Search Results (5,649)

The energy conservation, emission reduction, and electricity peak-load regulation requirements of smart cities have driven the establishment of virtual power plants (VPPs). Peer-to-peer (P2P) trading is applicable to distributed clean energy resources in VPPs and affects the transformation of the energy trading paradigm. To address the privacy protection and robustness issues of P2P trading, this paper presents a unified framework for point-to-point energy trading in VPPs. We comprehensively consider the multiple stages of verification and consensus in transactions and propose a complete framework. The proposed system can effectively protect participants’ private information and achieve identity authentication. Furthermore, we design a progressive settlement mechanism to ensure time-slot-level transaction parallelism. To verify the effectiveness of the method, we implemented the proposed system on IEEE bus benchmarks. The experiments covered the complete stages of authentication, consensus, and clearing. The overhead measurements showed that the overhead of the cryptographic components was reasonable and that the final signature adaptation introduced only a small computational overhead. Full article

The human ankle exhibits complex multiplanar behavior involving plantarflexion, dorsiflexion, inversion, eversion, and coupled orientation changes that are essential for balance and terrain adaptation. This work presents the design, kinematic evaluation, mechanical implementation, and control validation of a transtibial prosthesis based on a parallel robotic mechanism. Three candidate architectures, 3-SPS-1-S, 3-UPU-1-S, and 3-UCU-1-S, were compared through inverse and forward kinematics, orientational workspace, Jacobian conditioning, constructability, and mechatronic-integration criteria. The corresponding workspace coverages were 74.1%, 75.7%, and 72.2%, respectively. The 3-UPU-1-S architecture exhibited a median Jacobian condition number of 12.63, a 95th-percentile value of 20.56, no numerically singular configurations within the evaluated feasible workspace, and the highest VDI-2225 technical score (0.913), supporting its final selection. The selected mechanism was manufactured and integrated into a functional laboratory prototype with distributed ESP32-S3-based electronics, position sensing, inertial measurement, and closed-loop actuation. Experimental periodic tests showed that the decentralized PID controller achieved dominant-axis RMSE values of 3.53° in pitch during dorsiflexion–plantarflexion and 4.61° in roll during inversion–eversion. A revised formal LQRI controller was evaluated separately using the identified actuator-space model. Its nominal closed-loop system was asymptotically stable, with $\max \mathrm{Re}\left(\lambda \right)=-1.6896$, and robustness simulations showed mean-RMSE reductions of approximately 9.4–16.6% relative to PID across the evaluated perturbation scenarios. These results demonstrate the feasibility of the proposed 3-UPU-1-S mechanism as an integrated laboratory platform for multiplanar transtibial-prosthesis research while identifying the need for future dynamic load-bearing and user-centered validation. Full article

Casing assembly deviations can disrupt aero-engine support alignment and induce abnormal vibration. However, the mechanism by which bearing-seat coaxiality errors generate superharmonic responses remains insufficiently understood. This study investigates how such deviations propagate through support misalignment and a spline coupling to influence the vibration response of a coupled rotor–casing system. A geometric relationship is formulated to transform the misalignment of the support into equivalent parallel and angular initial offsets at the spline coupling. An additional excitation model for the spline coupling, accounting for meshing stiffness, transmitted torque, tooth-side clearance, unilateral tooth contact, and relative whirl motion, is incorporated into a reduced-order whole-engine dynamic model. The integrated model is evaluated under a range of experimentally measured coaxiality conditions. Numerical simulations predict critical response regions near 16,000 and 23,000 r/min, along with subcritical resonance peaks at approximately 7800 and 12,500 r/min that are attributed to superharmonic excitation mechanisms rather than conventional mass unbalance alone. Experimental results indicate that the 0.234 mm coaxiality condition yields larger vibration amplitudes, additional low-speed resonance peaks, and more pronounced 2X–4X harmonic components compared with the 0.069 mm condition, thereby corroborating the proposed assembly deviation mechanism under cold-state structural dynamic conditions. Full article

Accurate joint wind and photovoltaic power forecasting is essential for secure operation and dispatch in power systems. Wind and photovoltaic (PV) outputs depend strongly on meteorological conditions and exhibit stochastic fluctuations, multi-scale dynamics, and multivariable coupling. Existing models selectively model the relationships among heterogeneous meteorological variables, but struggle to capture temporal patterns across scales. This paper proposes a Multi-Scale Temporal Convolutional Gated iTransformer (MS-TCN-GiT) for joint wind and photovoltaic power forecasting. Parallel temporal convolutional branches and adaptive weighted fusion (MS-TCN) jointly learn short-term fluctuations and longer-term trends. A gated feature-selection mechanism (GiT) is embedded in the iTransformer variable-attention framework to screen and reweight meteorological variables dynamically. Point forecasts are combined with an error-statistics-based center-trajectory method that generates low-, moderate-, and high-output scenarios. Experiments on two years of State Grid microgrid data show that MS-TCN-GiT outperformed all evaluated baselines. Relative to TCN, it reduced capacity-weighted MAE and RMSE by approximately 23.2% and 22.2%, respectively, while increasing R² to 0.9373. The framework therefore provides accurate point forecasts and compact, interpretable uncertainty scenarios intended for subsequent dispatch. Full article

Against the background of AI penetration in C2C second-hand commerce, AI chatbots have emerged as vital operational tools for individual sellers. Prior technology adoption literature predominantly emphasizes positive acceptance drivers yet overlooks the simultaneous existence of benefit and risk perceptions, and cannot fully explain individual sellers’ paradoxical adoption intentions. This study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Threat Avoidance Theory (TTAT) to construct an integrated dual mediation model, adopting perceived effortlessness and perceived risk as parallel mediators. Online questionnaire surveys were distributed to individual sellers operating on Chinese C2C second-hand trading platforms. After screening and data cleaning, a final valid sample of 261 respondents was retained. Partial least-squares structural equation modelling (PLS-SEM) was employed to empirically test the proposed research model. The results demonstrate that personal factors (perceived busyness, desire for control, AI acceptance) and situational factors (product standardization, social influence, platform safeguards) jointly shape individual sellers’ dual perceptions. These antecedents exert differentiated impacts on the two mediating constructs, which subsequently predict individual sellers’ intentions to adopt AI chatbots. This research clarifies the balancing mechanism of positive and negative perceptions in technology decision-making. The framework reconciles conflicting psychological evaluations of intelligent systems, deepens the understanding of paradoxical adoption behaviors, and offers practical implications for optimizing intelligent services within C2C second-hand ecosystems. Full article

Programmed necrotic cell death (necroptosis) is a key antiviral defense pathway, yet its regulation during human cytomegalovirus (HCMV) infection remains incompletely defined in physiologically relevant cell types. The M45 protein of murine cytomegalovirus, a homologue of UL45, inhibits necroptosis and contributes to MCMV dissemination, providing a rationale for examining UL45 in HCMV. Previous studies of UL45 have largely relied only on fibroblasts or immortalized cell lines, which lack the necroptotic machinery necessary to reveal viral anti-necroptotic functions. To investigate the role of the HCMV UL45 protein, we generated a UL45 stop-frameshift mutant (UL45st) in the TB40E strain and compared its phenotype to that of the wild-type virus in primary human fibroblasts and monocyte-derived macrophages. In parallel, transfection-based assays in RIPK3-competent HT29 cells were used to assess the intrinsic cell-death-inhibitory activity of UL45. UL45st-infected primary macrophages exhibited significantly reduced numbers of viable cells and showed morphological features suggesting necroptotic death compared to wild-type-infected cells, whereas infected fibroblasts showed only modest defects. Expression of UL45 alone in HT29 cells partially protected cells from both apoptotic and RIPK3-dependent necroptotic stimuli, indicating a direct anti-death function of the protein. These findings demonstrate that HCMV UL45 promotes cellular survival in primary human macrophages and reveal that this function is likely associated with RIPK3-associated cell death. Collectively, this work identifies a never-before-shown function of HCMV UL45 as a key determinant of viral survival in myeloid compartment cells and underscores the importance of using physiologically relevant models to uncover cell-type-specific viral immune evasion mechanisms. Full article

Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide. Among its defining hallmarks, metabolic reprogramming, particularly aerobic glycolysis (the Warburg effect), emerged as a central driver of tumor progression, therapeutic resistance, and immune evasion. Over the past decades, substantial efforts have delineated the molecular architecture of metabolic reprogramming in GC, identifying key effector enzymes, including hexokinase 2 (HK2), pyruvate kinase M2 (PKM2), and lactate dehydrogenase A (LDHA), as well as upstream oncogenic signaling axes, such as the PI3K/AKT/mTOR pathway and hypoxia-inducible factor 1-alpha (HIF-1α), along with their interconnected regulatory networks. Despite extensive preclinical validation of these nodes, clinical translation remains elusive, with glycolysis-targeted monotherapies showing limited efficacy in early-phase trials. Yet, we contend that this persistent translational failure does not stem from invalid targets, but rather from a systemic underestimation of three fundamental roadblocks: (1) temporal metabolic plasticity that enables rapid compensatory adaptation and pathway switching; (2) spatial inter- and intra-tumoral metabolic heterogeneity that undermines uniform treatment strategies; and (3) a critical void in predictive and pharmacodynamic biomarkers essential for patient stratification and treatment monitoring. To overcome these barriers, we propose an integrated, forward-looking strategic framework that converges advanced diagnostics with next-generation therapeutic modalities. On the diagnostic front, we highlight spatial multi-omics for high-resolution metabolic cartography and artificial intelligence-driven integrative patient stratification to map heterogeneity and predict treatment response. On the therapeutic front, we examine strategies designed to circumvent metabolic plasticity, including dual-pathway inhibition, nodal targeting, exploitation of non-catalytic vulnerabilities, and tumor-penetrating nanocarriers for targeted metabolic intervention. Particular emphasis is placed on rational, mechanism-driven combination regimens, especially those synergizing glycolysis-targeted therapies with immunotherapy to remodel the suppressive tumor microenvironment—as well as hierarchical and parallel pathway combinations and the emerging metabolism–epigenetics axis, exemplified by lactate-mediated histone lactylation and α-ketoglutarate-dependent DNA demethylation. By shifting the therapeutic paradigm from static inhibition of single metabolic nodes toward dynamic, network-level intervention, this review provides a strategic roadmap for translating the vulnerabilities inherent in the glycolytic network into durable clinical benefit for patients with GC. This paradigm shift, we argue, is essential for advancing precision metabolic medicine beyond the current impasse. Full article

Accurate runoff forecasting serves as a fundamental basis for the scientific management of water resources and flood and drought risk mitigation. Owing to the nonlinearity, non-stationarity, and multi-scale temporal characteristics of runoff series, existing deep learning models still exhibit notable limitations in capturing long-term trends, responding to abrupt hydrological events, and ensuring model interpretability. The original Transformer relies on global self-attention, whose computational complexity increases quadratically with sequence length; it also has limited capacity to capture short-term local temporal dependencies such as rainfall–runoff relationships, and lacks prior constraints tailored to hydrological processes. To address these challenges, this study proposes a collaborative forecasting framework that integrates a gated convolutional Transformer (GCTrans) with an improved black kite algorithm (IBKA), enabling accurate, stable, and interpretable daily-scale runoff prediction. The GCTrans model consists of three customized modules: convolution-enhanced positional encoding (CEPE), which combines learnable positional encoding with local causal convolution to strengthen the temporal association of adjacent rainfall–runoff events, thereby providing a hydrologically meaningful positional reference for the attention mechanism; gated convolutional attention (GCA), which adopts a dual-path parallel architecture comprising global self-attention and local causal convolution to adaptively fuse long-term seasonal patterns with short-term storm-induced variations, thus capturing both baseflow evolution and flood peak responses; and a temporal gated output layer (TGOL), which performs adaptive feature weighting along the temporal dimension to selectively enhance the contribution of critical driving periods associated with extreme flood events, thereby improving the flood peak prediction accuracy. In addition, an improved black kite algorithm (IBKA) was developed by incorporating Tent chaotic initialization to enhance initial population diversity and introducing cosine adaptive inertia weights to dynamically balance global exploration and local exploitation, effectively alleviating premature convergence in high-dimensional hyperparameter spaces. Validation using data from the ME-Inland snowmelt-dominated watershed and the OR-Coastal storm-driven coastal watershed in the United States demonstrated that the GCTrans model consistently outperformed benchmark models including TCN, LSTM, Transformer, and Informer. After synergistic optimization with IBKA, both prediction accuracy and stability were further improved. SHAP-based interpretability analysis revealed that the model’s feature response patterns are statistically consistent with the rainfall–runoff generation mechanisms of the study basins: temperature-related drivers dominate in the inland watershed, while precipitation plays a dominant role in the coastal watershed, confirming the hydrological plausibility of the model’s decision-making logic. The integrated framework—encompassing model architecture, optimization algorithm, and interpretability—offers a valuable methodological reference for deep learning-based runoff forecasting in complex hydrological settings. Full article

The rapidly changing digitalization of the energy sector, fueled by smart grids, the Industrial Internet of Things (IIoT), Advanced Metering Infrastructure (AMI), and Supervisory Control and Data Acquisition (SCADA) systems, is leading to a great and symmetrical improvement in workflow and the ability to have real-time insights. In parallel, the massive adoption of low-power and resource-constrained devices that are still connected makes cybersecurity threats more serious and thus necessitates lightweight authentication mechanisms to have safe, secure, and symmetrical communicative. Selecting the right authentication method involves a challenging multi-criteria decision-making (MCDM) process where various factors such as security aspects, computation power needed, communication capabilities that can be delivered, and deployment-related aspects are considered together, along with inherent uncertainties in expert evaluations. This paper proposes a Hesitant Fuzzy (HF)-based hybrid method that combines the Analytic Network Process (ANP) with the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for the evaluation of lightweight authentication systems in the energy domain. This symmetrical HF-ANP is used for the modeling of the interrelations between major evaluation criteria such as security strength, computation efficiency, communication effectiveness, and deployment scalability. It can also handle the experts’ hesitant and uncertain preferences. The calculated weighting factors are then input to the HF-TOPSIS method to rank five different lightweight authentication protocols: Hash-Based Authentication, Elliptic Curve Cryptography (ECC)-Based Lightweight Authentication, Physical Unclonable Function (PUF)-Based Authentication, Blockchain-Assisted Lightweight Authentication, and Certificate-less Lightweight Authentication. Furthermore, sensitivity analysis and comparison analysis are conducted to verify the strength, symmetry, consistency, and reliability of the proposed framework. Our results indicate that the integrated HF-ANP and TOPSIS procedure provides a symmetrical, comprehensive, and systematic decision-making tool to appraise lightweight authentication techniques amid uncertainties. The proposed method will be very beneficial for different types of energy industrial players, system designers, and security experts to pick secure, efficient, and scalable methods of authentication to protect the critical energy facilities against the new generation of cyber threats. Full article

The Industrial Internet of Things (IIoT) is being used a lot in important areas like advanced manufacturing, smart energy systems, and intelligent cities. Watching for intrusion detection is very important for the safety of the IIoT. Nevertheless, multivariate sensor sequences frequently demonstrate pronounced physical coupling, non-stationarity, and periodicity concurrently, rendering it prone for detection models grounded in statistical correlation to erroneously classify normal collaborative variations as anomalies. Moreover, prevailing methods predominantly concentrate on single-domain representations within either the time or frequency domain, posing challenges in addressing both burst and periodic attacks concurrently. To address these challenges, this article introduces the Time–Frequency Dual-Branch Fusion and PCMCI-Based Association-Guided Network (TFPAG-Net) for IIoT Intrusion Detection. This model initially constructs a lightweight temporal convolutional network backbone employing depthwise separable convolutions. Subsequently, parallel branches in the time and frequency domains are established to respectively model local abrupt changes, long-range dependencies, and periodic spectral structures, with time–frequency feature fusion facilitated through a sample-dependent gating mechanism. Building on this, multi-scale temporal pyramids are employed to amalgamate fine, intermediate, and coarse-scale information. Furthermore, as an auxiliary refinement, a lagged conditional-dependence prior estimated from the training data via PCMCI is projected into a bounded attention bias to provide supplementary guidance for channel feature reweighting. Evaluations on Edge-IIoTset, X-IIoTID, and SWaT yield mean Macro-F1 scores of 0.9886, 0.9466, and 0.9607, respectively, over five predefined random seeds. Under the unified training protocol, TFPAG-Net ranks second on Edge-IIoTset and achieves the highest mean Macro-F1 on X-IIoTID and SWaT. Ablation experiments show dataset-dependent effects of the proposed components. On Edge-IIoTset and X-IIoTID, the final attention-stage improvement reflects the joint effect of SE-based modulation and the PCMCI-derived association prior. Accordingly, PCMCI is treated as an auxiliary association refinement rather than a principal contribution of TFPAG-Net. Additionally, TFPAG-Net maintains a moderate computational footprint, with approximately 0.29 M parameters, providing a favorable balance between model complexity and intrusion detection performance. Full article

Background/Objectives: Postoperative pain after endoscopic discectomy may reflect acute surgical nociception superimposed on pre-existing radicular symptoms. Molecular hydrogen (H₂) has antioxidant and anti-inflammatory properties, but clinical evidence for perioperative analgesia is limited. We evaluated the association between perioperative H₂ inhalation and postoperative pain and analgesic requirements. Methods: This open-label, non-randomized pilot study enrolled 37 patients in two consecutive periods (H₂, n = 25; control, n = 12). Because original outcome records for 12 H₂-period participants could no longer be retrieved at the time of manuscript preparation, the complete-case clinical analysis included 25 source-verifiable participants (H₂, n = 13; control, n = 12). H₂ was delivered via nasal cannula as a 66.7% H₂/33.3% O₂ source gas at 2 L/min (estimated inspired H₂ approximately 4%). Pain was evaluated with a numerical rating scale (NRS) preoperatively, at post-anesthesia care unit (PACU) arrival, at 1, 6, 12, and 24 h, and at 1 month. Longitudinal scores were analyzed using generalized estimating equations with Holm-adjusted comparisons. A parallel exploratory murine chronic constriction injury (CCI) experiment (n = 6/group) assessed mechanical allodynia over 14 days of daily H₂ inhalation. Results: The group-by-time interaction was significant (Wald χ²(6) = 56.68, p < 0.001). After Holm adjustment, pain scores were lower with H₂ at PACU arrival (difference, −3.21; adjusted p = 0.002), 1 h (−2.42; adjusted p = 0.004), and 1 month (−3.45; adjusted p < 0.001). Intraoperative fentanyl use and rescue analgesic use were also lower in the H₂ group; no adverse events were documented in the analyzed cohort. In the murine CCI experiment, H₂-treated mice showed higher ipsilateral paw withdrawal thresholds than air-exposed controls. Conclusions: Perioperative H₂ inhalation was associated with lower pain scores at selected time points and reduced analgesic requirements. These preliminary findings require confirmation in randomized trials. Trial registration: ClinicalTrials.gov NCT05476575. Full article

Studies of allergic rhinitis (AR) have increasingly recognized that reactive oxygen species (ROS) are not simply byproducts of oxidative metabolism but function as modulators of the type 2 inflammatory network during the pathogenesis of this disease. This narrative review summarizes the role of ROS in the pathophysiology of AR by analyzing studies that examined markers of systemic oxidative stress, dysfunction of the epithelial barrier, ROS derived from immune cells, mitochondrial redox signaling, and inflammasome-related pathways. Our structured search of the literature reviewed five major databases (PubMed, Scopus, EMBASE, Cochrane Library, and Google Scholar) and identified 17 eligible studies published between 2000 and 2026. Clinical studies suggest that patients with AR exhibit altered systemic redox homeostasis, including thiol–disulfide imbalance and increased lipid peroxidation. However, these findings are primarily from measurements of markers in peripheral blood, not nasal mucosa. Experimental studies consistently demonstrated that allergen exposure increased the levels of ROS in nasal epithelial and immune cells, disrupted the epithelial barrier, downregulated tight junction proteins, and activated inflammatory signaling pathways. Although direct nasal tissue data remain limited, evidence extrapolated from peripheral blood and bronchial challenge models suggests that eosinophils and neutrophils contribute to the generation of ROS during the late phase of the allergic response, thereby potentially amplifying and sustaining airway inflammation. There is also evidence that mitochondrial ROS and DUOX-dependent signaling contribute to epithelial dysfunction, including the release of damage-associated molecular patterns and activation of inflammasome pathways. In parallel, antioxidant defense mechanisms, such as the KEAP1/NRF2 axis and mitophagy-related pathways, appear to modulate disease severity by maintaining redox homeostasis. Experimental strategies such as ROS scavengers and oxidative stress-responsive drug delivery systems have shown early proof-of-concept potential in preclinical and pilot studies, but rigorous and large-scale clinical support is strictly required before any clinical application can be considered. Overall, current evidence indicates that ROS function in AR as context-dependent redox mediators rather than as primary causes. The biological effects of ROS appear to depend on site of synthesis, subcellular localization, and the balance between oxidant generation and antioxidant defenses. Further studies that directly assess the dynamics of nasal mucosal ROS and well-designed clinical trials are needed to clarify the translational relevance of these studies and the therapeutic potential of different treatments for AR. Full article

This study investigates the influence of interpass temperature on the dynamic tensile deformation behavior of 316L stainless steel. Specimens were fabricated using cold metal transfer (CMT)-based wire arc additive manufacturing (WAAM) at interpass temperatures of room temperature (RT), 200 °C, and 400 °C, and tested at nominal strain rates of 100 s⁻¹, 500 s⁻¹, and 1000 s⁻¹. Increasing interpass temperature promoted microstructural coarsening, reduced the continuity of the retained δ-ferrite network, and resulted in differences in the coincidence site lattice (CSL) Σ3 boundary fraction and the intensity of the ⟨001⟩ texture parallel to the build direction (BD), while decreasing the average hardness from 182 HV1 for RT to 173 HV1 and 161 HV1 for 200 °C and 400 °C, respectively. The RT condition exhibited the highest 0.2% proof stress, particularly at 100 s⁻¹ and 500 s⁻¹, whereas the tensile response tended to converge at 1000 s⁻¹. Digital image correlation (DIC) revealed that the local strain distribution depended on both interpass temperature and strain rate, with more homogeneous centerline strain profiles at the highest strain rate. Scanning electron microscopy (SEM) fractography showed predominantly ductile failure by dimple rupture and microvoid coalescence. The screening results indicate that interpass temperature influences the dynamic tensile response of WAAM-fabricated 316L through changes in the as-deposited microstructure and the resulting plastic strain distribution, rather than through a change in the dominant fracture mode. These findings highlight interpass temperature as a relevant processing parameter for balancing interlayer waiting time, as-deposited microstructure, and dynamic mechanical response in WAAM-fabricated 316L. Full article

Mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) represent a promising cell-free therapeutic strategy for cartilage regeneration and inflammation modulation in degenerative and inflammatory musculoskeletal disorders, including osteoarthritis (OA), rheumatoid arthritis (RA) and intervertebral disc degeneration (IVDD). MSC-EVs enhance cartilage repair by promoting chondrocyte proliferation, migration, survival and extracellular matrix (ECM) synthesis to maintain cartilage homeostasis. In parallel, they exert anti-inflammatory and anti-catabolic effects by suppressing inflammatory cytokines, matrix-degrading enzymes, oxidative stress and inflammasome activation. In OA, MSC-EVs regulate chondrocyte function, ECM remodelling, immune responses and tissue regeneration by modulating multiple signalling pathways, including NF-κB, PI3K/AKT, MAPK, Wnt/β-catenin and YAP signalling. In RA, MSC-EVs orchestrate innate and adaptive immune regulation, metabolic reprogramming and tissue repair pathways. In IVDD, MSC-EVs suppress chronic inflammation, reduce nucleus pulposus cell apoptosis, enhance cell proliferation and promote ECM synthesis, facilitating disc regeneration and functional restoration. Despite their therapeutic potential, several challenges hinder clinical translation. This review summarises current understanding of MSC-EV-mediated mechanisms in OA, RA and IVDD, and proposes strategies to enhance therapeutic efficacy and accelerate clinical application in musculoskeletal regenerative medicine. Full article

Against the backdrop of the coordinated advancement of the dual carbon goals and new-type urbanization, the traditional high-carbon development model has become a major constraint on urban green transformation. As a critical vehicle for digital technologies to empower low-carbon governance, smart cities have yet to receive a systematic evaluation of their long-term policy effects based on quasi-natural experiments. Using panel data from 280 prefecture-level cities from 2003 to 2023, this study takes the smart city pilot policy as a quasi-natural experiment. It adopts Interpretive Structural Modeling (ISM) to identify the key influencing factors and transmission paths of carbon emissions, and employs the progressive difference-in-differences (DID) model to assess the carbon emission reduction effects, dynamic evolutionary characteristics and urban heterogeneity of smart city construction. Furthermore, the mediation effect model is applied to clarify its underlying mechanisms. The empirical results show that smart city construction significantly curbs urban carbon emissions, and this finding remains valid after a series of robustness tests, including the parallel trend test, placebo test and PSM-DID. The emission reduction effect of the policy exhibits an obvious time lag: the effect is insignificant in the first and second years after policy implementation but turns significantly negative and continues to strengthen starting from the third year. Noticeable urban heterogeneity is also observed, with a more prominent emission reduction effect in eastern regions, central cities with high administrative ranks and large-sized cities. Mechanism analysis reveals that the conventional industrial pollution reduction pathway does not serve as the primary transmission channel. Instead, a suppression effect is identified, suggesting that smart cities achieve carbon abatement primarily through the digital empowerment of energy allocation efficiency—a pathway distinct from traditional end-of-pipe governance approaches. Unlike previous studies, this study combines ISM with a staggered DID framework to reveal the dynamic effects and transmission mechanisms of smart city policies. Full article