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Global waste sector dataset (1990–2050): scenario-based projections of generation, emissions, and socioeconomic drivers
Get PDFStrategy planning for global climate goals requires structured, multisectoral data linking environmental pressures with socioeconomic drivers across time and geography. However, internationally harmonized, machine-actionable datasets integrating waste generation, waste-related greenhouse gas (GHG) emissions, and socioeconomic indicators remain scarce. This study provides a harmonized, AI-ready dataset to support global analyses of municipal solid waste (MSW) and associated emissions. This FAIR2 dataset provides historical (1990–2020) and forecasted (2021–2050) national-level data for 43 countries, covering MSW generation, CO2, CH4, and N2O emissions, GDP per capita (PPP), and population. Forecasts were generated using an ensemble of fixed-effects regression models and artificial neural networks informed by economic and demographic trends. By linking MSW, emissions, and socioeconomic drivers within a standardized structure, the dataset enables analyses including benchmarking, equity assessments, and decoupling analysis. While limited to national aggregates and subject to scenario uncertainty, the dataset complies with FAIR2 principles, supporting reuse and traceability
Long-term cardiac effects of adrenalectomy versus surveillance in mild cortisol excess: 5-year results from the prospective ITACA study
Get PDFObjective: To determine whether cardiac remodelling associated with mild autonomous cortisol secretion (MACS) is reversible after treatment and how trajectories compare with non-functioning adrenal incidentalomas (NFAI). Design: Five-year prospective cohort study (ITACA; NCT04127552). Methods: Sixty patients (35 MACS, 25 NFAI) underwent clinical, biochemical, and echocardiographic evaluations at baseline and after 1 and 5 years. MACS was managed with either active surveillance (AS, n = 22) or unilateral adrenalectomy (ADRX, n = 13). Longitudinal changes were analysed with linear mixed-effects models. Results: At baseline, MACS had a higher prevalence of left-ventricular (LV) hypertrophy (46% vs 16%, P = .013) and diastolic dysfunction (34% vs 12%, P = .050), and greater LV mass index (LVMi) (median 100 vs 85 g/m², P = .011). Over time, the change in LVMi differed between NFAI, MACS-AS and MACS-ADRX (P = .004). At 1 year, LVMi fell by −14.8 g/m² (95%CI −28.7 to −0.9) after ADRX and rose by 13.7 g/m² (0.8 to 26.5) under AS. By 5 years, LVMi returned to baseline in both MACS subgroups, whereas NFAI increased by 22.4 g/m² (12.3 to 32.5; P < .001). Right-ventricular systolic excursion (TAPSE) improved only in AS (3.6 mm, 1.8 to 5.4; P = .001). Global LV systolic and diastolic indices deteriorated similarly across groups. Major adverse cardiac events occurred in 13.3% of MACS-AS, 12.5% of ADRX, and 5.6% of NFAI patients. Conclusions: MACS is associated with early concentric LV remodelling that regresses after adrenalectomy but rebounds within 5 years, leaving surgical and surveillance patients with comparable cardiac geometry. Under AS, remodelling stabilizes, whereas NFA continue a slow, progressive hypertrophic course. These findings support serial echocardiographic monitoring and underscore the need to test other cortisol-lowering therapies, alone or in combination with surgery, for durable cardioprotection
Optimization of various machine learning concepts to evaluate landslide susceptibility: XGBoost, k-NN and MLP using PSO algorithm
Get PDFLandslides significantly threaten natural and built environments, necessitating accurate prediction models for effective hazard mitigation. There is an urgent need to further improve the performance of machine learning algorithms in predicting landslide susceptibility by monitoring the impact of optimization algorithms on the performance of these models. This study evaluates the performance of various machine learning classifiers, including k-Nearest Neighbors (kNN), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), for landslide susceptibility mapping. Additionally, Particle Swarm Optimization (PSO) is employed to enhance model performance by optimizing hyperparameters. Mountainous areas in the eastern Mediterranean (the northern Kabir River basin in western Syria) were identified as a result of the high frequency of landslide events over the past two decades. Nineteen factors causing landslides were identified, with no factor excluded, as a result of a multicollinearity test. The results indicate that XGBoost achieves the highest performance among traditional models. When integrated with PSO, the PSO-XGBoost model further improves classification performance, demonstrating its robustness in handling complex spatial patterns. Feature importance analysis using SHAP confirms slope as the dominant factor, followed by TRI, rainfall, Aspect, TWI, and curvature, highlighting the role of topography and hydrology in landslide occurrence. Moderate lithology, NDVI, and LULC contributions and lower importance of Flow Accumulation and Soil Depth suggest complex environmental interactions. Model predictions show varying susceptibility distributions. PSO-MLP assigns the highest very high susceptibility (44.09%), while PSO-XGBoost provides a balanced classification (31.13%). The PSO-XGBoost model demonstrates superior predictive capability, offering reliable landslide susceptibility maps for disaster risk management and land-use planning
Evidence of Feedback Effects in Low-luminosity Active Galactic Nuclei Revealed by JWST Spectroscopy
Get PDFThis Letter presents an analysis of the infrared (∼3–28 μm) spectra extracted from the nuclear (r < 150 pc) regions of four low-luminosity active galactic nuclei (AGN), observed by JWST NIRSpec/integral field unit and MIRI/Medium Resolution Spectroscopy as an extension of the Galaxy Activity, Torus, and Outflow Survey. We find that, compared to higher-luminosity AGN, these low-luminosity AGN exhibit distinct properties in their emission of ionized gas, polycyclic aromatic hydrocarbons (PAHs), and molecular hydrogen (H2). Specifically, the low-luminosity AGN exhibit relatively weak high ionization potential lines (e.g., [Ne V] and [O IV]), and the line ratios suggest that fast radiative shocks (with vs of ∼100s km s−1) are the primary excitation source of ionized gas therein. Under the low-excitation conditions of their nuclear regions, these low-luminosity AGN generally exhibit a higher fraction of PAHs with large size (NC ≳ 200), reflecting the preferential destruction of smaller PAH molecules by AGN feedback. Furthermore, the H2 transitions in these low-luminosity AGN are not fully thermalized, with slow, plausibly jet-driven molecular shocks (with vs ≤ 10 km s−1) likely being the extra excitation source. Taken together with results from the literature, these findings indicate that feedback operates in both low- and high-luminosity AGN, although its impact varies with AGN luminosity. In particular, systematic variations in PAH band ratios are found across AGN, demonstrating the differing influence of feedback in AGN of varying luminosities and highlighting the potential of PAH band ratios as diagnostics for distinguishing kinetic- and radiative-mode AGN feedback
High-order finite element methods for three-dimensional multicomponent convection-diffusion
No full textWe derive and analyze a broad class of finite element methods for numerically simu6 lating the stationary, low Reynolds number flow of concentrated mixtures of several distinct chemical species in a common thermodynamic phase. The underlying partial differential equations that we discretize are the Stokes–Onsager–Stefan–Maxwell (SOSM) equations, which model bulk momentum transport and multicomponent diffusion within ideal and non-ideal mixtures. Unlike previous approaches, the methods are straightforward to implement in two and three spatial dimensions, and allow for high-order finite element spaces to be employed. The key idea in deriving the discretization is to suitably reformulate the SOSM equations in terms of the species mass fluxes and chemical potentials, and discretize these unknown fields using stable H(div)–L2 finite element pairs. We prove that the methods are convergent and yield a symmetric linear system for a Picard linearization of the SOSM equations, which staggers the updates for concentrations and chemical potentials. We also discuss how the proposed approach can be extended to the Newton linearization of the SOSM equations, which requires the simultaneous solution of mole fractions, chemical potentials, and other variables. Our theoretical results are supported by numerical experiments and we present an example of a physical application involving the microfluidic non-ideal mixing of hydrocarbons
Deep learning model predictive control for deep brain stimulation in Parkinson’s disease
Get PDFWe present a nonlinear data-driven Model Predictive Control (MPC) algorithm for deep brain stimulation (DBS) for the treatment of Parkinson’s disease (PD). Although DBS is typically implemented in open-loop, closed-loop DBS (CLDBS) uses the amplitude of neural oscillations in specific frequency bands (e.g. beta 13-30 Hz) as a feedback signal, resulting in improved treatment outcomes with reduced side effects and slower rates of patient habituation to stimulation. To date, CLDBS has only been implemented in vivo with simple algorithms such as proportional, proportional-integral, and thresholded switching control. Our approach employs a multi-step predictor based on differences of input-convex neural networks to model the future evolution of beta oscillations. The use of a multi-step predictor enhances prediction accuracy over the optimization horizon and simplifies online computation. In tests using a simulated model of beta-band activity response and data from PD patients, we achieve reductions of more than 20% in both tracking error and control activity in comparison with existing CLDBS algorithms. The proposed control strategy provides a generalizable data-driven technique that can be applied to the treatment of PD and other diseases targeted by CLDBS, as well as to other neuromodulation techniques
Challenges and pathways for matching corporate value-chain biodiversity losses and gains
No full textIn the context of ambitious global biodiversity goals, the need to compensate for the impact of corporate activities is no longer restricted to direct impacts but extends to the entire value-chain of corporates. This is challenging, considering the substantial uncertainties involved in measuring corporate value-chain biodiversity losses and gains, which render their comparison difficult. Corporates run the risk of taking inadequate action and making compensatory statements that are not supported by equivalent losses and gains, potentially exacerbating loss of biodiversity instead of supporting its recovery and leading to reputational and financial risks. Here, we highlight uncertainties that pertain to the metrics used for biodiversity loss and gain measurements and approaches that can be used to match these metrics. We then suggest a simple framework for corporates to evaluate the risk of making a compensatory claim, based on the level of uncertainty on value-chain biodiversity impacts, to reduce the risk of making inappropriate statements
Density dependence impacts our understanding of population resilience
No full textCurrent metrics of demographic resilience (e.g., resistance, recovery) summarize how populations respond to the frequent, varied disturbances that ecological systems experience. Much of the application of these metrics has focused on the potential response of populations represented by time-invariant, density-independent structured population models to hypothetical disturbances. Here, we show that density dependence has profound and complex impacts on our understanding of resilience. We examine resilience measures in a flexible structured model with five vital rate parameters (juvenile survival, adult survival, juvenile progression, adult retrogression, and adult reproductive output) with density dependence operating on one vital rate at a time. Depending on which vital rate was subject to density effects, existing measures of demographic resilience (compensation, resistance, and recovery time) either increased or decreased with population density. Moreover, the density-independent model under-predicted the recovery time of the corresponding density-dependent model, with a greater offset for species with longer generation times and higher iteroparity. Our findings demonstrate the importance of underlying non-linear processes when examining demographic resilience, particularly if we hope to predict how natural populations will respond to real disturbances