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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]

Background: Catheter-related bladder discomfort (CRBD) commonly occurs in patients who have indwelling urinary catheters while under general anesthesia. And moderate-to-severe CRBD can lead to significant adverse events and negatively impact patient health outcomes. However, current screening studies for patients experiencing moderate-to-severe CRBD after waking from general anesthesia are insufficient. Constructing predictive models with higher accuracy using multiple machine learning techniques for early identification of patients at risk of experiencing moderate-to-severe CRBD during general anesthesia resuscitation. Methods: Eight hundred forty-six patients with indwelling urinary catheters who were resuscitated in a post-anesthesia care unit (PACU). Trained researchers used the CRBD 4-level assessment method to evaluate the severity of a patient's CRBD. They then inputted 24 predictors into six different machine learning algorithms. The performance of the models was evaluated using metrics like the area under the curve (AUC). Results: The AUCs of the six models ranged from 0.82 to 0.89. Among them, the RF model displayed the highest predictive ability, with an AUC of 0.89 (95%CI: 0.87, 0.91). Additionally, it achieved an accuracy of 0.93 (95%CI: 0.91, 0.95), 0.80 sensitivity, 0.98 specificity, 0.94 positive predictive value (PPV), 0.92 negative predictive value (NPV), 0.87 F1 score, and 0.07 Brier score. The logistic regression (LR) model has achieved good results (AUC:0.87) and converted into a nomogram. Conclusions: The study has successfully developed a machine learning prediction model that exhibits excellent predictive capabilities in identifying patients who may develop moderate-to-severe CRBD after undergoing general anesthesia. Furthermore, the study also presents a nomogram, which serves as a valuable tool for clinical healthcare professionals, enabling them to intervene at an early stage for better patient outcomes. [ABSTRACT FROM AUTHOR]

Combination monoclonal broadly neutralizing antibodies (bnAbs) are currently being developed for preventing HIV-1 acquisition. Recent work has focused on predicting in vitro neutralization potency of both individual bnAbs and combination regimens against HIV-1 pseudoviruses using Env sequence features. To predict in vitro combination regimen neutralization potency against a given HIV-1 pseudovirus, previous approaches have applied mathematical models to combine individual-bnAb neutralization and have predicted this combined neutralization value; we call this the combine-then-predict (CP) approach. However, prediction performance for some individual bnAbs has exceeded that for the combination, leading to another possibility: combining the individual-bnAb predicted values and using these to predict combination regimen neutralization; we call this the predict-then-combine (PC) approach. We explore both approaches in both simulated data and data from the Los Alamos National Laboratory's Compile, Neutralize, and Tally NAb Panels repository. The CP approach is superior to the PC approach when the neutralization outcome of interest is binary (e.g., neutralization susceptibility, defined as inhibitory 80% concentration < 1 μg/mL). For continuous outcomes, the CP approach performs nearly as well as the PC approach when the individual-bnAb prediction algorithms have strong performance, and is superior to the PC approach when the individual-bnAb prediction algorithms have poor performance. This knowledge may be used when building prediction models for novel antibody combinations in the absence of in vitro neutralization data for the antibody combination; this, in turn, will aid in the evaluation and down-selection of these antibody combinations into prevention efficacy trials. [ABSTRACT FROM AUTHOR]

CONTEXT: Prognostic prediction models (PPMs) can help clinicians predict outcomes. OBJECTIVE: To critically examine peer-reviewed PPMs predicting delayed recovery among pediatric patients with concussion. DATA SOURCES: Ovid Medline, Embase, Ovid PsycInfo, Web of Science Core Collection, Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, Google Scholar. STUDY SELECTION: The study had to report a PPM for pediatric patients to be used within 28 days of injury to estimate risk of delayed recovery at 28 days to 1 year postinjury. Studies had to have at least 30 participants. DATA EXTRACTION: The Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies checklist was completed. RESULTS: Six studies of 13 PPMs were included. These studies primarily reflected male patients in late childhood or early adolescence presenting to an emergency department meeting the Concussion in Sport Group concussion criteria. No study authors used the same outcome definition nor evaluated the clinical utility of a model. All studies demonstrated high risk of bias. Quality of evidence was best for the Predicting and Preventing Postconcussive Problems in Pediatrics (5P) clinical risk score. LIMITATIONS: No formal PPM Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) process exists. CONCLUSIONS: The 5P clinical risk score may be considered for clinical use. Rigorous external validations, particularly in other settings, are needed. The remaining PPMs require external validation. Lack of consensus regarding delayed recovery criteria limits these PPMs. [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]

Applying latent growth mixture modeling (GMM), this study delves into the examination of self-esteem trajectories in a sample of 5,597 older adults over a nine-year period. Four distinct patterns of self-esteem changes have emerged: low, decreasing, increasing, and high. Additionally, the study explores the relationships between each trajectory and various predictors encompassing demographic factors, socioeconomic status, health, and interpersonal relationships. The findings highlight the significance of these factors in predicting the likelihood of an individual following a specific self-esteem trajectory. Notably, maintaining employment, fostering satisfactory social relationships, and being free of frequent depressive feelings emerged as strong predictors for the stability and increase of high self-esteem. Intriguingly, an average or above-average income was unexpectedly associated with lower levels of self-esteem. The study emphasizes the contribution of GMM to advancing aging research. [ABSTRACT FROM AUTHOR]

The present study investigated whether multicultural variables derived from the Multicultural Assessment‐Intervention Process model (shifting, gender roles, acculturation, perceived discrimination) could predict levels of internalized weight bias (IWB) or disordered eating behaviours in 429 Asian American adult women. A facet of shifting, White beauty conformity and mainstream acculturation were the only multicultural variables that significantly predicted all five body‐ and eating‐related outcomes (IWB, restraint, eating, shape and weight concerns). Gender roles and two aspects of perceived discrimination (social exclusion and stigmatization) also showed predictive ability for one or more of the outcome measures. Results highlight the relationship between multicultural and body‐ and eating‐related variables for Asian American women and underscore the salience of body shape and weight, the importance placed on conforming to Western culture and beauty standards and the detrimental effects of doing so for this population. Clinical implications and future research are discussed. [ABSTRACT FROM AUTHOR]

The purposes of the present study were a) to evaluate the construct validity of a questionnaire measuring parental physical activity (PA) orientation, and b) to examine the association between constructs of parental PA orientation and parental explicit PA modelling in parents of young children with disabilities. One hundred and thirty-five participants completed an online questionnaire. Exploratory factor analysis was used to examine the construct validity of a questionnaire for parental PA orientation. Exploratory factor analysis for parental PA orientation revealed a four-factor solution, accounting for 59.02% of the variance with eigenvalues of 7.14, 6.88, 4.00, and 2.35 for factor 1 (parent-perceived motor performance of their child), factor 2 (parental enjoyment of PA), factor 3 (parental importance of PA), and factor 4 (parental awareness of accessible PA program), respectively. Two hierarchical multiple regression models were developed and used to examine the association between constructs of parental PA orientation and parental explicit PA modelling. Parental enjoyment and importance of PA were significant factors predicting parental explicit PA modelling, controlling for child's age and parent's age. The results of the current study may be used for resources when developing an intervention for parental explicit PA modelling in parents of children with disabilities. [ABSTRACT FROM AUTHOR]

Congenital long QT syndrome (LQTS) diagnosis is complicated by limited genetic testing at scale, low prevalence, and normal QT corrected interval in patients with high-risk genotypes. We developed a deep learning approach combining electrocardiogram (ECG) waveform and electronic health record data to assess whether patients had pathogenic variants causing LQTS. We defined patients with high-risk genotypes as having ≥1 pathogenic variant in one of the LQTS-susceptibility genes. We trained the model using data from United Kingdom Biobank (UKBB) and then fine-tuned in a racially/ethnically diverse cohort using Mount Sinai BioMe Biobank. Following group-stratified 5-fold splitting, the fine-tuned model achieved area under the precision-recall curve of 0.29 (95% confidence interval [CI] 0.28–0.29) and area under the receiver operating curve of 0.83 (0.82–0.83) on independent testing data from BioMe. Multimodal fusion learning has promise to identify individuals with pathogenic genetic mutations to enable patient prioritization for further work up. [ABSTRACT FROM AUTHOR]

Simple Summary: Inflammation-based prognostic markers have been recognized as useful in a range of oncologic conditions. Given the high mortality rate associated with pancreatic ductal adenocarcinoma (PDAC), the demand for accurate prognostic tools is significant. Nonetheless, the literature still shows conflicting results regarding the effectiveness of the various available scoring systems. Our data confirm that NLR and PNI are useful biomarkers regarding overall survival in PDAC. Especially in advanced disease stages, they seem to be of considerable benefit as easily accessible tools for prognosis prediction. Purpose: Prognostic inflammation-based parameters have been reported as useful tools in various oncologic diseases. Pancreatic ductal adenocarcinoma (PDAC) is characterized by a high mortality rate, making reliable prognostic markers highly desirable. However, there is still inconsistency in the literature regarding the efficacy of the different available scores. Methods: A total of 207 patients, who underwent primary resection of PDAC from January 2000 to December 2018 at the University Hospital of Erlangen, were included in this retrospective single-center study. Different biomarkers, including the preoperative neutrophil–lymphocyte ratio (NLR), the platelet–lymphocyte ratio (PLR), the c-reactive protein (CRP)–albumin ratio (CAR), the lymphocyte–CRP ratio (LCR), the prognostic nutritional index (PNI) and the modified Glasgow prognostic score (mGPS) were analyzed for their ability to predict overall survival (OS). Results: In our cohort, the median overall survival was 20.7 months. Among the investigated biomarkers, NLR and PNI were identified as independent prognostic markers (Hazard Ratio (HR) 1.6 (1.0–2.5), p = 0.048 and HR 0.6 (0.4–0.9), p = 0.018), whereas PLR, CAR, LCR and mGPS did not reach significance in the multivariate analysis. Subgroup analysis revealed that the prognostic value of NLR and PNI is particularly evident in locally advanced tumor stages (pT3/4 and pN+). Conclusions: The NLR and PNI could serve as valuable tools for estimating prognosis in patients with PDAC undergoing pancreatic resection in curative intention, especially in locally advanced tumor stages. However, conflicting results in the current literature highlight the need for further prospective studies to validate these findings. [ABSTRACT FROM AUTHOR]

Millions of people around the world are affected by mental illness, yet a substantive portion of individuals with mental illness do not regularly seek help from medical professionals or utilize mental health services. Help-seeking involves an individual's communicative intention and behaviors to solicit advice and/or treatment and thus is essentially a communicative process. This manuscript reports a longitudinal study that examined predictors and outcomes of help-seeking in mental illness contexts. Three-wave data from 223 participants clinically diagnosed with one or more mental illness conditions were collected and analyzed. Results indicated that expected outcomes of help-seeking from counselors were significantly associated with participants' communication efficacy and target efficacy at Wave 1, which subsequently influenced motivation to seek help and actual help-seeking behaviors at Wave 2. Actual help-seeking behaviors predicted post-traumatic growth and cognitive reappraisal at Wave 3. Importantly, communication efficacy and target efficacy interacted in their effects on help-seeking motivation and behaviors. Theoretical implications for help-seeking communication processes and practical implications for mental health help-seeking are discussed. [ABSTRACT FROM AUTHOR]

Simple Summary: The incidence of metastatic melanoma is rising, making it imperative to identify patients who do not benefit from immunotherapy. This study aimed to develop a radiomic biomarker, using segmentations from 146 baseline and 146 first follow-up CT scans, to predict best overall response, progression-free survival, and overall survival across various immunotherapies. We volumetrically segmented the total tumour load, excluding cerebral metastases. This study also examined whether reducing the number of segmented metastases per patient affects predictive accuracy. The findings suggest that delta radiomics could enhance the prediction of best overall response, progression-free survival, and overall survival in metastatic melanoma patients undergoing first-line immunotherapy. Although volumetric whole tumour load segmentation is complex, it may provide predictive benefits. Background: The prevalence of metastatic melanoma is increasing, necessitating the identification of patients who do not benefit from immunotherapy. This study aimed to develop a radiomic biomarker based on the segmentation of all metastases at baseline and the first follow-up CT for the endpoints best overall response (BOR), progression-free survival (PFS), and overall survival (OS), encompassing various immunotherapies. Additionally, this study investigated whether reducing the number of segmented metastases per patient affects predictive capacity. Methods: The total tumour load, excluding cerebral metastases, from 146 baseline and 146 first follow-up CTs of melanoma patients treated with first-line immunotherapy was volumetrically segmented. Twenty-one random forest models were trained and compared for the endpoints BOR; PFS at 6, 9, and 12 months; and OS at 6, 9, and 12 months, using as input either only clinical parameters, whole-tumour-load delta radiomics plus clinical parameters, or delta radiomics from the largest ten metastases plus clinical parameters. Results: The whole-tumour-load delta radiomics model performed best for BOR (AUC 0.81); PFS at 6, 9, and 12 months (AUC 0.82, 0.80, and 0.77); and OS at 6 months (AUC 0.74). The model using delta radiomics from the largest ten metastases performed best for OS at 9 and 12 months (AUC 0.71 and 0.75). Although the radiomic models were numerically superior to the clinical model, statistical significance was not reached. Conclusions: The findings indicate that delta radiomics may offer additional value for predicting BOR, PFS, and OS in metastatic melanoma patients undergoing first-line immunotherapy. Despite its complexity, volumetric whole-tumour-load segmentation could be advantageous. [ABSTRACT FROM AUTHOR]

Chemical mechanical planarization (CMP) is vital for ensuring chip fabrication uniformity at nanometer scales. The emergence of a series of phenomenological CMP process models (Stine et al., 1997; Gbondo-Tugbawa, 2002; Xie, 2007; Vasilev, 2011) suggests that the existing model upgrade approach is largely based on a change in phenomenological model assumptions, demanding deep insights into complex process mechanisms and protracted period for accuracy improvements. To tackle this issue, this paper proposes a decoupling regression strategy for model upgrades. This strategy employs a data-driven approach to enhance the coupling relationships within the model, facilitating continuous improvement of simulation accuracy based on the existing model. It is capable of achieving improvements in model accuracy even in scenarios where modelers lack insight into complex process mechanisms. We validate our method by upgrading the Density Step Height (DSH) model to the Extend-DSH model to address poor erosion predictions at the 28nm node. Comparing model predictions with silicon data reveals that the Extend-DSH model aligns better with the measured data, reducing the root mean square error from 159.31Å to 6.89Å and increasing the coefficient of determination from -0.83561 to 0.6058, showcasing the effectiveness of the proposed chip-level CMP model upgrade method grounded in the decoupling regression strategy. [ABSTRACT FROM AUTHOR]

Atlases of normal genomics, transcriptomics, proteomics, and metabolomics have been published in an attempt to understand the biological phenotype in health and disease and to set the basis of comprehensive comparative omics studies. No such atlas exists for radiomics data. The purpose of this study was to systematically create a radiomics dataset of normal abdominal and pelvic radiomics that can be used for model development and validation. Young adults without any previously known disease, aged > 17 and ≤ 36 years old, were retrospectively included. All patients had undergone CT scanning for emergency indications. In case abnormal findings were identified, the relevant anatomical structures were excluded. Deep learning was used to automatically segment the majority of visible anatomical structures with the TotalSegmentator model as applied in 3DSlicer. Radiomics features including first order, texture, wavelet, and Laplacian of Gaussian transformed features were extracted with PyRadiomics. A Github repository was created to host the resulting dataset. Radiomics data were extracted from a total of 531 patients with a mean age of 26.8 ± 5.19 years, including 250 female and 281 male patients. A maximum of 53 anatomical structures were segmented and used for subsequent radiomics data extraction. Radiomics features were derived from a total of 526 non-contrast and 400 contrast-enhanced (portal venous) series. The dataset is publicly available for model development and validation purposes. [ABSTRACT FROM AUTHOR]

Orbital flybys have been extensively studied for spacecraft missions, resulting in effective mathematical and physical models. However, these models' applicability to natural encounters involving asteroids has not been explored. This paper examines the applicability of two such theories, patched conics (PC) and the Keplerian map (KM), to asteroid encounters. A review of the two methods will be provided, highlighting their assumptions and range of applicability. Simulations of asteroid–asteroid encounters will then be performed to evaluate their effectiveness in these scenarios. The simulation parameters are set by collecting data on actual asteroid–asteroid encounters, hereby presented, generally characterised by high close approach distances and small masses of the perturbing bodies, if compared to those used to build the flyby theories. Results show that the PC theory's effectiveness diminishes with increasing approach distances, aligning with its assumptions. Moreover, the prediction of the model is better in the geometric configurations where the flyby has major effects on the orbital energy change. The KM theory has shown good effectiveness for encounters occurring outside the sphere of influence of the perturbing body, even for very high distances. This research investigates flyby models' strengths and weaknesses in asteroid encounters, offering practical insights and future directions. [ABSTRACT FROM AUTHOR]

Grey systems theory can be used to predict the evolution of a system with insufficient information. Unfortunately, the most used version of the grey model (GM), namely, GM(1,1), works best when the system series have an increasing exponential rate. In any other case, the GM(1,1) produces inaccurate predictions. In this paper, we examine the mathematical formulation of the conventional GM(1,1) in order to propose a new GM that addresses its shortcomings through a new time response function. Examples are presented to demonstrate the flexibility and accuracy of the new model when implemented with series of various natures. Comparisons with other intelligent GM(1,1) show that the proposed model performs better than the reference models. [ABSTRACT FROM AUTHOR]

Background: Drawing on prospective data from the National Health and Nutrition Examination Survey (NHANES), our goal was to construct and validate a 5-year survival prediction model for individuals with cognitive impairment (CI). Methods: This study entailed a prospective cohort design utilizing information from the 2011-2014 NHANES dataset, encompassing individuals aged 40 years or older, with updated mortality status as of December 31, 2019. Predictive models within the derivation and validation cohorts were assessed using logistic proportional risk regression, column-line plots, and least absolute shrinkage and selection operator (LASSO) binomial regression models. Results: The study enrolled a total of 1,439 participants (677 men, mean age 69.75 ± 6.71 years), with the derivation and validation cohorts consisting of 1,007 (538 men) and 432 (239 men) individuals, respectively. The 5-year mortality rate stood at 16.12% (n = 232). We devised a 5-item column-line graphical model incorporating age, race, stroke, cardiovascular disease (CVD), and blood urea nitrogen (BUN). The model exhibited an area under the curve (AUC) of 0.772 with satisfactory calibration. Internal validation demonstrated that the column-line graph model displayed strong discrimination, yielding an AUC of 0.733, and exhibited good calibration. Conclusion: To sum up, our study successfully developed and internally validated a 5-item nomogram integrating age, race, stroke, cardiovascular disease, and blood urea nitrogen. This nomogram exhibited robust predictive performance for 5-year mortality in individuals with CI, offering a valuable tool for prognostic evaluation and personalized care planning. [ABSTRACT FROM AUTHOR]

Motion prediction has gotten much attention in the past few years. It has endless uses either in real life or in virtual life (gaming). The current dynamic motion prediction models, such as the Markov model, depend only on the object motions' history. They ignore the object's direction and speed, which play a key role in dynamic motion prediction. This paper introduces two new probability density functions (PDFs) for elliptic and ellipsoidal regions. Then, it introduces two new motion prediction models based on the new proposed PDFs. This paper proposes new motion detection models that depend not only on the motion's history but also on moving objects' speed and direction (objects' velocity). Moreover, it integrates the proposed models with the Markov model to attain a more precise prediction. Besides, it introduces several applications for the proposed models. [ABSTRACT FROM AUTHOR]

This research investigated the predictors of satisfaction for parents of pediatric patients after a clinical consultation. Specifically, we assessed whether perceptions of their provider's communication quality influenced the degree to which their (dis)satisfaction with consultation length associated with their provider rating and intent to recommend the provider's office. Using patient satisfaction survey data collected after initial clinical visits to a pediatric hospital (N = 12,004), we found that communication quality was a stronger predictor for those who were dissatisfied with their consultation length, whereas communication quality made a relatively smaller difference for those who were satisfied with their consultation length. Put another way, parents' dissatisfaction with their child's consultation length mattered little when they perceived their provider to be high in communication quality, but it reduced their ratings and intentions to recommend when they perceived their provider to be low in communication quality. These results suggest that providers' communication behaviors have the capacity to buffer patients' negative evaluations otherwise incurred from shorter than desired consultations. [ABSTRACT FROM AUTHOR]

Integrating the dualistic model of passion in the recovery literature, the present study investigates how playing video games after work hours can facilitate recovery from work stress. We predicted that passion for gaming would relate to gaming more hours in the evening. Next, we hypothesized that playing video games in the evening would relate to (a) feeling recovered the next morning through psychological detachment and (b) feeling vigorous the next morning through mastery experiences while gaming. We further hypothesized that harmonious passion would strengthen, and obsessive passion would weaken the links between gaming hours and (a) psychological detachment and (b) mastery. In total, 65 employees filled in short questionnaires in the mornings and evenings of at least five workdays (total n = 502). Results of multilevel hierarchical regression analyses supported the proposed mediation model, indicating that playing video games indeed helps replenish energy resources during leisure time. Findings additionally showed that gaming also predicted feeling recovered in the morning through mastery experiences. Players with a harmonious passion may benefit more from playing video games; harmonious (but not obsessive) passion strengthened the relationship between gaming and mastery experiences. [ABSTRACT FROM AUTHOR]

I evaluate the conception of direct retrieval as originally formulated in the Self-Memory System model (Conway & Pleydell-Pearce [2000]. The construction of autobiographical memories in the self-memory system. Psychological Review, 107(2), 261–288. ). In the hierarchical memory organisation proposed in the Self-Memory System model, direct retrieval is described as a bottom-up associative process. While its theoretical role within this model is clear, systematic empirical examination of direct retrieval, viewed as a natural and observable phenomenon, has been hampered by inconsistent operationalisations. Here, I suggest that direct retrieval should be treated as a theoretical concept, aiming at explaining the phenomenon of involuntary (spontaneously arising) autobiographical memories. I evaluate predictions derived from the concept of direct retrieval against findings on involuntary autobiographical memories obtained over the past 25 years. Most of these predictions are consistent with the evidence, notably, the enhanced episodic specificity and constructive nature of involuntary autobiographical memories. However, the theory also has critical limitations. It did not predict the frequent occurrence of involuntary recollections in daily life, exceeding the prevalence of voluntary memories. Additionally, it overlooked the early emergence of spontaneously arising event memories in ontogenesis and their presence in other species, such as great apes. Future advancements of the Self-Memory System model should integrate evolutionary perspectives to address these limitations. [ABSTRACT FROM AUTHOR]

The aim of this work was to examine the profile and treatment outcomes of patients with dual pathology depending on whether the patients were attending addiction centers or are being treated in a coordinated model by mental health services. Data from 7225 dual diagnosis patients were used, of whom 2417 (33.5%) received treatment in the mental health coordinated modality. Clinical information was taken from the patients' electronic health record. Differences were found in patients' sociodemographic and comorbidity profiles according to treatment modality. In general, coordinated care yielded favorable outcomes (higher attendance and lower dropout rates but no differences in retention). The logistic regression analysis identified predictors of patient profiles in coordinated care, emphasizing having a severe mental health disorder (OR = 3.878, 95% CI [3.443, 4.368]; p =.000), being referred by social/health services, or having retired status. Main differences were observed according to the comorbid diagnosis presented, particularly in cases in which the patient had impulse control, hyperkinetic, or cluster C personality disorder. While therapeutic outcomes are influenced by associated comorbidities, the disorders prognosis can be favorable with appropriate treatment. Furthermore, analysis of differences according to treatment modality allows for predicting the type of patient who will receive a particular service, which enables the development of tailored treatments. [ABSTRACT FROM AUTHOR]

Families must restructure and renegotiate rules, roles, and boundaries when a member is incarcerated. Nonincarcerated partners may act as gatekeepers and primary caretakers, however, a strong coparenting alliance can facilitate better family outcomes such as greater relationship satisfaction and cohesion. Structural family therapy (SFT) can help families conceptualize a healthy structure when experiencing the incarceration of a member. In this study, an actor-partner interdependence model (APIM) was estimated using structural equation modeling (SEM) to analyze 864 heterosexual romantic couples where the man was incarcerated, using data from the Multi-site Family Study on Incarceration, Parenting and Partnering (MFS-IP). We examined the relationship between 4-point scales measuring the perceived reliability of parenting responsibilities and internally consistent 8-item Dyadic Adjustment Scale (DAS) scores that capture relationship satisfaction and cohesion. Both actor and partner effects are significant for each dyad member; being able to rely on one's partner to follow through with their parenting responsibilities and having a partner who can rely on them are important predictors of greater relationship satisfaction and cohesion. Results emphasize the need for therapeutic services to strengthen romantic and coparenting relationships where one partner is incarcerated. Highlights: When a parent is incarcerated, families are tasked with restructuring. SFT allows for the theoretical understanding of structure, rules, and boundaries for families experiencing incarceration. We examined the relationship between the perceived reliability of parenting responsibilities and dyadic adjustment. Being able to rely on one's partner concerning parenting responsibilities predicts greater relationship satisfaction and cohesion and having a partner who can rely on the respondent predicts greater relationship satisfaction and cohesion. Results showcase the need for therapeutic services to strengthen incarcerated romantic and coparenting relationships. [ABSTRACT FROM AUTHOR]

The separator membrane is one of the four key materials of a lithium-ion battery, and the size of the separator membrane pores plays an important role on the internal resistance and safety of the battery. In this study, firstly, the relationship between the average pore size and the pore center to center distance with different porosity ratios in the separator membrane was analyzed with statistical theory. Then, a mathematical model was established to predict the probability of intersections and merging of the pores. In the end, the probability of intersections of pores with different porosity ratios were calculated and simulated; the differences between the results obtained from the two ways were 1.16%, 1.47% and 4.16%, when the porosity ratios were 21.36%, 40.16% and 53.34%, respectively, which indicated that the prediction model had good accuracy. [ABSTRACT FROM AUTHOR]

The traditional Model Predictive Control (MPC) algorithm, grounded in precise mathematical models, faces challenges attributed to uncertainties in vehicle model parameters and modeling errors. These limitations result in suboptimal accuracy of the path tracking controller, particularly in complex driving environments. To address this issue, a tube-based model predictive control strategy (Tube MPC) is proposed. Initially, nominal dynamic modeling of intelligent vehicles is conducted, followed by linearized derivations. Leveraging the resulting linearized nominal model, the MPC cost function is solved to derive the control law for the nominal system. Subsequently, an error model between the actual and nominal systems is established, integrating various target constraints for path tracking and formulating a robust objective function. Utilizing the linear matrix inequality optimization method, a state feedback-assisted control law is derived to mitigate steady-state lateral position error. This approach ensures a more precise alignment between the actual and nominal states, ultimately enhancing tracking accuracy. Finally, experimental validations are conducted in the Carsim/Simulink simulation environment, encompassing diverse vehicle speed conditions and driving scenarios to assess the proposed controller against traditional Model Predictive Control. The experimental results demonstrate that, while ensuring vehicle stability, this control method exhibits superior tracking accuracy. [ABSTRACT FROM AUTHOR]

Objective: This study examines Chinese international students' underlying beliefs and overall perception of the flu vaccination to inform effective health promotion efforts on U.S. college campuses. Participants: Data were collected in March 2020. Participants (N = 189) were recruited via email at a southeastern university in the U.S. Methods: Incorporating theory recommendations and recent vaccine-related research findings, this study designs a Four-Factor Measurement Model through confirmatory factor analysis (CFA) to examine Chinese international students' perception of flu vaccination. Results: Chinese international students' health beliefs, particularly their perceived susceptibility of the influenza virus, was the most important factor to explain their perception of flu vaccination. While normative beliefs were the least influential factor to account for Chinese international students' perception of flu vaccination, these students valued healthcare providers' opinions immensely. Conclusions: This study suggests the Four-Factor Measurement Model will help measure Chinese international students' perception of flu vaccination and can be applied to future vaccine-related research. [ABSTRACT FROM AUTHOR]

Deep brain stimulation(DBS) has become an effective intervention for advanced Parkinson’s disease(PD), but the exact mechanism of DBS is still unclear. In this review, we discuss the history of DBS, the anatomy and internal architecture of the basal ganglia (BG), the abnormal pathological changes of the BG in PD, and how computational models can help understand and advance DBS. We also describe two types of models: mathematical theoretical models and clinical predictive models. Mathematical theoretical models simulate neurons or neural networks of BG to shed light on the mechanistic principle underlying DBS, while clinical predictive models focus more on patients’ outcomes, helping to adapt treatment plans for each patient and advance novel electrode designs. Finally, we provide insights and an outlook on future technologies. [ABSTRACT FROM AUTHOR]

Low health literacy (LHL) significantly impacts patients' ability to participate actively in their healthcare. Registered nurses (RNs) play a crucial role in identifying LHL and addressing patient knowledge gaps and skill deficits. This correlational study examined the relationship between RNs' predictions of patients' health literacy levels (HLL) and the actual HLL of a predominately Hispanic patient population. In addition, personal factors (i.e., demographics) were analyzed to determine their influence on the nurse's predictions and patients' HLL. Data were collected from 84 participant patient–nurse couplets admitted to a medical-surgical unit in a rural setting located on the United States–Mexico border. In addition to demographic information collected via survey, RNs were asked to predict their patient's health literacy abilities while the Newest Vital Sign, a health literacy assessment tool, was deployed to determine the actual HLL of patients participating in the study. Data were analyzed using descriptive statistics, t -tests, and chi-square tests while a Spearman correlational model was used to examine the relationship between predicted HLL and actual HLL. Finally, a logistic regression model was used to analyze the relationship between personal factors and HL data for RNs and patients. Analysis of the data revealed that RNs consistently overestimated patients' abilities, as evidenced by the disparity between patients' actual HLL (mean 1.71) and predicted HLL (mean 4.26) by RNs, with a moderately strong positive relationship (rs =.418). Notably, higher academic preparation and years of experience did not enhance the RNs' ability to identify LHL while the highest level of education completed was the only statistically significant predictor of adequate health literacy in the patient population sampled. These findings emphasize the need to prioritize effective health literacy education in RN academic preparation and clinical practice to support the detection of LHL when a standardized health literacy assessment tool is not utilized in the clinical setting. By recognizing the presence of LHL, healthcare professionals can better support patients' needs and bridge the knowledge gap, ultimately improving patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Delayed extubation and transfer to the intensive care unit (ICU) in children undergoing major scoliosis surgery may increase postoperative complications, prolong hospital stay, and increase medical expenses; however, whether a child will require delayed extubation or transfer to the ICU after scoliosis orthopedic surgery is not fully understood. In this study, we reviewed the risk factors for delayed extubation and transfer to the ICU after scoliosis orthopedic surgery in children. Method: The electronic medical records of pediatric patients (≤ 18 years) who underwent posterior spinal fusion surgery between January 2018 and November 2021 were reviewed and analyzed. Patient characteristics (age, sex, body mass index, American Society of Anesthesiologists, ASA, grade, preoperative lung function, and congenital heart disease), preoperative Cobb angle, scoliosis type, correction rate, vertebral fusion segments, pedicle screws, surgical osteotomy, intraoperative bleeding, intraoperative allogeneic transfusion, intraoperative hemoglobin changes, intraoperative mean arterial pressure changes, intraoperative tidal volume (ml/kg predicted body weight), surgical time, postoperative extubation, and transfer to the ICU were collected. The primary outcomes were delayed extubation and transfer to the ICU. Multivariate logistic regression models were used to determine the risk factors for delayed extubation and ICU transfer. Results: A total of 246 children who satisfied the inclusion criteria were enrolled in this study, of whom 23 (9.3%) had delayed extubation and 81 (32.9%) were transferred to the ICU after surgery. High ASA grade (odds ratio [OR] 5.42; 95% confidence interval [CI] 1.49–19.78; p = 0.010), high Cobb angle (OR 1.04; 95% CI 1.02–1.07; p < 0.001), moderate to severe pulmonary dysfunction (OR 10.9; 95% CI 2.00–59.08; p = 0.006) and prolonged surgical time (OR 1.01; 95% CI 1.00–1.03; p = 0.040) were risk factors for delayed extubation. A high Cobb angle (OR 1.02; 95% CI 1.01–1.04; p = 0.004), high intraoperative bleeding volume (OR 1.06; 95% CI 1.03–1.10; p = 0.001), allogeneic transfusion (OR 3.30; 95% CI 1.24–8.83; p = 0.017) and neuromuscular scoliosis (OR 5.38; 95% CI 1.59–18.25; p = 0.007) were risk factors for transfer to the ICU. A high Cobb angle was a risk factor for both delayed extubation and ICU transfer. Age, sex, body mass index, number of vertebral fusion segments, correction rate, and intraoperative tidal volume were not associated with delayed postoperative extubation and ICU transfer. Conclusion: The most common risk factor for delayed extubation and ICU transfer in pediatric patients who underwent posterior spinal fusion was a high Cobb angle. Determining risk factors for a poor prognosis may help optimize perioperative respiratory management strategies and planning of postoperative care for children undergoing complicated spinal surgery. [ABSTRACT FROM AUTHOR]

Mathematical models of prediction provide theoretical support for basic rail automation. The three-point bending method for basic rails is characterized by its simplicity and flexibility, and, as such, it is widely used in bending processes. However, due to the significant curvature changes that occur after bending, it is not suitable for scenarios requiring large arc bending, and its range of achievable deflections is limited. This study focuses on four-point bending, dividing the bending process into three stages and using a power-law material hardening model to establish different bending moment expressions for each stage. We derived the relationships between curvature, elastic zone ratio, load, and deflection, ultimately creating a load–deflection model. Based on the simple springback law, we developed the final bending prediction model. Finite element simulations were conducted to simulate the bending process under various conditions, using top punch distances ranging from 200 mm to 400 mm and die distances ranging from 600 mm to 1000 mm. These simulations validated the advantages and accuracy of the four-point bending prediction model in large arc bending. Additionally, a four-point bending experimental setup was established under specified conditions. The experimental results were compared with the theoretical model calculations, showing errors within 0.2 mm and thus verifying the accuracy of the four-point bending prediction model. The mathematical model developed in this study provides theoretical support for the automation of basic rail bending. [ABSTRACT FROM AUTHOR]

This paper reviews an embodied or experientialist view of conceptual understanding. It focuses on George Lakoff and Mark Johnson's theory of embodied cognition and its framing of human conceptualization and reasoning in terms of embodied imagination. These ideas are summarized as ten basic claims: (a) objectivist assumptions are problematic; (b) many human categories have non-classical structure; (c) conceptual systems consist of cognitive models; (d) thinking utilizes frames, metonymies, and prototypes; (e) metaphor is prevalent and primarily conceptual; (f) image schemas structure our experiences; (g) the mind is embodied; (h) abstract thought is largely metaphorical; (i) truth is relative to embodied understanding; and (j) philosophy should be empirically responsible. Lakoff and Johnson's theory of embodied cognition offers a view of conceptual understanding that is cognitively realistic (or empirically responsible), biologically plausible, and self-critical, while providing adequate theories of meaning and truth grounded in embodied experience. [ABSTRACT FROM AUTHOR]

A decoupled model predictive control (MPC) power balancing strategy for a coupled inductor-based flying capacitor DC–DC converter (FCDC) is a proposed to solve the power imbalance caused by the parameter differences in the coupled inductor. The decoupled mathematical model of coupled inductor FCDC is firstly derived by analyzing the converter operation state under various modes. On this basis, the control relationship between inductor current and flying capacitor (FC) voltage is redefined and an MPC power balance strategy based on the inductor current with single-step optimization is proposed. The proposed MPC strategy not only achieves decoupled power balancing control but also solves multi-objective dynamic optimization control of the inductor current and FC voltage, greatly reducing the computation load. A detailed theoretical analysis of the proposed strategy is presented and the balancing performance is effectively verified through the experiments. [ABSTRACT FROM AUTHOR]

This study explored the relationship between the design of calf compression sleeves and the comfort of young women in long sitting and standing work environments. By studying the relationship between material elongation and comfort pressure, a prediction model was obtained. The characteristics of the calf models of 94 women were classified, and the influence of different calf characteristics on the pressure distribution and tensile value design was objectively analyzed through simulation tests. The samples were then produced based on the simulation results, and subjective and objective try-on tests and evaluations were carried out. The results show that the difference in the shape of the calf has a certain impact on the pressure distribution and comfort. The predicted value of the theoretical model is in good agreement with the actual test value. The sample can bring a suitable gradient pressure and meet the comfort requirements and safety standards. [ABSTRACT FROM AUTHOR]

This paper presents a centralized disturbance suppression strategy for distributed drive electric vehicles which is based on model predictive direct motion control. This strategy is capable of addressing issues such as parameter uncertainties and external disturbances in vehicles. Firstly, the paper provides a brief introduction to model predictive direct motion control. Secondly, it analyzes the impact of vehicle parameter uncertainties and external disturbances on the mathematical model. Finally, a centralized disturbance suppression strategy based on a sliding mode observer is proposed. Simulation results demonstrate that this strategy exhibits excellent disturbance rejection capabilities. [ABSTRACT FROM AUTHOR]

Patronizing speech and dehumanization both have negative impacts on the health and wellbeing of the recipients of these behaviors. This experiment applied Fiske’s stereotype content model, Haslam’s dual model of dehumanization, and Hummert’s model of patronizing speech to assess the effects of warmthand competence-enhancing messages about a person with dementia on perceptions of humanness and patronizing speech toward people with dementia. Results supported our predictions that warmth- and competence-enhancing messages would translate into general tendencies to humanize people with dementia as a group. Predicted effects on patronizing communication did not materialize, but there were some unanticipated ways in which warmth- and competence-enhancing messages did influence intentions to use patronizing speech. [ABSTRACT FROM AUTHOR]

Simple Summary: Despite recent advancements in experimental technology for genome-wide molecular profiling, our understanding of the dynamic mechanism underlying cancer drug resistance remains limited. In this study, we present an approach that combines mathematical modeling with the pseudotime analysis of single-cell time-series transcriptome data of drug-treated breast cancer cells. Our method identifies approximately 600 genes out of 6000 exhibiting multistable expression states, including RPS6KB1, a predictor of poor prognosis, cell survival, and growth in estrogen-receptor-positive breast cancers. The bifurcation analysis elucidates the regulatory mechanisms of the key regulatory genes, which can also be mapped into a molecular network based on cell survival and metastasis-related pathways, providing a comprehensive understanding of the interplay between signaling pathways and regulatory genes. Our method serves as a powerful tool for deciphering the complexities of drug resistance mechanisms in human diseases. Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560–680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such as RPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of LED short-circuit fault detection in signaling and lighting systems in the automotive industry. The conventional diagnostic method commonly implemented in newer vehicles relies on measuring the voltage drop across different LED branches and comparing it with threshold values indicating faults caused by open circuits or LED short circuits. With this algorithm, detecting cases of a few LEDs short-circuited within a branch, particularly a single malfunctioning LED, is particularly challenging. In this work, two easily implementable algorithms are proposed to address this issue within the vehicle's control unit. One is based on a mathematical prediction model, while the other utilizes a neural network. The results obtained offer a 100% LED short-circuit fault detection rate in the majority of analyzed cases, representing a significant improvement over the conventional method, even in scenarios involving a single malfunctioning LED within a branch. Additionally, the neural network-based model can accurately predict the number of failed LEDs. [ABSTRACT FROM AUTHOR]

Speech and language pathologists (SLPs) often rely on judgements of speech fluency for diagnosing or monitoring patients with aphasia. However, such subjective methods have been criticised for their lack of reliability and their clinical cost in terms of time. This study aims at assessing the relevance of a signal-processing algorithm, initially developed in the field of language acquisition, for the automatic measurement of speech fluency in people with aphasia (PWA). Twenty-nine PWA and five control participants were recruited via non-profit organizations and SLP networks. All participants were recorded while reading out loud a set of sentences taken from the French version of the Boston Diagnostic Aphasia Examination. Three trained SLPs assessed the fluency of each sentence on a five-point qualitative scale. A forward-backward divergence segmentation and a clustering algorithm were used to compute, for each sentence, four automatic predictors of speech fluency: pseudo-syllable rate, speech ratio, rate of silent breaks, and standard deviation of pseudo-syllable length. The four predictors were finally combined into multivariate regression models (a multiple linear regression — MLR, and two non-linear models) to predict the average SLP ratings of speech fluency, using a leave-one-speaker-out validation scheme. All models achieved accurate predictions of speech fluency ratings, with average root-mean-square errors as low as 0.5. The MLR yielded a correlation coefficient of 0.87 with reference ratings at the sentence level, and of 0.93 when aggregating the data for each participant. The inclusion of an additional predictor sensitive to repetitions improved further the predictions with a correlation coefficient of 0.91 at the sentence level, and of 0.96 at the participant level. The algorithms used in this study can constitute a cost-effective and reliable tool for the assessment of the speech fluency of patients with aphasia in read-aloud tasks. Perspectives for the assessment of spontaneous speech are discussed. [ABSTRACT FROM AUTHOR]

Annealed ASTM A510/A853 hypoeutectoid steel wire is a very useful and versatile material that finds its applications in the construction industry; this is mainly due to its mechanical properties, since this product can reach high ductility. In order to achieve the sought quality and homogeneity in this material, it is necessary to have a suitable control during operation. Important operational control parameters in batch annealing are: heating and cooling rates, holding temperature and cycle time. These parameters have a direct effect on the process, and it is essential to elucidate the way they impact in the final product. This work concerns the development of a mathematical model targeted at the prediction of mechanical properties in terms of the operational parameters, in the outer spirals of American Iron and Steel Institute/Society of Automotive Engineers (AISI/SAE) steel drawn wire coils after they have been subjected to batch annealing. The model addresses non-isothermically the involved phenomena that take place during the annealing process: recovery, recrystallization, and grain growth below and above Ac1. It predicts grain size, tensile strength, and yield strength values with very good accuracy. [ABSTRACT FROM AUTHOR]

Simple Summary: Metastasis, the spread of cancer cells to other parts of the body, is the leading cause of cancer-related deaths despite medical advances. The current methods used to study metastatic cancer face challenges in gathering enough data. To tackle this issue, we developed MetGen, a deep learning model that generates metastatic gene expression files using cancer and tissue samples. Our results show that the proposed model generated samples comparable to real data. The interpretability of the model could help researchers better understand cancer metastasis and lead to the discovery of new treatments to combat metastatic cancer. Despite significant advances in tumor biology and clinical therapeutics, metastasis remains the primary cause of cancer-related deaths. While RNA-seq technology has been used extensively to study metastatic cancer characteristics, challenges persist in acquiring adequate transcriptomic data. To overcome this challenge, we propose MetGen, a generative contrastive learning tool based on a deep learning model. MetGen generates synthetic metastatic cancer expression profiles using primary cancer and normal tissue expression data. Our results demonstrate that MetGen generates comparable samples to actual metastatic cancer samples, and the cancer and tissue classification yields performance rates of 99.8 ± 0.2% and 95.0 ± 2.3%, respectively. A benchmark analysis suggests that the proposed model outperforms traditional generative models such as the variational autoencoder. In metastatic subtype classification, our generated samples show 97.6% predicting power compared to true metastatic samples. Additionally, we demonstrate MetGen's interpretability using metastatic prostate cancer and metastatic breast cancer. MetGen has learned highly relevant signatures in cancer, tissue, and tumor microenvironments, such as immune responses and the metastasis process, which can potentially foster a more comprehensive understanding of metastatic cancer biology. The development of MetGen represents a significant step toward the study of metastatic cancer biology by providing a generative model that identifies candidate therapeutic targets for the treatment of metastatic cancer. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to externally validate a reported model for identifying patients requiring extended stay following lower limb arthroplasty in a new setting. Design: External validation of a previously reported prognostic model, using retrospective data. Setting: Medium-sized hospital orthopaedic department, Australia. Participants: Electronic medical records were accessed for data collection between Sep-2019 and Feb-2020 and retrospective data extracted from 200 randomly selected total hip or knee arthroplasty patients. Intervention: Participants received total hip or knee replacement between 2-Feb-16 and 4-Apr-19. This study was a non-interventional retrospective study. Main measures: Model validation was assessed with discrimination, calibration on both original and adjusted forms of the candidate model. Decision curve analysis was conducted on the outputs of the adjusted model to determine net benefit at a predetermined decision threshold (0.5). Results: The original model performed poorly, grossly overestimating length of stay with mean calibration of −3.6 (95% confidence interval −3.9 to −3.2) and calibration slope of 0.52. Performance improved following adjustment of the model intercept and model coefficients (mean calibration 0.48, 95% confidence interval 0.16 to 0.80 and slope of 1.0), but remained poorly calibrated at low and medium risk threshold and net benefit was modest (three additional patients per hundred identified as at-risk) at the a-priori risk threshold. Conclusions: External validation demonstrated poor performance when applied to a new patient population and would provide limited benefit for our institution. Implementation of predictive models for arthroplasty should include practical assessment of discrimination, calibration and net benefit at a clinically acceptable threshold. [ABSTRACT FROM AUTHOR]

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Zirconia is gradually used in manufacturing aerospace and other fields because of its excellent mechanical processing properties. Due to its hardness and brittleness, zirconia can easily cause excessive and unstable changes in grinding force during machining, resulting in extensive surface damage and poor surface quality. Therefore, it is important to establish an appropriate grinding force model and achieve accurate control and prediction of the grinding force. Ultrasonic-assisted grinding can reduce grinding forces, improve workpiece surface quality, and increase processing efficiency. However, the grinding force model for ultrasonic-assisted grinding is still not adequately investigated. In this study, a new method for predicting the axial force of ultrasonic-assisted grinding is proposed. Vickers hardness and indentation fracture theories combine two different methods: plastic removal and brittle fracture. Moreover, conventional and ultrasonic-assisted grinding tests are performed. Then, the relationship between grinding forces and process parameters is investigated using numerical statistical calculation methods. The grinding force data obtained from the test validate the theoretical model, with the average error between the theoretical and experimental values being 22.87%. The influence of machining variables on the grinding force is investigated, and experimental results of conventional al grinding and ultrasonic-assisted grinding are compared. It is found that the rotary ultrasonic-assisted grinding force can be reduced by up to 66.76%. [ABSTRACT FROM AUTHOR]

Since the basic rail of the switch needs to have a certain bending angle when the train changes direction, top bending is an important link in the production process of the basic rail. The three-point pressure top bending method is simple, flexible and widely used. In this study, the traditional three-point pressure bending is optimized, the influence of the pick width in the model is considered, a corresponding rebound model is established, and the model is applied to the pressure bending process of the basic rail. The bilinear strengthening model of the material was used to construct the bending moment expressions at different positions during the top bending process, and the relationship between the load and bending deflection in the elastic stage and elastic-plastic stage was obtained. The final top bending prediction model was obtained by combining the load-deflection model in the bending stage and the rebound stage. The correctness of the theoretical mathematical model was verified by establishing finite element simulations, and the theoretical calculation results were compared with the experimental results. The results showed that the top bend prediction optimization model established in this study had high feasibility and met the machining accuracy requirements. [ABSTRACT FROM AUTHOR]

Antibiotics are considered one of the most important contributions to clinical medicine in the last century. Due to the use and overuse of these drugs, there have been increasing frequencies of infections with resistant pathogens. One form of resistance, heteroresistance, is particularly problematic; pathogens appear sensitive to a drug by common susceptibility tests. However, upon exposure to the antibiotic, resistance rapidly ascends, and treatment fails. To quantitatively explore the processes contributing to the emergence and ascent of resistance during treatment and the waning of resistance following cessation of treatment, we develop two distinct mathematical and computer-simulation models of heteroresistance. In our analysis of the properties of these models, we consider the factors that determine the response to antibiotic-mediated selection. In one model, heteroresistance is progressive, with each resistant state sequentially generating a higher resistance level. In the other model, heteroresistance is non-progressive, with a susceptible population directly generating populations with different resistance levels. The conditions where resistance will ascend in the progressive model are narrower than those of the non-progressive model. The rates of reversion from the resistant to the sensitive states are critically dependent on the transition rates and the fitness cost of resistance. Our results demonstrate that the standard test used to identify heteroresistance is insufficient. The predictions of our models are consistent with empirical results. Our results demand a reevaluation of the definition and criteria employed to identify heteroresistance. We recommend that the definition of heteroresistance should include a consideration of the rate of return to susceptibility. [ABSTRACT FROM AUTHOR]

The development of mathematical models capable of predicting the lifespan of animals is growing. However, there are no studies that compare the predictive power of different sets of parameters depending on the age of the animals. The aim of the present study is to test whether mathematical models for life span prediction developed in adult female mice based on immune, redox, and behavioral parameters can predict life span in old animals and to develop new models in old mice. For this purpose, 29 variables, including parameters of immune function, redox state, and behavioral ones, were evaluated in old female Swiss mice (80 ± 4 weeks). Life span was registered when they died naturally. Firstly, we observed that the models developed in adults were not able to accurately predict the life span of old mice. Therefore, the immunity (adjusted R2 = 73.6%), redox (adjusted R2 = 46.5%), immunity-redox (adjusted R2 = 96.4%), and behavioral (adjusted R2 = 67.9%) models were developed in old age. Finally, the models were validated in another batch of mice. The developed models in old mice show certain similarities to those in adults but include different immune, redox, and behavioral markers, which highlights the importance of age in the prediction of life span. [ABSTRACT FROM AUTHOR]

In urban areas where populations commonly reside, particle mass concentrations in street canyons can pose significant risks to human health. This study aimed to investigate the diffusion mechanism of particle mass concentrations in urban street canyons by developing and applying a prediction model based on the mathematical modeling of physical processes. The prediction model considered factors such as the influence of traffic wind, natural wind, traffic flow, and other relevant variables influencing particle mass concentrations in street canyons. Field measurements were conducted in Dalian, China, to verify the feasibility of the model. Particle mass concentrations, traffic flow, temperature, relative humidity, and wind speed were measured on Shichang Street (a two-lane one-way road), Tangshan Street (a four-lane two-way road), and Shengli Road (a six-lane two-way road). The results indicated that the majority of traffic peaks occurred around 19:00 on all road types. The PM1.0 mass concentration was well diluted on the four-lane two-way road, with the least dilution observed on the two-lane one-way road. A strong correlation between the particle mass concentrations and traffic flow was discovered. Furthermore, a prediction model was established, accurately predicting the particle mass concentrations when the prediction step was from 5 to 15 s. The coefficient of determination (R2) between the predicted and measured values on the two-lane one-way road, four-lane two-way road, and six-lane two-way road was 0.9319, 0.6582, and 0.9238, respectively. Additionally, the prediction model allowed for a detailed analysis of traffic flow limitations, corresponding to the recommended World Health Organization (WHO) PM2.5 values. Overall, the findings of this study offer valuable insights for forecasting particle exposure levels in street canyons. [ABSTRACT FROM AUTHOR]

BACKGROUND: Outpatient oncology infusion centers (OICs) use acuity to quantify the complexity and intensity of care to improve staffing levels and equitable patient assignments. OIC interviews revealed inconsistent measurement of acuity and a mixture of use cases. No publications measured objective operational benefits beyond surveyed nurse satisfaction or compared different models of acuity. OBJECTIVES: This study assessed three acuity models across multiple centers to determine whether acuity was superior to patient volumes or patient hours in predicting the number of nurses needed to care for scheduled patients in an OIC, as well as the effect on objective metrics of missed nurse lunch breaks and patient wait times. A secondary end point was used to identify a superior model. METHODS: Classification machine learning models were built to assess the predictive value of three acuity models compared to patient hours and patient visits. FINDINGS: None of the tested acuity models were found to have statistically significant improvement to the prediction of needed OIC nurse staffing, patient wait times, or missed nurse lunch breaks. [ABSTRACT FROM AUTHOR]