bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-person Action Recognition Based on the HARNet Network in the Park Environment
Authors: Xie Shubiao , Liu Shumin , Chen Xingfeng , Zhang Xiaoming , Zhang Yunli , Dan Jiannan , Chen Shiyu , Wu Bingbing
Published: August 2023

Action recognition is crucial to public security, playing an important role in pedestrian protection. Recently, numerous approaches based on deep learning models have been reported for action recognition, which mainly has problems such as poor versatility, not good robustness, low recognition effects for low resolution, and occlusion in outdoor environments. This paper addresses this issue and aims to develop an approach for multi-person action recognition in the park environment. Frame extraction operation is first used to extract images from surveillance video in real-time, then YOLOv5 is adopted to detect and segment human targets from images. Thirdly, the AlphaPose algorithm is used to estimate the keypoint heatmap of the human body according to human targets. Finally, the heatmaps are preprocessed and then fused and classified by deep learning networks to complete the multi-person action recognition. In addition, an optimization method, i.e., Heatmap-based Action Recognition Network (HARNet), for ResNet is proposed in order to overcome the difficulty of information extraction from heatmaps, where the attention mechanism is considered. The approach presented here was tested on park surveillance video with multiple different scenarios, and the corresponding results verified its performance.
Transmission Line Instance Segmentation Algorithm Based on YOLACT
Authors: Chen Yong , Wang Yun-hui , Li Song , Li Meng
Published: August 2023

In the field of intelligent power patrol inspection, the transmission line is an important identification and detection target. The measurement of line spacing and ground distance are key technologies in the field of inspection. Therefore, it is necessary to quickly and accurately segment transmission lines. The transmission lines occupy a large span and vary widely in length. To improve the segmentation rate and accuracy of the transmission lines, we adopted EfficientNet as the main network. With the same accuracy, the number of parameters is reduced by 80% compared with ResNet 101. The network training cost is reduced, and the detection rate of the model is improved. To deal with the influence caused by the large change in transmission line length, we introduce adaptive anchor box calculation and the FPN + PAN structure. At the same time, multiple transmission lines are often overlapped, so the traditional NMS makes it easy to cause the omission or confusion of lines. We improve the NMS. Finally, we adopted the modified CIoU loss function to optimize the loss function. From the experimental results, our model has good performance for instance in segmenting transmission lines.
Tooth Surface Registration of Spiral Bevel Gear Based on Improved Genetic Algorithm
Authors: Liu Yongsheng , Hu Panru , Xu Zhibo , Chen Yixin , Tan Jiamin
Published: August 2023

To reduce the tooth surface measurement error of spiral bevel gear, a tooth surface registration method using an improved genetic algorithm (GA) was proposed according to the measuring principle of a coordinate measuring machine (CMM). To improve the performance of GA, the unit quaternion method was used to limit the initial population generation space, and then the simulated annealing algorithm was used to improve the search process. Finally, the measured and theoretical tooth surface was matched. Experimental results show that the improved genetic algorithm can compensate for more than 55% of the measurement error of the tooth surface.
Robotic Scheduling Strategies based on Machine Learning Algorithm
Authors: Wu Hongzhong , Xing Tenghui , Li Weiqing , Chen Hongbiao , Kang Yutao , Li Jiayang
Published: August 2023

Recently, robot scheduling has been widely concerned by numerous companies and researchers. They adopt an intelligent algorithm to design scheduling strategies for automated guided vehicle (AGV) and achieve required moving routes and certain aims. However, the existing robot scheduling strategies employ mathematical program models to arrange robots’ control and ignore machine issues that the robots may have. This will cause conflict and may lead the whole robot to a deadlock when the scale of conflict reaches a critical point. In this paper, we present a novel scheduling strategy for intelligent robots by training machine learning algorithms. Initially, we assume the robots contain a communication system, which can only receive signals from the central controller. Subsequently, we utilize the input parameters including robot conditions, working area, and working aims to train the machine learning algorithm. The work accomplishment ratio and conflict ratio are two essential indicators to measure our proposed method. Additionally, we also simulate the existing robot scheduling models to compare. From the extensive experimental results and comparison evaluations, we can conclude that our proposed method can achieve the robots scheduling tasks with reasonable costs. At last, we discuss the challenges of applying machine learning techniques to robotic scheduling and potential solutions and provide future directions in this field.
Garment Segmentation Network Based on Texture and Semantic Decoding Module
Authors: Liu Zhixian , Li Huiyin , Zhu Ping
Published: August 2023

With the great development of the apparel e-commerce industry, more and more people are engaged in this field. In reality, clothing is easily affected by background, perspective, occlusion, and other factors, so clothing segmentation is not accurate enough. This paper proposes a clothing segmentation network based on the texture and semantic decoding module of clothing. Based on the swing transformer network, this network proposes a clothing texture analysis module (CTAM) and a clothing semantic decoding module (CSDM). In the coding process, CTAM extracts clothing texture features to enhance spatial information, while CSDM uses mixed attention to enhance image context information during decoding to improve the accuracy of clothing segmentation. The experimental results show that compared with the original model, the average pixel accuracy of the proposed method on the LIP data set is increased by about 2%, and the average crossover ratio is increased by about 1.5%. The segmentation network significantly improves indistinguishable garment boundaries and the accuracy of garment image segmentation.
Research on the Identification Method of Radionuclide Based on the Lightweight Model
Authors: Yuan Chang
Published: August 2023

Traditional CNN has problems with significant parameters and is difficult to deploy in a resource-constrained portable spectrometer for radionuclide identification. We propose a lightweight neural network model based on GhostNet to address these problems. First, the Ghost-CS module is built by adding the channel mixing operation in the Ghost module to enhance the information exchange between different channels; Secondly, using the ECA module reduces the noise information in the spectrum. The results showed that the algorithm maintains a high recognition accuracy while the number of model parameters is greatly reduced, making it easier to implement in portable spectrometers.
The Ground Test System of Double Layer Air Floating Platform and Its Application
Authors: Wu Bojian , Zhao Yujie , Wang Song , Li Zongfeng
Published: August 2023

The suspension test system within the high microgravity scientific experiment cabinet is equipped with double-layer vibration isolation, effectively isolating external disturbances and creating a high microgravity environment within the inner body. Based on this characteristic, this paper proposes a double-layer plane air platform ground test system design. By using a high-precision acceleration sensor and gyroscope of the suspension test platform, the system coordinates the measurement of the double-layer air floating platform. The control mode and algorithm for double-layer vibration isolation in the suspension test platform are simulated through ground physics. Ground verification results from the double-layer plane air flotation test system show that the double-layer attitude control hardware of the suspension test platform meets the requirements, and the algorithm control effect is good, which provides sufficient experimental evidence for the normal operation of the suspension test system in orbit.
Numerical Simulation of Torque Variation During Paste Mixing and Kneading
Authors: Kang Zeqi , Tie Jun , Zhu Yan , Wang Zifeng , Li Guanlin
Published: August 2023

In the production process of aluminum electrolytic carbon anode, the kneading process is to fully mix asphalt and powder to obtain the best paste, because the kneading process is completed in a high-temperature kneading machine, there is no suitable automatic real-time judgment method for the uniformity of paste. Real-time online monitoring based on the viscoelastic change of paste during kneading is a potential method to judge the uniformity of paste. In this paper, the Discrete Element Method numerical simulation is used to study the variation law of stirring torque in the mixing and kneading wet mixing process, mainly examining the effect of viscosity on stirring torque. The results showed that the mixing and kneading torque varied periodically and the total torque of the stirrer was positively correlated with the viscosity.
Investigation on the Height of Fracture Zone in Goaf of Steep Coal Seam Based on Microseismic Monitoring
Authors: Yang Shouguo , Liu Haoxin , Huang Rumin , Li Jialin
Published: August 2023

The height of the fracture zone in the mined-out area is the basic parameter for controlling mine strata, preventing and controlling mine water, and arranging drilling holes for high-level gas extraction. Aiming at the development law of fracture zone in the steep seam and the limitation of traditional drilling inspection method, microseismic monitoring technology is used to investigate the height of fracture zone in goaf. Taking the 3103 south working face of a mine in Sichuan as an example, the scheme of microseismic monitoring and data collection of the working face is worked out. Through data analysis and processing, the range of the fracture zone in the goaf is 6.8 m– 34.2 m, and compared with the numerical simulation analysis results, the relevant results are basically consistent. It provides a new technical means for the investigation of “three zones” of overlying strata in the goaf of a steep seam.
Design of High-precision Water Quality Ammonia Nitrogen Online Detection Technology System Based on Micro-control Technology
Authors: Wang Tian , Wen Li , Ming-Gang Xu , Na He , Yong-Yue Ma
Published: August 2023

Based on Lambert’s theorem, this paper adopts the integration of micro-control technology and sequential injection technology and designs a water-quality ammonia nitrogen online detection system to improve the accuracy of ammonia nitrogen detection and to solve the demand for the determination of ammonia nitrogen index in the surface I to V water. The linearity of the assay data can be improved by controlling the variables in a control experiment, reducing the volume of the digestion cell, and reducing the inner diameter of the Teflon tube. Compared with the traditional ammonia nitrogen detection system, it has higher detection accuracy, and the number of reagents used and waste liquid generated have been greatly reduced, effectively improving the accuracy of ammonia nitrogen index detection. In this experiment, the determination of the ammonia nitrogen index was accomplished by salicylic acid spectrophotometry, and the range of determination covered the standard limits of ammonia nitrogen in a surface water environment from Class I to V water. The experimental results showed that: The regression model for the detection of ammonia index by the salicylic acid method was developed using the least squares fitting algorithm, and the linear range was from 0 to 2 mg/mL. The correlation coefficient of the experimental data for the 10 mm inner diameter tube was 0.9981 and that for the 15 mm inner diameter tube was 0.923. And the concentration and absorbance were both positively correlated, and the trend of the experimental data was consistent with the theoretical analysis. The system is designed to reduce operation and maintenance costs, improve detection accuracy, and efficiently analyze the ammonia nitrogen index in surface I-V water, providing technical support for environmental protection and surface water quality monitoring.
Fault Detection of Organic Heat Carrier Boilers Based on SeqGAN-CNN
Authors: Wang Yunhu , Zhang Guobao , Sun Tao , Zhang Yongchun , Huang Yongming , Liang Guoan
Published: August 2023

Organic heat carrier boilers are extensively used in industrial applications. However, the low accuracy of current fault detection methods, which primarily rely on statistical analysis and traditional machine learning methods, has become a significant challenge. This paper proposes a SeqGAN-CNN-based approach for detecting faults in organic heat carrier boilers. Firstly, SeqGAN is used to augment the dataset, solving the problem of limited fault sample data for training the CNN model. Then, the differential encoding method is utilized to transform one-dimensional monitoring data into two-dimensional grayscale images, resolving the issue that the CNN model’s input is generally multi-dimensional images while this study’s dataset is one-dimensional. Finally, the CNN model is trained and tested, and a simulation comparison is conducted with common industrial boiler fault detection methods. The experimental results demonstrate that the proposed method achieves higher accuracy, indicating that this method is accurate and effective for detecting faults in organic heat carrier boilers.
Design of an Intelligent Sensor System Based on Fluorescence Analysis
Authors: Li Wen , He Na , Wang Lei , Luo Xue-Ke , Tian Wang
Published: August 2023

In response to the current problems of serious pollution of typical organic substances in groundwater systems at petrochemical production sites, high cost of detection equipment, non-portability, low accuracy, and poor stability, a smart sensor for benzene and PAHs was proposed based on the fluorescence analysis method. The relationship curve between concentration and absorbance was derived using Lambert’s law from the different wavelengths and peaks of the absorbed light of different substances and was used to detect the concentration of organic substances. The experimental results showed that the online organic detection sensor based on fluorescence analysis had good stability for the detection of benzene and benzo(b)fluoranthene in groundwater systems at petrochemical production sites and could detect organic concentrations accurately and efficiently, providing data support for environmental protection and wastewater treatment. The method was low cost, simple in structure, high in detection accuracy, and good in interference immunity to meet analytical testing requirements.
Variational Auto-Encoder Reconstruction Networks for Classification of Hyperspectral and LiDAR Data
Authors: Zhai Ziming
Published: August 2023

The classification of remote sensing scene objects has been the subject of extensive studies in recent years due to the quick advancement of earth observation and remote sensing technology. More concentration, Hyperspectral images, and LiDAR data are complementary, and their combined use for data fusion can better mine the multi-dimensional features of ground objects in remote sensing scenes, which can effectively improve the classification accuracy and reliability of ground objects in remote sensing scenes. Single-modal remote sensing data frequently cannot fully meet the needs of ground feature classification due to the increasingly complex types of ground features. In order to solve this problem, we have developed two distinct multi-source fusion classification approaches using LiDAR and hyperspectral data and deep learning techniques. They are the reconstructed multi-layer perceptron network based on the variational auto-Encoder (encoding-decoding form) and the two-stream input convolutional neural network based on the cross-channel reconstruction mechanism. These two approaches can help us find more effective and deeper feature extraction methods and feature fusion methods in this research direction and design training. We adopted the network architecture model used in this area of research and used experimental data to demonstrate the effectiveness and superiority of the suggested network model.
The underwater obstacle avoidance method based on ROS
Authors: Zhang Jingguo , Lu Zongqing , Yu Chunliang , Shang Yingda , Chen Yuxiang
Published: August 2023

To achieve real-time obstacle avoidance for underwater robots, the artificial potential field method can be selected as the obstacle avoidance path planning algorithm. However, this method applies a large attractive force to the underwater robot when it is far from the target point, causing it to collide with obstacles. Additionally, when the underwater robot just enters the repulsion influence zone, the repulsion force that it experiences undergoes an abrupt change, resulting in a non-smooth obstacle avoidance path. Furthermore, developing the obstacle avoidance function based on software platforms such as MATLAB has a long algorithm verification cycle, and it is difficult to transfer the algorithm model to practical scenarios. Therefore, this paper proposes an underwater robot obstacle avoidance method based on ROS. Firstly, a dynamic adjustment factor is introduced into the potential field function of the artificial potential field method to redefine the obstacle influence zone, and an improved artificial potential field method is designed as the obstacle avoidance path planning algorithm for underwater robots. The experiment is conducted on the ROS simulation platform, and the results show that the improved algorithm can solve the two problems existing in the artificial potential field method. Secondly, nodes required for underwater robot obstacle avoidance are established based on the ROS operating system, and the improved artificial potential field method is integrated into the obstacle avoidance path planning node. Finally, the obstacle avoidance experiment of the Bluerov2 underwater robot is conducted in a pool, and the experimental results demonstrate that the proposed method can achieve real-time obstacle avoidance for underwater robots.
Design of Intelligent Voice Greenhouse System Based on STM32 MCU
Authors: Xie Yuhang
Published: August 2023

The traditional agricultural management mode no longer meets the needs of modern society, necessitating the development of modern agriculture. However, the limited education level in rural areas and the difficulty in popularizing complex IoT systems and internet platforms pose challenges to the development of intelligent agriculture. To address this, this paper proposes an intelligent voice greenhouse system based on a traditional STM32 single-chip microcomputer. The system utilizes various sensors to monitor environmental parameters and enable voice interactive control. This simple and practical approach promotes the use of smart agriculture, making it more accessible and easier to use. Physical testing has confirmed the effectiveness of this method, which will contribute to the development of intelligent agriculture in the future.
A Transformer-Based Model for Stratospheric Wind Field Prediction
Authors: Xu Yuhua , Jiao Xun , Kong Weisi
Published: August 2023

The stratospheric wind field environment is a key influence on the flight control of stratospheric aerostat, which is a popular field of research in recent years. In this paper, a Transformer-based stratospheric wind field prediction model is proposed for the construction of stratospheric wind field model and prediction, and the feasibility of the model is verified on a 5-year historical data set of the wind field in a place, then the parameters of the model are tuned by ablation studies, and finally, the Transformer prediction model is compared with GRU, LSTM and BP prediction models. The experimental results have shown that the proposed Transformer stratospheric wind prediction model can fully extract global features and its prediction accuracy is significantly higher than those of other prediction models.
Research on the New Energy Acceptance Capacity of Electric Networks Based on Random Optimization
Authors: Lu Mingyang , Jing Lantao , Ye Peng , Zhang Qiang , Wang Chao , Li Xinwei
Published: August 2023

According to the randomness of new energy power generation and power load in the power network, a new energy acceptance analysis method of power networks based on stochastic optimization is proposed. The algorithms for optimization based on the randomized simulation techniques is used, and the influence of the confidence level of different power network constraints and the combination of different new energy power generation access nodes is verified in IEEE-14. The results indicate that the model can fully consider the real-time operational fluctuations of new energy generation and power load in the electric network, and the solution algorithm is simple and easy to operate with fast calculation speed and small errors.
Improvement of knowledge tracing by integrating exercise features and relationships of concepts
Authors: Chen Lige , Liu Xianzhong
Published: August 2023

Knowledge Tracing (KT) is a challenging task in personalized learning where the objective is to track the progression of students’ understanding of the concepts over time based on their learning history. Typically, there are many more exercises than knowledge points. Tracing the state of the concepts seems a reasonable approach but ignores the additional information contained in the exercise itself. Moreover, there are numerous relationships between knowledge points, and the modification of one point’s state can affect related points. In this paper, we introduce a method that splits exercises into constituent knowledge points, and incorporates the exercise features to improve the differential representation of the exercises containing the same concepts. We also describe two ways of propagating knowledge point states: one-way and two-way, which update the state of the current knowledge point and its related knowledge points. Our model is tested on two real datasets, and the experimental results show that our model outperforms the existing methods.
Small-sample Engine Fault Diagnosis Method Based on IACGAN and DNNs
Authors: Tang Daijie , Bi Fengrong , Huang Meng , Shen Pengfei , Yang Xiao , Guo Mingzhi , Bi Xiaoyang
Published: August 2023

The reliability of engines, particularly aero engines, has become increasingly important in recent years. Accurate fault diagnosis can prevent accidents and minimize property damage. Deep neural network methods (DNNs) are commonly used for fault diagnosis, but their performance relies heavily on large amounts of high-quality training data. Unfortunately, obtaining high-quality engine fault data is challenging in practice. To address this problem, this paper proposes an improved auxiliary classifier generative adversarial network (IACGAN) that incorporates Wasserstein distance and a gradient penalty term. Meanwhile, a variable learning rate is also proposed to accelerate the model convergence. This approach effectively mitigates the problem of model gradient disappearance and expands one-dimensional time-series data. The proposed method was verified on a small aero-engine through a failure simulation test. The results show that the accuracy of DNN can be significantly raised by data enhancement of IACGAN, especially in the case of a limited number of samples. Therefore, this method shows promise as an auxiliary tool for DNN-based fault diagnosis.
Research on Performance Control of Intercalated Melt-blown Nonwoven Materials
Authors: Chen Enjun
Published: August 2023

This paper focuses on enhancing the performance of intercalated melt-blown nonwoven materials through performance control. The study explores whether the structural variables (thickness, porosity, and compression resilience ratio) and performance indicators (filtration resistance and efficiency) of intercalated products undergo significant changes, using analysis of variance (ANOVA). The experimental results indicate that intercalation significantly alters the product’s thickness, porosity, compression resilience ratio, and filtration efficiency. The study also establishes a decision tree regression model between process parameters (acceptable distance and hot air velocity) and structural variables, and a K-nearest neighbor regression model between structural variables and product performance indicators to investigate the relationship between process parameters and product performance during intercalation. Finally, the study determines the optimal performance indicators by controlling process parameters using the simulated annealing algorithm.
Kinematics Analysis and Modelling Based on 4 DOF Industrial Robot
Authors: Wei Yuli
Published: August 2023

With the rapid progress of science and technology, industrial manufacturing and processing technology have also developed rapidly. Industrial robots are widely used in many industrial fields. In this paper, based on the familiarity with the structure and principle of the 4 DOF industrial robot, combined with the virtual simulation platform, the kinematics analysis, modelling, and simulation of the 4 DOF industrial robot are studied. Firstly, the structure of the 4 DOF industrial robot is analyzed, the coordinate transformation is studied, and the coordinate transformation simulation is designed. Secondly, D-H parameter modeling and modelling simulation are achievements. Finally, the kinematic analysis and modeling are completed, and some research results are obtained.
Design of Hybrid Power Chassis System Based on Fuzzy Control
Authors: Luo Haocai , Zhang Changming
Published: August 2023

For wheeled robots working in environments such as sand and slopes, due to the many ups and downs in the environment, they often encounter working states such as acceleration and climbing. This often requires its power supply to provide instantaneous high power, resulting in a large current discharge of the power supply, which seriously affects its service life. To solve this problem, this paper will use a composite power supply composed of supercapacitors and batteries to supply power to the robot chassis and use fuzzy control to perform closed-loop regulation on the output power of the batteries.
Absorption and Crystallization of Carbon Dioxide Absorbed by Sodium Carbonate Solution at High Temperature
Authors: Zheng Mingzhen , Wang Yixiao , Fu Dong
Published: August 2023

The carbon dioxide produced by desulfurization in the metallurgical industry increases the carbon content in tail gas. However, the product of the decarbonization process can remove sulfur dioxide from tail gas. In this paper, the absorption and crystallization amounts of sodium carbonate solution reacting with carbon dioxide at high temperatures are explored through experiments, and the optimum concentration of sodium carbonate solution for absorbing carbon dioxide is determined. The purity of crystals is verified by analyzing the crystallization quality, absorption amount, and absorption curve.
Research on kinematic characteristics of spatial crank rocker mechanism based on MATLAB
Authors: Li Guoyu , Yang Yongxing , Mao Zijie , Chen Mengxue , Lu Xueyu
Published: August 2023

Many students trained in carbon-free trolley engineering to design smoothly but found that it was difficult to achieve the ideal results with a lot of time and effort in trajectory debugging. To deeply explore the characteristics of space crank rocker, through repeated experiments and theoretical analysis of carbon-free trolleys, and using MATLAB, SolidWorks, SPSS, and other software analysis, combined with projection geometric analysis to derive the equations of motion and derive the displacement, velocity, and acceleration characteristic curves. The conditions for the existence of the space crank rocker mechanism with an interlaced angle of 90° are derived, and the relationship between corner amplitude and main structural parameters is obtained. The size of the influence factor of each structural parameter was compared. In the commissioning, according to the characteristics of these parameters, the commissioning cycle of the carbon-free trolley can be greatly shortened.
An Energy Consumption Optimization Method for Intelligent Transfer Robots in a Low-temperature Environment
Authors: Guo Sicheng , Fan Shaosheng
Published: August 2023

The functional design and performance parameters of existing intelligent transfer robots (ITRs) have met the operational requirements for most scenarios at present. However, under the operation condition of low temperatures, the battery discharge capacity of ITRs will significantly reduce, resulting in a decrease in the efficiency of ITRs. Aiming at this problem, this paper proposes an energy consumption optimization method for intelligent transfer robots in a low-temperature environment, which can make the battery discharge capacity meet the best performance by adjusting the motion parameters of ITRs. Firstly, the optimal output current is obtained by analyzing the output characteristics of the battery in a low-temperature environment, and it is measured that the energy consumption of ITRs mainly depends on the energy used in the moving process. Then, it is proved that the moving speed of the ITR has the greatest influence on the output current of the battery based on comparative experiments. Next, the LSTM prediction model and fuzzy PID control are used to regulate the moving speed of the ITR, so that the output current of the robot battery can meet the optimal output performance in a low-temperature environment. Finally, extensive comparative experiments show that the energy consumption optimization method proposed in this paper can effectively improve the discharge capacity of robot batteries in a low-temperature environment.
Topology Optimization and Analysis of the Compound Boring and Milling Machining Center TX1600g Bed Structure
Authors: Yu Chengzhi , Shu Qilin
Published: August 2023

The overall performance of the machine tool bed of the boring and milling center has an important impact on the accuracy of the overall machining, in order to improve the machining accuracy and structural performance of the overall machine tool, the structure optimization design of the machine bed is carried out for the TX1600g composite boring and milling center. The maximum static displacement and mass of the bed were used as the evaluation index to optimize the topology and structure of the bed. An orthogonal test of 5 factors and 5 levels was designed, the maximum displacement generated by different positions in work was analyzed, the frequency under this working condition was explored, and modal analysis was conducted. The Hyperwork selective variable density method is used to optimize the beam structure with multiple objective topologies. By comparing and optimizing the performance parameters of the front and rear beams, the total static displacement is reduced by 9%, which proves the effectiveness of this method to solve the multi-objective optimization problem.
Improved BP neural network algorithm to SOC estimation of mine power supply
Authors: Dai Jianbo , Zhang Jinhao , Lin Yin , Ren Gaojian
Published: August 2023

Since Ni MH battery has the characteristics of high energy density and good stability, it is mainly used as a backup power supply for mining at present, but the accuracy of estimation the SOC of the power supply is poor. To enhance the precision of estimation and extend the lifespan of the battery, based on a single nickel-hydrogen battery, this paper applies a new BP neural network to rise the self-adaption and momentum terms. At the same time, the discharged power is included in the Input feature quantity of the estimation network. The temperature, current and terminal voltage are also included in the input characteristic quantity of the estimation network. There is output during training, and then the momentum term is adjusted based on the Variance of variance error by comparing the actual output. The error of the estimated network should be back propagated from the output to the input to correct the weight. The direction of change in the error determines the adaptive learning rate of the improved BP neural network. The speed of the algorithm is increased by 72% and the error rate is less than 5%, compared to the traditional BP neural network algorithm.
Presence of Antibiotic Resistance Genes in Bacteria Isolated from Raw Cow Milk obtained from Bowen University Dairy Farm.
Authors: Owoseni Abimbola A. , Adigun Taiwo O. , Asogbon Olajumoke H. , Atobatele Bukola O. , Adeleke Olufeyikemi A. , Nejo Yewande T.
Published: August 2023

Antibiotic resistance is becoming more prevalent thus posing a severe threat to global health. Bacteria were obtained from raw milk produced on the Bowen University campus using conventional methods. Morphological and biochemical testing were used to carry out phenotypic identification process. Six widely used broad-spectrum antibiotics were used in tests to determine their sensitivity. Bacteria resistant to four or fewer antibiotics were chosen for the amplification of antibiotic-resistance genes while using the 16S rRNA technique for sequencing. Eighty-one isolates were obtained, and the sensitivity patterns revealed 81.8% resistance to cefuroxime and 98.1% resistance to cefixime and amoxicillin/clavulanate (augmentin). Twelve out of the multidrug-resistant isolates were sequenced and they revealed 100% resistance to cefixime and amoxicillin/clavulanate (augmentin), 80% to cefuroxime, and 53.3% and 6.7% to gentamicin and ciprofloxacin respectively. Only ofloxacin showed a sensitivity of 100 percent. The sequenced isolates were identified as Pseudocitrobacter faecalis (-), Escherichia coli (-), Bacillus tropicus (+), Citrobacter koseri (-), Providencia burhodogranariea (-), Enterobacter hormaechei subsp. Xiangfangensis (-), Bacillus pumilus (+), Lysinibacillus boronitolerans (+), Klebsiella aerogenes (-), Klebsiella pneumoniae subsp. rhinoscleromatis (-) 2 isolates, and Acinetobacter baumannii. The TEM, SHV-1 and CTX-M genes were detected in the Citrobacter koseri isolate, while the TEM and SHV-1 genes were also found in the Escherichia coli isolate. The high rate of resistance in bacteria observed is alarming. Adequate milk storage procedures and antibiotic usage in dairy cattle farms must be regulated for safety. It is recommended that antibiotics should not be used in farm animals unless when highly necessary.
Molecular Characterization of Ixodid Ticks Infesting Cattle
Authors: Oladosu O O , Oyeku O G , Olatubi I V , Ayokunle D I , Okotie S O , Jibiri T M , Atobatele O E
Published: August 2023

There is great diversity of ticks with only three genera; Amblyomma, Hyalomma, and Rhipicephalus known to have highest impact on livestock health. Ticks suck blood and release toxic secretions from saliva through their mouth appendage into cattle resulting in anaemia, skin damage, inflammation, paralysis, reduced weight gain, and reduction in milk production. Proper tick identification is imperative to understand tick-borne pathogens as well as determine control measures. Molecular approach has made the identification of tick species easy. This study therefore used molecular tools to determine the identity of ticks collected from cattle in Iwo Osun state, Southwest Nigeria. Cross-sectional study of ticks was done in August 2022. Different species of ticks (n = 268) were collected from cows. The samples were collected in sterile tubes and stored in 70% ethanol before DNA extraction, amplification and sequencing. Ticks were morphologically identified using available keys. Amblyomma, n=170 (63.4%), Hyalomma, n=61 (22.8%) and Rhipicephalus, n=37 (13.8%) were identified. Molecular and phylogenetic analysis of the two morphotypes whose cytochrome c oxidase subunit 1 (COI) gene amplified revealed that they were Amblyomma variegatum and Rhipicephalus microplus. This study characterized Amblyomma variegatum and Rhipicephalus microplus ticks by sequencing the COI gene and it expands the knowledge of tick parasitofauna on cows in Iwo Osun state, Southwest Nigeria.
Factors influencing milk production among dairy farmers in Southwest, Nigeria
Authors: Komolafe P O , Ayandiji A
Published: August 2023

It is estimated that herds of cattle worth $18.2 million are reared and 82% of these animals are raised in an extensive system but Nigeria spent about $1.2 billion on milk importation, this indicates that the nation is still far from sustainability. The study examines the management practices of dairy farmers, sources of information, challenges, and factors that influence milk production in the study area. The study area covered Southwest, Nigeria. A multi-stage sampling technique was used to select 293 respondents. Data were analyzed using descriptive and influential statistics. The result shows that the majority of the respondents were between 51-60 years, married, with no formal education, and had 21-30 years of farming experience. The majority produced an average of 11-20 liters of milk per day, radio was their main source of information, challenges faced by dairy farmers were the health of the animal, climate change, and breed of the animals. The factor influencing the farmers’ decision is the milking of animals (P>0.032). It is concluded that dairy production in the study area is not thriving as expected due to the above challenges. Therefore, dairy farmers need more orientation, government support, and training on the intensive management system.
Efficacy of water extracts of mushrooms on multidrug-resistant bacteria isolated from raw cow milk
Authors: Owoseni Abimbola A , Adeleke Olufeyikemi A , Nejo Yewande T
Published: August 2023

The high rate of antibiotic-resistant bacteria observed in food samples has been alarming. The majority of the antibiotics now used in medicine have become ineffective against groups of bacteria. The use of bioactive substances from natural products against antibiotic-resistant bacteria is becoming attractive. Mushrooms are natural reservoirs of potent pharmaceuticals and are now the new interface for drug discovery. Aqueous extracts of five medicinal mushrooms namely Agaricus sp., Pleurotus pulmonarius, Pleurotus oestratus., Lentinus sp. and Ganoderma were prepared. Bacteria were isolated from raw cow milk using standard techniques and antibiotic sensitivity testing was carried out using 6 antibiotics. The efficacy of the mushroom extract was tested on 12 MAR bacteria using the disc diffusion method. It was observed that the aqueous extracts from the two Pleurotus spp were very active in killing the bacteria. Pleurotus oestratus was active against 5 isolates while Pleurotus pulmonarius was active against 3 isolates. Agaricus and Lentinus extracts were active against 4 isolates each while Ganoderma was active against only 3. Bacillus tropicus, Enterobacter hormaechei and Klebsiella pneumoniae isolates were totally resistant to the effects of the extracts. This study shows the effectiveness of mushroom aqueous extracts. This may be used to reduce bacterial load in food especially milk so as to prolong its shelf life in the natural state.
Effects of combined nitrogen-nutrient sources on lowland rice straw yield (a potential dairy feed) in a derived savannah ecology
Authors: Iyanda Olumayowa Joseph , Oyekanmi Akeem Abdullahi , Atayese Mufutau Olaoye , Adejuyigbe Christopher , Bamigboye Funmilayo
Published: August 2023

Low rice yields in farmers’ fields arise from inadequate nitrogen in most Nigerian soils. Fertilizer use has increased, particularly with the implementation of COVID-19. Straw is a by-product of cereal plants, consisting of dry stalks left over after the grain and chaff. It accounts for cereal crop yields, such as barley, oats, rice, rye, and wheat. The effects of greenhouse gas emissions from burning straw on the climate have been a cause for alarm. As a result, this study in Abeokuta, Southwest Nigeria, investigated the effects of six levels of integrated nutrient sources on NERICA L-34 and ARICA 3 through the analysis of variance in straw yield data. The experiment used a split-plot design, complete block randomization, and treatment arrangements in triplicate. At harvest, ARICA 3 produced significantly more straw (p = 0.05) (14.04 t/ha) than NERICA L-34 (10.44 t/ha) by combining 50 kg N/ha (NPK 15:15:15) with 50 kg N/ha (poultry manure) (10.12 t/ha). The study improved the straw yield (11.4 t ha−1) of ARICA 3 and NERICA L-34 by 50 kg N/ha (NPK 15-15-15) and 50 kg Nha−1 (poultry manure); thus, this straw could be used as dairy feed.
Study of the technical and financial aspects of traditional fads for the bajo tribe, Torokeku Village
Authors: Fajriah  , Nurdin I N , Isamu K T , Suwarjoyowirayatno  , Sari S F
Published: August 2023

Effective and efficient fishing is very much needed in capture fisheries business. FADs are tools that are widely used by fishermen to fulfill this. This study aims to describe the technical aspects and analyze the financial aspects of the traditional Bajo FAD business in Torokeku Village, South Konawe Regency, Southeast Sulawesi. The research method uses case studies with data collection techniques, namely direct measurements, following FAD installation activities on the seabed, following fishing operations, and interviewing fishermen. The results showed that technically the traditional FAD construction consisted of a FAD body consisting of 100 sticks of forest wood tied using a rope made of polyethylene and 50 stalks of coconut leaf attractor. The results of financial calculations in 1 year show that the investment cost is Rp. 10,000,000; fixed cost Rp. 3,100,000; variable fee Rp. 2,400,000; total cost Rp. 5,500,000; an average production value of Rp.60,000,000; profit Rp. 54,500,000; BCR 9.9; BEP sales Rp.3.229.167; the average profitability is 9.9% and the payback period is 0.18 (1 month 2 weeks). The results show that the traditional Bajo FADs are profitable. The profit-sharing system applied is 60% for fishing gear owners and 40% for FAD owners at each fishing operation.
Identification and productivity of lobster catching (Panulirus spp) in Aceh Jaya
Authors: Akmal Y , Irfannur I , Muliari M , Yunus M , Plumeriastuti H , Dhamayanti Y , Batubara A S
Published: August 2023

Lobster is a leading commodity in Aceh Province and it is one of the commodities with a high economic price. Besides, it is believed that Aceh Jaya Regency has superior fisheries commodities. This study aims to describe the composition of the catch, the relationship between the length and the weight and the productivity of the catch on six different types of lobster in Aceh Jaya. The study was conducted from June to August 2022 in the waters of Aceh Jaya, Setia Bakti District, Lhok Rigaih Village. The study focuses on the catch composition, length-weight relationship, and the measurement of the catch productivity. The types of samples taken were six species including Panulirus homarus, P. longipes, P. penicillatus, P. versicolor, P. ornatus and P. polyphagus. The composition of the total lobster catch was dominated by Panulirus penicillatus 23.94%, followed by P. versicolor 19.71%, P. homarus 18.30%, P. longipes 18.30%, P. ornatus 18.30%, and P. polyphagus 1.40%. The composition of the weight of the catch was dominated by Panulirus versicolor 23.20%, followed by P. longipes 22.79%, P. homarus 19.41%, P. penicillatus 17.42%, P. ornatus 17.02%, and P. polyphagus as 0.13%. The relationship between the length and the weight of these six types of lobster catch were allometric negatives. The productivity of lobster catch using gillnet fishing gear averaged 226.66 kg in April 2021, 293.51 kg in June, and 141.53 kg in December. The catch in January 2022 was 437.26 kg, meanwhile in February was 77.87 kg, followed by April 358.85 kg.
Application of avian pituitary gland extracts in semi artificial induced breeding of climbing perch Anabas testudineus
Authors: Maulida S , Rahayu S , Eriani K , Fadli N , Maharani M
Published: August 2023

The objective of the present study was to explore the potential of avian hypophysis to induce the breeding of climbing perch. The pituitary glandof local chicken and duck and its combination was tested at the same dose of 0.5 ml/kg body weight (BW) of broodfish. Therefore, the tested treatments as follows; treatment A = 0.5 ml/kg BW of Ovaprim (control; Treatment B= 0.5 ml/kg BW of chicken pituitary extract; Treatment C= 0.5 ml/kg BW of duck pituitary extract; Treatment D= 0.25 ml/kg BW of chicken pituitary extract + 0.25 ml/kg BW of duck pituitary extract. The ANOVA test showed that treatment of avian pituitary extracts had a significant effect on the latency period, the total number of ovulatory eggs, fertility, hatchability, and survival of climbing perch larvae (P<0.05). The highest number of ovulated eggs was obtained in treatment B, this value was significantly different from treatments C and D. The highest fertility and survival were also obtained in the treatment B, but were not significantly different from the treatment C. Meanwhile, the highest hatchability was also obtained in treatment B and was significantly different from other treatments.
Density of baree (Lingula anatina Lamarck, 1801) in Ulee Lheue beach, Indonesia
Authors: Daulay R E , Octavina C , Qalby P D
Published: August 2023

Lingula anatina or baree as a living fossil has an important role for coastal ecosystems where its presence and density are rarely recorded in Aceh. The density of L. anatina was carried out in the beach of Ulee Lheue, Meuraxa District, Banda Aceh City. The purpose of this study is to provide information about the existence of L. anatina to the public and the government to add to the literature review for similar research. Sampling was carried out using a 1m × 1m quadratic transect at each location where the L. anatina biota was found. The results of this study revealed that the density of L. anatina in Ulee Lheue beach was lower than the density in Ujong Pancu Beach. The density in Ulee Lheue beach is 2.5 ind/m2 while the density of L. anatina in Ujong Pancu Beach is 2.89 ind/m2.
Condition of Chaetodontidae fish based on living coral cover in Aceh Jaya Waters, Aceh Province
Authors: Ulfah M , Al Khumairi U K , Fazillah M R , Mustika V S , Azzahara F , Turnip I N , Yulizar  , Fadli N
Published: August 2023

Chaetodontidae is one of the fish families whose existence is important in coral reef ecosystems. This study aims to determine the condition of the Chaetodontidae fish and coral cover in the coral reef ecosystem area of Aceh Jaya District, Aceh Province. The study was conducted in March 2017 with 10 points of observasion location. Data retrieval is done using the visual census technique (VCT)-belt transect method to see the abundance of Chaetodontidae fish in the Point Intercept Transect (PIT) method to see coral cover. The result showed that the abundance of Chaetodontidae fish in the waters Aceh Jaya regency ranged from 187.5 to 5375 ind/ha. There ware 12 species of Chaetodontidae fish found, with the most species being Chaetodon Vagabundus, overall coral cover in the waters of Aceh Jaya regency ranged from 16.25 to 76.88% which was classified in the category of damaged to very good coral reef conditions. The results of the correlation analysis showed that the percentage of live cover had a weak and insignificant relationship with the abundance of Chaetodontidae fish. Not all locations have coral cover followed by high abundance of Chaetodontidae fish.