George Kolbe, Head of Life Insurance Marketing at Momentum discusses the recommended ways to prepare for unexpected occurrences.

Romit Maulik, an assistant professor in the Penn State College of Information Sciences and Technology, was granted a three-year, $360,000 Early Career Program Award from the Army Research Office. 

With the four-match series being levelled at 1-1, India will aim to bounce back in style in the third T20I on Wednesday in Centurion.

Comparing Logistic Regression and Decision Tree - Which of our models is better at predicting our outcome? Learn how to compare models using misclassification, area under the curve (ROC) charts, and lift charts with validation data. In part 6 and part 7 of this series we fit a logistic regression [...]

Getting Started with Python Integration to SAS Viya for Predictive Modeling - Comparing Logistic Regression and Decision Tree was published on SAS Users.

Learn how to fit a random forest and use your model to score new data. In Part 6 and Part 7 of this series, we fit a logistic regression and decision tree to the Home Equity data we saved in Part 4. In this post we will fit a Random [...]

Getting Started with Python Integration to SAS Viya for Predictive Modeling - Fitting a Random Forest was published on SAS Users.

Fitting a Gradient Boosting Model - Learn how to fit a gradient boosting model and use your model to score new data In Part 6, Part 7, and Part 9 of this series, we fit a logistic regression, decision tree and random forest model to the Home Equity data we [...]

Getting Started with Python Integration to SAS Viya for Predictive Modeling - Fitting a Gradient Boosting Model was published on SAS Users.

1. South Africa vs India Match Prediction - Who will win today’s 3rd T20I match between SA vs IND?  CricTracker
2. SA look to break spin stranglehold while India worry about depth  ESPNcricinfo
3. South Africa vs India 3rd T20 Playing 11, live match time (IST), streaming  Business Standard
4. Time running out for Abhishek, Avesh as Ramandeep-Vyshak waiting in wings: India vs South Africa 3rd T20I Probable XIs  Hindustan Times
5. India, SA eye series lead in Centurion before decisive T20I in Johannesburg  Cricbuzz

1. Chennai rain: IMD predicts heavy rainfall for Tamil Nadu. Are schools, colleges closed today?  Hindustan Times
2. Chennai rains today: Heavy downpour expected in many Tamil Nadu districts, IMD issues alert. Schools shut today?  Mint
3. Low pressure weakens, but heavy rain in Tamil Nadu may persist  The New Indian Express
4. Tamil Nadu weather: Schools shut in Chennai, IMD puts 17 districts in state on heavy rainfall alert  Business Today
5. Tamil Nadu rains: Schools, colleges closed in several districts amid heavy rain  The Hindu

Lower fertility rates will boost economic growth, according to a demographic model developed by Renaissance Capital. 

Escalation in protests across the globe in 2019 are forecast to persist into the new decade, according to Verisk Maplecroft report.

GENEVA — The U.N. weather agency is predicting that the phenomenon known as La Nina is poised to last through the end of this year, a mysterious “triple dip” — the first this century — caused by three straight years of its effect on climate patterns like drought and flooding worldwide. The World Meteorological Organization on Wednesday said La Nina conditions, which involve a large-scale cooling of ocean surface temperatures, have strengthened in the eastern and central equatorial Pacific with an increase in trade winds in recent weeks. The agency’s top official was quick to caution that the “triple dip” doesn’t mean global warming is easing. “It is exceptional to have three consecutive years with a La Nina event. Its cooling influence is temporarily slowing the rise in global temperatures, but it will not halt or reverse the long-term warming trend,” WMO Secretary-General Petteri Taalas said. La Nina is a natural and cyclical cooling of parts of the equatorial Pacific that changes weather patterns worldwide, as opposed to warming caused by the better-known El Nino — an opposite phenomenon. La Nina often leads to more Atlantic hurricanes, less rain and more wildfires in the western United States, and agricultural losses in the central U.S. Studies have shown La Nina is more expensive to the United States than the El Nino. Together El Nino, La Nina and the neutral condition are called ENSO, which stands for El Nino Southern Oscillation, and they have one of the largest natural effects on climate, at times augmenting and other times dampening the big effects of human-caused climate change from the burning of coal, oil and gas, scientists say.

Gang violence has ravaged Haiti, causing thousands of civilian deaths, displacements, and violations of international humanitarian law. Turmoil is expected to escalate following the removal of Haitian Prime Minister Garry Conille from office on November 11. On November 10, the Haitian government announced plans to replace incumbent prime minister Conille, with entrepreneur and former senate […]

Semi-mythical "earthquake lights" may be accompanied by changes to Earth's magnetic field. Now researchers say these changes could be used to forecast major tremors

An analysis of GPS data has revealed a slow and otherwise undetectable slip of tectonic plates that begins two hours before an earthquake - but detecting this in advance would require more accurate sensors

Few volcanos come with more risk than Mexico's Popocatépetl, situated near Mexico City.  To mitigate danger, volcanologist Chiara Maria Petrone is trying to predict its next eruption

A specialised algorithm could help autonomous vehicles track hidden objects, such as a pedestrian, a bicycle or another vehicle concealed behind a parked car

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Pulmonary hypertension (PH) is highly prevalent in patients with interstitial lung disease (ILD) and is associated with increased morbidity and mortality. Widely available noninvasive screening tools are warranted to identify patients at risk for PH, especially severe PH, that could be managed at expert centres. This review summarises current evidence on noninvasive diagnostic modalities and prediction models for the timely detection of PH in patients with ILD. It critically evaluates these approaches and discusses future perspectives in the field. A comprehensive literature search was carried out in PubMed and Scopus, identifying 39 articles that fulfilled inclusion criteria. There is currently no single noninvasive test capable of accurately detecting and diagnosing PH in ILD patients. Estimated right ventricular pressure (RVSP) on Doppler echocardiography remains the single most predictive factor of PH, with other indirect echocardiographic markers increasing its diagnostic accuracy. However, RVSP can be difficult to estimate in patients due to suboptimal views from extensive lung disease. The majority of existing composite scores, including variables obtained from chest computed tomography, pulmonary function tests and cardiopulmonary exercise tests, were derived from retrospective studies, whilst lacking validation in external cohorts. Only two available scores, one based on a stepwise echocardiographic approach and the other on functional parameters, predicted the presence of PH with sufficient accuracy and used a validation cohort. Although several methodological limitations prohibit their generalisability, their use may help physicians to detect PH earlier. Further research on the potential of artificial intelligence may guide a more tailored approach, for timely PH diagnosis.

Introduction:

High-quality primary care can reduce avoidable emergency department visits and emergency hospitalizations. The availability of electronic medical record (EMR) data and capacities for data storage and processing have created opportunities for predictive analytics. This systematic review examines studies which predict emergency department visits, hospitalizations, and mortality using EMR data from primary care.

The brain's capacity to predict and anticipate changes in internal and external environments is fundamental to initiating efficient adaptive responses, behaviors, and reflexes that minimize disruptions to physiology. In the context of feeding control, the brain predicts and anticipates responses to the consumption of dietary substances, thus driving adaptive behaviors in the form of food choices, physiological preparation for meals, and engagement of defensive mechanisms. Here, we provide an integrative perspective on the multisensory computation between exteroceptive and interoceptive cues that guides feeding strategy and may result in food-related disorders.

The SARS-CoV-2 frameshifting element (FSE) has been intensely studied and explored as a therapeutic target for coronavirus diseases, including COVID-19. Besides the intriguing virology, this small RNA is known to adopt many length-dependent conformations, as verified by multiple experimental and computational approaches. However, the role these alternative conformations play in the frameshifting mechanism and how to quantify this structural abundance has been an ongoing challenge. Here, we show by DMS and dual-luciferase functional assays that previously predicted FSE mutants (using the RAG graph theory approach) suppress structural transitions and abolish frameshifting. Furthermore, correlated mutation analysis of DMS data by three programs (DREEM, DRACO, and DANCE-MaP) reveals important differences in their estimation of specific RNA conformations, suggesting caution in the interpretation of such complex conformational landscapes. Overall, the abolished frameshifting in three different mutants confirms that all alternative conformations play a role in the pathways of ribosomal transition.

Organic anion transporting polypeptide (OATP) 1B1 and OATP1B3 are important hepatic transporters. We previously identified OATP1B3 being critically implicated in the disposition of abiraterone. We aimed to further investigate the effects of abiraterone on the activities of OATP1B1 and OATP1B3 utilizing a validated endogenous biomarker coproporphyrin I (CP-I). We used OATP1B-transfected cells to characterize the inhibitory potential of abiraterone against OATP1B-mediated uptake of CP-I. Inhibition constant (K_i) was incorporated into our physiologically based pharmacokinetic (PBPK) modeling to simulate the systemic exposures of CP-I among cancer populations receiving either our model-informed 500 mg or clinically approved 1000 mg abiraterone acetate (AA) dosage. Simulated data were compared with clinical CP-I concentrations determined among our nine metastatic prostate cancer patients receiving 500 mg AA treatment. Abiraterone inhibited OATP1B3-mediated, but not OATP1B1-mediated, uptake of CP-I in vitro, with an estimated K_i of 3.93 μM. Baseline CP-I concentrations were simulated to be 0.81 ± 0.26 ng/ml and determined to be 0.72 ± 0.16 ng/ml among metastatic prostate cancer patients, both of which were higher than those observed for healthy subjects. PBPK simulations revealed an absence of OATP1B3-mediated interaction between abiraterone and CP-I. Our clinical observations confirmed that CP-I concentrations remained comparable to baseline levels up to 12 weeks post 500 mg AA treatment. Using CP-I as an endogenous biomarker, we identified the inhibition of abiraterone on OATP1B3 but not OATP1B1 in vitro, which was predicted and observed to be clinically insignificant. We concluded that the interaction risk between AA and substrates of OATP1Bs is low.

Exercise significantly alters human physiological functions, such as increasing cardiac output and muscle blood flow and decreasing glomerular filtration rate (GFR) and liver blood flow, thereby altering the absorption, distribution, metabolism, and excretion of drugs. In this study, we aimed to establish a database of human physiological parameters during exercise and to construct equations for the relationship between changes in each physiological parameter and exercise intensity, including cardiac output, organ blood flow (e.g., muscle blood flow and kidney blood flow), oxygen uptake, plasma pH and GFR, etc. The polynomial equation P = a_iHRⁱ was used for illustrating the relationship between the physiological parameters (P) and heart rate (HR), which served as an index of exercise intensity. The pharmacokinetics of midazolam, quinidine, digoxin, and lidocaine during exercise were predicted by a whole-body physiologically based pharmacokinetic (WB-PBPK) model and the developed database of physiological parameters following administration to 100 virtual subjects. The WB-PBPK model simulation results showed that most of the observed plasma drug concentrations fell within the 5^th–95^th percentiles of the simulations, and the estimated peak concentrations (C_max) and area under the curve (AUC) of drugs were also within 0.5–2.0 folds of observations. Sensitivity analysis showed that exercise intensity, exercise duration, medication time, and alterations in physiological parameters significantly affected drug pharmacokinetics and the net effect depending on drug characteristics and exercise conditions. In conclusion, the pharmacokinetics of drugs during exercise could be quantitatively predicted using the developed WB-PBPK model and database of physiological parameters.

Early detection of drug-drug interactions (DDIs) can facilitate timely drug development decisions, prevent unnecessary restrictions on patient enrollment, resulting in clinical study populations that are not representative of the indicated study population, and allow for appropriate dose adjustments to ensure safety in clinical trials. All of these factors contribute to a streamlined drug approval process and enhanced patient safety. Here we describe a new approach for early prediction of the magnitude of change in exposure for cytochrome P450 (P450) CYP3A4-related DDIs of small-molecule anticancer drugs based on the model-based extrapolation of human-CYP3A4-transgenic mice pharmacokinetics to humans. Victim drugs brigatinib and lorlatinib were evaluated with the new approach in combination with the perpetrator drugs itraconazole and rifampicin. Predictions of the magnitude of change in exposure deviated at most 0.99- to 1.31-fold from clinical trial results for inhibition with itraconazole, whereas exposure predictions for the induction with rifampicin were less accurate, with deviations of 0.22- to 0.48-fold. Results for the early prediction of DDIs and their clinical impact appear promising for CYP3A4 inhibition, but validation with more victim and perpetrator drugs is essential to evaluate the performance of the new method.

The aim of this study was to validate a previously developed deep learning model in 5 independent clinical trials. The predictive performance of this model was compared with the international prognostic index (IPI) and 2 models incorporating radiomic PET/CT features (clinical PET and PET models). Methods: In total, 1,132 diffuse large B-cell lymphoma patients were included: 296 for training and 836 for external validation. The primary outcome was 2-y time to progression. The deep learning model was trained on maximum-intensity projections from PET/CT scans. The clinical PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, SUV_peak, age, and performance status. The PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, and SUV_peak. Model performance was assessed using the area under the curve (AUC) and Kaplan–Meier curves. Results: The IPI yielded an AUC of 0.60 on all external data. The deep learning model yielded a significantly higher AUC of 0.66 (P < 0.01). For each individual clinical trial, the model was consistently better than IPI. Radiomic model AUCs remained higher for all clinical trials. The deep learning and clinical PET models showed equivalent performance (AUC, 0.69; P > 0.05). The PET model yielded the highest AUC of all models (AUC, 0.71; P < 0.05). Conclusion: The deep learning model predicted outcome in all trials with a higher performance than IPI and better survival curve separation. This model can predict treatment outcome in diffuse large B-cell lymphoma without tumor delineation but at the cost of a lower prognostic performance than with radiomics.

[¹⁷⁷Lu]Lu-PSMA-617 was approved by the U.S. Food and Drug Administration for patients with prostate-specific membrane antigen (PSMA)–positive metastatic castration-resistant prostate cancer (mCRPC). Since the time of regulatory approval, however, real-world data have been lacking. This study investigated the efficacy, safety, and outcome predictors of [¹⁷⁷Lu]Lu-PSMA-617 at a major U.S. academic center. Methods: Patients with mCRPC who received [¹⁷⁷Lu]Lu-PSMA-617 at the Johns Hopkins Hospital outside clinical trials were screened for inclusion. Patients who underwent [¹⁷⁷Lu]Lu-PSMA-617 and had available outcome data were included in this study. Outcome data included prostate-specific antigen (PSA) response (≥50% decline), PSA progression-free survival (PFS), and overall survival (OS). Toxicity data were evaluated according to the Common Terminology Criteria for Adverse Events version 5.03. The study tested the association of baseline circulating tumor DNA mutational status in homologous recombination repair, PI3K alteration pathway, and aggressive-variant prostate cancer–associated genes with treatment outcome. Baseline PSMA PET/CT images were analyzed using SelectPSMA, an artificial intelligence algorithm, to predict treatment outcome. Associations with the observed treatment outcome were evaluated. Results: All 76 patients with PSMA-positive mCRPC who received [¹⁷⁷Lu]Lu-PSMA-617 met the inclusion criteria. A PSA response was achieved in 30 of 74 (41%) patients. The median PSA PFS was 4.1 mo (95% CI, 2.0–6.2 mo), and the median OS was 13.7 mo (95% CI, 11.3–16.1 mo). Anemia of grade 3 or greater, thrombocytopenia, and neutropenia were observed in 9 (12%), 3 (4%), and 1 (1%), respectively, of 76 patients. Transient xerostomia was observed in 23 (28%) patients. The presence of aggressive-variant prostate cancer–associated genes was associated with a shorter PSA PFS (median, 1.3 vs. 6.3 mo; P = 0.040). No other associations were observed between circulating tumor DNA mutational status and treatment outcomes. Eighteen of 71 (25%) patients classified by SelectPSMA as nonresponders had significantly lower rates of PSA response than patients classified as likely responders (6% vs. 51%; P < 0.001), a shorter PSA PFS (median, 1.3 vs. 6.3 mo; P < 0.001), and a shorter OS (median, 6.3 vs. 14.5 mo; P = 0.046). Conclusion: [¹⁷⁷Lu]Lu-PSMA-617 offered in a real-world setting after regulatory approval in the United States demonstrated antitumor activity and a favorable toxicity profile. Artificial-intelligence–based analysis of baseline PSMA PET/CT images may improve patient selection. Validation of these findings on larger cohorts is warranted.

This single-center, single-arm, phase II trial (ChiCTR2100050057) investigated the ability of ¹⁸F-labeled fibroblast activation protein inhibitor ([¹⁸F]AlF-NOTA-FAPI-04, denoted as ¹⁸F-FAPI) PET/CT to predict the response to neoadjuvant camrelizumab plus chemotherapy (nCC) in locally advanced esophageal squamous cell carcinoma (LA-ESCC). Methods: This study included 32 newly diagnosed LA-ESCC participants who underwent ¹⁸F-FAPI PET/CT at baseline, of whom 23 also underwent scanning after 2 cycles of nCC. The participants underwent surgery after 2 cycles of nCC. Recorded PET parameters included maximum, peak, and mean SUVs and tumor-to-background ratios (TBRs), metabolic tumor volume, and total lesion FAP expression. PET parameters were compared between patient groups with good and poor pathologic responses, and the predictive performance for treatment response was analyzed. Results: The good and poor response groups each included 16 participants (16/32, 50.0%). On ¹⁸F-FAPI PET/CT, the posttreatment SUVs were significantly lower in good responders than in poor responders, whereas the changes in SUVs with treatment were significantly higher (all P < 0.05). SUV_max (area under the curve [AUC], 0.87; P = 0.0026), SUV_peak (AUC, 0.89; P = 0.0017), SUV_mean (AUC, 0.88; P = 0.0021), TBR_max (AUC, 0.86; P = 0.0031), and TBR_mean (AUC, 0.88; P = 0.0021) after nCC were significant predictors of pathologic response to nCC, with sensitivities of 63.64%–81.82% and specificities of 83.33%–100%. Changes in SUV_max (AUC, 0.81; P = 0.0116), SUV_peak (AUC, 0.82; P = 0.0097), SUV_mean (AUC, 0.81; P = 0.0116), and TBR_mean (AUC, 0.74; P = 0.0489) also were significant predictors of the pathologic response to nCC, with sensitivities and specificities in similar ranges. Conclusion: ¹⁸F-FAPI PET/CT parameters after treatment and their changes from baseline can predict the pathologic response to nCC in LA-ESCC participants.

Understanding the differences between prognostic and predictive indices is imperative for medical research advances. We have developed a new prognostic measure that will identify the strengths, limitations, and potential applications in clinical practice.

BACKGROUND AND PURPOSE:

Periprocedural intracranial hemorrhage is one of common complications after stent placement for symptomatic intracranial atherosclerotic stenosis. This study was conducted to demonstrate predictors and long-term outcomes of periprocedural intracranial hemorrhage after stent placement for symptomatic intracranial atherosclerotic stenosis.

WASHINGTON CROSSING, Pennsylvania—At a campaign rally in the most important swing state in the country, anti-Trump activist George Conway told the Daily Beast why he thinks Kamala Harris can win over Republicans.

“She’s kind of done it already,” he said. “Look at all those people who voted for [Nikki] Haley when she was already done. I actually think there’s kind of a hidden Harris vote for Republicans who are just exhausted by Donald Trump.”

Turnout is another factor that plays to Democrat’s advantage, Conway predicted. “I also think that even the people who are still for Trump and won’t vote for Harris, I don’t think the turnout’s going to be great for him.”

Read more at The Daily Beast.

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LIVERPOOL midfielder Georginio Wijnaldum will be pivotal for Jurgen Klopp at Manchester City..

Like many innovators, Hagit Messer-Yaron had a life-changing idea while doing something mundane: Talking with a colleague over a cup of coffee. The IEEE Life Fellow, who in 2006 was head of Tel Aviv University’s Porter School of Environmental Studies, was at the school’s cafeteria with a meteorological researcher. He shared his struggles with finding high-resolution weather data for his climate models, which are used to forecast and track flash floods.

Predicting floods is crucial for quickly evacuating residents in affected areas and protecting homes and businesses against damage.

Hagit Messer-Yaron

Employer Tel Aviv University

Title Professor emerita

Member grade Life Fellow

Her colleague “said researchers in the field had limited measurements because the equipment meteorologists used to collect weather data—including radar satellites—is expensive to purchase and maintain, especially in developing countries,” Messer-Yaron says.

Because of that, she says, high-resolution data about temperature, air quality, wind speed, and precipitation levels is often inconsistent—which is a problem when trying to produce accurate models and predictions.

An expert in signal processing and cellular communication, Messer-Yaron came up with the idea of using existing wireless communication signals to collect weather data, as communication networks are spread across the globe.

In 2006 she and her research team developed algorithms that process and analyze data collected by communication networks to monitor rainfall. They measure the difference in amplitude of the signals transmitted and received by the systems to extract data needed to predict flash floods.

The method was first demonstrated in Israel. Messer-Yaron is working to integrate it into communication networks worldwide.

For her work, she received this year’s IEEE Medal for Environmental and Safety Technologies for “contributions to sensing of the environment using wireless communication networks.” The award is sponsored by Toyota.

“Receiving an IEEE medal, which is the highest-level award you can get within the organization, was really a surprise, and I was extremely happy to [receive] it,” she says. “I was proud that IEEE was able to evaluate and see the potential in our technology for public good and to reward it.”

A passion for teaching

Growing up in Israel, Messer-Yaron was interested in art, literature, and science. When it came time to choose a career, she found it difficult to decide, she says. Ultimately, she chose electrical engineering, figuring it would be easier to enjoy art and literature as hobbies.

After completing her mandatory service in the Israel Defense Forces in 1973, she began her undergraduate studies at Tel Aviv University, where she found her passion: Signal processing.

“Electrical engineering is a very broad topic,” she says. “As an undergrad, you learn all the parts that make up electrical engineering, including applied physics and applied mathematics. I really enjoyed applied mathematics and soon discovered signal processing. I found it quite amazing how, by using algorithms, you can direct signals to extract information.”

She graduated with a bachelor’s degree in EE in 1977 and continued her education there, earning master’s and doctoral degrees in 1979 and 1984. She moved to the United States for a postdoctoral position at Yale. There she worked with IEEE Life Fellow Peter Schultheiss, who was known for his research in using sensor array systems in underwater acoustics.

Inspired by Schultheiss’s passion for teaching, Messer-Yaron decided to pursue a career in academia. She was hired by Tel Aviv University as an electrical engineering professor in 1986. She was the first woman in Israel to become a full professor in the subject.

“Being a faculty member at a public university is the best job you can do. I didn’t make a lot of money, but at the end of each day, I looked back at what I did [with pride].”

For the next 14 years, she conducted research in statistical signal processing, time-delay estimation, and sensor array processing.

Her passion for teaching took her around the world as a visiting professor at Yale, the New Jersey Institute of Technology, the Institut Polytechnique de Paris, and other schools. She collaborated with colleagues from the universities on research projects.

In 1999 she was promoted to director of Tel Aviv University’s undergraduate electrical engineering program.

A year later, she was offered an opportunity she couldn’t refuse: Serving as chief scientist for the Israeli Ministry of Science, Culture, and Sports. She took a sabbatical from teaching and for the next three years oversaw the country’s science policy.

“I believe [working in the public sector] is part of our duty as faculty members, especially in public universities, because that makes you a public intellectual,” she says. “Working for the government gave me a broad view of many things that you don’t see as a professor, even in a large university.”

When she returned to the university in 2004, Messer-Yaron was appointed as the director of the new school of environmental studies. She oversaw the allocation of research funding and spoke with researchers individually to better understand their needs. After having coffee with one researcher, she realized there was a need to develop better weather-monitoring technology.

Hagit Messer-Yaron proudly displays her IEEE Medal for Environmental and Safety Technologies at this year’s IEEE Honors Ceremony. She is accompanied by IEEE President-Elect Kathleen Kramer and IEEE President Tom Couglin.Robb Cohen

Using signal processing to monitor weather

Because the planet is warming, the risk of flash floods is steadily increasing. Warmer air holds more water—which leads to heavier-than-usual rainfall and results in more flooding, according to the U.S. Environmental Protection Agency.

Data about rainfall is typically collected by satellite radar and ground-based rain gauges. However, radar images don’t provide researchers with precise readings of what’s happening on the ground, according to an Ensia article. Rain gauges are accurate but provide data from small areas only.

So Messer-Yaron set her sights on developing technology that connects to cellular networks close to the ground to provide more accurate measurements, she says. Using existing infrastructure eliminates the need to build new weather radars and weather stations.

Communication systems automatically record the transmitted signal level and the received signal level, but rain can alter otherwise smooth wave patterns. By measuring the difference in the amplitude, meteorologists could extract the data necessary to track rainfall using the signal processing algorithms.

In 2005 Messer-Yaron and her group successfully tested the technology. The following year, their “Environmental Monitoring by Wireless Communication Networks” paper was published in Science.

The algorithm is being used in Israel in partnership with all three of the country’s major cellular service providers. Messer-Yaron acknowledges, however, that negotiating deals with cellular service companies in other countries has been difficult.

To expand the technology’s use worldwide, Messer-Yaron launched a research network through the European Cooperation in Science and Technology (COST), called an opportunistic precipitation sensing network known as OPENSENSE. The group connects researchers, meteorologists, and other experts around the world to collaborate on integrating the technology in members’ communities.

Monitoring the effects of climate change

Since developing the technology, Messer-Yaron has held a number of jobs including president of the Open University of Israel and vice chair of the country’s Council for Higher Education, which accredits academic institutions.

She is maintaining her link with Tel Aviv University today as a professor emerita.

“Being a faculty member at a public university is the best job you can do,” she says. “I didn’t make a lot of money, but at the end of each day, I looked back at what I did [with pride]. Because of the academic freedom and the autonomy I had, I was able to do many things in addition to teaching, including research.”

To continue her work in developing technology to monitor weather events, in 2016, she helped found ClimaCell, now Tomorrow.io, based in Boston. The startup aims to use wireless communication infrastructure and IoT devices to collect real-time weather data. Messer-Yaron served as its chief scientist until 2017.

She continues to update the original algorithms with her students, most recently with machine learning capabilities to extract data from physical measurements of the signal level in communication networks.

A global engineering community

When Messer-Yaron was an undergraduate student, she joined IEEE at the suggestion of one of her professors.

“I didn’t think much about the benefits of being a member until I became a graduate student,” she says. “I started attending conferences and publishing papers in IEEE journals, and the organization became my professional community.”

She is an active volunteer and a member of the IEEE Signal Processing Society. From 1994 to 2010 she served on the society’s Signal Processing Theory and Methods technical committee. She was associate editor of IEEE Signal Processing Letters and IEEE Transactions on Signal Processing. She is a member of the editorial boards of the IEEE Journal of Selected Topics in Signal Processing and IEEE Transactions on Signal Processing.

In the past 10 years, she’s been involved with other IEEE committees including the conduct review, ethics and member conduct, and global public policy bodies.

“I don’t see my career or my professional life without the IEEE,” she says

Computer scientist and meteorologist Amy McGovern has studied the technology for two decades, and she weighs in with some answers

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THE weather bureau said it was not caught off guard by the intensity of the storm that tore through southeast Queensland on Sunday. An emergency situation was in place on the Sunshine Coast after the wild weather hit.

A specialised algorithm could help autonomous vehicles track hidden objects, such as a pedestrian, a bicycle or another vehicle concealed behind a parked car

Politicians say we're living in dangerous times, and Trump is a lot less willing than Biden to pay for Europe's defence.