BACKGROUND: Automatic transdiagnostic risk calculators can improve detection of individuals at risk of psychosis. However, they rely on a single point in time assessment and can be refined with dynamic modelling techniques that account for changes in risk over time.
METHODS: We included n=158,139 patients (n=5,007 events) receiving a first index diagnosis of a non-organic and non-psychotic mental disorder within Electronic Health Records from the SLaM NHS Foundation Trust between 01/01/2008 and 10/08/2021. A dynamic Cox landmark model was developed to estimate the 2-year risk of developing psychosis according to TRIPOD statement. The dynamic model included 24 predictors extracted at nine landmark points (baseline, 0, 6, 12, 24, 30, 36, 42, and 48 months): three demographic, one clinical, and 20 Natural Language Processing (NLP) based symptom and substance use predictors. Performance was compared to a static Cox regression model with all predictors assessed at baseline only, indexed via discrimination (C-index), calibration (calibration plots), and potential clinical utility (decision curves) in internal-external validation.
RESULTS: The dynamic model improves discrimination performance compared to the static model at baseline (dynamic: C-index=0.9; static: C-index=0.87) to the final landmark point (dynamic: C-index=0.79; static: C-index=0.76). The dynamic model was also significantly better calibrated (calibration slope=0.97-1.1) than the static model at later landmark points (≥24 months). Net benefit was higher in the dynamic compared to the static model at later landmark points (≥24 months).
CONCLUSIONS: These findings suggest that dynamic prediction models can improve detection of individuals at risk for psychosis in secondary mental health care.

A 1D model describing the dynamics of an injection moulding machine and the injection process is presented. The model describes an injection cylinder actuated by a dual-pump electro-hydraulic speed-variable drive and the filling, holding and cooling phases of the injection moulding process utilising amorphous polymers. The model is suggested as the foundation for the design of model-based pressure controllers of, e.g., the nozzle pressure. The focus is on using material, mould and machine properties to construct the model, making it possible to analyse and design the dynamic system prior to manufacturing hardware or conducting experiments. Both the presented model and the developed controller show good agreement with experimental results. The proposed method is general in nature and enables the design, analysis and evaluation of the machine, material and mould dynamics for controller design based solely on the physical properties of the system.

BACKGROUND: Selecting the time target for follow-up testing in lung cancer screening is challenging. We aim to devise dynamic, personalized lung cancer screening schema for patients with pulmonary nodules detected through low-dose computed tomography.
METHODS: We developed and validated dynamic models using data of pulmonary nodule patients (aged 55-74 years) from the National Lung Screening Trial. We predicted patient-specific risk profiles at baseline (R0) and updated the risk evaluation results in repeated screening rounds (R1 and R2). We used risk cutoffs to optimize time-dependent sensitivity at an early decision point (3 months) and time-dependent specificity at a late decision point (1 year).
RESULTS: In validation, area under receiver operating characteristic curve for predicting 12-month lung cancer onset was 0.867 (95 % confidence interval: 0.827-0.894) and 0.807 (0.765-0.948) at R0 and R1-R2, respectively. The personalized schema, compared with National Comprehensive Cancer Network (NCCN) guideline and Lung-RADS, yielded lower rates of delayed diagnosis (1.7% vs. 1.7% vs. 6.9 %) and over-testing (4.9% vs. 5.6% vs. 5.6 %) at R0, and lower rates of delayed diagnosis (0.0% vs. 18.2% vs. 18.2 %) and over-testing (2.6% vs. 8.3% vs. 7.3 %) at R2. Earlier test recommendation among cancer patients was more frequent using the personalized schema (vs. NCCN: 29.8% vs. 20.9 %, p = 0.0065; vs. Lung-RADS: 33.2% vs. 22.8 %, p = 0.0025), especially for women, patients aged ≥65 years, and part-solid or non-solid nodules.
CONCLUSIONS: The personalized schema is easy-to-implement and more accurate compared with rule-based protocols. The results highlight value of personalized approaches in realizing efficient nodule management.

This review emphasizes the significance of formulating control strategies for biological and advanced oxidation process (AOP)-based wastewater treatment systems. The aim is to guarantee that the effluent quality continuously aligns with environmental regulations while operating costs are minimized. It highlights the significance of understanding the dynamic behaviour of the process in developing effective control schemes. The most common process control strategies in wastewater treatment plants (WWTPs) are explained and listed. It is emphasized that the proper control scheme should be selected based on the process dynamic behaviour and control goal. This study further discusses the challenges associated with the control of wastewater treatment processes, including inadequacies in developed models, the limitations of most control strategies to the simulation stage, the imperative requirement for real-time data, and the financial and technical intricacies associated with implementing advanced controller hardware. It is discussed that the necessity of the availability of real-time data to achieve reliable control can be achieved by implementing proper, accurate hardware sensors in suitable locations of the process or by developing and implementing soft sensors. This study recommends further investigation on available actuators and the criteria for choosing the most appropriate one to achieve robust and reliable control in WWTPs, especially for biological and AOP-based treatment approaches.

Produced water (PW) is the largest by-product that comes out of the oil wells during oil and gas (O&G) field exploration. PW contains high-salt concentration along with other organic and inorganic components; therefore, PW must be treated before disposal. Electrocoagulation (EC) is an effective treatment method to remove pollutants from PW which has been the focus of many experimental studies; however, a mathematical model specifically for PW treatment by EC has not been developed yet. In this work, a comprehensive mathematical model has been developed to elucidate the role of EC operating parameters on the PW treatment performance and determine the mechanism for COD (Chemical Oxygen Demand) removal. The present model considers and identifies the dominant Al-hydroxy complex species and their contribution to the COD removal from synthetic PW samples by estimating their rate constants and comparing their magnitudes and investigates multi-scale modelling of the EC reactor. The influence of working parameters such as current density, initial pH, interelectrode distance, mixing speed and solution volume of PW on Al coagulant production and COD removal was investigated and modelled. The study estimates the rate constants of the reactions taking place for COD removal by EC process and by comparing their magnitudes identifies the dominant reactions and coagulant species involved in the process. The mathematical model prediction of COD removal fits well with the experimental data at 10 mA cm-2, 15 mA cm-2 and 20 mA cm-2 current density with R2 value of 0.96, 0.97 and 0.92, respectively and for dissolved Al concentration R2 value of 0.96, 0.99, and 0.97, respectively. The simulated results reproduced a good fit at initial pH of 6.1, 7.3 and 8.6 with R2 value of 0.92, 0.96 and 0.98, respectively for COD removal. The mathematical model and the experimental results showed the role of dominant Al-hydroxy complex species such as Al OH 2 + , Al OH 2 + , Al OH 3 , Al 2 OH 2 + 4 and Al OH 4 - in controlling the COD removal process. Under different operating conditions considered in the study, the model also predicted the COD removal performance of the EC reactors at different reactor volumes with R2 value of 0.96 for higher solution volume and larger reactor. The model presented and rate constants determined in the study will provide a theoretical basis for designing, scaling up and operating the EC reactor for oil-field PW treatment.

Thermal processing is a widely used method to ensure the microbiological safety of milk. Predictive microbiology plays a crucial role in quantifying microbial growth and decline, providing valuable guidance on the design and optimization of food processing operations. This study aimed to investigate the thermal inactivation kinetics of Listeria monocytogenes in milk under both isothermal and dynamic conditions. The thermal inactivation of L. monocytogenes was conducted under isothermal and non-isothermal conditions in sterilized and pasteurized milk, with and without background microbiota, respectively. Furthermore, a secondary model was developed between the shoulder effect and temperature, which was then integrated into the dynamic model. The results showed that L. monocytogenes grown in Tryptic Soy Yeast Extract Broth (TSBYE) prior to thermal inactivation exhibited higher heat resistance compared to cells grown in sterilized milk at isothermal temperatures of 60.0, 62.5, and 65℃. Moreover, the presence of background microbiota in milk significantly enhanced the heat resistance of L. monocytogenes, as evidenced by the increased D-values from 1.13 min to 2.34 min, from 0.46 min to 0.53 min, and from 0.25 min to 0.34 min at 60.0, 62.5, and 65 °C, respectively, regardless of whether the background microbiota was inactivated after co-growth or co-inactivated with L. monocytogenes. For non-isothermal inactivation, the one-step dynamic model based on the log-linear with shoulder model effectively described the microbial inactivation curve and exhibited satisfactory model performance. The model developed contributes to improved risk assessment, enabling dairy processors to optimize thermal treatment and ensure microbiological safety.

The objective of this study is to develop a mechanistic model to predict the long-term dynamic performance of High-Rate Activated Sludge (HRAS) process, including the removal of carbon (COD), nitrogen (N), and phosphorus (P). The model was formulated with inspiration from Activated Sludge Models No. 1 and 3 (ASM1 and ASM3) to incorporate essential mechanisms, such as adsorption and storage substrate, specific to HRAS systems. A stepwise protocol was followed for calibration with dynamic data from a pilot-scale HRAS plant. Sensitivity analysis identified influential model parameters, including maximum specific growth rate (μ), growth yield (YH), storage yield (YSTO), storage rate (kSTO), decay rate (b), and half saturation of the readily biodegradable substrate for growth (KS1). The calibrated model achieved prediction efficiencies above the normalized Mean Absolute Error (MAE) of 70 % for mixed liquor suspended solids (MLSS), total chemical oxygen demand (TCOD), soluble COD (SCOD), particulate COD (XCOD), total nitrogen (TN), ammonia nitrogen (SNH), total phosphorus (TP), soluble TP (STP), and particulate TP (XTP). Uncertainty analysis revealed that SCOD was underestimated. Based on the dynamic profiles of uncertainty bands and observed data, there is potential for improving the estimation of dynamic behavior in STP. The observed discrepancies may be attributed to variations in wastewater characteristics during the monitoring period, particularly concerning the phosphorus (P) fractions of the readily biodegradable substrate (SS) and soluble inerts (SI), which were not considered as dynamically changing parameters in the model.

BACKGROUND: This article assesses whether the European Union\'s (EU\'s) plant health regulations have had an impact on imports. A dynamic modelling approach was applied, using a two-step generalized method of moments estimator for panel data, and covering an 8-year period (2013-2020). The estimated equation includes volumes of trade, economic drivers, the trading partner, and variables capturing categories of import requirements (phytosanitary certificates, exemptions, restrictions) with regards to external border measures for enhanced biosecurity.
RESULTS: From the analysis we can conclude that the import regime and its recent changes have had a limited impact, if any, on trade flows of the affected products. The most significant impact is found for products classified as high-risk plants, while the extension of the phytosanitary certificate requirement to new products seems to have had negligible effects on trade.
CONCLUSIONS: Therefore, the plant protection regime for extra-EU trade seems to be not trade distorting while supplying a framework to enhance plant health in the EU. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Snow avalanches are gravitational processes characterised by the rapid movement of a snow mass, threatening inhabitants and damaging infrastructure in mountain areas. Such phenomena are complex events, and for this reason, different numerical models have been developed to reproduce their dynamics over a given topography. In this study, we focus on the two-dimensional numerical simulation tools RAMMS::AVALANCHE and FLO-2D, aiming to compare their performance in predicting the deposition area of snow avalanches. We also aim to assess the employment of the FLO-2D simulation model, normally used in water flood or mud/debris flow simulations, in predicting the motion of snow avalanches. For this purpose, two well-documented avalanche events that occurred in the Province of Bolzano (IT) were analyzed (Knollgraben, Pichler Erschbaum avalanches). The deposition area of each case study was simulated with both models through back-analysis processes. The simulation results were evaluated primarily by comparing the simulated deposition area with the observed one through statistical indices. Subsequently, the maximum flow depth, velocity and deposition depth were also compared between the simulation results. The results showed that RAMMS::AVALANCHE generally reproduced the observed deposits better compared to FLO-2D simulation. FLO-2D provided suitable results for wet and dry snow avalanches after a meticulous calibration of the rheological parameters, since they are not those typically considered in avalanche rheology studies. The results showed that FLO-2D can be used to study the propagation of snow avalanches and could also be adopted by practitioners to define hazard areas, expanding its field of application.

An important aspect in evaluating the resilience of hip replacement designs is testing their performance under adverse conditions that cause edge loading of the acetabular liner. The representation of edge loading conditions in finite element models is computationally challenging due to the changing contact locations, need for fine meshes, and dynamic nature of the system. In this study, a combined mesh and mass-scaling sensitivity study was performed to identify an appropriate compromise between convergence and solution time of explicit finite element analysis in investigating edge loading in hip replacement devices. The optimised model was then used to conduct a sensitivity test investigating the effect of different hip simulator features (the mass of the translating fixture and mediolateral spring damping) on the plastic strain in the acetabular liner. Finally, the effect of multiple loading cycles on the progressive accumulation of plastic strain was then also examined using the optimised model. A modelling approach was developed which provides an effective compromise between mass-scaling effects and mesh refinement for a solution time per cycle of less than 1 h. This \'Recommended Mesh\' model underestimated the plastic strains by less than 10%, compared to a \'Best Estimate\' model with a run time of ∼190 h. Starting with this model setup would therefore significantly reduce any new model development time while also allowing the flexibility to incorporate additional complexities as required. The polyethylene liner plastic strain was found to be sensitive to the simulator mass and damping (doubling the mass or damping had a similar magnitude effect to doubling the swing phase load) and these should ideally be described in future experimental studies. The majority of the plastic strain (99%) accumulated within the first three load cycles.