Search

• LGB-IPs is a new LGB-based optimal feature fusion model for the accurate prediction of STY phosphorylation sites. • LGB-IPs explores ten different feature descriptors and assesses its discriminative capability using five different classifiers. • Extensive cross-validation and independent assessment shows that LGB-IPs outperformed the single feature models consistently. SARS-CoV-2′s global spread has instigated a critical health and economic emergency, impacting countless individuals. Understanding the virus's phosphorylation sites is vital to unravel the molecular intricacies of the infection and subsequent changes in host cellular processes. Several computational methods have been proposed to identify phosphorylation sites, typically focusing on specific residue (S/T) or Y phosphorylation sites. Unfortunately, current predictive tools perform best on these specific residues and may not extend their efficacy to other residues, emphasizing the urgent need for enhanced methodologies. In this study, we developed a novel predictor that integrated all the residues (STY) phosphorylation sites information. We extracted ten different feature descriptors, primarily derived from composition, evolutionary, and position-specific information, and assessed their discriminative power through five classifiers. Our results indicated that Light Gradient Boosting (LGB) showed superior performance, and five descriptors displayed excellent discriminative capabilities. Subsequently, we identified the top two integrated features have high discriminative capability and trained with LGB to develop the final prediction model, LGB-IPs. The proposed approach shows an excellent performance on 10-fold cross-validation with an ACC, MCC, and AUC values of 0.831, 0.662, 0.907, respectively. Notably, these performances are replicated in the independent evaluation. Consequently, our approach may provide valuable insights into the phosphorylation mechanisms in SARS-CoV-2 infection for biomedical researchers. [ABSTRACT FROM AUTHOR]

During autonomous rendezvous and docking (AR&D) with a tumbling target, multiple unfavorable engineering constraints exist, such as constraints on the control, velocity, or collision avoidance. Meanwhile, many sources of uncertainty exist, including incomplete and inaccurate measurements, control deviation, dynamic uncertainty, and inertial parameter uncertainty of the tumbling target, which significantly complicate the design of the guidance and control algorithm. Considering these challenges, this paper proposes a robust identification, planning, and control unified framework for AR&D with a tumbling target, which incorporates an extended Kalman filter (EKF) with a robust tube-based model predictive control for tracking (TMPT) controller. First, the EKF is designed to estimate the system state and inertial parameters of the target and then predict the future behavior of the tumbling target. Additionally, the prediction information of the target is transmitted to the designed TMPT controller as a reference signal. This TMPT controller unites trajectory planning and control in a single layer that can ensure the stability and recursive feasibility of the algorithm with a short prediction horizon. Moreover, it can guarantee that the closed-loop system is robustly and asymptotically convergent to the reachable set whose center is the optimal reachable periodic trajectory concerning the docking port of the tumbling target. The unified framework represents a robustness-to-optimality control strategy that can accomplish the AR&D mission efficiently while considering multiple engineering constraints and uncertainties. Finally, the efficiency of the proposed control framework is verified through a numerical simulation. [ABSTRACT FROM AUTHOR]

The prediction method of MUF (Maximum Usable Frequency) has been extensively deliberated as a pivotal technology in HF (High Frequency) communication. To verify the reliability of MUF prediction models commonly used in HF communications, we evaluated the prediction accuracy of seven typical prediction models based on maximum observed frequency (MOFs) and predicted MUFs. The oblique-sounding (OS) circuit was from Changchun to Jingyang, with the vertical-sounding (VS) station situated in Beijing which is, approximately at the midpoint of this oblique-sounding (OS) circuit. The RMSE (root mean square error) and RRMSE (relative root mean square error) between the MOFs and predicted MUFs by the model were used to evaluate the accuracy of the models. The results of statistical analysis show that: (a) the prediction results of the seven models are consistent with the observed results in trend, among which the prediction results of the Lockwood model (LWM), the nonuniform ionospheric conditions MUF calculation model (NYM), and the model proposed by Istituto Nazionale di Geofisica e Vulcanologia (INGV) are in good agreement with the observed results; (b) In the four seasons, the seasonal distribution of the seven models is significantly different, and the INGV model has a better prediction effect; (c) The INGV model is superior to other models in relatively high solar activity epochs and relatively low solar activity epochs, followed by the LWM model, with little difference between the two models and close prediction accuracy; (d) The INGV model has a better prediction result in four different periods of a day, and the improved simplified MUF calculation model (MSPM) has a great prediction result in the periods of nighttime. [ABSTRACT FROM AUTHOR]

Background: Machine learning techniques (MLT) build models to detect complex patterns and solve new problems using big data. Aim: The present study aims to create a prediction interface for mothers breastfeeding exclusively for the first 6 months using MLT. Method: All mothers who had babies aged 6–24 months between 15.09.2021 and 15.12.2021 and to whom the surveys could be delivered were included. 'Personal Information Form' created by the researchers was used as a data collection tool. Data from 514 mothers participating in the study were used for MLT. Data from 70% of mothers were used for educational purposes, and a prediction model was created. The data obtained from the remaining 30% of the mothers were used for testing. Results: The best MLT algorithm for predicting exclusive breastfeeding for the first 6 months was determined to be the Random Forest Classifier. The top five variables affecting the possibility of mothers breastfeeding exclusively for the first 6 months were as follows: "the mother not having any health problems during pregnancy," "there were no people who negatively affected the mother's morale about breastfeeding," "the amount of water the mother drinks in a day," "thinking that her milk supply is insufficient," "having no problems breastfeeding the baby". Conclusions: Using created prediction model may allow early identification of mothers with a risk of not breastfeeding their babies exclusively for the first 6 months. In this way, mothers in the risk group can be closely monitored in the early period. [ABSTRACT FROM AUTHOR]

Machine learning as a data‐driven solution has been widely applied in the field of fatigue lifetime prediction. In this paper, three models for wideband fatigue life prediction are built based on three machine learning models, that is, support vector regression (SVR), Gaussian process regression (GPR), and artificial neural network (ANN). All the three prediction models use the parameter b of the well‐known Tovo–Benasciutti (TB) model as their outputs to realize fatigue life prediction and their generalization abilities are enhanced by employing numerous power spectrum samples with different bandwidth parameters and a variety of material properties related to fatigue life. Sufficient Monte Carlo numerical simulations demonstrate that the newly developed machine learning models are superior to the traditional frequency‐domain models in terms of life prediction accuracy and the ANN model has the best overall performance among the three developed machine learning models. Highlights: Three frequency‐domain models are devised based on machine learning.SVR, GPR, and ANN models are employed for random fatigue life predictions.Three models are suitable for predicting fatigue life under wideband random loads.Three models outperform existing frequency‐domain models in prediction accuracy. [ABSTRACT FROM AUTHOR]

Background: A quantitative approach to predict expected muscle activity and mandibular movement from non‐invasive hard tissue assessments remains unexplored. Objectives: This study investigated the predictive potential of normalised muscle activity during various jaw movements combined with temporomandibular joint (TMJ) vibration analyses to predict expected maximum lateral deviation during mouth opening. Method: Sixty‐six participants underwent electrognathography (EGN), surface electromyography (EMG) and joint vibration analyses (JVA). They performed maximum mouth opening, lateral excursion and anterior protrusion as jaw movement activities in a single session. Multiple predictive models were trained from synthetic observations generated from the 66 human observations. Muscle function intensity and activity duration were normalised and a decision support system with branching logic was developed to predict lateral deviation. Performance of the models in predicting temporalis, masseter and digastric muscle activity from hard tissue data was evaluated through root mean squared error (RMSE) and mean absolute error. Results: Temporalis muscle intensity ranged from 0.135 ± 0.056, masseter from 0.111 ± 0.053 and digastric from 0.120 ± 0.051. Muscle activity duration varied with temporalis at 112.23 ± 126.81 ms, masseter at 101.02 ± 121.34 ms and digastric at 168.13 ± 222.82 ms. XGBoost predicted muscle intensity and activity duration and scored an RMSE of 0.03–0.05. Jaw deviations were successfully predicted with a MAE of 0.9 mm. Conclusion: Applying deep learning to EGN, EMG and JVA data can establish a quantifiable relationship between muscles and hard tissue movement within the TMJ complex and can predict jaw deviations. [ABSTRACT FROM AUTHOR]

Numerous models have been developed to predict acute kidney injury (AKI) after noncardiac surgery, yet there is a lack of independent validation and comparison among them. We conducted a systematic literature search to review published risk prediction models for AKI after noncardiac surgery. An independent external validation was performed using a retrospective surgical cohort at a large Chinese hospital from January 2019 to October 2022. The cohort included patients undergoing a wide range of noncardiac surgeries with perioperative creatinine measurements. Postoperative AKI was defined according to the Kidney Disease Improving Global Outcomes creatinine criteria. Model performance was assessed in terms of discrimination (area under the receiver operating characteristic curve, AUROC), calibration (calibration plot), and clinical utility (net benefit), before and after model recalibration through intercept and slope updates. A sensitivity analysis was conducted by including patients without postoperative creatinine measurements in the validation cohort and categorising them as non-AKI cases. Nine prediction models were evaluated, each with varying clinical and methodological characteristics, including the types of surgical cohorts used for model development, AKI definitions, and predictors. In the validation cohort involving 13,186 patients, 650 (4.9%) developed AKI. Three models demonstrated fair discrimination (AUROC between 0.71 and 0.75); other models had poor or failed discrimination. All models exhibited some miscalibration; five of the nine models were well-calibrated after intercept and slope updates. Decision curve analysis indicated that the three models with fair discrimination consistently provided a positive net benefit after recalibration. The results were confirmed in the sensitivity analysis. We identified three models with fair discrimination and potential clinical utility after recalibration for assessing the risk of acute kidney injury after noncardiac surgery. [ABSTRACT FROM AUTHOR]

Preoperative planning has gained popularity in the management of reverse shoulder arthroplasty (RSA). Commercially available software provides 3-dimensional segmentation of scapula and humerus, as well as providing arc of motion for the implanted articulation and identifying potential areas of bony impingement. However, these software algorithms use a fixed scapula model, disregarding the preoperative clinical range of motion (C-ROM) of the patient, be it glenohumeral or scapulothoracic, as well as any soft tissue parameters. This study aims to compare the ROM based on preoperative planning software by using the implant position from postoperative computed tomography (CT) images (predicted ROM using preoperative planning software [P-ROM]), with the C-ROM assessed at minimum of 2 years of follow-up. Preoperative and postoperative CT scans of 46 patients who underwent primary RSA between 2017 and 2021 were analyzed. At the postoperative 2-year review, each patient was assessed for active ROM. Implant size and position based on operative notes and postoperative CT scans were used to replicate the performed surgery in the planning software. Abduction, flexion, and external rotation motion were simulated and recorded. The relationship between C-ROM and P-ROM was investigated using linear regression analysis, Pearson correlation coefficient, and paired t -test. P-ROM was significantly lower than C-ROM at 2 years postoperatively (P <.001), with an average discrepancy of 78° in abduction, 47° in flexion, and 37° in external rotation (C-ROM: abduction 155° ± 21° [80°-180°]; flexion 160° ± 17° [90°-180°]; external rotation 52° ± 14° [10°-80°] vs. P-ROM: abduction 77° ± 13° [53°-107°]; flexion 112° ± 25° [67°-180°]; external rotation 15° ± 21° [0°-79°]). The linear regression analysis indicated weak agreement between C-ROM and P-ROM (abduction R2 = 0.03; flexion R2 = 0.01; external rotation R2 = 0.04). Pearson's correlation coefficients revealed weak correlations of −0.18, 0.03, and 0.21 for abduction, flexion, and external rotation, respectively. P-ROM based on preoperative software in its current form does not allow the prediction of the C-ROM at 2 years of follow-up for patients undergoing RSA. [ABSTRACT FROM AUTHOR]

This article presents a hybrid model to predict the positions of the ball screw drive system of machine tool and then modify the trajectory through constructing a pre-compensation method to reduce servo errors in machine motion axes. To achieve this objective, a flexible control model is initially developed to characterize the ball screw drive system, and by leveraging this model, a high-bandwidth controller is constructed, with its physical representation, i.e. the state-space equation, being derived. Subsequently, a data-driven hybrid model is proposed to predict the positions of the ball screw drive system concerning the next multiple time steps from the current time step, and then the predicted positions associated with these steps are utilized as initial conditions to adjust and compensate for the physical model's prediction errors corresponding to these multiple time steps. As a result, a compensated trajectory with high tracking accuracy is generated. Finally, experiments confirm that the proposed prediction method offers superior prediction accuracy and enhanced adaptability, and the pre-compensated trajectory leads to reduced tracking errors. [Display omitted] • A hybrid model is constructed to pre-compensate servo error in the ball screw system. • A high bandwidth controller is designed by incorporating the feedback of the velocity. • A compensated trajectory with high tracking accuracy is generated. • The method can achieve a nice tracking accuracy and improve the bandwidth. • A series of comparative experiments validate the proposed method. [ABSTRACT FROM AUTHOR]

BACKGROUND: The American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Values (TLVs) for Lifting is a manual material handling (MMH) assessment method to identify weight limits that nearly all workers may be exposed to without developing work-related low back disorders (LBD). However, this assessment method only applies to lifting with the torso within 30° asymmetry of the sagittal plane. OBJECTIVE: Estimate TLV weight limits while lifting with torso asymmetry greater than 30° beyond the sagittal plane. METHODS: Lifting tasks were performed from various horizontal and vertical locations, at torso asymmetry angles of 0°, 15°, 30°, 45°, 60°, 75° and 90°, using ACGIH identified TLVs. Validated MMH assessment methods (NIOSH Lifting Equation, Ohio State University LBD Risk Model) were utilized to estimate TLVs at torso asymmetries greater than 30°. RESULTS: The current ACGIH TLVs resulted in low- to moderate-risk risk levels for torso asymmetries from 0° to 30°, and the risk incrementally increased as torso asymmetry increased to 90°. With the intention to keep the risk levels to that found at 30° torso asymmetry, lower TLV weight limits in the vertical and horizontal zones investigated were estimated for torso asymmetries from 45° to 90°. The resulting adjusted TLVs were consistent with weight limits identified for similar lifting conditions from other assessment methods that account for torso asymmetry. CONCLUSIONS: This research found current ACGIH-defined TLVs possess less than high-risk for LBD, and provided guidance to practitioners for reduced TLVs when torso asymmetry is greater than 30° from the sagittal plane. [ABSTRACT FROM AUTHOR]

The prediction of patient attendance in emergency departments (ED) is crucial for effective healthcare planning and resource allocation. This paper proposes an early warning system that can detect emerging trends in ED attendance, offering timely alerts for proactive operational planning. Over 13 years of historical ED attendance data (from January 2010 till December 2022) with 1,700,887 data points were used to develop and validate: (1) a Seasonal Autoregressive Integrated Moving Average with eXogenous factors (SARIMAX) forecasting model; (2) an Exponentially Weighted Moving Average (EWMA) surge prediction model, and (3) a trend persistence prediction model. Drift detection was achieved with the EWMA control chart, and the slopes of a kernel-regressed ED attendance curve were used to train various machine learning (ML) models to predict trend persistence. The EWMA control chart effectively detected significant COVID-19 events in Singapore. The surge prediction model generated preemptive signals on changes in the trends of ED attendance over the COVID-19 pandemic period from January 2020 until December 2022. The persistence of novel trends was further estimated using the trend persistence model, with a mean absolute error of 7.54 (95% CI: 6.77–8.79) days. This study advanced emergency healthcare management by introducing a proactive surge detection framework, which is vital for bolstering the preparedness and agility of emergency departments amid unforeseen health crises. [ABSTRACT FROM AUTHOR]

Migraine is one of the most common neurological disorders, characterized by moderate-to-severe headache episodes. Autonomic nervous system (ANS) alterations can occur at phases of migraine attack. This study investigates patterns of ANS changes during the pre-ictal night of migraine, utilizing wearable biosensor technology in ten individuals. Various physiological, activity-based, and signal processing metrics were examined to train predictive models and understand the relationship between specific features and migraine occurrences. Data were filtered based on specified criteria for nocturnal sleep, and analysis frames ranging from 5 to 120 min were used to improve the diversity of the training sample and investigate the impact of analysis frame duration on feature significance and migraine prediction. Several models, including XGBoost (Extreme Gradient Boosting), HistGradientBoosting (Histogram-Based Gradient Boosting), Random Forest, SVM, and KNN, were trained on unbalanced data and using cost-sensitive learning with a 5:1 ratio. To evaluate the changes in features during pre-migraine nights and nights before migraine-free days, an analysis of variance (ANOVA) was performed. The results showed that the features of electrodermal activity, skin temperature, and accelerometer exhibited the highest F-statistic values and the most significant p-values in the 5 and 10 min frames, which makes them particularly useful for the early detection of migraines. The generalized prediction model using XGBoost and a 5 min analysis frame achieved 0.806 for accuracy, 0.638 for precision, 0.595 for recall, and 0.607 for F1-score. Despite identifying distinguishing features between pre-migraine and migraine-free nights, the performance of the current model suggests the need for further improvements for clinical application. [ABSTRACT FROM AUTHOR]

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The support of athlete career excellence in sport is a growing societal issue. For professional athletes within North America, the factors that contribute to athlete career excellence are not well understood. The current investigation examined the relationships between three athlete career excellence resources: athletic identity; career engagement; and mental well-being. Using a non-random convenience sampling strategy, 72 professional athletes currently active in the sports of baseball, football, and ice hockey in North America participated in the study. It was found that athletic identity and career engagement significantly predicted mental well-being in North American professional athletes. Furthermore, both athletic identity and career engagement added significantly to the prediction model. The current investigation supports the conceptualisation and understanding of how North American professional athletes' career engagement and mental well-being contribute to athletes' striving for career excellence. Recommendations for further research and practical applications are considered. [ABSTRACT FROM AUTHOR]

A convolutional neural network is trained on a large dataset of suitably randomized film profiles and corresponding elastic energy densities ρ ɛ , computed by the finite element method. The trained model provides quantitative predictions of ρ ɛ for arbitrary profiles, surrogating its explicit calculation, and is used for the time integration of partial differential equations describing the evolution of strained films. The close match found between the neural network predictions and the "ground-truth" evolutions obtained by the finite element method calculation of ρ ɛ , even after tens-of-thousands of integration time-steps, validates the approach. A substantial computational speed up without significant loss of accuracy is demonstrated, allowing for million-steps simulations of islands growth and coarsening. The intriguing possibility of extending the domain size is also discussed. [ABSTRACT FROM AUTHOR]

The Atlantic Meridional Overturning Circulation (AMOC) is a significant component of the global ocean system, which has so far ensured a relatively warm climate for the North Atlantic and mild conditions in regions, such as Western Europe. The AMOC is also critical for the global climate. The complexity of the dynamical system underlying the AMOC is so vast that a long-term assessment of the potential risk of AMOC collapse is extremely challenging. However, short-term prediction can lead to accurate estimates of the dynamical state of the AMOC and possibly to early warning signals for guiding policy making and control strategies toward preventing AMOC collapse in the long term. We develop a model-free, machine-learning framework to predict the AMOC dynamical state in the short term by employing five datasets: MOVE and RAPID (observational), AMOC fingerprint (proxy records), and AMOC simulated fingerprint and CESM AMOC (synthetic). We demonstrate the power of our framework in predicting the variability of the AMOC within the maximum prediction horizon of 12 or 24 months. A number of issues affecting the prediction performance are investigated. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Temporary employment has spiked in OECD countries over the last 40 years and is now a common feature of their labor market landscape. A large body of empirical literature examines the spread of temporary employment, but no systematic review and interpretation of its findings in light of economic theory exists. This survey aims at filling this gap by interpreting the key empirical results based on the predictions of the macro models in markets with frictions developed to address specific features of temporary employment. Revisions of workhorse models used so far to analyze temporary employment are also suggested. (JEL J22, J31, J41, J81, J83, K31) [ABSTRACT FROM AUTHOR]

The current extension limit prediction model for offshore extended-reach well (ERW) in mudstone does not consider the formation collapse, which poses a huge risk to offshore drilling construction. To address this problem, this paper presents new open-hole extension limit prediction model for ERW. By considering formation collapse, rate of penetration (ROP), and annular pressure loss, the extension limit models during normal drilling and tripping were derived. The sensitivity of geological and engineering factors was evaluated by analyzing limits for wells 1H and 2H. The research results showed that: (1) The extension limit increases with the ROP and formation collapse duration, but decreases with the increase in mud weight, plastic viscosity, and flow rate. (2) In ERWs, the mud flow rate has a significant impact on the extension limit than the plastic viscosity of mud fluid. However, mud weight has the least impact compared to the two. (3) Considering various parameters, the predicted extension limit of well 1H, when the mud weight is 1.16 g/cm3 [9.67 ppg], is 1593.28 m [5225.96 ft] less than the limit when the collapse period is not considered. [ABSTRACT FROM AUTHOR]

Tight sandstone reservoirs are significant sources of natural gas reserves. As traditional reserves become increasingly scarce and costly, optimizing the development of these reservoirs becomes crucial. This study introduces a novel two-phase gas–water flow model for single wells, incorporating both Darcy and non-Darcy flow equations. These equations are derived from mass conservation and momentum principles for both gas and water phases. Using data from a real tight gas well, our model, which includes stress-sensitive phases for gas and water, outperforms traditional Darcy flow models. Specifically, the average relative deviations in daily production rates were 0.1815% for gas and − 0.2677% for water, which are significantly smaller compared to traditional Darcy flow models. Further application of the non-Darcy flow model reveals strategies to enhance well performance. For example, mitigating liquid lock damage within a 2 m radius near the well could restore the permeability from 0.045 to 0.143 mD, thereby tripling the daily gas production. This non-Darcy flow model is easy to implement and shows significant potential in forecasting production yields in tight sandstone reservoirs, highlighting its importance in the petroleum and natural gas industry. [ABSTRACT FROM AUTHOR]

Background: Integrating life-threatening clinical simulations improves learning outcomes. This study assessed nursing students' critical thinking factors before and after simulation, evaluated nursing clinical reasoning ability and learning satisfaction at two time points, and explored relationships and predictions among critical thinking, clinical reasoning, and satisfaction before and after simulation. Method: Surveys and focus groups were used for this mixed-methods study. Results: Quantitative findings revealed increased critical thinking scores for curiosity, skepticism, and systematicity; clinical reasoning; and satisfaction after simulation. Qualitative results supported these improvements and indicated enhanced curiosity for clinical knowledge and iterative phases of clinical reasoning. Students expressed satisfaction with the simulations. Objectivity significantly influenced clinical reasoning and satisfaction in nursing students following life-threatening simulations. Conclusion: Fostering a culture of critical thinking in life-threatening simulations is crucial. Educators must teach the importance of objectivity in clinical practice, encourage critical evaluation, and foster self-reflection in simulations. [J Nurs Educ. 2024;63(9):595–603.] [ABSTRACT FROM AUTHOR]

Filler components play a crucial role as thermo‐mechanical reinforcement to the epoxy‐based solder mask, ensuring effective insulation and passivation. This study investigates the impact of different inorganic filler types (talc, silica (SiO2), boron nitride (BN) and barium sulfate (BaSO4)) and loadings (5 and 15 wt%) on the tensile and thermal properties of the epoxy composites. The composites were blended, cast and thermally cured. Of these composites, only 15 wt% SiO2/epoxy composite exhibits notable increments in both tensile strength and modulus, surpassing unfilled epoxy by 11.65% and 31.9%, respectively. It also displays the highest elongation at break, supported by small particle size, high specific surface area and minimal void volume fraction. For thermal tests, the addition of all fillers increases the storage modulus in both glassy and rubbery regions, especially 15 wt% talc/epoxy composite with 30.09% increment at 25 °C. Additionally, all fillers raise the glass transition temperature (Tg), notably improving it by 9 °C in the 15 wt% BN/epoxy composite. Moreover, their inclusion generally enhances the onset decomposition temperature (Tonset) and reduces weight loss during decomposition. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Legal documents, like contracts or laws, are subject to interpretation. Different people can have different interpretations of the very same document. Large parts of judicial branches all over the world are concerned with settling disagreements that arise, in part, from these different interpretations. In this context, it only seems natural that during the annotation of legal machine learning data sets, disagreement, how to report it, and how to handle it should play an important role. This article presents an analysis of the current state-of-the-art in the annotation of legal machine learning data sets. The results of the analysis show that all of the analysed data sets remove all traces of disagreement, instead of trying to utilise the information that might be contained in conflicting annotations. Additionally, the publications introducing the data sets often do provide little information about the process that derives the "gold standard" from the initial annotations, often making it difficult to judge the reliability of the annotation process. Based on the state-of-the-art, the article provides easily implementable suggestions on how to improve the handling and reporting of disagreement in the annotation of legal machine learning data sets. [ABSTRACT FROM AUTHOR]

The stable and efficient regulation of voltage and frequency is a critical issue associated with the functioning of modern power system network having renewable integration. This manuscript focuses on the concurrent regulation of voltage and frequency in a power network comprising multiple generating sources. Herein, along with conventional thermal and diesel plants, renewable generations from different sources have been considered such as wind and solar photovoltaic (PV) resources. Further, in a fresh attempt, the authors have modeled wind and solar PV generation using two different stochastic modeling methods concerning combined voltage and frequency loops in the regulated power system model. Furthermore, the novel Leader Harris Hawks Optimized Model Predictive Controller (MPC‐LHHO) has been applied to concurrently minimize the voltage and frequency deviations in the given power network. To address the intermittent nature of load and renewable generations, various auxiliary frequency controllers, such as the unified power flow controller (UPFC) and electric vehicle (EV) integration with the grid, have been implemented. The robustness of the proposed control method has been successfully validated through diverse scenarios of random loadings. [ABSTRACT FROM AUTHOR]

An efficient supply chain (SC) management requires that decisions are taken to minimize the effects of parametric uncertainties and unpredictable external disturbances. In this article, we consider this problem with reference to a multi‐stage SC (MSSC) whose dynamics is characterized by the following elements of complexity: perishable goods with uncertain perishability rate, an uncertain future customer demand that is only known to fluctuate inside a given compact set. The problem we face is to define a resilient and robust Replenishment Policy (RP) such that at any stage the following requirements are satisfied: the fulfilled demand is maximized, overstocking is avoided, the bullwhip effect (BE) is mitigated. These objectives should be pursued despite the mentioned uncertainties and unexpected customer demand behaviors violating the bounds of the compact set. Robustness is here intended with respect to uncertainty on the perishability rate, and resiliency as the ability to quickly react to the mentioned unforeseen customer demands. We propose a method based on a distributed resilient robust model predictive control (DRRMPC) approach. Each local robust MPC (RMPC) involves solving a Min‐Max constrained optimization problem (MMCOP). To drastically reduce the numerical complexity of each MMCOP, we parametrize its solution by means of B‐spline functions. [ABSTRACT FROM AUTHOR]

This paper presents a real‐time optimization method for nonlinear model predictive control (NMPC) of systems governed by partial differential equations (PDEs). The NMPC problem to be solved is formulated by discretizing the PDE system in space and time by using the finite difference method. The proposed method is called the double‐layer Jacobi method, which exploits both the spatial and temporal sparsities of the PDE‐constrained NMPC problem. In the upper layer, the NMPC problem is solved by ignoring the temporal couplings of either the state or costate (Lagrange multiplier corresponding to the state equation) equations so that the spatial sparsity is preserved. The lower‐layer Jacobi method is a linear solver dedicated to PDE‐constrained NMPC problems by exploiting the spatial sparsity. Convergence analysis indicates that the convergence of the proposed method is related to the prediction horizon. Results of a numerical experiment of controlling a heat transfer process show that the proposed method can be two orders of magnitude faster than the conventional Newton's method exploiting the banded structure of NMPC problems. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid data‐driven and physics‐based simulation technique for seismic response evaluation of steel Buckling‐Restrained Braced Frames (BRBFs) considering brace fracture. Buckling‐Restrained Brace (BRB) fracture is represented by cumulative plastic deformation capacity. A dataset, consisting of 95 past BRB laboratory tests and 120 simulated BRB responses generated using the finite element method, is first developed. An Artificial Neural Network‐based (ANN) predictive model is then trained using the training dataset to estimate the cumulative plastic deformation of BRBs. The prediction capability of the ANN‐based predictive model is validated using the training dataset and an existing regression‐based predictive model. In the second part of the paper, an hybrid simulation technique combining the data‐driven model and physics‐based numerical modeling is presented to conduct the nonlinear time history analysis, followed by 1) validation against a full‐scale BRBF testing and 2) demonstration of the proposed simulation technique using a six‐story BRBF. The results confirm that the proposed predictive model can predict the BRB fracture with sufficient accuracy. Furthermore, the hybrid data‐driven physics‐based simulation technique can be used as a powerful tool for dynamic analysis of BRBFs considering BRB fracture. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There is no doubt that accurate short-term inbound metro ridership forecasting has always been a significant part in terms of intelligent transportation. Based on the excellent performance of deep learning models in dealing with processing time series data, a large number of researchers have adopted deep learning methods for short-term inbound metro ridership prediction. However, considering the non-linear and cyclical nature of inbound metro ridership, the prediction accuracy using only deep learning models is limited. To solve this problem, a hybrid prediction model combining Ensemble Empirical Mode Decomposition (EEMD), subsequences recombination, empirical wavelet transform (EWT) and long short-term memory (LSTM) network for inbound metro ridership which can greatly reduce the complexity of the original inbound metro ridership is proposed in this article. To prove the superiority of the proposed EEMD-EWT-LSTM-based model, we compare it with recent main forecasting models on three real datasets from the 2019 TIANCHI competition. The experimental results demonstrate that the reduction in WMAPE of the hybrid model almost reach to 10 percent compared to the non-hybrid models. [ABSTRACT FROM AUTHOR]

Trip purpose is a meaningful aspect of travel behaviour for the understanding of urban mobility. However, it is non-trivial to automatically obtain trip purposes. On one hand, trip purposes are naturally diverse and complicated, but the available predictive data sources are limited in real-world scenarios. On the other hand, since trip purpose labeling is costly and the development levels of cities are unbalanced, it is infeasible to access large-scale labeled data in less developed cities to train advanced prediction models. To narrow the gaps, this article presents A new Graph Embedding Network and active Domain Adaptation based framework (AGENDA) that only requires open data sources and is capable of predicting in both label-rich cities and label-scarce cities. Specifically, in label-rich source cities, we first use the vehicle's GPS trajectory and open POI check-ins to augment trip contexts. Then we establish a supervised graph embedding network with two attention mechanisms to extract the passenger's latent activity semantics and a classifier to predict trip purpose. To enable the prediction in label-scarce target cities, we further devise an active domain adaptation framework, in which adversarial domain adaptation is used to transfer the source-learned knowledge, and active learning is used to integrate human intelligence in the model training. A group of experiments are conducted with real-world datasets in Beijing and Shanghai. Evaluation results demonstrate that the proposed framework significantly outperforms existing trip purpose prediction algorithms, and could make accurate trip purpose prediction in label-scarce cities with much fewer labeling efforts. [ABSTRACT FROM AUTHOR]

We document considerable cross-investor variation in survey expectations about aggregate stock market returns. Although most investors are extrapolators who expect higher returns after a good market performance, some are contrarians who expect lower returns after a good performance. More notably, compared with extrapolators, contrarians have less persistent expectations that are corrected more quickly. We then develop a dynamic equilibrium model accounting for these differences in expectations and find that the equilibrium stock price exhibits short-term momentum and long-term reversal as in the data. Furthermore, we test the key predictions of our model linking the observable differences in extrapolators' and contrarians' expectations to aggregate stock market momentum and future stock performance and find supportive evidence for our model mechanism. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4960. [ABSTRACT FROM AUTHOR]

BACKGROUND: Biocontrol agents (BCAs) are alternatives to synthetic fungicides with low risk to the environment and human health. Although several studies on the biocontrol of gray mold in vineyards have been performed, it is necessary to improve the usage of BCAs in fields conditions. Therefore, in the present study, BCAs were used both in calendar‐based [based on four growth stages (GSs), i.e., flowering, pre‐bunch closure, veraison, and before harvest] and predictive model‐based strategies (only when Botrytis cinerea infection risk was predicted by the model). The BCAs applied during the seasons were selected considering the grapevine GSs. Treatments performed with BCAs were compared with synthetic fungicide treatments and an untreated control. The trials were conducted in three experimental vineyards with four epidemics. To evaluate the level of gray mold control of each treatment, disease severity was assessed at harvest and the presence of latent infection was evaluated. RESULTS: The integrative use of the predictive model and BCAs provided satisfactory levels of gray mold control, with gray mold severity levels significantly lower (P < 0.001) than those of the untreated control, which had severity values (< 7%) similar to those observed with synthetic fungicides following both calendar and model‐based strategies. CONCLUSIONS: The integrative use of the predictive model and BCAs represents a valid alternative to conventional methods of gray mold control in vineyards, with more than 75% reduction in fungicide usage. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Essays are one of the most used exams to assess students. Universitas Terbuka Indonesia (Open University) conducts three online essay exams within a week for each first-year course, accounting for 30% of the mid-test score. The university has over 500.000 students and hundreds of courses. However, the limited number of correctors resulted in a time-consuming and ineffective process of checking and scoring each student's essay response. Even score results can be subjective, unfair, lack detailed feedback from students, miss contextual and creative aspects, and be less reliable with complex or non-standard writing. This research proposes a hybrid approach of deep learning models and natural semantic grammar to improve and optimize an AES system, with the following steps: First, the datasets are collected from hundreds of students' answers, each representing a single question. The datasets are pre-processed and augmented to enhance the quantity and variety of the original data and scores. Second, the BERT approach was utilized to transform each text dataset into vector feature spaces using pre-trained model weights. Finally, a prediction score model was generated using the BiLSTM method. The experiment's results show that the model had an average Cohen's Kappa score of 0.749 and the highest Cohen's Kappa score of 0.91. This BERT-BiLSTM optimization model also has a better Cohen's Kappa Score average (0.820) than the ATT-CNN-LSTM, BERT-XLNET, R2BERT, and CNNBiLSTM models. After conducting a test on 46 lecturers, the results showed that the average time taken to examine one course for each student decreased to 1 minute and 2 seconds. Additionally, 92.75% of the lecturers found the process of checking responses to be fairer and more objective. In the results of the trial with 200 students, the mean percentage of question indications related to UI/UX, fairness, responsiveness, rubric, feedback, and transparency across students was 93.72%. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to define the evaluation criteria for Chinese female lower body shapes and simplify the evaluation process of shapewear, including girdles, shaping pants, etc. Design/methodology/approach: The study utilized a machine learning algorithm based on support vector regression and optimized by a genetic algorithm to construct an evaluation model for the contour beauty of Chinese female lower body shapes. A total of 64 virtual 3D models of women were measured. These models were rated by 42 raters using the Likert 9 psychological scale and data was obtained from 310 female samples. Findings: Eight factors were selected and used to create a regression prediction model. The model achieved an accuracy of 84.7% for the training samples and 86.6% for the test samples. Originality/value: The model can be utilized to assess the aesthetic appeal of the female lower body and to evaluate the shaping impact of shapewear. The research and evaluation of shapewear for the female lower body are of great significance, particularly in enhancing production efficiency. [ABSTRACT FROM AUTHOR]

Additive friction stir deposition (AFSD) is a solid-state additive manufacturing method that exploits frictional heat generation and severe plastic deformation to achieve metallurgical bonding during layer-by-layer deposition. AFSD can be used for additive manufacturing of bulk components or repair of aluminum alloys as the sub-solidus processing temperatures can be beneficial over fusion-based alternatives. Due to dynamic recrystallization during AFSD processing, significant evolution of the microstructure can occur which can lead to substantial gradients in grain and intermetallic particle size. In this study, we present a history dependent modeling framework to predict the microstructure evolution in the AFSD process through mesh-free numerical simulations. In this work, experimentally quantified relationships between grain and intermetallic particle size as well as temperatures and strain rates were incorporated into a smooth particle hydrodynamic simulation framework to predict the microstructure after AFSD processing. The SPH simulation approach showed reasonable agreement between the model predictions and experimental results of grain and intermetallic particle size distribution after AFSD processing. [ABSTRACT FROM AUTHOR]

Autonomous vehicles have limited computational resources and thus require efficient control systems. The cost and size of sensors have limited the development of self-driving cars. To overcome these restrictions, this study proposes an efficient framework for the operation of vision-based automatic vehicles; the framework requires only a monocular camera and a few inexpensive radars. The proposed algorithm comprises a multi-task UNet (MTUNet) network for extracting image features and constrained iterative linear quadratic regulator (CILQR) and vision predictive control (VPC) modules for rapid motion planning and control. MTUNet is designed to simultaneously solve lane line segmentation, the ego vehicle's heading angle regression, road type classification, and traffic object detection tasks at approximately 40 FPS for 228 × 228 pixel RGB input images. The CILQR controllers then use the MTUNet outputs and radar data as inputs to produce driving commands for lateral and longitudinal vehicle guidance within only 1 ms. In particular, the VPC algorithm is included to reduce steering command latency to below actuator latency, preventing performance degradation during tight turns. The VPC algorithm uses road curvature data from MTUNet to estimate the appropriate correction for the current steering angle at a look-ahead point to adjust the turning amount. The inclusion of the VPC algorithm in a VPC-CILQR controller leads to higher performance on curvy roads than the use of CILQR alone. Our experiments demonstrate that the proposed autonomous driving system, which does not require high-definition maps, can be applied in current autonomous vehicles. [ABSTRACT FROM AUTHOR]

Furnace temperature and oxygen content are important parameters reflecting the combustion inside a circulating fluidized bed (CFB) boiler. Accurately predicting boiler output is a complex task due to the high noise and nonsmoothness of actual boiler input and output data. In this paper, a new hybrid convolutional neural network (CNN), bidirectional long short-term memory (biLSTM) network, and squeezing and excitation (SE) network prediction model is proposed to significantly improve the prediction accuracy of oxygen content and furnace temperature by combining the advantages of multiple deep learning networks. This network can extract spatiotemporal characteristics of input parameters such as coal feed to effectively predict boiler furnace temperature and oxygen content. CNNs can extract complex features such as dynamic and static nonlinearities between multiple variables affecting the furnace temperature and oxygen content, as well as high noise. The biLSTM network layer can efficiently handle the temporal information of irregular trends in modeling time series components; SE can extract the important information between channels through the feature relationships between channels for better overfeature extraction. The CNN-biLSTM-SE model can effectively solve the problem of nonlinear mapping complexity between inputs and outputs. Experiments show that the proposed CNN-biLSTM-SE model outperforms existing methods. The experimental results showed that the average MAPE errors for oxygen content prediction were CNN-biLSTM-SE (0.038), CNN-biLSTM with attention mechanism (AM) (0.043), CNN-biLSTM (0.051), CNN-LSTM (0.051), biLSTM (0.051), RNN (0.051), LSTM(0.0052), and CNN(0.0054). Extensive experiments in CFB boilers with oxygen content and furnace temperature show that the proposed CNN-biLSTM-SE model achieves better results in terms of goodness-of-fit, generalization ability and accuracy. [ABSTRACT FROM AUTHOR]

Multi-label text classification (MLTC) aims to assign one or more labels to each document. Previous studies mainly use the label co-occurrence matrix obtained from the training set to establish the correlation between labels, but this approach ignores the noise in label co-occurrence, and applies the ungeneralizable label co-occurrence relationship to model testing and validation. In addition, labelling co-occurrence relationship globally lacks attention to a specific document, which results in the loss of the local label co-occurrence relationship. To address this issue, we introduced a new multi-label text classification model in this study, presenting CoocNet, which adopts a two-step label detection to effectively tackle the challenge of modeling label co-occurrence relations. The model first captures the global co-occurrence relationships of labels using the label co-occurrence matrix and denoises the label noise through the label denoising attention mechanism, and then uses a contrast learning strategy to capture the local label co-occurrence relationships among specific different documents. In particular, we unify the co-occurrence labeling into an auxiliary training task that runs parallel to the multi-label classification task. The new task supervises the learning of sentence representations for documents by leveraging the modeled label co-occurrence relationships, enhancing the model's generalization ability. Another novelty is that the auxiliary task is only active during model training, thereby preventing label co-occurrence relationships from interfering with the model's predictions outside the training phase. The experimental results on three benchmark datasets (Reuters-21578, AAPD, and RCV1) demonstrate that our model outperforms the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Introduction: A dynamic molecular biomarker that can identify early efficacy of immune checkpoint inhibitor (ICI) therapy remains an unmet clinical need. Here we evaluate if a novel circulating tumor DNA (ctDNA) assay, xM, used for treatment response monitoring (TRM), that quantifies changes in ctDNA tumor fraction (TF), can predict outcome benefits in patients treated with ICI alone or in combination with chemotherapy in a real-world (RW) cohort. Methods: This retrospective study consisted of patients with advanced cancer from the Tempus de-identified clinical genomic database who received longitudinal liquid-based next-generation sequencing. Eligible patients had a blood sample ≤ 40 days prior to the start of ICI initiation and an on-treatment blood sample 15–180 days post ICI initiation. TF was calculated via an ensemble algorithm that utilizes TF estimates derived from variants and copy number information. Patients with molecular response (MR) were defined as patients with a ≥ 50% decrease in TF between tests. In the subset of patients with rw-imaging data between 2 and 18 weeks of ICI initiation, the predictive value of MR in addition to rw-imaging was compared to a model of rw-imaging alone. Results: The evaluable cohort (N = 86) was composed of 14 solid cancer types. Patients received either ICI monotherapy (38.4%, N = 33) or ICI in combination with chemotherapy (61.6%, N = 53). Patients with MR had significantly longer rw-overall survival (rwOS) (hazard ratio (HR) 0.4, P = 0.004) and rw-progression free survival (rwPFS) (HR 0.4, P = 0.005) than patients with molecular non-response (nMR). Similar results were seen in the ICI monotherapy subcohort; HR 0.2, P = 0.02 for rwOS and HR 0.2, P = 0.01 for rwPFS. In the subset of patients with matched rw-imaging data (N = 51), a model incorporating both MR and rw-imaging was superior in predicting rwOS than rw-imaging alone (P = 0.02). Conclusions: xM used for TRM is a novel serial quantitative TF algorithm that can be used clinically to evaluate ICI therapy efficacy. [ABSTRACT FROM AUTHOR]

Background: Hospital-acquired pressure injuries (HAPIs) have a major impact on patient outcomes in intensive care units (ICUs). Effective prevention relies on early and accurate risk assessment. Traditional risk-assessment tools, such as the Braden Scale, often fail to capture ICU-specific factors, limiting their predictive accuracy. Although artificial intelligence models offer improved accuracy, their "black box" nature poses a barrier to clinical adoption. Objective: To develop an artificial intelligence–based HAPI risk-assessment model enhanced with an explainable artificial intelligence dashboard to improve interpretability at both the global and individual patient levels. Methods: An explainable artificial intelligence approach was used to analyze ICU patient data from the Medical Information Mart for Intensive Care. Predictor variables were restricted to the first 48 hours after ICU admission. Various machine-learning algorithms were evaluated, culminating in an ensemble "super learner" model. The model's performance was quantified using the area under the receiver operating characteristic curve through 5-fold cross-validation. An explainer dashboard was developed (using synthetic data for patient privacy), featuring interactive visualizations for in-depth model interpretation at the global and local levels. Results: The final sample comprised 28 395 patients with a 4.9% incidence of HAPIs. The ensemble super learner model performed well (area under curve = 0.80). The explainer dashboard provided global and patient-level interactive visualizations of model predictions, showing each variable's influence on the risk-assessment outcome. Conclusion: The model and its dashboard provide clinicians with a transparent, interpretable artificial intelligence–based risk-assessment system for HAPIs that may enable more effective and timely preventive interventions. [ABSTRACT FROM AUTHOR]

Background Following the Taliban's resurgence, Afghan girls were probited from attending school and university, likely causing adverse psychological effects. Therefore, the present study investigated depression, suicidal ideation, and quality of life (QoL) among Afghan girls banned from education by the Taliban. Methods A survey study assessed depression, suicidal ideation, and QoL among 426 females prohibited from attending secondary schools, high schools, and universities by the Taliban. The study used the CES-D 20 Scale to assess depression, and the WHOQOL-Bref Questionnaire to assess QoL. Associations between variables were tested with chi-square tests, and predictors of QoL were analyzed through multivariate regression analysis. Results Among the 426 participants, 87.6% exhibited depression symptoms and 49.8% reported suicidal thoughts. Significant predictors of overall QoL included monthly family income (B  = 0.147, P  = 0.001), recent others' behavior toward participants (B  = −0.247, P  < 0.001), physical illness (B  = 0.110, P  = 0.011), experiencing a recent traumatic event (B  = 0.108, P  = 0.015), depression (B  = −0.159, P  < 0.001) and suicidal ideation (B  = −0.187, P  < 0.001). Conclusions In Taliban-ruled Afghanistan, the results indicated that female students who have been prohibited from studying in classes have a high prevalence of depression and suicidal ideation. Having depression and suicidal ideation significantly predicted decreased quality of life among female students. [ABSTRACT FROM AUTHOR]

Early literacy skill development predicts later reading success, and development of skills in specific domains during the preschool years has been established as both a prerequisite and precursory for reading. Early literacy assessments typically include measures of separate skills across domains, and results can assist with determining where instructions may be most needed. When multiple areas of need are identified, understanding which skills to prioritize can be a challenge. Therefore, empirically identifying the relative contribution of each skill measured in preschool to subsequent reading success can promote more efficient systems of assessment. This study, conducted in the United States, examined the predictive validity of early literacy skills measured in preschool compared to skills measured in kindergarten, with a specific practical focus on identifying the most efficient predictive model for understanding reading readiness. Participants were 119 preschoolers (mean age = 66 months) who mostly spoke English as their primary language (79%). Results indicated early literacy and language skills in preschool are highly predictive of early reading in kindergarten, accounting for 59% of the variance in a reading composite score. The most parsimonious model indicated that first sounds, letter sounds, early comprehension, and expressive vocabulary measures adequately explained 52% of the variance in children's kindergarten reading performance. [ABSTRACT FROM AUTHOR]

In this research, the morphology, rheology, and mechanical performance of polyamide 6/polylactic acid/acrylonitrile–butadiene–styrene (PA6/ABS/PLA) ternary blends were studied. The optimum ABS percentage in PA6/ABS binary blend to achieve highly toughened blend was determined around 35 wt.%. The spreading coefficient (Sij) predictions showed that each of PLA and ABS can form separate phase in the PA6 matrix. The evaluation of the interfacial interactions in the ternary blends revealed a higher tendency of PLA to interact with PA6 chains (Sij:−1.20) rather than ABS (Sij:−0.20) which is in comply with higher work of adhesion of PLA/PA6 than PLA/ABS and PA6/ABS. By adding PLA to the binary blends of PA6/ABS, it was found that the ABS domain size decreased due to rheological change. The localization of each component in the ternary blends observed in SEM analysis was in agreement with spreading coefficient predictions. Morphological evaluations revealed higher interfacial interactions between PA6 and PLA in the blends with up to 20 wt.% PLA. The optimized blend ratio of ternary blends showed higher complex viscosity at low shear rates and higher values than the mixing rule prediction. The optimum flexural modulus of 2 GPa and impact resistance of 47 kJ/m2 were achieved at 20 wt.% PLA in PA6/ABS/PLA ternary blend. Highlights: The optimum PA6/ABS blend ratio to achieve high toughness was determined.The phase localization in the blends predicted via Spreading Coefficient.The interaction of PA6 and ABS enhanced by the addition of PLA in the blends.The mechanical properties of blends are correlated with rheological behavior.The flexural modulus of blends was higher than Halpin–Tsai model predictions. [ABSTRACT FROM AUTHOR]

Objective: To compare the Guy’s, S.T.O.N.E, and CROES nomograms for predicting stone-free status in patients who underwent percutaneous nephrolithotomy for renal stones. Materials and Methods: The data of 1114 patients who underwent percutaneous nephrolithotomy for renal calculi between 11/2008 and 08/2018 in our clinic were retrospectively reviewed. Various parameters evaluated by preoperative computed tomography and the scoring systems of the patients and postoperative stone-free status were compared. Results: Out of 1000 patients who met the study criteria. Gender, body mass index, and stone density were not statistically different between the group with residual stones and the stone-free group. However, stone size, number of renal accesses, duration of fluoroscopy usage, duration of operation, number of stones, and complication rate were significantly higher in the group with residual stones than in the stone-free group. A statistically significant correlation was found between the postoperative stone-free rate and scoring systems. The applicability and preoperative prediction ability of all three systems were evaluated by receiver operating characteristic analysis. The area under the curve (AUC) was detected in the Guy’s, CROES, and S.T.O.N.E scoring system (AUC: 0.642, 0.665, 0.592 respectively). Conclusion: In this study, where the perioperative and postoperative results of 1000 patients were evaluated, we found that all three scoring systems could predict the stone-free rate. “We believe that the use of these scoring systems before surgery can guide surgeons.” [ABSTRACT FROM AUTHOR]

Objective: Identification of at risk patients before surgery could facilitate improved clinical communication, care pathways and postoperative pain management Design: A retrospective cohort study was performed in all infants who had undergone cleft palate repair. Setting: Tertiary Institutional Participants: Infants < 36 months of age who underwent primary repair of cleft palate between March 2016 and July 2022 Intervention: Requirement for analgesic intervention in the post operative care unit Main outcome measure: Adverse perioperative event defined as pain or distress. Secondary outcomes were the incidence of airway obstruction, hypoxemia or unplanned intensive care admission. Results: Two hundred and ninety one patients (14.6 months,10.1 kg weight) were included. Cleft distribution included submucous (5.2%), Veau I (23.4%), Veau 2 (38.1%), Veau 3 (24.4%), and Veau 4 (8.9%). Overall 35% of 291 infants undergoing cleft palate repair experienced pain or distress requiring opiate intervention in the first hour after surgery. Infants with a Veau 4 cleft palate had 1.8 times and Veau 2 cleft palate had 1.5 times the risk of postoperative pain compared to infants with Veau 1 cleft palate (relative risk 1.82, 95%CI 1.04−3.18 and 1.49, 95%CI 0.96−2.32 respectively). The use of bilateral above elbow arm splints was significantly associated with postoperative pain or distress (odds ratio 2.23, 95%CI 1.01−5.16). Conclusions: Post operative pain requiring intervention in PACU is common despite adequate intraoperative multimodal analgesia, local anaesthesia infiltration and postoperative opiate infusions. Infants undergoing soft palate alone or submucous palate repair may require less perioperative opiates. [ABSTRACT FROM AUTHOR]

This review paper provides an exploration of various facets of creep behaviour in adhesives and adhesive joints, encompassing experimental procedures, prediction models, influential parameters and strategies to enhance resistance. The discussion extends to the interplay between fatigue and creep, emphasising recent advances over the last two decades. While avoiding redundancy with prior work on temperature and moisture degradation, the paper articulates connections between topics for a better understanding. A critical examination of load levels reveals that small variations significantly impact the creep life of adhesive joints, particularly prominent with epoxy adhesives. The adhesive type, joint geometry and substrate material are scrutinised, revealing distinct impacts on creep behaviour. The study underscores the critical role of adhesive thickness and overlap length, emphasising their relevance in determining the time to failure in bonded joints under creep conditions. Notably, the substrate material's role is highlighted. As the review delves into unexplored dimensions, it calls for further research to bridge existing gaps and refine our understanding of tertiary creep and time until failure. [ABSTRACT FROM AUTHOR]

Roadwork zones represent a hazardous place within the highway system, with higher crash rates and injury severity, often due to excessive speed and noncompliance with speed limits. On freeways, a common layout is to close a full roadway and to divert traffic onto the opposite roadway, with one lane closed to accommodate the redirected traffic, by driving through the median strip. In this scenario, the second chicane can be a hazardous point if it is not correctly signaled. This paper examines the need to install a speed monitoring display (SMD) before the returning chicane on a bypass. A two-phase study was conducted on a Spanish freeway where a roadway was temporarily closed. Two measurement points were established on the bypass, one in the middle and one at the end, prior to the return chicane through the median strip. During Phase 1, the portable SMD was installed and during Phase 2, it was removed. The average and the 85th percentile of the speed distribution at each point during both phases were compared. Additionally, mean difference tests were conducted and a speed prediction model was developed. With the SMD, drivers reduced their speed from the midpoint to the returning chicane, within the range of 7-10 km/h. Conversely, upon removal of the SMD, motorists increased their speed while driving through the bypass, resulting in excessive speed at the most hazardous point, the chicane leading back to the original roadway. The difference in mean speed between the two phases was 18 km/h at the returning chicane. In addition to the traffic calming measures implemented prior to entering roadwork zones on freeways, which are conveniently established in the standards; it is necessary to evaluate potentially dangerous areas of the layout and implement additional measures where required. Specifically, in the case of final chicanes of bypasses with reduced radii, it is recommended that a speed monitoring display be installed as a mandatory element in order to inform drivers of this challenging segment. Highway administrations around the world should maintain a SMD at the returning chicane of a bypass while roadworks last. [ABSTRACT FROM AUTHOR]

Increasing advancements in building digitization, smart sensing, and metering technologies have allowed large amounts of timeseries data to be collected for monitoring, analyzing, and controlling building systems. However, due to sensor or communication failures, the data collected are often incomplete and poor in quality. Data imputation approaches to replace the missing values, specifically based on either statistical or predictive models have been widely adopted for multivariate datasets in other domains. It is hence of interest to find an effective way to impute timeseries data collected from a building system. In this paper, we evaluate multiple data imputation approaches using data collected from a medium sized building situated in Stockholm, Sweden and a small commercial building from the ASHRAE RP-1312 research project. Sensors with widely varying characteristics from the case study buildings were selected to evaluate the imputation methods. The imputation accuracy and the impact of each chosen imputation method on information entropy, short-term building forecasting model performance, and fault detection strategy were evaluated. Results demonstrate that incorporating time-lagged cross correlations within a k -nearest neighbor ( k NN) model provide the most accurate imputations without affecting the quality of subsequent data analysis. [ABSTRACT FROM AUTHOR]

The objective of this research was to compare social, emotional, and cognitive determinants of depression and anxiety symptoms and mental wellbeing using the two-continua model of mental health and illness in two age groups. The two-continua model proposes that mental health and mental illness are not two ends of the same spectrum but are, in fact, two separate but related spectrums. This cross-sectional study used a multi-age group comparison approach to identify the relative predictive strengths of risk and protective factors for the two-continua model of mental health and mental illness. Participants (n = 458: 251 16–25 year olds, and 207 35–64 year olds) completed an online survey that measured the outcome variables of depression and anxiety symptoms and mental wellbeing. Independent variables from three conceptual areas in psychology—(1) positive psychology: optimism, pessimism, and accomplishment; (2) emotion regulation: cognitive reappraisal and expressive suppression; and (3) interpersonal theories: belonging and relationship with parent/child—were measured to ascertain the determinants of these outcome variables. The all-variables models explained 58–68% of the variance in depression symptoms, 77–80% in mental wellbeing, and 26–43% in anxiety symptoms. For both groups, the strongest predictor of mental wellbeing in these models was accomplishment. The strongest predictors in these models of mental illness symptoms differed between groups: belonging in the younger group and accomplishment in the older group. Programs targeting belonging and accomplishment could be highly effective in promoting mental wellbeing and reducing mental ill-health for these groups. Interventions require contextual investigation to locate drivers of mental wellbeing and illness for different age groups prior to implementation. [ABSTRACT FROM AUTHOR]