Senseonics (United States)

Separation of charge via noninductive ice-ice collisions in clouds is widely accepted as the primary mechanism behind cloud electrification. However, not all clouds end up with the same charge distributions, as observed in various field campaigns and laboratory experiments over the last several decades. The distribution of charge in a given thunderstorm controls the polarity, frequency, and other characteristics of lightning produced by that storm, but charge distribution is very difficult to measure directly, especially at statistically significant scales. Of particular interest to the lightning community is the relationship between thunderstorm environments and lightning characteristics, where the charge distribution of storms bridges the gap between the two. This study will use intracloud (IC) lightning data from the Earth Networks Total Lightning Network (ENTLN) to investigate the statistical relationships between the proportion of negative IC flash frequency to environmental parameters such as charge reversal temperature altitudes, cloud base height, cloud depth, and warm vs cold cloud depth fraction derived from global reanalysis data for multiple regions around the world

This study utilizes the Earth Networks Total Lightning Network (ENTLN) combined with the Geostationary Lightning Mapper (GLM) to investigate lightning activity in hurricanes relative to hurricane structure and evolution for 5 years of hurricane seasons. This combination enables a more complete estimate of lightning activity than one detection method can provide alone, including the derivation of the cloud flash fraction (CFF) for each hurricane. Additionally, lightning characteristics for each individual hurricane as well as average trends and correlations to hurricane wind speed and brightness temperatures are explored. Overall, this research has the potential to provide further insight into tropical cyclone intensification and weakening.

Optimal glycemic control in type 2 diabetes (T2D) remains an elusive goal despite an expanding range of effective treatments and technologies. Sustained, disciplined adherence to lifestyle modifications leads to positive outcomes in T2D, although converting medical recommendations into effective patient action is challenging in real-world practice. The emergence of AI and machine learning (ML) presents innovative opportunities to prompt effective lifestyle modifications and diabetes goal achievements in highly user-specific and user-friendly ways. The Twin Precision Treatment system is an AI-enabled bundled system of sensors and coaching that generates personalized recommendations through wearable Bluetooth-enabled technologies (including continuous glucose monitors), targeted laboratory data, Internet of Things, AI-ML algorithms, and human input. The authors designed a study to explore whether or not this bundled system intervention (INT) could help individuals — with T2D and managed in a primary care setting — achieve glycemic targets while concurrently de-escalating glucose-lowering medications. This single-center trial randomly assigned 150 adults with T2D and a body mass index (the weight in kilograms divided by the square of the height in meters) ≥27 to the INT (N=100) or a usual care (UC) control group (N=50). The primary end point was a hemoglobin A1c level (HbA1c) <6.5% (<48 mmol/mol; “target”) without glucose-lowering medications, except metformin, at 12 months; a secondary end point was the same outcome sustained for ≥90 days prior to 12 months. Other secondary end points included target attainment without any glucose-lowering medications at 12 months and ≥90 days prior to 12 months, as well as HbA1c and weight changes at 12 months. The primary end point was achieved by 71.0% (95% confidence interval [CI], 60.1 to 80.0) of INT participants versus 2.4% (95% CI, 0.5 to 11.6) of UC participants (P<0.001). Significantly more INT participants than UC participants achieved or sustained target ≥90 days prior to 12 months without glucose-lowering medications, except metformin (52.5% vs. 2.8%; P<0.001). The mean changes in HbA1c levels (−1.3% vs. −0.3%; P<0.001) and body weight (−8.6% vs. −4.6%; P<0.001) were significantly greater in the INT group than in the UC group. Other secondary end points were not significantly different. In post hoc analysis, overall use of glucose-lowering pharmacotherapy decreased markedly in the INT group but not the UC group. Quality-of-life and treatment satisfaction scores improved significantly from baseline in the INT group but not in the UC group (exploratory analyses). Overall, the AI-enabled bundled system of sensors and coaching facilitated significant improvements in glycemic control, weight loss, and quality of life versus UC, while allowing marked de-escalation of glucose-lowering pharmacotherapy.

Electrical propulsion and power systems in naval shipboard applications offer efficient propulsion arrangements for various types of vessels. Adoption has been propelled by the development of semiconductor switching devices since the 1980s. Presently these systems use a rectifier stage to convert the generator side AC system into a regulated DC bus that is distributed to drive the traction motors. The rectification stage typically features either passive diode bridge or thyristorbased line-commutated circuits. These rectifiers interface to the AC distribution using line-frequency transformers which provide galvanic isolation; but are bulky and deteriorate the power density of the overall system. This paper introduces a family of high-frequency isolated and direct AC-DC converters that tap into the emerging technologies of high-frequency high-power transformers and four-quadrant devices. Advantages include increased power density due to elimination of bulky line-frequency transformers, direct AC-DC conversion without multiple power conversion stages, and minimized filtering elements sized for switching frequency harmonics only. The paper also presents broader design and sizing aspects and simulation results of one of the selected candidates.

Emotions are very crucial for humans as they determine our ways of thinking, our actions, and even how we interrelate with other persons. Recognition of emotions plays a critical role in areas such as interaction between humans and computers, mental disorder detection, and social robotics. Nevertheless, the current emotion recognition systems have issues like noise interference, inadequate feature extraction, and integration of data for the multimodal context that embraces audio, video, and text. To address these issues, this research proposes an "Enhanced Trimodal Emotion Recognition Using Multibranch Fusion Attention with Epistemic Neural Networks and Fire Hawk Optimization." The proposed method begins with modality-specific preprocessing: Natural Language Processing (NLP) for text to address linguistic variations, Relaxed instance Frequency-wise Normalization (RFN) for the audio to minimize distortion of noise’s importance and iterative self-Guided Image Filter (isGIF) for the videos to enhance the image quality and minimize the artifacts. This preprocessing facilitates and optimizes data for feature extracting; an Inception Transformer for capturing the textual contexts; Differentiable Adaptive Short-Time Fourier transform (DA-STFT) to extract the audio's spectral and temporal features; and class attention mechanisms to emphasize important features in the videos. Following that, these features are combined through a Multi-Branch Fusion Attention Network to harmonize all the multifarious modalities into one. The last sanity check occurs through an Epistemic Neural Network (ENN), which tackles issues of uncertainty involved in the last classification, and the Fire Hawk algorithm is used to enhance the emotion recognition capabilities of the framework. Finally the proposed approach attains 99.5% accuracy with low computational time. Thus, the proposed method addresses important shortcomings of the systems developed previously and can be regarded as a contribution to the development of the multimodal emotion recognition field.

Abstract. The frequency and severity of fire weather have increased under climate change, particularly in high-latitude boreal forests. Lightning, a key ignition source globally, is also expected to become more frequent with climate change and could significantly increase burn area. Current research on lightning-ignited wildfires (LIW) has a long history in boreal ecosystems but has typically focused on North America due to better data availability, while the lack of publicly available data for Eurasia has hindered our comprehensive understanding of important characteristics of LIW, such as holdover time, lightning-ignition efficiency, frequency, and spatial distribution of lightning-ignited wildfires in boreal forests. This study introduces the Temporal Minimum Distance (TMin) method, a novel approach to matching lightning strikes with wildfires without requiring ignition location, that outperformed current methodologies. As a result, we developed a comprehensive dataset of lightning-ignited wildfires across the entire boreal forest from 2012 to 2022, encompassing 6,228 fires — 4,186 in Eurasia and 2,042 in North America — each over 200 hectares in size. This dataset provides new opportunities to model ignition and spread dynamics of boreal wildfires and offers deeper insights into lightning-driven fire activity globally.

The coastal snail Pirenella conica (Gastropoda: Potamididae) was observed and photographed in its mud flat habitat (Alaçati Bay, Karaburun Peninsula, Türkiye) and in the laboratory. The snails were amphibious; they moved about in damp sediment when the sea had receded and readily left the water in the laboratory to move on dry surfaces. The siphon, normally used for the intake of oxygenated water in submerged snails, remained open when snails were out of water, suggesting that they can also breathe air. Videos of snails taken from below explained the mechanics of their stop-and-go locomotion. Close-up photographs also revealed the presence of a pallial eye at the top of the siphonal canal. When a snail was partially buried in soft sediment, its pallial eye remained above the sediment. The pallial eye may orient the snail and help it detect potential dangers when its tentacular eyes are obstructed.

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