Search Results (2,006)

Scene Knowledge-guided Visual Grounding (SK-VG) is a multi-modal detection task built upon conventional visual grounding (VG) for human–computer interaction scenarios. It utilizes an additional passage of scene knowledge apart from the image and context-dependent textual query for referred object localization. Due to the inherent difficulty in directly establishing correlations between the given query and the image without leveraging scene knowledge, this task imposes significant demands on a multi-step knowledge reasoning process to achieve accurate grounding. Off-the-shelf VG models underperform under such a setting due to the requirement of detailed description in the query and a lack of knowledge inference based on implicit narratives of the visual scene. Recent Vision–Language Models (VLMs) exhibit improved cross-modal reasoning capabilities. However, their monolithic architectures, particularly in lightweight implementations, struggle to maintain coherent reasoning chains across sequential logical deductions, leading to error accumulation in knowledge integration and object localization. To address the above-mentioned challenges, we propose SplitGround—a collaborative framework that strategically decomposes complex reasoning processes by fusing the input query and image with knowledge through two auxiliary modules. Specifically, it implements an Agentic Annotation Workflow (AAW) for explicit image annotation and a Synonymous Conversion Mechanism (SCM) for semantic query transformation. This hierarchical decomposition enables VLMs to focus on essential reasoning steps while offloading auxiliary cognitive tasks to specialized modules, effectively splitting long reasoning chains into manageable subtasks with reduced complexity. Comprehensive evaluations on the SK-VG benchmark demonstrate the significant advancements of our method. Remarkably, SplitGround attains an accuracy improvement of 15.71% on the hard split of the test set over the previous training-required SOTA, using only a compact VLM backbone without fine-tuning, which provides new insights for knowledge-intensive visual grounding tasks. Full article

Electrochemical sensing arrays enable the spatial study of dopamine levels throughout brain slices, the diffusion of electroactive molecules, as well as neurotransmitter secretion from single cells. The integration of complementary metal-oxide semiconductor (CMOS) devices in the development of electrochemical sensing devices enables large-scale parallel recordings, providing beneficial high-throughput for drug screening studies, brain–machine interfaces, and single-cell electrophysiology. In this paper, an electrochemical sensor capable of recording at 40,000 frames per second using a CMOS sensor array with 1024 electrochemical detectors and a custom field-programmable gate array data acquisition system is detailed. A total of 1024 on-chip electrodes are monolithically integrated onto the designed CMOS chip through post-CMOS fabrication. Each electrode is paired with a dedicated transimpedance amplifier, providing 1024 parallel electrochemical sensors for high-throughput studies. To support the level of data generated by the electrochemical device, a powerful data acquisition system is designed to operate the sensor array as well as digitize and transmit the output of the CMOS chip. Using the presented electrochemical sensing system, both dopamine and hydrogen peroxide diffusions across the sensor array are successfully recorded at 40,000 frames per second across the 32 × 32 electrochemical detector array. Full article

This in vitro study evaluated the impact of simulated gastric acid exposure on the optical (ΔE₀₀, translucency parameter TP, contrast ratio CR) and surface (roughness Ra) properties of monolithic zirconia ceramics under varying sintering rates and surface treatments. Forty-eight disc specimens (10 mm × 10 mm × 1.0 mm) were randomly allocated into four groups (n = 12): slow sintering + polishing; slow sintering + glazing; fast sintering + polishing; and fast sintering + glazing. Specimens were aged in 0.06 M of HCl (hydrochloric acid) for 96 h, and all measurement parameters were assessed against white and black backgrounds before and after aging. Statistical analyses (Shapiro–Wilk, Kruskal–Wallis, Wilcoxon tests; α = 0.05) revealed that acid aging caused a significant increase in ΔE₀₀ across all groups (p < 0.05), with the smallest change observed in the fast-sintering + glazing group and the largest in the slow-sintering + glazing group. Contrast ratios remained high in all groups (CR > 0.92), while only the slow-sintering + glazing group exhibited a significant reduction in TP (p < 0.05). Surface roughness decreased following aging in all groups, with the lowest Ra detected in the fast-sintering + glazing group. These results suggest that fast sintering combined with glazing enhances color stability and yields smoother surfaces under acidic conditions, recommending this protocol particularly for patients at elevated risk of increased oral acidity. Full article

Current agentic AI frameworks such as LangGraph and AutoGen simulate autonomy via sequential prompt chaining but lack true multi-agent coordination architectures. These systems conflate semantic reasoning with orchestration, requiring LLMs at every coordination step and limiting scalability. By contrast, TB-CSPN (Topic-Based Communication Space Petri Net) is a hybrid formal architecture that fundamentally separates semantic processing from coordination logic. Unlike traditional Petri net applications, where the entire system state is encoded within the network structure, TB-CSPN uses Petri nets exclusively for coordination workflow modeling, letting communication and interaction between agents drive semantically rich, topic-based representations. At the same time, unlike first-generation agentic frameworks, here LLMs are confined to topic extraction, with business logic coordination implemented by structured token communication. This hybrid architectural separation preserves human strategic oversight (as supervisors) while delegating consultant and worker roles to LLMs and specialized AI agents, avoiding the state-space explosion typical of monolithic formal systems. Our empirical evaluation shows that TB-CSPN achieves 62.5% faster processing, 66.7% fewer LLM API calls, and 167% higher throughput compared to LangGraph-style orchestration, without sacrificing reliability. Scaling experiments with 10–100 agents reveal sub-linear memory growth (10× efficiency improvement), directly contradicting traditional Petri Net scalability concerns through our semantic-coordination-based architectural separation. These performance gains arise from the hybrid design, where coordination patterns remain constant while semantic spaces scale independently. TB-CSPN demonstrates that efficient agentic AI emerges not by over-relying on modern AI components but by embedding them strategically within a hybrid architecture that combines formal coordination guarantees with semantic flexibility. Our implementation and evaluation methodology are openly available, inviting community validation and extension of these principles. Full article

The increasing complexity and cost of manufacturing monolithic chips have driven the semiconductor industry toward chiplet-based designs, where smaller, modular chiplets are integrated onto a single interposer. While chiplet architectures offer significant advantages, such as improved yields, design flexibility, and cost efficiency, they introduce new security challenges in the horizontal hardware manufacturing supply chain. These challenges include risks of hardware Trojans, cross-die side-channel and fault injection attacks, probing of chiplet interfaces, and intellectual property theft. To address these concerns, this paper presents ChipletQuake, a novel on-chiplet framework for verifying the physical security and integrity of adjacent chiplets during the post-silicon stage. By sensing the impedance of the power delivery network (PDN) of the system, ChipletQuake detects tamper events in the interposer and neighboring chiplets without requiring any direct signal interface or additional hardware components. Fully compatible with the digital resources of FPGA-based chiplets, this framework demonstrates the ability to identify the insertion of passive and subtle malicious circuits, providing an effective solution to enhance the security of chiplet-based systems. To validate our claims, we showcase how our framework detects hardware Trojans and interposer tampering. Full article

To enable multi-channel parallel spectral analysis in array-based devices such as micro-light-emitting diodes (Micro-LEDs) and line-scan spectral confocal systems, the development of compact array spectrometers has become increasingly important. In this work, a novel spectrometer architecture based on a microlens array grating (MLAG) is proposed, which addresses the major limitations of conventional spectrometers, including limited parallel detection capability, bulky structures, and insufficient spatial resolution. By integrating dispersion and focusing within a monolithic device, the system enables simultaneous acquisition across more than 2000 parallel channels within a 10 mm × 10 mm unit consisting of an f = 4 mm microlens and a 600 lines/mm blazed grating. Optimized microlens and aperture alignment allows for flexible control of the divergence angle of the incident light, and the system theoretically achieves nanometer-scale spectral resolution across a 380–780 nm wavelength range, with inter-channel measurement deviation below 1.25%. Experimental results demonstrate that this spectrometer system can theoretically support up to 2070 independently addressable subunits. At a wavelength of 638 nm, the coefficient of variation (CV) of spot spacing among array elements is as low as 1.11%, indicating high uniformity. The spectral repeatability precision is better than 1.0 nm, and after image enhancement, the standard deviation of the diffracted light shift is reduced to just 0.26 nm. The practical spectral resolution achieved is as fine as 3.0 nm. This platform supports wafer-level spectral screening of high-density Micro-LEDs, offering a practical hardware solution for high-precision industrial inline sorting, such as Micro-LED defect inspection. Full article

To improve the efficiency and stability of the system, this paper proposes a monolithic integrated optical path design for a cavity optomechanical accelerometer based on a 250 nm top silicon thickness silicon-on-insulator (SOI) wafer instead of readout through U-shape fiber coupling. Finite Element Analysis (FEA) and Finite-Difference Time-Domain (FDTD) methods are employed to systematically investigate the performance of key optical structures, including the resonant modes and bandgap characteristics of photonic crystal (PhC) microcavities, transmission loss of strip waveguides, coupling efficiency of tapered-lensed fiber-to-waveguide end-faces, coupling characteristics between strip waveguides and PhC waveguides, and the coupling mechanism between PhC waveguides and microcavities. Simulation results demonstrate that the designed PhC microcavity achieves a quality factor (Q-factor) of 2.26 × 10⁵ at a 1550 nm wavelength while the optimized strip waveguide exhibits a low loss of merely 0.2 dB over a 5000 μm transmission length. The strip waveguide to PhC waveguide coupling achieves 92% transmittance at the resonant frequency, corresponding to a loss below 0.4 dB. The optimized edge coupling structure exhibits a transmittance of 75.8% (loss < 1.2 dB), with a 30 μm coupling length scheme (60% transmittance, ~2.2 dB loss) ultimately selected based on process feasibility trade-offs. The total optical path system loss (input to output) is 5.4 dB. The paper confirms that the PhC waveguide–microcavity evanescent coupling method can effectively excite the target cavity mode, ensuring optomechanical coupling efficiency for the accelerometer. This research provides theoretical foundations and design guidelines for the fabrication of high-precision monolithic integrated cavity optomechanical accelerometers. Full article

Silicon-based microcavity quantum dot lasers are attractive candidates for on-chip light sources in photonic integrated circuits due to their small size, low power consumption, and compatibility with silicon photonic platforms. However, integrating components like quantum dot lasers and photodetectors on a single chip remains challenging due to material compatibility issues and mode field mismatch problems. In this work, we have demonstrated monolithic integration of an InAs quantum dot microdisk light emitter, waveguide, and photodetector on a silicon platform using a shared epitaxial structure. The photodetector successfully monitored variations in light emitter output power, experimentally proving the feasibility of this integrated scheme. This work represents a key step toward multifunctional integrated photonic systems. Future efforts will focus on enhancing the light emitter output power, improving waveguide efficiency, and scaling up the integration density for advanced applications in optical communication. Full article

Cement-based batteries (CBBs) are an emerging category of multifunctional materials that combine structural load-bearing capacity with integrated electrochemical energy storage, enabling the development of self-powered infrastructure. Although previous reviews have explored selected aspects of CBB technology, a comprehensive synthesis encompassing system architectures, material strategies, and performance metrics remains insufficient. In this review, CBB systems are categorized into two representative configurations: probe-type galvanic cells and layered monolithic structures. Their structural characteristics and electrochemical behaviors are critically compared. Strategies to enhance performance include improving ionic conductivity through alkaline pore solutions, facilitating electron transport using carbon-based conductive networks, and incorporating redox-active materials such as zinc–manganese dioxide and nickel–iron couples. Early CBB prototypes demonstrated limited energy densities due to high internal resistance and inefficient utilization of active components. Recent advancements in electrode architecture, including nickel-coated carbon fiber meshes and three-dimensional nickel foam scaffolds, have achieved stable rechargeability across multiple cycles with energy densities surpassing 11 Wh/m². These findings demonstrate the practical potential of CBBs for both energy storage and additional functionalities, such as strain sensing enabled by conductive cement matrices. This review establishes a critical basis for future development of CBBs as multifunctional structural components in infrastructure applications. Full article

The increasing utilization of hard-to-cut materials in high-performance sectors such as aerospace and defense has pushed manufacturing systems to be flexible in processing large workpieces with a wide range of materials while also delivering high precision. Recent studies have highlighted the potential of integrating industrial robots (IRs) with electric discharge machining (EDM) to create a non-contact, low-force manufacturing platform, particularly suited for the accurate machining of hard-to-cut materials into complex and large-scale monolithic components. In response to this potential, a novel robotic EDM system has been developed. However, the manual programming and control of such a convoluted system present a significant challenge, often leading to inefficiencies and increased error rates, creating a scenario where the EDM process becomes unfeasible. To enhance the industrial applicability of this robotic EDM technology, this study focuses on a novel methodology to develop and validate a digital twin (DT) of the physical robotic EDM system. The digital twin functions as a virtual experimental environment for tool motion, effectively addressing the challenges posed by collisions and kinematic singularities inherent in the physical system, yet with proven 20-micron EDM gap accuracy. Furthermore, it facilitates a CNC-like, user-friendly offline programming framework for robotic EDM cutting path generation. Full article

A broadband, efficient monolithic microwave integrated circuit power amplifier (MMIC PA) in OMMIC’s 0.1 μm GaN-on-Si technology for 5G millimeter-wave communication is presented. This study concentrates on the output matching design, which has an important influence on the PA’s performance. A compact one-order synthesized transformer network (STN) is adopted to match the 50 Ω load to the extracted large-signal output model of the transistor. A dual-objective strategy is developed for parameter optimization, incorporating the impedance transformation trajectory inside the predefined optimal impedance domain (OID) that satisfies the required specifications, with approximation to selected optimal load impedances. By introducing a custom adjustment factor β into the error function, coupled with an automated iterative tuning process based on S-parameter simulations, desired broadband matching results can be rapidly achieved. The proposed two-stage PA occupies a small chip area of only 1.23 mm² and demonstrates good frequency consistency over the 24–31 GHz band. Continuous-wave characterization shows a flat small-signal gain of 19.7 ± 0.5 dB; both the output power (P_out) and the power-added efficiency (PAE) at the 4 dB compression point remain smooth, ranging from 32.3 to 32.7 dBm and 35.5% to 37.8%, respectively. The peak PAE reaches up to nearly 40% at the center frequency. Full article

A recently proposed method called “controlled swelling of monolithic films” was implemented to prepare ultra-high-molecular-weight polyethylene (UHMWPE) ultrafiltration membranes. For the first time, the effect of UHMWPE molecular weight (MW) on the structure and properties of the membranes prepared via this special case of thermally induced phase separation was studied in detail. The morphology and properties of the membranes were studied using SEM, DSC, liquid–liquid displacement porometry, and standard methods for the evaluation of mechanical properties, permeance, rejection, and abrasion resistance. High-quality membranes with a tensile strength of 5.0–17.8 MPa, a mean pore size of 25–50 nm, permeance of 17–107 L m⁻² h⁻¹ bar⁻¹, rejection of model contaminant (blue dextran) of 72–98%, and great abrasion resistance can be prepared only if the MW of the polymer in the initial monolithic film is sufficiently high. The properties of the membranes can effectively be controlled by changing the MW of the polymer and the mass fraction of the latter in the swollen film. Shrinkage is responsible for the variation in the membrane properties. The membranes prepared from a higher-MW polymer are more prone to shrinking after the removal of the solvent. Shrinkage decreases before rising again and minimizes with an increase in the polymer content in the swollen film. Full article

Thin-walled hemispherical shell structures are mainly used in the aerospace industry as energy absorbers. However, their thin walls frequently lead to stability problems. To create a stable structure, double-layered thin-walled hemispherical shell structures were developed. In this study, we investigated the deformation behaviors of these structures through both experimental and numerical methods. The shell span diameter is taken as 200 mm. Monolithic layers have thicknesses of 1.0 mm compared with double-layered shells which have thicknesses of 0.5 mm (inner)/0.5 mm (outer). We developed numerical models to simulate the structural responses of monolithic and double-layered spherical shell structures using ABAQUS/CAE^® V6.14 software. These models were validated against experimental results. Our results show that double-layered shells absorb more energy compared to monolithic shells. These insights provide a foundation for improved designs of hemispherical structures, ultimately enhancing their energy absorption performance. Full article

Background: Monolithic zirconia has attracted considerable interest in dentistry due to its favorable physical and mechanical properties, making it a promising alternative for crown fabrication. Nonetheless, a standardized finishing protocol for this material has yet to be established. Objective: This study aimed to evaluate the surface characteristics and in vitro biofilm formation of zirconia finished by either polishing or glazing. Methods: A total of 72 zirconia specimens were fabricated and divided into control, glazing, and polishing groups. Surface analysis included roughness, wettability, and surface free energy. Microbiological analysis included CFU (colony-forming units per mL) counts, microbial adhesion at 2, 4, 6, and 8 h, biofilm biovolume, and qualitative biofilm assessment via scanning electron microscopy (sEm). Results: The glazing group showed significantly greater roughness than the polishing (p = 0.006) and control (p = 0.016) groups, along with a lower contact angle (polishing—p = 0.002; control—p < 0.001) and higher surface energy (polishing—p = 0.005; control—p < 0.001). No significant differences were observed in CFU counts for the tested microorganisms (C. albicans, p = 0.158; L. casei, p = 0.610; S. mutans, p = 0.904). Regarding microbial adhesion, the polishing group showed a smaller biofilm-covered area compared to the control group for both total biofilm (p = 0.008) and viable biofilm (p = 0.005). no statistically significant difference was observed in biofilm biovolume (p = 0.082). Conclusions: These findings suggest that, despite the surface differences among the groups, biofilm formation was not significantly affected. Full article

This review investigates recent developments in cardiac mechano-electrical-fluid interaction (MEFI) modeling, with a focus on multiphysics simulation platforms and digital twin frameworks developed between 2015 and 2025. The purpose of the study is to assess how computational modeling methods—particularly finite element and immersed boundary techniques, monolithic and partitioned coupling schemes, and artificial intelligence (AI)-enhanced surrogate modeling—capture the integrated dynamics of cardiac electrophysiology, tissue mechanics, and hemodynamics. The goal is to evaluate the translational potential of MEFI models in clinical applications such as cardiac resynchronization therapy (CRT), arrhythmia classification, atrial fibrillation ablation, and surgical planning. Quantitative results from the literature demonstrate <5% error in pressure–volume loop predictions, >0.90 F1 scores in machine-learning-based arrhythmia detection, and <10% deviation in myocardial strain relative to MRI-based ground truth. These findings highlight both the promise and limitations of current MEFI approaches. While recent advances improve physiological fidelity and predictive accuracy, key challenges remain in achieving multiscale integration, model validation across diverse populations, and real-time clinical applicability. The review concludes by identifying future milestones for clinical translation, including regulatory model certification, standardization of validation protocols, and integration of patient-specific digital twins into electronic health record (EHR) systems. Full article