Search Results (1,245)

Quantum dots combine advantages such as strong processability via solution methods, wide color gamut coverage, and precise emission color coordinates, making them highly promising for applications in optoelectronic devices. However, they face limitations such as insufficient fluorescence intensity and low far-field extraction efficiency. Plasmonic nanocavities based on metallic nanostructures offer an efficient platform for regulating light–matter interactions. In this study, we constructed a tilted plasmonic nanocavity structure composed of a silver nanocube, CdSe/CdS nanorods, and a single-crystal silver microplate. An Al₂O₃ isolation layer prepared via atomic layer deposition was used to control the nanocavity gap, precisely matching the plasmonic resonance mode with the 620 nm fluorescence emission of the quantum dots. This coupling system significantly enhances the radiative rate in the emission band and the electric field strength in the excitation band, achieving a 187-fold luminescence enhancement of the quantum dot. Additionally, leveraging the nano-antenna effect, the fluorescence exhibits upward directional emission. Experimental and simulation results confirm the high-efficiency enhancement and directional control of quantum dot fluorescence by the tilted nanocavity, providing new insights for the integrated application of quantum dots in displays, quantum communication, and other fields. Full article

The 1626 nm laser is an essential component for conducting superlattice research on the strontium atomic clock platform. The superlattice constructed with the 1626 nm and 813 nm lasers will facilitate cutting-edge quantum information research focused on topological quantum states transport. We demonstrate an idler-resonant optical parametric oscillator that achieves 1626 nm laser output based on pump enhancement technology. Through a well-designed external cavity, a laser output of 127 mW at 1626 nm has been achieved, with a corresponding pump quantum conversion efficiency of 50% and a pump threshold of 110 mW. The long-term power stability of the output laser is ±1.5% per hour. Variations in the pump cavity modes under different experimental conditions have been measured, and the impedance matching process of the pump light within the cavity has been discussed. The 1626 nm laser and the associated technologies reported in this manuscript will provide optical support for the investigation of superlattice physics on the strontium optical lattice clock platform. Full article

This study presents a real-time framework for generating two-dimensional (2D) orthomosaic maps directly from UAV video. The method targets operational scenarios in which a continuously updated 2D overview is required during flight or immediately after landing, without relying on time-consuming offline photogrammetry workflows such as structure-from-motion (SfM) and multi-view stereo (MVS). The proposed procedure incrementally registers sparsely sampled video frames on standard CPU hardware using classical feature-based image registration. Each selected frame is converted to grayscale and processed under a fixed keypoint budget to maintain predictable runtime. Tentative correspondences are obtained through descriptor matching with ratio-test filtering, and outliers are removed using random sample consensus (RANSAC) to ensure geometric consistency. Inter-frame motion is modeled by a planar homography, enabling the mapping process to jointly account for rotation, scale variation, skew, and translation that commonly occur in UAV video due to yaw maneuvers, mild altitude variation, and platform motion. Sequential homographies are accumulated to warp incoming frames into a global mosaic canvas, which is updated incrementally using lightweight blending suitable for real-time visualization. Experimental results on three UAV video sequences with different durations, flight patterns, and scene targets report representative orthomosaic-style outputs and per-step CPU runtime statistics (mean, 95th percentile, and maximum), illustrating typical operating behavior under the tested settings. The framework produces visually coherent orthomosaic-style maps in real time for approximately planar scenes with sufficient overlap and texture, while clarifying practical failure modes under weak texture, motion blur, and strong parallax. Limitations include potential drift over long sequences and the absence of ground-truth references for absolute registration-error evaluation. Full article

The Dubai Government has made significant investments in digital learning through platforms such as Al Mawrid and Bayanati, enabling widespread access to employee training and upskilling. However, there remains a major gap in translating accumulated learning into intelligent workforce restructuring. This paper proposes “SkillChain DX,” a policy-driven framework that applies artificial intelligence (AI) to dynamically map employee-acquired skills to evolving job roles across departments, developed using a conceptual design science and policy analysis approach. The framework integrates blockchain to ensure secure, tamper-proof verification of skill credentials across diverse training platforms. To validate feasibility, a pilot prototype was implemented using sentence-transformer models for semantic skill inference and cryptographic hashing mechanisms for decentralized credential verification. Experimental evaluation across six controlled scenarios demonstrated an average role-matching accuracy of approximately 82%, blockchain transaction throughput exceeding 1000 operations per second, and near-instant credential verification with over 99% performance improvement compared to manual processes. The findings demonstrate that integrating AI-driven skill inference with decentralized credential verification can significantly enhance internal mobility, role alignment, and workforce planning at a policy level. The study benchmarks international practices and outlines a practical implementation path for the Dubai Government using only publicly available technologies and case studies, positioning SkillChain DX as one of the first integrated AI–blockchain policy frameworks tailored to public sector human resources (HR) transformation in Dubai. The proposed system framework bridges the current disconnect between training access and organizational transformation, supporting a proactive, transparent, and skills-first public sector, while offering actionable policy insights for future government HR modernization. Full article

The adoption of blockchain-based smart contracts for the trading of goods and services promises greater transparency, automation, and trustlessness, but also raises challenges related to payment integration and modularity. While business analysts (BAs) can express business logic and control flow using BPMN and decision rules using DMN, payment tasks that involve concrete transfers (on-chain, off-chain, cross-chain, or hybrid) require careful implementation by developers due to platform-specific constraints and semantic richness. To address this separation of concerns, we introduce a methodology within the context of the smart contract-as-a-service (SCaaS) approach that supports (1) identifying and mapping generic payment tasks in BPMN to pre-deployed payment smart contracts, (2) augmenting BPMN models with matching payment fragments from a pattern repository, and (3) automatically transforming the augmented models into smart contracts that invoke the appropriate payment services. Our approach builds on prior work in automated BPMN-to-smart contract transformation using Discrete Event–Hierarchical State Machine (DE-HSM) multi-modal modeling to capture process semantics and nested transactions, while enabling payment service reuse, extensibility, and the separation of concerns. We illustrate this methodology via representative use cases spanning conventional, DeFi, and cross-chain payments, and discuss the implications for modular contract deployment and maintainability. Full article

Accurate quantification of surface runoff is required for the appropriate design of storage structures, irrigation patterns, waterways, erosion control structures, water harvesting projects, and groundwater development schemes. However, the paucity of runoff data in Iraq and Iran is a serious obstacle. The soil conservation service–curve number (SCS–CN) method is applied in conjunction with remote sensing (RS) and geographic information system (GIS) to predict the surface runoff in the Shatt Al-Arab Region. In the present study, the Shatt Al-Arab Region is defined as the drainage areas and lateral sub-basins that contribute direct surface runoff to the main channel between Qurna city and the Arabian Gulf. Rainfall, land use/land cover (LULC), hydrologic soil group (HSG), and slope maps are developed in a GIS platform and processed to produce surface runoff for 35 years (1979–2013). The surface runoff ranges between 163 mm (2008) and 300 mm (1982) with an average of 233 mm yr⁻¹. The average annual surface runoff in the study area is 33.657 km³. A scatter plot constructed to visualize the connection between annual rainfall and annual runoff reveals a significant positive relation (coefficient of determination (r²) = 0.67, probability value (p) < 0.05). The runoff potential is low in the southern parts of the study area and gradually rises towards the northern parts. Cross-validation of the modeled annual runoff with the annual runoff data shows reasonably close matches (r² = 0.73, p < 0.001) demonstrating the procedure’s suitability. Full article

Manufacturing companies face significant challenges in leveraging artificial intelligence for equipment management due to high infrastructure costs and limited availability of labeled data for failures. While most manufacturing AI applications focus on structured sensor data, vast amounts of unstructured textual information containing valuable maintenance knowledge remain underutilized. This study presents a practical generative AI-based framework for structured information extraction that automatically converts unstructured equipment maintenance texts into predefined semantic fields to support predictive maintenance in manufacturing environments. We adopted and evaluated three representative generative models—Bidirectional and Auto-Regressive Transformers (BART) with KoBART, Text-to-Text Transfer Transformer (T5) with pko-t5-base, and the large language model Qwen—to generate structured outputs by extracting three predefined fields: failed components, failure types, and corrective actions. The framework enables the structuring of equipment management text data from Manufacturing Execution Systems (MES) to build predictive maintenance support systems. We validated the approach using a large-scale MES dataset consisting of 29,736 equipment maintenance records from a major automotive parts manufacturer, from which curated subsets were used for model training and evaluation. Our methodology employs Generative Pre-trained Transformer 4 (GPT-4) for initial dataset construction, followed by domain expert validation to ensure data quality. The trained models achieved promising performance when evaluated using extraction-aligned metrics, including exact match (EM) and token-level precision, recall, and F1-score, which directly assess field-level extraction correctness. ROUGE scores are additionally reported as a supplementary indicator of lexical overlap. Among the evaluated models, Qwen consistently outperformed BART and T5 across all extracted fields. The structured outputs are further processed through domain-specific dictionaries and regular expressions to create a comprehensive analytical database supporting predictive maintenance strategies. We implemented a web-based analytics platform enabling time-series analysis, correlation analysis, frequency analysis, and anomaly detection for equipment maintenance optimization. The proposed system converts tacit knowledge embedded in maintenance texts into explicit, actionable insights without requiring additional sensor installations or infrastructure investments. This research contributes to the manufacturing AI field by demonstrating a comprehensive application of generative language models to equipment maintenance text analysis, providing a cost-effective approach for digital transformation in manufacturing environments. The framework’s scalability and cloud-based deployment model present significant opportunities for widespread adoption in the manufacturing sector, supporting the transition from reactive to predictive maintenance strategies. Full article

To address the information-processing burden faced by consumers in green consumption markets due to complex carbon footprint labels, opaque certification standards, and vague descriptions of environmental benefits, this study proposes a generative artificial intelligence (GenAI)-based precision recommendation mechanism for green products. The mechanism aims to enhance cognitive fluency and promote low-carbon purchase decisions. An experimental system, termed Eco-GenRec, is developed by integrating large language models (LLMs), multimodal generation, and retrieval-augmented generation (RAG) techniques to enable personalized presentation of green product information. Based on inferred user cognitive styles, the system transforms product information into chart-based representations for analytical users or emotionally framed scenario narratives for intuitive users. This study is conducted on a web-based simulated shopping platform and employs a fully randomized design. A total of 1000 participants are randomly assigned to either a standardized information display group (control group) or an Eco-GenRec-generated display group (experimental group). Participants are drawn from diverse socioeconomic backgrounds and cover a wide age range. The sample exhibits substantial demographic diversity, which enhances the representativeness of the findings. Cognitive fluency and low-carbon purchase conversion rates are measured as the primary outcomes. The results show that the Eco-GenRec group achieves a significantly higher cognitive fluency score (M = 5.68, SD = 0.89) than the control group (M = 4.60, SD = 1.01). This represents an increase of 23.4% (t = 18.34, p < 0.001, effect size d = 1.17). In addition, the low-carbon purchase conversion rate in the experimental group (36.3%) is significantly higher than that in the control group (17.6%). The absolute increase of 18.7% is statistically significant (χ² = 70.28, p < 0.001, effect size Cramér’s V = 0.265). Under conditions of high cognitive-style matching, the conversion rate improvement reaches 27.2%. Mechanism analysis shows that cognitive fluency mediates the relationship between GenAI-based recommendations and purchase intention. By transforming abstract environmental parameters into intuitive and easily interpretable content, artificial intelligence reduces information-processing burden and activates positive affect and trust among consumers. Overall, this study empirically validates the effectiveness of GenAI in green product recommendation. It provides a practical pathway for addressing the “comprehension barrier” in green consumption and extends the theoretical boundaries of research on cognitive fluency and low-carbon decision-making. Full article

Background: With population aging and the increasing incidence of pancreatic and periampullary malignancies, more elderly patients are being considered for pancreaticoduodenectomy (PD). Although robotic pancreaticoduodenectomy (RPD) is steadily adopted, evidence regarding its safety in patients aged ≥ 75 years remains limited, particularly in centers with restricted access to robotic platforms. Materials and Methods: We conducted a retrospective single-center study including patients who underwent PD between January 2019 and September 2025. Outcomes after RPD were compared between patients aged < 75 and ≥75 years. In addition, elderly patients undergoing RPD were compared with elderly patients undergoing open pancreaticoduodenectomy (OPD) using 1:2 propensity score matching. The primary endpoint was major postoperative morbidity (Clavien–Dindo grade ≥ III). Results: Among 525 PDs, 130 (25%) were performed robotically, including 29 patients aged ≥ 75 years. Within the RPD cohort, age ≥ 75 years was not associated with an increased risk of major complications compared with younger patients (OR 0.68, 95% CI 0.23–1.76; p = 0.45), nor with higher 90-day mortality. In the propensity score-matched elderly cohort, major morbidity was similar between RPD and OPD (10% vs. 7%; p = 0.68). RPD was associated with a significantly lower 30-day readmission rate, despite a higher incidence of delayed gastric emptying, mainly driven by mild (grade A) cases. Conclusions: RPD appears to be safe in carefully selected patients aged ≥ 75 years, with morbidity and mortality comparable to those observed in younger RPD patients and in elderly patients undergoing open surgery. These findings support the selective use of RPD in elderly patients, even in centers with limited access to robotic platforms. Full article

Parallel inverter systems constitute the fundamental units of AC microgrids and distributed renewable energy generation systems, wherein accurate power sharing among units represents a critical challenge for stable operation. Conventional droop control fails to share the harmonic power in proportionality to the capacity of inverters due to disparities on line impedance, leading to circulating currents, degraded power quality, and reduced system load capability. To address these issues, this paper proposes a harmonic power-sharing control strategy based on perturbative virtual impedance injection. Under the premise that fundamental power sharing according to capacity ratios has been ensured, the strategy first converts the harmonic power information of each inverter into a small-signal perturbation, which is injected into the virtual impedance of its fundamental control loop. Subsequently, by detecting the resulting variations in fundamental power coefficients induced by this perturbation, a closed-loop feedback is constructed to adaptively adjust the virtual impedance value of each inverter at harmonic frequencies. This adjustment enables the automatic matching of the harmonic power distribution ratio to the inverter capacity ratio, ultimately achieving precise harmonic power sharing. The proposed strategy operates without requiring inter-unit communication links or sampling the voltage at the common coupling point, relying solely on local information, thereby enhancing system reliability. Finally, the effectiveness of the proposed control strategy in achieving harmonic power sharing under conditions of line impedance mismatch is validated through an RT-LAB hardware-in-the-loop platform. Full article

Aimed at the elevation ambiguity problem in array synthetic aperture radar (SAR) three-dimensional imaging, this paper proposes a novel ambiguity-resolving method based on wavefront modulation. By introducing measured plasma lens modulation phases and constructing an array SAR signal echo model incorporating wavefront modulation, the method effectively overcomes the physical size limitations of traditional array antennas. Theoretical analysis demonstrates that wavefront modulation significantly reduces the grating lobe level of the array pattern, equivalently increasing the number of array channels and thereby shortening the shortest baseline length, which enhances the system’s maximum unambiguous height. At the signal processing level, an observation equation based on compressed sensing is established, and target reconstruction is achieved using the Orthogonal Matching Pursuit (OMP) algorithm. Monte Carlo simulation results indicate that under the same signal-to-noise ratio conditions, when the observation range is extended to twice the theoretical maximum unambiguous height, the proposed method maintains a reconstruction success rate of over 95%, whereas the traditional method’s reconstruction success rate drops rapidly below 40% once the maximum unambiguous range is exceeded. This study also investigates the 3D reconstruction of spatial point targets and a rectangular building, with the analysis of their theoretical ambiguous positions confirming the method’s effectiveness in suppressing ambiguous targets in the vicinity of spatial point targets as well as in front of and behind the structure. This study provides a new technical approach to overcoming antenna size constraints on airborne platforms, with significant application value in fields such as digital elevation model construction and urban 3D imaging. Full article

Bovine viral diarrhea virus (BVDV) is a critical pathogen affecting the global cattle industry, causing severe economic losses primarily through persistent infection, immunosuppression, and reproductive failure. The virus exhibits substantial genetic diversity, with marked geographic variation in circulating subtypes, which complicates effective disease control. BVDV evades host immune responses by suppressing type I interferon signaling, impairing neutrophil function, and reprogramming host cellular metabolism, ultimately leading to the generation of persistently infected (PI) animals that serve as the principal reservoir for viral transmission. Current prevention and control strategies rely mainly on the identification and elimination of PI animals in combination with vaccination. However, conventional vaccines, including inactivated vaccines (IVs) and modified live vaccines (MLVs), have notable limitations, such as suboptimal subtype matching, interference by maternal antibodies, and safety concerns associated with MLV use in pregnant cattle. Emerging vaccine platforms, including mRNA vaccines, subunit vaccines, and multi-epitope vaccines, offer promising alternatives owing to their improved safety profiles, rapid design and production, and potential to elicit broad and robust immune responses. Future BVDV vaccine development should integrate artificial intelligence-driven design strategies with high-throughput sequencing and molecular epidemiological surveillance to enable the rational development of multivalent and multi-epitope vaccines. In addition, coordinated implementation of strain monitoring, PI animal clearance, and enhanced biosecurity practices will be essential for establishing a comprehensive and sustainable BVDV prevention and control framework. Full article

This paper proposes a low-complexity joint parallel timing recovery loop, which is well-suited for large-bandwidth terahertz (THz) communication systems. Specifically, the loop is jointly composed of a modified matched filter (MMF) and a timing error detector (TED), where sampling point offset correction is achieved by deleting, holding, or retaining data in parallel data caches (DCs), and timing phase error compensation is implemented by sliding the coefficients of the MMF. The feasibility of the proposed loop is verified using both Gardner and O&M TED. Numerical simulation results demonstrate that the loop operates efficiently, with a performance loss of less than 0.1 dB compared to the theoretical bit error rate (BER) curve. Furthermore, the loop is implemented on a THz field-programmable gate array (FPGA) platform, successfully realizing parallel demodulation of 15 Gbps 64QAM high-speed signals at 220 GHz. Notably, the proposed loop effectively reduces hardware resource consumption under a parallel architecture, providing a viable solution to address the current shortage of on-board resources in high-speed THz communication systems. Full article

Background/Objectives: Nanoemulsions (NEs) are a promising platform for transdermal drug delivery (TDD); however, how the polarity of the active pharmaceutical ingredient (API) influences NE structure–performance relationships remains insufficiently understood. This study aimed to systematically compare the transdermal delivery behavior of a hydrophilic API, salbutamol hemisulphate (log P ≈ 0.1), and a lipophilic API, ibuprofen (log P ≈ 3.3), incorporated into compositionally matched nanoemulsion systems. Methods: Kolliphor EL–based NEs were prepared using identical excipients, with systematic variation of oil, surfactant, and water ratios. Thirty-six formulations were produced for each API. Physical stability, droplet size, and viscosity were characterized, and in vitro skin permeation studies were conducted using excised mouse skin. Flux and cumulative permeation were quantified, and statistical analyses were performed to identify key compositional drivers of permeation. Results: Ibuprofen-containing NEs exhibited superior physical stability compared to salbutamol formulations, likely due to interfacial interactions that imparted surfactant-like behavior. Both APIs formed nanoscale droplets, with salbutamol formulations ranging from 16 to 507 nm and ibuprofen formulations spanning 12–563 nm, more frequently yielding sub-100 nm droplets. Viscosity values covered broad ranges (3–9532 mPa·s for salbutamol; 13.4–9759 mPa·s for ibuprofen), with salbutamol generating an extended high-viscosity domain at 50% (w/w) surfactant and ibuprofen showing a narrower viscosity maximum at 30–40% surfactant. Salbutamol NEs achieved high fluxes (up to 374 µg/cm²·h) and cumulative permeation of approximately 80% of the applied dose, whereas ibuprofen formulations showed markedly lower fluxes (maximum 32 µg/cm²·h) and cumulative permeation below 6%. High surfactant levels suppressed permeation for both APIs, but the dominant positive drivers differed: balanced oil–water ratios for salbutamol and hydration-dependent diffusional resistance for ibuprofen. Conclusions: These findings demonstrate that API polarity and interfacial portioning behavior decisively govern NE performance, providing a framework for rational tailoring of oil–surfactant–water ratios to maximize transdermal delivery efficiency. Full article

Forensic DNA profiling commonly relies on polymerase chain reaction (PCR) amplification followed by capillary electrophoresis (CE) or massively parallel sequencing (MPS), which requires expensive, laboratory-based equipment that depends on a stable power supply and is unsuitable for field applications. Here, we present a proof-of-concept assay that uses recombinase polymerase amplification (RPA) combined with exo probe detection for rapid, isothermal genotyping of insertion–deletion (InDel) markers. To the best of our knowledge, this study represents the first demonstration of forensic DNA typing using RPA coupled with exo probes. The reaction proceeds at 39 °C and combines amplification and detection in a single 20 min step. Thirteen DNA samples were genotyped in triplicate across eight InDel loci using allele-specific fluorescent probes. Genotypes were derived from differential endpoint fluorescence between matched and mismatched probes. Compared with benchmark genotyping, 97.07% of genotypes (n = 307) were correct at 1 ng DNA input. Accurate profiles were reliably obtained for DNA inputs as low as 250 pg, and partial profiles were still detectable at 31 pg. The results demonstrate that RPA-based InDel genotyping is fast, sensitive, and reproducible. With further optimization, such as refined probe design and selection of robust loci, the assay has clear potential to achieve complete accuracy and to be integrated into portable lab-on-a-chip platforms for rapid, field-deployable forensic identification. Full article