This paper explores the role of Large Language Models (LLMs) in promoting sustainable behavior, specifically in overcoming procrastination. Despite widespread recognition of the need for sustainable behavior change, individuals often struggle to break free from entrenched, unsustainable habits. LLMs, such as OpenAI’s GPT-4, represent a significant breakthrough in artificial intelligence and are increasingly used in behavior change interventions. This study introduces ProactiMate, a chatbot built using Motivational Interviewing (MI) principles and a Chain of Models approach for prompt engineering, designed to help users combat procrastination. Our research compares four LLMs (GPT-3.5 Turbo, LLaMA-3.2, Qwen-2.5, and SmolLM-1.7B) for output influence on procrastination avoidance, and assesses the impact of hyperparameters (temperature and top-p values) on procrastination avoidance. The findings reveal that GPT-3.5 outperforms other models across various evaluation metrics, and higher temperature and top-p values lead to more effective procrastination avoidance from automatic evaluation. According to expert evaluations, Qwen-2.5 and GPT-3.5 Turbo demonstrated notable effectiveness in fostering user engagement and motivation for addressing procrastination, with GPT-3.5 Turbo particularly distinguished by its capacity to provide strategies that help maintain long-term motivation. And GPT’s output aligns well with both automatic evaluation metrics and human evaluation. The results provide insights into the most effective ways to use LLMs in chatbot design, offering solutions for future usability testing.

Early skin cancer detection is essential to improve treatment success and reduce mortality rates. This study is an experimental and exploratory study of the performance of various deep Convolutional Neural Network (CNN) architectures in dermatoscopic image-based skin cancer classification. Eight popular CNN models—ResNet18, ResNet50, ResNet101, DenseNet121, EfficientNet-B0, MobileNet V3 Large, ConvNeXt Tiny, and Xception—were tested on a large-scale dataset of 401,062 images and the size ±130×130 pixels. Experiments were conducted with various combinations of hyperparameters, such as batch size, learning rate, and number of epochs, to measure the stability of the models and their effects on accuracy, sensitivity, precision, and computational efficiency. One crucial observation is that some models, such as ResNet and Xception, exhibit symptoms of overfitting after a certain number of epochs (e.g., >40), where the training accuracy increases to 99.98%, but the validation accuracy decreases or stagnates. In contrast, models such as ConvNeXt-Tiny and DenseNet121 show stable performance up to the 100th epoch with validation accuracy approaching 99.87% and F1-score only reaching 34%. This is due to the F1-score testing and the data imbalance in the Indeterminate, Benign, and Malignant classes. The analysis also includes GPU memory usage and training time, showing that ResNet101 and ConvNeXt-Tiny require high resources (over 300 MB of total memory and more than 700 seconds per epoch). In comparison, lightweight models such as MobileNetV3-Large and EfficientNet-b0 are more efficient ( <150 MB of memory and <350 seconds per epoch) with competitive classification performance. DenseNet121 recorded the highest F1 score (34.92%) with efficient memory consumption and training time. In contrast, ResNet101 and ConvNeXt require high computational resources without significant improvement in the metrics. MobileNetV3 and EfficientNet-B0 excel in GPU duration and memory efficiency. The training discussion shows that large hidden dimensions do not guarantee better performance and model stability is more affected by architecture depth and training configuration. This study emphasizes the importance of comprehensively evaluating models’ accuracy, efficiency, stability, and ability to handle data imbalance in automated medical diagnosis systems.

Audio Source Splitting refers to the procedure of decomposing a mixed audio signal into its constituent components. This technique enables numerous applications, including creative music production, educational tools, karaoke, transcription, and music analysis. Although deep learning-based source separation techniques have showed promise recently, they frequently fail to achieve high precision and clean separation, especially in complicated audio combinations. These methods typically rely on either temporal or spectral features, limiting their ability to fully capture the intricate dynamics of audio signals. To address these limitations, proposed the Amalgamation Audio Features (A2F), a hybrid representation combining temporal and spectral features. Then, Proposed the Audio Strips Network (ASNet), a novel framework designed to achieve clean and precise separation of individual audio sources with enhanced performance. ASNet utilized A2F, to separate sources more effectively. The model is trained and evaluated on the MUSDB, DSD100 and MUSDB18-HQ dataset, a benchmark for music source separation, and its standard measures like the Signal-to-Distortion Ratio (SDR) and Signal-to-Interference Ratio (SIR) are used to examine performance. The evaluated results demonstrate that ASNet outperforms existing methods regarding quality of separation, robustness, and efficiency of computation. This advancement benefits musicians through high-quality remixing and creativity while aiding researchers in improving Deep Learning and hybrid audio processing models. By integrating innovative architectural design and feature extraction techniques, ASNet represents an important advancement in the area of audio source splitting.

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) carries the risk of immune-related complications, including rare alloimmune phenomena. Type 1 diabetes (T1D), a chronic disease marked by pancreatic beta-cell destruction, is rarely observed after allo-HSCT, and its pathogenesis in this setting remains unclear. We report two pediatric cases of T1D that developed several years after allo-HSCT for primary immunodeficiencies and review clinical and HLA data. A review of relevant literature was also conducted to put these findings in perspective. Both patients developed T1D 6.5 to 9.5 years after allo-HSCT. In both cases, pancreatic autoantibodies were detected. Neither donor had a history of autoimmune disease. Literature review suggests that post-HSCT T1D may arise through alloimmune mechanisms rather than a manifestation of graft-versus-host disease. Improving outcomes may depends on close multidisciplinary follow-up. Development of screening protocols and early detection of potential immunological processes are required.

We present an interpretable machine learning framework for divorce prediction that integrates causal inference and counterfactual reasoning to generate actionable insights. Using a dataset of 170 couples (85 divorced, 85 married) assessed via the Divorce Predictors Scale (54 behavioral features), we identify 16 causally significant predictors using Double Machine Learning with Causal Forests. Notable drivers include humiliating language during arguments (ATE: +24.4%) and shared entertainment preferences (ATE: –20.8%). We train four gradient-boosting models—XGBoost, LightGBM, CatBoost, and HistGradientBoosting—and achieve high performance, with XGBoost yielding 97.9% accuracy and CatBoost achieving a ROC-AUC of 0.99. Our models outperform prior approaches, including a BERT-based Random Forest (accuracy: 81.0%) and also outperforms the state-of-the-art transformer model (FT-Transformer accuracy: 97.0%), while providing greater interpretability. Our framework uniquely combines SHAP values for local and global explanations (e.g., humiliation contributing 0.11 units toward an individual’s divorce prediction), DiCE for generating diverse and plausible counterfactuals (e.g., reducing humiliation flipped the prediction to marital stability), and Bayesian Neural Networks to estimate uncertainty (±9.3% standard deviation). The entire system is accessible via an open-source Google Colab notebook, allowing users to simulate personalized interventions. This research contributes to the fields of responsible AI, computational and informational social science by demonstrating that high-accuracy prediction can be meaningfully combined with interpretability and actionability in sensitive domains. All reproducibility resources are provided via a publicly available GitHub repository.

Adenosine recognized for its immunomodulatory, anti-inflammatory, and anti-cancer properties, has limited clinical and commercial applications due to low production levels in Ganoderma lucidum. Enhancing the expression of key biosynthetic genes presents a viable strategy to boost adenosine production through microbial fermentation. This study focused on the cloning, characterization, and overexpression of the key biosynthetic gene 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase ( GlATIC) in G. lucidum. The 1803-bp GlATIC cDNA was inserted into the plasmid pCAMBIA1302 and introduced into G. lucidum protoplasts via PEG-mediated transformation. Protoplasts were prepared using lywallzyme and driselase. Transformants were analyzed for GlATIC expression levels and adenosine content. The transformant OE- GlATIC-14 exhibited a 7.4-fold increase in GlATIC expression compared to the wild-type (WT) strain on the fourth day of culture. Adenosine content in OE- GlATIC-14 increased by 130.6%, reaching 3970.89 µg/g, compared to the WT strain. Additionally, AMP and guanosine levels were elevated in OE- GlATIC-14. The adenosine enhancement rate achieved in this study surpasses that of genetically engineered Bacillus subtilis strains, with the adenosine content being the highest reported among G. lucidum and other fungi. Overexpression of GlATIC in G. lucidum significantly enhances adenosine production and represents a promising strategy for optimizing microbial fermentation of adenosine in G. lucidum.

In this study, a unified formulation is derived for fatigue crack nucleation under general unbalanced cyclic loading profiles, and it is combined with the integrated creep-fatigue theory (ICFT) to give a physics-based theoretical description from very low cycle fatigue to high cycle fatigue, rather than building up by empirical rules. Using several engineering alloys as illustrative examples, the model has been shown to effectively include the effects of mean stress /ratcheting strain, and monotonic plastic damage in metal fatigue. For small-amplitude loading cases, it works equivalently well as the SWT model, but it goes beyond the empirical data-collapsing method to predict fatigue life based on the material basic physical/mechanical properties such as the elastic modulus, Poisson’s ratio, surface energy, Burgers vector without the need of data regression. Therefore, it offers a unified tool for fatigue design and analysis.

Birds can show patterns of sexual size dimorphism as early as during the nestling stage. This raises the question of how the faster growing sex might reconcile the energetic and nutritional needs of a faster growth rate with resource allocation to other important life functions, such as the development of innate immune function. Innate immunity represents the main line of defence against diseases, and while some innate immune defences are already present at hatching, substantial development occurs throughout the nestling stage. Hence, this development may compete for resource allocation with growth, potentially affecting nestlings in a sex-specific way in species showing sexual size dimorphism at early age. However, little is known about how sex might shape life-history strategies early into the life cycle. In this two-year study, we molecularly determined the sex of Great tit (Parus major) nestlings. We measured morphometrics (mass, wing and tarsus) and carried out innate immunity assays (Hemolysis-hemagglutination assay, Bacteria Killing Assay, and Haptoglobin assay). We then compared size, mass and immune function among sexes shortly before fledging, likely reflecting the outcome of relative resource allocation during ontogeny. We also carried out a brood size manipulation experiment to simulate resource limitation in the nest. We found that male nestlings grew to a larger size at day 14 than their female siblings. However, we also found some indication that males developed a better immune defense than females albeit their faster growth. Thus, males manage to invest more heavily in both growth rate and immune defence, probably depending on males being dominant to females in the competition for parental feeding, resulting in higher resource acquisition.

This paper introduces the core formulation of Dimensional Relativity Theory within the Knot of Light framework. By extending the conventional relation , we generalize massenergy equivalence across variable dimensional spaces using dimensionally modulated light speed and rhythm indices.

Organisms in ecosystems continuously release a myriad of organic matter molecules that undergo microbial and abiotic transformation, processes that critically influence carbon storage and climate feedbacks. Yet, a systematic understanding of what determines the transformation and persistence of organic matter across spatiotemporal scales remains elusive. We propose an emerging framework, termed "functional chemogeography", to understand transformation and persistence based on the chemical traits of organic matter molecules. This framework extends beyond a sole focus on intrinsic traits, which remain relatively constant across spatiotemporal scales, to emphasize extrinsic traits such as biochemical transformations and environmental responses, which vary spatiotemporally and are shaped by both intrinsic traits and the environment. When upscaled to the assemblage-level using functional diversity indices, these extrinsic traits reveal a significant, and in some cases superior, capacity than intrinsic traits to explain biogeochemical processes, as demonstrated through a case study in China's lakes. By integrating trait-based perspectives into predictive models, this framework helps bridge chemical complexity with ecosystem biogeochemistry, thereby advancing our ability to predict the fate of global organic carbon under environmental change.

In resource-constrained environments, deploying deep learning models for real-time image classification poses significant challenges due to limited computational power and memory. Existing solutions often rely on full-precision (FP32) models, which are computationally expensive and impractical for embedded systems. This study addresses the problem of efficient deployment of deep learning models by evaluating the impact of 16-bit Floating Point (FP16) quantization on the performance of MobileNetV3Large for mung bean seed defect classification. The proposed solution targets the limitations of current approaches, which offer high accuracy but at the cost of large model size and slow inference speeds. A dataset comprising 6,598 high-resolution images was constructed, with samples classified into five defect categories: broken, immature, infected, normal, and rotten. The baseline FP32 MobileNetV3Large model achieved a test accuracy of 94.85% with a model size of 16.2MB and an inference speed of 3.5 frames per second (FPS). After applying FP16 quantization, the model size was reduced to 8.27MB and inference speed increased to 8 FPS. This demonstrates a significant improvement in memory and speed efficiency. Although there was a minor accuracy drop to 93.86% (a reduction of 0.9%), the trade-off is acceptable for real-time applications on embedded platforms. These findings highlight the practical advantages of FP16 quantization for deploying lightweight yet accurate deep learning models in resource-constrained environments. The results support its viability for real-time agricultural applications such as automated seed sorting.

This study examined whether elementary children’s executive functions (EFs) moderated associations between observed autonomy-supportive and controlling parenting during math homework help and children’s math achievement and anxiety one year later. In total, 170 parent-child dyads completed a second-grade assessment ( M age = 8.02), and 111 returned in third grade ( M age = 9.18). Half of the children were girls (48%) and the sample was predominantly white (78%). Results indicated that autonomy-supportive parenting was associated with higher math achievement for all children ( β = 0.264). Significant interactions emerged between controlling parenting and children’s EFs for their math achievement ( β = 0.165) and math anxiety ( β = 0.190). Children with high EFs were protected against the negative effects of controlling parenting for children’s math achievement. In contrast, children with low EFs demonstrated moderate math anxiety regardless of controlling parenting, whereas children with high EFs demonstrated a positive association between controlling parenting and increased math anxiety.

Hypertrophic cardiomyopathy (HCM) is characterised by dynamic LVOT obstruction and is frequently associated mitral valve abnormalities commonly leading to mitral regurgitation. We describe a case of HCM with dynamic as well as fixed LVOTO due to the presence of a subaortic membrane along with concomitant mitral stenosis due to anomalous insertion of papillary muscles. This case represents a very rare scenario of mitral inflow and mitral outflow obstruction along with coexistent fixed and dynamic LVOTO in a case of HCM.

Understanding how rainfall and snowmelt influence baseflow, the groundwater-fed component of streamflow, is essential for sound water resources management. Current approaches that aim to uncover the spatial couplings between these processes and baseflow are limited. The most commonly used methods include geochemical tracers and hydrologic models. A key limitation of the first is cost, while the second is limited by the need for simplifying assumptions. This study developed a data-driven approach which leverages satellite Earth observation data and ground-based data to assess the degree to which baseflow is influenced by upstream rainfall and snowmelt in California’s Sierra Nevada. The procedure involved: (1) separation of baseflow from streamflow time series using a low-pass filtering technique, (2) application of time series and information theory methods to identify the areas which have the greatest impacts on baseflow through both rainfall and snowmelt, and (3) characterization of the elevation zones which have a prevailing influence on baseflow. Results suggest that areas which have the strongest impact on baseflow through rainfall and snowmelt are not necessarily the areas which experience the highest annual rates of snowmelt or rainfall; snowmelt occurring in the 3000 – 3700 m elevation range was found to be the most important driver of baseflow.

Aims: High quality feedback is crucial for helping medical students understand and apply core concepts of pharmacology, yet personalised feedback is resource intensive to produce. Artificial intelligence (AI) offers a potential solution, but little is known about students’ perspectives on AI-generated feedback. This study investigated how medical students perceived and made use of AI feedback in a formative assessment while studying fundamental pharmacology. Methods: We used a qualitative approach to explore how third year medical students perceived AI score and feedback after completing a formative test containing eight short-answer questions on core concepts in pharmacology. Data were collected using focus groups (N=11). In an iterative thematic approach, the transcripts were analysed, and themes identified. Results: Three themes representing factors that affect students’ experiences with AI-generated feedback were identified in the analyses: 1) trustworthy and accessible feedback information, 2) aligning the feedback with the study program, and 3) student feedback literacy. Conclusion: Our findings illustrate the complex interplay between technological, contextual, and individual factors in shaping the effectiveness of AI-supported formative assessment. Students found the AI-generated feedback to be useful and mostly reliable, but raised concerns regarding AI being overly positive, the timing and mandatory nature of the assessment, and the workload required to engage with lengthy narrative feedback comments. While AI tools have the potential to provide reliable, personalised and effective feedback, its implementation needs to ensure that students are equipped with feedback literacy and that the educational program incentivises meaningful engagement with feedback.

Sand dams are small, reinforced barriers constructed across seasonal and ephemeral streams which trap water in sediments deposited s. For these reservoirs to provide sustainable and dependable water supplies or valuable sand for other purposes, they should primarily fill with coarse sand rather than fine sediments. Excessive accumulation of fine sediments in sand dam reservoirs limits recharge and recoverable water. We describe a novel approach to preventing the accumulation of fine sediments in sand dam reservoirs by geomorphic management of reservoir sedimentation. We propose building sand dams with outlets at the foot of the dam to selectively trap coarse sediments (>0.125 mm; Rouse number = 2.5) across a range of flows and sediment transport rates. An optimal outlet had an “Eiffel Tower” shape which maintains the desired Rouse number, ensuring finer particles will pass out of the reservoir remaining suspended, while coarser particles settle. HEC-RAS simulations confirm that these designs promote uniform coarse sediment deposition within the reservoir and perform effectively, with minimal deviation (with an MSE value of less than 1%) from the target Rouse number. Alternative circular and rectangular base cutouts were also found to provide good performance across a wide range of flows and would be easier to construct and have less impact of the strength of the dam.

In this paper, we propose an algorithm that integrates a dynamic leader election (DLE) mechanism and model-free reinforcement learning (RL). The algorithm aims to address the issue of fixed leaders restricting reactive power flow between buses during heavy load variations in islanded microgrids, while also overcoming the challenge of obtaining model parameters such as resistance and inductance in practical microgrids. The proposed method consists of two main components: a dynamic leader election algorithm and a model-free reinforcement learning algorithm. First, we establish a voltage containment control and reactive power error model for alternating current (AC) microgrids and construct a corresponding value function based on this error model. Second, a dynamic leader election algorithm is designed to address the issue of fixed leaders restricting reactive power flow between buses due to preset voltage limits under unknown or heavy load conditions. The algorithm adaptively selects leaders based on bus load conditions, allowing the voltage limits to adjust accordingly, thereby regulating reactive power flow between buses. Then, to address the difficulty of accurately acquiring parameters such as resistance and inductance in microgrid lines, a model-free reinforcement learning method is introduced. By constructing a value function based on voltage and reactive power errors, this method relies solely on real-time measurements of voltage and reactive power data, without requiring specific model parameters, to realize accurate reactive power sharing and voltage containment control. Ultimately, simulation experiments on AC microgrids are conducted to validate the effectiveness of the proposed algorithm.

Introduction: The study aims to analyze the potential relationships between climate change, renewable energy development, and the intensification of drought phenomena in industrial regions of Central and Eastern Europe, in order to develop an integrated strategy to prevent negative climate impacts.Methods: Meteorological data for the period 1990-2025 were analyzed using ERA5 and MERRA-2 reanalysis. Statistical methods were applied to detect trends in climate indicators and their possible correlation with energy infrastructure development. Numerical modeling of atmospheric circulation was performed using the WRF model, including uncertainty analysis and validation of parameterizations for regional conditions.Results: A tendency toward decreasing precipitation in continental regions of Central and Eastern Europe was detected. Analysis of literature sources shows that large-scale wind farms can potentially influence local meteorological conditions; however, their impact on regional climate remains the subject of scientific discussion. Modeling suggests that optimizing the spatial distribution of energy infrastructure can reduce potential impacts on atmospheric circulation, although the causal relationship requires further research.Conclusions: Based on the research results, an integrated strategy for climate change adaptation and drought phenomena was proposed, which includes optimizing the spatial distribution of energy infrastructure, diversifying renewable energy sources, and developing water resource protection systems.

This paper formulates a velocity modulation model based on phase density within the Knot of Light framework. Phase density () governs the flow capacity of resonance structures, impacting the effective speed of signal and energy propagation. The presented equation enables modeling of velocity under varying topological resonance densities.

A document by Ali Chamani. Click on the document to view its contents.

Correspondence Title: Blocking or anti-idiotypic antibodyThe elegant work reported by Trifonova et al (1) describing the creation of specific allergen peptides, and their use in the construction of a recombinant vaccine is quite remarkable. The IgG antibodies produced by the administration of that vaccine, and their subsequent interference with IgE antibodies’ allergenic abilities offers enhanced effectiveness compared to other therapeutic approaches.However, I wonder how the authors determined the target of the IgG antibodies they created…Antibody-Antigen interactions have been likened to a tumbler-lock and key model. The antigen’s surface is represented by the key; the antibodies’ amino acid patterned surface complementary to the antigen’s surface, the pins of the tumbler-lock.However, the Fab region of the antibody molecule is not a 2-dimensional structure, any more than the pins of the tumbler-lock are.With the tumbler lock, the inner surface of pins would be patterned complementary to the surface of the key. However, the opposite ends of those pins would be patterned like the surface of the key.Likewise, the inner surface of the exposed paratope of the IgE’s Fab would be patterned complementary to the surface of the antigen. The outer surface of the exposed paratope of the IgE Fab portion, however, would be “like” the surface of the antigen … “like” in the sense of more similar than the paratopic outer surface of any IgE antibody targeting any other antigen.An IgG antibody is then introduced into the system targeting the same antigenic surface the IgE antibody is targeting.How would one know whether the surface which binds with the IgG antibody is the surface of the antigen the IgE antibody is targeting (IgG acting as a blocking antibody), or the outer surface of the IgE’s paratope (IgG acting as an anti-idiotypic antibody)?Either one could have interfered with the functional ability of the IgE antibody and would have produced the observed results.ReferenceTrifonova D, Curin M, Focke-Tejkl M, et al. Recombinant Hypoallergenic Cat Allergy Vaccines. Allergy . Published online April 3, 2025. doi.org/10.1111/all.16542Kind regards, Elliott Hurwitz, MDKeywordsallergenallergen-specific immunotherapy (AIT)allergyBlocking antibodyIdiotypic antibodymolecular allergy vaccine

Acute exercise has been shown to positively impact working memory, yet the influence of cognitive demand exercise on these effects remains unclear. The study investigated the effects of acute exercise with high (HE) and low (LE) cognitive demand exercise on working memory performance and neurophysiological indices in young adults. A three-arm crossover randomized design in which 31 participants completed three 20-minute conditions: HE, LE, and active control (AC). Working memory was assessed via a 2-back task, using mean reaction time (mean-RT), standard deviation reaction time (SD-RT), and accuracy along with the measurement of P300 (amplitude and latency) before and after each condition. Results showed significant reductions in 2-back reaction time following both HE (mean-RT: p = .005, d = -0.55; SD-RT: p = .008, d = -0.51) and LE (mean-RT: p = .030, d = -0.41; SD-RT: p = .030, d = -0.42) compared to AC, with no significant differences between HE and LE. Notably, HE showed larger effects than LE, suggesting a potential advantage of higher cognitive demand. No changes were found in accuracy or P300 amplitude and latency across conditions. These findings suggested that 20 minutes of moderate-to-vigorous interval training enhanced working memory performance regardless of cognitive demand. However, the absence of corresponding P300 amplitude and latency changes indicated that the underlying neural mechanisms remain to be clarified.

Background: Imipenem-cilastatin is one of the commonly used antibacterial drugs for the treatment of severe infections in clinical practice. Since its launch, apart from seizures, other neuropsychiatric adverse events(AEs) have been rarely reported. This study comprehensively evaluates the neuropsychiatric AEs of imipenem-cilastatin in the real world through the FDA Adverse Event Reporting System (FAERS) database. Methods: This study searched the FAERS database for neuropsychiatric AEs related to imipenem-cilastatin from the first quarter of 2004 to the fourth quarter of 2024. We evaluated the association between imipenem-cilastatin and neuropsychiatric AEs through various disproportionality analysis methods. Results: We obtained a total of 1822 patients and 2899 reports of neuropsychiatric AEs related to imipenem-cilastatin from the data. The common AEs included epilepsy, seizure, delirium, dysphoria, and mental disorder. Additionally, we identified some unexpected signals such as uraemic encephalopathy, persecutory delusion, logorrhoea, and staring. The median age of patients with overall neuropsychiatric AEs with imipenem-cilastatin was 70 years, and the median time to onset of overall neuropsychiatric AEs with imipenem-cilastatin was 2 days, with median onset times of 2 days for seizures and 3 days for delirium-related AEs,no statistical differences in median onset times were found in gender and age subgroups. Conclusion: This study provides real-world insights into the use of imipenem-cilastatin, which is an important supplement to clinical application. Elderly patients should be closely monitored for neuropsychiatric AEs during early use of imipenem-cilastatin

I propose an advanced cyber-attack, the Ordinal Metamorphic Evasion (OME), exploiting transfinite state evolutions within the recently introduced Alpay Algebra framework [1]. This attack leverages Alpay Algebra's operators (⊕, φ, χ, Ξ, ∇) to hide malicious payloads at an ordinal-limit state, remaining undetectable through all finite transformations. I then formalize defenses by modeling the system in the same algebraic category and deriving invariants using Alpay's axioms. Using the fixed-point and monotonicity theorems of Alpay Algebra, I prove that the OME attack inevitably produces a detectable fixed point under realistic evaluation conditions, enabling a universal categorical defense. My results demonstrate Alpay Algebra's universality: every state transition, morphism, and fixed-point argument is internal to the algebraic system. This research provides the first Alpay-based threat model and a rigorous mathematical proof of security, aimed to inform both academia and industry on the foundational strength of Alpay Algebra in cybersecurity threat modeling and protection.