Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
The report documents that “the technology bottleneck that defined twentieth‑century development… has substantially closed” and that the new constraint is “absorptive capacity: whether an economy’s institutions, grids, balance sheets, and human capital can turn access into adoption fast enough to matter.” Across five domains—renewable energy, mobile‑money finance, internet connectivity, artificial intelligence, and electric mobility—the Global South is experiencing historically rapid diffusion. Renewable capacity growth in Asia, Africa, and the Middle East now outpaces Europe; mobile money has become the dominant financial rail for Africa; and EV adoption has crossed meaningful thresholds in emerging markets. Yet diffusion is uneven, shaped less by technology availability and more by capital access, regulatory readiness, and fiscal space. The report introduces three analytical constructs: D‑coefficient — a composite measure of absorptive capacity determining how much diffused technology becomes realized development gain. P‑integral — the cumulative burden of debt service, currency depreciation, climate shocks, and fiscal deficits that suppress investment capacity. Compound vulnerability multiplier — the interaction effect of simultaneous stresses that magnify constraints on adoption. These constructs explain why Africa, despite strong renewable growth rates, received only “2% of global clean energy investment… while debt‑servicing costs alone consumed more than 85% of the continent’s energy‑investment envelope.” The report’s core claim: Diffusion access is nearly universal; diffusion speed is not. Economies now diverge based on whether they can convert cheap, widely available technology into productive capacity. Three broad groups emerge: High‑absorptive‑capacity emerging economies (India, Vietnam, Brazil, Gulf states) converting diffusion into rapid convergence. Middle group with uneven, technology‑specific convergence shaped by targeted institutional bottlenecks. Compound‑vulnerability economies (many in Sub‑Saharan Africa) where debt, currency risk, and climate exposure prevent diffusion from translating into development. The report concludes that a genuinely convergent clean‑technology century requires four interventions: Large‑scale currency‑risk and country‑risk hedging instruments. Debt‑service restructuring tied to absorptive‑capacity investment. Universal connectivity and digital‑payments infrastructure. Financing to replicate proven Global‑South delivery models rather than transplanting advanced‑economy ones. The final binding term: “Capital or technology deployed without a corresponding D‑coefficient gain… is not diffusion, it is inventory.”
This paper describes the formulation of a numerical model for simulating environmentally driven one-dimensional (1D) ground movements of expansive clay. The formulation is based on a finite-element model that simulates the redistribution of matric suction through a diffusion-type equation, explicitly accounting for volume changes due to wetting and drying of the clay. We synthesize and modify highly nonlinear constitutive relationships for (1) hysteretic soil water retention; (2) reversible soil shrinkage and expansion of clay; and (3) hydraulic conductivity, explicitly incorporating desiccation cracks through a multidomain framework and assuming a critical surface crack depth. These models are well-calibrated to published laboratory tests on a reference expansive clay, Denver bentonite. We demonstrate capabilities of the proposed formulation to simulate the response of a homogeneous expansive clay to periods of drying and wetting, considering the initial matric suction, saturated hydraulic conductivity of the intact clay, and critical crack depth as three primary sources of uncertainty. We compare ensemble model simulations with measured ground movements from an instrumented expansive clay test site in Texas over a 3-year period using detailed records of potential evapotranspiration and precipitation. By assigning weights to the ensemble simulations based on their performance, we constrain the ranges of the three key uncertain parameters. The results showed very reasonable first-order agreement with the measured data and highlight the potential of the proposed formulation. We anticipate that more reliable predictions can be achieved through direct measurements of actual in situ evaporation rates and local soil properties.
Mahdi Seyyedan, Jiali Ma, Ivo Rosa Montenegro et al.· Journal of Geotechnical and...· 1 citation
A rational design for next-generation thermo-responsive nanocarriers is proposed, in which polymer chemistry, nanoparticle structure, experimental characterization, and mechanistic modelling are integrated from the earliest stages of material development.
M. Schifone, Giuseppe Nunziata, Filippo Rossi· Advances in Colloid and Inte...· 1 citation
This paper develops the economics of artificial intelligence as a single connected structure, from the physics of the production function to the aggregate growth constraint and the valuation of the firms building and adopting it. Part I derives the cost of capability from scaling laws, shows why deployed models are systematically overtrained, and estimates the task-success slope directly from 23,235 public evaluation runs: $\hat\beta=0.83$ with no detectable release-date trend. Part II treats market structure: minimum efficient scale, the two-tier equilibrium in which open weights contest the trailing edge but never the frontier, and inference as a capacity-constrained short-run market that rations rather than prices. Part III is the core. We replace the standard automation assignment rule with one that prices reliability, obtaining an automation calendar $t_{\mathrm{aut}}=t_{1/2}+(\tau/\beta)\log_2\gamma$ in which verification cost, not task difficulty, sets the date; derive optimal checkpoint spacing $k^\star\approx\sqrt{v_{\mathrm{ver}}/\lambda}$; and prove the exact best-of-$k$ result. Against a sound verifier, sampling divides the reliability lag by $k$ in the small-$k$ regime and does better outside it; against an unsound verifier, it leaves an error floor that no amount of sampling removes. Part IV aggregates: diffusion inherits its time dispersion from verification costs, and revenue growth is governed by the density of tasks at the current threshold. Part V proves a Baumol bound --- with elasticity of substitution below one, aggregate growth converges to that of the least automatable essential input --- and states three jointly necessary conditions for explosive growth. Part VI treats measurement, policy, and financial markets. Part VII states the investment bridge: technological importance, industry profit, and security return are distinct objects, and a coherent valuation must respect the automation calendar, rent migration, capital consumption, and expectations already in price. The full valuation architecture is reserved for a separate companion paper. Part VIII states eighteen open problems.
Miquel Noguer Alonso· Zenodo (CERN European Organi...· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.