Aurelia Butler

Matrix, process, or something else? The structure of an organisation is of little significance for its success, as long as there is systems intelligence, according to a study conducted by doctoral student Juha Törmänen together with Professor Esa Saarinen, and Professor Emeritus Raimo P. Hämäläinen, based on a survey of 470 British and US citizens in 2018-2019.
Systems Intelligence is a concept created by Saarinen and Hämäläinen connecting human sensitivity and engineering thinking, which takes comprehensive interaction between individuals and their environments into account. It examines people and organisations through systemic perception, attitude, reflection, positive engagement, attunement, spirited discovery, wise action, and effective responsiveness.
The researchers ascertained how well these different dimensions of Systems Intelligence explained an organisation’s success.
‘A systems intelligent organisation is successful. By its nature, a systems intelligent organisation is also one that is capable of learning and development. The employees of a systems intelligent organisation have models of behaviour and action, which enable learning’, Törmänen says.
About 60 percent of the respondents were employees and about 40 percent were in a managerial or leadership position. The respondents evaluated issues pertaining to their organisations on if people there are warm and accepting of one another, and if they bring out the best in others. The metrics of Systems Intelligence were compared with the Dimensions of the Learning Organization Questionnaire (DLOQ), which is the most common scale used for evaluating learning organizations. Questions from both metrics were placed randomly in the questionnaire. In addition, respondents were asked to choose from among ten options on how successful the organisations that they represent are in their fields.
The study revealed that Systems Intelligence and the DLOQ are approximately equally good at explaining an organisation’s success. A respondent who evaluates the organisation as being successful, giving it the highest evaluation, typically gave the organisation higher marks in both the dimensions of Systems Intelligence and the different areas of the DLOQ. More

When Android and Apple operating system updates started giving users the option to put their smartphones in dark mode, the feature showed potential for saving the battery life of newer phones with screens that allow darker-colored pixels to use less power than lighter-colored pixels.
But dark mode is unlikely to make a big difference to battery life with the way that most people use their phones on a daily basis, says a new study by Purdue University researchers.
That doesn’t mean that dark mode can’t be helpful, though.
“When the industry rushed to adopt dark mode, it didn’t have the tools yet to accurately measure power draw by the pixels,” said Charlie Hu, Purdue’s Michael and Katherine Birck Professor of Electrical and Computer Engineering. “But now we’re able to give developers the tools they need to give users more energy-efficient apps.”
Based on their findings using these tools they built, the researchers clarify the facts about the effects of dark mode on battery life and recommend ways that users can already take better advantage of the feature’s power savings.
The study looked at six of the most-downloaded apps on Google Play: Google Maps, Google News, Google Phone, Google Calendar, YouTube and Calculator. The researchers analyzed how dark mode affects 60 seconds of activity within each of these apps on the Pixel 2, Moto Z3, Pixel 4 and Pixel 5. More

The Biomedical Genomics laboratory at IRB Barcelona has developed a computational tool that identifies cancer driver mutations for each tumour type. This and other developments produced by the same lab seek to accelerate cancer research and provide tools to help oncologists choose the best treatment for each patient. The study has been published in the journal Nature.
Each tumour — each patient — accumulates many mutations, but not all of them are relevant for the development of cancer. Researchers led by ICREA researcher Dr. Núria López-Bigas at IRB Barcelona have developed a tool, based on machine learning methods, that evaluates the potential contribution of all possible mutations in a gene in a given type of tumour to the development and progression of cancer.
In previous work that is already available to the scientific and medical community, the laboratory developed a method to identify those genes responsible for the onset, progression, and spread of cancer. “BoostDM goes further: it simulates each possible mutation within each gene for a specific type of cancer and indicates which ones are key in the cancer process. This information helps us to understand how a tumour is caused at the molecular level and it can facilitate medical decisions regarding the most appropriate therapy for a patient,” explains Dr. López-Bigas, head of the Biomedical Genomics lab. In addition, the tool will contribute to a better understanding of the initial processes of tumour development in different tissues.
The new tool has been integrated into the IntOGen platform, developed by the same group and designed to be used by the scientific and medical community in research projects, and into the Cancer Genome Interpreter, also developed by this group and which is more focused on clinical decision-making by medical oncologists.
BoostDM currently works with the mutational profiles of 28,000 genomes analysed from 66 types of cancer. The scope of BoostDM will grow as a result of the foreseeable increase in publicly accessible cancer genomes.
An advance founded on evolutionary biology
To identify the mutations involved in cancer, the scientists based themselves on a key concept in evolution, namely positive selection. Mutations that drive the growth and development of cancer are found in higher numbers in distinct samples, compared to those that would occur randomly. More

Computers have been able to quickly process 2D images for some time. Your cell phone can snap digital photographs and manipulate them in a number of ways. Much more difficult, however, is processing an image in three dimensions, and doing it in a timely manner. The mathematics are more complex, and crunching those numbers, even on a supercomputer, takes time.
That’s the challenge a group of scientists from the U.S. Department of Energy’s (DOE) Argonne National Laboratory is working to overcome. Artificial intelligence has emerged as a versatile solution to the issues posed by big data processing. For scientists who use the Advanced Photon Source (APS), a DOE Office of Science User Facility at Argonne, to process 3D images, it may be the key to turning X-ray data into visible, understandable shapes at a much faster rate. A breakthrough in this area could have implications for astronomy, electron microscopy and other areas of science dependent on large amounts of 3D data.
The research team, which includes scientists from three Argonne divisions, has developed a new computational framework called 3D-CDI-NN, and has shown that it can create 3D visualizations from data collected at the APS hundreds of times faster than traditional methods can. The team’s research was published in Applied Physics Reviews, a publication of the American Institute of Physics.
CDI stands for coherent diffraction imaging, an X-ray technique that involves bouncing ultra-bright X-ray beams off of samples. Those beams of light will then be collected by detectors as data, and it takes some computational effort to turn that data into images. Part of the challenge, explains Mathew Cherukara, leader of the Computational X-ray Science group in Argonne’s X-ray Science Division (XSD), is that the detectors only capture some of the information from the beams.
But there is important information contained in the missing data, and scientists rely on computers to fill in that information. As Cherukara notes, while this takes some time to do in 2D, it takes even longer to do with 3D images. The solution, then, is to train an artificial intelligence to recognize objects and the microscopic changes they undergo directly from the raw data, without having to fill in the missing info.
To do this, the team started with simulated X-ray data to train the neural network. The NN in the framework’s title, a neural network is a series of algorithms that can teach a computer to predict outcomes based on data it receives. Henry Chan, the lead author on the paper and a postdoctoral researcher in the Center for Nanoscale Materials (CNM), a DOE Office of Science User Facility at Argonne, led this part of the work. More

Researchers at North Carolina State University demonstrated a low-cost technique for retrieving nanowires from electronic devices that have reached the end of their utility and then using those nanowires in new devices. The work is a step toward more sustainable electronics.
“There is a lot of interest in recycling electronic materials because we want to both reduce electronic waste and maximize the use we get out of rare or costly materials,” says Yuxuan Liu, first author of a paper on the work and a Ph.D. student at NC State. “We’ve demonstrated an approach that allows us to recycle nanowires, and that we think could be extended to other nanomaterials — including nanomaterials containing noble and rare-earth elements.”
“Our recycling technique differs from conventional recycling,” says Yong Zhu, corresponding author of the paper and the Andrew A. Adams Distinguished Professor of Mechanical and Aerospace Engineering at NC State. “When you think about recycling a glass bottle, it is completely melted down before being used to create another glass object. In our approach, a silver nanowire network is separated from the rest of the materials in a device. That network is then disassembled into a collection of separate silver nanowires in solution. Those nanowires can then be used to create a new network and incorporated into a new sensor or other devices.”
The new recycling technique takes into account the entire life cycle of a device. The first step is to design devices using polymers that are soluble in solvents that will not also dissolve the nanowires. Once a device has been used, the polymer matrix containing the silver nanowires is dissolved, leaving behind the nanowire network. The network is then placed in a separate solvent and hit with ultrasound. This disperses the nanowires, separating them out of the network.
In a proof-of-concept demonstration, the researchers created a wearable health sensor patch that could be used to track a patient’s temperature and hydration. The sensor consisted of silver nanowire networks embedded in a polymer material. The researchers tested the sensors to ensure that they were fully functional. Once used, a sensor patch is normally discarded.
But for their demonstration, the researchers dissolved the polymer in water, removed the nanowire network, broke it down into a collection of individual nanowires, and then used those nanowires to create a brand-new wearable sensor. While there was minor degradation in the properties of the nanowire network after each “life cycle,” the researchers found that the nanowires could be recycled four times without harming the sensor’s performance. More

Army researchers developed a new machine learning-based framework to enhance the security of computer networks inside vehicles without undermining performance.
With the widespread prevalence of modern automobiles that entrust control to onboard computers, this research looks toward to a larger Army effort to invest in greater cybersecurity protection measures for its aerial and land platforms, especially heavy vehicles.
In collaboration with an international team of experts from Virginia Tech, the University of Queensland and Gwangju Institute of Science and Technology, researchers at the U.S. Army Combat Capabilities Development Command, known as DEVCOM, Army Research Laboratory devised a technique called DESOLATOR to help optimize a well-known cybersecurity strategy known as the moving target defense.
“The idea is that it’s hard to hit a moving target,” said Dr. Terrence Moore, Army mathematician. “If everything is static, the adversary can take their time looking at everything and choosing their targets. But if you shuffle the IP addresses fast enough, then the information assigned to the IP quickly becomes lost, and the adversary has to look for it again.”
DESOLATOR, which stands for deep reinforcement learning-based resource allocation and moving target defense deployment framework, helps the in-vehicle network identify the optimal IP shuffling frequency and bandwidth allocation to deliver effective, long-term moving target defense.
According to Army computer scientist and program lead Dr. Frederica Free-Nelson, achievement of the former keeps uncertainty high enough to thwart potential attackers without it becoming too costly to maintain, while attainment of the latter prevents slowdowns in critical areas of the network with high priority.
“This level of fortification of prioritized assets on a network is an integral component for any kind of network protection,” Nelson said. “The technology facilitates a lightweight protection whereby fewer resources are used for maximized protection. The utility of fewer resources to protect mission systems and connected devices in vehicles while maintaining the same quality of service is an added benefit.”
The research team used deep reinforcement learning to gradually shape the behavior of the algorithm based on various reward functions, such as exposure time and the number of dropped packets, to ensure that DESOLATOR took both security and efficiency into equal consideration.
“Existing legacy in-vehicle networks are very efficient, but they weren’t really designed with security in mind,” Moore said. “Nowadays, there’s a lot of research out there that looks solely at either enhancing performance or enhancing security. Looking at both performance and security is in itself a little rare, especially for in-vehicle networks.”
In addition, DESOLATOR is not limited to identifying the optimal IP shuffling frequency and bandwidth allocation. Since this approach exists as a machine learning-based framework, other researchers can modify the technique to pursue different goals within the problem space.
“This ability to retool the technology is very valuable not only for extending the research but also marrying the capability to other cyber capabilities for optimal cybersecurity protection,” Nelson said.
Story Source:
Materials provided by U.S. Army Research Laboratory. Note: Content may be edited for style and length. More

In the evolving field of cancer biology and treatment, innovations in organ-on-a-chip microdevices allow researchers to discover more about the disease outside the human body. These organs-on-chips serve as a model of the state an actual cancer patient is in, thus allowing an opportunity to finding the correct treatment before administering it to the patient. At Texas A&M University, researchers are pushing these devices to new levels that could change the way clinicians approach cancer treatment, particularly ovarian cancer.
The team has recently submitted a patent disclosure with the Texas A&M Engineering Experiment Station.
“We claim several novelties in technological design as well as biological capabilities that didn’t exist in prior organs-on-chips,” said Dr. Abhishek Jain, lead researcher and assistant professor in the Department of Biomedical Engineering.
Jain also has a joint appointment in the College of Medicine at Texas A&M.
Jain’s device — the ovarian tumor microenvironment-chip (OTME-Chip) — focuses on platelets, tiny blood cells that help the body form clots to stop bleeding. The microdevice, about the size of a USB, models the properties of a tumor in the lab. Researchers then can recreate events within platelets circulating in the blood as they approach the tumor and make it more potent and metastatic.
“We are creating a platform technology using the organ-on-a-chip approach where tumor biology can be advanced, and new drugs can be identified by recreating the platelet-tumor and platelet-tumor-drug interactions under the influence of flow, supporting blood vessels and the extracellular matrix,” Jain said. More