Cortisol is an essential steroid hormone that is involved in numerous physiological processes such as the stress response, regulation of blood pressure, immune modulation, and regulation of the sleep cycle. Cortisol levels can vary based on several factors. Cortisol imbalances can indicate adrenal disorders, such as Cushing’s Syndrome and Addison’s Disease. Cortisol imbalance can also lead to disruption in the sleep cycle, increased stress, and metabolic disorders. Given these facts, accurate and accessible cortisol monitoring is crucial for diagnostics and overall health.Standard methods for monitoring cortisol levels involve enzyme-linked immunosorbent assays or liquid chromatography-tandem mass spectrometry. These methods, while reliable are performed in laboratory settings using expensive equipment and take significant time to produce results. Reliable on-site or at-home detection methods of cor are unavailable, but would be important tools

WiFi-based indoor positioning has been a widely researched area for the past five years, with systems traditionally relying on signal telemetry data including Received Signal Strength Indicator (RSSI), Channel State Information (CSI), and Fine Timing Measurement (FTM). However, adoption in practice has remained limited due to environmental challenges including signal fading, multipath effects, and interference that significantly impact positioning accuracy. Existing machine learning approaches typically require extensive manual feature engineering, preprocessing steps like filtering and data scaling, and struggle with missing or incomplete telemetry data while lacking flexibility across heterogeneous environments. Furthermore, there is currently no unified model capable of handling variations in telemetry data formats from different WiFi device vendors, use-case requirements, and environmental conditions, forcing practitioners to develop separate models for each specific deployment scenario.

Pressure injuries (commonly called bedsores or pressure ulcers) represent one of the most persistent and costly challenges in healthcare, affecting over 2.5 million US patients and costing almost $27B in 2019. Hospital-acquired pressure injury events occur in about 3% in general populations and about 6% in intensive care units (ICUs). Current prevention strategies still rely on the Braden Scale risk assessment tool as the gold standard. Developed in the 80s, it is used to stratify patients into risk categories based on factors like sensory perception, moisture, mobility, and friction. The Braden score directly informs turning frequency as the standard of protocol. Unfortunately, medical staff adherence to turning protocols remains low at ~50% nationally, creating a gap between prescribed care and actual implementation. Technologies to help assess by sensing pressure injuries have limitations, including discontinuous monitoring requiring manual interpretation, and lack of objective mobility metrics. These fail to account for the complex interplay between pressure distribution, patient movement patterns, and individual risk factors. The Braden-scoring approach is particularly problematic as it does not account for the presence of existing pressure injuries or patient-specific factors, and has been shown to have inadequate validity for ICU patients. Additionally, current pressure mapping systems are typically large, expensive, and require specialized training, limiting their practical deployment in routine clinical care.

Vehicle platooning technology is an evolving segment within the broader movement towards more intelligent transportation, specifically relating to autonomous vehicles. Some early concepts dates back to the 1970s with projects like Electronic Route Guidance System developed by the U.S. Federal Highway Administration, which used a destination-oriented approach with roadside units to decode vehicle inputs and provide routing instructions. Subsequent initiatives such as the California Partners for Advanced Transportation Technology program demonstrated vehicles traveling in close formation guided by magnets embedded in roadways. The landscape has since evolved from individual vehicle automation concepts to more sophisticated vehicle-to-vehicle (V2V) communication schemes to enable coordinated movements. More recent industry developments have been driven by advancements in 5G technology, V2V communication protocols, and enhanced safety requirements. Current systems face control stability challenges, particularly as platoon size increases, with research showing that system stabilizability degrades and can lose stability entirely in infinite vehicle formations. Moreover, issues with V2V communication reliability persist, including frequent intermittent connectivity problems and wireless interference, limiting wider adoption. Additional challenges include the fundamental trade-off between fuel efficiency and safety margins, where shorter inter-vehicle distances improve aerodynamic benefits but increase collision risk.

Outdoor wireless sensor and camera networks are important for environmental monitoring and public-safety surveillance, yet their real-world deployment still relies heavily on expert intuition and exhaustive simulations that fail to scale in many landscapes. Traditional coverage-maximization techniques evaluate every candidate position for every node while factoring in every other node, the task complexity becomes intractable as node count or terrain granularity grows. The challenge is sharper in three-dimensional topographies where ridges, valleys, and plateaus block line-of-sight and invalidate two-dimensional heuristics. Moreover, once nodes are in the field, relocating them is slow and costly if new blind spots emerge or missions evolve.

Multimode optical fiber was first introduced in astrophotonics applications as “light pipes” to transport light from telescopes to instruments. The integration of multimode optical fiber helped to maximize light collection but offered little control over the propagation modes from the collected light, which affects the quality and speed of light transmission. Single-mode optical fiber used in interferometry proved invaluable for spatial filtering and wavefront correction, providing a stable, reliable, and flexible way to guide light in precision sensing and imaging. Photonic lanterns were conceived in the early 2000s to help bridge a gap between the light-gathering efficiency of multimode optical fiber and the precision of single-mode optical fiber. Photonic lantern devices have reasonably addressed the efficient conversion needs between multimode/ multi-modal and multiple single-mode light paths. However, challenges remain with respect to improving and scaling of photonic lantern devices, including coupling efficiency/losses, bandwidth limitations, and high-order mode (>20) capabilities.

Advances in biological research have been greatly influenced by the development of organoids, a specialized form of 3D cell culture. Created from pluripotent stem cells, organoids are effective in vitro models in replicating the structure and progression of organ development, providing an exceptional tool for studying the complexities of biology. Among these, cerebral cortex organoids (hereafter "organoid") have become particularly instrumental in providing valuable insights into brain formation, function, and pathology. Despite their potential, organoid experiments present several challenges. Organoids require a rigorous, months-long developmental process, demanding substantial resources and meticulous care to yield valuable data on aspects of biology such as neural unit electrophysiology, cytoarchitecture, and transcriptional regulation. Traditionally the data has been difficult to collect on a more frequent and consistent basis, which limits the breadth and depth of modern organoid biology. Generating and measuring organoids depend on media manipulations, imaging, and electrophysiological measurements. Historically are labor- and skill-intensive processes which can increase risks associated with experimental validity, reliability, efficiency, and scalability.

A cornerstone of bacterial molecular biology is the ability to genetically manipulate the microbe under study. Manipulating the genomes of bacteria is critical to many fields. Such manipulations are made by genetic engineering, which often requires new pieces of DNA to be added to the genome. It is often difficult to move genes into a recalcitrant destination organism due to surveillance systems (CRISPR, Restriction Modification) of the destination/host which degrade invading DNA . It may be commercially desirable to evade these systems in the destination organism. However, evading these systems may require significant experimental effort to design and implement.