Search Results (596)

Ion-acoustic waves in dense quantum plasmas are strongly influenced by Fermi degeneracy, Landau quantization, and finite-temperature effects, and in many relevant environments, they also experience memory and nonlocal transport processes that cannot be captured within the planar integer Korteweg-de Vries (KdV) paradigm. In the present work, we revisit this problem by considering a two-fluid, partially degenerate electron-ion plasma in which electron trapping in the presence of a quantizing field and finite temperature is taken into account. Starting from the normalized fluid-Poisson system appropriate for such magnetized quantum plasmas, the reductive perturbation technique is used to derive the planar integer KdV equation for weakly nonlinear ion-acoustic disturbances. Within this integer-order KdV framework, we recast the evolution equation as a planar dynamical system, construct the associated Hamiltonian and effective Sagdeev-like potential, and demonstrate the existence of compressive solitary waves and nonlinear periodic modes via homoclinic and periodic phase-space orbits. Exact multi-soliton solutions and interaction states are then obtained by combining Hirota’s direct bilinear method with generalized Wronskian representations, allowing us to describe not only standard one-, two-, and three-soliton profiles but also positon-negaton interactions relevant to magnetized, partially degenerate plasmas. To incorporate hereditary and history-dependent effects that arise from anomalous transport and nonlocal temporal response in dense environments, we extend the model by introducing a Caputo time-fractional derivative, thereby obtaining a time-fractional KdV (FKdV) equation that continuously connects the classical KdV limit to fractional dynamics. The FKdV equation is analyzed using the Tantawy technique. This semi-analytical iterative scheme yields rapidly convergent series approximations for the fractional ion-acoustic soliton and provides explicit control of the approximation error. The fractional solutions show that varying the order of the Caputo derivative modifies the amplitude, width, and temporal relaxation of the solitary structures and can even split the pulse into two distinct lobes, in contrast with the nearly rigid propagation predicted by the integer-order KdV equation. Taken together, these results clarify how Landau quantization, finite electron temperature, and fractional-order memory jointly shape the morphology, robustness, and interaction properties of ion-acoustic structures in strongly magnetized quantum plasmas of astrophysical and high-energy-density laboratory interest. Full article

Generative AI, retrieval-augmented architectures, and multi-source automated analytical tools are now being deployed in increasingly exacting risk-analytic environments. Yet faster processing has not yielded commensurate reductions in false alarms, missed alarms, hallucinated outputs, or failures of responsibility attribution. Against that background, this study develops a biomimetic framework that integrates collective sensing with immune-inspired verification for analyzing complex risk information. Using an openly documented two-layer data architecture that combines authentic public-source samples with rule-generated, synthetic derivative samples, the study links biological-to-engineering mechanism translation, multi-objective optimization, National Institute of Standards and Technology (NIST)-aligned evaluation, and a governance-compatibility index within a single auditable design chain. The present evidence indicates that risk level continues to show a stable positive association with threat scores. At the same time, fabricated relations, despite their smaller aggregate volume, are more likely to accumulate in high-risk intervals. These patterns suggest that structural perturbations are more consequential than mere high-frequency noise in distorting judgment. More importantly, the study establishes the empirical and methodological conditions for a formal comparison of recognition quality, system resilience, and governance compatibility. Taken together, the paper offers a testable biomimetic mechanism model and a reproducible evaluative blueprint for auditable optimization in complex risk-information analysis. Full article

Federated Learning (FL) is a machine learning technique that preserves data privacy and security by training models directly on decentralized edge network devices. This generates substantial communication overhead due to the repeated exchange of model updates across numerous edge network devices. Quantization has tackled this challenge by reducing communication overhead and computational costs by quantizing model updates. Although selecting the most suitable quantization level to balance communication efficiency and model accuracy is challenging, failing to achieve this balance results in excessive compression, leading to accuracy degradation due to the lossy nature of the quantization technique. This challenge was tackled in this paper via a Quantile-based lossless compression method named Pcodec, which implements lossless compression in the FL context. Pcodec is a Quantile-based lossless compression algorithm designed for numerical data that utilizes mode identification with delta encoding and binning, where binning groups similar values into entropy-coded bins and stores the exact offset within each bin, thus achieving high compression ratios and efficient processing speed. Using MNIST and CIFAR-10 datasets and models such as CNN and ResNet18, we demonstrate that Pcodec achieves up to 58.19% size reduction with no accuracy loss compared to standard quantization methods. The experiments showed that the proposed Quantile-based compression approach in FL reduces up to 2.81× the communication overhead between each server and edge network device while maintaining the accuracy. In comparison to quantization, the Quantile approach reduced the communication overhead by 2.74×, tackling the main challenge of FL context by reducing communication overhead with a remarkably high compression ratio while maintaining the model’s accuracy. Full article

The gut microbiota is increasingly recognized as an important factor in the pathogenesis of type 1 diabetes (T1D), although its exact role in disease initiation and progression remains uncertain. Earlier interpretations considered alterations in intestinal microbial composition as secondary effects of immune dysregulation or metabolic disturbance. Recent longitudinal studies, however, suggest that specific microbial changes occur before the onset of islet autoimmunity, indicating a potential contributory role in the early phases of disease development. In this narrative review article, the gut–pancreas axis (GPA) is described as a dynamic and reciprocal system in which microbial, metabolic, and immune processes influence each other to shape β-cell outcomes. Evidence from human cohorts and experimental models links early life reductions in microbial diversity, impaired intestinal barrier function, and decreased production of short-chain fatty acids (SCFAs) to altered immune activation and β-cell damage. Microbiota transferred from individuals at risk for T1D has been shown to accelerate disease in animal models, supporting a possible causal relationship. Although experimental models support mechanistic links between microbiota alterations and autoimmune diabetes, current human evidence remains largely associative. Together, these findings suggest that microbial and immune networks interact in a feedback manner that can sustain immune tolerance or promote autoimmunity depending on environmental and host factors. Understanding T1D as a state of disrupted microbial and immune integration provides a basis for restoring gut–pancreas communication and preserving β-cell integrity. Full article

The Poisson exponentially weighted moving average (PEWMA) control chart rests upon the equidispersion assumption of the pure Poisson distribution, a structural symmetry condition stipulating that the process mean and variance are equal. In manufacturing environments characterized by latent process heterogeneity, this assumption is systematically violated: the resulting distributions are inherently asymmetric, heavily right-skewed, and overdispersed. This structural asymmetry renders standard PEWMA control limits artificially narrow, inducing a substantial inflation of false alarm rates. This paper introduces the Poisson mixture EWMA (PM-EWMA) control chart, which models the latent heterogeneous structure of count data as a finite Poisson mixture distribution, with parameters estimated via the Expectation–Maximization (EM) algorithm without requiring prior labeling of process states. The optimal number of components is determined via the Bayesian Information Criterion (BIC) as the primary criterion, supplemented by the Akaike Information Criterion (AIC), its bias-corrected variant (AICc), and the log-likelihood ratio diagnostic. The PM-EWMA chart incorporates the exact mixture variance, accounting for both within-component and between-component variability, into the EWMA control limit structure, thereby providing a theoretically justified correction under the fitted Poisson mixture assumption. A Monte Carlo simulation study comprising 495 factorial configurations benchmarks the PM-EWMA chart against both the standard PEWMA chart and the negative binomial EWMA (NB-EWMA) chart with oracle dispersion calibration, confirming stable in-control ARL performance and demonstrating improved discrimination relative to the misspecified PEWMA baseline. Empirical validation using fabric defect count data from two textile manufacturers in Türkiye, with Overdispersion Indices of 6.01 and 2.74, respectively, demonstrates false alarm reductions ranging from 40.9% to 89.2% relative to the standard PEWMA chart, depending on the smoothing parameter and degree of overdispersion. Full article

Role-Based Access Control (RBAC) is the de-facto mechanism for preserving Kubernetes and other cloud-native container platforms, however real deployments occasionally drift away from the principle of least privilege as clusters, teams, and services improve. This paper introduces an automated RBAC hardening framework that formulates least-privilege policy design as a limited optimization problem over RoleBindings and ClusterRoleBindings. The objective combines (i) a permission-risk score for namespaced and cluster-scoped actions with (ii) an operational complexity term that discourages overly large binding sets. Solid limitations encode functional requirements as well as practical security policies, which includes namespace allowlists, role scoping rules, administrative restrictions on cluster-wide bindings, binding budgets, and separation-of-duty requirements expressed by utilizing capability classes. To allow optimizer-agnostic search while protecting Kubernetes RBAC semantics, we analyze candidate policies by utilizing a unified penalty-based fitness function that compines risk, complexity, and constraint violations into a single scalar value. We utilized ten metaheuristic as a benchmark including baseline search paths on a Kubernetes-inspired instance and report feasibility and least-privilege quality metrics (precision, recall, F1, and over-privilege ratio) parallel to RB/CRB counts and excess risk as a structural indicators. Outcomes present that feasibility is the prime challenge, and is restricted to a subset of optimizers reliably arrives to entirely feasible and compact arrangements within the exact budget, indicating the practicality of metaheuristic enhancement for systematic RBAC reduction in containerized cloud computing environments. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly proposed to improve surgical planning, guidance, and postoperative surveillance. Yet many promising applications remain disconnected from the full surgical pathway and the feasible limitations of clinical deployment. In contrast to prior reviews that primarily catalog AI use cases, this review combines the literature to define the translational pathway—from label design through staged validation to workflow integration—required for clinically deployable computed tomography (CT)-based surgical AI. CT and particularly computed tomography angiography (CTA) are especially usable sources for surgical AI because they provide a standardized three-dimensional anatomic model that is already embedded in many clinical workflows. In autologous breast reconstruction, deep inferior epigastric perforator (DIEP) flap CTA offers an unusually strong model system: the anatomy is discrete, surgeon decisions are actionable, and downstream operative and postoperative outcomes are measurable. These characteristics make DIEP reconstruction suitable not only for technical model development, but also for exacting testing of how CT-based AI should be annotated, validated, displayed, and governed. Methods: This focused narrative review combines evidence across the surgical workflow, spanning preoperative planning and risk stratification, intraoperative support, and postoperative monitoring. Reporting standards, implementation frameworks, governance, and regulatory sources were also considered when directly relevant to clinical deployment. Results: Across the available literature on breast reconstruction with the DIEP flap, preoperative CTA has been associated with reductions in operative time of approximately 54–76 min in individual studies. Semi-automated perforator mapping can reduce review time from 2 to 3 h to approximately 30 min. Intraoperative extended-reality tools and surgeon-facing navigation systems illustrate the importance of the ‘last mile’ of translation, while postoperative monitoring models show how imaging-linked data can support a closed-loop learning system. Across these stages, recurring limits include target mismatch, weak external validation, protocol variability, inconsistent reporting, limited subgroup analysis, and inadequate integration of economic and governance considerations. Conclusions: We argue that the next important step is not a generic autonomous model, but a clinically deployable DIEP-CTA-AI program. The practical blueprint proposed here is staged: structured anatomical labels, separate imaging, surgeons’ decisions, and outcome reference standards, dense intermediate endpoints, retrospective and external validation, reader studies, prospective silent deployment, and workflow-impact assessment. If implemented in this way, DIEP flap CTA can serve as a practical blueprint for CT-based AI translation in surgery more broadly. Full article

Background and Objectives: Paramedian cervical interlaminar epidural steroid injection (CESI) is commonly used for cervical radicular pain and is considered safer than the transforaminal approach. However, its clinical effectiveness may be influenced by contrast distribution patterns, although these may not fully reflect actual drug delivery. This study aimed to evaluate the association between needle trajectory, foraminal or periradicular contrast distribution patterns, and short-term pain reduction following paramedian cervical interlaminar CESI. Materials and Methods: This single-center retrospective study included 109 patients who underwent paramedian cervical interlaminar CESI. Needle trajectory was classified as inward or outward. Contrast distribution was graded based on anteroposterior (AP) spread patterns. Pain intensity was assessed using a numeric rating scale (NRS) at baseline and 2 weeks after the procedure. Group comparisons were performed using Welch’s t-test and chi-square or Fisher’s exact test, as appropriate. Effect sizes were calculated using Cohen’s d and η². Multivariable linear regression analysis was performed adjusting for age, sex, baseline NRS, and target level. Results: The outward trajectory group demonstrated a significantly higher proportion of Grade 2 contrast spread compared to the inward group (69.8% vs. 8.9%, p < 0.001). Higher AP contrast spread grades were associated with greater pain reduction (β = 0.83, 95% CI: 0.44–1.22, p < 0.001; η² = 0.14). In addition, patients in the outward trajectory group showed greater NRS reduction than those in the inward group (2.96 vs. 1.71, mean difference: 1.25, 95% CI: 0.74–1.76, p < 0.001; Cohen’s d = 0.96). In multivariable analysis, needle trajectory remained significantly associated with pain reduction, whereas AP contrast spread grade was not independently associated. Conclusions: Needle trajectory was associated with contrast distribution patterns and short-term pain reduction following paramedian cervical interlaminar CESI. An outward-directed trajectory was associated with greater foraminal or periradicular contrast spread and greater pain reduction. These findings suggest that needle trajectory may represent a clinically relevant procedural factor influencing clinical outcomes. Full article

Objective: Atherosclerotic renal artery stenosis (ARAS) is increasingly prevalent among aging populations and in patients with diabetes, hyperlipidemia, aortoiliac obstructive disease, coronary artery disease, and/or hypertension. Patients with severe ARAS are at a substantially elevated risk of cardiovascular disease, recurrent congestive heart failure, stroke, ischemic nephropathy, and chronic kidney disease. Therefore, the ARAS-TR study aims to evaluate the effect of renal artery stenting on the clinical outcomes in patients with severe ARAS and renovascular hypertension. Materials: This study was conducted as a multicenter, prospective study between July 2024 and September 2025. It encompassed 278 patients with angiographically confirmed severe ARAS who underwent renal artery stent implantation. Patients were subsequently monitored for 6 months. A paired-samples t-test was used to compare continuous variables pre- and post-intervention, while categorical variables were analyzed using the Pearson chi-square test and Fisher’s exact test. Results: The mean age of the patients was 63.6 [±13.4] years, and the male gender ratio was 52.5%. After renal artery stenting, systolic and diastolic blood pressures decreased significantly at the 6-month follow-up compared with the pre-procedure levels (SBP 166.99 [21.24] vs. 135.40 [15.69], p < 0.001; DBP 96.28 [13.03] vs. 80.39 [11.03], p < 0.001, respectively). GFR (61.23 [28.33] vs. 63.35 [26.36], p = 0.029) and creatinine (1.40 [0.93] vs. 1.29 [0.66], p = 0.004) levels improved compared to baseline. The mean number of antihypertensive drugs required for patients to remain normotensive decreased significantly (3.19 [1.04] vs. 2.48 [1.13], p < 0.001) during the follow-up period. Conclusions: Percutaneous renal artery intervention appears to be a promising and safe strategy for carefully selected high-risk patients presenting with severe ARAS, renovascular hypertension, and non-atrophic kidneys. In this specific clinical context, restoring renal artery patency through percutaneous stenting was associated with improved renal function and observed reduction in the burden of antihypertensive drugs required to sustain normotension. Full article

Background/Objectives: Early identification of breast cancer patients who are likely or unlikely to benefit from neoadjuvant chemotherapy (NAC) remains clinically important because ineffective treatment may delay definitive surgery and expose patients to unnecessary toxicity. Quantitative ultrasound (QUS) radiomics offers a contrast-free and repeatable method for extracting tissue-sensitive imaging biomarkers from raw ultrasound data. This study aimed to evaluate whether baseline QUS radiomic features and early treatment-induced changes could predict a pathologic response to NAC in a real-world single-center cohort. Methods: We designed a prospective observational study including 96 consecutive women with biopsy-proven stage II–III breast cancer treated with NAC at Victor Babes University of Medicine and Pharmacy Timisoara. All patients underwent standardized QUS examinations before treatment and again at week 2. The response was defined pathologically at surgery as residual cancer burden class 0/I versus II/III. Clinical, histopathologic, and QUS variables were compared between responders and non-responders. Group comparisons used Student’s t test, Mann–Whitney U test, chi-square testing, and Fisher’s exact test where appropriate. Multivariable logistic regression was used to identify independent predictors of response. Model discrimination was summarized using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results: Forty-three patients (44.8%) were classified as responders and 53 (55.2%) as non-responders. Responders had higher baseline Ki-67 values (47.8 ± 13.1% vs. 41.9 ± 13.0%, p = 0.033), lower baseline homogeneity (0.3 ± 0.1 vs. 0.4 ± 0.1, p = 0.010), and higher peritumoral heterogeneity (0.9 ± 0.1 vs. 0.8 ± 0.2, p = 0.027). At week 2, responders showed larger increases in mid-band fit (3.0 ± 0.8 vs. 1.2 ± 0.8 dB, p < 0.001), greater entropy change (0.7 ± 0.2 vs. 0.2 ± 0.2, p < 0.001), more pronounced spectral intercept reduction (−3.5 ± 1.4 vs. −1.2 ± 1.3, p < 0.001), and greater tumor shrinkage (−24.3 ± 7.0% vs. −11.1 ± 5.7%, p < 0.001). In multivariable analysis, Δ MBF and Δ entropy remained independent predictors of pathologic response. The combined clinical-plus-QUS model achieved an AUC of 0.89. Conclusions: Baseline microstructural heterogeneity and very early QUS-derived treatment changes were strongly associated with the pathologic response to NAC. These findings support the potential role of QUS radiomics as a low-cost, repeatable early-response biomarker in breast cancer. Full article

With the expansion of offshore oil and gas exploration into deep-water regions, the efficient scheduling of platform supply vessels (PSVs) is critical to offshore operations. The platform supply vessel routing and scheduling problem (PSVRSP) is an NP-hard combinatorial optimization problem, which is further complicated by uncertainty in offshore demand. Existing studies reveal a methodological gap: exact optimization algorithms have rarely been applied to this problem, as most prior research relies on heuristic methods that cannot guarantee optimality. To address this gap, this study proposes a novel enhanced branch-and-price (B&P) algorithm for the platform supply vessel routing and scheduling problem with uncertain demand (PSVRSP-UD). The proposed approach integrates NG-route labeling, a group-representative label mechanism, and a two-level branching strategy to efficiently obtain globally optimal solutions under demand uncertainty. A scenario-based mixed-integer linear programming (MILP) model is formulated, in which demand uncertainty is captured using Latin hypercube sampling (LHS) combined with Cholesky decomposition and sample-based reduction (SBR). Based on Dantzig–Wolfe decomposition, the proposed B&P algorithm integrates NG-route labeling and a two-level branching strategy to achieve global optimization. Computational experiments show that the B&P algorithm outperforms CPLEX in both computational efficiency and solution quality. Sensitivity analyses examine the impacts of scenario number, demand fluctuation, time window tightness, and weight coefficients on the results. The new results in this study can provide a practical decision-support tool for offshore logistics operations. Full article

Background/Objectives: A marked anteroposterior gradient of nasal fossa pneumatisation over the posterior maxillary alveolar base has been documented at the second premolar level, yet whether this gradient extends to the second upper molar (M2)—the primary site for posterior implant rehabilitation—remains uncharacterised. We aimed to quantify this gradient by classifying pneumatisation patterns above the maxillary alveolar base at M2 (Type 1: pure antral; Type 2: antral with palatine recess; Type 3: Big Nose pattern with combined antral and nasal involvement), assess bilateral symmetry and sex distribution, and compare findings with published second premolar data. Methods: A retrospective study was conducted on 165 cone-beam computed tomography scans (330 sides) from a Romanian population. Patterns were classified as Type 1 (pure antral), Type 2 (antral with palatine recess), or Type 3 (Big Nose pattern). Bilateral symmetry was assessed using Cohen’s kappa, and sex differences using Fisher’s exact test. Results: Type 1 was observed in 93.3% of sides, Type 2 in 4.2%, and Type 3 in 2.4%. Bilateral symmetry was 98.8% (kappa = 0.904), with all Type 3 cases occurring bilaterally. No significant sex difference was found (p = 0.363), although Type 3 showed a non-significant male predominance (OR = 4.55; p = 0.305). The Big Nose pattern was 6.8-fold less prevalent at M2 than at the second premolar level. Conclusions: A 6.8-fold reduction in Big Nose prevalence from the second premolar (16.2%) to M2 (2.4%) confirms a pronounced anteroposterior gradient in nasal fossa involvement over the posterior maxillary alveolar base—the central finding of this study. At M2, the maxillary sinus dominates exclusively in 97.6% of sides, rendering standard sinus floor elevation highly predictable. The invariable bilaterality of the Big Nose pattern at M2 supports contralaterally symmetrical surgical planning. These findings provide a gradient-based clinical framework: nasal-floor-aware augmentation planning is essential anteriorly (premolar region), whereas standard sinus augmentation protocols are reliably applicable at M2. Full article

The problem of optimizing a causal objective function emerged in recent work, where the behavior of objects needs to be expressed in terms of interventional or counterfactual probabilities. A key example is the unit selection problem introduced by Li and Pearl, where the goal is to find the individuals who maximize a benefit function that scores their characteristics (called units) using counterfactual probabilities. Previous work on unit selection focused mainly on this specific objective function and on identifying its value using bounds. We complement this line of work by developing a theory that treats unit selection as a computational problem, assuming a fully specified causal model is available and a more general class of objective functions. At the core of our treatment is a novel reduction that transforms the computation of a broad class of causal objective functions into a classical associational probability on a meta-model called the objective model. Based on this reduction, we propose the first exact algorithm for finding the optimal units by applying Variable Elimination (VE) on the objective model. We then characterize the complexity of causal unit selection, showing that it is ${\mathrm{NP}}^{\mathrm{PP}}$-complete, and that the runtime of VE must be exponential in the constrained treewidth of the objective model, which is larger and denser than the original input model. To address this challenge, we compile the objective model into a special class of tractable arithmetic circuits, allowing the optimal units to be computed in time linear in the circuit size. Finally, we present experiments demonstrating the substantial speedup from the circuit-based method over the VE-based method, and the speedup from the VE-based method over a baseline search method, together with a case study on a real-world ecology problem. Full article

Continuous maize cropping is often associated with yield decline and soil degradation, yet the temporal responses of rhizosphere bacterial communities to prolonged monocropping remain incompletely understood. Here, we used a continuous maize cropping chronosequence representing 0, 1, 2, 3, 6, 7, and 8 years of cropping to evaluate soil physicochemical properties, maize yield, rhizosphere bacterial community composition, and BugBase-predicted phenotypes using 16S rRNA gene amplicon sequencing. Available potassium declined progressively with cropping duration, whereas alkali-hydrolyzable nitrogen (AN) increased and available phosphorus (AP) changed nonlinearly. Soil pH declined in the later stages of the chronosequence. Maize yield declined progressively with prolonged cropping, with reduction of 46–55% in the 6–8 years treatments relative to earlier within-plot peaks. Bacterial alpha diversity changed nonlinearly, with Shannon diversity peaking at C3, declining at C6, and partially recovering at C7–C8. Because years 4 and 5 were not sampled, the exact shape of the transition between C3 and C6 remains unknown. Community composition also shifted with cropping duration, including a relative decline in Proteobacteria and enrichment of Actinobacteria in the longer-duration treatments. At the genus level, Arthrobacter increased in the later stages of the chronosequence. Redundancy analysis indicated broad associations between community composition and soil variables, although the phylum-level model was only marginally significant. BugBase-predicted phenotypes also varied across treatments, but these functional inferences should be interpreted cautiously because they were derived from 16S-based predictions. Overall, our findings support nonlinear changes in rhizosphere bacterial communities along the continuous maize cropping chronosequence and suggest an unresolved transition between C3 and C6, followed by partial stabilization at later stages. However, due to the missing data for years 4–5 and the inherent limitations of the chronosequence design, the existence and timing of a proposed mid-term transition remain tentative. These findings highlight the need for complete annual sampling to resolve successional trajectories. Full article

Background/Objectives: Vertebral body stenting (VBS) and balloon kyphoplasty (BKP) are widely used for the treatment of osteoporotic vertebral fractures (OVFs). However, it remains unclear whether the theoretical biomechanical advantages of VBS translate to superior clinical or radiographic outcomes. This study aimed to compare VBS and BKP with respect to clinical outcomes, radiographic parameters, and complications. Methods: In this multicenter retrospective comparative cohort study, 123 patients with OVF treated with VBS (n = 24) or BKP (n = 99) were analyzed. VBS was indicated for complex fracture patterns, including severe endplate injury, split-type fractures, and absence of interbody sclerosis; other fractures were treated with BKP. Pain outcomes, operative parameters, cement volume and leakage, and radiographic measures of vertebral kyphosis angle (VKA) and local kyphosis angle (LKA) were assessed. For group comparisons, we used independent-samples t tests or Mann–Whitney U tests for continuous variables and chi-squared or Fisher’s exact tests for categorical variables. Results: Baseline demographics and bone mineral density were comparable between groups. Surgical time was longer for VBS (39 ± 6 vs. 35 ± 9 min, p = 0.007). Both procedures produced significant pain reductions (p < 0.001), and postoperative VAS did not differ between VBS and BKP (18 ± 11 vs. 13 ± 12 mm, p = 0.06). Although VKA immediately after surgery was lower for VBS (4.8 ± 4.4° vs. 7.0 ± 4.9°, p = 0.03), the magnitude of correction, VKA, and LKA at final follow-up were comparable. Cement volume was similar (6.4 ± 1.4 vs. 6.7 ± 1.9 mL, p = 0.45), but cement leakage occurred more frequently with VBS (54% vs. 24%, p = 0.005). Rates of adjacent vertebral fracture (13% vs. 26%, p = 0.12) and revision surgery (4% vs. 8%, p = 0.44) were comparable between groups. Conclusions: Despite VBS being reserved for more complex fracture morphologies with split-type fractures and severe endplate defects, while BKP was generally used for uncomplicated OVF cases, VBS provided pain relief and radiographic correction comparable to BKP. Full article