Search Results (27,699)

Millimeter-wave (mmWave)/terahertz (THz) devices relying on conventional semiconductor technologies face significant performance bottlenecks, constraining their use in next-generation electronic systems. To address these challenges, this work demonstrates high-performance THz Schottky barrier diodes (SBDs) based on aligned carbon nanotube (ACNT) arrays, and the realization of a D-band second-harmonic frequency multiplier. The ACNT-SBDs exhibit superior electrical and radio-frequency (RF) characteristics, achieving a forward current density of 0.14 mA·μm⁻¹ at −1.3 V and an intrinsic cutoff frequency (f_C) of 506 GHz. The developed small-signal model of diodes shows close agreement with measurements, with S-parameter relative errors below 0.7% from 100 MHz to 67 GHz. The implemented 154 GHz D-band multiplier achieved a maximum output power of −18.97 dBm and a minimum conversion loss of 27.92 dB, outperforming previously reported frequency multipliers based on carbon nanotubes or two-dimensional (2D) materials. This study not only establishes the outstanding high-frequency response, nonlinear efficiency, and integration potential of ACNT-based devices but also provides a promising technical pathway for future THz communication and sensing applications. Full article

The rapid increase in organic waste generation poses significant environmental challenges and highlights the limitations of conventional waste management practices. In this context, black soldier fly (Hermetia illucens) larvae (BSFL) have emerged as a promising biological tool for valorizing organic residues within circular bioeconomy frameworks. This review provides an integrated analysis of BSFL-based bioconversion systems, focusing on the biological characteristics of BSFL, suitable organic waste streams, and the key process parameters influencing waste reduction efficiency, larval biomass production, and frass (the residual material from larval bioconversion) yield. The performance of BSFL in converting organic waste is assessed with emphasis on substrate characteristics, environmental conditions, larval density, and harvesting strategies. Environmental and economic implications are discussed in comparison with conventional treatments such as landfilling, composting, and anaerobic digestion. Special attention is given to the nutritional composition of BSFL and the valorization of larvae as sustainable protein and lipid sources for animal feed and emerging human food applications, while frass is highlighted as a nutrient-rich organic fertilizer and soil amendment. Finally, current challenges related to scalability, safety, regulation, and social acceptance are highlighted. By linking waste management, resource recovery, and sustainable protein production, this review clarifies the role of BSFL and frass in resilient and resource-efficient food and waste management systems. Full article

To construct a high-performance avermectin (Avm) carrier system, this study utilized the advantages of stereocomplex (SC) crystal formation between poly (L-lactic acid) (PLLA) and poly (D-lactic acid) (PDLA) to prepare Avm-loaded stereocomplex polylactic acid (SC-PLA) nanoformulations via the emulsion solvent evaporation method. The results showed the successful formation of SC-PLA after introducing PDLA into the PLLA matrix, and the influence of SC-PLA crystallinity enabled the fabrication of tunable Avm@SC-PLA nanospheres with a regular spherical morphology. Avm@SC-PLA exhibited controlled release characteristics and possessed pH-responsive properties with specific release behaviors under pH 5.5, 7.4, and 8.0 conditions. The Avm@SC-PLA sustained-release nano system had a series of advantages, including controllable particle size, efficient drug loading, excellent sustained-release performance, good UV-shielding ability, high stability, favorable spreadability, and strong affinity for different leaves. In conclusion, the Avm@SC-PLA nanoformulation not only achieves effective loading and stable encapsulation of Avm but also possesses good structural stability and environmental responsiveness. It provides a novel PLA-based carrier strategy for the efficient delivery of Avm and holds potential application value in the pesticide and pharmaceutical fields. Full article

Internal wall grinding of pipes constitutes a critical pretreatment procedure in the anti-corrosion repair operations of Prestressed Concrete Cylinder Pipes (PCCP). To address the limitations of low efficiency and poor safety associated with traditional manual internal wall grinding in PCCP anti-corrosion repair, this study presents the design of a support-wheel-type internal wall grinding robot for pipes. The robot’s structure comprises a walking support module and a grinding module: the walking module employs four sets of circumferentially equally spaced (90° apart) independent-support wheel groups. Through an active–passive collaborative adaptation mechanism regulated by pre-tensioned springs and lead screws, the robot can dynamically conform to the inner wall of the pipe, ensuring stable locomotion. The grinding module is connected to the walking module via a slewing bearing and is equipped with three roller-type steel brushes. During operation, the grinding module revolves around the pipe axis, while the roller brushes rotate simultaneously, generating a composite three-helix grinding trajectory. Mathematical models for the robot’s obstacle negotiation, bend traversal, and grinding motion were established, and multi-body dynamics simulations were conducted using ADAMS for verification. Additionally, a physical prototype was developed to perform basic functional tests. The results demonstrate that the robot’s motion characteristics are highly consistent with theoretical analyses, exhibiting stable and reliable operation, excellent pipe traversability, and robust driving capability, thus meeting the requirements for internal wall grinding of PCCP pipes. Full article

As a globally significant food crop, the assessment of wheat quality is essential for ensuring food security and enhancing the processing quality of agricultural products. Conventional methods for assessing wheat kernel quality are often inefficient and markedly subjective, which hampers their ability to accurately distinguish the complex and diverse phenotypic characteristics of wheat kernels. To tackle the aforementioned issues, this study presents an enhanced recognition method for defective wheat kernels, based on the EfficientNet-B1 architecture. Building upon the original EfficientNet-B1 network structure, this approach incorporates the lightweight attention mechanism known as CBAM (Convolutional Block Attention Module) to augment the model’s capacity to discern features in critical regions. Simultaneously, it modifies the classification head structure to facilitate better alignment with the data, thereby enhancing accuracy. The experiment employs a self-constructed dataset comprising five categories of wheat kernels—perfect wheat kernels, insect-damaged wheat kernels, scab-damaged wheat kernels, moldy wheat kernels, and black germ wheat kernels—which are utilized for training and validation purposes. The results indicate that the enhanced model attains a classification accuracy of 99.80% on the test set, reflecting an increase of 2.6% compared to its performance prior to the enhancement. Furthermore, the Precision, Recall, and F1-score all demonstrated significant improvements. The proposed model achieves near-perfect performance on several categories under controlled experimental conditions, with particularly high precision and recall for scab-damaged and insect-damaged kernels. This study demonstrates the efficacy of the enhanced EfficientNet-B1 model in the recognition of defective wheat kernels and offers novel technical insights and methodological references for intelligent wheat quality assessment. Full article

The operation status of transmission line insulators, such as damage, zero-value, pollution, and deterioration, affect the safe operation of power grids. Non-contact detection technology judges the operating status of transmission line insulators through indirect means such as electrical, thermal, acoustic, and image signals. Due to its advantages of rapidity and high efficiency, it has been widely accepted by operation departments. This paper summarizes existing non-contact detection technologies for transmission line insulator conditions, including acoustic wave detection, electric field detection, infrared/ultraviolet imaging detection and spectral detection. It analyzes the principle, characteristics, and application scenarios of each non-contact detection technology. Combined with the rapid development of artificial intelligence technology, the paper looks forward to future new detection methods, such as those integrating deep learning, multi-component comprehensive detection, and multi-source data-driven detection. Finally, the challenges faced by the detection of Ultra-High Voltage (UHV) transmission lines are analyzed. This study provides a reference for the research and development of non-contact detection technology for transmission line insulators. Full article

The tool-point frequency response function (FRF) of a spindle–tool system plays a crucial role in predicting machining stability. Among the factors influencing the FRF, the interface characteristics between the spindle and the tool holder are particularly significant, especially when different holder designs are used. This study focused on identifying these interface characteristics for two common tool holder types—BT and BBT—to improve FRF prediction accuracy. The receptance coupling substructure analysis (RCSA) method was employed in conjunction with finite element modeling (FEM) to characterize the spindle–tool holder interfaces without needing extensive experimental tapping tests. Finite element models were developed to generate receptance components for various tool holder–tool assemblies, enabling efficient and accurate coupling within the RCSA framework. The identified interface parameters were applied to predict the tool-point FRFs of the cutter clamped in a BT tool holder with different overhang lengths. The predicted and measured tool compliances differed by 3–4.6%, demonstrating high agreement and reliability. The proposed methodology provides a powerful tool for predictive modeling of dynamic behavior in spindle–tool systems under varying tooling conditions, enhancing process planning and evaluation of the cutting stability in high-precision machining. Full article

Mixing processes in stirred tanks are widely applied across various industries, but still offer significant potential for optimization. A promising strategy is the use of double-stage impeller setups instead of conventional single impellers. While multi-impeller configurations are common in tall vessels, their benefits for standard tanks with a height-to-diameter ratio of 1 are largely unexplored. This study systematically investigates the flow fields of single, identical, and mixed double-stage configurations of a Rushton turbine, a pitched-blade turbine, and a retreat curve impeller. To ensure balanced power input in mixed configurations, a refined method for harmonizing specific power via impeller diameter adjustment is proposed. Stereo particle image velocimetry is applied to visualize flow fields, supported by refractive-index matching to enable measurements in a dished-bottom tank. The results reveal substantial flow deficiencies in single-impeller setups. In contrast, double-impeller setups generate novel and significantly improved velocity fields that offer clear advantages and demonstrate strong potential to enhance process efficiency across various mixing applications. These findings provide new experimental insights into the characteristics of dual impellers and form a valuable basis for the design and scale-up of stirred tanks, contributing to more efficient, reliable, and sustainable mixing processes. Full article

Fluidic thrust vectoring control (FTVC) enables highly agile flight without the mechanical complexity of traditional vectoring nozzles. However, a robust onboard identification of the jet deflection state remains challenging when only limited measurements are available. This study proposes a sparse reconstruction of the pressure field method for a wedge passive FTVC nozzle and validates the approach experimentally on a low-speed jet platform. By combining the proper orthogonal decomposition (POD) algorithm with an ${\mathcal{l}}_1$-regularized compressed sensing method, a full Coanda wall pressure distribution is reconstructed from the sparse measurements. A genetic algorithm is then employed to optimize the wall pressure tap locations, identifying an optimal layout. With only four pressure taps, the local pressure coefficient errors were maintained within $\left|\Delta Cp\right|$ < 0.02. In contrast, conventional Kriging interpolation requires increasing the sensor count to 13 to approach the reconstruction level of the proposed POD–compressed sensing method using 4 sensors, yet still exhibits a reduced fidelity in capturing key flow structure characteristics. Overall, the proposed approach provides an efficient and physically interpretable strategy for pressure field estimation, supporting lightweight, low-maintenance, and precise fluidic thrust vectoring control. Full article

In this study, the effects of molecular structure parameters of polycarboxylate ether (PCE)-based grinding aids (GAs) on grinding efficiency, cement properties, and powder flowability were systematically investigated. Existing literature indicates that only limited attention has been given to a comprehensive evaluation of the combined influence of PCE molecular weight, main chain-to-side chain ratio, and side chain characteristics on the grinding process and powder behavior. Within this framework, seven different PCE-based GAs were synthesized by systematically varying the main chain length, side chain length, and side chain/main chain ratio. The structural characterization of the synthesized additives was carried out using Fourier transform infrared spectroscopy (FTIR) and gel permeation chromatography (GPC). Subsequently, the grinding efficiency, particle size distribution (PSD), and powder flowability of cements produced at two different GA dosages were evaluated in detail. The results demonstrated that increasing the GA dosage generally enhanced grinding efficiency and led to a narrower particle size distribution. An increase in main chain length at a constant side chain length improved grinding performance, whereas PCEs with a medium main chain length exhibited superior powder flowability. In contrast, increasing the side chain length alone had a limited effect on grinding efficiency. Considering all structural parameters collectively, the PCE5 additive—characterized by medium main and side chain lengths and a low side chain/main chain ratio—exhibited the most balanced and overall highest performance. Full article

The relentless rise in global plastic consumption has intensified the challenge of managing plastic waste pollution. Current conventional recycling technologies face significant limitations in processing efficiency and environmental compatibility, hindering the effective recovery of plastic resources. Against this background, microwave pyrolysis technology has emerged as a promising solution, leveraging its dual advantages of thermal and non-thermal effects. This technology enables uniform and rapid heating, substantially reducing processing time and energy consumption. Its characteristics open new pathways for the high-value conversion of waste plastics. Through this approach, waste plastics can be efficiently transformed into valuable products such as pyrolysis oil, hydrogen gas, and solid carbon, demonstrating broad application prospects. This paper first systematically reviews the shortcomings of existing plastic pyrolysis technologies. It then delves into the operational mechanisms, process characteristics, and key influencing factors of microwave-assisted pyrolysis. Finally, it examines current challenges and issues while outlining future research directions, offering insights for the sustainable resource utilisation of waste plastics. Full article

Deep reinforcement learning (DRL) has been widely adopted to solve decision-making problems in complex environments, demonstrating high performance across various domains. However, DRL-based FPS agents are typically trained with a traditional, monolithic policy that integrates heterogeneous functionalities into a single network. This design hinders policy interpretability and severely limits structural flexibility, since even minor design changes in the action space often necessitate complete retraining of the entire network. These constraints are particularly problematic in game development, where behavioral characteristics are distinct and design updates are frequent. To address these issues, this study proposes a Modular Reinforcement Learning (MRL) framework. Unlike monolithic approaches, this framework decomposes complex agent behaviors into semantically distinct action modules, such as movement and attack, which are optimized in parallel with specialized reward structures. Each module learns a policy specialized for its own behavioral characteristics, and the final agent behavior is obtained by combining the outputs of these modules. This modular design enhances structural flexibility by allowing selective modification and retraining of specific functions, thereby reducing the inefficiency associated with retraining a monolithic policy. Experimental results on the 1-vs-1 training map show that the proposed modular agent achieves a maximum win rate of 83.4% against a traditional monolithic policy agent, demonstrating superior in-game performance. In addition, the retraining time required for modifying specific behaviors is reduced by up to 30%, confirming improved efficiency for development environments that require iterative behavioral updates. Full article

This paper presents a hierarchical model architecture for the analysis and optimization of Fuel Cell Electric Vehicles (FCEVs). The model encompasses the levels of cell, stack, and complete vehicle, which are interconnected through clearly defined transfer parameters. At the cell level, electrochemical and thermodynamic processes are mapped, the results of which are aggregated at the stack level into characteristic maps such as current–voltage curves and efficiency profiles. These maps serve as interfaces to the vehicle level, where the electric powertrain—comprising the fuel cell, energy storage, electric motor, and auxiliary consumers—is integrated. Special attention is given to the trade-off between the lifetime and dynamics of the fuel cell, which is methodically captured through variable parameter vectors. The transfer parameters enable consistent and scalable modelling that considers both detailed cell and stack information as well as vehicle-side requirements. On this basis, various vehicle configurations can be evaluated and optimized with regard to efficiency, lifetime, and drivability. Full article

Porcine reproductive and respiratory syndrome (PRRS) is one of the costliest diseases in swine production, causing >$1.2 billion USD in annual losses in the United States. Oral fluids are widely used for PRRS virus (PRRSV) surveillance, accounting for 42% of nearly 480,000 PRRSV RT-qPCR cases submitted to six Midwestern U.S. laboratories between 2020 and 2025. Despite this reliance, few studies have applied appropriate methodological approaches to compare the performance of commercial extraction and PRRSV RT-qPCR protocols for oral fluid specimens. In this study, we evaluated nine extraction-amplification protocols for PRRSV RNA detection, based on three commercial extraction kits and three commercial RT-qPCR assays. For each protocol, performance was evaluated using 314 oral fluid samples of known status (215 positive, 99 negative), collected under controlled conditions from 72 pigs assigned to five groups inoculated with contemporary PRRSV isolates and from one negative control group. The Cq values were normalized as efficiency-standardized Cqs (ECqs) and then analyzed by receiver operating characteristic (ROC) analysis. The mean amplification efficiencies ranged from 67 to 92%, repeatability from 0.98 to 0.99, and overall reproducibility was 0.91. The ROC AUCs ranged from 0.916 to 0.986, with significant pairwise differences (p < 0.05). At optimal ECq cutoffs, sensitivities ranged from 83 to 98.1% with 100% specificity. Normalization enabled objective protocol comparisons and statistically valid diagnostic cutoffs and supports future improvements in PRRSV diagnostics. Full article

Out-of-distribution (OOD) detection is essential for ensuring the reliability of machine learning models deployed in safety-critical applications. Existing methods often rely solely on statistical properties of feature distributions while ignoring the geometric structure of learned representations. We propose TopoAD, a topology-aware OOD detection framework that leverages Euler Characteristic Curves (ECCs) extracted from intermediate convolutional activation maps and fuses them with standardized energy scores. Specifically, we employ a computationally efficient superlevel-set filtration with a local estimator to capture topological invariants, avoiding the high cost of persistent homology. Furthermore, we introduce task-adaptive aggregation strategies to effectively integrate multi-scale topological features based on the complexity of distribution shifts. We evaluate our method on CIFAR-10 against four diverse OOD benchmarks spanning far-OOD (Textures), near-OOD (SVHN), and semantic shift scenarios. Our results demonstrate that TopoAD-Gated achieves superior performance on far-OOD data with 89.98% AUROC on Textures, while the ultra-lightweight TopoAD-Linear provides an efficient alternative for near-OOD detection. Comprehensive ablation studies reveal that cross-layer gating effectively captures multi-scale topological shifts, while threshold-wise attention provides limited benefit and can degrade far-OOD performance. Our analysis demonstrates that topological features are particularly effective for detecting OOD samples with distinct structural characteristics, highlighting TopoAD’s potential as a sustainable solution for resource-constrained applications in texture analysis, medical imaging, and remote sensing. Full article