Search Results (1,018)

Background/Objectives: Oral mucosal wounds are frequently encountered in daily dental practice and are often difficult to manage because of continuous exposure to saliva, mastication, and mechanical irritation. This technical note describes the clinical practicality of an oral liquid bandage (ORAPLA) as a film-forming protective barrier for traumatic and surgical oral mucosal wounds. Methods: ORAPLA was applied in four clinical scenarios: a traumatic lip bite injury, a postoperative mucosal defect following leukoplakia excision, a biopsy wound for suspected oral squamous cell carcinoma (OSCC), and aphthous stomatitis. Clinical observations included patient-reported symptom relief, film retention, and the clinical appearance of epithelialization at follow-up (1–2 weeks). Results: In all cases, ORAPLA formed a thin protective film immediately after application and was typically observed to remain on the wound surface for approximately 5–6 h under routine daily activities. Patients reported prompt subjective pain relief, and no adverse events were observed. Epithelialization proceeded without clinically evident secondary infection during the follow-up period. Conclusions: In this small descriptive case series, ORAPLA was feasible to apply, well tolerated, and provided temporary mechanical protection with immediate subjective comfort. Controlled studies using standardized outcome measures are warranted. Full article

Additive manufacturing enables lightweight sandwich structures with complex cellular cores, but the selection of material and process settings typically involves trade-offs among mechanical performance, cost, and sustainability. This study proposes an integrated multi-criteria decision-making framework to identify the most suitable configuration for Fused Filament Fabrication (FFF) sandwich structures featuring a gyroid triply periodic minimal surface (TPMS) core. Eight alternatives are evaluated by combining two materials (PLA and PLA–Flax biocomposite) with two extrusion temperatures (200 °C and 220 °C) and two infill densities (20% and 30%). Mechanical performance is represented by flexural strength obtained from three-point bending tests reported in a previously published experimental campaign, while economic and environmental indicators are quantified through material cost and printing energy consumption, respectively. Criteria weights are derived using the Analytic Hierarchy Process (AHP) based on expert judgment and consistency-ratio verification, and the alternatives are ranked using the TOPSIS method. The results highlight a clear dominance of PLA-based configurations under the adopted weighting scheme, with PLA printed at 200 °C and 20% infill emerging as the best compromise solution. PLA–Flax options are penalized by higher material cost, higher printing-process energy demand, and lower flexural strength in the investigated conditions. The proposed AHP–TOPSIS workflow supports transparent, data-driven selection of AM process–material combinations for gyroid sandwich structures, and it can be readily extended by including additional sustainability metrics (e.g., CO₂-equivalent) and application-specific constraints. A sensitivity analysis under alternative weighting scenarios further confirms the robustness of the obtained ranking. Full article

In this study, an improved particle swarm optimization (PSO) algorithm is designed to construct a weighting model for line-of-sight (LOS) and non-line-of-sight (NLOS) channels in an ultra-wideband (UWB) indoor positioning system. In the proposed algorithm, the particle position represents candidate weight vectors, and the fitness function is defined by the 3D positioning error over multiple test points. An optimized weight modeling framework is proposed for a multi-anchor, three-dimensional UWB indoor positioning system under LOS and NLOS channels. First, the three-dimensional positioning problem is formulated as a multilateration model, and the tag coordinates are estimated via a linearized matrix equation solved by the least-squares method, which explicitly links anchor geometry and ranging errors to the positioning accuracy. To evaluate the proposed method, extensive ranging and positioning experiments are conducted in a realistic indoor environment using up to eight anchors with different LOS/NLOS configurations, including dynamic scenarios with varying numbers of NLOS anchors. The results show that, compared with the conventional unweighted multi-anchor scheme, the PSO-based weighting model can reduce the average 3D positioning error by more than 30% in typical LOS-dominant settings and significantly suppress error bursts in severe NLOS conditions. These findings demonstrate that the combination of mathematical modeling, least-squares estimation, and swarm intelligence optimization provides an effective tool for designing intelligent engineering positioning systems in complex indoor environments, which aligns with the development of smart factories and industrial Internet-of-Things (IIoT) applications. Full article

With the increasingly prominent issues of energy shortage and environmental pollution, the development of clean energy materials has become a core topic in the academic community. SnSe, as a material with moderate bandgap, a high light absorption coefficient, and environmental friendliness, has shown broad application prospects in the fields of photovoltaics and thermoelectrics. However, pure SnSe thin films have inherent defects, low carrier concentration, and high recombination rates, which limit their photoelectric conversion efficiency. This article provides a detailed overview of the characteristics of band engineering control technology, defect control technology, and carrier concentration control technology, as well as the improvements in the characteristics of SnSe thin films that they bring. This article systematically reviews the research progress on doping control technology for SnSe thin films characteristics in recent years and analyzes and discusses the differences in typical doping elements on SnSe thin films characteristics, such as optical bandgap and absorption coefficient, and applicable application scenarios, such as photovoltaics, near-infrared/infrared detection, and thermoelectric and flexible optoelectronic devices. Furthermore, the interaction between the doping mechanism of dopants and natural defects, as well as the influence of the structural parameters of doped films on doping efficiency, were analyzed, and a predictive design route for the doping mechanism of SnSe films was proposed. Finally, the influence of different atomic fractions on the characteristics of SnSe thin films was discussed. Low atomic fractions are beneficial for bandgap tuning and absorption enhancement; high atomic fractions can easily introduce phase separation and non-radiative recombination. It is suggested that future researchers can continue to focus on the precise control of atomic fractions, exploration of new element co-doping, and industrial large-scale production applications, providing theoretical guidance for the design and application of SnSe thin films in photothermal devices. Full article

With the growing development of facial micro-expression recognition technology, its practical application value has attracted increasing attention. In real-world scenarios, facial micro-expression recognition typically involves cross-dataset evaluation, where training and testing samples come from different datasets. Specifically, cross-dataset micro-expression recognition employs multi-dataset composite training and unseen single-dataset testing. This setup introduces two major challenges: inconsistent feature distributions across training sets and data imbalance. To address the distribution discrepancy of the same category across different training datasets, we propose a plug-and-play batch regularization learning module that constrains weight discrepancies across datasets through information-theoretic regularization, facilitating the learning of domain-invariant representations while preventing overfitting to specific source domains. To mitigate the data imbalance issue, we propose an Action Unit (AU)-guided generative adversarial network (GAN) for synthesizing micro-expression samples. This approach uses K-means clustering to obtain cluster centers of AU intensities for each category, which are then used to guide the GAN in generating balanced micro-expression samples. To validate the effectiveness of the proposed methods, extensive experiments are conducted on CNN, ResNet, and PoolFormer architectures. The results demonstrate that our approach achieves superior performance in cross-dataset recognition compared to state-of-the-art methods. Full article

Urban integrated energy systems (UIESs) are increasingly exposed to uncertainties arising from wind and photovoltaic variability, load fluctuations, and equipment failures, highlighting the need for refined reserve assessment and coordinated operation. This study develops a unified framework that jointly models renewable and load deviations together with a load-dependent failure probability model, using Monte Carlo sampling and K-means scenario reduction to obtain representative system states. A reserve-capacity-oriented optimisation model is formulated to minimise total operating cost—including thermal generation, energy-storage operation, and reserve cost—while satisfying power balance, reserve adequacy, unit operating limits, and state-of-charge constraints. Application to a UIES comprising a 1000 kW load, 800 kW photovoltaic unit, 100 kW wind turbine, five thermal power units (total capacity 1000 kW), and a 250 kW/370 kWh energy storage system shows that reserve requirements fluctuate between −100 kW (downward) and 500 kW (upward) across different scenarios, with uncertainty-driven reserves dominating and failure-related reserves remaining below 100 kW. The optimisation results indicate coordinated operation between thermal units and storage, with storage absorbing surplus renewable output, supporting peak shaving, and providing most upward and all downward reserves. The total operating costs under typical summer and winter scenarios are 2264.02 CNY and 3122.89 CNY, respectively, confirming the method’s ability to improve reserve estimation accuracy and support economical and reliable UIES operation under uncertainty. Full article

Thermo-hydrodynamic (THD) lubrication is a key mechanism in injection pumps, where frictional heating and heat transfer strongly influence lubrication performance. Accurate numerical modeling remains challenging due to the nonlinear coupling of temperature- and pressure-dependent fluid properties and the high computational cost of iterative solvers. The rising relevance of bio-hybrid fuels, however, demands the investigation of a great number of fuel mixtures and conditions, which is currently infeasible with traditional solvers. Physics-informed neural networks (PINNs) have recently been applied to lubrication problems; existing approaches are typically restricted to stationary cases or rely on data to improve training. This work presents a novel, purely physics-based PINN framework for solving coupled, transient THD lubrication problems in injection pumps. By embedding the Reynolds equation, energy conservation laws, and temperature- and pressure-dependent fluid models directly into the loss function, the proposed approach eliminates the need for any simulation or experimental data. The PINN is trained solely on physical laws and validated against an iterative solver for 16 transient test cases across two fuels and eight operating scenarios. The good agreement of PINN and iterative solver demonstrates the strong potential of PINNs as efficient, scalable surrogate models for transient THD lubrication and iterative design applications. Full article

The exploitation of hydrokinetic resources represents a sustainable and efficient alternative for renewable energy generation. This study presents the design and real-world implementation of a compact hydrokinetic system capable of converting rainwater runoff into electricity within smart homes. Unlike conventional large-scale hydrokinetic technologies, this system was specifically engineered for intermittent, low-flow conditions typical of residential rainwater collection networks. The turbine was manufactured using 3D-printed biodegradable materials to promote environmental sustainability and facilitate rapid prototyping. Through CFD simulations and laboratory testing, the system’s hydraulic behaviour and energy conversion efficiency were validated across different flow scenarios. The complete system, consisting of four turbines rated at 120 W each, was integrated into a real smart home without structural modifications. From an academic perspective, this study contributes a quantitatively validated hybrid hydrokinetic–low-head framework for residential rainwater energy recovery, addressing intermittent and low-flow urban conditions insufficiently explored in existing literature. Field tests demonstrated that the hydrokinetic system provides complementary energy during rainfall events, generating up to 6000 Wh per day and enhancing household energy resilience, particularly during periods of low solar availability. The results confirm the technical feasibility, sustainability, and practical viability of decentralized hydrokinetic energy generation for residential applications. Full article

Mobile application workloads are inherently driven by user interactions and are characterized by short execution phases and frequent behavioral changes. These properties make it difficult for traditional micro-architecture analysis approaches, which typically assume stable execution behavior, to accurately capture performance bottlenecks in realistic mobile scenarios. To address this challenge, this paper presents CharSPBench, an interaction-aware micro-architecture characterization framework for analyzing mobile benchmarks under representative user interaction scenarios. CharSPBench organizes micro-architecture performance events in a structured and semantically consistent manner. It further enables systematic attribution of performance bottlenecks across different interaction conditions. The framework further supports intensity-based workload analysis to identify workload tendencies, such as memory-intensive and frontend-bound behaviors, under interaction-driven execution. Using the proposed framework, 126 micro-architecture performance events are systematically organized. This process leads to the identification of 19 key, semantically non-redundant features, further grouped into five major micro-architecture subsystems. Based on this structured representation, eight representative interaction-dependent micro-architecture insights are extracted to characterize performance behavior across mobile benchmarks. These quantitative results demonstrate that CharSPBench complements existing micro-architecture analysis techniques and provides practical support for interaction-aware benchmark design and mobile processor performance evaluation. Full article

Deep reinforcement learning (DRL) has demonstrated considerable potential for autonomous collision avoidance in unmanned surface vessels (USVs). However, its application in complex multi-agent maritime environments is often limited by challenges such as convergence issues and high computational costs. To address these issues, this paper proposes an expert-guided DRL algorithm that integrates a Dual-Priority Experience Replay (DPER) mechanism with a Hybrid Reciprocal Velocity Obstacles (HRVO) expert module. Specifically, the DPER mechanism prioritizes high-value experiences by considering both temporal-difference (TD) error and collision avoidance quality. The TD error prioritization selects experiences with large TD errors, which typically correspond to critical state transitions with significant prediction discrepancies, thus accelerating value function updates and enhancing learning efficiency. At the same time, the collision avoidance quality prioritization reinforces successful evasive actions, preventing them from being overshadowed by a large volume of ordinary experiences. To further improve algorithm performance, this study integrates a COLREGs-compliant HRVO expert module, which guides early-stage policy exploration while ensuring compliance with regulatory constraints. The expert mechanism is incorporated into the Soft Actor-Critic (SAC) algorithm and validated in multi-vessel collision avoidance scenarios using maritime simulations. The experimental results demonstrate that, compared to traditional DRL baselines, the proposed algorithm reduces training time by 60.37% and, in comparison to rule-based algorithms, achieves shorter navigation times and lower rudder frequencies. Full article

The current-mode logic (CML) driver has evolved alongside integrated circuit (IC) technology. Its typical structure contains a tail current source, differential amplifying transistors, and load resistors. It is widely used in modern optical transceivers and other serial link transceivers, and is compatible with various processes, including CMOS, SiGe BiCMOS, and InP DHBT. The basic performance indicators of CML driver include gain, bandwidth, power, and total harmonic distortion (THD). For different application scenarios, different tail currents and load resistance are required. Nowadays, as the performance requirements for drivers in various applications continue to increase, more techniques need to be employed to balance high speed, high output amplitude, high linearity, and low power, such as bandwidth expansion techniques, linearity improvement techniques, and gain control techniques. In this review, the electrical characteristics of basic CML circuits are highlighted and compared with other interface level standards. The advancement of CML drivers is summarized. Emerging CML structures and performance enhancement technologies are introduced and analyzed. Design considerations are concluded in terms of the challenges faced by high-speed drivers. The review provides comparative study and comprehensive reference for designers. Full article

Effective engineering modeling requires simultaneously addressing feature selection and hyperparameter interdependence, a challenge exacerbated by high-dimensional data characteristics in complex engineering modeling. Traditional optimization methods typically address these two aspects separately, which limits overall model performance. This study introduces a hybrid framework to enhance the performance of extreme gradient boosting (XGBoost) in engineering applications. The framework comprises two main phases: first, preliminary feature selection guided by prior domain knowledge and statistical analysis to reduce data dimensionality while preserving interpretability; second, a discrete symbiotic organisms search algorithm with adaptive feature mutation (DMSOS) simultaneously optimizes feature subsets and XGBoost hyperparameters. The DMSOS employs a discretization strategy to separate feature selection from hyperparameter tuning, facilitating focused searches within distinct spaces. An adaptive mutation mechanism dynamically adjusts exploration intensity based on iteration progress and feature importance. Additionally, evaluations on 1414 field-measured blasting vibration data demonstrate that the proposed DMSOS-XGBoost model achieves superior prediction performance, with an r² of 0.96696 and RMSE of 0.02636, outperforming models optimized via traditional sequential approaches. Further interpretability analysis highlights spatial geometry and explosive load as critical features, offering actionable insights for environmental risk management. This research provides a valuable methodological reference for engineering modeling scenarios requiring simultaneous optimization of features and hyperparameters. Full article

Landslide dams, typically composed of newly deposited, loose, and heterogeneous materials, are highly susceptible to failure induced by overtopping and seepage, particularly under extreme hydrological conditions. Accurate prediction of such breaching processes is essential for flood risk management and emergency response, yet existing models generally consider only a single failure mechanism. This study develops a mathematical model to simulate landslide dam breaching under the coupled action of overtopping and seepage erosion. The model integrates surface erosion and internal erosion processes within a unified framework and employs a stable time-stepping numerical scheme. Application to three real-world landslide dam cases demonstrates that the model successfully reproduces key breaching characteristics across overtopping-only, seepage-only, and coupled erosion scenarios. The simulated breach hydrographs, reservoir water levels, and breach geometries show good agreement with field observations, with peak outflow and breach timing predicted with errors generally within approximately 5%. Sensitivity analysis further indicates that the model is robust to geometric uncertainties, as variations in breach outcomes remain smaller than the imposed parameter perturbations. These results confirm that explicitly accounting for the coupled interaction between overtopping and seepage significantly improves the representation of complex breaching processes. The proposed model therefore provides a reliable computational tool for analyzing landslide dam failures and supports more accurate hazard assessment under multi-mechanism erosion conditions. Full article

Metal–organic frameworks (MOFs) typically exist in the form of powders or dispersed crystals, which limits their direct application in practical engineering scenarios that require monolithic structures and processability. To address this issue, the present study successfully anchored MOF (zeolitic imidazolate framework-8, ZIF-8) nanocrystals within a porous silicon carbide (p-SiC) substrate via a facile in situ growth strategy, achieving both stable macroscopic loading and intimate microscopic interfacial bonding. The resulting ZIF-8/p-SiC composite exhibits a hierarchical porous structure, with a specific surface area approximately 183 times higher than that of the raw p-SiC, alongside a substantially enhanced CO₂ adsorption capacity. By utilizing a low-cost p-SiC support and mild ZIF-8 synthesis conditions, this work demonstrates excellent reproducibility and scalability, providing a facile and effective pathway for fabricating MOF/porous media composite systems that possess both superior mechanical properties and tailored pore structures. Additionally, the developed MOF/p-SiC composites can serve as controllable rock-analog porous media, offering new perspectives for investigating MOF-rock interfacial interactions and CO₂ geological sequestration mechanisms, thereby establishing an organic link between fundamental materials science and geological engineering applications. Full article

Considering the rapid iteration of you-only-look-once (YOLO)-series algorithms, this paper aims to provide a data-driven performance spectrum and selection guide for the latest YOLO series algorithm (YOLOv8 to YOLOv13) in printed circuit board (PCB) automatic optical inspection (AOI) through systematic benchmarking. A comprehensive evaluation of the six state-of-the-art YOLO series algorithms is conducted on a standardized dataset containing six typical PCB defects: missing hole, mouse bite, open circuit, short circuit, spur, and spurious copper. An innovative dual-cycle comparative experiment (100 rounds and 500 rounds) is designed, and a systematic assessment is performed across multiple dimensions, including accuracy, efficiency, and inference speed. The experimental results have revealed significant variations in algorithm performance with training cycles: under short-term training (100 rounds), YOLOv13 achieves leading detection performance (mAP50 = 0.924, mAP50-95 = 0.484) with the fewest parameters (2.45 million); after full training (500 rounds), YOLOv10 achieves the highest overall accuracy (mAP50 = 0.946, mAP50-95 = 0.526); additionally, YOLOv11 shows the optimal speed-accuracy balance after long-term training, while YOLOv12 excels in short-term training; moreover, “open circuit” and “spur” are evaluated as the most challenging defect categories to detect. The findings given in this paper indicate the absence of a universally applicable “all-in-one” algorithm and propose a clear algorithm selection roadmap: YOLOv10 is recommended for offline analysis scenarios prioritizing extreme accuracy; YOLOv13 is the top choice for applications requiring rapid iteration with tight training time constraints; and YOLOv11 is the best option for high-throughput online inspection PCB production lines. Full article