bluuebunny/crossref_metadata_2025_split huggingface dataset:
The fourth fundamental circuit element: principle and applications
Authors: Sun Young 
Published: January 2022

Abstract
The relationships between four basic circuit variables—voltage (v), current (i), charge (q), and magnetic flux (ϕ)—have defined three fundamental circuit elements: resistors, capacitors, and inductors. From a viewpoint of symmetry, there is a fourth fundamental circuit element defined by the relationship between charge and magnetic flux. Historically, a device called a memristor was considered to be the fourth element, but it has caused intense controversy because a memristor is conceived based on a nonlinear i–v relationship rather than a direct q-ϕ relationship. Alternatively, a direct correlation between charge (q) and magnetic flux (ϕ) can be built up by employing the magnetoelectric (ME) effects; i.e. magnetic field control of electric polarization and electric field control of magnetization. In this review, recent progress on the principle and applications of the fourth circuit element based on ME effects is summarized. Both the fourth linear element and nonlinear memelement, termed transtor and memtranstor, respectively, have been proposed and experimentally demonstrated. A complete relational diagram of fundamental circuit elements has been constructed. A transtor with a linear ME effect can be used in a variety of applications, such as energy harvesters, tunable inductors, magnetic sensors, gyrators, transformers, etc. A memtranstor, showing a pinched hysteresis loop, has great potential in developing low-power nonvolatile electronic devices. The principle is to utilize the states of the ME coefficient α
E = dE/dH, instead of resistance, magnetization, or electric polarization, to store information. Both nonvolatile memory and logic devices can be implemented by using memtranstors, which provides a route toward logic-in-memory computing systems. In addition, artificial synaptic devices that are able to mimic synaptic behaviors have also been realized using memtranstors. The fourth circuit element and memelement based on ME effects provide extra degrees of freedom to broaden circuit functionalities and develop advanced electronic devices.
Thermoinduced structural-transformation and luminescent conversion in hybrid manganese halides
Authors: Jiang Chunli , Luo Qianqian , Luo Chunhua , Lin Hechun , Peng Hui 
Published: January 2022

Abstract
We report here the synthesis of hybrid manganese halide crystals, (C4H7N2)MnCl3 · H2O and (C4H7N2)2MnCl4, by using the same organic ligand 2-methylimidazole. Upon heating treatment, the red-emissive (C4H7N2)MnCl3 · H2O crystal is structurally transformed into green-emissive (C4H7N2)2MnCl4 crystal in situ. The crystal structural analysis reveals that the [MnCl5 · H2O]3− octahedra chains decompose into mono [MnCl4]2− tetrahedra, accompanied by the departure of H2O molecules. Upon cooling in air or water vapor, the crystal structure and luminescence of (C4H7N2)2MnCl4 are transformed to those of (C4H7N2)MnCl3 · H2O. The in situ conversion of luminescence between (C4H7N2)MnCl3 · H2O and (C4H7N2)2MnCl4 provides new insight into the potential application of hybrid manganese halides.
Enhanced thermoelectric performance of defect engineered monolayer graphene
Authors: Kim Woochang , Lee Wonseok , Lee Seung-Mo , Kim Duckjong , Park Jinsung 
Published: January 2022

Abstract
We propose a method of improving the thermoelectric properties of graphene using defect engineering through plasma irradiation and atomic layer deposition (ALD). We intentionally created atomic blemishes in graphene by oxygen plasma treatment and subsequently healed the atomistically defective places using Pt-ALD. After healing, the thermal conductivity of the initially defective graphene increased slightly, while the electrical conductivity and the square of the Seebeck coefficient increased pronouncedly. The thermoelectric figure of merit of the Pt-ALD treated graphene was measured to be over 4.8 times higher than the values reported in the literature. We expect that our study could provide a useful guideline for the development of graphene-based thermoelectric devices.
Equivalent circuit modeling and analysis of aerodynamic vortex-induced piezoelectric energy harvesting
Authors: Jia Jinda , Shan Xiaobiao , Zhang Xingxu , Xie Tao , Yang Yaowen 
Published: January 2022

Abstract
Low-speed wind energy has potential to be captured for powering micro-electro-mechanical systems or sensors in remote inaccessible place by piezoelectric energy harvesting from vortex-induced vibration. Conventional theory or finite-element analysis mostly considers a simple pure resistance as interface circuit because of the complex fluid-solid-electricity coupling in aeroelastic piezoelectric energy harvesting. However, the output alternating voltage should be rectified to direct voltage to be used in practical occasions, where the theoretical analysis and finite-element analysis for complex interface may be cumbersome or difficult. To solve this problem, this paper presents an equivalent circuit modeling (ECM) method to analyze the performance of vortex-induced energy harvesters. Firstly, the equivalent analogies from the mechanical and fluid domain to the electrical domain are built. The linear mechanical and fluid elements are represented by standard electrical elements. The nonlinear elements are represented by electrical non-standard user-defined components. Secondly, the total fluid-solid-electricity coupled mathematical equations of the harvesting system are transformed into electrical formulations based on the equivalent analogies. Finally, the entire ECM is established in a circuit simulation software to perform system-level transient analyses. The simulation results from ECM have good agreement with the experimental measurements. Further parametric studies are carried out to assess the influences of wind speed and resistance on the output power of the alternating circuit interface and the capacitor filter circuit. At wind speed of 1.2 m s−1, the energy harvester could generate an output power of 81.71 μW with the capacitor filter circuit and 114.64 μW with the alternating circuit interface. The filter capacitance is further studied to ascertain its effects on the stability of output and the settling time.
Degradation mechanism of the superconducting performance of a Bi2212 cable under magnetic fields
Authors: Zhang Zhen-Chuang , Yang Dong-Sheng , Zhou Hai-Shan , Qin Jing-Gang , Luo Guang-Nan 
Published: January 2022

Abstract
In magnetic confinement fusion reactors, superconducting magnet systems are essential for generating and controlling high magnetic fields. To increase the magnetic field, new superconducting materials such as Bi2212 (Bi2Sr2CaCu2O8+x
) have been selected in the design of magnet systems. However, the stability of the Bi2212 superconductor under magnetic fields must be studied for the routine and safe operation of magnet systems. In this work, the stability and degradation mechanism of a Bi2212 cable under magnetic fields were investigated. With a magnetic field of 5.8 T, the cable carrying 29 kA was exerted with a force of ∼168.2 kN m−1. In the core area of the cable, moved wires were detected by computed tomography. The macroscopic movement of the wires would vary with the axial position, which could be related to the twist structure. Then, the cable was decomposed, and the acquired wires were tested under 12 T at 4.2 K by four-probe method. The results indicated that the inner wires had relatively lower critical currents, which should be the reason for the degradation of cable performance. Scanning electron microscope images of the superconducting phase within the wires confirmed that cracks existed in the superconducting phase of the inner wires, while intact crystals were found in that of the outer wires. The variation in microstructures gave rise to changes in the wire performance.
Ultracold neutron storage simulation using the Kassiopeia software package
Authors: Bogorad Z , Mohanmurthy P , Formaggio J A 
Published: January 2022

Abstract
The Kassiopeia software package was originally developed to simulate electromagnetic fields and charged particle trajectories for neutrino mass measurement experiments. Recent additions to Kassiopeia also allow it to simulate neutral particle trajectories in magnetic fields based on their magnetic moments. Two different methods were implemented: an exact method that can work for arbitrary fields and an adiabatic method that is limited to slowly-varying fields but is much faster for large precession frequencies. Additional interactions to simulate reflection of ultracold neutrons (UCNs) from material walls and to allow spin–flip pulses were also added. These tools were used to simulate neutron precession in a room temperature neutron electric dipole moment experiment and predict the values of the longitudinal and transverse relaxation times as well as the trapping lifetime. All three parameters are found to closely match the experimentally determined values when simulated with both the exact and adiabatic methods, confirming that Kassiopeia is able to accurately simulate neutral particles. This opens the door for future uses of Kassiopeia to prototype the next generation of atomic traps and UCN experiments.
Waveform processing using neural network algorithms on the front-end electronics
Authors: Miryala S. , Mittal S. , Ren Y. , Carini G. , Deptuch G. , Fried J. , Yoo S. , Zohar S. 
Published: January 2022

Abstract
In a multi-channel radiation detector readout system, waveform sampling, digitization, and raw data transmission to the data acquisition system constitute a conventional processing chain. The deposited energy on the sensor is estimated by extracting peak amplitudes, area under pulse envelopes from the raw data, and starting times of signals or time of arrivals. However, such quantities can be estimated using machine learning algorithms on the front-end Application-Specific Integrated Circuits (ASICs), often termed as “edge computing”. Edge computation offers enormous benefits, especially when the analytical forms are not fully known or the registered waveform suffers from noise and imperfections of practical implementations. In this work, we aim to predict peak amplitude from a single waveform snippet whose rising and falling edges containing only 3 to 4 samples. We thoroughly studied two well-accepted neural network algorithms, Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) by varying their model sizes. To better fit front-end electronics, neural network model reduction techniques, such as network pruning methods and variable-bit quantization approaches, were also studied. By combining pruning and quantization, our best performing model has the size of 1.5 KB, reduced from 16.6 KB of its full model counterpart. It can reach mean absolute error of 0.034 comparing to that of a naive baseline of 0.135. Such parameter-efficient and predictive neural network models established feasibility and practicality of their deployment on front-end ASICs.
Multi-threshold window discriminator based on SAR logic
Authors: Jirsa J. , Marcisovsky M. , Jakovenko J. 
Published: January 2022

Abstract
The new X-ray imaging detectors allow capturing an X-ray image in various photon energy ranges in one shot. This technique is called X-ray color imaging, and it is becoming a promising method in fields such as medical imaging, computed tomography, and non-destructive material testing. To measure the energy spectrum in one shot, discriminant circuits need to be integrated into the pixel front-end electronics. Several solutions of in-pixel discriminators exist. However, current designs suffer from a low number of discrimination bins and need to adjust each threshold separately, leading to relatively complicated calibration procedures. This work introduces a novel design of a multi-threshold window discriminator based on successive approximation register logic. This circuit realizes in-pixel binning to ten equidistant windows. Two variables are used for tuning the multi-threshold window discriminator: offset of the first window and width of the windows. Setting these parameters allows the user to fulfill the need of the target application.
A synthetic view of rainfall intensification in the West African Sahel
Authors: Chagnaud G , Panthou G , Vischel T , Lebel T 
Published: January 2022

Abstract
The West African Sahel has been facing for more than 30 years an increase in extreme rainfall with strong socio-economic impacts. This situation challenges decision-makers to define adaptation strategies in a rapidly changing climate. The present study proposes (i) a quantitative characterization of the trends in extreme rainfall at the regional scale, (ii) the translation of the trends into metrics that can be used by hydrological risk managers, (iii) elements for understanding the link between the climatology of extreme and mean rainfall. Based on a regional non-stationary statistical model applied to in-situ daily rainfall data over the period 1983–2015, we show that the region-wide increasing trend in extreme rainfall is highly significant. The change in extreme value distribution reflects an increase in both the mean and variability, producing a 5%/decade increase in extreme rainfall intensity whatever the return period. The statistical framework provides operational elements for revising the design methods of hydraulic structures which most often assume a stationary climate. Finally, the study shows that the increase in annual maxima of daily rainfall is more attributable to stronger storm intensities (80%) than to more frequent storm occurrences (20%), reflecting a major rainfall regime shift in comparison to those observed in the region since 1950.
Crop-climate feedbacks boost US maize and soy yields
Authors: Coffel Ethan D , Lesk Corey , Winter Jonathan M , Osterberg Erich C , Mankin Justin S 
Published: January 2022

AbstractUS maize and soy production have increased rapidly since the mid-20th century. While global warming has raised temperatures in most regions over this time period, trends in extreme heat have been smaller over US croplands, reducing crop-damaging high temperatures and benefiting maize and soy yields. Here we show that agricultural intensification has created a crop-climate feedback in which increased crop production cools local climate, further raising crop yields. We find that maize and soy production trends have driven cooling effects approximately as large as greenhouse gas induced warming trends in extreme heat over the central US and substantially reduced them over the southern US, benefiting crops in all regions. This reduced warming has boosted maize and soy yields by 3.3 (2.7–3.9; 13.7%–20.0%) and 0.6 (0.4–0.7; 7.5%–13.7%) bu/ac/decade, respectively, between 1981 and 2019. Our results suggest that if maize and soy production growth were to stagnate, the ability of the crop-climate feedback to mask warming would fade, exposing US crops to more harmful heat extremes.
A low-profile consolidated metastructure for multispectral signature management
Authors: Gupta Nitish Kumar , Singh Gaganpreet , Wanare Harshawardhan , Ramakrishna S Anantha , Srivastava Kumar Vaibhav , Ramkumar J 
Published: January 2022

AbstractThis work pertains to the design, numerical investigation, and experimental demonstration of an optically transparent, lightweight, and conformable metastructure that exhibits multispectral signature management capabilities despite its extremely low-profile configuration. In comparison to the existing hierarchical approaches of designing multispectral stealth solutions, attention has been paid to accommodate the conflicting requirements of radar and infrared (IR) stealth using a single metasurface layer configuration, which required a few constraints to be incorporated during the design stage to ensure compatibility. This methodology promulgates the desired multispectral response with minimal manufacturing footprint and facilitates an efficient integration with the other existing countermeasure platforms. The resulting design exhibits a polarization-insensitive and incident angle stable broadband microwave absorption with at least 90% absorption ranging from 8.2 to 18.4 GHz. Concomitantly it also exhibits an averaged IR emissivity of 0.46 in the 8–14µm long-wave IR regime, along with high optical transparency (71% transmission at 632.8 nm). Notably, the total thickness of the metastructure stands at 0.10λL(λLcorresponds to the wavelength at lowest frequency). The metastructure has been fabricated with indium tin oxide coated polyethylene terephthalate sheets, on which the frequency selective pattern is machined using Excimer laser micromachining, and the performances are verified experimentally. Furthermore, a hybrid theoretical model has been developed that not only provides crucial insights into the operation of metastructure but also presents a methodical semi-analytical approach to design.
Facile green synthesis of ZnO–CuO nanocomposites using areca catechu leaves and their in vitro antidiabetic and cytotoxicity studies
Authors: Shwetha U R , Latha M S , Betageri Virupaxappa S , Pujar G H , Rajith Kumar C R , Kiran M S , Sunita M S , Gokavi Nagappa B , Kollur Shiva Prasad 
Published: January 2022

Abstract
The fabrication and diverse applications of mixed oxides have received immense interest due to numerous prospects for better functional performance in tuning their properties compared to the basic metal oxides. Herein, we report synthesis of ZnO-CuO nanocomposites (NCs) using a simple and green route solution combustion method. The as-prepared ZnO-CuO NCs have been characterised through x-ray diffraction (XRD), energy dispersive x-ray (EDX) with scanning electron microscopy (SEM) and transmission electron microscopy (TEM) techniques. The results revealed that as-prepared ZnO-CuO NCs have spherical and rod-shaped structures with an average size between 10 and 30 nm. Further, ZnO-CuO NCs were tested for antidiabetic and anticancer properties. Amylase inhibition and MTT assays were carried out with different concentrations of NCs. The biological results depicted that the as-prepared nanocomposites exhibited significant cytotoxic effects with IC50 value of 13.29 μg mg−1. These observations further showed that the newly synthesised ZnO–CuO NCs are interesting and promising nanomaterials in pharmaceutical and healthcare sector.
Machinability study of Aluminium 6082 reinforced with boron carbide and titanium by stir casting method using Abrasive water jet machining process
Authors: Umanath K , Nithyanandhan T , Ajayan Adarsh , Devika D , Gokul Prasath M , Aravind Kumar S , Ganeesh R 
Published: January 2022

Abstract
Aluminium Metal Matrix Composite (AMMC) has broad uses in the medical, aerospace, and automobile industries, which have long sought lightweight materials with superior designs and improved properties to improve performance. This analysis has aimed to prepare an AMMC to investigate its machining and mechanical properties. The AMMC is produced using a stir casting process by reinforcing boron carbide and titanium with aluminium 6082. The material’s mechanical properties are studied by using wear test, hardness test, and corrosion test. The wear rate increases when the load increases by varying the load and time with speed as a constant. It is found that the hardness of a material is increased due to titanium and boron carbide as the reinforcement particle in the fabricated AMMC. Using the pitting corrosion technique, the corrosion occurs on the AMMC under the estimated time at room temperature. In order to illustrate the machining characteristics of the aluminium metal matrix composite, an Abrasive Water Jet Machining (AWJM) process has been used. The experiments use L9 orthogonal Array using Taguchi’s method and ANOVA analysis. The input parameters considered are Traverse rate, Stand-off distance, and Nozzle diameter. To find the optimum value of circularity, cylindricity, and surface roughness by varied input parameters. The respective graphs are also plotted. Scanning electron microscopic analysis was performed on the wear-tested specimen and machining surface of the material to determine the distribution of reinforced material and investigate the material’s fracture mechanism. It is found that wear tracks, voids, delamination, micro pits, embedded garnet abrasive particles are located on the machined surface of the AMMC.
Fast prediction of distances between synthetic routes with deep learning
Authors: Genheden Samuel , Engkvist Ola , Bjerrum Esben 
Published: January 2022

Abstract
We expand the recent work on clustering of synthetic routes and train a deep learning model to predict the distances between arbitrary routes. The model is based on a long short-term memory representation of a synthetic route and is trained as a twin network to reproduce the tree edit distance (TED) between two routes. The machine learning approach is approximately two orders of magnitude faster than the TED approach and enables clustering many more routes from a retrosynthesis route prediction. The clusters have a high degree of similarity to the clusters given by the TED-based approach and are accordingly intuitive and explainable. We provide the developed model as open-source.