聚变科学与工程资源库
检索物理代码、实验数据、代理模型、工程几何、技术文档与验证资料，并核对来源、版本和使用条件。
Data supporting: Nonstationary Behavior of Resistivity–Moisture Relationships in Time-Lapse ERT Monitoring of Sandy Soils
ZenodoThis dataset contains processed data supporting the figures and results presented in the manuscript: "Nonstationary Behavior of Resistivity–Moisture Relationships in Time-Lapse ERT Monitoring of Sandy Soils". The dataset includes: - Processed electrical resistivity tomography (ERT) data (e.g., Δlogρ) used to generate spatial profiles and temporal analyses - Soil moisture time series from in situ sensors - Rainfall data associated with selected recharge events - Event-based summaries and derived quantities used in the analysis (e.g., inferred water-content changes) All data are provided in a format suitable for reproducing the figures and key results of the study. Raw ERT data and inversion outputs are not included in this dataset but are available from the corresponding author upon reasonable request.
Remaining useful life estimation for predictive battery maintenance with improved recurrent singular spectrum analysis algorithm
Mahanakorn University of TechnologyAs the global electric vehicle (EV) battery market is projected to reach a valuation of over USD 100 billion by the end of 2026, the demand for sophisticated battery management systems (BMS) has become more critical than ever. Accurate remaining useful life (RUL) prediction is essential for ensuring vehicle safety, optimizing maintenance, and evaluating retired batteries for second-life applications. However, existing prognostic methods often struggle to balance computational efficiency with predictive accuracy, especially during the early stages of battery usage. This research proposes combined weighted similarity-based and recurrent singular spectrum analysis (CWS-RSSA), a hybrid forecasting framework that integrates RSSA with a similarity-based approach through a weighted logistic switching mechanism. The algorithm is designed to be computationally lightweight, making it suitable for resource-constrained BMS hardware. The proposed method was validated using NASA and a large-scale dataset from MIT-Stanford consisting of 124 lithium-ion cells. Experimental results demonstrate that CWS-RSSA is capable of early-stage prediction with a relative error of 19.8%, whereas existing methods are unable to provide predictions. In later stages, once sufficient data becomes available, the algorithm achieves near-perfect accuracy with a negligible relative error on the NASA dataset and an average relative error of only 0.14% across the 124 MIT-Stanford batteries. Furthermore, the algorithm demonstrates robust performance in handling capacity regeneration phenomena. These findings suggest that CWS-RSSA represents a scalable and practical advancement for battery health management, supporting the transition toward a sustainable circular energy economy and providing a reliable foundation for second-life battery certification.
A scalable hybrid deep learning framework for mining actionable knowledge from large-scale and uncertain Twitter data
REVA UniversityExisting deep learning approaches often exhibit limitations in contextual comprehension, high computational overhead, and restricted generalization when processing large-scale, tweet-level, and semantically ambiguous text. Moreover, deploying such computationally intensive models in real-time internet of things (IoT)-enabled monitoring systems and embedded platforms introduces additional constraints related to latency, memory footprint, and energy efficiency. To address these challenges, this work proposes a scalable hybrid deep learning framework (SHDLF). The proposed framework effectively captures semantic, syntactic, and temporal dependencies in both short and long social media texts through a novel integration of transformer-based representations and attention-driven feature fusion mechanisms. The architecture is designed with a modular and parallelizable structure to facilitate hardware-aware optimization and potential deployment on embedded and reconfigurable computing platforms, enabling efficient edge-level processing of high-velocity Twitter streams. Extensive experimental evaluations conducted on a large benchmark Twitter dataset demonstrate that SHDLF consistently outperforms state-of-the-art models, including convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and baseline bidirectional encoder representations from transformers (BERT)-based architectures, in terms of accuracy, F1-score, and robustness under noisy conditions. The results confirm that SHDLF offers a robust, scalable, and computationally efficient solution for extracting reliable sentiment insights from noisy and dynamically evolving social media data.
Design and characterization of a flexible ultra-low-power analog front-end circuit using organic thin-film transistors for wearable electrocardiogram monitoring
Al Mansour University CollegeThe shift from reactive to proactive healthcare has accelerated the development of flexible, comfortable, and energy-efficient wearable health monitoring systems. This study addresses a critical technological gap: the inherent trade-off between the mechanical flexibility of organic thin-film transistors (OTFTs) and their limited electrical performance (low charge carrier mobility) compared to rigid silicon-based technologies. To overcome this, we outline the system-level design of a fully configurable analog front-end (AFE) utilizing OTFTs for precise electrocardiogram (ECG) measurement. A theoretical transfer-function-based co-design approach is used to balance gain, noise rejection, and power efficiency via high-fidelity MATLAB/Simulink continuous-time simulations. Simulation results demonstrate that the proposed AFE achieves a 40 dB gain, a precise diagnostic bandwidth of 0.5–150 Hz, a common-mode rejection ratio (CMRR) of 65 dB, and an ultra-low power consumption of 33 µW. These metrics strictly align with standard clinical ECG requirements, outperforming current state-of-the-art all-organic architectures primarily in power efficiency. Consequently, the input signal-to-noise ratio (SNR) is significantly enhanced by 24.53 dB (from 8.01 to 32.54 dB). The novelty of this work lies in achieving silicon-like clinical functionality within an allorganic structure at minimal power. This establishes a new benchmark for flexible electronics and paves the way for invisible, skin-like devices for continuous cardiovascular monitoring.
Analysis of VHF Pulse Frequency and Relative Power Across Pre-, During-, and Post-Branching Phases of Lightning Channels
College of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou, ChinaThis dataset contains the data associated with Figures 3–6 of the manuscript entitled “Analysis of VHF Pulse Frequency and Relative Power Across Pre-, During-, and Post-Branching Phases of Lightning Channels.” The dataset provides the data used to analyze the VHF pulse frequency and relative power characteristics of lightning channels during the pre-branching, branching, and post-branching phases. The data are organized into separate folders corresponding to Figures 3, 4, 5, and 6 of the manuscript. A README file is included in each folder, providing detailed descriptions of the data files, variables, formats, and their relationship to the corresponding figure.
Dual-Stage Toxic Comment Detection System: Binary and Multi-Class Classification
ZenodoThis paper presents our latest evaluation on a unified toxic comment dataset (merged from the Jigsaw Toxic Comment Classification Chal- lenge and the Unintended Bias in Toxicity datasets), outlining both performance metrics and future enhancements. We detail the dataset merging process and quality checks, discuss evaluation results for our dual-stage model (bi- nary and multiclass classification), incorporate an advanced deep neural network (DNN) ap- proach with semantic embeddings and engi- neered features, and propose steps to expand our system to address dialect and demographic fairness.
Eye-tracking dataset: Seeing without reporting (visual attention to vulnerable individuals)
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Simulation Results of a Nitrogen Rejection Unit
Tecnológico de MonterreyThe details regarding the data description are provided in the README file. The data corresponding to Figure 2 were obtained from a gas chromatography analysis of the gas plant. The data corresponding to Figures 3, 5, 9, and 10 were recorded in real time using the Aspen HYSYS® simulation environment. The data are provided in raw form to facilitate reproducibility of the results presented in the submitted manuscript.
Bone strength analyzer and monitoring device for lower limb external fixation
Universiti Malaysia Pahang Al-Sultan AbdullahThe procedure for external fixator removal in lower limb fractures is typically based on radiographic data, subjects’ patients to ionizing radiation, and provides minimal real-time information about the healing process. This project suggests a sensor-based Internet-of-Things apparatus, which will measure bone strength during the recuperating process based on load cells, HX711 amplifiers, and a Wemos ESP8266 microcontroller. The system provides real-time feedback in the form of LED indicators and a buzzer, whereas remote monitoring is supported by the Blynk dashboard. Measurement accuracy of over 90% was carried out as per the experimental validation conducted under the simulation of various loads (3-9 kg) and with clear stage indicators of critical, partial, and full recovery. The device offers continuous monitoring and objective bone healing evaluation with no radiation in comparison to conventional imaging. The limitation of the study is the limited range of loads in prototype testing, which could affect accuracy in particular situations. However, the results represent the possible clinical relevance of introducing real-time biomechanical surveillance into the process of fracture treatment, hence contributing to safer rehabilitation and more reasonable decisions related to the fixator removal.
Sampling Effort Data
ZenodoOpen access data on Mosquito Alert participation and sampling effort. This dataset contains data on participation and sampling effort in the Mosquito Alert citizen science system. It can be used for a variety of purposes, including (a) to adjust estimates of mosquito population densities and human-mosquito encounters based on sampling effort, and (b) to better understand the dynamics of citizen scientists' participation. The data is organized spatially by grids of "sampling cells," drawn at intervals of 0.05 degree and 0.025 degree latitude and longitude, and it is based on optional anonymous background tracks from the Mosquito Alert app. The dataset includes raw track counts aggregated in sampling cells, along with estimates of sampling effort based on a model of participants' propensity to send any report as a function of the time elapsed since they first began participating. The repository is hosted on both Zenodo and GitHub and contains the following files: sampling_effort_daily_cellres_025.csv.gz - Daily participation and sampling effort in 0.025 degree sampling cells. sampling_effort_daily_cellres_025_metadata.json - Metadata for the 0.025 degree sampling cell data. sampling_effort_daily_cellres_05.csv.gz - daily participation and sampling effort in 0.05 degree sampling cells. sampling_effort_daily_cellres_05_metadata.json - Metadata for the 0.05 degree sampling cell data. CITATION.cff - Shows how to cite this dataset. LICENSE - License for this dataset. .gitignore - Specifies which files are excluded from the git repository. README.md - This file. .github/workflows/auto-release.yml - Script used for automating releases from GitHub.
Graph neural network-based biomedical misinformation detection with semantic consistency analysis
SRM University - Ramapuram CampusConventional misinformation detection approaches primarily rely on textual features and deep learning (DL) classifiers, which often fail to capture complex relationships among biomedical entities and the underlying scientific context of health claims. To address this limitation, this study proposes a graph neural network (GNN)-based biomedical misinformation detection framework that integrates knowledge graph propagation with semantic consistency verification. Initially, key biomedical entities such as diseases, treatments, and biological processes are extracted and mapped into a structured biomedical knowledge graph (BKG) to represent semantic relationships. A graph attention network (GAT) is then employed to model relational dependencies and propagate contextual information across connected entities, enabling the detection of hidden inconsistencies in biomedical claims. The proposed model is evaluated using benchmark biomedical misinformation datasets, including Reliable COVID-19 News Dataset, 2021 (ReCOVery), COVID-19 Healthcare Misinformation Dataset, 2020 (CoAID), and 2018–2020 biomedical health news corpus (HealthStory). Experimental results demonstrate that the proposed framework achieves an average detection accuracy of 96.3%, outperforming conventional long shortterm memory (LSTM), convolutional neural networks (CNN), and transformer-based models in terms of precision, recall, and F1-score. The findings highlight that integrating structured biomedical knowledge with graph-based reasoning significantly enhances the reliability and interpretability of misinformation detection systems.
Code for: Phenological Fluctuations Induce Seasonal Asymmetric Warming in Boreal Forests
ZenodoCode accompanying the manuscript "Phenological Fluctuations Induce Seasonal Asymmetric Warming in Boreal Forests" (Nature Communications, 2026). The repository contains the Google Earth Engine JavaScript scripts for seasonal compositing of the MODIS products, quality masking, the persistent-forest mask and computation of the leaf area change and change-rate layers, and the Python and R scripts for the paired-grid matching, the inverse distance weighting climate-background removal, the moving-window Huber regression, the asymmetry index, the structural equation modelling and the robustness analyses (block bootstrap, baseline stratification and truncation sensitivity). All input data are public MODIS products and the FLUXNET2015 dataset, as listed in the Data Availability statement of the paper.
Sparse Quotient-Threshold Transform (SQTT): benchmark data, reference code, and reproducibility package
University of PhayaoThis version 1.0.0 research compendium supports the computational evaluation of the Sparse Quotient-Threshold Transform (SQTT), an exact method for computing all outputs of a knapsack-threshold transform and its additive generalized-assignment extension. The compendium contains the MATLAB reference implementation, declared exact comparators, instance generators, correctness checks, the frozen primary experimental protocol, analysis scripts, preservation-friendly data tables, figure-source data, rendered figures, and SHA-256 artifact manifests. The empirical data comprise 1,788 completed full-benchmark case-method trials over 610 generated cases and a balanced 30-run scalar scalability panel for k = 18 and k = 20. The balanced panel is a post-inspection exploratory supplement and is not part of the prespecified primary benchmark. Eight accepted k = 22 observations are supplied in a separate exploratory resource-boundary dataset: three completed outcomes and five verified memory-limit outcomes. These k = 22 observations are not included in the primary runtime comparisons, and no missing runtime is imputed. The statistical unit is the generated case rather than an individual timing repeat. Timing repeats are summarized within each case. Paired log-speed ratios are defined as ln(baseline time / SQTT time), and uncertainty is reported using stratified case-level percentile bootstrap 95% confidence intervals. The archive includes the derived case-level dataset, summary tables, performance-profile curves, scaling summaries, resource-boundary summaries, and an independent correctness gate covering 2,000 scalar and 500 generalized-assignment cases with zero failures. Code is released under the MIT License. Data, tables, figures, protocols, and documentation are released under Creative Commons Attribution 4.0 International. File-level provenance and SHA-256 checksums are provided in the package manifest.
Vision-Language Models for Low-Vision Navigation Assistance in Cluttered Environments
Birla Institute of Technology and Science, PilaniNavigating busy, unstructured roads is a daily challenge for people with visual impairments, while many assistive systems do not explicitly account for the clutter and heterogeneous traffic found in Indian street environments. We present an end-to-end vision-to-audio navigation pipeline that transforms monocular video frames into priority-ordered spoken directional commands. The reported system combines a YOLOv8n detector fine-tuned on the Indian Driving Dataset (IDD), MiDaS DPT-LeViT monocular depth estimation, DeepSORT tracking, field-of-view-aware spatial reasoning, temporal smoothing, and an eight-layer rule-based navigation planner. The planner maps perceived hazards to concise commands such as STOP, AVOID, MOVE LEFT/RIGHT, and CONTINUE FORWARD, while a smart text-to-speech gate suppresses passive descriptions when an urgent command is pending. An optional BLIP-based visual validation stage is used in image mode. On the reported 60-frame manually annotated Indian-street evaluation set, the complete pipeline achieves 97.9% hazard recall, 92.8% distance accuracy, 0.407 walkable-corridor IoU, and 84.6 ms average latency per frame. On the IDD detection test set, fine-tuning improves mAP@50 from 0.389 for the COCO- pretrained baseline to 0.542, with mAP@50–95 increasing from 0.224 to 0.351. Ablations show that field-of-view-aware depth calibration is critical for proximity estimation, while temporal tracking is important for instruction stability and motion-aware warnings. The system is deliberately modular: the perception and spatial reasoning stack supplies grounded state information, while deterministic planning converts that state into actionable audio rather than relying on unconstrained generative narration.
IMPLEMENTATION OF THE "JEMPUT SAMPAH TERIMA DUIT" (JUMPA MADU) WASTE COLLECTION APP TO PROMOTE CLEANLINESS IN LUBUK PAKAM SUBDISTRICT, DELI SERDANG REGENCY
Universitas Sumatra UtaraWaste management in Indonesia has become increasingly complex due to population growth, lifestyle changes, and inadequate source-based management. In response, the Deli Serdang Regency government introduced a digital innovation, Jumpa Madu (Jemput Sampah Terima Duit), to enhance waste management efficiency through a technology-based system combined with economic incentives. This study aims to examine the implementation of the Jumpa Madu application in Lubuk Pakam District, focusing on community participation, implementation effectiveness, and the challenges encountered. A descriptive qualitative method with a case study approach was employed. Data were collected through observations, interviews, and documentation involving key stakeholders, including the Environmental Agency, the BERSERI central waste bank, and community users. The findings indicate that community participation is relatively high, particularly among active users who recognize the economic benefits of waste sorting. The application effectively facilitates waste collection scheduling and increases the economic value of segregated waste. However, several challenges persist, including limited digital literacy, constrained technical resources, and uneven access to information. This study concludes that Jumpa Madu represents a promising innovation in strengthening circular economy-based waste management at the local level. Its optimal implementation requires stronger cross-sector collaboration, continuous public education, and improved regulatory and digital infrastructure support.
PID-GraphVAE: Physics-informed disentangled graph VAE for cascade-aware equation discovery
ZenodoOfficial implementation of "From compression to discovery: representations whose structure reveals the governing equations". PID-GraphVAE (physics-informed disentangled graph variational autoencoder) is a co-design of representation learning and sparse equation discovery: a graph VAE partitions its latent space into physics-anchored subspaces along a candidate energy-cascade hierarchy, a cascaded mutual-information objective preserves the cross-scale couplings, the inter-subspace topology is discovered from the latent dynamics, and a cascade-aware sparse identification distills closed-form reduced-order models. This archive is the frozen snapshot accompanying the submission (release v1.0-submission). Active development continues at the GitHub repository linked below. The CFD datasets, trained model weights, latent trajectories, and identification records are deposited separately.
Implementation of support vector machine on LVMDP panel with overheating protection system
Shipbuilding Institute of Polytechnic SurabayaElectricity is a critical requirement in industrial operations, where the continuity and stability of power distribution directly affect safety and productivity. The low voltage main distribution panel (LVMDP) functions as the main node of electrical power distribution; however, conventional LVMDP systems generally lack intelligent protection mechanisms capable of detecting overheating-related fire hazards and initiating preventive action before failure occurs. This study proposes an intelligent monitoring and protection system for LVMDP panels that combines real-time multi-sensor monitoring, support vector machine (SVM) based hazard classification, and an automatic shutdown mechanism. The main contribution of this work lies in the integration of predictive thermal risk detection with autonomous protective action, enabling the system not only to monitor panel conditions but also to respond immediately to hazardous states before they escalate into fire incidents. SVM was selected because of its strong capability to classify complex and nonlinear patterns from sensor data with high reliability. The developed system continuously evaluates panel conditions and triggers autoshutdown when an overheating risk is identified, thereby improving preventive protection compared with conventional alarm-based monitoring systems. Experimental results show that the sensor measurements achieved error rates mostly below 5% compared with calibrated instruments, indicating good accuracy. In addition, the SVM model obtained an overall accuracy of 93%, with a macro-average F1-score of 92% and a weightedaverage F1-score of 93%. These results demonstrate that the proposed system is effective for early detection and active protection of LVMDP panels against overheating hazards.
Data and Code for: Forward-Only Temporal-Coherence Occupancy Regimes in Atlantic Tropical Cyclone Intensity Evolution (1851–2024)
PTEM Labs# Version 4.0 record description This package reproduces the corrected V2 analysis for “Forward-Only Temporal-Coherence Occupancy Regimes in Atlantic Tropical Cyclone Intensity Evolution (1851–2024).” The algebraic reduction from pairwise agreement to a common-sign representation was numerically neutral when applied to unchanged historical state semantics. Two subsequent activation corrections were material: no active label or occupancy is assigned to the first coherent advisory, and confirmation requires the same nonzero direction at consecutive advisories, so reversal resets confirmation. The 54,174-row public materialization and portable code regenerate the V2 detector, RI labels, descriptive stratification, five-advisory comparator, signed timing, separate null-family summaries, duration models, and all current manuscript tables and figures. Results and claims were regenerated and narrowed. RI is a downstream outcome only. This package is descriptive and is not an operational forecast model or physical storm-structure retrieval. Version 3.2 is retained as historical lineage and is superseded for reproduction of the current manuscript. The exact environmental model matrix is not redistributed because explicit redistribution authority was not locally verified; the schema, formulas, hashes, and expected outputs are supplied.
WORKLOAD AND BURNOUT AMONG HEALTHCARE WORKERS: THE ROLE OF WORK-FAMILY CONFLICT AS A MEDIATOR AND HUMAN RESOURCE MANAGEMENT RESOURCES AS BUFFERS: A SYSTEMATIC REVIEW
Universitas TanjungpuraThis systematic review examines the relationship between workload and burnout among healthcare workers, with work-family conflict (WFC) as a mediating mechanism and HRM resources as buffering factors. Following PRISMA guidelines, English-language studies published between 2015 and 2026 were screened, and 23 articles met the inclusion criteria. Of these, 21 reported a positive association between high workload and burnout, especially emotional exhaustion. Among 16 studies assessing WFC, 14 identified it as a mediator. Additionally, 18 studies found that organizational support, supportive leadership, family-friendly policies, adequate staffing, and positive work climates reduced burnout. Burnout thus reflects interacting work demands, family conflict, and organizational resources
ANALYSIS OF THE EFFECTIVENESS OF RAILWAY SERVICE IN LAMPUNG ON THE TANJUNG KARANG STATION – KOTABUMI STATION ROUTE
Universitas LampungThis study aims to analyze the effectiveness of railway services in Lampung on the Tanjung Karang–Kotabumi route from the users’ perspective using a quantitative survey-based approach. The research focuses on Rajabasa (economy class) and Kuala Stabas (premium class) train services. Data were collected through Likert-scale questionnaires and secondary sources using an accidental simple random sampling technique. The variables include traveler characteristics, travel characteristics, and transportation system facilities. Data validity and reliability were tested before being analyzed using the Classification and Regression Tree (CART) method. The results indicate that service effectiveness is strongly influenced by the quality of interaction between staff and passengers as well as the condition of physical carriage facilities. Staff attributes emerge as the most dominant factor with the highest satisfaction scores, while management aspects remain the primary weakness. Differences in facility quality between premium and economy classes and accessibility issues in premium services were also identified. The CART model reveals that integrated operational and service quality is the key determinant, with staff attributes as the strongest predictor. The optimal model was obtained at a 1.5 threshold with high accuracy.
Artificial Intelligence in service learning: A systematic mapping review and future research agenda for higher education
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THE EFFECTIVENESS OF STUDENT-CENTERED LEARNING
ZenodoStudent-centered learning (SCL) is an educational approach that places learners at the center of the teaching and learning process. Unlike traditional teacher-centered instruction, student-centered learning emphasizes learners’ active participation, independence, collaboration, critical thinking, problem-solving, and responsibility for their own learning. This article examines the effectiveness of student-centered learning and its influence on students’ academic achievement, motivation, engagement, communication skills, and independent learning abilities. The study is based on an analysis of theoretical and empirical research conducted by scholars in the field of education. The literature demonstrates that student-centered approaches can create a more interactive and motivating learning environment and encourage students to take greater responsibility for their educational progress. However, successful implementation requires appropriate teacher preparation, effective classroom management, sufficient learning resources, and consideration of individual learner differences. The article concludes that student-centered learning can significantly contribute to the quality of education when it is systematically planned and appropriately adapted to learners’ needs.
A METHODOLOGICAL FRAMEWORK FOR DEVELOPING STUDENTS' PROFESSIONAL ENGLISH SPEAKING SKILLS THROUGH DIGITAL EDUCATIONAL TECHNOLOGIES: THE CASE OF ECOLOGY EDUCATION
[Position], [Department], Namangan State University, Namangan, UzbekistanGraduates of ecology and environmental science programmes increasingly work in internationalised professional settings in which English is the working language of environmental impact assessment, transboundary water management, climate finance and scientific collaboration. Yet the oral component of their language preparation remains the weakest link: conventional English for Specific Purposes (ESP) instruction offers each learner only minutes of authentic speaking time, rehearses decontextualised topics rather than professional genres, and provides feedback that is delayed, impressionistic and rarely discipline-sensitive. This article develops, on theoretical grounds, a methodological framework for forming the professional English speaking skills of ecology students through digital educational technologies. Drawing on ESP needs-analysis theory, Content and Language Integrated Learning (CLIL), sociocultural and output-based accounts of second language development, and technology-integration models (SAMR, TPACK), the study proposes the Digitally Mediated Professional Speech Development (DMPSD) model. The model comprises four interlocking components — a genre-based content architecture, a three-tier taxonomy of digital tools differentiated by didactic function, a five-stage instructional cycle, and a multi-source assessment system anchored in CEFR mediation descriptors — governed by seven design principles. The article specifies nine professional oral genres for the ecology field, maps each to appropriate digital mediation, and analyses the conditions, risks and teacher-competence requirements of implementation. The framework is offered as a theoretically motivated design that is directly operationalisable in curricula and empirically testable; the limitations of a purely conceptual study and an agenda for experimental validation are set out in the conclusion.
The Chronos Stability Coordinate x: Historical Provenance, Ratio-Share Reconciliation, and a Minimal Primitive Shell Closure
ZenodoThis working preprint consolidates the development of the Chronos stability quantity historically denoted by x or chi and clarifies how its mathematical meaning changed across earlier stages of the research program. Earlier Chronos work used the same notation for more than one type of quantity, including a direct active-to-restoring ratio and a bounded stability selector. This paper separates those roles explicitly. It reserves rho for the positive direct response ratio and x for the bounded normalized stability coordinate derived from that ratio. This resolves an important notation collision and shows why several earlier numerical values near 0.55128 should not be treated as identical versions of the same coordinate. The paper then develops a minimal four-phase shell construction. Four equally spaced phase channels are decomposed into one uniform mode and a three-dimensional nonuniform active sector. A canonical transition operator derived from the four-cycle supplies both the unit active-channel strength and the normalized active rank used in the reduced model. The four-phase symmetry also determines the equal phase allocation used in the overlap term. Combining this phase structure with the first nonzero spherical response produces a primitive connected stability response through the minimal quartic shell-action class. A self-consistency condition then selects a unique attracting fixed point for the bounded stability coordinate, giving a current value near 0.5512827409. The paper proves uniqueness and rapid convergence of this fixed point within the stated reduced selector model. The work also establishes a clear model boundary. Repeated return operations do not generate new reduced return classes, and the primitive selector closes through quartic order within the stated minimal dimension-four shell-action construction. However, more general higher-dimensional effective operators could introduce additional primitive terms, so the result is not presented as a universal theorem for every possible microscopic theory. A further section translates the shell result into the spectral and Wheeler-DeWitt language used elsewhere in the Chronos program. The algebraic translation is exact once the shell and spectral ratios are identified, but the physical identification between those two constructions remains a model bridge rather than an empirical result. The paper therefore serves both as a provenance record and as a mathematical consolidation. It preserves the earlier history of the Chronos stability quantity while establishing a stricter modern convention: rho denotes the direct response ratio, x denotes its bounded representation, and the present value of x is the selected root of the explicit minimal shell closure rather than a decimal retroactively imposed on earlier formulas.