Abstract: Understanding customer purchasing behavior is essential for businesses to optimize marketing strategies and improve customer retention. This study employs machine learningbased clustering techniques to segment customers based on transactional data. By leveraging Recency, Frequency, and Monetary (RFM) analysis, the study compares multiple clustering algorithms to identify distinct customer groups. Experimental results demonstrate that the proposed approach effectively categorizes customers, enabling data-driven decision-making for targeted marketing. These findings highlight the potential of unsupervised learning methods in enhancing business intelligence and customer relationship management.

Abstract: This study investigated the degradation and contamination behavior of 41 real-world operational Marine Diesel Fuel samples, conforming to ELOT ISO 8217:2024 (DFA category). Samples were sourced directly from land-based supply tanks. To assess fuel degradation, a comprehensive suite of parameters was evaluated, including fuel characteristics such as viscosity and density. Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) was employed for elemental analysis to determine the content of wear and other metallic contaminants. Elevated concentrations of various metals were detected, suggesting potential leaching from system components within the storage infrastructure. Notable elemental concentrations included Iron (Fe up to 1.38 mg/kg), Copper (Cu up to 0.401 mg/kg), Lead (Pb up to 0.358 mg/kg), Aluminum (Al up to 0.218 mg/kg), Zinc (Zn up to 1.331 mg/kg), Nickel (Ni up to 0.172 mg/kg), Calcium (Ca up to 8.054 mg/kg), Sodium (Na up to 0.332 mg/kg), Phosphorous (P up to 0.602 mg/kg), and Silicon (Si up to 8.249 mg/kg). The presence of these contaminants in marine fuels, if bunkered, poses a significant risk of impaired engine performance, including injector fouling and ash formation. Critically, this study suggests that FAME content is not the primary driver of the observed oxidation and subsequent metallic degradation.

Abstract: We present a geometric framework for understanding the parameter structure of the StandardModel. Starting from the Grassmannian manifold Gr(k,N)—the space of k-dimensional subspaces inan N-dimensional vector space—we demonstrate that two fundamental observables, the weak mixingangle and the gauge-gravity hierarchy, uniquely select the integers (k, n) = (3, 13) with N = k+n =16. This selection is not approximate but exact: no other integer pair satisfies both constraintssimultaneously within experimental tolerances. We provide complete mathematical proofs of globaluniqueness, analyze the robustness of the selection across tolerance variations, and show that theresulting Grassmannian dimension D = k(N −k) = 39 determines the hierarchy between the Planckand electroweak scales. The framework makes over forty parameter-free predictions for StandardModel quantities, with a mean accuracy of 0.1%. We discuss the physical interpretation, connectionsto gauge theory, and implications for the hierarchy problem.

Abstract: This study revisits the Efficient Markets Hypothesis by employing a GRU-D neural network to predict stock return distributions across global equity markets, accounting for missing and irregular data. It examines whether stock returns exhibit statistically significant departures from purely random behavior. By combining price, technical and fundamental inputs, it tests both weak and semi-strong market efficiency. We implement the GRU-D model on a global dataset of stock returns, where daily returns are classified into quartiles. Model performance is assessed using Micro-Average Area Under the Curve (AUC) and Relative Classifier Information (RCI). Robustness checks include sub-sample tests across countries and sectors, an examination of the Covid-19 sub-period, and a price-memory persistence analysis. The results reveal that the GRU-D model achieves a ranking accuracy of approximately 75% when classifying returns, with a statistical significance at the 99.99% confidence level, and exhibits modest but robust deviations from strict market efficiency. These deviations persist for up to 200 trading days. Notably, the findings indicate that the GRU-D model is more robust during the Covid-19 period. These findings are consistent with the Adaptive Markets Hypothesis and underscore the relevance of machine-learning frameworks, particularly those designed for imperfect data environments, for identifying time-varying departures from strict market efficiency in global equity markets.

Abstract: The growing demand for cost-efficient digital transactions has driven the need for scalable and low-cost payment solutions. Traditional blockchain-based transactions suffer from high fees and slow processing times, making decentralized off-chain payment networks a promising alternative. In this paper, we propose SpeedyMurmurs, an AI-enhanced decentralized routing algorithm that significantly reduces payment processing costs and transaction delays. Our approach optimizes payment routing efficiency through embedding-based path discovery, reducing routing overhead by up to two orders of magnitude and cutting transaction processing times by over 50 percent compared to existing blockchain networks. By leveraging machine learning-driven transaction optimization, our system dynamically selects the most cost-effective paths for digital payments while maintaining user privacy and security. Experimental results demonstrate that SpeedyMurmurs reduces transaction fees and computational costs, making decentralized payment systems more financially viable. This research highlights the role of AI-powered routing strategies in minimizing costs and improving efficiency in modern payment networks.

Abstract:

Background: The outer membrane impermeability of multidrug-resistant (MDR) Gram-negative bacteria, particularly Escherichia coli, remains a primary barrier to antibiotic efficacy. Overcoming this challenge requires strategies that transcend traditional lipophilicity-driven drug design. Methods: This study presents the rational design and in silico validation of ‘Armored-Trojan-1,’ a novel siderophore–beta-lactam conjugate engineered to exploit the bacterial iron-acquisition pathway. Using a generative in silico approach, we designed a high-affinity catechol siderophore with a beta-lactam warhead. To address the metabolic instability limiting previous "Trojan Horse" candidates, we introduced a sterically hindered alpha-methyl ether linker designed to prevent premature periplasmic hydrolysis. Results: Physicochemical profiling indicates that while the candidate exceeds standard passive diffusion thresholds (TPSA > 190 Ų), its polarity is optimized for active transport via the FhuA receptor. A steric and dimensional compatibility audit demonstrates that the molecule fits within the transporter channel without occlusion. Furthermore, structure-based database analysis validates the candidate as a previously undescribed chemical entity. Conclusion: These findings provide a validated computational blueprint for the development of sterically stabilized conjugates, offering a viable strategy to bypass intrinsic resistance mechanisms in Gram-negative pathogens.

Abstract: Fire resistance is a critical aspect of passive fire protection, particularly in door systems that must maintain integrity under extreme conditions. This paper presents the thermal and structural performance of a single-leaf sandwich fire door, with the goal of improving its fire resistance through numerical optimization. An initial numerical assessment identified the door frame as the thermally weakest component, guiding the subsequent optimization process. Then, a one-way coupled transient thermal–structural Finite Element Method (FEM) analysis was performed using ANSYS Mechanical to evaluate the influence of frame material, frame geometry, and insulation type and placement on the door–frame assembly when exposed to fire. Results show that the frame material plays a decisive role where aluminum alloys performed poorly, whereas wooden frames significantly reduced temperatures in both the door and frame by approximately 55% relative to the original configuration. Additional improvements were achieved by increasing frame thickness and placing rock wool within the thermal break, resulting in temperature reductions of 58.3% in the door and 57.3% in the frame. However, these thermal improvements had limited impact on structural deformation, which remained nearly unchanged.

Abstract: Correlation of in vivo morphological and functional changes in the degenerating retina in a large animal model of retinitis pigmentosa (RP) has not been characterized longitudinally. Herein, spectral domain optical coherence tomography (SD-OCT) was used to monitor the dynamic morphological changes in the Pro23His rhodopsin transgenic (TgP23H) pig model of RP and was correlated with electroretinography (ERG) in the rapid, early phase of photoreceptor degenera-tion. TgP23H and wild type (Wt) hybrid pig littermates at the ages of P30, P60, and P90 were studied. The thickness of different retinal layers was quantified using SD-OCT and compared with histology. Retinal function was evaluated with ERG at corresponding time points. In the Wt pig, retinal morphology on SD-OCT was consistent throughout the observation period. In the TgP23H pig, the retinal thickness decreased significantly from P30 to P90. Moreover, the relative intensity of the ellipsoid zone (EZ) progressively decreased, while the intensity of the interdigita-tion zone-retinal pigment epithelium (IZ-RPE) progressively increased during this period. Mor-phological changes in SD-OCT corresponded with histology, as well as the progressively de-creased amplitude of the ERG photopic a- and b-waves in TgP23H pigs. Thus, retinal degenera-tion can be quantified using SD-OCT by measuring retinal thickness and the intensity of the EZ and IZ-RPE bands in the TgP23H pig. The SD-OCT results correspond with the histologic and ERG assessments of retinal degeneration. These data provide a foundation for future preclinical studies investigating potential new therapeutic strategies in a large animal model of retinitis pigmentosa.

Abstract: Social connection has been consistently associated with slower decline in cognitive and functional abilities, reduced behavioral and psychological symptoms of dementia (BPSD), and delayed institutionalization in people with dementia, as demonstrated by multiple longitudinal and epidemiological studies (Crooks et al., 2008; Röhr et al., 2020). However, conventional interventions that promote social participation and interaction are often designed on the assumption that individuals retain sufficient motivation, attention, executive function, and interpersonal coordination skills—capacities that are typically compromised in people with dementia, making implementation and continuation of such interventions difficult. Consequently, interventions that are theoretically effective often fail to function adequately in real-world clinical and care settings. This paper aims to reconceptualize social connection interventions for people with dementia by systematically distinguishing between “core active components that drive therapeutic effects” and “ancillary elements that impose excessive cognitive or operational burdens.” Based on an integration of observational studies, intervention research, and neuropsychiatric evidence, we propose an implementation-adapted model comprising three minimal components: (1) brief daily face-to-face interactions lasting approximately 5–10 minutes; (2) support to ensure at least weekly contact with someone outside the household; and (3) person-centered communication that emphasizes name usage, eye contact, and affirmative responses. These components do not directly modify the neurodegenerative pathology of dementia. However, they hold essential value in mitigating “accelerating factors” of disease progression—such as social isolation, apathy, depression, and BPSD (Kitwood, 1997; Tible et al., 2017). As a low-cost, non-pharmacological intervention that prioritizes feasibility, safety, and sustainability, this model is considered to have high practical utility for both clinical practice and long-term care policy.

Abstract:

Vector-borne plant diseases represent a complex phytosanitary challenge. Mathematical models serve as a key tool for analyzing integrated management strategies, enabling more effective control of these pests. A dynamical system is presented to model the infection of Tahiti lime (Citrus x latifolia) with the bacterium Candidatus Liberibacter asiaticus (CLas), transmitted mainly by infected adults of the psyllid D. citri, which causes the citrus greening—Huanglongbing (HLB). The proposed model is based on the D. citri-HLB pathosystem, basic interactions between bacteria, vector psyllid hosts, trees and a vector parasitoid wasp. It consists of nine ordinary differential equations that model the rates of change of the numbers of infected and uninfected vector nymphs and adult females, of infected and uninfected trees of high and low productivity, and of the parasitoid Tamarixia radiata, recommended for the biological control of D. citri. The parameters of the model are identified from extant literature or otherwise estimated, in both cases being adjusted to Colombian conditions. A mathematical analysis of a simplified model is carried out, and simulations are conducted to demonstrate the effect of different types of control easures.

Abstract: Vehicular Ad-hoc Networks (VANETs) face critical challenges regarding intermittent connectivity and latency due to high node mobility, often resulting in a performance trade-off between reactive and proactive routing paradigms. This study aims to resolve these inherent limitations and ensure reliable communication in volatile environments. We propose a novel context-aware framework, the Dynamic Hybrid Routing Protocol (DHRP), which integrates Ad hoc On-Demand Distance Vector (AODV) and Optimized Link State Routing (OLSR). Distinguished by a predictive multi-criteria switching logic and a hysteresis-based stability mechanism, the proposed method employs a synergistic cross-layer framework that adapts transmission power and routing strategy in real time. Validated through extensive simulations using NS-3 and SUMO, experimental results demonstrate that the protocol outperforms traditional baselines and contemporary benchmarks across all key metrics. Specifically, the system maintains a Packet Delivery Ratio (PDR) exceeding 90%, ensures end-to-end delays remain under the safety-critical 40 ms threshold, and achieves energy savings of up to 60%. In conclusion, DHRP successfully resolves the routing performance dichotomy, providing a scalable, energy-efficient foundation for next-generation Intelligent Transportation Systems (ITS) in which reliable safety messaging is paramount.

Abstract: The field of agentic artificial intelligence is transitioning from reasoning-centric architectures toward systems explicitly designed for reliability under uncertainty. Current agent frameworks, while effective in controlled environments, exhibit cognitive rigidity—an inability to proactively correct planning trajectories when confronted with unexpected faults. This paper introduces Adapt-Plan, a foundational hybrid architecture that reformulates planning as a control-theoretic process by elevating the Planning Efficiency Index (PEI) from a post-hoc evaluation metric to a real-time control signal. Through dual-mode planning (strategic and tactical) and Extended Dynamic Memory (EDM) for selective experience consolidation, Adapt-Plan enables agents to detect behavioral drift early and initiate corrective adaptation before catastrophic degradation occurs. Controlled validation across 150 episodes demonstrates PEI=0.91 ± 0.06 and FRR=78% ± 4.2% (95% CI [74%, 82%], p < 0.001, Cohen’s d = 2.18 vs. ReAct), establishing the algorithmic viability of metric-driven adaptation. Comprehensive ablation studies isolate component contributions, revealing that PEI-guided control accounts for 31% of performance gains. These architectural principles were subsequently validated at scale through rigorous certification frameworks, confirming that PEI-driven control generalizes to deployment-grade reliability when augmented with safety protocols. This work establishes the conceptual foundation for reliable agentic AI through the tight integration of architecture, metrics, and control.

Abstract: Background: Cardiac rehabilitation (CR) is a key component of secondary prevention after acute coronary events, coronary and valve interventions, and device implantation, yet participation and longterm adherence remain suboptimal. Digital technologies offer the potential to extend CR beyond the centrebased model and to support more flexible, patientcentred care. Methods: This narrative review synthesizes original clinical studies published between 2005 and 2025 that evaluated the use of digital technologies as an integral part of CR in adults after myocardial infarction, revascularization, valve procedures or implantation of cardiac devices. Interventions were grouped into four categories: mobile health (mHealth) and telerehabilitation, virtual reality (VR) and exergaming, virtual education platforms, and other multicomponent digital CR solutions. Only original studies with clinical, functional, or patientreported outcomes were included. Results: Twenty-one studies on the categories mentioned above met the eligibility criteria. mHealthenabled homebased or hybrid CR programs consistently achieved improvements in functional capacity and physical activity that were broadly comparable to centrebased CR, with generally high adherence. VR and exergaming interventions were feasible and safe, produced at least similar functional gains, and showed more consistent benefits as far as anxiety levels and engagement levels. Virtual education platforms delivered knowledge and produced behaviour change similar to traditional education and, in some studies, supported better control of blood pressure and lipids. Comprehensive digital CR platforms improved riskfactor profiles and quality of life to a degree comparable with facetoface CR. Conclusions: Digital technologies can credibly support core objectives of CR in highrisk patients and expand access, but must be implemented as a complement to, rather than a replacement for, multidisciplinary, patientcentred rehabilitation.

Abstract:

Background: Accurate prognostic assessment remains crucial in metastatic renal cell carcinoma (mRCC), especially as treatment options have expanded beyond vascular endothelial growth factor (VEGF)–targeted therapies to include immune checkpoint inhibitors (ICIs) and ICI–TKI combinations. The widely used IMDC classification shows important limitations in the modern therapeutic era, highlighting the need for complementary prognostic tools. In this context, the Meet-URO and CANLPH scores—incorporating clinical, inflammatory, and nutritional markers have emerged as promising alternatives. Objective: To evaluate and compare the prognostic performance of the Meet-URO and CANLPH scoring systems in a real-world mRCC cohort predominantly treated with first-line tyrosine kinase inhibitor (TKI) monotherapy due to limited access to ICI-based combinations. Methods: This retrospective single-center study included 112 patients with mRCC. The Meet-URO score was calculated for all patients, while the CANLPH score was assessed in 56 patients with complete laboratory data. CAR, NLR, and PHR were computed using baseline pre-treatment measurements. Overall survival (OS) and progression-free survival (PFS) the latter defined exclusively for first-line therapy—were estimated using the Kaplan–Meier method. Correlations between inflammatory markers and survival outcomes were analyzed using Spearman’s rho. Results: Meet-URO demonstrated clear prognostic stratification across all five categories, with the most favorable outcomes in score group 2 and progressively poorer OS and PFS in higher-risk groups. CANLPH also showed meaningful survival discrimination, with the highest inflammatory group (score 3) exhibiting markedly reduced OS and PFS. CAR was the strongest individual predictor of survival, while NLR and PHR showed weaker associations. Conclusion: Both Meet-URO and CANLPH provide strong, complementary prognostic information in mRCC, even in a cohort largely treated with TKI monotherapy. Their integration into routine risk assessment may enhance clinical decision-making, particularly in resource-limited settings.

Abstract: L-asparaginase (L-Aspase) enzyme has found applications in medicine for treatment of various cancers. Herein, we report single-molecule study of thermal denaturation of L-Aspase within 25°C to 60°C temperature range by atomic force microscopy (AFM) and by single-molecule sensing with a (solid state nanopore)-based electrical detector (SSNPED). AFM has allowed us to reveal a thermally induced changes in aggregation state of L-Aspase and in its adsorbability on mica. At the same time, the configuration of the enzyme’s globule spatial conformation has been found to alter according to data obtained with the SSNPED. Our results reported open up opportunities for further development of anti-cancer drugs.

Abstract: Background/Objectives: Nutritional risk screening is critical in the management of gynaecologic oncology (GO) surgical patients. Malnutrition is prevalent in this population and is associated with poorer surgical outcomes, including increased morbidity, prolonged hospital stays, and reduced survival rates. Nevertheless, the optimal nutritional screening tools for this patient group remain undefined. Methods: We conducted a narrative review to critically appraise commonly used nutritional screening and assessment tools in surgical GO patients. To highlight practical challenges in accurately identifying at-risk individuals, we incorporated findings from our recent clinical audit. Results: Several nutritional screening and assessment tools were identified. The results varied considerably between tools. The presence of ascites and rapid deterioration in oral intake were frequently overlooked, leading to under-recognition of malnutrition. These issues were corroborated by our audit findings. Emerging strategies including determining body composition from routine preoperative CT scans show promise. Conclusions: Accurate nutritional assessment is imperative to improve surgical outcomes in surgical GO patients. As currently no gold standard currently exists for this population, bespoke approaches to address disease-specific nutritional considerations are urgently needed to identify those at risk and allow for timely nutritional interventions. Integrating CT-based body composition analysis can provide an objective solution, thus requiring further investigation.

Abstract:

Background/Objectives: IgA nephropathy (IgAN) is the most common primary glomerulopathy worldwide; it is characterized by a complex pathophysiology involving several inflammatory pathways. The Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway may be critical in this process. This study aimed to investigate the role of this pathway in IgAN and examine related tissue inflammatory markers. Methods: We analyzed 63 biopsy-confirmed patients with IgAN and performed immunohistochemical analysis on renal samples. A panel of antibodies targeting the JAK/STAT pathway, including JAK2, JAK3, p-STAT, STAT3, and MAPK/ERK, was used for this analysis. Six kidney tumor border samples were used as controls. Additionally, CD68 staining was used to evaluate tissue inflammation in the kidney biopsies. Results: Patients with IgAN showed a significantly higher cellular density of JAK3 staining at the glomerular level compared to controls, indicating JAK3 activation (p < 0.0002). Nevertheless, the correlation between JAK3 positivity in glomeruli and clinical parameters such as the initial and final estimated glomerular filtration rate (eGFR) and proteinuria was not statistically significant. Identical results were obtained with CD68+ macrophage counts in the glomerular compartment, which did not show any correlation with clinical parameters, while CD68+ tubulointerstitial staining demonstrated a significant correlation with both initial (p = 0.002) and final eGFRs (p = 0.0014), proteinuria (p = 0.010), and interstitial fibrosis (p < 0.001), as well as with renal disease progression (p = 0.005). Conclusions: Patients with IgAN exhibited activation of the JAK/STAT pathway, in contrast to controls. Macrophage CD68 staining in the tubulointerstitial area increased and was associated with clinical and laboratory parameters such as eGFR and proteinuria. Additionally, MEST-C histological parameters, such as segmental glomerulosclerosis (S0/S1), tubular atrophy/interstitial fibrosis (T0/T1/T2), and crescents (C0/C1/C2), were associated with a higher number of CD68+ cells.

Abstract:

Accurately estimating actual evapotranspiration (ETa) is essential for sustainable water management, particularly in semi-arid regions. Although the SAFER algorithm provides a practical remote sensing-based approach, its sensitivity to parameter settings and reduced performance during dry periods limit its reliability. This study develops four parametric ETa models—two linear (LM-I, LM-II) and two nonlinear (NLM-I, NLM-II)—and recalibrates SAFER coefficients via a simulation/optimization (S/O) approach. Models were evaluated using Landsat-8 data (LST, NDVI, αₛ) and reference evapotranspiration (ETo), and compared with machine learning methods: Random Forest (RF), Bagged Trees (BT), Support Vector Machines (SVM), and Generalized Additive Models (GAM). Results indicate that nonlinear models better capture the physical behavior of ET processes and outperform linear models across key metrics. In particular, the NLM-II model achieved R² = 0.8295 and RMSE = 0.4913 on the test set, surpassing SAFER (R² = 0.8195, RMSE ≈ 0.5713), LM-II, and the best soft computing model, BT (R² = 0.8137, RMSE = 0.5084). Its physically grounded structure ensures stable, interpretable predictions that accurately reflect water–energy interactions and seasonal dynamics. These findings demonstrate that compact, physically based nonlinear parametric models provide a robust, operationally practical solution for ETa estimation under sparse Landsat-based datasets, outperforming both linear and black-box machine learning approaches.

Abstract: Estimating surface soil moisture in semi-arid regions is challenging due to its high spatial and temporal variability, the scarcity of in-situ measurements, and the limitations of optical sensors in the presence of cloud cover and vegetation cover. Synthetic Aperture Radar (SAR) sensors, such as Sentinel-1, overcome these constraints by operating in the microwave domain and providing high-resolution data regardless of atmospheric conditions or daylight availability. This enables the application of inverse semi-empirical models, notably the Hallikainen model for the soil dielectric constant and the Dubois model for backscattering. This study proposes an integrated methodology applied to the municipality of Villaconejos (Madrid, Spain) over the period 2015–2025. The approach was initially calibrated on a pilot plot near Balcón del Tajo using field measurements of soil moisture and soil texture data (sand and clay content) obtained from the SoilGrids platform. Starting from Sentinel-1 VV and VH backscatter coefficients, the combined Hallikainen–Dubois model is inverted through an iterative search over a range of volumetric soil moisture values (0.02–0.45 m* m*) and surface roughness values (0.85–2 cm), selecting the parameter pair that minimises the difference between modelled and observed backscatter. The calibrated methodology is then extrapolated across the entire municipality of Villaconejos using Empirical Bayesian Kriging Regression Prediction (EBK-RP), incorporating topographic covariates (digital elevation model, slope, aspect), hydrological covariates (Topographic Wetness Index, TWI), and vegetation covariates (NDVI). The results include annual and seasonal maps of near-surface volumetric soil moisture (0–5 cm depth) at 10 m resolution and, after a geostatistical downscaling procedure, at 2 m resolution. Additional outputs comprise analyses of temporal variations between wet and dry periods and spatial patterns related to land use and topography. The developed methodology provides continuous, high-resolution, operational, and low-cost soil moisture estimates, representing a valuable tool for water resource management and agro-environmental monitoring in semi-arid regions.

Abstract:

The integration of artificial intelligence (AI) in precision agriculture marks a transformative step toward sustainable, efficient, and data-driven farming practices. By merging AI with predictive analytics and autonomous monitoring systems, agriculture is empowered to achieve higher crop yields and maintain robust soil health. AI-driven models process vast datasets from sensors, drones, and IoT devices to predict crop performance, recommend targeted interventions, and enable real-time monitoring of field conditions. This synergy not only allows for early detection of threats such as pests or nutrient deficiencies but also ensures optimized resource utilization, reducing environmental impact. The adoption of these intelligent systems paves the way for a resilient agricultural landscape that can adapt to the challenges posed by climate variability and the growing global food demand, ultimately fostering productivity and long-term ecological sustainability.