Abstract: Standard cryptocurrency transaction cost models assume flat geometry and assign execution cost as a proportional fee. This paper tests whether replacing flat-fee models with a unified Riemannian execution cost framework improves out-of-sample prediction of realised execution costs. The framework identifies execution slippage as the geodesic arc length on the Fisher information manifold of a Markov-switching GARCH maximum-entropy model, augmented by a joint curvature-topological fragmentation alarm derived from the same parameter vector. Ablation confirms that each geometric component contributes uniquely: removing the geodesic increases mean squared prediction error by 2.9%, removing topological data analysis by 2.1%, and removing curvature by 1.5%. No subset matches the full framework. On five major cryptocurrency markets (BTC, ETH, XRP, LTC, BCH) over 2,253 daily observations, the integrated framework achieves the lowest prediction error on all five assets and is the sole model retained in the Model Confidence Set at the 10% significance level against six benchmarks, including Amihud, Kyle λ, and Almgren and Chriss. A joint curvature-topological alarm fires a median of two days before price-based circuit breaker thresholds across four crisis episodes, including the Terra collapse of May 2022 and the FTX bankruptcy of November 2022. The framework requires no additional data or free parameters beyond the upstream estimation pipeline.

Abstract: Diabetic peripheral neuropathy (DPN) remains a leading cause of disability in diabetes, yet current care is largely symptomatic. Increasing evidence places early dysfunction of the blood-nerve barrier (BNB)—a core element of the peripheral nerve neurovascular unit (PNVU)—at the intersection of metabolic stress and neuroinflammation. This review synthesizes a redox-centered model of BNB failure in DPN: (i) chronic hyperglycemia and dyslipidemia overwhelm endogenous antioxidant defenses, driving reactive oxygen species (ROS) imbalance; (ii) ROS-associated endothelial activation promotes endothelial-immune crosstalk, leukocyte recruitment, and macrophage polarization; and (iii) progressive loss of tight-junction and barrier homeostasis increases paracellular permeability and exposure of nerves to pro-inflammatory and neurotoxic mediators. We then evaluate incretin-based therapies—GLP-1 receptor agonists, DPP-4 inhibitors, and emerging multi-agonists—as potential modulators of PNVU/BNB stress. Beyond glucose and weight effects, these agents may dampen oxidative and inflammatory signaling, engage antioxidant pathways (e.g., Nrf2), and potentially support molecular determinants of BNB integrity via indirect metabolic unloading and possible GLP-1R-dependent vascular-immune actions. By reframing DPN as a neurovascular-immune disorder driven by redox imbalance, we highlight barrier-focused biomarkers and hypothesis-generating therapeutic opportunities that require clinical validation.

Abstract: The Warburg effect is a metabolic phenomenon observed in cancer cells and is characterized by aerobic glycolysis instead of mitochondrial oxidative phosphorylation as the primary mechanism of cellular energy generation. The exact benefit of such a metabolic switch is poorly understood, as aerobic glycolysis is thermodynamically more inefficient than mitochondrial oxidative phosphorylation. Here, we present a case of a 40-year-old male with advanced stage 4 hepatocellular carcinoma with chronically low glucose levels measured in the 20s to 40s and completely asymptomatic. Upon examination, findings of sympathetic hyperactivity in the setting of hypoglycemia were absent, and mentation was completely intact. This occurred in the absence of any states or medications known to induce hypoglycemia; concurrently, the patient demonstrated hyperphagia, suggesting increased metabolic demand in the setting of an immense, overwhelming tumor burden. During these hypoglycemic intervals, the patient's coagulation profile, including PT and international normalized ratio, remained within normal limits, suggesting sufficient residual hepatic parenchyma and glucogenic capacity. The patient's glucose remained extremely low, refractory to correction with multiple dextrose, D5, and D10 administrations. This suggests chronic systemic habituation to malignant cell consumption of serum glucose leading to adaptations to this hypoglycemia in highly metabolically active organs, such as the brain, heart, liver, and kidneys. This report highlights the clinical utility of recognizing this metabolic state in the setting of advanced-stage malignancy with significant tumor burden and how it affects hospital glucose management. Its early recognition will lead to improvements in meeting the patient's metabolic demands while avoiding paradoxical exacerbation of lactic acidosis when providing guideline-directed oncological treatment. This metabolic state may function as a surrogate marker, in conjunction with serum markers and imaging studies, for clinical identification of advanced stage malignancies and for treatment escalation.

Abstract: Background/Objectives: Artificial intelligence (AI) has emerged as a promising tool to improve the detection and characterization of small bowel (SB) lesions through endoscopic imaging. This scoping review aimed to map and synthesize the available evidence regarding the diagnostic performance of AI systems applied to capsule endoscopy (CE) and device-assisted enteroscopy for the identification of SB lesions with potential malignant or premalignant relevance. Methods: A scoping review was conducted following the frameworks of Arksey and O’Malley, Levac, and Joanna Briggs Institute recommendations, and reported according to PRISMA-ScR guidelines. Searches were performed in PubMed, Scopus, and Embase. Original studies evaluating AI systems on SB endoscopic images or videos and reporting quantitative diagnostic metrics were included. Data extraction covered study characteristics, imaging modality, AI task, diagnostic performance, and methodological limitations. Results: A total of 13 studies were included, with a predominance of retrospective designs (9/13; 69.2%), followed by one prospective study (1/13; 7.7%), and one pilot study (1/13; 7.7%). Most studies originated from China (4/13; 30.8%, including an international collaboration with Denmark) and Japan (4/13; 30.8%). CE was the predominant imaging modality (9/13; 69.2%), followed by device-assisted enteroscopy (2/13; 15.4%) and upper gastrointestinal endoscopy (1/13; 7.7%). Automated lesion detection was the main application of artificial intelligence, reported in most studies (11/12; 91.7%), frequently combined with diagnostic classification tasks (8/12; 66.7%). Most models were based on convolutional neural networks, including architectures such as ResNet50 (29), Single Shot Multibox Detector (28), YOLOv5 (30), and nnU-Net (32). Diagnostic performance was consistently high, with sensitivities ranging from 81.2% to 98.6%, specificities from 88.6% to 99.8%, and area under the ROC curve values approaching 1.0 in several studies (22–30,33). Conclusion: AI improves the detection of SB lesions and significantly reduces endoscopic reading times, particularly in CE. Nevertheless, current evidence remains insufficient to support the use of AI as a screening tool for SB cancer, mainly due to the predominance of retrospective studies and the lack of robust prospective multicenter validation.

Abstract: Background: Assessing the quality of ICU care directly from the patient’s perspective is challenging. Therefore, it is essential to train health professionals with resources that allow them to evaluate family satisfaction, with reliable and valid instruments, capable of supporting staff, managers and family members in the management of care centered on the person and family. The Family Satisfaction with Care in the Intensive Care Unit 24 (FS-ICU 24) is the most reliable and valid measure of family satisfaction. Recently the FS-ICU was revised and little psychometric evaluation has been done on the FS-ICU 24R. Objective: This study aimed to evaluate the psychometric properties of FS-ICU 24R using the version, translated and adapted to Brazilian Portuguese. Methods: The data was collected with 200 family members of patients admitted to Intensive Care Units in three hospitals in the state of Rio Grande do Sul between October 2021 and January 2022. The coefficient α-Cronbach was used to determine the internal consistency of FS-ICU 24R. The validity of the revised questionnaire and its subscales was performed by factorial (construct), discriminant, and convergent validity. The construct was tested by exploratory factor analysis with Varimax orthogonal rotation. The relationship between observed variables and latent variables was evaluated through Confirmatory Factor Analysis, with testing of models suggested in exploratory factor analysis and by the main author of the questionnaire, through adequacy indexes and comparison of the models. Results: The evaluation of internal consistency showed a α-Cronbach of 0.97. The results of the exploratory factor analysis showed that the number of items was preserved; however, instead of the two factors present in the original questionnaire, three were retained, explaining 74.20% of the data variance, with a Kaiser-Meyer-Olkin indicator of 0.95 and eigenvalue of 1.10. In the Confirmatory Factor Analysis, the three factors presented factorial loadings above 0.70; the values of the Average Variance extracted were above 0.5, and composite validity indexes above 0.70. The instrument maintained the same items but distributed in three factors (the first factor “overall satisfaction”, includes satisfaction with the atmosphere, communication, and support of professionals to the family; the second factor ‘family satisfaction with patient care’, and the third ‘satisfaction with the decision-making process'). Conclusions: We conclude that the FS-ICU 24R questionnaire was valid and reliable to evaluate the satisfaction of families in Intensive Care Units but that the use of sub-scale scores may be dependent on context or version of the questionnaire.

Abstract: Background: Current international hereditary angioedema (HAE) guidelines and treat-to-target recommendations consistently emphasize accurate diagnosis, early on-demand treatment of attacks, appropriate short-term prophylaxis for procedures, individualized long-term prophylaxis (LTP), and routine assessment of disease control and quality of life. Published information on how these recommendations are implemented in Latin America remains limited. We therefore aimed to assess the extent to which a Latin American expert panel converges with recommendations across current international HAE guidelines and consensus documents and to identify the principal barriers affecting real-world implementation in the region. Methods: A scientific committee developed and externally validated a 74-item questionnaire (domains summarized in Supplementary Table S1) and conducted a 2-round Delphi process among Latin American HAE experts. Consensus items were rated on a 9-point agreement scale, with consensus defined a priori as at least two-thirds of panelists scoring within a 3-point band in the agreement (7-9) or disagreement (1-3) range. Descriptive items captured expert-reported diagnostic testing, acute and preventive treatment patterns, monitoring practices, and implementation determinants using 5-point Likert scales. Instrument validation (July-November 2024) demonstrated high inter-rater agreement (Kendall W = 0.84; p < 0.001) and excellent internal consistency (Cronbach alpha = 0.92). Results: Thirty experts from 10 countries completed the process (Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Panama, Peru, Uruguay, Venezuela). Panel consensus broadly converged with recommendations across current international HAE guidelines and consensus documents on core diagnostic work-up (C4 plus C1-INH antigen and function; 84% consensus), selective use of genetics (97% consensus), first-line targeted LTP (pdC1-INH, Lanadelumab, Berotralstat; 94% consensus), retention of on-demand therapy for breakthrough attacks and for patients without access to LTP, routine monitoring, and management in special populations. The principal novel finding was an implementation feasibility gap: frequent or constant interruptions in prophylaxis supply were reported by 19/30 experts (63%), including 13/23 (57%) practicing in settings where public or insurance coverage was reported. Across the panel, cost (19/30; 63%) and medicine availability (8/30; 27%) were the most commonly cited barriers, with additional constraints related to diagnostic testing access and administrative pathways. Conclusions: Latin American expert consensus largely converges with recommendations across current international HAE guidelines and consensus documents on diagnosis, acute management, prophylaxis selection, monitoring, and special-population care. The dominant divergence is not clinical disagreement but implementation feasibility—particularly access to diagnostic testing, financing mechanisms, medicine availability, and continuity of supply. These findings provide region-specific evidence to contextualize international recommendations and support implementation strategies tailored to Latin America.

Abstract: Background and Objectives: Myocardial injury after noncardiac surgery (MINS) is a major determinant of perioperative morbidity and mortality. Its largely silent clinical course often makes early diagnosis difficult and challenging. Cardiac Cycle Efficiency (CCE), is a new parameter that reflects the energy efficiency of the cardiovascular system. This study aimed to evaluate the relationship between intraoperative CCE values and postoperative myocardial injury. Materials and Methods: This prospective observational study included 50 adult patients. The CCE parameters, including baseline CCE, minimum CCE, mean CCE, ΔCCE, and the duration and percentage of CCE<0, were continuously recorded. In all patients, high-sensitivity troponin I (hs-TnI) levels were measured on the postoperative days 1, 2, and 3. The primary endpoint was defined as exceeding the 99th percentile upper limit of the hs-TnI values. Results: Postoperative troponin elevation above the 99th percentile upper reference limit was identified in 11 patients (22%); none of these patients had accompanying ischemic symptoms or new ECG changes. Comparison of CCE-derived parameters between the elevated and normal troponin groups yielded no statistically significant differences for any variable (MinCCE p=0.87, MeanCCE p=0.74, DeltaCCE p=0.69, CCE index p=0.50, time with CCE<0 p=0.19, CCE<0 percentage p=0.51). Spearman rank correlation analysis similarly demonstrated no significant association between any CCE parameter and peak troponin levels; the closest trend was observed for MinCCE (r=–0.244, p=0.08), which nonetheless did not reach statistical significance. On ROC curve analysis, none of the CCE parameters exhibited meaningful discriminative ability, with the highest AUC recorded for cumulative time with CCE below zero (AUC=0.63, 95% CI: 0.43–0.83, p=0.19). Conclusions: Intraoperative CCE parameters failed to predict postoperative troponin elevation in patients at low-to-moderate risk undergoing elective noncardiac surgery. These findings indicate that CCE is not a reliable, standalone predictive marker in this patient population. Studies involving higher-risk patient groups and larger sample sizes are required.

Abstract: Rising complexity in cyber-physical systems development exposes challenges in the consistent and reusable specification of graphical domain-specific languages (DSLs). Despite the benefits of model-based systems engineering (MBSE), the absence of a standardized, lifecycle-wide specification process results in semantic inconsistencies, tool dependence, and limited interoperability. While our previous work has addressed individual stages of DSL definition, a comprehensive, standards-based process integrating these stages remains missing. Building on these foundations, this paper introduces a unified language specification process for graphical DSLs grounded in established standards---the Meta-Object Facility (MOF), Unified Modeling Language (UML), Web Ontology Language (OWL), and Resource Description Framework (RDF). The process integrates three core artifacts: a tool-independent ontology capturing domain semantics, a MOF-conformant metamodel unifying abstract syntax, semantics, and concrete syntax, and a UML-profile-based implementation. To support and exemplify this process, a prototypical toolchain is introduced that enables automated transformations between these artifacts, thereby facilitating the consistent propagation of semantics from ontology to implementation. The applicability of the proposed process is demonstrated through both a top-down automotive case and a bottom-up cybersecurity DSL, illustrating its cross-domain generalizability. By explicitly structuring and connecting ontology, metamodel, and implementation, this work contributes a semantically consistent, machine-interpretable, and tool-independent specification process for graphical DSLs in MBSE.

Abstract: Retroviruses induce immunosuppression in their infected hosts. This phenomenon is well described for the immunodeficiency viruses, with human immunodeficiency virus type 1 (HIV-1) representing the best-studied example, but it also occurs in other retroviral infec-tions. Immunosuppressive properties were first characterized in murine leukemia viruses (MuLV). Additional well-studied examples include feline leukemia virus (FeLV) and koala retrovirus (KoRV). Investigations into the mechanisms underlying retrovirus-induced immunosuppression revealed that not only inactivated viral particles, but also their purified transmembrane envelope (TM) proteins exhibit immunosuppressive activity. However, in certain retroviral infections, additional viral proteins contribute to the immunosup-pression in vivo. Within the TM proteins, a highly conserved region, designated the immunosuppressive (isu) domain, was identified. Synthetic peptides corresponding to this domain suppress a wide range of in vitro immune responses by regulating Ras-Raf-MEK-MAPK and PI3K-AKT-mTOR pathways. They modulate cytokine release and alter gene expression in immune cells, mirroring the activity of the corresponding TM protein. Mutations in the sequence abrogate the effect. Numerous TM proteins have demonstrated immunosuppressive activity in vivo, in a tumor rejection model, and muta-tions within the isu domain also abrogate this function. These studies have important implications for reproduction, particularly through the immunosuppressive syncytins in the placenta; for tumor development, where similar mechanisms may protect cancer cells from the host immune system; as well as for vaccine development and xenotransplantation. Notably, immunization with TM proteins carrying mutations in the isu domain elic-its stronger immune responses compared with the wild-type proteins. Finally, the potential of retroviral TM proteins to protect xenotransplants from immune rejection will be discussed.

Abstract: The flotation of oxidized lead–zinc ores presents a significant challenge due to the low floatability of oxidized minerals and their weak interaction with conventional reagents. This study investigates the influence of the electrochemical parameters of the pulp (redox potential, Eh, and pH) on the flotation kinetics of oxidized lead–zinc ore from the Koskuduk deposit. It was established that the use of sodium sulfide Na₂S leads to the selective activation of lead-bearing minerals (Pb recovery up to 40.74%) with low zinc recovery (~12%). The use of a polysulfide-lime system S:CaO:H₂O is proposed, providing more uniform and stable sulfidization of the mineral surface. It is shown that the application of this reagent increases recovery to 65.10% for lead and 56.89% for zinc. It was established that the maximum recoveries are achieved within an Eh range of -120 to -180 mV at pH 11-12. Kinetic studies demonstrated that the main contribution to metal recovery occurs within the first 2-6 minutes of flotation. The obtained results indicate that flotation efficiency is determined both by the type of reagent and by the electrochemical state of the pulp, and that the use of polysulfide systems represents a promising approach for the processing of oxidized lead-zinc ores.

Abstract: In a previous work the authors initiated a study on mutation semigroups, where elementary mutation operations were encoded as total maps on finite sets and analyzed through structural, algebraic, and computational methods. Here we address several of the open problems raised therein. First, we investigate the algebraic characterization of generator sets that force the existence of constant or low-rank maps, linking these conditions to classical results on synchronizing automata. Second, we analyze the computational complexity of contraction-based heuristics, identifying cases where polynomial-time criteria are achievable and others where hardness results emerge. Finally, we extend the finite theory to parameterized and infinite families of mutations, drawing connections with quasispecies models in biology and interpreting image contractions as mechanisms of error suppression and genomic stability. By combining algebraic definitions, structural theorems, and algorithmic analyses, we provide a refined toolkit for understanding mutation collapse and its theoretical and biomedical implications.

Abstract: Background/Objectives: To investigate the effects of a 12-week multimodal intervention (MMI [resistance + motor-cognitive dual-task training]) versus a unimodal intervention (UMI [resistance training]) on gait and cognitive performance in people with glaucoma during single-task (ST) and dual-task (DT) walking. Methods: In this randomized controlled pilot study, fifteen glaucoma patients (MMI: n=8, UMI: n=7) completed 24 supervised intervention sessions over 12-weeks. Spatio-temporal gait parameters (stride length, gait velocity, minimum toe clearance [MTC], and their respective coefficient of variance [CoV]) were assessed using inertial measurement units (sampling frequency 100Hz) during ST and DT walking. During DT walking, the participants performed three cognitive tasks: reaction time task, N-Back task, and letter fluency task. Each cognitive task was performed with two levels of difficulty. Repeated measures analysis of covariance (TIME x INTERVENION x CONDITION) was conducted to analyze the data. Results: No significant group or time effects were observed for ST gait or cognitive performance. Independent of intervention, dual-task costs (DTC) improved for MTC (p=0.188,η_p²=0.205) and MTCCoV (p=0.021, η_p²=0.713) over time. UMI showed greater improvements over time than MMI for DTC MTC (p=0.168,η_p²=0.223) and DTC MTCCoV (p=0.047,η_p²=0.407).Conclusion: This pilot study revealed that both MMI and UMI decreased DTC MTC and DTC MTCCoV in glaucoma patients. However, UMI appeared more effective than MMI to improve gait performance in glaucoma patients. Thus, resistance training might be a promising intervention to improve gait performance in glaucoma patients, with the ultimate aim to reduce the risk of falls.

Abstract: Highway–rail grade crossing (HRGC) safety analysis is often based on raw incident counts or site-level models that do not control for exposure and ignore spatial dependence. This limits the ability to identify where risk is structurally concentrated across the rail network. The problem is important because misidentifying high-risk environments leads to inefficient allocation of limited safety resources and weakens corridor-level intervention strategies. This study introduces accumulated incidents per crossing (AIPX), an exposure-normalized metric that measured cumulative incident burden at the county level over a 51-year period (1975–2025). The study developed an algorithmic framework that integrates data reconciliation with spatial autocorrelation analysis, distributional modeling, and nonparametric machine learning to identify and interpret high-intensity risk environments. Global Moran’s I indicates statistically significant positive spatial autocorrelation (I = 0.359, p = 0.001), confirming that incident intensity is spatially clustered rather than random. Local indicators identify coherent high and low intensity county clusters. Distributional analysis shows that AIPX in high intensity clusters follows heavy-tailed behavior best represented by lognormal and Johnson SU distributions, indicating concentrated risk in a small subset of counties. Machine learning models achieve strong classification performance (AUC ≈ 0.85), with explainability methods consistently identifying temperature, train direction, crossing warning configuration, train composition, and track class as dominant associated features. These variables function as proxies for exposure intensity and network structure rather than causal drivers. The findings demonstrate that HRGC risk is a regional, network-driven phenomenon concentrated along freight-intensive corridors. The study provides a transparent and transferable framework that supports corridor-level prioritization of safety interventions and more effective allocation of infrastructure investments.

Abstract: The article analyzes the linear-quadratic optimal stabilization problem in the so-called "critical case", namely, the situation is considered when the spectrum of the system matrix contains purely imaginary eigenvalues or when the standard conditions of positive definiteness of the weight matrices of the quality functional are violated. Methods for regularizing critical problems by perturbing the system matrices and the functional are investigated, and algorithms for decomposing multidimensional problems into a set of one-dimensional canonical systems are proposed. The results are of practical importance for constructing optimal synthesis in various engineering and economic systems, in particular, the results can be used for stabilizing unmanned aerial vehicles, robotic complexes and intelligent power grids.

Abstract: This study examines the perspectives of coffee industry stakeholders on blockchain traceability systems in Colombia and Switzerland. Adopting the Unified Theory of Acceptance and Use of Technology (UTAUT) as an interpretive framework, the study analyzes these perceptions through the constructs of performance expectancy, effort expectancy, social influence, and facilitating conditions. Using a quantitative cross-sectional design, we survey 360 participants, comprising 60 coffee supply chain companies and 300 consumers. Results reveal that 78.3% of stakeholders consider traceability essential, yet only 46.7% are familiar with blockchain. Stakeholders identify three primary benefits: improved transparency (91.7%), fraud prevention (88.3%), and enhanced security (86.7%). However, significant barriers persist: high implementation costs (95%), limited expertise (91.7%), and lack of awareness (93.3%). Geographic differences emerge: Colombian stakeholders prioritize cost reduction and fraud prevention, while Swiss participants focus on data management and privacy protection. Among consumers, 62.7% express interest in provenance information, 56.7% indicate willingness to pay for blockchain systems, and 59% show interest in tipping farmers. The study classifies benefits and drawbacks across nine dimensions, providing a comprehensive framework for understanding the multidimensional impacts of blockchain on coffee supply chains.

Abstract: Urban mobility planning in smart cities requires sophisticated simulation tools, yet their complexity often creates a technical barrier for non-expert stakeholders. This paper presents a novel architecture that integrates generative artificial intelligence with digital twin technology to create an accessible and robust decision-support system. The framework employs a conversational AI agent based on Gemini 2.5 Flash Lite to interpret natural language intentions and translate them into validated simulation parameters. A critical safety layer, built using Pydantic, ensures that the agent’s stochastic outputs adhere to strict technical schemas and urban logic before execution. The underlying digital twin, developed with SimPy, NetworkX, and OSMnx, features a multi-source data integration strategy that includes demographic density (INE), tourism activity (ISTAC), and high-resolution traffic statistics (TomTom) to calibrate vehicle behavior. The architecture was validated through a Technology Readiness Level (TRL) 4 proof-of-concept in Las Palmas de Gran Canaria, simulating multimodal scenarios including buses, the future MetroGuagua (BRT), and pedestrian flows. Results demonstrate a 95.99% success rate in intent recognition and configuration mapping, with end-to-end execution times under 20 minutes for a 19-hour simulated day. This study demonstrates that LLM-driven orchestration, coupled with automated data pipelines and a decoupled microservice architecture, can democratize access to urban simulation, fostering more inclusive, agile, and evidence-based smart city governance.

Abstract: We propose a Generalized Multivariate Functional Additive Mixed Model (GMFAMM) for the simultaneous bias correction of five hydroclimatic variables derived from the NASA POWER satellite product: minimum temperature (Tmin), maximum temperature (Tmax), relative humidity (HR), solar radiation (Rad), and precipitation occurrence (Pbin). The GMFAMM extends the univariate functional framework by incorporating a shared latent Gaussian process Λ0i(t) that captures cross-variable thermodynamic dependence. A systematic experimental grid of more than 200 model configurations across four distributional families (Gaussian, Gamma, Poisson, Binomial), two effect structures (linear and smooth P-splines), and four nested covariate sets is evaluated on a strict chronological 70/30 hold-out – seven training years (2016–2022) and three hold-out years (2023–2025) – to identify the optimal marginal specification for each variable. The value of joint modelling is quantified through a two-stage cross-residual approximation to the GMFAMM shared latent process, which constitutes a conservative lower bound on the gains achievable by the full simultaneous model: out-of-sample RMSE is reduced by 53% for Tmin, 38% for Tmax, and 51% for relative humidity relative to the independent GAMM baseline. These gains are physically interpretable through the Clausius-Clapeyron thermodynamic coupling documented in the residual cross-correlation analysis. The trained model artefacts are deployed in ColClim, an open-access R Shiny web application that queries the NASA POWER API and the Open-Meteo forecast service for any user-selected location in Colombia, applies the GMFAMM correction pipeline, and delivers both historical bias-corrected time series and short-range (1–16 day) forecasts across the five variables.

Abstract: As Intelligent Transportation Systems (ITS) transition towards automated ecosystems, the deployment of advanced wireless charging technologies becomes a critical infrastructure requirement. Central to the management of these networks is the Open Charge Point Protocol (OCPP), which ensures interoperability across diverse hardware vendors. However, the reliance on digital communication for power transfer introduces significant cybersecurity vulnerabilities. This paper presents a methodology for evaluating the impact of cyber-threats on urban transport services, with a specific focus on the communication layers that support these Advanced Wireless Power Transfer (WPT) environments. Utilising Stochastic Petri net (SPN) ontology, we model the operational states of an Electric Vehicle (EV) service—including the activation and the arrival phases—to quantify how protocol-level vulnerabilities affect service reliability. We introduce an Extended Vulnerability List (EVL) and analyse two distinct scenarios: a public transport service and a weather forecasting integration. Our results demonstrate that as wireless charging moves towards standardization, the security of the OCPP-based backbone is a fundamental necessity for preventing service disruption. The proposed assessment framework provides a roadmap for securing the next generation of dynamic wireless charging infrastructures against evolving cyber-physical threats.

Abstract: Forecasting crash severity is critical for emergency response, infrastructure spending & risk communication. Although machine learning has been widely applied to this problem, three shortcomings prevent its practical application: poorly calibrated probability scores, SHAP-based explanations whose faithfulness has not been verified, and models never tested in different regions. The proposed framework, termed SAE-XCrash (Safety-Aware and Explainable Crash Severity Prediction), considers all three using two public datasets - US-Accidents (7.0 million records, 2016-2023) and UK STATS19 (approximately 1,010,000 records, 2016-2022). Notably, the US-Accidents severity label refers to traffic disruption duration, not injury outcome, and results should be interpreted accordingly. Previously unknown label-schema drift led to a revised binary target with Severity 4 as only the positive class; strict temporal splits are used throughout. FIVE classifiers are compared. Post hoc isotonic Calibration reduces expected calibration. Error by 97.3 percent while maintaining negligible discrimination loss. A four-step SHAP audit confirms that explanations genuinely reflect model behavior: deletion-based per-budget faithfulness gaps exceed the 0.05 threshold at every feature budget (min gap=0.066, p<0.0001), though the aggregate trapezoidal AUC is borderline due to scale compression at AUPRC≈0.13, and insertion gaps are statistically significant at more than ten percent of features. Explanation stability holds under conservative noise levels but degrades at realistic perturbation magnitudes mainly in spatially sparse geohash cells. In a three-tier cross-dataset transfer experiment - zero-shot, recalibration and full retraining - spatial memorization is the major generalization barrier while temporal features transfer smoothly between jurisdictions.

Abstract: Objective: To synthesise the pathobiology of cortical spreading depolarization (CSD) and critically appraise current and emerging pharmacological strategies specifically targeting migraine aura prevention. Background: Migraine with aura affects 25–30% of patients, and the aura phenomenon remains a substantial unmet preventive need. Calcitonin gene-related peptide (CGRP) monoclonal antibodies do not readily cross the blood–brain barrier and frequently fail to suppress CSD, the neurophysiological substrate of aura.Methods: A literature search of PubMed, Embase, and the Cochrane Library (inception through January 2026) identified studies on CSD pathophysiology, preclinical CSD suppression, and clinical efficacy of candidate agents. Evidence quality was assessed with GRADE; risk of bias with Cochrane RoB 2 (RCTs) and ROBINS-I (observational); narrative synthesis followed SWiM. De novo quantitative estimations (post-hoc power analyses, sample-size projections, worst-case sensitivity analyses) were used as methodological tools, not as original empirical data. Results: CSD pathogenesis is organised into four phases: pre-CSD vulnerability, initiation, glial propagation, and neuro-inflammatory transduction. Lamotrigine and memantine target initiation and have the most advanced clinical evidence; both lack aura-specific RCTs. A 2024 network meta-analysis ranked memantine favourably (50% responder rate OR 5.58, 95% CI 2.41–12.92) but no contributing trial stratified by aura. An a priori sample-size calculation indicates 214 enrolled patients (170 evaluable; NNT≈4.9; n/(1−d) for 20% attrition) for a definitive aura-specific memantine RCT. Tonabersat—a Cx36/Cx43 gap-junction modulator—reduced aura attacks from 3.2 to 1.0 per 12 weeks in Phase 2; a worst-case intention-to-treat sensitivity analysis confirms that this signal survives even 16.6% unaccounted attrition. Spironolactone (pannexin-1 inhibition) and amiloride (ASIC1a) remain preclinical or pilot-stage. Tissue-selective KATP antagonists (Kir6.1/SUR2B) and the anti-PACAP-38 antibody Lu AG09222 represent the most promising pipeline agents. Conclusion: The therapeutic gap for migraine aura prevention reflects correctable methodological choices, not a lack of biological tractability. Four mechanism-based drug classes—NMDA-receptor antagonists, pannexin-1 inhibitors, gap-junction modulators, and KATP antagonists—offer entry points for aura-specific prevention. Adequately powered, aura-enriched RCTs with validated CSD biomarkers (DC-EEG co-registered against electrocorticography; neuron-derived extracellular vesicles) and pre-specified falsifiability thresholds are now the rate-limiting step. Seven testable methodological predictions are proposed.