Search Results (6,953)

Background: Head and neck squamous cell carcinoma (HNSCC) responds poorly (<20%) to immune checkpoint blockade. Since sialylation suppresses anti-tumour immunity through Siglec signalling independently of the PD-L1/PD-1 axis, we mapped its prognostic landscape in HNSCC by integrating prognostic modelling, molecular subtyping, and AI-guided drug discovery. Methods: Transcriptomic data from seven GEO cohorts and TCGA-HNSC (n = 501) were analysed with a 1204-gene sialylation compendium. The Sialylation Niche Index (SNI) was built in TCGA-HNSC from 117 machine-learning algorithm combinations (nominally 101), with survival-based feature selection and model fitting confined to the training set; model selection used TCGA-HNSC and the external model-selection cohort GSE42743 (n = 74 with overall survival), and the three independent test cohorts were scored without re-fitting (E-MTAB-8588, n = 83; GSE65858, n = 270; and GSE41613, n = 97). Key model genes were characterised by single-cell RNA sequencing (168,742 cells), spatial transcriptomics, and AI-guided screening of 249,455 compounds. Results: Thirty-one prognostic sialylation-associated DEGs defined an immunosuppressive subtype (C1) and an immune-active subtype (C2) with divergent survival. The SNI achieved a C-index of 0.88 in TCGA-HNSC, 0.68 in GSE42743, and 0.58–0.62 in the three independent test cohorts and remained independently prognostic after adjustment for age and other clinicopathological variables in multivariable analysis. SHAP analysis identified HSPH1 (risk-associated) and ST6GALNAC1 (protective) as principal contributors with opposing microenvironmental associations. Conclusions: Sialylation constitutes a distinct immunosuppressive axis in HNSCC, complementary to PD-1 blockade. The SNI provides a biologically anchored prognostic framework whose cross-platform transfer requires recalibration; putative candidate compounds require experimental validation. Full article

With the continuous advancement of infrared detection imaging technology and the increasing demand for applications, there is a need for integrated applications of infrared imaging (IR imaging) systems. The design of these applications aims to achieve programmability, modularity, and intelligence in IR imaging verification systems through a high-performance integrated hardware architecture. Therefore, in the present study, an infrared image (IR image)-processing system-level SoC was designed for integrated application in IR imaging systems. The chip integrates a CPU, an IR image-processing coprocessor, Cameralink image output, and dedicated peripheral control interfaces, with a maximum real-time data-processing capability of over 1.9 Gbps. The chip can realize real-time image processing such as infrared detector control, IR image acquisition, adaptive non-uniformity correction, adaptive blind pixel recognition and replacement, image equalization (enhancement), image stretching and shrinking, pseudocolor conversion, image flipping and mirroring, and image windowing, simplifying the structure of infrared detection and signal processing in the IR imaging system and laying a technical foundation for the implementation of the integrated design of IR imaging systems. The chip is implemented using 40 nm CMOS process technology, with a size of 4000 × 6800 μm, an operating temperature of 218.15~398.15 K, and a frame rate of 200 Hz. The test results show that, after processing with the chip, the non-uniformity and blind pixel rate of the image can be reduced to 0.13% and 0.15%, respectively; the image stretching and shrinking rate can reach 25%; the image can be windowed at any size; and the expected requirements are achieved in IR image processing. At the same time, the system’s power consumption is less than 0.5 W, its packaging weight is less than 0.4 g, and its volume is reduced by 75% compared with traditional systems, meeting the application requirements of systems with high volume, power consumption, cost, and performance requirements. Furthermore, the proposed system has high engineering application value and prospects, promoting the further development of infrared imaging technology and more intelligent IR image algorithms. Full article

Objectives: Colorectal adenomatous polyps are well-recognized precancerous lesions of colorectal cancer. Patients who undergo endoscopic polypectomy still face a high risk of polyp recurrence, and individualized risk stratification remains challenging in routine clinical practice. Dyslipidemia has been implicated in adenoma development, but its role in recurrence and the predictive value of machine learning tools are understudied in Chinese populations. This study aimed to identify independent risk factors for adenoma recurrence and compare the performance of eight machine learning prediction models. Methods: This single-center retrospective cohort study included 769 patients who underwent colonoscopic polypectomy and completed at least one surveillance colonoscopy. A non-random site-based split was used to derive a training cohort (n = 539, Endoscopy Center) and an independent internal test cohort (n = 230, Colorectal Center). Univariate and multivariate Cox proportional hazards regression were applied to identify independent predictors of recurrence. Eight machine learning models were constructed using the selected predictors, and their discriminative performance, calibration, and clinical net benefit were comprehensively evaluated. Results: Abnormal high-density lipoprotein cholesterol (HDL-C), higher baseline polyp count, and larger total polyp volume were independent risk factors for adenoma recurrence. In the training set, the gradient boosting machine (GBM) achieved the highest AUC of 0.874, followed by XGBoost (AUC = 0.866); in the test set, GBM and XGBoost maintained favorable discriminative performance with AUCs of 0.863 and 0.849, respectively. The two models delivered comparable clinical net benefit across clinically relevant probability thresholds. XGBoost demonstrated acceptable calibration (Hosmer-Lemeshow p = 0.065), whereas GBM showed statistically significant miscalibration (p = 0.043). Conclusions: Abnormal HDL-C and greater baseline polyp burden are independent predictors of earlier colorectal adenoma recurrence. Machine learning models, particularly gradient boosting algorithms, achieve favorable discriminative performance and may serve as complementary tools for post-polypectomy risk stratification, though further calibration optimization is warranted prior to clinical application. Full article

To address the challenges of low localization accuracy for insulator defects in complex inspection scenarios and excessive model redundancy that hinders edge deployment, this paper proposes FS-YOLO, a lightweight defect detection algorithm. First, a C3k2 ConvFormer Gated Linear Unit (C3k2-CFGLU) module replaces the original backbone, using depthwise separable convolution and gated linear units to enhance defect feature representation while reducing computation. Second, a Multi-Branch Multi-Scale Feature Pyramid Network (MBMSFPN) neck improves defect target perception via multi branch auxiliary connections that strengthen high-level and low-level feature interaction. Third, a Shared Convolution Detection Head (SCDH) reduces parameters through multi-scale shared convolutions and group normalization, and incorporates a localization quality estimation mechanism to offset lightweight induced accuracy loss. Finally, layer adaptive sparsity for magnitude-based pruning (LAMP) removes redundant channels, compressing model size and improving efficiency. Experiments show that FS-YOLO achieves 93.0% precision, 88.3% recall, 92.9% mAP@0.5, and 62.9% mAP@0.5:0.95, with only 0.88M parameters and 4.1 GFLOPs. Compared with the baseline, parameters and computation drop by 65.9% and 34.9%, while mAP@0.5 increases by 5.7%. Against other mainstream YOLO variants, FS-YOLO offers superior accuracy efficiency trade offs, offering a promising reference for the intelligent development of power line inspection. Full article

This paper concerns the optimization of the tribological properties of carbonitrided steel, a matter of particular significance in the design of highly stressed, hardened mechanical components. The study aims to explore the simultaneous treatment of microhardness, normal load and number of passes as jointly optimized design variables for the multi-pass scratch friction response of carbonitrided AISI 4130 steel. It employs a full-factorial response-surface (RSM) approach, in conjunction with a desirability-function optimization and an artificial neural network (ANN) model, with the objective of both explaining and predicting this response. The microstructural and mechanical properties of carbonitrided AISI 4130 steel were first examined by looking at how the steel wears in tribological applications. The microstructure of the carbonitrided steel was analyzed using optical microscopy and X-ray diffraction (XRD), with varying carbon-potential and tempering parameters. As the tempering temperature increased, the carbonitrided steel demonstrated a decrease in microhardness, consistent with progressive relief of quenching-induced stresses and decomposition of retained austenite. The surface microhardness ranged from 630 HV0.1 (C12, tempered at 550 °C) to 980 HV0.1 (C2), against 270 HV0.1 for untreated steel. To investigate the friction coefficient, a multi-pass scratching approach was employed, utilizing a full-factorial design of experiments (4 × 4 × 4 combinations of hardness, normal load and number of passes). The experimental data were analyzed by response surface methodology (RSM) with a desirability-function approach, and by an artificial neural network (ANN) trained with the standard back-propagation algorithm. The correlation coefficient (R²) of 0.993 demonstrates a strong agreement between the model predictions and the experimental results. The findings establish a validated, transferable quantitative relationship between carbonitriding-induced hardness gradients and adhesive-wear friction behavior, providing a predictive framework that can be extended to other case-hardened low-alloy steels subjected to multi-pass sliding degradation. Full article

(1) Background: To address the challenges in ground-penetrating radar (GPR) B-scan profiles—where targets typically exhibit hyperbolic signatures, small scales, low contrast, and severe background clutter—this paper proposes an efficient and accurate target detection algorithm based on YOLOv8n, termed YOLOv8n-PCPG. (2) Methods: First, a Pinwheel-shaped Convolution (PSConv) is integrated into the backbone to capture directional hyperbolic boundaries. Second, a lightweight CSP-PMSFA module replaces the standard C2f structures in the neck, reducing computational redundancy while preserving feature integrity. Finally, a structurally reparameterized GC-downsampling module is introduced to enhance cross-channel feature interaction without adding inference latency. (3) Extensive experiments on a self-constructed GPR B-scan dataset show that the proposed model achieves a Precision of 97.2%, a Recall of 97.3%, an mAP@0.5 of 98.8%, and an mAP@0.75 of 84.3%. Compared with the baseline YOLOv8n, the parameter count and GFLOPs are reduced by 13.0% and 7.4%, respectively, while achieving a real-time detection speed of 155.78 FPS, outperforming existing mainstream detectors. (4) Conclusions: These results demonstrate that YOLOv8n-PCPG achieves a strong trade-off between detection accuracy and computational efficiency, providing effective technical support for subsurface hyperbolic signature identification. Full article

The second-life utilization of retired power batteries is critically constrained by high cell-to-cell variability in capacity and internal resistance, which severely reduces the usable capacity of repacked modules. To overcome this challenge, this paper presents a multi-stage screening and grouping strategy that balances accuracy with practical efficiency. The method comprises four progressive steps: static Euclidean distance-based pre-screening, 0.1C low-rate reference capacity calibration, 0.5C operating-condition re-screening, and serpentine sorting for final grouping. A total of 389 retired 18650 ternary lithium-ion batteries from a single batch were studied. First, 89 cells were pre-screened using voltage–internal resistance Euclidean distance, from which 16 cells were selected for 0.1C calibration to establish a low-rate reference capacity baseline. Subsequently, 52 cells were re-screened from the remaining 300 and tested at a 0.5C rate. Finally, the 52 cells were assembled into 13 groups via serpentine sorting and uniformly calibrated to 50% SOC. A benchmark conversion coefficient β, defined as the ratio of the mean 0.5C capacity to the mean 0.1C capacity, and a comprehensive consistency index (CQI) were established for evaluation. Results show that the mean 0.1C capacity is 2835.2 mAh with β = 0.9681. After serpentine grouping, the capacity range across the 13 groups is only 16.69 mAh, with a coefficient of variation of 0.0407%—significantly outperforming random grouping—and the CQI reaches 0.985. The proposed method reduces the total capacity testing time from approximately 21.9 days to about 2.5 days, improving efficiency by approximately 88%. In contrast to prior work focusing solely on algorithmic improvements, this study, for the first time, integrates static outlier exclusion, small-sample-rate mapping, and serpentine balanced grouping into a closed-loop engineering workflow, providing a deterministic, rule-based solution for the entire screening-to-grouping pipeline in second-life applications. The method requires neither complex instrumentation nor sophisticated algorithms and exhibits strong robustness against common measurement errors, offering an economical, reliable, and easily replicable engineering solution for retired battery second-life utilization. Full article

This paper proposes a Priority–Urgency Index (PUI) mechanism to address the multi-objective conflict problem in electric vehicle charging scheduling at public fast-charging stations. The mechanism dynamically quantifies the charging urgency of each vehicle based on remaining dwell time, current state of charge (SoC), and target SoC, enabling differentiated service prioritization under resource scarcity. The PUI is systematically integrated into four multi-agent reinforcement learning (MARL) algorithms. To handle the time-varying number of vehicle entities caused by random arrivals and departures, the chargers are modeled as fixed agents; a partially observable Markov decision process (POMDP) is formulated, and a centralized training with decentralized execution (CTDE) architecture is adopted. On this basis, a state-aware dynamic threshold mechanism is introduced to distinguish urgency levels of charging tasks, and an adaptive reward function is designed to accommodate complex operating conditions. Empirical comparisons show that PUI-MAPPO (multi-agent proximal policy optimization) achieves the best performance among all PUI-enhanced variants. Under extreme supply–demand conditions—such as resource-scarce and heavy-traffic scenarios—PUI-MAPPO improves the target-SoC fulfillment rate and net revenue by up to 42.7% and 23.2%, respectively, and reduces the cumulative grid-limit exceedance by 22.8% to 47.3%, relative to the first-come, first-served (FCFS) baseline. Ablation studies further validate the individual effectiveness of the PUI urgency mechanism, the dynamic threshold framework, and the adaptive reward function. Full article

This study extends an Empirical Orthogonal Function (EOF)-based diagnostic framework to a global scale to characterize oceanic precipitation systems during hemispheric summer, applied to multi-channel Polarization Corrected Temperature (PCT) observations from the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The EOF framework condenses multi-channel microwave variability into a two-dimensional coordinate space defined by the first two principal components ($P{C}_1$ and $P{C}_2$), providing a physically interpretable structural representation of precipitation systems. $P{C}_1$ represents bulk hydrometeor loading; its global distribution is characterized by elevated values in the ITCZ, Asian monsoon regions, and major midlatitude storm-track belts. $P{C}_2$ modulates the relative contributions of upper-level ice scattering and lower-level liquid emission, characterizing vertical phase partitioning within the column. Collocated GPM Dual-frequency Precipitation Radar (DPR) observations demonstrate that the $PC$ coordinates correspond systematically to three-dimensional reflectivity structures across both convective and stratiform regimes. This partitioning exhibits a systematic meridional contrast, shifting toward greater ice-phase contribution in the tropics and greater liquid-phase contribution across the midlatitudes—consistent with DPR-observed reflectivity profiles, storm-top height, and the Ice-to-Rain Path Ratio (IRPR). The EOF framework provides an observation-based reference for evaluating microphysical representations in numerical models and satellite precipitation retrieval algorithms. Full article

Background: Oxeiptosis is a ROS-induced, caspase-independent form of regulated cell death, but its role in osteoarthritis (OA) remains largely unexplored. This study aimed to identify oxeiptosis-associated molecular markers in OA synovium and establish an oxeiptosis-based molecular classification system. Methods: This study integrated five publicly available GEO datasets for comprehensive analysis. GSE55235 and GSE55457 were used as discovery cohorts to identify DEGs between OA and normal synovial tissues. The GSE206848 dataset was utilized to perform correlation analysis with the key oxeiptosis regulators, including KEAP1, PGAM5, AIFM1, and CUL3, thereby establishing an oxeiptosis-associated gene set. These genes were intersected with OA-related DEGs to obtain ORDEGs. Subsequently, LASSO regression, SVM-RFE, and RF algorithms were jointly applied to identify hub differential genes. Based on the identified oxeiptosis-related feature genes, molecular subtypes of OA were constructed, with the GSE46750 dataset used for machine learning-based feature selection. Consensus clustering was then performed based on the selected features to identify distinct OA molecular subtypes. The immune microenvironment characteristics and potential regulatory networks of different subtypes were further investigated using CIBERSORT, ESTIMATE, and WGCNA. Finally, an independent GSE89408 cohort was employed for external validation. Results: A total of 159 common differentially expressed genes were identified from the two OA synovial cohorts, which were mainly enriched in cellular response to hydrogen peroxide, the MAPK signaling pathway, the PI3K-Akt signaling pathway, and NF-κB-related inflammatory processes. Further screening identified seven ORDEGs, including OTUD4, GATM, KTN1, FGGY, ACACB, RERE, and APLP2. Machine learning analysis ultimately identified ACACB and FGGY as potential molecular features of OA. Molecular clustering based on oxeiptosis-related features demonstrated that OA samples could be stably classified into two subtypes, C1 and C2. The C2 subtype exhibited higher ORDEG scores, increased ACACB expression levels, greater M1 macrophage infiltration, and higher ESTIMATE scores, indicating oxidative stress-related transcriptional characteristics and enhanced inflammatory features, whereas FGGY was mainly highly expressed in the C1 subtype. WGCNA further revealed that the salmon module closely associated with the C2 subtype was mainly enriched in calcium signaling, focal adhesion, cytoskeletal remodeling, and cell adhesion-related pathways. In the independent GSE89408 validation cohort, exploratory clustering analysis again identified two potential molecular subtypes, and FGGY remained significantly differentially expressed between the two subtypes. In addition, ACACB and FGGY showed AUC values of 0.735 and 0.647, respectively, for distinguishing OA from normal synovial tissues. Conclusion: This study establishes an oxeiptosis-associated molecular subtyping framework for OA synovium and identifies ACACB and FGGY as candidate oxeiptosis-associated genes. Further analyses revealed distinct immune microenvironment characteristics and transcriptional regulatory patterns associated with different oxeiptosis states. External validation provided partial support for the reproducibility of these molecular patterns, particularly the FGGY-related features. These findings provide transcriptomic evidence for exploring molecular heterogeneity in OA and generate hypotheses for future mechanistic and clinical validation studies. Full article

This article presents a multi-objective optimization framework for fluorescent organic light-emitting diodes (OLEDs), combining numerical simulation with an analysis of exciton populations and the associated loss mechanisms. An ITO/NPB/Alq₃:C545T/Alq₃/LiF–Al structure was modeled, calibrated, and validated against experimental electro-optical characteristics. The influence of the emissive layer thickness ($t_{\mathrm{EML}}$) and the C545T doping concentration ($D_p$) was systematically studied across 25 configurations, with $t_{\mathrm{EML}}$= 20–40 nm and $D_p=1$–$9\%$, under two operating conditions: $J=0.15$ A/cm² and $L=5000$ cd/m². Increasing the dopant concentration led to a marked deterioration in current efficiency (${\eta }_c$) and power efficiency (${\eta }_p$), together with a progressive localization of singlet excitons near the interface between the emissive layer (EML) and the hole transport layer (HTL) in structures with thinner EMLs. This exciton localization was accompanied by an increased contribution from non-radiative deactivation pathways, which were incorporated into a dopant singlet-exciton loss fraction, $F_{\mathrm{loss},d}$. The systematic increase in this loss fraction with increasing dopant concentration and its overall inverse relationship with ${\eta }_c$ and ${\eta }_p$ motivated the development of a three-objective formulation that maximizes ${\eta }_c$ and ${\eta }_p$ while minimizing $F_{\mathrm{loss},d}$. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) identified compromise solutions at the lowest dopant concentration, $D_p=1\%$, with $t_{\mathrm{EML}}=24$–25 nm under both operating conditions. Multi-objective Particle Swarm Optimization (MOPSO) identified a trade-off region comparable to that obtained by NSGA-II, providing a cross-algorithm consistency check of the reported numerical results. The proposed methodology thus establishes a physically grounded link between device design, the spatial redistribution of excitons, the modeled loss pathways, and the macroscopic performance of OLEDs, thereby providing an interpretable framework for the multi-objective optimization of fluorescent OLEDs prior to fabrication. Full article

Manual sorting is still the mainstream scheme for component identification in down feather quality evaluation, which suffers from low detection efficiency and poor stability. To address these drawbacks, this paper proposes a fine-grained component detection algorithm based on YOLO12 for down feather quality classification. Five typical down feather components are selected as detection targets, including discolored feathers, down filaments, immature down, feathers and pure down. A dedicated down feather object detection dataset consisting of 1140 images is established accordingly. To improve the feature representation capacity of the model for tiny objects, faint boundary features and subtle distinctions between analogous categories, this work integrates the Convolutional Gated Linear Unit (CGLU) into the A2C2f module of YOLO12. While maintaining the original feature aggregation pathways and residual architecture, the conventional MLP feed-forward branch within ABlock is replaced with convolutional gated transformation. Experimental results demonstrate that the proposed A2C2f-CGLU model achieves precision of 96.50%, recall of 94.49%, mAP50 of 98.05% and mAP50-95 of 57.89% with the optimal weights on the validation set. Compared with the original YOLO12, the mAP50-95 metric is elevated by 3.04 percentage points, and the overall performance surpasses two comparative variants, A2C2f-DFFN and A2C2f-KAN. Visualizations of PR curves, confusion matrices and real test samples validate that the proposed method effectively enhances the recognition stability of tiny down feather targets and similar classes. This research provides a visual inspection foundation for subsequent component proportion calculation, quality grade discrimination and the development of intelligent detection systems. Full article

This work presents an incremental zoom analog-to-digital converter (ADC) readout integrated circuit (IC) and field-programmable gate array (FPGA)-based digital calibration system for high-precision capacitive micro electro-mechanical system (MEMS) pressure sensors, aimed at enhancing pressure readout resolution, pressure measurement accuracy, and long-term output stability. In the readout IC, the zoom ADC employs coarse-fine quantization, achieving high readout accuracy while relaxing requirements on integrator output swing and front-end linearity. On the digital side, a sparrow-search-algorithm-optimized Gaussian process regression (SSA-GPR) model constructs a nonlinear pressure–temperature mapping with limited calibration samples, while a segmented aging-compensation scheme based on dual-channel periodic self-test dynamically corrects aging-induced offset and sensitivity drifts. Together, these three techniques form a complete signal-conditioning chain addressing weak capacitance variation readout, pressure–temperature coupling, and long-term drift. Measured results show 0.11 Pa_RMS output root mean square (RMS) noise under a 205.2 ms conversion time, 37.44 Pa mean absolute error over −40 °C to 85 °C and 30 kPa to 120 kPa, and ±30 Pa residual error after 85 °C/1000 h aging, confirming the effectiveness of the proposed readout IC and digital calibration system. Full article

Background: Morbidity associated with anastomotic leak (AL) can be reduced by timely detection. Local electrophysiological parameters are highly sensitive to pathophysiological processes (inflammation/ischemia) underlying AL and may enable early post-op monitoring ahead of clinical signs. A new approach to early leak detection includes post-operative measurement of bioelectric activity of the GI tract. The method has been implemented in a medical device that uses electrodes embedded in a standard surgical drain to continuously monitor local bowel myoelectric activity following GI surgery. Methods: A multicenter pilot study evaluated the performance of the device to support detection of clinically recognizable anastomotic leaks on post-operative day 3. The device’s technology collected data that an algorithm classified to predict AL. Data were collected for a duration of 3–10 days, while the surgical drain was in place (without affecting routine care). Pilot endpoints were safety and performance analyses: sensitivity and specificity. Results: Fifty patients from 6 medical centers (mean age: 63.6 ± 13.3 years; 38% male) undergoing elective anterior resection (laparoscopic (N = 26), robotic (N = 16), open (N = 8)) for cancer (N = 44), diverticulitis (N = 4), and rectal prolapse (N = 2) were included. Average anastomotic height was 9 ± 5 cm from the anal verge. Grade C ALs were observed in 6.0% (N = 3) of the patients, resulting in increased hospitalization time (6.4 ± 2.4 days with no leak vs. 15.0 ± 7.9 days with Grade C leak). In this N = 50 cohort, the device had 100% sensitivity and 100% specificity at POD3 in prediction of Grade C leaks. Given the small number of events (three Grade C leaks, seven leaks overall), these results are preliminary. No device-related serious adverse events were observed, and the standard drain form factor contributed to positive usability feedback. Conclusions: Pilot data suggest that the bioelectric sensing drain device is safe and warrants further evaluation as a potential adjunct for AL detection. The small study sample size and wide confidence intervals for key outcomes require confirmation in a pivotal diagnostic accuracy study with a fixed thresholding algorithm. Full article

The rapid decarbonisation of urban districts is accelerating the deployment of rooftop photovoltaic (PV) systems, household battery energy storage systems (BESSs) and electric vehicles (EVs). However, the high variability of urban micro-generation creates substantial forecasting errors at the urban–grid interface, imposing high balancing costs on distribution system operators (DSOs) and local energy communities. Traditional metrics fail to assess how effectively internal peer-to-peer (P2P) flexibility offsets these forecast mismatches prior to grid settlement. To address this gap, this paper presents a novel methodological framework and a non-parametric indicator: the Community Imbalance Neutralisation Index (CINI). Formulated in a generalised, scalable matrix structure applicable to any heterogeneous urban neighbourhood (N households), CINI quantifies the relative reduction in net community-level imbalance relative to the cumulative sum of uncoordinated individual forecast errors. CINI ranges from 0 per cent (no collective mitigation) to 100 per cent (perfect internal neutralisation). Complementing this index, an adaptive day-ahead scheduling algorithm is introduced to determine the optimal community energy purchase requirement (E_forecast). The proposed framework is numerically evaluated using a high-resolution synthetic benchmark annual dataset with 15 min intervals (35,040 intervals) representing a Central European urban residential cluster equipped with diverse combinations of PV, BESS and managed EV charging infrastructure. The simulation results demonstrate that active cVPP coordination reduces annual grid-facing imbalance energy from 188.73 MWh to 143.88 MWh, increasing the annual CINI score from 57.22% to 67.39% (+10.17 percentage points) compared with the uncoordinated baseline. Notably, the framework reveals a ‘Winter Flexibility Paradox’, achieving its highest relative efficacy during the winter months (+13.88 percentage points in December). Furthermore, sensitivity analyses show that scaling flexibility up to 40 kW achieves a CINI score of 91.22%, revealing diminishing marginal returns and critical technological saturation thresholds. The proposed CINI metric and E_forecast dispatch algorithm provide city planners, municipal energy managers and DSOs with a transparent diagnostic tool to design dynamic socio-economic tariff incentives, optimise urban micro-grid sizing, prevent free-rider dynamics, and foster resilient, self-balancing smart cities. Full article