The study examined a population rather than searching for a single unusual quasar. That distinction addresses a central difficulty in astronomy: several individual candidates for recoiling black holes have been proposed, but observations of one object can have alternative explanations. The new analysis instead asked whether the signature predicted for an entire population appears consistently across a very large catalogue. The findings were published in the peer-reviewed journal Monthly Notices of the Royal Astronomical Society, and the authors describe the work as the first observational test of a prediction formulated at ELTE in 2016.

Supermassive black holes at the centres of large galaxies can have masses of millions or billions of times the Sun’s mass. When two galaxies merge, their central black holes approach one another, form a binary system, emit gravitational waves and eventually merge into a larger remnant. During the final moments of that coalescence, gravitational waves may be emitted unequally in different directions. The resulting recoil can propel the remaining black hole at several million kilometres per hour. If the recoil is too weak to remove the black hole from the galaxy altogether, it can enter a long, pendulum-like orbit around the galactic nucleus, moving rapidly as it crosses the centre before slowing, turning back and eventually returning again.

The quasar sample provided two measurements needed to test that model. The first was the black hole’s velocity relative to its host galaxy. Gas close to the black hole moves with it, while gas at a somewhat greater distance does not move in exactly the same way. The light from these regions is therefore shifted slightly relative to one another, allowing the black hole’s motion to be inferred. The information is analogous to the change in the perceived pitch of a siren as an ambulance approaches or recedes: small shifts in the emitted light reveal whether the central source is moving relative to the surrounding system.

The second measurement concerned dust. Dust surrounding a black hole makes a quasar’s light appear redder than that of comparable objects, providing an indication that more dust is obscuring the active nucleus. In a computer model published in 2016, Peter Raffai, Zoltán Haiman and Zsolt Frei predicted that quasars containing recoiling black holes should show a velocity–dust relationship. According to the model, a recoiling black hole moves fastest at the galactic centre, where the dust is also densest. Quasars with faster-moving black holes should therefore, on average, be redder because they contain more obscuring dust.

For the observational test, the researchers selected approximately 100,000 quasars from the Sloan Digital Sky Survey catalogue for which sufficiently high-quality data were available. They determined the black-hole velocity and the redness of each quasar, then tested whether the two measurements varied together. The predicted relationship emerged: quasars containing faster-moving black holes were, on average, redder and therefore more dust-obscured. Natural differences among individual quasars make the pattern difficult to recognize in any single system, but its statistical strength becomes apparent across the enormous sample.

The sharpest contrast occurred among the dustiest quasars. The proportion of heavily dust-obscured objects was three times higher among quasars containing the fastest-moving black holes than among those containing the slowest-moving black holes. Bence Bécsy, lead author of the new study and an assistant professor at the University of Birmingham, said the central advance was the search for statistical traces of a population rather than proof based on one candidate. “It is very difficult to prove that the black hole in a single quasar has been ejected, because other phenomena can produce similar signatures,” Bécsy said. “What is new about our research is that, rather than looking for individual candidates, we searched statistically for traces of an entire population.”

Several validation tests examined whether the result depended on the team’s choices about data selection or measurement. The relationship remained consistent when the researchers varied those procedures. It disappeared, however, when they used only light from gas clouds that do not follow the black hole’s motion. The result therefore depended specifically on the emission from the region that moves with the black hole, as expected if the measured velocity offset is encoding black-hole recoil. The authors nevertheless cautioned that other physical processes cannot be completely excluded, and said that further observations and more detailed models would be needed.

The most natural explanation for the observed relationship is that some quasars in the sample contain supermassive black holes displaced by recoil following an earlier merger. If that interpretation is correct, the strength of the statistical signal suggests that two black holes merged relatively recently in roughly half of the quasars studied. The authors emphasised that this estimate remains uncertain. It is an inference based on the strength of the population-level relationship, not a direct count of mergers or proof that every displaced black hole in the catalogue arose from a recent coalescence. Other explanations for part of the signal have not been completely ruled out, so the result does not yet provide a definitive merger rate.

The findings may nevertheless inform the future of gravitational-wave astronomy. The European Space Agency plans to launch the LISA space observatory in 2035, with three spacecraft orbiting in formation and separated by 2.5 million kilometres. LISA is intended to detect gravitational waves directly from supermassive black-hole mergers, the events that can produce the recoils examined in the quasar data. If the observed relationship is caused by ejected black holes, it indicates that such recent mergers occur in a significant proportion of quasars and therefore helps determine how many comparable events LISA may detect. ELTE researchers have worked on LISA preparations since 2006, including future work on waveform models intended to extract information about black-hole masses, distances and final fates from the signals.

The project also demonstrates a direct link between an earlier theoretical prediction and a large observational test. Raffai, Haiman and Frei had used computer models in 2016 to show how the signatures of oscillating, recoiling black holes might be detected statistically in active galactic nuclei. Ten years later, Bécsy led the test with Raffai, Haiman, Andor Budai and Frei, using real quasar observations. Bécsy had completed his physics degree and doctoral thesis at ELTE under Raffai’s supervision before moving to Birmingham. “We have now detected in real observational data the relationship we predicted years ago,” Raffai said. The result provides strong statistical support for the predicted population signature while leaving the histories of individual black holes, the precise frequency of mergers and the implications for future gravitational-wave detections to be tested through additional observations and modelling.

Subject of Research: Statistical detection of supermassive black-hole recoil in a large quasar sample

Article Title: Predicted a decade ago, now discovered

Article References: Predicted a decade ago, now discovered. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: supermassive black holes, quasars, galaxy mergers, black-hole recoil, gravitational waves, Sloan Digital Sky Survey, dust obscuration, active galactic nuclei, LISA, astrophysics, Predicted, decade

The research, led by Sophie Tribolet and Vincent Rigo of the neonatology division at University Hospital of Liège, together with a large team of collaborators across the country, drew on data from the SUPREMEneo prospective cohort. In June 2024, the investigators captured every laryngoscopy performed in all 19 neonatal intensive care units in Belgium, making it an exhaustive national snapshot rather than a sample from a handful of willing centers. The analysis focused on 142 laryngoscopies: 91 were performed to intubate newborns for mechanical ventilation, and 51 were performed to administer surfactant through a less invasive technique known as LISA, or less invasive surfactant administration, which has become the preferred approach for many preterm infants with respiratory distress syndrome. Fourteen of the intubations occurred in immediate life-threatening situations, where speed understandably takes precedence over comfort.

The headline numbers are sobering. Premedication was given before 56 of 77 non-emergency intubations, a rate of 73 percent, and before 35 of 51 LISA procedures, a rate of 69 percent. In other words, roughly three in ten elective, non-urgent laryngoscopies were carried out on unsedated newborns. The study also identified exactly where and when the practice falters. Premedication was significantly less likely when intubation was urgent, defined as needing to happen within ten minutes, and when the procedure took place in the delivery room rather than in the neonatal unit. For LISA procedures, no such delivery room penalty appeared, suggesting that the gap is not simply about location but about the perceived urgency and logistics surrounding intubation specifically.

Who performs the procedure also matters, in ways that differ between the two types of laryngoscopy. For LISA, premedication rates varied according to the operator’s professional status and their previous laryngoscopy experience, with statistical significance at p equal to 0.04 and 0.05 respectively. For intubation, the rates differed according to whether the operator had previously performed a successful laryngoscopy, at p equal to 0.01, but not according to operator status. These patterns hint at a cultural and educational dimension: clinicians who are more experienced, or more confident, may be more comfortable taking the extra minutes needed to sedate a baby, while those under pressure or in training may default to proceeding without drugs.

The pharmacology itself revealed remarkable heterogeneity. There was no single standard regimen across the country. The most commonly used drug was propofol, a short-acting anesthetic agent that provides rapid sedation but requires careful monitoring of blood pressure and breathing. Atropine, a drug given to protect against the slowing of heart rate that laryngoscopy can provoke, was administered before 37 of 128 non-life-threatening procedures, or 29 percent. Interestingly, atropine was given far more frequently in combination with fentanyl, an opioid, with or without a muscle relaxant, than alongside propofol, a difference that was highly statistically significant. This split reflects two competing philosophies of neonatal premedication: one favoring anesthetic agents with predictable short duration, the other favoring analgesia-based combinations with cardiovascular protection.

Perhaps the most actionable finding concerned protocols. Units that had a specific written premedication protocol behaved very differently from those without one. In units without protocols, 70 percent of non-emergency intubations and a staggering 90 percent of LISA procedures went ahead without premedication. In units with protocols, those figures fell to 24 percent and 53 percent respectively, differences that were statistically significant. The message is blunt: simply having a protocol, even before considering what it says, dramatically changes behavior. Standardization appears to convert premedication from an individual judgment call, vulnerable to habit and time pressure, into a default practice that clinicians must actively decide to bypass.

The researchers also asked clinicians why they skipped premedication, and the answers paint a nuanced picture. In 24 percent of unsedated laryngoscopies, the stated reason was the perception that the infant was comfortable or did not need it, a judgment that the authors implicitly challenge given the well-documented physiological stress response to laryngoscopy. Lack of venous access accounted for 19 percent of cases, a genuine practical obstacle in tiny premature infants whose veins are difficult to cannulate, though it raises questions about whether alternative routes or timing of line placement could help. Unit protocol was cited in 16 percent of cases, implying that some existing protocols may themselves be inadequate or permissive of omission, and personal habits accounted for 11 percent, an admission that routine rather than reasoning drives some decisions.

The context for these findings is a decade-long international debate. The American Academy of Pediatrics and the Canadian Paediatric Society both recommend premedication before elective neonatal intubation, and the French Neonatology Society has issued similar guidance. Yet audits in multiple countries have repeatedly shown that adherence lags far behind the recommendations. Part of the hesitation stems from legitimate clinical concerns: premedication drugs can cause hypotension, prolong the procedure if the baby becomes too sedated, and in the case of muscle relaxants, leave the infant apneic and dependent on the operator’s skill to secure the airway quickly. In units where operators have variable experience, these risks can feel more immediate than the longer-term harms of pain. The Belgian data showing that operator experience correlates with premedication rates suggests these concerns are being weighed differently by different hands at the bedside.

The stakes extend beyond the moment of discomfort. Repeated painful procedures in the neonatal period have been associated in longitudinal research with altered pain processing, differences in brain development, and poorer neurodevelopmental outcomes, although the evidence base for the benefits of any specific premedication regimen remains incomplete, which is itself part of why practice varies so widely. LISA, which avoids intubation and mechanical ventilation in many preterm infants, was developed partly to reduce procedural burden, but it still requires the same laryngoscopy to visualize the glottis and pass a thin catheter, so the pain problem does not disappear with the gentler technique. The Belgian study’s finding that 90 percent of LISA procedures in protocol-less units were done without premedication indicates that the shift toward less invasive surfactant delivery has not automatically carried analgesia with it.

The authors conclude that substantial inter-center variability persists, and that the development and implementation of premedication protocols may improve compliance and ultimately enhance the quality and safety of laryngoscopies in newborns. Because the study captured every NICU in a single country over a defined month, it offers an unusually complete picture of the gap between guideline and bedside, and its protocol findings provide a concrete lever for change: units seeking to improve need not wait for new drugs or new evidence, but can start by writing down, agreeing on, and enforcing a standard approach. For the smallest patients, who cannot advocate for themselves and whose pain is easy to underestimate, that administrative act may be one of the simplest available protections.

Subject of Research: Premedication practices before neonatal laryngoscopy in neonatal intensive care units

Article Title: Premedication practices prior to neonatal laryngoscopy: a nationwide observational study in Belgian NICUs

Article References: Tribolet, S., Legros, L., Laval, N., Derriks, F., Naessens, P., Debuf, M.-J., Van Poucke, J., Godart, V., Guevorkian, D., Schögler, A., Perceval, C., Ceulemans, L., Reibel, R., Plaskie, K., Debeer, A., Hocq, C., Rohaert, C., Avino, D., De Buyst, J., … Tauzin, M. (2026). Premedication practices prior to neonatal laryngoscopy: a nationwide observational study in Belgian NICUs. BMC Pediatrics. https://doi.org/10.1186/s12887-026-07813-3

Image Credits: AI Generated

DOI: 10.1186/s12887-026-07813-3

Keywords: neonatology, laryngoscopy, premedication, intubation, LISA, surfactant, NICU, propofol, pain management, preterm infants, clinical protocols, Belgium

The study assembles an unusually rich set of official data for England’s local authorities. It combines 2024 local-authority total fertility rates from the Office for National Statistics with Census 2021 measures of the share of residents holding Level 4 or above qualifications, roughly equivalent to a degree or higher. To this it adds residence-based housing affordability ratios, earnings data, the English Indices of Deprivation 2019, internal migration estimates, official 2022-based population projection outputs and local-authority boundary files. All of these sources are publicly available, meaning the analysis relies on no restricted or newly collected personal data. The design allows the education-fertility relationship to be tested not just as a statistical average but as a geographic pattern.

The central statistical result is stark. In adjusted models that control for housing affordability, earnings and deprivation, the share of highly educated residents remains strongly and negatively associated with the local total fertility rate. In other words, places with more degree-holders have fewer births per woman, and this is not simply because educated people live in expensive or deprived areas. The education gradient survives when the usual economic suspects are held constant. This matters because much demographic research treats the education-fertility link as a property of individuals aggregated without regard to place; here it appears as a property of places themselves.

To test whether these patterns are geographically organised, the study applies local indicators of spatial association, a technique introduced by geographer Luc Anselin that identifies clusters of similar values among neighbouring areas. The results show that raw fertility rates, educational composition and even the residuals of adjusted fertility models all remain spatially clustered. Neighbouring local authorities tend to resemble one another in their fertility levels and in the strength of the education association. Statistical dependence of this kind violates the assumptions of ordinary regression, which is why the study turns to spatial autoregressive models that explicitly account for neighbouring areas influencing one another.

The low-fertility, high-education regime is most clearly visible in London. Boroughs including Camden, Islington, Westminster, Southwark, Lambeth and Wandsworth form a concentrated block of places where high qualification levels and very low period fertility coincide. These are areas characterised by dense professional labour markets, high housing costs and large populations of young adults in education and early-career employment. The clustering suggests that postponement behaviour is transmitted or reinforced locally, through shared labour markets, housing markets and social environments, rather than emerging independently in each place from a national average trend.

The spatial autoregressive analysis adds a technical but important nuance. Impact decomposition in these models separates the effect of local education levels from the effect of education in neighbouring areas. The education association splits into a direct local component and an indirect spillover component, meaning that a local authority’s fertility is shaped partly by the educational composition of the places around it. This supports what the author calls a spatial-demographic interpretation of fertility postponement: the phenomenon behaves like a regional system, not a collection of isolated jurisdictions. Policies aimed at supporting families may therefore need to be designed at spatial scales larger than a single borough.

A second strand of the study carries a warning for anyone who uses official population projections in local statistical models. Projection outputs, such as projected working-age growth and old-age dependency ratios, already encode assumptions about migration, age structure and fertility. Using them as independent explanatory variables in regression models risks circularity, because the projected outcomes are partly built from the very behaviours being explained. The study therefore analyses projection models separately, as projection-sensitivity exercises rather than causal claims. This methodological caution is relevant well beyond England, wherever local researchers combine official forecasts with current outcomes.

The appendix diagnostics reinforce this distinction. For the current-fertility spatial autoregressive model, residual spatial autocorrelation is reduced but not fully eliminated, which the author reads as evidence that current fertility retains genuine spatial structure deserving substantive analysis. For the projection-sensitivity models, residuals are much closer to spatial independence, consistent with the interpretation that projected outcomes are strongly shaped by spatially organised demographic assumptions rather than by the covariates in the models. In plain terms, current fertility has a geography of its own, while projections largely inherit the geography of their assumptions.

The broader significance lies in what this means for how England understands its demographic future. If low fertility is concentrated in identifiable urban regimes, then national averages conceal sharp local divergence, and the places driving the decline are also the places absorbing much of the country’s internal migration and economic growth. Fertility postponement has long been linked to rising educational enrolment and the expansion of higher education, and the persistence of a two-child family ideal across Europe suggests that much of the decline reflects timing rather than completed family size. But the spatial clustering found here indicates that timing decisions are made in contexts, and those contexts differ profoundly across the country.

For planners, the message is that fertility postponement is not only a life-course process aggregated to places but a regional-planning issue. Housing affordability, labour market structure and educational composition interact across administrative boundaries, and the spillover effects documented in the study mean that neighbouring authorities share demographic fates. For demographers, the message is methodological: spatial dependence must be modelled, and official projections must be handled with care to avoid treating assumed futures as observed data. As England’s birth rates continue to fall, the geography of that decline, clustered in educated urban England and radiating through neighbouring areas, may prove as consequential as its overall magnitude.

Subject of Research: Spatial patterns of fertility postponement and their relationship to education across English local authorities

Article Title: Spatial regimes of fertility postponement in England : education, local clustering and projection sensitivity across local authorities

Article References: Bhagat, S. (2026). Spatial regimes of fertility postponement in England : education, local clustering and projection sensitivity across local authorities. Journal of Population Research, 43(4), Article 32. https://doi.org/10.1007/s12546-026-09440-2

Image Credits: AI Generated

DOI: 10.1007/s12546-026-09440-2

Keywords: fertility postponement, total fertility rate, spatial demography, education gradient, local authorities, England, London, LISA, spatial autoregressive model, population projections, housing affordability, place-based demography

The team’s approach operates within the parametrized post-Einsteinian, or ppE, framework, a widely used model-independent scheme that decorates the standard general-relativistic waveform with extra amplitude and phase corrections. Each correction is tagged with a post-Newtonian order, a measure of how early or late in the inspiral the deviation becomes important. Positive post-Newtonian corrections modify fine phase structure near merger, while negative-order corrections — such as those from a massive graviton, a slowly changing gravitational constant, or friction from surrounding matter — accumulate slowly over long stretches of low-frequency inspiral. By varying these corrections and asking how precisely a detector network could measure them, researchers can forecast the discovery potential of instruments that have not yet been built.

The technical heart of the work lies in the Fisher-matrix formalism, a standard forecasting tool that estimates parameter uncertainties from the curvature of the likelihood surface around a fiducial signal. Constructing the Fisher matrix requires derivatives of the waveform with respect to every model parameter — chirp mass, symmetric mass ratio, spin combinations, luminosity distance, coalescence time and phase, plus the deformation parameter encoding the non-Einsteinian effect. Traditionally these derivatives are computed by finite differences, nudging each parameter slightly and measuring the waveform’s response. That procedure is slow and introduces step-size systematics: too large a step misses local structure, too small a step drowns in numerical noise.

Wu and colleagues sidestepped the problem entirely. Working with the frequency-domain TaylorF2 inspiral waveform, they derived closed-form expressions for all waveform derivatives, exploiting a logarithmic decomposition that splits each derivative into an amplitude part and a phase part. Because the ppE corrections enter as simple power laws in frequency, the derivatives of the non-Einsteinian terms can be written explicitly for any modified gravity theory whose leading correction maps onto the ppE parameter space. The result is a Fisher matrix built from real-valued frequency integrals — no finite differences, no step-size tuning, no imaginary-component bookkeeping in the most streamlined formulation.

The speedup is dramatic. In benchmark tests on a dual Xeon Platinum server, the fully analytic formulation reduced runtime to roughly 30.8 percent of the finite-difference method, and a further-optimized variant that plugs the amplitude and phase derivatives directly into the Fisher integral cut runtime to about 21.6 percent. For studies requiring thousands or millions of repeated Fisher evaluations — large parameter scans, population synthesis, or Markov-chain analyses — that factor of four or five transforms what is computationally prohibitive into routine work, while eliminating a class of numerical artifacts altogether.

Armed with this machinery, the authors surveyed an unusually broad landscape: space-based detectors LISA, Taiji and TianQin operating in the millihertz band; the current ground-based network of LIGO, Virgo and KAGRA; the third-generation Einstein Telescope; and multiband scenarios in which a stellar-mass binary is tracked by a space observatory years before merger and then caught by ground-based instruments as it plunges to coalescence. They layered on six black-hole population models, from light-seed and heavy-seed massive black hole binaries to power-law stellar-mass populations and the latest LVK catalog, ensuring that their forecasts reflect realistic distributions of masses, spins and distances rather than single idealized sources.

The results expose clean scaling laws. Uncertainty in the deformation parameter grows linearly with luminosity distance, and the dependence on post-Newtonian order follows analytic ratios involving the chirp mass and frequency-weighted integrals over the detector noise curve. A striking exception appears at zeroth post-Newtonian order, where the correction mimics the leading Einsteinian phase term and becomes severely degenerate with the binary’s intrinsic parameters — except, intriguingly, for space-based observations of stellar-mass binaries, whose signals drift so slowly across the detector band that different orders act as near-constant rescalings, suppressing that degeneracy entirely.

The complementarity between detector classes emerges as a central theme. For corrections entering at positive post-Newtonian order, ground-based detectors excel because they resolve high-frequency phase structure, and the Einstein Telescope outperforms current instruments at every order thanks to its lower starting frequency and superior sensitivity. For negative-order corrections, the tables turn: low-frequency coverage and long observation times dominate, giving space-based detectors a decisive edge. The most extreme case is dynamical friction from matter surrounding the binary, entering at minus 5.5 post-Newtonian order, where space-based observations outperform ground-based ones by more than ten orders of magnitude because weak dissipative effects build into measurable phase shifts only over years of coherent tracking.

Applied to specific theories, the framework yields concrete forecasts. For dynamical Chern–Simons gravity, a parity-violating extension entering at 2PN order, ground-based stellar-mass binary observations could tighten the characteristic length scale from the current order of one hundred million kilometers down to roughly ten kilometers — an improvement of many orders of magnitude over satellite-based bounds. Scalar–tensor theories and Einstein–dilaton–Gauss–Bonnet gravity, both producing dipole radiation at minus 1PN order, show contrasting patterns: massive black hole binaries favor the former, while stellar-mass systems constrain the latter more strongly, demonstrating that theories entering at the same order can behave differently because their corrections correlate differently with binary properties. Multiband observations prove especially powerful for noncommutative gravity and charged black holes, combining low-frequency constraints on masses and spins with high-frequency phase information to break parameter degeneracies.

For a nonzero graviton mass, entering at minus 3PN order, space-based observations of massive black hole binaries provide the strongest constraints, since stellar-mass sources observed from the ground start at too high a frequency to accumulate propagation effects efficiently. Time-varying gravitational constant and mass scenarios at minus 4PN order favor multiband and space-based stellar-mass observations, with gravitational-wave bounds on the mass variation expected to become meaningful for the first time. Taken together, the study delivers a transparent, computationally efficient and systematically extensible toolkit for planning precision tests of gravity, underscoring that the next generation of detector networks — ground, space and multiband combined — will probe classes of non-Einsteinian physics, particularly low-order and environmental effects, that no other experiment can reach.

Subject of Research: Parametrized post-Einsteinian tests of general relativity using analytic Fisher-matrix derivatives of gravitational waveforms

Article Title: Tests of general relativity using analytic derivatives of parametrized post-Einsteinian gravitational waveforms within the Fisher-matrix framework

Article References: Wu, J., Sun, M., & Li, J. (2026). Tests of general relativity using analytic derivatives of parametrized post-Einsteinian gravitational waveforms within the Fisher-matrix framework. The European Physical Journal C, 86(9), Article 1098. https://doi.org/10.1140/epjc/s10052-026-16251-5

Image Credits: AI Generated

DOI: 10.1140/epjc/s10052-026-16251-5

Keywords: general relativity, gravitational waves, parametrized post-Einsteinian framework, Fisher matrix, modified gravity, binary black holes, LISA, Einstein Telescope, multiband observations, post-Newtonian corrections, dynamical Chern-Simons gravity, dynamical friction

The research team, led by Arshdeep Singh Atwal and Aarush Lal of CSK Himachal Pradesh Krishi Vishvavidyalaya, Palampur, focused on cauliflower, a nutrient-hungry cool-season crop that dominates the vegetable economy of the Shivalik foothill zone. Indora block, spanning roughly 298 square kilometres between elevations of 261 and 765 metres, receives about 1000 millimetres of rain annually and enjoys a semi-humid subtropical climate that has made it an important vegetable belt. Yet the region’s agriculture is constrained by tiny landholdings, erratic water availability, and fertiliser use that is often both low and imbalanced. Continuous cropping, excessive tillage, residue removal and skewed fertiliser regimes have accelerated nutrient mining and organic matter depletion, making systematic soil characterisation an urgent priority rather than an academic luxury.

Between August 2022 and 2023, the researchers sampled the top 20 centimetres of soil from ninety-nine farming households growing the Megha cauliflower variety on plots of at least 500 square metres, covering twenty-two village councils and thirty-seven villages. The samples, air-dried and sieved, were subjected to a full battery of standard analyses: bulk density by core sampler, texture by hydrometer, water holding capacity by the Keen and Raczkowski box method, pH by glass electrode, organic carbon by dichromate wet oxidation, available nitrogen by alkaline potassium permanganate, phosphorus by Olsen’s method, potassium by flame photometry, and sulphur by turbidimetry, alongside exchangeable calcium and magnesium. The result was one of the most comprehensive physico-chemical datasets ever assembled for this corner of the lower Himalayas.

The descriptive statistics painted a picture of generally healthy but unevenly distributed fertility. Soil pH ranged from 6.34 to 7.56 with a mean of 7.16, placing the soils in the neutral to slightly alkaline range that suits vegetable production well. Electrical conductivity, averaging 0.26 decisiemens per metre, stayed safely below the 0.8 threshold considered problematic for crops. Organic carbon ranged from 7.00 to 13.60 grams per kilogram, a medium-to-high level the authors attribute to the regular addition of farmyard manure and plant residues by local growers. Available nitrogen averaged 284 kilograms per hectare, phosphorus a modest 12.15 kilograms per hectare, and potassium 280 kilograms per hectare, while sulphur, calcium and magnesium varied considerably, with phosphorus showing the highest relative variability of all measured properties at a coefficient of variation of 38.28 percent.

Correlation analysis revealed the web of interdependencies that governs how these soils behave. Bulk density, a measure of compaction, correlated positively with particle density and available potassium, the latter likely reflecting potassium-bearing minerals such as feldspars and micas that simultaneously increase particle packing and nutrient supply. It correlated strongly and negatively with porosity, as expected, since denser soils have less pore space. Most strikingly, organic carbon correlated strongly and positively with available nitrogen at 0.65, confirming that organic matter is the engine of nitrogen supply in these systems, releasing mineralisable nitrogen in proportion to its abundance. Sulphur tracked positively with pH, consistent with enhanced mineralisation of organic sulphur under near-neutral conditions, while exchangeable calcium and magnesium rose together with indicators of improved fertility and microbial activity.

To compress this multivariate complexity into interpretable structure, the team applied principal component analysis, which extracted six components with eigenvalues above one that together explained 69.43 percent of the total variance. The first component, dominated by bulk density alone, signalled that soil structural condition, shaped by management decisions about tillage and compaction, is the single strongest axis of differentiation across the landscape. The second was driven by organic carbon, reflecting the heavy reliance of smallholders on organic manure as their primary nutrient pathway. Later components captured particle density with phosphorus and potassium, the pH-sulphur-calcium-magnesium cluster tied to base saturation and leaching dynamics, electrical conductivity, and finally nitrogen with porosity. A complementary hierarchical cluster analysis grouped the ninety-nine sampling sites into seven major clusters, whose long branch linkages pointed to substantial multivariate dissimilarity driven by topography, parent material and divergent management environments.

The study’s most innovative contribution came from spatial statistics. Using Global Moran’s I, a measure of spatial autocorrelation computed in the GeoDa software with a six-nearest-neighbour weight matrix and 999 permutations, the researchers tested whether each soil property was randomly distributed or spatially structured. Six properties passed the significance test: pH, electrical conductivity, organic carbon, porosity, nitrogen and sulphur. Positive Moran’s I values for these attributes mean that similar values cluster together on the map, so high-pH fields neighbour high-pH fields and nitrogen-poor patches form coherent zones rather than isolated points. The remaining properties, including bulk density, water holding capacity, phosphorus, potassium, calcium and magnesium, appeared spatially random, a finding the authors link to the region’s complex topography, which creates microclimatic and edaphic variation over short distances, and to individual farm management decisions that override natural gradients.

Local Indicator of Spatial Association analysis then pinpointed exactly where the clusters lie. Sulphur expressed the strongest clustering of any property, with 47 significant sampling sites and 23.23 percent of the study area falling into the high-high category, concentrated toward the north, a pattern the authors attribute to gypsiferous parent material, higher organic matter and fertilisers such as single superphosphate. Electrical conductivity formed high-value clusters in the north, where erratic and low rainfall allows salts to accumulate, while nitrogen showed high clusters in the central and northern regions and low clusters toward the south-west, a patchiness that implicates nutrient management practices as the dominant driver rather than any natural gradient. The team even notes that naturally occurring Acacia catechu trees, which fix atmospheric nitrogen, may act as local nitrogen hotspots. Porosity was the only variable with more low-low than high-high area, with compacted zones in the northern half pointing to the cumulative effect of seasonal tillage and field traffic.

Translated into practice, the cluster typology becomes a management blueprint. Low-low zones, where a property is significantly depressed and surrounded by similarly depleted neighbours, are the highest-priority targets: nitrogen-poor clusters call for increased fertilisation or green manures such as Sesbania or Trifolium, organic-carbon-poor zones for farmyard manure, compost and legume cover crops between cauliflower seasons, and sulphur-deficient patches for sulphur-coated urea and oilseed cakes. Porosity low-low zones indicate compaction that could be remedied with subsoiling or deep tillage combined with organic amendments. High-high clusters, by contrast, may need optimisation to avoid toxicity-like conditions, while high-low outliers, anomalously enriched sites within deficient neighbourhoods, deserve investigation to understand how they arose. To visualise all of this, the team generated continuous fertility surfaces using inverse distance weighting in QGIS with a weighting power of two and a fine spatial resolution of 0.0001 degrees, producing thematic maps of every measured property clipped to the block boundary.

The authors are candid about the limitations: the survey covered a single block, a single season, and only the surface layer, so the findings cannot be generalised to other agro-climatic zones without further work. Even so, the framework is deliberately replicable, and the maps now serve as a baseline against which future soil health monitoring, climate-driven fertility shifts and the effects of changing nutrient regimes can be measured. For a region where farmers manage fragments of land under unpredictable monsoon rainfall, the message is quietly transformative: soil fertility in the lower Himalayas is not a uniform backdrop but a structured, mappable landscape of hotspots and deficits, and knowing where those zones lie is the first step toward feeding crops precisely what they need, where they need it, while protecting fragile mountain soils for the long term.

Subject of Research: Spatial variability and geostatistical mapping of soil physico-chemical properties in vegetable-cultivated Himalayan soils

Article Title: Spatial characterization and geostatistical analysis of soil properties in vegetable cultivated soils of Indora block (Kangra district), Himachal Pradesh, India

Article References: Atwal, A. S., Lal, A., Kapoor, R., Sandal, S. K., Sharma, R., Sepehya, S., & Kumari, P. (2026). Spatial characterization and geostatistical analysis of soil properties in vegetable cultivated soils of Indora block (Kangra district), Himachal Pradesh, India. Discover Soil, 3(1), Article 116. https://doi.org/10.1007/s44378-026-00271-4

Image Credits: AI Generated

DOI: 10.1007/s44378-026-00271-4

Keywords: soil science, spatial variability, geostatistics, Moran's I, LISA, precision agriculture, Himalayan farming, cauliflower, soil fertility, nutrient management, Himachal Pradesh, organic carbon

The team focused on a specific and mathematically elegant scenario: a magnetically charged black hole, generated within nonlinear electrodynamics, embedded in what astronomers call a Hernquist dark matter halo. The Hernquist profile is a widely used analytic model for the distribution of dark matter in galaxies and galactic bulges, characterized by two numbers: a characteristic density and a scale radius. Because the profile has a finite total mass and can be handled analytically, it serves as an ideal benchmark for studying how an extended matter distribution modifies the geometry of spacetime around a compact object. The resulting composite solution, which the authors call the MHDM black hole, combines the magnetic charge of the black hole with the gravitational influence of the halo in a single metric function.

At the heart of the analysis lies the effective potential, the quantity that determines whether a particle orbiting the black hole remains bound, escapes to infinity, or plunges to its doom. The researchers found that increasing either of the two halo parameters, the density or the scale radius, lowers the potential everywhere and suppresses its peak, enlarging the region in which stable bound motion is possible. The magnetic charge, by contrast, does the opposite: it reinforces the potential structure and raises the peak in a subtle but detectable way. In other words, dark matter and magnetic charge act as competing influences on the spacetime, a tug-of-war that leaves measurable traces in every aspect of orbital dynamics.

Two special radii anchor the analysis. The first is the marginally bound orbit, the boundary beyond which a particle with the escape energy can no longer remain tied to the black hole. The second is the innermost stable circular orbit, or ISCO, the famous threshold inside of which no stable circular motion exists. For an ordinary Schwarzschild black hole the ISCO sits at six times the black hole mass, but the study shows that both the halo density and the scale radius push the ISCO and its associated angular momentum to larger values, while reducing the orbital energy required there. The magnetic charge shifts everything back in the opposite direction, partially undoing the halo’s influence. When the authors mapped the full allowed parameter space for bound orbits in the energy–angular momentum plane, they found that the magnetically charged halo black hole neatly interpolates between the pure Schwarzschild case and the uncharged halo case.

The most visually striking part of the study concerns periodic orbits, the closed trajectories that serve as building blocks for all bound motion around a black hole. When the ratio of a particle’s azimuthal frequency to its radial frequency is a rational number, the orbit closes on itself after a finite number of cycles, producing the celebrated zoom–whirl behavior in which the particle loops rapidly around the black hole before swinging out and back. The authors classified these orbits using three integers, known as the zoom, whirl, and vertex numbers, and computed the energies at which each class of periodic orbit exists for different combinations of halo density, scale radius, and magnetic charge. Their results show that increasing the halo parameters systematically lowers the energy required for periodic orbits while raising the average angular momentum, and that richer orbital structures emerge as the zoom number grows.

Periodic orbits are more than mathematical curiosities. Because any generic bound orbit can be approximated arbitrarily closely by a periodic one, they provide a natural vocabulary for describing the gravitational waveforms emitted by orbiting bodies. The researchers also examined what happens when the energy is perturbed away from the exact periodic value by as little as one part in ten thousand. The result is precession: the orbital ellipse slowly rotates, and the trajectory never exactly repeats. This exquisite sensitivity to initial conditions underscores how finely tuned the closed orbits are, and how even minute deviations translate into a secular drift that would be encoded in the emitted radiation.

To connect these orbital dynamics with observations, the team modeled an extreme mass-ratio inspiral, the system in which a stellar-mass compact object spirals into a supermassive black hole over hundreds of thousands of orbital cycles. Such systems are prime targets for the Laser Interferometer Space Antenna, or LISA, the planned space-based gravitational wave observatory. Using the adiabatic approximation and the numerical kludge waveform methodology, the authors integrated the geodesic equations and constructed the gravitational wave polarizations from the quadrupole formula. The resulting waveforms display a clear correlation with the orbital motion: the amplitude swells when the small object whips past the black hole at close range and fades as it retreats to larger radii, producing a characteristic modulation across each orbital cycle.

The comparison between different spacetimes is where the physics becomes genuinely exciting. In the presence of the Hernquist halo, the periodic orbits stretch to larger spatial extents, the gravitational wave amplitude drops, and the oscillation period lengthens, reflecting the slower orbital timescale of the enlarged trajectories. When the black hole carries magnetic charge, the halo’s dispersive effect is partially suppressed: the orbits become more compact, the wave amplitude recovers, and the oscillation period shortens back toward the Schwarzschild value. The magnetic charge thus acts as a kind of counterweight, driving the system back toward vacuum-like behavior. Both polarizations, the plus and cross modes, carry these signatures, meaning that a sufficiently sensitive detector could in principle disentangle the contributions of the halo and the charge from a single observed signal.

The final step was to translate the time-domain waveforms into the frequency domain and compare the characteristic strain with LISA’s sensitivity curve. For a central black hole of one million solar masses, a small companion with a mass ratio of one hundred-thousandth, and a source at two megaparsecs, the dominant peaks of the signal for all three spacetime configurations fall comfortably within LISA’s most sensitive band, between roughly one thousandth and one hundredth in strain. The dark matter halo shifts the spectral peak toward lower frequencies while leaving the amplitude nearly unchanged, whereas the magnetic charge produces only a weak shift toward higher frequencies with negligible amplitude variation. Increasing the black hole mass shifts the entire spectrum to lower frequencies in accordance with the expected inverse scaling, preserving the overall structure of the peaks.

What emerges from this study is a coherent picture in which the invisible architecture of a galaxy’s dark matter halo could be read directly from the ripples in spacetime produced by orbiting compact objects. If LISA and its successors achieve their design sensitivity, the fine structure of gravitational waveforms from extreme mass-ratio inspirals may one day reveal not just the mass and spin of the central black hole, but also the density and extent of the dark matter surrounding it, and perhaps even exotic electromagnetic properties of the black hole itself. For now, the work stands as a detailed theoretical roadmap, demonstrating that the marriage of black hole physics and dark matter astrophysics is written into every wiggle of the waveform.

Subject of Research: Gravitational wave signatures of periodic orbits around a magnetically charged black hole embedded in a Hernquist dark matter halo

Article Title: Gravitational wave signatures and periodic orbits of a charged black hole in a Hernquist dark matter halo

Article References: Heidari, N., Araújo Filho, A. A., & Lobo, I. P. (2026). Gravitational wave signatures and periodic orbits of a charged black hole in a Hernquist dark matter halo. The European Physical Journal C, 86(9), Article 1104. https://doi.org/10.1140/epjc/s10052-026-16322-7

Image Credits: AI Generated

DOI: 10.1140/epjc/s10052-026-16322-7

Keywords: black holes, dark matter, Hernquist halo, gravitational waves, periodic orbits, zoom-whirl, LISA, extreme mass-ratio inspiral, nonlinear electrodynamics, ISCO, magnetic charge, theoretical physics

Ernesto F. L. Amaral and Nereyda Y. Ortiz Osejo de Lemus of Texas A&M University analyzed American Community Survey data spanning 2005 to 2024, covering adults aged eighteen and older across the entire country. Their central question was deceptively simple: are the places where migration rates run high the same for everyone, and do the groups with the strongest regional concentrations also have the highest individual likelihood of moving? The answer, they found, is frequently no—a discrepancy with significant implications for how researchers and policymakers understand population redistribution in America.

The backdrop to the study is what demographers have called the Great American Migration Slowdown. Between 1968 and 2009, annual interstate migration rates fell from 3.6 percent to just 1.6 percent, and between 1982 and 2015, moves across state and county lines dropped by roughly half. The new analysis confirms this trajectory continued into recent years: overall migration rates bottomed out at 4.57 percent in 2012, recovered modestly through 2019, dipped during the COVID-19 pandemic, and settled back down to 4.58 percent by 2024. The decline is pronounced compared with other developed countries and is thought to reflect reduced responsiveness to regional economic differences, growing place attachment, and shifts in job mobility.

But the aggregate numbers conceal enormous variation. Hispanic, non-Hispanic African American, and non-Hispanic Native American populations consistently showed lower migration rates than non-Hispanic whites and non-Hispanic Asians throughout the two decades studied, with foreign-born Hispanics recording the lowest rates of any group. Foreign-born individuals of all backgrounds moved less often than their U.S.-born counterparts, reflecting differences in settlement histories, social networks, and access to information about destinations. When the researchers modeled individual migration odds statistically, controlling for age, education, income, marital status, children, homeownership, employment, and region of residence, these group differences persisted rather than disappearing.

The spatial half of the analysis used a technique called Local Indicators of Spatial Association, or LISA, which identifies statistically significant clusters of high or low values across neighboring geographic areas. Applied to Migration Public Use Microdata Areas—the finest geography available in the public census microdata—the method revealed striking regional signatures. In 2019, hotspots of high internal migration appeared across parts of the West, including northern California, Nevada, Utah, southern Idaho, and northwestern Wyoming, along with central Texas and northern Florida. By 2024, most of the western hotspots had faded, while Texas and Florida concentrations endured. A long corridor of low migration, stretching from Texas through the Midwest to the Northeast, remained stable across the entire period and even expanded into California and New Mexico.

Those patterns, however, look very different depending on which demographic group is mapped. Non-Hispanic African Americans showed pronounced high-migration clusters across southeastern regions, alongside widespread low-migration zones in the Midwest, Northeast, and West. Hispanic clustering concentrated in California, the Mountain West, Texas, and Florida. Non-Hispanic Native Americans displayed high-migration clusters in the Northwest, Southwest, and parts of the Great Plains, including Arizona, Utah, New Mexico, Colorado, Oklahoma, and Alaska—areas where Indigenous populations are geographically concentrated. Non-Hispanic Asians, by contrast, were characterized mainly by low-migration clusters in southern and Midwestern regions, with some expansion into the Northwest by 2024.

Nativity proved to be a crucial modifier of these spatial patterns. U.S.-born Hispanics showed broad high-migration clustering across western and southern areas, while foreign-born Hispanics displayed far fewer hotspots, concentrated primarily in Texas and Florida by 2024. The contrast was equally stark for African Americans: U.S.-born Black migrants exhibited a broad southeastern pattern, whereas foreign-born Black migrants showed tightly localized clusters in eastern metropolitan areas, including Florida, Atlanta, and Washington, DC—patterns likely reflecting distinct immigrant origins, settlement networks, and labor market incorporation. Foreign-born whites, meanwhile, were marked predominantly by low-migration clusters across central regions of the country.

Perhaps the study’s most striking finding is that regional concentration and individual mobility are genuinely distinct dimensions of migration. Non-Hispanic African Americans and Hispanics both showed lower individual odds of moving than non-Hispanic whites after adjustment, yet both groups displayed strong, persistent regional hotspots. Conversely, non-Hispanic Asians showed relatively high individual mobility despite maps dominated by low-migration clusters. A group can be regionally concentrated precisely because most of its members who do move go to the same places, or because past settlement histories anchor its geography, without its members being frequent movers overall. The authors are careful to note that their LISA maps describe spatial clustering at selected points in time and cannot disentangle the cumulative flows, network effects, and historical settlement processes that may have produced them.

The research also has limitations that the authors acknowledge candidly. The analysis is associational rather than causal, and the logistic regression models do not formally account for spatial autocorrelation, which could lead to underestimated standard errors. The aggregated geographic units limit the ability to study very local dynamics, and detailed subgroup-specific flow analysis at the county level would require restricted Census data. The 2020 ACS, disrupted by pandemic data-collection problems, was tested in robustness checks and found not to alter the main results. Still, the authors argue the two-pronged approach—descriptive spatial clustering paired with adjusted individual-level models—offers a descriptive framework that previous single-lens studies could not provide.

What emerges is a compelling guide for future research. The findings point directly to places and populations where localized studies could uncover the mechanisms behind the patterns: Hispanic migration in Texas and Florida, African American migration in the Southeast, foreign-born African American settlement in Atlanta and Washington, DC, and Native American migration across the Southwest, Great Plains, and Alaska. The authors suggest combining administrative county-to-county flow data with survey microdata, and applying spatially explicit methods such as geographically weighted regression, to test whether the relationships they identify vary across places. As the United States continues to grow more demographically diverse while its residents grow more rooted, understanding precisely where and how different communities redistribute themselves—and why geography and mobility do not always move in tandem—has become an essential question for demographers, economists, and anyone concerned with regional inequality.

Subject of Research: Spatial patterns and demographic heterogeneity of internal migration in the United States by nativity and race/ethnicity

Article Title: Internal migration, demographic heterogeneity, and spatial patterns in the United States

Article References: Amaral, E. F. L., & Ortiz Osejo de Lemus, N. Y. (2026). Internal migration, demographic heterogeneity, and spatial patterns in the United States. Journal of Population Research, 43(3), Article 27. https://doi.org/10.1007/s12546-026-09434-0

Image Credits: AI Generated

DOI: 10.1007/s12546-026-09434-0

Keywords: internal migration, demographic heterogeneity, spatial clustering, LISA, nativity, race and ethnicity, American Community Survey, population redistribution, spatial demography, United States, logistic regression, migration slowdown

The research, conducted by Juarez Antônio da Silva Júnior of the Federal University of Pernambuco, examined landslide occurrences recorded by Recife’s Civil Defense between 2015 and 2024. Rather than treating each neighborhood as an isolated unit, as conventional frequency counts and ordinary statistical models tend to do, the study measured whether landslides in one neighborhood were related to landslides in adjacent ones. The central tool was the Global Moran’s Index, a measure of spatial autocorrelation that quantifies whether similar values—here, landslide counts—sit closer together in space than chance would predict. Positive, significant values indicate clustering; values near zero indicate randomness.

The results were unambiguous. In most years of the historical series, the Global Moran’s Index was positive and statistically significant, ranging from moderate values of roughly 0.17 to 0.27 between 2015 and 2017, dipping during an attenuation period from 2018 to 2020, and then strengthening again to a peak of about 0.33 in 2023. In other words, throughout nearly the entire decade, neighborhoods with many landslides tended to be surrounded by other neighborhoods with many landslides, while quiet zones stayed quiet together. The risk, the analysis shows, is not diffuse across the city but concentrated in stable geographic poles.

To locate those poles precisely, the study turned to Local Indicators of Spatial Association, or LISA, a technique proposed by geographer Luc Anselin in 1995 that decomposes global autocorrelation into neighborhood-level clusters. LISA classifies each area into categories such as High-High, where a high-incidence neighborhood is surrounded by similarly high neighbors, or Low-Low, where low values cluster together, along with outlier categories where a unit diverges from its surroundings. The spatial weighting was built using Queen-type contiguity, meaning neighborhoods were considered neighbors if they shared a boundary or a vertex—an approach well suited to the irregular geometries of urban districts.

The local analysis revealed two large, statistically significant High-High hotspots at opposite ends of the municipality. In the North Zone, the neighborhoods of Guabiraba, Passarinho, Dois Unidos, Nova Descoberta, and Linha do Tiro emerged as chronic hotspots, classified as High-High in four to six of the ten years analyzed. In the South Zone, COHAB, Ibura, Jordão, and Barro showed recurring clusters, particularly in 2017, 2018, and above all 2022—a year of exceptional rainfall that produced the highest landslide count in the entire series, with some neighborhoods exceeding 50 occurrences and LISA values greater than 1 across broad contiguous areas. By contrast, central and coastal districts such as Boa Viagem, Pina, Graças, and Derby were dominated by Low-Low patterns, confirming consistently low risk in the flat, highly urbanized heart of the city.

Beyond mapping clusters in individual years, the study introduced two temporal indicators derived from the LISA results: the frequency of High-High classification and a persistence ranking that distinguishes chronic hotspots, significant in at least five of ten years, from episodic and intermittent ones. This persistence analysis showed that the northern hotspot is not a product of any single disaster but a structural condition—vulnerability that is rooted in time as well as space. Descriptive statistics reinforced the picture: neighborhoods such as Dois Unidos displayed extreme interannual swings, recording no cases in 2018, 104 cases in 2019, and only 2 in 2020, while Sen’s slope estimates suggested a modest overall downward trend in several historically critical areas, possibly reflecting municipal interventions.

The spatial patterns aligned closely with independent evidence. When the identified clusters were overlaid on landslide susceptibility maps prepared by the Geological Survey of Brazil (SGB/CPRM) and on the city’s continuous landslide monitoring map, the correspondence was strong. The CPRM maps classify the hilly terrain of the North and South zones as highly susceptible to mass movements, and it is precisely there that the chronic hotspots sit. The city’s own monitoring points, concentrated in the North, Northeast, and South administrative regions, coincide with the High-High clusters. This convergence between empirical occurrence data and model-based susceptibility mapping strengthens confidence that the statistical signals reflect genuine geotechnical and social conditions rather than artifacts of reporting.

The underlying drivers are as much social as geological. Recife, home to nearly 1.5 million people at a density of more than 6,800 per square kilometer, combines a humid tropical climate with steep slopes occupied by self-built housing. Studies cited in the paper document chaotic occupation, narrow streets, inadequate drainage, vegetation loss, and construction in permanent preservation areas in neighborhoods such as Passarinho, where urban growth reached 41 percent between 1975 and 2022 while vegetation cover fell by 30 percent. An estimated 207,000 residents live in at-risk areas, and Recife was ranked the fifth most impacted city by flash floods and landslides in terms of population in CEMADEN studies following the catastrophic May 2022 rains, which affected more than 200,000 inhabitants and caused dozens of deaths.

The methodological lesson extends beyond Recife. Spatial autocorrelation analysis has proven its value in fields from dengue epidemiology in Nepal to hepatitis A mapping in Indonesia and vegetation fragmentation studies in Zimbabwe, and recent work has integrated LISA with machine learning models to improve landslide prediction in China. By demonstrating that a decade of municipal civil defense records, freely available under an open data license and processed with open-source Python libraries such as GeoPandas and PySAL, can yield actionable risk intelligence, the study offers a replicable template for other cities with rugged terrain in the Brazilian Northeast and beyond.

The practical implications are direct. The author recommends prioritizing slope containment works, expanded drainage, vegetation recovery, control of irregular occupation, and relocation of families in imminent danger in the chronic northern hotspots, while reinforcing containment structures, permanent geotechnical monitoring, and contingency planning in the southern cluster. As climate change intensifies extreme rainfall events across the region, distinguishing chronic from episodic risk areas becomes essential for allocating scarce public resources. The decade of data from Recife delivers a clear message: landslide risk has an address, and prevention efforts should go precisely there.

Subject of Research: Spatiotemporal autocorrelation of landslide occurrences across neighborhoods of Recife, Brazil, from 2015 to 2024 using the Global Moran's Index and LISA.

Article Title: Spatiotemporal autocorrelation of landslides in neighborhoods of Recife, Brazil (2015–2024) based on the Moran and Lisa index

Article References: da Silva Júnior, J. A. (2026). Spatiotemporal autocorrelation of landslides in neighborhoods of Recife, Brazil (2015–2024) based on the Moran and Lisa index. Discover Geoscience, 4(1), Article 354. https://doi.org/10.1007/s44288-026-00722-z

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00722-z

Keywords: landslides, spatial autocorrelation, Moran's Index, LISA, Recife, Brazil, spatial hotspots, urban risk, geotechnical instability, disaster risk management, GIS, susceptibility mapping