bluuebunny/crossref_metadata_2025_split huggingface dataset:
Operation Strategy Optimization of Energy Storage Virtual Synchronous Machine Using Big Data Analysis Technology
Authors: Xiang Zhicai , Chen Yang , Li Yunda , Li Zhaoming , Man Yangyang 
Published: December 2023

Abstract
With the increasing combination of energy with the power grid, energy storage systems play a crucial role in maintaining grid stability and ensuring reliable power supply. In recent years, the concept of energy storage virtual synchronous machine (VSM) has emerged as an effective and flexible method for mimicking the behavior of synchronous generators in energy storage systems. This paper presents a new approach to optimize the operation strategy of energy storage VSM using big data analytics techniques. The proposed method utilizes data analysis to enhance the performance and efficiency of energy storage systems, contributing to overall grid reliability and resilience. Neural networks are employed as an efficient big data analytics technique, and in this study, they are applied to evaluate and optimize the operation strategy of energy storage VSM. Firstly, this paper evaluates the operation strategy of energy storage VSM using a deep belief network (DBN) as the underlying network. Secondly, an improved whale optimization algorithm (WOA) is proposed. IWOA initializes the initial population by employing a mapping strategy, laying the foundation for global search. Furthermore, IWOA introduces a nonlinear strategy to balance the global and local exploration capabilities, while avoiding premature convergence based on diversified mutation strategies. IWOA demonstrates significant improvements in search speed and convergence accuracy, with a strong ability to escape local optima. Thirdly, this paper utilizes IWOA to initialize the weights and thresholds of the DBN network, constructing IWOA-DBN. The network is then utilized to evaluate and optimize the operation strategy of energy storage VSM based on the evaluation results. The feasibility and correctness of IWOA-DBN are validated through systematic experiments.
An improved genetic algorithm with density-guiding operator based on the location selection model of emergency logistics center
Authors: Cao Jinjing , Fang Huang 
Published: December 2023

Abstract
In disaster relief efforts, establishing logistical channels with the affected population is crucial. This necessitates the construction of temporary logistics distribution centers. However, rapidly selecting suitable locations for these temporary logistics centers presents a considerable challenge. An inability to swiftly provide viable relief strategies can have significant negative implications for both the government’s reputation and the overall effectiveness of the relief efforts. In terms of problem modeling, the majority of researchers base their models on economic variables, with few considering the issue of panic among the disaster-affected populace. However, it is critical to address not only the widespread panic but also to focus on proactive rescue operations for those who are excessively panic-stricken or less mobile due to the disaster. In this article, we create an improved genetic algorithm with a density-guiding operator based on the location selection model of emergency logistics centers to address these issues. During variation iteration, the algorithm introduces population density as a guide and makes the algorithm’s parameters adaptive and dynamic, which can successfully choose a superior option. Finally, numerical tests are run on the Jiading District of Shanghai data set to confirm the algorithm’s efficacy. The final improvement of the adaptive algorithm is 13.21%.
An Intelligent Manufacturing Models Recommendation Model Based on Knowledge Graph and Recommendation Algorithms
Authors: Zhang Liwei , Hou Kaixuan 
Published: December 2023

Abstract
With the concept of intelligent manufacturing, a variety of intelligent manufacturing models emerge. But companies don’t have enough knowledge to choose the suitable intelligent manufacturing model. To solve this problem, based on lean design principles, combined with expert knowledge and expertise, we propose an Intelligent Manufacturing Models Recommendation Model (referred to as IMMR Model). First, we categorize customer requirements into real need, customer preference and customer expectation to accurately identify customer requirements. Then, we use knowledge graph to build a professional knowledge base for intelligent manufacturing, and form customized intelligent manufacturing models based on lean design. Finally, a recommendation system is constructed by combining an ontology rule-based recommendation algorithm and a similarity-based recommendation algorithm to recommend suitable intelligent manufacturing models for different customer requirements.
Parameter Optimization of Energy Storage Virtual Synchronous Machine Based on Particle Swarm Optimization Algorithm
Authors: Cui Rongxi , Du Shanhui , Li Zhongyu , Chen Yang , Zhang Xu 
Published: December 2023

Abstract
Energy transformation is a severe challenge and major demand faced by China’s sustainable development, and new energy development has become a key driving force for energy transformation. The issue of system stability is brought to light by the steadily rising share of renewable energy sources like wind and solar, which in turn places greater demands on the inertia support capacity of power plants. In order to replicate the dynamic equations of a conventional synchronous machine and add inertia and damping to the system, a new technology known as virtual synchronous machine (VSM) control has arisen. Therefore, improving the system’s absorption capacity of new energy generation and ensuring its safe and stable operation are both greatly aided by the study of energy storage VSM control technology’s grid stability and parameter design methods. Although VSM control adds inertia to a system, which is good for stability, it also complicates the system’s ability to withstand tiny disturbances. This paper investigates the small disturbance stability of VSM-controlled power generating and storage systems. The stability analysis method of storage VSM when subjected to random power sources is investigated further, and a method for optimising the system’s parameters is proposed. VSM with optimised parameters can provide sufficient inertia and damping under complex operating conditions, as shown by simulation results in real-world systems. Finally, the proposed method’s efficacy was assessed with the help of an enhanced binary particle swarm optimisation (EBPSO) algorithm, and the outcomes confirmed its superiority.
Energy Efficiency Prediction of Energy Storage Virtual Synchronous Machine Based on Long Short-Term Memory Network
Authors: Li Zhongyu , Liu Xu , Lin Yu , Xu Xiaohua , Wang Xiangfa 
Published: December 2023

Abstract
The integration of renewable energy sources in the power grid has led to an increased demand for energy storage systems to manage the intermittency and variability of these sources. Among various energy storage technologies, Energy Storage Virtual Synchronous Machines (ESVSMs) have emerged as a promising solution for enhancing grid stability and energy efficiency. However, optimizing the performance of ESVSMs requires accurate energy efficiency predictions. This paper proposes a energy efficiency prediction network (EEPNet) to achieve energy efficiency prediction of ESVSMs. Firstly, considering the peculiarity of storing virtual synchronous machine efficiency fluctuations, this paper improves the traditional Inception structure. This allows for the extraction of information at different scales and its fusion to obtain a better representation. Secondly, this paper introduces residual connections in the structure. This not only avoids the difficulties in model training caused by deep networks but also helps to integrate the original information with the extracted information. Thirdly, building upon the improved Inception structure, this work combines long short-term memory (LSTM) and attention mechanisms to construct a complete network architecture, further enhancing the performance of the entire model. Finally, the effectiveness and validity of the model are validated through comprehensive experiments.
Application of Multi-sensory interaction design Based on Machine Learning in Virtual Reality
Authors: Zheng Liming 
Published: December 2023

Abstract
Virtual reality (VR) technology is well-known to the public for its unparalleled sense of environmental immersion, which is accompanied by a large amount of human-computer interaction between users and objects in the virtual environment. Users transmit and obtain information through their senses, so the processing mechanism of information in various senses under the background of VR technology has become the foundation of research. In the process of natural information dissemination, users’ senses are synergistically involved, so whether there is a correlation between various senses that affects users’ final perception has become an important theoretical support for studying multi-sensory interaction. Facial expression recognition is an important component of multi-sensory interaction technology. Therefore, this article mainly uses machine learning algorithms to achieve real-time detection of facial targets in images or videos, as well as classification and recognition of facial expressions. To meet the task requirements of the expression recognition module, this article selects the DeepID network model based on deep learning in the field of facial recognition as the basic model for the expression recognition function. This study utilizes the excellent feature extraction ability of the original network for facial images and introduces residual modules and attention mechanisms, making the modified network model more suitable for facial expression multi classification recognition tasks. The recognition accuracy of the facial expression classification algorithm proposed by this work reaches 97.2%.
Detection of cracks in aircraft structures using digital image processing technology
Authors: Zheng Hao 
Published: December 2023

Abstract
Full-scale aircraft structural fatigue testing exposes structural weaknesses by simulating the use of the aircraft in service and monitoring the development and expansion of fatigue cracks to guide the optimisation of aircraft structural design, the determination of service life and the development of maintenance and repair programmes. If crack damage is not detected in time, the uncontrolled expansion of the crack may lead to structural damage and prolong the test cycle. Therefore, it is crucial to achieve accurate detection of early cracks in fatigue testing of aircraft structures. Digital image technology is a new technology supported by a number of technical and theoretical achievements. In practical application, it relies on the support of computer functions, by converting image information into two-dimensional digital signals, thus realising various operations. In this paper, digital image processing techniques are used to deepen the backbone feature extraction network structure and improve the prediction scale of small targets, thus improving the performance of the model in terms of feature extraction and small defect detection. Experimental results show that the method can achieve accurate detection of small cracks, providing an effective technical approach for the automatic detection of cracks in aircraft structural fatigue tests. The technique of detecting cracks in aircraft structures using digital image processing also provides a new idea for research into improving aircraft endurance testing, which can be further developed with the aid of image technology.
Climate change adaptation and mitigation in agriculture: a review of the evidence for synergies and tradeoffs
Authors: Barbieri Lindsay , Bittner Carolyn , Wollenberg Eva , Adair E Carol 
Published: December 2023

Abstract
Agricultural practices that both support climate change mitigation and facilitate adaptation to a changing climate are critical for reducing greenhouse gas emissions while ensuring food security. This need has led to many claims regarding the potential for a variety of agricultural practices to achieve synergies between mitigation and adaptation in agriculture. However, the evidence for climate change mitigation and adaptation synergies in agriculture remains mixed. To evaluate such claims, we examined the evidence for these synergies by conducting a systematic review of peer-reviewed literature that make claims about outcomes for both climate change adaptation and mitigation in agriculture. Based on 87 articles identified, we show that synergistic outcomes are claimed more frequently than tradeoffs for all practices, yet the evidence was stronger for mixed and conflicting outcomes than for synergies. Indeed, claims of synergistic outcomes may be overstated, because these publications more often relied on secondary data rather than empirically evaluating adaptation and mitigation outcomes. We also show important gaps in the consideration and assessment of climate change adaptation and mitigation objectives and outcomes. This review highlights the critical need for more robust research, evidence, and evaluation of the adaptation and mitigation outcomes of agricultural practices, and the need to clarify the contexts of such results, in order to effectively support policies and practices that aim to promote synergistic outcomes and avoid conflicting outcomes.
Modelling plastic fluxes with INCA-macroplastics in the Imus catchment: impacts of long-term accumulation and extreme events
Authors: Clayer François , Norling Magnus D , Fürst Kathinka , Hurley Rachel , Creencia Glenn Bryan A , Msojica Daeniel Zarene P , Dizon Jose Carlo R , Lin Yan , Nizzetto Luca , Sedigo Noel A , Olsen Marianne , Veiteberg Braten Hans Fredrik 
Published: December 2023

Abstract
Plastic environmental pollution is threatening water resources, aquatic ecosystems, and human wellbeing but is still highly uncertain with global fluxes to sea of 0.4–13 Mt\yr, and up to 517 Mt of mismanaged plastics on land. Catchment modelling tools are required to challenge current knowledge, simulate impacts of management initiatives, and complement global and observation-based studies. Here we present the first spatiotemporally explicit model for mismanaged plastic mobilization and transport from land to sea from the INtegrated CAtchment (INCA) family. INCA-Macroplastics encompasses all components of the catchment, is driven by available data (weather, population, solid waste) and enables calibration and validation against diverse observations (river monitoring, household surveys). INCA-Macroplastics was applied to the Imus River, Philippines, one of World’s most polluted rivers. Given large uncertainties on catchment plastic retention, two calibrations and two emission scenarios were developed to describe catchment plastic fluxes, residence time and stocks over 1990–2020. Plastic fluxes to the sea are highly variable over years and seasons (55–75% exported during the wet season) and have increased exponentially over 1990–2020 from 5–100 to 2000–15000 tons\yr. INCA-Macroplastics is the first model handling plastic accumulation on land and highlights the importance of extreme flooding events in mobilizing and transporting legacy plastics. Model outputs explicitly show that current land plastic pollution can impact fluxes to the ocean for up to 30 years into the future. INCA-Macroplastics is useful to provide tailored recommendations for local monitoring, testing waste management scenarios and pointing towards future research avenues.
A biological dipole variability in the Indian Ocean
Authors: Pathirana Gayan , Noh Kyung Min , Lee Dong-Geon , Lee Huiji , Kug Jong-Seong 
Published: December 2023

Abstract
The Indian Ocean Dipole (IOD) is a prominent mode of climate variability in the tropical Indian Ocean (IO). It exerts a significant influence on biological activities in this region. To elucidate the biological response to the IOD, previous research has introduced the biological dipole mode index (BDMI). However, the delineation of the region by the BDMI has limitations in capturing IOD-induced chlorophyll variations in the IO. By analyzing observational data and historical simulations from a Coupled Model Intercomparison Project model, this study shows that chlorophyll anomalies in the IO exhibit a dipole pattern in response to IOD. During the developing and mature phases of the positive IOD, we observe a substantial decrease in chlorophyll in the south-southwest of India, contrasting with a pronounced increase in the southeast of the IO. This response is attributed to anomalous southeasterly winds induced by IOD, which enhance nutrient upwelling in the southeastern IO and suppress it in the south-southwest of India, resulting in corresponding changes in surface chlorophyll blooms. Based on this finding, we propose a new biological dipole index that more robustly explains the surface chlorophyll response to IOD in the tropical IO. This study highlights the profound influence of IOD on oceanic chlorophyll and underscores the importance of a more comprehensive understanding of the associated biophysical interactions.
Domain walls and vector solitons in the coupled nonlinear Schrödinger equation
Authors: Snee David D J M , Ma Yi-Ping 
Published: December 2023

Abstract
We outline a program to classify domain walls (DWs) and vector solitons in the 1D two-component coupled nonlinear Schrödinger (CNLS) equation without restricting the signs or magnitudes of any coefficients. The CNLS equation is reduced first to a complex ordinary differential equation (ODE), and then to a real ODE after imposing a restriction. In the real ODE, we identify four possible equilibria including ZZ, ZN, NZ, and NN, with Z(N) denoting a zero (nonzero) value in a component, and analyze their spatial stability. We identify two types of DWs including asymmetric DWs between ZZ and NN and symmetric DWs between ZN and NZ. We identify three codimension-1 mechanisms for generating vector solitons in the real ODE including heteroclinic cycles, local bifurcations, and exact solutions. Heteroclinic cycles are formed by assembling two DWs back-to-back and generate extended bright-bright (BB), dark-dark (DD), and dark-bright (DB) solitons. Local bifurcations include the Turing (Hamiltonian–Hopf) bifurcation that generates Turing solitons with oscillatory tails and the pitchfork bifurcation that generates DB, bright-antidark, DD, and dark-antidark solitons with monotonic tails. Exact solutions include scalar bright and dark solitons with vector amplitudes. Any codimension-1 real vector soliton can be numerically continued into a codimension-0 family. Complex vector solitons have two more parameters: a dark or antidark component can be numerically continued in the wavenumber, while a bright component can be multiplied by a constant phase factor. We introduce a numerical continuation method to find real and complex vector solitons and show that DWs and DB solitons in the immiscible regime can be related by varying bifurcation parameters. We show that collisions between two DB solitons with a nonzero phase difference in their bright components typically feature a mass exchange that changes the frequencies and phases of the two bright components and the two soliton velocities.
Fluctuating asymmetry patterns of common carp (Cyprinus carpio) in fish hatchery center of Kabat, Banyuwangi, Indonesia
Authors: Lutfiyah L , Budi D S 
Published: December 2023

Abstract
A decrease in common carp (Cyprinus carpio) fry quality has occurred in many hatcheries indicated by fry abnormality increase, which causes low survival and growth rates. This research aims to obtain quantitative information of asymmetry level on common carp fry in the Fish Hatchery Centre (FHC) of Kabat as a reference for the breeding program. A total of 200 common carp fries with a total length of 5-7 cm were obtained from FHC of Kabat. The observed magnitude (FAm) and number (FAn) are fluctuating asymmetry. To calculate both of them; bilateral meristic characteristics were measured are pectoral fins radii, ventral fins radii, and scales on the lineal lateralis number. We found that there was an incidence of asymmetric fluctuations in FHC of Kabat with the highest FAm and FAn were obtained in lateral line scales. This implies inbreeding which causes a decrease in hatchery productivity, so the introduction of brood stock from other areas is required.
Effect of feeding a combination of silage of fish offal and fermented tofu dregs with different doses on the population and biomass of silkworms (Tubifex tubifex)
Authors: Sari V N , Lutfiyah L , Sulmartiwi L 
Published: December 2023

Abstract
Silkworm (Tubifex tubifex) is one of the natural food that is often used in fish farming. Determining the success of silkworm cultivation is strongly influenced by the culture media, which is generally rice mud. Rice field mud is a living medium for silk worms in nature, but the low nutrient content requires the addition of silage of fish offal and fermentation of tofu dregs. This study used a maintenance medium dose of 100% rice field mud (control), 75% fish offal silage + 25% tofu dregs fermentation (P1), 50% fish offal silage + 50% tofu dregs fermentation (P2), and 25% of tofu silage. fish + 75% tofu dregs fermentation (P3). The main parameters in this study were the growth of biomass and population of silkworms during 21 days of rearing. While the supporting parameters of this research are water quality parameters which include temperature, pH, and DO. Analysis of the data in this study using Analysis of Variance (ANOVA) 1 factors. The results of the calculation of optimal biomass and population were obtained in 25% silage of fish offal + 75% fermented tofu dregs with an absolute biomass value of 18.6 grams and an absolute population value of 8,698 individuals.
Study of underwater sound effects relation to current velocity and Siganus sp. population in seaweed cultivation area
Authors: Kasim M , Musaruddin M , Mansur LK , Palupi RD , Jalil W , Takwir A 
Published: December 2023

Abstract
Underwater sound is one of the interesting phenomena in the ocean. Underwater sounds can impact marine organisms, especially fish and mammals. One of the organisms that can be influenced by underwater sound is the Siganus fish. This study aims to determine sound propagation with a certain level of fish pest repellents about the surface currents velocity and Siganus canaliculatus. population in seaweed cultivation areas. This research was conducted in a seaweed cultivation area in Baubau City, Southeast Sulawesi, Indonesia, in May-July 2022. The tool used is a seaweed pest repellent Ver.04 (Usirikan Ver 03). Underwater sound, at a strength level of 76.19-83.09 dB, can propagate quickly at 60 m in diameter of the sea area. The farthest distance multiplied at a strength of 62.41-69.30 dB can reach 150 m in diameter of the sea area. The fish population (S. canaliculatus) was not found in the seaweed cultivation area, with a sound level of 62.41 dB. The population of Siganus fish is around 2-4 ind/m2 at a sound level of 48.62 dB. Underwater sound with a sound level above 50 dB can impact the population of S. canaliculatus. Seaweed fish repellent can significantly affect behavior. of S. canaliculatus