Hasil untuk "Electronics"

A. Yariv

R. V. Ommering, F. V. Linden, J. Kramer et al.

Zhongqing Wei, Debin Wang, Suenne Kim et al.

S. Ko, H. Pan, C. Grigoropoulos et al.

R. W. Johnson, John L. Evans, P. Jacobsen et al.

G. Eda, M. Chhowalla

H. Klauk

Ali Emadi, Sheldon S. Williamson, A. Khaligh

G. Aad, M. Ackers, F. Alberti et al.

Qicai Li, Hongbin Liu, Dongcheng Cai et al.

As the Chinese Spallation Neutron Source enters Phase II, the increase in proton beam power will lead to a further boost in the intensity of pulsed neutron beams. To address the demand for higher event-rate readout electronics for energy-resolved neutron imaging detectors, we have developed a high-performance readout electronics system based on the Timepix4 chip. The prototype electronics system comprises a Timepix4 chip board and a high-performance digital board, which are interconnected through a custom FMC interface. The advantage of this system is its ability to achieve the full bandwidth readout of 160 Gbps for a single Timepix4 chip. The electronics system, based solely on a single ZYNQ-MPSOC chip, is capable of fully meeting the required performance specifications within a compact form factor of 8 cm x 30 cm. Furthermore, the system features a high-capacity external SODIMM memory interface (supporting up to 32 GB), which ensures stable data readout through a single 40 Gbps QSFP+ interface. As of the present moment, notable progress has been achieved, including the successful establishment of 16 data channels between Timepix4 and FPGA that operate error-free and stably at a speed of 5.12 Gbps, which is half of the maximum theoretical speed of 10.24 Gbps. The threshold standard deviation across all pixels is less than 50 e- after equalization. And the clear structural results obtained from X-ray experiments indicate that the functionality is essentially complete, allowing further testing.

Duroyon Matthieu, Susini Patrick, Misdariis Nicolas et al.

Electric vehicles are now part of the everyday automotive landscape. The resulting sonic experience is a major challenge for driver comfort. Despite this challenge being known, no solution reaching general consensus has yet been proposed. This might be due to the lack of a common culture of the sound or the expected sonic target in electric vehicles, in opposition to what existed for thermal engine. This work proposes a decisive tool to enhance communication on sound description in the electric car cabin. Inspired by soundscape studies, the methodology consists in using a semi-structured questionnaire oriented toward sound description and judgment with 12 acousticians working on electric vehicles. A verbal analysis identifies 11 specific sound names describing this sonic environment. Definitions that include three levels of description: causal, reduced and hedonic as well as audio illustrations, are proposed for each sound name. The lexicon is validated by the same group of acousticians and available online.

Ran Zhang, Xiaoping Wu, Xude Zhang et al.

With the rapid development of information technology, personalized education has become a key direction for improving the quality of online learning and optimizing individualized learning paths. However, accurately recommending appropriate courses and exercises for diverse learners remains a significant challenge. Existing recommendation methods often struggle with effectively modeling learner interests, addressing the cold-start problem, and dynamically adapting recommendation strategies to meet personalized needs. To address these limitations, this paper proposes RL-TBTNet, a novel teaching optimization recommendation framework that integrates a bidirectional Transformer, BERT, and reinforcement learning (DQN). The model first vectorizes user behavior data, learning content, and knowledge base information. Transformer layers are employed for feature encoding, while BERT extracts deep semantic representations to form individualized feature vectors. These features are then fused via Transformer-based processing to predict optimal learning content. In addition, a DQN-based reinforcement learning module models dynamic shifts in user interests, enabling adaptive refinement of learning trajectories over time. Experimental evaluations on public datasets show that RL-TBTNet outperforms existing Transformer-based methods such as BST in terms of key metrics like HR and NDCG, particularly excelling in cold-start scenarios. Ablation studies further confirm the effectiveness of semantic enhancement through BERT and reinforcement-driven optimization. These results demonstrate the framework’s potential as a robust and adaptive solution for personalized educational content recommendation, offering both practical value and theoretical insights for the development of intelligent education systems.

Jianshi Sun, Shouhang Li, Zhen Tong et al.

Wurtzite gallium nitride (GaN) has great potential for high-frequency and high-power applications due to its excellent electrical and thermal transport properties. However, enhancing the performance of GaN-based power electronics relies on heavy doping. Previous studies showed that electron-phonon interactions have strong effects on the lattice thermal conductivity of GaN due to the Fröhlich interaction. Surprisingly, our investigation reveals weak effects of electron-phonon interactions on the lattice thermal conductivity of n-type GaN at ultra-high electron concentrations and the impact of the Fröhlich interaction can be ignored. The small phonon-electron scattering rate is attributed to the limited scattering channels, quantified by the Fermi surface nesting function. In contrast, there is a significant reduction in the lattice thermal conductivity of p-type GaN at high hole concentrations due to the relatively larger Fermi surface nesting function. Meanwhile, as p-type GaN has relatively smaller electron-phonon matrix elements, the reduction in lattice thermal conductivity is still weaker than that observed in p-type silicon. Our work provides a deep understanding of thermal transport in doped GaN and the conclusions can be further extended to other wide-bandgap semiconductors, including $β$-Ga2O3, AlN, and ZnO.

Rakesh Rajendran Nair, Laura Teuerle, Jakob Wolansky et al.

The need to reduce the environmental impact of inorganic electronic systems is pressing. Although the field of organic electronics provides a potential solution to this issue, research and optimization is still majorly carried out on glass or plastic substrates. Additionally, the fabrication of organic devices requiring transparent electrodes is fraught with complex techniques and expensive materials which limit widespread implementation and sustainability goals. Here, we show that the quasi-fractal lignocellulose structures extracted from natural leaves can be successfully modified to be used as biodegradable substrates as well as electrodes for optoelectronic applications. Chemically coating the microstructures of these leaf skeletons with metals results in quasi-transparent, flexible electrodes having sheet resistances below 1 ohm/sq. and a concomitant current carrying capacity as high as 6 A over a 2.5*2.5 cm2 leaf electrode, all while maintaining broadband optical transmittance values of around 80%.

Binwei Deng, Hucheng Chen, Kai Chen et al.

A prototype Liquid-argon Trigger Digitizer Board (LTDB), called the LTDB Demonstrator, has been developed to demonstrate the functions of the ATLAS Liquid Argon Calorimeter Phase-I trigger electronics upgrade. Forty Analog-to-Digital converters and four FPGAs with embedded multi-gigabit-transceivers on each Demonstrator need high quality clocks. A clock distribution system based on commercial components has been developed for the Demonstrator. The design of the clock distribution system is presented. The performance of the clock distribution system has been evaluated. The components used in the clock distribution system have been qualified to meet radiation tolerance requirements of the Demonstrator.

Uihoon Jung, Miseong Kim, Jaewon Jang et al.

Abstract With increasing demand for wearable electronics capable of computing huge data, flexible neuromorphic systems mimicking brain functions have been receiving much attention. Despite considerable efforts in developing practical neural networks utilizing several types of flexible artificial synapses, it is still challenging to develop wearable systems for complex computations due to the difficulties in emulating continuous memory states in a synaptic component. In this study, polymer conductivity is analyzed as a crucial factor in determining the growth dynamics of metallic filaments in organic memristors. Moreover, flexible memristors with bio‐mimetic synaptic functions such as linearly tunable weights are demonstrated by engineering the polymer conductivity. In the organic memristor, the cluster‐structured filaments are grown within the polymer medium in response to electric stimuli, resulting in gradual resistive switching and stable synaptic plasticity. Additionally, the device exhibits the continuous and numerous non‐volatile memory states due to its low leakage current. Furthermore, complex hardware neural networks including ternary logic operators and a noisy image recognitions system are successfully implemented utilizing the developed memristor arrays. This promising concept of creating flexible neural networks with bio‐mimetic weight distributions will contribute to the development of a new computing architecture for energy‐efficient wearable smart electronics.

Asini Kumar Baliarsingh, Sangram Keshori Mohapatra , Pabitra Mohan Dash

This article introduces hybrid Arithmetic Optimization Algorithm (AOA) and Local Unimodal Sampling (hAOA-LUS)-based fractional order (FO) proportional derivative (PD) cascaded with one plus proportional integral (1+PI) controller for automatic generation control of power system with renewable energy sources (RES) and electric vehicle (EV). The control-area 1 has thermal, hydro, gas, and wind power generators and the same true for control area 2, which uses thermal, hydro, gas, and solar energy sources. Initially, Proportional-Integral-Derivative (PID) controllers are taken into consideration and it has been demonstrated that hAOA-LUS outperforms as compared to the AOA, Particle Swarm Optimization, and Generic Algorithm (GA). The assessing of overshoots, undershoots, and different integral errors of frequency and tie-line power deviations after step load perturbations in each area allows for performance comparison of the proposed power system. In the next stage, EVs are considered in each area and the controller parameters are optimized by hAOA-LUS techniques in the presence of RES and EV. A comparative analysis is carried out by hAOA-LUS-tuned FO PD(1+PI) controller with PID as well integer ordered PD(1+PI) for various cases so as to validate the superiority of the anticipated controller. The results from MATLAB and OPAL-RT are compared in order to verify the authenticate feasibility of method.

Felix E. B. Brettner, Stefanie Gier, Annika Haessler et al.

Abstract Inflammation plays a critical role in the pathophysiology of many diseases, and dysregulation of the involved signaling cascades often culminates in uncontrollable disease progression and, ultimately, chronic manifestation. Addressing these disorders requires balancing inflammation control while preserving essential immune functions. Cyclodextrins (CDs), particularly β-CD, have gained attention as biocompatible biomaterials with intrinsic anti-inflammatory properties, and chemical modification of their backbone offers a promising strategy to enhance their physicochemical properties, adaptability, and therapeutic potential. This study evaluated and characterized the immunomodulatory effects of amphiphilic CD derivatives, which self-assemble into nanoparticles, compared to soluble parent β-CD. In a human macrophage model, CD nanoparticles demonstrated superior anti-inflammatory activity, with derivative-specific effects tied to their physicochemical properties, surpassing the soluble β-CD control. Alongside the downregulation of key pro-inflammatory markers, significant reductions in inflammasome activation and changes in lipid profiles were observed. The findings of this study underscore the potential of cyclodextrin-based nanoparticles as versatile biomaterials for treating the complex pathophysiology of various acute and chronic inflammation-associated disorders.

Peiyao Guo, Shahab Dehghan, Vladimir Terzija et al.

With the increasing share of wind power in the energy sector, many countries start to cut back supporting policies for wind power and shift towards market-oriented schemes, challenging the profitability of wind farms. Energy storage offers a flexible solution to enhance their profitability. This work explores different wind-related storage investment modes, including 1) direct ownership, 2) cooperative, and 3) competitive modes in a market-based environment. For the direct ownership mode, a bilevel single-leader-single–follower Stackelberg game model is proposed, where wind farms invest in and operate storage facilities strategically to maximize their profits in the upper level, while the lower-level problem represents the system operator’ s market-clearing process. A cooperative game framework is presented for the cooperative mode, that wind farms and storage investors agree on a profit allocation rule, i.e., Shapley value or Nucleolus to collaborate in investing and bidding as a coalition. The competitive mode is interpreted as a multi-leader-single-follower Stackelberg game, describing an independent investor investing in and operating storage facilities in competition with wind farms. Case studies conducted on a 6-bus and the IEEE 30-bus test systems demonstrate that storage facilities directly invested in by wind farms are the best option for maximizing their profits, resulting in up to an 8.7% increase. The cooperative option provides a suboptimal increase of up to 3.1%, diversifying the costs and risks associated with storage investments. In contrast, the competitive mode can diminish wind farms’ profitability, with up to a 30.6% decrease in profits.

P. Bujak, I. Kulszewicz-Bajer, M. Zagórska et al.