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

Machine learning at the extreme edge has enabled a plethora of intelligent, time-critical, and remote applications. However, deploying interpretable artificial intelligence systems that can perform high-level symbolic reasoning and satisfy the underlying system rules and physics within the tight platform resource constraints is challenging. In this paper, we introduce TinyNS, the first platform-aware neurosymbolic architecture search framework for joint optimization of symbolic and neural operators. TinyNS provides recipes and parsers to automatically write microcontroller code for five types of neurosymbolic models, combining the context awareness and integrity of symbolic techniques with the robustness and performance of machine learning models. TinyNS uses a fast, gradient-free, black-box Bayesian optimizer over discontinuous, conditional, numeric, and categorical search spaces to find the best synergy of symbolic code and neural networks within the hardware resource budget. To guarantee deployability, TinyNS talks to the target hardware during the optimization process. We showcase the utility of TinyNS by deploying microcontroller-class neurosymbolic models through several case studies. In all use cases, TinyNS outperforms purely neural or purely symbolic approaches while guaranteeing execution on real hardware.

To succeed in the global market, firms must prioritize quality over individual goals and preferences. Acceptance sampling is one of the two primary approaches for ensuring quality, which is used in statistical process control for attributes inspection of the product. In acceptance sampling based on predetermined acceptance criteria for inspection the lot is either accepted or not accepted. This research introduces a Bayesian double group sampling plan (BDGSP) for estimating average number of nonconforming products. Based on acceptance criteria the Poisson distribution is used to construct the likelihood function for both nonconforming and conforming products. To calculate the average probability of acceptance, the gamma distribution is used as a suitable prior distribution of the Poisson distribution. For various suggested producer's and consumer's risk levels four distinct quality regions are calculated. The producer's risk is related to acceptable quality level and consumer's risk is related to and limiting quality level. Operating characteristic curves are utilized to track the effects of changes in the values of the specified parameters. The applicability of the proposed plan for current industrial strategies is demonstrated using a real dataset. [ABSTRACT FROM AUTHOR]

Introduction: Precise understanding of proliferative activity in breast cancer holds significant value in the monitoring of neoadjuvant treatment, while current immunostaining of Ki-67 from biopsy or resected tumour suffers from partial sampling error. Multi-compartment model of transverse relaxation time has been proposed to differentiate intra- and extra-cellular space and biochemical environment but susceptible to noise, with recent development of Bayesian algorithm suggested to improve robustness. We hence hypothesise that intra- and extra-cellular transverse relaxation times using Bayesian algorithm might be sensitive to proliferative activity. Materials and methods: Twenty whole tumour specimens freshly excised from patients with invasive ductal carcinoma were scanned on a 3 T clinical scanner. The overall transverse relaxation time was computed using a single-compartment model with the non-linear least squares algorithm, while intra- and extra-cellular transverse relaxation times were computed using a multi-compartment model with the Bayesian algorithm. Immunostaining of Ki-67 was conducted, yielding 9 and 11 cases with high and low proliferating activities respectively. Results: For single-compartment model, there was a significant higher overall transverse relaxation time (p = 0.031) in high (83.55 ± 7.38 ms) against low (73.30 ± 11.30 ms) proliferating tumours. For multi-compartment model, there was a significant higher intra-cellular transverse relaxation time (p = 0.047) in high (73.52 ± 10.92 ms) against low (61.30 ± 14.01 ms) proliferating tumours. There was no significant difference in extra-cellular transverse relaxation time (p = 0.203) between high and low proliferating tumours. Conclusions: Overall and Bayesian intra-cellular transverse relaxation times are associated with proliferative activities in breast tumours, potentially serving as a non-invasive imaging marker for neoadjuvant treatment monitoring. [ABSTRACT FROM AUTHOR]

Spatiotemporal point process models have a rich history of effectively modeling event data in space and time. However, they are sometimes neglected due to the difficulty of implementing them. There is a lack of packages with the ability to perform inference for these models, particularly in python. Thus we present BSTPP a python package for Bayesian inference on spatiotemporal point processes. It offers three different kinds of models: space-time separable Log Gaussian Cox, Hawkes, and Cox Hawkes. Users may employ the predefined trigger parameterizations for the Hawkes models, or they may implement their own trigger functions with the extendable Trigger module. For the Cox models, posterior inference on the Gaussian processes is sped up with a pre-trained Variational Auto Encoder (VAE). The package includes a new flexible pre-trained VAE. We validate the model through simulation studies and then explore it by applying it to shooting data in Chicago. [ABSTRACT FROM AUTHOR]

Background: An estimated 75.8 under-five deaths per 1000 live births occurred in sub-Saharan Africa. This study aimed to investigate the prevalence and factors associated with under-five mortality in Sub-Saharan Africa. Methods: This study was based on secondary data sources from 33 Sub-Saharan countries' recent Demography and Health surveys from 2010 to 2020. A weighted sample of 360,397 under-five children was included in the study. Bayesian multilevel binary logistic regression was fitted using the brms R package. Besides, leave one out information criteria was used for model comparison. The adjusted odds ratio (AOR) and its 95% credible interval (CrI) were reported for significant factors associated with under-five mortality. Clinical trial number: not applicable. Results: The prevalence of under-five mortality in sub Saharan Africa was 62 per 1000 live births (95%CI (56.29, 68.29). In sub region of SSA, it was 65 in central, 52 in eastern, 50 in southern and 73 in western region per 1000 live births. Multiple birth (AOR = 5.27; 95%CrI: 4.72, 5.87), number of under-five children 3 to 5 (AOR = 3.31; 95%CrI: 3.01, 3.60), caesarean section delivery (AOR = 1.64; 95%CrI: 1.47, 1.83), being unmarried (AOR = 1.16; 95%CrI 1.08, 1.26), using unimproved toilet (AOR = 1.08;95%CrI: 1.02, 1.16), birth order of 4th to 6th (AOR = 1.18; 95%CrI:1.1, 1.25),were risk factors of under-five mortality. Whereas, being female (AOR = 0.86; 95%CrI: 0.82, 0.91), preceding birth interval of 24–35 months (AOR = 0.61; 95%CrI: 0.57, 0.65) and above 36 months (AOR = 0.48; 95%CrI: 0.43, 0.49), ANC visit (AOR = 0.80; 95%CrI: 0.74 0.86), contraceptive use (AOR = 0.57; 95%CrI: 0.53, 0.61), were preventive factors of under-five mortality. Conclusion: Under-five mortality remains the highest in sub-Saharan Africa. Most of the risk factors of under-five mortality were found to be preventable. Policymakers and other stakeholders should enhance maternal education, lengthen birth interval, ANC visit, improved toilet facilities and, giving special attention to small size child and cesarean section delivery to reduce under-five mortality. [ABSTRACT FROM AUTHOR]

Objective: To demonstrate the application of Bayes' theorem to diagnostic testing in clinical settings, especially with respect to rare diseases, enhancing an understanding of pre-test probability and its implications. Conclusion: Bayes' theorem enables the revision of the conditional probabilities of an event occurring when new information is acquired. It demonstrates that when the prevalence of a disease is very low, there are a high number of false positives, thereby reducing the clinical utility and cost benefit profile of the diagnostic test, even in the presence of relatively high sensitivities and specificities of the chosen test. [ABSTRACT FROM AUTHOR]

Nonprofit organizations are vital to the implementation of social policy and provide myriad benefits to those nearby, yet few studies consider founding patterns in small areas. Conceptualizing ecological processes at the local level and in the context of developmental regimes, this article investigates nonprofit founding events among neighborhoods with a unique data set aggregated by census tract from 2010 to 2016 in one northeast Ohio county (United States) using a hierarchical Bayesian model. The results broadly support the density dependence hypothesis, however, suggest high density is required to reduce founding rates in small areas. The results indicate nonprofit founding rates are lower in tracts with higher levels of economic disadvantage and higher shares of Latino residents. The paper calls for further research into nonprofit population dynamics among small areas and for policymakers to closely consider nonprofit founding events, as founding determines the future of a region's nonprofit infrastructure. [ABSTRACT FROM AUTHOR]