Model Validation Research Articles

Development and validation of an early diagnosis model for bone metastasis in non-small cell lung cancer based on serological characteristics of the bone metastasis mechanism

Bone metastasis significantly impact the prognosis of non-small cell lung cancer (NSCLC) patients, reducing their quality of life and shortening their survival. Currently, there are no effective tools for the diagnosis and risk assessment of early bone metastasis in NSCLC patients. This study employed machine learning to analyze serum indicators that are closely associated with bone metastasis, aiming to construct a model for the timely detection and prognostic evaluation of bone metastasis in NSCLC patients. The derivation cohort consisted of 664 individuals with stage IV NSCLC, diagnosed between 2015 and 2018. The variables considered in this study included age, sex, and 18 specific serum indicators that have been linked to the occurrence of bone metastasis in NSCLC. Variable selection used multivariate logistic regression analysis and Lasso regression analysis. Six machine learning methods were utilized to develop a bone metastasis diagnostic model, assessed with Area Under the Curve (AUC), Decision Curve Analysis (DCA), sensitivity, specificity, and validation cohorts. External validation used 113 NSCLC patients from the Medical Alliance (2019-2020). Furthermore, a prospective validation study was conducted on a cohort of 316 patients (2019-2020) who were devoid of bone metastasis, and followed-up for at least two years to assess the predictive capabilities of this model. The model's prognostic value was evaluated using Kaplan-Meier survival curves. Through variable selection, 11 serum indictors were identified as independent predictive factors for NSCLC bone metastasis. Six machine learning models were developed using age, sex, and these serum indicators. A random forest (RF) model demonstrated strong performance during the training and internal validation cohorts, achieving an AUC of 0.98 (95% CI 0.95-0.99) for internal validation. External validation further confirmed the RF model's effectiveness, yielding an AUC of 0.97 (95% CI 0.94-0.99). The calibration curves demonstrated a high level of concordance between the anticipated risk and the observed risk of the RF model. Prospective validation revealed that the RF model could predict the occurrence of bone metastasis approximately 10.27±3.58 months in advance, according to the results of the SPECT. An online computing platform (https://bonemetastasis.shinyapps.io/shiny_cls_1model/) for this RF model is publicly available and free-to-use by doctors and patients. This study innovatively employs age, gender, and 11 serological markers closely related to the mechanism of bone metastasis to construct an RF model, providing a reliable tool for the early screening and prognostic assessment of bone metastasis in NSCLC patients. However, as an exploratory study, the findings require further validation through large-scale, multicenter prospective studies. This work is supported by the National Natural Science Foundation of China (NO.81974315); Shanghai Municipal Science and Technology Commission Medical Innovation Research Project (NO.20Y11903300); Shanghai Municipal Health Commission Health Industry Clinical Research Youth Program (NO.20204Y034).

Harnessing Computational Modeling for Efficient Drug Design Strategies

Abstract: Computational modeling has become a crucial tool in drug design, offering efficiency and cost-effectiveness. This paper discusses the various computational modeling techniques used in drug design and their role in enabling efficient drug discovery strategies. Molecular docking predicts the binding affinity of a small molecule to a target protein, allowing the researchers to identify potential lead compounds and optimize their interactions. Molecular dynamics simulations provide insights into protein-ligand complexes, enabling the exploration of conformational changes, binding free energies, and fundamental protein-ligand interactions. Integrating computational modeling with machine learning algorithms, such as QSAR modeling and virtual screening, enables the prediction of compound properties and prioritizes potential drug candidates. High-performance computing resources and advanced algorithms are essential for accelerating drug design workflows, with parallel computing, cloud computing, and GPU acceleration reducing computational time. The paper also addresses the challenges and limitations of computational modeling in drug design, such as the accuracy of scoring functions, protein flexibility representation, and validation of predictive models. It emphasizes the need for experimental validation and iterative refinement of computational predictions to ensure the reliability and efficacy of designed drugs.

Formulation, Implementation, and Validation of a 3D Damage-Plasticity Cohesive-Interface Model with Multiple Yield Surfaces for Cyclic Modeling of Mortar Joints

Formulation, Implementation, and Validation of a 3D Damage-Plasticity Cohesive-Interface Model with Multiple Yield Surfaces for Cyclic Modeling of Mortar Joints

Effects of environmental humidity on the gap of mortise-tenon joints: experimental and numerical investigations

Mortise-tenon (MT) joints play a vital role in the lateral deformability and seismic performance of traditional timber structures. However, moisture exposure can cause shrinkage-induced gaps and then affect the durability of MT joints. Limited knowledge exists on the humidity response of MT joints. To investigate the effect of environmental humidity on the gaps between mortise and tenon, multiple measurements of moisture content (MC) and shrinkage deformation were conducted on straight MT joints that were subjected to humidity variations. Different ambient conditions and exposure levels were taken into account. Three-dimensional numerical models were established to simulate the humidity response of MT joints. Model validation was then performed by comparing the simulation and experimental results in terms of moisture distribution and gaps. On this basis, quantitative analysis was made to reveal the development mechanism and main influence factors of moisture-induced gaps. Results show that straight MT joints are vulnerable to environmental humidity changes. The observed gap is estimated to cause a decrease of up to 23.1% in the initial elastic stiffness. Although only contributed by the shrinkage deformation of tenons, the vertical gap is theoretically larger than the horizontal one which is attributed to a combination of both mortise and tenon. A clear linear relationship is found between the vertical gaps and the MC decrease, with the product of the radial shrinkage coefficient and tenon height as the theoretical ratio. Controlling the moisture exposure is considered the most effective way to reduce moisture-induced gaps in MT joints.

Development and internal validation of machine-learning models for predicting survival in patients who underwent surgery for spinal metastases.

A retrospective study. This study aimed to develop machine-learning algorithms for predicting survival in patients who underwent surgery for spinal metastasis. This study develops machine-learning models to predict postoperative survival in spinal metastasis patients, filling the gaps of traditional prognostic systems. Utilizing data from 389 patients, the study highlights XGBoost and CatBoost algorithms̓ effectiveness for 90, 180, and 365-day survival predictions, with preoperative serum albumin as a key predictor. These models offer a promising approach for enhancing clinical decision-making and personalized patient care. A registry of patients who underwent surgery (instrumentation, decompression, or fusion) for spinal metastases between 2004 and 2018 was used. The outcome measure was survival at postoperative days 90, 180, and 365. Preoperative variables were used to develop machine-learning algorithms to predict survival chance in each period. The performance of the algorithms was measured using the area under the receiver operating characteristic curve (AUC). A total of 389 patients were identified, with 90-, 180-, and 365-day mortality rates of 18%, 41%, and 45% postoperatively, respectively. The XGBoost algorithm showed the best performance for predicting 180-day and 365-day survival (AUCs of 0.744 and 0.693, respectively). The CatBoost algorithm demonstrated the best performance for predicting 90-day survival (AUC of 0.758). Serum albumin had the highest positive correlation with survival after surgery. These machine-learning algorithms showed promising results in predicting survival in patients who underwent spinal palliative surgery for spinal metastasis, which may assist surgeons in choosing appropriate treatment and increasing awareness of mortality-related factors before surgery.

Inflammation-based lung adenocarcinoma molecular subtype identification and construction of an inflammation-related signature with bulk and single-cell RNA-seq data.

The role of inflammation is increasingly understood to have a central influence on therapeutic outcomes and prognosis in lung adenocarcinoma (LUAD). However, the detailed molecular divisions involved in inflammatory responses are yet to be fully elucidated. Our study identified two main inflammation-oriented LUAD grades: the inflammation-low (INF-low) and the inflammation-high (INF-high) subtypes. Both presented with unique clinicopathological features, implications for prognosis, and distinctive tumor microenvironment profiles. Broadly, the INF-low grade, marked by its dominant immunosuppressive tumor microenvironment, was accompanied by less favorable prognostic outcomes and a heightened prevalence of oncogenic mutations. In contrast, the INF-high grade exhibited more optimistic clinical trajectories, underscored by its immune-active environment. In addition, our efforts led to the conceptualization and empirical validation of an inflammation-centric predictive model with considerable predictive potency. Our study paves the way for a refined inflammation-centric LUAD classification and fosters a deeper understanding of tumor microenvironment intricacies.

Validation of a synthetic simulation model of endoscopic rectus sheath plication.

Literature reviews outline minimally invasive approaches for abdominal diastasis in patients without skin excess. However, few surgeons are trained in endoscopic rectus sheath plication, and no simulated training programs exist for this method. This study aimed to develop and validate a synthetic simulation model for the training of skills in this approach under the Messick validity framework. A cross-sectional study was carried out to assess the participants' previous level of laparoscopic/endoscopic skills by a questionnaire. Participants performed an endoscopic plication on the model and their performance was evaluated by one blinded observer using the global rating scale OSATS and a procedure specific checklist (PSC) scale. A 5-level Likert survey was applied to 5 experts and 4 plastic surgeons to assess Face and Content validity. Fifteen non-experts and 5 experts in abdominal wall endoscopic surgery were recruited. A median OSATS score [25 (range 24-25) vs 14 (range 5-22); p < 0.05 of maximum 25 points] and a median PSC score [11 (range 10-11) vs 8 (range 3-10); p < 0.05 of maximum 11 points] was significantly higher for experts compared with nonexperts. All experts agreed or strongly agreed that the model simulates a real scenario of endoscopic plication of the rectus sheath. Our simulation model met all validation criteria outlined in the Messick framework, demonstrating its ability to differentiate between experts and non-experts based on their baseline endoscopic surgical skills. This model stands as a valuable tool for evaluating skills in endoscopic rectus sheath plication.

Development and internal validation of a clinical prediction model for osteopenia in Chinese middle-aged and elderly men: a prospective cohort study

BackgroundEarly identification of patients at risk of osteopenia is an essential step in reducing the population at risk for fractures. We aimed to develop and validate a prediction model for osteopenia in Chinese middle-aged and elderly men that provides individualized risk estimates.MethodsIn this prospective cohort study, 1109 patients who attend regular physical examinations in the Second Medical Centre of Chinese PLA General Hospital were enrolled from 2015.03 to 2015.09. The baseline risk factors included dietary habits, exercise habits, medical histories and medication records. Osteopenia during follow-up were collected from Electronic Health Records (EHRs) and telephone interviews. Internal validation was conducted using bootstrapping to correct the optimism. The independent sample T-test analysis, Mann_Whitney U test, Chi-Square Test and multivariable Cox regression analysis were utilized to identify predictive factors for osteopenia in Chinese middle-aged and elderly men. A nomogram based on the seven variables was built for clinical use. Concordance index (C-index), receiver operating characteristic curve (ROC), decision curve analysis (DCA) and calibration curve were used to evaluate the efficiency of the nomogram.ResultsThe risk factors included in the prediction model were bone mineral density at left femoral neck (LNBMD), hemoglobin (Hb), serum albumin (ALB), postprandial blood glucose (PBG), fatty liver disease (FLD), smoking and tea consumption. The C-index for the risk nomogram was 0.773 in the prediction model, which presented good refinement. The AUC of the risk nomogram at different time points ranged from 0.785 to 0.817, exhibiting good predictive ability and performance. In addition, the DCA showed that the nomogram had a good clinical application value. The nomogram calibration curve indicated that the prediction model was consistent.ConclusionsOur study provides a novel nomogram and a web calculator that can effectively predict the 7-year incidence risk of osteopenia in Chinese middle-aged and elderly men. It is convenient for clinicians to prevent fragility fractures in the male population.

Predicting Hospitalization Risk Among Home Care Residents in the United Kingdom: Development and Validation of a Machine Learning-Based Predictive Model

Preventable hospital admissions in elderly home care residents are a major socioeconomic burden, whilst early detection of deterioration may improve outcomes. Our goal was to develop and validate a machine learning-based algorithm to predict hospitalization risk among home care users. Our primary outcome was hospitalization. An existing risk score (1-5) was assessed for its discriminatory capacity over time. We subsequently developed a new machine learning model using carer concerns, service user demographics, and other home care data between January and July 2021. We randomly selected 150 service user records for validation, which were evaluated by both the model and 10 clinicians (9 doctors and 1 nurse) to compare prediction time and accuracy to human experts. Comparison between model and human was via area under the receiver operating characteristic curve (AUC). A score of 5 conferred an 8x higher likelihood of hospitalization in the subsequent 7 days (15.4% vs 1.8%, p &lt; .05), compared to a score of 1. The new model and risk score increased performance, detecting 182 hospitalizations/month (3.7x chance). The AUC for the model was significantly higher than for clinicians (0.87 vs 0.41-0.57, respectively; p &lt; .05). The model took &lt;1 minute, while clinicians typically took over 40 minutes. A risk prediction model using carer concerns and other home care data features detects 3.7x more hospitalizations than chance. The model is faster and more accurate than human clinicians, enabling low-cost scale-up. This study supports linking the model to a triage and intervention service to reduce preventable hospitalizations in the home care sector.

Theoretical and Experimental Analysis of the Effect of Vaporization Heat on the Interaction between Laser and Biological Tissue

A theoretical model, based on the classical Pennes’ bioheat theory, incorporating various boundary conditions, was established and compared to analyze the influence of the latent heat of vaporization via simulation. The aim was to elucidate the extent of its influence. The thermal damage rate, governed by the vaporization heat of biological tissue, is introduced as a key factor. Functional relationships between temperature and incident laser power, spatial position, and time are derived from the classical Pennes’ bioheat equation. According to the theoretical model, numerical simulations and experimental validations are conducted using Comsol Multiphysics 6.0, considering the tissue latent heat of vaporization. The model incorporating the latent heat of vaporization proved more suitable for analyzing the interactions between laser and biological tissue, evident from the degree of fit between simulated and experimental data. The minimum deviations between theoretical and experimental observations were determined to be 2.43% and 5.11% in temperature and thermal damage, respectively. Furthermore, this model can be extended to facilitate the theoretical analysis of the impact of vaporization heat from different primary tissue components on laser-tissue interaction.

Development of parametric window and optimization of process parameters to predict bead profile in magnetically controlled gas tungsten arc welding

A novel methodology has been proposed to tame the arc shape by adopting an external magnetic field, resulting weld profile as required. The co-axial magnetic field developed by specially designed electromagnets is superimposed on the welding arc. It was found that 0-0-S-N configuration provided more penetration than conventional gas tungsten arc welding. A parametric window has been developed for the selected configuration to obtain the desired bead geometry. The experiments were performed using process parameters as suggested by the design matrix, developed using response surface method technique. Mathematical models were evolved from experimental data for penetration and bead width. The evolved model for penetration is adequate up to 99.72% confidence level and for bead width is 99.98% confidence level. The effects of process parameters have been presented in a graphical manner for better understanding. The penetration achieved with the magnetically controlled GTAW process is 3.92 mm, which is 30% more than that achieved with conventional GTAW. The bead width increases initially, up to a certain limit, and then reduces with an increase in excitation current. Further, the experiments have been conducted on the optimized parameters for the validation of models. The refined grains were obtained due to magnetic stirring of the molten pool, which is desirable for improvement in mechanical properties of welds. The average grain size was reduced from 42.55 to 31.03 µm. The improved microstructure containing more amount of acicular ferrite was obtained with magnetically controlled arc.

SWAT Model Calibration and Validation with Remotely Sensed Evapotranspiration data in Maklang-Tuyungbi-Taret lok Ungauged Basin in Manipur, India

SWAT Model Calibration and Validation with Remotely Sensed Evapotranspiration data in Maklang-Tuyungbi-Taret lok Ungauged Basin in Manipur, India

A combined uncertainty and sensitivity analysis of melting in a cubical cavity, Part A: Model validation

Latent heat thermal energy storage systems exploit the solid–liquid phase transition of a material to store thermal energy almost isothermally with high energy density. Numerical methods are increasingly being used for the targeted design of these storage systems. The problem, however, is that the results obtained with these methods – despite a frequently shown near-perfect agreement between validation experiments and simulation results – are subject to large uncertainty. To illustrate the extent of this problem and to show approaches to solving it, a combined uncertainty and sensitivity analysis of the melting process of a paraffin (octadecane) in a cuboid heated from one side is carried out in this two-paper series. In part A, we present the model and validate it; in part B we use it to carry out the combined uncertainty and sensitivity analysis. The validation of the model is performed with the help of experimental results. For this purpose, we measure the liquid phase fraction, the position of the phase boundary, the velocity field in the liquid and the temperature inside the test capsule. We make sure that the experimental results are reproducible. Systematic deviations are discussed and, if possible, the results are corrected for their value. When measuring the velocity field using particle image velocimetry (PIV), the greatest systematic deviation is due to the inhomogeneous refractive index field in the liquid phase. Finally, it is shown that, in the present case, the influence is very low if constant material properties evaluated at the mean temperature are taken for each phase compared to a temperature-dependent implementation. An exception is the density. If the density change during melting is not taken into account the deviation of the melt fraction lies between 6 and 8%. In summary, the validation of the numerical model is very satisfactory and demonstrates the suitability of the model for performing the investigations of the part B paper.

Pest Detection and Classification in Peanut Crops

Recent advancements in image processing have significantly improved pest detection and classification in peanut crops. Our study introduces an innovative approach that optimizes image features for accurate pest identification. Leveraging insights from successful image analysis methodologies, our model employs a tailored architecture for pest detection, segmentation, and classification tasks. By integrating dual branch segment representations and a dual-layer transformer encoder, we aim to enhance image representations and consolidate pest image segments of varying sizes. We evaluate our approach using three distinct pest datasets—Aphids, Wireworm, and Gram Caterpillar—ensuring comprehensive analysis and model validation. Prior to training, we preprocess the datasets extensively, employing feature extraction techniques and addressing image quality issues. We then apply normalization procedures to standardize the data for seamless integration into our model architecture. Our methodology focuses on extracting key features through self-attention mechanisms and standardized scaling processes to enhance predictive capabilities. Comprehensive experimentation demonstrates the superiority of our approach, outperforming established benchmarks in pest detection and classification with high accuracy rates. In summary, our study presents a novel framework that optimizes feature extraction and enhances predictive accuracy in pest detection and classification for peanut crops, addressing the unique challenges of agricultural pest identification.

Establishment and validation of a predictive model for tracheotomy in critically ill patients and analysis of the impact of different tracheotomy timing on patient prognosis

BackgroundIn critically ill patients receiving invasive mechanical ventilation (IMV), it is unable to determine early which patients require tracheotomy and whether early tracheotomy is beneficial.MethodsClinical data of patients who were first admitted to the ICU and underwent invasive ventilation for more than 24 h in the Medical Information Marketplace in Intensive Care (MIMIC)-IV database were retrospectively collected. Patients were categorized into successful extubation and tracheotomy groups according to whether they were subsequently successfully extubated or underwent tracheotomy. The patients were randomly divided into model training set and validation set in a ratio of 7:3. Constructing predictive models and evaluating and validating the models. The tracheotomized patients were divided into the early tracheotomy group (< = 7 days) and the late tracheotomy group (> 7 days), and the prognosis of the two groups was analyzed.ResultsA total of 7 key variables were screened: Glasgow coma scale (GCS) score, pneumonia, traumatic intracerebral hemorrhage, hemorrhagic stroke, left and right pupil responses to light, and parenteral nutrition. The area under the receiver operator characteristic (ROC) curve of the prediction model constructed through these seven variables was 0.897 (95% CI: 0.876–0.919), and 0.896 (95% CI: 0.866–0.926) for the training and validation sets, respectively. Patients in the early tracheotomy group had a shorter length of hospital stay, IMV duration, and sedation duration compared to the late tracheotomy group (p < 0.05), but there was no statistically significant difference in survival outcomes between the two groups.ConclusionThe prediction model constructed and validated based on the MIMIC-IV database can accurately predict the outcome of tracheotomy in critically ill patients. Meanwhile, early tracheotomy in critically ill patients does not improve survival outcomes but has potential advantages in shortening the duration of hospitalization, IMV, and sedation.

An Intelligent Model and Methodology for Predicting Length of Stay and Survival in a Critical Care Hospital Unit

This paper describes the design and methodology for the development and validation of an intelligent model in the healthcare domain. The generated model relies on artificial intelligence techniques, aiming to predict the length of stay and survival rate of patients admitted to a critical care hospitalization unit with better results than predictive systems using scoring. The proposed methodology is based on the following stages: preliminary data analysis, analysis of the architecture and systems integration model, the big data model approach, information structure and process development, and the application of machine learning techniques. This investigation substantiates that automated machine learning models significantly surpass traditional prediction techniques for patient outcomes within critical care settings. Specifically, the machine learning-based model attained an F1 score of 0.351 for mortality forecast and 0.615 for length of stay, in contrast to the traditional scoring model’s F1 scores of 0.112 for mortality and 0.412 for length of stay. These results strongly support the advantages of integrating advanced computational techniques in critical healthcare environments. It is also shown that the use of integration architectures allows for improving the quality of the information by providing a data repository large enough to generate intelligent models. From a clinical point of view, obtaining more accurate results in the estimation of the ICU stay and survival offers the possibility of expanding the uses of the model to the identification and prioritization of patients who are candidates for admission to the ICU, as well as the management of patients with specific conditions.

A structured process for the validation of a decision-analytic model: application to a cost-effectiveness model for risk-stratified national breast screening.

Decision-makers require knowledge of the strengths and weaknesses of decision-analytic models used to evaluate healthcare interventions to be able to confidently use the results of such models to inform policy. A number of aspects of model validity have previously been described, but no systematic approach to assessing the validity of a model has been proposed. This study aimed to consolidate the different aspects of model validity into a step-by-step approach to assessing the strengths and weaknesses of a decision-analytic model. A pre-defined set of steps were used to conduct the validation process of an exemplar early decision-analytic-model-based cost-effectiveness analysis of a risk-stratified national breast cancer screening programme [UK healthcare perspective; lifetime horizon; costs (£; 2021)]. Internal validation was assessed in terms of descriptive validity, technical validity and face validity. External validation was assessed in terms of operational validation, convergent validity (or corroboration) and predictive validity. The results outline the findings of each step of internal and external validation of the early decision-analytic-model and present the validated model (called 'MANC-RISK-SCREEN'). The positive aspects in terms of meeting internal validation requirements are shown together with the remaining limitations of MANC-RISK-SCREEN. Following a transparent and structured validation process, MANC-RISK-SCREEN has been shown to have satisfactory internal and external validity for use in informing resource allocation decision-making. We suggest that MANC-RISK-SCREEN can be used to assess the cost-effectiveness of exemplars of risk-stratified national breast cancer screening programmes (NBSP) from the UK perspective. A step-by-step process for conducting the validation of a decision-analytic model was developed for future use by health economists. Using this approach may help researchers to fully demonstrate the strengths and limitations of their model to decision-makers.

Prostate intra-fraction motion recorded by transperineal ultrasound.

Infra-fraction motion of the prostate was recorded during 3.423 fractions of image guided radiotherapy (IGRT) in 191 patients, 14 of which were treated by intensity modulated radiation therapy (IMRT), and 177 of which were treated by volumetric arc therapy (VMAT). The prostate was imaged by three-dimensional and time-resolved transperineal ultrasound (4D-US) of type Clarity by Elekta AB, Stockholm, Sweden. The prostate volume was registered and the prostate position (center of volume) was recorded at a frequency of 2.0 samples per second. This raw data set contains a total of 1.985.392 prostate and patient couch positions over a time span of 272 hours, 52 minutes and 34 seconds of life radiotherapy as exported by the instrument software. This data set has been used for the validation of models of prostate intra-fraction motion and for the estimation of the dosimetric impact of actual intra-fraction motion on treatment quality and side effects. We hope that this data set may be reused by other groups for similar purposes.

Vertical vibration model for the roll system of a six-high rolling mill based on the Timoshenko beam theory

During the production of cold-rolled strips, the high-speed operation of rolling mills generates vibrations that significantly impact the production efficiency, the strip quality, and the rolling mill service life. Therefore, investigating the vibration characteristics of rolling mills is of paramount importance. Previous studies predominantly treated the rolls as point masses, overlooking variations in the vibrations along the lengths of the rolls. In this study, the working, intermediate, and backup rolls were regarded as continuous elastic bodies, and they were simplified as Timoshenko beams. The modal superposition method was used to solve the vertical vibration equations for the rolls of a six-high rolling mill; thus, the natural frequencies and modal shapes for the rolls were obtained for both free and forced vibrations. A finite-element model validation confirmed that the accuracy of the novel Timoshenko beam six-high rolling mill vibration model introduced in this paper is significantly higher than that of the traditional Euler beam model when the roller length-to-diameter ratio is relatively small. Further analysis based on this model indicates that the forced vibration of the rolls is primarily influenced by fluctuations in rolling force. The amplitude of forced vibration is affected by factors such as the friction coefficient in the deformation zone, rolling speed, and cumulative rolling distance of the work rolls. These findings provide a precise representation of the characteristics of forced vibrations in rolling mills and offer valuable theoretical insight to ensure stable rolling mill operation.

Manure temperature prediction for slurry storage in Sweden: Model validation including effects of shading, snow cover and mixing

Measuring and modelling manure temperatures are crucial for estimating greenhouse gas emissions from liquid manure storage. The manure temperature was recorded at various depths in two swine slurry storage tanks situated in Vallentuna (VA) and Örsundsbro (OR) in Sweden. These data were used to assess the effectiveness of a revised mechanistic model for estimating manure temperatures, which incorporates the effects of wall shading, snow cover, and manure input mixing. The average manure temperatures were higher than air temperatures in the summer and fall. This indicated that using air temperature would result in an underestimation of methane emissions when applying the 2019 IPCC Refinement methodology. The revised model estimated manure temperatures for spring, summer, fall, and winter as 4.8, 16.1, 7.8, and 2.6 °C at the VA tank and 11.6, 17.1, 9.5, and 3.6 °C at the OR tank. The root mean square errors between daily simulated and observed temperatures in the summer decreased in both tanks due to incorporating shadow effect into the revised model. Fall estimates did not improve, possibly because of uncertainties from slurry removal and higher precipitation inputs. Sensitivity analysis indicated that solar radiative heat input was reduced with higher tank walls and smaller tank diameters when applying the revised model. Wall shading may influence manure temperatures in tanks with small diameters at high-latitude locations. This study offers insights into understanding the relationship between manure temperatures and its thermal balance influenced by latitude, storage design, snow cover and mixing, and its implications for accurately estimating methane emissions.