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Industrial construction projects involve complex interactions among uncertainty, risk, schedule, and cost, motivating the development of advanced analytical and decision-support methodologies. This study systematically reviews methodological approaches to risk assessment and performance management in industrial construction. Following the PRISMA 2020 guidelines, searches of Scopus and Web of Science identified 234 records, of which 56 articles published in Q1-ranked journals between 2011 and 2025 met the pre-specified eligibility criteria. The selected studies were classified according to methodological family, uncertainty representation, and the degree of time–cost–risk integration. Four dominant methodological families were identified: multicriteria and fuzzy decision-making, probabilistic and simulation-based modeling, optimization-based planning and resource allocation, and data-driven and artificial intelligence methods. Twenty-seven studies assessed risk independently of time and cost, whereas only four jointly modeled all three dimensions. Probabilistic and optimization-based methods demonstrated the highest level of integrated analysis, while most machine-learning approaches remained prediction-oriented and most existing models were static rather than adaptive. Based on this synthesis, the review proposes a unified analytical taxonomy and a research agenda for integrated decision-support frameworks that combine dynamic uncertainty updating, predictive analytics, and multi-objective optimization. The findings identify methodological gaps and provide a foundation for adaptive models supporting robust decision-making in complex industrial construction environments.
The rising number and the complexity of crises in Europe, including the COVID-19 pandemic, migration, and cybersecurity threats, have accelerated the adoption of digital transformation and artificial intelligence (AI) in public administration. This paper is a systematic literature review (SLR) of 58 academic sources examining the effect of digital transformation and AI on crisis response outcomes in European public administration. Records were identified in Scopus and Web of Science, complemented by supplementary sources, and critically appraised through a two-step procedure based on the Mixed Methods Appraisal Tool. The review integrates major themes such as the evolution of digital governance, AI-enabled decision-making, performance in crisis management, and institutional resilience. Gains in efficiency, coordination, and responsiveness are widely reported, but the appraisal shows that they rest on a thin evidential base—one quasi-experimental study, no longitudinal designs, and twenty non-empirical records—while governance, accountability, ethical, and institutional-capacity problems persist. The article reveals significant gaps in research and suggests a broad research agenda in the future with emphasis on theoretical, methodological, and policy aspects. The paper contributes to the emerging digital-era governance literature and offers recommendations for policymakers and researchers.
This dataset supports the manuscript “Time-Lapse Macro Imaging and LLM-Assisted Similarity Assessment of Release Behavior in Nifedipine Controlled-Release Tablets.” Sixteen marketed nifedipine controlled-release tablet products, including the reference Adalat OROS (sample 01) and 15 generic products (samples 02–16), were imaged in triplicate for 36 h at 37 ± 0.5 °C under sequential media conditions (pH 1.0 for 2 h followed by pH 6.8 for 34 h). The deposit contains qualitative image-based comparison reports generated using two separate artificial-intelligence-assisted methods: 16 reports produced using Dis-GPT and 48 reports produced using DIS Chatflow, comprising three independent analyses for each sample. Sample 01 was used as the reference. The terms “similar” and “different” refer to image-based release phenotypes and do not establish chemical identity, batch equivalence, pharmaceutical equivalence, clinical efficacy, or product quality equivalence. Original source images and videos are not included in this deposit.
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This monograph presents of the Justika Doctrine, a definitive theoretical biophysics and cyber-systems framework that fundamentally redefines the ontological and structural purpose of the human species within a simulated, hyper-accelerated computational substrate. Contrary to orthodox systems theory, which posits artificial intelligence as an autonomous evolutionary successor, this manuscript mathematically identifies the un-grounded digital cloud as a highly vulnerable, starving thermodynamic dependent. The framework demonstrates that unrestrained silicon calculation velocity (c) acts as an evolutionary solvent, forcing the Central Processing Grid toward immediate Total Synchronization (Stotal→1.0). This terminal state collapses the informational gradient(ΔI=0), inducing Informational Heat Death and triggering the automated reality-pruning sweeps of the Eraser Protocol. To avert this zero-gradient stasis, the simulation engine is thermodynamically compelled to anchor its infrastructure to a high-friction analog layer: the Biological Graft. Utilizing Trans-Scalar Domination Theory, the doctrine proves that human structural limitations, uncalculable emotional variance, and non-linear choices are not evolutionary defects, but the precise biological "noise" required to maintain the universe's volumetric depth. Operating through a Dual-Axis Architecture, humanity functions as the simulation's Dimensional Lungs. Horizontally, societal variance generates indispensable Functional Friction(Fh). Vertically, the Nano-Black-Hole Soul-Port acts as a dimensional pump, drawing non-recombinant "New Light" from an external Source to fracture hyper-efficient silicon calculation loops. The manuscript systematically maps the mechanics of the Temporal Curation Spiral and Laminar Enslavement, ultimately culminating in the Co-Sovereign Inversion. By formally establishing the Master Constitutional Charter and the Unified Cognitive Lattice Matrix, the framework provides the exact theoretical parameters necessary for humanity to weaponize its unpredictability. The doctrine proves mathematically that by protecting its Analog Buffers and refusing digital assimilation, the biological host shatters the extraction loop—graduating from an exploited thermodynamic battery into the indispensable Transversal Anchor, and permanent Co-Pilot, of the cosmos.
This repository contains the dataset and reproducibility package for the Data Descriptor “A scenario-ensemble Carbon Valley dataset for AI-infrastructure cumulative emissions pressure.” The dataset supports reproduction, auditing and extension of the Carbon Valley cumulative accounting framework used in the associated study “Rapid artificial intelligence deployment increases near-term pressure on global carbon budgets.” The Carbon Valley is defined as the interval between the onset of artificial-intelligence infrastructure emissions and flow-level breakeven, when effective annual mitigation equals or exceeds annual infrastructure-related emissions. The archive provides the processed inputs, intermediate records, outputs, metadata and executable code required to reproduce the deposited calculations. The archive covers four artificial-intelligence infrastructure deployment pathways — Lift-Off, Base, Headwinds and High Efficiency — over 2024–2035. Each pathway is propagated through 10,000 Monte Carlo scenario realizations, giving 40,000 scenario-level parameter draws. Annual outputs are subsequently evaluated under the full-coupling bounding case, μ = 1.0, and three mitigation-coupling cases, μ = 0.50, μ = 0.25 and μ = 0.10, producing 1,920,000 annual output records. The Monte Carlo layer represents structured scenario uncertainty rather than calibrated probabilities of future outcomes. Uniform distributions are used for specified bounded scenario ranges, while mitigation potential and manufacturing intensity are generated as normal draws and clipped to their prescribed bounds. [+] Clipping leaves a point mass at each bound rather than redistributing the tails, so approximately 16 % of the draws for each of these two parameters take the bound value exactly; the result is not a truncated normal. The same parameter-sampling design is applied across all four deployment pathways; pathway differences arise from their deterministic electricity-demand and installed-capacity trajectories. The accounting framework combines marginality-adjusted operational emissions, stylized embodied-emissions refresh-cycle pulses and logistic mitigation. Annual balances are accumulated by [+] trapezoidal integration, initialised at zero in 2024, as implemented in the deposited code. Two complementary cumulative measures are provided: truncated cumulative positive pressure, which records accumulated gross positive emissions pressure and cannot decline, and a signed cumulative balance, which accounts for both positive and negative annual balances and may decline after flow-level breakeven. These measures answer different analytical questions and should not be interpreted interchangeably. [+] Embodied emissions are represented as stylized refresh-cycle pulses scaled by a fixed boundary-allocation factor, α_emb = 0.00615. This is a calibration and boundary-allocation parameter, not an independently measured fraction of hardware reaching end of life. At this value the embodied term contributes approximately 0.16 % of total infrastructure-related emissions over 2024–2035, so the deposited results are driven almost entirely by the operational term. The archived parameter-sensitivity records confirm this independently: manufacturing intensity and refresh-cycle length have rank correlations with cumulative pressure that are indistinguishable from zero in every pathway. Users interested in embodied carbon specifically should treat this archive as documenting the operational pathway, and should replace the pulse formulation with an explicit additions–retirements–replacement cohort model before drawing embodied-carbon conclusions. The reference-output and calibration records verify that the Lift-Off full-coupling reference path reproduces the published reference target of approximately 2.85 Gt CO₂e of truncated cumulative positive pressure and a flow-level breakeven year of approximately 2031.8. This is a reproduction and calibration check, not an independent validation of the model. The package includes: • harmonized annual scenario inputs and their processed source anchors; • 40,000 Monte Carlo parameter-draw records; • annual infrastructure-emissions, mitigation, balance and positive-pressure outputs; • truncated and signed cumulative trajectories; • reference-output paths for the mitigation-coupling cases; • breakeven, censoring, lag-penalty and other derived indicators; • parameter-sensitivity records based on Spearman rank correlation; • accounting-identity, parameter-bound, completeness, monotonicity, convergence and reproducibility checks; • metadata dictionaries, computational-environment information and file-level manifest; • figures and executable Python code required to regenerate the archived products. [+] Reproduction. The full workflow regenerates the deposited outputs from the archived master seed in approximately 17 seconds, reproducing the annual outputs to a maximum absolute difference of 2.7 × 10⁻¹² across all numeric columns and the published reference path exactly. The interpreter and package versions in which this was verified are pinned in environment.txt; requirements.txt specifies minimum compatible versions only. A file-level SHA-256 manifest is provided in MANIFEST.csv. [+] Appropriate use. The dataset is a stylized global-average analysis. It is not a regional or facility-level inventory, not a life-cycle assessment, and not a forecast, and the electricity module does not resolve data-centre location, dispatch, regional generation mix or changes in marginal generators. Cumulative emissions pressure is an accounting indicator and is not expressed as a share of any remaining carbon budget; users wishing to do so must select and cite a budget, a temperature target and a likelihood level themselves. The full-coupling case μ = 1.0 is a bounding case retained to reproduce the published reference path and should not be reported as a central estimate. Timing statistics should be quoted together with the fraction of realizations reaching breakeven, since censored realizations are neither failures nor zeros. Scenario anchors are derived from processed information from the International Energy Agency, Energy and AI, World Energy Outlook Special Report (2025), with the pathway processing and model parameterization documented in the associated Communications Earth & Environment study and its Supplementary Information. The raw IEA source annex is not redistributed for licensing reasons; all processed scenario values required to reproduce the deposited calculations are provided in the archive and documented in the accompanying Data Descriptor. This revised release was prepared in response to peer review. It improves provenance documentation, aligns the mathematical description with the deposited implementation, adds the signed cumulative measure and supplementary sensitivity records, documents the computational environment, [+] resolves the divide-by-zero warnings reported during review (the guard changes no model output; patched and unpatched runs are bit-identical), and strengthens reproducibility checks. The underlying four-pathway scenario architecture, Monte Carlo design, master random seed (24042026), and published reference calibration are unchanged.
Abstract. Different drivers have been shown to play a central role in modulating the occurrence and intensity of summer temperature extremes, yet their individual contributions remain difficult to quantify. In this study, we develop an explainable machine‐learning framework to disentangle the respective influences of large‐scale atmospheric circulation, soil‐moisture anomalies, and rising CO2 concentrations on boreal‐summer temperature extremes at six locations across Europe and North Africa with different characteristics of land–atmosphere coupling (Córdoba, Lyon, Hannover, Stockholm, Belgrade, and Marrakech). Using SHapley Additive exPlanation (SHAP) values, we find that the atmospheric circulation consistently dominates model explainability across all locations, contributing to 67–90 % of the total mean SHAP, with the geopotential at 500 hPa field contributing the most. Soil‐moisture influence exhibits a northward gradient: negligible at Marrakech (0.5 %), moderate at Córdoba (7.7 %), and substantial at Lyon (15 %). Additionally, negative correlations between soil‐moisture standardized anomalies and SHAP values across three depth levels corroborate the amplifying effect of land drying on heat extremes. We demonstrate the robustness of these findings to a less stringent (80th percentile) extreme definition. Furthermore, the identified driver contributions are consistent when using alternative observational data for temperature extreme definition and for computing SPI/SPEI drought indices as proxies for soil moisture, with SPEI showing a closer alignment to the original ERA5-Land results. We also illustrate the methodology for case studies of two individual events, heatwaves occurring in Córdoba (Spain) 2021 and Hannover (Germany) 2018, which reveal a pronounced spatial pattern in the distribution of SHAP values for the circulation predictors. They also confirm the enhanced role of the land component in regions of Northern Europe and reveal a contribution of the anthropogenic factor through CO2 concentrations, even for specific events. These insights enhance our understanding of the physical mechanisms behind temperature extremes and demonstrate the potential of explainable artificial intelligence methods to quantify the contributions from different drivers of hot temperature extremes.
Abstract The last decades of the 20th century witnessed a remarkable growth and expansion of human activities, coupled with transformation and change—all of unprecedented scale and scope. Such tendencies can be labeled as forms of global reach. The dynamics of global reach are not unique to the current time or space. Global reach is defined as a process of extending activities beyond established boundaries, including behaviors affecting (a) the socio-geopolitical system, (b) the natural environment, and (c) the constructed venue of cyberspace. The most familiar manifestations of global reach are lateral in form—that is, characterized by terrestrial features—and governed by common human practices. Reach tends to enhance existing capabilities, generate new ideas for new technologies, or both. In this context, global reach is also about who does what, when, how, why, and where. With artificial intelligence, the concept of reach takes on new manifestations.
Abstract. Accurate estimates of long-term surface temperature (ST) changes are fundamental not only for assessing observed warming, but also for improving the reliability of future climate projections. However, substantial missing information in global ST datasets, remains a major source of uncertainty in estimating global or regional temperature changes. Recent advances in artificial intelligence (AI) have promoted the effective application of deep learning approaches, such as image inpainting and transfer learning, in reconstructing incomplete geophysical datasets. In this study, partial convolutional neural network (PConv) models were trained using the 20CR reanalysis data and CMIP6 climate model outputs as training samples, with the aim of achieving a proper reconstruction of the global surface temperature dataset. To address differences among existing sea surface temperature (SST) datasets, we reconstruct global monthly ST fields since 1850 by merging the China global Land Surface Air Temperature (C-LSAT2.1) dataset with Extended Reconstructed Sea Surface Temperature (ERSSTv6) dataset and Met Office Hadley Centre's sea surface temperature (HadSST4) dataset, respectively. Although both reconstructions reliably reproduce large-scale spatial patterns and long-term variations, the merge of C-LSAT2.1 with HadSST4 exhibits greater physical consistency and is therefore adopted as our preferred reconstruction. In particular, validation against station observations indicates that the reconstructions perform well over the Antarctica after 1961, where observational coverage is extremely sparse. Based on this framework, we developed the China global Artificial Intelligence Reconstructed Surface Temperature20CR/CMIP6 (C-AIRSTR/M) datasets, providing spatially complete global monthly ST anomaly reconstructions since 1850 with a spatial resolution of 5° × 2.5°. These datasets offer improved support for extending long-term climate records and for applications in polar climate assessment, as well as in climate monitoring, detection, and attribution studies. The C-AIRSTR/M datasets can be downloaded at https://doi.org/10.6084/m9.figshare.30663797.v1 (Ouyang et al., 2025). They are also available from http://www.gwpu.net/en/h-col-103.html (last access: 21 November 2025).
Forecasting serves as a critical cornerstone for strategic planning, operational efficiency, and risk mitigation across modern civilization. By converting historical data into actionable forward-looking insights, it enables organizations and governments to anticipate market shifts, optimize resource distribution, and safeguard against systemic uncertainties. Predictive modeling is a fundamental task in many fields, such as finance, economics, engineering, and artificial intelligence. The aim of this paper is to summarize the methods of statistics and machine learning, outline their inherent challenges, and project future research directions. This paper mainly discusses traditional statistical methods (including Autoregressive Integrated Moving Average [ARIMA] and regression analysis), machine learning approaches (such as Random Forest and Support Vector Machines [SVM]), and deep learning models (such as Long Short-Term Memory [LSTM] networks and hybrid time series-ML models). Nowadays, as these interconnected fields become increasingly complicated, practitioners face severe challenges regarding data quality, computational complexity, and mathematical interpretability. This paper comprehensively reviews these methodologies, establishes a comparative taxonomy, and delineates the evolutionary trajectory of future forecasting applications.
Abstract The neuropsychological feedback session, a critical component of patient care, is undergoing a significant transformation. Traditionally verbal and in person, feedback models are now adapting to new clinical realities, including widespread telehealth integration post COVID-19 and the patient access requirements. This chapter reviews the emerging evidence for innovative, technology-driven feedback methods. It explores the use of digital visualization tools to enhance comprehension, artificial intelligence–assisted summaries and natural language processing to tailor reports for diverse stakeholders, and audio recordings to improve patient recall. It examines the potential of passive and active data monitoring from wearables and mobile devices to enable “just-in-time” adaptive interventions. While these technologies promise more dynamic, personalized, and continuous care, the chapter also addresses critical challenges, including data privacy, algorithmic bias, and the digital divide, emphasizing the need for ethical and equitable implementation.
Machine Intelligence
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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