bluuebunny/crossref_metadata_2025_split huggingface dataset:
Global disparities in urban green space use during the COVID-19 pandemic from a systematic review
Authors: Kleinschroth Fritz , Savilaakso Sini , Kowarik Ingo , Martinez Paloma Julia , Chang Yuyang , Jakstis Kristen , Schneider Jessica , Fischer Leonie K. 
Published: January 2024

AbstractThe COVID-19 pandemic disrupted urban resilience and challenged the use of urban green space (UGS). Previous studies lack consensus on whether UGS use increased or decreased during and after lockdowns and how this related to policy, economic conditions and UGS types. In a systematic review, we screened >3,000 articles in 5 languages, identifying 177 articles on UGS use changes in 60 countries. The cities studied show diverging changes in UGS use. Generally, decreases occurred where COVID-19 policies were stricter and the gross domestic product per capita was lower, including in most of the few studied areas of the Global South. All studies on private gardens and 60% on forests and other natural areas showed increases, while 77% of studies conducted on public parks indicated decreased use. The global disparity in UGS use was exacerbated during the COVID-19 pandemic, demonstrating the need to enhance green infrastructure for healthy cities and to extend it beyond public parks.
Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion
Authors: Shakarchy Amit , Zarfati Giulia , Hazak Adi , Mealem Reut , Huk Karina , Ziv Tamar , Avinoam Ori , Zaritsky Assaf 
Published: January 2024

AbstractCells modify their internal organization during continuous state transitions, supporting functions from cell division to differentiation. However, tools to measure dynamic physiological states of individual transitioning cells are lacking. We combined live-cell imaging and machine learning to monitor ERK1/2-inhibited primary murine skeletal muscle precursor cells, that transition rapidly and robustly from proliferating myoblasts to post-mitotic myocytes and then fuse, forming multinucleated myotubes. Our models, trained using motility or actin intensity features from single-cell tracking data, effectively tracked real-time continuous differentiation, revealing that differentiation occurs 7.5–14.5 h post induction, followed by fusion ~3 h later. Co-inhibition of ERK1/2 and p38 led to differentiation without fusion. Our model inferred co-inhibition leads to terminal differentiation, indicating that p38 is specifically required for transitioning from terminal differentiation to fusion. Our model also predicted that co-inhibition leads to changes in actin dynamics. Mass spectrometry supported these in silico predictions and suggested novel fusion and maturation regulators downstream of differentiation. Collectively, this approach can be adapted to various biological processes to uncover novel links between dynamic single-cell states and their functional outcomes.
Intermittent short-duration reoxygenation relieves high-altitude pulmonary hypertension via NOX4/H2O2/PPAR-γ axis
Authors: Li Shaohua , Lyu Qiang , Shi Qixin , Bai Yungang , Ren Xinling , Ma Jin 
Published: January 2024

Abstract
High-altitude pulmonary hypertension (HAPH) is a severe and progressive disease that can lead to right heart failure. Intermittent short-duration reoxygenation at high altitude is effective in alleviating HAPH; however, the underlying mechanisms are unclear. In the present study, a simulated 5,000-m hypoxia rat model and hypoxic cultured pulmonary artery smooth muscle cells (PASMCs) were used to evaluate the effect and mechanisms of intermittent short-duration reoxygenation. The results showed that intermittent 3-h/per day reoxygenation (I3) effectively attenuated chronic hypoxia-induced pulmonary hypertension and reduced the content of H2O2 and the expression of NADPH oxidase 4 (NOX4) in lung tissues. In combination with I3, while the NOX inhibitor apocynin did not further alleviate HAPH, the mitochondrial antioxidant MitoQ did. Furthermore, in PASMCs, I3 attenuated hypoxia-induced PASMCs proliferation and reversed the activated HIF-1α/NOX4/PPAR-γ axis under hypoxia. Targeting this axis offset the protective effect of I3 on hypoxia-induced PASMCs proliferation. The present study is novel in revealing a new mechanism for preventing HAPH and provides insights into the optimization of intermittent short-duration reoxygenation.
AML‐Net: Attention‐based multi‐scale lightweight model for brain tumour segmentation in internet of medical things
Authors: Zeeshan Aslam Muhammad , Raza Basit , Faheem Muhammad , Raza Aadil 
Published: January 2024

AbstractBrain tumour segmentation employing MRI images is important for disease diagnosis, monitoring, and treatment planning. Till now, many encoder‐decoder architectures have been developed for this purpose, with U‐Net being the most extensively utilised. However, these architectures require a lot of parameters to train and have a semantic gap. Some work tried to make a lightweight model and do channel pruning that made a small receptive field which compromised the accuracy. The authors propose an attention‐based multi‐scale lightweight model called AML‐Net in Internet of Medical Things to overcome the above issues. This model consists of three small encoder‐decoder architectures that are trained with different scale input images along with previously learned features to diminish the loss. Moreover, the authors designed an attention module which replaced the traditional skip connection. For the attention module, six different experiments were conducted, from which dilated convolution with spatial attention performed well. This attention module has three dilated convolutions which make a relatively large receptive field followed by spatial attention to extract global context from encoder low‐level features. Then these fine features are combined with the decoder's same layer of high‐level features. The authors perform the experiment on a low‐grade‐glioma dataset provided by the Cancer Genome Atlas which has at least Fluid‐Attenuated Inversion Recovery modality. The proposed model has 1/43.4, 1/30.3, 1/28.5, 1/20.2 and 1/16.7 fewer parameters than Z‐Net, U‐Net, Double U‐Net, BCDU‐Net and CU‐Net respectively. Moreover, the authors’ model gives results with IoU = 0.834, F1‐score = 0.909 and sensitivity = 0.939, which are greater than U‐Net, CU‐Net, RCA‐IUnet and PMED‐Net.
Heterogeneous decentralised machine unlearning with seed model distillation
Authors: Ye Guanhua , Chen Tong , Hung Nguyen Quoc Viet , Yin Hongzhi 
Published: January 2024

AbstractAs some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalised IoT service providers have to put unlearning functionality into their consideration. The most straightforward method to unlearn users' contribution is to retrain the model from the initial state, which is not realistic in high throughput applications with frequent unlearning requests. Though some machine unlearning frameworks have been proposed to speed up the retraining process, they fail to match decentralised learning scenarios. A decentralised unlearning framework called heterogeneous decentralised unlearning framework with seed (HDUS) is designed, which uses distilled seed models to construct erasable ensembles for all clients. Moreover, the framework is compatible with heterogeneous on‐device models, representing stronger scalability in real‐world applications. Extensive experiments on three real‐world datasets show that our HDUS achieves state‐of‐the‐art performance.
Block sparse vector recovery for compressive sensing via ℓ1−αℓq$\ell _1-\alpha \ell _q$‐minimization Model
Authors: Shi Hongyan , Xie Shaohua , Wang Jiangtao 
Published: January 2024

AbstractThis paper solves the problem of block sparse vector recovery using the block ‐minimization model. Based on the block restricted isometry property (B‐RIP) condition, exact block sparse vector recovery result is obtained. The theoretical bound for the block ‐minimization model are also obtained when measurements are depraved by the noises.
S‐parameter extraction for electromagnetic modelling of memristor‐based crossbar array circuits
Authors: Yu Junwei , Ma Hanzhi , Tao Tuomin , Zhang Ling , Li Da , Li Er‐Ping 
Published: January 2024

AbstractThis article presents an efficient S‐parameter extraction method for memristor‐based crossbar array circuits. The proposed approach involves converting the array into an equivalent circuit and analytically solving the circuit to obtain the S‐parameters. Memristor‐based crossbar array circuit simulations and corresponding experimental measurements are conducted to verify the high accuracy and low computation complexity of the established method. These numerical results indicate that the authors’ S‐parameter extraction method has the potential for evaluating signal integrity performance in neuromorphic chips, thus aiding their design and optimization for diverse applications.
Unmanned aerial vehicle localization using angle of departure from a single base station and dead‐reckoning
Authors: Rajasekaran Akash , Meles Mehari , Virrankoski Reino , Jäntti Riku 
Published: January 2024

AbstractLocalization in GNSS‐denied environments for unmanned aerial vehicles (UAVs) has recently gained significant interest from the research community. Most of the research is focused primarily on visual localization. This paper examines an algorithm which employs angle of departure and UAVs payload sensor data for UAV localization. First the algorithm uses multiple angle of departures from a single base station and a travel calculated by applying dead‐reckoning on the UAVs inertial measurement unit (IMU), to compute UAV location in 2D coordinates. The 2D location estimate is then fed into a modified extended Kalman filter, which employs the estimate, IMU and barometer data to compute the 3D coordinates for UAV. For the simulation, simulation‐in‐the‐loop accompanied by Arducopter and MAVLink is used to simulate different trajectories and collect the required data for the algorithm. The algorithm is validated by comparing the extended Kalman filter estimates with IMU dead‐reckoned positions. Three simulations were performed, consisting of linear, zigzag, and curved trajectories. A percentile error of 2.5 and 4 m is achieved for the x‐coordinate and y‐coordinate, respectively, on the zigzag and curved trajectories. Interestingly, the linear trajectory showed a larger localization error in its y‐coordinate.
High frequency resonance characteristics analysis of voltage‐source virtual synchronous generator and suppression strategy
Authors: Li Zhi , Liu Mingbo , Gong Yu , Li Xudong 
Published: January 2024

AbstractThe voltage‐source virtual synchronous generator is a grid‐forming type power electronic control technology and is able to support converters that have the ability of inertial, primary frequency and voltage regulation. Firstly, the authors build the small‐signal model of the voltage‐source virtual synchronous generator and conventional converters, compare and analyse the characteristics of the high‐frequency resonance modes, the results show that the voltage‐source virtual synchronous generator has almost the same high‐frequency resonance characteristics as the conventional converter. Secondly, the different control parameters and power grid strength that affect the high‐frequency resonance characteristic are analysed with eigenvalue, and the mechanism of voltage‐source VSG that caused high‐frequency resonance is derived. The leading factor that causes high‐frequency resonance is power grid strengths, and the authors proposed introducing virtual impedance into the front‐end channel of the current inner loop control strategy to suppress resonance for the voltage‐source virtual synchronous generator based on the theoretical analysis with the small‐signal model and eigenvalue analysis. In addition, the electromagnetic transient simulation model and RT‐LAB controller‐in‐loop platform are built, and the simulation and experiment results indicate the effectiveness of the virtual impedance control strategy.
Electromagnetic optimization of piezoelectric ceramic and its application in mechanical antenna
Authors: Gao Song , Wang Liwei , Lu Jingtong , Zhang Shuai , Sun Hongyu , Huang Kai , Xu Jianchun , Hao Yanan 
Published: January 2024

AbstractMechanical antennas have garnered considerable attention due to their ability to address the challenges posed by the significant size and high energy consumption associated with traditional electric antennas when operating at low frequencies. Here, a compact and structurally stable mechanical antenna design is presented. The proposed antenna is constructed using cylindrical piezoelectric ceramics, which have dimensions smaller than 1/1000 of the wavelength. The scrutiny of the influence exerted by the antenna feed area and material thickness on the radiation performance was undertaken, followed by an exhaustive discourse on these effects. Experimental measurements demonstrate the practical functionalities of signal coding, transmission, and reception in the low‐frequency communication system. Notably, at a frequency of 288 kHz, a single proposed antenna achieves an effective information transmission distance of 60 m using binary information coding. This remarkable outcome underscores the potential of this design in facilitating the development of portable and cost‐effective wireless communication equipment for low‐frequency applications.
Deep learning method for predicting electromagnetic emission spectrum of aerospace equipment
Authors: Zhang Yuting 
Published: January 2024

AbstractThis paper proposes a deep learning method to predict the electromagnetic emission spectrum in the electromagnetic compatibility (EMC) test of aerospace products. A threshold‐based data decomposition method is used to propose the spike signal, reconstruct the original test data, and solve the contradiction between the overfitting and prediction accuracy of the deep learning method to deal with the EMC test spectrum. Using a long short‐term memory neural network architecture for predicting electromagnetic emission spectrum, the Bayesian optimization method is used to optimize the network hyperparameter, and the acquisition function is introduced into the loss function to improve the comprehensive training optimization of deep learning network. Apply the method to three numerical examples: signal line current conduction emission, power line voltage conduction emission, and electric field radiation emission. The analysis results indicate that at a 95% confidence level, the predicted electromagnetic emission spectrum is basically consistent with the test results. The prediction results can be used as the basis for EMC evaluation of aerospace electronic equipment.
Application of the support vector machines method for metal analyse by an eddy current system
Authors: Abramovych Anton , Zaitsev Ievgen , Piddubnyi Volodymyr , Bereznychenko Viktoriia 
Published: January 2024

AbstractThe eddy current method of control analyzed and the possibility of its use for the identification of metals and alloys substantiated. It is shown that this can be done by modernizing the eddy current radio engineering system with dynamic scanning of metal samples, which consists in the use of modern microcontrollers with high‐precision ADC operation. The structural of the modernized system is presented, some aspects of processing the received response signal are considered, which made it possible to highlight the information coefficients that would represent the metal or alloy as a generalized image. It is proposed to use machine learning methods to compare an unknown sample of a metal or alloy under study with those available in the collected database, which is shown on the example of the support vector machine method, which allows for real‐time identification of metals and alloys, to carry out express control, without human intervention in the identification process.
An integrated fuse information fusion method for infrared imaging detector and range finder based on improved particle filter
Authors: Wei Daozhi , Li Ning , Li Junwei 
Published: January 2024

AbstractAiming at the traditional integrated fuse information fusion method that cannot simultaneously obtain high‐precision burst control parameters such as burst delay time, azimuth angle, and detection angle, the infrared imaging detector and range finder information fusion method based on particle filtering with BAS algorithm is proposed. Under the centralized information fusion structure that can obtain higher accuracy, first, use a faster linear interpolation method to realize the time alignment of the two asynchronous sensors. Second, for the strong non‐linear observation model in the missile target rendezvous model, use a particle filter framework that is not affected by the nonlinearity of the system function and non‐gaussian noise. Finally, Utilizing the single search BAS algorithm with low computational complexity to solve the problem of particle degradation in particle filtering and improve the accuracy of burst control. The experiment shows that in a typical simulation environment, the optimized information fusion method improves the burst delay time accuracy by 61.82%, azimuth accuracy by 66.28%, detection angle accuracy by 36.15%, and simulation time is shorter compared to traditional methods.
Bispectral optical cavity based on twin metamirrors
Authors: Shelling Neto Liam , Dickmann Johannes , Sauer Steffen , Kroker Stefanie 
Published: January 2024

In this work, we introduce a new bispectral optical cavity concept for which we design twin pairs of highly reflective, ultra-low noise metamirrors. Metasurfaces, artificial structures composed of periodic or quasi-periodic arrays of nanostructures, offer unprecedented control over light properties, paving the way for new applications in areas from high-precision optical metrology to quantum science. Custom phase and an ultra-high reflection coefficient make these metasurfaces an ideal candidate to surpass traditional multilayer mirrors as metamirrors in precision interferometry, particularly by also minimizing thermal noise. The focusing metamirrors designed in this study expect to reflect 99.95% and 99.96% of the incoming light at both, 1064 nm and 1550 nm wavelength. Their planar counterparts even reach theoretical reflectivities of 99.9999% (1064 nm) and 99.9995% (1550 nm). These specialized metamirrors enable bispectral low-noise optical cavities, which would reduce the number of cavities in optical experiments or could be used as a versatile transfer cavity for frequency locking.
The intersection of medieval studies and Indigenous studies: A Norse-Saami case study
Authors: Wang Solveig Marie 
Published: January 2024

AbstractMedieval texts reveal the normalised presence of Saami peoples in medieval Fennoscandia, suggesting close interactions involving trade, relationships, rituals, and magic. Despite growing recognition of these relations, the Saami remain overlooked in general studies of the Middle Ages, often relegated to symbolic roles or footnotes. As a result, Saami characters are typically depicted as the exotic Other within Norse society, often being stripped of agency and humanity in historical narratives. To counter these biases and distorted narratives, an essential step is analysing exclusionary structures in medieval literature and critically reviewing existing research on Saami representation. This process challenges dehumanising portrayals and confronts present-day stereotypes. The present study aims to ‘re-humanize’ (as Paulette F. C. Steeves puts it) the medieval Saami past by using decolonising frameworks and perspectives offered by the so-called ‘Indigenous turn’ of medieval studies, bridging medieval studies and Indigenous studies within a Norse context.
Role of green finance and carbon accounting in achieving sustainability
Authors: Wu Juan 
Published: January 2024

AbstractThis study delves into the influence of carbon accounting and green finance on the sustainable development index within a dataset of 500 Chinese publicly listed companies over the period from 2010 to 2020. Leveraging analysis conducted through the fully modified OLS methodology, the research findings unveil that the adoption of carbon accounting positively impacts the sustainable development index among these Chinese-listed firms. However, the number of employees engaged in these companies exhibits an adverse effect on the sustainable development index, and the consumption of resources, including water and electricity, demonstrates an inverse relationship with the overall sustainability of the firms. In light of these findings, the research suggests several central policies to bolster sustainable development. These include expanding green finance markets by harnessing financial technology (fintech) and big data, advancing corporate sustainability management, investing in green energy projects, and establishing an efficient green taxation system.
Communication tools and their support for integration in transdisciplinary research projects
Authors: Fischer Cornelia , Radinger-Peer Verena , Krainer Larissa , Penker Marianne 
Published: January 2024

AbstractThis study investigated how different communication tools support integration in transdisciplinary research. Ten digital and analogue tools with different communication directions and degrees of participation were tested in a 3.5-year transdisciplinary research project. Based on an exploratory operationalisation of the social-organisational, cognitive-epistemic and communicative dimensions of integration, we compared the tools’ integration support as perceived by 80 practitioners, 6 scientists and 3 integration experts. The multi-methods approach involved three surveys at different project phases, an ex-post poster assessment and interviews. The study showed that a variety of tools can serve diverse actors’ needs with varying preconditions and can play a supportive role for integration. Throughout the research process, the project website was identified as the central information platform for all groups. A living document in the form of a large hand-drawn poster and sketchnotes provided quick and understandable overviews and were particularly relevant for the communicative and the social-organisational dimension of integration. Digital videoconferences performed best in the cognitive-epistemic dimension and were found to be effective for information exchange, while online voting, emails and minutes were perceived to be less relevant. The involvement of integration experts with diverse communication and visualisation skills can support adaptive, context-specific and dynamic choices of communication tools, making project outcomes accessible to a variety of actors in a timely and transparent way throughout the project. The communication tools that were perceived by the actors to be most supportive were those that used visual and textual sign systems and facilitated a strong group identity. Therefore, we conclude that future research should include and operationalise a fourth, emotional dimension of integration.