加利福尼亚州立大学

Accurate prediction of saturated flow-boiling heat transfer coefficients remains challenging because wall nucleation, two-phase convection, confinement, vapor shear, liquid-film depletion, and dryout-region deterioration interact across fluids, tube sizes, and operating conditions. This study develops a dryout-aware empirical correlation for saturated flow boiling in smooth circular micro/mini tubes using a curated literature database of 18,833 heat transfer coefficient measurements from 78 sources. The correlation is formulated in closed form using dimensionless variables informed by a preceding machine-learning feature-importance study. Separate branches are developed for micro pre-dryout, mini pre-dryout, micro dryout-region, and mini dryout-region data, allowing the model to distinguish confinement-sensitive pre-dryout behavior from vapor-convection and residual-liquid effects after dryout incipience. The pre-dryout form combines heat-flux/thermodynamic forcing, inertial two-phase convection, and confinement/film-structure terms, while the dryout-region form combines vapor convection with a residual liquid/film/droplet contribution. Gravity/orientation and thermophysical-descriptor factors are included through continuous dimensionless groups, and a targeted micro-pre-dryout correction is introduced to capture confined high-shear behavior. The correlation achieved an overall mean absolute percentage error of 17.81%, median absolute percentage error of 13.35%, and root mean square error of 2.04 kWm−2K−1 across the full curated database, with 82.58% and 95.25% of the data predicted within strict ±30% and ±50% error bands, respectively. Regime-resolved MAPE values were 17.61%, 17.99%, 16.86% and 17.47% for micro pre-dryout, mini pre-dryout, micro dryout-region, and mini dryout-region data, respectively. For pre-dryout data, the current model reduced MAPE relative to Liu–Winterton, Kim–Mudawar, Bertsch, and Sun–Mishima correlations evaluated on the same curated dataset. For the full evaluated dataset, the current model achieved 17.81% compared with 31.19% for the Bertsch composite correlation used as a full-quality-range stress test. The results demonstrate that a physics-informed, dryout-aware empirical structure can improve accuracy over conventional operating-variable correlations while retaining interpretability and requiring no wall-temperature input.

Background and objective This study presents a computationally and physiologically grounded framework for electromyography (EMG) simulation to examine how assumed cortical and subcortical inputs shape bilateral EMG and intermuscular coherence during bimanual coordination. Methods Simulated first dorsal interosseous (FDI) EMG was generated for a 1:2 force coordination task (approximately 40% maximum voluntary contraction) using motor unit models incorporating (i) variable cortical and subcortical drive weighting, (ii) narrowband versus broadband cortical drive (13–30 vs. 13–60 Hz), and (iii) asymmetric bilateral coupling. Intermuscular EMG–EMG wavelet coherence was computed across α, β, low-γ, and high-γ bands. Model outputs were validated against experimental human EMG at the signal level, comparing simulated and recorded bipolar first dorsal interosseous traces across eight amplitude and frequency-domain signal features. Results Coherence magnitude increased with greater modeled neural crosstalk and cortical weighting. The frequency band most sensitive to drive weighting depended on cortical bandwidth: β-band coherence (13–30 Hz) best differentiated cortical and subcortical influences under narrowband drive, whereas low-γ (30–60 Hz) was most informative under broadband conditions. α-band coherence showed minimal sensitivity to subcortical modulation in this context. Conclusions Within this simulation framework, intermuscular coherence was sensitive to modeled bilateral coupling and cortical-subcortical weighting, with diagnostic sensitivity depending on the spectral bandwidth of cortical drive. Thus, the model should be interpreted as a hypothesis generating, signal level framework rather than a direct neural measurement. This open-source framework bridges computational modeling and biomarker signal analysis, supporting reproducible, hypothesis driven biomarker development for neuromotor disorders such as Parkinson’s disease, spinal cord injury, and stroke.

This paper studies a two-server split–merge queue with positively dependent subtask service times modeled through a latent-factor bivariate Erlang construction. An exact characterization of the split–merge completion time is obtained, including explicit formulas for its first two moments and the resulting mean waiting time. Under fixed marginal service-time distributions, independence is shown to stochastically increase the completion time and hence overestimate mean waiting time. Numerical illustrations show that this benchmark gap can be substantial.

Across many immigrant destination countries, recent immigration law enforcement has expanded in scope and salience, intensifying public debate over deportation, criminal punishment, and the treatment of immigrants in the criminal legal system. Drawing on focal concerns theory, the current study uses the U.S. as an empirical case and a 4 (country of origin: Syria, China, Russia, or Mexico) & times; 2 (crime severity: felony drug offense or misdemeanor trespassing) & times; 2 (possibility of judicial error: certain or uncertain witness) & times; 2 (legality: valid work visa or expired tourist visa) online survey experiment with a national sample of U.S. adults to examine public preferences for deportation and criminal punishment of immigrants arrested for non-immigration offenses. Results indicate that legality is significantly associated with both deportation and imprisonment preferences, and that respondents rely on overlapping considerations when evaluating immigrants' deportability and criminal culpability. Although the uncertainty of evidence reduces imprisonment preferences, it has no effect on deportation preferences, suggesting greater public tolerance for errors and mistakes in the context of immigrant removal. Together, these findings suggest that punishment decisions involving immigrants are shaped in part by concerns about undocumented status, even when individuals are formally processed through the criminal legal system.

Abstract The current study establishes how lean tenets contribute to the circular economy (CE) with exploration of lean tools with circular economy core principles and framework development. While there seem to be synergies between the concepts of lean and CE, the construction management research fraternity has not fully tapped the huge research potential in this domain. To address this gap, a two-phased exploratory qualitative research design is proposed. The first phase involves mapping lean tools with 10 dimensions of CE in the context of the built environment sector through content analysis and expert validation ( n = 3 ). Continuous improvement and added value tools of lean attained maximum realization of CE dimensions. The second phase involves semistructured interviews of experts ( n = 9 ) on an understanding of the synergies between lean and CE through thematic analysis (TA). The current study identifies predominant lean tools that realize CE dimensions and develops a conceptual model of a lean-driven circular economy (LdCE) framework for construction projects. Also, the lean tenets and their allied tools enabled value optimization through effective mapping of non-value-added processes and minimizing wasteful aspects. These findings are intended to inform engineers, construction managers, and legal practitioners about actionable strategies for promoting sustainable, legally compliant project execution. This study proves to accelerate sustainable construction through a lean and CE integrated framework in developing nations. Analyzing the associativity of the lean tools in realizing the CE dimensions and the strategies of a lean-driven circular economy through models is a novel concept with this research.