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Depression, age, and weight were three factors that the artificial intelligence model identified as predictive of an insomnia diagnosis

A machine learning model can effectively predict a patient’s risk for a sleep disorder using demographic and lifestyle data, physical exam results, and laboratory values, according to a new study published this week in the open-access journal PLOS ONE by Samuel Y. Huang of Virginia Commonwealth University School of Medicine, and Alexander A. Huang of Northwestern Feinberg University School of Medicine, US.

The prevalence of diagnosed sleep disorders among American patients has significantly increased over the past decade. This trend is important to better understand and reverse since sleep disorders are a significant risk factor for diabetes, heart disease, obesity, and depression.

In the new work, the researchers used the machine learning model XGBoost to analyze publicly available data on 7,929 patients in the US who completed the National Health and Nutrition Examination Survey. The data contained 684 variables for each patient, including demographic, dietary, exercise, and mental health questionnaire responses, as well as laboratory and physical exam information.

Overall, 2,302 patients in the study had a physician diagnosis of a sleep disorder. XGBoost could predict the risk of sleep disorder diagnosis with a strong accuracy (AUROC=0.87, sensitivity=0.74, specificity=0.77), using 64 of the total variables included in the full dataset. The greatest predictors for a sleep disorder, based on the machine learning model, were depression, weight, age, and waist circumference.

The authors conclude that machine learning methods may be effective first steps in screening patients for sleep disorder risk without relying on physician judgment or bias. 

Samuel Y. Huang adds: “What sets this study on the risk factors for insomnia apart from others is seeing not only that depressive symptoms, age, caffeine use, history of congestive heart failure, chest pain, coronary artery disease, liver disease, and 57 other variables are associated with insomnia, but also visualizing the contribution of each in a very predictive model.”

Dense urban areas amplify the effects of higher temperatures, due to the phenomenon of heat islands in cities. This makes cities more vulnerable to extreme climate events. Large investments in the electricity network will be necessary to cool us down during heatwaves and keep us warm during cold snaps, according to a new study led by Lund University in Sweden.

“Unless we account for extreme climate events and continued urbanization, the reliability of electricity supply will fall by up to 30%. An additional outlay of 20-60 percent will be required during the energy transition to guarantee that cities can cope with different kinds of climate,” says Vahid Nik, Professor of Building Physics at Lund University.

The study presents a modeling platform that ties together climate, building, and energy system models to facilitate the simulation and evaluation of cities’ energy transition. The aim is to secure the cities’ resilience against future climate changes at the same time as the densification of urban areas is taking place. In particular, researchers have looked closely at extreme weather events (e.g. heatwaves and cold snaps) by producing simulations of urban microclimates. 

“Our results show that high-density areas give rise to a phenomenon called urban heat islands, which make cities more vulnerable to the effects of extreme climate events, particularly in southern Europe. For example, the outdoor temperature can rise by 17% while the wind speed falls by 61%. Urban densification – a recommended development strategy to reach the UN’s energy and climate goals – could make the electricity network more vulnerable. This must be taken into consideration when designing urban energy systems, says Kavan Javanroodi, Assistant Professor in Building and Urban Physics.

“The framework we have developed connects future climate models to buildings and energy systems at the city level, taking the urban microclimate into account. For the first time, we are getting to grips with several challenges around the issues of future climate uncertainty and extreme weather situations, focussing in particular on what are known as ‘HILP’ or High Impact Low Probability events”, says Vahid Nik.

There is still a large gap between future climate modeling and building and energy analyses and their links to one another. According to Vahid Nik, the model now being developed makes a great contribution to closing that gap. 

“Our results answer questions like ‘How big an effect will extreme weather events have in the future, given the predicted pace of urbanization and several different future climate scenarios?’, ‘How do we take them and the connections between them into account?’ and ‘How does the nature of urban development contribute to exacerbating or mitigating the effects of extreme events at the regional and municipal level?’ “

The results show that the peaks in demand in the energy system increase more than previously thought when extreme microclimates are taken into account, for example with an increase in cooling demand of 68% in Stockholm and 43% in Madrid on the hottest day of the year. Not considering this can lead to incorrect estimates of cities’ energy requirements, which can turn into power shortages and even blackouts. 

“There is a marked deviation between the heat and cooling requirements shown in today’s urban climate models, compared to the outcomes of our calculations when urban morphology, the physical design of the city, is more complex. For example, if we fail to take into account the urban climate in Madrid, we could underestimate the need for cooling by around 28%,” says Kavan Javanroodi.

Vahid Nik explains that an increasing number of countries have become interested in extreme weather events, energy issues, and their impact on public health. At the same time, there are no methods of quantifying the effects of climate change and planning for adapting to them, especially when it comes to extreme weather events and climate variations across space and time. 

“Our efforts can contribute to making societies more prepared for climate change. Future research should aim to examine the relationship between urban density and climate change in energy forecasts. Furthermore, we ought to develop more innovative methods of increasing energy flexibility and climate resilience in cities, which is a major focus of research for our team at the moment,” says Vahid Nik.

The multi-functional filter could help advance autonomous driving and the Internet of Things

Researchers have developed a new chip-sized microwave photonic filter to separate communication signals from noise and suppress unwanted interference across the full radio frequency spectrum. The device is expected to help next-generation wireless communication technologies efficiently convey data in an environment that is becoming crowded with signals from devices such as cell phones, self-driving vehicles, internet-connected appliances, and smart city infrastructure. 

“This new microwave filter chip has the potential to improve wireless communication, such as 6G, leading to faster internet connections, better overall communication experiences, and lower costs and energy consumption for wireless communication systems,” said researcher Xingjun Wang from Peking University. “These advancements would, directly and indirectly, affect daily life, improving the overall quality of life and enabling new experiences in various domains, such as mobility, smart homes, and public spaces.”

In the Photonics Research journal co-published by Chinese Laser Press and Optica Publishing Group, the researchers describe how their new photonic filter overcomes the limitations of traditional electronic devices to achieve multiple functionalities on a chip-sized device with low power consumption. They also demonstrate the filter’s ability to operate across a broad radio frequency spectrum extending to over 30 GHz, showing its suitability for envisioned 6G technology.

“As the electro-optic bandwidth of optoelectronic devices continues to increase unstoppably, we believe that the integrated microwave photonics filter will certainly be one of the important solutions for future 6G wireless communications,” said Wang. “Only a well-designed integrated microwave photonics link can achieve low cost, low power consumption, and superior filtering performance.”

Stopping interference

6G technology is being developed to improve upon currently-deployed 5G communications networks. To convey more data faster, 6G networks are expected to use millimeter wave and even terahertz frequency bands. As this will distribute signals over an extremely wide frequency spectrum with an increased data rate, there is a high likelihood of interference between different communication channels.

To solve this problem, researchers have sought to develop a filter to protect signal receivers from various types of interference across the full radio frequency spectrum. To be cost-effective and practical for widespread deployment, this filter needs to be small, consume little power, achieve multiple filtering functions, and be integrated into a chip. However, previous demonstrations have been limited by their few functions, large size, limited bandwidth, or requirements associated with electrical components.

For the new filter, researchers created a simplified photonic architecture with four main parts. First, a phase modulator serves as the input of the radio frequency signal, which modulates the electrical signal onto the optical domain. Next, the double-ring acts as a switch to shape the modulation format. An adjustable microring is the core unit for processing the signal. Finally, a photodetector serves as the output of the radio frequency signal and recovers the radio frequency signal from the optical signal.

“The greatest innovation here is breaking the barriers between devices and achieving mutual collaboration between them,” said Wang. “The collaborative operation of the double-ring and microring enables the realization of the intensity-consistent single-stage-adjustable cascaded-microring (ICSSA-CM) architecture. Owing to the high reconfigurability of the proposed ICSSA-CM, no extra radio frequency device is needed for the construction of various filtering functions, which simplifies the whole system composition.”

Demonstrating performance

To test the device, researchers used high-frequency probes to load a radio frequency signal into the chip and collected the recovered signal with a high-speed photodetector. They used an arbitrary waveform generator and directional antennas to simulate the generation of 2Gb/s high-speed wireless transmission signals and a high-speed oscilloscope to receive the processed signal. By comparing the results with and without using the filter, the researchers demonstrated the filter’s performance.

Overall, the findings show that the simplified photonic architecture achieves comparable performance with lower loss and system complexity compared with previous programmable integrated microwave photonic filters composed of hundreds of repeating units. This makes it more robust, energy-efficient, and easier to manufacture than previous devices.

The researchers plan to further optimize the modulator and improve the overall filter architecture to achieve a high dynamic range and low noise while ensuring high integration at both the device and system levels.