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If the first few frames are any indicator of a blockbuster movie, hold the 2035 Best Picture Oscar for the Vera C. Rubin Observatory and its ambitious new 10-year project. On June 23, 2025, scientists at the state-of-the-art facility in the mountains of north-central Chile gave the public its first glimpses into the capabilities of its 8.4-meter Simonyi Survey Telescope, equipped with the world’s largest digital camera—a 3.2 megapixel, 6,600-pound behemoth that can photograph the whole southern sky every few nights. Its task is a decade-long lapse record-called the Legacy Survey of Space and Time (LSST). The first shots on that journey have left both the general public and astronomical community in awe, revealing in rich detail a mind-boggling number of galaxies, stars, asteroids and other celestial bodies. “The amount of sky it covers, even in just one image, is unprecedented,” said David Chuss, PhD, chair of the Department of Physics, who viewed the first images with colleagues at an organized watch party. “It’s such high-precision, beautiful detail,” added Kelly Hambleton Prša, PhD, associate professor of Astrophysics and Planetary Sciences. “It’s just mind-blowing.” What Makes Rubin and LSST So Unique? Simply, this revolutionary instrument, embarking on an equally revolutionary initiative, will observe half the sky to a greater depth and clarity than any instrument ever has before. Consider this: "The Cosmic Treasure Chest” image released by Rubin contains 1,185 individual exposures, taken over seven nights. Each one of those individual exposures covers 10 square degrees of night sky, which is about the same as looking up at 45 full moons positioned around one another. It may seem like a small size, but click the image yourself, and zoom in and out. The amount of sky captured in that range—enough to show roughly 10 million galaxies—is astounding. Per the Observatory, “it is the only astronomical tool in existence that can assemble an image this wide and deep so quickly.” “At the end of 10 years, Rubin will have observed 20 billion galaxies, and each night in that time frame it will generate 20 terabytes of data,” Dr. Hambleton Prša said. “And, because Rubin has so many different filters, we get to see the same objects in so many different ways.” According to Dr. Hambleton Prša and Dr. Chuss, the power and precision of the Rubin LSST, combined with the shear area of the sky that will be observed, will allow for an incredibly in-depth study of myriad objects, processes and events in ways nobody has ever studied them before. “For example, in our galaxy, we expect to observe only two supernovae per century,” Dr. Hambleton Prša said. “But we're observing 20 billion galaxies. For someone studying this phenomenon, the number of supernovae that they’re going to observe will be off the charts. It is an exquisite survey.” It will also provide insight into the universe’s oldest and most puzzling enigmas. “Rubin is able to look back into our universe at times when it was much smaller during its expansion and really address some of these incredible mysteries out there, like dark energy,” Dr. Chuss said. “We know the universe is expanding and that this expansion is accelerating. Rubin will trace the history of that acceleration and, from that, provide insight into the physics of the mysterious dark energy that appears to be driving it.” To enhance the technological capabilities of its instrument, scientists were invited to contribute towards the selection of the observing strategy of the telescope. The Rubin team took into consideration continual input from the astrophysics community, separated into what they call “science collaborations.” To achieve this, the Rubin team generated proposed simulations for collecting observations, which the science collaborations then assessed for their specific science goals. “The Rubin team then iterated with the science collaborations, taking into account feedback, to ultimately obtain the best strategy for the largest number of science cases,” Dr. Hambleton Prša said. Dr. Hambleton Prša is the primary contact for the Pulsating Star Subgroup, which is part of the Transients and Variable Stars Science Collaboration, the science collaboration that focuses on objects in the sky that change with time. She was the lead author among 70 co-authors on the roadmap for this science collaboration, underscoring the significant scale of community participation for each of these areas. Joined Under One Sky Dr. Hambleton Prša, Dr. Chuss and other members of the Astrophysics and Planetary Sciences Department and Department of Physics at Villanova have a vested interest in Rubin and the LSST project. In April, the two departments joined forces to launch The Villanova One Sky Center for Astrophysics, co-directed by the two faculty members. With goals to elevate the University's longstanding record of research eminence in astronomy and astrophysics and create opportunities for more students to access the disciplines, the Center partnered with the Rubin Observatory to help realize the mission. Both Villanova and Rubin share a similar vision on expanding access to this broad field of study. Fortuitously, the launch of The Villanova One Sky Center coincided with the initial data released from Rubin. What will result, Dr. Chuss says, will be a “truly awesome impact on both our Center and institution.” Dr. Hambleton Prša will advance her own research of pulsating stars, and Andrej Prša, PhD, professor of Astrophysics and Planetary Science and the primary contact for the Binary Star Subgroup, will broaden his study of short-period binary stars. Joey Neilsen, PhD, associate professor of Physics, will expand his research in black hole astrophysics. Becka Phillipson, PhD, an assistant professor of Physics, who recently led a proposal for Villanova to join the Rubin LSST Discovery Alliance, aims to increase the scope of her study of chaotic variability of compact objects. Dr. Chuss, who generally works on infrared and microwave polarimetry, which is “outside the wavelength ranges of Rubin” is interested in its complementarity with other observations, such as those of the cosmic microwave background—the oldest light in the universe—and the evolution of the large-scale structure of the universe. Subjects, he says, which are “exactly in the wheelhouse for Rubin.” Other faculty members are interested in topics such as how Rubin’s observations may change the knowledge of both the history and structure of our solar system and the population of Milky Way satellite galaxies. That is not to mention, Dr. Hambleton Prša points out, the daily 20 terabytes of data that will become available for students and postdoctoral researchers under their tutelage, who will be heavily involved in its analysis for their own projects and ideas. “This partnership is going to greatly increase our opportunities and elevate our profile,” Dr. Chuss said. “It will make our program even more attractive for faculty, postdocs and students to come and to share their knowledge and expertise. “Together, we will all have access to an incredible movie of this epoch of our universe, and the knowledge and surprises that come with it along the way.”
The Impact of Counterfeit Goods in Global Commerce
Introduction Counterfeiting has been described as “the world’s second oldest profession.” In 2018, worldwide counterfeiting was estimated to cost the global economy between USD 1.7 trillion and USD 4.5 trillion annually, as well as resulting in more than 70 deaths and 350,000 serious injuries annually. It is estimated that more than a quarter of US consumers have purchased a counterfeit product. The counterfeiting problem is expected to be exacerbated by the unprecedented shift in tariff policy. Tariffs, designed as an import tax or duty on an imported product, are often a percentage of the price and can have different values for different products. Tariffs drive up the cost of imported brand name products but may not, or only to a lesser extent, impact the cost of counterfeit goods. In this article, we examine the extent of the global counterfeit dilemma, the role experts play in tracking and mitigating the problem, the use of anti-counterfeiting measures, and the potential impact that tariffs may have on the flow of counterfeit goods. Brand goods have always been a target of counterfeits due to their high price and associated prestige. These are often luxury goods and clothing, but can also be pharmaceuticals, cosmetics, and electronics. The brand name is an indication of quality materials, workmanship, and technology. People will pay more for the “real thing,” or decide to buy something cheaper that looks “just as good.” In many cases, “just as good” is a counterfeit of the brand name product. A tariff is an import tax or duty that is typically paid by the importer and can drive up the cost of imported brand name products. For example, a Yale study has shown that shoe prices may increase by 87% and apparel prices by 65%, due to tariffs. On the other hand, counterfeit products don’t play by the rules and can often avoid paying tariffs, such as the case of many smaller, online transactions, shipped individually. Therefore, we expect to see an increase in counterfeit products as well as a need to increase efforts to reduce the economic losses of counterfeiting. The Scale of the Counterfeit Problem In their 2025 report, the Organisation for Economic Co-operation and Development (OECD) and the European Union Intellectual Property Office (EUIPO), estimated that in 2021, “global trade in counterfeit goods was valued at approximately USD 467 billion, or 2.3% of total global imports. This absolute value represents an increase from 2019, when counterfeit trade was estimated at USD 464 billion, although its relative share decreased compared to 2019 when it accounted for 2.5% of world trade. For imports into the European Union, the value of counterfeit goods was estimated at USD 117 billion, or 4.7% of total EU imports.” In a 2020 report, the US Patent and Trademark Office (USPTO) estimated the size of the international counterfeit market as having a “range from a low of USD 200 billion in 2008 to a high of USD 509 billion in 2019.” According to the OEDC / EUIPO General Trade-Related Index of Counterfeiting for economies (GTRIC-e), China continues to be the primary source of counterfeit goods, as well as Bangladesh, Lebanon, Syrian Arab Republic, and Türkiye. Based on customs seizures in 2020-21, the most common items are clothing (21.6%), footwear (21.4%), and handbags, followed by electronics and watches. Based on the value of goods seized, watches (23%) and footwear (15%) had the highest value. However, it should be noted that items that are easier to detect and seize are likely to be overrepresented in the data. Although the share of watches declined, and electronics, toys, and games increased, it remains unclear whether this represents a long term trend or just a short term fluctuation. In general, high value products in high demand continue to be counterfeited. Data from the US Library of Congress indicates that 60% – 80% of counterfeit products are purchased by Americans. The US accounts for approximately 5% of the world’s consumers; however, it represents greater than 20% of the world’s purchasing power. Though it is still possible to find counterfeit products at local markets, a large number of counterfeit goods are obtained through online retailers and shipped directly to consumers as small parcels classified as de minimis trade. This allows for the duty-free import of products up to USD 800 in value. Counterfeit items may be knowingly or unknowingly purchased from online retailers and shipped directly to consumers, duty-free. Purchased products can be shipped via postal services, classified as de minimis trade. Approximately 79% of packages seized contained less than 10 items. Given the size and volume of the packages arriving daily, many or most will evade scrutiny by customs officials. This means of import is increasing over time. In 2017-19 it was 61% of seizures. By 2020-21, it was 79%. Economic Impact of Counterfeiting The scale of the counterfeiting problem has significant impacts on the US economy, US business interests, and US innovations in lost sales and lost jobs. Moreover, counterfeit products are often made quickly and cheaply, using materials that may be toxic. The companies producing these goods may not dispose of waste properly and may dump it into waterways, causing significant environmental consequences. Counterfeit products from electrical equipment and life jackets to batteries and smoke alarms may be made without adhering to safety standards or be properly tested. These products may fail to function when you need it and may lead to fire, electric shock, poisoning, and other accidents that can seriously injure and even kill consumers. Counterfeit cosmetics and pharmaceuticals can also lead to injuries by either including unsafe ingredients or by failing to provide the benefits of the real product. The Tariff Counterfeit Connection Tariffs may be seen as a tax on consumers and raise the price of imported products that are already the target of counterfeiters such as luxury leather products and apparel. It’s commonly understood that raising prices on genuine products can only drive up the demand for counterfeit goods. In general, consumers will have less disposable income and the brand goods they desire will cost more which is bound to increase the demand for counterfeit goods. Although recent changes removing the USD 800 tax exemption on de minimis shipments from China and Hong Kong will make it more expensive for counterfeiters to ship their goods internationally, tariffs are typically applied as a percentage of the cost of an object. This will cause the price of more expensive legitimate goods to increase even more than the cheaper counterfeit goods and likely make the counterfeit products even more attractive economically. Therefore, we expect to see an increase in counterfeit products as well as an increase in efforts to reduce the economic losses of counterfeiting. The Role of Technical Experts in Counterfeit Detection Technical experts play an important role in both the prevention and detection of counterfeits and helping to identify counterfeiting entities. Whether counterfeit money, clothing, shoes, electronics, cosmetics or pharmaceuticals, the first step in fighting counterfeits is detecting them. In some cases, the counterfeit product is obvious. A leather product may not be leather, a logo may be wrong, packaging may have a spelling mistake, or a holographic label may be missing. These products may be seized by customs. However, some counterfeit products are very difficult to detect. In the case of a counterfeit memory card with less than the stated capacity or a pharmaceutical that contains the wrong active ingredient, technical analysis may be needed to identify the parts. Technical analysis may also be used to try and identify the source of the counterfeit goods. For prevention measures, manufacturers may use radio frequency identification (RFID) or Near Field Communication (NFC) tags within their products. RFID tags are microscopic semiconductor chips attached to a metallic printed antenna. The tag itself may be flexible and easy to incorporate into packaging or into the product itself. A passive RFID requires no power and has sufficient storage to store information such as product name, stock keeping unit (SKU), place of manufacture, date of manufacture, as well as some sort of cryptographic information to attest to the authenticity of the tag. A simple scanner powers the tag using an electromagnetic field and reads the tag. If manufacturers include RFID tags in products, an X-ray to identify a product in a de minimis shipment (perhaps using artificial intelligence technology) and an RFID scanner to verify the authenticity of the product can be used to efficiently screen a large number of packages. Many products also may be marked with photo-luminescent dyes with unique properties that may be read by special scanners and allow authorities to detect legitimate products. Similarly, doped hybrid oxide particles with distinctive photo-responsive features may be printed on products. These particles, when exposed to laser light, experience a fast increase in temperature which may be quickly detected. For either of these examples, the ability to identify legitimate products, or – due to the absence of marking – track counterfeit products, allows authorities to map the flow of the counterfeit goods through the supply chain as they are manufactured, shipped, and are exported and imported to countries. For many years, electronic memory cards such as SD cards and USB sticks have been counterfeited. In many cases, the fake card will have a capacity much smaller than listed. For example, a 32GB memory card for a camera may only hold 1GB. Sometimes, these products may be identified by analyzing the packaging for discrepancies from the brand name products. In other cases, software must be used to verify the capacity and performance of each one, which is time-consuming when analyzing a large number of products. Forensic investigators, comprised of forensic accountants and forensic technologists, are heavily involved in efforts to combat this illicit trade. By analyzing financial records, supply-chain data, and transaction histories, they trace the origins and pathways of counterfeit products. Their work often involves identifying suspicious procurement patterns, shell companies, and irregular inventory flows that signal counterfeit activity. Forensic investigators often begin by mapping the counterfeit supply chain, an intricate web that often spans continents. Using data analytics, transaction tracing, and inventory audits, they identify anomalies in procurement, distribution, and sales records. These methodologies help pinpoint the origin of counterfeit goods, the intermediaries involved, and the final points of sale. By reconstructing the flow of goods and money, forensic investigators can begin to unmask activities. Cross-border partnerships are essential for tracking assets, sharing insights, and coordinating with financial regulators. Public-private partnerships further enhance the effectiveness of anti-counterfeiting efforts. Forensic investigators often serve as bridges between government agencies, brand owners, and financial institutions, facilitating the exchange of key information. These partnerships increase information-sharing, streamline investigations, and amplify the impact of enforcement actions. A promising development in this space is the World Customs Organization’s Smart Customs Project, which integrates artificial intelligence to detect and intercept counterfeit goods. Forensic investigators can leverage this initiative by analyzing AI-generated alerts and incorporating them into broader financial investigations, which allows for faster and more accurate identification of illicit networks. Jurisdictional complexity is a major hurdle in anti-counterfeiting efforts. Forensic investigators work closely with legal teams to navigate these challenges to ensure that investigations comply with local laws, and evidence is admissible and can withstand scrutiny in court, especially when dealing with offshore accounts and international money laundering schemes. Forensic investigators follow the money, tracing illicit profits through bank accounts, shell companies, and cryptocurrency transactions. Their findings not only help recover stolen assets but also support disputes by providing expert testimony that quantifies financial losses and identifies the bad actors. Conclusion Imitations of brand name products have become more convincing, harder to detect, and the sources of the counterfeit goods more difficult to identify. While counterfeiting clearly has evolved because of technological advancements, e-commerce, and the growing sophistication of bad actors, the process has now been complicated even further by the unpredictable tariff and trade policies that are affecting businesses worldwide. Consequently, companies need to take a multi-faceted approach to these new challenges introduced into the counterfeiting of products by tariffs. By engaging high-tech product authentication measures, utilizing technology-based alerts about counterfeits, and retaining the specialized skills of forensic investigators and other experts, companies will be able to navigate the risks posed by the complex and changing relationship between tariffs and counterfeit goods. To learn more about this topic and how it can impact your business or connect with James E. Malackowski simply click on his icon now to arrange an interview today. To connect with David Fraser or Matthew Brown - contact : Kristi L. Stathis, J.S. Held +1 786 833 4864 Kristi.Stathis@JSHeld.com
NASA Grant Funds Research Exploring Methods of Training Vision-Based Autonomous Systems
Conducting research at 5:30 a.m. may not be everybody’s first choice. But for Siddhartha Bhattacharyya and Ph.D. students Mohammed Abdul, Hafeez Khan and Parth Ganeriwala, it’s an essential part of the process for their latest endeavor. Bhattacharyya and his students are developing a more efficient framework for creating and evaluating image-based machine learning classification models for autonomous systems, such as those guiding cars and aircraft. That process involves creating new datasets with taxiway and runway images for vision-based autonomous aircraft. Just as humans need textbooks to fuel their learning, some machines are taught using thousands of photographs and images of the environment where their autonomous pupil will eventually operate. To help ensure their trained models can identify the correct course to take in a hyper-specific environment – with indicators such as centerline markings and side stripes on a runway at dawn – Bhattacharyya and his Ph.D. students chose a December morning to rise with the sun, board one of Florida Tech’s Piper Archer aircraft and photograph the views from above. Bhattacharyya, an associate professor of computer science and software engineering, is exploring the boundaries of operation of efficient and effective machine-learning approaches for vision-based classification in autonomous systems. In this case, these machine learning systems are trained on video or image data collected from environments including runways, taxiways or roadways. With this kind of model, it can take more than 100,000 images to help the algorithm learn and adapt to an environment. Today’s technology demands a pronounced human effort to manually label and classify each image. This can be an overwhelming process. To combat that, Bhattacharyya was awarded funding from NASA Langley Research Center to advance existing machine learning/computer vision-based systems, such as his lab’s “Advanced Line Identification and Notation Algorithm” (ALINA), by exploring automated labeling that would enable the model to learn and classify data itself – with humans intervening only as necessary. This measure would ease the overwhelming human demand, he said. ALINA is an annotation framework that Hafeez and Parth developed under Bhattacharyya’s guidance to detect and label data for algorithms, such as taxiway line markings for autonomous aircraft. Bhattacharyya will use NASA’s funding to explore transfer learning-based approaches, led by Parth, and few-shot learning (FSL) approaches, led by Hafeez. The researchers are collecting images via GoPro of runways and taxiways at airports in Melbourne and Grant-Valkaria with help from Florida Tech’s College of Aeronautics. Bhattacharyya’s students will take the data they collect from the airports and train their models to, in theory, drive an aircraft autonomously. They are working to collect diverse images of the runways – those of different angles and weather and lighting conditions – so that the model learns to identify patterns that determine the most accurate course regardless of environment or conditions. That includes the daybreak images captured on that December flight. “We went at sunrise, where there is glare on the camera. Now we need to see if it’s able to identify the lines at night because that’s when there are lights embedded on the taxiways,” Bhattacharyya said. “We want to collect diverse datasets and see what methods work, what methods fail and what else do we need to do to build that reliable software.” Transfer learning is a machine learning technique in which a model trained to do one task can generalize information and reuse it to complete another task. For example, a model trained to drive autonomous cars could transfer its intelligence to drive autonomous aircraft. This transfer helps explore generalization of knowledge. It also improves efficiency by eliminating the need for new models that complete different but related tasks. For example, a car trained to operate autonomously in California could retain generalized knowledge when learning how to drive in Florida, despite different landscapes. “This model already knows lines and lanes, and we are going to train it on certain other types of lines hoping it generalizes and keeps the previous knowledge,” Bhattacharyya explained. “That model could do both tasks, as humans do.” FSL is a technique that teaches a model to generalize information with just a few data samples instead of the massive datasets used in transfer learning. With this type of training, a model should be able to identify an environment based on just four or five images. “That would help us reduce the time and cost of data collection as well as time spent labeling the data that we typically go through for several thousands of datasets,” Bhattacharyya said. Learning when results may or may not be reliable is a key part of this research. Bhattacharyya said identifying degradation in the autonomous system’s performance will help guide the development of online monitors that can catch errors and alert human operators to take corrective action. Ultimately, he hopes that this research can help create a future where we utilize the benefits of machine learning without fear of it failing before notifying the operator, driver or user. “That’s the end goal,” Bhattacharyya said. “It motivates me to learn how the context relates to assumptions associated with these images, that helps in understanding when the autonomous system is not confident in its decision, thus sending an alert to the user. This could apply to a future generation of autonomous systems where we don’t need to fear the unknown – when the system could fail.” Siddhartha (Sid) Bhattacharyya’s primary area of research expertise/interest is in model based engineering, formal methods, machine learning engineering, and explainable AI applied to intelligent autonomous systems, cyber security, human factors, healthcare, explainable AI, and avionics. His research lab ASSIST (Assured Safety, Security, and Intent with Systematic Tactics) focuses on the research in the design of innovative formal methods to assure performance of intelligent systems, machine learning engineering to characterize intelligent systems for safety and model based engineering to analyze system behavior. Siddhartha Bhattacharyya is available to speak with media. Contact Adam Lowenstein, Director of Media Communications at Florida Institute of Technology at adam@fit.edu to arrange an interview today.
Red Light Cameras Emerge as a Politically Divisive Issue
Lawrence Levy, associate vice president and executive dean of the National Center for Suburban Studies, is featured in a Newsday article about red-light camera programs and how they are emerging as a divisive political issues on Long Island. He likened the red-light camera program to that of congestion pricing for its “good government motive” aimed to improve traffic safety, charging drivers who violate the law while gaining money to help pay for the county police department. The issue, Levy said, is “a real tough one for politicians to gauge because of the mix of potential court cases and legislative actions that could be taken and the general mood of the public about anything that could be seen as a tax by another name.”
A recent study on dangerous driving conducted by CAA South Central Ontario (CAA SCO) revealed that more than half of Ontario motorists, 55 per cent, admit to engaging in risky and unsafe driving behaviours in the past year. According to the survey, this number increases to 61 per cent amongst young drivers aged 18 to 34. “Dangerous driving behaviours, such as speeding, distracted driving, and aggressive driving, continue to pose significant risks on our roads,” says Michael Stewart, community relations consultant for CAA SCO. “These actions not only endanger the lives of the drivers themselves but also put all road users at risk. We must prioritize road safety by promoting responsible driving habits." Of those surveyed, the top five dangerous driving behaviours that motorists engaged in are, 1. Speeding (41 per cent) 2. Distracted driving (20 per cent) 3. Unsafe lane changes (9 per cent) 4. Aggressive driving (8 per cent) 5. Running red lights (7 per cent) In contrast, those surveyed say they frequently witnessed others driving dangerously far more often. 1. Speeding (84 per cent) 2. Unsafe lane changes (76 per cent) 3. Aggressive driving (76 per cent) 4. Distracted driving (73 per cent) 5. Running red lights (56 per cent) “The data tells us that it is far more prevalent for people to see others driving dangerously rather than admitting that they themselves are carrying out the same behaviour,” adds Stewart. The survey found that speeding continues to be the leading concern for Ontario motorists, especially on roads with higher speed limits. According to the study, 86 per cent of motorists feel safe on residential streets, compared to only 68 per cent on posted 110-kilometre-per-hour highways. “Ontarians frequently witness dangerous driving behaviours, especially on highways,” says Stewart, “the important thing to remember is that the risk of collision can increase when travelling at higher speeds.” Four out of 10 Ontario drivers will actively avoid roads with ASE Ontario drivers favour stricter fines and penalties to combat the increasing amount of dangerous driving across the province. This year alone, 35 per cent of Ontarians indicated that penalties and fines for speeding and stunt driving have influenced their driving behaviour – up seven per cent from last year. The study also found that more than three-quarters of Ontario drivers (78 per cent) believe that automated speed enforcement (ASE) can help deter drivers from speeding, as 70 per cent of Ontario drivers admit that they will slow down in the presence of an ASE camera. “It is no surprise that Ontario motorists are in favour of stricter penalties as speeding and dangerous driving continue to be an issue,” says Stewart, “what is surprising, however, is that despite the support for ASE, four out of 10 Ontario drivers will actively avoid roads where an ASE is present, an increase from last year.” According to the survey, Ontarians support the presence of ASE on all types of streets, especially near schools (84 per cent) and community centres (81 per cent). A total of 17 per cent of Ontario drivers, equivalent to more than 1.5 million individuals, have received a ticket from an ASE camera in the past. “CAA SCO continues to advocate for road safety for all road users,” says Stewart. “Our hope is that through education and awareness of the repercussions, we can begin to see a steady decrease in dangerous driving across all roads.” For more information, please visit www.caasco.com/speeding The online survey was conducted by DIG Insights from April 1 to April 16, 2024, with 1,509 Ontario drivers aged 18 and older. Based on the sample size of n=1,509 and with a confidence level of 95 per cent, the margin of error for this research is +/- 2%.)
Career advice: Expert provides tips for acing job interviews
Later this month, a whole new class of graduating seniors will hit the job market. University of Delaware career expert Jill Gugino Panté has advice for them and others seeking employment that can help boost confidence and chances for success when entering job interviews. Panté, director of the Lerner Career Services Center in UD's Lerner College of Business & Economics, provided the following tips: Hand write your answers to common interview questions. Research shows that people have a better chance of remembering information if it’s handwritten. So, rather than typing answers out on your computer, grab an “interview prep” notebook and start writing down your stories to have a better chance of remembering them when it comes time to interview. Practice saying your answers out loud. Written communication is very different from verbal communication. Once you have your answers written down, practice them out loud. This allows you to warm up your voice and get used to talking about yourself out loud. You can do this alone in a room or with a trusted friend who will give you honest feedback. Fan out notes around your screen. Now that most interviews have moved to a virtual format, take advantage of having some extra help. Put post it notes around your screen instead of in a notebook at your side so your eyes and attention stay toward the camera. Exercise beforehand. Of course, you don’t want to run a marathon right before your interview, but you can do some quick exercises to get your blood and endorphins pumping which can have a positive effect on the body and mind. I personally like to dance to an upbeat song that triggers happy memories. To set up an interview with Panté, visit her profile and click on the "contact button." This will send a message directly to her.
Researcher develops microrobots to battle cancer with unique precision
Magnetic robots that can target cancer cells are nothing new. But the patented microrobots developed by the University of Delaware's Sambeeta Das can be guided with a magnetic field to deliver medication to cells – or to destroy infectious cells, such as cancer – inside the body. To mark the launch of National Inventors Month, Das, assistant professor of mechanical engineering, shared her journey toward invention. Q: Tell us about your patented invention on microrobots for cancer research. What problem were you trying to solve? Das: One of the biggest issues with cancer research is the ability to target cancer cells without harming healthy cells. Cancer cells are sneaky, and they have evolved ways of hiding from the body’s immune cells. A big part of our research focuses on targeting, specifically precision targeting. We want to be able to target a single cell in a mass of cells, whether that is a single cell in a mass of cancer cells or whether it is a single abnormal cell surrounded by healthy cells. To do this, we use magnetic microrobots that can be driven inside the body by magnetic fields to a particular cell location. Magnetic fields are biocompatible, meaning they are not harmful to biological tissues, and our microrobots are very small, around 20 microns, which is about the size of a single bacteria cell. We can load our microrobots with various drugs and modify their surface in such a way that when the robots come in contact with the cells we are targeting, they can kill the target cell or perform some other function. Q: How is this solution unique? Das: Other people have made magnetic microrobots, but our system is unique since it allows us to do automatic targeting with a lot of precision. For example, a person operating our microrobots can just point to a cell and our system will drive the microrobot there. Additionally, the instrument we have made and patented is an all-in-one portable device that can be used anywhere. We don’t need a separate microscope, camera or software, it is all built in and very user friendly. Anyone can use it. This makes it super portable, which means quick solutions for health practitioners. In addition, poor and resource challenged areas can also be accessed with this portable solution. Q: What drives you toward invention? Das: I like to solve problems, and I like seeing something come together from nothing. I am very interested in problems that affect human health and longevity, particularly those that affect the common person. Q: How do you approach solving a problem, and whose support has been critical along the way? Das: One thing I have realized is that it is imperative to ask the right question to solve a problem. You must really get to the core of the issue. The second thing is to always keep the end user in mind. So, it’s kind of a two-pronged approach—looking from both ends of the problem. For support, I would say my team members and my collaborators. Their support has been invaluable in helping me solve the problems that I want to solve. In fact, my graduate students keep a running list of crazy ideas that they have come up with. It helps us look at problems in a unique way and come up with innovative solutions. Q: Not every invention makes it. How do you deal with failure? Das: The way that I start working on a problem is to assume that whatever we do, we are going to fail. I always tell my students that their first couple of experiments or designs will always fail. But failure is essential because it will teach you what not to do. And knowing what not to do is sometimes the critical part of the invention process. The failures inform us about the ways of not doing something which means now there is another way of doing something. Q: What is the best advice you’ve ever received? Das: The best career advice I’ve ever received is that there is always another way. If you run into roadblocks there is always another answer, there is always another opportunity. So we just need to keep going and trying new and crazy ideas. Q: How are inventive minds created – is it innate or can it be developed? How do you encourage innovation among your students? Das: That’s an interesting question and honestly, I am not sure. I do believe in what Edison said, “Genius is 1% inspiration and 99% perspiration.” He is a known inventor, so I would go with his interpretation on this. As for my students, I give them lots of freedom. I think freedom is essential in encouraging innovation. The freedom to come up with crazy ideas without anyone saying that won't work and the freedom to fail—multiple times. Das is available for interviews to talk about her microrobots and other projects at UD. To reach her, visit her profile and click the "contact" button.
MEDIA RELEASE: CAA Insurance Company Addresses Escalating Auto Theft Crisis Across Canada
CAA Insurance Company is deeply concerned with the auto theft crisis unfolding across Canada. According to industry experts, in 2022, auto theft exceeded $1.2 billion in claims, a number that is only expected to rise if things do not change quickly. “Consumers are at a tipping point, and they will soon feel the tangible effects of the auto theft crisis,” says Elliott Silverstein, Director of Government Relations CAA Insurance. “If the rate of vehicle theft does not decrease, it will lead to an increase in auto-related costs that could become unbearable for drivers in Ontario, many of whom are already struggling with affordability.” Current Impact on Consumers The ongoing shortage of microchips and vehicle availability is intensifying the situation, making vehicle rentals and replacements both time-consuming and costly for consumers, with wait times for new vehicles sometimes exceeding a year. With interest rates remaining high, the cost of purchasing or leasing a new vehicle will further burden consumers. However, what is most troubling is that as consumers take necessary precautions, thieves are exploring other more aggressive ways to steal cars, which include home invasions. "Getting your car stolen will not only disrupt your daily life but there is also considerable emotional distress it takes on your life as well. We believe the surge in auto theft cases demands a united front," adds Silverstein. Call to Action CAA Insurance believes everyone has a role to play in combatting auto theft and is urging stakeholders – including government, insurers, and vehicle manufacturers – to collaborate and develop a plan to combat this issue. “The impacts of auto theft are significant. For the insurance industry, it is the equivalent of addressing a year-round catastrophic incident (like a flood or tornado) with no visible end in sight,” adds Silverstein. Technology advancements have far surpassed vehicle standards, which haven’t been updated since 2007 in Canada, making it more difficult to reinforce technology-based solutions like immobilizers and mandate their inclusion in new vehicles. Preventive Measures and Tips for Consumers However, our data shows that consumers can make simple adjustments to safeguard their vehicles. To help mitigate the risk of vehicle theft, CAA Insurance recommends the following preventive measures for consumers: Secure your parked vehicle with a steering-wheel lock, brake pedal lock, or wheel lock, such as “The Club” to secure your parked vehicle. Secure your car key fob by storing it in a Faraday box or pouch to prevent signal hacking. Consider a professionally installed after-market immobilizer. Lock your doors (both car and home) and park your vehicle inside if you have a garage. If you own more than one vehicle, it's recommended to park the less valuable one nearer to the street. Install motion sensors and a camera on your driveway to capture any activity. Cover the VIN (Vehicle Identification Number) so it's not visible on the dashboard. Store a GPS tracker (ex, Air Tag) to track your vehicle should it be stolen. Ensure items are out of sight, and do not leave valuables in your vehicle. Always avoid leaving your vehicle unattended while it is running. CAA Insurance urges individuals to report any suspicious activity to police and avoid confrontations with thieves.
Optical research illuminates a possible future for computing technology
Nathaniel Kinsey, Ph.D., Engineering Foundation Professor in the Department of Electrical and Computer Engineering (ECE), is leading a group to bring new relevance to a decades-old computing concept called a perceptron. Emulating biological neuron functions of the messenger cells within the body’s central nervous system, perceptrons are an algorithmic model for classifying binary input. When combined within a neural network, perceptrons become a powerful component for machine learning. However, instead of using traditional digital processing, Kinsey seeks to create this system using light with funding from the Air Force Office of Scientific Research. This “nonlinear optical perceptron” is an ambitious undertaking that blends advanced optics, machine learning and nanotechnology. “If you put a black sheet outside on a sunny day, it heats up, causing properties such as its refractive index to change,” Kinsey said. “That’s because the object is absorbing various wavelengths of light. Now, if you design a material that is orders of magnitude more complex than a sheet of black plastic, we can use this change in refractive index to modify the reflection or transmission of individual colors – controlling the flow of light with light.” Refractive index is an expression of a material’s ability to bend light. Researchers can harness those refractive qualities to create a switch similar to the binary 1-0 base of digital silicon chip computing. Kinsey and collaborators from the U.S. National Institute of Standards and Technology, including his former VCU Ph.D. student Dhruv Fomra, are currently working to design a new kind of optically sensitive material. Their goal is to engineer and produce a device combining a unique nonlinear material, called epsilon-near-zero, and a nanostructured surface to offer improved control over transmission and reflection of light. Kinsey’s prior research has demonstrated that epsilon-near-zero materials combine unique features that allow their refractive index to be modified quite radically – from 0.3 to 1.3 under optical illumination – which is roughly equivalent to the difference between a reflective metal and transparent water. While an effective binary switch, the large change in index requires a lot of energy (~1 milli-Joules per square centimeter). By combining epsilon-near-zero with a specifically designed nanostructure exhibiting surface lattice resonance, Kinsey hopes to achieve a reduction in the required energy to activate the response. The unique response of a nanostructure exhibiting surface lattice resonance allows light to effectively be bent 90 degrees, arriving perpendicular to the surface while being split into two waves that travel along the surface. When a large area of the nanostructure is illuminated, the waves traveling along the surface mix, where they interfere constructively or destructively with each other. This interference can produce strong modification to reflection and transmission that is very sensitive to the geometry of the nanostructure, the wavelength of the incident light and the refractive index of the surrounding materials. The mixing of optical signals along the surface can also selectively switch regions of the epsilon-near-zero material thereby performing processing operations. A key aspect of Kinsey’s work is to build nonlinear components, like diodes and transistors, that use optical signals instead of electrical ones. Transistors and other traditional electronic components are nonlinear by default because electrical charges strongly interact with each other (for example, two electrons will tend to repel each other). Creating optical nonlinear components is challenging because photons do not strongly interact, they just pass through each other. To correct for this, Kinsey employs materials whose properties change in response to incident light, but the interaction is weak and thus requires large energies to utilize. Kinsey’s device aims to reduce that energy requirement while simultaneously shaping light to perform useful operations through the use of the nanostructured surface and lightwave interference. The United States Department of Defense sees optical computing as the next step in military imaging. Kinsey’s work, while challenging, has potential to yield an enormous payoff. “Let’s say you want to find a tank within an image,” Kinsey said, “Using a camera to capture the scene, translate that image into an electrical signal and run it through a traditional, silicon-circuit-based computer processor takes a lot of processing power. Especially when you try to detect, transfer, and process higher pixel resolutions. With the nonlinear optical perceptron, we’re trying to discover if we can perform the same kinds of operations purely in the optical domain without having to translate anything into electrical signals.” Linear optical systems, like metasurfaces and photonic integrated circuits, can already process information using only a fraction of the power of traditional tools. Building nonlinear optical systems would expand the functionality of these existing linear systems, making them ideal for remote sensing platforms on drones and satellites. Initially, the resolution would not be as sharp as traditional cameras, but optical processing built into the device would translate an image into a notification of tanks, troops on the move, for example. Kinsey suggests optical-computing surveillance would make an ideal early warning system to supplement traditional technology. “Elimination or minimization of electronics has been a kind of engineering holy grail for a number of years,” Kinsey said, “For situations where information naturally exists in the form of light, why not have an optical-in and optical-out system without electronics in the middle?” Linear optical computing uses minimal power, but is not capable of complex image processing. Kinsey’s research seeks to answer if the additional power requirement of nonlinear optical computing is worthwhile given its ability to handle more complex processing tasks. Nonlinear optical computing could be applied to a number of non-military applications. In driverless cars, optical computing could make better light detection and ranging equipment (better known as LIDAR). Dark field microscopy already uses related optical processing techniques for ‘edge detection’ that allows researchers to directly view details without the electronic processing of an image. Telecommunications could also benefit from optical processing, using optical neural networks to read address labels and send data packets without having to do an optical to electrical conversion. The concept of optical computing is not new, but interest (and funding) in theory and development waned in the 1980s and 1990s when silicon chip processing proved to be more cost effective. Recent years have seen many advancements in computing, but the more recent slowdown in scaling of silicon-based technologies have opened the door to new data processing technologies. “Optical computing could be the next big thing in computing technology,” Kinsey said. “But there are plenty of other contenders — such as quantum computing — for the next new presence in the computational ecosystem. Whatever comes up, I think that photonics and optics are going to be more and more prevalent in these new ways of computation, even if it doesn’t look like a processor that does optical computing.” Kinsey and other researchers working in the field are in the early stages of scientific exploration into these optical computing devices. Consumer applications are still decades away, but with silicon-based systems reaching the limit of their potential, the future for this light-based technology is bright.
A new study conducted on behalf of CAA South Central Ontario (CAA SCO), found that 51 per cent of Ontario drivers label speeding as a ‘big problem’ within the province – that number has crept up three per cent compared to last year. “Speeding continues to be the most common dangerous driving behaviour that drivers are both witnessing and engaging in,” says Michael Stewart, community relations consultant, Government and Community Relations, CAA SCO. While many have witnessed motorists speeding, they don’t believe they are the issue The study also found that 81 per cent of Ontario drivers have witnessed others speeding but only 38 per cent admit to doing it themselves. After their main concern of speeding, other common dangerous driving behaviours that drivers see and admit to doing include: Aggressive driving Unsafe lane changes Distracted driving Running stop signs and red lights Among those who admit to speeding, almost two thirds (63 per cent) drive between 10-19 km/hr over the speed limit. “It may seem harmless to drive an additional 10 or 15 km/hr above the posted speed limit, but the risk outweighs the benefit,” says Stewart. According to the Traffic Injury Research Foundation, travelling even 10 km/hr over the speed limit increases the likelihood of a collision by 60 per cent, while saving the average driver only four minutes on their commute. “Drivers are urged to be considerate of their speed and drive according to speed limits to keep themselves and others safe on the road,” says Stewart. Most drivers say they believe photo radar helps deter speeding, but many try to avoid it. While 77 per cent of Ontario drivers believe that Automated Speed Enforcement (ASE) can help deter speeding, one in four drivers try to avoid roads with an ASE. It was also found that 44 per cent are likely to increase their speed after passing an ASE camera. According to the survey, 1.5 million Ontario drivers have received a ticket from an ASE camera. Steep penalties remain for excessive speeding. The rise in speeding and stunt driving prompted the Ontario government to introduce tougher fines and penalties in 2021, through the Moving Ontarians More Safely Act. Drivers caught by police travelling 50 km/hr or more over the speed limit, or 40 km/hr or more on roads with a speed limit less than 80 km/hr, face: An immediate licence suspension for 30 days and their vehicle impounded for 14 days. If convicted, drivers face a minimum fine of $2,000, up to a maximum of $10,000. A first conviction will also net a minimum one-year licence suspension, while a third would carry a lifetime driving ban. “If you come across an aggressive driver who is speeding, the best thing you can do is stay calm, focus on your driving and do not engage with the other driver,” says Stewart. “If someone is driving erratically or you believe their behaviour could be an immediate danger to others, safely pull over and call 911, or report them online when you get home.”