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Purpose: The relationship between external load and creatine-kinase (CK) response at the team/position or individual level using Global Positioning Systems (GPS) has been studied. This study aimed to compare GPS-derived and Football Movement Profile (FMP) –derived CK-prediction models for national-team soccer players. The second aim was to compare the performance of general and individualized CK prediction models. Methods: Four hundred forty-four national-team soccer players (under 15 [U15] to senior) were monitored during training sessions and matches using GPS. CK was measured every morning from whole blood. The players had 19.3 (18.1) individual GPS-CK pairs, resulting in a total of 8570 data points. Machine learning models were built using (1) GPS-derived or (2) FMP-based parameters or (3) the combination of the 2 to predict the following days' CK value. The performance of general and individual-specific prediction models was compared. The performance of the models was described by R2 and the root-mean-square error (RMSE, in units per liter for CK values). Results: The FMP model (R2 =.60, RMSE = 144.6 U/L) performed similarly to the GPS-based model (R2 =.62, RMSE = 141.2 U/L) and the combination of the 2 (R2 =.62, RMSE = 140.3 U/L). The prediction power of the general model was better on average (R2 =.57 vs R2 =.37) and for 73% of the players than the individualized model. Conclusions: The results suggest that FMP-based CK-prediction models perform similarly to those based on GPS-derived metrics. General machine learning models' prediction power was higher than those of the individual-specific models. These findings can be used to monitor postmatch recovery strategies and to optimize weekly training periodization. [ABSTRACT FROM AUTHOR]

This article presents the possibility of controlling/managing the frequency selection ability of membrane-based broadband frequency sensors (MB2FS), exploiting systematic selection of membrane stiffness. MB2FS is a bio-inspired system mimicking the geometry and functionality of the basilar membrane (BM) in the mammalian cochlea. The actual BM is tapered in geometry (both length wise and thickness wise), which makes the stiffness of the membrane uniformly varied over its length. Because of varied stiffness, different locations of the BM show resonance deflection at different frequency inputs, which allow the BM to select/sense the entire sonic frequency band within its 35 mm length. While actual BM and conventional MB2FS possess homogeneous stiffness over the domain length, in this article, a comprehensive insight is provided of how the frequency selection ability of the sensors can be manipulated and controlled, predictively, using functionally graded structural stiffness. Therefore, this work is not intended to develop an artificial BM, rather is focused on developing frequency sensors inspired by the membrane stiffness of BM, which plays a vital role in the spatial selection of acoustic frequencies. The study is performed using a numerically validated predictive model developed in a semi-analytical interface to explain the effects of MB2FS stiffness variations. Based on biological occurrence of stiffness in natural BM, three functions (logarithmic, linear, and exponential) are assumed to predict the FSP. While a random sonic frequency band of 10–12 kHz is targeted in this study to demonstrate the stiffness grading principle of MB2FS, a similar process (e.g., choosing the appropriate stiffness distribution of the beam) can be used to develop both sonic and ultrasonic frequency sensors. This study presents a detailed framework of how the sensing parameters of a specific frequency band (e.g., sensing location of band start and end frequencies and membrane segment width necessary to sense the entire frequency band) are dependent on the function coefficients. Finally, a comprehensive guideline is provided to predictively determine the function coefficients for user-defined frequency selection parameters. While existing state-of-the-art only allows designing MB2FS for a specific frequency band, the work presented in this study will open the opportunity to select multiple frequency bands of an MB2FS without altering its geometric configuration. [ABSTRACT FROM AUTHOR]

Purpose: Injury prevention is a crucial aspect of sports, particularly in high-performance settings such as elite female football. This study aimed to develop an injury prediction model that incorporates clinical, Global-Positioning-System (GPS), and multiomics (genomics and metabolomics) data to better understand the factors associated with injury in elite female football players. Methods: We designed a prospective cohort study over 2 seasons (2019–20 and 2021–22) of noncontact injuries in 24 elite female players in the Spanish Premiership competition. We used GPS data to determine external workload, genomic data to capture genetic susceptibility, and metabolomic data to measure internal workload. Results: Forty noncontact injuries were recorded, the most frequent of which were muscle (63%) and ligament (20%) injuries. The baseline risk model included fat mass and the random effect of the player. Six genetic polymorphisms located at the DCN, ADAMTS5, ESRRB, VEGFA, and MMP1 genes were associated with injuries after adjusting for player load (P <.05). The genetic score created with these 6 variants determined groups of players with different profile risks (P = 3.1 × 10−4). Three metabolites (alanine, serotonin, and 5-hydroxy-tryptophan) correlated with injuries. The model comprising baseline variables, genetic score, and player load showed the best prediction capacity (C-index:.74). Conclusions: Our model could allow efficient, personalized interventions based on an athlete's vulnerability. However, we emphasize the necessity for further research in female athletes with an emphasis on validation studies involving other teams and individuals. By expanding the scope of our research and incorporating diverse populations, we can bolster the generalizability and robustness of our proposed model. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, many production scenarios utilise sensors to monitor manufacturing processes, resulting in massive data that can be leveraged to build machine-learning models to improve production efficiency and quality. In semiconductor manufacturing, where many sensors are employed to monitor the production process, the resulting multi-sensor data poses challenges for constructing a virtual metrology (VM) model to assess wafer quality. Furthermore, building separate prediction models for each sensor can result in a loss of redundancy present in the multi-sensor data. Therefore, this paper proposes a multi-source AdaBoost with a cross-weight sampling method for predicting the critical dimension required in VM. Compared with the existing AdaBoost algorithm in regression, the proposed method adapts well to multi-source data by incorporating a cross-weight sampling distribution. Thus, when updating the sampling weight distribution, the impact of both individual and multi-source data is considered to re-sample high-quality observations. Based on the numerical results from the synthetic datasets and case study in semiconductor manufacturing, the proposed method outperforms benchmark methods. Additionally, owing to the redundancy of multi-sensor data, the proposed method demonstrates robust performance regardless of the noise level in the semiconductor VM data. [ABSTRACT FROM AUTHOR]

Bike fitting aims to optimize riders' positions to improve their performance and reduce the risk of injury. To calculate joint angles, the location of the joint centers of the lower limbs needs to be identified. However, one of the greatest difficulties is the location of the hip joint center due to the frequent occlusion of the anterior superior iliac spine markers. Therefore, the objective of this study was to validate a biomechanical model adapted to cycling (modified pelvic model, MPM), based on the traditional pelvic model (TPM) with an additional lateral technical marker placed on the iliac crests. MPM was also compared with a widely used model in cycling, trochanter model (TM). Thirty-one recreational cyclists pedaled on a roller bike while the movement was captured with a 7-camera VICON system. The position of the hip joint center and knee angle were calculated and compared with the TPM continuously (along 10 pedaling cycles) and discreetly at 90° and 180° crank positions. No significant differences were found in the position of the hip joint center or in the knee flexion/extension angle between the TPM and the MPM. However, there are differences between TPM and TM (variations between 4.1° and 6.9° in favor of the TM at 90° and 180°; P <.001). Bland–Altman graphs comparing the models show an average difference or bias close to 0° (limits of agreement [0.2 to −8.5]) between TPM and MPM in both lower limbs and a mean difference of between −4° and −7° (limits of agreement [−0.6 to −13.2]) when comparing TPM and TM. Given the results, the new cycling pelvic model has proven to be valid compared with the TPM when performing bike fitting studies, with the advantage that the occluded markers are avoided. Despite its simplicity, the TM presents measurement errors that may be relevant when making diagnoses, which makes its usefulness questionable. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to cross-validate a recently proposed equation for the prediction of maximal oxygen uptake ( V ˙ O 2 max) in cycling exercise by using the average power output normalized by the body mass from a 5-minute time trial (RPO5-min) as the independent variable. Further, the study aimed to update the predictive equation using Bayesian informative prior distributions and meta-analysis. Methods: On different days, 49 male cyclists performed an incremental graded exercise test until exhaustion and a 5-minute time trial on a stationary cycle ergometer. We compared the actual V ˙ O 2 max with the predicted value obtained from the RPO5-min, using a modified Bayesian Bland–Altman agreement analysis. In addition, this study updated the data on the linear regression between V ˙ O 2 max and RPO5-min, by incorporating information from a previous study as a Bayesian informative prior distribution or via meta-analysis. Results: On average, the predicted V ˙ O 2 max using RPO5-min underestimated the actual V ˙ O 2 max by −6.6 mL·kg–1·min–1 (95% credible interval, −8.6 to −4.7 mL·kg–1·min–1). The lower and upper 95% limits of agreement were −17.2 (−22.7 to −12.3) and 3.8 (−1.0 to 9.5) mL·kg–1·min–1, respectively. When the current study's data were analyzed using the previously published data as a Bayesian informative prior distribution, the accuracy of predicting sample means was found to be better when compared with the data combined via meta-analyses. Conclusions: The proposed equation presented systematic bias in our sample, in which the prediction underestimated the actual V ˙ O 2 max. We provide an updated equation using the previous one as the prior distribution, which could be generalized to a greater audience of cyclists. [ABSTRACT FROM AUTHOR]

The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a dataset of the melting points of 476 non-metallic crystalline binary compounds using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180 K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability. [ABSTRACT FROM AUTHOR]

The transformation of production mode leads to the need to strictly ensure the order in-time delivery. The global control ability of manufacturing workshops has become a necessary capability for enterprises. The real-time accurate Order Remaining Completion Time (ORCT) prediction allows managers to master the production schedule fluctuation, which provides criteria for workshop management. Due to the widespread deployment of Internet of Things (IoT) devices in the workshop, the difficulty of systematic processing and analysis of multi-dimensional heterogeneous data has been the main pain point of ORCT prediction. To tackle these problems, a Manufacturing Big Data (MBD)-driven ORCT prediction method based on Salp Swarm Algorithm (SSA) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. An attribute selection algorithm combined with max-relevance and min-redundancy and regularisation is used to mine key data from MBD. An SSA-BiLSTM model is proposed to achieve efficient and accurate ORCT prediction. The prediction model is supported by BiLSTM, and the hyperparameters are optimised by SSA. Finally, a case study about ORCT prediction in the IoT-enabled manufacturing workshop is presented. The result verifies that the proposed ORCT prediction method has obvious advantages over the other three traditional methods in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Nanoconfined water plays an important role in broad fields of science and engineering. Classical molecular dynamics (MD) simulations have been widely used to investigate water phases under nanoconfinement. The key ingredient of MD is the force field. In this study, we systematically investigated the performance of a recently introduced family of globally optimal water models, OPC and OPC3, and TIP4P/2005 in describing nanoconfined two-dimensional (2D) water ice. Our studies show that the melting points of the monolayer square ice (MSI) of all three water models are higher than the melting points of the corresponding bulk ice Ih. Under the same conditions, the melting points of MSI of OPC and TIP4P/2005 are the same and are ∼90 K lower than that of the OPC3 water model. In addition, we show that OPC and TIP4P/2005 water models are able to form a bilayer AA-stacked structure and a trilayer AAA-stacked structure, which are not the cases for the OPC3 model. Considering the available experimental data and first-principles simulations, we consider the OPC water model as a potential water model for 2D water ice MD studies. [ABSTRACT FROM AUTHOR]

Solar interfacial evaporation, as a novel practical freshwater production method, requires continuous research on how to improve the evaporation rates to increase water production. In this study, sets of data were obtained from molecule dynamics simulation and literature, in which the parameters included height, diameter, height–radius ratio, evaporation efficiency, and evaporation rate. Initially, the correlation between the four input parameters and the output of the evaporation rate was examined through traditional pairwise plots and Pearson correlation analysis, revealing weak correlations. Subsequently, the accuracy and generalization performance of the evaporation rate prediction models established by neural network and random forest were compared, with the latter demonstrating superior performance and reliability confirmed via random data extraction. Furthermore, the impact of different percentages (10%, 20%, and 30%) of the data on the model performance was explored, and the result indicated that the model performance is better when the test set is 20% and all the constructed model converge. Moreover, the mean absolute error and mean squared error of the evaporation rate prediction model for the three ratios were calculated to evaluate their performance. However, the relationship between the height- radius ratio and optimal evaporation rate was investigated using the enumeration method, and it was determined that the evaporation efficiency was optimal when the height–radius ratio was 6. Finally, the importance of height, diameter, height– radius ratio, and evaporation efficiency were calculated to optimize evaporator structure, increase evaporation rate, and facilitate the application of interfacial evaporation in solar desalination. [ABSTRACT FROM AUTHOR]

We propose a new work design model, SMART work design, that identifies five higher order categories of work characteristics, including stimulating work characteristics (task variety, skill variety, information processing requirements, and problem‐solving requirements), mastery work characteristics (job feedback, feedback from others, and role clarity), autonomous work characteristics (decision‐making autonomy, timing autonomy, and method autonomy), relational work characteristics (social support, task significance, and beneficiary contact), and tolerable work characteristics (low levels of: role overload, work–home conflict, and role conflict). Higher order confirmatory factor analysis of working participants provided initial evidence of this structure (Study 1, N = 1107), which was replicated in an additional dataset (Study 2, time 1, N = 709). To provide further evidence, we examined Study 2 data across three waves (N = 573) to show that each higher order factor at time 1 predicted time 3 job satisfaction either directly or via the theorized time 2 mediators (challenge appraisals, work meaningfulness, fulfillment of relatedness needs, and activated negative affect). In Study 3 (N = 108), employees' scores on specific higher order variables correlated with leader ratings of performance in the expected ways. The SMART work design model provides a unique integrating and multidimensional theory of work design that extends beyond existing models. The model can be used to facilitate the synthesis of research knowledge and guide scholars and practitioners to diagnose and address contemporary work design challenges. [ABSTRACT FROM AUTHOR]

We present a theoretical model to study the effect of counterion size on the effective charge, size, and thermodynamic behavior of a single, isolated, and flexible polyelectrolyte (PE) chain. We analyze how altering counterion size modifies the energy and entropy contributions to the system, including the ion-pair free energy, excluded volume interactions, entropy of free and condensed ions, and dipolar attraction among monomer–counterion pairs, which result in competing effects challenging intuitive predictions. The PE self-energy is calculated using the Edwards–Muthukumar Hamiltonian, considering a Gaussian monomer distribution for the PE. The condensed ions are assumed to be confined within a cylindrical volume around the PE backbone. The dipolar and excluded volume interactions are described by the second and third virial coefficients. The assumption of freely rotating dipoles results in a first-order coil–globule transition of the PE chain. A more realistic, weaker dipolar attraction, parameterized in our theory, shifts it to a second-order continuous transition. We calculate the size scaling-exponent of the PE and find exponents according to the relative dominance of the electrostatic, excluded volume, or dipolar effects. We further identify the entropy- and energy-driven regimes of the effective charge and conformation of the PE, highlighting the interplay of free ion entropy and ion-pair energy with varying electrostatic strengths. The crossover strength, dependent on the counterion size, indicates that diminishing sizes favor counterion condensation at the expense of free ion entropy. The predictions of the model are consistent with trends in simulations and generalize the findings of the point-like counterion theories. [ABSTRACT FROM AUTHOR]

Multi-scale predictive models for the shock sensitivity of energetic materials connect energy localization ("hotspots") in the microstructure to macro-scale detonation phenomena. Calculations of hotspot ignition and growth rely on models for chemical reaction rates expressed in Arrhenius forms; these chemical kinetic models, therefore, are foundational to the construction of physics-based, simulation-derived meso-informed closure (reactive burn) models. However, even for commonly used energetic materials (e.g., HMX in this paper) there are a wide variety of reaction rate models available. These available reaction rate models produce reaction time scales that vary by several orders of magnitude. From a multi-scale modeling standpoint, it is important to determine which model best represents the reactive response of the material. In this paper, we examine three global Arrhenius-form rate models that span the range of reaction time scales, namely, the Tarver 3-equation, the Henson 1-equation, and the Menikoff 1-equation models. They are employed in a meso-informed ignition and growth model which allows for connecting meso-scale hotspot dynamics to macro-scale shock-to-detonation transition. The ability of the three reaction models to reproduce experimentally observed sensitivity is assessed by comparing the predicted criticality envelope (Walker–Wasley curve) with experimental data for pressed HMX Class V microstructures. The results provide a guideline for model developers on the plausible range of time-to-ignition that are produced by physically correct Arrhenius rate models for HMX. [ABSTRACT FROM AUTHOR]

Background: Maximal lower-body strength can be assessed both dynamically and isometrically; however, the relationship between the changes in these 2 forms of strength following resistance training is not well understood. Purpose: To systematically review and analyze the effects of resistance training on changes in maximal dynamic (1-repetition-maximum back squat, deadlift, and power clean) and position-matched isometric strength (isometric midthigh pull and the isometric squat). In addition, individual-level data were used to quantify the agreement and relationship between changes in dynamic and isometric strength. Methods: Databases were systematically searched to identify eligible articles, and meta-analysis procedures were performed on the extracted data. The raw results from 4 studies were acquired, enabling bias and absolute reliability measures to be calculated using Bland–Altman test of agreement. Results: Eleven studies met the inclusion criteria, which resulted in 29 isometric–dynamic change comparisons. The overall pooled effect was 0.13 in favor of dynamic testing; however, the prediction interval ranged from g = −0.49 to 0.75. There was no evidence of bias (P =.825) between isometric and dynamic tests; however, the reliability coefficient was estimated to be 16%, and the coefficient of variation (%) was 109.27. Conclusions: As a range of future effects can be expected when comparing isometric to dynamic strength changes following resistance training, and limited proportionality exists between changes in these 2 strength qualities, there is strong evidence that isometric and dynamic strength represent separate neuromuscular domains. These findings can be used to inform strength-assessment models in athlete populations. [ABSTRACT FROM AUTHOR]

Since the beginning of COVID-19, more than 13,036,550 people have been infected, and 571,574 died because of the disease by July 13, 2020. Developing new methodologies to predict the COVID-19 pandemic will help policymakers plan to contain the spread of the virus. In this research, we develop a Stochastic Fractal Search algorithm combined with a mathematical model to forecast the pandemic. To enhance the algorithm, we employed a design of the experiments approach for tuning.We applied our algorithm to public datasets to model the COVID-19 pandemic in Canada in the upcoming months. Our algorithm predicts the number of symptomatic, asymptomatic, life-threatening, recovered, and death cases. The outcomes reveal that asymptomatic cases play the main role in the transmission of the virus.Wealso show that increasing the testing capacity would enhance the detection of asymptomatic cases and limit community transmission. Moreover, we performed sensitivity analyses to discover the effects of changes in transmission rates on pandemic growth. The sensitivity analyses provide a realistic overview of the future number of cases if the transmission rates change due to the emergence of new variants or change in social measures. Considering the outcomes, we provide several managerial insights to minimize community transmission. [ABSTRACT FROM AUTHOR]

It is not currently possible to directly and noninvasively measure in vivo patellofemoral joint contact force during dynamic movement; therefore, indirect methods are required. Simple models may be inaccurate because patellofemoral contact forces vary for the same knee flexion angle, and the patellofemoral joint has substantial out-of-plane motion. More sophisticated models use 3-dimensional kinematics and kinetics coupled to a subject-specific anatomical model to predict contact forces; however, these models are time consuming and expensive. We applied a principal component analysis prediction and regression method to predict patellofemoral joint contact forces derived from a robust musculoskeletal model using exclusively optical motion capture kinematics (external approach), and with both patellofemoral and optical motion capture kinematics (internal approach). We tested this on a heterogeneous population of asymptomatic subjects (n = 8) during ground-level walking (n = 12). We developed equations that successfully capture subject-specific gait characteristics with the internal approach outperforming the external. These approaches were compared with a knee-flexion based model in literature (Brechter model). Both outperformed the Brechter model in interquartile range, limits of agreement, and the coefficient of determination. The equations generated by these approaches are less computationally demanding than a musculoskeletal model and may act as an effective tool in future rapid gait analysis and biofeedback applications. [ABSTRACT FROM AUTHOR]

This prospective study aimed to determine which specific mobility tests were the most accurate for predicting falls in physically active older adults living in the community. Seventy-nine physically active older adults who met the American College of Sports Medicine physical activity guidelines volunteered. Participants were assessed and followed up for 12 months. Mobility assessments included the 30-s sit-to-stand test, five times sit-to-stand test, single-task timed-up-and-go test (TUG), motor dual-task TUG (Mot-TUG), and cognitive dual-task TUG (Cog-TUG). Mot-TUG and Cog-TUG performances were moderately correlated with number of falls (r =.359, p <.01 and r =.372, p <.01, respectively). When Mot-TUG, Cog-TUG, or Age were included as fall predictors, discrimination scores represented by the area under the receiver operating characteristic curve (AUC) were AUC (Mot-TUG) = 0.843 (p <.01), AUC (Cog-TUG) = 0.856 (p <.01), and AUC (Age) = 0.734 (p <.05). The cutoff point for Cog-TUG was 10.98 s, with test sensitivity of 1.00 and specificity of 0.66. Fall predictors for different populations may be based on different test methods. Here, the dual-task TUG test more accurately predicted falls in older adults who met American College of Sports Medicine's physical activity guidelines. [ABSTRACT FROM AUTHOR]

We develop an approach that combines the estimation of monthly firm-level expected returns with an assignment of firms to (possibly) latent groups, both based on observable characteristics, using machine learning principles with linear models. The best-performing methods are flexible two-stage sparse models that capture group-membership predictive relationships. Portfolios formed to exploit such group-varying predictions based on a parsimonious set of characteristics deliver economically meaningful returns with low turnover. We propose statistical tests based on nonparametric bootstrapping for our results, and detail how different characteristics may matter for different groups of firms, making comparisons to the existing literature. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate statistical models developed for predicting medal-winning performances for international swimming events and generate updated performance predictions for the Paris 2024 Olympic Games. Methods: The performance of 2 statistical models developed for predicting international swimming performances was evaluated. The first model employed linear regression and forecasting to examine performance trends among medal winners, finalists, and semifinalists over an 8-year period. A machine-learning algorithm was used to generate time predictions for each individual event for the Paris 2024 Olympic Games. The second model was a Bayesian framework and comprised an autoregressive term (the previous winning time), moving average (past 3 events), and covariates for stroke, gender, distance, and type of event (World Championships vs Olympic Games). To examine the accuracy of the predictions from both models, the mean absolute error was determined between the predicted times for the Budapest 2022 World Championships and the actual results from said championships. Results: The mean absolute error for prediction of swimming performances was 0.80% for the linear-regression machine-learning model and 0.85% for the Bayesian model. The predicted times and actual times from the Budapest 2022 World Championships were highly correlated (r =.99 for both approaches). Conclusions: These models, and associated predictions for swimming events at the Paris 2024 Olympic Games, provide an evidence-based performance framework for coaches, sport-science support staff, and athletes to develop and evaluate training plans, strategies, and tactics, as well as informing resource allocation to athletes, based on their potential for the Paris 2024 Olympic Games. [ABSTRACT FROM AUTHOR]

Portevin–Le Chatelier effect, associated with dynamic strain aging, is widely found in various alloys, and the maximum serration magnitude from the tensile stress–strain curve can be considered as the point closest to the failure threshold. Due to the difficulty to define solute and solvent atoms in multicomponent alloys, it is a great challenge to explore the serration mechanism. In this study, the atoms that play the role of pinning are defined as solute atoms. Aided by mean-field theory, an in situ pinning model coupled with the twinning effect in a CoNiV alloy with low stacking fault energies is successfully established, which can well predict the failure threshold, i.e., the maximum serration magnitude. The present study paves a new way to correlate the serration dynamics and in situ pinning, and further predicts the failure threshold upon loading for multicomponent high- and medium-entropy alloys. [ABSTRACT FROM AUTHOR]

Structural integrity and robustness are key parameters to evaluate microfabrication techniques. Bending and collapsing of 2D/3D microstructures are commonly noted in solvent-involved procedures, e.g., liquid-based post-treatment in wet-etching, lithography, and Two Photon Polymerizations (TPPs). Such structural failures are caused by excessive solution-imposed capillary forces, where multiple kinds of liquids may intensively participate. Current pieces of the literature focus on the mechanical one-axis models to illustrate their deformation process. To date, there exists an emerging demand for dual-axis models to satisfy rapidly developed micro/nano-engineerings. Here, utilizing polymer micro-pillars distributed in a square array as an illustration example, a dual-axis beam-sway model is proposed considering the influences of structure arrangement as well as the solvent. Specifically, a simplified criterion for judging structural stability is identified. For verifications, the TPP-based experimental data show excellent consistency with model predictions. All in all, the extended model offers reliable guidance for the fabrication of delicate structures and further benefits the optimization of related microfabrication processes. [ABSTRACT FROM AUTHOR]

Spasticity is a common impairment within pediatric neuromusculoskeletal disorders. How spasticity contributes to gait deviations is important for treatment selection. Our aim was to evaluate the pathophysiological mechanisms underlying gait deviations seen in children with spasticity, using predictive simulations. A cluster analysis was performed to extract distinct gait patterns from experimental gait data of 17 children with spasticity to be used as comparative validation data. A forward dynamic simulation framework was employed to predict gait with either velocity- or force-based hyperreflexia. This framework entailed a generic musculoskeletal model controlled by reflexes and supraspinal drive, governed by a multiobjective cost function. Hyperreflexia values were optimized to enable the simulated gait to best match experimental gait patterns. Three experimental gait patterns were extracted: (1) increased knee flexion, (2) increased ankle plantar flexion, and (3) increased knee flexion and ankle plantar flexion when compared with typical gait. Overall, velocity-based hyperreflexia outperformed force-based hyperreflexia. The first gait pattern could mostly be explained by rectus femoris and hamstrings velocity-based hyperreflexia, the second by gastrocnemius velocity-based hyperreflexia, and the third by gastrocnemius, soleus, and hamstrings velocity-based hyperreflexia. This study shows how velocity-based hyperreflexia from specific muscles contributes to different spastic gait patterns, which may help in providing targeted treatment. [ABSTRACT FROM AUTHOR]

Data has become a high-value commodity in manufacturing. There is a growing realisation that the data-driven applications could become strong differentiators of manufacturing enterprises. To guide the developments in digitisation, a widely accepted framework is needed. In the absence of the universal framework, the components making a digital enterprise are captured in an example framework that is introduced in the paper. The adoption of new technology and software solutions has increased complexity of manufacturing systems. In addition, new product introductions have become more frequent and the demand more variable. A digital space enables optimisation and simulation of decisions before their realisation in the physical space. Predictive modelling with its time dimension is a valuable actor in the digital space. Three challenges of predictive modelling such as model complexity, model interpretability, and model reuse are identified in this paper. The coverage of each challenge in the literature is illustrated with the recently published papers. The main aspects of these challenges and the synthesis of the developments in digital manufacturing are articulated in the form of eight observations that could guide the future research. [ABSTRACT FROM AUTHOR]

A model predicting the number of emission sites and total current from a porous conical electrospray emitter as functions of voltage is derived. A pressure balance between capillary and electric forces is used to determine an onset criterion for individual menisci, and an ionic emission scaling law is invoked to predict the current each meniscus emits. These submodels are integrated over a phenomenological meniscus size distribution and the area of the emitter to yield a model for emitter performance as a function of five free parameters, two for the ionic emission submodel and three for the meniscus size distribution. Bayesian inference is applied to determine these model parameters from an existing dataset [Dressler et al., J. Propul. Power 38, 809 (2022)]. The model predictions after training are compared to the experimental data, and it is found that the majority of the data are within a 90% credible interval. The ability of the model to capture key trends in the experimental data is attributed to the interplay of two effects: the distribution over meniscus size on the emitter and the position-dependent electric field. The calibrated model results also suggest that the emitter surface is wetted by a series of large but sparsely distributed pools of propellant. The performance and extensibility of the model are examined within the context of model-based design for porous electrospray array thrusters. [ABSTRACT FROM AUTHOR]

Background: Laboratory assessment of maximal oxygen uptake ( V ˙ O 2 max) is physically and mentally draining for the athlete and requires expensive laboratory equipment. Indirect measurement of V ˙ O 2 max could provide a practical alternative to laboratory testing. Purpose: To examine the relationship between the maximal power output (MPO) in an individualized 7 × 2-minute incremental test (INCR-test) and V ˙ O 2 max and to develop a regression equation to predict V ˙ O 2 max from MPO in female rowers. Methods: Twenty female club and Olympic rowers (development group) performed the INCR-test on a Concept2 rowing ergometer to determine V ˙ O 2 max and MPO. A linear regression analysis was used to develop a prediction of V ˙ O 2 max from MPO. Cross-validation analysis of the prediction equation was performed using an independent sample of 10 female rowers (validation group). Results: A high correlation coefficient (r =.94) was found between MPO and V ˙ O 2 max. The following prediction equation was developed: V ˙ O 2 max (mL·min−1) = 9.58 × MPO (W) + 958. No difference was found between the mean predicted V ˙ O 2 max in the INCR-test (3480 mL·min−1) and the measured V ˙ O 2 max (3530 mL·min−1). The standard error of estimate was 162 mL·min−1, and the percentage standard error of estimate was 4.6%. The prediction model only including MPO, determined during the INCR-test, explained 89% of the variability in V ˙ O 2 max. Conclusion: The INCR-test is a practical and accessible alternative to laboratory testing of V ˙ O 2 max. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the indirect measurement of 1-repetition-maximum (1RM) free-weight half-squat in high-level sprinters using the load–velocity relationship. Methods: Half-squat load and velocity data from 11 elite sprinters were collected in 2 separate testing sessions. Approximately 24 hours prior to the first testing session, sprinters completed a fatiguing high-intensity training session consisting of running intervals, staircase exercises, and body-weight exercises. Prior to the second testing session, sprinters had rested at least 48 hours. Two different prediction models (multiple-point method, 2-point method) were used to estimate 1RM based on the load and either mean or peak concentric velocity data of submaximal lifts (40%–90% 1RM). The criterion validity of all methods was examined through intraclass correlation coefficients, coefficient of variation (CV%), Bland–Altman plots, and the SEM. Results: None of the estimations were significantly different from the actual 1RM. The multiple-point method showed higher intraclass correlation coefficients (.91 to.97), with CVs from 3.6% to 11.7% and SEMs from 5.4% to 10.6%. The 2-point method showed slightly lower intraclass correlation coefficients (.76 to.95), with CVs 1.4% to 17.5% and SEMs from 9.8% to 26.1%. Bland–Altman plots revealed a mean random bias in estimation of 1RM for both methods (mean and peak velocity) ranging from 1.06 to 13.79 kg. Conclusion: Velocity-based methods can be used to roughly estimate 1RM in elite sprinters in the rested and fatigued conditions. However, all methods showed variations that limit their applicability for accurate load prescription for individual athletes. [ABSTRACT FROM AUTHOR]

Are employees more likely to identify with their organization and engage in helping behaviors on its behalf if the coworkers who make up their social networks identify and help the organization? We draw on social information processing theory, social learning theory, and research on diffusion of innovation to develop a model that predicts how relational (advice ties) and structural (structural equivalence) factors predict organizational identification and organizational citizenship. We argue that OI is neither a matter of individual perceptions generated in isolation, nor a simple function of the number of ties that an employee has, but that it is instead an outcome of relations and interactions with coworkers, as reflected in the social structure of the employing organization. We tested our model by conducting a social network study of employees in a construction company. Our findings show that (1) employees tend to have similar levels of OI to the OI of their advice ties; (2) employees who are structurally equivalent tend to demonstrate similar levels of OCB; (3) similarity in OI mediates the relationship between advice ties and similarity in OCB; and (4) advice ties with high and moderate levels of OI are most strongly associated with similarity in OCB. In a supplementary analysis we show that the social influence model that we tested offers a better explanation for the role that the social context plays in shaping OI than does a "more ties, more OI" model. We discuss implications for theory and practice. [ABSTRACT FROM AUTHOR]

Recently, additive manufacturing (AM) has received increased attention due to its high energy consumption. By extracting hidden information or highly representative features from energy-relevant data, knowledge distillation (KD) reduces predictive model complexity and computational load. By using almost predetermined and fixed models, the distillation process restricts students and teachers from learning from one model to another. To reduce computational costs while maintaining acceptable performance, a teacher assistant (TA) was added to the teacher-student architecture. Firstly, a teacher ensemble was combined with three baseline models to enhance accuracy. In the second step, a teacher ensemble (TA) was formed to bridge the capacity gap between the ensemble and the simplified model. As a result, the complexity of the student model was reduced. Using geometry-based features derived from layer-wise image data, a KD-based predictive model was developed to evaluate the feasibility and effectiveness of two independently trained student models. In comparison with independently trained student models, the performance of the proposed method has the lowest RMSE, MAE, and training time. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to discuss the use of topological indices (TIs) to anticipate the physical and biological aspects of innovative drugs used in the treatment of Alzheimer's disease. Degree-based topological indices are generated using edge partitioning to assess the drugs Tacrine, Donepezil, Ravistigmine, Butein, Licochalcone-A and Flavokqwain-A. Furthermore, using linear regression, a quantitative structure–property relationship (QSPR) model is developed to predict the characteristics such as boiling point (BP), flash point (FP), molar volume (MV), molecular weight, complexity and polarizability. The findings show that topological indices have the potential to be used as a tool for drugs discovery and design in the field of Alzheimer's disease treatment. [ABSTRACT FROM AUTHOR]

This work proposes a new data-driven model to estimate and predict pH dynamics in freshwater raceway photobioreactors. The resulting model is based purely on data measured from the reactor and divides the pH dynamics into two different behaviors. One behavior is described by the variation of pH due to the photosynthesis phenomena made by microalgae; and the other comes from the effect of CO 2 injections into the medium for control purposes. Moreover, it was observed that the model parameters vary throughout the day depending on the weather conditions and reactor status. Thus, a decision tree algorithm is also developed to capture the parameter variation based on measured variables of the system, such as solar radiation, medium temperature, and medium level. The proposed model has been validated for a data set of more than 100 days during 10 months in a semi-industrial raceway reactor, covering a wide range of weather and system scenarios. Additionally, the proposed model was used to design an adaptive control algorithm which was also experimentally tested and compared with a classical fixed parameter control approach. • Simple regression tree model able to adapt to a wide variety of conditions. • The model is capable of successfully predicting pH under a different system conditions. • The simple structure of the model facilitates the adaptive tuning of control algorithms. • More than 100 days for 10 months for calibration and validation purposes. [ABSTRACT FROM AUTHOR]

Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of language models in software engineering tasks, promoting their use in production environments and ensuring that deployed software is defect-free efficiently. [ABSTRACT FROM AUTHOR]

In recent years, the vigorous development of e-commerce has also greatly promoted the development of the fashion multimedia field. Fashion compatibility modeling plays a very important role in a series of applications, such as fashion retrieval and personalized recommendation. Because fashion compatibility prediction usually contains multiple modal information, in this paper, we present a type-aware fashion compatibility prediction model based on a hybrid attention mechanism that is centered on using a priori category information for the fusion of multimodal information. Specifically, the model first designs a hybrid attention mechanism to fully explore the inner dependency of the visual modality while enriching with textual modalities to obtain a joint feature representation of multimodal information. Furthermore, the model matches fashion items as a distance measurement problem, and the joint feature representation of multimodal information realizes type-aware characteristics by the design of a distance measurement function with category-aware embedding. The experimental results obtained on the publicly available Polyvore and Polyvore-D datasets demonstrate the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Acute mountain sickness (AMS) is a well‐studied illness defined by clinical features (e.g., headache and nausea), as assessed by the Lake Louise score (LLS). Although obvious in its severe form, early stages of AMS are poorly defined and easily confused with common travel‐related conditions. Measurement of hypoxaemia, the cause of AMS, should be helpful, yet to date its utility for identifying AMS susceptibility remains unclear. This study quantified altitude‐induced hypoxaemia in individuals during an ascent to 4800 m to determine the utility of nocturnal pulse oximetry measurements for prediction of AMS. Eighteen individuals (36 ± 16 years of age) ascended to 4800 m over 12 days. Symptomology of AMS was assessed each morning via LLS criteria, with participants categorized as either AMS‐positive (LLS ≥ 3 with headache) or AMS‐negative. Overnight peripheral oxygen saturations (ov‐SpO2${{S}_{{\mathrm{p}}{{{\mathrm{O}}}_2}}}$) were recorded continuously (1 Hz) using portable oximeters. Derivatives of these recordings were compared between AMS‐positive and ‐negative subjects (Mann–Whitney U‐test). Exploratory analyses (Pearson's) were conducted to investigate relationships between overnight parameters and AMS severity. Overnight derivatives, including ov‐SpO2${{S}_{{\mathrm{p}}{{{\mathrm{O}}}_2}}}$, heart rate/ov‐SpO2${{S}_{{\mathrm{p}}{{{\mathrm{O}}}_2}}}$, variance, oxygen desaturation index, hypoxic burden and total sleep time at <80% SpO2${{S}_{{\mathrm{p}}{{{\mathrm{O}}}_2}}}$, all differed significantly between AMS‐positive and ‐negative subjects (all P < 0.01), with cumulative/relative frequency plots highlighting these differences visually. Exploratory analysis revealed that ov‐SpO2${{S}_{{\mathrm{p}}{{{\mathrm{O}}}_2}}}$ from 3850 m was correlated with peak LLS at 4800 m (r = 0.58–0.61). The findings highlight the potential for overnight oximetry to predict AMS susceptibility during ascent to high altitude. Further investigation is required to develop, evaluate and optimize predictive models to improve AMS management and prevention. [ABSTRACT FROM AUTHOR]

Summary Background Methods Results Conclusions Acute post‐surgical pain is managed inadequately in many patients undergoing surgery. Several prognostic risk prediction models have been developed to identify patients at high risk of developing moderate to severe acute post‐surgical pain. The aim of this systematic review was to describe and evaluate the methodological conduct of these prediction models.We searched MEDLINE, EMBASE and CINAHL for studies of prognostic risk prediction models for acute post‐surgical pain using predetermined criteria. Prediction model performance was evaluated according to discrimination and calibration. Adherence to TRIPOD guidelines was assessed. Risk of bias and applicability was independently assessed by two reviewers using the prediction model risk of bias assessment tool.We included 14 studies reporting on 17 prediction models. The most common predictors identified in final prediction models included age; surgery type; sex or gender; anxiety or fear of surgery; pre‐operative pain intensity; pre‐operative analgesic use; pain catastrophising; and expected surgical incision size. Discrimination, measured by the area under receiver operating characteristic curves or c‐statistic, ranged from 0.61 to 0.83. Calibration was only reported for seven models. The median (IQR [range]) overall adherence rate to TRIPOD items was 62 (53–66 [47–72])%. All prediction models were at high risk of bias.Effective prediction models could support the prevention and treatment of acute post‐surgical pain; however, existing models are at high risk of bias which may affect their reliability to inform practice. Consideration should be given to the goals, timing of intended use and desired outcomes of a prediction model before development. [ABSTRACT FROM AUTHOR]

Accurately predicting traffic accident severity is crucial for road safety. However, existing studies lack interpretability in revealing the relationship between accident severity and key factors. To address this issue, we propose a new interpretable analytical framework. The framework utilizes XGBoost and SHAP to select effective factors. Then the AISTGCN model is constructed by improving the STGCN through the local attention mechanism to predict the severity of the accident. Finally, DeepLIFT is used to interpret the forecasts and identify key factors. Our experiments using real-world UK accident data demonstrate that our proposed AISTGCN outperforms baseline models in outcome prediction with an accuracy of 0.8772. The computation time was reduced and the reliability of predictions was enhanced through screening for effective factors. Furthermore, DeepLIFT provides more reasonable explanations when explaining accidents of different severity, indicating that vehicle count significantly impacts. Our framework aids in developing effective safety measures to reduce accidents. [ABSTRACT FROM AUTHOR]

The Conditional Colour-Magnitude Distribution (CCMD) is a comprehensive formalism of the colour–magnitude–halo mass relation of galaxies. With joint modelling of a large sample of SDSS galaxies in fine bins of galaxy colour and luminosity, Xu et al. inferred parameters of a CCMD model that well reproduces colour- and luminosity-dependent abundance and clustering of present-day galaxies. In this work, we provide a test and investigation of the CCMD model by studying the colour and luminosity distribution of galaxies in galaxy groups. An apples-to-apples comparison of group galaxies is achieved by applying the same galaxy group finder to identify groups from the CCMD galaxy mocks and from the SDSS data, avoiding any systematic effect of group finding and mass assignment on the comparison. We find an overall nice agreement in the conditional luminosity function (CLF), the conditional colour function (CCF), and the CCMD of galaxies in galaxy groups inferred from CCMD mock and SDSS data. We also discuss the subtle differences revealed by the comparison. In addition, using two external catalogues constructed to only include central galaxies with halo mass measured through weak lensing, we find that their colour-magnitude distribution shows two distinct and orthogonal components, in line with the prediction of the CCMD model. Our results suggest that the CCMD model provides a good description of halo mass-dependent galaxy colour and luminosity distribution. The halo and CCMD mock catalogues are made publicly available to facilitate other investigations. [ABSTRACT FROM AUTHOR]

The far-ultraviolet (FUV) flare activity of low-mass stars has become a focus in our understanding of the exoplanet atmospheres and how they evolve. However, direct detection of FUV flares and measurements of their energies and rates are limited by the need for space-based observations. The difficulty of obtaining such observations may push some works to use widely available optical data to calibrate multiwavelength spectral models that describe UV and optical flare emission. These models either use single temperature blackbody curves to describe this emission, or combine a blackbody curve with archival spectra. These calibrated models would then be used to predict the FUV flare rates of low-mass stars of interest. To aid these works, we used TESS optical photometry and archival HST FUV spectroscopy to test the FUV predictions of literature flare models. We tested models for partially (M0–M2) and fully convective (M4–M5) stars, 40 Myr and field age stars, and optically quiet stars. We calculated FUV energy correction factors that can be used to bring the FUV predictions of tested models in line with observations. A flare model combining optical and NUV blackbody emission with FUV emission based on HST observations provided the best estimate of FUV flare activity, where others underestimated the emission at all ages, masses, and activity levels, by up to a factor of 104 for combined FUV continuum and line emission and greater for individual emission lines. We also confirmed previous findings that showed optically quiet low-mass stars exhibit regular FUV flares. [ABSTRACT FROM AUTHOR]

This study investigated how equipment and sex affect the prediction accuracy of the maximum number of repetitions performed to failure (RTF) using the fastest mean velocity of the set (MVfastest). Sixteen men and twelve women completed four sessions (two using free-weight equipment and two sessions using the Smith machine). Each session involved three sets of repetitions to failure against the 65%, 75%, and 85% of the one-repetition maximum, interspersed by 10-min of rest. The goodness-of-fit of the individualized RTF-MVfastest relationships was comparable between both equipment types and sexes (P ≥0.510). Moreover, there were not significant differences in the MVfastest associated with RTF between equipment types (P ≥0.258). However, the MVfastest associated with RTF was higher for men than for women in repetitions 6 to 15 (P ≤0.043; ES≥0.69). In addition, the absolute errors when predicting RTF showed no significant differences between equipment types and loads (P <0.444). Specifically, these RTF estimates were within an acceptable range for men (<2 repetitions), but not for women (≥2 repetitions) (main effect of sex: P ≤0.018; ES≥0.58). These findings suggest that individualized RTF-MVfastest equations estimate the RTF with an acceptable precision in men during bench press exercises in both equipment types but exhibit lower precision for women. [ABSTRACT FROM AUTHOR]

Well workover operation in some blocks of the Tarim Basin resulted in mud leakage to the gas-producing formation, resulting in damage to gas well production. Therefore, refracturing of mud leakage wells in the future is necessary. To explore the means of refracturing to promote the recovery of gas well productivity, studying the evolution law of the production-induced stress field is necessary. Therefore, in this study, a prediction model of the production-induced stress field of fractured high-yield gas wells was established, and the evolution law of the stress field under the set production conditions of high-yield gas wells was clarified. The results show that with an increase in matrix permeability, main fracture conductivity, and natural fracture density, the decrease in the amplitude of stress decreases under the set production. At lower matrix permeability and natural fracture density, the low-stress areas on both sides and the front end of the main fracture are larger. At a higher main fracture conductivity, the low-stress area at both sides and the front end of the main fracture is larger. The change in the in situ stress of the gas well is smaller under set production than constant-pressure production. [ABSTRACT FROM AUTHOR]

This paper was to develop a prediction framework for obtaining the rheological properties of extracted and recovered asphalt binders utilizing mixture performance and other factors such as in-place air voids, pavement structure, traffic level, and aging duration. A total of 21 field projects consisting of 66 pavement sections were included in the analysis, which were located in different climatic zones with various RAP contents, traffic levels, pavement structures, pavement age, and material properties. Results indicate that the neural network can be successfully adopted to predict cracking-related binder properties consisting of effective asphalt binder, G*sin(δ) and binder fracture energy, and rutting-related binder properties including high temperature performance grade (PG), MSCR Jnr3.2, and R3.2. Both prediction and validation models worked well for pavement sections with varied contents of RAP when new data was introduced. [ABSTRACT FROM AUTHOR]

Service-based systems (SBSs) are a unique category of software systems that dynamically combine various third-party services at runtime to deliver complex and adaptive functionality. This dynamic composition introduces a high level of unpredictability and uncertainty, creating potential anomalies and exerting significant pressure on system maintenance. To tackle this challenge, the conventional approach involves employing prediction-based proactive self-adaptation. Despite the prevalence of existing approaches emphasizing prediction accuracy, the critical aspect of "earliness" in predictions is often overlooked. Striking a balance between early and accurate predictions is paramount in practice. In response, we propose Proactive Self-Adaptation based on Ensemble Prediction (PSA-EP) to effectively balance the trade-off between the prediction earliness and accuracy of in SBSs. At the heart of PSA-EP lies an ensemble prediction model built upon a deep neural network and an enhanced long short-term memory (DNN-ELSTM) architecture. PSA-EP is crafted to empower SBSs to adapt to the inherent unpredictability and instability, facilitating the achievement of their adaptation goals. This adaptation mechanism not only enables effective prediction and analysis of adaptation goal violations but also addresses the reliability and performance of service level agreements (SLAs) governing quality of service (QoS). We evaluated the performance of PSA-EP in a decentralized tele-assistance system using four key metrics, and the experimental results, examined from various perspectives, underscore its exceptional performance. [ABSTRACT FROM AUTHOR]

Objective No available scale, at the time of initial evaluation for necrotizing enterocolitis (NEC), accurately predicts, that is, with an area under the curve (AUC) ≥0.9, which preterm infants will undergo surgery for NEC stage III or die within a week. Study Design This is a retrospective cohort study (n = 261) of preterm infants with <33 weeks' gestation or <1,500 g birth weight with either suspected or with definite NEC born at Parkland Hospital between 2009 and 2021. A prediction model using the new HASOFA score (Hyperglycemia, Hyperkalemia, use of inotropes for Hypotension during the prior week, Acidemia, Neonatal Sequential Organ Failure Assessment [nSOFA] score) was compared with a similar model using the nSOFA score. Results Among 261 infants, 112 infants had NEC stage I, 68 with NEC stage II, and 81 with NEC stage III based on modified Bell's classification. The primary outcome, surgery for NEC stage III or death within a week, occurred in 81 infants (surgery in 66 infants and death in 38 infants). All infants with pneumoperitoneum or abdominal compartment syndrome either died or had surgery. The HASOFA and the nSOFA scores were evaluated in 254 and 253 infants, respectively, at the time of the initial workup for NEC. Both models were internally validated. The HASOFA model was a better predictor of surgery for NEC stage III or death within a week than the nSOFA model, with greater AUC 0.909 versus 0.825, respectively, p < 0.001. Combining HASOFA at initial assessment with concurrent or later presence of abdominal wall erythema or portal gas improved the prediction surgery for NEC stage III or death with AUC 0.942 or 0.956, respectively. Conclusion Using this new internally validated prediction model, surgery for NEC stage III or death within a week can be accurately predicted at the time of initial assessment for NEC. Key Points No available scale, at initial evaluation, accurately predicts which preterm infants will undergo surgery for NEC stage III or die within a week. In this retrospective cohort study of 261 preterm infants with either suspected or definite NEC we developed a new prediction model (HASOFA score). The HASOFA-model had high discrimination (AUC: 0.909) and excellent calibration and was internally validated. [ABSTRACT FROM AUTHOR]