bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of InP/ZnS quantum dots aggregation on the kinetics of birefringence recorded in thin azopolymer composite films
Authors: Stoilova A , Dimov D , Trifonova Y , Mateev G , Lilova V , Nazarova D , Nedelchev L 
Published: August 2024

Abstract
The article describes the preparation of new thin film composite materials based on the azopolymer (poly[1-[4-(3-carboxy-4-hydroxyphenylazo)benzenesulfonamido]-1,2-ethanediyl, sodium salt]) doped with three different concentration of InP/ZnS quantum dots with size of 7.5 nm. Clusters of aggregated InP/ZnS were microscopically observed in the fabricated composite samples. Birefringence (Δn) was induced in the films at two different wavelengths of the pump laser (355 nm and 444 nm) and the obtained higher values of Δnmax for the samples doped with quantum dots up to 2 wt% in comparison to the non-doped film and the sample with the highest nanocrystals concentration are discussed, based on the measured fluorescence spectra, in terms of possible local energy transfer between the azobenzene chromophores and the InP/ZnS quantum dots.
Galactic dynamics in the presence of scalaron: a perspective from f(R) gravity
Authors: Mohan Gayatri , Dev Goswami Umananda 
Published: August 2024

Abstract
We consider f(R) modified gravity theory incorporating the chameleon mechanism to address galactic dynamics. By employing the metric formalism and utilizing a conformal transformation, we simplify the field equations and describe the extra degree of freedom f

R
via a scalar field (scalaron) with chameleonic behavior. A recently proposed f(R) model is analyzed to illustrate this behavior effectively. Subsequently, the rotational velocity equation including the scalaron’s contribution is derived for a test particle in a static, spherically symmetric spacetime. Then we generate rotation curves and fit them to observational data of thirty seven galaxies using two fitting parameters, M
0 and r

c
, the total mass and core radius of a galaxy respectively.
A four-element metasurface loaded high gain MIMO antenna for X-band applications
Authors: Sundaravadivel P , Rajesh Kumar D , Prithivirajan V , Anish Pon Yamini K 
Published: August 2024

Abstract
This research presents a novel four-port metamaterial-backed Multiple Input Multiple Output (MIMO) antenna system designed for X-band applications. Each modified circular-shaped antenna element is meticulously printed on a 1.6 mm thick FR4 substrate, resonating within the 9.2 to 9.9 GHz frequency range, encompassing a significant portion of the X-band spectrum. To enhance overall gain, a tilted square-shaped 4 × 4-unit cell is strategically printed on the ground plane of the substrate. The proposed MIMO antenna achieves a maximum gain of up to 8 dBi across the operating spectrum, coupled with an impressive radiation efficiency of up to 80%. Fabrication of the designed antenna is performed, and comprehensive parameter measurements validate its performance, exhibiting a close correlation with simulated results. Essential MIMO parameters such as ECC (<0.01), TARC (>10 dB), CCL (<0.3), and diversity gain are calculated to affirm the antenna’s proficiency further. The results show that the proposed antenna could be a suitable contender for X-band applications.
On the Vegard’s law compliance for CsxFA(1-x)PbI3 perovskite thin films
Authors: Torres-Herrera D Mateus , Ramírez-Iturbe Olaf , Nava-Sánchez Rosa , Morales-Acevedo Arturo 
Published: August 2024

Abstract
Perovskite-based solar cells (PSCs) have demonstrated remarkable high power conversion efficiency in recent years. However, the use of mono-organic cations (such as Methylammonium or Formamidinium) limits the potential for large-scale development due to potential degradation under environmental conditions. The incorporation of multi-cations has emerged as a strategy to enhance both performance and stability. The cesium (Cs) cation represents a solid alternative for partial substitution of Formamidinium. However, the initial concentration of precursors in the solution is often reported without establishing the final concentration of the cation present in the thin films. Herein, the incorporation of Cs cations into the FAPbI3 structure to produce a CsxFA(1-x)PbI3 perovskite with different values of x using a one-step spin coating process is demonstrated. Assessing the structural and optical properties, it is demonstrated that CsxFA(1-x)PbI3 films behave according to Vegard’s law for values of x between 0 and 0.66. In particular, CsxFA(1-x)PbI3, with an x concentration of 0.33 exhibits a cubic lattice parameter of 6.28 Å, lower than that for FAPbI3 but higher than that for CsPbI3. This concentration showed stability of the dark phase under ambient conditions for extended periods. In addition, this material has a bandgap of 1.5 eV, making it suitable for use in solar cells.
Label-free biochemical sensor for hydrogen peroxide detection enabled by a misaligned square coreless fiber
Authors: Liu Xiao , Feng Qianying , Wu Jixuan , Song Binbin , Sun Hanchao , Wang Jifang 
Published: August 2024

Abstract
Hydrogen peroxide (H2O2) is a common chemical substance that is widely used in many industries. Within a certain concentration range, hydrogen peroxide is hazardous and toxic. Therefore, it is crucial to accurately determine the concentration of hydrogen peroxide for safety and diagnostic purposes. Herein, a label-free biochemical sensor is proposed and experimentally demonstrated for highly efficient H2O2 detection. The biosensor is based on a misaligned square coreless fiber, which is fabricated using ultrasonic vibrational cutting technology. The surface of the square coreless fiber is functionalized with catalytic enzymes through covalent bonding, allowing for the accurate detection of H2O2 concentration. The experimental results show that label-free biochemical sensor exhibits a sensitivity of 0.0083 nm μM−1 within the H2O2 concentration range of 0–800 μM. A low limit of detection of 7.95 × 10−4 mol L−1 is also obtained during the detection of H2O2 solution. Furthermore, the developed optical sensor provides a biochemical platform for determining enzyme-catalyzed reactions and can be applied in the fields of food safety, health monitoring, and environmental protection.
Electromagnetic effects on solitons propagation of the (3+1)-dimensional extended Zakharov-Kuznetsov dynamical model with applications
Authors: Shehzad Khurrem , Wang Jun , Arshad Muhammad , Ghamkhar Madiha 
Published: August 2024

Abstract
This paper investigates wave solutions and electromagnetic wave phenomena governed by the (3+1)-dimensional extended Zakharov-Kuznetsov equation (EZKE) utilizing the Sardar sub-equation method. With a focus on electromagnetic wave generation and propagation, we rigorously analyze fundamental properties, soliton solutions, and dynamic behaviors of the EZKE. Through this analytical technique, we unravel the complex interplay among various wave types, including solitary waves and electromagnetic structures, elucidating their formation mechanisms and interaction dynamics. Furthermore, we delve into the stability characteristics of the EZKE, enhancing our understanding of its mathematical and physical implications. Our findings not only contribute to theoretical insights into nonlinear wave phenomena in (3+1)-dimensional space but also hold practical significance in plasma physics, nonlinear optics, and electromagnetic wave propagation. This study advances the development of innovative wave manipulation and control techniques, with applications ranging from plasma confinement in fusion devices to the design of advanced photonic devices for telecommunications and sensing purposes.
A molecular dynamics study on mechanical performance and deformation mechanisms in nanotwinned NiCo-based alloys with nano-precipitates under high temperatures
Authors: Yu 于 Zihao 子皓 , Wang 王 Hongyu 鸿宇 , Sun 孙 Ligang 李刚 , Li 李 Zhihui 志辉 , Zhu 朱 Linli 林利 
Published: August 2024

Abstract
Molecular dynamics simulations are performed to investigate the mechanical behavior of nanotwinned NiCo-based alloys containing coherent L12 nano-precipitates at different temperatures, as well as the interactions between the dislocations and nano-precipitates within the nanotwins. The simulation results demonstrate that both the yield stress and flow stress in the nanotwinned NiCo-based alloys with nano-precipitates decrease as the temperature rises, because the higher temperatures lead to the generation of more defects during yielding and lower dislocation density during plastic deformation. Moreover, the coherent L12 phase exhibits excellent thermal stability, which enables the hinderance of dislocation motion at elevated temperatures via the wrapping and cutting mechanisms of dislocations. The synergistic effect of nanotwins and nano-precipitates results in more significant strengthening behavior in the nanotwinned NiCo-based alloys under high temperatures. In addition, the high-temperature mechanical behavior of nanotwinned NiCo-based alloys with nano-precipitates is sensitive to the size and volume fraction of the microstructures. These findings could be helpful for the design of nanotwins and nano-precipitates to improve the high-temperature mechanical properties of NiCo-based alloys.
Visualizing extended defects at the atomic level in a Bi2Sr2CaCu2O8+δ superconducting wire
Authors: Hu 胡 Kejun 柯钧 , Wang 王 Shuai 帅 , Li 李 Boyu 泊玉 , Liu 刘 Ying 影 , Ge 葛 Binghui 炳辉 , Song 宋 Dongsheng 东升 
Published: August 2024

Abstract
The microstructure significantly influences the superconducting properties. Herein, the defect structures and atomic arrangements in high-temperature Bi2Sr2CaCu2O8+δ
(Bi-2212) superconducting wire are directly characterized via state-of-the-art scanning transmission electron microscopy. Interstitial oxygen atoms are observed in both the charge reservoir layers and grain boundaries in the doped superconductor. Inclusion phases with varied numbers of CuO2 layers are found, and twist interfaces with different angles are identified. This study provides insights into the structures of Bi-2212 wire and lays the groundwork for guiding the design of microstructures and optimizing the production methods to enhance superconducting performance.
SAFE-OPT: a Bayesian optimization algorithm for learning optimal deep brain stimulation parameters with safety constraints
Authors: Cole Eric R , Connolly Mark J , Ghetiya Mihir , Sendi Mohammad E S , Kashlan Adam , Eggers Thomas E , Gross Robert E 
Published: August 2024

Abstract

Objective. To treat neurological and psychiatric diseases with deep brain stimulation (DBS), a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects. Approach. In this study we develop SAFE-OPT, a Bayesian optimization algorithm designed to learn subject-specific safety constraints to avoid potentially harmful stimulation settings during optimization. We prototype and validate SAFE-OPT using a rodent multielectrode stimulation paradigm which causes subject-specific performance deficits in a spatial memory task. We first use data from an initial cohort of subjects to build a simulation where we design the best SAFE-OPT configuration for safe and accurate searching in silico. Main results. We then deploy both SAFE-OPT and conventional Bayesian optimization without safety constraints in new subjects in vivo, showing that SAFE-OPT can find an optimally high stimulation amplitude that does not harm task performance with comparable sample efficiency to Bayesian optimization and without selecting amplitude values that exceed the subject’s safety threshold. Significance. The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of DBS.
Wasserstein generative adversarial network with gradient penalty and convolutional neural network based motor imagery EEG classification
Authors: Xiong Hui , Li Jiahe , Liu Jinzhen , Song Jinlong , Han Yuqing 
Published: August 2024

Abstract

Objective. Due to the difficulty in acquiring motor imagery electroencephalography (MI-EEG) data and ensuring its quality, insufficient training data often leads to overfitting and inadequate generalization capabilities of deep learning-based classification networks. Therefore, we propose a novel data augmentation method and deep learning classification model to enhance the decoding performance of MI-EEG further. Approach. The raw EEG signals were transformed into the time-frequency maps as the input to the model by continuous wavelet transform. An improved Wasserstein generative adversarial network with gradient penalty data augmentation method was proposed, effectively expanding the dataset used for model training. Additionally, a concise and efficient deep learning model was designed to improve decoding performance further. Main results. It has been demonstrated through validation by multiple data evaluation methods that the proposed generative network can generate more realistic data. Experimental results on the BCI Competition IV 2a and 2b datasets and the actual collected dataset show that classification accuracies are 83.4%, 89.1% and 73.3%, and Kappa values are 0.779, 0.782 and 0.644, respectively. The results indicate that the proposed model outperforms state-of-the-art methods. Significance. Experimental results demonstrate that this method effectively enhances MI-EEG data, mitigates overfitting in classification networks, improves MI classification accuracy, and holds positive implications for MI tasks.
A UWB indoor positioning method based on triple filtering fingerprint matching algorithm
Authors: Yang Jiaqi , Gao Junbo , Sun Wei , Jing Xin 
Published: August 2024

Abstract
In recent years, ultra-wideband (UWB) has gradually become a research hot spot in the field of indoor positioning because of its various advantages. Although UWB has such excellent performance in normal environments, the non-line-of-sight propagation of signals in complex indoor environments and the multi-path effect caused by obstacles will affect its positioning accuracy. To solve this problem, we use the fingerprint positioning method and optimize the previously commonly used k-nearest neighbor algorithm in the online matching phase. In this paper, we proposed a triple-filtered k-nearest neighbor algorithm based on sample distance weighting (TFWKNN). Experimental results show that the mean calculation error of TFWKNN is 63.4% less than the k-nearest algorithm, and the proposed algorithm has better prediction stability than other commonly used machine learning regression algorithms.
Causal V: A five-phase causal mining methodology for real-world accident data
Authors: Zhao Jiangnan 
Published: August 2024

Abstract
Amid rapid advancements in autonomous vehicle technology, traffic accidents due to Autonomous Driving Systems (ADS) flaws persist. The critical task of attributing causality to accidents and forecasting potential incidents based on situational data stands at the forefront of enhancing vehicular safety. Addressing these challenges, this study introduces Casual V, a systematic five-phase V-shape approach to constructing a causal Bayesian network (CBN) from massive real-work crash data. To evaluate the proposed approach, a CBN was constructed from more than 300,000 Maryland accident records. Experiments demonstrate that our method is capable of effectively constructing a CBN that provides deep insights into the causal relationships within vehicular accident data.
Bone-on-a-chip simulating bone metastasis in osteoporosis
Authors: Lee Sunghan , Kim Young Gyun , Jung Hyo-Il , Lim Ji Seok , Nam Ki Chang , Choi Han Seok , Kwak Bong Seop 
Published: August 2024

Abstract
Osteoporosis is the most common bone disorder, which is a highly dangerous condition that can promote bone metastases. As the current treatment for osteoporosis involves long-term medication therapy and a cure for bone metastasis is not known, ongoing efforts are required for drug development for osteoporosis. Animal experiments, traditionally used for drug development, raise ethical concerns and are expensive and time-consuming. Organ-on-a-chip technology is being developed as a tool to supplement such animal models. In this study, we developed a bone-on-a-chip by co-culturing osteoblasts, osteocytes, and osteoclasts in an extracellular matrix environment that can represent normal bone, osteopenia, and osteoporotic conditions. We then simulated bone metastases using breast cancer cells in three different bone conditions and observed that bone metastases were most active in osteoporotic conditions. Furthermore, it was revealed that the promotion of bone metastasis in osteoporotic conditions is due to increased vascular permeability. The bone-on-a-chip developed in this study can serve as a platform to complement animal models for drug development for osteoporosis and bone metastasis.
Enhancing mechanical properties of lanthanide zirconates through the cold sintering assisted sintering process
Authors: Cruz-Sánchez Brianda , Díaz-Guillén Juan C , Hdz-García Hector M , Perez-Cortes Pedro , Fuentes Antonio F 
Published: August 2024

Abstract
This study evaluates the impact of incorporating varying contents (10–40 wt%) and molar concentrations (0.001–1 M) of citric acid solutions, as transient liquid phases in the Cold Sintering Assisted Sintering (CSAS) process of dysprosium zirconate (Dy2Zr2O7). CSAS processed samples achieved relative densities up to 98% of the theoretical maximum and significantly increased Vickers microhardness by over 2.5 times, compared to the traditional ‘press and fired’ sintering method. The Dy2Zr2O7 crystal structure remained consistent with the fluorite-type, with no secondary phases detected. Our findings underscore the benefits of using CSAS to enhance the mechanical strength of Dy2Zr2O7, while reducing the lengthy processing times at very high temperatures typically required for sintering refractory materials such as lanthanide zirconates.
An integrated expert recommender system approach to environmental service priorities in renewable energy
Authors: Yüksel Serhat , Dinçer Hasan , Hacıoğlu Ümit , An Jaehyung , Mikhaylov Alexey , Karpyn Zuleima 
Published: August 2024

Abstract
The purpose of this study is to analyze the investment success of renewable energy generation projects design. A novel model has been constructed for this purpose. At the first stage, collaborative filtering methodology is taken into consideration to complete missing evaluations. After that, M-SWARA based on QUSFSs with golden cut is used to compute the weights of these factors. Finally, the components of the service design are ranked by TOPSIS approach. The main contribution of the paper is that a new methodology (M-SWARA) has been created in this study by making improvements to SWARA. With the help of this new model, causal directions between the indicators can also be examined. Similarly, collaborative filtering methodology is taken into consideration to complete missing evaluations. In this process, the decision makers are allowed to leave the questions they wanted blank. This situation is considered as the superiority of the proposed model compared to many previous models in the literature. The findings indicate that cost is the most significant factor for the success of renewable energy investments because it gets the highest weight (.261). The ranking results also demonstrate that product is the most essential component of the service design of renewable energy investments. Therefore, solving the high-cost problem is of vital importance to increase these investments. First, renewable energy companies can reduce costs with more effective financial management. To carry out this process effectively, a finance department consisting of qualified personnel is needed. Thanks to this team, current situations in the financial markets will be better followed and this will play an important role in reducing costs.
Household air pollution disparities between socioeconomic groups in Chicago
Authors: Krakowka William Isaac , Luo Jiajun , Craver Andrew , Pinto Jayant M , Ahsan Habibul , Olopade Christopher S , Aschebrook-Kilfoy Briseis 
Published: August 2024

Abstract

Purpose: To assess household air pollution levels in urban Chicago households and examine how socioeconomic factors influence these levels. Methods: We deployed wireless air monitoring devices to 244 households in a diverse population in Chicago to continuously record household fine particulate matter (PM2.5) concentration. We calculated hourly average PM2.5 concentration in a 24-hour cycle. Four factors—race, household income, area deprivation, and exposure to smoking—were considered in this study. Results: A total of 93085 h of exposure data were recorded. The average household PM2.5 concentration was 43.8 μg m−3. We observed a significant difference in the average household PM2.5 concentrations between Black/African American and non-Black/African American households (46.3 versus 31.6 μg m−3), between high-income and low-income households (18.2 versus 52.5 μg m−3), and between smoking and non-smoking households (69.7 versus 29.0 μg m−3). However, no significant difference was observed between households in less and more deprived areas (43.7 versus 43.0 μg m−3). Implications: Household air pollution levels in Chicago households are much higher than the recommended level, challenging the hypothesis that household air quality is adequate for populations in high income nations. Our results indicate that it is the personal characteristics of participants, rather than the macro environments, that lead to observed differences in household air pollution.
Robust Left-Right Hashing Scheme for Ubiquitous Computing
Authors: Tripathi Rajeev Ranjan Kumar , Singh Pradeep Kumar , Singh Sarvpal 
Published: August 2024

Abstract
Ubiquitous computing systems possess the capability to collect and process data, which is subsequently shared with other devices. These systems encounter resource challenges such as memory constraints, processor speed limitations, power consumption considerations, and the availability of data storage. Therefore, maintaining data access and query processing speed in ubiquitous computing is challenging. Hashing is crucial to search operations and has caught the interest of many researchers. Several hashing techniques have been proposed and Cuckoo Hashing is found efficient to use in several applications. There are two variants of Cuckoo Hashing: Parallel Cuckoo Hashing and Sequential Cuckoo Hashing. Cuckoo Hashing suffers from challenges like high insertion latency, inefficient memory usage, and data migration. This paper proposes two hashing schemes: Left-Right Hashing and Robust Left-Right Hashing that successfully address and solve the major challenges of Sequential Cuckoo Hashing. The proposed schemes adopt the Combinatorial Hashing technique after modification and use this with a new collision resolution technique called Left-Right Random Probing. Left-Right Random Probing is a variant of random probing and uses prime numbers and Fibonacci numbers. In addition, this paper proposes a new performance indicator, degree of dexterity to estimate the performance of hashing techniques. Sequential Cuckoo Hashing suffers from hidden switching costs which are identified and its estimation is given by a new performance indicator called, T.R.C./Key. Performance of Sequential Cuckoo Hashing is order dependent.