Search Results (6,715)

Multi-environment evaluation of yield performance and stability is essential for the rational recommendation of new cultivars. This study evaluated the elite winter wheat (Triticum aestivum L.) cultivar Zhoumai 49—a third-generation descendant of the founder parent Zhou 8425B—using national trial data from the southern Huang-Huai irrigated wheat region: the 2020–2021 and 2021–2022 regional trials (18 cultivars at 23 and 22 locations; checks Zhoumai 18 and Zhoumai 36) and the 2022–2023 production trial (8 cultivars at 22 locations; check Zhoumai 36). The aims were to quantify the relative contributions of genotype, environment, and genotype-by-environment interaction to grain yield, to assess the yield performance, stability, and adaptability of Zhoumai 49 relative to the check cultivars within the national testing system, and to dissect the yield component mechanism underlying its high yield. Variance decomposition, genotype plus genotype-by-environment interaction (GGE) biplot, weighted average of absolute scores (WAASB) and yield-integrated WAASBY indices, Eberhart–Russell regression, path analysis, genotype-by-trait (GT) biplot, and random forest approaches were combined. Environmental effects accounted for 76.2–92.7% of the yield variation, with entry-mean heritability of 0.90–0.94. Zhoumai 49 ranked 2nd and 1st in mean yield in the two regional trial years (8720.6 and 9897.6 kg ha⁻¹), outyielding the two checks by 7.33–9.31%, and ranked 1st in the production trial (8752.1 kg ha⁻¹; +6.98%). Its WAASBY ranked 1st, 1st, and 3rd across the three years; the Eberhart–Russell coefficients (b = 1.011–1.056) with non-significant deviations in the regional trials indicated an average-responsive, widely adapted type with high responsiveness under favorable production conditions. Kernels per spike was the core advantage trait (Z = 1.20–1.69; random forest importance = 0.514), showing inter-annual compensation with the other components. Zhoumai 49 is recommended for medium-to-high-fertility irrigated fields in the southern Huang-Huai region. Full article

High-dimensional non-normal longitudinal data are ubiquitous across fields such as genomics, biomedicine, microbiome research, and the social sciences. Such data often combine non-normal responses, within-subject dependence and sparse population effects. We develop a structured variational Bayesian procedure: variational Bayesian empirical likelihood with spike-and-slab priors, which combines an empirical-likelihood-motivated working kernel with two Laplace shrinkage branches. The variational family links each inclusion indicator to its coefficient and local scale, and it uses probability-weighted updates for global shrinkage. A scalar empirical-likelihood weighting implementation provides tractable computation, and conditional working-evidence scores compare random-effect covariance structures. Simulation studies examine selection, estimation, prediction, interval coverage, and sensitivity to numerical and prior settings. Two longitudinal microbiome applications yield interpretable conditional associations. The resulting framework provides an explicit computational construction for sparse mixed-model analysis, with numerical results characterizing its operating behavior. Full article

Accurate estimation of the state of charge (SOC) of lithium-ion batteries is essential for energy management and safety control in battery management systems (BMSs). Conventional methods, such as Coulomb counting and open-circuit voltage methods, are constrained by error accumulation and slow response. This study proposes a data-driven SOC estimation method based on multi-frequency electrochemical impedance spectroscopy (EIS) feature-point extraction. Based on an EIS-SOC dataset, representative impedance frequency feature points are identified through Pearson correlation analysis, redundancy screening, and validation-based sequential forward selection. The corresponding real and imaginary impedance components are extracted to construct the input feature vector. The whale optimization algorithm (WOA) then optimizes the initial weights and biases of a backpropagation (BP) neural network, establishing a nonlinear mapping between EIS features and SOC. Experimental results on the predefined test cells show that, averaged over three runs, the proposed WOA-BP model with extracted multi-frequency impedance features achieves a root mean square error (RMSE) of 2.02%, a mean absolute error (MAE) of 1.41%, a maximum absolute error (MaxAE) of 5.84%, and a coefficient of determination ($R^2$) of 0.9937. In comparison to traditional BP, PSO-BP, and LightGBM, the proposed method exhibits commendable overall estimation accuracy, which suggests the potential for EIS-based SOC estimation of lithium-ion batteries. Full article

Pantograph arcing in electrified railway systems is influenced by vehicle operating state because traction, coasting, regenerative braking, and rheostatic braking produce different electrical interactions among the overhead supply, pantograph, onboard filter, traction converter, and braking circuits. Distinguishing these conditions is important for condition-monitoring systems that must separate operating-state changes from arc-related disturbances. This study develops and evaluates a leakage-safe framework for classifying pantograph-arc operating mode as braking or traction/coasting using 13 independent 3 kV DC recordings acquired from a Trenitalia E464 locomotive. Seven recordings represented braking and six represented the source dataset’s composite traction/coasting category. Each recording contributed seven equally weighted 200 ms arc-centered windows, producing 91 analysis windows while retaining the complete recording as the independent unit. The primary analysis used pantograph voltage, pantograph current, and filter voltage; braking-rheostat current was excluded to prevent a direct operating-mode shortcut. Four engineered-feature classifiers and two temporal networks were compared under nested leave-one-recording-out validation. The selected RBF-SVM achieved event-level balanced accuracy of 0.9167, a Macro-F1 of 0.9212, a Matthews correlation coefficient of 0.8539, an ROC-AUC of 1.0000, and a Brier score of 0.0267. A held-out-recording perturbation analysis identified voltage-skewness, current-distribution, and signed-energy descriptors as quantitative contributors, supporting a distributed multivariate interpretation. In matched sensitivity runs, removing filter voltage reduced Brier loss by 0.0059, but the 95% bootstrap interval crossed zero (−0.0004 to 0.0169; exact paired p = 0.3796), so the apparent perfect classification does not establish channel dispensability. These results support operating-mode-aware condition monitoring while emphasizing the small sample, absence of synchronized force or camera validation, and need for external validation before operational use. Full article

An evolutionary knowledge update framework—self-training with confidence-gated pseudo-labels, elastic weight consolidation, and a rollback rule—was implemented for intracranial region segmentation in Time-of-Flight MR Angiography (TOF-MRA). A DeepLabV3+ model trained on 16 labeled cases consumed 1000 unlabeled examinations from the Japan Medical Image Database over ten generations; six configurations on two separately trained seeds gave eight runs, each evaluated on a four-case training holdout that also drove candidate selection. Seven runs produced no candidate above their seed; the eighth retained a model +0.0007 above it—not distinguishable from re-training stochasticity and not matched by consistent improvement in task-relevant maximum-intensity-projection measures. The rollback rule itself did not protect the deployed model: acceptance is tested against the highest recorded score, whereas what acceptance replaces is the stored checkpoint. With a tolerance of 0.005, in the permissive Naive configuration, the two separated—8 of 10 and 10 of 10 degraded candidates were admitted, and the deployed model fell by 0.0046 and 0.0033 while the recorded best never moved; with a tolerance of zero, the two moved together. The confidence score gating pseudo-labels is confounded by foreground fraction (r = −0.370) and on labeled slices tended to rank less accurate model-generated labels higher; the Dice similarity coefficient (DSC) aggregation convention alone moves the absolute score of identical predictions by 0.0152. Acceptance criteria for continual updating must be stated in terms of the checkpoint that is retained. Full article

Controlling the titanium concentration in weld deposits plays an important role in establishing weld metal microstructure. This work tested the effect of the reaction between the steel melt pool and liquid slag (molten flux) on titanium control during submerged-arc welding. Laboratory equilibration experiments confirmed that welding conditions are oxidizing towards titanium, with an expected equilibrium distribution coefficient of titanium between slag and metal of around 1000. Analysis of multilayer weld deposits confirmed the low recovery of titanium, but also a change in oxide inclusion composition in response to the titanium recovery in the weld metal. Both an approximate analytical model and a transient model considering full equilibration at the steel–slag interface demonstrated that titanium recovery is poorer if the steel–slag reaction proceeds further towards equilibrium. The relative importances—for the titanium concentration in the weld metal—of the rate of the steel–slag reaction and dilution of the added wire by remelted material are quantified with kinetic weighting factors. Considering these relative weights leads to general guidelines for control of the weld deposit composition. Full article

This study presents a methodological framework for the parametric assessment of residential construction costs, designed to support decision-making processes through systematic cost monitoring and continuous updating. The approach is grounded in a Work Breakdown Structure (WBS) decomposition, organising the building process into hierarchical intervention categories and working clusters that enable clear cost attribution and aggregation at each level. The framework is applied to fourteen residential case studies located predominantly in the Municipality of Rome, Italy, organised in two phases: Phase I (five cases, with one excluded as anomalous) and Phase II (nine additional cases). A logical–deductive procedure introduces homogenization coefficients for accessory surfaces relative to the primary usable area, enabling the derivation of consistent, comparable unit construction costs and analysis of the relative economic weight of each cost component. Results are validated against official parametric benchmarks and delivered through operational tools—including interactive project sheets and GIS-based maps—that support both analytical interpretation and territorial comparisons. The framework is designed to be readily updatable through the integration of new cost inputs. Overall, the proposed model constitutes an applied instrument for monitoring and analysing construction costs in relation to building typology and territorial context, providing concrete support for the planning and economic management of residential construction. Full article

Accurate food quality prediction remains challenging when labeled samples are limited, and the relationships between measurable characteristics and target quality attributes are nonlinear and heterogeneous. To address this problem, this study proposes an adaptive fuzzy recurrent stochastic configuration network that integrates adaptive fuzzy rule-number selection, rule-coupled recurrent subreservoirs, global-residual-guided stochastic configuration, and regularized analytical output-weight learning. The proposed framework was evaluated on the Mackey–Glass benchmark and two practical food quality prediction tasks involving litchi soluble sugar content and wine quality. On the Mackey–Glass benchmark, it achieved a root mean square error of $4.47\times {10}^{-4}$, representing a $37.8\%$ reduction relative to the conventional fuzzy recurrent stochastic configuration network. For litchi sugar prediction, it achieved a root mean square error of 0.7200, a mean absolute error of 0.5660, a mean absolute percentage error of 2.632%, and a coefficient of determination of 0.9352, with the root mean square error reduced by approximately $10.2\%$ relative to the conventional fuzzy recurrent stochastic configuration network. For white and red wine, the corresponding root mean square errors were 0.1829 and 0.1655, with coefficients of determination of 0.9513 and 0.9677, respectively. These results indicate that the proposed framework provides favorable predictive performance under the evaluated limited-sample settings and predefined data partitions, supporting its applicability to nonlinear food quality prediction with limited labeled data. Full article

Safe and efficient rail–road intermodal transportation of hazardous materials is threatened by uncertain node disruptions. A risk assessment model integrating the Gaussian plume model is developed to estimate casualty risks on arcs and at nodes. Treating node disruptions as stochastic train service delays, a multi-objective scenario-based robust optimization model is formulated to minimize the total risk, cost, and carbon emissions, and the model is solved via the augmented ε-constraint method according to the complexity of the proposed model. The key innovation lies in embedding node disruption uncertainties into a robust optimization framework for hazmat intermodal transportation and constructing a detailed risk assessment. A real-life problem in China validates the proposed model and approach. The results show that disruption scenarios cause significantly later delivery times compared to deterministic conditions, and the trade-offs among risk, cost, and carbon emissions shift notably. Sensitivity analyses indicate that disruption probability, disruption scale, and risk deviation weight coefficient substantially affect the optimal plan. Computational tests on different scales demonstrate the stability of the proposed approach. These findings offer practical guidance for railway authorities and hazmat carriers. Full article

This study examines how weighting and aggregation affect environmental, social, and governance (ESG) assessments within a shared provider architecture. A balanced panel of 50 Construction Materials firms over 2019–2024 is reconstructed from ten London Stock Exchange Group (LSEG) category scores. Stage I forms three pillars using annual entropy weights. Stage II re-estimates entropy weights and applies Grey Relational Analysis (GRA), producing a 100-scaled relational index. Composite Pearson correlations with LSEG range from 0.908 to 0.944 and Spearman correlations from 0.938 to 0.966. However, mean absolute errors of 7.48–8.74 points and concordance correlation coefficients of 0.779–0.847 indicate limited numerical agreement. Under the specified common min–max normalization and displayed score scales, the entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) yields smaller numerical discrepancies in every year. GRA provides stronger rank association under these conditions. Equal Stage-I category weighting also improves benchmark correspondence. Rankings are relatively insensitive to individual firm omission and alternative temporal weights but more sensitive to Environmental Innovation omission. Varying the GRA distinguishing coefficient largely preserves ranks while mechanically shifting score levels. A 2000-replicate firm-trajectory bootstrap assesses uncertainty. The framework provides a transparent diagnostic of reconstruction consistency rather than independent validation of corporate sustainability performance. Full article

Objectives: Developing sensitive imaging biomarkers is critical for capturing early brain alterations in Alzheimer’s disease. This exploratory study aimed to evaluate how multiparametric magnetic resonance imaging (MRI) measures, including chemical exchange saturation transfer (CEST) imaging, can detect early cognitive impairment and to compare their diagnostic performance across multiple brain regions. Methods: 18 cognitively normal participants and 18 patients with mild cognitive impairment or mild dementia were recruited to undergo CEST and other MRI sequences. Imaging metrics included CEST measures (amide proton transfer-weighted or APTw, APT^#, nuclear Overhauser enhancement or NOE^#), alongside conventional measures (T₁, T₂) and advanced measures (magnetization transfer ratio or MTR, quantitative susceptibility mapping or QSM, apparent diffusion coefficient or ADC). Quantitative analyses were performed in the hippocampus, caudate, putamen, posterior cingulate cortex, and amygdala. Associations between MRI measures and cognition were evaluated using Pearson correlation analysis, and the diagnostic performance of single-parameter and multiparameter models were assessed using logistic regression and receiver operating characteristic analysis. Results: Among all evaluated MRI measures, APTw, APT^#, NOE^#, MTR, T₁, and QSM demonstrated statistically significant group differences (cognitively normal vs. mild cognitive impairment or mild dementia), with medium to very large effect sizes in at least one region. Across all evaluated regions, APT^# and NOE^# signals were consistently and positively associated with clinical dementia severity (based on the Clinical Dementia Rating), whereas MTR showed consistently negative associations. In contrast, APTw, T₁, T₂, QSM, and ADC exhibited weaker or more region-dependent associations. Correlations with the Mini-Mental State Examination were generally weaker across MRI measures and regions. Among these, APT^# showed the strongest and most consistent associations with clinical severity, and was the best single-parameter classifier in most regions. Conclusions: Multiparametric MRI with CEST imaging shows potential for detecting microstructural and molecular alterations associated with cognitive dysfunction in individuals at risk for early cognitive decline. Full article

Graphene has emerged as a revolutionary two-dimensional (2D) nanomaterial, profoundly altering the fields of materials science and mechanical engineering. Driven by its superior mechanical, electrical, and thermal properties, including a Young’s modulus of approximately 1 TPa, high electrical conductivity, and high thermal conductivity, researchers have extensively explored its potential as a reinforcing filler in various composites. The thin composite steel/graphene-reinforced polymer/steel sandwich structure proposed in this study can be applied in the development of precision electromechanical devices due to its advantages of high dimensional stability, high rigidity in a confined space, light weight, and ability to reduce micro-vibrations. This study evaluates the effect of adding graphene to the alkyd-based polymer core layer on the mechanical properties and fracture mechanisms of a sandwich system with a total measured thickness of 0.35 ± 0.01 mm. Through small punch tests (SPTs) with controlled die and punch geometry coefficients, the local load-bearing behavior of the material was investigated in detail. Experimental results show that the dispersion of graphene enhances the flexural stiffness of the core layer, thereby improving the flexural stiffness of the entire structure and increasing the maximum load by 12.6% (from 0.79 ± 0.03 kN to 0.89 ± 0.04 kN). However, this structure exhibits a clear mechanical trade-off as the fracture strain decreases from 1.80 ± 0.08 mm to 1.50 ± 0.09 mm, resulting in a slight 6.1% decrease in approximated energy absorption capacity. Morphological observations at the fracture groove suggest a shift in the fracture mechanism from macroscopic ductile tearing with large plastic deformation to localized abrupt brittle shear plug, accompanied by instantaneous elastic energy release and delamination. These findings indicate that the graphene-reinforced sandwich structure is suitable for thin-film applications requiring high static rigidity, but careful consideration is needed in environments subject to dynamic impact. Full article

Deep marine carbonate formations are affected by multi-stage tectonism and dissolution, causing frequent wellbore instability and complex drilling events. This study integrates electrical imaging logs (FMI), core observations, and environmental scanning electron microscopy (SEM) to characterize the multi-scale structural features of the target formation. These features include macroscopic fractures 1~5 mm wide, mesoscopic dissolution vugs 3–10 mm in diameter, and microscopic loose grain boundaries. Mechanical parameters were obtained via a multi-field coupled rock testing platform, identifying a pronounced confining-pressure strengthening effect. A continuous well-log inversion model with correlation coefficients > 0.89 was established. By incorporating a weak-plane slip criterion and leakage mechanism, a four-pressure profile prediction model was developed. Field application in Well PT-101 demonstrated that natural fractures and vugs drastically reduced the local leakage pressure equivalent density from a theoretical matrix baseline of 2.10~2.20 g/cm³ down to 1.22~1.35 g/cm³. This degradation narrows the safe mud-weight window to near zero in localized anomaly zones. The predicted pressure profiles matched precisely with field records, including multiple gas invasions, a lost-circulation event at 5784 m, and a severe loss-kick coexistence at 5775 m. These results provide a quantitative basis for wellbore structure optimization and precise mud-weight design in deep fractured-vuggy carbonates. Full article

Fractional-order differential operators accentuate weak edges and fine texture more effectively than their integer-order counterparts, but in image enhancement the fractional machinery is confined almost entirely to the spatial stage, while the tonal stage remains integer-order and heuristic. This paper places both stages on fractional foundations and derives what has so far been assumed. The tonal stage is built from the fractional-order Chebyshev and Legendre functions ${\phi }_k^{(\alpha )}(I)=p_k(2I^{\alpha }-1)$ generated by the substitution $x\mapsto x^{\alpha }$; the spatial stage is a Grünwald–Letnikov fractional differential mask whose weights follow from the definition together with a zero-sum requirement, and which reduces to the classical eight-neighbour Laplacian sharpener exactly at order one. Five analytic results characterise the construction: a uniform bound on the deviation from the identity; a criterion on the slope profile that is necessary and sufficient for strict monotonicity simultaneously at every order $\alpha >0$, so that intensity folding is excluded; the invariance of the total contrast gain under α, which shows that the fractional order relocates contrast rather than creating it; a closed-form law for where it is relocated; and the exact admissibility threshold ${\nu }^{*}=3/2$, proved in both directions, beyond which the spatial mask ceases to amplify a band of frequencies and begins to attenuate it. A further proposition shows that monotonicity at every order is incompatible with preservation of the intensity range, so that clipping is intrinsic to this class and must be bounded rather than assumed away. Coefficients are obtained from a linear program instead of by trial. The resulting algorithm is deterministic and cheap: a monotone 256-entry lookup table followed by a fixed 5 × 5 convolution, $O\left(N\right)$ in the pixel count, with no image-dependent parameter and with monotonicity enforced only at intensities an 8-bit sensor can represent. On 18 images under four acquisition conditions (72 test cases, with a disjoint tuning split), the fractional family reaches operating points that the integer-order operator does not reach anywhere in the swept budget and order ranges, by a margin of +0.14 dB on average and at most +0.85 dB of no-reference contrast at matched fidelity. All code, parameters and metrics are released, and every reported number is regenerated by that code. Full article

Tailings storage facility (TSF) site selection requires the simultaneous consideration of economic, engineering, safety, and social factors. Taking a copper-polymetallic mine in northern China as an engineering case, this study develops a feasibility-stage decision-support framework combining the analytic hierarchy process (AHP) and the technique for order preference by similarity to an ideal solution (TOPSIS) to evaluate six pre-screened candidate sites using 11 indicators. The relative closeness coefficients of Schemes I–VI were 0.5345, 0.4996, 0.4949, 0.3543, 0.4407, and 0.6125, respectively, resulting in the ranking VI > I > II > III > V > IV. Scheme VI was therefore identified as the preferred alternative. Robustness analyses considering weight uncertainty, ordinal-indicator coding, normalization, and alternative ranking procedures generally preserved the preferred-site identification; Scheme VI ranked first in 99.31% of 100,000 Monte Carlo simulations, and MOORA and EDAS produced the same complete ranking as TOPSIS. The ranking was also consistent with the project feasibility-study recommendation. The study provides a transparent and robustness-oriented workflow for comparative TSF site selection at the feasibility stage rather than proposing a new MCDM algorithm. Full article