Verizon (United States)

The papers in this Special Issue highlight significant progress toward fully convergent sensing and communication in optical networks, underscoring the growing role of these technologies in enabling autonomous, resilient, and environmentally aware fiber-optic networks. We present a brief introduction followed by an overview of the topics covered in the papers.

Vibration sensing based on forward transmission is an emerging topic for network protection and environmental monitoring, especially in long-haul submarine cables and urban fiber networks. However, previous field trials of this approach have mainly focused on localizing strong events under controlled or relatively quiet conditions. In this work, we investigate the capability of forward-transmission vibration sensing to detect weak signals in noisy environments. We demonstrate a highsensitivity vibration sensing system operating over an 80-km deployed live urban fiber loop without optical amplifiers. The system is enhanced by adaptive time-frequency masking and inband laser phase noise suppression techniques to improve sensitivity and noise robustness. It has successfully identified and localized weak real-world vibration events with peak-to-peak amplitude lower than 20 rad, such as construction activity near a manhole and even footsteps on handhole lids. Field trial results confirm its robust performance under dynamic environments, including road traffic-induced ground vibrations and aerial cable disturbances. To the best of our knowledge, this is the first demonstration of weak vibration event detection using forward transmission in urban fiber networks. It remarks a significant step towards practical distributed vibration sensing in smart city applications.

Predicting which patients will benefit from a therapy is one of the most practical promises of transcriptomics, but the literature is crowded with gene expression response signatures that do not generalize. A main reason is methodological: many signatures are trained on one cohort, one platform, and one response definition, so the reported genes partly reflect study specific noise, batch effects, and hidden confounding. This manuscript presents an AI assisted meta analysis workflow that aggregates evidence across independent cohorts to derive a consensus gene expression signature that is both biologically interpretable and prediction oriented. The approach combines standardized per gene effect sizes (Hedges g), random effects meta analysis, heterogeneity screening, and nested, cohort aware machine learning. We outline data acquisition from open repositories, harmonization, batch adjustment, and label mapping, then build a meta signature and train parsimonious classifiers using elastic net and gradient boosting. Model evaluation uses leave one study out validation to approximate real world deployment, with calibration and decision curve analysis to connect performance to clinical utility. The workflow is designed to be auditable: every inclusion decision is recorded, and all transformations are reversible and reproducible. By treating signatures as meta analytic objects rather than single study artifacts, the framework reduces overfitting, exposes context specific failure modes, and yields gene sets that map to known treatment response biology. We close with reporting guidance aligned to PRISMA, TRIPOD, and current best practice for transcriptomic predictors. A melanoma anti PD 1 case study is used to illustrate implementation and expected outputs.

We present methods and field trial results demonstrating an integrated distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) system for manhole localization, condition diagnostics, and anomaly detection in pre-deployed telecommunication fiber networks. The proposed system leverages ambient environmental signals, such as vibrational patterns from traffic and day-night temperature fluctuations, and machine learning techniques for automated detection. By combining DAS waterfall traces with temperature measurements from DTS, we achieve improved classification accuracy. Experimental results from three real-world testbeds in Texas and New Jersey show a significant improvement in classification accuracy-from 78.9% and 89.5% using DAS and DTS alone, respectively, to 94.7% via cross-referenced analysis. We propose a structured prediction formulation for manhole localization based on a U-Net architecture with a gated attention mechanism, where the label of each fiber location in the waterfall image is predicted using both its neighboring context and within-patch discriminative features. The method also supports cross-route generalization for manhole localization and enables condition diagnostics, identifying issues such as cable exposure and water ingress. These results highlight the potential for scalable deployment of fiber sensing solutions for real-time, continuous monitoring of telecom infrastructure.

Abstract Date Presented 04/03/2025 Chaviza, the deaf community of Zanzibar, hosted 21 Canadian Occupational Performance Measure interviews, reviewed for cultural relevance. Narrative needs assessments were performed with emergent supports, and barriers were identified for increased inclusion in the greater population. Primary Author and Speaker: Geela Spira Contributing Authors: Abdalla Baafarijiy

Background: Current hypertension guidelines recommend combining both the office (clinic) BP and out-of-office (home) BP to diagnose hypertension. This study was designed to explore masked hypertension in the young adult population of a defined community by home blood pressure monitoring (HBPM). Methodology: It was a community-based, prospective, observational study conducted by the hypertension study group, Chattagram International Medical College at Ward number 4 (Chandgaon), Chattogram City Corporation, Chattogram from May 15, 2023, to September 14, 2023. Young adults of 18 to 35 years with no history of hypertension, not on any antihypertensive, with clinic BP measurement <140/90 mm Hg and who consented were included in the study. After taking HBPM recording twice daily for seven successive days, the participant returned to the clinic with the completed HBPM chart and returned the devices. The data were analyzed; masked hypertension was labelled when clinic blood pressure measurement was less than 140/90 mmHg and average HBPM monitoring was ≥135/85 mm Hg. Result: A total of 327 participants were enrolled, and 11 cases were excluded. Among 316 participants, 55% were male and 45% were female, mean age was 26.77±5.6 years. Among all, 12 (3.8%) participants were proven to have masked hypertension. Among those with masked hypertension, the mean age was 31.42±7.09 years, males and females were 58% and 42% respectively, the mean BMI was 28.53±5.25kg/m2, mean home blood pressure monitoring (HBPM) was 139.96/88.79 (±1.85/2.98) mm Hg. The factors significantly related to masked hypertension were age >30 years, high BMI (>25 Kg/m2), high clinic BP, sedentary lifestyle, diabetes mellitus, positive family history and smoking. Conclusion: This study emphasizes home blood pressure monitoring for identifying masked hypertension in selected cases; future research is recommended to develop optimal screening strategies. J Bangladesh Coll Phys Surg 2025; 43: 190-196