Tellabs (Canada)

Continuous glucose monitoring (CGM) devices provide critical real-time data but remain minimally invasive and require frequent replacement. This study presents a novel, personalized machine learning approach for non-invasive glucose monitoring using radiofrequency (RF) spectroscopy to address these limitations. To simulate real-world usage and ensure clinical relevance, we developed a model for a single individual using data collected during standardized meals. The model was trained and tested on data collected on separate days, ensuring that the training and test sets are drawn from distinct, non-overlapping time periods. A comprehensive machine learning pipeline was validated using 3,101 spectral features (400-3500 MHz) combined with contextual data to predict glucose levels. Our best-performing model, multi-layer perception regressor (MLP), achieved a Mean Absolute Relative Difference (MARD) of 11.6%. These findings demonstrate that a personalized machine learning model holds potential to predict glucose non-invasively. This highlights a promising path toward a more user-friendly and sustainable solution for continuous glucose management

Reinforcement learning (RL) is a machine learning method in which a model optimizes its decision-making strategy based on rewards or penalties received for the actions it takes in an environment, often simulated. An example of an optimized process could be work scheduling in a restaurant, with the cost function being the absolute error of the difference between the scheduled and actual delivery times of an order. In task planning, RL stands out for its ability to handle problems requiring a complex sequence of actions, where traditional planning algorithms may struggle. RL models can effectively explore the solution space, adjusting their decisions to changing conditions, which enables dynamic and adaptive task execution management. RL is a broad class encompassing various approaches to achieving a goal, and in this research, we focus on selected ones. Three popular RL methods named DQN, SARSA and TD-AC have been implemented and evaluated. The study was conducted in a simulated environment designed to replicate a "delivery-based" restaurant business model. The kitchen simulation model has been developed based on 65,845 recorded food preparation processes performed in 30 restaurants located throughout Poland. A rule-based, queue-driven model (FIFO) served as the baseline for absolute quality comparison of the generated schedules. The results show that, for the defined problem, the quality of the scheduling outcomes varies significantly depending on the choice of learning algorithm. Notably, the hybrid approach performed best under simulation conditions, considerably reducing the total completion time in a scenario reflecting the operations of a small, typical restaurant.

Most Micro-expression recognition (MER) methods tend to explore only single modality (key frames or optical flow), ignoring intermediate frames, which usually results in the omission of critical temporal cues. Meanwhile, the generalization of MER deep models remains limited due to insufficient annotated micro-expression (ME) samples. To address these challenges, this paper proposes an efficient self-supervised MER scheme based on deep learning, the cross-modal hierarchical temporal-spatial masked autoencoder framework, named CHTMAE. Specifically, CHTMAE constructs a reconstruction task via both optical flow- and image-based masked autoencoders (MAE) for the multi-feature representation on motion and spatial features from unlabeled ME data. To obtain spatiotemporal fusion related to the action unit (AU) in the priors of MEs, this work designs the Hierarchical Temporal-Spatial Feature Guided Enhancement Component (H-TSGEC). Generally, the H-TSGEC consists of a cascade of Temporal-Spatial Feature Guided Enhancement Units (TSGEUs) which further comprise the Spatiotemporal Guided Cross-Attention Module (SGCAM) and the Frame Token Attention Module (FTAM). By cascading multiple TSGEUs, the CHTMAE achieves progressive fusion for diverse motion representation on subtle MEs. Extensive experimental results validate the effectiveness of the CHEMAE model, whose performance surpasses that of state-of-the-art MER methods. The source codes for this model are available on: https://github.com/cc99forever/CHTMAE99/tree/master.

This study explores the feasibility and advantages of implementing a Communications-Based Train Control (CBTC) signalling system on the Copenhagen Metro. As urban rail transit faces increasing passenger demand and the impending obsolescence of its current signalling system, CBTC emerges as a promising solution to enhance operational efficiency. Through microsimulation, the study compares the existing system with CBTC, focusing on key performance indicators such as headway adherence, running time reliability, and delay management. Results demonstrate that CBTC significantly improves service reliability, capacity, energy efficiency, and operational robustness. Additionally, the study identifies Vanløse station as a critical bottleneck in capacity utilization, which can be alleviated through CBTC deployment. This research underscores the potential of CBTC to transform urban rail systems, providing insights into its application in brownfield projects.

PURPOSEMaster protocols represent transformations, enabling multiple therapies or diseases under a single protocol. These designs streamline therapeutic development by reducing redundancies. Suited for evolving fields such as oncology and global emergencies such as the COVID-19 pandemic, master protocols have been exemplified by studies such as RECOVERY, Solidarity, and I-SPY 2, which accelerated effective treatment identification (with I-SPY 2 focused on molecular subtypes). Recent oncology examples, such as MORPHEUS, evaluate immunotherapy combinations with shared controls. Despite advantages, their application in early-phase oncology remains underutilized amid growing regulatory emphasis on randomization for robust evidence.METHODSUS Food and Drug Administration (FDA) Oncology Center of Excellence (OCE) initiatives, such as Project Optimus and Project FrontRunner, emphasize randomization in early-phase oncology trials. However, these initiatives pose challenges, including larger sample sizes, patient and physician reluctance to randomization, and high failure rates from poor accrual. To address these, this article adapts master protocol designs to consolidate early-phase trials for novel therapeutics sharing a common backbone therapy, integrating hybrid controls from published standard-of-care data to minimize randomization to the control arm.RESULTSBy consolidating trials under a shared standard-of-care control arm, the proposed master protocol design reduces total sample size by as much as 55% when compared with independent trials (with control arm sizes 2.4-2.9 times lower), lowers costs and duration, and enhances enrollment through reduced randomization to controls. Incorporating hybrid controls from prior studies and sharing information among arms with common background standard-of-care further improve efficiency, increasing power (reaching 90% overall) while controlling type I error rates acceptable levels.CONCLUSIONMaster protocol designs with hybrid controls and Bayesian information sharing enable the efficient integration of randomization into early-phase oncology trials, enhancing efficiency, cost-effectiveness, and patient-centricity while aligning with FDA OCE initiatives.

Repetitive head impacts due to heading in soccer may bear the potential to induce brain changes. To investigate how brain development is affected by heading, effects of heading exposure on neurocognitive and vestibular performance in adolescent soccer players were prospectively examined. In this longitudinal cohort study, male and female high-level youth soccer players of different ages were enrolled in two seasons. Age-matched athletes of other ball sports were included as controls. Before and after each season, neurocognitive performance (six domain scores), vestibulo-ocular reflex, dynamic visual acuity, and postural control (sway velocity) were objectively assessed in all athletes. Every soccer training and match during the observation period was videotaped to analyze individual heading exposure. Associations between heading frequency (total, in duels, >20-m flight distance) and pre- to post-changes were investigated via the Spearman correlation. A total of 103 soccer and 51 control athletes (9-19 yr) participated. Neurocognitive scores and sway velocity significantly improved in each season in soccer and control athletes. There were no associations between total heading numbers and changes in any parameter. Over the first season, the more headers played in duels ( r = -0.255; 95% confidence interval (CI), -0.474 to -0.006; P = 0.04) and from >20-m distance ( r = -0.299; 95% CI, -0.510 to -0.055; P = 0.02), the less psychomotor speed improvement occurred. In the second season, improvements in reaction time were significantly smaller with higher numbers of headers played in duels ( r = 0.375; 95% CI, 0.043 to 0.632; P = 0.02) and from >20-m distance ( r = 0.359; 95% CI, 0.025 to 0.621; P = 0.03). Potential high-impact headers may have affected neurocognitive improvements, exhibiting small to moderate effects. Therefore, more high-impact headers may be more important to consider than solely the overall heading exposure in discussions about adverse effects in youth soccer.

Keith Barnes died at age 92 on 3 January 2025. He devoted his whole working life to PMG and Telecom. He rose from the ranks in the Queensland State Administration to become Chief Planning Engineer in Telecom Headquarters. Keith did it his way, but his story is broadly typical of many others who contributed to the great infrastructure-building era of telecommunications in the post-war era. What is not typical is Keith’s last major assignment, which was to lead the in-country team from Telecom Australia to provide a wide range of professional advice and support to Saudi Telecom. The assignment was unique, big and challenging. In this article the authors pay tribute to Keith and the team, and reflect upon the Saudi project itself.

Abstract Cyclic peptides are potentially therapeutic in clinical applications, due to their great stability and activity. Yet, designing and identifying potential cyclic peptide binders targeting specific targets remains a formidable challenge, entailing significant time and resources. In this study, we modified the powerful RFdiffusion model to allow the cyclic peptide structure identification and integrated it with ProteinMPNN and HighFold to design binders for specific targets. This innovative approach, termed cycledesigner, was followed by a series of scoring functions that efficiently screen. With the combination of effective cyclic peptide design and screening, our study aims to further broaden the scope of cyclic peptide binder design.

РОЗВИТОК ІНФОРМАЦІЙНИХ СИСТЕМ І ТЕХНОЛОГІЙ В БАНКАХАнотація.Подано огляд етапів розвитку інформаційних систем і технологій у діяльності банківських установ.Досліджено еволюцію інформаційних систем, які застосовують банки,від розробки простих СУБД, що ґрунтувалися на системах управління персональними базами даних (Clipper, dBase, Foxpro та ін.), і до найновітніших систем, які базуються на клієнт-серверних рішеннях промислових СУБД (Oracle, Informix, Sybase, MS SQL Server).Доведено, що інформаційні системи і технології сприяють автоматизації банківських бізнес-процесів, підвищують їх ліквідність, покращують ефективність менеджменту банківськими ризиками.Система SWIFT забезпечує безпеку міжбанківських операцій, застосовуючи різні комбінації методів страхування ризику, зменшуючи банківські ризики, пов'язані з втратою документів, помилками в адресації, фальсифікації платіжних документів.УкраїнаСВІФТ дозволяє українським банкам виходити на міжнародний банківський рівень.Простежено різницю між цими системами стосовно банківських повідомлень: оперативна пересилка і зберігання розрахункових документів; функції ж системи розрахунків пов'язані безпосередньо з дотриманням взаємних вимог і зобов'язань.Визначено тенденції розвитку інформаційних систем і технологій, які використовують банківські установи