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In South Korea, issues related to production and environmental concerns are arising from marine fish cage farm. To address this, a modeling study was conducted to derive management plan. The model used a combination of hydrodynamic, fish growth, and ecosystem (water quality and sediment) models. The hydrodynamic model utilized Environmental Fluid Dynamics Code. The fish growth model was developed specifically for this study’s purpose. The ecosystem model used was Ecosystem Model for Marine Management. This modelling approach is the first of its kind in South Korea. It incorporated pollution sources such as wasted feed and fish feces as state variables, which were integrated into the ecosystem model. Furthermore, the fish growth model considered fish mortality based on aquaculture conditions (water temperature, dissolved oxygen, stocking density). Although the negative effects of high stocking densities are well-documented, no previous fish modeling cases have considered this. Model validation results showed good reproducibility of annual fish growth and production for each cage. Notably, the model reproduced the phenomenon in the 2-year-olds fish of reduced production per cage followed by an increase due to the consideration of density-dependent mortality. Additionally, spatial and temporal reproducibility of water quality and sediment parameters, including total organic carbon and sedimentation rate, were satisfactory. A limitation of this study remains the lack of sufficient observational data. Scenario analysis for the management plan yielded the following results: Firstly, different age classes require specific management approaches due to varying impacts of feeding, excrement production, and growth-limiting factors. Secondly, optimizing feed input can improve environmental conditions and economic benefits. Thirdly, regulating stocking density is more efficient than adjusting the number of cages. This model provides a tool for deriving management plans for marine fish cage farming based on an integrated understanding of the aquaculture ecosystem. Furthermore, it can be utilized for various purposes, such as assessing the range of influence of organic matter, calculating resting periods for fish farms, and evaluating sustainability.

Abstract Non-fungible tokens (NFTs), which are immutable and transferable tokens on blockchain networks, have been used to certify the ownership of digital images often grouped in collections. Depending on individual interests, wallets explore and purchase NFTs in one or more image collections. Among many potential factors of shaping purchase trajectories, this paper specifically examines how visual similarities between collections affect wallets’ explorations. Our model characterizes each wallet’s explorations with a Lévy flight and shows that wallets tend to favor collections having similar visual features to their previous purchases while their behaviors vary widely. The model also predicts the extent to which the next collection is close to the most recent collection of purchases with respect to visual features. These results are expected to enhance and support recommendation systems for the NFT market.

Abstract The gravity model of human mobility has successfully described the deterrence of travels with distance in urban mobility patterns. While a broad spectrum of deterrence was found across different cities, yet it is not empirically clear if movement patterns in a single city could also have a spectrum of distance exponents denoting a varying deterrence depending on the origin and destination regions in the city. By analyzing the travel data in the twelve most populated cities of the United States of America, we empirically find that the distance exponent governing the deterrence of travels significantly varies within a city depending on the traffic volumes of the origin and destination regions. Despite the diverse traffic landscape of the cities analyzed, a common pattern is observed for the distance exponents; the exponent value tends to be higher between regions with larger traffic volumes, while it tends to be lower between regions with smaller traffic volumes. This indicates that our method indeed reveals the hidden diversity of gravity laws that would be overlooked otherwise.

This study presents a two-dimensional analytical method for fast optimization, taking into consideration the influence of the eddy current in a magnet and iron loss within a coaxial magnetic gear. Subdomain modeling was utilized to obtain vector potentials in the air-gap, magnet, and modulation regions by solving Maxwell’s equations. After that, the magnet, rotor, and modulation losses were predicted and then compared using a finite element method simulation within three topologies with gear ratios ranging from five to six. The authors improved the machine performance, specifically the torque density, by employing a multi-objective function with particle swarm optimization. The flux density obtained using subdomain modeling in just 0.5 s benefits the optimization process, resulting in a torque-density optimal model after around 3 h. A 3/19/16 prototype targeting a low-speed, high-torque, permanent generator application was fabricated to verify the analytical and simulation results.

As the performance of permanent-magnet synchronous generators improves, research on device noise is also becoming important. This study analyzes the causes of electromagnetic noise, such as the back electromotive force, cogging torque, torque ripple, unbalanced magnetic force, and harmonics that occur during operation for each DC and AC load. In particular, it shows that the influence of the waterfall diagram changes depending on the harmonic order of the torque ripple. By combining 2D finite element analysis and 3D mechanical analysis, electromagnetic analysis can be used to analyze the noise-generating relationship. Based on the analysis results, the electromagnetic noise generation results depending on the load are presented. The electromagnetic performance and mechanical reliability of the designed model were verified by comparing the finite element method and experimental results.

As rare-earth permanent magnet prices continue to fluctuate, interest in non-rare-earth permanent magnet-assisted synchronous reluctance motors (PMa-SynRMs) is increasing. However, non-rare-earth PMa-SynRMs have a multi-layer structure with interior permanent magnets to compensate for the low magnetic fields. Therefore, the rotor has several design parameters, and electromagnetic analysis is time-consuming. To overcome these challenges, magnetic equivalent circuit analysis was used to perform fast electromagnetic analysis of non-rare-earth PMa-SynRMs. In addition, the validity of the proposed method was confirmed through a comparison of the obtained results with those of finite element analysis.

This study investigates a subdomain method for performing the characteristic analysis of an axial flux permanent magnet motor (AFPM). Initially, the AFPM was simplified for the analytical model and analysis regions were defined to apply the subdomain method. Subsequently, a general solution was derived for the governing equations of each analysis region using the variable separation method. Subsequently, the undetermined coefficients of the general solution were obtained from the matrix using appropriate boundary conditions, and the magnetic flux density in the air-gap region was derived based on the defined magnetic vector potential. Finally, the induced voltage and electromagnetic torque of the analytical model were calculated. The analytical results were verified using the finite element method and the experimental results.

Industrial greenhouse mushroom cultivation is currently promising, due to the nutritious and commercial mushroom benefits and its convenience in adapting smart agriculture technologies. Traditional Device-Cloud protocol in smart agriculture wastes network resources when big data from Internet of Things (IoT) devices are directly transmitted to the cloud server without processing, delaying network connection and increasing costs. Edge computing has emerged to bridge these gaps by shifting partial data storage and computation capability from the cloud server to edge devices. However, selecting which tasks can be applied in edge computing depends on user-specific demands, suggesting the necessity to design a suitable Smart Agriculture Information System (SAIS) architecture for single-crop requirements. This study aims to design and implement a cost-saving multilayered SAIS architecture customized for smart greenhouse mushroom cultivation toward leveraging edge computing. A three-layer SAIS adopting the Device-Edge-Cloud protocol, which enables the integration of key environmental parameter data collected from the IoT sensor and RGB images collected from the camera, was tested in this research. Implementation of this designed SAIS architecture with typical examples of mushroom cultivation indicated that low-cost data pre-processing procedures including small-data storage, temporal resampling-based data reduction, and lightweight artificial intelligence (AI)-based data quality control (for anomalous environmental conditions detection) together with real-time AI model deployment (for mushroom detection) are compatible with edge computing. Integrating the Edge Layer as the center of the traditional protocol can significantly save network resources and operational costs by reducing unnecessary data sent from the device to the cloud, while keeping sufficient information.

Abstract Recreational gatherings are sources of the spread of infectious diseases. Understanding the dynamics of recreational gatherings is essential to building effective public health policies but challenging as the interaction between people and recreational places is complex. Recreational activities are concentrated in a set of urban areas and establish a recreational hierarchy. In this hierarchy, higher-level regions attract more people than lower-level regions for recreational purposes. Here, using customers’ motel booking records which are highly associated with recreational activities in Korea, we identify that recreational hierarchy, geographical distance, and attachment to a location are crucial factors of recreational gatherings in Seoul, Republic of Korea. Our analyses show that after the COVID-19 outbreak, people are more likely to visit familiar recreational places, avoid the highest level of the recreational hierarchy, and travel close distances. Interestingly, the recreational visitations were reduced not only in the highest but also in low-level regions. Urban areas at low levels of the recreational hierarchy were more severely affected by COVID-19 than urban areas at high and middle levels of the recreational hierarchy.

Abstract Urban green space is thought to contribute to citizen happiness by promoting physical and mental health. Nevertheless, how urban green space and happiness are related across many countries with different socioeconomic conditions has not been explored. By measuring the urban green space score (UGS) from high-resolution satellite imagery of 90 global cities covering 179,168 km2 and 230 million people in 60 developed countries, we find that the amount of urban green space and GDP are correlated with a nation’s happiness level. More specifically, urban green space and GDP are each individually associated with happiness. Yet, only urban green space is related to happiness in the 30 wealthiest countries, whereas GDP alone can explain happiness in the subsequent 30 countries in terms of wealth. We further show that the relationship between urban green space and happiness is mediated by social support and that GDP moderates this relationship. These findings corroborate the importance of maintaining urban green space as a place for social cohesion to support people’s happiness.

A surface-mounted permanent magnet synchronous motor (SPMSM) is an electric motor with a simple magnetic circuit design, fast responsiveness, linear torque–current characteristics, speed–voltage characteristics, and constant operating speed. SPMSMs use only magnetic torque; however, interior PMSMs (IPMSMs) have high power densities because they can use reluctance torque. In addition, when flux-weakening control is used, the operating range is wide compared with the SPMSM. This study presents a comparative analysis of the characteristics of SPMSM and bar-type IPMSM. Characteristic analyses are performed by setting the same stator shape, rated speed, number of turns, winding specifications, voltage limit, and magnet usage in a pole/slot combination of six poles and 27 slots. Next, we compare the no-load back electromotive force, cogging torque, and loss characteristics, and perform a characteristic analysis of each model while satisfying the design specifications. No-load and load tests are performed using a back-to-back system. The results of the analysis and experimental results are in good agreement, and the reliability of the analysis results is guaranteed. The SPMSM is approximately 8.5% superior to the IPMSM in terms of core loss, and the eddy current loss is greater than that of the IPMSM.

Permanent magnet synchronous machines (PMSMs) with an overhang structure can increase power density by compensating for the increased magnetic energy of permanent magnets. To analyze the overhang structure, a three-dimensional (3D) analysis of PMSMs is essential. However, 3D analysis takes a long time and the modeling process is complicated in the initial design stage. To overcome these problems, a magnetic equivalent circuit technique is applied to the 2D model. In this paper, an optimal design method for PMSMs with an overhang structure is proposed based on the semi 3D magnetic equivalent circuit (MEC) and random walk method. By using semi 3D MEC, it is possible to quickly analyze PMSM and obtain accurate electromagnetic analysis results considering the overhang effect. Moreover, the volume and weight of PMSM can be minimized by optimizing the rotor’s four design parameters using a random walk algorithm. To obtain high efficiency, the objective function is selected so that copper loss is minimized under the same constraints. The validity of the proposed design technique is verified by comparing the analysis results of semi 3D MEC and 3D finite element method for the derived optimal model.

Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks. Most existing embedding algorithms assign a single vector to each node, implicitly assuming that a single representation is enough to capture all characteristics of the node. However, across many domains, it is common to observe pervasively overlapping community structure, where most nodes belong to multiple communities, playing different roles depending on the contexts. Here, we propose persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based on their structural contexts. Using link prediction-based evaluation, we show that our framework is significantly faster than the existing state-of-the-art model while achieving better performance.

We investigate the dynamics of aggressive order in the financial market to further understand volatility. To analyze aggressive order, market orders in the order book are scrutinized. The market orders have different degrees of aggressiveness; therefore, we categorize market orders into four types: types Zero, One, A, and B, of which type B is the most aggressive. To examine the dynamics and impacts of each type of order, we use both macro- and micro-level approaches. From the macroscopic perspective, the burstiness and memory of type B is highly correlated with volatility. When traders face a financial crisis, they place bursty aggressive orders, and the orders are more predictable than usual. From the microscopic perspective, we additionally focus on the influence of the orders, particularly the price impact and resilience. The aggressive order has a greater impact than others, even when the price change of the aggressive order is smaller. Moreover, the aggressive order delivers more information on price because the aggressive order has a higher price impact than the execution cost.