bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unravelling the magnetodielectric characteristics of strain-coupled PMN-PT/FSMA multiferroic heterojunction toward flexible MEMS applications
Authors: Arora Diksha , Kumar Pradeep , Kaushlendra Kumar , Kaur Davinder 
Published: July 2022

Abstract
Flexible microelectromechanical (MEMS) devices are poised to scaffold technological innovations in the fields of wearable sensors, implantable health monitoring systems and touchless human-machine interaction. Here, we report the magnetoelectric properties of cost-effective and room-temperature sensitive 0.67Pb(Mg1/3Nb2/3)O3-0.33PbTiO3/Ni50Mn35In15 (PMN-PT/ferromagnetic shape memory alloy (FSMA)) multiferroic heterostructure integrated on flexible stainless steel substrate via RF/DC magnetron sputtering technique. The growth of the pure perovskite phase of PMN-PT without any pyrochlore impurity is confirmed by the dominant (002) orientation of the tetragonal PMN-PT. The double logarithmic plot of current density with electric field validates the Ohmic conduction mechanism with low leakage current density of ∼10−6 A cm−2. The anomaly observed in temperature-dependent dielectric and ferroelectric characteristics of the heterostructure overlap with the martensite transformation regime of the bottom Ni–Mn–In (FSMA) layer. The PMN-PT/Ni–Mn–In multiferroic heterostructure exhibits a significant magnetodielectric effect of ∼3% at 500 Oe and can be used as an ultra-sensitive room-temperature magnetic field sensor. These results have been explained by an analytical model based on strain-mediated magnetoelectric coupling between interfacially coupled PMN-PT and Ni–Mn–In layers of the multiferroic heterostructure. Furthermore, the excellent retention of magnetodielectric response up to 200 bending cycles enhances its applicability towards flexible MEMS devices. Such PMN-PT based multiferroic heterostructures grown over the flexible substrate can be a potential candidate for piezo MEMS applications.
Theoretical analysis and modelling of degradation for III–V lasers on Si
Authors: Liu Jianzhuo , Tang Mingchu , Deng Huiwen , Shutts Samuel , Wang Lingfang , Smowton Peter M , Jin Chaoyuan , Chen Siming , Seeds Alywn , Liu Huiyun 
Published: July 2022

Abstract
InAs/GaAs quantum-dot (QD) lasers offer a promising method to realise Si-based on-chip light sources. However, the monolithic integration of III–V materials on Si introduces a high density of threading dislocations (TDs), which limits the performance of such a laser device in terms of device lifetime. Here, we proposed a kinetic model including a degradation term and a saturation term to simulate the degradation process caused by the TDs in the early stage of laser operation. By using a rate equation model, the current density in the wetting layer, where the TDs concentrate, is calculated. We compared the rate of degradation of QD lasers with different cavity lengths and of quantum-well lasers, where both are directly grown on Si substrates, by varying the fitting parameters in the calculation of current densities in the kinetic model.
Fault monitoring and diagnosis of high-pressure heater system based on improved particle swarm optimization and probabilistic neural network
Authors: Wu Zheng , Zhang Yue , Dong Ze , Li Dingshuai 
Published: July 2022

Abstract
The high-pressure heater system is an important part of the return heat system of thermal power units, which can significantly reduce the boiler fuel consumption and is of great significance to the safe and economic operation of the units. Taking into consideration the issues that the high-pressure heater system data has strong non-linear characteristics and the fault diagnosis accuracy is low, this paper proposes a hybrid model-based fault monitoring and diagnosis method for a high-pressure heater system. Firstly, an improved particle swarm optimization algorithm (IEDPSO) is proposed. A differential evolution operation is introduced to enhance particle diversity, and the inertia weight coefficients and learning factor parameters are improved to optimize the particle position and velocity update process. The problem that PSO tends to fall into local optimum at the late stage of iterative optimization search is solved. Numerical simulation experiments demonstrate that IDEPSO has high convergence speed and accuracy in the optimization process of the test function. Secondly, a fault monitoring and diagnosis method based on a hybrid kernel principal component analysis (KPCA)-IDEPSO-probabilistic neural network (PNN) model is proposed. Non-linear features are extracted using KPCA for fault monitoring. The IDEPSO algorithm is used to iteratively find the best PNN to improve the fault diagnosis accuracy. Simulation experiments prove that compared with the traditional PNN model, the fault diagnosis accuracy of the KPCA-IDEPSO-PNN model is improved by 4.9% and the number of fault misclassifications is reduced by 34, effectively improving the fault diagnosis accuracy of the high-pressure heater system and ensuring the safe and stable operation of thermal power units.
Micro-force sensing techniques and traceable reference forces: a review
Authors: Yang Yong , Zhao Meirong , Yinguo Huang , Zhang Hong , Guo Ning , Zheng Yelong 
Published: July 2022

Abstract
Micro-force measurement with high resolution, accuracy, and reliability is of interest in a broad range of applications including gravitational-wave detection, intelligent healthcare, bionic robotics, and micromanipulation. Herein, the researchand development in recent years of micro-force sensors based on various principles is reviewed thoroughly, presenting their characteristics and applications, as well as summarizing their advantages and limitations. The most indispensable component of force sensors, elastic sensitive elements, is underlined. Next, four kinds of not widely used but promising sensors are also introduced briefly. Finally, the traceable reference forces are analyzed, concluding with a future perspective into the corresponding challenges and opportunities of micro-force sensors for future research. This review aims at providing references for developing micro-force sensors and improving their performance.
Prediction of static strength properties of carbon fiber-reinforced composite using artificial neural network
Authors: Sharan Agam , Mitra Mira 
Published: July 2022

Abstract
In this paper, an artificial neural network (ANN) based model is developed considering the significant parameters affecting the strength properties of the fiber-reinforced composite. The model utilizes the experimental data obtained from Composite Materials Handbook, Volume 2—Polymer Matrix composites material properties (Military Handbook 17-1F). The data is extracted for unidirectional carbon fiber reinforced composite (CFRP) which represents the mean data obtained from experimentally tested specimens in batches. The dataset consists of 74 samples with eight input parameters: fiber strength, matrix strength, number of plies, loading axis, temperature, volume fraction, void percentage and thickness of ply. The output of the ANN model is the strength of the composite. The hyper-parameter of the ANN model is tuned and selected optimally. The network architecture arrived at is 8-[4]-1 with training function as Levenberg–Marquardt and activation function as tan-sigmoid in the hidden layer and pure-linear in the output layer. The agreement between the prediction from the developed model and experimental data is satisfactory, indicating the model’s applicability and efficacy. The trend analysis with respect to the input parameters is also carried out to verify that the model captures the mechanics-based behavior of CFRP.
B, P-co-doped PdCu nanothorn assemblies for enhanced oxygen reduction electrolysis
Authors: Wang Hongjing , Chen Hongyong , Yin Shuli , Mao Qiqi , Li Chunjie , Xu You , Li Xiaonian , Wang Ziqiang , Wang Liang 
Published: July 2022

AbstractNonmetal doping is a promising strategy to improve electrocatalytic performance of noble metal based catalysts for oxygen reduction reaction (ORR). Herein, we report a facile method to fabricate PdCuBP nanothorn assemblies (PdCuBP NTAs) by co-doping B and P into pre-synthesized PdCu NTAs using NaBH4and NaH2PO2as B source and P source, respectively. The metal-nonmetal structure and multi-branched morphology can optimize oxygen adsorption energy and avoid catalyst migration, agglomeration and Ostwald ripening. As such, the obtained PdCuBP NTAs exhibit efficient activity and excellent long-term stability for ORR. This research offers an excellent strategy for co-doping nonmetal elements into metal nanocrystals with controllable composition and structure to improve electrocatalytic ORR performance.
Engineering the electronic structure of Ni3FeS with polyaniline for enhanced electrocatalytic performance of overall water splitting
Authors: Zou Yang , Wu Yong-Zheng , Huang Yuan , Liu Jia-Lin , Liu Hong , Wang Jian-Jun 
Published: July 2022

Abstract
Developing highly efficient and stable electrocatalysts for oxygen evolution reaction is of significant importance for applications in energy conversion and storage. Modulation of electronic structure of catalysts is critical for improving the performance of the resulting electrodes. Here, we report a facile way to engineer the electronic structure of Ni3FeS by coating a thin polyaniline (PANI) layer for improving electrocatalytic activity for overall water splitting. Experimental investigations unveil that the strong electronic interactions between the lone electron pairs of nitrogen in PANI and d orbitals of iron, nickel in Ni3FeS result in an electron-rich structure of Ni and Fe, and consequently optimize the adsorption and desorption processes to promote the OER activity. Remarkably, the resulting PANI/Ni3FeS electrode exhibited much enhanced OER performance with a low overpotential of 143 mV at a current density of 10 mA·cm−2 and good stability. Promisingly, coupled with the reported MoNi4/MoO2 electrode, the two-electrode electrolyzer achieved a current density of 10 mA·cm−2 with a relatively low potential of 1.55 V, and can generate oxygen and hydrogen bubbles steadily driven by a commercial dry battery, endowed the composite electrocatalyst with high potential for practical applications.
Manipulating liquid metal flow for creating standalone structures with micro-and nano-scale features in a single step
Authors: Shastri Vijayendra , Talukder Santanu , Roy Kaustav , Kumar Praveen , Pratap Rudra 
Published: July 2022

Abstract
Standalone structures with periodic surface undulations or ripples can be spontaneously created upon flowing a liquid metal, e.g. Ga, over a metallic film, e.g. Pt, Au, etc, through a complex ‘wetting-reaction’-driven process. Due to the ability of 3-dimensional patterning at the small length scale in a single step, the liquid metal ‘ripple’ flow is a promising non-conventional patterning technique. Herein, we examine the effect of a few process parameters, such as distance away from the liquid reservoir, size of the liquid reservoir, and the geometry, thickness, and width of substrate metal film, on the nature of the ripple flow to produce finer patterns with feature sizes of ≤ 2 μm. The height and the pitch of the pattern decrease with distance from the liquid reservoir and decrease in the reservoir volume. Furthermore, a decrease in the thickness and width of the substrate film also leads to a decrease in the height and pitch of the ripples. Finally, the application of an external electric field also controls the ripple patterns. By optimizing various parameters, standalone ripple structures of Ga with the height and pitch of ≤ 500 nm are created. As potential applications, the ripple patterns with micro-and nano-scopic features are demonstrated to produce a diffraction grating and a die for micro-stamping.
Efficiency enhancement of triple absorber layer perovskite solar cells with the best materials for electron and hole transport layers: numerical study
Authors: Belarbi Mousaab , Zeggai Oussama , Khettaf Sami , Louhibi-Fasla Souad 
Published: July 2022

Abstract
In this paper, a novel perovskite solar cell (PSC) with a triple absorber layer is numerically simulated using Solar Cell Capacitance One-Dimensional software. The initial simulation of the structure (FTO/TiO2/CsSnI3/CsSnGeI3/Cs3Sb2Br9/spiro-OMeTAD/Au) reveals that by combining cesium tin triiodide (CsSnI3), cesium tin-germanium triiodide (CsSnGeI3) and cesium antimony bromide (Cs3Sb2Br9) as triple absorber layer, we obtain a higher efficiency (31.81%) than the single (CsSnI3), and double (CsSnI3/CsSnGeI3) layer structures, whose efficiencies are 12.87% and 29.41%, respectively. Then, to optimize the proposed structure, different parameters like; thicknesses of the triple absorber layer, different materials of electron transport layer (ETL) and hole transport layer (HTL), thicknesses of ETL and HTL, as well as the operating temperature have been investigated. The optimized structure (0.4/0.1/0.1 µm of CsSnI3/CsSnGeI3/Cs3Sb2Br9 as triple absorber layer; 0.1 µm of tungsten trioxide WO3 as ETL and 0.35 µm of copper(I) oxide Cu2O as HTL, as well as an optimum temperature of 300 K) shows a remarkable photovoltaic parameters i.e. J
SC = 32.640 774 mA cm−2, V
OC= 1.2442 V, FF = 89.17% and η = 36.21% (which corresponds to an improvement of 4.4% compared to the initial proposed structure (31.81%)). This study’s simulation results open a better route toward fabricating highly efficient PSCs.
Chemical sensing and actuation properties of polypyrrole coated fibers
Authors: Beregoi Mihaela , Beaumont Samuel , Jinga Sorin Ion , Otero Toribio F , Enculescu Ionut 
Published: July 2022

Abstract
Designing complex electrochemical artificial muscles aims towards novel devices which besides excellent actuation capabilities should also present the ability to self-sense the modification of environmental parameters. In order to improve efficiency, mimicking the structure of natural muscles, synthetic actuators should have a similar fibrillary morphology. The importance of using materials based on fiber building blocks in actuators aimed at soft robotics field was demonstrated in the present report by comparing a fibrillary artificial muscle with one based on a classical film structure. Nylon electrospun fiber meshes and films were covered in the same conditions with a thin polypyrrole (PPy) layer. The fibrillary electrospun web morphology mimics that of natural muscles and the structure performs a fast, ample bending movement in liquid electrolyte when switching an applied electric potential between −0.6 and +0.6 V. Using the same actuation conditions, no movement of a film based artificial muscle was observed. In order to check the sensing ability of both fibrillary and film like electroactive architectures, their response i.e. PPy reaction when potential cycles were applied in different concentrations of LiClO4 electrolyte were recorded. The obtained results suggest that the ion exchange of the fibrillary artificial muscle is more efficient due to its higher active surface and such devices could work also as dual device (sensor/artificial muscle).
Multiple lump molecules and interaction solutions of the Kadomtsev–Petviashvili I equation
Authors: Zhao Zhonglong , He Lingchao 
Published: July 2022

Abstract
In this paper, a modified version of the solution in form of a Gramian formula is employed to investigate a new type of multiple lump molecule solution of the Kadomtsev–Petviashvili I equation. The high-order multiple lump molecules consisting of M N-lump molecules are constructed by means of the Mth-order determinant and the non-homogeneous polynomial in the degree of 2N. The interaction solutions describing P line solitons radiating P of the M N-lump molecules are constructed. The dynamic behaviors of some specific solutions are analyzed through numerical simulation. All the results will enrich our understanding of the multiple lump waves of the Kadomtsev–Petviashvili I equation.
The Quasar Candidate Catalogs of DESI Legacy Imaging Survey Data Release 9
Authors: He Zizhao , Li Nan 
Published: July 2022

Abstract
Quasars can be used to measure baryon acoustic oscillations at high redshift, which are considered as direct tracers of the most distant large-scale structures in the universe. It is fundamental to select quasars from observations before implementing the above research. This work focuses on creating a catalog of quasar candidates based on photometric data to provide primary priors for further object classification with spectroscopic data in the future, such as the Dark Energy Spectroscopic Instrument (DESI) Survey. We adopt a machine learning algorithm (Random Forest, RF) for quasar identification. The training set includes 651,073 positives and 1,227,172 negatives, in which the photometric information are from DESI Legacy Imaging Surveys (DESI-LIS) and Wide-field Infrared Survey Explore (WISE), and the labels are from a database of spectroscopically confirmed quasars based on Sloan Digital Sky Survey and the Set of Identifications & Measurements and Bibliography for Astronomical Data. The trained RF model is applied to point-like sources in DESI-LIS Data Release 9. To quantify the classifier’s performance, we also inject a testing set into the to-be-applied data. Eventually, we obtained 1,953,932 Grade-A quasar candidates and 22,486,884 Grade-B quasar candidates out of 425,540,269 sources (∼5.7%). The catalog covers ∼99% of quasars in the to-be-applied data by evaluating the completeness of the classification on the testing set. The statistical properties of the candidates agree with that given by the method of color-cut selection. Our catalog can intensely decrease the workload for confirming quasars with the upcoming DESI data by eliminating enormous non-quasars but remaining high completeness. All data in this paper are publicly available online.
A-PASS: an automated pipeline to analyze simultaneously acquired EEG-fMRI data for studying brain activities during sleep
Authors: Zou Guangyuan , Liu Jiayi , Zou Qihong , Gao Jia-Hong 
Published: July 2022

Abstract

Objective. Concurrent electroencephalography and functional magnetic resonance imaging (EEG-fMRI) signals can be used to uncover the nature of brain activities during sleep. However, analyzing simultaneously acquired EEG-fMRI data is extremely time consuming and experience dependent. Thus, we developed a pipeline, which we named A-PASS, to automatically analyze simultaneously acquired EEG-fMRI data for studying brain activities during sleep. Approach. A deep learning model was trained on a sleep EEG-fMRI dataset from 45 subjects and used to perform sleep stage scoring. Various fMRI indices can be calculated with A-PASS to depict the neurophysiological characteristics across different sleep stages. We tested the performance of A-PASS on an independent sleep EEG-fMRI dataset from 28 subjects. Statistical maps regarding the main effect of sleep stages and differences between each pair of stages of fMRI indices were generated and compared using both A-PASS and manual processing methods. Main results. The deep learning model implemented in A-PASS achieved both an accuracy and F1-score higher than 70% for sleep stage classification on EEG data acquired during fMRI scanning. The statistical maps generated from A-PASS largely resembled those produced from manually scored stages plus a combination of multiple software programs. Significance. A-PASS allowed efficient EEG-fMRI data processing without manual operation and could serve as a reliable and powerful tool for simultaneous EEG-fMRI studies on sleep.
Reply to Comment on ‘Egypt’s water budget deficit and suggested mitigation policies for the Grand Ethiopian Renaissance Dam filling scenarios’
Authors: Heggy Essam , Sharkawy Zane , Abotalib Abotalib Z 
Published: July 2022

Abstract
The reply herein shows the factual incorrectness of the comment by Eladawy, Asefa and El Nour, which is primarily supported by miscitations and misinterpretations of published research, as well as by claims from social media and unreliable TV interviews. The comment mistakenly ommit the different components of the calculated total water budget deficit in our paper and accordingly claims that it is primarily aimed to create a ‘water panic’, a term that derives from a non-quantitative and subjective reading of the abstract. We show in detail that the comment misrepresents the aim, approach, results, and interpretations reported in our original paper and does not provide any verification on the reproducibility of our results, nor offers any alternative interpretations or suggested impacts on our conclusions which remain valid and unchallenged by the above comment.
Statistical mechanics analysis of generalized multi-dimensional knapsack problems
Authors: Nakamura Yuta , Takahashi Takashi , Kabashima Yoshiyuki 
Published: July 2022

Abstract
Knapsack problem (KP) is a representative combinatorial optimization problem that aims to maximize the total profit by selecting a subset of items under given constraints on the total weights. In this study, we analyze a generalized version of KP, which is termed the generalized multidimensional knapsack problem (GMDKP). As opposed to the basic KP, GMDKP allows multiple choices per item type under multiple weight constraints. Although several efficient algorithms are known and the properties of their solutions have been examined to a significant extent for basic KPs, there is a paucity of known algorithms and studies on the solution properties of GMDKP. To gain insight into the problem, we assess the typical achievable limit of the total profit for a random ensemble of GMDKP using the replica method. Our findings are summarized as follows: (1) when the profits of item types are normally distributed, the total profit grows in the leading order with respect to the number of item types as the maximum number of choices per item type x
max increases while it depends on x
max only in a sub-leading order if the profits are constant among the item types. (2) A greedy-type heuristic can find a nearly optimal solution whose total profit is lower than the optimal value only by a sub-leading order with a low computational cost. (3) The sub-leading difference from the optimal total profit can be improved by a heuristic algorithm based on the cavity method. Extensive numerical experiments support these findings.
Brownian non-Gaussian diffusion of self-avoiding walks
Authors: Marcone Boris , Nampoothiri Sankaran , Orlandini Enzo , Seno Flavio , Baldovin Fulvio 
Published: July 2022

Abstract
Three-dimensional Monte Carlo simulations provide a striking confirmation to a recent theoretical prediction: the Brownian non-Gaussian diffusion of critical self-avoiding walks. Although the mean square displacement of the polymer center of mass grows linearly with time (Brownian behavior), the initial probability density function is strongly non-Gaussian and crosses over to Gaussianity only at large time. Full agreement between theory and simulations is achieved without the employment of fitting parameters. We discuss simulation techniques potentially capable of addressing the study of anomalous diffusion under complex conditions like adsorption- or Theta-transition.