bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimizing energy density in dielectric elastomer generators: a reliability-dependent metric
Authors: Taine Emmanuel , Andritsch Thomas , Saeedi Istebreq A , Morshuis Peter H F 
Published: October 2024

Abstract
Dielectric elastomer generators (DEGs) are soft transducers capable of converting mechanical energy into electrostatic energy. Increasing the mechanical stretch amplitude and the electric field imposed to the DEG leads to higher energy conversion at the cost of a reduced lifetime. Here, mechanical fatigue and electrical degradation were assessed on a silicone-based DEG, and the outcome was used to build an electro-mechanical reliability model. A novel metric, termed levelized energy density, has been introduced to carefully balance the conflicting objectives of high energy output and long-term reliability. Through a multi-dimensional anaylsis of this index, the optimal operating parameters (stretch amplitude and electric field) that maximize energy conversion can be derived. Energy densities reported in literature are generally obtained after pushing the DEG close to their intrinsic limits for a limited number of cycles. In our approach, more realistic values in the endurance domain are presented, which typically leads to a 9-fold decrease in energy density for a design life of 1 million cycles. This article not only addresses the challenge of optimizing DEG performance but also emphasizes the importance of considering realistic operational conditions to enhance reliability, ultimately contributing to the practical and sustainable deployment of these soft transducers in various applications.
Block-based color image encryption algorithm by a novel memristor chaotic system and new RNA computation
Authors: Yang Yang , Yang Degang 
Published: October 2024

Abstract
The security of images is closely related to the protection of information privacy. We proposed a novel 5D memory resistive chaotic system (5D-MRCS), which exhibits good chaotic characteristics. Therefore, we employed it to design an image encryption algorithm aimed at ensuring secure image transmission. To further enhance the complexity of the algorithm and obtain more chaotic sequences, we combine the 5D-MRCS with the Hodgkin-Huxley (HH) model and use this combination in algorithm design. Initially, we combine the plain image with the hash function SHA-384 to devise and generate the secret key. Subsequently, the algorithm determines whether to pad the plain image based on different block size requirements. Then, we use multiple chaotic sequences generated by the 5D-MRCS and HH model to perform the global image permutation operation. Our designed permutation algorithm includes two parts: Block-based permutation and a new pixel-level permutation. Next, the scrambled image undergoes block-based random RNA diffusion, incorporating two newly proposed methods in the RNA operations, ultimately resulting in the ciphertext image. The algorithm’s NPCR, UACI, information entropy, and other security performance metrics are very close to the ideal values, and it possess characteristics such as resistance to differential, cutting, chosen plaintext, and noise attacks. Compared with other algorithms, it still has some advantages across multiple images and demonstrates excellent image encryption performance.
Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning
Authors: Park Seungbin , Lipton Megan , Dadarlat Maria C 
Published: October 2024

Abstract

Objective. Brain–machine interfaces (BMIs) aim to restore sensorimotor function to individuals suffering from neural injury and disease. A critical step in implementing a BMI is to decode movement intention from recorded neural activity patterns in sensorimotor areas. Optical imaging, including two-photon (2p) calcium imaging, is an attractive approach for recording large-scale neural activity with high spatial resolution using a minimally-invasive technique. However, relating slow two-photon calcium imaging data to fast behaviors is challenging due to the relatively low optical imaging sampling rates. Nevertheless, neural activity recorded with 2p calcium imaging has been used to decode information about stereotyped single-limb movements and to control BMIs. Here, we expand upon prior work by applying deep learning to decode multi-limb movements of running mice from 2p calcium imaging data. Approach. We developed a recurrent encoder–decoder network (LSTM-encdec) in which the output is longer than the input. Main results. LSTM-encdec could accurately decode information about all four limbs (contralateral and ipsilateral front and hind limbs) from calcium imaging data recorded in a single cortical hemisphere. Significance. Our approach provides interpretability measures to validate decoding accuracy and expands the utility of BMIs by establishing the groundwork for control of multiple limbs. Our work contributes to the advancement of neural decoding techniques and the development of next-generation optical BMIs.
E-SAT: an extreme learning machine based self attention approach for decoding motor imagery EEG in subject-specific tasks
Authors: Abbasi Muhammad Ahmed , Abbasi Hafza Faiza , Yu Xiaojun , Aziz Muhammad Zulkifal , Yih Nicole Tye June , Fan Zeming 
Published: October 2024

Abstract

Objective. Despite substantial advancements in Brain–Computer Interface (BCI), inherent limitations such as extensive training time and high sensitivity to noise largely hinder their rapid development. To address such issues, this paper proposes a novel extreme learning machine (ELM) based self-attention (E-SAT) mechanism to enhance subject-specific classification performances.
Approach. Specifically, for E-SAT, ELM is employed both to improve self-attention module generalization ability for feature extraction and to optimize the model’s parameter initialization process. Meanwhile, the extracted features are also classified using ELM, and the end-to-end ELM based setup is used to evaluate E-SAT performance on different motor imagery (MI) EEG signals.
Main results. Extensive experiments with different datasets, such as BCI Competition III Datasets IV-a, IV-b and BCI Competition IV Datasets 1, 2a, 2b, 3 are conducted to verify the effectiveness of the proposed E-SAT strategy. Results show that E-SAT outperforms several state-of-the-art and existing methods in subject-specific classification on all the datasets. An average classification accuracy of 99.8%, 99.1%, 98.9%, 75.8%, 90.8%, and 95.4% respectively is achieved for each datasets which demonstrate an improvement of 5%–6% compared to the existing methods. In addition, Kruskal Wallis test is performed to demonstrate the statistical significance of E-SAT and the results indicate significant difference with a 95% confidence level. Significance. The experimental results not only show outstanding performance of E-SAT in feature extraction, but also demonstrate that it helps achieve the best results among nine other robust classifiers. In addition, results in this study also demonstrate that E-SAT achieves exceptional performance in both binary and multi-class classification tasks, as well as for noisy and non-noisy datasets.
Feeling senseless sensations: a crossmodal EEG study of mismatched tactile and visual experiences in virtual reality
Authors: Lehser Caroline , Hillyard Steven A , Strauss Daniel J 
Published: October 2024

Abstract

Objective. To create highly immersive experiences in virtual reality (VR) it is important to not only include the visual sense but also to involve multimodal sensory input. To achieve optimal results, the temporal and spatial synchronization of these multimodal inputs is critical. It is therefore necessary to find methods to objectively evaluate the synchronization of VR experiences with a continuous tracking of the user. Approach. In this study a passive touch experience was incorporated in a visual-tactile VR setup using VR glasses and tactile sensations in mid-air. Inconsistencies of multimodal perception were intentionally integrated into a discrimination task. The participants’ electroencephalogram (EEG) was recorded to obtain neural correlates of visual-tactile mismatch situations. Main results. The results showed significant differences in the event-related potentials (ERP) between match and mismatch situations. A biphasic ERP configuration consisting of a positivity at 120 ms and a later negativity at 370 ms was observed following a visual-tactile mismatch. Significance. This late negativity could be related to the N400 that is associated with semantic incongruency. These results provide a promising approach towards the objective evaluation of visual-tactile synchronization in virtual experiences.
Effect of detachment on Magnum-PSI ELM-like pulses: direct observations and qualitative results
Authors: Federici Fabio , Lipschultz Bruce , Akkermans Gijs R.A. , Verhaegh Kevin , Reinke Matthew L. , Classen Ivo G.J. ,   Magnum-PSI Team
Published: October 2024

Abstract
Conditions similar to those at the end of the divertor leg in a tokamak were replicated in the linear plasma machine Magnum-PSI. The neutral pressure in the target chamber is then increased to cause the target to transition from an attached to a detached state. Superimposed to this steady state regime, edge localised mode (ELM)-like pulses are reproduced, resulting in a sudden increase in plasma temperature and density, such that the heat flux increases transiently by half an order of magnitude. Visible light emission, target thermography, and Thomson scattering are used to demonstrate that the higher the neutral pressure the more energy is removed from the ELM-like pulse in the volume. If the neutral pressure is sufficiently high, the ELM-like pulse can be prevented from affecting the target and the plasma energy is fully dissipated in the volume instead (ID 4 in table 1). The visible light images allow the division of the ELM-plasma interaction process of ELM energy dissipation into 3 ‘stages’ ranging from no dissipation to full dissipation (the target plasma is detached). In the second publication related to this study, spectroscopic data is analysed with a Bayesian approach, to acquire insights into the significance of molecular processes in dissipating the plasma energy and particles.
Application of the VMM ASIC for SiPM-based calorimetry
Authors: Bearden I. , Buchakchiev V. , Buhl A. , Dufke L. , Isidori T. , Jia S. , Kozhuharov V. , Loizides C. , Muller H. , Pfeiffer D. , Rauch M. , Rusu A. , Simeonov R. 
Published: October 2024

Abstract
Highly integrated multichannel readout electronics is
crucial in contemporary particle physics experiments. A novel
silicon photomultiplier readout system based on the VMM3a ASIC was
developed, for the first time exploiting this chip for calorimetric
purposes. To extend the dynamic range the signal from each SiPM
channel was processed by two electronics channels with different
gain. A fully operational prototype system with 256 SiPM readout
channels allowed the collection of data from a prototype of the
ALICE Forward Hadron Calorimeter (FoCal-H Prototype 2). The design
and the test beam results using high energy hadron beams are
presented and discussed, confirming the applicability of VMM3a-based
solutions for energy measurements in a high rate environment.
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superfields and geometry revisited
Authors: Hull Chris , Zabzine Maxim 
Published: October 2024

Abstract
We take a fresh look at the relation between generalised Kähler geometry and

N
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supersymmetric sigma models in two dimensions formulated in terms of (2, 2) superfields. Dual formulations in terms of different kinds of superfield are combined to give a formulation with a doubled target space and both the original superfield and the dual superfield. For Kähler geometry, we show that this doubled geometry is Donaldson’s deformation of the holomorphic cotangent bundle of the original Kähler manifold. This doubled formulation gives an elegant geometric reformulation of the equations of motion. We interpret the equations of motion as the intersection of two Lagrangian submanifolds (or of a Lagrangian submanifold with an isotropic one) in the infinite dimensional symplectic supermanifold which is the analogue of phase space. We then consider further extensions of this formalism, including one in which the geometry is quadrupled, and discuss their geometry.
Spatial photoinduced doping of graphene/hBN heterostructures characterized by quantum Hall transport
Authors: Le Son T , Mai Thuc T , Munoz Maria F , Hight Walker Angela R , Richter Curt A , Hanbicki Aubrey T , Friedman Adam L 
Published: October 2024

Abstract
Doped semiconductors are a central and crucial component of all integrated circuits. By using a combination of white light and a focused laser beam, and exploiting hexagonal boron nitride (hBN) defect states, heterostructures of hBN/Graphene/hBN are photodoped in-operando, reproducibly and reversibly. We demonstrate device geometries with spatially-defined doping type and magnitude. After each optical doping procedure, magnetotransport measurements including quantum Hall measurements are performed to characterize the device performance. In the unipolar (p+–p–p+ and n–n+–n) configurations, we observe quantization of the longitudinal resistance, proving well-defined doped regions and interfaces that are further analyzed by Landauer–Buttiker modeling. Our unique measurements and modeling of these optically doped devices reveal a complete separation of the p- and n-Landau level edge states. The non-interaction of the edge states results in an observed ‘insulating’ state in devices with a bi-polar p–n–p configuration that is uncommon and has not been measured previously in graphene devices. This insulating state could be utilized in high-performance graphene electrical switches. These quantitative magnetotransport measurements confirm that these doping techniques can be applied to any two-dimensional materials encapsulated within hBN layers, enabling versatile, rewritable circuit elements for future computing and memory applications.
Comparison study on SMC and grain-oriented laminated steel core for small-size axial flux permanent-magnet synchronous machines
Authors: Karabulut Yiğit , Meşe Erkan , Ayaz Murat , Aktaş Serkan 
Published: October 2024

Abstract
This study aims to compare the soft magnetic composite (SMC) and grain-oriented (GO) steel stator axial flux permanent-magnet synchronous machine (AFPMSM) in terms of performance and iron losses. Stator cores are manufactured using both materials to perform experimental performance tests. The produced machines are designed for pump propulsion systems in left ventricular assist devices as an application area. The machines are modeled with several analytical equations, and iron losses and performance tests are carried out with AFPMSMs, finite element methods, and experimental setups. Our findings show that the torque density is higher in the GO steel stator AFPMSMs that can produce 15.07 percent more torque. GO steel material experimentally has 20.33 percent less iron loss as SMC material since the loss value per kilogram for SMC material is higher than that for GO steel. In addition, the saturation value of the SMC material is lower than that of the GO material according to the magnetic flux density value.
High altitude dilemmas: assessing academic air travel reduction measures by comparing reduction potential and willingness
Authors: Bagutti Rocco , Wenger Ariane , Ranacher Peter 
Published: October 2024

Abstract
Many researchers consider air travel crucial for a successful career and fly frequently to attend conferences, participate in meetings, teach classes, and conduct fieldwork, which creates significant greenhouse gas (GHG) emissions that often account for the largest share of a university’s emission total. Several universities have attempted to address this issue by implementing measures to reduce GHG emissions from academic air travel. Ideally, these measures are both effective in reducing emissions and acceptable to the academic staff willing to endorse them. We evaluated five air travel reduction measures and compared their GHG emission reduction potential to academics’ willingness to implement them. We calculated reduction potentials based on empirical flight data from the University of Zurich’s Department of Geography and conducted an online survey among the department’s academic staff to assess willingness. On average, academic staff were willing to implement all five measures. Measures restricting travel showed the highest reduction potential but the lowest willingness to adopt among academic staff. Conversely, measures involving the transition to alternative travel modes showed higher willingness but lower reduction potentials. Our results suggest that there is no straightforward answer for universities aiming to reduce their air travel emissions. In addition to implementing more acceptable low-impact measures, universities must explore examples of good practices and endeavor to remove obstacles to high-impact measure implementation.
Deep-millet: a deep learning model for pearl millet disease identification to envisage precision agriculture
Authors: Johnson I , Mary X Anitha , Winifred Raj A Peniel , Chalmers J , Karthikeyan M , J Andrew 
Published: October 2024

Abstract
Plants are integral to human sustenance, serving as fundamental sources of sustenance, materials, and energy, crucial for economic prosperity. However, their productivity and yield are increasingly threatened by pests and diseases, exacerbated by shifting climatic conditions. Pearl millet, a vital crop in Africa and Asia, is particularly susceptible to a range of diseases including downy mildew, rust, ergot, smut, and blast, posing significant risks to crop yield and quality. Timely and accurate disease identification is paramount for effective management strategies. Traditional methods of disease detection relying on visual identification are laborious, costly, and often require specialized expertise, presenting formidable challenges for farmers. In this study, we propose a novel mobile application integrating a robust Deep Learning (DL) model for the automated identification of pearl millet leaf diseases, employing advanced computer vision techniques. A Convolutional Neural Network (CNN) architecture, named Deep Millet, was trained on a comprehensive dataset comprising 3441 field images depicting pearl millet leaves in both healthy and diseased states. It consists of fewer but more effective layers, which are optimized to extract the most pertinent features from the RGB images Comparative analysis against pre-trained models, including AlexNet, ResNet50, InceptionV3, Xception, NasNet mobile, VGG16, and VGG19, was conducted to evaluate the performance of the proposed model. Results demonstrate that Deep Millet achieved superior accuracy, completing training in a mere 240 s and yielding an impressive accuracy rating of 98.86%, surpassing current state-of-the-art models.
Harmony and the Land
Authors: Lewis Colin J , Kling Jennifer 
Published: October 2024

Abstract
Discussions of land management in contemporary analytic political philosophy often take place through the lens of environmental protection, wealth management, or climate change. Such scholarship overlooks the distinctly communal issues and benefits associated with land management that are brought out by a close analysis of the well-field system elaborated by the early Confucian Mengzi. Shared management of the land can help increase prosociality and reduce interpersonal and intercommunal fractiousness, and so help ameliorate political polarization. The chapter argues that, understood broadly, such lessons are applicable to contemporary multicultural liberal democracies. This is true even though such polities diverge wildly from ancient China in terms of their economic, social, and political structures.
Proud Vermin
Authors: Lewis Colin J , Kling Jennifer 
Published: October 2024

Abstract
Contemporary arguments about private paramilitary organizations often focus on the threat of physical violence they pose to the state: if such organizations garner enough physical power, then they can overtake the state via violent coup. Inspired by the legalist scholar Han Feizi’s position, this chapter contends that such organizations also represent a sociopolitical, existential threat to the state. Specifically, their tendency for ideological expansion and subsequent gathering of political influence undermines state institutions, even without the use of overt physical force. Consequently, the sociopolitical enterprise of having a unified, stable state is incompatible with the existence of, and public political support for, private paramilitary organizations, regardless of their actual or potential physical power. This argument succeeds regardless of the moral status of such paramilitary groups. Such groups, when they match the essential components of the description Han Feizi provides, are practically and politically antithetical to the integrity of the state.
Histomorphologic spectrum of nodal marginal zone lymphoma as defined by its methylome
Authors: Spada Francesca , Rosenwald Andreas , Klapper Wolfram , Feller Alfred C , Fend Falko , Ott German , Fürstberger Axel , Barth Thomas F E , Möller Peter 
Published: October 2024

Abstract

Objectives
Primary nodal marginal B-cell lymphoma (NMZL) is rare and histologically very variable. Its large-cell presentation is difficult to distinguish from nodal diffuse large B-cell lymphoma (nDLBCL) due to the absence of specific markers for nodal marginal zone lymphomas in general.

Methods
Using a comprehensive cohort of NMZLs and a control cohort of nDLBCLs, we conducted a methylome analysis on subgroups of both.

Results
The methylomes were strikingly different between the cohorts but unexpectedly homogeneous within the NMZL cohort. This allowed us to describe the morphologic spectrum of NMZL in all its value ranges. The considerable overlap in growth pattern and cytology of NMZL with nDLBCL was explored morphometrically, leading to an operational tool for separating both by a simple measurement of cell size and nuclear size. This was integrated in a hierarchical approach, including a scoring system for the parameter growth pattern, follicular colonization, follicular dendritic network, IgD expression, and Ki-67 rate, and led to a proposal for a classifier that we present here.

Conclusions
This methylome-based study extends the morphological spectrum of NMZL towards large cell morphology and offers a conventional way to distinguish it from nDLBCL.