Abstract: With the progressive implementation of China's dual-carbon strategy, the proportion of renewable energy in the power system continues to rise. Large-scale renewable energy clusters for centralized power transmission have been established in regions such as Northeast and Northwest China, while offshore wind power in coastal areas of East China is experiencing rapid growth. The inherent intermittency and stochastic variability of wind and solar resources introduce significant uncertainty into power output, leading to frequent operational challenges within renewable energy clusters, including voltage fluctuations and reactive power oscillation. To address these challenges, this paper proposes a multi-mode adaptive coordinated reactive power control strategy for renewable energy clusters. The research framework proceeds as follows: First, two key indicators characterizing the voltage dynamic behavior of renewable energy clusters are analyzed, namely, voltage sensitivity and the Multi-plant Short-Circuit Ratio (MRSCR). Subsequently, based on the physical implications of these indicators, conventional control modes, including constant voltage, constant reactive power, and constant power factor control, are refined and adaptively deployed, forming an integrated multi-mode coordinated control strategy. Finally, the effectiveness of the proposed strategy is verified through a closed-loop, co-simulation testing platform that combines digital simulation with physical hardware-in-the-loop elements. The results indicate that, compared to conventional automatic voltage control (AVC) methods, the proposed strategy demonstrates enhanced adaptability to frequently changing grid operating conditions and contributes more effectively to the mitigation of voltage oscillation issues in renewable energy clusters.

Abstract: Background: Microbiome-targeted interventions have shown promise for metabolic health, yet clinical evidence remains inconsistent, particularly across stages of metabolic disease. This study evaluated the metabolic effects, safety, and tolerability of EDC-HHA01, a microbiome-informed, non-pharmacologic intervention, in adults with prediabetes (PD) or Type 2 Diabetes (T2DM). Methods: In a randomized, double-blind, placebo-controlled clinical trial, participants received EDC-HHA01 or placebo for six months. The study was adequately powered (≥80%) for the primary endpoint. Outcomes included changes in glycated hemoglobin (HbA1c), indices of insulin resistance, markers of metabolic endotoxemia, safety-related laboratory parameters, and exploratory patient-reported measures. Analyses were stratified by metabolic status and background metformin use. Results: In participants with PD, EDC-HHA01 supplementation was associated with a statistically and clinically meaningful reduction in HbA1c compared with placebo, supported by concordant improvements in fasting insulin, insulin resistance indices, and reductions in endotoxemia markers. In participants with T2DM, changes were directionally similar but attenuated and did not reach statistical significance. The intervention was well tolerated, with no serious adverse events, high adherence, and no clinically relevant adverse changes in renal or lipid parameters. Exploratory patient-reported outcomes indicated favorable acceptability but were not interpreted as efficacy endpoints. Conclusions: EDC-HHA01 was associated with biologically coherent, stage-dependent metabolic effects, most evident in PD. These findings support further investigation of microbiome-informed strategies as metabolic support in early-stage dysregulation.

Abstract: Background: Breast cancer is one of the leading causes of female mortality, especially if diagnosed in late stages. While mammography is the cornerstone of screening, its diagnostic accuracy is limited by tumor heterogeneity and subjective interpretation. Objective: Herein, we explored the potential of radiomics and machine learning to improve the diagnostic accuracy of mammograms and personalise patient management in breast cancer. Methods: We manually segmented tumours and lymph nodes to analyse mammograms of the open-source INbreast dataset, which comprised multiple cases of benign and malignant breast masses with and without lymphadenopathy. Ra- diomics features (morphological, texture, wavelet) were extracted using PyRadiomics. Stratified sampling ensured balanced class representation. Then, we trained ML classifiers (XGBoost, CatBoost, LightGBM, etc.) to detect malignancy from the extracted radiomical features. Random Forest classifier was used to prognosticate the molecular subtype of the tumour from radiomical findings. Results: Significant radiomic differences were observed between benign and malignant lesions. Combining features of breast mass and lymph node yielded the highest classification accuracy (up to 99%) in detecting malignancy. The Random Forest model achieved 90.8% accuracy in identifying Luminal A molecular subtypes, with first-order and shape-based features contributing most to model perfor- mance. Conclusion: Radiomics-based ML models significantly improve diagnostic accuracy and enable non-invasive prediction of breast cancer subtypes. This approach supports precision oncology by enhancing screening efficiency and informing personalized treatment strategies.

Abstract: Background: Digital workflows have significantly improved accuracy and predictability in implant prosthodontics; however, full-arch rehabilitations in completely edentulous patients remain challenging due to the need for precise implant position transfer and passive fit of prosthetic frameworks. Although artificial intelligence (AI)–assisted digital workflows have shown promising results in vitro, prospective clinical evidence remains limited. This study aimed to clinically evaluate the accuracy of a novel AI-integrated digital impression workflow (SmartX) for all-on-X full-arch implant-supported rehabilitations. Methods: This prospective observational case series included 10 completely edentulous patients rehabilitated with all-on-X implant-supported full-arch prostheses. Digital impressions were obtained using extended SmartFlag scan bodies combined with an AI-assisted SmartX workflow and an intraoral scanner. Clinical accuracy was evaluated using visual and tactile inspection, the one-screw (Sheffield) test, and a screw resistance test, all recorded as dichotomous outcomes (acceptable passive fit: yes/no). Secondary outcomes included implant and prosthesis survival rates and the incidence of biological or technical complications. Patients were followed for a minimum of 6 months. Results: A total of 54 implants were placed (mean: 5.4 implants per patient), with no dropouts during follow-up. All cases demonstrated acceptable passive fit according to all accuracy assessments (100% positive outcomes). Progressive screw tightening revealed a consistent angular displacement of approximately 60° in all cases. Implant and prosthesis survival rates were 100%, and no biological or technical complications were observed during the follow-up period. Conclusions: Within the limitations of this prospective clinical study, the SmartX AI-assisted digital workflow combined with extended scan bodies demonstrated high clinical accuracy for full-arch implant-supported rehabilitations. This approach appears to be a reliable and clinically feasible option for digital full-arch prosthodontics. Further studies with larger sample sizes and longer follow-up periods are required to confirm these findings.

Abstract: Extended-spectrum β-lactamase–producing E. coli (ESBL-EC) threatens public health by driving widespread antimicrobial resistance transmission in environmental and agricultural settings. This study examined the prevalence, genetic determinants, and phylogenetic relationships of ESBL-EC isolated from municipal wastewater treatment plants (WWTPs) and farm environments in southeastern North Carolina. A cross-sectional survey was conducted between May and September 2025 across two WWTPs and two farms (cattle and poultry). We sampled influent and effluent wastewater, plus fecal and water specimens collected from chickens, ducks, and cattle. Antimicrobial susceptibility testing was performed using Kirby–Bauer disk diffusion method against nine drugs, while PCR and sequencing were used for genotypic characterization. Phylogenetic analysis assessed genetic relatedness among isolates. ESBL-EC was detected in 27.4% (n = 124) of 452 samples, with the highest prevalence in chickens (31.5%), followed by WWTP influents (28.2%), ducks (18.5%), and cattle (12.1%). Dominant resistance genes included blaCMY-2 (71.8%), blaCTX-M-1 and blaOXA (54% each), and blaSHV (29.8%). Co-occurrence of blaCMY-2 with blaCTX-M-1 and blaOXA was observed in poultry isolates. Phylogenetic analysis revealed clonal relatedness between poultry and cattle isolates. These findings highlight poultry as a key reservoir and emphasize the need for One Health surveillance to mitigate cross-reservoir transmission of resistant E. coli.

Abstract: This empirical study examines how FinTech innovation is adopted, scaled, and sustained in a small and highly regulated market, such as Latvia. The triangulated analytical framework is applied in this study, integrating Rogers’ Innovation Diffusion Theory IDT [1], De Meyer’s Innovation Ecosystem framework [2], and the Value Chain Theory [3], [4]. This framework enables the exploration of the interaction between innovation characteristics, ecosystem relationships, and restructuring in the value chain. The data was collected from FinTech leaders, conventional financial institutions (banks), regulators, and associations, and it was analysed thematically. Based on the interviews with stakeholders, the relative advantage of Latvian FinTechs lies in their flexibility, speed, and trialability; however, the adoption barrier is the complexity of regulation and unevenness in infrastructure and institutional readiness. The authors found strong collaboration among the ecosystem's players but limited proactive regulatory engagement. This research provides a replicable model for cross-border or cross-sector analysis to assess the progress of innovation in regulatory and Environmental, Social and Governance ESG integration.

Abstract: Background/Objectives: High doses of pilocarpine to rats induce status epilepticus (SE) and reproduce the main characteristics of mesial temporal epilepsy. This model is considered highly isomorphic with the human disease, reason why it has been applied to elucidate the process of epileptogenesis. Methods: Two-dimensional electrophoresis (2-DE) was employed to study the hippocampal differential expression of proteins in rats exhibiting spontaneous recurrent seizures induced by pilocarpine. Two groups were studied: rats treated with pilocarpine (360mg/kg, N=6), and rats treated with saline (N=6). Both groups were analyzed 90 days after SE onset. Hippocampi homogenized in a lysis buffer were used to perform 2-DE. Interactome for differentially expressed proteins was performed using STRING database. Results: Protein spots analyzed by PDQuest software revealed forty proteins differentially expressed in epileptic rats compared to control (p< 0.05), among them thirty-seven were successfully identified. LC-ESI-MS/MS results analyzed with MASCOT MS/MS ion search and IPI protein database showed twenty-nine up-regulated proteins in epileptic rats while six proteins were down-regulated and two proteins were expressed only in the control animals. The differentially expressed proteins integrated the domains of neuronal hyperexcitability, energy failure, synaptic dysregulation, and post-status epilepticus remodeling (confidence scores ≥0.90–0.99). Conclusions: The differentially expressed proteins showed high-confidence protein-protein interaction modules directly linked to the molecular pathogenesis of epilepsy. The simultaneous failure of the identified pathophysiological domains drives the transition from acute seizures to chronic, drug-refractory epilepsy. The protein complexes identified represent high-value, translation-ready candidate nodes for next-generation antiepileptogenic and disease-modifying therapies.

Abstract: Advances in generative AI have brought advanced tools to tax & legal practice, but with them comes AI hallucinations, which are fabricated citations, quotes, or facts that appear plausible but are entirely false. In 2023, a notable U.S. case (Mata v. Avianca) revealed this risk when attorneys, relying on ChatGPT for research, submitted a brief containing fictitious case law and were sanctioned as a result. This incident revealed substantial risks for the tax & legal profession. Increased AI use in tax & legal submissions and decision drafting subsequently led to numerous similar global incidents. By late 2025, a collection of various datasets logged nearly 800 cases of AI-related citation errors or hallucinations” in at least 25 countries, with a marked increase in 2025 alone. These cases span court filings by lawyers and pro se litigants, as well as orders drafted by judges or tribunals. This development necessitates an examination of professional responsibility and procedural fairness concerning AI-generated falsehoods. This article analyzes how courts and administrative bodies across jurisdictions have responded to AI-generated hallucinations in tax & legal submissions and decisions, and what these responses indicate regarding emerging verification standards under existing law. The analysis compares incidents from the United States, Canada, the United Kingdom, India, Israel, and other jurisdictions, focusing on the imposition or withholding of sanctions, the treatment of various actors (e.g., lawyers, self-represented parties, experts, judges), and the adaptation of tax & legal doctrines to this challenge.

Abstract: Direct-to-consumer (D2C) brands increasingly rely on A/B testing to optimizepaid social advertising, yet common execution errors—inconsistent attribution,underpowered samples, mid-test edits, and metric flexibility—undermine inferentialvalidity.[13] This paper develops a methodological framework for decision-gradeexperimentationonMeta(Facebook)Adsthatbalancesstatisticalrigorwithbusinessguardrails. We synthesize established practices in online experiments[3, 12, 13] andoperationalize them into a prioritized test sequence, integrating power analysis,Sample Ratio Mismatch (SRM) diagnostics,[21, 23] optional CUPED variancereduction,[6] and economic guardrails (e.g., ROAS thresholds, delivery balance,frequency parity).Evidence and scope. All analyses use a synthetic/simulated dataset calibratedto D2C benchmarks; no live-traffic data are analyzed. The simulation demonstratesthe analytical workflow and decision logic without making empirical claims aboutreal-world effectiveness. The contribution is methodological: a reproducibletemplate practitioners can adapt and validate in production.

Abstract: Misdeclared hazardous cargo inside sealed maritime containers continues to drive ship fires, port disruptions, and avoidable losses for crews, carriers, and coastal communities. Ports and carriers already use non-intrusive inspection (NII) imaging and document checks, but these systems are often treated as separate queues rather than a single integrated decision system. This paper proposes a practical multimodal framework that fuses radiographic sensing with shipping document intelligence to flag hazardous misdeclaration risks without opening a container. The approach combines a vision encoder that learns density-aware patterns from X-ray or gamma imagery with a language model that extracts and normalizes claims from bills of lading, manifests, and booking descriptions. A fusion layer learns cross-modal consistency, so the system can react when what the scan suggests does not match what the paperwork claims. The design is grounded in port constraints, including strict throughput targets, noisy and delayed labels, long-tailed hazard categories, and an operational need for clear explanations that can be audited. We define a data lifecycle that turns inspections, holds, claims, and incident investigations into structured feedback, without requiring constant full unpacking of cargo. We describe a low-latency edge deployment pattern that reduces backhaul and helps avoid unnecessary centralized compute, which matters in regions where water and power constrain expansion planning. A simulation-driven evaluation plan is provided, including realistic cost-sensitive metrics that focus on recall at fixed false-alarm rates, because ports pay real costs for every extra secondary inspection. The paper positions the framework relative to DHS work on AI-enabled paradigms for non-intrusive screening and recent industry adoption of AI screening for dangerous goods in booking workflows.

Abstract: The volume of scientific submissions continues to climb, outpacing the capacity of qualified human referees and stretching editorial timelines. At the same time, modern large language models (LLMs) offer impressive capabilities in summarization, fact checking, and literature triage, making the integration of AI into peer review increasingly attractive—and, in practice, unavoidable. Yet early deployments and informal adoption have exposed acute failure modes. Recent incidents have revealed that hidden prompt injections embedded in manuscripts can steer AI-generated reviews toward unjustifiably positive judgments. Complementary studies have also demonstrated brittleness to adversarial phrasing, authority and length biases, and hallucinated claims. These episodes raise a central question for scholarly communication: when AI reviews science, can we trust the AI referee? This paper provides a security- and reliability-centered analysis of AI-assisted peer review. We map attacks across the review lifecycle—training and data retrieval, desk review, deep review, rebuttal, and system-level. We instantiate this taxonomy with four treatment-control probes on a stratified set of ICLR 2025 submissions, using two advanced LLM-based referees to isolate the causal effects of prestige framing, assertion strength, rebuttal sycophancy, and contextual poisoning on review scores. Together, this taxonomy and experimental audit provide an evidence-based baseline for assessing and tracking the reliability of AI-assisted peer review and highlight concrete failure points to guide targeted, testable mitigations.

Abstract: The integration of outdoor camera images with three-dimensional (3D) geographic information on the observed scene has an interest in many applications of video acquisition. To solve this data fusion problem, camera images have to be matched with the 3D geometry provided by the geographic information system (GIS). This paper proposes to use, for a camera of known geographical position, a dense local azimuth-elevation map (LAEM) derived from a gridded digital elevation model (DEM) as data representation to ease the matching operation between GIS data and the image. Such a map assigns to each regularly sampled azimuth and elevation angles pair the geographic point derived from the DEM viewed in this direction. The problem of computing the LAEM from the DEM is closed to the problem of surface rendering, for which solutions exist in computer graphics. However, rendering software cannot be used directly, since their view directions are constrained by the pin-hole camera model and apparent colour rather than position of the viewed point is assigned to the viewing direction. This paper therefore also proposes a specific algorithm for the computation of the LAEM from the DEM. A MATLAB implementation of the algorithm is also provided, which is tailored to process the DEM data set swissALTI3D from the Swiss Federal Office of Topography swisstopo.

Abstract: In the current study, we propose a novel reduced-order model for the drag coefficient of a circular cylinder model that can be either fixed or undergoing an oscillatory linear motion in the cross-flow direction, the streamwise direction, or at an arbitrary tilt angle. Thus, the proposed model is not restricted to a single geometric setting of the cylinder. The model establishes a proper nonlinear coupling between the drag coefficient and the lift coefficient, such that the drag coefficient can be restructured using the simple reduced-order model, given the time signal of the lift coefficient. The proposed model is able to capture both the mean component of the drag coefficient, as well as the oscillatory component of it. We derived closed-form expressions to estimate the model parameters from a training dataset. The model was tested and found to be performing satisfactorily under different motion modes. We generated the training data using computational fluid dynamics simulation for a circular cylinder at a low Reynolds number of 300. The computational fluid dynamics solver used was successfully validated by comparison against independent published data. The current study is viewed as a contribution to the fields of nonlinear dynamics, fluid mechanics, and computational mathematics.

Abstract: Generative diffusion models have emerged as a powerful class of models in machine learning, yet a unified theoretical understanding of their operation is still developing. This paper provides an integrated perspective on generative diffusion by connecting the information-theoretic, dynamical, and thermodynamic aspects. We demonstrate that the rate of conditional entropy production during generation (i.e. the generative bandwidth) is directly governed by the expected divergence of the score function's vector field. This divergence, in turn, is linked to the branching of trajectories and generative bifurcations, which we characterize as symmetry-breaking phase transitions in the energy landscape. Beyond ensemble averages, we demonstrate that symmetry-breaking decisions are revealed by peaks in the variance of pathwise conditional entropy, capturing heterogeneity in how individual trajectories resolve uncertainty. Together, these results establish generative diffusion as a process of controlled, noise-induced symmetry breaking, in which the score function acts as a dynamic nonlinear filter that regulates both the rate and variability of information flow from noise to data.

Abstract: Extracellular vesicles (EVs) reflect their spatiotemporal cellular origins and, when released into the peripheral bloodstream, represent sources of biomarkers for processes occurring in inaccessible tissues, such as the brain. Positive selection by immunoaffinity isolation targeting a specific EV surface marker can be used to preferentially enrich for EVs from a particular cell type over other EVs in circulation. However, the case of L1CAM immunocapture for enrichment of brain-derived neuronal EVs has exemplified the biological and technical influences affecting the outcomes of positive selection, which are discussed here. These include findings from the wider (non-EV) literature showing that soluble L1CAM is a binding partner of the common EV marker CD9. Additional studies show that CD9 is not expressed by the vast majority of neurons in the brain, but it is highly expressed by neurons of the peripheral nervous system, which could affect the interpretation of studies demonstrating the co-localisation of L1CAM and CD9 on EVs to assert isolation of EVs derived from neurons in the brain. Next, emerging negative selection strategies are discussed which, when combined with positive selection, could overcome some of the challenges associated with enriching for EV-based biomarkers. These strategies include depleting EVs on a cell-by-cell type basis, as well as targeting common EV markers to simultaneously deplete diverse, undesired EV populations whilst selecting for the EVs of interest. An example is given of how CD9 depletion could enrich and select for brain-derived neuronal EVs. Additionally, studies that have used negative selection alone to select for EVs, and the advantages of this approach for biomarker detection, are highlighted. Finally, based on these insights, considerations for the choice of future positive and negative selection targets are discussed, including how understanding the nuances of EV heterogeneity could facilitate the process of EV-based biomarker discovery and their translation to the clinic.

Abstract: The continued scaling of large-scale AI and HPC systems increasingly encounters limits that are not imposed by raw compute capacity, but by the dynamics of interconnects that bind distributed components into a single execution fabric. As system size, heterogeneity, and synchronization demands grow, performance degradation manifests in non-linear and often opaque ways, leading to a collapse of effective cost per performance despite sustained investment in additional hardware. Classical performance and network metrics, while necessary, fail to capture the structural origins of these effects and therefore provide limited guidance for architectural or economic decision-making. This article argues that interconnect-induced instability should not be understood as a collection of incidental faults or implementation bugs, but as an emergent structural property of tightly coupled, large-scale runtime systems. We analyze how latency drift, synchronization loss, and non-local coupling effects propagate through operator dependencies and give rise to hidden economic costs, including re-runs, over-provisioning, and diminished result usability. The contribution of this work is a structural problem analysis that reframes stability as a first-order economic variable rather than a secondary performance artifact. The methodology is deliberately conceptual and analytical, avoiding implementation details or prescriptive solutions. By isolating the structural mechanisms underlying cost-per-performance collapse, this analysis establishes a foundation for structure-oriented approaches to runtime stability control in AI and HPC infrastructures.

Abstract:

Chronic lymphocytic leukemia (CLL) is a dynamic malignancy in which intraclonal subfractions differ in activation history and responsiveness to microenvironmental signals. Here, we investigated the expression and inducibility of IL-12 family receptor subunits (IL-23R, IL-12Rβ1, IL-12Rβ2) and the related receptor complexes in recirculating CLL cells, with a focus on CXCR4/CD5-defined fractions: the proliferative fraction (PF; CXCR4^dim/CD5^bright; most recently divided, tissue-emigrated cells) and the resting fraction (RF; CXCR4^bright/CD5^dim; older, quiescent cells). At baseline, IL-12Rβ1 was enriched in the PF and was associated with a higher proportion of cells expressing IL-23R and IL-12R receptor complexes. Concomitantly, RT-qPCR disclosed higher IL-12Rβ1 mRNA levels. Following antigen-independent activation with CpG or CpG + IL-15, there was a marked increase of IL-23R and IL-12Rβ1 but not of IL-12Rβ2 surface expression, resulting in preferential upregulation of the IL-23R complex over the IL-12R complex. Fraction-specific analyses showed stronger induction of IL-23R and IL-23R complex expression in PF compared with RF. These findings identify an intraclonal bias toward IL-23 responsiveness in the CLL cells with a phenotype of recently divided, tissue-emigrated cells and suggest the IL-23/IL-23R axis as a potential therapeutic target.

Abstract:

Follicular lymphoma (FL) is the second most common form of non-Hodgkin’s lymphoma (NHL) and accounts for about 5% of all hematological malignancies. Despite therapeutic advances, FL follicular lymphoma remains an incurable disease, with frequent relapses and increasingly shorter disease control intervals. Bispecific antibodies (bsAbs) are molecules that target two different epitopes or antigens. The mechanism of action is determined by the molecular targets and structure of the bsAbs. Several bsAbs have already changed the therapeutic landscape of hematological malignancies and some solid tumors. In particular, in this article we review the general principles on follicular lymphoma and established and innovative therapies including bsAbs, in particular the bsAb mosunetuzumab, a new bispecific antibody that acts on CD3 epitopes of T lymphocytes and CD20 epitopes of B lymphocytes with the aim of inducing T lymphocyte-mediated elimination of malignant B lymphocytes, its safety and efficacy with the analysis of no. 3 patients who completed treatment with the drug mosunetuzumab in the A.O. Pia Fondazione di Culto e Religione ‘Card. G. Panico’, Tricase (Lecce).

Abstract: BACKGROUND In Kenya, end-stage renal disease is a significant public health burden treated primarily with hemodialysis in county hospitals, yet comprehensive outcome data from these routine settings are scarce. AIM To evaluate one-year clinical outcomes and identify independent predictors of mortality among ESRD patients undergoing hemodialysis at a Kenyan county hospital. METHODS We conducted a retrospective cohort study of all patients who initiated hemodialysis for ESRD at Murang'a County Referral Hospital between January 2024 and January 2025. Data on demographics, clinical characteristics, comorbidities, and treatment parameters were extracted from hospital electronic medical records and dialysis unit records. Cox proportional hazards regression was used to identify factors associated with one-year mortality. RESULTS Of 79 patients analysed (median age 62.0 years, IQR 48.0-74.0; 65.8% male), the one-year all-cause mortality rate was 34.2% (27/79). The cohort demonstrated a heavy reliance on central venous catheters (89.9%, 71/79) rather than arteriovenous fistulas (10.1%, 8/79). 3 Non-survivors were significantly older (median 73.0 vs 58.0 years, p<0.001) and had lower baseline haemoglobin (7.1 vs 8.6 g/dL, p=0.008). In multivariable analysis, older age (aHR 1.05 per year, 95% CI 1.01-1.09, p=0.012) and central venous catheter use (aHR 3.12, 95% CI 1.08-9.01, p=0.036) remained independent predictors of mortality. Lower eGFR and hemoglobin were significant in univariate analysis but not in the adjusted model. Comorbidities, including HIV and diabetes, did not reach statistical significance. CONCLUSION This study found high one-year mortality in Kenyan hemodialysis patients, with older age and catheter use showing strong associations with death. The near-universal use of CVCs is a marker of systemic challenges in pre-dialysis care, underscoring the urgent need for vascular access programs and improved care strategies to improve survival.

Abstract: Background/Objectives: Driving cessation is associated with adverse health outcomes. Proactive support that extends safe driving while preparing for life after driving cessation has been emphasized, but empirical evidence remains limited. This study examined the effects of a proactive class for older drivers on awareness and behavior related to driving and mobility (Study 1) and on longitudinal changes in on-road driving behavior (Study 2). Methods: The proactive class was implemented as a municipal program, including information provision, training activities, group discussions, and optional on-road driving evaluations. Study 1 included 71 older drivers who attended the class at least five times annually and completed an anonymous questionnaire assessing perceived changes in awareness and behavior. Study 2 included 29 participants who completed standardized on-road driving evaluations at baseline and at a 1-year follow-up. Paired t tests or Wilcoxon signed-rank tests with effect sizes were applied. Results: In Study 1, participants reported increased awareness of safe driving, greater confidence in continuing to drive, heightened risk perception, initiation of health-related behaviors, trial use of public transportation, and increased healthcare utilization, particularly ophthalmology visits. In Study 2, total scores on the on-road driving skill test improved significantly at follow-up (Cohen’s dz = 0.805), with reductions in errors related to braking, vehicle control, and overspeeding. No significant changes were observed in physical, cognitive, or daily functioning, except for a reduction in driving simulator accidents. Conclusions: A proactive, continuous driving transition support class may facilitate multidimensional behavioral change and improve on-road driving performance among older drivers, supporting safer mobility and healthier aging. This study provides an initial conceptual and empirical foundation for proactive driving transition support delivered during the driving continuation phase, which will be examined in a future randomized controlled trial.