bluuebunny/crossref_metadata_2025_split huggingface dataset:
A parallel quantum eigensolver for quantum machine learning
Authors: Yang Fan , Zhao Dafa , Wei Chao , Chen Xinyu , Wei Shijie , Wang Hefeng , Long Guilu , Xin Tao 
Published: December 2023

Abstract
Eigensolvers have a wide range of applications in machine learning. Quantum eigensolvers have been developed for achieving quantum speedup. Here, we propose a parallel quantum eigensolver (PQE) for solving a set of machine learning problems, which is based on quantum multi-resonant transitions that simultaneously trigger multiple energy transitions in the systems on demand. PQE has a polylogarithmic cost in problem size under certain circumstances and is hardware efficient, such that it is implementable in near-term quantum computers. As a verification, we utilize it to construct a collaborative filtering quantum recommendation system and implement an experiment of the movie recommendation tasks on a nuclear spin quantum processor. As a result, our recommendation system accurately suggests movies to the user that he/she might be interested in. We further demonstrate the applications of PQE in classification and image completion. In the future, our work will shed light on more applications in quantum machine learning.
One approach for selection of materials available for 3D printing of a dosimetry pelvic phantom: numerical simulation
Authors: Grigorieva A A , Bulavskaya A A , Bushmina E A , Kuyvalaynen V A , Miloichikova I A , Stuchebrov S G 
Published: December 2023

Abstract
Pelvic organ tumors are a serious problem in the field of oncology and account for one-third of the total number of the most common locations of neoplasms. Radiation therapy is often used to treat these types of cancer. A necessary procedure in the application of radiation therapy is the verification of the dosimetric plan to ensure maximum accuracy of radiation and minimize the negative effects of overexposure. To achieve these goals, dosimetry phantoms are used to mimic the structure and properties of biological tissues. Additive technologies can be an effective method for manufacturing such phantoms due to their high-precision ability to replicate both body geometry and anatomical structures. Today, the search and development of materials suitable for both additive technologies and assessing the interaction of ionizing radiation with matter is a relevant task. In this study, models of biological tissues and organs, as well as materials for 3D printing, were created. The features of Co-60 photon beam interaction with these materials were analyzed by numerical simulation. The calculated results demonstrated the suitability of plastic materials based on polylactide for simulating individual biological tissues and human organs from the point of view of interaction with ionizing radiation.
Study of the Development of Sound Waves Generated by Shock Waves
Authors: Harutyunyan V G , Aramyan A R , Aramyan G R , Alexanyan H A , Sargsyan N H , Khachikyan L E , Harutyunyan G A 
Published: December 2023

Abstract
The work is devoted to studies of the time development of sound waves generated by shock waves. It is shown that the shock wave, propagating, generates an acoustic wave whose frequency changes over time. That change is related to the change in the speed of propagation of the shock wave.
Dynamical behaviour of phase transitions in liquid crystals under ultrasound field
Authors: Bezhanova L S , Vasilyan M S , Atanesyan A K 
Published: December 2023

Abstract
The study investigates the influence of an acoustic field on the process of interface formation, phase boundary propagation kinetics, and isotropic phase nucleation in a nematic liquid crystal (NLC) during the NLC → isotropic liquid (IL) phase transition. Experimental studies were conducted using acoustically "rigid" drops and both "rigid" and "soft" cells of sandwich type, with one substrate being a piezo quartz’s X-cut plate. The thickness of the investigated LC layers with planar and homotropic orientations was 20, 50, and 100 μm. The frequency of the ultrasonic field ranged from 3.77 to 4 MHz. Additionally, we computed the values of isotropic germ’s radius in NLC during the NLC → IL phase transition both without and under the ultrasonic field. The obtained data indicate an increased probability of forming a germ of an isotropic phase during the NLC → IL phase transition under the influence of ultrasound. These findings align well with the experimentally observed trends in the changes of phase transition temperature, specifically with the depression of the NLC → IL phase transition temperature in the presence of an ultrasound field.
Generator of Monodisperse Droplets and Some Applications
Authors: Harutyunyan S H , Khachikyan L E , Harutyunyan G A , Margaryan A L , Baghdasaryan D E , Muradyan T R 
Published: December 2023

Abstract
A device for generation of monodisperse droplets with adjustable size and generation frequency is presented. The solid particles are formed by usage of drying techniques of droplets of different solutions in the glass tube. Experimental investigation are performed in order to create periodical structures (particularly photonic crystals) using these solid particles. For focusing and “trapping” of particles thermo-phoretic force is applied. For fabrication of opal photonic crystals self-assembly techniques is used.
Remote Sensing Image Segmentation of Wetlands in Macau Based on Machine Learning
Authors: Li Cheng , Cui Hanwen , Tian Xiaolin 
Published: December 2023

Abstract
Wetlands are essential components of our natural ecosystems, performing critical functions that contribute to biodiversity conservation, water purification, climate regulation, and shoreline protection. Remote sensing classification of wetlands, wetland resource surveys and other wetland research is booming, for the development of environmental protection policies continue to provide reliable data. Macau is a coastal city with rich wetland resources, but there is no relevant research on wetlands in Macau. In this paper, we use the high spatial resolution remote sensing data from Gaofen-2 satellite to extract the wetlands of Macau SAR to fill the gap of related research. This paper used two distinct machine learning classifiers, including Random Forest (RF) and Support Vector Machine (SVM), to analyse the high spatial resolution remote sensing data of GF-2 satellite in order to extract wetlands in Macau Special Region Area. In this research, the accuracy of RF model is much higher than that of SVM model, comparing the indexes of overall accuracy (OA) and Kappa coefficient. After analysing the resultant images generated after segmentation, the image segmentation result of RF model is better than that of SVM model. As for the RF model, the overall accuracy (OA) is 0.9941 and Kappa coefficient is 0.9899.
Based on Zabbix-Prometheus Group Classification Alarm System
Authors: Ling Chen , Xiaoming Wang , Ping Xue , Yong Zhang , Xuezhi Zhao , Xianzhou Cheng , Mengyao Fan , Yueqi Gu , Shuang Xiao 
Published: December 2023

Abstract
The efficient operation of the system is the foundation for improving business level in the rapid development of information technology. Based on the data integration of the Zabbix-Prometheus system, it achieves information data storage, data collection, grouping and classification linkage alarm functions. The Zabbix-Prometheus grouping and classification alarm integration system can effectively solve the problems of a large amount of redundant alarm information, no priority of alarm information, and inability to silence. It has established a multi-platform integrated alarm service system. It has implemented linkage alarm functions such as email, SMS, and WeChat.
Research on Temperature Control of Plastic Laser Welding Based on Improved Snake Optimization Algorithm
Authors: Zhou Keliang , Xiong Juntao , Yang Caihong 
Published: December 2023

Abstract
Aiming at the nonlinear and time-varying characteristics of plastic laser welding temperature control system, and the problems of low accuracy and large overshoot when using the traditional proportional-integral-derivative (PID) controller, an improved snake optimization (ISO) is proposed to optimize the PID parameters. Firstly, the elite reverse learning strategy is introduced into the snake optimization (SO) to generate the reverse solution, and new individuals are generated through comparison to improve the convergence speed of the algorithm; Simultaneously introducing the water wave dynamic adaptive factor to enhance the algorithm’s iterative optimization ability. By iterating the algorithm, a set of suitable PID parameters are obtained to control the temperature control system. The simulation results show that compared to traditional PID controllers, ISO-PID reduces the adjustment time by approximately 0.42 seconds and does not cause overshoot.
Online Rolling Optimization for Energy Efficiency in Smart Homes
Authors: Zhukui Tan , Shengyong Feng , Ruifeng Zhang , Cheng Yang , Bin Liu 
Published: December 2023

Abstract
To cope with the variability of electricity consumption patterns, this study proposes an online rolling optimization-based energy efficiency management strategy for smart homes, which considers user preferences on energy saving and electricity comfort. A weight parameter is used to balance these two objectives and users can set them according to their own electricity preferences. The strategy employs predictive models based on historical data and future inputs to forecast system outputs, and applies feedback correction to compensate prediction errors. In order to better express the power consumption satisfaction of users, two measures of user satisfaction are introduced: utility comfort and temperature comfort. Finally, the simulation result shows that the proposed method achieves an average energy reduction rate of 13.97%, which demonstrates that our strategy can achieve significant reductions in power consumption while enhancing user satisfaction.
Booster-Driven Consistency Training Framework for Semi-Supervised Learning
Authors: Ma Menghao , Xiong Feng , Xu Ye , Gao Luxiong , Yang Huafang , Zou Bingyu 
Published: December 2023

Abstract
The paper proposes an inductive semi-supervised learning method, called Booster-Driven Consistency Training (BDCT). In our work, we extend consistency training by designing a “booster” module for aggregating multi-view information, preventing the networks from collapsing into each other, and constructing better targets in parallel with the existing consistency training method. By booster, we mean that the one network is treated as the booster to help the training of the other two co-trained and superior networks, “spacecraft”. Furthermore, BDCT integrates smoothness enforcing into the designed fast feedback loop to ensure the effectiveness and the robustness of training. Meanwhile, adversarial examples are exploited to maintain the diversity among the networks that are learned to be smooth on the low dimensional manifold. BDCT demonstrates satisfactory performance in comparison with state-of-the-art semi-supervised deep learning methods and extensive experiments validate the effectiveness of the “booster” module.
Real-time hazard detection and mitigation on construction sites based on artificial neural networks
Authors: Wang Jianqi 
Published: December 2023

Abstract
As construction site safety problems appear more and more often, China, as a global infrastructure power, gives high importance and attention to them, so the research of construction site safety early warning technology is very necessary. In this paper, we introduce the technology related to the topic and analyze the early warning process. The anchor nodes are classified by K-nearest neighbor algorithm, and the optimal number of anchor nodes are selected to participate in the positioning. By optimizing the weights of the weighted prime positioning algorithm to make it more rationalized, it is experimentally proven to have better accuracy and higher efficiency than the existing personnel positioning algorithm; it is expected to be able to provide smooth and stable, timely and accurate early warning, predict ten common construction risks, and effectively avoid the construction risks.
Non-invasive detection of renal disease biomarkers through breath analysis
Authors: Khokhar Manoj 
Published: December 2023

Abstract
Breath biomarkers are substances found in exhaled breath that can be used for non-invasive diagnosis and monitoring of medical conditions, including kidney disease. Detection techniques include mass spectrometry (MS), gas chromatography (GC), and electrochemical sensors. Biosensors, such as GC-MS or electronic nose (e-nose) devices, can be used to detect volatile organic compounds (VOCs) in exhaled breath associated with metabolic changes in the body, including the kidneys. E-nose devices could provide an early indication of potential kidney problems through the detection of VOCs associated with kidney dysfunction. This review discusses the sources of breath biomarkers for monitoring renal disease during dialysis and different biosensor approaches for detecting exhaled breath biomarkers. The future of using various types of biosensor-based real-time breathing diagnosis for renal failure is also discussed.
Fast and high precision phase recovery technology of single-shot ineterferogram based on depth convolution neural network
Authors: Kuang Yu , Li Jiawen , Liu Fengwei , Wu Yongqian , Zhang Rongzhu 
Published: December 2023

Abstract
Phase retrieval from single interferogram is of great interest for the possibility of dynamic phase measurement. However, it is a very complicated process in reality since the unknowns and knows are highly unequal. In this paper, we propose a fast phase recovery method from single interferogram based on deep convolution neural network. The network is trained based on supervised learning to achieve the purpose of quickly obtaining unwrapped phase results from a single interferogram. To improve the detection accuracy, a modified set establishment model has been propose to improve the practicability of the fringe data. The simulation and experimental results show that the root mean square value of residual phase extraction error by this method is closed to 0.01λ (λ = 632.8 nm), and the constructed depth convolution neural network model has significant flexibility and effective generalization ability for phase recovery of single frame interference fringe.
Optimal MEA structure and operating conditions for fuel cell reactors with hydrogen peroxide and power cogeneration
Authors: Yang Jie , Ding Ruimin , Liu Chang , Xu Qinchao , Liu Shanshan , Yin Xi 
Published: December 2023

Abstract
The cogeneration of hydrogen peroxide (H2O2) and power in proton exchange membrane fuel cell (PEMFC) reactors via two-electron oxygen reduction reaction on the cathode is an economical, low-carbon, and green route for the on-site production of H2O2. However, in practice, the H2O2 that cannot be collected timely will accumulate and self-decompose in the catalyst layer (CL), reducing the H2O2 generation efficiency. Thus, accelerating the mass transport of H2O2 within the cathode CL is critical to efficient H2O2 generation in PEMFC. Herein, we investigated the effects of the membrane electrode assembly (MEA) fabrication process, cathode CL thickness, and cathode carrier water flow rate on H2O2 generation and cell performance in a PEMFC reactor. The results show that the catalyst-coated membrane-type MEA exhibits high power output due to its lower proton transport resistance. However, the formed CL with a dense structure significantly limits H2O2 collection efficiency. The catalyst-coated gas diffusion electrode (GDE)-type MEA formed macroporous structures in the cathode CL, facilitating carrier water entry and H2O2 drainage. In particular, carbon cloth GDE with thin CL could construct rich macroscopic liquid channels, thus maximizing the generation of H2O2, but will impede fuel cell performance. These results suggest that the construction of a well-connected interface between CL and proton exchange membrane (PEM) in MEA and the establishment of a macroscopic pore structure of the CL are the keys to improve the cell performance and H2O2 collection efficiency.
Atomic layer deposition in advanced display technologies: from photoluminescence to encapsulation
Authors: Chen Rong , Cao Kun , Wen Yanwei , Yang Fan , Wang Jian , Liu Xiao , Shan Bin 
Published: December 2023

Abstract
Driven by the growing demand for next-generation displays, the development of advanced luminescent materials with exceptional photoelectric properties is rapidly accelerating, with such materials including quantum dots and phosphors, etc. Nevertheless, the primary challenge preventing the practical application of these luminescent materials lies in meeting the required durability standards. Atomic layer deposition (ALD) has, therefore, been employed to stabilize luminescent materials, and as a result, flexible display devices have been fabricated through material modification, surface and interface engineering, encapsulation, cross-scale manufacturing, and simulations. In addition, the appropriate equipment has been developed for both spatial ALD and fluidized ALD to satisfy the low-cost, high-efficiency, and high-reliability manufacturing requirements. This strategic approach establishes the groundwork for the development of ultra-stable luminescent materials, highly efficient light-emitting diodes (LEDs), and thin-film packaging. Ultimately, this significantly enhances their potential applicability in LED illumination and backlighted displays, marking a notable advancement in the display industry.
Classifying legal systems
Authors: Ragone Sabrina , Smorto Guido 
Published: December 2023

Abstract
Chapter 2 describes how comparative law highlights commonalities and differences in law across space and time. The chapter focuses on taxonomies and on the gathering of the numerous legal systems worldwide into a limited number of types based on common patterns. It traces the evolution from conventional classifications of national legal systems into well-defined legal families to a more open-ended and inclusive understanding of legal traditions and their varying influences on local laws. The chapter then describes how comparative law has proved to be a powerful means of understanding how, when, and by whom changes in law occur, and of grasping the rules and patterns of legal change.
An evolving field
Authors: Ragone Sabrina , Smorto Guido 
Published: December 2023

Abstract
This short closing chapter briefly considers the tendency of comparative law to move away from the original limitations of the discipline to widen its field of enquiry—Western and state laws as the conventional units of analysis, written rules as the sole legal sources, functionalism as the only possible methodology, and private law as the main field of study—and it reflects on the strengths and weaknesses of the growing diversity of laws, topics, sources, methods, and aims in comparative legal studies.
After TWAIL’s Success, What Next? Afterword to the Foreword by Antony Anghie
Authors: Becker Lorca Arnulf 
Published: December 2023

Abstract
In the span of two decades, Third World approaches to international law (TWAIL) experienced a meteoric rise, becoming not only one of the most interesting but also one of the dominant approaches to international law. This Afterword to the Foreword by Antony Anghie reflects upon the rise of TWAIL and its significance to the discipline of international law. I argue that having become part of the disciplinary mainstream, TWAIL ‘civilizes’ international law, making it more difficult for international lawyers to ignore or dismiss the colonial origins and legacies of their field. As TWAIL leaves a mark on international law, new spaces for international legal action by the peoples of the global South might have been opened. Does greater action weaken TWAIL’s central insights about colonial origins and legacies? Maybe, and if so, a mainstream TWAIL opens also disciplinary space for other critical approaches that shine light on Third World experiences of international law that point not just to oppression but also to North/South engagement and, potentially, Southern resistance.
A chromosome-level assembly of the seed beetle Callosobruchus maculatus genome with annotation of its repetitive elements
Authors: Arnqvist Göran , Westerberg Ivar , Galbraith James , Sayadi Ahmed , Scofield Douglas G , Olsen Remi-André , Immonen Elina , Bonath Franziska , Ewels Philip , Suh Alexander 
Published: December 2023

Abstract
Callosobruchus maculatus is a major agricultural pest of legume crops worldwide and an established model system in ecology and evolution. Yet, current molecular biological resources for this species are limited. Here, we employ Hi-C sequencing to generate a greatly improved genome assembly and we annotate its repetitive elements in a dedicated in-depth effort where we manually curate and classify the most abundant unclassified repeat subfamilies. We present a scaffolded chromosome-level assembly, which is 1.01 Gb in total length with 86% being contained within the 9 autosomes and the X chromosome. Repetitive sequences accounted for 70% of the total assembly. DNA transposons covered 18% of the genome, with the most abundant superfamily being Tc1-Mariner (9.75% of the genome). This new chromosome-level genome assembly of C. maculatus will enable future genetic and evolutionary studies not only of this important species but of beetles more generally.
Distinct neurogenetic mechanisms establish the same chemosensory valence state at different life stages in Caenorhabditis elegans
Authors: Banerjee Navonil , Rojas Palato Elisa J , Shih Pei-Yin , Sternberg Paul W , Hallem Elissa A 
Published: December 2023

Abstract
An animal's preference for many chemosensory cues remains constant despite dramatic changes in the animal's internal state. The mechanisms that maintain chemosensory preference across different physiological contexts remain poorly understood. We previously showed that distinct patterns of neural activity and motor output are evoked by carbon dioxide (CO2) in starved adults vs dauers of Caenorhabditis elegans, despite the two life stages displaying the same preference (attraction) for CO2. However, how the distinct CO2-evoked neural dynamics and motor patterns contribute to CO2 attraction at the two life stages remained unclear. Here, using a CO2 chemotaxis assay, we show that different interneurons are employed to drive CO2 attraction at the two life stages. We also investigate the molecular mechanisms that mediate CO2 attraction in dauers vs adults. We show that insulin signaling promotes CO2 attraction in dauers but not starved adults and that different combinations of neurotransmitters and neuropeptides are used for CO2 attraction at the two life stages. Our findings provide new insight into the distinct molecular and cellular mechanisms used by C. elegans at two different life stages to generate attractive behavioral responses to CO2.