Abstract: This study develops a regime-switching credit-risk forecasting model for consumer-finance portfolios that dynamically adapts to changing macroeconomic conditions. Using monthly data from 2016–2024 covering 3.8 million loan accounts and 27 macro indicators, the model identifies two latent economic regimes—expansion and stress—via a Markov-switching structure. A hybrid system integrating gradient boosting with regime-specific probability calibration is used to predict 90-day delinquency. Results show that incorporating regime states reduces the mean absolute forecasting error by 22.7% compared with non-regime models, and improves early-warning lead time by 3.4 months on average. Stress testing under simulated recession conditions indicates a potential 31.5% increase in portfolio-wide default probability. The findings demonstrate the effectiveness of regime-aware models for credit-risk management in volatile environments.

Abstract: Today, Artificial Intelligence (AI) is rapidly transforming digitalstorytelling through advances in text generation, multimodalsynthesis, and interactive narrative systems. Large LanguageModels (LLMs), vision-language models, and generative mediamodels make it possible for the creators to design adaptivemultimedia content stories, images, and auditory environmentsthat can be created with less manual work. This paper suggests aconceptual framework for understanding practicing AI-enabledstorytelling as human-AI collaborative production. Instead ofdiscussing an actual implemented model, the paper synthesizesexisting research in AI, narrative theory, and digital mediato introduce the AI-Assisted Storytelling Model (AASM) asan analytical and organizational framework. The paper talksabout narrative aspects, multimodal alignment, Interactivity,applications, and ethical issues to be supported/reviewed futureempirical and creative research.

Abstract:

Artificial intelligence (AI), particularly generative AI, is rapidly reshaping medical education worldwide. While AI-enabled tools offer significant opportunities for personalized learning, feedback automation, and clinical reasoning support, they simultaneously challenge foundational principles of assessment integrity and professional conduct. Traditional assessment models—largely predicated on individual authorship, knowledge recall, and observable performance are increasingly strained by AI systems capable of generating sophisticated responses, analyses, and clinical narratives. This disruption has prompted urgent reconsideration of what constitutes academic honesty, valid assessment, and professional identity formation in contemporary medical training. This article critically examines the intersection of AI, assessment integrity, and professionalism in medical education from a global perspective, with particular attention to the experiences and emerging lessons from the Gulf Cooperation Council (GCC). The GCC provides a distinctive context characterized by rapid digital transformation, centralized accreditation and licensing systems, high-stakes assessments, and strong sociocultural norms governing professional behavior. These features make the region an instructive case for understanding how medical education systems respond to AI-driven challenges at scale. Drawing on international literature, policy documents, and regional practices, this paper argues that AI should be understood not merely as a technological tool but as a normative disruptor that compels a re-examination of assessment validity, ethical responsibility, and professional identity. The article proposes a shift from reactive prohibition toward principled integration of AI within assessment and professionalism frameworks. It concludes by outlining future-oriented recommendations for educators, institutions, and regulators aimed at preserving trust, fairness, and professional standards in an AI-augmented educational landscape.

Abstract: The large digital archives of the American Physical Society (APS) offer an opportunity to quantitatively analyze the structure and evolution of scientific communication. In this paper, we perform a comparative analysis of the language used in eight APS journals (Phys. Rev. A, B, C, D, E, Lett., X, Rev. Mod. Phys.) using methods from statistical linguistics. We study word rank distributions (from monograms to hexagrams), finding that they are consistent with Zipf’s law. We also analyze rank diversity over time, which follows a characteristic sigmoid shape. To quantify the linguistic similarity between journals, we use the rank-biased overlap (RBO) distance, comparing the journals not only to each other, but also to corpora from Google Books and Twitter. This analysis reveals that the most significant differences emerge when focusing on content words rather than the full vocabulary. By identifying the unique and common content words for each specialized journal, we develop an article classifier that predicts a paper’s journal of origin based on its unique word distribution. This classifier uses a proposed “importance factor” to weigh the significance of each word. Finally, we analyze the frequency of mention of prominent physicists and compare it to their cultural recognitions ranked in the Pantheon dataset, finding a low correlation that highlights the context-dependent nature of scientific fame. These results demonstrate that scientific language itself can serve as a quantitative window into the organization and evolution of science.

Abstract: Manual assembly of multi-pin cable harnesses remains vulnerable to miswiring when conductors are visually indistinguishable. This paper presents an industrial case study of a quick-connect harness composed of two connectors (receptacle-type and pin-type) linked by 16 black conductors (2.5 mm²; 200 mm length), where the dominant failure mode is a two-wire swap that breaks correct pin-to-pin mapping and may cause downstream equipment damage. In the baseline state, end-of-line verification relied on visual inspection only (1 min/unit), resulting in an internal nonconformity rate of 4% (repairable). To achieve the operational goal of zero defects (zero escapes), we propose and integrate an electronic pin-to-pin continuity and mapping fixture as a deterministic End-of-Line (EOL) quality gate implementing poka-yoke logic (“no PASS—no shipment”) and enabling structured traceability records. Using a before–after workload model that includes mandatory retest after rework, the fixture reduces test time to 0.33 min/unit. For a monthly volume of 1500 units, total quality workload (test + rework + retest) decreases from 31 h/month to 13.58 h/month, releasing 17.42 h/month. Global quality productivity increases from 48.39 units/h to 110.46 units/h (+128%). The proposed architecture couples deterministic electrical verification with data logging aligned to digital thread and data-driven quality management concepts to sustain continuous improvement and prevent customer escapes.

Abstract: Student dropout remains a critical challenge for higher education institutions, with significant implications for resource allocation, academic planning, and institutional sustainability. This study applies machine learning techniques to predict student non-continuation and attrition, with the objective of supporting data-driven retention strategies. Using a publicly available higher education student dataset (4,424 records, 34 features, multi-class outcome), a structured analytical pipeline was implemented, incorporating Winsorization for outlier mitigation, SMOTE for class imbalance handling, and targeted feature engineering. Model performance was assessed using a 5-fold nested cross-validation framework. Four classifiers, Extra Trees, Random Forest, Gradient Boosting, and Logistic Regression, were trained on an optimized subset of 28 features. Among these, the Extra Trees model achieved the strongest performance, attaining a mean AUC of 0.96 (±0.0053) and an accuracy of 87.4% (±0.012). Model interpretability was enhanced through SHAP analysis, which identified cumulative approved academic units and tuition fee payment status as the most influential predictors of student outcomes. The findings underscore the value of early predictive analytics for informing proactive institutional interventions, particularly in academic monitoring and financial support, to strengthen student retention frameworks.

Abstract: Tight junction (TJ) proteins such as Junctional Adhesion Molecule-A (JAM-A), claudins, and occludin play increasingly recognised roles in cancer biology beyond their structural functions, influencing tumour proliferation, invasion, metastasis and therapy resistance. Understanding how these proteins modulate tumour progression in vivo requires models that are both physiologically relevant and ethically viable. The Chick Chorioallantoic Membrane (CAM) Xenograft model has emerged as a powerful and cost-effective in vivo system that aligns with the 3Rs (Replacement, Reduction, and Refinement), offering unique advantages such as vascular accessibility, rapid tumour growth kinetics and im-munotolerance. This review explores how the CAM model can be leveraged to study the mechanistic role of TJ proteins in tumour-stroma interactions, angiogenesis, extracellular matrix (ECM) remodelling and mechanotransduction, including the YAP/TAZ pathway. While limitations remain, particularly with respect to immune modelling and long-term studies, recent advances in imaging, genetic manipulation and integration of pa-tient-derived xenografts (PDXs) are expanding the model’s translational relevance. Standardising methodologies and embracing new molecular tools will further elevate the utility of this approach as a complementary platform to traditional rodent models, with significant promise for TJ-focused cancer research and therapeutic innovation.

Abstract: Natural disasters disrupt maritime operations, yet their environmental consequences remain underexplored. This study quantifies CO₂ emission changes following the February 2023 İskenderun Bay earthquakes (Mw 7.7 and 7.6) using AIS-derived port visit data and graph neural network modeling. Analyzing 25,837 port visits across a 36-month period (January 2022–December 2024), we compared emissions during baseline (pre-earthquake), acute disruption (February–June 2023), and recovery phases. Results revealed a statistically significant 35.9% increase in per-visit CO₂ emissions during the acute phase (t = 11.79, p < .001, Cohen's d = 0.27), driven by extended port visit durations (from 77.87 to 105.82 hours). Counterfactual analysis estimated 27,574 tonnes of excess CO₂ emissions directly attributable to earthquake disruption. Network analysis showed 23.8% reduction in edge density during the acute phase. The Temporal Graph Attention Network model achieved R² = 0.985 (baseline) and R² = 0.997 (recovery) in predicting emission patterns, while acute phase showed predictability collapse (R² = −1.591). These findings demonstrate that seismic events generate significant environmental externalities beyond immediate physical damage, with implications for disaster preparedness, port resilience planning, and maritime emission accounting under frameworks such as the EU MRV Regulation.

Abstract: The paper states that biogas plants are of particular importance in the development of renewable energy sources, and their efficiency is largely determined by the accuracy and reliability of parameter measurements during the production process. Sensors that determine temperature, pressure, pH, humidity, methane (CH₄) and hydrogen sulfide (H₂S) concentrations, gas flow, and oxidation-reduction potential (ORP) form the basis of the monitoring system. However, during operation, they are affected by nonlinear dependence, noise, drift, and errors that reduce the reliability of measurements. To solve this problem, mathematical modeling and sensor optimization methods are proposed. The study proposes a mathematical model that describes the correlations between the physicochemical characteristics of the environment and the output signals of the sensors. Based on this model, an analysis of the sensitivity of the measurement channels was carried out, critical areas where accuracy is significantly reduced were identified, and methods for compensating for errors were proposed. To improve the reliability of the results, intelligent data processing was used, including artificial neural networks, which allow adaptive adjustment of output data and calibration in real-time monitoring mode. The proposed approach improves measurement accuracy and the stability of the sensor system to external influences, which is also of practical importance for monitoring and controlling biogas plants. A mathematical model was proposed that takes into account the physicochemical dependence on environmental parameters (temperature, pressure, pH, Ch₄ and H₂S concentrations, humidity, gas flow, and redox potential) and sensor response. Based on this, a sensitivity analysis of the measurements was performed to identify areas of maximum error. Intelligent data processing using artificial neural networks was used to compensate for systematic errors and sensor drifts, which allowed for real-time calibration and correction of sensor readings.

Abstract: Background: Child sexual exploitation and abuse have been an existing global phe-nomenon. However, with increasing dependency on digital transformation, mobile de-vices, and the internet, the emphasis has shifted to child online sexual exploitation and abuse (COSEA), leading to an exponential growth of perpetrators. A 2020 report indi-cated a 200% increase in child sex abuse forums that are linked to the internet. Existing literature has emphasized child protection challenges, online attacks, and using surveys and questionnaires to gather and draw inferences regarding grooming tactics and the-matic analysis. Social Issues, such as the lack of reporting platforms, limited sharing of threat information, cyber awareness, and social engagement and support, pose serious challenges for children, parents, and law enforcement. Several papers exist that have used the term Online Child Sexual Exploitation and Abuse (OCSEA). However, our paper considers Child Online Sexual Exploitation and Abuse (COSEA) as we explore and look at it from the challenges of a child going online and accessing the internet. Methods: The paper explores COSEA challenges and examines how perpetrators deploy MITRE Tactics, Techniques, and Procedures (TTPs) against victims to understand attack motives and establish potential attributions for cyber threat intelligence gathering and cyber profiling. The paper acknowledges existing research by considering the changing threat landscape and the evolving attack surface. It aims to contribute to the body of knowledge on adversarial TTPs and current trends, and to understand the threat actor’s mindset and motives. Results: The results demonstrate that analyzing TTPs facilitates the establishment of attributions and the determination of the adversary’s intents, motives, opportunities, and methods. The novelty contributions of this research are threefold. First, we explore existing challenges in online child abuse and exploitation by identifying and discussing what constitutes child abuse and exploitation, how COSEA manifests, and the attack methods used by perpetrators to exploit their victims. Secondly, we used the MITRE TTP and subjective judgment approach to identify the TTPs and determine how these factors make the child complicit. Finally, we discuss the strategies required to address the challenges and the stakeholder role in mitigating COSEA Conclusion: The paper has considered TTPs from a technical perspective to understand the perpetrator's motives. The paper considers factors that could influence the victim, such as money, societal norms, and deterrence, including education, laws, regulations, and recommendations for threat information-sharing platforms and collaborations among stakeholders.

Abstract: Extracellular vesicles released by Leishmania spp. (LEVs) are increasingly recognized as key mediators of parasite communication and host immune modulation. Lipids, although central to LEV biogenesis and function, remain understudied in the context of Leishmania pathogenicity. Here, we investigated the presence and distribution of lipid-rich structures in Leishmania (L.) amazonensis promastigotes and intracellular amastigotes using transmission electron microscopy (TEM), scanning electron microscopy (SEM), and fluorescence microscopy with Bodipy staining. Promastigotes at the stationary phase exhibited abundant vesicle accumulation in the flagellar pocket, Golgi-associated autophagic structures, and lipid body–like inclusions. Infected BALB/c peritoneal macrophages contained amastigotes within parasitophorous vacuoles, also displaying lipid-rich compartments. Bodipy staining confirmed the presence of neutral lipid bodies in promastigotes, supporting their involvement in LEV formation. These findings suggest that lipid-enriched LEVs contribute to membrane remodeling, intracellular survival, and host cell modulation. Our study provides experimental evidence supporting lipid-centered mechanisms in Leishmania LEV biology and highlights potential targets for therapeutic intervention.

Abstract: Backgound/Objective: Breast cancer (BC) management has traditionally relied on static clinicopathologic and immunohistochemical biomarkers (hormone receptor status, HER2 expression, and proliferative activity assessed at diagnosis. However, these biomarkers are typically evaluated at a single time point and may not reflect therapy-induced mo-lecular evolution. This study evaluates whether longitudinal molecular profiling before and after treatment better characterizes tumor dynamics and provides clinically ac-tionable insights into treatment response, resistance, and prognosis. Methods: Thirty-two patients with invasive breast carcinoma were analyzed using his-topathology, immunohistochemistry, tissue-based next-generation sequencing, and plasma circulating tumor DNA (ctDNA) analysis. Paired tumor tissue and plasma sam-ples were collected before and after treatment when available. Changes in biomarker expression, molecular subtype, and genomic alterations were assessed to characterize molecular plasticity under therapeutic pressure. Results: The cohort had a median age of 54 years (range 29–86), predominantly invasive ductal carcinoma (>85%) and high-grade disease. Hormone receptor–positive tumors accounted for 78.1%. Molecular subtypes were Luminal A (34.4%), Luminal B HER2− (40.6%), Luminal B HER2+ (6.3%), HER2-enriched (6.3%), and triple-negative breast cancer (12.5%). Initial tissue sequencing identified PI3K/AKT pathway alterations in 28.1% of cases. Post-treatment analyses revealed substantial molecular discordance, including progesterone receptor loss (33.3%), HER2 status changes (33.3%), and Ki67 variability (77.8%). Plasma ctDNA analysis was informative in 53.1% of patients and identified additional clinically relevant alterations, including FGFR1 amplification and BRCA1/2 variants not detected in tissue. Conclusion: BC molecular profiles are dynamic and frequently altered by therapy. Longitudinal molecular assessment reveals clinically actionable changes overlooked by static subtyping, supporting a dynamic model of molecular classification, highlighting the potential value of adaptive molecular subtyping to improve treatment stratification and resistance monitoring.

Abstract: Large language models (LLMs) have significantly advanced artificial intelligence, yet their high com-putational, energy, and privacy costs pose substantial challenges. In contrast, Small Language Models(SLMs), typically with fewer than 15 billion parameters, have emerged as efficient alternatives. Thissurvey provides a comprehensive analysis of the SLM landscape, tracing their evolution and examiningarchitectural innovations that enhance efficiency. A novel multi-axis taxonomy is introduced to classifySLMs by genesis, architecture, and optimization goals, offering a structured framework for this field.Performance benchmarks are reviewed exhaustively, demonstrating that while LLMs excel in broadknowledge tasks, state-of-the-art SLMs match or exceed larger models in domains such as mathematicalreasoning and code generation. The analysis concludes that the future of AI lies in hybrid ecosystems,where specialized SLMs manage most tasks locally, escalating complex queries to cloud-based LLMs.This tiered approach promises scalability, privacy, and the democratization of AI.

Abstract: Background and Clinical Significance: Diffuse Large B-Cell Lymphoma (DLBCL) is a morphologically and molecularly heterogeneous lymphoproliferative disorder that originates from a clonal B-cell ancestor, that can either arise de novo or transform from an indolent B-cell lymphoma. It represents 30% of adult lymphoma cases. Patients usually present with rapidly enlarging lymph nodes or mass(es) at a single site or multiple sites. Myofibroblastoma (MFB) is a benign mesenchymal tumor of the mammary stroma composed of fibroblasts and myofibroblasts. These entities do not often present concurrently. Case presentation: An 80-year-old man with a history of Diffuse Large B-Cell Lymphoma (DLBCL) IV-BS stage with a high-risk International Prognostic Index (IPI). The patient underwent treatment with R-CHOP regimen. In the post therapeutic evaluation, an 18F‐Fluorodeoxyglucose (18F-FDG) positron emission tomography with computed tomography (PET‐CT) scan revealed a nodular solid lesion with a faintly increase metabolic standardized uptake value (SUVmax) of 3 in the upper outer quadrant of his left breast. The patient has not presented symptoms or any complications since surgery (12 months) and remains in complete remission (CR). Conclusions: Given the potential diagnostic pitfall and therapeutic implications of MFB in the context of DLBCL, a conscientious evaluation by a multidisciplinary team (MDT) is highly recommended.

Abstract: The broad hierarchy of fermion masses in the Standard Model, spanning six orders of magnitude, is conventionally attributed to ad hoc Yukawa couplings. This work explores a possible geometric interpretation arising from a discrete $\mathbb{Z}_3$-graded vacuum structure, derived from a finite-dimensional (19D: $12+4+3$) Lie superalgebra with exact triality symmetry. Within this framework, the vacuum is organized into a two-layer lattice: a finite \textbf{Core Lattice} (44 vectors) that yields gauge unification with $\sin^2 \theta_W = 0.25$, and an infinite \textbf{Extended Lattice} ($\mathbb{Z}^3$) that may generate the fermion mass spectrum through a geometric seesaw-like relation $m \propto L^{-2}$. By associating specific integer lattice vectors with known fermions, we find that the resulting mass scales appear to align with those of the top quark, bottom quark, tau lepton, charm quark, muon, down quark, and electron. For instance, the electron mass is obtained within 4.6\% (0.488 MeV compared to the observed 0.511 MeV) across a $10^6$ range. Observed deviations for heavier quarks are qualitatively consistent with QCD renormalization effects, suggesting the lattice might correspond to bare parameters. These numerical coincidences, while intriguing, do not constitute a proof and may reflect mathematical serendipity. The approach offers a complementary geometric perspective that unifies forces and matter within a single algebraic setting, extending previous work on the Weinberg angle and other constants from the same structure.

Abstract: We demonstrate a new quantitative method to the sieve of Eratosthenes, which is an alternative to the sieve of Legendre. In this method, every element of a given set is sifted out once only; and therefore, this method is free of the Mobius function and of the parity barrier. Using this method, we prove that every sufficiently large even number is the sum of two primes, and that every even number is the difference of two primes in infinitely many ways.

Abstract: This study investigated whether ambient biogenic volatile organic compounds (bVOCs) scent profiles emitted by botanic glasshouse vegetation influence quantifiable human health and wellbeing outcomes, extending evidence previously obtained in clinical settings. Over 11 months in 2024 (January–December), human participant trials were conducted at the Oxford Botanic Garden to compare the physiological and psychological impacts of 30-minute exposures in five different vegetation-rich glasshouses, each characterised by a distinct and complex bVOCs profile, with those of a plant-free control room containing minimal bVOCs. Pre- and post-intervention assessments were conducted on 43 participants using the State-Trait Anxiety Inventory (STAI), heart-beat rate (beats per minute), and heart rate variability (HRV), a widely used index of autonomic regulation. Glasshouse exposure produced significant reductions in STAI anxiety scores and decreases in heart-beat rate, while HRV indices remained stable relative to the control condition. Distinct scent profiles in the glasshouses included volatiles previously associated with therapeutic effects in clinical settings, suggesting that such vegetated environments may deliver meaningful physiological and psychological benefits. Overall, these findings highlight the potential public health value of aromatic plant species and the importance of incorporating them into urban green space planning and policy.

Abstract: Housing inequalities remain a major challenge for contemporary urban governance, as they combine economic, social, spatial, and demographic dynamics that are difficult to capture through single indicators. This paper develops a data-driven assessment of housing inequalities in Portugal between 2015 and 2025, drawing on official national and European statistics and applying a Business Intelligence (BI) and urban analytics frame-work oriented towards policy monitoring. Data from Statistics Portugal and Eurostat are integrated through an analytical pipeline including automated extraction via public APIs, data enrichment, and visual analytics. The workflow follows a CRISP-DM–inspired structure, creating a set of normalized indicators to capture different dimensions of housing conditions. The results point to a structurally polarized housing market. Housing valuations increased across all regions, but at uneven rates, reinforcing territorial disparities rather than convergence. Metropolitan and tourism-oriented regions experienced faster appreciation and indirect effects, while year-over-year growth in completed dwellings slowed after 2021–2022, indicating an un-even supply response. Beyond its empirical findings, the primary contribution of this study lies in demonstrating how BI and data science methodologies can be operationalized to monitor housing inequalities using official statistics. The proposed framework is replicable and can be adapted to other territorial and policy contexts.

Abstract: HIV infection is a nowadays pathology that affect persons from all of the world. Nowadays are known different strategies related HIV infection. Pathogenesis of the HIV-infection and cancer is a great problem, with a complexity directions in research. More important in HIV infection to patients, is also to take into consideration others things, such as prevention, diagnosis, monitoring, treatment, including control measures. This previously mentioned, should be supported by statistical studies that report on restricted or extended geographical areas, to the level of social class and age. In our written text we try to describe from our opinion, strategies in HIV, infection, status knowing as a social and as a healthcare problem.

Abstract: Contemporary psychotherapy faces a profound paradox: while empirical evidence confirms the clinical significance of spirituality for resilience, established theoretical frameworks often lack a process-based mechanism to integrate this dimension beyond narrative content or cultural coping. This article addresses this gap by introducing a "spiritual self-pattern" into the Resonance-Inference Model (RIM), conceptualizing it not as a metaphysical construct, but as a fundamental neurocognitive imperative for biological self-organization.Drawing on the Free Energy Principle and spatiotemporal neuroscience, we define the spiritual self-pattern as the system’s highest-order regulator, instantiated within the brain’s slowest Intrinsic Neural Timescales (INTs). These deep temporal structures function as "long-term priors," integrating sensory and emotional data over vast durations—akin to the psychic "climate" that contextualizes the "weather" of momentary affect. We posit that this pattern maintains mental health by modulating the E-I balance (Excitatory-Inhibitory criticality) between predictive confidence (elation) and corrective sensitivity (anxiety) via top-down precision weighting.Within this framework, "meaning" is redefined as the successful integration of sensory chaos into these long-term temporal models, preserving the functional integrity of consciousness against existential entropy. We distinguish spiritual resonance—a state of "Bayesian binding" characterized by metastable synchronization—from spiritual dissonance, where pathological precision leads to the "frozen priors" seen in fanaticism or the systemic collapse of existential despair. By shifting the focus to mechanisms of temporal integration, this model offers a precise grammar for spiritually integrated psychotherapy, framing the therapist as a "criticality manager" dedicated to restoring the client's capacity for global self-organization.