Search Results (271)

High-throughput DNA sequencing has revealed that the extensive diversity of Vitis vinifera L. cultivars is structured into several major genetic lineages. Identifying phenotypic traits that reflect these genetic relationships remains an important challenge for grapevine taxonomy, germplasm characterization, and archaeobotany. Here, we present an automated computational workflow implemented in R that reconstructs seed outlines from Elliptic Fourier Descriptors (EFDs) and calculates the J index, a measure of similarity to predefined reference morphotypes, together with curvature profiles along the seed outline. The workflow is reproducible and suitable for high-throughput morphometric analysis. A dataset comprising 2895 seeds from 143 populations representing 85 V. vinifera cultivars was initially organized into three groups according to available pedigree information and previously characterized seed morphotypes. Geometric reference models representing the Hebén, Savagnin Blanc, and Muscat morphotypes were then used to calculate J index values for each population. Based on these values, 74 populations were assigned unambiguously to one of the three reference morphotypes, whereas 69 showed similar affinities to two or three models and were therefore classified as intermediate or undifferentiated. The three primary morphometric groups comprised 41 populations assigned to the Hebén morphotype, 12 to the Savagnin Blanc morphotype, and 21 to the Muscat morphotype. Curvature analysis of the reconstructed seed outlines was subsequently used to characterize local geometric variation and to identify representative populations within each group. The resulting morphotypes showed substantial correspondence with major grapevine haplotypic lineages while also revealing intermediate phenotypes associated with the complex genetic and developmental history of cultivated grapevine. Overall, the workflow provides an objective and reproducible approach for high-throughput seed phenotyping and represents a complementary phenotypic tool for investigating grapevine genetic diversity, germplasm characterization, and the relationships between seed morphology and genetic lineage. Full article

Manufacturing-as-a-service (MaaS) platforms require fast and accurate manufacturing cost estimation to support quotation and resource allocation across distributed networks. However, existing machine learning approaches are often constrained by limited data availability, proprietary information, and insufficient interpretability. This work presents an engineering-based framework for generating high-fidelity synthetic data to develop scalable and interpretable cost estimation models, culminating in a SHAP-interpreted XGBoost model. A parameterized prismatic workpiece was sampled using a Hammersley sequence design of experiments, producing 1355 geometrically feasible parts with representative drilling and pocket milling features. For each configuration, cutting tools, machining parameters, and CNC toolpaths were automatically generated and optimized through an integrated SolidWorks, MATLAB, and VERICUT workflow. Over 70,000 physics-based simulations were performed to obtain reference values for machining time, tool usage, and manufacturing cost. The resulting dataset integrates geometric descriptors and optimized process parameters, enabling the training and evaluation of machine learning models. The proposed methodology demonstrates that engineering-driven synthetic data provides a physically consistent foundation for fast and interpretable AI-enabled cost estimation. While applied to a specific family of three-axis milled parts, this approach serves as a proof-of-concept for predictive micro-services required in next-generation platform-based manufacturing systems. Full article

Artificial intelligence can extend the functional capabilities of embedded sensors by extracting application-level information from physical measurements. This study investigates whether footstep-induced floor vibrations acquired with a single triaxial MEMS accelerometer contain sufficient information to characterize pedestrian path orientation and travel sense. A custom sensing platform based on an ADXL355 accelerometer and an ESP32 microcontroller was developed to acquire the structural vibration response at 4 kSPS. Lightweight temporal features were processed using a Random Forest classifier. The primary assessment used leakage-aware event-level cross-validation, with complete footsteps as the data-partitioning units. Under this more conservative protocol, discrimination of the complete A–K movement-label set was poor, whereas a compact 12-dimensional descriptor representation achieved 73.18% accuracy, 71.52% balanced accuracy, and 70.98% macro-F1 for X/Y path-orientation classification. Reliable positive/negative travel-sense discrimination could not be demonstrated from isolated footsteps. For historical comparison, the original sample-level procedure yielded 97.09% accuracy, but this value is retained only as a within-sequence reference because densely sampled observations contain strongly overlapping information. The findings provide proof-of-concept evidence that a single floor-mounted MEMS accelerometer can capture coarse pedestrian path-orientation information without cameras or spatially distributed vibration-sensor networks. Feature extraction and classifier inference were performed offline; broader validation across participants, sessions, floor structures, and realistic disturbances is required before deployment as an embedded edge AI sensing node. Full article

Morphometric information provides quantitative descriptors of cetacean size and shape and may support future assessments of individual condition and population status when combined with appropriate biological calibration. However, conventional contact-based measurements are difficult to apply to free-ranging Yangtze finless porpoises (Neophocaena asiaeorientalis). UAV imagery offers a non-contact means of acquiring porpoise morphometric data, but automated workflows for converting UAV observations into reliable body-surface measurements remain limited. To address this gap, an oriented detection-guided SAM2 morphometric workflow, termed ODG-SAM2-Morph, was developed to automatically extract body-surface morphometric parameters from UAV imagery. The workflow integrates YOLO26-OBB for oriented target localization, SAM2 for prompt-guided body-surface segmentation, and differentiated morphometric extraction strategies for complete-body and partial-body samples. Guided by oriented detections, the small SAM2 model with the R-Box + 1FG prompt generated body-surface masks with mean IoU, mean Dice, Precision, and Recall values of 0.824, 0.901, 0.883, and 0.932, respectively. For complete-body samples, automatically extracted body-length and body-width parameters showed preliminary agreement with manual measurements. For 114 partial-body samples, the automatically extracted maximum visible body width agreed reasonably well with manual measurements, with a MAE of 3.32 cm and a MAPE of 11.93%. Continuous-sequence analysis further showed that the representativeness of maximum visible body width depended on trunk exposure, contour clarity, body posture, and inter-frame stability. By converting UAV observations into quantitative image-derived morphometric measurements, ODG-SAM2-Morph provides a practical basis for non-contact morphometric monitoring of Yangtze finless porpoises under natural survey conditions. Full article

The Internet of Vehicles (IoV) relies on Basic Safety Messages (BSMs) for cooperative awareness, yet these broadcasts remain vulnerable to falsification, replay, flooding, Sybil-based, and motion-manipulation attacks. This paper proposes a temporal-window-aware physics-informed edge-oriented Intrusion Detection System (IDS) for 20-class IoV misbehavior detection under two simulation-based BSM observability regimes: ideal noise-free kinematics and realistic noise-inclusive observables reconstructed using the sensor-error components supplied separately by VeReMi Extension. Accordingly, “realistic” denotes a noise-inclusive simulation condition rather than real-world validation. From VeReMi Extension streams, the framework derives a compact 20-feature representation capturing kinematics, timing, replay cues, pseudonym dynamics, position-consistency residuals, zero-pattern behavior, and long-horizon motion indicators. These features are normalized with a training-only robust scaler, organized into sender-specific temporal windows, and classified using a lightweight three-layer stacked Long Short-Term Memory (LSTM) with residual temporal pooling. Four implementation variants are evaluated: dense Keras, default-optimized TensorFlow Lite, pruning-only Keras, and pruning-plus-compression TensorFlow Lite. Temporal sensitivity identifies $T=40$ as the best robustness–latency compromise under the realistic noise-inclusive regime. At $T=40$, the final pruned-and-compressed TensorFlow Lite model achieves $99.60\%$ accuracy and $99.09\%$ macro-F1 under ideal observability, and $99.38\%$ accuracy and $98.67\%$ macro-F1 under realistic noise-inclusive observability, with an $88.38$ KB footprint and $0.1283$ ms controlled-runtime latency. Large-scale Central Processing Unit (CPU) benchmarks on $\mathrm{150,000}$ noise-inclusive test sequences provide a platform-dependent runtime reference, with the pruned TensorFlow Lite model reaching $99.14\%$ accuracy, $98.18\%$ macro-F1, and $3.544$ ms average latency on a multi-core Intel Xeon CPU. To complement this high-throughput evaluation, edge-deployment potential is profiled using the official C++ TensorFlow Lite benchmark tool. When evaluated using a single CPU thread without batching, the final artifact achieves an unbatched per-sequence latency of $1.356$ ms, corresponding to less than $1.4\%$ of the standard 100 ms BSM generation interval. An architecture-width ablation identifies the $64/32/32$ recurrent stack as the performance–resource knee point: expanding it to $128/64/64$ improves validation macro-F1 by only $0.0019$ percentage points while increasing TensorFlow Lite footprint and latency by factors of $2.46$ and $2.32$, respectively. A training-time architecture-preserving feature-family ablation confirms that engineered descriptors are essential: raw kinematics alone reduce noise-inclusive macro-F1 from $98.67\%$ to $67.49\%$, with pseudonym dynamics and position-consistency cues producing the largest individual degradations. Full article

We develop a five-variable coupled dynamical system modeling the structural transition of contemporary capitalism toward technofeudalism. The state variables: feudal capital ($F$), productive capital ($K$), elite overproduction ($E$), systemic debt ($D$), and natural resources ($R$) interact through Lotka–Volterra competition, logistic saturation, and bilinear feedback, supplemented by a bounded logistic Gini diagnostic ($G$). We define collapse operationally as the simultaneous breach of three or more of five reference thresholds (feudal dominance, elite crisis, debt insolvency, resource exhaustion, extreme inequality). Because the inequality threshold is already met at $t_0$, $T_c$ is set by the feudal-dominance crossing and is definitional in part: excluding $G$, or counting only indicators not yet breached at $t_0$, yields $\approx 2053$ (debt insolvency), and varying the rule spans 2031–2075. Parameters are set within theory-motivated ranges against sparse historical points over 1990–2023; because the inverse problem is severely ill-conditioned, they are not treated as precisely estimated, and the model is exploratory rather than a forecasting instrument. The baseline indicates a threshold-driven transition on the order of one generation, with an indicative collapse time $T_c\approx 2044$ (central 90% conditional sensitivity range $\approx$ 2039–2052 under a ±30% independent-perturbation design); we report these as conditional descriptors of model behavior, not predictive probabilities. Across a 5000-member ensemble, the robust finding is qualitative: no realization avoided collapse before 2070, constraining the direction and ordering of the dynamics rather than the inevitability of any date. Sobol analysis identifies the feudal expansion rate $\alpha$ and crowding coefficient $\delta$ as dominant drivers; a frozen-Jacobian eigenvalue trace is positive at the 2024 state (${\lambda }_{max}=+0.016>0$) and turns negative around 2030; reported as an instantaneous growth diagnostic along a nonequilibrium trajectory, not as a stability criterion. In the model, no single-domain reform prevents collapse, whereas coordinated antitrust, ecological, and financial intervention yields superadditive delay (>56 years). No observations are withheld from the parameter specification, so every figure is in-sample, and the dates are ordinal markers of sequence, not forecasts. Extending the HANDY and Goodwin–Keen tradition, this is, to our knowledge, the first dynamical-systems operationalization of technofeudalism: a diagnostic laboratory with explicit, falsifiable thresholds. Full article

Gravity matching provides an absolute position reference for correcting the accumulated errors of an inertial navigation system (INS) during long-endurance underwater navigation. Its performance, however, can deteriorate when the available sampling data are limited, the gravity field is weakly distinctive, or the measurements are contaminated by noise. To improve matching accuracy and robustness under these conditions, this paper proposes a gravity-matching navigation method based on an adaptive-scale feature descriptor (ASFD). A coarse-to-fine framework is first established by introducing the feature extraction mechanism of SURF into gravity sequence matching. An adaptive multi-scale descriptor is then constructed to screen candidate positions efficiently. During fine matching, the matching position estimates obtained using phase correlation, random sample consensus, and least squares are integrated through a reliability-aware adaptive fusion strategy. Simulation experiments evaluate the effects of measurement length, measurement accuracy, and regional gravity-field characteristics, followed by validation using three independent shipborne gravity survey trajectories acquired in different matching areas with two marine gravimeters. Across the three trajectories, the ASFD achieves APEs of 0.80–1.13 n miles, representing reductions of approximately 23.1–50.9% relative to the best-performing traditional methods, with MSRs of 89–99% at the 2 n mile threshold. These results indicate that the ASFD maintains relatively high and consistent gravity-matching accuracy under different simulated and measured-data conditions. Full article

The accuracy and consistency of error-state Kalman filtering for SINS/GNSS integrated navigation depend critically on properly tuned process and measurement noise covariance matrices. In dynamic operation, these covariances can be non-stationary: inertial uncertainty changes with maneuver intensity, vibration, and sensor-bias instability, whereas GNSS measurement quality changes with satellite geometry, multipath, obstruction, and signal loss. Fixed-covariance and classical adaptive filters can therefore become overconfident or insufficiently responsive during abrupt maneuvers and degraded GNSS reception. We propose a physics-informed constrained neural covariance estimation (PC-NCE) framework that augments, rather than replaces, the error-state Kalman filter (ESKF) by estimating bounded process and measurement covariance-scale parameters online. The framework maps IMU-window sequences, GNSS-quality indicators, innovation statistics, and motion-state descriptors through a CNN-BiLSTM-attention network to filter-admissible ${\mathit{Q}}_{\mathit{k}}$ and ${\mathit{R}}_{\mathit{k}}$ parameterizations injected into a closed-loop ESKF. Training enforces positivity, bounds, temporal smoothness, and innovation–consistency regularization. In a reproducible filter-level MATLAB scenario suite, PC-NCE improved covariance-scale tracking and selected consistency ratios relative to fixed and unconstrained neural baselines, whereas position RMSE gains were scenario-dependent. These results provide a simulation-level proof of concept supplemented by an initial held-out measured-trajectory evaluation; broader validation using a full 15-state SINS/GNSS implementation and additional field datasets remains necessary. By treating neural networks as uncertainty-perception layers rather than black-box state estimators, PC-NCE retains the interpretability and engineering safeguards of classical Kalman filtering. Full article

This study aimed to review the scientific literature on dysbiosis characterized by Staphylococcus aureus enrichment and its implications for atopic dermatitis and other dermatoses. A narrative literature review was conducted using the PubMed database with the descriptors “Staphylococcus aureus” and “dysbiosis”, covering publications from January 2014 to May 2026. A total of 372 articles were identified, of which 38 met the inclusion criteria and were qualitatively synthesized. Most studies (n = 31) specifically examined the association between S. aureus–related dysbiosis and atopic dermatitis, with bacterial enrichment primarily assessed using 16S rRNA gene sequencing. The evidence explored the potential roles of distinct bacterial species in exerting protective or exacerbating effects on dermatoses, as well as the mechanisms underlying skin barrier dysfunction and the development of dermatological lesions. Additionally, emerging innovative therapeutic perspectives for atopic dermatitis were discussed, particularly strategies aimed at modulating or reducing S. aureus abundance. Collectively, the findings indicate that increased S. aureus abundance represents a significant risk factor for multiple dermatological conditions; however, the complexity of host–microbiome interactions and the directionality of these relationships remain incompletely understood. The identification of specific microbial biomarkers may enable earlier diagnosis and support the development of targeted therapeutic interventions for dermatological diseases associated with S. aureus overabundance. Full article

Rigid carbon frameworks in organic semiconductors restrict the tunability of their electronic and spin properties. Peri-metalation of polyacenes with coinage metals, particularly gold and copper, offers a route to electronic regimes not attainable in conventional organic systems, yet navigating this hybrid space often requires exhaustive quantum chemical sampling. This study integrates density functional theory with a small-data scientific machine learning framework to show that global electronic trends, including bandgaps, ionization energies, and electron affinities, can be captured by minimalist, coordinate-free descriptors. The hybrid architecture combines an analytical baseline defined only by inverse ring size and Au/Cu counts with coordinate-free residual learning that utilizes discrete metal sequence and topology descriptors. The original 53-descriptor residual model offers a chemically comprehensive representation, whereas a reduced 4-descriptor model assesses the persistence of principal predictive trends following significant dimensionality reduction. By circumventing explicit atomic coordinates, geometric parameters, orbital energies, wavefunctions, and interaction energies as model inputs, both models successfully recover chemically meaningful electronic properties and trends across the polyacene series while remaining sensitive to subtle local sequence effects. Systematic model–DFT deviations serve as diagnostic indicators, revealing that Cu-rich extended acenes represent a regime where the learned size and composition scaling is quantitatively insufficient, thereby necessitating further electronic structure analysis. While gold metalation yields stable, predictable electronic structures, copper incorporation drives the system into “emergent” regimes characterized by extreme bandgap narrowing and near-degenerate singlet–triplet states. This work establishes a framework in which machine learning performance itself marks the boundary of simple chemical trends, offering a rational approach to the discovery of low-bandgap, spin-sensitive hybrid semiconductors. Full article

Augmented reality (AR) enhances the real world scene by overlaying virtual information onto it. Vision-based 3D tracking and registration is the key technology for ensuring the fusion of virtual and real content in monocular AR systems. Existing mainstream visual tracking and registration methods are susceptible to illumination variations, motion blur, target occlusion, and dynamic background interference in complex scenarios. They also suffer from low computational efficiency, cumulative pose errors, and insufficient stability, making them difficult to deploy on low power edge devices such as embedded systems and mobile terminals. To address these issues, this paper proposes a lightweight monocular AR 3D tracking and registration method that integrates ORB-FREAK features, mismatching outlier filtering, background weighted mean shift, and template-based relocalization. The method does not rely on depth sensors or neural network inference, enabling efficient and accurate lightweight pose estimation. Specifically, we first combine the ORB (Oriented FAST and Rotated BRIEF) descriptor with the FREAK (Fast Retina Keypoint) algorithm for feature detection and initial matching. Hamming distance is used for coarse filtering of mismatched point pairs, and an ascending sort combined with an iterative sequential sampling strategy is applied to solve the optimal homography matrix, significantly improving the accuracy and efficiency of matrix estimation. Then, distance constraints among feature points are imposed on the target registration region to optimize the selection, and camera pose is computed based on the matching between 2D feature points and their corresponding 3D spatial coordinates, eliminating the error accumulation problem of conventional algorithms. Real-time feature matching is further used to correct the optical flow tracking sequence and camera pose, ensuring the continuity of the AR tracking process. Finally, a background weighted mean shift algorithm is introduced to narrow the feature detection range and suppress background interference, complemented by a template-matching relocalization module and a dynamic model update strategy, which effectively enhance the robustness of continuous tracking and registration under complex conditions. Experimental results demonstrate that, in extreme scenarios such as low light conditions, high speed motion, and occlusion, the proposed method achieves AR 3D tracking and registration success rates of 86.7%, 82.3%, and 78.5%, respectively. It exhibits superior performance in pose estimation accuracy and anti-interference capability in complex environments, with significantly reduced computational overhead. Moreover, it can achieve robust and continuous AR 3D tracking and registration on low power edge devices, effectively adapting to demanding AR application scenarios and providing reliable technical support for lightweight AR applications. Full article

Visible foliar damage in maize caused by fall armyworm(Spodoptera frugiperda)is commonly assessed through manual field scouting, a labor-intensive process that is difficult to scale across large planting areas. Multispectral and hyperspectral sensing can support more objective assessment but remain costly and impractical for routine field use. This exploratory case study evaluated whether low-cost red–green–blue (RGB) imagery can provide preliminary indicators of visible foliar damage associated with natural S. frugiperda infestation. RGB video was recorded in maize fields in Jerez, Zacatecas, Mexico, yielding seven field-acquired sequences and 302 extracted frames. The pipeline combined hue–saturation–value (HSV)-based foliar segmentation with four visible-spectrum indices—Excess Green (ExG), Excess Red (ExR), the Visible Atmospherically Resistant Index (VARI), and the Green Leaf Index (GLI)—an ExG-ratio damage threshold, and a 17-feature descriptor per frame used to train a Random Forest (RF) severity classifier. The study is positioned relative to RGB, Unmanned Aerial Vehicle (UAV)-based, deep learning, and multimodal approaches through its emphasis on traceability, low acquisition cost, and sequence-aware validation. Using the recovered canonical HSV/ExG-ratio pipeline, sequence-level mean damage ranged from 0.1086% to 0.4511%, with maximum frame-level damage up to 7.7401%. Severity labels were percentile-derived from the canonical damage index, yielding 100 Low, 99 Medium, and 103 High samples. Under a stratified frame-level split, the RF baseline reached 76.9% accuracy and a macro F1-score of 0.759. Under leave-one-sequence-out validation, performance decreased to 57.3% overall accuracy and 0.577 macro F1-score, indicating sequence-level dependence and supporting a conservative interpretation of classifier generalization. A zero-shot comparison using the Segment Anything Model (SAM) on a curated ten-frame-per-sequence subset produced higher damage estimates (SAM 1.30–11.67% versus HSV 0.82–2.61% on the same frames), suggesting HSV segmentation may under-detect pale or bleached tissue. These results provide preliminary, exploratory evidence that low-cost RGB indices can capture information associated with visible foliar damage in the studied recordings, without establishing agronomic validation, generalization beyond this dataset, or readiness for field deployment. Full article

In some scenarios, motorsport simulators have been used to enable the controlled acquisition of dense telemetry with high similarity to real-world data, reducing cost when assessing driving performance. However, although popular, performance analyses traditionally treat human control as deterministic and overlook the stochasticity of driving behavior. In fact, existing coaching methods which improve driving performance have to deal with two distinct outcomes: a driver who restructures his race control strategy and a driver who merely repeats it faster. This article presents a Behavior-First framework for interpretable driver behavior analysis that separates them. We characterize control signals with two information-theoretic descriptors: Jensen–Shannon divergence, which quantifies distributional distance from a proficiency-matched reference and whose square root satisfies the triangle inequality, and Permutation Entropy to measure the ordinal complexity of the input sequence. A deterministic, physics-informed heuristic layer then identifies kinematic performance gaps and emits structured tokens that a Large Language Model translates into natural-language coaching narratives. We evaluated the framework in an exploratory case study. The three beginners who received generated coaching messages and the single uncoached comparison participant exhibited different lap-time and information-theoretic trajectories. Because the groups were small and non-randomized, these observations describe within-driver evolution and do not estimate a causal coaching effect. Full article

Bitter peptides are a practical barrier in food-grade protein hydrolysates, fermented products, and peptide-based supplements because they can compromise flavor before nutritional or functional value is realized. Sensory panels and mass-spectrometry-based identification remain reliable, but their throughput is limited for early screening of large peptide pools. Existing predictors usually emphasize either interpretable hand-crafted descriptors or deep sequence representations, whereas these two information sources may be complementary for food-oriented bitter peptide screening. Here, we propose iBitter-HF, a hybrid feature embedding method that integrates seven classes of hand-crafted descriptors with Unified Representation (UniRep) features. Light Gradient Boosting Machine (LGBM)-based feature-importance ranking was used to organize the candidate embeddings, and eXtreme Gradient Boosting (XGB) was used for classification of the selected feature subset. On the public BTP640 benchmark, the finalized 135-feature model achieved 96.9% accuracy on the independent test set. Literature-based comparison indicated competitive performance relative to eight reported bitter peptide predictors, and dimensionality reduction visualization suggested clearer local organization of bitter and non-bitter peptides after feature optimization. These results support iBitter-HF as a computational aid for sequence-level bitter peptide screening and debittering-oriented design of protein hydrolysates. Full article

Peak secant stiffness degradation in reinforced concrete (RC) columns governs how lateral stiffness is redistributed and where deformation concentrates during repeated earthquake loading. Fixed stiffness-reduction factors and prescribed degradation functions cannot simultaneously account for member properties and deformation demand. This study develops a hierarchical framework for predicting the deformation-dependent peak secant stiffness of rectangular RC columns. Separate specimen-level models estimate the first-reference peak secant stiffness, K₀, and the drift capacity, θ_u, from mechanical descriptors. A physics-guided cumulative sequence model then predicts the normalized stiffness-degradation path, with deformation demand normalized by the predicted drift capacity, θ_u. Non-negative degradation increments ensure bounded, monotonic stiffness loss. On the test set, the selected K₀ predictor achieved R² = 0.935, MAE = 4.098 kN/mm, and RMSE = 7.666 kN/mm; the selected θ_u predictor achieved R² = 0.927, MAE = 0.346 percentage points, and RMSE = 0.512 percentage points. With normalization based on predicted drift capacity, the degradation submodel achieved R² = 0.9272, MAE = 0.0542, and RMSE = 0.0788, substantially outperforming constant, linear, exponential, and power-law global functions. At frame level, the predicted $\hat{\mathrm{K}}$₀, $\hat{\mathsf{\theta }}$_u, and degradation ratio are incorporated into an equivalent secant-stiffness procedure. Column shear is obtained directly from the updated stiffness and interstory displacement, without a separate strength-degradation law. Comparisons with OpenSeesPy fiber-frame analyses of 12 two-, three-, and four-story frames yielded mean errors of 8.26–21.88% for base shear, 11.42–24.54% for story shear, and 10.19–23.51% for column shear; the mean absolute error in structural stiffness ratio ranged from 0.019 to 0.068. These comparisons establish a numerical consistency benchmark for the adopted frame configurations and modeling assumptions. The method is intended for stiffness-based peak-response assessment, not as a complete hysteretic constitutive model. Full article