Search Results (22,515)

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with poor long-term survival despite advances in surgical techniques, systemic therapies, and perioperative management. High rates of systemic recurrence following curative-intent resection suggest that many patients harbor minimal residual disease (MRD), microscopic tumor burden that persists postoperatively and remains undetectable by conventional diagnostic tools. Recent advances in liquid biopsy technologies, particularly circulating tumor DNA (ctDNA) analysis, alongside detailed characterization of the PDAC mutational landscape, offer a promising non-invasive approach for MRD detection. Emerging evidence indicates that MRD status can serve as a sensitive prognostic biomarker, identify patients at high risk of relapse, and guide personalized perioperative therapy, including optimization of adjuvant treatment. This review summarizes current knowledge on the biology and detection of MRD in PDAC, its implications for perioperative risk stratification and treatment decision-making, and discusses future directions for integrating MRD assessment into clinical practice to enable more precise, individualized patient management. Full article

This study provides a chronological analysis of how Extended Reality (XR) interaction techniques have evolved from early controller-centered interfaces to natural hand- and gaze-based input and, more recently, to multimodal input, with a particular focus on the role of XR devices. We collected 46 user study–based XR interaction papers published between 2016 and 2024, including only studies that explicitly defined their interaction techniques and reported quantitative and/or qualitative evaluation results. For each study, we documented the XR hardware and software development kits (SDKs) used as well as the input modalities applied (e.g., controller, hand tracking, eye tracking, wrist rotation, multimodal input). These data were analyzed in relation to a device and SDK timeline spanning major platforms from the HTC Vive and Oculus Rift to the Meta Quest Pro and Apple Vision Pro. Using frequency summaries, heatmaps, correspondence analysis, and chi-square tests, we quantitatively compared input modality distributions across device generations. The results reveal three distinct stages of XR interaction development: (1) an early controller-dominant phase centered on the Vive/Rift (2016–2018), (2) a transitional phase marked by the widespread introduction of hand- and gaze-based input through the Oculus Quest, HoloLens 2, and the Hand Tracking SDK (2019–2021), and (3) an expansion phase in which multisensor and multimodal input became central, driven by MR-capable devices such as the Meta Quest Pro (2022–2024). These findings demonstrate that the choice of input modalities in XR research has been structurally shaped not only by researcher preference or task design but also by the sensing configurations, tracking performance, and SDK support provided by devices available at each point in time. By reframing XR interaction research within the technological context of device and SDK generations—rather than purely functional taxonomies—this study offers a structured analytical framework for informing future multimodal and context-adaptive XR interface design and guiding user studies involving next-generation XR devices. Full article

Bone regeneration remains difficult due to the complex bone microenvironment and the limited healing capacity of large defects. Biomimetic strategies offer promising solutions by using advanced 3D scaffolds guided by natural tissue cues. Recent advances in additive manufacturing, nanotechnology, and tissue engineering now allow the fabrication of hierarchical scaffolds that closely mimic native bone. Smart scaffold systems combine materials with biochemical and mechanical signals. These features improve vascularization, enhance tissue integration, and support better regenerative outcomes. Bio-inspired materials also help connect inert implants with living tissues by promoting vascular network formation and improving cell communication. Multiscale design approaches recreate bone nano- to macro-level structure and support both osteogenic activity and immune regulation. Intelligent and adaptive scaffolds are being developed to respond to physiological changes and enable personalized bone repair. This review discusses the current landscape of biomimetic scaffold design, fabrication techniques, material strategies, biological mechanisms, and translational considerations shaping next-generation bone regeneration technologies. Future directions focus on sustainable, clinically translatable biomimetic systems that can integrate with digital health tools for improved treatment planning. Full article

Magnetooptics (MO) explores light—matter interactions in magnetized media and has advanced rapidly with progress in materials science, spectroscopy, and integrated photonics. This review highlights recent developments in fundamental principles, experimental techniques, and emerging applications. We revisit the canonical MO effects: Faraday, MO Kerr effect (MOKE), Voigt, Cotton—Mouton, Zeeman, and Magnetic Circular Dichroism (MCD), which underpin technologies ranging from optical isolators and high-resolution sensors to advanced spectroscopic and imaging systems. Ultrafast spectroscopy, particularly time-resolved MOKE, enables femtosecond-scale studies of spin dynamics and nonequilibrium processes. Hybrid magnetoplasmonic platforms that couple plasmonic resonances with MO activity offer enhanced sensitivity for environmental and biomedical sensing, while all-dielectric magnetooptical metasurfaces provide low-loss, high-efficiency alternatives. Maxwell-based modeling with permittivity tensor ($\epsilon$) and machine-learning approaches are accelerating materials discovery, inverse design, and performance optimization. Benchmark sensitivities and detection limits for surface plasmon resonance, SPR and MOSPR systems are summarized to provide quantitative context. Finally, we address key challenges in material quality, thermal stability, modeling, and fabrication. Overall, magnetooptics is evolving from fundamental science into diverse and expanding technologies with applications that extend far beyond current domains. Full article

Essential oils (EOs) have been extensively studied as potential alternatives for insect pest management. In recent years, research on these natural compounds has increased substantially. However, despite numerous studies demonstrating the insecticidal properties of EOs under laboratory conditions, their practical application remains limited. This discrepancy highlights a significant gap between experimental findings and the development of commercially viable products. Several factors have been proposed as the basis for this gap, including the absence of positive controls to compare their effectiveness (i), the imperative need to develop new formulations (ii), and the potential toxicity of many to non-target organisms (iii). This work focuses on why the information obtained in the laboratory has not translated into the biopesticide market. A key issue is the difficulty of applying laboratory knowledge in adapting to field-like scenarios, such as spray quality (droplet size and volume), the nature of the application solvent used in the sprayer tank, and the way the insect is exposed to the insecticide (i.e., the type of laboratory bioassay selected). This challenge is primarily due to researchers’ limited understanding of the application techniques used in field settings to manage specific insect pests. Many laboratory bioassays designed to measure effectiveness do not accurately reflect field conditions; instead, they often create scenarios that artificially enhance effectiveness. This results in an unrealistically high effectiveness estimate of the true potential of EOs in controlling the targeted insects. Full article

Privacy-preserving machine learning (PPML) constitutes a core element of responsible AI by supporting model training and inference without exposing sensitive information. This survey presents a comprehensive examination of the major cryptographic PPML techniques and introduces a unified taxonomy covering technical models, verification criteria, and evaluation dimensions. The study consolidates findings from both survey and experimental works using structured comparison tables and emphasizes that recent research increasingly adopts hybrid and verifiable PPML designs. In addition, we map PPML applications across domains such as healthcare, finance, Internet of Things (IoT), and edge systems, indicating that cryptographic approaches are progressively transitioning from theoretical constructs to deployable solutions. Finally, the survey outlines emerging trends—including the growth of zero-knowledge proofs (ZKPs)-based verification and domain-specific hybrid architectures—and identifies practical considerations that shape PPML adoption in real systems. Full article

In recent years, with the rapid development of intelligent communication technologies, anti-jamming techniques based on deep learning have been widely adopted in unmanned aerial vehicle (UAV) systems, yielding significant improvements. Most existing studies primarily focus on intelligent anti-jamming decision-making for single UAVs. However, in UAV swarm systems, single-agent decision models often suffer from data isolation and inconsistent frequency usage decisions among nodes within the same task subnet, caused by asynchronous model updates. Although data sharing among UAVs can partially alleviate model update issues, it introduces significant communication overhead and data security challenges. To address these problems, this paper proposes a novel multi-UAV cooperative intelligent anti-jamming decision-making method, termed Federated Learning-Hierarchical Deep Q-Network (FL-HDQN). First, an adaptive model synchronization mechanism is integrated into the federated learning framework. By sharing only local model parameters instead of raw data, UAVs collaboratively train a global model for each task subnet. This approach ensures decision consistency while preserving data privacy and reducing communication costs. Second, to overcome the curse of dimensionality caused by multi-domain interference parameters, a hierarchical deep reinforcement learning model is designed. The model decouples multi-domain optimization into two levels: the first layer performs time–frequency domain decisions, and the second layer conducts power and modulation-coding domain decisions, ensuring both real-time performance and decision effectiveness. Finally, simulation results demonstrate that, compared with state-of-the-art intelligent anti-jamming models, the proposed method achieves 1% higher decision accuracy, validating its superiority and effectiveness. Full article

Collaborative multi-UAV swarms are central to many missions. This review covers the most recent two years. It organizes the literature with a scenario-aligned taxonomy. The taxonomy has 12 cells (Path/Distribution/Coverage × offline/online × static/dynamic). Nine cells are well populated and analyzed. For each, representative techniques, reported limitations, and scenario-appropriate use are summarized. Cross-scenario trade-offs are made explicit. Key examples include scalability vs. energy efficiency and centralized vs. decentralized (hybrid) architectures. The review also links offline pre-planning to online execution through architecture choices, digital-twin validation, and safety-aware collision avoidance in cluttered airspace. Unlike prior algorithm-centric or bibliometric surveys, this work applies a scenario-conditioned taxonomy, ties best-suited method families to each populated cell, and surfaces reported limitations alongside trade-offs. The result is deployment-oriented guidance that maps methods to mission context. Finally, five near-term priorities are highlighted: (i) compute-aware real-time adaptivity on resource-constrained platforms; (ii) scalable multi-objective scheduling with coupled motion and cooperative control; (iii) bandwidth-aware, conflict-resilient intra-swarm communication with reliability guarantees; (iv) certifiable planning for dense urban low-altitude corridors; and (v) energy-aware, hierarchical planners that couple offline pre-planning with online replanning. Full article

Tissue regeneration is essential for wound healing, organ function restoration, and overall patient recovery. Its success significantly impacts medical procedures in fields like internal medicine and orthopedics, enhancing patient quality of life. Recent advances in regenerative medicine, particularly the combination of advanced drug delivery systems (DDS) and bioengineering, have enabled customized methods to improve tissue regeneration outcomes. However, conventional tissue engineering techniques have drawbacks, often using static scaffolds that lack the dynamic properties of real tissues, leading to subpar healing outcomes. The use of 3D printing and other advanced scaffolding techniques allows for the creation of bio functional scaffolds that deliver bioactive molecules at precise locations and times. The optimal integration of biological systems with enhanced material properties for personalized treatment options remains unclear. There is a need for more research into the complex interactions between cellular biology, drug delivery, and material technology to improve tissue regeneration. Despite progress in developing bioactive scaffolds and localized drug delivery methods, the interactions among different scaffold materials, bioactive agents, and cellular behaviors within the regenerative ecosystem are not fully understood. While there is extensive research on 3D-printed scaffolds in tissue engineering, there is a lack of studies integrating bio printing with in vivo biological reactions in real time. Limited research on the dynamic integration of patient-specific parameters in regeneration methods highlights the need for customized approaches that consider individual physiological differences and the complex biological environment at injury sites. Additionally, challenges arise when translating laboratory results into effective therapeutic applications, underscoring the necessity for interdisciplinary collaboration and innovative design approaches that align advanced material properties with biological needs. Full article

Migraine is a prevalent and disabling neurological disorder with multifactorial origins and complex clinical manifestations. While pharmacologic therapies remain the cornerstone of management, a growing body of evidence highlights the role of extracranial peripheral nerve compression as a significant contributor to migraine pathophysiology in selected patients. This recognition has expanded the therapeutic role of plastic surgery, offering anatomically targeted interventions that complement or surpass traditional medical approaches for refractory cases. From a plastic surgeon’s perspective, optimal migraine care begins with accurate identification of clinical patterns, trigger-site mapping, and the judicious use of diagnostic tools such as nerve blocks and botulinum toxin. Surgical decompression techniques, including endoscopic and open approaches, address compression of the supraorbital, supratrochlear, zygomaticotemporal, greater and lesser occipital, auriculotemporal, and intranasal contact-point trigger sites. Adjunctive strategies such as autologous fat grafting further enhance outcomes by providing neuroprotective cushioning and modulating local inflammation through adipose-derived stem cell activity. Recent advances, including neuromodulation technologies, next-generation biologics, and innovations in surgical visualization, underscore the ongoing shift toward precision-based, mechanism-driven therapy. As understanding of migraine heterogeneity deepens, the integration of surgical expertise with modern neuroscience offers a comprehensive and personalized therapeutic framework. Plastic surgeons, equipped with detailed knowledge of peripheral nerve anatomy and minimally invasive techniques, play an increasingly pivotal role in the multidisciplinary management of refractory migraine. Full article

Recently, the kinetic improvement of the nitrogenation reaction of lithium hydride (LiH) to form lithium imide (Li₂NH) by adding a scaffold was reported. The scaffold prevents agglomeration of Li₂NH and maintains the activity of LiH, achieving a reduction in reaction temperature and an increase in reaction rate. In this work, a Li–Si alloy, Li₂₂Si₅, was used as a starting material to form nano-sized LiH dispersed in a Li alloy matrix. Lithium nitride (Li₃N) is generated by the reaction between Li₂₂Si₅ and N₂ to form Li₇Si₃, and then Li₃N is converted to LiH with ammonia (NH₃) generation during heat treatment under H₂ flow conditions. Since Li₃N is formed at the nano-scale on the surface of alloy particles, LiH generated from the above nano-Li₃N is also nano-scale. The differential scanning calorimetry results indicate that direct nitrogenation of LiH in the alloy matrix occurred from around 280 °C, which is much lower than that of the LiH powder itself. Such a highly active state might be achieved due to the nano-crystalline LiH confined by the Li alloy as a self-transformed scaffold. From the above experimental results, the nano-confined LiH in the alloy matrix was recognized as a potential NH₃ synthesis technique based on the LiH-Li₂NH type chemical looping process. Full article

The thyroid cancer incidence has been continuously rising over the last decades. Recently, intelligent cancer detection software are gaining popularity, due to their high diagnostic accuracy and subsequent direct benefits in avoiding unnecessary surgical interventions. This study introduces a novel hybrid computer-aided diagnosis (CAD) system that combines convolutional neural networks (CNNs) and molecular data analysis to achieve comprehensive and reliable thyroid cancer diagnostics. The system consists of two key modules: The first is a CNN-based model leveraging transfer learning, processes ultrasound images to classify patients as either “healthy” or “with a thyroid nodule.” In cases where a nodule is detected, the second module utilizes molecular data to predict the malignancy risk, providing a probability score for clinical decision support. Different image augmentation techniques (traditional ones as well as novels) were carried out to enhance the robustness of the system. The combination of two independent modules makes it possible to use them decoupled, while used together they provide a powerful, in-depth diagnosis of thyroid cancer. The proposed system demonstrates strong performance: the ultrasound-based CNN module achieves an accuracy of 93.65%, with a sensitivity of 100% and a specificity of 69.23%. For the gene analysis component, the model achieves a training mean squared error (MSE) of 4.24 × 10⁻⁵ and a testing MSE 6.31 × 10⁻³. These results underscore the system’s competitive performance with existing thyroid cancer detection CAD systems in both diagnostic performance and the depth of insights provided, supporting clinicians in making informed, reliable decisions in thyroid cancer management. Full article

Ultra-precision single-point diamond turning (SPDT) remains the core process for fabricating optical-grade surfaces with nanometric roughness and sub-micrometer form accuracy. However, machining hard-to-cut or brittle materials such as high-entropy alloys, metals, ceramics, and semiconductors is limited by severe tool wear, high cutting forces, and brittle fracture. To overcome these challenges, a new generation of non-conventional assisted and hybrid SPDT platforms has emerged, integrating multiple physical fields, including mechanical, thermal, magnetic, chemical, or cryogenic methods, into the cutting zone. This review comprehensively summarizes recent advances in hybrid non-conventional assisted SPDT platforms that combine two or more assistive techniques such as ultrasonic vibration, laser heating, magnetic fields, plasma or gas shielding, ion implantation, and cryogenic cooling. The synergistic effects of these dual-field platforms markedly enhance machinability, suppress tool wear, and extend ductile-mode cutting windows, enabling direct ultra-precision machining of previously intractable materials. Recent key case studies are analyzed in terms of material response, surface integrity, tool life, and implementation complexity. Comparative analysis shows that hybrid SPDT can significantly reduce surface roughness, extend diamond tool life, and yield optical-quality finishes on hard-to-cut materials, including ferrous alloys, composites, and crystals. This review concludes by identifying major technical challenges and outlining future directions toward optimal hybrid SPDT platforms for next-generation ultra-precision manufacturing. Full article

In recent years, the RRT* algorithm has been widely applied in industrial fields because of its asymptotic optimality. However, the traditional RRT* algorithm exhibits limitations in terms of convergence speed and quality of generated paths, and its path exploration capability in complex environments remains inadequate. To address these issues, this study proposes a self-adaptive bidirectional APF-RRT* (SA-Bi-APF-RRT*) algorithm. Specifically, a hierarchical node expansion mechanism is established, enabling dynamic adjustment of the new node expansion strategy. Furthermore, a bidirectional artificial potential field (APF) guidance strategy is introduced to enhance obstacle avoidance performance. An obstacle range density evaluation module, which autonomously adjusts APF parameters according to the density distribution of surrounding obstacles, is then incorporated. Additionally, the algorithm integrates a segmented greedy approach with Bézier curve fitting techniques to achieve simultaneous optimization of path length and smoothness, while ensuring path safety. Finally, the proposed algorithm is compared against RRT*, GB-RRT*, Bi-RRT*, APF-RRT*, and Bi-APF-RRT*, demonstrating superior adaptability and efficiency in environments characterized by low iteration counts and high obstacle density. Results indicate that the SA-Bi-APF-RRT* algorithm constitutes a promising optimization solution for USVs path planning tasks. Full article

Transcranial focused ultrasound stimulation (tFUS), an emerging non-invasive neuromodulation technique, has garnered growing attention owing to its high spatial resolution and precise targeting capability for deep brain structures. A body of evidence demonstrates that tFUS can effectively modulate neural activity in specific brain regions, inducing excitatory or inhibitory effects, and it is an important means to reshape neural functions. Ultrasound parameters are crucial in determining the transcranial ultrasound modulation effects. However, there is still controversy over which parameters can regulate neural excitability or inhibition, and there are significant differences in the parameters used in previous studies, which have limited the clinical application of transcranial ultrasound to some extent. Therefore, a systematic clarification of parameter–effect relationships is urgently needed to enable qualitative and quantitative understanding of ultrasound-induced neuromodulation, which is essential for achieving reliable and reproducible outcomes. This paper intends to review the effects of different tFUS parameters and their combinations on the excitability and inhibition of brain neural activities as well as the possible mechanisms. By integrating recent findings from both animal models and human clinical studies, we also discuss critical safety issues related to tFUS, aiming to provide a theoretical basis for future transcranial focused ultrasound modulation treatments for various neurological diseases such as stroke, Parkinson’s disease, dementia, epilepsy, pain disorders, and disorders of consciousness while providing reference value for selecting tFUS treatment regimens. Full article