The study, reported in BMC Infectious Diseases, obtained monthly cutaneous leishmaniasis surveillance data from regional health organizations and meteorological variables from regional meteorological organizations. Investigators used Pearson correlation analysis to examine relationships among climatic variables and disease incidence. They then applied seasonal autoregressive integrated moving average models with exogenous variables, or SARIMAX, to evaluate temporal relationships between monthly incidence and climatic factors.

Rainfall was strongly associated with other climatic measures over the study period. Annual rainfall was positively correlated with maximum relative humidity (r = 0.80) and minimum relative humidity (r = 0.75), negatively correlated with sunshine duration (r = −0.73), and very strongly positively correlated with minimum and mean temperature (r = 0.99). Cutaneous leishmaniasis incidence itself had little correlation with annual rainfall (r = 0.01) and a weak negative correlation with minimum temperature (r = −0.23). The long-term trend in incidence showed a moderate positive correlation with disease occurrence (r = 0.38).

The selected SARIMAX models included moving-average coefficients ranging from 0.38 to 0.45 and autoregressive coefficients ranging from 0.58 to 0.62. In the fitted models, annual rainfall had a negative regression coefficient, while temperature, relative humidity and sunshine duration had positive coefficients. These coefficients indicated associations with temporal variation in cutaneous leishmaniasis incidence, with the models linking climatic factors to the observed time patterns rather than demonstrating that any single variable directly produces disease changes. The contrast between the minimal direct correlation of rainfall with incidence and its negative model coefficient also shows that simple pairwise association and time-series modeling captured different aspects of the data.

The authors emphasize that the ecological associations cannot be interpreted as proof that climate directly causes changes in cutaneous leishmaniasis. Transmission may also depend on vector ecology, reservoir dynamics, human behavior and disease-control interventions. They present SARIMAX as a possible framework for characterizing temporal patterns and supporting surveillance planning in endemic areas. The study’s scope was limited to monthly surveillance and meteorological data from Kashan and Aran and Bidgol counties during 2014–2024, so its conclusions concern relationships observed in that region and period. The analysis was ecological rather than a test of individual-level risk, and it did not evaluate the effects of a clinical intervention.

Subject of Research: Ecological associations between climate variability and cutaneous leishmaniasis incidence in central Iran

Article Title: Ecological analysis of Cutaneous leishmaniasis incidence and climatic variability in Kashan and Aran – Bidgol, Iran (2014–2024)

Article References: Delavari, M., Moradi, A., Niknejad, H., Arghavani, A., Modabber, A. H., & Arani, M. H. (2026). Ecological analysis of Cutaneous leishmaniasis incidence and climatic variability in Kashan and Aran – Bidgol, Iran (2014–2024). BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14311-5

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14311-5

Keywords: cutaneous leishmaniasis, climate variability, ecological analysis, Iran, SARIMAX, surveillance, temperature, humidity, rainfall, time-series modeling, Ecological, analysis

The team started from a well-known reference design, the Volturn-US-S platform developed at the University of Maine, paired with the International Energy Agency’s 15 megawatt reference wind turbine. That turbine is a monster by any measure, with a rotor diameter of 240 metres and a hub height of 150 metres, and it rests on a steel platform built around a central column connected to three outer columns through pontoons and upper supports. The researchers defined six continuous design variables, including the main platform diameter, the diameters of the central and outer columns, and the height, width and thickness of the pontoons, and then let an optimization algorithm search that design space for the lightest structure that could still survive.

The search itself was conducted with a genetic algorithm, a computational technique that mimics biological evolution. A population of candidate platform designs, each described by its six geometric parameters, is evaluated for fitness, and the fittest individuals are combined through simulated binary crossover and polynomial mutation to produce offspring designs. Infeasible solutions that violate physical constraints are handled with a penalty scheme that lets the algorithm compare them purely on how badly they fail. Because the algorithm is inherently random, the team ran the entire optimization ten separate times, each starting from a population of one hundred individuals and running for up to one thousand generations, to gather statistics and confirm the results were not a fluke of a single lucky run.

Evaluating each candidate design required a physics model, and for that the researchers turned to RAFT, an open-source frequency-domain code that models the full floating wind turbine system as a rigid body with six degrees of freedom: surge, sway, heave, roll, pitch and yaw. Rather than simulating every turbulent gust and extreme wave in the time domain, which would be computationally prohibitive across thousands of design iterations, RAFT linearizes the dynamic equations around a mean steady state, computing how the platform responds to each excitation frequency under steady winds and stochastic sea states described by a JONSWAP wave spectrum. Aerodynamic loads on the rotor come from a steady-state blade-element momentum solver, while mooring forces are handled with a quasi-static approach. The code has been validated against higher-fidelity tools such as OpenFAST and showed good agreement, making it an ideal engine for rapid design iteration.

Crucially, the optimization was not allowed to produce a flimsy platform that would capsize or batter its turbine. The team imposed hard constraints on maximum platform offset, platform pitch, and nacelle acceleration at the tower top, along with limits on the fore-aft bending moment at the tower base and the static stress at the critical junction where the central column meets the pontoons. These quantities were computed for a set of representative load cases, including the turbine’s rated wind speed and harsh sea states typical of European waters, with the worst result across all cases checked against each limit. A static stress analysis using a free-body approach estimated bending stresses at the pontoon base from structure weight and buoyancy loads. The outcome was striking: primary steel mass fell from 3916 tonnes to 3273 tonnes, a reduction of roughly 16 percent, while every dynamic and structural constraint remained satisfied. The optimized design features slightly larger outer columns and pontoons but a smaller overall platform diameter.

With a leaner platform in hand, the researchers confronted a problem that engineering alone cannot solve: the price of steel. Commodity markets have been violently volatile in recent years, particularly after the global pandemic, and steel prices feed directly into the capital expenditure of any floating wind project, since primary steel accounts for around 83 percent of total platform cost. To bring order to this uncertainty, the team built a hybrid time series forecasting tool that blends two complementary models. The first is XGBoost, a gradient-boosted decision tree algorithm that excels at capturing short-term stochastic fluctuations in market data. The second is SARIMAX, a seasonal autoregressive statistical model that tracks longer-term trends and seasonal cycles while incorporating exogenous variables, external economic indicators such as rebar and copper prices, coal costs, and London Metal Exchange indices that act as leading indicators for steel.

The selection of those supporting variables was itself a rigorous filtering exercise. Candidate exogenous datasets were compared against the target hot-rolled coil steel prices using correlation metrics, monthly lag testing, and rolling stability analysis, with multiple transformations including log-differences applied to the data. Only indicators that passed thresholds for predictive strength, stability, sign consistency and non-redundancy made it into the final model. When tested against hindcast data covering the turbulent market period from 2022 to 2024, the hybrid approach clearly outperformed either model alone. Without supporting data, forecasts failed to revert to the mean after the 2020 price spike; with them, the hybrid model achieved a mean absolute error of 187.5, kept its average bias close to zero, and produced the most consistent performance across root mean square error and symmetric mean absolute percentage error metrics. Interestingly, the statistical SARIMAX model actually performed best in the earliest forecast months, while the machine learning model showed a tendency to overpredict and SARIMAX alone to underpredict, suggesting the two errors partially cancel in the blend.

The forecasting results were then translated into money. Monthly steel price predictions were aggregated into yearly values, converted to marine-grade steel prices, and multiplied by the platform mass to yield capital costs, with other components such as secondary steel, ballast and controls costed from published industry references. For a one gigawatt floating wind farm using a net capacity factor of 0.4925, the team calculated the levelized cost of energy over a thirty-year project lifetime with an eight percent discount rate. The numbers that emerged carry a sobering message for the industry: uncertainty in steel prices alone swings total capital expenditure by roughly 150 to 200 million euros at the gigawatt scale, a variation of 40 to 50 percent across the forecast scenarios. The XGBoost forecast, which sees prices rebounding 10 to 15 percent, represents a market recovery upper bound, while the SARIMAX forecast with its continued downward drift forms the lower bound, and the hybrid model provides a balanced baseline.

Against that backdrop of market turbulence, the engineering gains look meaningful but partial. The optimized platform achieved cost savings of approximately 10 to 15 percent across all scenarios, and structural optimization reduced the levelized cost of energy by around one euro per megawatt-hour or more in high-price scenarios. The overall LCoE variation across the steel price forecasts was more moderate, at roughly 3 to 5 euros per megawatt-hour, because the long project lifetime and high discount rate dampen the effect of upfront capital swings. The authors are careful to note the limits of their structural analysis, which uses a relatively simple static stress check rather than a full dynamic finite-element assessment, and they deliberately selected the least successful of their ten optimization runs to build in a safety margin. Still, the framework demonstrates that design improvements are possible for reference platforms while respecting real load constraints, and that hybrid forecasting can give developers and investors a defensible range of future costs.

What makes this study notable is its refusal to treat engineering and economics as separate silos. Floating offshore wind grew rapidly from its first full-scale prototype in 2009 to 88 megawatts of installed capacity by 2023, but progress has since stagnated as costs have risen in a difficult macroeconomic climate. To compete in renewable energy auctions, floating wind must cut costs across dimensions, materials and site-specific design, and the floating substructure has been identified as one of the highest-potential areas for savings. By showing that a genetic algorithm can carve 643 tonnes of steel out of a commercial-scale platform, and that machine learning can put credible bounds on the commodity price risk that dominates the remaining bill, the research offers the industry a template for making both the hardware and the financial case for floating wind more robust at the same time. The forecasting code, the genetic algorithm and the RAFT model are all openly available, inviting other teams to push the approach further.

Subject of Research: Structural and economic optimization of a 15 MW floating offshore wind platform using genetic algorithms and time series forecasting of steel prices

Article Title: Economic and design optimization of a 15 MW floating offshore wind platform using time series forecasting

Article References: White, C., Benifla, V., Cândido, J., & Gato, L. M. C. (2026). Economic and design optimization of a 15 MW floating offshore wind platform using time series forecasting. Wind Energy Science, 11(9), 3703-3717. https://doi.org/10.5194/wes-11-3703-2026

Image Credits: AI Generated

DOI: 10.5194/wes-11-3703-2026

Keywords: floating offshore wind, semi-submersible platform, genetic algorithm, time series forecasting, steel price volatility, levelized cost of energy, 15 MW wind turbine, structural optimization, XGBoost, SARIMAX, CAPEX, renewable energy economics

The research, published in the journal Air Quality, Atmosphere & Health, was led by İbrahim Sarbay of Gaziosmanpaşa Training and Research Hospital in İstanbul together with colleagues at İzmir Katip Çelebi University. The team assembled daily emergency department visit counts from two tertiary hospitals, one in İstanbul and one in İzmir, spanning October 2023 through June 2025. After careful temporal alignment, 625 daily observations were available at each site. Alongside attendance figures, the researchers matched five meteorological variables: temperature, atmospheric pressure, humidity, wind speed, and precipitation. The goal was simple and ambitious: to test rigorously whether weather data add meaningful predictive power to forecasting models, or whether simpler, calendar-driven approaches suffice.

Methodologically, the study was designed as a genuine bake-off between statistical and machine-learning approaches. The authors first compared univariate forecasting models that used only the historical series of daily visits, then built multivariable models in four flavors: SARIMAX, a seasonal autoregressive integrated moving average framework that can absorb external predictors; dynamic regression, which models the target series as a function of calendar and weather covariates; and two ensemble tree methods, random forest and XGBoost, which are widely used in applied machine learning for tabular forecasting problems. Accuracy was measured with a battery of standard metrics, including root mean square error, mean absolute error, and the percentage-based measures MAPE, sMAPE, and MASE, which normalize error against a naive benchmark.

The statistical verdict was delivered with Diebold-Mariano tests, a formal method for deciding whether one forecast is significantly more accurate than another, rather than merely better by a rounding error. Robustness checks added further depth: rolling-origin validation, which repeatedly re-estimates models as new data arrive, mimicking real operational deployment; hyperparameter sensitivity analysis; ensemble averaging; and a cross-center generalization test in which models trained at one hospital were applied to the other.

The headline result is striking in its simplicity. Among univariate models, a neural network autoregression approach performed best in İstanbul, while the Theta method, a decomposition-based statistical forecaster, won in İzmir. But when calendar and weather covariates entered the picture, models relying on calendar structure alone delivered the best holdout accuracy in both cities. Dynamic regression achieved a mean absolute percentage error of 5.88 percent in İstanbul, and SARIMAX reached 4.42 percent in İzmir, both with calendar-only inputs. These are clinically meaningful accuracies, precise enough to inform staffing rosters and bed planning.

Weather augmentation, meanwhile, behaved erratically. In İstanbul, adding meteorological variables significantly worsened the SARIMAX and dynamic regression forecasts, with p-values of .013 and .015 respectively. The sole bright spot was XGBoost, where weather inputs produced a significant improvement, with p = .005. In İzmir, weather made no significant difference for any model. The authors are candid that the effect of weather is model- and center-specific, and that no universal weather dividend emerged.

Equally consequential is the finding that models do not travel. When a model tuned at one hospital was deployed at the other, errors ballooned severalfold, with mean absolute percentage errors climbing to roughly 39 percent. Local attendance culture, catchment demographics, hospital-specific referral patterns, and idiosyncratic scheduling apparently imprint themselves so deeply on each site’s demand curve that a model calibrated elsewhere is nearly useless. For health systems hoping to buy a single, generic forecasting product, this is a cautionary datapoint: locally tuned systems outperform imported ones.

There is an important caveat the authors themselves flag. In the holdout analyses where weather appeared to help, the models were fed observed future meteorological values as exogenous inputs. In a real operational setting, tomorrow’s weather is not observed but forecast, and forecast error would erode any weather advantage. The weather-augmented results therefore represent an idealized upper bound on the incremental value of meteorological information, not a fully prospective simulation of a live deployment.

The broader scientific context makes the finding less paradoxical than it first appears. Epidemiological literature has long documented links between ambient temperature and morbidity, seasonal patterns in respiratory viral infections, and correlations between weather and trauma admissions. Weather does influence health. But those influences are largely entangled with the seasons, and the calendar already encodes the seasons. Day-of-week effects, holidays, paydays, and seasonal disease waves explain most of the predictable variance in emergency demand, leaving little residual signal for humidity or wind speed to capture once the calendar is modeled properly.

For hospital operations, the practical message is clear and refreshingly frugal. Daily emergency department attendance can indeed be forecast with an accuracy useful for staffing and resource planning, with errors around four to six percent at both Turkish centers. But the winning ingredients are not exotic weather feeds or elaborate machine-learning pipelines; they are each hospital’s own attendance history and the disciplined use of calendar structure. The most valuable weather forecast for an emergency department, it turns out, may simply be the knowledge that the calendar rarely lies.

Subject of Research: Time-series forecasting of daily emergency department visit volume using calendar and meteorological variables

Article Title: Forecasting emergency department visit volume using meteorological variables: a multicenter time-series study

Article References: Sarbay, İ., Coban, B., Efgan, M. G., Kanter, E., Özel, Z., Kaymaz, E., Bıyıklı, S., Çalık, M., & Tekindal, M. A. (2026). Forecasting emergency department visit volume using meteorological variables: a multicenter time-series study. Air Quality, Atmosphere & Health, 19(9), Article 205. https://doi.org/10.1007/s11869-026-02097-z

Image Credits: AI Generated

DOI: 10.1007/s11869-026-02097-z

Keywords: emergency department, forecasting, time series, meteorological variables, machine learning, SARIMAX, XGBoost, dynamic regression, calendar effects, hospital operations, Türkiye, emergency