Connected and automated vehicles, which can interact vehicle to vehicle (V2V) and between vehicles and roadway infrastructure like traffic signals and stop signs (V2I), promise to save energy and improve safety. In a new study published in Transportation Research Part B, engineers from Michigan Technological University propose a modeling framework for V2V and V2I cooperative driving.

Cooperative driving helps cars and their drivers safely and efficiently navigate. The framework uses an eco-driving algorithm that prioritizes saving fuel and reducing emissions. The automated algorithm calculates location-based traffic control devices and roadway constraints using maps and geographic information. The research is led by Kuilin Zhang, associate professor of civil and environmental engineering and affiliated associate professor of computer science at Michigan Tech, along with Shuaidong Zhao ’18, now a senior quantitative analyst at National Grid.

For the past three years, Houghton, Michigan, has been home to roadside units installed on five of the city’s traffic signals that make V2I communication possible. Zhang conducted a simulation analysis using real traffic signal phasing and timing messages from the Ann Arbor connected vehicle test environment and plans to expand testing in the Houghton area.

“The whole idea of cooperative driving automation is that the signals in the intersection tell your car what’s happening ahead,” Zhang said. “The sensor at the intersection can benefit all connected vehicles passing through the intersection. The automated eco-driving algorithm improves the driving decisions of the connected and automated vehicles.”

The simulation results show that the cooperative automated eco-driving algorithm saves energy — 7% under light traffic and 23% under heavy traffic along the corridor.

“The stop and go, stop and go, it may use a lot of energy,” Zhang said. “The concept of eco-driving incorporates how the vehicle makes driving decisions using data not only from vehicles in front of it, but also with information given from a traffic signal.”

Zhang’s model pulls in high-definition (HD) maps, which use a connected vehicle’s hardware and software to provide down-to-the-centimeter accuracy in navigation. HD maps incorporate multiple types of environmental sensing: long-range radar, lidar, camera footage, short/medium-range radar and ultrasound.

Zhang said for autonomous driving, it’s important to know landmarks to control the car’s driving, as well as hill grades; using a hill to slow or accelerate a car can also increase energy savings. It’s easy to conserve energy on a straight highway; on busy arterial streets with traffic and stoplights, energy conservation isn’t so simple. On city streets, Zhang and Zhao’s online predictive connected and automated eco-driving model considers traffic control devices and road geometry constraints under light and heavy traffic conditions.

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Materials provided by Michigan Technological University. Original written by Kelley Christensen. Note: Content may be edited for style and length.

The Mediterranean monk seal is classified as “endangered,” with a total population of about 700.

The new study — by the University of Exeter and the Society for the Protection of Turtles (SPOT) — used camera-traps to confirm breeding in caves in northern Cyprus, with at least three pups born from 2016-19 at one cave.

Only certain caves are suitable for monk seal breeding and resting, so — although the numbers are small — the researchers say urgent action is needed to protect these caves.

“This area of coastline in being developed rapidly, especially for construction of hotels,” said Dr Robin Snape, of the Centre for Ecology and Conservation on Exeter’s Penryn Campus in Cornwall.

“A survey of the coast in 2007 found 39 possible breeding caves, and some of these have already been destroyed.

“The main breeding site we identify in this study currently has no protected status, and we are working with local authorities to try to change this.”

Lead author Dr Damla Beton, of SPOT, added: “Another major threat to monk seals in this area is bycatch (accidental catching by fisheries).

“We are working with fishers and government ministries to ensure protection areas at sea, because at present no measures are implemented to mitigate bycatch in the core areas used by these seals.”

The team has now established long-term monitoring of the breeding caves, aiming to determine the size of this seal population.

The study, carried out in collaboration with the Middle Eastern Technical University with the support of the local authorities, received funding from the MAVA Foundation.

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Materials provided by University of Exeter. Note: Content may be edited for style and length.

Although biochemical and optical methods exist, mechanical techniques are the most widely used. They indirectly assess ripeness based on the fruit’s firmness. In turn, firmness is quantified by observing the vibrations that occur on the fruit when mechanical energy is accurately delivered through devices such as hammers, pendulums, or speakers. Unfortunately, these approaches fall short for softer fruits, which are more readily damaged by the contact devices used.

In a recent study published in Foods, a team of scientists from Shibaura Institute of Technology (SIT), Japan, addressed this issue through an innovative method for measuring the firmness of soft fruits using laser-induced plasma (LIP). This work is a sort of follow-up of a previous study in which LIP was used to quantify the firmness of harder fruits.

But what is LIP and how is it used? Plasma is a state of matter similar to the gaseous state but in which most particles have an electric charge. This energetic state can be produced in normal air by focusing a high-intensity laser beam onto a small volume. Because the generated plasma “bubble” is unstable, it immediately expands, sending out shockwaves at ultrasonic speeds. Professor Naoki Hosoya and colleagues at SIT had successfully used LIP shockwaves generated close to the surface of apples to excite a type of vibration called ₀S₂ mode, colloquially referred to as “football mode vibration” because of how the resulting deformation looks on spherical bodies. They then verified that the frequency of the ₀S₂ mode vibrations was correlated with the firmness of the fruit.

However, soft fruits do not exhibit ₀S₂ mode vibrations, so the team had to analyze an alternative type of oscillation: Rayleigh waves. These are waves that occur exclusively on the surface of bodies without penetrating far into the interior. Using Kent mangoes, a setup for generating LIP, and commercially available laser-based vibrometers, the scientists verified that the velocity at which Rayleigh waves propagate is directly related to the firmness of the mangoes. Because the propagation velocity markedly decreases with storage time, it provides a reliable way to indirectly assess ripeness.

The team went further and looked for the best position on the mangoes’ surface to determine the velocity of Rayleigh waves. Mangoes, as well as other soft fruits, have large seeds inside, which can alter the propagation of surface waves in ways that are detrimental to measurements. “The results of our experiments indicate that Rayleigh waves along the ‘equator’ of the mango are better for firmness assessment compared to those along the ‘prime meridian’,” explains Hosoya. The experiments also revealed that cavities within the fruit’s flesh or decay can greatly affect the results of the measurements. Thus, as Hosoya adds, they will keep investigating which is the best area to measure firmness in mangoes using their novel approach.

In short, the team at SIT has engineered an innovative strategy to assess the ripeness of soft fruits from outside. “Our system,” remarks Hosoya, “is suitable for non-contact and non-destructive firmness assessment in mangoes and potentially other soft fruits that do not exhibit the usual ₀S₂ mode vibrations.” Further refinement of such firmness assessment methods will hopefully make them more reliable and accessible for the agricultural industry. With any luck, their widespread adoption will ensure that fruits reach your plate only when the time is ripe!

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Materials provided by Shibaura Institute of Technology. Note: Content may be edited for style and length.

The microbial community, or microbiome, of the gut is known to influence human health. Previous studies have focused on determining the types of microbes that are present. Unfortunately, this information is not enough to understand the microbiome.

“The gut environment is shaped by small molecules known as metabolites, which are excreted by the microbial community,” said Sergei Maslov (BCXT/CABBI), a professor of bioengineering and Bliss faculty scholar. “Although it is possible to measure these metabolites experimentally, it is cumbersome and expensive.”

The researchers had previously published a study where they used experimental data from other studies to model the fate of metabolites as they pass through the gut microbiome. In the new study, they have used the same model to predict new microbial processes that have not been determined before.

“What we eat passes into our gut, and there is a cascade of microbes which release metabolites,” said Akshit Goyal, a postdoctoral fellow at MIT and a collaborator of the Maslov lab. “Biologists have measured these molecules in human stools, we have shown that you can use computational models to predict the levels of some.”

Measuring every metabolite and trying to understand which microbe might be releasing it can be challenging. “There is a large universe of possible cross-feeding interactions. Using this model, we can aid experiments by predicting which ones are more likely to occur in the gut,” Goyal said.

The model was also supported by genomic annotations, which explain which microbial genes are responsible for processing the metabolites. “We are confident of our modelling predictions because we also checked whether the microbes contain the genes necessary for carrying out the associated reactions. About 65% of our predictions were supported by this information,” said Veronika Dubinkina, a PhD student in bioengineering.

The researchers are now working to improve the model by including more experimental data. “Different people have different strains of gut microbes. Although these different strains have many genes in common, they differ in their capabilities,” Dubinkina said. “We need to collect more data from patients to understand how different microbial communities behave in different hosts.”

“We are also interested in determining how fast the microbes consume and secrete the metabolites,” said Tong Wang, a PhD student in physics. “Currently the model assumes that all the microbes consume metabolites at the same rate. In reality, the rates are different and we need to understand them to capture the metabolite composition in the gut.”

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Materials provided by Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign. Original written by Ananya Sen. Note: Content may be edited for style and length.