Search Results (914)

Germanium (Ge) incorporation profoundly modifies the structural and electronic characteristics of carbon fullerenes, giving rise to a diverse landscape of substitutional, exohedral, and endohedral Ge–fullerene architectures. Although experimental studies demonstrate that Ge can be introduced into fullerene matrices through nuclear recoil implantation and arc-discharge synthesis, only exohedral germylated derivatives have been structurally confirmed to date. Substitutional germanium-doped fullerene (Ge-C₆₀) species remain experimentally elusive, with available evidence relying largely on radiochemical signatures and indirect spectroscopic data. In contrast, computational investigations provide a detailed and coherent picture of germanium doping across fullerene sizes, showing that Ge induces significant cage distortion, breaks local symmetry, narrows the highest occupied molecular orbital–lowest unoccupied molecular orbital (HOMO–LUMO) gap, and enhances charge localization at the dopant site. These electronic perturbations strongly increase the affinity of Ge-doped fullerenes for external guest molecules, leading to enhanced adsorption energies and distinct optical and transport responses in exohedral complexes. Theoretical studies of endohedral systems further indicate that Ge atoms or small clusters could form stable encapsulated species with unique electronic properties. Collectively, current evidence positions germanium-doped fullerenes as electronically versatile nanostructures with potential applications in sensing, optoelectronics, catalysis, and nanomedicine, while highlighting the need for definitive experimental synthesis and structural validation of substitutional Ge-fullerene derivatives. Full article

This work presents a fused-filament fabrication (FFF) hot end that combines an unrestricted-rotation C-axis with a rectangular-slot nozzle and an induction-heated melt sleeve. The architecture replaces the popular resistive cartridge and heater block design with an external coil that induces eddy-current heating in a thin-walled sleeve, threaded to the heat break and nozzle, reducing thermal mass and eliminating wired sensors across the rotating interface. A contactless infrared thermometer targets the nozzle tip; the temperature is regulated by frequency-modulating the inverter around resonance, yielding stable control. The hot end incorporates an LPBF-manufactured nozzle, which transitions from a circular inlet to a rectangular outlet to deposit broad, low-profile strands at constant layer height while preserving lateral resolution. The concept is validated on a desktop Cartesian platform retrofitted to coordinate yaw with XY motion. A twin-printer testbed compares the proposed hot end against a stock cartridge-heated system under matched materials and environments. With PLA, the induction-heated, rotating hot end enables printing at 170 °C with defect-free flow and delivers substantial reductions in job time (22–49%) and energy per part (9–39%). These results indicate that the proposed approach is a viable route to higher-throughput, lower-specific-energy material extrusion. Full article

This article presents a novel methodology for assessing the visibility and reachability of cultural heritage objects within urban structures, tested through a pilot study in Kaunas New Town (Naujamiestis), Lithuania. While heritage protection policies usually emphasize architectural composition, details, and external visual protection zones, interior urban views and functional spatial dynamics remain underexplored. Building upon Space Syntax theory and John Peponis’s concepts of distributive and correlative situational codes, this study integrates detailed visibility analysis with graph-based accessibility modeling. Visibility was quantified through a raster-based viewshed analysis of building footprints and street-based observation points, producing a normalized visibility index. Reachability was examined using a new graph indicator based on the ratio of reachable polygon area to perimeter (A²/P), further weighted by the area of adjacent buildings to reflect the potential for urban activity. Validation against independent datasets (population, companies, and points of interest) confirmed the superior explanatory power of the proposed indicator over traditional centralities. By combining visibility and reachability in a bivariate matrix, the model provides insights into heritage objects’ dual roles as landmarks, everyday hubs, or hidden sites, and offers predictive capacity for evaluating urban transformations and planning interventions. Full article

Background: Chronic Obstructive Pulmonary Disease (COPD) represents a significant global health burden, characterized by complex diagnostic and management challenges. Artificial Intelligence (AI) presents a powerful opportunity to enhance clinical decision-making and improve patient outcomes by leveraging complex health data. Objectives: This scoping review aims to systematically map the existing literature on AI applications in COPD. The primary objective is to identify, categorize, and summarize research into three key domains: (1) Diagnosis, (2) Clinical Symptoms, and (3) Clinical Outcomes. Methods: A scoping review was conducted following the Arksey and O’Malley framework. A comprehensive search of major scientific databases, including PubMed, Scopus, IEEE Xplore, and Google Scholar, was performed. The Population–Concept–Context (PCC) criteria included patients with COPD (Population), the use of AI (Concept), and applications in healthcare settings (Context). A global search strategy was employed with no geographic restrictions. Studies were included if they were original research articles published in English. The extracted data were charted and classified into the three predefined categories. Results: A total of 120 studies representing global distribution were included. Most datasets originated from Asia (predominantly China and India) and Europe (notably Spain and the UK), followed by North America (USA and Canada). There was a notable scarcity of data from South America and Africa. The findings indicate a strong trend towards the use of deep learning (DL), particularly Convolutional Neural Networks (CNNs) for medical imaging, and tree-based machine learning (ML) models like CatBoost for clinical data. The most common data types were electronic health records, chest CT scans, and audio recordings. While diagnostic applications are well-established and report high accuracy, research into symptom analysis and phenotype identification is an emerging area. Key gaps were identified in the lack of prospective validation and clinical implementation studies. Conclusions: Current evidence shows that AI offers promising applications for COPD diagnosis, outcome prediction, and symptom analysis, but most reported models remain at an early stage of maturity due to methodological limitations and limited external validation. Future research should prioritize rigorous clinical evaluation, the development of explainable and trustworthy AI systems, and the creation of standardized, multi-modal datasets to support reliable and safe translation of these technologies into routine practice. Full article

Background/Objectives: Differential learning (DL) amplifies natural fluctuations in movement execution and, in its more extreme forms, facilitates repetition-free training with minimal external feedback. While increasingly recognized in the field of skill acquisition, its application in anterior cruciate ligament (ACL) rehabilitation remains underexplored. Methods: This study examined the application of DL in the rehabilitation of an 18-year-old trained basketball player following left-ACL reconstruction. The athlete completed a 42-week rehabilitation program in which DL principles were incorporated throughout the pre-operative, early, mid-, and late phases, culminating in return to sport. Training included differential mobility work, motor control, plyometric exercises, and sport-specific drills. Functional recovery was evaluated using single-leg hop tests, change-of-direction tasks, and sprint performance, while self-reported knee function was monitored via the International Knee Documentation Committee (IKDC) questionnaire. Results: Results indicated substantial improvements in both functional performance and psychological readiness. The IKDC score increased from 13.8% at baseline to 95.4% postoperatively, reaching the normal functional range. An ACL-RSI score of 85.2%, and inter-limb asymmetries were reduced to below 10%. Strength, agility, and sprint performance exceeded pre-injury levels. Conclusions: DL again shows potential as an effective approach to facilitating recovery and return to sport after ACL reconstruction, but larger controlled studies are needed for validation. Full article

Background/Objectives: Quantitative computed tomography (CT) metrics are widely used to assess pulmonary involvement and to predict short-term severity in coronavirus disease 2019 (COVID-19). However, it remains unclear whether baseline artificial intelligence (AI)-based quantitative high-resolution computed tomography (HRCT) metrics of pneumonia burden provide incremental prognostic value for in-hospital composite adverse outcomes beyond routine clinical factors, or whether these imaging-derived markers carry any exploratory signal for long-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection among hospitalized patients. Most existing imaging studies have focused on diagnosis and acute-phase prognosis, leaving a specific knowledge gap regarding AI-based quantitative HRCT correlates of early deterioration and subsequent reinfection in this population. To evaluate whether combining deep learning-derived, quantitative, HRCT features and clinical factors improve prediction of in-hospital composite adverse events and to explore their association with long-term reinfection in patients with COVID-19 pneumonia. Methods: In this single-center retrospective study, we analyzed 236 reverse-transcription polymerase chain reaction (RT-PCR)-confirmed COVID-19 patients who underwent baseline HRCT. Median follow-up durations were 7.65 days for in-hospital outcomes and 611 days for long-term outcomes. A pre-trained, adaptive, artificial-intelligence-based, prototype model (Siemens Healthineers) was used for pneumonia analysis. Inflammatory lung lesions were automatically segmented, and multiple quantitative metrics were extracted, including opacity score, volume and percentage of opacities and high-attenuation opacities, and mean Hounsfield units (HU) of the total lung and opacity. Patients were stratified based on receiver operating characteristic (ROC)-derived optimal thresholds, and multivariable Cox regression was used to identify predictors of the composite adverse outcome (intensive care unit [ICU] admission or all-cause death) and SARS-CoV-2 reinfection, defined as a second RT-PCR-confirmed episode of COVID-19 occurring ≥90 days after initial infection. Results: The composite adverse outcome occurred in 38 of 236 patients (16.1%). Higher AI-derived opacity burden was significantly associated with poorer outcomes; for example, opacity score cut-off of 5.5 yielded an area under the ROC curve (AUC) of 0.71 (95% confidence interval [CI] 0.62–0.79), and similar performance was observed for the volume and percentage of opacities and high-attenuation opacities (AUCs up to 0.71; all p < 0.05). After adjustment for age and comorbidities, selected HRCT metrics—including opacity score, percentage of opacities, and mean HU of the total lung (cut-off −662.38 HU; AUC 0.64, 95% CI 0.54–0.74)—remained independently associated with adverse events. Individual predictors demonstrated modest discriminatory ability, with C-indices of 0.59 for age, 0.57 for chronic obstructive pulmonary disease (COPD), 0.62 for opacity score, 0.63 for percentage of opacities, and 0.63 for mean total-lung HU, whereas a combined model integrating clinical and imaging variables improved prediction performance (C-index = 0.68, 95% CI: 0.57–0.80). During long-term follow-up, RT-PCR–confirmed reinfection occurred in 18 of 193 patients (9.3%). Higher baseline CT-derived metrics—particularly opacity score and both volume and percentage of high-attenuation opacities (percentage cut-off = 4.94%, AUC 0.69, 95% CI 0.60–0.79)—showed exploratory associations with SARS-CoV-2 reinfection. However, this analysis was constrained by the very small number of events (n = 18) and wide confidence intervals, indicating substantial statistical uncertainty. In this context, individual predictors again showed only modest C-indices (e.g., 0.62 for procalcitonin [PCT], 0.66 for opacity score, 0.66 for the volume and 0.64 for the percentage of high-attenuation opacities), whereas the combined model achieved an apparent C-index of 0.73 (95% CI 0.64–0.83), suggesting moderate discrimination in this underpowered exploratory reinfection sample that requires confirmation in external cohorts. Conclusions: Fully automated, deep learning-derived, quantitative HRCT parameters provide useful prognostic information for early in-hospital deterioration beyond routine clinical factors and offer preliminary, hypothesis-generating insights into long-term reinfection risk. The reinfection-related findings, however, require external validation and should be interpreted with caution given the small number of events and limited precision. In both settings, combining AI-based imaging and clinical variables yields better risk stratification than either modality alone. Full article

Background: Laryngeal and hypopharyngeal cancers (LHCs) exhibit heterogeneous outcomes after definitive radiotherapy (RT). Large language models (LLMs) may enhance prognostic stratification by integrating complex clinical and imaging data. This study validated two pre-trained LLMs—GPT-4o-2024-08-06 and Gemma-2-27b-it—for outcome prediction in LHC. Methods: Ninety-two patients with non-metastatic LHC treated with definitive (chemo)radiotherapy at Linkou Chang Gung Memorial Hospital (2006–2013) were retrospectively analyzed. First-order and 3D radiomic features were extracted from intra- and peritumoral regions on pre- and mid-RT CT scans. LLMs were prompted with clinical variables, radiotherapy notes, and radiomic features to classify patients as high- or low-risk for death, recurrence, and distant metastasis. Model performance was assessed using sensitivity, specificity, AUC, Kaplan–Meier survival analysis, and McNemar tests. Results: Integration of radiomic features significantly improved prognostic discrimination over clinical/RT plan data alone for both LLMs. For death prediction, pre-RT radiomics were the most predictive: GPT-4o achieved a peak AUC of 0.730 using intratumoral features, while Gemma-2-27b reached 0.736 using peritumoral features. For recurrence prediction, mid-RT peritumoral features yielded optimal performance (AUC = 0.703 for GPT-4o; AUC = 0.709 for Gemma-2-27b). Kaplan–Meier analyses confirmed statistically significant separation of risk groups: pre-RT intra- and peritumoral features for overall survival (for both GPT-4o and Gemma-2-27b, p < 0.05), and mid-RT peritumoral features for recurrence-free survival (p = 0.028 for GPT-4o; p = 0.017 for Gemma-2-27b). McNemar tests revealed no significant performance difference between the two LLMs when augmented with radiomics (all p > 0.05), indicating that the open-source model achieved comparable accuracy to its proprietary counterpart. Both models generated clinically coherent, patient-specific rationales explaining risk assignments, enhancing interpretability and clinical trust. Conclusions: This external validation demonstrates that pre-trained LLMs can serve as accurate, interpretable, and multimodal prognostic engines for LHC. Pre-RT radiomic features are critical for predicting mortality and metastasis, while mid-RT peritumoral features uniquely inform recurrence risk. The comparable performance of the open-source Gemma-2-27b-it model suggests a scalable, cost-effective, and privacy-preserving pathway for the integration of LLM-based tools into precision radiation oncology workflows to enhance risk stratification and therapeutic personalization. Full article

Background and Objectives: Artificial intelligence (AI) has shown promising performance in skin-lesion classification; however, its fairness, external validity, and real-world reliability remain uncertain. This systematic review and meta-analysis evaluated the diagnostic accuracy, equity, and generalizability of AI-based dermatology systems across diverse imaging modalities and clinical settings. Materials and Methods: A comprehensive search of PubMed, Embase, Web of Science, and ClinicalTrials.gov (inception–31 October 2025) identified diagnostic accuracy studies using clinical, dermoscopic, or smartphone images. Eighteen studies (11 melanoma-focused; 7 mixed benign–malignant) met inclusion criteria. Six studies provided complete 2 × 2 contingency data for bivariate Reitsma HSROC modeling, while seven reported AUROC values with extractable variance. Risk of bias was assessed using QUADAS-2, and evidence certainty was graded using GRADE. Results: Across more than 70,000 test images, pooled sensitivity and specificity were 0.91 (95% CI 0.74–0.97) and 0.64 (95% CI 0.47–0.78), respectively, corresponding to an HSROC AUROC of 0.88 (95% CI 0.84–0.92). The AUROC-only meta-analysis yielded a similar pooled AUROC of 0.88 (95% CI 0.87–0.90). Diagnostic performance was highest in specialist settings (AUROC 0.90), followed by community care (0.85) and smartphone environments (0.81). Notably, performance was lower in darker skin tones (Fitzpatrick IV–VI: AUROC 0.82) compared with lighter skin tones (I–III: 0.89), indicating persistent fairness gaps. Conclusions: AI-based dermatology systems achieve high diagnostic accuracy but demonstrate reduced performance in darker skin tones and non-specialist environments. These findings emphasize the need for diverse training datasets, skin-tone–stratified reporting, and rigorous external validation before broad clinical deployment. Full article

Background/Objectives: Prevalence and burden of chronic obstructive pulmonary disease (COPD) are projected to increase in the coming decades. Although prognostic models for disease progression and exacerbation risk have proliferated, especially with the advent of machine learning (ML), their methodological rigor, generalizability, and predictive performance remain inconsistent. This study aimed to systematically review prognostic models for disease progression in adults with COPD, including traditional regression-based methods and ML techniques, evaluating model performance, sources of heterogeneity and methodological issues. Methods: PubMed and Embase were searched for all studies that developed and/or validated prognostic models for mortality (overall and cause-specific), exacerbations, or hospitalizations in adults with COPD over a time window of 1–5 years. Methodological quality was appraised using PROBAST. Model performance was summarized descriptively, and discrimination (c-statistic) was meta-analyzed for externally validated models with sufficient homogeneity. Results: Eighty-seven studies presenting 193 prognostic models across 96 unique cohorts were included. Only 7% of models were based on ML. Thirty-eight percent of records were validations of multidimensional indices. All-cause mortality (n = 85), severe exacerbations (n = 38) and moderate/severe exacerbations (n = 16) were the most frequently studied outcomes. Meta-analysis of exacerbation models was hampered by insufficient homogeneity (median c 0.74). As for mortality, BODE outperformed other indices (pooled c 0.75). Over 40% of studies were flawed by a high risk of bias. Conclusions: Despite a comprehensive literature search and thorough data extraction, we were able to provide a meaningful quantitative synthesis only for externally validated mortality models, as pooling results for other individual outcomes was precluded by substantial heterogeneity. Our findings highlight the predominance of regression approaches, the limited use of ML, the presence of persistent methodological limitations and the need for more robust, validated models capable of handling complex, multimodal patient data. Full article

Background: The need to improve gait emulation in people with amputation has driven the development of customized prosthetic mechanisms. This study focuses on the design and validation of a mechanism for external knee joint prostheses, based on the trajectory of the Instantaneous Center of Rotation (ICR) of a healthy knee. Objective: The objective is to design a mechanism that accurately reproduces the evolution of the ICR trajectory, thereby improving stability and reducing the user’s muscular effort. Methods: An exploratory methodology was employed, utilizing computer-aided design (CAD), kinematic simulations, and rapid prototyping through 3D printing. Multiple configurations of four- and six-bar mechanisms were evaluated to determine the ICR trajectory and compare it with a reference model obtained in the laboratory from a specific subject, using MATLAB-2023a and the Fréchet distance as an error metric. Results: The results indicated that the four-bar mechanism, with the incorporation of a simple gear train, achieved a more accurate emulation of the ICR trajectory, reaching a minimum error of 6.87 mm. Functional tests confirmed the effectiveness of the design in terms of stability and voluntary control during gait. It can be concluded that integrating the mechanism with the gear train significantly enhances its functionality, making it a viable alternative for the development of external knee prostheses for people with transfemoral amputation, based on the ICR of the contralateral leg. Full article

Background/Objectives: Immunohistochemical (IHC) profiles assessed on core biopsies guide initial therapy in breast cancer; however, paired changes between biopsy and surgical specimens may alter treatment pathways. We aimed to quantify paired biomarker dynamics (ER, PR, HER2, Ki-67) and the proportion of patients undergoing clinically actionable reclassification. Methods: We conducted a single-center retrospective study of 79 patients with paired pre- and post-surgical IHC for ER, PR, HER2 (0/1+/2+/3+ with reflex ISH for 2+), and Ki-67 (20% cut-off). Paired categorical shifts were tested with McNemar’s test; agreement was quantified using Cohen’s κ (95% CI); and multivariable logistic regression examined correlates of neoadjuvant chemotherapy (NACT). Two-sided p < 0.05 denoted statistical significance. Results: Post-surgical reassessment showed measurable conversions: PR-negative increased from 15.19% to 27.85%, while PR-positive decreased 84.81%→72.15%; HER2 3+ contracted 11.39%→6.33% with a parallel rise in 2+ (equivocal) 17.72%→24.05%; Ki-67 < 20% rose 37.97%→56.96%, whereas the >30% category was absent post-surgery. McNemar tests indicated significant paired shifts for PR (p = 0.016) and Ki-67 (p = 0.009); agreement was substantial for ER (κ = 0.70) and lower for PR (κ = 0.49), HER2 (κ = 0.43), and Ki-67 (κ = 0.29). High proliferation (Ki-67 ≥ 20%) independently predicted NACT (OR = 4.36, 95% CI 1.48–12.80). Conclusions: Paired IHC reassessment from biopsy to surgery reveals biomarker conversions that can reclassify therapeutic eligibility (e.g., anti-HER2 candidacy, endocrine strategies). These data support selective confirmation of IHC on resection specimens in routine practice and provide Δ-metrics to inform decision-making; external validation in prospective cohorts is warranted. Full article

Background: Cerebellar infarction complicated by obstructive hydrocephalus is a life-threatening condition. External ventricular drainage (EVD) has traditionally been regarded as hazardous due to concerns about precipitating upward transtentorial herniation, whereas suboccipital decompressive craniectomy (SDC) remains the definitive life-saving treatment. The optimal role and sequencing of these interventions remain controversial. Methods: A scoping review was conducted in accordance with PRISMA-ScR guidelines. PubMed/MEDLINE was systematically searched from inception to September 2025. Eligible studies included adult patients with cerebellar infarction and acute obstructive hydrocephalus managed with EVD, with or without SDC. Data on study design, patient characteristics, interventions, complications, and outcomes were extracted and narratively synthesized. Results: Forty studies were included, encompassing multicenter registries, retrospective cohorts, case series, and international guidelines. Evidence suggests that EVD alone can be effective in selected patients with preserved or moderately impaired consciousness, while outcomes in comatose patients are improved with SDC or combined approaches. Importantly, this scoping review integrates current evidence with a representative institutional case to provide a practical clinical context. Radiographic signs of upward transtentorial herniation before EVD were common, but clinically significant deterioration was infrequent. Prognostic factors for surgical decision-making included infarct volume (practical threshold 25–35 mL), location (vermian or bilateral infarcts), brainstem involvement, and level of consciousness. International guidelines increasingly recognize EVD as a valid treatment option, particularly as initial therapy for hydrocephalus. Conclusions: EVD should no longer be regarded as an absolute contraindication in cerebellar infarction with obstructive hydrocephalus. Controlled drainage can suffice in carefully selected patients, whereas SDC remains indispensable in cases with severe mass effect or brainstem compression. A pragmatic stepwise approach—beginning with cautious EVD and escalating to SDC when indicated—may optimize outcomes. Further multicenter studies are required to refine patient selection criteria and establish standardized management algorithms. Full article

Urban street sweeping infrastructure plays a critical role in municipal solid waste management by mitigating particulate matter resuspension and preventing contaminant mobilization into water bodies, thereby supporting public health and environmental sustainability. The primary objective of this study is to develop a dynamic evaluation model for urban street sweeping services in four localities of Bogotá, Colombia. Operating system variables are integrated with citizens’ perceptions to capture their coupled socio-environmental behavior. The methodology comprised four phases: a global literature review, a citizen-perception survey, the development of a dynamic simulation model integrating perceptions, and a statistical analysis of all collected data. The results demonstrate that technical efficiency in street sweeping operations, measured through the street cleanliness index, is insufficient to ensure service sustainability without incorporating citizen perception metrics. The model reveals that geometric, spatial, and climatic factors reduce the street cleanliness index by up to 100%, highlighting infrastructure vulnerability to external conditions. Model validation exposes a critical gap between operational cleanliness and citizen perception, with decreases of up to 64.2% in comprehensive service evaluation. The inclusion of perception indicators (Cronbach’s α = 0.770) underscores the significance of variables such as service punctuality and personnel attitude in determining citizen satisfaction and overall service assessment. The dynamic model constitutes a robust decision-support tool for optimizing resource allocation, mitigating socio-environmental impacts, and strengthening institutional legitimacy in urban infrastructure maintenance. Nevertheless, limitations in representing external factors (informal commerce and illegally parked vehicles) and spatial heterogeneity in cleanliness indices suggest future research directions incorporating stochastic modeling approaches and longitudinal studies on citizen perception dynamics. Full article

Permanent Magnet Synchronous Generators (PMSGs) have become increasingly important in industrial applications such as wind turbine systems due to their high efficiency and power density. However, their operational reliability can be affected by asymmetries such as static eccentricity (SE) and load current unbalance (UnB), which exhibit similar spectral features and are therefore difficult to differentiate using conventional techniques such as Motor Current Signature Analysis (MCSA). Stray flux analysis provides an alternative diagnostic approach, yet single-point measurements often lack the sensitivity required for accurate fault discrimination. This study introduces a diagnostic methodology based on the Space Vector Stray Flux (SVSF) for identifying static eccentricity (SE) and load current unbalance (UnB) faults in PMSG-based systems. The SVSF is derived from three external stray flux sensors placed 120° electrical degrees apart and analyzed through symmetrical component decomposition, focusing on the +5f_s positive-sequence harmonic. Two-dimensional Finite Element Analysis (FEA) conducted on a 36-slot/12-pole PMSG model shows that the amplitude of the +5f_s harmonic increases markedly under static eccentricity, while it remains nearly unchanged under load current unbalance. To validate the simulation findings, comprehensive experiments have been conducted on a dedicated test rig equipped with high-sensitivity fluxgate sensors. The experimental results confirm the robustness of the proposed SVSF method against practical constraints such as sensor placement asymmetry, 3D axial flux effects, and electromagnetic interference (EMI). The identified harmonic thus serves as a distinct and reliable indicator for differentiating static eccentricity from load current unbalance faults. The proposed SVSF-based approach significantly enhances the accuracy and robustness of fault detection and provides a practical tool for condition monitoring in PMSG. Full article

In the construction of ultra-high voltage (UHV) transformation substations, mass concrete is highly susceptible to temperature-induced cracking due to thermal gradients arising from the disparity between internal hydration heat and external environmental conditions. Such cracks can severely compromise the structural integrity and load-bearing capacity of foundations, making accurate temperature prediction and effective thermal control critical challenges in engineering practice. To address these challenges and enable real-time monitoring and dynamic regulation of temperature evolution, this study proposes a novel hybrid forecasting model named CPO-VMD-SSA-Transformer-GRU for predicting temperature behavior in mass concrete. First, sine wave simulations with varying sample sizes were conducted using three models: Transformer-GRU, VMD-Transformer-GRU, and CPO-VMD-SSA-Transformer-GRU. The results demonstrate that the proposed CPO-VMD-SSA-Transformer-GRU model achieves superior predictive accuracy and exhibits faster convergence toward theoretical values. Subsequently, four performance metrics were evaluated: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²). The model was then applied to predict temperature variations in mass concrete under laboratory conditions. For the univariate time series at Checkpoint 1, the evaluation metrics were MAE: 0.033736, MSE: 0.0018812, RMSE: 0.036127, and R²: 0.98832; at Checkpoint 2, the values were MAE: 0.016725, MSE: 0.00091304, RMSE: 0.019114, and R²: 0.96773. In addition, the proposed model was used to predict the temperature in the rising stage, indicating high reliability in capturing nonlinear and high-dimensional thermal dynamics in the whole construction process. Furthermore, the model was extended to multivariate time series to enhance its practical applicability in real-world concrete construction. At Checkpoint 1, the corresponding metrics were MAE: 0.56293, MSE: 0.34035, RMSE: 0.58339, and R²: 0.95414; at Checkpoint 2, they were MAE: 0.85052, MSE: 0.78779, RMSE: 0.88757, and R²: 0.91385. These results indicate significantly improved predictive performance compared to the univariate configuration, thereby further validating the accuracy, stability, and robustness of the multivariate CPO-VMD-SSA-Transformer-GRU framework. The model effectively captures complex temperature fluctuation patterns under dynamic environmental and operational conditions, enabling precise, reliable, and adaptive temperature forecasting. This comprehensive analysis establishes a robust methodological foundation for advanced temperature prediction and optimized thermal management strategies in real-world civil engineering applications. Full article