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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

In make-to-order manufacturing workshops, accurate prediction value of production progress (PP) is a significant reference index for dynamic optimisation of production process and on-time delivery of production orders. The implementation of big data and Industrial Internet of Things (IIoT) in manufacturing workshops makes it possible to obtain large amounts of production data which can affect PP. However, the particularities of massive historical order data are not fully excavated and the amount of target order data is insufficient to support the training of high-precision prediction model, which will result in bad training approximation and generalisation. To overcome these shortcomings, a PP prediction approach consisting of two models with transfer learning (TL) is proposed. TL can avoid the training of PP prediction model from scratch every time. Consequently, computational efficiency can be greatly improved. A convolutional neural network (CNN) model with TL is devised to excavate the comprehensive features from historical and current orders. Additionally, a long short-term memory network (LSTM) model with TL is constructed to fit the nonlinear relation of the features provided by CNN-TL model for PP prediction. In order to validate the performance of the proposed PP prediction approach, comparative experiments of eight algorithms are conducted in an IIoT-enabled manufacturing workshop. [ABSTRACT FROM AUTHOR]

Objectives: The aim of the present study was to evaluate the effect on systemic inflammation of subgingival instrumentation (SI) with or without antibiotics. Moreover, systemic parameters were compared between periodontally healthy (PH) individuals and periodontitis patients. Method and materials: Patients with generalized periodontitis: stage III and PH individuals were recruited. Forty eight periodontitis patients were randomly allocated to each treatment group; systemic antibiotics for seven days after completion of SI (AB group), or SI alone (SI group). Periodontal parameters, serum high-sensitivity C-reactive protein (hsCRP), and hematological parameters were assessed at baseline and at week 8. Multivariate analysis was applied to analyze predictive effect of treatment allocated and improvement in periodontal parameters on change in systemic parameters. Results: At baseline, hsCRP, total leukocyte count (TLC), neutrophil, and monocyte count were significantly higher in periodontitis patients. There was comparable reduction in neutrophil count in both treatment groups. At week 8, change in periodontal parameters was similar in treatment groups, except for probing pocket depth (PPD). Improvement in both PPD and clinical attachment level (CAL) and CAL alone was predictive of change in TLC and lymphocyte count, respectively. Conclusion: This study failed to demonstrate the significant benefit of systemic antibiotics as an adjuvant to SI on improvement in periodontal inflammation and systemic inflammatory parameters, despite significantly higher reduction in PPDs. [ABSTRACT FROM AUTHOR]

Purpose: To develop and validate the Brazilian Adductor Performance Test (BAPT) for predicting hip adductor muscle injuries in elite soccer athletes. Methods: A total of 108 soccer athletes were assessed, followed up for 3 months, and evaluated for a history of adductor injury 6 months before BAPT evaluation. The Shapiro–Wilk test was used as the normality test. The Mann–Whitney U test was used to compare BAPT scores between injured and uninjured athletes. Binary logistic regression was performed to identify the athletes' chances of injury based on their BAPT scores. A receiver operating characteristic curve was used to determine the cutoff point for the number of repetitions in the BAPT and Spearman bivariate correlation and identify factors potentially related to the test score. Furthermore, the intraclass correlation coefficient was used to determine interexaminer agreement. The level of significance was set at 95%. Results: The BAPT scores for hip adductor injury history did not differ significantly (P =.08). A significant deficit was identified in the BAPT scores of the injured athletes at the 3-month follow-up (P =.001). The cutoff point identified was 33 repetitions. Low BAPT scores increased the chance of injury by 20% (odds ratio, 1.20%; P =.001). The interexaminer agreement was.96 (P =.001). Conclusion: BAPT can be used to identify athletes most likely to sustain hip adductor muscle injuries, indirectly reducing the rate of this injury in soccer clubs. [ABSTRACT FROM AUTHOR]

BACKGROUND: Timely discharge to post-acute care (PAC) settings, such as skilled nursing facilities, requires early identification of eligible patients. We sought to develop and internally validate a model which predicts a patients likelihood of requiring PAC based on information obtained in the first 24 h of hospitalization. METHODS: This was a retrospective observational cohort study. We collected clinical data and commonly used nursing assessments from the electronic health record (EHR) for all adult inpatient admissions at our academic tertiary care center from September 1, 2017 to August 1, 2018. We performed a multivariable logistic regression to develop the model from the derivation cohort of the available records. We then evaluated the capability of the model to predict discharge destination on an internal validation cohort. RESULTS: Age (adjusted odds ratio [AOR], 1.04 [per year]; 95% Confidence Interval [CI], 1.03 to 1.04), admission to the intensive care unit (AOR, 1.51; 95% CI, 1.27 to 1.79), admission from the emergency department (AOR, 1.53; 95% CI, 1.31 to 1.78), more home medication prescriptions (AOR, 1.06 [per medication count increase]; 95% CI 1.05 to 1.07), and higher Morse fall risk scores at admission (AOR, 1.03 [per unit increase]; 95% CI 1.02 to 1.03) were independently associated with higher likelihood of being discharged to PAC facility. The c-statistic of the model derived from the primary analysis was 0.875, and the model predicted the correct discharge destination in 81.2% of the validation cases. CONCLUSIONS: A model that utilizes baseline clinical factors and risk assessments has excellent model performance in predicting discharge to a PAC facility.

Abstract Purpose The debate surrounding factors influencing postoperative flatus and defecation in patients undergoing colorectal resection prompted this study. Our objective was to identify independent risk factors and develop prediction models for postoperative bowel function in patients undergoing colorectal surgeries. Methods A retrospective analysis of medical records was conducted for patients who undergoing colorectal surgeries at Peking University People’s Hospital from January 2015 to October 2021. Machine learning algorithms were employed to identify risk factors and construct prediction models for the time of the first postoperative flatus and defecation. The prediction models were evaluated using sensitivity, specificity, the Youden index, and the area under the receiver operating characteristic curve (AUC) through logistic regression, random forest, Naïve Bayes, and extreme gradient boosting algorithms. Results The study included 1358 patients for postoperative flatus timing analysis and 1430 patients for postoperative defecation timing analysis between January 2015 and December 2020 as part of the training phase. Additionally, a validation set comprised 200 patients who undergoing colorectal surgeries from January to October 2021. The logistic regression prediction model exhibited the highest AUC (0.78) for predicting the timing of the first postoperative flatus. Identified independent risk factors influencing the time of first postoperative flatus were Age (p