UNASSIGNED: In the use of medical device procedures, learning effects have been shown to have a significant impact on the outcome, and are a critical component of medical device safety surveillance. To support estimation of these effects, we evaluated our methods for modeling these rates within several different actual datasets representing patients treated by physicians clustered within institutions to show the flexibility of this method across applications.
UNASSIGNED: In order to estimate the learning curve effects, we employed our unique modeling for the learning curves to incorporate the learning hierarchy between institution and physicians, and then modeled them within established methods that work with hierarchical data such as generalized estimating equations (GEE). Within the actual datasets, we looked at two device types and also two procedure types which had not been observed before: off pump coronary artery bypass (CABG) experience, and radial access experience. We also tried mediation analyses within the GEE framework for these various devices/procedures as well.
UNASSIGNED: We found that the choice of shape used to produce the \"learning-free\" dataset would still be dataset specific depending upon needs for modeling fast or slow learning but that in general the power series or logarithmic shapes would be better for modeling slower learning while exponential may be better for faster learning. Mediation analysis also showed promise in adapting the modeling of the learning curve.
UNASSIGNED: In showing the flexibility of using our method in various applications; this time utilizing more than one possible procedure done per patient so that each physician had more volume, we were able to show the flexibility of applying our method in different data applications to allow for more accurately capturing the learning curve rates in physicians nested within institutions. This can, therefore, be used across the board for device and procedure safety.

UNASSIGNED: To identify factors that influence the severity of tinnitus via a hierarchical multiple linear regression model.
UNASSIGNED: The study was a retrospective cross-sectional analysis. The study included 331 patients experiencing tinnitus as their primary concern, who visited Shanghai Changzheng Hospital of the Navy Medical University between 2019 and 2021. Data on general health status and disease characteristics were collected from all patients. With their consent, participants underwent audiological evaluatons and completed questionnaires to analyze the characteristics of their tinnitus and the factors influencing its severity.
UNASSIGNED: The correlation analysis showed a positive relationship between tinnitus frequency, tinnitus loudness, SAS scores, and PSQI scores with THI scores (P < 0.05) among nine examined variables (gender, handedness, employment status, age, BMI, tinnitus frequency, tinnitus loudness, SAS scores, and PSQI scores). The variables that were extracted from the multiple regression were; for the constant; β = -51.797, t = -4.484, P < 0.001, variable is significant; for the tinnitus loudness; β = 0.161, t = 2.604, P < 0.05, variable is significant; for the tinnitus frequency; β = 0.000, t = 1.269, P = 0.206, variable is not significant; for the SAS scores; β = 1.310, t = 7.685, P < 0.001, variable is significant; for the PSQI scores; β = 1.680, t = 5.433, P < 0.001, variable is significant. Therefore, the most accurate model for predicting severity in tinnitus patients is a linear combination of the constant, tinnitus loudness, SAS scores, and PSQI scores, Y(Tinnitus severity) = β 0 + β 1 (Tinnitus loudness) + β 2 (SAS scores) + β 3 (PSQI scores). β 0, β 1, β 2, and β 3 are -51.797, 0.161, 1.310 and 1.680, respectively.
UNASSIGNED: Tinnitus severity is positively associated with loudness, anxiety levels, and sleep quality. To effectively manage tinnitus in patients, it is essential to promptly identify and address these accompanying factors and related symptoms.

Data-driven fault diagnosis, identifying abnormality causes using collected industrial data, is one of the challenging tasks for intelligent industry safety management. It is worth noting that practical industrial data are usually related to a mixture of several physical attributes, such as the operating environment, product quality and working conditions. However, the traditional models may not be sufficient to leverage the coherent information for diagnostic performance enhancement, due to their shallow architecture. This paper presents a hierarchical matrix factorization (HMF) that relies on a succession of matrix factoring to find an efficient representation of industrial data for fault diagnosis. Specifically, HMF consecutively decomposes data into several hierarchies. The intermediate hierarchies play the role of analysis operators which automatically learn implicit characteristics of industrial data; the final hierarchy outputs high-level and discriminative features. Furthermore, HMF is also extended in a nonlinear manner by introducing activation functions, referred as NHMF, to deal with nonlinearities in practical industrial processes. The applications of HMF and NHMF to fault diagnosis are evaluated by the multiple-phase flow process. The experimental results show that our models achieve competitive performance against the considered shallow and deep models, consuming less computing time than deep models.

Skin is the largest protective tissue of the body and is at risk of damage. Hence, the design and development of wound dressing materials is key for tissue repair and regeneration. Although silk fibroin is a known biopolymer in tissue engineering, its degradation rate is not correlated with wound closure rate. To address this disadvantage, we mimicked the hierarchical structure of skin and also provided antibacterial properties; a hydrogel with globular structure consisting of silk fibroin, pluronic F127, and curcumin was developed. In this regard, the effect of pluronic and curcumin on the structural and mechanical properties of the hydrogel was studied. The results showed that curcumin affected the particle size, crystallinity, and ultimate elongation of the hydrogels. In vitro assays confirmed that the hydrogel containing curcumin is not cytotoxic while the diffused curcumin and pluronic provided a considerable bactericidal property against Methicillin-resistant Staphylococcus aureus. Interestingly, presence of pluronic caused more than a 99% reduction in planktonic and adherent bacteria in the curcumin-free hydrogel groups. Moreover, curcumin improved this number further and inhibited bacteria adhesion to prevent biofilm formation. Overall, the developed hydrogel showed the potential to be used for skin tissue regeneration.

Training studies typically investigate the cumulative rather than the analytically challenging immediate effect of exercise on cognitive outcomes. We investigated the dynamic interplay between single-session exercise intensity and time-locked recognition speed-accuracy scores in older adults with Alzheimer\'s dementia (N = 17) undergoing a 24-week dual-task regime. We specified a state-of-the-art hierarchical Bayesian continuous-time dynamic model with fully connected state variables to analyze the bi-directional effects between physical and recognition scores over time. Higher physical performance was dynamically linked to improved recognition (-1.335, SD = 0.201, 95% Bayesian credible interval [BCI] [-1.725, -0.954]). The effect was short-term, lasting up to 5 days (-0.368, SD = 0.05, 95% BCI [-0.479, -0.266]). Clinical scores supported the validity of the model and observed temporal dynamics. Higher physical performance predicted improved recognition speed accuracy in a day-by-day manner, providing a proof-of-concept for the feasibility of linking exercise training and recognition in patients with Alzheimer\'s dementia.
UNASSIGNED: Hierarchical Bayesian continuous-time dynamic modeling approachA total of 72 repeated physical exercise (PP) and integrated recognition speed-accuracy (IRSA) measurementsPP is dynamically linked to session-to-session variability of IRSAHigher PP improved IRSA in subsequent sessions in subjects with Alzheimer\'s dementiaShort-term effect: lasting up to 4 days after training session.

OBJECTIVE: To compare ganglion cell complex (GCC) and retinal nerve fiber layer (RNFL) rates of change (RoC) in eyes with central or moderate to advanced glaucoma.
METHODS: Prospective cohort study.
METHODS: A total of 918 matched macular and RNFL OCT scan pairs from 109 eyes (109 patients) enrolled in the Advanced Glaucoma Progression Study with ≥2 years of follow-up and ≥4 OCT scans.
METHODS: We exported GCC and RNFL thickness measurements in 49 central macular superpixels and 12 RNFL clock-hour sectors, respectively. We applied our latest Bayesian hierarchical longitudinal model to estimate population and subject-specific baseline thickness (intercepts) and rates of change (RoC) in macular superpixels and RNFL sectors. Global RNFL and GCC RoC were analyzed in a single bivariate longitudinal model to properly compare them accounting for the correlation between their RoC.
METHODS: Proportion of significant negative (deteriorating) and positive (improving) RoC expressed in μm/year. Standardized RoC were calculated by dividing RoC by the corresponding population SD. Analyses were repeated in eyes with visual field mean deviation (MD) ≤-6 and > -6 dB.
RESULTS: Average (SD) 24-2 visual field MD and follow-up length were -8.6 (6.3) dB and 4.2 (0.5) years, respectively. Global RNFL RoC (-0.70 µm/year) were faster than GCC (-0.44 µm/year) (P < .001); corresponding normalized RoC were not significantly different (P = .052). In bivariate analysis, patients with a significant negative global RNFL RoC (n = 63, 57%) or GCC (n = 56, 51%) frequently did so for both outcomes (n = 49, 45%). The average proportion of significantly decreasing RNFL sectors within an eye was 30.7% in eyes with MD > -6 dB compared to 20.5% in those with MD ≤ -6 dB (P = .014); the proportions for GCC superpixels were 21.1% versus 18.7%, respectively (P = .63).
CONCLUSIONS: Both GCC and RNFL measures can detect structural progression in glaucoma patients with central damage or moderate to advanced glaucoma. The clinical utility of RNFL imaging decreases with worsening severity of glaucoma.

This contribution describes a Decision Tree intended to guide the selection of statistical models and data reduction procedures in key comparisons (KCs). The Decision Tree addresses a specific need of the Inorganic Analysis Working Group (IAWG) of the Consultative Committee (CC) for Amount of Substance, Metrology in Chemistry and Biology (CCQM), of the International Committee for Weights and Measures (CIPM), and it is likely to address similar needs of other working groups and consultative committees. Because the portfolio of KCs previously organized by the CCQM-IAWG affords a full range of opportunities to demonstrate the capabilities of the Decision Tree, the majority of the illustrative examples of application of the Decision Tree are from this working group. However, the Decision Tree is widely applicable in other areas of metrology, as illustrated in examples of application to measurements of radionuclides and of the efficiency of a thermistor power sensor. The Decision Tree is intended for use after choices will have been made about the measurement results that qualify for inclusion in the calculation of the key comparison reference value (KCRV), and about the measurement results for which degrees of equivalence should be produced. Both these choices should be based on substantive considerations, not on purely statistical criteria. However, the Decision Tree does not require that the measurement results selected for either purpose be mutually consistent. The Decision Tree should be used as a guide, not as the sole and autonomous determinant of the model that should be selected for the measurement results obtained in a KC, or of the procedure that should be employed to reduce these results. The scientists running the KCs ultimately have the freedom and responsibility to make the corresponding choices that they deem most appropriate and that best fit the purpose of each KC. The Decision Tree involves three statistical tests, and comprises five terminal leaves, which correspond to as many alternative ways in which the KCRV, its associated uncertainty, and the degrees of equivalence (DoEs) may be computed. This contribution does not purport to suggest that any of the KCRVs, associated uncertainties, or DoEs, presented in previously approved final reports issued by working groups of the CCs should be modified. Neither do the alternative results question existing, demonstrated calibration and measurement capabilities (CMCs), nor do they support any new CMCs.

Function as a Service (FaaS) is highly beneficial to smart city infrastructure due to its flexibility, efficiency, and adaptability, specifically for integration in the digital landscape. FaaS has serverless setup, which means that an organization no longer has to worry about specific infrastructure management tasks; the developers can focus on how to deploy and create code efficiently. Since FaaS aligns well with the IoT, it easily integrates with IoT devices, thereby making it possible to perform event-based actions and real-time computations. In our research, we offer an exclusive likelihood-based model of adaptive machine learning for identifying the right place of function. We employ the XGBoost regressor to estimate the execution time for each function and utilize the decision tree regressor to predict network latency. By encompassing factors like network delay, arrival computation, and emphasis on resources, the machine learning model eases the selection process of a placement. In replication, we use Docker containers, focusing on serverless node type, serverless node variety, function location, deadlines, and edge-cloud topology. Thus, the primary objectives are to address deadlines and enhance the use of any resource, and from this, we can see that effective utilization of resources leads to enhanced deadline compliance.

Despite a remarkable reduction in global poverty and famines, substantial childhood malnutrition continues to persist. In 2017, over 50 million and 150 million young children suffered from acute malnutrition (wasting) and chronic malnutrition (stunting), respectively. Yet, the measurable impact of determinants is obscure. We evaluate proposed socio-environmental related determinants of stunting and wasting across Kenya and Nigeria and quantify their effectiveness. We combine health and demographic data from Kenya and Nigeria Demographic Health Surveys (2003, 2008-2009, 2013, 2014) with spatially explicit precipitation, temperature, and vegetation data. Geospatial and disaggregated data help to understand better who is at risk and where to target mitigation efforts. We evaluate the responsiveness of malnutrition indicators using a four-level random intercept hierarchical generalized logit model. We find that spatial and hierarchical relationships explain 28% to 36% of malnutrition outcome variation. Temporal variation in precipitation, temperature, and vegetation corresponds with more than a 50% change in malnutrition rates. Wasting is most impacted by mother\'s education, family wealth, clinical delivery, and vaccinations. Stunting is most impacted by family wealth, mother\'s education, clinical delivery, vaccinations, and children asymptomatic of fever, cough, or diarrhea. Remotely monitored climatic variables are powerful determinants, however, their effects are inconsistent across different indicators and locations.

Animal songs differ from calls in function and structure, and have comparative and translational value, showing similarities to human music. Rhythm in music is often distributed in quantized classes of intervals known as rhythmic categories. These classes have been found in the songs of a few nonhuman species but never in their calls. Are rhythmic categories song-specific, as in human music, or can they transcend the song-call boundary? We analyze the vocal displays of one of the few mammals producing both songs and call sequences: Indri indri. We test whether rhythmic categories (a) are conserved across songs produced in different contexts, (b) exist in call sequences, and (c) differ between songs and call sequences. We show that rhythmic categories occur across vocal displays. Vocalization type and function modulate deployment of categories. We find isochrony (1:1 ratio, like the rhythm of a ticking clock) in all song types, but only advertisement songs show three rhythmic categories (1:1, 1:2, 2:1 ratios). Like songs, some call types are also isochronous. Isochrony is the backbone of most indri vocalizations, unlike human speech, where it is rare. In indri, isochrony underlies both songs and hierarchy-less call sequences and might be ancestral to both.