Search Results (53,088)

Accurate and non-destructive volume estimation of agricultural products is essential for precision agriculture, yet remains challenging when transitioning from controlled laboratory conditions to complex orchard environments. Although 2D image-based volume estimation methods provide a cost-effective and scalable solution, existing studies are fragmented and lack a unified perspective on their real-world applicability. This review presents a systematic synthesis of 2D image-based volume estimation methods, explicitly framed through the laboratory-to-orchard transition. We categorized existing volume estimation approaches according to the sensing modality into monocular RGB-based approaches and depth-assisted methods, and further reviewed them based on the image processing methods. A key finding is that high-precision geometric estimation can be achieved in laboratory environments, whereas deep learning and RGB-D fusion have driven a shift from conventional geometric modeling toward data-driven and hybrid learning frameworks in orchard settings. However, 2D image-based volume estimation remains fundamentally limited by scale ambiguity, severe occlusion, and sensitivity to illumination and background variability in real orchard environment. Overall, this review provides a unified perspective for understanding volume estimation methodology across environments and offers guidance for developing robust, scalable, and field-deployable volume estimation systems for real-world agricultural applications. Full article

The Late Eocene Huizhou-A sandy conglomeratic system in the Pearl River Mouth Basin presents a highly heterogeneous reservoir system shaped by intense synsedimentary fault activity and variable depositional processes. Utilizing 3D seismic interpretation, well log analysis, and core calibration, this study reconstructs the tectono-sedimentary evolution, facies distribution, and diagenetic modifications controlling reservoir quality. Results show that the best reservoir quality is not confined to proximal fan-delta coarse-grained deposits near steep boundary faults, but occurs mainly in fan-delta front and braided-river-delta deposits, especially braided- and turbidite-channel microfacies. These reservoirs benefit from better sorting, favorable grain size, and higher textural maturity, whereas proximal clastic-flow deposits are poorer due to heterogeneity, poor sorting, and compaction. Reservoir quality is also depth-dependent: upper Enping reservoirs are mainly controlled by maturity, while lower Enping reservoirs are more influenced by grain size. Semi-quantitative analysis identifies the 7–11 km transport-distance zone as the optimal fairway for vertically stacked high-quality reservoirs. This approach not only guides exploration and development in the Huizhou Sag but also offers a transferable predictive model for similar steep slope lacustrine rift basins with comparable tectono-sedimentary settings worldwide. Full article

Predicting the evolution of microstructure and field quantities under varying processing and loading conditions is a central challenge in computational materials science and metal additive manufacturing (AM). While deep learning (DL) methods offer ultra-fast prediction capabilities post-training, existing models often struggle with poor spatial and temporal extrapolation, high parameter burdens, and an inability to effectively integrate diverse conditioning parameters alongside high-dimensional input fields. To address these bottlenecks, we propose a novel conditional generative adversarial network (CPGAN), which is designed to seamlessly ingest both initial fields and governing condition parameters. The CPGAN framework offers three distinct advantages: (1) it accurately maps the combined effects of initial states and process conditions onto evolved fields; (2) it demonstrates robust extrapolation capabilities across diverse spatial and temporal scales, including the unique ability to natively generate high-resolution rectangular domains; and (3) it achieves superior predictive accuracy and training stability compared to standard convolutional baselines by effectively suppressing spurious artifacts. We validate CPGAN’s performance against rigorous physics-based ground truths across three representative engineering applications: porosity evolution in selective laser sintering (SLS), spatial distribution of 2D von Mises stress fields in solid structures, and the spatiotemporal evolution of grain growth. The results confirm that CPGAN is a highly adaptable and efficient surrogate model, capable of simulating continuous structural and morphological evolutions even when driven by highly non-uniform spatial or temporal kinetics. Full article

Ceramic additive manufacturing offers strong potential for fabricating geometrically complex and application-specific components, yet achieving reliable dimensional fidelity remains challenging because dimensional deviation is governed by highly coupled material, process, thermal, and environmental factors. To address this problem, this study proposes an uncertainty-aware computational framework for dimensional error prediction in ceramic 3D printing under variable material and process conditions. The contribution is positioned as a system-level integration of established learning, uncertainty estimation, calibration, and reliability-interpretation components within a ceramic additive manufacturing dimensional-error prediction workflow, rather than as a fundamental methodological breakthrough. The validation is conducted using the publicly available Ceramic 3D Printing Process Control Dataset, a 1000-sample tabular dataset, and the resulting findings are therefore interpreted as dataset-specific computational evidence rather than direct proof of industrial deployment readiness. The methodology begins with a structured data-driven preprocessing pipeline that transforms the Ceramic 3D Printing Process Control Dataset into a multi-condition feature space through data cleaning, one-hot material encoding, min–max normalization, and engineered descriptors capturing extrusion–speed balance, thermal gradients, cooling intensity, deposition density, and material-conditioned interactions. A multi-branch deep computational architecture is then developed to encode material, process, thermal-environmental, and engineered-feature streams separately, followed by adaptive cross-condition fusion to learn nonlinear dependencies across ceramic printing regimes. To improve reliability beyond deterministic regression, the framework jointly models aleatoric and epistemic uncertainty and incorporates calibration refinement to align predictive confidence with observed error behavior, thereby enabling preliminary reliability-oriented interpretation of stable and high-risk operating conditions. Experimental results demonstrate that the full model achieves the best overall within-dataset performance, with a test MAE of 0.0118, RMSE of 0.0172, $R^2=0.999$, MAPE of 1.74%, calibration error of 0.003, PICP of 0.996, reliability score of 0.992, and a stable prediction rate of 98.7%. Although these values indicate strong predictive behavior under the current structured dataset, the exceptionally high $R^2$ should be interpreted cautiously because external experimental validation, larger measured datasets, and cross-machine ceramic printing trials are still required. These findings show that the proposed framework provides an effective system-level computational strategy for dataset-specific reliability-aware dimensional quality prediction in ceramic additive manufacturing and offers a preliminary data-driven foundation for uncertainty-aware intelligent process optimization. Full article

Background: Electroencephalographic (EEG) functional connectivity analysis requires multiple signal-processing, source-modelling, and statistical steps that can limit its adoption in clinician-led randomised controlled trials (RCTs). NeuroStat was developed as a prototype research tool to integrate this workflow; formal usability validation with clinician end-users has not yet been conducted. Methods: NeuroStat is an open-source Python/PyQt6 desktop application that integrates automated artefact removal (a Generalised Eigenvalue Decomposition for Artefact Identification [GEDAI] pathway and a traditional Artefact Subspace Reconstruction (ASR)/Independent Component Analysis (ICA)/ICLabel pathway), boundary element model (BEM) source localisation using the Desikan–Killiany atlas (68 cortical regions), Phase Lag Index (PLI) connectivity estimation across five canonical frequency bands, and RCT-oriented statistical analysis. Evaluation separated sensor-space and source-space claims: a sensor-level simulation (repeated across five independent random seeds) tested preprocessing robustness, a repeated source-space simulation tested recovery of a known cortical parcel-pair contrast after forward projection and inverse reconstruction, a PhysioNet benchmark tested posterior Desikan–Killiany alpha PLI in 20 healthy adults, and an illustrative application to 20 sessions from a published chiropractic RCT demonstrated real-world workflow applicability. Results: In the sensor-level simulation benchmark, the Traditional pathway achieved a mean absolute error of $0.168\pm 0.017$ PLI units and root mean squared error of $0.219\pm 0.045$ (mean ± SD across five independent random seeds) across all artefact conditions. In the source-space simulation, reconstructed alpha PLI for the known bilateral lateral-occipital parcel pair exceeded anterior control edges across 60 repeated condition runs (mean known-control difference = 0.105 PLI units, 95% CI 0.096–0.114; $t\left(59\right)=22.61$, $p<0.001$). In the PhysioNet source-space benchmark, posterior Desikan–Killiany alpha PLI was higher during eyes-closed than eyes-open rest (Cohen’s $d=0.85$, $p=0.001$; 16/20 subjects showing the expected direction) after ICLabel-enabled preprocessing. In the pilot RCT application, all 20 sessions completed processing without manual intervention, with default-mode network alpha PLI showing a pre-to-post change of +0.071 in the intervention group versus +0.015 in the active control group. Conclusions: NeuroStat integrates preprocessing, source-space construction, connectivity estimation, and statistical reporting within a parameter-logged desktop workflow for EEG functional connectivity studies. Current evidence supports initial technical feasibility, sensor-level preprocessing robustness for one pathway in controlled simulations, source-space recovery of a known parcel-level contrast, source-space sensitivity to an expected posterior alpha resting-state contrast, and error-free processing across 20 real RCT sessions in a pilot workflow demonstration. Formal usability testing, test–retest reliability analysis, participant-specific source-model validation, and clinical-population validation remain necessary before clinician-facing or trial-deployment claims can be made. Full article

Objectives: Neuroinflammation is recognized as a significant characteristic of Alzheimer’s disease (AD). Currently, there is a notable absence of effective pharmacological agents to prevent or treat neuroinflammatory processes associated with AD. Heat shock protein 70 (HSP70) is pivotal in the progression of neuroinflammation. In this study, we explored the potential of maltol, a Maillard reaction product derived from red ginseng, as a therapeutic agent for neuroinflammation. Methods: In vitro, HMC3 microglial cell models were developed to examine the regulatory effects of gradient concentrations of maltol (12.5, 25, 50 μM) on the TLR4/MyD88/NF-κB p65 signaling pathway, neuroinflammation, and pyroptosis. Analyses of the GEO database and Gene Set Enrichment Analysis (GSEA) were performed to identify the core targets of maltol, followed by HSP70 gene silencing experiments to validate the targeted regulatory mechanism. Results: Maltol significantly mitigated LPS-induced neuronal damage and cognitive deficits in mice. It effectively suppressed microglia-mediated neuroinflammation and pyroptosis, reversed oxidative stress-induced neuronal ferroptosis, and inhibited neuronal apoptosis. In vitro experiments demonstrated that maltol obstructed TLR4/MyD88 binding, thereby inhibiting NF-κB p65-mediated neuroinflammation and pyroptosis, while also alleviating excessive ROS accumulation to enhance oxidative stress and ferroptosis. Bioinformatics analysis identified HSP70 as a crucial target for the anti-inflammatory and antioxidant effects of maltol. Subsequent gene silencing experiments confirmed that maltol exerted its inhibitory effects on LPS-induced neuroinflammation and pyroptosis in an HSP70-dependent manner. Conclusions: Maltol exhibits significant protective effects against Alzheimer’s disease-related neuroinflammation, oxidative stress, pyroptosis, and ferroptosis through the targeting of HSP70. This study elucidates the molecular mechanisms by which maltol improves neuroinflammatory injury and provides a novel theoretical foundation and therapeutic strategy for the intervention of Alzheimer’s disease neuroinflammation using traditional Chinese medicine. Full article

Background/Objectives: Immune surveillance is increasingly recognized as a modifier of myeloproliferative neoplasm (MPN) initiation and evolution, yet the contribution of the NKG2D receptor and its ligands MICA/MICB to CALR-mutated disease remains unclear. Methods: We performed high-resolution next-generation sequencing genotyping of MICA and MICB in 43 patients with CALR-mutated MPN (WHO 2022 criteria) and compared the allele and haplotype distributions with those of 156 healthy Bulgarian controls and 85 patients with JAK2 V617F-positive MPN. Associations were tested using age- and sex-adjusted additive generalized linear models; bi-locus haplotypes were evaluated using haplotype score methods. In a genotyped subgroup (35 CALR-mutated MPN patients and 105 controls), functional KLRK1 (NKG2D) polymorphisms were analyzed for haplotype-level associations. We also performed 700 ns molecular dynamics simulations of selected MICA variants in complex with NKG2D and reanalyzed publicly available single-cell RNA-sequencing data (GSE117826) and RNA-sequencing data from CRISPR/Cas9-edited CALR-mutant iPSC-derived megakaryocytes to evaluate MICA/MICB expression. Results: MICA*004:001 was significantly associated with CALR-mutated MPN versus controls (p = 0.004; Bonferroni-adjusted p = 0.047), while MICB*008:001 showed only nominal association. Exploratory haplotype analyses identified a MICA*009:01-MICB*004:001 haplotype associated with CALR-mutated status (p = 0.008) and a KLRK1 G-A-G-T haplotype (rs1049174-rs2617160-rs2246809-rs2617170) associated with increased CALR-mutated MPN risk (OR = 3.61; p = 0.029). Transcriptomic reanalysis indicated a higher fraction of CALR-mutant stem and progenitor cells expressing detectable MICA/MICB transcripts, and heterozygous CALR-mutant megakaryocytes exhibited higher MICA expression than the wild type. Conclusions: Together, these data support an exploratory immunogenetic and transcriptomic link between the NKG2D-MICA/MICB axis and CALR-mutated MPN, but direct protein-level and functional studies are required before mechanistic or therapeutic conclusions can be drawn. Full article

This study introduces a Caputo fractional-order version of the SEIRS-V model to investigate the spreading dynamics of worms within wireless sensor networks. Traditional integer-order worm propagation models describe the instantaneous evolution of network states; however, they do not adequately account for memory and hereditary characteristics that may influence the transmission dynamics. Consequently, their ability to represent realistic network behavior can be limited in systems where past states affect current propagation patterns. The framework divides sensor nodes into susceptible, exposed, infectious, recovered, and vaccinated classes, while explicitly incorporating worm transmission rates, temporary loss of immunity, and the impact of preventive security measures under limited resource conditions. A detailed theoretical examination is performed, covering the existence, boundedness, and uniqueness of solutions of the fractional-order system. The coupled nonlinear fractional system is solved semi-analytically by means of the Fractional Reduced Differential Transform (FRDT) technique. To confirm accuracy and robustness, the identical system is also discretized and solved using the finite difference scheme (FDS). Unlike previous studies on worm propagation models in wireless sensor networks, which are mainly limited to equilibrium point analysis and qualitative investigations without deriving explicit solutions, the present work develops an approximate semi-analytical solution for the fractional-order SEIRS-V system using the FRDTM. Comparisons between the two solution sets demonstrate excellent agreement and high precision. Numerical outcomes are presented through a series of 2D graphical profiles that illustrate the time-dependent behavior of each compartment and reveal the sensitivity of worm propagation and suppression to variations in the fractional order and key model parameters. The integrated theoretical and computational findings underscore the strong protective role of vaccination in mitigating worm outbreaks and offer valuable guidelines for strengthening cybersecurity measures in wireless sensor networks. Full article

Background: Dedicated artificial intelligence (AI) systems for fracture detection already exist, yet general-purpose multimodal models are increasingly accessible to clinicians despite not being developed or formally validated as medical devices. Their behavior in focused orthopedic imaging tasks remains insufficiently characterized. Purpose: To characterize how two accessible general-purpose multimodal models interpret AP pelvis radiographs with hip fractures, focusing on context dependence, overconfidence, and complementary error patterns within a surgically confirmed positive-only cohort. This was a behavioral characterization study of a fracture-positive cohort, not a diagnostic accuracy evaluation. Methods: In April 2026, we retrospectively studied 214 surgically confirmed hip fractures on AP pelvis radiographs using two general-purpose multimodal models under six prompting conditions. In runs A–D, the models were explicitly told that a hip fracture was present and were asked to classify it; in runs E–F, they were not told whether a hip fracture was present. Each image was rerun de novo in a separate chat session through vendor APIs using a fixed base prompt and no image preprocessing. We recorded hip-fracture detection, correct laterality, coarse fracture pattern, intracapsular displacement, AO/OTA grading, subtrochanteric identification, and self-reported confidence. Because the cohort contained hip fractures only, we report fracture-detection rates and classification performance within a positive-only cohort rather than full diagnostic-accuracy metrics. Results: Using the more conservative endpoint of hip-fracture detection with correct laterality, GPT-5.4 was correct in 79.0% and 86.4% of cases in runs E and F, whereas Gemini was correct in 80.4% and 93.5%, respectively. When outputs from both models were combined, this endpoint reached 89.7% in run E and 96.7% in run F, indicating complementary rather than redundant error patterns. Incorrect laterality cues markedly degraded performance, from 90.7% to 66.4% in GPT-5.4 and from 97.7% to 57.0% in Gemini. Performance remained limited for treatment-relevant subtyping, particularly AO/OTA grading and subtrochanteric identification. Both models frequently remained highly confident when wrong, and self-reported confidence did not reliably distinguish correct from incorrect outputs. Conclusions: Accessible general-purpose multimodal models showed partial capability for coarse hip-fracture interpretation, but they remained context-sensitive, unreliable for treatment-relevant subtyping, and highly confident even when incorrect. Their complementary error patterns are hypothesis-generating rather than evidence of clinical readiness. On the basis of these findings, we do not support unvalidated or uncontrolled clinical use of such models. As access to these tools expands, explicit usage boundaries, minimum performance expectations, repeated local revalidation, and sustained human oversight become increasingly necessary. Full article

Traditional over-the-air (OTA) testing typically requires the device under test (DUT) to be positioned at the geometric center of the anechoic chamber, which limits the flexible evaluation of modern wireless terminals. Although the spherical vector wave (SVW) method provides a rigorous electromagnetic mode expansion, its direct use in eccentric testing scenarios is prone to coefficient-domain overfitting. In the conventional coefficient-domain formulation, the increased involvement of high-order evanescent modes can lead to overfitting of physically insignificant coefficients, resulting in unstable and oscillatory reconstruction. To explain this behavior, an analytical periodicity model is developed and validated by numerical simulations, showing good agreement across all tested configurations. To overcome this limitation, this paper develops a unified 3D eccentric spatial–spectral composite operator for eccentric field synthesis by directly incorporating the three-dimensional offset into the field evaluation process. The proposed operator maps probe excitation weights to the translated 3D local test-zone field samples, thereby reformulating the synthesis problem from coefficient-domain fitting to field-domain matching. This field-domain formulation naturally downweights high-order modal components with negligible local-field contributions, thereby improving numerical stability. Numerical simulations in a 3D multi-probe anechoic chamber (MPAC) demonstrate that, under significant eccentric conditions, the conventional SVW method essentially fails, while the plane wave synthesis (PWS) method achieves less accurate reconstruction than the proposed scheme. In contrast, the proposed scheme maintains stable, oscillation-free reconstruction and consistently outperforms PWS by 5 to 15 dB across all evaluated scenarios. This work provides a promising solution for flexible 3D OTA evaluation of large-scale wireless terminals. Full article

Multiple Myeloma (MM) is a hematological malignancy characterized by the clonal proliferation and survival of neoplastic plasma cells (PCs) within the bone marrow (BM), where disease progression is critically supported by interactions with the BM tumor microenvironment (TME). Despite significant advances in therapeutic strategies, MM remains incurable, underscoring the need for improved preclinical models to better understand the disease biology and therapeutic response. This review summarizes current and emerging MM treatment approaches and critically examines the development of models designed to more accurately recapitulate interactions between MM-PCs and the surrounding BM niche. We describe established and emerging modeling platforms, with emphasis on advanced three-dimensional (3D) culture systems and highlight their unique contributions to the preclinical assessment of both existing and novel therapies. The advantages of 3D models, including in vitro and in silico systems, over traditional two-dimensional (2D) models are discussed, alongside a comparative evaluation of scaffold-free and scaffold-based approaches. In addition, the benefits and recent advances in the customization of BM niche simulation using microfluidic technologies and organ-on-a-chip platforms are reviewed. The application of 3D models in MM research is increasingly enabling the study of disease pathogenesis, progression, drug resistance and precision-medicine approaches (informed by biomarker discovery). Although standardized preclinical approaches for evaluating MM therapeutics are currently lacking, the growing imperative to reduce reliance on preclinical animal models highlights the importance of alternate systems. Consequently, the development and adoption of physiologically relevant models that accurately recapitulate MM-PC interactions with the BM TME will be critical for advancing future therapeutic strategies in MM. Full article

Accurate object detection remains a major challenge in autonomous systems and surveillance, particularly when objects are partially or fully obscured by occlusions. To address this issue, we revisit FuDensityNet as a multimodal detection framework that jointly leverages 2D RGB images and 3D LiDAR point clouds for robust feature representation. The model integrates spatial and depth cues through low-rank tensor fusion (LRTF) and incorporates an Occlusion Rate (OR) assessment module that estimates the degree of occlusion and dynamically selects the most suitable detection pathway to preserve performance. Experiments on the KITTI and NuScenes datasets indicate that this adaptive strategy improves robustness under high occlusion while maintaining competitive accuracy in less challenging conditions. In particular, FuDensityNet attains 76.6% AP for car detection under “Hard” conditions on KITTI and outperforms several RGB-only and RGB–LiDAR baselines. Owing to its adaptive and modular design, FuDensityNet remains compatible with both 2D and 3D detection pipelines, making it a practical option for real-world environments where visual obstructions are frequent. Full article

3D magnetotelluric (MT) inversion based on the Gauss–Newton (GN) framework plays an important role in deep mineral exploration by imaging subsurface electrical conductivity structures. However, large-scale 3D MT inversion remains computationally expensive due to the high cost of sensitivity-matrix construction. To address this challenge, we develop a stochastic Gauss–Newton (SGN) framework that reduces computational cost through random data subsampling while preserving the practical convergence behavior of GN inversion. In the proposed framework, only a randomly selected subset of data is used to approximate the GN search direction. By exploiting a key property of MT forward modelling, namely that responses at all receivers are obtained simultaneously for each frequency, the line search is performed using the full dataset, ensuring stable convergence of the inversion process. The SGN framework is validated using both a synthetic multiblock model and a field dataset from the Akebasitao area in Xinjiang, China. The recovered models remain highly consistent with those obtained using conventional full-data Gauss–Newton inversion across a wide range of sampling ratios. For the synthetic example, reducing the sampling ratio from 100% to 10% decreases peak memory consumption from approximately 433 GB to 242 GB and reduces runtime from 86.8 h to 23.9 h while maintaining comparable inversion quality. Similar computational savings are achieved for the field-data inversion. The field application successfully recovers the major conductive structures along the margins of the intrusion that are associated with hydrothermal alteration and fluid activity, highlighting the capability of SGN to delineate geologically meaningful targets relevant to deep mineral exploration. These results demonstrate that SGN provides an efficient and scalable approach for large-scale 3D MT inversion. Full article

[d=LE]Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty.Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human-automation interaction. Recent studies suggest that transition performance should not be assessed only through takeover timing or response speed since control resumption quality also depends on traffic complexity, driver readiness, automation limitations, and situational awareness recovery. [d=LE]This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery.This paper motivates and defines a trustworthy companion AI (TCAI) layer for human-aware transition support in automated driving. The TCAI is conceived as a bounded, supervised, and explainable advisory agent that supports the driver without entering the safety-critical vehicle-control loop. It reasons over structured semantic state abstractions derived from a human digital twin, a vehicle digital twin, and a context/road digital twin, exposing driver readiness, automation capability, and contextual urgency in a form that supports traceable, uncertainty-aware, and degradation-aware assistance. [d=LE]Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery.Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, and conversational assistance, we propose a conceptual architecture in which the TCAI coordinates multimodal assistance across different interaction conditions, including vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The companion does not actuate the vehicle; its outputs are constrained by runtime governance, policy enforcement, and deterministic fallback mechanisms. [d=LE]The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty.The paper concludes with a research roadmap for validating the proposed architecture under planned transitions, urgent handovers, and degraded or adversarial conditions. Although motivated by SAE Level 3 automation, the approach may also inform fallback-related Level 4 scenarios. Full article

Background: Vitamin D deficiency is highly prevalent in older adults and has been increasingly recognised as a potential contributor to cognitive decline, depressive symptomatology, and functional impairment. However, the clinical significance of specific 25-hydroxyvitamin D thresholds in relation to this multidomain geriatric phenotype remains incompletely characterised. Methods: We conducted a cross-sectional study of 1438 consecutive patients aged 65 years or older admitted for comprehensive geriatric assessment at a tertiary centre in Cluj-Napoca, Romania, between January 2023 and November 2025. Serum 25-hydroxyvitamin D [25(OH)D] was categorised as deficient (<20 ng/mL), insufficient (20–30 ng/mL), or sufficient (≥30 ng/mL). Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE), depressive symptoms using the Geriatric Depression Scale (GDS-30 and GDS-SF), and functional status using Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL). Multivariable linear regression analyses were adjusted for age, body mass index, serum albumin, and estimated glomerular filtration rate (eGFR). Results: Suboptimal vitamin D status was highly prevalent in this geriatric cohort, with 43.3% of participants meeting criteria for frank deficiency (<20 ng/mL). Lower 25(OH)D concentrations were significantly associated with worse cognitive performance, greater depressive symptom burden, and higher functional dependency. Serum 25(OH)D correlated positively with MoCA and MMSE scores and inversely with ADL, IADL, and GDS scores. In adjusted models, vitamin D remained independently associated with MoCA, IADL, and GDS. Stratified analyses suggested that the main clinical deterioration occurred below 20 ng/mL, while the 20–30 ng/mL range behaved as an intermediate phenotype closer to sufficiency than to frank deficiency. Conclusions: In this large cohort of hospitalised older adults, serum 25(OH)D deficiency below 20 ng/mL was independently associated with poorer cognition, more depressive symptoms, and greater functional impairment. These findings support routine vitamin D assessment in geriatric practice and suggest that the <20 ng/mL threshold identifies a clinically relevant high-risk phenotype. Full article