Search Results (851)

Photobioreactor (PBR) microalgae cultivation involves complex multivariate physicochemical interactions in which subtle disturbances may affect multiple parameters simultaneously before visible culture degradation occurs. Conventional monitoring approaches based on manual sampling and univariate threshold inspection are often inadequate for capturing these coupled temporal dynamics in cultivation environments. However, the relative effectiveness of different unsupervised anomaly detection (AD) methods for modeling complex multivariate environmental data remains insufficiently understood, highlighting the need for a systematic comparative evaluation. This study aims to evaluate three unsupervised AD models, namely Isolation Forest (IForest), One-Class Support Vector Machine (OC-SVM), and Local Outlier Factor (LOF), for monitoring C. vulgaris PBR cultivation using multivariate real-time IoT sensor data. The observations comprising oxidation-reduction potential (ORP), electrical conductivity (EC), potential of hydrogen (pH), and water temperature were collected and analyzed as multivariate time-series data. Percentile-based thresholds (P90, P95, and P99), derived from the normal training data, were evaluated to examine the trade-off between anomaly sensitivity and false alarm reduction. Within the threshold-sensitivity analysis, IForest achieved a recall of 0.9750 and an F1-score of 0.9656 at P95, together with an ROC-AUC of 0.9957 and a PR-AUC of 0.9959. Temporal anomaly profiling further revealed that anomaly clusters were concentrated during the adaptation and stationary growth phases. These findings demonstrate the potential of unsupervised AD models as practical tools for data-driven monitoring and anomaly profiling in microalgae PBR cultivation systems. Full article

Timely bridge-deck pavement inspection must cover large areas while preserving the image detail needed to recognize local distress. This study develops a macro-to-micro workflow with separate LiDAR screening and YOLOv8s image-detection branches. Airborne LiDAR data from the Jingzhou Yangtze River Highway Bridge supported deck extraction and geometric screening. Processing included statistical outlier removal, coordinate normalization, voxel sampling, local plane fitting, residual and normal-discrepancy screening, and spatial clustering. Deck extraction retained 179,914 of 311,784 representative points. Geometric screening based on residual and normal discrepancy identified 17,075 suspected points. Region filtering followed by bounding-box export yielded 423 points in seven candidate regions for targeted inspection. The 2025 inspection report documented pavement distress, and site personnel confirmed corresponding distress within the screened regions. The image branch used the official image-level splits of UAV-PDD2023. The epoch-146 checkpoint achieved the highest validation mAP50 and was evaluated on the official-test split. Official-test precision reached 0.85591, with a recall of 0.85882, mAP50 of 0.88556, and mAP50–95 of 0.58195. The model detected all six pavement-distress classes under the fixed benchmark protocol. The two branches support wide-area geometric screening and detailed image analysis for bridge-deck pavement inspection. Full article

Few outages have confirmed labels in power distribution data, which makes anomaly ranking difficult. Semi-Supervised Local Outlier Factor with Positive-Unlabeled Learning (SSLOF-PU) combines local density with similarity to confirmed outages. The method shares similarity information among close observations and uses the resulting scores to rank anomalies. Calibration sets alarm thresholds without changing the ranking. We evaluated the method on one utility dataset and seven public datasets, using 20 paired runs per dataset. SSLOF-PU had the highest mean average precision (MAP) among the compared approaches on all eight datasets. Its numerical gains over the strongest comparator ranged from 1.4% to 6.1%. None of the paired differences remained statistically significant after Holm–Bonferroni correction. On the utility dataset, MAP was 0.52 ± 0.04, with a 95% confidence interval of 0.501 to 0.539. Examining the top 50 of 10,000 observations achieved an alarm hit rate of 0.700 and a recall of 0.350. Removing graph propagation decreases MAP to 0.39; deleting learned similarity bandwidths reduced it to 0.45. Increasing the calibration sample from 500 to 5000 reduced the standard deviation of the alarm threshold from 0.048 to 0.014. These results present the method’s potential for ranking observations under limited inspection budgets. Full article

Background: Outcomes for patients with osteosarcoma (OS) presenting with metastases at diagnosis or relapsed remain poor with five-year survival rate of only 20–30%. Risk stratification at diagnosis remains limited, and no validated prognostic liquid biopsy analyte currently exists for this disease. OS lacks the recurring mutational hotspots and epithelial surface markers that underpin liquid biopsy strategies developed for carcinomas, necessitating fundamentally different approaches to circulating biomarker detection. Methods: Peripheral blood samples from 25 pediatric and young adult OS patients at clinically apparent disease timepoints (n = 29 samples) and 76 adult healthy donors (ND) were processed without cell enrichment using fluorescent whole-slide imaging. An outlier detection pipeline employing deep contrastive learned features classified rare cells across eight immunophenotypic categories defined by DAPI, vimentin (V), cytokeratin (CK), and CD45/CD31 (CD) expression. From the same patient cohort, plasma cell-free DNA (cfDNA) was analyzed by ichorCNA-based tumor fraction estimation across 42 samples (10 clinically apparent disease timepoints shared with the rare cell cohort and 32 alternative timepoints) from ten patients, and single-cell copy number alteration (scCNA) profiling was performed on 80 rare cells from five patients. Results: Rare cells were detected at a median of approximately 127 cells/mL (range: 29–1114) in OS patients at clinically apparent disease timepoints versus 34 cells/mL (range: 4–225) in healthy donors. DAPI-only (D) cells, identified using a data-driven optimal cutpoint (38.32 cells/mL) that is subject to optimization bias and requires independent validation, were associated with relapse-free survival in this exploratory, single-center OS cohort (log-rank p = 0.0003), with high D cell burden associated with markedly shorter relapse-free survival (median 393 days, 95% CI: 106–572) compared to patients with low D cell counts (median 835 days, 95% CI: 494–1671). D cells also showed the broadest clinical associations, correlating with metastatic burden and disease subgroup. The Vimentin⁺/CD45⁻ (D|V) population was the strongest discriminator between OS patients and healthy donors (p = 4.64 × 10⁻¹²) but did not distinguish relapse-free survival groups within the OS cohort (log-rank p = 0.8367). Genomic analyses at the plasma, single-cell, and tumor tissue levels were largely uninformative. Conclusions: Enrichment-free rare cell profiling identifies a DAPI-only circulating rare cell population whose abundance is associated with relapse-free survival in this exploratory, single-center OS cohort, accessible by nuclear morphology alone without biomarker enrichment. This is a novel, hypothesis-generating observation that, together with the mesenchymal-forward D|V population characterized here, motivates a larger, independent, prospective validation cohort and dedicated single-cell molecular (proteomic) characterization of the D-cell population, rather than establishing DAPI-only rare cell enumeration as a validated liquid biopsy stratification tool at this stage. Full article

Active signal-source seeking by swarms of unmanned aerial vehicles (UAVs) requires reliable waypoint decisions under measurement corruption, misleading response peaks, and geometric constraints. We propose MI-PSO-Adaptive, a high-level waypoint planning framework based on particle swarm optimization (PSO). The framework combines repeated sampling with robust aggregation, adaptive exploration with archive-based social target selection, candidate waypoint guidance, and geometric constraint repair. We evaluated the framework in a three-dimensional simulation using a five-sample measurement budget, waypoint limits, obstacles, and reproducible random seed protocols. In the default unimodal Gaussian field, MI-PSO-Adaptive achieved a mean localization error of 35.36 m and a success rate of 94%; model-matched Gaussian estimation baselines remained more accurate. In the fixed Gaussian mixture field, it achieved a mean localization error of 54.03 m and a success rate of 66%. In contrast, the other evaluated optimization methods and unimodal-model estimation baselines failed to reach the target region. Under 5% outlier contamination, robust aggregation achieved a mean localization error of 157.80 m and a success rate of 52%, outperforming arithmetic mean MI-PSO, although the error distribution remained heavy tailed. Functional ablation showed that adaptive exploration with social target selection helped the swarm escape misleading interference peaks, whereas candidate waypoint guidance further reduced localization errors after effective exploration. The method is therefore intended for discrete high-level waypoint planning under the tested model-mismatch conditions rather than serving as a general replacement for model-based estimation or a guarantee of low-level flight feasibility. Full article

Ground temperature prediction is critical for ensuring the long-term operational stability and energy efficiency of ground-source heat pump (GSHP) systems, which are essential for the sustainable utilization of shallow geothermal energy and the stability of the underground ecological environment. However, existing models are predominantly developed for shallow depths (typically within 5 m) for agricultural or permafrost applications, leaving the 50–100 m depth range relevant to GSHP systems underexplored. This study addresses this gap by constructing differentiated prediction models for two typical data types: multi-parameter short-period data and single-parameter long-period data. For Sample I, random forest and support vector regression (SVR) models were developed with hyperparameters optimized by the Sparrow Search Algorithm. For Sample II, ARIMA and SARIMA models were established. A standardized preprocessing workflow integrating box plot-based outlier detection, Newton interpolation, and Min-Max normalization was applied to both datasets. The results show that random forest significantly outperforms SVR, while SARIMA with seasonal components substantially improves upon ARIMA by capturing annual ground temperature periodicity. These findings provide quantitative guidance for model selection in GSHP engineering, particularly for the 50–100 m depth range, enabling accurate prediction of deep ground temperature for thermal balance assessment and early warning of thermal imbalance risks, thereby supporting the sustainable operation of GSHP systems and the efficient utilization of shallow geothermal energy. Full article

To address the challenges in large-scale packaging and printing workshops, where the dense arrangement of electromechanical equipment leads to large-scale scenes and prominent repetitive structures, and where frequent dynamic disturbances impair the accuracy and efficiency of cooperative mapping by multiple mobile robots, this paper proposes a grid map fusion method that integrates multi-resolution cascading with optimal transport. The method constructs a coarse-to-fine pyramid hierarchy: at the coarse-resolution level, an initial estimate of the global alignment trend is obtained, and a cross-layer prior transfer mechanism maps the prior information to the finer level, with log-domain Sinkhorn iterations ensuring numerical stability in fine matching; at each resolution level, Oriented FAST and Rotated BRIEF (ORB) features are extracted, and a hybrid cost matrix is formulated by combining appearance information with local topological signatures, while virtual nodes are introduced to improve robustness against missing correspondences and dynamic outliers; high-confidence correspondence candidates are selected based on bidirectional normalized confidence and the maximum consensus criterion, and the Random Sample Consensus (RANSAC) algorithm is employed to estimate the two-dimensional rigid-body transformation; finally, a globally consistent grid map is generated through standard log-odds probability fusion. Experiments on a self-constructed packaging and printing workshop dataset demonstrate that the proposed method maintains registration accuracy while effectively reducing the computational burden of large-scale map fusion, thus providing a feasible technical pathway for global environment modeling and dynamic task scheduling in multi-robot cooperative operations. Full article

This paper presents a forecasting strategy for distributed photovoltaic (PV) systems using the echo state network (ESN), aiming to improve forecasting performance with data noise at sampling points of dispersed PV data collection devices. In real-world scenarios, noisy and non-normally distributed data caused by sensor malfunctions, human errors, and extreme weather lead to outliers, thereby undermining the reliability of power generation forecasting algorithms. Therefore, we propose an outlier-robust echo state network (OR-ESN) that leverages the combined strengths of ridge regularization and least absolute shrinkage and selection operator (LASSO) regularization to achieve enhanced generalizability and sparsity. Furthermore, by adopting the ${\mathsf{\ell }}_1$-norm as the loss function, the model achieves robust performance in the presence of noise. In addition, as distributed PV systems are increasingly preferred because of their flexibility, efficiency, and economic advantages, we extend the OR-ESN to the distributed outlier-robust echo state network (DOR-ESN) to better meet the forecasting needs of distributed PV systems. Furthermore, the distributed average consensus protocol is introduced and combined with the ADMM algorithm to enhance communication efficiency during the training process of the distributed ESN network for power prediction. The experimental results show that the OR-ESN and DOR-ESN demonstrate excellent predictive performance. Specifically, the OR-ESN increases the resistance to perturbations due to noise in distributed PV power forecasting. Moreover, the DOR-ESN builds on this foundation to significantly improve the accuracy and adaptability of power predictions for distributed PV systems. Full article

Quantitative evidence synthesis has become a central tool for integrating findings across multiple studies, multi-centre trials, and multi-source cohort data. However, the identification and interpretation of non-replicable, outlying, and influential studies remain insufficiently addressed in practice, despite their potential to substantially affect the robustness and credibility of meta-analytic conclusions. In this paper, we clarify the conceptual distinctions between non-replicability, statistical outlyingness, and study influence, emphasizing that these concepts are related but not interchangeable. We then review the standard principles and procedures of model diagnostics for detecting outlying and influential studies in meta-analysis, together with their underlying statistical rationale. Building on recent methodological developments, we further discuss several practical and methodological refinements, including approaches for handling imprecise and correlated sampling variances, robust diagnostic procedures, and graphical tools for facilitating the identification and interpretation of unusual studies. Finally, we summarize recent advances in outlier and influence diagnostics and provide recommendations for the cautious interpretation and evaluation of studies identified as potentially non-replicable, outlying, or influential within meta-analytic frameworks. Full article

In the UK, contaminated land risk assessments using the Contaminated Land Exposure Assessment (CLEA) model rely on soil organic matter (SOM) values to determine acceptable thresholds for contamination caused by anthropogenic pollution for human health. Soil organic carbon (SOC), total organic carbon (TOC) or loss on ignition (LOI) are routinely used as a proxy for SOM in the industry, both by contaminated land consultants and laboratories who rely on conversions such as the Van Bemmelen factor. Many standard laboratory methods for measuring SOC or TOC do not differentiate between natural organic carbon and petroleum hydrocarbons. This study investigates the interference of total petroleum hydrocarbons (TPHs) with SOC measurements by analysing 2377 brownfield soil samples. A positive correlation was observed between the two variables; when TPH concentrations increase by 1000 mg/kg, the reported SOC increases by 0.46 percentage points when outlier samples (>10,000 mg/kg TPH or >30% SOC) are excluded. When converted to SOM for risk assessment purposes using the Van Bemmelen factor, the calculated SOM rises by 0.79 percentage points per 1000 mg/kg TPH. This can push soils into higher assessment bands, generating less stringent generic assessment criteria (GAC). While the entire 0.46-percentage point increase cannot be accounted for entirely by a TPH rise of 1000 mg/kg (or 0.1%), it is hypothesised that other contaminants containing carbons are likely to rise alongside TPH and contribute to the increase in SOC. This study finds that relying on SOC as a proxy for SOM in hydrocarbon-impacted soils masks the absence of the natural organic matter required to sorb contaminants, leading to an underestimation of human health risks in contaminated land risk assessments. Full article

Zinc is an essential trace element for human and plant growth and development. Exploiting naturally Zn-rich soil resources represents an important pathway to develop green characteristic agriculture, improve the nutritional quality of agricultural products, and advance agricultural sustainability aligned with the United Nations Sustainable Development Goals (SDGs2, 3 and 15). To clarify the geochemical characteristics and bioavailability of soil zinc in cultivated lands of the mid-low hilly region in southwestern Henan, and to scientifically evaluate the exploitation potential of Zn-rich land resources, this study selected the farming area of Fangcheng County, Henan Province, as its research object. Based on 1:50,000 land-quality geochemical survey data, 1602 topsoil samples and 61 paired wheat-grain–root-zone soil samples were collected. Multiple statistical approaches, including descriptive statistics, Universal Kriging spatial interpolation, Pearson correlation analysis and segmented regression, were adopted to systematically investigate Zn concentrations, spatial-distribution patterns, element paragenesis relationships and wheat Zn-accumulation characteristics in cultivated soils. Furthermore, the resource potential of Zn-rich land was assessed according to the Specification for Delineation and Identification of Natural Zn-Rich Land (DD2025-04). The results showed the following: (1) The mean Zn concentration of topsoil was 72.3 mg/kg (N = 1602, after removing 3σ outliers), which was higher than the national soil background value (67 mg/kg). With a coefficient of variation of 23.0%, soil Zn exhibited moderate enrichment and relatively uniform distribution. Its spatial pattern was dominated by geomorphic types, showing a north-high–south-low trend. Significant Zn-enrichment advantages were found in hilly areas, yellow-cinnamon soils, and regions covered by alluvial–proluvial and residual-slope parent materials, whereas Zn concentrations were markedly low in strongly alkaline soils. (2) Soil Zn was significantly positively correlated with Cd (r = 0.62), Cu (r = 0.51), V (r = 0.54) and Fe₂O₃ (r = 0.57), and moderately positively correlated with Se (r = 0.41) and Mo (r = 0.43). These correlations indicate homologous soil-forming parent-material sources and Zn-adsorption–enrichment mechanisms mediated by Fe-Mn oxides. The paragenetic relationship between soil Zn and Cd implies that heavy-metal ecological risks should be considered simultaneously during Zn-rich-land exploitation to guarantee ecological sustainability of cultivated land. (3) The average Zn concentration in wheat grains was 28.48 mg/kg, and the mean bioconcentration factor (BCF) was 0.42 (N = 61, averaged from individual sample-pair ratios), demonstrating a moderate Zn-accumulation capacity of wheat. Only a moderate correlation existed between total soil Zn and wheat-grain Zn, suggesting that high total soil Zn concentrations do not necessarily produce high-Zn crops. (4) Soil pH regulated Zn bioavailability: BCF was positively correlated with pH for samples with pH < 6.5 (R² = 0.116, p = 0.013), while a non-significant negative trend was observed for the subgroup pH ≥ 6.5 (R² = 0.269, p = 0.153). Wheat presented relatively high Zn-accumulation potential under neutral-pH conditions. Nevertheless, the observational dataset cannot confirm pH = 6.5 as a strict optimum threshold, which needs further validation through field-controlled experiments. Either extremely acidic or alkaline conditions may reduce available Zn supply via Zn leaching loss or hydroxide precipitation. (5) In accordance with DD2025-04, Zn-rich soils accounted for 21.85% of the study area, and Zn-enriched wheat grains occupied 21.31%, indicating abundant reserves of Zn-rich land resources. These findings can provide geochemical support for the scientific utilization of Zn-rich cultivated land, industrial layout of Zn-enriched wheat, and precise cultivated-land quality management in the hilly areas of southwestern Henan, promoting sustainable utilization of soil resources and nutrition-sensitive agricultural development. Full article

Reliable ground-based aerosol observations are essential for evaluating satellite aerosol products across heterogeneous surfaces and aerosol regimes. This study established a six-site CW193 sun photometer network in China with quality-controlled observations from January 2023 to May 2026 and assessed CW193 AOD consistency using collocated CE318 observations at Fujin and Lijiang. A consistent processing chain including Langley calibration, preprocessing, triplet consistency screening, 440 nm daily-stability screening, temporal smoothness tests, and statistical outlier removal was applied, and the sensitivity to QC threshold perturbations was examined. The quality-controlled observations were then used to evaluate the FY-3F/MERSI-III daily AOD product and three MODIS products (MCD19A2, MOD04_L2, and MYD04_L2). Across 440–1020 nm, CW193 and CE318 showed strong agreement (R = 0.979–0.999; slopes = 0.989–1.019; RMSE = 0.007–0.024), with no evident long-term drift during the available comparison periods. At Lijiang, Hebi, and Qingdao, FY-3F/MERSI-III yielded R values of 0.220, 0.664, and 0.702 and RMSE values of 0.128, 0.157, and 0.183, respectively. MODIS performance varied strongly among sites and products; MCD19A2 yielded the lowest RMSE at Dunhuang, Kashgar, and Lijiang, whereas MYD04_L2 yielded the lowest RMSE at Hebi and Qingdao. The small MOD04_L2 and MYD04_L2 matchup samples at Fujin precluded a robust ranking. All three MODIS products substantially underestimated AOD at Kashgar. Regional CW193 observations further distinguished high-AOD, low-AE conditions at Kashgar from low AOD, high-AE conditions at Lijiang. QC threshold perturbations affected data retention more than the principal aerosol and satellite evaluation statistics. These results support CW193 as a regional ground-based reference for satellite AOD evaluation while emphasizing the importance of site conditions, collocation strategy, sampling, and the empirical nature of AOD–AE optical regimes. Full article

Background: Metamorphic malware is among the most persistent adversarial challenges in cybersecurity: it rewrites its own instruction stream on every propagation, preserving functional semantics while presenting a syntactically distinct binary that defeats signature-based and many learning-based detectors. Methods: We propose MetaGNN-Sec, a graph-augmented neural framework that detects metamorphic malware from program structure rather than surface bytes. The framework composes four components, each addressing a distinct facet of the problem: (i) control-flow graph (CFG) extraction with semantic opcode embeddings; (ii) a heterogeneous graph neural network (hGNN) operating over program-dependence graphs that capture mutation-stable control- and data-flow invariants; (iii) an adversarial training loop derived from the Wasserstein generative adversarial network (WGAN) that hardens the classifier against adaptive evasion mutations; and (iv) a quantum-kernel anomaly layer implemented in PennyLane for separation of heavily obfuscated outliers in a high-dimensional feature space. Results: Experiments are conducted on two public corpora—VirusShare 2024 and a SOREL-20M subset—comprising 200,175 binary samples in total (155,175 malware and 45,000 benign), in agreement with the corpus totals reported in Datasets Section of this paper. MetaGNN-Sec achieves a detection rate of 97.83%, a false-positive rate of 0.41%, and an $F_1$ score of 0.978 on held-out metamorphic families, exceeding the next-best baseline (MalConv+) by 4.6 percentage points on clean data and degrading by only 5.4 points under adaptive adversarial evasion (versus 17–31 points for the baselines). The quantum-kernel module contributes a further 1.2 pp reduction in false-negative rate, concentrated on the most heavily mutated families. Conclusions: The framework provides a heterogeneous PDG representation with a conditional score-shift bound under graph-edit-bounded mutations, a WGAN hardening loop that delivers measurable adversarial robustness, a quantum-kernel pre-filter with an explicit cost/benefit characterization, and a reproducible, near-real-time pipeline suitable for enterprise endpoint deployment. Full article

This study proposes a multi-stage decomposition framework for auxiliary power prediction of underwater vehicles to support energy management. Isolation Forest first removes outliers from the load record; Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) then decomposes it, and the modes are aggregated by Sample Entropy (SE) into high-, medium-, and low-frequency sub-sequences; Variational Mode Decomposition (VMD) further refines the highest-entropy sub-sequence; and Long Short-Term Memory (LSTM) networks predict each sub-sequence and sum the results. Validation on measured data adopts a leak-free protocol in which normalization, outlier detection, and decomposition never access future observations and all models are evaluated on the original, unmodified test targets; as the test segment’s mean load level is lower, bias-corrected metrics are reported alongside raw ones. The proposed method attains the best bias-corrected R² (0.330) and NRMSE (0.109) among seven models, reducing NRMSE by 1.0% to 15.9% versus all benchmarks, while its MAPE (9.01%) stays within 0.71 percentage points of the best baseline; five-seed repetitions confirm that this advantage is not an artifact of a single training run, a capacity-matched comparison shows that it does not stem from the larger parameter count, and a 16-combination sweep indicates low sensitivity to the VMD hyperparameters. On the public Shifts vessel power dataset, the framework remains competitive (R² = 0.885 ± 0.077 over three seeds), although the EMD–LSTM and direct baselines are superior there owing to richer exogenous features, delimiting the applicability of the decomposition cascade. 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