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This paper studies the time dependence of corrosion resistance in belitic calcium sulfoaluminate (BCSA) cement concrete. BCSA is a hydraulic cement that sets rapidly and has a lower carbon footprint than portland cement (PC). Some studies suggest that BCSA concrete is highly susceptible to chloride-induced corrosion, but these claims are based on testing at 28 days. No later-age results have been reported. We con昀椀rm that BCSA cement concrete exhibits poor corrosion resistance when tested at 28 days, which can be attributed to an immature microstructure with weak resistance to chloride ingress and a pore solution that does not support passivation. We further show that corrosion resistance improves signi昀椀cantly as the BCSA system matures, achieving good corrosion resistance between 90 and 180 days. This improvement relates to the slow hydration of belite, which results in signi昀椀cant later-age microstructure re昀椀nement and probably alters the pore solution in a way that promotes or enhances steel passivation. 

We describe an inexpensive and accessible instructional setup that explores particle trapping with a planar linear ion trap. The planar trap is constructed using standard printed circuit board manufacturing and is designed to trap macroscopic charged particles in air. Trapping, shuttling, and splitting are demonstrated to students using these particles, which are visible to the naked eye. Students control trap voltages and can compare properties of particle motion with an analytic model of the trap using a computer vision program for particle tracking. Learning outcomes include understanding the design considerations for planar AC traps, mechanisms underpinning particle ejection, the physics of micromotion, and methods of data analysis using standard computer vision libraries. 

This paper studies the alkali-silica reaction (ASR) in rapid-strength belitic calcium sulfoaluminate (BCSA) cement systems. Theoretically, its low alkalinity and high alumina content should make BCSA less prone to ASR than portland cement (PC), but little experimental evidence has been published, and the theorized mechanisms have not been examined critically. We examine this problem using expansion tests, microstructural analysis, and pore solution analysis. Accelerated expansion tests show increased expansion in BCSA mortars with reactive aggregates, but we argue that the test conditions are unsuitable for the cement. Long-term expansion tests show a significant reduction in expansion in BCSA mortars with reactive aggregates, but later-age measurements still exceed ASTM C1778 limits and microstructural investigations indicate ASR damage. Curiously, BCSA mortars with nonreactive aggregates also expanded significantly, but no ASR damage was observed. BCSA pore solutions had ten times more aluminum than PC and one-tenth as much calcium. While the pH was sufficiently high to initiate ASR, the alkali reserves can be half or less than in PC. Overall, BCSA cement is not immune to ASR, but it is more resistant than PC. This is mostly related to the lower alkalinity of the cement and, to a lesser degree, to the abundance of alumina and shortage of soluble calcium. 

Abstract BackgroundAtrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Importantly, obstructive sleep apnea is highly prevalent among AF patients (60–90%); therefore, electrocardiogram (ECG) analysis from polysomnography (PSG), a standard diagnostic tool for subjects with suspected sleep apnea, presents a unique opportunity for the early prediction of AF. Our goal is to identify individuals at a high risk of developing AF in the future from a single-lead ECG recorded during standard PSGs. MethodsWe analyzed 18,782 single-lead ECG recordings from 13,609 subjects at Massachusetts General Hospital, identifying AF presence using ICD-9/10 codes in medical records. Our dataset comprises 15,913 recordings without a medical record for AF and 2,056 recordings from patients who were first diagnosed with AF between 1 day to 15 years after the PSG recording. The PSG data were partitioned into training, validation, and test cohorts. In the first phase, a signal quality index (SQI) was calculated in 30-second windows and those with SQI<0.95 were removed. From each remaining window, 150 hand-crafted features were extracted from time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1,800 features. We then updated a pre-trained deep neural network and data from the PhysioNet Challenge 2021 using transfer-learning to discriminate between recordings with and without AF using the same Challenge data. The model was applied to the PSG ECGs in 16-second windows to generate the probability of AF for each window. From the resultant probability sequence, 13 statistical features were extracted. Subsequently, we trained a shallow neural network to predict future AF using the extracted ECG and probability features. ResultsOn the test set, our model demonstrated a sensitivity of 0.67, specificity of 0.81, and precision of 0.3 for predicting AF. Further, survival analysis for AF outcomes, using the log-rank test, revealed a hazard ratio of 8.36 (p-value of 1.93 × 10⁻⁵²). ConclusionsOur proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite a modest precision indicating the presence of false positive cases. This approach could potentially enable low-cost screening and proactive treatment for high-risk patients. Ongoing refinement, such as integrating additional physiological parameters could significantly reduce false positives, enhancing its clinical utility and accuracy.