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Delivery systems smaller than 50 nm are advantageous for cancer prevention. In this study, curcumin was dissolved in shellac micelles following co-dissolving at pH 13.0 and neutralization using glucono-delta-lactone. With 5% w/v shellac and 0.5-5 mg/mL curcumin, the loading capacity and encapsulation efficiency were up to 8.0 and 92.6%, respectively, and the nanocapsules had an average diameter of 20 nm. Differential scanning calorimetry, FTIR spectroscopy, and fluorescence spectroscopy results confirmed the encapsulation of curcumin in an amorphous state in shellac micelles. The neutral nanocapsule dispersions maintained the particle dimension and had less than 10% curcumin degradation during 4 week storage at 4 °C. Nanoencapsulating curcumin enhanced in vitro bioavailability and antiproliferation activity against colon cancer cells. After simulated digestions, ∼60% of the nanoencapsulated curcumin was not available for intestinal absorption, nanocapsules retained their structure, and nanoencapsulated curcumin remained active against colon cancer cells, indicating the potential delivery for colorectal cancer prevention.

Orbital angular momentum (OAM) has made it possible to regulate classical waves in novel ways, which is more energy- or information-efficient than conventional plane wave technology. This work aims to realize the transition of antenna radiation mode through the rapid design of an anisotropic dielectric lens. The deep learning neural network (DNN) is used to train the electromagnetic properties of dielectric cell structures. Nine variable parameters for changing the dielectric unit structure are present in the input layer of the DNN network. The trained network can predict the transmission phase of the unit cell structure with greater than 98% accuracy within a specific range. Then, to build the corresponding relationship between the phase and the parameters, the gray wolf optimization algorithm is applied. In less than 0.3 s, the trained network can predict the transmission coefficients of the 31 × 31 unit structure in the arrays with great accuracy. Finally, we provide two examples of neural network-based rapid anisotropic dielectric lens design. Dielectric lenses produce the OAM modes +1, −1, and −1, +2 under TE and TM wave irradiation, respectively. This approach resolves the difficult phase matching and time-consuming design issues associated with producing a dielectric lens.

In this article, we study resource optimization issues for hybrid energy harvesting massive multiple-input–multiple-output (MIMO) systems, where renewable energy harvested from surrounding environments is introduced as additional energy supply to the base station (BS) together with the grid power. Our purpose is to maximize system energy efficiency with the preferential use of renewable energy under several practical restrictions, such as limited battery capacity, energy causality, quality-of-service (QoS) requirement, and transmit power constraints. An offline policy is first presented under ideal assumptions, i.e., noncausal knowledge of the channel state information and energy harvesting dynamics are known in advance, which causes it to become a performance upper bound. In particular, the offline policy is designed in an iterative manner with the use of the fractional programming technique. Moreover, inspired by the offline policy, we exploit statistical information to present two practical online algorithms. Specifically, the Markov chain method predicts prospective information through use of the finite-state Markov model, whereas the timing detection scheme focuses only on the resource optimization of the current time slot. Numerical results illustrate that both online algorithms perform closely to the offline policy, which indicates that they can achieve a good tradeoff between performance and implementation complexity.

In practical scenarios, there are always array errors, which would increase the sidelobe level (SLL) of the array and distort the performance of the electronic devices consequently. Nevertheless, most of the reported works do not take these unavoidable errors into consideration when implementing array beampattern synthesis. To remedy this problem, a low sidelobe beampattern synthesis approach is proposed in the presence of array errors based on the bat algorithm (BA). In particular, the covariance matrix of the sidelobe region in the presence of array errors is incorporated into the optimization problem. Generally, there are many factors that would contribute to array errors in practical scenarios. Therefore, to reduce the influence of the uncertainty characteristic of the errors on the low-SLL beampattern synthesis, the statistical mean method is utilized to obtain a robust calculation of the covariance matrix in the presence of array errors. Theoretical analysis using signal processing technique and electromagnetic simulation using Ansoft HFSS workbench are combined to testify the effectiveness of the proposed approach. Numerical results show that peak SLLs (PSLLs) of around −22dB and −20dB can be respectively achieved in the above two simulation situations with the obtained weight vectors.

This paper investigates the application of nonorthogonal multiple access (NOMA) in millimeter-Wave (mmWave) communications to address resource allocation issues for multiuser downlink transmission. Inspired by the K-means clustering algorithm, a NOMA user grouping policy is first employed in accordance with the channel correlation between users. The first-stage decoding order for each NOMA group is then determined by merely utilizing the users’ channel gain to formulate a joint power allocation and hybrid beamforming optimization problem. Specifically, we use signal-to-leakage-plus-noise ratio (SLNR) as the performance index of the optimization problem to reduce the computational complexity, because it can decouple the design of the beamforming and power allocation issues so they can be executed iteratively. Under given beamforming matrix, the power allocation issue is expressed as a quadratic programming (QP) problem, and then transformed into a convex problem by introducing an auxiliary-positive real variable to solve through the Lagrange multiplier method. On the contrary, the beamforming optimization problem is non-convex and very difficult to solve due to the constant modulus constraints in the hybrid architecture. However, the optimum solution of the ideal full-digital beamforming can be acquired primarily by generalized eigenvalue decomposition (GED). In this condition, two hybrid beamforming algorithms are proposed, in which normalized phase matching and equal gain transmission are performed respectively in the analog domain. On this basis, a second-stage decoding order can be employed according to the effective channel gain of users to perform successive interference cancellation (SIC) successfully. Numerical results show that the proposed joint power allocation and hybrid beamforming algorithms are superior to the classical mmWave-NOMA and orthogonal multiple access (OMA) schemes not only in lower implementation complexity, but also in better spectrum efficiency and energy efficiency performance.

Abdominal aortic aneurysm (AAA) rupture is one of the most common causes of mortality in cardiovascular diseases, but currently there is no approved drug for AAA treatment or prevention in the clinic. Naringenin (NGN) has been reported to have anti-AAA effects. However, water solubility and in vivo absorption of NGN are not satisfactory, which leads to its low bioavailability, thus affecting its pharmacological effects. In this project, the improving effects of isonicotinamide (INT) co-crystal and hydroxy propyl methyl cellulose (HPMC) or polyvinyl pyrrolidone (PVP) on the solubility, in vivo absorption, and anti-AAA effects of NGN were evaluated.In the current study, co-crystals of naringenin-isonicotinamide (NGN-INT) were prepared, and effects of PVP or HPMC on precipitation rate, supersaturation, and bioavailability of NGN were explored. In addition, with or without HPMC supply, the effects of NGN-INT co-crystal on anti-AAA efficacy of NGN were investigated on an elastase-induced AAA mouse model, and the results were compared with the efficacy of the NGN crude drug.Our results demonstrate that NGN-INT formulation, compared to the NGN crude drug, enhanced the dissolution rate of NGN and significantly increased Csubmax/suband AUCsub(0-∞)/subof NGN by 18 times and 1.97 times, respectively. Addition of PVP or HPMC in NGN-INT co-crystal further increased bioavailability of NGN in NGN-INT. The in vivo pharmacodynamic study showed that NGN-INT with HPMC significantly improved the inhibitory effects of NGN against AAA.NGN-INT significantly improved the absorption and aortic protective effects of NGN. The supersaturation-prolonging effect of HPMC further enhanced bioavailability and anti-AAA effects of NGN-INT.

The three-dimensional (3D) sea urchin-like CuO microspheres structures were successfully synthesized by a simple hydrothermal method. With the hydrothermal temperature increases, the morphology of ...

A large amount of data transmission is one of the challenges faced by communication systems. In this paper, we revisit the intelligent receiver consisting of a neural network, and we find that the intelligent receiver can reduce the data at the transmitting end while improving the decoding accuracy. Specifically, we first construct a smart receiver model, and then design two ways to reduce the data at the transmitter side, namely, end-of-transmitter data trimming and equal-interval data trimming, to investigate the decoding performance of the receiver under the different trimming methods. The simulation results show that the receiver still has an accurate decoding performance with a small amount of trimming at the end of the transmitter data, while the decoding performance of the smart receiver is better than that of the conventional receiver with complete data when the data is trimmed at equal intervals. Moreover, the receiver with equally-spaced data cropping has a lower BER when the data at the transmitter side is reduced by the same data length. This paper provides a new solution to reduce the amount of data at the transmitter side.

ZnO nanorods arrays, with high crystal quality has been successfully synthesized by sol-gel assisted hydrothermal method and their photoelectric property has been studied for toward a UV detection....

In this study, the distributed optical fiber is used to monitor the overburden changes during the coal mining process, and the average frequency shift change degree of optical fiber is introduced as the index to judge the periodic pressure. The ARMA-SVR model composed of auto-regressive moving average (ARMA) and support vector machine regression (SVR) is established to predict the rock pressure behavior through the prediction of the frequency shift change of optical fiber. Firstly, a similar material model is built to obtain the data of the frequency shift of the optical fiber during the excavation of the working face; Then, the frequency shift change sequence of optical fiber is converted into a time series. Finally, a combined model is built. The linear auto-correlation part of the frequency shift change sequence is predicted by ARMA model, and the nonlinear residual part is predicted by SVR model to jointly complete the prediction of the frequency shift change of optical fiber. The experimental results show that the combined model proposed in this paper is superior to the single auto-regressive moving average model in different data sets, and the ARMA-SVR combined model can effectively predict the change of optical fiber frequency shift and provide a new idea for the prediction of rock pressure in the mining process.

Unlike conventional phased array (PA), frequency diversity array (FDA) can perform the beampattern synthesis not only in an angle dimension but also in a range dimension by introducing an additional frequency offset (FO) across the array aperture, thus greatly enhancing the beamforming flexibility of an array antenna. Nevertheless, an FDA with uniform inter-element spacing that consists of a huge number of elements is required when a high resolution is needed, which results in a high cost. To substantially reduce the cost while almost maintaining the antenna resolution, it is important to conduct a sparse synthesis of FDA. Under these circumstances, this paper investigated the transmit–receive beamforming of a sparse-fda in range and angle dimensions. In particular, the joint transmit–receive signal formula was first derived and analyzed to resolve the inherent time-varying characteristics of FDA based on a cost-effective signal processing diagram. In the sequel, the GA-based low sidelobe level (SLL) transmit–receive beamforming of the sparse-fda was proposed to generate a focused main lobe in a range-angle space, where the array element positions were incorporated into the optimization problem. Numerical results showed that 50% of the elements can be saved for the two linear FDAs with sinusoidally and logarithmically varying frequency offsets, respectively termed as sin-FO linear-FDA and log-FO linear-FDA, with only a less than 1 dB increment in SLL. The resultant SLLs are below −9.6 dB, and −12.9 dB for these two linear FDAs, respectively.

Caffeic acid phenethyl ester (CAPE) has various biological activities but low water solubility and poor bioavailability. In this study, CAPE was encapsulated in skim milk powder (SMP) by spray drying warm aqueous ethanol solutions with different mass ratios of SMP and CAPE. The loading capacity and encapsulation efficiency were up to 10.1 and 41.7%, respectively. Differential scanning calorimetry and X-ray diffraction results confirmed the loss of crystallinity of CAPE after encapsulation. Fourier-transform infrared and fluorescence spectroscopy results indicated the hydrophobic binding between CAPE and caseins. Scanning electron microscopy and static light scattering results showed spherical capsules with an average diameter of around 26 μm. The CAPE loaded in SMP microcapsules showed significantly improved in vitro bioaccessibility and antiproliferation activity against human colon cancer cells compared to free CAPE. The simple, scalable, and low-cost approach in the present study may be significant for industrial encapsulation of CAPE and other lipophilic bioactive compounds.

There is growing evidence that out-of-school factors, such as physical and mental health, family support, and social and emotional development, significantly affect student learning (Berliner 2009). To address challenges related to poverty, schools are being charged with serving as a focal point in providing and coordinating support services for students and their families (Adelman and Taylor 2002; Dryfoos 2002). In many schools these support services are provided in fragmented ways that do not address the needs of all students or engage teachers in connecting these services to the academic mission of the school (Walsh and DePaul 2008). An emerging school-based model, broadly termed "comprehensive student support" (Walsh et al. 2016), is designed to overcome such fragmentation. In this paper, we build upon previous effectiveness work with an economic evaluation of a successful support model, City Connects. We find that the benefits of the program exceed the costs, indicating that the program is a sound investment and should be considered an option to address the needs of students and to prevent future crises from disrupting their learning.

Al doped and Cu doped ZnO NRAs/Graphene composite material are synthesized by introducing the concentrations of 0%, 2%, 4%, 6%, 8% using hydrothermal method, and their structure and morphology are ...

TiO2 materials are of great interest in different applications because of the controllable preparation of different structures. In this paper, the TiO2 and TiO2/graphene films with different phase ...

The conventional polar format algorithm (CPFA) is widely used for synthetic aperture radar (SAR) because of its simple and efficient operations. However, due to its wavefront curvature assumption, the CPFA’s depth-of-focus (DOF) is extremely small, which greatly limits the scene size, especially for high-resolution and highly squinted (HRHS) SAR. To solve this problem, an extended PFA (EPFA) is proposed in this study, re-deriving mapping functions by expanding the range history into slant- and cross-range components according to the forms of real data storage. This allows the full use of storage data, which the CPFA cannot achieve due to the large approximations introduced by the projection of echo data onto the ground. The wavefront curvature error is then analyzed and eliminated using a space-variant phase compensation function. Due to the high accuracy of expansion in the slant range plane and the space-variant correction processing, the EPFA has a larger DOF than the CPFA. The EPFA is also more suitable for undulating terrains since it avoids the projection of real data onto the ground plane performed in the CPFA. Using comparative analyses of simulated data and real-world images, the results suggest that the proposed EPFA achieves better focusing effects than the CPFA and is particularly useful for HRHS SAR.

Multiple-input multiple-output (MIMO) systems suffer from high BER in the mining environment. In this paper, the mine MIMO depth receiver model is proposed. The model uses densely connected convolutional networks for feature extraction and constructs multiple binary classifiers to recover the original information. Compared with conventional MIMO receivers, the model has no error accumulation caused by processes such as decoding and demodulation. The experimental results show that the model has better performance than conventional decoding methods under different modulation codes and variations in the number of transmitting terminals. Furthermore, we demonstrate that the model can still achieve effective decoding and recover the original information with some data loss at the receiver.

With the advancement of an intellectual and numerical society, the coal mining industry has also begun to change to intelligence. As an important aspect of intelligent coal mine construction, coal mine communication has put forward more stringent standards for communication quality. For the complex communication environment in mines, the transmission of communication signals is always damaged by various noises and interferences, resulting in serious distortion of the communication signals received at the receiving end. Therefore, the use of traditional receivers for information recovery has the problem of high bit error rate (BER), which cannot meet the standard of intelligent coal mine construction. Based on this, the aim of this research is to combine convolutional neural networks (CNN) and multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) communication systems to design an intelligent receiver model for complex mine communication systems. At the receiver side, CNNs are used to take the place of all the information processing processes. First, features are extracted from the received IQ signal by the convolutional neural network, and then the original information bit is recovered using a multi-label classifier to finally realize end-to-end information restoration. The experimental results show that the intelligent receiver model designed in this research has more accurate information recovery capability in the complex mine channel environment compared with the traditional receiver. In addition, they also verify that the intelligent receiver can still recover information effectively when the traditional receiver cannot recover information properly in the case of partial loss of received data.

ZnO and ZnO/Graphene thin films were deposited on Cu substrate using a low pressure chemical vapor deposition (LPCVD) and the magnetron sputtering method. The impacts of graphene layer growth and a...

Sum ( $\Sigma$ ) and difference ( $\Delta$ ) beampatterns are commonly utilized to estimate the bearings of targets in the radar system. However, the nulls of $\Delta $ -pattern are not always aligned with those of $\Sigma $ -pattern, and this phenomenon would become more severe in practical scenario since the array errors exist. Meanwhile, the sidelobe level (SLL) of the array would also increase due to the unavoidable errors. The above-described factors would dramatically decrease the performance of radar in the adaptive suppression of interference. To increase the output signal-to-interference-plus-noise ratio of a radar system, this paper presents an approach to design the low SLL $\Sigma \Delta $ -patterns where the nulls of the $\Delta $ -pattern, excluding the central null, are always aligned with those of the $\Sigma $ -pattern in the presence of errors. Two schemes are addressed to obtain the covariance matrix for null-alignment constraint, which is exploited to force nulling in desired angles accordingly. In the sequel, the matrix tapering method is adopted to broaden the nulls and enhance the robustness for the possible errors. With the proposed approach, the low SLL and nulls alignment can be achieved simultaneously. Numerical examples are provided and discussed to demonstrate the effectiveness of the proposed solution, including the results based on a linearly spaced microstrip array antenna.

There are many unavoidable array errors in practical scenarios, which would drastically increase the sidelobe level (SLL) and distort the performance of radar systems accordingly. In this paper, an effective beampattern synthesis approach is proposed to realize a low peak sidelobe level (PSLL) and wide-nulling in the presence of array errors, thus improving the consequent performance of the radar. In particular, the covariance matrix of the sidelobe region (CMSR) is incorporated into the optimization. Considering the randomness of array errors, the statistical mean method is adopted to obtain a more accurate calculation of the CMSR in the presence of array errors based on a Monte Carlo trial. To efficiently and effectively solve the optimization problem, an advanced metaheuristic algorithm, i.e., the water cycle algorithm (WCA), is adopted when seeking the corresponding optimal weight vectors. Numerical results are provided and discussed to demonstrate the effectiveness of the proposed approach including the results based on a wideband linearly spaced magneto-electric (ME) dipole array.

Aiming at receiving the precision of the field intensity in the underground roadway, this paper proposes a network model based on SVR-LSTM hybrid proposal forecast model, with the real data of roadway, forecast receiving field of roadway, combined with the advantages of the two models, the prediction results of two kinds of single model of weight distribution, and the weights are optimized. The simulation results show that the SVR-LSTM hybrid network model has higher prediction accuracy than the adaptive regression model and the single network model.

The critical environment of the underground mines is a risky zone for mining applications and it is very hazardous to engage the miners without a sophisticated communication system. The existing wired networks are susceptible to damage and the wireless radio systems experience severe fading that restricts the complete access to the entire assembly of a mine. Wireless optical communication is a better approach that can be incorporated in the erratic atmosphere of underground mines to overcome such issues, as lights are already used to illuminate the mine galleries. This study is focused on investigating the characteristics of visible light communication (VLC) in an underground coal mine. The entire scope of VLC is elaborated along with the influence of coal dust particles and the scattering model. The impact of coal dust clouds on visibility and attenuation is analyzed for visible light transmission. The shadowing effect generated by the pillars and mining machinery is estimated by employing the bimodal Gaussian distribution (BGD) approach in coal mines. The characteristic model of VLC for underground coal mines is presented by classifying the area of the mine into mine gallery and sub-galleries. The transmission links of VLC are categorized as the line of sight (LOS) link for direct propagation and the non-LOS (NLOS) link for reflected propagation. The scenarios of LOS and NLOS propagation are considered for each evaluating parameter. Furthermore, the performance of the proposed framework is examined by computing the received signal power, path loss, delay spread (DS), and signal to noise ratio (SNR).

In view of the current situation that the recognition rate of digital signal modulation recognition method is unsatisfactory at low Signal-to-Noise Ratio(SNR), a recognition method based on high-order cumulants and wavelet transform is proposed to realize the automatic modulation recognition of 8 kinds of digital signals such as 2ASK, 4ASK, 8ASK, 2PSK, 4PSK, 8PSK, 16QAM and 32QAM. Based on the high-order cumulants principle and wavelet transform theory, the characteristic parameters f1∼f5 are constructed by the elaborate analysis of the characteristic extraction of these signals. Through simulation experiments, the characteristic parameter changes of different types of modulation signals at different SNR are obtained, and design the classifier of Back Propagation (BP) neural network to classify the signals. The simulation results show that this method can improve the average correct recognition rates of 8 digital modulation signals reaching up to above 97% when the SNR is higher than 0dB, which greatly improves the signal recognition performance at low SNR.

Raw almonds could be contaminated by pathogens, but the current pasteurization practice using propylene oxide in the U.S. has flammability and carcinogenicity concerns. Supercritical carbon dioxide (scCO2) is a water-free technology and is a solvent of essential oils that are effective antimicrobial preservatives. The objective of this study was to investigate the possibility of combining scCO2 and thyme oil (TO) to reduce Escherichia coli K12 inoculated on raw almonds. Raw almonds inoculated with ∼6 log CFU/g E. coli K12 were batch-treated with scCO2 alone or the combination of presoaking in pure TO followed by scCO2 treatments at different combinations of temperature, pressure, and duration. Compared to scCO2 alone treatments, the combination of TO and scCO2 treatments significantly improved the disinfection effectiveness. Temperature had the most significant effect on the log reduction. At 70 °C, the log reduction by the combination treatment was over 4-log CFU/g and the maximum reduction was 5.16 log CFU/g. The findings suggest that the combination of TO and scCO2 may be a potential water-free technology to meet the requirement of over 4-log reduction of target microorganism for almond and other tree nut products.

coal is an important energy. Coal mining needs a series of equipment, among which communication equipment can ensure the safety of underground workers in the mining process and improve production efficiency. Aiming at the problems of multipath fading and anti-interference of signal transmission in coal mine, researchers propose to optimize the quality of signal transmission in coal mine with OFDM technology. Compared with traditional OFDM technology, this paper proposes a method of mine OFDM intelligent receiver based on deep learning, which aims to use deep learning technology to replace the signal recovery process of the receiver of traditional OFDM technology. The simulation results show that in terms of bit error rate, the signal recovery of the receiver based on deep learning technology is obviously better than that of the traditional OFDM receiver. This will improve the economic benefits of coal mining in the future.

In view of the serious refraction and reflection of radio wave transmission in the complex environment of coal mines, serious multipath fading occurs, which affects the performance of the communication system. Therefore, we presents a simulated annealing algorithm to optimize the kernel function and regularization function parameters in LS-SVM, and establishes a wireless channel path loss model based on LS-SVM. Comparing the optimized and improved model based on the SA algorithm with the traditional model, the MATLAB simulation experiment results show that the SA+LS-SVM model is significantly superior to the pre-optimized model and the traditional model in prediction accuracy, and can more effectively predict the fading change to the channel Condition.

In the complex and dynamically varying underwater acoustic (UWA) channel, cooperative communication can improve throughput for UWA sensor networks. In this paper, we design a reasonable relay selection strategy for efficient cooperation with reinforcement learning (RL), considering the characteristics of UWA channel variation and long transmission delay. The proposed scheme establishes effective state and reward expression to better reveal the relationship between RL and UWA environment. Meanwhile, simulated annealing (SA) algorithm is integrated with RL to improve the performance of relay selection, where exploration rate of RL is dynamically adapted by SA optimization through the temperature decline rate. Furthermore, the fast reinforcement learning (FRL) strategy with pre-training process is proposed for practical UWA network implementation. The whole proposed SA-FRL scheme has been evaluated by both simulation and experimental data. The simulation and experimental results show that the proposed relay selection scheme can converge more quickly than classical RL and random selection with the increase of the number of iterations. The reward, access delay and data rate of SA-FRL can converge at the highest value and are close to the ideal optimum value. All in all, the proposed SA-FRL relay selection scheme can improve the communication efficiency through the selection of the relay nodes with high link quality and low access delay.

Plant peptide hormones play various roles in plant development, pathogen defense and abiotic stress tolerance. Plant elicitor peptides (Peps) are a type of damage-associated molecular pattern (DAMP) derived from precursor protein PROPEPs. In this study, we identified nine PROPEP genes in the broccoli genome. qRT-PCR analysis indicated that the expression levels of BoPROPEPs were induced by NaCl, ABA, heat, SA and P. syringae DC3000 treatments. In order to study the functions of Peps in salinity stress response, we synthesized BoPep4 peptide, the precursor gene of which, BoPROPEP4, was significantly responsive to NaCl treatment, and carried out a salinity stress assay by exogenous application of BoPep4 in broccoli sprouts. The results showed that the application of 100 nM BoPep4 enhanced tolerance to 200 mM NaCl in broccoli by reducing the Na+/K+ ratio and promoting accumulation of wax and cutin in leaves. Further RNA-seq analysis identified 663 differentially expressed genes (DGEs) under combined treatment with BoPep4 and NaCl compared with NaCl treatment, as well as 1776 genes differentially expressed specifically upon BoPep4 and NaCl treatment. GO and KEGG analyses of these DEGs indicated that most genes were enriched in auxin and ABA signal transduction, as well as wax and cutin biosynthesis. Collectively, this study shows that there was crosstalk between peptide hormone BoPep4 signaling and some well-established signaling pathways under salinity stress in broccoli sprouts, which implies an essential function of BoPep4 in salinity stress defense.

Caffeic acid phenethyl ester (CAPE) has various biological activities but low water solubility and poor bioavailability. In this study, CAPE was encapsulated in skim milk powder (SMP) by spray drying warm aqueous ethanol solutions with different mass ratios of SMP and CAPE. The loading capacity and encapsulation efficiency were up to 10.1 and 41.7%, respectively. Differential scanning calorimetry and X-ray diffraction results confirmed the loss of crystallinity of CAPE after encapsulation. Fourier-transform infrared and fluorescence spectroscopy results indicated the hydrophobic binding between CAPE and caseins. Scanning electron microscopy and static light scattering results showed spherical capsules with an average diameter of around 26 μm. The CAPE loaded in SMP microcapsules showed significantly improved