Search Results (212)

The European crafts ecosystem faces critical structural threats, declining practitioner numbers, weakening intergenerational transmission, limited digital literacy, and competition from industrial imitation. Existing online craft communities are narrowly material-specific and structurally ill-suited to the cross-disciplinary dialogue required for systemic sector transformation. This paper presents the design, iterative development, and pilot evaluation of the Craeft Community, a multi-stakeholder Virtual Community of Practice (VCoP) developed within the Horizon Europe CRAEFT project. Three research questions guided the study: how a multi-stakeholder VCoP should be structured to overcome disciplinary fragmentation; to what extent a stewarded digital forum can operationalize Situated Learning and Communities of Practice theory; and what factors facilitate or inhibit engagement and post-funding sustainability. Using design-based research, the platform evolved through four iterative phases, culminating in restructuring from a material-based architecture into five transversal thematic pillars, driven by survey evidence from 151 European craft professionals and systematic stakeholder feedback. The pilot phase yielded 86 registered members, 31 posts, and 27 interactions, with Transmission & Training as the most engaged pillar. Qualitative analysis reveals substantive cross-disciplinary discourse alongside a structural Effort-Engagement Gap, a persistent tension between forum participation demands and the gravitational pull of mainstream social media. The study demonstrates that a thematically organized, stewarded VCoP can meaningfully operationalize apprenticeship-based learning in digital settings, advancing craft heritage preservation, economic resilience, and hybrid professional identity formation at the intersection of craft and technology. Full article

Background/Objectives: Self-management intervention strategies are recommended to address the needs of breast cancer survivors (BCSs) who have cardiovascular diseases (CVDs). Despite the benefits, these strategies are often suboptimally implemented, resulting in inadequate cardiovascular management among BCS with CVDs. The project aims to develop a nurse-led and GP-supported self-management (NGPS) intervention to reduce cardiovascular risks in BCS with CVDs and evaluate its feasibility and potential effects in primary care settings using the double diamond co-design process and the Medical Research Council (MRC) Framework for Developing and Evaluating Complex Interventions. Methods: The study will be conducted in two phases. In phase I, an evidence-based and co-designed NGPS protocol with end-users will be preliminarily developed based on the identified research evidence, relevant theories, practice guidelines and the current practice standards in Australia. The co-design workshop(s) will involve healthcare professionals and BCSs with CVDs to further refine and validate the developed preliminary protocol. In phase II, a one-group pre-post pilot study will evaluate the feasibility of the intervention and study procedures, such as recruitment, retention, adherence, acceptability, and safety as primary outcomes. The study will also preliminarily explore the effectiveness of the intervention such as physiological measures as secondary outcomes to inform a future large-scale trial. Phase II also involves follow-up qualitative interviews to explore participants’ experiences of the pre-post pilot study. The participants will be recruited from primary medical centres in Victoria. Conclusions: The findings will provide a co-designed evidence-based intervention for primary care nurses and General Practitioners (GPs) to promote long-term health outcomes for patients with both breast cancer (BC) and CVDs. Clinical registration: ClinicalTrial.gov (registration No. NCTO7313397). Full article

Against the background of China’s large-scale, fast-paced, and uniformly constructed urban renewal, Lingnan arcade buildings are confronted with unprecedented conservation challenges, a contextual feature that endows this research with unique academic value in the international arena. Drawn from the authors’ practical participation in a government-led arcade conservation project in Zhao’an, three pragmatic research gaps unaddressed by existing theories are identified. Through a systematic comparative analysis of conservation and sustainable renewal models, as well as specific adaptive reuse cases of arcade buildings in four representative Lingnan cities, Macao, Hong Kong, Guangzhou and Shantou, three targeted recommendations corresponding to the aforementioned dilemmas are proposed to inform future practices: 1. Arcade conservation practices should not be restricted to superficial facade restoration. Instead, a well-defined intervention framework shall be established prior to the implementation of any conservation works. 2. Sufficient emphasis shall be placed on the material and structural authenticity throughout the design and construction phase. 3. A sustainable long-term operation mechanism shall be formulated post restoration to facilitate the revival of the inherent vitality of arcade buildings. Full article

The transition towards circular practices in the construction sector requires integrated approaches addressing both material resource procurement and the fragmentation of local stakeholder networks. This study presents the ACT-FLOW Framework, a multi-level approach enabling territorial circular processes by integrating actor networks and material flows. The framework comprises three interconnected levels (strategies, processes, and indicators) supporting the definition, implementation, and evaluation of circular practices across building and territorial scales with an iterative refinement phase. Methodologically, it was developed through a Design Science Research approach (DSR) articulated into four steps: (1) define the scope and boundaries; (2) develop a knowledge base; (3) structure the framework and its components; and (4) validate and apply it to a real case, the Circular Design Polito Lab, a research infrastructure currently under development by the Politecnico di Torino (Italy). The results demonstrate how the framework supports stakeholder coordination, structures circular workflows, and enhances circular performance monitoring. The primary limitation is that the case study has not yet been realized; consequently, it is not feasible to conduct an ex post but only an ex ante evaluation of the results. Future research will assess the framework’s capacity to foster ecosystemic conditions for circular construction practices through its longitudinal application across project phases. Full article

Background: Electroencephalographic (EEG) functional connectivity analysis requires multiple signal-processing, source-modelling, and statistical steps that can limit its adoption in clinician-led randomised controlled trials (RCTs). NeuroStat was developed as a prototype research tool to integrate this workflow; formal usability validation with clinician end-users has not yet been conducted. Methods: NeuroStat is an open-source Python/PyQt6 desktop application that integrates automated artefact removal (a Generalised Eigenvalue Decomposition for Artefact Identification [GEDAI] pathway and a traditional Artefact Subspace Reconstruction (ASR)/Independent Component Analysis (ICA)/ICLabel pathway), boundary element model (BEM) source localisation using the Desikan–Killiany atlas (68 cortical regions), Phase Lag Index (PLI) connectivity estimation across five canonical frequency bands, and RCT-oriented statistical analysis. Evaluation separated sensor-space and source-space claims: a sensor-level simulation (repeated across five independent random seeds) tested preprocessing robustness, a repeated source-space simulation tested recovery of a known cortical parcel-pair contrast after forward projection and inverse reconstruction, a PhysioNet benchmark tested posterior Desikan–Killiany alpha PLI in 20 healthy adults, and an illustrative application to 20 sessions from a published chiropractic RCT demonstrated real-world workflow applicability. Results: In the sensor-level simulation benchmark, the Traditional pathway achieved a mean absolute error of 0.168 ± 0.017 PLI units and root mean squared error of 0.219 ± 0.045 (mean ± SD across five independent random seeds) across all artefact conditions. In the source-space simulation, reconstructed alpha PLI for the known bilateral lateral-occipital parcel pair exceeded anterior control edges across 60 repeated condition runs (mean known-control difference = 0.105 PLI units, 95% CI 0.096–0.114; t(59) = 22.61, p < 0.001). In the PhysioNet source-space benchmark, posterior Desikan–Killiany alpha PLI was higher during eyes-closed than eyes-open rest (Cohen’s d = 0.85, p = 0.001; 16/20 subjects showing the expected direction) after ICLabel-enabled preprocessing. In the pilot RCT application, all 20 sessions completed processing without manual intervention, with default-mode network alpha PLI showing a pre-to-post change of +0.071 in the intervention group versus +0.015 in the active control group. Conclusions: NeuroStat integrates preprocessing, source-space construction, connectivity estimation, and statistical reporting within a parameter-logged desktop workflow for EEG functional connectivity studies. Current evidence supports initial technical feasibility, sensor-level preprocessing robustness for one pathway in controlled simulations, source-space recovery of a known parcel-level contrast, source-space sensitivity to an expected posterior alpha resting-state contrast, and error-free processing across 20 real RCT sessions in a pilot workflow demonstration. Formal usability testing, test–retest reliability analysis, participant-specific source-model validation, and clinical-population validation remain necessary before clinician-facing or trial-deployment claims can be made. Full article

Egon Steinmann’s industrial architecture represents a significant yet insufficiently researched contribution to the development of post-war industrial architecture in Croatia. This paper examines his industrial projects designed between 1947 and 1965 within the context of post-war industrialization and modernization in socialist Yugoslavia. Based on archival documents, historical photographs, field observations, and comparative analysis, the paper first identifies Steinmann’s broader industrial work and then examines six selected industrial complexes in Zagreb. The case studies are compared in terms of their urban context, spatial organization, structural systems, production logistics, daylighting strategies, and architectural expression, highlighting differences between heavy industrial facilities and food-processing plants. A comparison of historical and contemporary orthophotos is further used to evaluate the long-term spatial transformation and adaptability of these industrial sites. The findings demonstrate that Steinmann’s designs were characterized by rational planning, large-span and flexible structures, integration of technological and transport requirements, and the capacity for phased expansion. The continued industrial use and preservation of many of these complexes confirm the lasting value of his architectural and planning concepts, contributing to a broader understanding of Croatian industrial architecture and socialist industrial modernism of the 1950s and 1960s. Full article

Field-Programmable Gate Array (FPGA) technology allows for the creation of unique hardware implementations based on mass-produced chips. The process of project prototyping for such systems using Hardware Description Languages (HDLs) remains complex, even with modern tools. The comparison of HDL coding styles, for example, a behavioral case statement against a structural binary-tree decomposition, shows that the choice is capable of affecting post-implementation timing and area. The performed study, using the open-source yosys/nextpnr toolchain, shows that the validity of such a comparison is decided by the fabric domain. Logic that falls through to generic Look-Up Table (LUT) mapping is governed by the mapper’s heuristic fixed point rather than by source intent: on the crossbar, the behavioral and structural netlists become identical in cell composition; on the priority encoder, the verdict reverses; and on the barrel shifter, the LUT area collapses, so the comparison does not isolate the coding-style variable. It was measured that the keep_hierarchy attribute restores a meaningful comparison at ~17% LUT cost (N = 8) and provides a structural invariant to the ABC mapper variant, but the behavioral result is mapper-sensitive and the N = 4 verdict reverses under the legacy -noabc9 mapper (Cohen’s d from +2.4 to −1.6). By contrast, logic that involves a dedicated primitive before LUT mapping—an adder bound to the carry chain or a multiplier bound to a DSP block—yields source-meaningful verdicts that do not reverse with a mapper. Replication on a second fabric (Lattice iCE40) confirms that this behavior is fabric- rather than vendor-specific. The main contribution of this work is the proposed first fabric-domain characterization of synthesis canonicalization as a methodological hazard for cross-HDL FPGA studies on open-source toolchains, which identifies the two-phase synthesis mechanism that delimits it and supplies a decision rule (inspect post-synthesis composition) to identify whether a given comparison is susceptible. Full article

Background: The coronavirus disease 2019 (COVID-19) pandemic significantly disrupted cancer screening, diagnosis, and care. This phase of the Hungarian Cancer Epidemiology (HUN-CANCER-EPI) study evaluated trends in cancer incidence and mortality in Hungary during the pre-COVID (2011–2019), COVID (2020–2021), and post-COVID (2022–2023) periods. Methods: Nationwide data from the Hungarian National Health Insurance Fund database were analysed. Age- and sex-adjusted incidence and mortality trends from 2011 to 2019 were modeled using Poisson regression. Changes from trends in 2020–2023 were compared to pre-COVID projections with 95% confidence intervals. Results: From 2011 to 2019, age-standardised cancer incidence declined by 1.9% (95% CI: 1.3% to 2.4%) annually in males and by 1.0% (95% CI: 0.6% to 1.4%) in females. During 2020–2021, incidence dropped sharply below the expected: in 2020 (−12.8% in males and −11.8% in females) and in 2021 (−11.7% and −7.9%, respectively). The largest declines affected prostate, melanoma, and kidney cancer. Rapidly progressing tumors like pancreatic and esophageal showed smaller decreases. By 2023, partial incidence rebounds were observed for prostate cancer, kidney cancer, and melanoma, likely reflecting the recovery of pandemic-delayed diagnoses. Lung and liver cancers showed no rebound. The steepest drops were in males aged 70+, with incomplete recovery. Mortality stayed near expected levels overall, with some exceptions, like melanoma, where the rebound in incidence coincided with increased mortality rates in 2023, which may reflect delayed diagnosis, although this cannot be directly confirmed. Conclusions: The pandemic had lasting, cancer-type-specific impacts on incidence patterns, particularly affecting screening-dependent, slow-growing tumors. Mortality remained largely stable overall during the available follow-up, highlighting the need for targeted recovery strategies and strengthened healthcare system resilience. Full article

Background/Objectives: The trajectory of influenza hospitalization burden from pre-COVID-19 pandemic baseline through post-pandemic recovery remains poorly characterized at the national level. This study characterized phase-stratified burden and seasonal structure, quantified racial and ethnic disparities, and assessed whether post-pandemic seasons represent anomalous departures from pre-pandemic expectations. Methods: Sixteen complete seasons of FluSurv-NET surveillance data (2009–2010 through 2024–2025; 509 observation weeks) were analyzed across pre-pandemic, disruption, and recovery phases using OLS regression with effect-size estimation, bootstrapped age-adjusted rate ratios, seasonal-trend decomposition (STL), Prophet time-series forecasting, and Isolation Forest anomaly detection. Results: Mean peak weekly hospitalization rate nearly doubled from pre-pandemic to recovery (5.1 to 11.1 per 100,000), cumulative seasonal burden increased from 46.3 to 87.0 per 100,000, and median peak timing advanced from MMWR week 9 to week 50. STL decomposition revealed a marked shift from weak pre-pandemic seasonality (Fs = 0.14) to substantially stronger annual regularity (Fs = 0.98) across three recovery seasons, with threefold amplitude increase. Non-Hispanic Black persons had rate ratios of 1.72, 2.16, and 1.99 relative to White persons across phases; American Indian and Alaska Native persons showed the highest disruption-phase ratio (2.24, 95% CI 1.90–3.53), based on two contributing seasons. A flat-growth Prophet model detected first exceedance in February 2020, outperforming a linear-growth specification on held-out validation. Isolation Forest identified 2017–2018, 2023–2024, and 2024–2025 as robust anomalies across all contamination thresholds. Conclusions: Post-COVID-19 pandemic influenza recovery is characterized by intensified and restructured seasonality, persistent racial and ethnic disparities, and anomalous burden exceeding pre-pandemic projections, identified independently by time-series forecasting and unsupervised anomaly detection. Full article

Post-Quantum Cryptography (PQC) migration to National Institute of Standards and Technology (NIST) Federal Information Processing Standards (FIPS) 203, 204, and 205 under the National Security Agency (NSA) Commercial National Security Algorithm Suite (CNSA) 2.0 is a multi-year, multi-domain transformation across cloud, enterprise, embedded, operational technology (OT), tactical, and national-security systems. Anthropic’s Claude Mythos Preview (April 2026) introduces artificial intelligence (AI)-accelerated cybersecurity capabilities that intersect this migration directly, performing autonomous reasoning against previously unknown vulnerabilities in production software—a qualitative departure from signature-based and static and dynamic application security testing (SAST/DAST) tooling. Drawing on federal guidance from NIST, NSA, the Office of Management and Budget (OMB), and the Cybersecurity and Infrastructure Security Agency (CISA), and on independent analyses from the Centre for Emerging Technology and Security (CETaS) and the UK AI Security Institute, we present a lifecycle and architecture analysis of how Mythos-class models alter PQC migration timelines, risk surfaces, lifecycle dependencies, and architectural constraints. Modeling Mythos as both accelerator and destabilizer, we derive an analytic projection of a compressed two-to-four-year migration window for highest-exposure systems, against traditional baselines of five-to-ten years for small organizations and twelve-to-fifteen-plus years for large enterprises. The compression collapses human-labor bottlenecks in discovery, planning, and code modification, not cryptography itself. We propose a lifecycle-aligned migration model, an updated cost model, and governance requirements for frontier-model access. The binding constraint shifts domain-conditionally: defender capacity at adversary tempo governs software-analytical phases, while non-compressible external cadence governs embedded and regulated domains. Full article

The continued loss and degradation of wetlands pose major challenges to global waterbird conservation. In response, large-scale wetland restoration projects have been widely implemented worldwide, yet their long-term ecological effectiveness has not been sufficiently evaluated. Here, we assessed the long-term impacts of wetland restoration on waterbird communities at Chongming Dongtan Wetland, China, using 17 years of monitoring data spanning pre-restoration, restoration, and post-restoration phases. Our results suggest that the Ecological Control of Spartina alterniflora and Improvement of Bird Habitats substantially enhanced waterbird diversity, with both species richness and total abundance increasing significantly after restoration. Restored artificial wetlands supported particularly high abundances of waterbirds, confirming their role as critical supplementary habitats alongside natural tidal flats. Notably, different waterbird guilds exhibited pronounced seasonal shifts in habitat use: the Anatidae predominated during the wintering period, whereas Waders dominated during spring and autumn migrations, and the degree of reliance on artificial versus natural wetlands varied markedly between guilds and across seasonal cycles. Beyond local effects, we detected a clear spillover effect, whereby increases in waterbird abundance and species richness were also observed in adjacent non-restored natural intertidal mudflats following restoration. In addition, several threatened and nationally protected species were recorded exclusively during the post-restoration phase, indicating improved habitat suitability for conservation-priority taxa. Overall, our findings highlight that wetland restoration can generate both local and landscape-scale biodiversity benefits, emphasizing the importance of incorporating habitat heterogeneity, seasonal habitat requirements, and spillover effects into coastal wetland restoration and management strategies. Full article

Qinghai Lake, the largest endorheic saline lake in China, has undergone a pronounced hydrological regime shift from a multi-decadal decline to a rapid post-2004 recovery, reflecting strong hydroclimatic non-stationarity in the northeastern Tibetan Plateau (TP). This paper supplements the current water level and lake area status of Qinghai Lake to provide basic background for future prediction. Reliable forecasting of such climate sensitive lake systems remains difficult because conventional statistical models often fail to capture non-linear fluctuations, whereas standalone deep learning models may overlook long-term deterministic evolution. To address this challenge, we developed a serial decomposition GeoAI framework that integrates autoregressive integrated moving average (ARIMA), one-dimensional convolutional neural networks (1D-CNNs), and long short-term memory (LSTM) networks for non-stationary water level forecasting. Using annual water level observations from 1960 to 2025, the ARIMA component was first used to extract the low-frequency deterministic trend, after which the CNN-LSTM module reconstructed the nonlinear residual variability. The model was trained on the 1960–2012 period and validated over 2013–2025, which represents the most dynamic expansion stage of Qinghai Lake. The hybrid framework outperformed the benchmark models, achieving a Root Mean Square Error (RMSE) of 0.2033 m, Mean Absolute Error (MAE) of 0.1727 m, and Mean Squared Error (MSE) of 0.0413 m² during validation. The decomposition strategy effectively reduced phase lag and amplitude attenuation, improving both predictive accuracy and process interpretability. Multi-step forecasting for 2026–2056 suggests that Qinghai Lake will continue to rise, reaching approximately 3204.08 m by 2056, although the growth rate is projected to slow as negative hydrological feedback strengthen. By explicitly separating deterministic climate scale signals from nonlinear short-term variability, the proposed framework provides a robust and transferable geoinformation based tool for forecasting water level dynamics and supporting adaptive management in climate sensitive, data scarce lake basins. Full article

Background and Objectives: The SARS-CoV-2 pandemic disrupted oral and maxillofacial surgery (OMS) services worldwide because of the high aerosol-generating nature of head-and-neck procedures, restricted access to elective dental care, and systemic reallocation of hospital resources. Continuous longitudinal multi-year data covering both the pandemic and the post-pandemic phases from regional Romanian (and more broadly central and southeastern European) emergency centers remain scarce. We aimed to quantify the impact of the pandemic on OMS activity in a large Romanian regional referral center and to evaluate post-pandemic resilience. Materials and Methods: We conducted a retrospective single-center study of all inpatient admissions to the OMS Clinic of a tertiary emergency hospital in western Romania between 1 January 2018 and 31 December 2024. Three periods were pre-specified: pre-pandemic (2018–2019), pandemic (2020–2022) and post-pandemic (2023–2024). A Newey–West segmented interrupted-time-series (ITS) regression and a negative-binomial monthly count model with Fourier seasonality were fitted; length of hospital stay was further analyzed with a multivariable gamma-log generalized linear model adjusted for age, sex, county, primary ICD-10 chapter and total ICD-10 codes. Variables analyzed included case volume, demographics, primary and secondary ICD-10 diagnoses, length of hospital stay (LOS), case complexity (total ICD-10 codes per admission) and in-hospital mortality. Results: A total of 11,628 inpatient admissions corresponding to 8084 unique patients (56.5% male; mean age 52.2 ± 19.2 years) were analyzed. Compared with the pre-pandemic baseline (mean 2037 admissions/year), annual volume dropped by 45.1% in 2020, 44.0% in 2021 and 32.3% in 2022, with a nadir of −76% during the first state of emergency (April 2020; n = 34 admissions). Recovery was rapid; 2024 exceeded the pre-pandemic baseline by +10.1% on raw counts and by +16.2% on admissions per 100,000 catchment population using year-specific INS denominators. The segmented ITS regression confirmed an immediate level drop of −114.2 admissions/month in March 2020 (95% CI −133.1 to −95.3; p < 0.001) and a positive post-intervention slope of +2.06 admissions/month (95% CI 1.23–2.88; p < 0.001), with observed monthly volume returning to the counterfactual projection by October 2023. The case mix shifted significantly (χ² = 406.9, p < 0.0001); elective benign neoplasm admissions were reduced from 7.2% to 2.0%, while neoplasms of uncertain behavior nearly doubled from 15.7% to 27.5%. Case complexity increased during the pandemic (mean ICD codes 4.08 ± 2.42 vs. 3.44 ± 2.30; p < 0.001); after exclusion of administrative codes (whole Z chapter and U07.x), the difference attenuated to 3.34 vs. 3.17 codes (still p < 0.001 by Kruskal–Wallis), indicating that the largest portion of the unadjusted increase was driven by the new mandatory pre-admission SARS-CoV-2 screening code Z11.5 rather than true clinical complexity. Notably, the clinically interpretable proxy R63.3 (feeding difficulty) independently rose from 41.5% to 53.1%. The crude median LOS did not differ between the pre-pandemic and pandemic periods (3.07 vs. 3.06 d; p = 0.19) and dropped significantly post-pandemic (2.22 d; p < 0.001); however, after multivariable adjustment for case mix, age, sex, county and code count, the LOS was 15.7% shorter during the pandemic (adjusted ratio 0.84, 95% CI 0.82–0.87; p < 0.001) and 22.8% shorter post-pandemic (adjusted ratio 0.77, 95% CI 0.75–0.80; p < 0.001) relative to baseline. Conclusions: The pandemic caused a severe but transient contraction of OMS activity accompanied by increased case complexity and a marked shift away from elective surgery. Inpatient volume returned to and exceeded the pre-pandemic baseline by 2024. These results support the value of standing pandemic-preparedness protocols, sustained access to preventive dental care, and integrated tele-triage pathways for future public-health crises. Full article

Adaptive reuse of post-industrial heritage is often studied through technical performance, formal intervention strategies, or decision-support models. While these approaches clarify important aspects of reuse, they give limited attention to how projects evolve through the combined effects of architectural decisions, governance arrangements, financing mechanisms, policy instruments, social programs, and inherited fabric. This paper examines adaptive reuse as a time-structured project trajectory. It applies a hybrid methodology combining within-case reconstruction and comparative cross-case analysis to seven European projects in Brussels, Essen, Rotterdam, San Sebastián, Florence, Vienna, and Barcelona. The cases are analyzed across six dimensions: Asset & Context, Governance & Finance, Circularity, Social & Cultural, Policy & Design, and Outcomes & Transfer. The comparison shows that adaptive capacity depends on the alignment of governance, project time, and intervention strategy. Governance determines who can revise decisions and under what conditions; adaptation time is produced through funding horizons, approval procedures, institutional continuity, and civic or public stewardship; and strategies of retention, replacement, reversible insertion, and incremental occupation distribute future risk differently across project phases. From this synthesis, the paper extracts ten conditional lessons that frame adaptive reuse as configuration knowledge: transferable insights whose relevance depends on the interaction among governance capacity, temporal sequencing, inherited fabric, financing, policy support, and social objectives. The paper argues that knowledge transfer in adaptive reuse should be understood as disciplined translation across comparable constraints, not as the replication of models, rankings, or best-practice templates. Full article

To address the monitoring needs for carbon emissions during the construction phase, this paper proposes an intelligent analysis method based on construction activities. Fine-grained monitoring of construction carbon emissions is achieved through the collaborative application of a carbon emission quantification model, a digital twin monitoring model, and a Long Short-Term Memory (LSTM) prediction model. Firstly, based on three dimensions—time, space, and elements—the method constructs a quantification model for construction carbon emissions grounded in construction activities. This model accurately captures the dynamic relationships between material transportation losses, construction machinery usage, and carbon emissions. Secondly, leveraging digital twin technology, an integrated monitoring model is established, unifying three dimensions: element information, temporal processes, and model hierarchy. This model enables continuous data acquisition during the construction period. Finally, a Long Short-Term Memory (LSTM) neural network is introduced to enhance the accuracy of carbon emission predictions. Using a public building in Beijing as a case study, the research demonstrates that, with the traditional inventory method as a baseline (which exhibited a deviation of 18–25% from actual emissions verified through post-construction reconciliation), the proposed activity-based model reduced the calculation deviation for core division works to within 3.2%, an absolute reduction of approximately 15–22% points. The LSTM prediction model achieves an overall short-term prediction accuracy of 89%, with the Mean Absolute Percentage Error (MAPE) reaching approximately 11% across the full validation set. For a representative two-week forecasting case, the model yields a MAPE of 4.3%, with a deviation of 23 tCO₂e between predicted and actual emissions. This provides a viable technical pathway for carbon emission monitoring during the construction phase of building projects. Full article