An Underwater Target Detection Algorithm Based on Attention Mechanism and Improved YOLOv7

For underwater robots in the process of performing target detection tasks,the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model,which is prone to issues like error detection,omission detection,and poor accuracy.Therefore,this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7)underwater target detection algorithm.To improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase,we have added a Convolutional Block Attention Module(CBAM)to the backbone network.The Reparameterization Visual Geometry Group(RepVGG)module is inserted into the backbone to improve the training and inference capabilities.The Efficient Intersection over Union(EIoU)loss is also used as the localization loss function,which reduces the error detection rate and missed detection rate of the algorithm.The experimental results of the CER-YOLOv7 algorithm on the UPRC(Underwater Robot Prototype Competition)dataset show that the mAP(mean Average Precision)score of the algorithm is 86.1%,which is a 2.2%improvement compared to the YOLOv7.The feasibility and validity of the CER-YOLOv7 are proved through ablation and comparison experiments,and it is more suitable for underwater target detection.

An improved typhoon monitoring model based on precipitable water vapor and pressure

The potential of monitoring the movement of typhoons using the precipitable water vapor(PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence,based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV(ERA5-PWV) and the Global Navigation Satellite System-derived PWV(GNSS-PWV) were compared with the reference radiosonde PWV(RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure(P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode(IMTM) with different models(i.e.,IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction(NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network(CMOTN). The results show that the root mean square(RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV,and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.

Anti-aging performance improvement and enhanced combustion efficiency of boron via the coating of PDA

Boron is an ambitious fuel in energetic materials since its high heat release values,but its application is prohibited by low combustion efficiency and oxidization during storage.The polydopamine(PDA)was introduced into boron particles,investigating the impact of PDA content on the energetic behavior of boron.The results indicated that the PDA coating formed a fishing net structure on the surface of boron particles.The heat release results showed that the combustion calorific value of B@PDA was higher than that of the raw boron.Specifically,the actual combustion heat of boron powder in B@10%PDA increased by 38.08%.Meanwhile,the DSC peak temperature decreased by 100.65℃under similar oxidation rate compared to raw boron.Simultaneously,the B@PDA@AP and B@AP composites were prepared,and their combustion properties were evaluated.It was demonstrated that B@10%PDA@AP exhibited superior performance in terms of peak pressure and burning time,respectively.The peak pressure is 12.43 kPa more than B@AP and burning time is 2.22 times higher than B@AP.Therefore,the coating of PDA effectively inhibits the oxidization of boron during storage and enhances the energetic behavior of boron and corresponding composites.

Deterioration Reason and Improvement Measure of the Retarding Effect of Protein Retarder on Phosphorus Building Gypsum

The retarding effect of protein retarder on phosphorus building gypsum(PBG)and desulfurization building gypsum(DBG)was investigated,and the results show that protein retarder for DBG can effectively prolong the setting time and displays a better retarding effect,but for PBG shows a poor retarding effect.Furthermore,the deterioration reason of the retarding effect of protein retarder on PBG was investigated by measuring the pH value and the retarder concentration of the liquid phase from vacuum filtration of PBG slurry at different hydration time,and the measure to improve the retarding effect of protein retarding on PBG was suggested.The pH value of PBG slurry(<5.0)is lower than that of DBG slurry(7.8-8.5).After hydration for 5 min,the concentration of retarder in liquid phase of DBG slurry gradually decreases,but in liquid phase of PBG slurry continually increases,which results in the worse retarding effect of protein retarder on PBG.The liquid phase pH value of PBG slurry can be adjusted higher by sodium silicate,which is beneficial to improvement in the retarding effect of the retarder.By adding 1.0%of sodium silicate,the initial setting time of PBG was efficiently prolonged from 17 to 210 min,but little effect on the absolute dry flexural strength was observed.

IGED:Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data volume.Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization capabilities.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

A spatiotemporal evolution model of a short-circuit arc to a secondary arc based on the improved charge simulation method

The initial shape of the secondary arc considerably influences its subsequent shape.To establish the model for the arcing time of the secondary arc and modify the single-phase reclosing sequence,theoretical and experimental analysis of the evolution process of the short-circuit arc to the secondary arc is critical.In this study,an improved charge simulation method was used to develop the internal-space electric-field model of the short-circuit arc.The intensity of the electric field was used as an independent variable to describe the initial shape of the secondary arc.A secondary arc evolution model was developed based on this model.Moreover,the accuracy of the model was evaluated by comparison with physical experimental results.When the secondary arc current increased,the arcing time and dispersion increased.There is an overall trend of increasing arc length with increasing arcing time.Nevertheless,there is a reduction in arc length during arc ignition due to short circuits between the arc columns.Furthermore,the arcing time decreased in the range of 0°-90°as the angle between the wind direction and the x-axis increased.This work investigated the method by which short-circuit arcs evolve into secondary arcs.The results can be used to develop the secondary arc evolution model and to provide both a technical and theoretical basis for secondary arc suppression.

Intramolecular Hydrogen Bond Improved Durability and Kinetics for Zinc‑Organic Batteries

Organic compounds have the advantages of green sustainability and high designability,but their high solubility leads to poor durability of zinc-organic batteries.Herein,a high-performance quinone-based polymer(H-PNADBQ)material is designed by introducing an intramolecular hydrogen bonding(HB)strategy.The intramolecular HB(C=O⋯N-H)is formed in the reaction of 1,4-benzoquinone and 1,5-naphthalene diamine,which efficiently reduces the H-PNADBQ solubility and enhances its charge transfer in theory.In situ ultraviolet-visible analysis further reveals the insolubility of H-PNADBQ during the electrochemical cycles,enabling high durability at different current densities.Specifically,the H-PNADBQ electrode with high loading(10 mg cm^(-2))performs a long cycling life at 125 mA g^(-1)(>290 cycles).The H-PNADBQ also shows high rate capability(137.1 mAh g^(−1)at 25 A g^(−1))due to significantly improved kinetics inducted by intramolecular HB.This work provides an efficient approach toward insoluble organic electrode materials.

Optimization of jamming formation of USV offboard active decoy clusters based on an improved PSO algorithm

Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.

An Improved JSO and Its Application in Spreader Optimization of Large Span Corridor Bridge

In this paper,given the shortcomings of jellyfish search algorithmwith low search ability in the early stage and easy to fall into local optimal solution,this paper introduces adaptive weight function and elite strategy,improving the global search scope in the early stage and the ability to refine the local development in the later stage.In the numerical study,the benchmark problem of dimensional optimization with a 10-bar truss structure and simultaneous dimensional shape optimization with a 15-bar truss structure is adopted,and the corresponding penalty method is used for constraint treatment.The test results show that the improved jellyfish search algorithm can provide better truss sections as well as weights.Because when the steel main truss of the large-span covered bridge is lifted,the site is limited and the large lifting equipment cannot enter the site,and the original structure does not meet the problem of stress concentration and large deformation of the bolt group,so the spreader is used to lift,and the improved jellyfish search algorithm is introduced into the design optimization of the spreader.The results show that the improved jellyfish algorithm can efficiently and accurately find out the optimal shape and weight of the spreader,and throughMidas Civil simulation,the spreader used canmeet the requirements of weight and safety.

Data-driven diagnosis of high temperature PEM fuel cells based on the electrochemical impedance spectroscopy: Robustness improvement and evaluation

Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.

Research on Defect Detection of Wind Turbine Blades Based on Morphology and Improved Otsu Algorithm Using Infrared Images

To address the issues of low accuracy and high false positive rate in traditional Otsu algorithm for defect detection on infrared images of wind turbine blades(WTB),this paper proposes a technique that combines morphological image enhancement with an improved Otsu algorithm.First,mathematical morphology’s differential multi-scale white and black top-hat operations are applied to enhance the image.The algorithm employs entropy as the objective function to guide the iteration process of image enhancement,selecting appropriate structural element scales to execute differential multi-scale white and black top-hat transformations,effectively enhancing the detail features of defect regions and improving the contrast between defects and background.Afterwards,grayscale inversion is performed on the enhanced infrared defect image to better adapt to the improved Otsu algorithm.Finally,by introducing a parameter K to adjust the calculation of inter-class variance in the Otsu method,the weight of the target pixels is increased.Combined with the adaptive iterative threshold algorithm,the threshold selection process is further fine-tuned.Experimental results show that compared to traditional Otsu algorithms and other improvements,the proposed method has significant advantages in terms of defect detection accuracy and reducing false positive rates.The average defect detection rate approaches 1,and the average Hausdorff distance decreases to 0.825,indicating strong robustness and accuracy of the method.

Dynamic Characteristics of Functionally Graded Timoshenko Beams by Improved Differential Quadrature Method

This study proposes an effective method to enhance the accuracy of the Differential Quadrature Method(DQM)for calculating the dynamic characteristics of functionally graded beams by improving the form of discrete node distribution.Firstly,based on the first-order shear deformation theory,the governing equation of free vibration of a functionally graded beam is transformed into the eigenvalue problem of ordinary differential equations with respect to beam axial displacement,transverse displacement,and cross-sectional rotation angle by considering the effects of shear deformation and rotational inertia of the beam cross-section.Then,ignoring the shear deformation of the beam section and only considering the effect of the rotational inertia of the section,the governing equation of the beam is transformed into the eigenvalue problem of ordinary differential equations with respect to beam transverse displacement.Based on the differential quadrature method theory,the eigenvalue problem of ordinary differential equations is transformed into the eigenvalue problem of standard generalized algebraic equations.Finally,the first several natural frequencies of the beam can be calculated.The feasibility and accuracy of the improved DQM are verified using the finite element method(FEM)and combined with the results of relevant literature.

Image encryption algorithm based on multiple chaotic systems and improved Joseph block scrambling

With the rapid development of digital information technology,images are increasingly used in various fields.To ensure the security of image data,prevent unauthorized tampering and leakage,maintain personal privacy,and protect intellectual property rights,this study proposes an innovative color image encryption algorithm.Initially,the Mersenne Twister algorithm is utilized to generate high-quality pseudo-random numbers,establishing a robust basis for subsequent operations.Subsequently,two distinct chaotic systems,the autonomous non-Hamiltonian chaotic system and the tentlogistic-cosine chaotic mapping,are employed to produce chaotic random sequences.These chaotic sequences are used to control the encoding and decoding process of the DNA,effectively scrambling the image pixels.Furthermore,the complexity of the encryption process is enhanced through improved Joseph block scrambling.Thorough experimental verification,research,and analysis,the average value of the information entropy test data reaches as high as 7.999.Additionally,the average value of the number of pixels change rate(NPCR)test data is 99.6101%,which closely approaches the ideal value of 99.6094%.This algorithm not only guarantees image quality but also substantially raises the difficulty of decryption.

Value of improved nursing measures and enhanced nursing management to reduce the occurrence of adverse events in pediatric infusion

BACKGROUND Intravenous infusion is a common method of drug administration in clinical practice.Errors in any aspect of the infusion process,from the verification of medical orders,preparation of the drug solution,to infusion by nursing staff,may cause adverse infusion events.AIM To analyzed the value of improving nursing measures and enhancing nursing management to reduce the occurrence of adverse events in pediatric infusion.METHODS The clinical data of 130 children who received an infusion in the pediatric department of our hospital from May 2020 to May 2021 were analyzed and divided into two groups according to the differences in nursing measures and nursing management:65 patients in the control group received conventional nursing and nursing management interventions,while 65 patients in the observation group received improved nursing measure interventions and enhanced nursing management.The occurrence of adverse events,compliance of children,satisfaction of children’s families,and complaints regarding the transfusion treatment were recorded in both groups.RESULTS The incidence of fluid extravasation and infusion set dislodgement in the observation group were 3.08%and 1.54%,respectively,which were significantly lower than 12.31%and 13.85%in the control group(P<0.05),while repeated punctures and medication addition errors in the observation group were 3.08%and 0.00%,respectively,which were lower than 9.23%and 3.08%in the control group,but there was no significant difference(P>0.05).The compliance rate of children in the observation group was 98.46%(64/65),which was significantly higher than 87.69%(57/65)in the control group,and the satisfaction rate of children’s families was 96.92%(63/65),which was significantly higher than 86.15%(56/65)in the control group(P<0.05).The observation group did not receive any complaints from the child’s family,whereas the control group received four complaints,two of which were due to the crying of the child caused by repeated punctures,one due to the poor attitude of the nurse,and one due to medication addition errors,with a cumulative complaint rate of 6.15%.The cumulative complaint rate of the observation group was significantly lower than that of the control group(P<0.05).CONCLUSION Improving nursing measures and enhancing nursing management can reduce the incidence of fluid extravasation and infusion set dislodgement in pediatric patients,improve children’s compliance and satisfaction of their families,and reduce family complaints.

Research on Evacuation Path Planning Based on Improved Sparrow Search Algorithm

Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Firstly,the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm.Secondly,the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima.Finally,the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm,improving its local search ability.To evaluate the effectiveness of the proposed algorithm,the Whale Algorithm,Gray Wolf Algorithm,Improved Gray Wolf Algorithm,Sparrow Search Algorithm,and MSSA Algorithm were employed to solve various test functions.The accuracy and convergence speed of each algorithm were then compared and analyzed.The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms.To further validate the enhanced algorithm’s capabilities for path planning,evacuation experiments were conducted using different maps featuring various obstacle types.Additionally,a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building.Both the sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multiexit evacuation path planning.The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm,showcasing its greater advantages and higher application potential.

Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

This paper focuses on wireless-powered communication systems,which are increasingly relevant in the Internet of Things(IoT)due to their ability to extend the operational lifetime of devices with limited energy.The main contribution of the paper is a novel approach to minimize the secrecy outage probability(SOP)in these systems.Minimizing SOP is crucial for maintaining the confidentiality and integrity of data,especially in situations where the transmission of sensitive data is critical.Our proposed method harnesses the power of an improved biogeography-based optimization(IBBO)to effectively train a recurrent neural network(RNN).The proposed IBBO introduces an innovative migration model.The core advantage of IBBO lies in its adeptness at maintaining equilibrium between exploration and exploitation.This is accomplished by integrating tactics such as advancing towards a random habitat,adopting the crossover operator from genetic algorithms(GA),and utilizing the global best(Gbest)operator from particle swarm optimization(PSO)into the IBBO framework.The IBBO demonstrates its efficacy by enabling the RNN to optimize the system parameters,resulting in significant outage probability reduction.Through comprehensive simulations,we showcase the superiority of the IBBO-RNN over existing approaches,highlighting its capability to achieve remarkable gains in SOP minimization.This paper compares nine methods for predicting outage probability in wireless-powered communications.The IBBO-RNN achieved the highest accuracy rate of 98.92%,showing a significant performance improvement.In contrast,the standard RNN recorded lower accuracy rates of 91.27%.The IBBO-RNN maintains lower SOP values across the entire signal-to-noise ratio(SNR)spectrum tested,suggesting that the method is highly effective at optimizing system parameters for improved secrecy even at lower SNRs.

Improved IChOA-Based Reinforcement Learning for Secrecy Rate Optimization in Smart Grid Communications

In the evolving landscape of the smart grid(SG),the integration of non-organic multiple access(NOMA)technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy management.However,the open nature of wireless channels in SG raises significant concerns regarding the confidentiality of critical control messages,especially when broadcasted from a neighborhood gateway(NG)to smart meters(SMs).This paper introduces a novel approach based on reinforcement learning(RL)to fortify the performance of secrecy.Motivated by the need for efficient and effective training of the fully connected layers in the RL network,we employ an improved chimp optimization algorithm(IChOA)to update the parameters of the RL.By integrating the IChOA into the training process,the RL agent is expected to learn more robust policies faster and with better convergence properties compared to standard optimization algorithms.This can lead to improved performance in complex SG environments,where the agent must make decisions that enhance the security and efficiency of the network.We compared the performance of our proposed method(IChOA-RL)with several state-of-the-art machine learning(ML)algorithms,including recurrent neural network(RNN),long short-term memory(LSTM),K-nearest neighbors(KNN),support vector machine(SVM),improved crow search algorithm(I-CSA),and grey wolf optimizer(GWO).Extensive simulations demonstrate the efficacy of our approach compared to the related works,showcasing significant improvements in secrecy capacity rates under various network conditions.The proposed IChOA-RL exhibits superior performance compared to other algorithms in various aspects,including the scalability of the NOMA communication system,accuracy,coefficient of determination(R2),root mean square error(RMSE),and convergence trend.For our dataset,the IChOA-RL architecture achieved coefficient of determination of 95.77%and accuracy of 97.41%in validation dataset.This was accompanied by the lowest RMSE(0.95),indicating very precise predictions with minimal error.

Path Planning of Multi-Axis Robotic Arm Based on Improved RRT*

An improved RRT∗algorithm,referred to as the AGP-RRT∗algorithm,is proposed to address the problems of poor directionality,long generated paths,and slow convergence speed in multi-axis robotic arm path planning.First,an adaptive biased probabilistic sampling strategy is adopted to dynamically adjust the target deviation threshold and optimize the selection of random sampling points and the direction of generating new nodes in order to reduce the search space and improve the search efficiency.Second,a gravitationally adjustable step size strategy is used to guide the search process and dynamically adjust the step-size to accelerate the search speed of the algorithm.Finally,the planning path is processed by pruning,removing redundant points and path smoothing fitting using cubic B-spline curves to improve the flexibility of the robotic arm.Through the six-axis robotic arm path planning simulation experiments on the MATLAB platform,the results show that the AGP-RRT∗algorithm reduces 87.34%in terms of the average running time and 40.39%in terms of the average path cost;Meanwhile,under two sets of complex environments A and B,the average running time of the AGP-RRT∗algorithm is shortened by 94.56%vs.95.37%,and the average path cost is reduced by 55.28%vs.47.82%,which proves the effectiveness of the AGP-RRT∗algorithm in improving the efficiency of multi-axis robotic arm path planning.

Selection criteria of MPOB-Angola germplasm collection for yield improvement of the oil palm

Oil palm germplasm collected from Angola,Africa in 1991 were subjected to genetic variability potential studies.The collection was planted in the form of open-pollinated families as trials at the Malaysian Palm Oil Board(MPOB)Kluang Research Station,Johor,Malaysia,in 1994.Dura palms from 52 families and tenera palms from 44 families of MPOB-Angola were evaluated for their bunch yield and bunch quality components.The objectives of this study were to determine the genetic variability among the families and performance of MPOB-Angola germplasm for yield improvement.The analysis of variance(ANOVA)revealed highly significant differences between the dura and tenera families for most of the traits,suggesting the presence of high genetic variability,which is essential for breeding programmes.Among the duras,family AGO 02.02 displayed the best yield performance,with a high fresh fruit bunch,oil yield and total economic product at 240.40,29.46 and 37.93 kg palm^(-1)year^(-1),respectively.As for the teneras,family AGO 03.04 recorded the highest FFB yield and oil yield at 249.25 and 45.22 kg palm^(-1)year^(-1),respectively.Besides that,several families with big fruit sizes or producing a mean fruit weight of 14-17 g were also identified.Both dura and tenera from AGO 01.01 recorded the highest oil to bunch(O/B)of 17.76%and 28.65%,respectively.These findings will facilitate the selection of palms from the MPOB-Angola germplasm for future breeding programmes.

Introduction of Western Medicines into China and Improvement on Decoctions during Modern China

During the late Qing dynasty(1840 A.D.-1912 A.D.),a large quantity of Western medicines entered China,which continuously impacted the traditional Chinese medicine(TCM)market and revealed the shortcomings of Chinese medicines.Some personages in the TCM community followed the trend of learning from the West,and attempted to reform TCM,with the improvement on decoction becoming an important aspect of this effort.Through debates and trials,the improvement on decoction underwent three stages of conceptual evolution:“taking Chinese medicines as the foundation and referring to the dosage forms of Western medicines”,“introducing Western techniques to serve the preparation of decoctions”and“integrating the theories of TCM and Western medicine to improve decoctions”.The study highlights the effective complementarity between modern TCM and Western medicine in the field of pharmacy,and provides valuable experience and support for the reevaluation of the value of TCM in contemporary society.