. However, there have not been any formal validation efforts. This study aims to formally evaluate the face and content validity of the LMA. Methods: A survey was emailed to 12 board-certified lifestyle medicine experts asking them to rank items in the LMA on a 1-4 scale for content relevance and clarity. Content and face validity were quantitatively determined using the item-level content validity index (I-CVI), scale-level content validity index (S-CVI), and item-level face validity index (I-FVI), scale-level face validity index (S-FVI), respectively. Literature accepted thresholds of I-CVI/I-FVI ≥.79 and S-CVI/S-FVI average ≥ .80 were used. Results: Eleven experts returned evaluations of the 21-item LMA. All 21 items had I-CVI for relevance ≥.91 and I-FVI ≥.81 with excellent kappa values. The S-CVI/I-FVI average for relevance and clarity were .99 and .95, respectively. Conclusion: The 21-item LMA is a brief global lifestyle medicine tool that has demonstrated excellent content and face validity.

Improved ant colony optimization for safe path planning of AUV.

In order to address the autonomous underwater vehicle navigation challenge for dam inspections, with the goal of enabling safe inspections and reliable obstacle avoidance, an improved smooth Ant Colony Optimization algorithm is proposed for path planning. This improved algorithm would optimize the smoothness of the path besides the robustness, avoidance of local optima, and fast computation speed. To achieve the goal of reducing turning time and improving the directional effect of path selection, a corner-turning heuristic function is introduced. Experimental simulation results show that the improved algorithm performs best than other algorithms in terms of path smoothness and iteration stability in path planning.

Coagulation process for the removal of color and turbidity from wet coffee processing industry wastewater using bio-coagulant: Optimization through central composite design.

The growing problem of industrial pollution in developing countries, especially Ethiopia, has sparked serious issues about the quality of the water, particularly when it comes to the effluent from wet coffee processing industries. In response, this study investigates the potential of utilizing natural coagulants, Acanthus sennii C., Moringa stenopetala B., and Aloe vera L., either individually or in combination, for the treatment of coffee effluent. Methodologically, the study systematically varies operational parameters, including coagulant dose, pH levels, stirring speed, and stirring time, to evaluate their impact on coagulation efficiency. Experimental data undergo statistical analysis, employing ANOVA, while computational optimization techniques are employed using Design Expert software to determine optimal conditions. Notably, the blended form of the three coagulants emerges as particularly promising, yielding optimal conditions of 0.750 g/L coagulant dosage, pH 8.76, agitation speed of 80.73 rpm, and agitation time of 19.23 min. Under these optimized conditions, the blended coagulant achieves remarkable removal efficiencies, approximately 99.99% for color and 98.7% for turbidity. These findings underscore the efficiency of natural coagulants, particularly in blended form, for sustainable wastewater treatment in wet coffee processing.

Outcomes with guideline-directed medical therapy and cardiac implantable electronic device therapies for patients with heart failure with reduced ejection fraction.

Background: Limited real-world evidence exists for outcomes with contemporary guideline-directed medical therapy (GDMT) or GDMT with implantable cardioverter-defibrillator (ICD)/cardiac resynchronization therapy defibrillator (CRT-D) therapy for patients with heart failure with reduced ejection fraction (HFrEF) and left ventricular ejection fraction (LVEF) ≤35%. Objective: The present study aimed to assess survival associated with GDMT or GDMT with ICD/CRT-D therapy. Methods: This retrospective observational study included real-world de-identified data from January 1, 2016, to December 19, 2023, from 24 U.S. institutions per participating institutional agreements (egnite Database; egnite, Inc.). Patients with a diagnosis of HFrEF and an echocardiographic study documenting LVEF ≤35% were included for analysis. Results: Of 43,591 patients with eligible index event of LVEF ≤35%, prescription history through ≥1 year preindex, and no ICD/CRT-D therapy preindex, mean ± standard deviation age at index was 71.2 ± 13.2 years; 14,805 (34.0%) patients were female. At 24 months, an estimated 99.1% (95% confidence interval [CI] 99.0%-99.2%), 89.9% (95% CI 89.7%-90.1%), 54.8% (95% CI 54.4%-55.2%), and 17.2% (95% CI 16.9%-17.5%), had ≥1, 2, 3, or all 4 GDMT classes prescribed, respectively; an estimated 15.7% (95% CI 15.3%-16.1%) had device placement. Of those without a device, by 24 months, an estimated 45.1% (95% CI 44.4%-45.7%) had a documented LVEF >35%. Counts of GDMT classes prescribed as well as ICD/CRT-D device therapy were associated with lower mortality risk in this population, even after adjustment for patient age, sex, and comorbidities. Conclusion: Both GDMT classes prescribed and device therapy were independently associated with lower mortality risk, even in the presence of more GDMT options for this more contemporary population.

Markov decision process design: A framework for integrating strategic and operational decisions.

We consider the problem of optimally designing a system for repeated use under uncertainty. We develop a modeling framework that integrates the design and operational phases, which are represented by a mixed-integer program and discounted-cost infinite-horizon Markov decision processes, respectively. We seek to simultaneously minimize the design costs and the subsequent expected operational costs. This problem setting arises naturally in several application areas, as we illustrate through examples. We derive a bilevel mixed-integer linear programming formulation for the problem and perform a computational study to demonstrate that realistic instances can be solved numerically.

Optimization of fermentation conditions and quality evaluation of Chaenomeles sinensis glutinous rice wine.

The present study was conducted to optimize fermentation conditions for preparation of Chaenomeles sinensis Glutinous Rice Wine (CRW). The dynamic changes of main substances in the liquor during fermentation process, aroma components, biologically active substances and antioxidant capacity in the CRW after 6 months of aging were tested. The results showed that under optimized conditions, the yield and alcohol content of wine was 44.97 and 20.00%, respectively. After aging, 64 aroma components were detected in the wine, mainly alcohols and esters. The alcohol content of the CRW was 14.8%. Polyphenols and flavonoids reached 0.23 g/L and 0.037 g/L respectively. Furthermore, the CRW had an excellent free radical scavenging ability. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-024-05934-0.

Dynamic simulation and projection of ESV changes in arid regions caused by urban growth under climate change scenarios.

Spatial simulation and projection of ecosystem services value (ESV) changes caused by urban growth are important for sustainable development in arid regions. We developed a new model of cellular automata based grasshopper optimization algorithm (named GOA-CA) for simulating urban growth patterns and assessing the impacts of urban growth on ESV changes under climate change scenarios. The results show that GOA-CA yielded overall accuracy exceeding 98%, and FOM for 2010 and 2020 were 43.2% and 38.1%, respectively, indicating the effectiveness of the model. The prairie lost the highest economic ESVs (192 million USD) and the coniferous yielded the largest economic ESV increase (292 million USD) during 2000-2020. Using climate change scenarios as urban future land use demands, we projected three scenarios of the urban growth of Urumqi for 2050 and their impacts on ESV. Our model can be easily applied to simulating urban development, analyzing its impact on ESV and projecting future scenarios in global arid regions.

Optimization of radiation doses for open lumbar spinal fusion using C-arm fluoroscopy and impact on radiation-induced cancer: a pilot study.

PURPOSE: Intraoperative fluoroscopy use is essential during spinal fusion procedures. The amount of radiation dose should always be minimized. This study aimed to determine the feasibility of halving the frame rate from 12.5 to 6.25 frames per second (fps) and to quantify the reduction in the risk of developing radiation-induced cancer. METHODS: This pilot study included 34 consecutive patients operated for open lumbar posterolateral fusion (PLF) with or without transforaminal lumbar interbody fusion (TLIF). C-arm modes were changed from half-dose (12.5 frames per second (fps), group I) to quarter-dose (6.25 fps, group II). Age, body mass index, surgical procedure, number of treated levels, and complications were collected. Kerma area product (KAP), cumulative air kerma (CAK), and fluoroscopy time were compared. Effective dose and radiation-induced cancer risk were estimated. RESULTS: Eighteen and 16 patients were, respectively, included in group I and II. Demographic, surgical data, and fluoroscopy time were similar in both groups. However, CAK, KAP, and effective dose were significantly lower in group II, respectively, 0.56 versus 0.41 mGy (p = 0.03), 0.09 versus 0.06 Gy cm2 (p = 0.04), and 0.03 versus 0.02 mSv (p = 0.04). Radiation-induced cancer risk decreased by 47.7% from 1.49 × 10-6 to 7.77 × 10-7 after optimization. No complications were recorded in either group. CONCLUSION: This study demonstrates the feasibility of setting 6.25 fps for TLIF with and without PLF. By halving the fps, radiation-induced cancer risk could be almost divided by two, without compromising surgical outcome. Finally, after optimization, the risk of developing radiation-induced cancer was less than one in a million.

High-resolution cell imaging using white light phase shifting interferometry and iterative phase deconvolution.

An optimization algorithm is presented for the deconvolution of a complex field to improve the resolution and accuracy of quantitative phase imaging (QPI). A high-resolution phase map can be recovered by solving a constrained optimization problem of deconvolution using a complex gradient operator. The method is demonstrated on phase measurements of samples using a white light based phase shifting interferometry (WLPSI) method. The application of the algorithm on real and simulated objects shows a significant resolution and contrast improvement. Experiments performed on Escherichia coli bacterium have revealed its sub-cellular structures that were not visible in the raw WLPSI images obtained using a five phase shifting method. These features can give valuable insights into the structures and functioning of biological cells. The algorithm is simple in implementation and can be incorporated into other QPI modalities .

Statistically derived proxy potentials accelerate geometry optimization of crystal structures.

The crystal structures of known materials contain the information about the interatomic interactions that produced these stable compounds. Similar to the use of reported protein structures to extract effective interactions between amino acids, that has been a useful tool in protein structure prediction, we demonstrate how to use this statistical paradigm to learn the effective inter-atomic interactions in crystalline inorganic solids. By analyzing the reported crystallographic data for inorganic materials, we have constructed statistically derived proxy potentials (SPPs) that can be used to assess how realistic or unusual a computer-generated structure is compared to the reported experimental structures. The SPPs can be directly used for structure optimization to improve this similarity metric, that we refer to as the SPP score. We apply such optimization step to markedly improve the quality of the input crystal structures for DFT calculations and demonstrate that the SPPs accelerate geometry optimization for three systems relevant to battery materials. As this approach is chemistry-agnostic and can be used at scale, we produced a database of all possible pair potentials in a tabulated form ready to use.

Scan-Free and Fully Automatic Tracking of Native Knee Anatomy from Dynamic Stereo-Radiography with Statistical Shape and Intensity Models.

Kinematic tracking of native anatomy from stereo-radiography provides a quantitative basis for evaluating human movement. Conventional tracking procedures require significant manual effort and call for acquisition and annotation of subject-specific volumetric medical images. The current work introduces a framework for fully automatic tracking of native knee anatomy from dynamic stereo-radiography which forgoes reliance on volumetric scans. The method consists of three computational steps. First, captured radiographs are annotated with segmentation maps and anatomic landmarks using a convolutional neural network. Next, a non-convex polynomial optimization problem formulated from annotated landmarks is solved to acquire preliminary anatomy and pose estimates. Finally, a global optimization routine is performed for concurrent refinement of anatomy and pose. An objective function is maximized which quantifies similarities between masked radiographs and digitally reconstructed radiographs produced from statistical shape and intensity models. The proposed framework was evaluated against manually tracked trials comprising dynamic activities, and additional frames capturing a static knee phantom. Experiments revealed anatomic surface errors routinely below 1.0 mm in both evaluation cohorts. Median absolute errors of individual bone pose estimates were below 1.0 ∘ or mm for 15 out of 18 degrees of freedom in both evaluation cohorts. Results indicate that accurate pose estimation of native anatomy from stereo-radiography may be performed with significantly reduced manual effort, and without reliance on volumetric scans.

Prediction of bitcoin stock price using feature subset optimization.

In light of recent cryptocurrency value fluctuations, Bitcoin is gradually gaining recognition as an investment vehicle. Given the market's inherent volatility, accurate forecasting becomes crucial for making informed investment decisions. Notably, previous research has utilized machine learning methods to enhance the accuracy of Bitcoin price predictions. However, few studies have explored the potential of employing diverse modeling methods for sampling with varying data formats and dimensional characteristics. This study aims to identify the internal feature subset that yields the highest returns in forecasting Bitcoin's price. Specifically, Bitcoin's internal features were categorized into four groups: currency data, block details, mining information, and network difficulty. Subsequently, a long short-term memory (LSTM) artificial neural network was employed to predict the next day's Bitcoin closing price, utilizing various categorizations of feature subsets. The model underwent training using two and a half years of historical data for each feature. The findings revealed a mean absolute error rate of 6.38% when modeling with the block details category features. This enhanced performance primarily stemmed from the positive relationship between Bitcoin price and this data subset's low ambiguity. Experimental results underscored that, compared to other investigated feature subsets, the categorization of block detail features provided the most accurate Bitcoin price predictions, laying the foundation for future research in this domain.

Innovate and Validate: Design-Led Simulation Optimization to Test Centralized Registration Feasibility in a Multispecialty Clinic.

OBJECTIVE: This study utilizes a design-led simulation-optimization process (DLSO) to refine a hybrid registration model for a free-standing outpatient clinic. The goal is to assess the viability of employing DLSO for innovation support and highlight key factors influencing resource requirements. BACKGROUND: Manual registration in healthcare causes delays, impacting patient services and resource allocation. This study addresses these challenges by optimizing a hybrid centralized registration and adopting technology for efficiency. METHOD: An iterative methodology with simulation optimization was designed to test a proof of concept. Configurations of four and five registration options within a hybrid centralized system were explored under preregistration adoption rates of 30% and 50%. Three self-service kiosks served as a baseline during concept design and test fits. RESULTS: Centralized registration accommodated a daily throughput of 2,000 people with a 30% baseline preregistration rate. Assessing preregistration impact on seating capacity showed significant reductions in demand and floor census. For four check-in stations, a 30%-50% preregistration increase led to a 32% seating demand reduction and a 26% decrease in maximum floor census. With five stations, a 50% preregistration reduced seating demand by 23% and maximum floor census by 20%. CONCLUSION: Innovating introduces complexity and uncertainties requiring buy-in from diverse stakeholders. DLSO experimentation proves beneficial for validating novel concepts during design.

Enhanced removal of rare earth elements from aqueous media: exploring the potential of AM-3 and AM-4 titanosilicates.

This study investigates the impact of three key variables on the performance of nanoporous AM-3 and layered AM-4 titanosilicates in removing nine REEs (Y, La, Ce, Pr, Nd, Eu, Gd, Tb, and Dy) from natural mineral water and identifies optimal operational conditions using Response Surface Methodology (RSM). The experimental conditions were determined by a Box-Behnken Design of 3 factors-3 levels (pH 4, 6, and 8; sorbent dose 20, 100, and 180 mg/L; and element concentration 1, 3, and 5 µmol/L). Three-dimensional response surfaces were used to assess the linear, quadratic, and interaction influences of each factor on the REEs' removal percentage. The pH was the most significant factor in the removal process using AM-3, while the sorbent dose was more important for AM-4. The results highlighted the sorbents' strong capacity for REE removal. The optimal operating conditions obtained by RSM were applied to aqueous solutions with salinity 10 (common in coastal and transitional systems) and 30 (average seawater salinity). The results showed that AM-3 has a strong potential for removing REEs in solutions with salinity 10 and 30, while AM-4 was less efficient due to competition between REEs and other ions present in the solution.

Research on factor analysis and method for evaluating grouting effects using machine learning.

The evaluation of grouting effects constitutes a critical aspect of grouting engineering. With the maturity of the grouting project, the workload and empirical characteristics of grouting effect evaluation are gradually revealed. In the context of the Qiuji coal mine's directional drilling and grouting to limestone aquifer reformation, this study thoroughly analyzes the influencing factors of grouting effects from geological and engineering perspectives, comparing these with various engineering indices associated with drilling and grouting. This led to the establishment of a "dual-process, multi-parameter, and multi-factor" system, employing correlation analysis to validate the selected indices' reasonableness and scientific merit. Utilizing the chosen indices, eight high-performing machine learning models and three parameter optimization algorithms were employed to develop a model for assessing the effectiveness of directional grouting in limestone aquifers. The model's efficacy was evaluated based on accuracy, recall, precision, and F-score metrics, followed by practical engineering validation. Results indicate that the "dual-process, multi-parameter, multi-factor" system elucidates the relationship between influencing factors and engineering parameters, demonstrating the intricacy of evaluating grouting effects. Analysis revealed that the correlation among the eight selected indicators-including the proportion of boreholes in the target rock strata, drilling length, leakage, water level, pressure of grouting, mass of slurry injected, permeability properties of limestone aquifers before being grouted, permeability properties of limestone aquifers after being grouted-is not substantial, underscoring their viability as independent indicators for grouting effect evaluation. Comparative analysis showed that the Adaboost machine learning model, optimized via a genetic algorithm, demonstrated superior performance and more accurate evaluation results. Engineering validation confirmed that this model provides a more precise and realistic assessment of grouting effects compared to traditional methods.

Designing of high entropy alloys with high hardness: a metaheuristic approach.

The near-infinite compositional space of high-entropy-alloys (HEAs) is a huge resource-intensive task for developing exceptional materials. In the present study, an algorithmic framework has been developed to optimize the composition of an alloy with chosen set of elements, aiming to maximize the hardness of the former. The influence of phase on hardness prediction of HEAs was thoroughly examined. This study aims to establish generalized prediction models that aren't confined by any specific set of elements. We trained the HEA identification model to classify HEAs from non-HEAs, the multi-labeled phase classification model to predict phases of HEAs also considering the processing route involved in the synthesis of the alloy, and the hardness prediction model for predicting hardness and optimizing the composition of the given alloy. The purposed algorithmic framework uses twenty-nine alloy descriptors to compute the composition that demonstrates maximum hardness for the given set of elements along with its phase(s) and a label stating whether it is classified as HEA or not.

The Effect of Using a Remote Patient Management Platform in Optimizing Guideline-Directed Medical Therapy in Heart Failure Patients: A Randomized Controlled Trial.

BACKGROUND: Guideline-directed medical therapy (GDMT) remains underutilized in patients with heart failure with reduced ejection fraction, leading to morbidity and mortality. OBJECTIVES: The Medly Titrate (Use of Telemonitoring to Facilitate Heart Failure Mediation Titration) study was an open-label, randomized controlled trial to determine whether remote medication titration for patients with heart failure with reduced ejection fraction was more effective than usual care (UC). METHODS: In this study, 108 patients were randomized to remote GDMT titration through the Medly heart failure program (n = 56) vs UC (n = 52). The primary outcome was the proportion of patients completing GDMT titration at 6 months. Secondary outcomes included the number of clinic visits and time required to achieve titration, patient health outcomes, and health care utilization, including urgent clinic/emergency department visits and hospitalization. RESULTS: At 6 months, GDMT titration was completed in 82.1% (95% CI: 71.2%-90.8%) of patients in the intervention arm vs 53.8% in UC (95% CI: 41.1%-67.7%; P = 0.001). Remote titration required fewer in-person (1.62 ± 1.09 vs 2.42 ± 1.65; P = 0.004) and virtual clinic visits (0.50 ± 1.08 vs 1.29 ± 1.86; P = 0.009) to complete titration. Median time to optimization was shorter with remote titration (3.42 months [Q1-Q3: 2.99-4.04 months] vs 5.47 months [Q1-Q3: 4.14-7.33 months]; P < 0.001). The number of urgent clinic/emergency department visits (incidence rate ratio of remote vs control groups: 0.90 [95% CI: 0.53-1.56]; P = 0.70) were similar between groups, with a reduction in all-cause hospitalization with remote titration (incidence rate ratio: 0.55 [95% CI: 0.31-0.97]; P = 0.042). CONCLUSIONS: Remote titration of GDMT in heart failure with reduced ejection fraction was effective, safe, feasible, and increased the proportion of patients achieving target doses, in a shorter period of time with no excess adverse events compared with UC. (Use of Telemonitoring to Facilitate Heart Failure Mediation Titration [Medly Titrate]; NCT04205513).

Optimizing hydropower scheduling through accurate power load prediction: A practical case study.

Hydropower stations that are part of the grid system frequently encounter challenges related to the uneven distribution of power generation and associated benefits, primarily stemming from delays in obtaining timely load data. This research addresses this issue by developing a scheduling model that combines power load prediction and dual-objective optimization. The practical application of this model is demonstrated in a real-case scenario, focusing on the Shatuo Hydropower Station in China. In contrast to current models, the suggested model can achieve optimal dispatch for grid-connected hydropower stations even when power load data is unavailable. Initially, the model assesses various prediction models for estimating power load and subsequently incorporates the predictions into the GA-NSGA-II algorithm, specifically an enhanced elite non-dominated sorting genetic algorithm. This integration is performed while considering the proposed objective functions to optimize the discharge flow of the hydropower station. The outcomes reveal that the CNN-GRU model, denoting Convolutional Neural Network-Gated Recursive Unit, exhibits the highest prediction accuracy, achieving R-squared and RMSE (i.e., Root Mean Square Error) values of 0.991 and 0.026, respectively. The variance between scheduling based on predicted load values and actual load values is minimal, staying within 5 (m3/s), showcasing practical effectiveness. The optimized scheduling outcomes in the real case study yield dual advantages, meeting both the demands of ship navigation and hydropower generation, thus achieving a harmonious balance between the two requirements. This approach addresses the real-world challenges associated with delayed load data collection and insufficient scheduling, offering an efficient solution for managing hydropower station scheduling to meet both power generation and navigation needs.

Potential of photon counting computed tomography derived spectral reconstructions to reduce beam-hardening artifacts in chest CT.

PURPOSE: Aim of the recent study is to point out a method to optimize quality of CT scans in oncological patients with port systems. This study investigates the potential of photon counting computed tomography (PCCT) for reduction of beam hardening artifacts caused by port-implants in chest imaging by means of spectral reconstructions. METHOD: In this retrospective single-center study, 8 ROIs for 19 spectral reconstructions (polyenergetic imaging, monoenergetic reconstructions from 40 to 190 keV as well as iodine maps and virtual non contrast (VNC)) of 49 patients with pectoral port systems undergoing PCCT of the chest for staging of oncologic disease were measured. Mean values and standard deviation (SD) Hounsfield unit measurements of port-chamber associated hypo- and hyperdense artifacts, bilateral muscles and vessels has been carried out. Also, a structured assessment of artifacts and imaging findings was performed by two radiologists. RESULTS: A significant association of keV with iodine contrast as well as artifact intensity was noted (all p < 0.001). In qualitative assessment, utilization of 120 keV monoenergetic reconstructions could reduce severe and pronounced artifacts completely, as compared to lower keV reconstructions (p < 0.001). Regarding imaging findings, no significant difference between monoenergetic reconstructions was noted (all p > 0.05). In cases with very high iodine concentrations in the subclavian vein, image distortions were noted at 40 keV images (p < 0.01). CONCLUSIONS: The present study demonstrates that PCCT derived spectral reconstructions can be used in oncological imaging of the thorax to reduce port-derived beam-hardening artefacts. When evaluating image data sets within a staging, it can be particularly helpful to consider the 120 keV VMIs, in which the artefacts are comparatively low.

Development of iterative optimization technology: Selecting pure component spectra using a small-scale feed frame simulator.

The application of spectroscopic process analytical technology (PAT) for in-line data collection offers advantages to modern pharmaceutical manufacturing. Partial least squares (PLS) models are the preferred approach for predicting API potency from PAT data, particularly near-infrared (NIR) spectra. However, the calibration burden of PLS models is sometimes considered prohibitive. Pure component approaches, such as iterative optimization technology (IOT), have a reduced calibration burden for PAT applications. The IOT algorithm is dependent on several assumptions, including the harmonization of spectral collection conditions for pure component and mixture spectra. Collecting pure components under identical conditions to mixture spectra does not guarantee accurate predictions, and not all pure components are suitable for individual processing. This IOT assumption must be addressed to facilitate IOT application in PAT systems. In this work, IOT predicted API potency from in-line NIR spectra using combinations of stagnant and dynamic pure component spectra. A small number of mixture samples called a development set guided the selection of representative pure component spectral sets. Several model performance metrics from the development set predictions identified optimal pure component spectral sets for prediction of test sets. The combination of IOT and a development set generated accurate API potency predictions and potentiates the application of IOT in challenging pharmaceutical manufacturing settings. The IOT assumption of similar collection conditions should not be regarded as an assumption, but rather a consideration that the pure component spectral collection conditions should be representative of the mixture spectra to ensure appropriate predictions.