bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-branch Context Awareness Network for Prostate MRI Segmentation
Authors: Sang Zijiang , Shao Yeqin , Xu Changyan 
Published: August 2023

Abstract
Prostate image segmentation is a precondition for the diagnosis of prostate diseases and subsequent treatment. However, the blurry organ edges and low image contrast make accurate segmentation difficult. In order to overcome the difficulties, this paper proposes an innovative Multi-branch Context Awareness Network (MBCA-Net) for prostate MRI segmentation. MBCA-Net uses the 3D UNet as the backbone network with an encoder-decoder framework. To improve the feature extraction capacity of the network, a multi-branch residual module with different convolution kernels is proposed. To better make use of extracted features and reduce semantic ambiguity, a context awareness module is used to fuse low-level features in the encoder and high-level features in the decoder in a full process manner. To improve the stability of the model during training, a mixed loss function is adopted. Experiments on the public dataset PROMISE12 show that our model’s Dice coefficient is 90.19%. Compared with other advanced techniques, the proposed model has better segmentation results.
Image stitching method based on feature optimization and circular function weighted fusion
Authors: Hou Yufang , Jiang Pinqun , Xia Haiying , Song Shuxiang , Luo Jiarong 
Published: August 2023

Abstract
To address the issues of ghosting and chromatic gaps in parallax image stitching, this paper proposes a novel method based on feature optimization and circular function weighted fusion. The method employs the Scale-Invariant Feature Transform (SIFT) algorithm with dimensionality reduction optimization to extract point features, while the Grid-based Motion Statistics (GMS) algorithm is utilized to eliminate mismatched points. To further enhance the number of features, line features are also introduced. A local warping model is employed to guide the deformation of the grid image. Finally, a nonlinear fading-in and fading-out fusion model with circular function weighting is proposed for better image transition. Experimental results demonstrate that the proposed method is effective in dealing with ghosting and chromatic gaps compared to several existing methods.
Short-term PV Power Prediction Based on SAGA-FCM and XGBoost
Authors: Zhong Min , Zhou Yucai 
Published: August 2023

Abstract
The stable and economic operation of the power grid cannot be achieved without accurate prediction of PV plant power. To address the problem of unstable clustering quality of the fuzzy c-mean clustering algorithm, this paper proposes to combine the genetic algorithm and simulated annealing algorithm to optimize the FCM clustering algorithm, and then combine it with XGBoost to construct a short-term PV power generation prediction model. Firstly, the robustness of the clustering algorithm is improved. Secondly, the differences in power generation characteristics under different weather types are considered, and the prediction models under three typical weather types are separately trained. Finally, to verify the improvement of the model on prediction accuracy, different clustering methods are used to compare the prediction results, and the results show that the algorithm proposed in this paper has higher accuracy compared with the other three methods.
A Novel PCB Defect Detection Method Based on Digital Image Processing
Authors: Zhang Guangxin , Cao Yu 
Published: August 2023

Abstract
Printed circuit board (PCB) has a wide range of applications, and its automated quality inspection is a vital part of economic production. The time overhead of the current PCB defect detection method will increase with the increase in the number of detection targets. To ensure the stability of detection performance, this paper proposes a novel PCB detection method based on color threshold segmentation. Experiments show that this method can effectively detect and analyse various information about solder pads and solder pastes, among which the detection accuracy rate of solder pads is 99.4%, the missed detection rate is 0.4%, and the detection accuracy rate of solder paste is 99.3%, The missed detection rate is 0.03%. At the same time, the method in this paper has better robustness and stability than other PCB defect detection methods, and can better meet the needs of industrial production.
Design of a Wind Power Forecasting System Based on Deep Learning
Authors: Sun Qitao , Duan Lianda , Liang Hao , Zhao Chaofan , Lu Nana 
Published: August 2023

Abstract
In recent years, wind energy, as a ubiquitous, easy-to-capture cost-effective clean energy, is accounting for a sharp increase in the installed capacity of China’s new energy grid. However, its stochastic volatility has always brought challenges to the generation scheduling of wind farms. To better optimize the energy management level of wind farms and improve the stability of wind power grid connection, this paper proposes a design of a wind power forecasting system based on deep learning. Our system architecture mainly includes data pre-processing, power prediction, and data application modules. The data pre-processing module will correct the numerical weather forecast (NWP) updated twice daily to get the wind turbine generator (WTG) hub height weather forecast and resample the SCADA data in a 15-minute time resolution. The power prediction module will periodically perform the ultra-short-term and short-term forecasting results and record them in the database. On one hand, the data application module will present the power prediction results and SCADA data to the web page for display. On the other hand, it will provide data to the energy dispatching department for reference. The experiment shows the effect of using a residual channel attention network (RCAN) to correct the NWP and the influence of two different RNN cores, including GRU and LSTM, on the prediction of ultra-short-term wind power results of the DeepAR model. The experimental results show that the proposed RCAN can effectively correct the results of the numerical weather forecast to a single WTG, and the DeepAR model using the GRU core can achieve better performance in the test set than the DeepAR model using the LSTM core. Thus, we choose GRU as the core of the model of our power prediction module.
A Generative Design Method for Typical Structural Parts of Aircraft
Authors: Chu Tao , Pei Yang , Cheng Puqiang 
Published: August 2023

Abstract
Typical structural parts of aircraft are the main frame of the aircraft and the carrier of other parts and equipment. Their good design is the basis to ensure the overall performance of aircraft and reduce maintenance costs. If the selection estimation, parameter setting, and model normative inspection of typical structural parts are carried out manually, the design quality and efficiency of large quantities of structural parts will be low especially. In this paper, a generative design method for typical structural parts of aircraft is proposed. Based on the introduction of the design framework, the intelligent design of 3D models for typical structural parts of aircraft is elaborated by taking shear slices of aircraft as an example. It includes the intelligent recommendation of the structural scheme, intelligent design of structural parameters, and intelligent generation of the 3D model. Engineering implementation shows that the proposed method greatly improves the design efficiency and the overall quality of the aircraft.
Automatic Cooperative Robot Intelligent Production Line
Authors: Feng Wenxuan , Xiao Junxiang , Sun Huajie 
Published: August 2023

Abstract
Due to the diversification of consumer demand, “fast iteration, less batch, and flexible delivery” has become an inevitable choice for enterprises to have market competitiveness. In order to survive in the market competition, it is necessary to transform the traditional manufacturing line. This paper mainly puts forward and introduces a set of reconstruction schemes. The revamp of the production line will incorporate new collaborative robots. The TM5-900 robot produced by Daming was used in the scheme. Compared with the artificial situation, the man-machine cooperative production line improves the production efficiency of products, improves the balance rate, and reduces the smoothness index, which makes resource allocation more reasonable and production more stable. In addition, the man-machine cooperative scheme can realize the rapid change of product types to meet customer needs. The robot follows the operating frequency of humans so that humans can achieve the best comfort in the operation.
Research on the Control Method of Anti-vibration Hammer Fastening Robot Operation for High-voltage Transmission Lines
Authors: Wu Wenjie , Wang Xuhong 
Published: August 2023

Abstract
The problem of low efficiency and high danger is addressed by mainly relying on the manual tightening of the anti-vibration hammer. A transmission line anti-vibration hammer fastening robot is designed, and its operation control method is focused on the study. Firstly, we use the visual YOLOV-5 algorithm to detect the anti-vibration hammer in real time to achieve rough positioning and then cooperate with the depth camera to obtain the accurate 3D coordinates of the anti-vibration hammer bolts. After obtaining the position of the anti-vibration hammer bolt, a fuzzy PID controller is used to control the robot’s operation motion to ensure the stability and accuracy of the operation. Finally, the tightening experiment of the anti-vibration hammer under the transmission line verifies that the proposed operation control method can meet the requirements of efficiency and reliable tightening of the anti-vibration hammer.
Numerical Simulation of Atomization Characteristics of Perfluorohexanone under Low-Pressure Environment
Authors: Wang Zhiyuan , He Yuanhua , Huang Jiang 
Published: August 2023

Abstract
Since perfluorohexanone can play a role in cooling and extinguishing the heat loss of lithium batteries, it is urgent to study the atomization characteristics of perfluorohexanone in different ambient pressures (49 kPa, 75 kPa, 101 kPa) to ensure the flight safety of all-electric aircraft in the future and the storage and use the safety of lithium-ion battery in high plateau areas. The process of the primary and secondary breakup was studied by using the VOF-DPM model, and the influence of different environmental pressures on the development of atomization and its atomization characteristics was analyzed. The results showed that the primary breakup of the perfluorohexanone liquid sheet was caused by Kelvin Helmholtz/Rayleigh-Taylor (KH-RT), while the secondary breakup was mainly caused by RT instability. Under different environmental pressures, the Sauter Mean Diameter of (D32) along the spray axis decreases sharply at first and then increases slowly. As the air density decreases with the decrease of air pressure, D32 gradually increases, resulting in the decrease of atomization mass. Moreover, when the atomization drops from 101 kPa to 49 kPa, D32 of droplets increases by 38% to 51%, which is conducive to providing evidence for the effectiveness of perfluorohexanone in fire extinguishing under low-pressure conditions.
Mathematical Modelling of Lumpy Skin Disease in Dairy Cow
Authors: Falowo O D , Owolabi J A , Oludoun O Y , Akingbade R 
Published: August 2023

Abstract
Lumpy skin disease (LSD) is a threatnening disease caused by virus that affects large ruminant animals mainly cattle. LSD is native in many African and Asian countries, having existed for over one hundred years in Africa where it originally started and has recently expanded to the Middle East region. A disease model consisting of six-dimensional system of ordinary differential equations was formulated to properly understand the spread and control of LSD. The classes considered includes the susceptible cow, vaccinated cow, infectious cow, recovered cow, susceptible mosquito and infectious mosquito. This paper captures two modes of transmission of the disease, direct contact and contact through mosquito bites. Inclusion of vaccination parameter makes this model different from existing literature. We show the model is well-posed through positivity and boundedness of the state variables. The basic reproduction number R0 of the model was derived and the disease-free equilibrium points was shown to be locally asymptotically stable when R0 < 1. Sensitivity analysis to identify the most sensitive parameters was conducted and the vaccination parameter was varied using matplotlib module in Python to check the effect of vaccination on both vaccinated and infectious cow. Increase in the vaccination parameter cause the number of vaccinated cow to increase and hence leads to reduction in the number of infected cow.
Transmission Analysis of Salmonella Dublin of Diary Calves
Authors: Oludoun O Y , Adeniyi M O , Ogunlaran O M , Akinola E I , Abiodun O E 
Published: August 2023

Abstract
Salmonellosis is an extremely contagious disease caused by bacteria of the genus Salmonella which affects both humans and animals. Salmonella is a cause for concern in the cattle business because it is a zoonotic disease that can cause serious invasive infections in people and besides, infected herds suffer economic and welfare losses. Consequently, a SIRB (Susceptible - Infected - Recovered-Bacteria) model is being constructed to study the transmission process of the Salmonella Dublin disease in a diary calf. The associated basic reproduction number and stability theory are used to investigate the stability analysis of the model equilibrium points. Numerical simulations was carried out to investigate the effect of the sensitive parameters on the basic reproduction number R
0. Also, the impact of environmental bacteria on Salmonella transmisssion dynamics and the eradication threshold was shown to help reduce the spread of the disease.
Effect of selected plant extracts on phytochemical, antioxidant, physicochemical and sensory properties of yoghurt
Authors: Makinde F M , Afolabi M O , Ajayi O A , Olatunji O M , Eze E E 
Published: August 2023

Abstract
Health conscious consumers are investing in food products containing functional ingredients. The study investigates the effect of addition of selected plant extracts on phytochemical, antioxidant activity, physicochemical and consumer acceptability of yoghurt. Yoghurts were produced from 70% raw cow milk and 30% skimmed milk powder with 10% (v/v) of Cymbopogon citratus leaf, Carica papaya leaf, Sorghum bicolour stalk or Ocimum gratisimum infusion added before the fermentation stage. The yoghurt sample without the infusion served as the control. The yoghurt samples were analysed 1 day after production for phytochemical and antioxidant properties and after 1, 7, 14 and 21 days of refrigeration at 4°C for titratable acidity, pH, syneresis, colour and sensorial quality using standard methods. Total polyphenol content ranged from 0.04 to 0.12 mg GAE/100mL and alkaloid content ranged from 1.25% to 3.36% with the least value reported in the control sample, respectively. Similarly, antioxidant activity ranged from 13.88% to 45.74% with the highest value in yoghurt with the pawpaw leaf extract. The addition of various plant extracts resulted in lower pH, increased syneresis but no effect on the titratable acidity during storage period. The colour attributes (L*, a*, b*) showed significant differences (p≤0.05) among the samples over the storage period. Considering overall consumer acceptability, the control sample was most preferred. Functional milk product has potential to expand the application of endangered species of plant food material in the dairy industry.
Temperature Forecasting for Iwo City, Nigeria Using Statistical Models and Selected Machine Learning Algorithms
Authors: Aweda Francis O. , Adeola Solomon O. , Adeniji Adetunji A. , Adebayo Segun , Akinpelu Jacob A. , Jegede Olusanya O. 
Published: August 2023

Abstract
Time series modeling and forecasting using a machine learning algorithm approach for Iwo City, a western city in Nigeria, is a technique used to forecast the future occurrence of diverse data for the study. The purpose of this study is to use time series models and some machine learning approaches to predict the temperature in the future for climate change. The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) Web service provided the information used in this study. The dataset underwent training and testing in an 80/20 per cent manner. Using seasonal auto-regressive integrated moving average (SARIMA) and twenty-four (24) different Machine Learning algorithms of regression performance of the temperature, future predictions of Iwo station, were made using training data that spans the years 1980 to 2012 and test data that spans the years 2013 to 2021. The results reveal that the standard deviation value ranges from 2.4×10-05 to 0.031, while the optimum model for the SARIMA (1, 0, 0) and Akaike’s Information Criterion (AIC) spans from 961.090 to 1341.831. Furthermore, the results of the statistical analysis show that the maximum average temperature recorded in April was 303.68K, the minimum temperature recorded in January was 293.23K with a maximum standard deviation of 1.84 in February, and the minimum standard deviation for the temperature was 0.42 in September. For the machine learning algorithm, Exponential GPR shows the highest R2 of 0.19 while the least Ensemble Boosted Trees (R2= -46.24). In terms of the forecasting performance of these machine learning algorithms based on RMSE, the best forecasting model was Medium Neural Network (RMSE = 1.8112). The result also reveals that Fine Gaussian gave the least MAE = 1.19, while the least MSE = 2.7534 meaning that these machine learning algorithms outperformed other models. As a result, the report recommends that Nigerian meteorological management establishes additional research centers to collect data and conduct research.
Biomass and phycocyanin production from microalgae Spirulina platensis using POME slurry waste
Authors: Astriandari A , Syamsu K , Setyaningsih D 
Published: August 2023

Abstract
Microalgae cultivation medium was an important component which is related to the economic aspects and its effects on cell life in culture. Synthetic medium was widely used but because of its less economical, its necessary to look for alternative substitute medium. This alternative substitute medium could come from industrial and household waste. Some organic medium for microalgae cultivation of Spirulina platensis have been widely studied. One source of organic waste from other industries is derived from oil palm such as POME (Palm Oil Mill Effluent) Slurry waste. Spirulina platensis known as one of microalgae which have high protein component on biomass and phycocyanin as biopigment. Biomass and pigment production is influenced by the medium and photoperiod. The research objective was to determine the influence of medium (Walne as synthetic medium and Slurry POME as organic medium) and photoperiod culture to productivity of biomass and phycocyanin. Result of this research is POME Slurry medium has a fairly good ability and could competes with Walne for the production of biomass and phycocyanin so that it can be used as an alternative medium.
Life support systems and aquatic communities in public aquariums
Authors: Prasetiawan N R , Kurniasih R A , Ma’muri  , Setiawan A , Bramawanto R , Mulyadi U , Nugraha A R 
Published: August 2023

AbstractThe Pangandaran Integrated Aquarium and Marine Research Institute (PIAMARI) was developed in Pangandaran, and is equipped with public aquarium facilities. The Main Aquarium is an oceanarium in the form of a box, with a volume of 1.238 million liters, and accommodates various marine biota. This paper aims to provide an overview of the life support system used and the aquatic communities in the Main Aquarium. An inventory of principal components and equipment, the composition of biota, plankton, and ectoparasites was conducted in the oceanarium. The Main Aquarium is supported by filtration using a sand filter, protein skimmer, and an ozone generator. Water quality is maintained using the filtration combined with partial water changes. There are nine species of fish, including sharks and rays, and two species of turtles that are kept in the oceanarium, where most of the collections are omnivores, with IUCN LC conservation status and the rest NT to CR. Seven plankton genera were identified in the oceanarium (H’:1.78 and E: 0.90). There wasCryptocaryonectoparasite infestation on the gills and skin ofTrachinotus blochiiwith open lesions. Fish collections show different behavior and consume the fresh and artificial feed. Apart from being an edu-tourism facility, the oceanarium can act as a place for living and ex-situ culture for endangered marine biota.
Study of ammonia removal in aquaponic system with Chinese flowering cabbage (Brassica chinensis (var. parachinensis)) and mechanical filter for tilapia cultivation
Authors: Mahabror D , Zulkarnain R 
Published: August 2023

Abstract
Water quality is one of the important factors to increase the success of fish farming production apart from disease factors and fish seed quality. Oreochromis niloticus or commonly called tilapia is one of the fish that is widely cultivated in Indonesia. The challenges of increasing intensive aquaculture production are stocking density and high protein feed while maintaining good water quality. High stocking densities and high protein feed can increase ammonia in the water which can cause death in fish, for that some quality control of aquaculture can be done, namely using mechanical filters (Granular activated carbon and biofoam) and aquaponic. The difference between these two methods was in the media used, mechanical filters used granular activated carbon and biofoam, while aquaponics used Chinese flowering cabbage (caisin) (Brassica chinensis var. parachinensis). To determine the effectiveness of reducing ammonia from the two methods, this study will compare water quality parameters with mechanical filters and aquaponic filter methods by using plant medi, namely (Brassica chinensis var. parachinensis). This research was conducted for 30 days to measure several water quality parameters including temperature, DO, pH, TDS and total ammonia nitrogen (TAN). The results during the study showed that the recirculating aquaponic system was better than the recirculating system with a mechanical filter (Granular activated carbon and biofoam) for SR, FCR and TDS parameters while the temperature, pH, DO and TAN parameters for 30 days were not much different between the recirculating aquaponic system and the recirculating system using a mechanical filter (Granular activated carbon and biofoam).
Sustainable stingray stock (Dasyatis sp.) in Malacca Strait, Serdang Bedagai Regency
Authors: Manurung V R , Nasution S F P , Hasibuan J S , Fadhilah A , Husaini H 
Published: August 2023

Abstract
Sustainable stingray stock catches and increasing the potential that these fish populations may become overfished this may have a negative impact on the size of fish stocks. In order to estimate stock models and prospective resources of stingrays in the seas of the Malacca Strait, the descriptive technique was utilized to collect secondary data. The necessary to conduct stock dynamics research that examines the exploitation rate of stingrays. Based on the Schaefer model, obtained the value of sustainable potential (MSY) for Stingray resources (Dasyatis sp.) at Tanjung Beringin fishing port of 37,24625 tons/year and the optimum effort value of 148,6119 trips/ years. The percentage of stingray resource utilization rate which landed in Tanjung Beringin TPI during the last 5 years has an average value of 0,925% and an effort level of 1,234%. TAC is 29,797 tons/year, shows that fishing can still be increased to get more optimal results. The value of the allowable catch must be below the MSY value which is 80%. The average allowable catch of stingray (Dasyatis sp.) is 80% of the MSY. This means that fishermen’s catches can still be optimized, but must not exceed MSY limit.