Search Results (318)

Background/Objectives: This updated systematic review and meta-analysis provide a focused synthesis of contemporary evidence on clinical outcomes reported after the Latarjet procedure and Free Bone Block (FBB) techniques for anterior shoulder instability, focusing on recurrence, patient-reported outcome measures (PROMs), return to sport, complications, and osteoarthritis progression. Given that most available studies report single-procedure cohorts, between-technique comparisons were interpreted in the context of indirect evidence and study-level heterogeneity. Methods: A systematic review and meta-analysis were conducted according to PRISMA 2020 guidelines. The updated search included studies published from 2019 to May 2024 and was integrated with 70 studies from the previous review that were re-screened according to the same eligibility criteria. Eligible studies reported outcomes for the Latarjet or FBB procedure, with a minimum 2-year follow-up and at least five patients in the relevant treatment cohort. Risk of bias was assessed using the RoB 2 and MINORS tools. Outcomes were synthesized using random-effects meta-analysis. Pooled estimates were calculated separately for each procedure, and between-technique contrasts were treated as exploratory and descriptive when appropriate. Heterogeneity was assessed using I². Results: Ninety-eight studies with 6043 patients and 6071 shoulders were included: 72 on Latarjet, 23 on FBB, and 3 direct comparative studies. Both procedures were associated with low recurrence rates, improved PROMs, and comparable return-to-sport rates. Recurrence was 7% for Latarjet and 5% for FBB. Return to sport was 66% after Latarjet and 65% after FBB. Complication rates were 5% for Latarjet and 8% for FBB, while osteoarthritis progression was 12% and 9%, respectively. PROMs improved after both techniques, although differences between procedures should be interpreted cautiously because of the indirect nature of most comparisons and substantial heterogeneity across studies. Conclusions: Both Latarjet and FBB procedures were associated with generally favorable outcomes for anterior shoulder instability with bone loss in the included studies. The available evidence suggests broadly comparable clinical outcomes, with possible differences in complication profile, recurrence pattern, and osteoarthritis progression. However, these findings should be interpreted considering differences in patient characteristics, follow-up duration, surgical technique, graft type, and outcome definitions across studies. Current evidence does not allow definitive conclusions regarding the superiority of one technique over the other, but it provides useful descriptive outcome profiles to inform clinical decision-making and guide future direct comparative research. Full article

Fiber optic gyroscopes (FOGs) are core sensors in inertial navigation systems, and their miniaturization and integration are currently hot research topics. This work presents an FOG system driven by a silicon photonics integrated circuit (PIC). The PIC, based on a 90 nm silicon-on-insulator (SOI) process, integrates core components such as polarizers, 3 dB couplers, and phase modulators within a compact footprint of 3 × 0.45 mm². These components exhibit excellent performance over a wide spectral range and play a crucial role in high-performance FOG systems. Experimental results show that the proposed FOG system can definitively measure the small angular velocity of the Earth’s rotation (±7.5 °/h). Further Allan variance analysis reveals that the FOG system has an angular random walk (ARW) of 0.00358 °/h^1/2 and a bias instability (BIS) of 0.1185 °/h. These results demonstrate the application potential of silicon photonics-based FOG systems. Full article

Background/Objective: Intracranial arteriovenous malformations (AVMs) are high-flow cerebrovascular lesions associated with a significant risk of intracranial hemorrhage, neurological morbidity, and mortality. Current management strategies, including microsurgical resection, endovascular embolization, stereotactic radiosurgery, and conservative observation, remain limited by procedural risk and uncertain long-term outcomes. Beta-blockers, particularly propranolol, have recently attracted interest as potential adjunctive therapies because of their vasoconstrictive, antiangiogenic, and vascular remodeling properties. This review evaluates the mechanistic rationale and current evidence regarding beta-blocker use in intracranial AVMs. Methods: A comprehensive literature review was conducted using PubMed, Scopus, and Google Scholar databases through January 2026 using combinations of the terms “arteriovenous malformation,” “AVM,” “beta-blocker,” “propranolol,” “angiogenesis,” “hemorrhage,” and “cerebral cavernous malformation.” Eligible studies included experimental investigations, translational studies, observational cohorts, case reports, clinical trials, systematic reviews, and meta-analyses evaluating beta-blocker use in intracranial AVMs or related vascular malformations. Studies unrelated to cerebrovascular lesions, duplicate reports, and non-English publications were excluded. Given the heterogeneity and limited volume of available AVM-specific literature, findings were synthesized narratively rather than through formal systematic review methodology. Discussion: Preclinical studies suggest that beta-blockers modulate molecular pathways implicated in AVM pathophysiology, including VEGF, HIF-1α, SDF1α/CXCR4, MMP-9, and Notch-associated signaling. These mechanisms may reduce abnormal angiogenesis, endothelial instability, and pathological vascular remodeling. Clinical evidence, however, remains limited to retrospective studies, perioperative reports, and indirect evidence from cerebral cavernous malformations. Observational studies have reported associations between beta-blocker exposure and certain favorable AVM characteristics, including lower rates of hemorrhagic presentation and less complex angioarchitecture. However, these findings are highly susceptible to confounding, reverse causation, and selection bias and should not be interpreted as evidence of disease modification. Conclusions: Beta-blockers cannot currently be recommended as definitive therapy for intracranial AVMs. Their established role remains perioperative hemodynamic control, while potential disease-modifying effects require validation through prospective studies and randomized clinical trials. Full article

Background/Objectives: Vision Transformers (ViTs) have demonstrated impressive performance in dealing with large-scale natural image datasets. They have started to be used in medical image classification problems as well. However, how they behave under real-world conditions, such as data scarcity and extreme class imbalance, has not been well investigated. In this study, we examine the feasibility of using a standard Vision Transformer Base model that learned from scratch how to classify skin lesion images into multiple classes using the ISIC 2019 dataset. Methods: The Vision Transformer architecture was trained from scratch using stratified splitting of the data, class-balanced cross-entropy loss, multi-seed initialization, and control of hyperparameters such as patch size and dropout rate. The evaluation of the Vision Transformer architecture was performed using a hold-out test set with metrics such as accuracy, macro-F1, weighted-F1, and analysis of the confusion matrix. Results: Across all configurations, the training exhibited substantial instability and consistent overfitting behavior, with an average accuracy gap between validation and test sets of 22.7%. Test accuracy ranged from 8.0% to 37.8%, showing high sensitivity to initialization. For minority classes, the F1-score remained very low (F1 < 0.05) even though the classes were balanced in the loss function. Conclusions: The results indicate that a standard ViT-Base model trained from scratch can exhibit pronounced instability and a tendency toward majority-class bias when applied to multi-class skin lesion classification under conditions of extreme class imbalance and data scarcity. The findings point to the limitations of using simple transformer models without pre-training or other forms of inductive bias in scarce data settings. Full article

Landslide detection has largely relied on supervised learning or DEM-based representations, which can limit rapid deployment and generalization across heterogeneous terrain. In this work, we present a zero-shot, fully unsupervised framework that identifies landslide-like geomorphic instability candidates from raw UAV-mounted LiDAR, removing the need for labeled data, pre-event baselines, or rasterized terrain abstractions. Our approach is motivated by the observation that landslides manifest as localized geometric inconsistencies in the terrain surface. We capture this through a multi-scale formulation that combines point-level and cluster-level indicators of instability. At the point level, a PCA-based residual depth metric reduces slope-induced bias and highlights surface discontinuities, while local concavity captures terrain depletion patterns. At the cluster level, geomorphometric descriptors such as curvature concentration, surface roughness, elevation discontinuity, and slope variation are extracted using density-aware 3D clustering and integrated through adaptive feature fusion. The resulting probabilistic instability field enables spatially coherent delineation of landslide scars, including rupture boundaries, displaced material, and emerging failure regions. In addition, the detected patches provide useful priors for post-event susceptibility analysis without requiring temporal observations. Experiments across diverse geomorphic settings show that the proposed method improves detection of subtle terrain disturbances compared to DEM-based pipelines and supervised learning approaches, while remaining robust to noise and terrain variability. Overall, this work demonstrates that geometry-driven, unsupervised inference on raw 3D data can serve as a practical and scalable alternative for near real-time landslide detection using UAV-based systems. Full article

Background/Objectives: Colistin (COL), administered as a prodrug colistimethate sodium (CMS), is commonly used to treat infections caused by multidrug-resistant Gram-negative bacteria in critically ill patients. Given high CMS instability, very complex and variable pharmacokinetics (PK) and high incidence of toxicity, therapeutic drug monitoring (TDM) of active COL might play an important role. This study aimed to develop and validate an accessible immunoassay-based approach for COL monitoring in human serum. Methods: A direct competitive enzyme-linked immunosorbent assay (dcELISA) was developed using polyclonal (pAb) anti-polymyxin antibody alongside a polymyxin B–horseradish peroxidase conjugate. CMS conversion to COL along with serum deproteinization was achieved using 5% trichloroacetic acid (TCA) treatment at 37 °C. Assay accuracy and precision were assessed by spike-and-recovery experiments in healthy volunteer serum. The assay was applied to serum samples from critically ill patients with burns or pneumonia receiving CMS therapy. The reliability of the measurements was confirmed by parallel dcELISA based on a reference monoclonal antibody (mAb) against fragmented polymyxin molecule. Results: Both ELISA formats demonstrated high sensitivity, with limits of detection of 0.053 ng/mL (pAb) and 0.047 ng/mL (mAb). TCA treatment achieved maximal CMS hydrolysis under tested conditions within one hour. Clinical sample analysis showed excellent agreement between the two assays (R² = 0.996), with Bland–Altman analysis revealing a minimal bias of 3.7%. Exploratory PK analysis in burn patients demonstrated increased total drug volume of distribution (45.7–64.9 L) and clearance (8.3–16.3 L/h). Conclusions: This is the first report of ELISA for COL TDM in critically ill patients. The method offers acceptable analytical performance and practical simplicity, with potential to broaden TDM access beyond specialist centers. Full article

Background: Carotid atherosclerosis and plaque instability are major drivers of ischemic stroke. The NLRP3 inflammasome and interleukin-1 (IL-1) pathway are recognized as key upstream regulators of atherogenesis. This systematic review aims to synthesize the available evidence regarding the impact of NLRP3 and IL-1-mediated inflammation on plaque vulnerability and clinical outcomes in patients undergoing carotid endarterectomy (CEA). Materials and Methods: A systematic search was performed relying on MEDLINE, Scopus, and Web of Science for studies assessing NLRP3 inflammasome and IL-1-mediated biomarkers in adult patients undergoing CEA. Data extraction and risk-of-bias evaluation were independently performed using the National Heart, Lung, and Blood Institute (NHLBI) Quality Assessment Tool. Due to substantial methodological heterogeneity, a narrative synthesis was conducted. Results: Sixteen studies involving 1677 participants were included. Evidence demonstrates that NLRP3 inflammasome components (NLRP3, ASC, Caspase-1) and IL-1 family cytokines (IL-1β, IL-18) are consistently elevated in unstable plaques and in symptomatic patients compared to asymptomatic counterparts. These markers were associated with histological features of instability, such as intraplaque haemorrhage, large lipid cores and extensive macrophage infiltration. While some data suggest a link between these biomarkers and Major Adverse Cardiovascular Events (MACE), most studies were limited by a lack of adjusted multivariable modelling and an overall unclear risk of bias. Conclusions: NLRP3/IL-1/-mediated inflammation is a promising biomarker axis for carotid plaque vulnerability and symptomatic disease. However, small, heterogeneous cohorts and limited adjusted analyses highlight the need for larger, well-designed studies to refine risk stratification and guide targeted anti-inflammatory strategies in carotid atherosclerosis. Full article

Behavioral addiction in digital environments is an increasingly relevant neurobehavioral phenomenon characterized by persistent engagement with high-frequency, algorithmically optimized reward stimuli. Although neural correlates of addictive behaviors have been widely studied, current models only partly explain how modern reinforcement environments reorganize behavior at the systems level. This review introduces Reward Instability Theory, a conceptual dynamical systems framework proposing that behavioral addiction may emerge as an attractor-like state within distorted reward landscapes shaped by high-density and high-variance reinforcement signals. The model shifts focus from static behavioral descriptions toward a systems account of motivation involving reinforcement learning, salience attribution, executive control, and environmental reward structure. We propose that digital environments may increase reinforcement density and reward variance, promoting dominant reward peaks and reducing behavioral diversity. To formalize these dynamics, we outline the Behavioral Reward Instability Index (BRII) as a heuristic systems construct integrating individual reward sensitivity, environmental reinforcement structure, and behavioral variability. The framework also situates established addiction models—including incentive sensitization, habit formation, and allostatic regulation—within a shared dynamical architecture. In addition, digital phenotyping is discussed as a potential empirical strategy for testing reward instability, while acknowledging limitations related to signal noise, ecological validity, bias, and privacy. This model is intended to explain problematic patterns characterized by reduced behavioral flexibility, persistence despite negative consequences, and functional impairment, rather than all forms of frequent digital use. Attractor-like terminology is used throughout as a conceptual heuristic to describe behavioral persistence and reduced flexibility, rather than as evidence of formally verified mathematical attractors. Full article

Reliability degradation in semiconductor devices originates from microscopic stochastic processes such as defect motion, diffusion, bond rearrangement, and charge trapping occurring under electrical and thermal stress. Experimental degradation measurements, however, often exhibit smooth empirical scaling behavior, particularly power-law time dependences extending across many orders of magnitude in time. This tutorial reviews the thermodynamic and kinetic foundations underlying these observations and explains how empirical power-law degradation behavior can emerge from the collective interaction of many microscopic stochastic processes. The discussion begins with irreversible thermodynamics, random walk transport, diffusion, and Arrhenius kinetics and then connects these microscopic concepts to the macroscopic degradation trends commonly observed in semiconductor reliability experiments. Attention is given to the interpretation of stress-dependent power-law degradation kinetics and their implications for accelerated lifetime extrapolation. Practical limitations associated with conventional logarithmic degradation analysis are examined, including baseline sensitivity, logarithmic instability near the measurement floor, and systematic curvature that may remain hidden despite high goodness-of-fit metrics. Methods based on transformed-coordinate linearization and curvature-sensitive extraction are discussed together with their implications for time-to-failure extrapolation and activation-energy interpretation. Experimental studies of phenomena such as bias temperature instability frequently show degradation behavior in which the time exponent depends systematically on voltage and temperature stress conditions. Under such conditions, the reciprocal exponent $m=1/n$ can significantly amplify stress acceleration during lifetime extrapolation. This work provides a conceptual framework connecting microscopic stochastic degradation physics with the empirical methods commonly used in practical semiconductor reliability analysis and long-term lifetime prediction. Full article

Silicon micromachining serves as the foundational enabling technology for high-precision MEMS inertial sensors. However, the relentless pursuit of enhanced sensitivity and multi-functionality in emerging applications encounters a fundamental bottleneck when confined to two-dimensional scaling. The evolution toward complex three-dimensional (3D) stacking architectures is an inevitable trajectory for devices including MEMS inertial sensors, yet performance is constrained by the limitations of conventional processes in fabricating and integrating intricate 3D hollow structures. Specifically, uniformity in large-area deep silicon etching, structural integrity of convex corners in wet etching, and residual stress induced by multi-layer wafer bonding have emerged as critical, shared challenges. To address these issues, this paper proposes a triple-layer wafer-level stacking scheme that synergistically combines wet/dry hybrid etching with low-temperature adhesive bonding. This stacking scheme incorporates an innovative linear compensation model for wet-etched convex corners, enabling high-precision fabrication of complex corner structures under deep etching conditions. Furthermore, a collaborative strategy involving temporary bonding and plasma flow-field optimization improves the uniformity and integrity of dry etching for large perforated structures. A low-temperature triple-layer wafer-level stacking process is developed, encompassing precise adhesive dispensing, optical alignment, and a stepped low-temperature curing profile, thereby achieving highly symmetric 3D integration with controlled adhesive distribution. The efficacy of this stacking scheme is validated through the fabrication of a symmetrically stacked triple-layer MOEMS accelerometer sensing element. Test results demonstrate a noise floor as low as 0.40 µg/√Hz and a bias instability of 1.81 µg over 10 min. Compared with a double-layer counterpart, improved performance is obtained. The wafer-level stacking scheme established in this work not only provides a viable pathway for pushing the manufacturing limits of high-precision inertial devices but also offers a generic methodology for tackling complex hollow structure formation and low-temperature integration, holding referential value for broader applications in high-precision 3D microsystems. Full article

Radiomics enables quantitative medical image analysis by converting imaging data into structured, high-dimensional feature representations for predictive modeling. Despite methodological developments and encouraging retrospective results, radiomics continue to face persistent challenges related to feature instability, limited reproducibility, validation bias, and restricted clinical translation. Existing reviews largely focus on application-specific outcomes or isolated pipeline components, with limited analysis of how interdependent design choices across acquisition, preprocessing, feature engineering, modeling, and evaluation collectively affect robustness and generalizability. This survey provides an end-to-end analysis of radiomics pipelines, examining how methodological decisions at each stage influence feature stability, model reliability, and translational validity. This paper reviews radiomic feature extraction, selection, and dimensionality reduction strategies; classical machine and deep learning–based modeling approaches; and ensemble and hybrid frameworks, with emphasis on validation protocols, data leakage prevention, and statistical reliability. Clinical applications are discussed with a focus on evaluation rigor rather than reported performance metrics. The survey identifies open challenges in standardization, domain shift, and clinical deployment, and outlines future directions such as hybrid radiomics–artificial intelligence models, multimodal fusion, federated learning, and standardized benchmarking. Full article

End-to-end autonomous driving remains fragile in long-tail scenarios, while incorporating vision-language models (VLMs) introduces substantial deliberation latency that cannot interfere with the real-time planning loop. We present ThinkDrive, a dual-process driving framework designed under explicit real-time queuing constraints. The framework contains four coordinated components. First, a Scene Complexity Estimator regulates System-2 activation through a trigger cool-down mechanism, allowing at most one asynchronous request every $\lceil L_2/\Delta t\rceil$ frames and thereby preventing queue saturation under a System-2 latency of $L_2=565$ ms. Second, a multi-modal System-1 planner generates $K_1=5$ candidate trajectories within 44 ms and is trained with winner-takes-all imitation learning together with explicit score supervision. Third, a two-stage Causal-CoT module uses the VLM to identify risk agents and predict a preferred spatial goal $G_{\mathrm{VLM}}$, after which a single batched scm_rollout selects the safest candidate and extracts its endpoint as a world-coordinate goal anchor ${\mathbf{g}}_{S2}$. Fourth, a Goal-Anchor Replanning module transforms ${\mathbf{g}}_{S2}$ into the current ego frame and selects the candidate whose waypoint at the remaining time horizon is closest to the transformed goal. This design avoids coordinate-space mixing, mitigates bias caused by mismatched temporal horizons, and prevents semantic instability across replanning cycles. On nuPlan test14-hard, ThinkDrive with InternVL2-8B and a 6.8% trigger rate achieves 74.9 PDMs, outperforming AdaThinkDrive at 73.1 while maintaining a nominal latency of 44 ms. Full article

Low-refresh-rate driving is an effective way to reduce the power consumption of active-matrix organic light-emitting diode (AMOLED) displays. However, in conventional low-temperature polycrystalline silicon (LTPS) thin-film transistor (TFT) pixel circuits, leakage current through switching TFTs can disturb the stored gate voltage of the driving TFT during the long emission period. This causes the time-dependent variation in driving current and visible flicker. In this study, a novel pixel circuit for leakage suppression in low-refresh-rate driving is presented. Bias aging was first applied to reduce the leakage current of the LTPS TFT, and a device model was then built from the characteristics measured at 60 °C. Based on this model, the leakage-induced instability of a conventional 7T1C pixel circuit was analyzed. To suppress this effect, a new 8T1C pixel circuit was proposed. The key idea is to reduce the source–drain voltage of the leakage-sensitive switching TFT during the emission period by raising the initial line potential to a level close to the storage node potential. Simulation results show that the proposed circuit greatly reduces the time-dependent variation in both the driving TFT gate voltage and the driving current compared with the conventional 7T1C circuit. Perceptual evaluation based on human visual sensitivity also confirms stable low-refresh-rate operation down to 20 Hz over the practical gray range. These results show that the proposed circuit is an effective solution for moderate low-refresh-rate operation without relying on low-temperature polycrystalline silicon and oxide (LTPO) technology. Full article

This work presents a reliability-oriented design methodology for LC oscillators, focusing on Colpitts configurations implemented by a bipolar transistor. A nonlinear steady-state analytical framework is used as a practical design tool to determine the resonant tank parameters, loop gain conditions, and the dependence of oscillation amplitude on the operating conditions. Based on this analysis, a systematic sizing procedure is developed, in which the LC tank is designed and statistically characterized prior to amplifier and bias selection. The influence of component tolerances, temperature variation, supply voltage deviation, and load changes is quantified through statistical Monte Carlo analysis. To overcome the amplitude instability observed in the classical Colpitts topology, an automatic gain control (AGC) block is introduced that directly regulates the transistor transconductance, eliminating the need for individual amplitude adjustment. The simulation results demonstrate that, while the conventional Colpitts oscillator exhibits output amplitude variations of approximately ±30% under realistic parameter deviations, the proposed AGC-enhanced design limits worst-case amplitude variation to within ±10% using only standard tolerance components. A hardware prototype was developed to experimentally validate the methodology over wide variations in resonant tank parameters, amplifier bias conditions, and external load. The combined analytical, statistical, and experimental results confirm that the proposed approach simplifies the design process, improves robustness, and enables predictable, trimming-free oscillator operation suitable for mass-produced electronic systems. Full article

Ultrafast physical random bit generators (PRBGs) are essential components for modern applications in secure communication, quantum cryptography, encrypted optical fiber sensing and artificial intelligence. While optical chaos-based PRBGs offer high-speed capabilities, conventional systems often rely on discrete components that suffer from system complexity and environmental instability. This paper proposes and experimentally demonstrates a robust, integrated solution using a two-section mutual injection DFB laser. The device was fabricated using the reconstruction equivalent chirp (REC) technique, which provides precise control over grating phase variation while utilizing low-cost, high-volume fabrication methods. The laser sections, each measuring 450 μm in length, were designed with a free-running wavelength difference of 0.3 nm to ensure a flat optical spectrum and enhanced chaotic dynamics. By optimizing the bias currents, we achieved a chaos RF bandwidth of 20.1 GHz. Notably, the resulting chaotic signal lacks time-delayed signatures, which simplifies the randomness extraction process. To generate random bits, the chaotic waveform was sampled by an 8-bit analog-to-digital converter at 100 GSa/s. Following post-processing through delay-subtracting and the extraction of the four least significant bits (4-LSBs), we realized a total physical random bit rate of 400 Gb/s. The randomness of the generated sequence was successfully verified using the NIST SP 800-22 statistical test suite. This approach offers a compact, energy-efficient, and high-performance integrated chaotic source suitable for secure communication and high-performance computation. Full article