Abstract: Rapid urbanization has dramatically transformed land use land cover (LULC) patterns and ecosystem services in megacities of the Global South, with major implications for carbon storage and climate regulation. This study investigates LULC dynamics, associated carbon storage changes, and future urban growth in the Dhaka Metropolitan Area from 1994 to 2044. Multi-temporal Landsat imagery was used to classify six LULC categories for 1994, 2004, 2014, 2019, and 2024. Carbon storage was quantified using the InVEST model by integrating LULC maps with literature-based carbon density values representing four carbon pools. Future LULC scenarios for 2034 and 2044 were simulated using a Cellular Automata–Artificial Neural Network (CA-ANN) model within the MOLUSCE framework, incorporating six biophysical and infrastructural driving factors. Model validation produced an overall accuracy of 70.13% with satisfactory kappa indices. Results reveal that built-up expansion occurred mainly at the expense of vegetation and water bodies, causing nearly a 40% decline in total carbon storage between 1994 and 2024, with water bodies contributing the largest share of carbon loss. Process-based analysis identified dominant transformation clusters driven by urban reclamation and hydrological loss, alongside vegetation clearance and land degradation. Future projections indicate continued urban growth and further carbon depletion by 2044. These findings highlight the urgent need to conserve wetlands and urban green spaces and to integrate carbon-sensitive strategies into sustainable urban planning.

Abstract: Organizations are adopting generative AI and AI decision support in professional work while evidence on their effects is still accumulating. Empirical studies document substantial productivity gains in some settings and losses in others, and practitioners carry assumptions formed during early use into everyday professional work. Research on the resulting problems is spread across human factors, human-computer interaction, fairness research, empirical economics and law, and no consolidated framework for professional use was found. This paper proposes such a framework. Its format is inspired by the Fallacies of Distributed Computing, a short list of false assumptions well known in software engineering; its content is specific to professional AI usage. Through an integrative review of peer-reviewed studies, randomized and preregistered trials, court decisions, provider documentation and regulation, nine fallacies are derived: (1) the output is correct; (2) capability is uniform across tasks; (3) the human reviewer is a reliable safeguard; (4) perceived productivity equals actual productivity; (5) AI removes work; (6) accountability moves to the tool or vendor; (7) skills and system understanding stay intact when work is delegated; (8) results transfer from benchmarks and pilots; and (9) the model is available. For each fallacy, the paper explains why the assumption persists, presents the evidence against it, states the conditions under which it does hold, and grades the strength of the evidence. It then analyzes how fallacies can compound, with particular attention to agent-built software systems that may become difficult to maintain when AI access is lost. The paper closes with implications for practice, a research agenda and a self-assessment instrument.

Abstract: Goldbach’s original conjecture was posed more than two centuries ago. Is it true that: all positive even integers (≥4) can be stated as the sum of two prime numbers? Based on asymmetry of occurrences of prime and composite numbers under some even integer N≥4, here of Goldbach Conjecture (GC) is analyzed and then a proof is outlined.

Abstract: Credit-risk models may become unreliable when longitudinal observations are incomplete and predictive uncertainty is ignored. We propose a Structured Dynamic Bayesian Network and Bayesian Neural Network (Structured DBN + BNN) combining borrower-conditioned latent-state transitions, differentiable filtering, complementary GRU and direct-input encoders, and variational Bayesian discrimination. Training-time masking improves robustness to missing evidence, while reconstruction regularizes the latent states. At inference, Monte Carlo sampling, validation-based temperature scaling, predictive entropy, and mutual information support calibrated prediction and uncertainty-aware triage. On the Default of Credit Card Clients dataset, ten paired seeded evaluations show competitive clean-data discrimination, with an AUROC of 0.7828. Under 30% MCAR missingness, the model achieves an AUROC of 0.7623 and achieves a higher mean AUROC than the principal baselines drawn from recurrent, Bayesian, and Transformer model families; similar robustness is observed under MAR, MNAR, and recent-month removal. At 10% selective coverage, entropy-based ranking reduces the mean error to 3.51%. These findings show that the framework’s main benefit is robust, uncertainty-aware prediction when temporal evidence is incomplete, rather than uniformly superior discrimination on complete data.

Abstract: Background: Terminal sterilization is generally preferred for parenteral products when the formulation and container can tolerate the process. Retatrutide is a thermolabile peptide, making conventional moist heat unsuitable and creating a need to evaluate a nonthermal terminal modality. Objective: To establish an in silico decision framework for electron beam terminal sterilization of a 10 mg/mL retatrutide multidose cartridge and to define a stability indicating reversed phase high performance liquid chromatography method for post irradiation control. Methods: Moist heat, dry heat, ethylene oxide, sterile filtration with aseptic processing, and electron beam irradiation were compared by product specific criteria. A model electron accelerator configuration used a UELR-10-15-S-1 unit No. 128170401 at 9.5 MeV, 1.19 mA, 50 cm scan width, and 1.75 m/min conveyor speed. Three virtual batches were assigned dose maps, bioburden distributions, verification tests, sterility outcomes, and RP HPLC results. A Monte Carlo survivor model estimated the probability of a nonsterile unit. Results: The model selected electron beam irradiation as a candidate terminal method, subject to experimental qualification. Simulated minimum and maximum absorbed doses were 15.1 to 15.4 kGy and 18.1 to 18.4 kGy, respectively. All three virtual batches met the model criterion of a probability of a nonsterile unit not greater than 10 to the power of minus 6. Post irradiation retatrutide assay was 97.2 to 97.8 percent, while total related substances were 2.10 to 2.39 percent. Conclusions: The simulated dataset supports a bounded development hypothesis but does not demonstrate sterility, peptide integrity, process validation, or suitability for human use. Accelerator electrical settings do not define absorbed dose. Confirmatory work requires calibrated dose mapping, microbiological dose substantiation, validated RP HPLC and orthogonal structural and biological methods.

Abstract: MYRRHA at SCK CEN in Mol/Belgium will be a pre-industrial large-scale Accelerator Driven System for unparalleled research opportunities in spent nuclear fuel treatment, nuclear medicine, and fundamental and applied physics. The Belgian government is funding MYRRHA’s first phase, MINERVA, for a staged implementation and operation. It covers the design, construction, and commissioning of the continuous wave superconducting RF, high power proton linac up to 100 MeV, as well as dedicated user target stations. The MINERVA proton linac will accommodate 30 identical cryomodules to boost the beam energy delivered by the normal-conducting frontend from 16.6 MeV to 100 MeV. Each cryomodule will contain two superconducting RF single-spoke cavities immersed in a superfluid Helium bath at 2 K. The design and architecture of the associated cryogenic system is derived from the stringent linac reliability requirements imposed by the future subcritical nuclear reactor. We present the resulting design basis, architecture, and detailed component design of the MINERVA Cryogenic System, the consolidation from prototype to series, and its current procurement and implementation status.

Abstract: Biomedical Knowledge Graphs (KGs) such as PrimeKG and Hetionet offer vast structured repositories for clinical discovery; however, conventional Graph Neural Networks (GNNs) suffer from black-box opacity, while standard Retrieval-Augmented Generation (RAG) paradigms face catastrophic hallucinations and biological implausibility. To resolve this critical trade-off, this paper proposes BioAgent-RAG, a multi-agent orchestrated retrieval-augmented generation framework tailored for complex biomedical reasoning and transparent graph traversal. By integrating autonomous sub-agents for query decomposition, path-based KG grounding, and cross-verification, BioAgent-RAG systematically enhances biological plausibility and reduces reasoning hallucinations. Comprehensive empirical evaluations demonstrate that BioAgent-RAG substantially outperforms existing competitive baselines, RD2-KG, KGiA, and RAG-LLMs. Specifically, on the PrimeKG benchmark, it achieves an ROC-AUC of 0.952, PR-AUC of 0.814, MRR of 0.637, and superior Recall@10 and Recall@50 of 0.715 and 0.868, respectively (p < 0.01). Moreover, evaluated on biological plausibility, BioAgent-RAG yields a Biomedical Factuality Score (BFS) of 0.887 on PrimeKG (0.894 on Hetionet) and a Path Reasoning Score (PRS) of 0.841 (0.862 on Hetionet), demonstrating strong preliminary correlation (0.912) with domain-expert evaluations. These findings suggest that BioAgent-RAG serves as a robust, explainable computational screening tool to prioritize drug repurposing candidates prior to in vitro validation.

Abstract: Closed-loop acoustic stimulation can enhance slow-wave sleep and sleep spindle activity, but evidence has largely been obtained under controlled laboratory conditions. Translat-ing this approach to the home environment is important for developing scalable interven-tions for the myriad of disorders featuring deep sleep reduction, including insomnia. We evaluated the feasibility and electrophysiological effects of modeling-based closed-loop neurostimulation (M-CLNS) using a self-applied wireless EEG headband (ZMax) during unsupervised home use in adults with self-reported maintenance insomnia symptoms. Sixty-two participants completed two habituation nights followed by two counterbalanced five-night periods of stimulation or sham. Forty-nine participants contributed 330 record-ings that met signal-quality criteria. Acoustic stimuli were phase-locked to the onset of the positive slow oscillation deflection (0°). Across 230,139 phase-targeting events, stimula-tion was delivered close to the intended phase for both stimulation (mean −10.2° ± 74.5°) and sham (−4.1° ± 72.4°). Compared with sham, stimulation elicited a significant stimu-lus-locked SO response and increased spectral power in both the SO (0.5–4 Hz) and sleep-spindle (9–14 Hz) bands. Sleep architecture was not measurably altered. These findings demonstrate the feasibility of phase-targeted acoustic stimulation using wearable EEG during repeated unsupervised home use. This scalable approach provides a founda-tion for longitudinal studies investigating whether sustained enhancement of SO activity can improve sleep and daytime functioning or alleviate insomnia symptoms.

Abstract: Let X be an n-dimensional idempotent evolution algebra over a field k, and let ρ_X: Aut(X) → S_n be the natural permutation representation determined by a natural basis. Costoya, Mayorga, and Viruel proved that, for n ≥ 3, 2-transitivity of the image of ρ_X implies that ρ_X is faithful, whereas, for n ≥ 5, 4-transitivity forces the image to be the full symmetric group. We classify the proper 3-transitive case left open by these results.If ρ_X(Aut(X)) ≤ S_n is 3-transitive, ρ_X(Aut(X)) ≠ S_n, and n ≥ 5, then n = q + 1 for a prime power q = p^m ≡ 1 (mod 3), the field k contains a nontrivial cube root of unity, and char(k) ≠ p. Moreover, after a monomial change of natural basis, the structure matrix of X is a cubic-character matrix C_θ = (c_xy)x,y∈P¹(F_q), c_xx = 0, c_xy = θ(det(v_x,v_y)) for x ≠ y, where v_x = (x,1)^T for x ∈ F_q, v_∞ = (1,0)^T, and θ: F_q× → k^× is a nontrivial character of order 3. The key rigidity comes from a compatibility relation derived from the evolution-algebra automorphism equation. Its exponent 2 reflects the squaring of a natural basis element: if χ is the character induced by the monomial lifts on a point stabilizer and s interchanges two points, then χ(s^-1ks) = χ(k)² for k in their common stabilizer. In the projective action this combines with inversion to force χ³ = 1.Conversely, every such cubic-character matrix defines an idempotent evolution algebra precisely when char k≠ p; indeed C_θC_θ^-1 = qI. If X_θ denotes the resulting algebra, then Aut(X_θ) ≅ PGL₂(q) ⋊ Gal(F_q/F_p)_θ, where the subscript denotes the subgroup of field automorphisms fixing θ. Finally, for every nontrivial character η: F_q× → k^× of order 3, \( X_\theta\cong X_\eta \quad\Longleftrightarrow\quad \theta \) and η lie in the same Frobenius orbit. Thus the 4-transitivity threshold of Costoya–Mayorga–Viruel cannot be lowered to 3 in general. For q = 4, the construction yields the unique five-dimensional isomorphism class with automorphism group A₅, and dimension 5 is minimal.

Abstract: Staphylococci are the major pathogens associated with bovine mastitis, and the antimicrobial resistance, biofilm formation, and virulence factors may promote infection persistence. Forty-two Staphylococcus spp. isolates ob-tained from the milk of cows with clinical and subclinical mastitis were analyzed from farms in the Almaty, Zhetysu, and Kyzylorda regions of the Republic of Kazakhstan. The isolates were identified using MALDI-TOF MS, after which their hemolytic and DNase activities, ability to form biofilms, and susceptibility to antimicrobial agents were determined. PCR was used to detect the presence of antibiotic resistance genes (blaZ, mecA, tetM, tetK, ermB, aac(6′)/aph(2″)), as well as genes associated with adhesion and biofilm formation (clfA, icaA, icaD, aap, fnbpA) and virulence (hla, hlb, sea, seb, tst). S. aureus was detected in 17 (40.5%) cases, and S. epidermidis in 12 (28.6%). Bio-film formation was detected in 22 (52.4%) isolates. Resistance to penicillin G and ampicillin was detected in 41 (97.6%) isolates, and the most common genetic determinants were clfA (73.8%), blaZ (47.6%), and mecA (35.7%). Overall, these findings show that resistance- and persistence-related traits are common among Staphylococcus spp. isolates associated with bovine mastitis.

Abstract: Background: Nanotechnology has expanded biomedical applications in chronic kidney disease (CKD), including diagnostics, monitoring, targeted therapy, and extracorporeal management. However, heterogeneity in materials, experimental models, and translational development complicates assessment of clinical progress. This review characterized major CKD nanotechnology applications and their documented translational stages, supported by complementary bibliometric analysis. Methods: English-language original studies indexed in PubMed/MEDLINE between July 1, 2021, and July 1, 2026, were organized into 11 overlapping thematic categories and classified according to their highest documented translational stage. Bibliometrix/Biblioshiny characterized publication, journal, institutional, and geographic patterns, while VOSviewer analyzed MeSH term co-occurrence. Results: Of 288 records identified, 160 studies across 91 journals were included. The most frequent categories were nanodiagnostics and biosensors (53 studies), nanotherapy for renal fibrosis (51), kidney-targeted drug delivery (28), and redox nanomedicine, inflammation, and oxidative stress (27). Diagnostic platforms more frequently reached human-sample validation or early clinical evaluation, whereas therapeutic approaches remained predominantly preclinical. Extracorporeal nanotechnologies were largely experimental, with limited human-sample evaluation. The 10 most productive journals accounted for 55 studies (34.4%), illustrating the concentration of publications within a multidisciplinary field. Conclusions: CKD nanomedicine exhibits substantial technological diversity but uneven translational development. Progress toward clinical implementation requires standardized physicochemical characterization, reproducibility, independent validation, long-term safety assessment, manufacturing scalability, and rigorous comparisons with established standards of care using clinically meaningful outcomes.

Abstract: Depression among Black patients is shaped by the interaction of clinical symptoms, cultural meanings, social conditions, and experiences of care. This narrative review examines evidence among African American and other Black populations, including Black–White comparisons and within-Black differences, on symptom presentation, measurement, persistence, disability, comorbidity, and social and cultural correlates, across the United States and selected Canadian, European, and African settings. Targeted searches identified population surveys, clinical studies, psychometric investigations, qualitative research, intervention trials, and relevant reviews. James S. Jackson's research agenda provides a starting point: understanding comparatively favorable findings in the context of historical and continuing adversity. Several United States surveys report lower lifetime prevalence of interview-defined major depression among Black than White adults, while symptom scales often indicate greater depressive burden among Black adults. This Black–White mental health paradox varies by outcome and population; some studies identify greater persistence and disability among Black adults with depression. Studies of hope, positive affect, coping resources, and physical health suggest that the correlates of depression may also differ across groups, although no single mechanism explains the pattern. Evidence on distinctive symptom presentation is mixed. Some studies identify Black–White differences or within-Black variation in bodily symptoms, self-criticism, or interpersonal experiences, whereas others find substantial similarity after accounting for severity and social or medical conditions. Psychiatric and physical comorbidity among Black adults with depression varies across samples, including in comparisons with White adults. Associations of depressive symptoms or MDD with smoking, disease self-management, self-rated health, inflammation, and mortality differ across studies and populations; colorism, trauma, sexuality, gender, and migration further identify within-Black heterogeneity. The review examines associations of discrimination, financial strain, employment conditions, family relationships, and gendered expectations with depressive symptoms, MDD, and treatment use among Black populations. Studies of Black populations also examine political representation and insurance expansion in relation to self-reported mental health, and loneliness, personality, and distinct church-based relationships in relation to depressive symptoms or MDD. Social support, religious involvement, mastery, and socioeconomic resources are associated with depressive symptoms or MDD in several studies, with variation across measures, social positions, and national settings. Studies also identify disparities in medication initiation and selection, communication, continuity, and care quality; stigma, discrimination in care, and medical debt are relevant to treatment access and experience. Cultural assessment and accessible, sustained depression care may improve recognition and engagement. Future research should distinguish symptom burden from diagnosed disorder, assess longitudinal course directly, and avoid treating Black identity as a single cultural or biological category.

Abstract: Governments across the Global South now promote cultural festivals as tourism assets, but few of them measure what those festivals actually do. This article asks whether the official data that already exist in Ghana are adequate for evaluating the tourism role of two festivals in Accra: Homowo, the harvest festival of the Ga people, and the Chale Wote Street Art Festival, which began in 2011. The study draws on 21 official and statistical sources, 84 months of international arrivals, the 2023 national domestic tourism survey, licensed-enterprise registers, and a corpus of 53 media and official texts. Each source is assessed against five criteria: relevance, resolution, consistency, coverage and access. August arrivals run about 10 per cent above an average month, yet they cannot be distinguished statistically from July (p = 0.50). Domestic culture and festival trips cluster in July to September but make up only about 1 per cent of overnight trips. Thirty-two inconsistencies were logged across sources, among them a change in the basis of 2023 receipts that roughly halves the apparent growth between 2023 and 2024. Four gaps, in resolution, coverage, consistency and attribution, rule out any estimate of festival effects. Yet the national tourism authority already surveys December in GH, Kwahu Easter and PANAFEST. The obstacle, then, is not capacity. It is an asymmetry in which the state promotes festivals it does not measure. The article offers a fitness-for-use framework for festival-tourism data in data-scarce destinations and sets out practical steps for closing the gap.

Abstract: Quartz sand is used throughout oil and gas well engineering, including hydraulic fracturing, wellbore sand control, high-temperature cementing and produced-water treatment, but each use imposes distinct require-ments on particle size, morphology, strength, reactivity and cleanliness. Following the performance chain from mineral source, washing and size classification, storage and transport, slurry injection and fracture placement to long-term service, this review discusses proppant transport and settling, bridging and bed-load migration, breakage and wall embedment under closure stress, fines migration, fracture-fluid residues, mul-tiphase and non-Darcy flow, stress cycling and time-dependent damage. Natural quartz sand, pre-cured and curable resin-coated sands, ceramic proppants and mixed proppants are compared. Material selection should be based on target-well long-term conductivity tests and life-cycle economics. The review also considers gravel packing, where grain-size matching to formation sand, screen compatibility and packing completeness are critical, and high-temperature well cementing, where finely ground silica regulates the calcium-to-silicon ra-tio of hydration products and mitigates strength retrogression. API Std 19C and ISO 13503-2:2024 provide the basic characterization framework, whereas ISO 13503-5:2006 can organize long-term conductivity compari-son but does not provide an absolute in-situ conductivity. A project-level test matrix, minimum reporting set and batch-traceability loop are proposed for resource assessment, product development, procurement and field verification.

Abstract: Adaptive gradient clipping can improve training without establishing that its feedback signals are useful. We examine Dynamics-Aware Gradient Clipping (DAGC), a bounded threshold controller driven by clipping exposure and consecutive-gradient alignment. Its exposure term tightens clipping when clipping is already frequent. We show that an interior target-exposure equilibrium is unstable in a stationary mean-field model, even with exponential smoothing. We then compare the frozen controller with tuned clipping rules, an alignment ablation, and a predetermined threshold schedule on FashionMNIST and CIFAR-10, using validation-only calibration and twelve paired confirmation seeds in each of three conditions.These controls separate threshold scheduling from evidence for an online feedback mechanism; the analysis does not claim convergence of the coupled training dynamics.

Abstract: Educational institutions recognizing the widespread value of artificial intelligence agents may desire to adopt the emerging technologies within their own organizations. However, the deployment of custom agents involves risks for the institutions in the form of prompt injection attacks, hallucinations, embarrassing responses, data leaks, RAG poisoning, and more. Accountability structures, proper identity and access management, and legal compliance standards (FERPA, AI Disclosure Laws, and ADA accessibility) must be maintained on an administrative level to ensure the institution can safely and efficiently deploy agentic technology. When it comes to the initial deployment of the agent, technical teams should implement guardrails, RAG databases, and rate limiting to protect against many of the associated agentic-specific risks, excluding traditional software risks. Finally, subject matter experts can review existing metrics and agent traces to adjust RAG database documents, token limits, tools, and more to ensure optimal agentic output.

Abstract: Large Language Model-guided Evolutionary Search (LLM-ES), exemplified by AlphaEvolve, has recently demonstrated impressive performance in solving goal-driven problems with extremely large and complex search spaces. Unlike conventional LLM approaches based on single-shot or few-shot sampling, LLM-ES introduces population-based evolution to iteratively generate more novel and higher-quality solutions. Compared with traditional Evolutionary Computing algorithms, LLM-ES treats LLMs as semantic variation operators. This design extends the search space from low-dimensional vectors to expressive and language-defined structures such as code and programs. Meanwhile, the rich prior knowledge embedded in LLMs significantly improves the efficiency of evolutionary search in complex solution spaces. As a result, LLM-ES effectively exploits the test-time scaling capability of LLMs and achieves state-of-the-art performance in many open-ended problems. In this survey, we provide a systematic overview of this emerging field. We first formalize the problem setting and the general pipeline of LLM-ES. We then review existing works from several key perspectives, including population design, prompt builder, LLM model selection, evaluation design, and exploration-exploitation control. Finally, we summarize representative applications and discuss open challenges and future research directions.

Abstract: Change detection in high-resolution remote sensing imagery is susceptible to adversary effects of within-class spectral heterogeneity, shadows, and complex spatial structures. Meanwhile, change detection methods based on land-cover posterior probabilities generally rely on reliable semantic training information, making complete manual interpretation costly. To address these issues, this study develops a change detection framework that integrates SEEDS-guided SAM object construction and active learning-based interpretation with FCM-CSBN-CVAPS posterior probability inference. First, representative points of SEEDS superpixels are used as prompts for SAM to generate candidate objects and construct a mutually exclusive object partition. Second, based on the spectral statistical features of the superpixels and an XGBoost classifier, active learning is employed to select highly uncertain samples for limited manual annotation, thereby obtaining object-level semantic information. Finally, FCM is used to characterize pixel-level spectral uncertainty, while CSBN incorporates spatial context to estimate posterior probabilities of land-cover classes. Bi-temporal change measurement and changed-area extraction are then performed in the posterior probability space. Experimental results show that the proposed framework achieves stable change detection performance with only approximately 10% manual annotation. On DS1, DS2, and DS3, the Kappa coefficients reach 83.87%, 86.05%, and 90.21%, respectively, while the corresponding F1 scores are 87.62%, 91.78%, and 91.31%, outperforming the compared methods. These results indicate an effective balance between false-alarm suppression and missed-detection control. In adaptive evaluation experiments, the mean Kappa and F1 scores reach 84.73% and 87.88%, respectively, further confirming the effectiveness and stability of the proposed framework across diverse remote sensing scenarios.

Abstract: Microfluidics has emerged as an important platform for manipulating and analyzing small volumes of fluids, cells, biomolecules, and particles with high precision. Its ability to control microscale flows and integrate multiple analytical steps has enabled diverse biomedical technologies, including lab-on-a-chip, point-of-care diagnostics, organ-on-chip, and microphysiological systems. This review summarizes the fundamental principles of biomedical microfluidics, including microscale fluid dynamics, laminar flow, diffusion, and key dimensionless parameters, together with major platform formats such as continuous-flow, droplet-based, and digital microfluidics. Recent fabrication approaches, including photolithography, soft lithography, molding, laser-based methods, 3D printing, micromilling, and paper- and lamination-based techniques, are also discussed. The review highlights applications in cancer and liquid biopsy, infectious disease diagnostics, cardiovascular and neurological disorders, organ-on-chip models, drug screening and toxicity testing, pharmaceutical synthesis, drug delivery, single-cell analysis, and regenerative and reproductive medicine. Particular attention is given to the growing use of microfluidics for personalized and human-relevant biomedical research. The review further examines the major challenges associated with microfluidic biomedical systems, including fabrication reproducibility, material compatibility, biological complexity, standardization, scalability and manufacturing, clinical validation, regulatory requirements, and integration with advanced computational approaches such as artificial intelligence and machine learning. Finally, future perspectives are discussed, with particular emphasis on personalized medicine, decentralized diagnostics, human-relevant drug development, multi-organ microphysiological systems, wearable and implantable microfluidics, and automated biomedical platforms.

Abstract: This study tests whether frozen connected multiscale paths across a 99-event development cohort form a higher-order population dynamical object. Structural paths occur in 98/99 events, but rarity is graded rather than binary: 58 path-bearing events satisfy the frozen rare-path criterion and 40 do not, while node-count complexity correlates with the frozen rarity coordinate (Spearman ρ = 0.6619802493). A search-aware discrete-family model fails (7,565/9,999 null exceedances; p = 0.7566), motivating a move from event labels to event-internal geometry.Intrinsic registration yields a 97-event atlas with 1,077 nodes and 980 transitions. PRE and POST dynamics are strongly asymmetric: PRE contains 730 nodes and 633 transitions across 90 events with macro R² = 0.131234 and minimum-fold R² = 0.106708, whereas POST contains 347 nodes and 250 transitions across 92 events with macro R² = 0.033268 and minimum-fold R² = -0.104449. Rate balance, re-entry, multistate flow, run index, state age, history, and frozen forward replay establish a recurrent memory-conditioned process.Broad physical-covariate screening produces large apparent fits, but leverage and search-aware controls reject them as population laws. Three-phase organization survives removal of physical terms: the physics-free F0 score is 0.5952381984 versus 0.5970683812 for the best physical candidate. Only after this structural freeze does a state-first physical search survive full selection correction, with 5/9,999 primary null exceedances (p = 0.0006) and 0/9,999 fixed-score exceedances (p = 0.0001).The frozen score then modifies transition rate with γ = 0.3162354634393393, so a one-unit increase in S multiplies the conditional hazard by exp(γ) = 1.371953262430. The frozen clock gives 3/999 primary exceedances under the plus-one rule (p = 0.004). All four developmental views retain positive likelihood gain, and Holdout5 retains positive no-refit likelihood gain in W1–W4; W1–W3 reproduce the frozen ordering direction, while W4 is underpowered at four scorable events.A transition-local depletion interval has generic maximum-gap strength (p = 0.99) but specific frozen transition-local placement at the tested resolution (p = 0.01). The surviving object is therefore an effective, history-bearing population state system with intrinsic registration and a source-dependent traversal-rate modifier.