Electricity markets are experiencing increasing volatility due to the growing integration of renewable energy, which results in more complex planning and decision-making and highlights the importance of accurate forecasting. Medium-term load forecasting poses challenges, such as lead-time-dependent error propagation, evolving seasonal effects, and cross-market structural heterogeneity. This paper evaluates three deep learning architectures as base learners: Long Short-Term Memory, Gated Recurrent Unit, and Convolutional Neural Network integrated with Long Short-Term Memory. Given the lead-time-dependent performance variability of individual models, a lead-time-conditioned convex stacked ensemble framework is proposed. The ensemble learns horizon-specific blending weights from out-of-fold predictions, constrained through a Softmax transformation to ensure a convex combination of base forecasts. The framework is evaluated using rolling weekly forecasts on datasets from Spain, France, Greece, and Portugal. The results show that the proposed ensemble model consistently outperforms the standalone models. It reduces the average error considering all error metrics and countries by about 10%. Furthermore, it provides improved forecast stability across lead times, adapts to different market conditions, and offers reliable predictions for medium-term load forecasting.

This memorandum places into the public scientific record an empirical validation of the bivariate scaling law Vₑₐₒ(μ, σ) = μ^γ • F(σ/μ^κ) in human cardiac dynamics, using open-access electrocardiographic data from the PhysioNet repository. Using a proprietary extraction mechanism calibrated for biological oscillators, the scaling relationship was applied to ten subjects drawn from two clinically distinct cohorts: five healthy individuals and five individuals diagnosed with congestive heart failure (CHF). Across 444,118 healthy beats and approximately 537,000 CHF beats, the bivariate scaling law was found to hold in both populations, with healthy subjects exhibiting a mean goodness-of-fit of R² = 0.9279 ± 0.017 and CHF subjects exhibiting R² = 0.9004 ± 0.013. A statistically significant reduction in geometric integrity (R²; p = 0.031) was observed in the diseased cohort, alongside a trend toward exponent migration in the scaling exponent p (p = 0.066). These results are consistent with the theoretical prediction that diseased biological oscillators undergo a measurable shift in their stochastic scaling structure as system integrity degrades. This document establishes a timestamped empirical baseline for the cardiac application of the RGL framework and connects these findings to the Biological Feigenbaum Spectrum (BFS) established in prior work (Romberger, 2026). This work is a preprint. The original data record and timestamped version are archived at Zenodo: https://doi.org/10.5281/zenodo.18825703 GitHub repository: https://github.com/devinromberger123-prog/rgl-scaling Software DOI: https://doi.org/10.5281/zenodo.19212391

This memorandum documents the computational extraction of the stochastic scaling exponent governing the spectral gap of a nonlinear dynamical system transitioning to criticality via the quasiperiodic route. Utilizing a discretized transfer operator methodology on the critical sine circle map, we observe a highly stable, parameter-independent power-law scaling in the subdominant eigenvalue under varying stochastic perturbation amplitudes. This document places the empirically derived scaling exponent into the public scientific record to establish a timestamped baseline for quasiperiodic stochastic vulnerability. This work is a preprint. The original data record and timestamped version are archived at Zenodo: https://doi.org/10.5281/zenodo.18825703 GitHub repository: https://github.com/devinromberger123-prog/rgl-scaling        Software DOI: https://doi.org/10.5281/zenodo.19212391

This memorandum serves as a predictive roadmap for the application of the universal bivariate scaling law, V_eff(μ, σ) = μ^γ * F(σ / μ^κ), across divergent topological routes to chaos. By isolating the universal scaling architecture from class-specific geometric constants, we identify the solved eigenvalues for established universality classes and explicitly define the unmapped stochastic eigenvalues required to resolve higher-order and non-unimodal critical transitions. A validation taxonomy is provided to delineate confirmed empirical baselines from theoretical predictions. This work is a preprint. The original data record and timestamped version are archived at Zenodo: https://doi.org/10.5281/zenodo.18825703 GitHub repository: https://github.com/devinromberger123-prog/rgl-scaling Software DOI: https://doi.org/10.5281/zenodo.19212391

This document records a mathematical scaling relationship describing how stochastic perturbations interact with proximity to critical transitions in nonlinear dynamical systems. An empirically observed bivariate scaling form is stated, along with a derived perturbation threshold relationship and a hub-dependent geometric correction factor. A validation taxonomy is provided to delineate confirmed empirical baselines from theoretical predictions. This memorandum places the mathematical relationships into the public scientific record to establish definitive prior art and ensure open accessibility.This work is a preprint. The original data record and timestamped version are archived at Zenodo: https://doi.org/10.5281/zenodo.18825703 GitHub repository: https://github.com/devinromberger123-prog/rgl-scaling        Software DOI: https://doi.org/10.5281/zenodo.19212391

OBJECTIVE: To investigate factors associated with postpartum depression symptoms one year after operative vaginal delivery (OVD). DESIGN: Secondary analysis of the prospective observational INSTRUMODA cohort. SETTING: 111 French maternity units. POPULATION: Nulliparous women who underwent OVD between April 2021 and March 2022, and completed two questionnaires, the first one during the days following delivery and the second at one year. METHODS: Statistical analyses accounted for one-year attrition and missing data. Logistic regression models were constructed using a hierarchical approach, with sequential introduction of covariate groups. MAIN OUTCOME MEASURES: Postpartum depression symptoms defined as an Edinburgh Postnatal Depression Scale score ≥13. RESULTS: The prevalence of postpartum depression symptoms at one year was 21.3% (95%CI [18.4–24.2]). Non-modifiable factors associated with postpartum depression symptoms one year after an OVD were pre-pregnancy body mass index (≥30kg/m 2: adjusted OR=2.37 [1.33–4.24]; 25–30kg/m 2: aOR=2.03 [1.30–3.17]; reference: 18–25kg/m 2) and need for psychological support during postpartum hospital stay (aOR=3.78 [1.64–8.76]). Potentially modifiable factors were high anxiety level prior to OVD (aOR=1.84 [1.05–3.22]), feeling of not consenting to the OVD (aOR=1.58 [1.01–2.49]), and marked difficulties in bonding with the child during the postpartum stay (aOR=3.10 [1.04–9.29]). The obstetrical context (indication for OVD, OVD difficulty and morbidity composites) was not found to be associated with higher depression symptoms at one year. CONCLUSIONS: One woman in five suffers from postpartum depression symptoms one year after OVD. Associated factors include both individual characteristics and aspects of maternal experience, highlighting potential targets for prevention.

Objective: To clarify the specific causes of inconsistencies between prenatal fetal genetic results and preimplantation genetic testing (PGT) outcomes via ECIT. Design: A retrospective clinical study focusing on embryo origin tracing and PGT result discrepancy analysis. Setting: Institute of the Women, Children, and Reproductive Health, Shandong University. Sample: 16 patients with successful pregnancies after PGT-selected embryo transfer (April 2021–June 2024); clinical samples with discordant fetal genetic and PGT results were analyzed. Methods: PGT results were first rechecked to confirm accurate embryo transfer. ECIT was then used for pregnancy embryo origin tracing and discrepancy clarification. Main Outcome Measures: Identification of PGT-prenatal diagnosis inconsistency causes (natural conception, embryonic mosaicism); confirmation of embryo origin in sequential transfers and optimal implantation window. Results: Among 15 discordant cases, 2 gestational embryos were not PGT-selected or cycle-matched, but genetically related to the mother, confirming natural conception. The remaining 13 cases involved consistent embryo origin between gestation and PGT transfer, verifying accurate targeted embryo transfer with discrepancies likely attributed to embryonic mosaicism. In one sequential PGT transfer case, miscarriage tissue was confirmed as the day-5 transferred embryo. Conclusions: This study introduces a novel pregnancy embryo tracing technology applicable to a variety of clinical scenarios. It can be used to analyze the causes of fetal abnormalities and pregnancy loss, define the optimal implantation window in sequential transfer, and further refine and optimize the clinical PGT quality control system. Funding: This study was supported by Key R&D Program of Shandong Province, China（2023ZLGX02）; Key R&D Program of Shandong Province, China （2023CXPT010）. Key words: Embryo Traceability | Kinship Identification | Window of Implantation | PGT Quality

IntroductionCardiac sarcoidosis (CS) is an infiltrative heart disease caused by granulomatous inflammation in the myocardium. About 5% of individuals with sarcoidosis exhibit noticeable cardiac involvement. Typical manifestations consist of severe conduction disorders, ventricular arrhythmias (VAs), or dysfunction of the left ventricle (LV) 3. To assess the probability of a person having CS, a blend of multimodal imaging and collaborative efforts across various disciplines is essential. ECG and echocardiography possess limited sensitivity. Cardiac magnetic resonance (CMR) imaging and fluorine-18 fluorodeoxyglucose (FDG)–positron emission tomography (PET) are essential imaging techniques for the precise diagnosis of CS3.Current data show that the prognosis of cardiac sarcoidosis (CS) has significantly improved with the use of implantable cardioverter-defibrillators and modern heart failure treatment. Recently, the quantity of people with implantable cardioverter-defibrillator (ICD) devices for inherited cardiomyopathy and congenital heart conditions has risen significantly. Consequently, several women of childbearing age now have such devices, prompting inquiry into whether these patients face a higher risk of negative outcomes or complications during pregnancy and presenting a significant challenge in managing pregnancy.

Introduction: Stillbirth is a major public health problem, especially in low- and middle-income countries where problems with health systems contribute to high perinatal mortality . Understanding trends and determinants of stillbirths is essential for designing targeted interventions. Kitwe Teaching Hospital (KTH), a major referral hospital in Northern Zambia, experiences a significant burden of stillbirths. However, comprehensive local data on their patterns and risk factors are limited. We conducted a study to describe the prevalence, trends, and characteristics of stillbirths at Kitwe Teaching Hospital from 2020 to 2023. Methodology: A retrospective descriptive study was conducted using a total enumeration of medical records of stillbirths at Kitwe Teaching Hospital between 2020 and 2023. A complete case analysis was used for the records that were finally included in the study. Maternal and fetal characteristics were extracted by using a standardized form and analyzed by using SPSS version 23. Results: Of 7723 births, 278 stillbirths were reported, resulting in an overall stillbirth rate of 36 per 1000 births. The rate declined from 43 per 1,000 in 2020 to 30 per 1,000 in 2023. A Chi-square test for trend (Cochran–Armitage) showed a borderline statistically significant downward trend in stillbirth rates over the study period (χ 2 = 3.83, p = 0.05). Most mothers were aged 25-29 years (31.3%), were multigravida (56.5%), and were HIV positive (20.1%). Vaginal delivery was predominant (74.8%), and common pregnancy complications were antepartum hemorrhage (9.7%) and severe preeclampsia (5.4%). Among stillborn babies, 55.4% were female, most had birth weights between 1,500-2,499 grams (37.1%) and 43.9% occurred at late preterm gestation (36 weeks). Conclusion: Stillbirth rates at Kitwe Teaching Hospital show a gradual decline, yet the burden remains significant. These findings can guide targeted interventions to further reduce stillbirths at the Kitwe Teaching Hospital.

This paper presents a fully-integrated frequency-modulated continuous-wave (FMCW) transceiver chip operating from 55 to 66 GHz, fabricated in a 65 nm silicon-on-insulator (SOI) CMOS process. The chip incorporates a wide-tuning-range voltage-controlled oscillator (VCO) with four digitally selectable states, a driver amplifier, a power amplifier, a low-noise amplifier, and passive mixers, providing a compact solution for high-resolution millimeter-wave radar and short-range communication systems. An innovative hybrid digital-analog VCO tuning architecture, combined with co-optimized transmit/receive front-end design, enables robust noise and linearity performance within a small core area. Measurement results demonstrate a transmitter saturated output power of 12 dBm and an output return loss better than 8 dB across the band. The receiver achieves 10 dB of gain, a 9.8 dB noise figure, and an input 1‑dB compression point of ‑18 dBm. The integrated VCO exhibits a phase noise better than –87.71 dBc/Hz at 1 MHz offset.

Predicting patient response to cancer immunotherapy remains a critical challenge due to the complex and heterogeneous nature of the tumor microenvironment. Current singlemodal analyses often fail to capture the intricate details influencing outcomes. To address this, we propose MAFNet, a novel Multi-modal Attention Fusion Network, designed to integrate diverse patient-derived data including pathological images, genomics, and transcriptomics. MAFNet incorporates a Hierarchical Attention Fusion Module (HAFM) for tailored feature encoding, a Transformer-based Cross-Modal Interaction Learning (CMIL) component to model intermodal dependencies, and a Multi-task Self-supervised Pre-training strategy for robust representation learning. Evaluated on TCGA Lung Adenocarcinoma and Melanoma cohorts, and validated on an external GEO dataset, MAFNet achieved superior immunotherapy response prediction, significantly outperforming single-modal deep learning models and simple concatenation-based fusion methods. It further demonstrated strong generalizability, high interpretability through attention visualizations, and effective overall survival prediction. An ablation study confirmed the critical contribution of each innovative component, while human evaluation highlighted its clinical plausibility and utility. Although computationally more intensive, MAFNet's enhanced predictive accuracy, generalizability, and interpretability position it as a powerful decision-support tool for advancing personalized immunotherapy.

AbstractAdoption is increasingly seen as a lifelong experience, with cascading effects from early life experiences into adulthood. Consequently, support needs can often persist long after adoption. Current post-adoption support has largely been informed by infant studies and developmental theories such as attachment theory, emphasizing the importance of early bond forming with adoptive caregivers. Recent evidence and cohort studies as adoptees have grown older suggests that support needs, especially beyond early childhood are both broad and complex. This systematic review aimed to consolidate research about adoptee support needs as reported by adoptees and/or their families. The review included international, English-language primary studies published between 2002-2025 that reported on the lived post-adoption experiences of adoptees and their families, with particular attention to areas of unmet support. Following a review of 120 full texts, 76 were included. Support needs were identified for areas of attachment, identity, neurodevelopment conditions, mental health and trauma, education, grief/loss, and contact with birth families. Participants reported that neurodevelopmental problems and the role of early adversity and trauma on later mental health outcomes were not well understood among service providers, which created a barrier to diagnoses and interventions received. Practice implications and recommendations for service providers are discussed.

The enormous mismatch between quantum-field-theory estimates of vacuum energy and the observed dark-energy density-the "vacuum catastrophe"-is revisited as a geometric effect of dimensional crossover. We consider a scenario in which space is effectively four-dimensional at sufficiently small scales and transitions to three-dimensional behavior at larger scales. Combining the measured darkenergy density with dimensional analysis, we estimate the crossover length and find it must lie extremely close to the Planck scale, far below nuclear or subatomic dimensions. Thus, if dimensional crossover contributes to the cosmological constant problem, it must be confined to Planckian distances. The main contribution of this note is to reframe the discrepancy not as a failure of QFT but as a stringent empirical constraint on higher-dimensional models: the observed dark-energy density functions as evidence for dimensional stratification [8,9].

A document by Grold Otieno Mboya. Click on the document to view its contents.

This study proposes a structural interpretation of low-density, high-temperature hydrogen-rich gas in the halo environment of the Andromeda galaxy under the framework of SOSD-1. The central hypothesis of this work is that some diffuse halo gas structures may not be solely products of internal galactic processes such as stellar feedback, supernova-driven outflows, or recycled fountain flows, but may instead represent primordial hydrogen clouds that were not fully incorporated during early galaxy formation. The main purpose of this paper is not merely to restate that diffuse halo gas exists, since that has already been widely discussed in previous studies, but to reinterpret the origin of a subset of such gas from a structural perspective. The present work argues that the physical state of gas alone cannot determine origin. Low density, high temperature, and even low metallicity may support the primordial interpretation, but these properties are not sufficient by themselves because similar states may also emerge through other astrophysical pathways. For this reason, the paper distinguishes between _state-based conditions_ and _structural origin criteria_. A key element of this study is the conceptual clarification of why low-density, high-temperature gas does not necessarily undergo rapid macroscopic expansion. In ordinary human intuition, high temperature is associated with strong heat, expansion, and energetic activity. However, that intuition is derived from dense environments where particle collisions are frequent. In a highly rarefied halo environment, individual particles may possess high kinetic energy while remaining dynamically decoupled from one another due to extremely low collision rates. Thus, high temperature does not directly imply collective expansion. This distinction is essential to understanding how hot gas may remain diffuse without immediate dispersal. The paper also argues that galaxy formation should not be regarded as a perfectly efficient process in which all primordial gas is inevitably accreted into the galaxy. The early universe was not characterized by complete density uniformity, and therefore not all gas parcels would have experienced identical gravitational conditions. Some regions could become efficiently bound and incorporated, whereas others would remain only weakly bound, marginally coupled, or dynamically separated from the central gravitational collapse. This provides the basis for considering the survival of non-merged primordial gas in halo regions. In addition, the study addresses why such structures may not have been clearly recognized in past observations. Diffuse low-density gas produces weak signals, broad spatial distributions, and low contrast against the background. For this reason, some structures may historically have been treated as noise, background fluctuation, or low-significance artifacts. This does not imply that the gas did not exist; rather, it suggests that the detection and classification frameworks of earlier observations may not have been optimized to distinguish physically real diffuse gas from statistical or instrumental background components. The historical transition in astronomy, in which previously ambiguous diffuse gaseous signals came to be recognized as physically meaningful structures, supports this broader methodological point. Finally, this study introduces both falsification conditions and structural validation conditions. The falsification conditions are derived from the internal logic of the hypothesis itself; if these conditions are violated, the primordial interpretation becomes structurally unsustainable. By contrast, the validation conditions proposed here are not claims that current observations have already confirmed the hypothesis, but rather predictive structural criteria that future observations can use to test it. In this sense, the present work is intended as a structural framework for origin classification rather than as a claim of completed observational proof.

This article complements the article on FinanceLLMsBackRL [1]. In this supplement, we focus on the specific aspect of the singularity (Figure 7) of the 3D Navier-Stokes equations as applied to the Finan-ceLLMsBackRL method. We seek to understand how the singularity approach to the 3D Navier-Stokes equations governs the overall configuration of the methods developed in the FinanceLLMsBackRL article [1].

A document by Shuanglin Lei. Click on the document to view its contents.

A document by Jordan Barton. Click on the document to view its contents.

Coral reefs are among the most biodiverse ecosystems on Earth and rely on endosymbiosis with Symbiodiniaceae, a diverse family of dinoflagellates essential for coral nutrition and which also play an important role in thermal tolerance. Despite their ecological importance, resolving species boundaries within Symbiodiniaceae remains challenging, partly due to limited phylogenetic signal in commonly used genetic markers and the absence of broadly accepted criteria for species delimitation. Refining taxonomic resolution is therefore critical for understanding symbiont diversity, distributions, and evolutionary relationships. In this study, we delimit two closely related Cladocopium species using a meta-haplotype framework applied to two hypervariable loci—the ITS2 region and the psbA non-coding region (psbAncr). Slowly evolving markers fail to differentiate Cladocopium proximale (SCF049; described here) and its sister species C. proliferum, yet consistent and biologically meaningful variation is captured across both hypervariable regions. By integrating divergence across multiple organellar and nuclear regions, we demonstrate that C. proximale and C. proliferum represent distinct species. More broadly, closely related Symbiodiniaceae such as these provide important systems for examining speciation processes, including potential ecological differentiation and reproductive compatibility. Our results highlight the value of combining multiple hypervariable markers to improve taxonomic accuracy in Symbiodiniaceae and strengthen the basis for identifying and describing closely related species within this ecologically crucial group.

[1]¿p#1 This study presents a systematic methodology for the preliminary tuning of a proportional–integral–derivative (PID) controller implemented within a distributed control system (DCS) for regulating the bottom temperature of a stabilizer column in a catalytic reforming unit of a petroleum refinery industry controlled by TIC-3051-1. To enable stable automatic operation, real-time historical process plant data comprising approximately 300 records of process and manipulated variables were extracted for 17 minutes from the DeltaV DCS panel. The transfer function tf2 of the process was identified through MATLAB system identification techniques. The developed model was subsequently integrated into a Simulink-based control framework. Automatic PID tuning was then performed to obtain appropriate proportional (Kp), integral (Ki), and derivative (Kd) gains, which were estimated as 0.611, 0.022, and 4.16, respectively, and further, these were converted into DCS-compatible controller parameters as Kc = 0.63, Ti = 26 seconds, and Td = 6.4 seconds. 14 hours of operational trends demonstrated that the tuned controller maintained the set-point temperature of 238.4℃ within an acceptable range over extended operation. The results confirm that data-driven preliminary tuning using MATLAB–Simulink provides an effective and practical approach for improving temperature control performance in refinery DCS applications.

Classical information retrieval metrics such as Mean Reciprocal Rank (MRR) evaluate only the position of the first relevant item, ignoring both deeper relevant results and the structural quality of the retrieved set within the corpus graph. We introduce MRR-Top0, a novel ranking metric that extends MRR to the entire top-k result list by weighting each relevant item's reciprocal rank with a topology factor T q,i. This factor combines three graph signals computed on the corpus feature graph: Personalized PageRank (random-walk affinity from the query anchor), a conductance penalty (subgraph cohesion), and a modularity gain (community alignment). The resulting score evaluates both relevance order and structural coherence in a single, bounded, label-agnostic quantity. MRR-Top0 is applicable to any retrieval system operating over an embedding space equipped with a graph Laplacianincluding spectral vector databases, RAG pipelines, and topology-aware search enginesand provides a computationally cheap proxy for assessing whether a ranking reflects the learned manifold structure of the dataset, not merely geometric proximity. We present the formal definition, explain every constituent, discuss key properties and practical guidance, and situate the metric within the broader landscape of graph-aware retrieval evaluation.

Having introduced Graph Wiring-(1) a technique that leverages the Graph Laplacian computed in feature space to provide semantically-aware search over high-dimensional vector corpora-and MRR-Top0 (2) as a topology-aware retrieval metric for evaluating it; this paper proceeds to demonstrate formally and empirically that the feature-space Laplacian produced by ArrowSpace (3) carries structural information in the sense of epiplexity (4), so that it is not plain graph metadata but a reusable context-bound semantic artifact. Using the CVE 1999-2025 vulnerability corpus as a case study, we instantiate ArrowSpace as a spectral vector-search engine, wrap its feature-space Laplacian in a Laplacian-constrained Gaussian Markov Random Field (LGMRF), and evaluate the resulting two-part Minimum Description Length (MDL) code. The model achieves a compression ratio of 38.4× over raw float32 storage, passes all three structural-information diagnostic tests. Furthermore it is demonstrated that the same Laplacian object as computed by Graph Wiring supports six distinct algorithm families (search, classification, anomaly detection, diffusion, dimensionality reduction, and data valuation) without additional learning. The two accompanying Jupyter notebooks are intended as a reproducible reference pattern for applying epiplexity measurement in algorithm design for large-scale data engineering for LLMs and ML operations.

In this research, the energy consumption models of Bitcoin, Ethereum, and Dogecoin are analyzed using Explainable Artificial Intelligence (XAI) models aided by the three stages of analysis involving Digiconomist data from 2022 to 2025: (1) exploratory data analysis for the nature of energy consumption, (2) model identification of influential variables using Random Forest models enhanced with SHAP values, and (3) an LSTM transfer learning method for predicting the energy consumption of Ethereum and Dogecoin using a model developed with Bitcoin data. The initial results show that while both assets vary largely when it comes to their normal usage level, Ethereum sees a sharp drop after the changeover from Proof-of-Work to Proof-of-Stake as a mechanism. The XAI analysis indicates that energy use is largely a consequence of past use, seasonality, and annual patterns. In addition to this, the models show a high level of accuracy for Dogecoin (R²: 88.4%, MAPE: 13.45%) and Ethereum (R²: 86.2%, MAPE: 11.47%) when it comes to predicting energy usage using the concepts of transfer learning.

Construction estimators play a huge role in the project's bidding process. A good estimate is one that appropriately considers the scope of work of the project; this helps reduce the chances of an unforeseen expense or risk that could delay the project. Estimators face many challenges because they must take into account the market conditions, project timeline, labor cost, material cost, and contingencies of the project when they are working on the construction estimate. A single mistake from their side can cause the project to go over budget, so staying up to date with the previous construction estimates is extremely important since the data can help the estimators with future references and help them find ways to improve and prevent unforeseen expenses. This research was collected from a journal to demonstrate the risk factors that impact a construction project estimate. The data was gathered from a questionnaire and then labeled on a chart to illustrate the impact the risk factor causes on the project cost and the risk factor's occurrence level.