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"Growth Predictor": A new predictive modelling and quantitative microbial risk assessment tool

FOOD RESEARCH INTERNATIONAL 2025年 209卷 116329页

作者： Skandamis, Panagiotis N. Agr Univ Athens Dept Food Sci & Human Nutr Lab Food Qual Control & Hyg Iera Odos 75 Athens 11855 Greece

A new predictive modelling and quantitative microbial risk assessment (QMRA) software, developed in R, is available on-line (https://***/Microbial-Growth-Predictor-Dashboard/). Primary model fitting is carried out with the Baranyi model. Secondary fitting and growth simulations are based on gamma models with or without interactions. The same gamma terms can be used for fitting and growth simulations under static or dynamic conditions. One of the novel features is the use of normal distributions to describe the variability in T, pH, aw, the levels of a single inhibitor and the inter-strain variability in growth limits. The QMRA is comprised of four consecutive modules from primary production until consumption. In addition to prevalence, the modules may also consider partition, mixing and cross-contamination. Variability can be introduced through a variety of probability distributions, for initial contamination, re-contamination, storage time and temperature, product characteristics, serving size and maximum population density. Fixed or variable log reductions during cooking, may be introduced as user-defined values or probability distributions, respectively, or estimated by a Bigelow thermal inactivation model. The trilinear primary growth model is used for estimating log changes, based on mu max obtained by gamma models. The QMRA outputs include graphical distribution of ingested dose and probability of illness (Pill), as well as tabular estimates. The user may select built-in dose-response models, or define the parameters values of exponential, beta-Poisson, beta-binomial and binomial dose-response models. The tool is intended for multiple users from the scientific community, the authorities and the food industry, for growth simulations and high-resolution risk assessments.

关键词： predictive modelling QMRA Risk assessment Gamma models Fitting Cardinal values Software

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A review of predictive modelling and drone remote sensing technologies as a tool for detecting clandestine burials

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FORENSIC SCIENCE INTERNATIONAL 2025年 367卷 112375-112375页

作者： Koopman, Marissa Milliet, Quentin Champod, Christophe Univ Lausanne Fac Law Criminal Justice & Publ Adm Sch Criminal Justice Lausanne Switzerland

The search for missing people is a complex and intensive undertaking. predictive models (such as RAG mapping and geographic profiling) in combination with drone-mounted technologies can improve these searches by driving down time and monetary costs, gathering new types of data and reducing the need for investigators to expose themselves to dangerous environments. Promising technologies to discover traces of clandestine burials in the landscape are LiDAR, RGB photography, multispectral and hyperspectral imaging, as well as infrared/ thermal photography. This review covers the existing literature on these techniques and discusses future opportunities and directions.

关键词： Remote sensing UAVs Clandestine graves Missing persons Photography Imaging LiDAR Forensic science Detection predictive modelling RAG Geographic profiling

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Machine learning or traditional statistical methods for predictive modelling in perioperative medicine: A narrative review

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JOURNAL OF CLINICAL ANESTHESIA 2025年 102卷 111782-111782页

作者： Mann, Jason Lyons, Mathew O'Rourke, John Davies, Simon Sheffield Teaching Hosp NHS Fdn Trust Royal Hallamshire Hosp Anaesthesia & Operating Serv C FloorGlossop Rd Sheffield S11 2JF S Yorkshire England Univ Edinburgh Anaesthesia Edinburgh Scotland York & Scarborough Teaching Hosp York England Hull York Med Sch Ctr Hlth & Populat Sci Anaesthesia Heslington England

Prediction of outcomes in perioperative medicine is key to decision-making and various prediction models have been created to help quantify and communicate those risks to both patients and clinicians. Increasingly, machine learning (ML) is being favoured over more traditional techniques to improve prediction of outcomes, however, the studies are of varying quality. It is also not known whether any increase in predictive performance using ML algorithms transpires into a clinically meaningful benefit. This coupled with the difficulty in interrogating ML algorithms is a potential cause of concern within the medical community. In this review, we provide a concise appraisal of studies which develop perioperative predictive ML models and compare predictive performance to traditional statistical models. The search strategy, title and abstract screening, and full-text reviews produced 37 studies for data extraction. Initially designed as a systematic review but due to the heterogeneity of the population and outcomes, was written in the narrative. Perioperative ML and traditional predictive models continue to be developed and published across a range of populations. This review highlights several studies which show that ML can enhance perioperative prediction models, although this is not universal, and performance for both methods remain context dependent. By focusing on relevant patient-centred outcomes, model interpretability, external validation, and maintaining high standards of reporting and methodological transparency, researchers can develop ML models alongside traditional methods to enhance clinical decision-making and improve patient care.

关键词： Machine learning Medical statistics predictive modelling Perioperative medicine Anaesthesiology

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Predicting treatment success and failure using routine outcome data: The role of therapist effects in dynamic predictive modelling

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COUNSELLING & PSYCHOTHERAPY RESEARCH 2025年第1期25卷

作者： Mahon, Daryl Minami, Takuya Brown, Jeb Outcomes Matter Wicklow Ireland Univ Massachusetts Boston MA USA ACORN USA Ctr Clin Informat Boston MA USA ACORN USA Ctr Clin Informat Boston MA USA

Background: The goal of this research was to identify how early in psychotherapy a reliable prediction regarding the probability of premature termination, treatment failure or treatment success can be made, and which variables are most predictive. This research breaks new ground by including data on each clinician's historical outcomes (therapist effects) to explore to what degree predictions and decision rules might differ with therapist effectiveness. Materials & Methods: Data was drawn from a large online database of clients regularly completing self-report outcome questionnaires. The sample included clinicians (n = 1020) treating clients (n = 68,690) with an intake score in the clinical range during a treatment episode of between 2 and 10 sessions. Variables available at the first session and later sessions were tested for predictive validity for status at subsequent sessions using general linear regression models and logistic regression models. Results: The results suggest that therapy outcomes can be predicted as early as the second session with a good degree of accuracy, 65%. The analyses revealed that predictions made as early as the second session remain valid even for clients taking up to 10 sessions to complete treatment. Adding therapist effects to the predictive model provides a much more nuanced understanding of the results. By session two, therapists in the top quartile demonstrate effect sizes comparable to the effect sizes of bottom quartile therapists at session ten. Conclusion: The probability of client success, failure or early termination varies significantly depending on the clinician. Implications for training and supervision are discussed.

关键词： alliance effects feedback informed treatment measurement-based care predicting outcomes predictive modelling routine outcome monitoring therapist effects

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Machine learning-based predictive modelling of biodiesel production from animal fats catalysed by a blast furnace slag geopolymer

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RESULTS IN ENGINEERING 2025年 25卷

作者： Mwenge, Pascal Rutto, Hilary Vaal Univ Technol Fac Engn & Technol Dept Chem & Met Engn ZA-1900 Vanderbijlpark South Africa

Climate change and fossil fuel depletion have driven the need for environmentally friendly, low-carbon-emitting fuels. Biodiesel has emerged as a cleaner and sustainable renewable energy source. Yet its production is confronted by challenges such as high feedstock costs and wastewater generation from conventional catalysts. To address these issues, alternative feedstocks like animal fats and eco-friendly catalysts such as geopolymers have gained attention. However, a knowledge gap exists in machine learning (ML) applications for biodiesel production from animal fats catalysed by blast furnace slag (BFS) geopolymer. This study bridges this gap by employing predictive modelling methods: artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) to predict biodiesel yield. Four input parameters were studied: methanol-to-oil ratio (6-18 mol/ mol), catalyst ratio (5-15 wt. %), reaction time (3-7 h) and temperature (30-70 degrees C), with yield as output variable. The highest yield, 97.06 %, was achieved. MATLAB 2024b's NN toolbox and neuro-fuzzy designer toolbox were used to implement the models. Evaluation metrics were used to assess the models' effectiveness;both ANN and ANFIS demonstrated reliable performances, with ANFIS outperforming ANN. ANFIS achieved an R2 of 0.9857 and a lower mean squared error (MSE) of 2.9386, compared to ANN's R2 of 0.9781 and MSE of 6.2106. This study highlights the potential use of ML in biodiesel production from animal fats catalysed by BFS geopolymer, offering a significant contribution to the field while reducing environmental impacts. Future research should explore biodiesel's physicochemical properties and BFS geopolymer catalysts' reusability.

关键词： Biodiesel Blast furnace slag geopolymer Machine learning predictive modelling Animal fats

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predictive modelling of instant whole milk powder functional performance across three industrial plants

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JOURNAL OF FOOD ENGINEERING 2019年 252卷 1-9页

作者： Depree, N. Prince-Pike, A. Young, R. Wilson, D., I Univ Auckland Dept Chem & Mat Engn Auckland New Zealand Auckland Univ Technol Dept Elect Engn Auckland New Zealand

The efficient production of instant whole milk powders with stringent functional requirements is the economic goal for large-scale milk powder plants. However, the physical predictors of functional properties are not sufficiently characterised to ensure quality at the time of manufacture, and given high production rates, the time delays for testing can lead to significant amounts of devalued product if functional tests are subsequently failed. This work combines big-data collection and alignment with multivariate methods on a re-sampled, balanced data set to provide a real-time proxy measurement_ A unique dataset was constructed to compare three large-scale plants of different designs, overcoming significant challenges in alignment of disparate data sources, across six years of production. Models were clearly able to differentiate plant and process changes, and by appropriate weighting of the unusual results, were able to predict functional test results, especially the production of off-spec product. These models can be used in real-time to significantly lower the likelihood of producing powder that will fail customer functional tests, and therefore substantially reduce cost of quality failure.

关键词： Big data Data analysis Milk powder Dairy Multivariate predictive modelling

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predictive modelling techniques on the encapsulation size of spherical capsules for rapid cool thermal energy storage system

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JOURNAL OF ENERGY STORAGE 2024年 83卷

作者： Subramaniyan, Malarmannan Ponnusamy, Chandrasekaran SRM Inst Sci & Technol Fac Engn & Technol Dept Mech Engn Kattankulathur 603203 Tamil Nadu India

This study aims to study the solidification behaviour of the deionized water in spherical capsules widely found in the beds of thermal energy storage systems used for building air conditioning applications. More particularly this study deals with different encapsulation sizes, volume percentage of phase change material, and operating bath temperatures, to explore the merits of the larger size encapsulation over the smaller one. The major constraint associated with the solidification process is the increase in thermal resistance during the augmentation of solidified phase change material. The varying encapsulation size is the most influential factor, and its relationship with the bath temperatures and volume percentage of the phase change material during the solidification process is revealed. A Box-Behnken experimental design is adopted for the tests with the operating bath temperatures (-6 degrees C, -9 degrees C, and -12 degrees C) and different volumes of phase change material (50 %, 75 %, and 100 %) and varying spherical sizes (50 mm, 75 mm, and 100 mm). Using response surface regression, mathematical models were formulated to forecast the critical parameters of solidified front velocity, solidified mass rate, freezing time, and energy stored in the phase change material. Charging 50 % of phase change material volume in the 100 mm capsule exhibits a significant improvement in solidified front velocity, solidified mass rate, freezing time, and energy stored compared to charging 100 % volume of phase change material in the 50 mm and 75 mm capsules. Subcooling is eliminated for the 100 mm capsules without nucleating agents or nanoparticles. Charging 50 % of phase change material volume will significantly reduce the solidification duration by 74.96 % compared to the 100 % phase change material volume and increasing the size of the spherical capsule will enhance the energy storage capacity in the range of 4.5 and 7.9 times. The constructed predictive models exhibit a

关键词： Phase change material Deionized water Encapsulation size of spherical capsule predictive modelling Response surface methodology

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predictive modelling of Colour Strength and Ink Penetration (%) of Inkjet-Printed Cotton

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FIBERS AND POLYMERS 2018年第7期19卷 1435-1443页

作者： Faisal, Saira Tronci, Aurelio NED Univ Engn & Technol Text Engn Dept Karachi 75270 Pakistan Univ Cagliari Dept Mech Chem & Mat Engn I-09124 Cagliari Italy

Recent times have seen a rising interest in inkjet printing of textiles due to potential benefits it offers over conventional printing methods such as lean set-up costs and enabling cost effective short runs. The growing interest has made pretreatment of fabric to be the subject of much recent research. The aim of the study was to develop a model which can predict the colour strength and ink penetration (%) of inkjet-printed cotton as a function of pre- and post- treatment process variables. The independent variables investigated were concentration of thickener, urea, and alkali and steaming time. The experimental plan was based on the full factorial design. predictive models were constructed by modelling the values of the independent variables and their coefficient of regression. It can be concluded that most significant predictor affecting the colour strength was concentration of urea followed by concentration of thickener;whereas, for ink penetration (%), the most influential predictor was concentration of thickener followed by concentration of urea and steaming time. The adequacy of predictive models was evaluated by analysis of variance, coefficient of determination (R-2) and residual analysis and found to be accurate at 95 % confidence level.

关键词： Inkjet printing Pretreatment Colour strength Ink penetration (%) predictive modelling

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predictive modelling of surface roughness in fused deposition modelling using data fusion

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2019年第12期57卷 3992-4006页

作者： Wu, Dazhong Wei, Yupeng Terpenny, Janis Univ Cent Florida Dept Mech & Aerosp Engn Orlando FL 32816 USA Univ Cent Florida Dept Ind Engn & Management Syst Orlando FL 32816 USA Penn State Univ Dept Ind & Mfg Engn University Pk PA 16802 USA

To realise high quality, additively manufactured parts, real-time process monitoring and advanced predictive modelling tools are crucial for accelerating quality assurance in additive manufacturing. While previous research has demonstrated the effectiveness of physics- and model-based diagnosis and prognosis for additive manufacturing, very little research has been reported on real-time monitoring and predictive modelling of the surface roughness of additively manufactured parts. This paper presents a data fusion approach to predicting surface roughness in fused deposition modelling (FDM) processes. The predictive models are trained using random forests (RFs), support vector regression (SVR), ridge regression (RR), and least absolute shrinkage and selection operator (LASSO). A real-time monitoring system is developed to monitor the health condition of a FDM machine in real-time using multiple sensors. RFs, SVR, RR, and LASSO are demonstrated on the condition monitoring data collected from these sensors. To integrate the data sources, a feature-level data fusion method is introduced. Experimental results have shown that the predictive models trained by the machine learning algorithms are capable of predicting the surface roughness of additively manufacturing parts with very high accuracy. The prediction accuracy can be further improved using the data fusion method.

关键词： predictive modelling additive manufacturing condition monitoring surface roughness data fusion

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predictive modelling of angiotensin converting enzyme inhibitory dipeptides

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FOOD CHEMISTRY 2012年第4期133卷 1349-1354页

作者： Norris, Roseanne Casey, Fergal FitzGerald, Richard J. Shields, Denis Mooney, Catherine Univ Limerick Dept Life Sci Limerick Ireland Univ Coll Dublin Complex & Adapt Syst Lab Conway Inst Biomol & Biomed Res Dublin 2 Ireland Univ Coll Dublin Sch Med & Med Sci Dublin 2 Ireland

The ability of docking to predict angiotensin converting enzyme (ACE) inhibitory dipeptide sequences was assessed using AutoDock Vina. All potential dipeptides and phospho-dipeptides were docked and scored. Peptide intestinal stability was assessed using a prediction amino acid clustering model. Selected dipeptides, having AutoDock Vina scores <= -8.1 and predicted to be 'stable' intestinally, were characterised. using LIGPLOT and for ACE-inhibitory potency. Two newly identified ACE-inhibitory dipeptides, Asp-Trp and Trp-Pro, having Vina scores of -8.3 and -8.6 gave IC50 values of 258 +/- 4.23 and 217 +/- 15.7 mu M, respectively. LIGPLOT analysis indicated no zinc interaction for these dipeptides. Phospho-dipeptides were predicted to have a good affinity for ACE. However, the experimentally determined IC50 results did not correlate since, for example, Trp-pThr and Pro-pTyr, having Vina scores of -8.5 and -8.1, respectively, displayed IC50 values of >500 mu M. While docking allowed identification of new ACE inhibitory dipeptides, it may not be a fully reliable predictive tool in all cases. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： ACE inhibition Dipeptides predictive modelling AutoDock Vina Intestinal stability

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