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In order to achieve fast and accurate detection of Gannan navel orange fruits with different ripeness levels in a natural environment under all-weather scenarios and then to realise automated harvesting of Gannan navel oranges, this paper proposes a YOLOv5-NMM (YOLOv5 with Navel orange Measure Model) object detection model based on the improvement in the original YOLOv5 model. Based on the changes in the phenotypic characteristics of navel oranges and the Chinese national standard GB/T 21488-2008, the maturity of Gannan navel oranges is tested. And it addresses and improves the problems of occlusion, dense distribution, small target size, rainy days, and light changes in the detection of navel orange fruits. Firstly, a new detection head of 160 × 160 feature maps is constructed in the detection layer to improve the multi-scale target detection layer of YOLOv5 and to increase the detection accuracy of the different maturity levels of Gannan navel oranges of small sizes. Secondly, a convolutional block attention module is incorporated in its backbone layer to capture the correlations between features in different dimensions to improve the perceptual ability of the model. Then, the weighted bidirectional feature pyramid network structure is integrated into the Neck layer to improve the fusion efficiency of the network on the feature maps and reduce the amount of computation. Lastly, in order to reduce the loss of the target of the Gannan Navel Orange due to occlusion and overlapping, the detection frame is used to remove redundancy using the Soft-NMS algorithm to remove redundant candidate frames. The results show that the accuracy rate, recall rate, and average accuracy of the improved YOLOv5-NMM model are 93.2%, 89.6%, and 94.2%, respectively, and the number of parameters is only 7.2 M. Compared with the mainstream network models, such as Faster R-CNN, YOLOv3, the original model of YOLOv5, and YOLOv7-tiny, it is superior in terms of the accuracy rate, recall rate, and average accuracy mean, and also performs well in terms of the detection rate and memory occupation. This study shows that the YOLOv5-NMM model can effectively identify and detect the ripeness of Gannan navel oranges in natural environments, which provides an effective exploration of the automated harvesting of Gannan navel orange fruits. [ABSTRACT FROM AUTHOR]

Organic afterglow is a fascinating phenomenon with exceptional applications. However, it encounters challenges such as low intensity and efficiency, and typically requires UV-light excitation and facile intersystem crossing (ISC) due to its spin-forbidden nature. Here, we develop a novel strategy that bypasses the conventional ISC pathway by promoting singlet-triplet transition through the synergistic effects of the intra/intermolecular heavy-atom effect in aromatic crystals, enabling the direct population of triplet excited states from the ground state. The resulting materials exhibit a bright organic afterglow with a remarkably enhanced quantum efficiency of up to 5.81%, and a significantly increased organic afterglow lifetime of up to 157 microseconds under visible light. Moreover, given the high-efficiency visible-light excitable organic afterglow emission, the potential application is demonstrated in lifetime-resolved, color-encoded, and excitation wavelength-dependent pattern encryption. This work demonstrates the importance of the direct population method in enhancing the organic afterglow performance and red-shifting the excitation wavelength, and provides crucial insights for advancing organic optoelectronic technologies that involve triplet states. [ABSTRACT FROM AUTHOR]

In this study, the flexural bearing behavior of corroded reinforced concrete (RC) beams reinforced with U-type Textile Reinforced Concrete (TRC) was investigated using a four-point bending loading method. Nine test beams were produced: one original beam, three RC beams with corrosion alone, and five corroded beams strengthened with U-type TRC. The analysis focuses on assessing the impacts of the steel corrosion degree and the number of textile layers on various aspects of the bending behavior, such as failure modes, bearing capacity, and load displacement curves, in U-type TRC-strengthened corroded beams. The experimental results revealed three distinct failure modes in the U-type TRC-strengthened corroded beams. TRC effectively enhanced the bearing capacity. With sufficient textile layers, it can be restored to the level of the original RC beams. Moreover, in the cases of severe corrosion in RC beams, the bearing capacity increased more significantly. The TRC also enhanced the ductility. Finally, a calculation equation for the ultimate bearing capacity of U-type TRC-strengthened corroded beams was presented and validated, demonstrating consistent alignment with the experimental data. [ABSTRACT FROM AUTHOR]

In the face of complex scenarios, the information insufficiency of classification tasks dominated by a single modality has led to a bottleneck in classification performance. The joint application of multimodal remote sensing data for surface observation tasks has garnered widespread attention. However, issues such as sample differences between modalities and the lack of correlation in physical features have limited the performance of classification tasks. Establishing effective interaction between multimodal data has become another significant challenge. To fully integrate heterogeneous information from multiple modalities and enhance classification performance, this paper proposes a dual-branch cross-Transformer feature fusion network aimed at joint land cover classification of hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data. The core idea is to leverage the potential of convolutional operators to represent spatial features, combined with the advantages of the Transformer architecture in learning remote dependencies. The framework employs an improved self-attention mechanism to aggregate features within each modality, highlighting the spectral information of HSI and the spatial (elevation) information of LiDAR. The feature fusion module based on cross-attention integrates deep features from two modalities, achieving complementary information through cross-modal attention. The classification task is performed using jointly obtained spectral and spatial features. Experiments were conducted on three multi-source remote sensing classification datasets, demonstrating the effectiveness of the proposed model compared to existing methods. [ABSTRACT FROM AUTHOR]

The current work reveals the influence of loading parameters on the crack growth behavior of a Zr/Ti/steel composite plate with a crack normal to the interface by using an experiment and the finite element method. The Chaboche model was first used to study cyclic plastic evolution in composite materials. The results reveal that an increase in Fmax, Fm, and Fa can promote da/dN; meanwhile, an increase in R will reduce da/dN. The plastic strain accumulation results indicate that Fm mainly contributes to the tensile strain and compressive stress after the first cycle. Additionally, Fa increases the stress range and compression stress and greatly improves the plastic strain accumulation degree in subsequent loading cycles. The Fmax can significantly increase the stress amplitude and plastic strain accumulation level. When R increases, the plastic strain accumulation increases a little, but the stress amplitude and compression stress decrease greatly. Furthermore, it is also found that the elastic–plastic mismatch also affects the plastic evolution, that is, strengthening or weakening the effect of the loading parameters. [ABSTRACT FROM AUTHOR]

Nowadays, interactive recommendation systems (IRS) play a significant role in our daily life. Recently, reinforcement learning has shown great potential in solving challenging tasks in IRS, since it can focus on long-term profit and can capture the dynamic preference of users. However, existing RL methods for IRS have two typical deficiencies. First, most state representation models use left-to-right recurrent neural networks to capture the user dynamics, which usually fail to handle the long and noisy sequential data in real life. Second, an IRS always needs to handle millions of items, leading to a large discrete action space in RL settings, which has not been fully addressed by the inefficient existing works. To bridge these deficiencies, in this paper, we propose attention-based tree recommendation (ATRec), an efficient tree-structured policy with attention-based state representation for IRS. ATRec uses an attention-based state representation model to effectively capture the user's dynamic preference hidden in the long and noisy sequence of behaviors. Moreover, to improve the learning efficiency, we propose an efficient tree-structured policy representation method, in which a complete tree is devised to represent the policy, and a novel parameter-sharing strategy is introduced. Extensive experiments are conducted on three real-world datasets and the results show the proposed ATRec obtains 42.3% improvement over some of the state of the arts methods in the hit rate and 21.4% improvement in the mean reciprocal rank of the top 30 ranked items. Additionally, the learning and decision efficiency can also be improved at an average of 35.5%. [ABSTRACT FROM AUTHOR]

Mixed mode fracture is a widely studied topic, while the coupling effects of mixed mode cracking are unclear. In this paper, elastic fracture behaviors and the coupling effects of the mixed mode cracks are studied in detail based on the finite element method, experimental study and linear elastic fracture mechanics. Results show that there always exist II-III coupling effects at the crack tips of mixed mode cracks, which have many effects on the crack tip field and crack propagation behavior. It is found that a mode II component at the tip of a mixed mode crack is the main reason for crack deflection, while the mode III components show no effect. For any mixed mode crack, mode II components at the crack tip can be divided as that by mode II loading which causes plane crack propagation, and by the coupling effect which causes spatial crack propagation. On this basis, a new fracture criterion suitable for any mixed mode crack is proposed, combined with the coupling effect and the linear elastic superposition principle. The research in this paper provides a solution to the problem of an II-III coupling effect in mixed mode fracture research and further promotes the development of fracture mechanics. [ABSTRACT FROM AUTHOR]

As nano-zinc oxide (Nano-ZnO), a new type of nanomaterial, has antioxidant and intestinal protection effects, we hypothesized that dietary Nano-ZnO could modulate poor meat quality, oxidative stress and disturbed gut microbiota in the finishing pig model of naturally occurring intrauterine growth retardation (IUGR). A total of 6 normal-born weight (NBW) and 12 IUGR piglets were selected based on birth weight. The pigs in the NBW group received a basal diet, and IUGR pigs were randomly divided into two groups and treated with basal diet and 600 mg/kg Nano-ZnO-supplemented diet. Dietary Nano-ZnO ameliorated IUGR-associated declined meat quality by lowering the drip loss48h, cooking loss, shearing force and MyHc IIx mRNA expression, and raising the redness (a*), peak area ratio of immobilized water (P22), sarcomere length and MyHc Ia mRNA expression. Nano-ZnO activated the nuclear factor erythroid 2-related factor 2-glutamyl cysteine ligase (Nrf2-GCL) signaling pathway by promoting the nuclear translocation of Nrf2, increasing the GCL activities, and mRNA and protein expression of its catalytic/modify subunit (GCLC/GCLM), thereby attenuating the IUGR-associated muscle oxidative injury. Additionally, the composition of IUGR pigs' cecal microbiota was altered by Nano-ZnO, as seen by changes in Shannon and Simpson indexes, the enhanced UCG-005, hoa5-07d05 gut group and Rikenellaceae RC9 gut group abundance. The UCG-005 and hoa5-07d05 gut group abundance were correlated with indicators that reflected the meat quality traits and antioxidant properties. In conclusion, Nano-ZnO improved the IUGR-impaired meat quality by altering water holding capacity, water distribution and the ultrastructure of muscle, activating the Nrf2-GCL signaling pathway to alleviate oxidative status and regulating the cecal microbial composition. [ABSTRACT FROM AUTHOR]

We demonstrate that the conductivity of graphene on thin-film polymer substrates can be accurately determined by reflection-mode air-plasma-based THz time-domain spectroscopy (THz-TDS). The phase uncertainty issue associated with reflection measurements is discussed, and our implementation is validated by convincing agreement with graphene electrical properties extracted from more conventional transmission-mode measurements. Both the reflection and transmission THz-TDS measurements reveal strong non-linear and instantaneous conductivity depletion across an ultra-broad bandwidth (1–9 THz) under relatively high incident THz electrical field strengths (up to 1050 kV/cm). [ABSTRACT FROM AUTHOR]

Aqueous zinc-ion batteries (AZIBs) are promising for large-scale energy storage systems due to their high safety, large capacity, cost-effectiveness, and environmental friendliness. However, their commercialization is currently hindered by several challenging issues, including cathode degradation and zinc dendrite growth. Recently, metal-organic frameworks (MOFs) and their derivatives have gained significant attention and are widely used in AZIBs due to their highly porous structures, large specific surface area, and ability to design frameworks for Zn2+ shuttle. Based on preceding contributions, this review aims to generalize two design principles for MOF-based materials in AZIBs: cathode preparation and anode protection. For cathode preparation, we mainly introduce novel MOF-based electrode materials such as pure MOFs, porous carbon materials, metal oxides, and their compounds, focusing on the analysis of the specific capacity of AZIBs. For anode protection, we systematically analyze MOF-based materials used as 3D Zn architecture, solid electrolyte interfaces, novel separators, and solid-state electrolytes, highlighting the improvement in the cyclic stability of Zn anodes. Finally, we propose the future development of MOF-based materials in AZIBs. Our work can give some clues for raising the practical application level of aqueous ZIBs. [ABSTRACT FROM AUTHOR]

We report on studies of the dwell fatigue crack growth (FCG) behavior of commercial pure titanium (CP-Ti) TA2 weld joints at 25 °C and 200 °C. Taking into account the effects of load ratio and dwell temperature, the impact of dwell on FCG behavior and the associated fracture mechanism are clearly demonstrated. Meanwhile, in order to illustrate creep–fatigue interaction, finite element simulations are also performed to analyze the evolution of stress and strain field near the crack tip. The results show that with the increase in dwell temperature, the FCG rate is increased. Furthermore, the effect of dwell on FCG behavior is more pronounced at higher load ratios. Finite element simulation results indicate that dwell induces creep stress relaxation and leads to an increase in the equivalent plastic strain near the crack tip. With the increase of dwell temperature and load ratio, the more pronounced creep deformation may lead to a creep-dominated FCG behavior. As consistent with the above analysis, the examination of the fracture surface reveals that more cavities and secondary cracks may be observed because of the increased creep deformation at the higher dwell temperature and load ratio. [ABSTRACT FROM AUTHOR]

The hydropower unit commitment (HUC) is often indirectly considered by combining individual units in a hydropower plant into a plant-based generator to alleviate the dimensional difficulty in short-term hydropower scheduling of cascaded reservoirs. This work presents three procedures to combine operating zones of units in a hydropower plant into the plant-based operating zones, including an exhaustive method (M01) that enumerates all the possible combinations, a discrete method (M03) that investigates discrete values to see if they are in an operating zone, and a bound oriented method (M04) that explores new operating zones with an optimization solver. The procedures are compared with a previous one (M02) that merges the operating zones of a unit one by one in order. The experiments of the methods in 11 case studies involving a variety of hydropower plants reveal that the second method (M02) should always be recommended due to its strength, which is even more prominent than any other methods in dealing with large-scale problems, and errors occur when using interpolation to estimate the plant-based operating zones at a water head between two sampling water heads. [ABSTRACT FROM AUTHOR]

A monthly hydropower scheduling determines the monthly flows, storage, and power generation of each reservoir/hydropower plant over a planning horizon to maximize the total revenue or minimize the total operational cost. The problem is typically a complex and nonlinear optimization that involves equality and inequality constraints including the water balance, hydraulic coupling between cascaded hydropower plants, bounds on the reservoir storage, etc. This work applied the Zoutendijk algorithm for the first time to a medium/long-term hydropower scheduling of cascaded reservoirs, where the generating discharge capacity is handled with an iterative procedure, while the other head-related nonlinear constraints are represented with exponential functions fitting to discrete points. The procedure starts at an initial feasible solution, from which it finds a feasible improving direction, along which a better feasible solution is sought with a one-dimensional search. The results demonstrate that the Zoutendijk algorithm, when applied to six cascaded hydropower reservoirs on the Lancang River, worked very well in maximizing the hydropower production while ensuring the highest firm power output to be secured. [ABSTRACT FROM AUTHOR]

The influence of pre-strain on the fatigue crack growth behavior of Zr702/TA2/Q345R composite plate is studied by experiments and the finite element method. The crack perpendicular to the interface and the through-wall crack are considered at the same time. For the crack perpendicular to the interface, the monotonic plastic zone and cyclic plastic zone at the crack tip are used to study the influence of pre-strain on the plastic zone. Furthermore, the influence of pre-strain on the evolution of the plastic damage at the crack tip is analyzed in detail by studying the variation in the initial plastic energy and equivalent plastic strain. For the through-wall crack, the effect of pre-strain on the propagation behavior of cracks on both sides and the whole crack is studied systematically. The results show that the strengthening of the cracks on the Zr702 side is significantly higher than that on the Q345R side, so the inhibitory effect of pre-strain on the whole crack of the through-wall specimen is mainly due to the increase in the resistance to crack growth on the Zr702 side. [ABSTRACT FROM AUTHOR]

Proteins are the fundamental biological macromolecules which underline practically all biological activities. Protein–protein interactions (PPIs), as they are known, are how proteins interact with other proteins in their environment to perform biological functions. Understanding PPIs reveals how cells behave and operate, such as the antigen recognition and signal transduction in the immune system. In the past decades, many computational methods have been developed to predict PPIs automatically, requiring less time and resources than experimental techniques. In this paper, we present a comparative study of various graph neural networks for protein–protein interaction prediction. Five network models are analyzed and compared, including neural networks (NN), graph convolutional neural networks (GCN), graph attention networks (GAT), hyperbolic neural networks (HNN), and hyperbolic graph convolutions (HGCN). By utilizing the protein sequence information, all of these models can predict the interaction between proteins. Fourteen PPI datasets are extracted and utilized to compare the prediction performance of all these methods. The experimental results show that hyperbolic graph neural networks tend to have a better performance than the other methods on the protein-related datasets. [ABSTRACT FROM AUTHOR]

With the expansion of wind power grid integration, the challenges of sharp fluctuations and high uncertainty in preparing the power grid day-ahead plan and short-term dispatching are magnified. These challenges can be overcome through accurate short-term wind power process prediction based on mining historical operation data and taking full advantage of meteorological forecast information. In this paper, adopting the ERA5 reanalysis dataset as input, a short-term wind power prediction framework is proposed, combining light gradient boosting machine (LightGBM), mutual information coefficient (MIC) and nonparametric regression. Primarily, the reanalysis data of ERA5 provide more meteorological information for the framework, which can help improve the model input features. Furthermore, MIC can identify effective feature subsets from massive feature sets that significantly affect the output, enabling concise understanding of the output. Moreover, LightGBM is a prediction method with a stronger ability of goodness-of-fit, which can fully mine the effective information of wind power historical operation data to improve the prediction accuracy. Eventually, nonparametric regression expands the process prediction to interval prediction, which significantly improves the utility of the prediction results. To quantitatively analyze the prediction results, five evaluation criteria are used, namely, the Pearson correlation coefficient (CORR), the root mean square error (RMSE), the mean absolute error (MAE), the index of agreement (IA) and Kling–Gupta efficiency (KGE). Compared with support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost) models, the present framework can make full use of meteorological information and effectively improve the prediction accuracy, and the generated output prediction interval can also be used to promote the safe operation of power systems. [ABSTRACT FROM AUTHOR]

In order to conduct a data-driven load forecasting modeling and its application in optimal control of air-conditioning system, this study used a hotel's central air conditioning system as the research object. Based on the data of the hotel energy management system, the load-forecasting model of the central air conditioning system based on support vector regression (SVR) was established by MATLAB. Based on the working principle of a chiller, chilled water pump, cooling water pump, and cooling tower, the energy consumption models were established, respectively. Finally, based on the load-forecasting results and the equipment energy consumption model, the energy consumption optimization objective function of the hotel water system was established, the objective function was solved to optimize the operating parameters of the water system at different load rates, the operation control strategy for each piece of equipment was obtained, and the energy-saving analysis was carried out. The results show that in the range of a load rate of 25~90%, the optimization strategy has an energy-saving effect, and the system's energy-saving rate is the highest when the load rate is 25.4%. The average energy-saving rate of the system is 12.4%. [ABSTRACT FROM AUTHOR]

End concrete cover separation is one of the most common failure modes for RC beams strengthened with external FRP reinforcement. The premature failure mode significantly restricts the application of FRP materials and could incur serious safety problems. In this paper, an innovative stress field-based analytical approach is proposed to assess the failure strength of end concrete cover separation and the conventional plane-section analysis is extended to evaluate the corresponding carrying capacity of FRP-strengthened RC beams. First, the dowel action of reinforcement and the induced concrete splitting, reflecting the interaction between concrete, steel and FRP, are considered in establishing the geometrical relationships of stress field for cracked concrete block. Then, the cracking angle and innovative failure criterion, considering the arrangement of steel and FRP reinforcement and cracking status of concrete and its softening effect, are derived to predict the occurrence of concrete cover separation and related mixed modes of debonding failure. Subsequently, an extended sectional analytical approach, in which the components of effective tensile strain of FRP resulted from flexural and shear actions are both considered, is presented to evaluate the carrying capacity of strengthened beams. Finally, the proposed calculational model is effectively validated by experimental results available in the literature. [ABSTRACT FROM AUTHOR]

Through proper arranging of a hybrid combination of longitudinal fiber reinforced polymer (FRP) bars and steel bars in the tensile region of the beam, the advantages of both FRP and steel materials can be sufficiently exploited to enhance the flexural capacity and ductility of a concrete beam. In this paper, a methodology for the flexural strength design of hybrid FRP-steel reinforced concrete (RC) beams is proposed. Firstly, based on the mechanical features of reinforcement and concrete and according to the latest codified provisions of longitudinal reinforcement conditions to ensure ductility level, the design-oriented allowable ranges of reinforcement ratio corresponding to three common flexural failure modes are specified. Subsequently, the calculation approach of nominal flexural strength of hybrid FRP-steel RC beams is established following the fundamental principles of equilibrium and compatibility. In addition to the common moderately-reinforced beams, the proposed general calculation approach is also applicable to lightly-reinforced beams and heavily-reinforced beams, which are widely used but rarely studied. Furthermore, the calculation process is properly simplified and the calculation accuracy is validated by the experimental results of hybrid FRP-steel RC beams in the literature. Finally, with the ductility analysis, a novel strength reduction factor represented by net tensile steel strain and reinforcement ratio is proposed for hybrid FRP-steel RC beams. [ABSTRACT FROM AUTHOR]

Ship targets are high-value military and civilian targets with broad application prospects. However, the precise focusing of ships is still a difficult issue because of their complicated six-degree-of-freedom motions on the sea surface. This paper focused on investigating the effect of ship six-degree-of-freedom oscillation on Synthetic Aperture Radar imaging. Firstly, based on the six-degree-of-freedom motions, the accurate range models for ship linear oscillation and angular oscillation were built, and the superiority was verified by comparing them with the models described in published literature. Secondly, we used the Taylor formula and Bessel function to expand the phase error introduced by ship oscillation, then their effects on imaging were further analyzed. Finally, based on the measured ship attitude data, we generated the semi-physical echoes of the oscillatory ship to validate the analysis throughout this article. Based on the proposed range model, we also made some tentative on the phase compensation method by fitting ship attitude angles with multiple sinusoidal functions. [ABSTRACT FROM AUTHOR]

As the Geosynchronous Earth Orbital Synthetic Aperture Radar (GEO SAR) allows a wide area viewing combined with a short revisit cycle, it is suitable for many applications that require high timeliness, such as natural disaster monitoring, weather supervision, and military reconnaissance. However, the ultralong integration time and the invalidation of "stop-and-go" assumption caused by the raise of orbital height also greatly increase the difficulty of signal processing. In this paper, a generalized method for calculating the accurate propagation distance between a GEO satellite and a target with ultralong integration time is proposed. This range model is mainly composed of an accurate pulse transmitting distance and an error compensation term for "stop-and-go" assumption failure. The transmitting distance is obtained by Taylor expansion, and the specific derivation process of the general formula of the mth-order expansion is given, in this paper. As for the compensation term, this is achieved by approximately calculating the pulse receiving distance based on twice Taylor expansion, the first expansion is for fast-time and the other is for slow-time. Finally, a series of simulation experiments were conducted to verify the effectiveness and superiority of this new range model for an ultralong integration time. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) has become one of the most important means of information acquisition in today's society and shows great potential in many fields. Target identification and classification of SAR images are also the focus of research. With the vigorous development of deep learning, many researchers apply this method to SAR target classification to obtain a more automatic process and more accurate results. In this paper, a novel deep forest model constructed by multi-grained cascade forest (gcForest), which is different from the traditional neural network (NN) model, is employed to classify ten types of SAR targets in the moving and stationary target acquisition and recognition (MSTAR) dataset. Considering that the targets of input images may be off-center and of different sizes in practical applications, two improved models based on varying weights by image features have been put forward, and both obtain better results. A series of experiments have been conducted to optimize model parameters, and final results with the MSTAR dataset illustrate that the two improved models are both superior to the original gcForest model. This is the first attempt to classify SAR targets using the non-NN model. [ABSTRACT FROM AUTHOR]

We presented the colorimetric and electrochemical methods for determination of the dipeptidyl peptidase-IV (DPP-IV) activity and screening of its inhibitor using gold nanoparticle (AuNP) as the probe. In the colorimetric assay, the substrate peptide with a sequence of Arg-Pro-Arg induced the aggregation and color change of AuNPs, whereas cleavage of the peptide by DPP-IV prevented the aggregation of AuNPs. Furthermore, the aggregation of AuNPs in the solution was easily initiated on a solid/liquid (electrode/electrolyte) surface, which induced a decrease in the electron-transfer resistance. However, once the peptide was clipped by DPP-IV, the assembly of AuNPs on electrode surface was prevented. Consequently, a higher electron-transfer resistance was observed. The colorimetric and electrochemical assays allowed for the determination of DPP-IV with the detection limits of 70 μU/mL and 0.55 μU/mL, respectively. Meanwhile, the proposed methods were used to determine DPP-IV inhibitor with satisfactory results. Both the colorimetric and electrochemical methods are simple, rapid and sufficiently sensitive for DPP-IV activity assay and inhibitor screening. The results also demonstrated that the AuNP-based colorimetric assay could be converted into an enhanced surface tethered electrochemical assay with improving sensitivity. The simple detection principle may be extended to the design of other peptidases biosensors with easy manipulation procedures.