The research was conducted by a team from Semmelweis University, the Budapest University of Technology and Economics, and the Hungarian National Ambulance Service. Drawing on one of the most comprehensive datasets assembled for this purpose, the investigators analyzed more than 114,000 cases of out-of-hospital cardiac arrest that occurred across Hungary between November 2018 and December 2023. They then compared the daily counts of these events with a battery of meteorological indicators, including air temperature, wind speed, atmospheric pressure, humidity, and air quality measurements. The goal was not simply to confirm that weather and cardiac arrest are connected, but to determine whether that connection is strong and consistent enough to serve as the basis for a predictive tool.

The stakes are considerable. Out-of-hospital cardiac arrest is one of the leading causes of death in developed countries. In Europe, an estimated 67 to 170 cases occur per 100,000 people each year, and survival remains below 10 percent. Because every minute without circulation dramatically reduces the chances of survival, the speed of emergency response is critical. If emergency medical services could anticipate a spike in case numbers even a few days in advance, they could adjust staffing, reposition ambulances, and prepare hospital resources accordingly. Public awareness campaigns could also be timed to coincide with high-risk periods, reinforcing the importance of rapid intervention and bystander resuscitation, both of which are decisive factors in survival.

The analysis produced a strikingly clear signal. Lower temperatures emerged as one of the most important weather-related risk factors examined. For every 1 degree Celsius decrease in average temperature, the daily number of out-of-hospital cardiac arrests increased by 1.4 percent. That may sound modest, but across a population of millions and over an entire cold season, the cumulative effect translates into a substantial additional burden on emergency services. The seasonal comparison reinforced the pattern: nearly 18 percent more cardiac arrests occurred in winter than in summer, a gap that aligns with the temperature findings and underscores how strongly cold conditions shape cardiovascular emergencies at the population level.

Importantly, the researchers found that it is not only sudden drops in temperature that pose a risk, but lower temperatures in general. This distinction matters for how early warning systems might be designed. A system tuned exclusively to detect abrupt cold snaps would miss the broader, slower-moving risk that accompanies persistently cold days. At the same time, rapid changes remain particularly significant: the study identified a daily temperature drop of more than 5 degrees Celsius as a potential warning signal, one that could be flagged within an early warning framework as a day of heightened concern. In practice, this means both the level of cold and the pace at which it arrives carry predictive information.

Perhaps the most consequential discovery was that the effects of weather changes are not immediate. The number of out-of-hospital cardiac arrests may rise up to three days after the relevant weather conditions occur. This lag between meteorological trigger and clinical outcome is precisely what makes prediction feasible. Weather forecasts are already reliable on a one-to-three-day horizon, so if the physiological and behavioral consequences of cold exposure take days to manifest in case counts, forecast data can be fed into a model that estimates demand before the surge materializes. As Dr. Endre Zima, Professor at the Heart and Vascular Center of Semmelweis University and lead researcher of the study, explained, a previous study by the group examined how extreme cold and heat affect the incidence of out-of-hospital cardiac arrest, and this new work went a step further by investigating whether weather patterns could predict when higher-than-average numbers of cases are likely to occur. If proven feasible, he noted, ambulance services and hospitals could prepare for increased demand several days in advance.

To translate these associations into a practical tool, the team built a predictive model that uses meteorological data to estimate the expected daily number of out-of-hospital cardiac arrests. Dr. Ádám Pál-Jakab, resident physician and PhD student at the Heart and Vascular Center of Semmelweis University and first author of the study, described how combining meteorological and ambulance service data allows the model to predict one to three days in advance when case numbers are likely to rise above average. He emphasized an important limitation and strength of the approach at once: the model does not estimate an individual person’s risk, but rather the number of cases expected nationwide. This population-level framing is what makes the tool useful for health system planning rather than personal diagnosis, and it reflects the statistical nature of the underlying analysis, which treats cardiac arrest counts as a time series shaped by environmental conditions.

Building such a model was far from straightforward. The researchers had to determine not only which atmospheric parameters exerted the greatest influence on cardiac arrest numbers, but also how to use them in a way that produces reliable estimates. Dr. Brigitta Szilágyi, Associate Professor at the Budapest University of Technology and Economics and Corvinus University of Budapest and co-author of the study, identified one of the biggest challenges as determining which days could genuinely be considered outliers, and then examining whether these anomalous days were associated with weather-related factors. Only after establishing that connection could the team construct a model that uses previous data to estimate expected case numbers. This careful separation of true anomalies from ordinary variation is a cornerstone of sound time-series modeling, and it helps ensure that the predictions rest on genuine signal rather than noise.

The next step, according to the researchers, could be the development of an operational early warning system that ingests weather forecasts and issues alerts when conditions associated with elevated cardiac arrest risk are expected. For ambulance services and hospitals, such a system would support capacity planning and help manage expected increases in demand, from scheduling additional crews to ensuring that emergency departments are ready for an influx of resuscitation cases. For the public, the potential applications are equally meaningful. In the future, the system could alert people with cardiovascular disease and their families to higher-risk periods, encouraging them to pay closer attention to warning symptoms and to seek medical attention sooner when necessary. Simple behavioral adjustments during flagged periods, such as avoiding strenuous outdoor exertion in severe cold, could complement the system-level benefits.

The study was conducted as part of the National Multidisciplinary Laboratory for Climate Change, with the University of Pannonia serving as consortium leader, a detail that situates the work within a broader research effort to understand how a changing climate affects human health. As global temperatures become more volatile and extreme weather events grow more frequent, the ability to anticipate the health consequences of atmospheric conditions will only grow in importance. This Hungarian study demonstrates that the connection between weather and cardiac arrest is not merely a statistical curiosity but a predictable, quantifiable relationship with a built-in delay that medicine can exploit. If early warning systems built on this research prove effective in practice, the humble weather forecast could become a routine component of cardiovascular prevention and emergency preparedness, turning days of cold air into days of warning.

Subject of Research: Predicting out-of-hospital cardiac arrest incidence from meteorological conditions using nationwide time-series data

Article Title: Weather data could warn of cardiac arrest risk several days in advance

Article References: Weather data could warn of cardiac arrest risk several days in advance. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: out-of-hospital cardiac arrest, weather forecasting, temperature, early warning system, emergency medical services, cardiovascular disease, public health, time-series analysis, cold exposure, Hungary, Semmelweis University, predictive modeling

The research, published in Results in Engineering by Johannes Rathgen and Vincent Oettel of the iBMB Division of Concrete Construction, tackles a problem that has haunted bridge engineers for decades. Shear failure is among the most treacherous failure modes in structural concrete: unlike ductile flexural failure, which announces itself through large deflections and visible sagging, shear failure typically arrives suddenly and without significant warning, governed by the unstable propagation of diagonal cracks inclined toward the load. Design codes handle this uncertainty conservatively, and analyses of extensive experimental databases show that current approaches can substantially overestimate the danger for beams with little or no shear reinforcement. The German team’s calculations, for instance, underestimated the measured failure loads of three low-reinforced beams by factors approaching 1.85, confirming that the true reserve of these structures is often much larger than the codes admit. Monitoring, the authors argue, can unlock that reserve safely by revealing how a structure actually behaves under load.

To test that idea, the researchers cast six reinforced concrete beams spanning a deliberate range of shear reinforcement ratios, from one beam with no stirrups at all to others provided with between 1.3 and 8.1 times the minimum ratio required by the German National Annex to Eurocode 2. All beams used normal-strength concrete with a maximum aggregate size of 16 millimeters, B500B steel bars of 20 millimeters as longitudinal reinforcement, and B500A stirrups of 6 and 8 millimeters. Each beam was loaded to failure in three-point bending, with the load applied at midspan and a shear span of 90 centimeters, deliberately chosen to prevent direct load transfer to the supports. The material properties were carefully characterized on the day of testing: mean cylinder compressive strengths ranged from 36.3 to 56.5 newtons per square millimeter, and mean yield strengths of the reinforcement were determined by tensile testing according to EN ISO 15630-1.

The instrumentation was the heart of the experiment. The team used a LUNA ODiSI 6104 interrogator working on the principle of optical frequency domain reflectometry, a technique that turns a single polyimide-coated optical fiber into thousands of distributed strain measurement points with a gage pitch of just 0.65 millimeters, acquired at 1 hertz. Two contrasting sensor configurations were compared in the shear-critical regions near the supports. In one region, the fiber was bonded in an inclined loop at 45 degrees, aligned with the principal tensile stresses of the uncracked beam so that emerging shear cracks would cross it nearly perpendicularly. In the other region, the fiber ran in three horizontal sections parallel to the beam’s bottom surface, spaced 6 centimeters vertically. The horizontal layout is far simpler to install and requires no prediction of the expected crack pattern, but it sacrifices some sensitivity because inclined cracks intersect it at oblique angles. To keep the fragile fibers alive through large crack openings, the researchers bonded them with a slightly elastic UV-curing resin, allowing controlled debonding instead of catastrophic fiber breakage.

The results were striking. In the beam without shear reinforcement, which failed at 145 kilonewtons, the inclined sensor detected the critical shear crack at approximately 76 percent of the failure load and pinpointed its position to within a centimeter at sensor coordinate 8.2 centimeters. Continued loading produced a pronounced and largely continuous rise in measured strain, an unmistakable early indication of impending failure. In the beams with low shear reinforcement, the picture was even more encouraging: the crack that ultimately governed failure was first flagged at 58 percent of the ultimate load in one beam, 52 percent in another, and 77 percent in a third with an asymmetric stirrup arrangement. The team condensed these observations into a shear-failure warning index, defined as the ratio of the detection load to the failure load, offering a quantitative measure of how much warning a monitoring system can realistically provide before brittle collapse.

Perhaps the most consequential finding concerns what happens after the load is removed. Because bridge inspections are performed under service loads rather than at the extreme levels that may have formed cracks, many shear cracks close up and become invisible to the naked eye. The fiber optic measurements, however, reliably detected and localized cracks even after they had largely closed upon unloading. This means the sensors can supply a structural record that visual inspection cannot, capturing evidence of past cracking events that would otherwise vanish. For the vast population of older bridges that show no visible distress despite calculated shear deficits, this capability could fundamentally change how condition assessments are made, replacing worst-case assumptions with measured structural response.

The study also revealed that detecting cracks is the easy part; classifying them is the real challenge. Flexural cracks form roughly perpendicular to the beam axis, while shear cracks incline at around 45 degrees, but a crack that begins as a benign flexural crack can gradually rotate and develop into the critical flexural-shear crack that destroys the beam. In one test, the top horizontal sensor section registered a crack at 63 percent of the failure load that looked entirely flexural; only when a rapidly propagating shear crack merged with it did the fatal crack emerge, becoming detectable in the lower sections at 77 to 79 percent of the failure load. The researchers therefore propose a conceptual two-step classification procedure: first, estimate crack inclination from the pattern of strain peaks across adjacent, closely spaced sensor sections of a loop-shaped fiber; second, track potentially critical cracks near the supports over successive load levels, watching for continuous strain growth that signals dangerous propagation.

That classification framework, the authors emphasize, remains a concept rather than a validated method, but it points toward something genuinely transformative: an automated, condition-based early warning system for shear-deficient bridges. Combined with predefined warning and action levels, the approach could drive a traffic-light scheme in which green indicates all measured strains remain within expected ranges, yellow triggers additional assessment, and red demands prompt investigation, weight restrictions, or closure. A fixed strain threshold of 1 per mille proved suitable under laboratory conditions for separating crack-induced strain peaks from measurement noise, and its simplicity makes it attractive for automated data evaluation, though the researchers caution that it is a configuration-specific criterion whose robustness under field conditions, varying materials, and environmental exposure still requires validation.

The broader implications reach well beyond the laboratory. Strengthening a deficient bridge with ultra-high performance concrete overlays or carbon fiber reinforced polymer strips demands long planning lead times, temporary closures, and considerable expense, while wholesale replacement is simply infeasible given limited personnel, machinery, and materials. Distributed fiber optic sensing, by contrast, can be installed on existing structures with minimal intervention and no major structural modification, enabling both long-term and large-scale monitoring of the regions where shear failure would begin. The study also found that sensor placement matters more than orientation: multiple horizontal sensor sections stacked near the support, particularly the lowest section in the tension zone, caught cracks earlier than expected, and the horizontal configuration avoids the complex principal-stress trajectory calculations that the inclined layout demands. Notably, no clear relationship emerged between the crack-to-fiber intersection angle and early detection performance, weakening the case for the more elaborate inclined installations.

Laboratory beams are not bridges, and the authors are careful about the limits of their work. Only four of the six tests ended in shear failure, some measurement data were lost to a defective remote module and sensor damage, and the findings apply strictly to the tested configurations and loading conditions. Long-term and variable loading, environmental effects, and full-scale field trials remain necessary before the method can be trusted on live structures. The Braunschweig team is already extending the work to haunched beams, whose tapered support regions alter the stress distribution and crack patterns in ways that complicate sensor placement. Still, the core message stands: light traveling through a fiber thinner than a human hair can reveal the silent, invisible cracks that precede one of structural engineering’s most sudden failure modes, and in doing so may help keep thousands of aging bridges open safely while the slow work of renewal catches up.

Subject of Research: Monitoring shear crack formation in reinforced concrete beams using distributed fiber optic sensors

Article Title: Experimental investigation of fiber optic sensors in shear-critical regions of reinforced concrete beams for monitoring shear behavior

Article References: Experimental investigation of fiber optic sensors in shear-critical regions of reinforced concrete beams for monitoring shear behavior. (n.d.). https://doi.org/10.1016/j.rineng.2026.113052

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113052

Keywords: fiber optic sensors, structural health monitoring, reinforced concrete, shear failure, bridge assessment, distributed fiber optic sensing, crack detection, Eurocode 2, shear reinforcement, concrete beams, early warning system, TU Braunschweig

The magnitude of the intrusion is modest by volcanic standards. Stamps compares the volume to about 400 Olympic-size swimming pools, an amount that is not in itself a cause for alarm. The magma, according to the team’s numerical modeling, does not appear to be migrating closer to the crater, and the reservoir it is entering already existed prior to the event. Yet the significance of the finding extends well beyond the volume of rock involved. It demonstrates that the monitoring infrastructure installed on the volcano can detect subtle, transient deformation signals and, more importantly, that the team can interpret those signals with sufficient confidence to attribute them to specific subsurface processes. That capability lies at the heart of any credible early-warning effort for a volcano that alternates between gentle and violent behavior.

The detection itself was made possible by a ground-based geodetic network that Stamps’ group began building a decade ago. In 2016, the team installed six Global Navigation Satellite System instruments on the flanks and surroundings of Ol Doinyo Lengai. These continuously operating stations track the horizontal and vertical motions of the ground surface to a precision of about one millimeter, a level of sensitivity that allows researchers to distinguish genuine deformation of the volcanic edifice from noise introduced by atmospheric effects or equipment drift. The GNSS network is complemented by two broadband seismic stations that record earthquake activity across a wide range of frequencies, and the continuous instrumentation is supplemented by episodic benchmark measurements taken during field campaigns.

The combination proved decisive in 2024. When the GNSS stations recorded an uplift signal around the volcano, the team turned to numerical modeling to determine what subsurface source could reproduce the observed pattern of surface movement. By fitting deformation models to the geodetic data, the researchers concluded with a high degree of certainty that the signal was generated by an influx of magma into an already existing magma reservoir roughly three kilometers underground. The result validated both the sensitivity of the network and the interpretive framework the team has developed over more than ten years of observation at the site. The work was published on September 18, 2026, in Frontiers in Earth Science, with the DOI 10.3389/feart.2026.1881885.

Ol Doinyo Lengai occupies a singular position in volcano science. Its carbonatite lavas erupt at temperatures far lower than the silicate lavas produced by virtually every other volcano on the planet, and during quiet periods the volcano maintains an active lava lake that bubbles and flows effusively within its summit crater. This effusive behavior, however, is only half of the volcano’s personality. On average, Ol Doinyo Lengai produces an explosive eruption every 10 to 15 years, and these explosive episodes can pose serious hazards to the communities that live on and around its slopes. Stamps notes that the team expects another explosive eruption within their lifetime, and the central motivation of the research program is to give residents and authorities enough lead time to prepare and respond to evacuation decisions made by the Tanzania Geological Survey.

Understanding what distinguishes a benign magma intrusion from a precursor to violence is the crux of the forecasting problem, and the 2024 event provided a valuable calibration point. According to Stamps, the last time the volcano erupted explosively, the event was preceded by a magnitude 5.9 earthquake. That historical sequence suggests a set of warning indicators that the team now watches for: a significant earthquake followed by observable changes in surface deformation, and, most critically, evidence that magma is moving from a deeper storage zone to a shallower one over time. Such upward migration indicates that magma is ascending the volcanic conduit, and it is precisely the kind of progression that would elevate concern. The 2024 intrusion, by contrast, showed magma entering an existing reservoir without any sign of ascent toward the crater, which is why the team assessed it as low risk.

One of the most stubborn uncertainties in volcanology is timing. Some volcanoes erupt explosively every time they erupt, making their behavior relatively predictable once unrest is detected. Ol Doinyo Lengai is more complicated, alternating between effusive and explosive eruptions in a pattern that is not fully understood. A key reason the team continues to monitor the volcano intensively is to constrain this particular volcano’s time delay, the interval between detectable precursory activity and an actual explosive eruption. Every additional episode of recorded deformation, seismicity, and magma transport adds to a decade-long baseline of observations that gradually reveals how the volcano’s subsurface system transitions from storage to ascent to eruption. The longer and richer the record becomes, the more skillfully the team can match patterns of surface observation to processes occurring underground.

The monitoring effort at Ol Doinyo Lengai is also notable for its ground-based character. Satellite-based observations, including those contributed through NASA, provide valuable context and broad spatial coverage of deformation across the region. But Stamps emphasizes that her team is currently the only group conducting continuous ground-based GNSS monitoring and consistent ground-based seismic monitoring at the volcano. Ground instruments offer continuous temporal sampling and millimeter-level precision that orbital observations alone cannot match, particularly for small, short-lived deformation episodes like the 2024 bulge, which might be missed or ambiguously characterized by intermittent satellite acquisitions. The redundancy and complementarity of the two approaches strengthen the overall early-warning picture.

The practical payoff of this work accrues directly to the people who live in the shadow of the volcano. More than a decade of observations has given the team a substantially better understanding of which underground magma movements correspond to which surface signals, and that empirical link is the foundation of any effort to anticipate when the volcano is edging closer to an explosive eruption. For residents of the surrounding area, the difference between a well-instrumented volcano and a poorly instrumented one can be measured in the time available to act. The Tanzania Geological Survey, which holds formal responsibility for evacuation decisions, now has access to a continuously refreshed, quantitatively interpreted stream of deformation and seismicity data from one of the rift’s most active and most unpredictable volcanoes.

The 2024 bulge may have been modest, equivalent to 400 swimming pools of magma settling into a reservoir it already knew, but as a test of scientific and operational readiness it was passed convincingly. A transient deformation signal was detected in near real time, modeled rigorously, attributed to a specific source at a specific depth, and correctly evaluated as non-threatening. Each such episode sharpens the interpretive tools that will be needed when the volcano’s behavior changes in more consequential ways. Given the volcano’s average recurrence interval of 10 to 15 years between explosive eruptions, the question is not whether the next one will come but whether the warning signs will be recognized in time. On the evidence of this study, the instruments are in place, the baseline record is deepening, and the science of translating millimeters of ground motion into meaningful forecasts is steadily maturing at one of Earth’s most remarkable volcanoes.

Subject of Research: Magma intrusion and surface deformation monitoring at Ol Doinyo Lengai volcano in Tanzania

Article Title: Interpreting a volcano’s ‘bulges’ and predicting the next explosive eruption

Article References: Interpreting a volcano’s ‘bulges’ and predicting the next explosive eruption. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Ol Doinyo Lengai, volcano monitoring, magma intrusion, GNSS, Tanzania, explosive eruption, East African Rift, surface deformation, carbonatite lava, seismic monitoring, early warning system, Frontiers in Earth Science