Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has advanced Large Language Models (LLMs) reasoning by providing objective feedback, yet it remains fundamentally dependent on external verifiers, which limits self-regulated reasoning and generalization. We propose a shift toward internalization, relocating verification from external infrastructure into model-internal signals. We formalize this paradigm through a four-dimensional taxonomy: Probabilistic, Uncertainty, Process, and Interaction Internalization. This taxonomy captures how verifier-free reinforcement learning (VFRL) derives evaluative signals from likelihood, uncertainty, intermediate reasoning steps, and candidate interactions. This perspective enables dense, scalable, and model-driven supervision while highlighting characteristic failure modes such as proxy misalignment, miscalibration, local-process errors and preference drift. Our analysis systematizes recent VFRL methods, delineates their strengths and limitations, and outlines research directions for building reliable, auditable, and self-supervised reasoning agents.

Abstract: The standard interpretation of quantum measurement on entangled systems holds that measuring one particle nonlocally collapses the wavefunction of its spacelike-separated partner. We argue that this conclusion rests on a false presupposition: that subsystems of entangled systems possess independent ontic states. If the global wavefunction is the sole ontic object (ψ-ontic holism), then for entangled systems there is no “state of B” to be affected by measurement at A. The reduced density matrix of a subsystem, while operationally useful, is not ontologically real for non-factorizable states. Measurement is a local dynamical process — concretely modeled by continuous spontaneous localization (CSL) — that destroys one local wavefunction component at the measurement site. The global state factorizes as a consequence, and subsystem ontology emerges for the first time. The transition of the distant particle’s reduced density matrix from mixed to pure reflects this emergence of separability, not a physical change at the distant location. We show that this framework is consistent with no-signaling, with the contextuality required by the Kochen–Specker and GHZ theorems, and with the local measurement axiom (Postulate M) proposed in a companion paper. Bell’s theorem does not force nonlocality upon our framework because it presupposes outcome determinism — the assignment of definite values to observables prior to measurement — which several quantum mechanical theorems and our ψ-ontic ontology explicitly deny. Decoherence, which is ubiquitous in nature, provides the mechanism by which the global wavefunction factorizes and classical separability emerges. The apparent nonlocality of quantum mechanics is thus reinterpreted as nonseparability: the fundamental ontology is holistic, but the dynamics are local.

Abstract: Background: Autism spectrum disorder (ASD) affects approximately 1-2% of children worldwide, yet its etiology remains incompletely understood. Emerging evidence suggests that offspring of parents with autoimmune diseases show elevated autism prevalence. Notably, children of parents or mothers with immune-related conditions, including psoriasis (OR 1.59), maternal type 1 diabetes (HR 2.36 in one large cohort study), and rheumatoid arthritis (OR 1.51), show elevated ASD-associated risk estimates.Hypothesis: I propose that autism may be conceptualized as an immune-metabolic disorder in which multiple pro-inflammatory cytokines—including TNF-α, IL-6, IL-1β, and IFN-γ—act through distinct molecular pathways yet converge on a common endpoint of mitochondrial dysfunction and cerebral energy deficiency. This convergence implies that it is the cumulative prenatal inflammatory burden, rather than any single cytokine, that drives the energy deficit. The resulting energy shortage may impair four critical processes: (1) synaptic pruning during neurodevelopment, (2) real-time social cognition including gaze processing and emotion recognition, (3) protein synthesis of critical synaptic scaffolding molecules, and (4) flexible hierarchical predictive inference. The last domain offers a unifying bioenergetic interpretation of restricted repetitive behaviors and insistence on sameness as a behavioral compensation for chronic cerebral energy constraint. Crucially, the resulting mitochondrial dysfunction is proposed to persist beyond birth, with the gap between cerebral energy demand and supply widening during the rapid brain growth of the first postnatal years. This developmental trajectory may help explain the typical emergence of clinical symptoms between 12 and 24 months of age, the selective vulnerability of high-metabolism brain regions, and the regressive pattern observed in a substantial subset of ASD cases.The proposed mechanism is a chronic low-grade pro-inflammatory cytokine state—clinically silent, yet biologically consequential—arising from inherited inflammatory susceptibility and/or direct fetal exposure to elevated maternal inflammatory signaling during pregnancy. Unlike high-grade inflammatory states in which maternal and fetal survival are acutely threatened, low-grade cytokine elevations may proceed without conspicuous symptoms or detectable clinical signs, particularly when chronic. Although seemingly quiet, such a state may be insufficient to endanger maternal or fetal survival, yet sufficient to disrupt fetal brain bioenergetics during sensitive gestational windows—producing neonates who appear outwardly healthy at term while their neurodevelopmental trajectories have already been altered.I further propose that the well-documented "firstborn effect" in autism reflects maternal immune maladaptation during primigravid pregnancies. Additionally, for cases without parental autoimmune history, a speculative secondary mechanism is proposed: mitonuclear incompatibility, in which paternally inherited nuclear genes encoding mitochondrial proteins may be imperfectly matched to the maternally inherited mitochondrial genome, impairing mitochondrial function and thereby generating endogenous pro-inflammatory (DAMP-driven) signaling.Implications: This framework may provide an integrative account of disparate observations about autism pathophysiology by uniting prenatal initiation with postnatal persistence into a single developmental trajectory. It suggests that pro-inflammatory immune pathways and mitochondrial dysfunction merit further investigation as candidate targets for risk modification, with the prenatal period offering opportunities for identification of high-risk pregnancies through parental autoimmune or inflammatory disease, and the early postnatal period offering an additional window for longitudinal characterization of mitochondrial and bioenergetic trajectories. If supported by sufficient subsequent evidence, prenatal cytokine monitoring and prospective postnatal mitochondrial assessment—neither of which is currently part of routine care—may merit consideration by the medical community as complementary candidate strategies for autism risk research.

Abstract: Artificial intelligence (AI) is increasingly influencing nanopharmaceutical development by supporting the transition from empirical formulation screening toward predictive, data-driven, and translationally oriented design. Nanocarrier-based therapeutics are governed by nonlinear relationships among material composition, physicochemical attributes, manufacturing parameters, biological identity, pharmacokinetics, toxicity, and therapeutic performance. In this review, we examine how AI can contribute to nanopharmaceutical development from predictive formulation design to clinical translation. We synthesize current applications of machine learning, deep learning, physics-informed modeling, hybrid mechanistic–AI approaches, and automated optimization workflows, with emphasis on critical quality attribute modeling, multi-objective optimization, design of experiments, quality-by-design, process analytical technology, digital twins, and continuous manufacturing. We also discuss applications involving nano–bio interactions, pharmacokinetics, toxicity, immunogenicity, and precision nanomedicine. AI-based approaches can support rational nanocarrier design, identify nonlinear formulation–property relationships, guide optimization, improve process understanding, and integrate heterogeneous experimental, biological, and manufacturing datasets across diverse nanopharmaceutical platforms. These methods are particularly relevant for modeling protein corona formation, cellular uptake, intracellular trafficking, biodistribution, pharmacokinetics, toxicity, immunogenicity, and patient-specific responses. However, translational implementation remains limited by fragmented datasets, inconsistent reporting standards, limited interpretability, insufficient external validation, uncertain predictions, poorly defined applicability domains, and evolving regulatory expectations for adaptive computational models. Overall, AI should be viewed not only as an optimization tool, but also as a translational framework connecting formulation science, biological prediction, manufacturing control, and clinical implementation. Future progress will depend on standardized data infrastructures, explainable and externally validated models, uncertainty quantification, applicability-domain definition, hybrid mechanistic–AI frameworks, regulatory-ready documentation, and clinically relevant case studies.

Abstract: The paper focusses upon the challenge of uptake and scaling of sustainable housing development models for the UK. It introduces a newly created Place Building System as an emergent framework designed to challenge the dominant, volume-led practices of national housebuilders and support the transition towards more socially equitable, environmentally regenerative, and economically resilient forms of development. The Place Building System was developed through a transdisciplinary, participatory methodology, engaging a Steering Group of senior UK development stakeholders. This co-creation arena enabled iterative learning, critical interrogation, and the evolution of both the Place Building System and its supporting infrastructure: the Regional Building Foundation. Framed through Geels’ Multi-Level Perspective (MLP) of sociotechnical transitions, the Place Building System is understood as a niche innovation searching for traction, with the capacity to challenge and reconfigure entrenched regime logics within the housing development sector. Mobilising the MLP framework, the paper demonstrates how systemic change in housing development may unfold through the interaction of niche innovations, landscape pressures, and regime destabilisation and reorientation, while recognising the institutional, cultural, and structural shifts required for such a transition to take root. In doing so, it specifies how regime-level lock-ins associated with finance, planning, and project governance condition niche maturation and shape the pathways through which regime reconfiguration may occur. Empirical illustrations show how place building principles operate as niche practices offering alternative sociotechnical configurations. By integrating empirical insight with transition theory, this paper contributes a novel conceptualisation of place building for sustainability as a systemic intervention and considers how it might be scaled to gain traction and reshape the future of the housing development sector in the UK.

Abstract: This study examines how investment priorities for sustainable rural development are shaped when financial, environmental, social, and institutional criteria are evaluated simultaneously. Using the Analytic Hierarchy Process (AHP), the study assesses six investment alternatives: eco-tourism, agro-tourism, renewable energy, digital tourism, sustainable agriculture, and cultural tourism. The results reveal the dominance of financial performance and risk considerations, which together account for more than two-thirds of total decision weight. Renewable energy emerges as the highest-ranked investment alternative, whereas agro-tourism and sustainable agriculture remain under-prioritized despite their environmental and social benefits. A comparative scenario analysis demonstrates that policy-oriented weighting structures substantially alter investment rankings, increasing the attractiveness of locally embedded and sustainability-oriented activities. The findings suggest a structural divergence between market-driven capital allocation and broader rural development objectives. By integrating sustainable finance and rural development within a multi-criteria decision-making framework, the study provides practical insights for investors and policymakers seeking to align investment decisions with long-term sustainability goals.

Abstract: Epilepsy is a challenging brain disease that requires significant clinical findings. (1) Background: The aim of this study is to improve the success rate of epilepsy detection using a newly developed method by optimizing the high-dimensional dataset obtained from brain MRI images. Standard machine learning models fall short of achieving the desired success in high-dimensional datasets. To achieve this, we aimed to develop an optimized hybrid model by combining the local classification power of the k-Nearest Neighbor classifier and the anomaly detection success of the Negative Selection Algorithm. (2) Methods: Cortical and subcortical brain regions were analyzed to examine volumetric differences. A dataset was created by identifying regions statistically significant for epilepsy. This dataset was then optimized using the Scatter Search Snake Optimization algorithm. The performances of six different machine learning models trained on this optimized dataset were compared. (3) Results: The standard and popular models, SVM (78.70%), kNN (77.30%), RF (77.30%), MLP (74.30%), and NSA (93.33%), demonstrated a success rate. In contrast, the proposed hybrid model, kNN-NSA (98.67%), demonstrated a detection success rate. (4) Conclusions: The optimized hybrid kNN-NSA approach, which considers local density in such high-dimensional datasets and tolerates outliers within the self-data, appears to outperform traditional methods. Furthermore, this study has demonstrated that volumetric differences in regions not previously reported in the literature, such as WM-hypointensities, ventral DC, and choroid plexus, may be effective in the decision-making process for diagnosing epilepsy, as they are also found to be significant.

Abstract: Catalonia has a Mediterranean climate with intense, long drought or rainy periods, which are difficult to manage. The variable topographic and sea conditions also contribute to modulating the extreme atmospheric regime. The aim of this research is to model the precipitation regime of the region based on different radar and lightning fields. To conduct the analysis, several points have been selected with different heights to evaluate the monthly values and establish the common yearly patterns. It has been observed that there are two principal behaviours: single and double maxima modes. Single peak usually occurs during warm months (mainly July and August), meanwhile the bimodal maxima are concentrated on Spring (March) and Autumn (October or November).

Abstract: Vegetation in watercourses is increasingly recognized as a vital Nature-Based Solution (NBS) for ecosystem preservation. However, from a hydraulic perspective, rigid emergent vegetation also increases flow resistance, which can lead to higher water levels if not properly managed. To calculate the water surface profiles, it is essential to assess the drag coefficient values. Numerous formulas have been proposed in the literature for staggered and random arrangements, while relatively fewer ones have been suggested for linear configurations. Generally, these formulas were derived under uniform flow conditions, and it remains uncertain whether they are relevant for steady-flow conditions, which are more commonly encountered in natural settings. Consequently, the reliability of the estimated drag coefficients is often questionable. In this study, four formulas from the literature, derived for linear arrangements, and one formula for staggered and random arrangements, were employed to simulate thirty-six water surface profiles from two experimental series. These profiles span a broad range of vegetation densities, from 0.008 to 0.42. The profiles belong to accelerated subcritical flow (M2 type), and the standard step method was applied for their simulations. A comparison between the experimental and computed profiles was performed using several statistical parameters, particularly the Taylor diagram. One of the equations analyzed was applied over the aforementioned range of vegetation density, and the computed profiles exhibited a root mean square relative error of approximately 6% compared to the experimental data. The best-performing equation is dependent solely on vegetation density and is likely applicable to higher Reynolds numbers than those encountered in the experimental conditions. Finally, providing a reliable tool for estimating flow resistance is crucial for the successful implementation of vegetative NBSs, allowing engineers to perfectly balance flood discharge capacity with environmental sustainability.

Abstract: Cortisol, as a crucial biomarker reflecting psychological stress and physiological status, requires rapid and sensitive detection for health assessment and disease diagnosis. Conventional methods are time-consuming, operationally complex, and costly, limiting their use for point-of-care testing. This study reports a flexible, aptamer-based capacitive biosensor that exploits alternating current electrokinetics for ultrafast detection of cortisol in small-volume samples. Aptamers are immobilized via Au-S self-assembly on gold interdigitated electrodes on a PET substrate, and ACEK-induced fluid motion and dielectrophoresis rapidly enrich cortisol at the electrode interface, producing measurable interfacial capacitance changes ΔC/C₀. Experimental results demonstrate detection limits of 0.412 ng/mL in PBS and 0.337 ng/mL in artificial sweat, with response times within 1 minute and excellent linear response across 1-1000 ng/mL concentrations. Requiring only 10 μL of sample, the sensor exhibits good repeatability, specificity, and interference resistance, making it suitable for rapid cortisol level detection. To enhance detection stability, this study designed and integrated a microfluidic chip, enabling efficient sample delivery and stable detection. The system demonstrates strong interference resistance, revealing potential applications in health management and disease monitoring.

Abstract:

The bacterial proteome is a highly dynamic landscape rather than a static reflection of the genome. Recent research revealed that proteome complexity extends far beyond canonical gene annotation, with N-terminal (Nt-)proteoforms emerging as an important underexplored additional regulatory layer. These molecular variants originate from a single genetic locus through alternative translation initiation at internal or external in-frame start sites, thereby generating N-terminal heterogeneity that can influence protein stability, subcellular localization, interaction networks, and the stoichiometric assembly of multiprotein complexes. While recent advances in riboproteogenomics, N-terminomics, and computational annotation strategies have enabled proteoform mapping at single-amino acid resolution, rapid high-throughput discovery currently outpaces downstream functional characterization. This review discusses the technological advances driving Nt-proteoform discovery, including emerging ribosome profiling and proteogenomic approaches, and further evaluates strategies for the functional characterization of Nt-proteoform. Particular emphasis is placed on the transition from conventional plasmid-based heterologous expression systems toward precise genome-engineering approaches that enable selective manipulation of alternative translation initiation events within their native genomic context. Such targeted strategies are essential to bridge the gap between Nt-proteoform identification and functional understanding, ultimately uncovering how individual bacterial genomic loci can encode proteoforms with distinct and potentially polarized roles in bacterial physiology and pathogenesis.

Abstract: This paper proposes a new four-parameter statistical distribution based on the neutrosophic Gompertz (NGo-G) family and the extended Nadarajah-Haghighi distribution in neutrosophic logic called neutrosophic Gompertz Nadarajah-Haghighi (NGoNH) distribution to deal with uncertain and indeterminate data, known as neutrosophic data. The basic distribution functions and some properties are derived and the parameters of the proposed distribution are estimated using three different methods. To compare the performance of the different estimation methods, numerical simulations are performed using the evaluation criteria: MSE, RMSE, and bias. NGoNH distribution is applied to real data set representing the monthly minimum and maximum temperatures in Lahore, Pakistan, for the period 2016-2020. To verify the consistency of the data with the proposed distribution, the properties of the neutrosophic data are tested and the three components (True, Indeterminate, False) are plotted. In addition, the performance of the proposed distribution is compared with six other neutrosophic distributions using some information criteria and some goodness-of-fit tests. The extent to which the data fit the proposed distribution is plotted to demonstrate its effectiveness. The results indicate that the proposed distribution provides a better fit to neutrosophic data than other distributions, which enhances its effectiveness in analyzing data with uncertainty.

Abstract: This study examines the role of smart digital systems in shaping the tourist experience, with a particular focus on perceived safety, comfort, and convenience, and their relationship with satisfaction and future intention to use digital services. The research was conducted on a sample of 104 respondents using a questionnaire and a quantitative approach. The results show that perceived comfort and convenience have the strongest impact on satisfaction with the tourist experience, while perceived safety, although significant, has a weaker effect. Satisfaction, perceived usefulness, and comfort significantly influence the intention to use smart digital services. The findings also indicate that experience with digital technologies positively affects perceived safety and comfort, while gender differences were not statistically significant. The results highlight the importance of developing simple, intuitive, and user-oriented digital solutions in tourism.

Abstract: This work presents an advanced Structural Health Monitoring (SHM) system for the refined identification of structural micro-defects, achieving a relative dimensional error of less than 1% for characterizing high-aspect-ratio damage geometries. The system integrates coaxial microscopic imaging with a precision motorized scanning stage. To ensure high-fidelity measurements in early-stage warning applications, depth is determined using a focus variation method driven by a robust data fusion strategy. By capturing a sequence of images along the Z-axis, the focal planes of the defect’s surface orifice and internal base are automatically identified using a data fusion algorithm based on a consensus evaluation of three parallel sharpness metrics (Tenengrad, Laplacian, and Brenner variants). The Z-axis scanning module, featuring encoder feedback and bi-directional compensation, achieves a repeated positioning error of ±0.5µm. For lateral damage assessment, the system’s high magnification provides an effective sampling resolution of 0.09µm. The equivalent diameter of the focused orifice image is calculated through a robust data fusion pipeline involving adaptive thresholding, morphological filtering, and sub-pixel ellipse fitting, which serves as a highly sensitive indicator for early-stage structural deformation. The entire process can be completed within five minutes, demonstrating a rapid, highly accurate, and localized optical inspection solution that generates high-precision dimensional data crucial for Digital Twin modeling in aerospace and precision engineering.

Abstract: Electrical power systems are exposed to interacting electrical, thermal, environmental, and resource-related faults such as leakage current, voltage and frequency deviations, overcurrent, harmonic distortion, phase-sequence error, humidity, fire, wind-speed variability, water insufficiency, and solar-resource loss. This study introduces a software-based database-generation and smart-learning framework that converts 22 candidate risk factors into six normalized severity levels and then maps the simultaneous system state to low-, medium-, and high-level protection decisions. The main novelty is that the software does not only evaluate existing measurements; it also produces a literature- and standards-informed synthetic database when long-term real field measurements are not yet available. The database is generated by defining variable limits, sampling realistic operating states, computing severity labels, and storing input-output pairs that can later train or validate predictive maintenance models. The proposed framework therefore links protection logic, database construction, and reusable training data in a single workflow. The results show how simulated annual operating scenarios can be transformed into structured risk records, warning classes, and shutdown decisions, supporting early fault detection, maintenance planning, and resilience improvement in renewable-integrated electrical networks.

Abstract: Background: Management of post-extraction sockets with buccal dehiscence in the esthetic zone remains clinically challenging, particularly when immediate implant placement is indicated. Conventional approaches often rely on guided bone regeneration (GBR) with biomaterials, which may increase surgical complexity and morbidity. This case report evaluates the clinical and radiographic outcomes of a fully autologous approach combining leukocyte- and platelet-rich fibrin (L-PRF) and connective tissue graft (CTG) in conjunction with immediate implant placement. Methods: A 50-year-old healthy patient presenting with a fractured maxillary lateral incisor and buccal bone dehiscence underwent atraumatic extraction, immediate implant placement, and simultaneous site management using L-PRF membranes and CTG. Clinical and radiographic evaluations were performed at baseline (T0) and after 12 months (T1). Cone-beam computed tomography (CBCT) was used to assess horizontal bone thickness (HBT) at multiple apico-coronal levels and vertical evaluation parameters, including nasal floor–crest distance (NF–AC) and buccal dehiscence height (BDH). Clinical outcomes included keratinized tissue width (KT), gingival thickness (GT), and patient-reported outcome measures (PROMs). Results: Radiographic analysis demonstrated increased HBT at all levels (+1,4 mm at 12 mm, +3,1 mm at 15 mm, +3,2 mm at 18 mm, and +2,6 mm at 21 mm). NF-AC showed a marked increase (+2,7 mm) and substantial reduction of the buccal dehiscence defect. Clinically, KT increased by +2,0 mm and GT by +1,8 mm. Healing was uneventful, with minimal postoperative discomfort and high patient satisfaction. Conclusions: Within the limitations of a single case, the combined use of L-PRF and CTG with immediate implant placement resulted in favorable clinical and radiographic outcomes, suggesting that a fully autologous, biologically driven approach may represent a viable alternative to conventional GBR in selected cases. Further controlled studies are required to confirm these findings.

Abstract: Recently, several studies have investigated the validity of General Relativity’s predictions. Gravitational waves provide an ideal probe for testing the theory in the strong field regime. In this work, we consider a class of modified-gravity theories, specifically f(R), and scrutinize their predictions for the gravitational-wave emission from the coalescence of two astrophysical compact objects. We also assess the impact of next-generation gravitational-wave detectors on the ability to test these extensions of General Relativity.

Abstract: The interleaved parallel Buck–Boost converter can reduce the output voltage ripple and has been widely used in engineering practice. The application of fractional-order theory has a significant influence on model accuracy and power converter performance. Based on fractional calculus theory and the state-space averaging method, this paper establishes a fractional-order mathematical model of the CCM interleaved parallel Buck–Boost converter. The steady-state operating point and ripple characteristics of the converter under the Caputo fractional-order definition are analyzed and compared with those under other fractional-order definitions. Fractional-order energy storage elements are constructed, and a fractional-order circuit simulation model of the converter is established for comparative simulation analysis. Finally, experiments are carried out to verify the effectiveness of the theoretical analysis.

Abstract: Middle school is a key window for building core academic skills and the learning routines students carry into later grades, yet many students still fall behind because help is often limited and comes too late, after they have already been stuck for a while. Learning Management Systems (LMSs) are now standard infrastructure for distributing materials, collecting work, assessing students’ tasks, and recording grades, but in most deployments they still behave more like workflow tools than instructional supports. The result is the usual bottleneck: students keep practicing through confusion, teachers triage questions, and feedback that could have corrected the misunderstanding arrives after the misconception has already hardened. To address this gap, we propose an AI-integrated LMS for middle school instruction, paired with a longitudinal study design to test whether sustained, bounded AI support changes outcomes through high school and into post-high school pathways. The proposed platform adds policy-gated AI assistance to everyday coursework, delivering formative feedback and hinting, recommending spaced review and adaptive practice based on mastery, and providing teacher-facing dashboards that summarize misconception patterns and flag sustained struggle. Because the platform is intended for minors, the design is privacy-first, using data minimization, role-based access control, age-appropriate response constraints, and auditable logs of AI interactions. Beyond short-term performance, the evaluation plan links fine-grained learning traces (attempts, revisions, help-seeking, and pacing) to institutional outcomes where feasible, so we can separate tool adoption effects from longer-run changes in learning trajectories.

Abstract: The Kidney Disease: Improving Global Outcomes (KDIGO) classification of chronic kidney disease (CKD) is based on the cause of disease, the category of estimated glomerular filtration rate (eGFR), and the category of albuminuria. This framework is indispensable for risk stratification, yet it does not always identify the functional and hemodynamic mechanism that maintains the current filtration level and drives future progression. In particular, a normal or only moderately reduced eGFR does not exclude relative hyperfiltration of the remaining nephrons, and albuminuria reflects not only glomerular barrier injury but also the limited capacity of the proximal tubule to endocytose and metabolically process filtered proteins. In this conceptual review, we propose a functional-hemodynamic extension of the KDIGO CGA model: Cause + GFR + Albuminuria + Functional Renal Reserve + Blood Pressure. Within this framework, functional renal reserve (FRR) is considered a dynamic stress test of nephron reactivity, whereas blood pressure acts as an essential hemodynamic and therapeutic modifier that influences the safety, sequencing, and intensity of renoprotection. Detailed antihypertensive treatment is beyond the scope of this article; the KDIGO 2021 recommendation of a target systolic blood pressure below 120 mmHg in adults with CKD and elevated blood pressure, when tolerated and measured in a standardized manner, is used as a clinical reference point. A central element of the proposed algorithm is FRR. A zero or negative FRR under standardized testing may indicate an "actionable hyperfiltration phenotype": a clinically meaningful state in which total eGFR does not reflect the true workload imposed on individual nephrons. Depending on the combination of urinary albumin-to-creatinine ratio, eGFR, FRR, blood pressure, metabolic phenotype, and tubular overload markers, the proposed approach may support RAAS-blockade-first, SGLT2-inhibitor-first, early dual therapy, or staged triple renoprotection. A mechanistic distinction is also emphasized. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) directly inhibit proximal tubular sodium and glucose reabsorption, but their key anti-hyperfiltration effect is mediated by increased sodium delivery to the macula densa, restoration of tubuloglomerular feedback, and increased afferent arteriolar tone. By contrast, renin-angiotensin-aldosterone system inhibitors (RAASi) predominantly modulate the efferent/postglomerular compartment and may improve downstream peritubular perfusion, a mechanism potentially relevant to albumin and protein handling by the proximal tubule. The proposed model does not replace KDIGO; rather, it adds physiological phenotyping to the existing risk map. More precise evaluation of proteinuria, including the albumin-to-total-protein ratio, first-morning urine sampling, low-molecular-weight proteins, and tubular markers, may prevent misclassification of physiological, orthostatic, postglomerular, or tubular proteinuria as progressive glomerular CKD. The model requires prospective validation but may help develop practical algorithms for personalized renoprotection, particularly in patients with low or moderate albuminuria, normal or moderately reduced eGFR, diabetes, obesity, hypertension, solitary kidney, reduced nephron mass, or an uncertain progression trajectory.