bluuebunny/crossref_metadata_2025 huggingface dataset:
Study of dynamic analysis of separation devices in gas drying
Authors: Gurbanov Abdulaga , Sardarova Ijabika , Damirova Javida
Published: February 2025

The article considers the main types of separation equipment used at gas production and gas transportation enterprises. The most common types of separators are given. A new combined design of a gravitational cyclone separator with mesh elements has been developed. A description of the internal structure of the developed separator is given. The characteristics of the initial gas and the gas after separation are given. The effect of gas velocity on the process of separation efficiency is shown. A new direction in the field of gas separation is proposed - the implementation of gas separation in the pipelines themselves. The table shows the results of calculations to determine the rate of gas separation in vertical and horizontal pipelines. The main feature of the principle of operation of the proposed pipe separators, in contrast to the existing gravitational separators operating on the principle of jet breaking, is, on the contrary, in the continuity of the jet of gas and liquid phase.
Consideration of shear deformation in the calculation of bent structural elements
Authors: Khamzaev Inomjon , Umarov Elmurad , Khaydarova Ozodakhon , Muminov Jalolidin , Ortiqaliyev Bobojon
Published: February 2025

The article studies the effect of experimental stresses on the deformation of structural elements subject to bending. It is known that the bending element of the structure is a beam, the main problem is to check their uniformity, for this it is necessary to determine the displacements caused by external forces, this task is carried out on the basis of integrating the main differential equation of the bent axis of the beam. But in general, the bending of the beam is significantly affected by tensile stresses arising on the surfaces of its cross section. Shear deformation caused by these stresses can in some cases significantly affect the deformation of the beam, that is, its composure. It is known that when deriving the main differential equation of the bent axis of the beam, only the effect of the bending moment is taken into account. In this case, the axis of the beam is curved, there is a stretching of the longitudinal layers from the neutral on one side and compression on the other. It is based on the hypothesis that the cross sections of the beam are straight, and the straight sections remain perpendicular to its bent axis. The article derives a differential equation for a curved beam axis taking into account the influence of shear deformation.
Theoretical aspects of the process of technical magnetization of rare earth terbium ferrite garnet in the temperature range of magnetic compensation
Authors: Sharipov Mirzo , Hayitov Dilshod , Ergasheva Nilufar , Zokirova Zarkhal , Fayzieva Zarifa
Published: February 2025

The article presents an interpretation of the results obtained from studies of the process of technical magnetization of terbium ferrite garnet near the magnetic compensation temperature point, which were carried out within the framework of the thermodynamic theory of domain structures. The model used for changing the domain position of a rare-earth magnet with changes in the magnetic field and temperature makes it possible to qualitatively describe the identified patterns of the process of technical magnetization of ferrite garnet during a transition near the temperature of magnetic compensation.
Automation and control of the vacuum block
Authors: Melikov Elchin , Maharramova Tamella , Safarova Aygun
Published: February 2025

Based on a comprehensive study of technological processes occurring in the vacuum block for an installation of the ELOU-AVT type, the features of the complex technological complex under consideration as a control object were analyzed. In this regard, a statement for the optimization task of a block under study has been developed, taking into account restrictive conditions on control and input parameters. Taking into account the compiled mathematical models for the quantitative and qualitative characteristics of the process under consideration and the algorithm for their gradient adaptation, to numerically solve the problem of optimizing the functioning for this block, the classical Lagrange method multipliers is used. In a wide range of changing conditions in input disturbing factors in quantity and quality, as well as insufficient operational quality information on the selected petroleum products, the proposed method and principles of development algorithm for controlling the process under study allows for prompt preliminary local regulation modes correction and the selection of new optimal modes for adaptive control as a whole. This circumstance leads to an increase in the economic production efficiency as a whole.
The methodology for reactive power control to ensure voltage quality using fuzzy logic
Authors: Kholiddinov Ilkhombek , Sharobiddinov Mirzokhid , Kholiddinova Mashkhurakhon , Komolddinov Sokhib , Qodirov Afzal , Tukhtasinov Saidislomkhon
Published: February 2025

The article discusses the relevance of various models in addressing the challenges of solving electrical supply issues, particularly in the context of monitoring and optimizing power distribution networks. Researchers describe unique algorithms proposed for different aspects of power systems, including voltage regulation and enhancement, such as reactive compensation and optimal capacitor deployment. The benefits of utilizing fuzzy logic for controlling reactive power and improving regulation are highlighted, along with assessments of future developments in this field. Comparisons are made with conventional methods, emphasizing the advantages of fuzzy control. The article provides descriptions of results obtained in real-world conditions. It serves as an interesting resource for specialists in electrical engineering, automation, and researchers engaged in utilizing fuzzy models in engineering systems.
Estimation of the error in 3D control of products with complex geometric profiles using the method of multi-view phase triangulation
Authors: Dvoynishnikov Sergey , Zuev Vladislav , Meledin Vladimir
Published: February 2025

The analysis of achievable measurement errors of threedimensional geometry was performed during the implementation of the phase triangulation method in a multi-view setup. The essence of the method lies in measuring a stationary object from different perspectives using one or multiple measurement systems, and then merging the measured point clouds into a single closed three-dimensional model. The main focus was on measuring convex objects, as the phase triangulation method predominantly operates with convex objects. The limitation arises from the fact that for complex-profile objects with arbitrary geometry, "dead" zones will inevitably be present, which cannot be measured using methods based on phase-based triangulation with spatially modulated illumination. The primary attention in the study was directed towards elongated objects, as they are challenging to measure from a single viewpoint, and multi-view methods of phase triangulation enable measurements with virtually no fundamental limitations on the geometric dimensions of the object being measured. Mathematical modeling was conducted in the study to assess measurement errors when merging point clouds into a unified coordinate system. The obtained results demonstrated the potential use of multi-view phase triangulation methods even in industrial conditions for measuring elongated objects with complex three-dimensional geometry and arbitrary light-scattering properties.
Lithium Isotopic Analysis Using ERIAD (Electrospray with in-source collisional atomization)
Authors: Dyachenko Artyem , Samsonova Natalia , Gall Nikolay
Published: February 2025

The paper considers a number of physical processes occurring during electrospraying of analytical solution, ion transport in the ERIAD interface (electrospraying with atomization in the ion source) and in a specialized mass analyzer, and providing analytical characteristics of the device required for lithium isotope analysis. Registration of lithium isotope signals (6Li and 7Li), is carried out simultaneously on a two-channel receiver based on a doublet of microchannel plates. The gas-dynamic interface is constructed according to the Kontorowitz-Gray type scheme; it is gasdynamically "long", i.e. the distance between the nozzle and the skimmer is ~4 times larger than the size of the "Mach barrel" at operating pressure (200 Pa). It is shown that the achieved accuracy of analysis in determining the fraction of 7Li isotope in the mixture is 0.03%, which corresponds to the requirements of technological control in lithium isotope enrichment by homogeneous sorption methods.
A Study on the Impact of Obstacle Size on Training Models Based on DQN and DDQN
Authors: Lu Siyu , Tao Ye , Zeng Junwei , Zuo Qihuan
Published: February 2025

This paper presents a comparative analysis of Deep Q-Network (DQN) and Double Deep Q-Network (DDQN) algorithms in a simulated car racing environment, focusing on how variations in obstacle density affect each algorithm’s learning and trajectory planning, with the goal of enhancing adaptability and safety in autonomous driving systems. In the Gymnasium car racing environment, each episode generates a unique track where obstacles of varying sizes, colours, and quantities are introduced to test the agent's adaptability. Collisions result in immediate penalties and termination of the episode, while avoiding obstacles grants rewards. The model applies penalties to encourage fast completion and minimize the number of frames used. Various parameters such as obstacle size and complexity influence the agent's performance, promoting efficient learning and policy optimization using both DQN and DDQN algorithms under different configurations. After our experiment, comparing with DQN, we found that DDQN could be a better algorithm in car racing scenarios as in some extreme environments, DQN would not very accurately estimate future reward, therefore, agent could not make better decisions because of the misguided evaluation. While DDQN introduces another network for evaluation, which could better decrease overestimation and explore more possible actions to improve training performance. In this experiment, we evaluated both DQN and DDQN algorithms for obstacle avoidance in reinforcement learning, revealing DDQN’s superior performance, especially in complex environment. Initially, DQN and DDQN performed similarly with smaller obstacles, but DDQN gained higher rewards as training progressed. In the case of large obstacles, DDQN always outperforms DQN, which proves the enhancement of strategy optimization. Despite these advantages, DDQN also has training fluctuations that indicate areas for future research, such as enhancing stability, exploring multi-agent learning, and adapting to more complex environments.
Adaptability of Deep Q-Networks in Autonomous Driving: Influences of Variable Obstacle Dynamics and Road Conditions
Authors: Sun Yuanjun
Published: February 2025

Autonomous vehicles (AVs) have garnered significant attention due to their potential for enhancing transportation accessibility, and traffic safety. Despite numerous advancements, current AV systems still face challenges in fully autonomous operation under real-world conditions. This study modifies the CarRacing-v2 environment from the OpenAI Gymnasium to simulate realistic road conditions with obstacles. Deep Q- Network (DQN) combined with Convolutional Neural Networks (CNN) is utilized for perception and path planning. The approach investigates the impact of varying obstacle sizes, shapes, movement speeds, and road surface conditions on the agent’s performance. The model is trained over 3000 episodes, with the evaluation focusing on cumulative rewards. The results show that the agent's performance is inversely related to obstacle size; the smaller the obstacle, the poorer the performance, which can be attributed to insufficient feature extraction. Irregular-shaped obstacles and reduced surface traction both caused a delayed learning response but will eventually achieve the desired reward. Variations in obstacle colors showed that red obstacles caused confusion due to similarities with red curbs of the road, indicating the model’s over-dependence on luminance. In general, the agent shows adaptability to various road conditions, but enhancements in perception are needed for complex visual cues.
Explore the Impact of Smart Transportation on Urban Transportation Planning
Authors: Xu Jingkai
Published: February 2025

With the increase and concentration of vehicles and population, a lot of people are beginning to focus on urban traffic problems, and smart transportation, as a means to effectively solve people's traffic problems, has received more and more attention. This article introduces smart transportation and the role it can play. It also introduces some core technologies and methods of smart transportation from three important perspectives: 5G technology, big data technology, and public transportation priority. The passage also takes Singapore's advanced smart transportation as an example for in-depth exploration and then draws the advantages compared with traditional traffic planning through comparison. Finally, it shows that smart transportation is of great significance for countries around the world to solve modern traffic problems and proposes an important direction for the development of urban transportation in the 21st century. All signs indicate that smart transportation will play an increasingly important role in future urban construction.
Application of Multi-Armed Bandit Algorithm in Quantitative Finance
Authors: Chen Chengxun , Liu Xuanyuan , Ma Yanyan , Zuo Xiaole
Published: February 2025

The volatility and diversity of financial markets make it challenging for a single portfolio achieve better returns, therefore, adjustable portfolios based on the risk tolerance of clients are highly demanded. However, traditional portfolio strategies cannot meet this requirement. Regarding this issue, the paper combines Fuzzy C-means (FCM) with the Upper Confidence Bound (UCB) algorithm, Genetic Algorithm (GA) optimizing UCB parameters (GA-UCB) and UCB redefining the fitness of GA (UCB-GA) to construct an investment portfolio strategy that can be dynamically adjusted. The research methodology is as follows: the assets are grouped by FCM, using UCB to find the best cluster among the groups; UCB, UCB-GA, and GA-UCB are used to refine the weight distribution of the best cluster. The result shows that the cumulative return of the cluster recommended by the UCB is significantly higher than that recommended by FCM, the Sortino Ratio is improved by 1.18, and the Maximum Drawdown is reduced by 8%. In terms of the weights of the optimal cluster; the portfolio strategy from GA-UCB has the highest cumulative return of approximately 250% in algorithms. The Sortino Ratio of the GA-UCB is the largest at 3.23, which is 1.5 and 1.63 higher than the UCB and the UCB-GA, respectively. In addition, the Maximum Drawdown of the GA-UCB is 26%, which is 1% lower than UCB-GA and 3% lower than UCB. Combining FCM and GA- UCB can improve investment return and stability by adjusting the portfolio weight, which leads to better return risk ratios.
Optimizing Click-Through Rates in Online Advertising Using Thompson Sampling
Authors: Zhao Ben
Published: February 2025

Advertisers must optimize ad selection to increase click-through rates (CTR) in an unpredictable environment as a result of the quick expansion of online advertising. Despite its effectiveness, traditional A/B testing is inefficient when it comes to dynamic user behavior and an ever- changing ad pool. This study integrates user behavior and ad context data using the TS algorithm to optimize ad selection through dynamic prediction of ad click-through rates. This allows for well-informed ad choices even when data is sparse and ad performance fluctuates. Although there were some missing pieces in the dataset utilized for the experiment, TS selected the Ad5 ad about 200 times in 200 trials. This suggests that the TS method can continue to achieve high accuracy and robustness in selection while optimizing the click-through rate in the presence of missing data. As a result, TS can adjust to complex data settings and performs better in advertising optimization. The study's findings indicate that the TS algorithm can give advertisers a practical tool for ad selection, allowing them to optimize their marketing efforts in a dynamic context.
Precision Agriculture Optimization based on Multi-Armed Bandits Algorithm: Wheat Yield Optimization under Different Temperature and Precipitation Conditions
Authors: Huang Qikang
Published: February 2025

Climate change and the growing unpredictability of environmental elements such as temperature and precipitation present considerable challenges to contemporary agriculture. Data-driven algorithms present promising solutions by offering more precise tools for optimizing crop yields and resource efficiency to tackle these challenges. Among these approaches, the multi-armed bandit (MAB) algorithm effectively balances exploration and exploitation, showcasing considerable potential for optimizing agricultural decision-making. This study investigates four widely utilized Multi-Armed Bandits (MAB) algorithms: Explore Then Commit (ETC), Upper Confidence Bound (UCB), Asymptotically Optimal UCB, and Thompson Sampling (TS). The objective is to optimize wheat yield under varying temperature and precipitation conditions while also assessing the effectiveness of these different algorithms in achieving this goal. The experiment demonstrates that the UCB algorithm is optimal for analyzing data on total precipitation during the growth of wheat. . Furthermore, the TS algorithm significantly outperforms others in analyzing flat temperature data throughout the wheat growth period. Therefore, the Asymptotically Optimal UCB algorithm can identify the most suitable rainfall conditions for wheat growth in a changing environment. In contrast, the TS algorithm can determine the optimal temperature requirements for wheat growth under similar environmental fluctuations. These insights assist agricultural practitioners in timely adjusting their strategies to enhance crop yield. Additionally, it provides a model for those who want to use the MAB algorithm to improve agricultural yields.
Active Inference-Driven Multi-Armed Bandits: Superior Performance through Dynamic Correlation Adjustments
Authors: Lin Xiaoqi
Published: February 2025

In recent years, Multi-Armed Bandit (MAB) algorithms have gained substantial attention due to their effectiveness in real-world applications, such as recommendation systems, autonomous systems, and dynamic resource allocation. Traditional MAB algorithms, such as UCB and Thompson Sampling, often lack mechanisms to incorporate correlations between arms, limiting their adaptability and optimality in complex environments. This paper presents a novel MAB framework that integrates Active Inference through a dynamic Adaptive Influence Factor (AIF) mechanism. The AIF mechanism builds correlation matrices to capture inter-arm dependencies and dynamically adjusts exploration strategies through an influence factor, γ, which adapts over time based on pull counts. This adaptive exploration enhances decision-making in sparse and uncertain environments by leveraging correlations. The proposed framework is evaluated on movie recommendation data, with AIF-based algorithms, particularly AIF-TS, significantly outperforming traditional and correlated bandit approaches in settings with high data sparsity. These results demonstrate that dynamically adjusting exploration based on inter-arm relationships substantially improves performance in real-world applications, where data quality and relationships are often variable. The findings suggest that incorporating inter-arm correlations with active inference can lead to more efficient and effective decision-making in adaptive systems, highlighting the potential of AIF-based MAB algorithms in addressing real- world challenges.
Review of Social Media Sentiment and Contextual Bandit Models in Stock Market Investment
Authors: Miao Ruicheng
Published: February 2025

In recent years, social media has become an important channel for investors to obtain market information, and investors are increasingly concerned about how emotional signals obtained from social media affect the stock market. With the profound impact of social platforms on market sentiment, studying how to effectively use these real-time emotional signals has become a hot issue in the financial field.This review explores the use of social media sentiment analysis and contextual bandit models in stock market investment. With platforms like Twitter and Reddit impacting market sentiment, incorporating these signals into investment strategies offers a dynamic edge. Traditional strategies rely on historical data, but real-time sentiment provides valuable insights into market fluctuations.This paper examines the application of multi-armed bandit (MAB) algorithms, particularly their contextual variant, in financial decision-making. By leveraging social media sentiment, contextual bandit models balance exploration and exploitation to adjust investment portfolios dynamically. The review also addresses key challenges, such as handling noise and non-stationarity in sentiment data, and discusses how these methods can improve decision-making in uncertain market environments, ultimately helping investors maximize returns and minimize risks.
Construction and Optimization of a Prediction Model for Shenzhen Component Index Price Changes Based on a BP Neural Network
Authors: Che Yuexuan
Published: February 2025

In the increasingly complex and dynamic global financial markets, the Shenzhen Component Index (Shenzhen Index), as one of the core indices of China's securities market, has garnered extensive attention from both investors and researchers. Price swings in the Shenzhen Index are due to many different complex reasons making it quite difficult to predict prices accurately. With the growth of AI technology, the Backpropagation Neural Network (BP Neural Network), known for its strong non-linear matching and adaptive qualities, is becoming a more significant tool used in predicting market movements. This research is aimed at improving and building a BP Neural Network model, trying to increase how well it can predict Shenzhen Index price changes. In this paper, efforts are made to optimize the structure and parameters of the BP Neural Network model to offer more precise guidance to investors and fill certain blanks in the existing literature. It is thus represented from the results that the use of the BP neural network is very effective for the leading price fluctuation prediction of the Shenzhen Index. This paper offers a theoretical and practical guide which may be beneficial for subsequent applications within the area of financial time series prediction.
Financial Customer Behavior Prediction Based on Machine Learning: A Comprehensive Investigation
Authors: Zhang Xinyue
Published: February 2025

Predicting customer behavior has become a critical component in shaping effective financial strategies. As customers' expectations evolve and their behavior becomes increasingly complex, traditional methods struggle to keep up with the demands for accuracy and efficiency in analysis. This paper reviews the financial customer behavior prediction technology based on machine learning (ML), emphasizing its importance in the formulation of financial industry strategies. The paper first introduces how the machine learning is applied in financial customer behavior prediction, including data collection, preprocessing, feature extraction and model selection. Then, by comparing deep learning and traditional machine learning models, their applications and effects in customer churn and loan prediction are explored. The paper also discusses challenges such as model interpretability, data distribution differences and privacy protection, and looks forward to future research directions, such as integrating machine learning techniques, tools to improve model interpretability, and transfer learning strategies. Finally, the paper summarizes the positive impact of machine learning in financial customer behavior prediction.
Utilizing Web Scraping Technology to Monitor Public Opinion on Environmental Policies
Authors: Cui Yue
Published: February 2025

Web scraping, an automated tool for extracting and analyzing large amounts of data from websites and social media platforms, has become an essential practice for researchers and policymakers. This paper examines the application of web scraping technology to monitor public sentiment towards environmental policies. In an age where digital platforms are crucial to public discourse, governments and researchers have increasingly utilised web scraping for real-time sentiment monitoring. This approach presents considerable ethical dilemmas, especially with user privacy and data security. This paper rigorously analyses these obstacles, emphasising concerns such as data integrity, user consent, and adherence to legislation. Contemporary academic methodologies illustrate the technical advantages of web scraping while simultaneously emphasising its ethical quandaries. This study proposes various methods for reconciling the necessity of data access with the safeguarding of user privacy, including the implementation of anonymisation techniques and the formulation of explicit ethical frameworks. The findings highlight the imperative for responsible web scraping to guarantee ethical adherence while optimising its effectiveness in analysing environmental debate.
Fraseología y léxico de la orfebrería en contextos turísticos (francés-español)
Authors: Jiménez Carmen Trinado
Published: February 2025

La orfebrería es una parte fundamental del patrimonio artístico español, y su conservación es objeto de interés del Instituto
del Patrimonio Cultural de España (Mejías Álvarez, 2021, p. 161). No obstante, apenas se ha
abordado la orfebrería desde un enfoque lingüístico. En el presente trabajo se estudia la fraseología en un corpus en francés y español de
textos turísticos sobre orfebrería. En primer lugar, se analizan las distintas tipologías textuales a los que se enfrenta a diario un
turista. En segundo lugar, se recopilan textos turísticos relacionados con la orfebrería en francés y en español siguiendo la
metodología propuesta por Vargas Sierra (2006) y Torruella y
Llisteri (1999). Por último, llevaremos a cabo un análisis de la fraseología de nuestro corpus prestando especial atención a las
colocaciones de acuerdo con el enfoque semántico propuesto por Buendía Castro (2020).
CA-SLA
Authors: Pekarek Doehler Simona
Published: February 2025

Second language interactional competence has for long remained out of the focal concerns of
research on second language acquisition (SLA). Over the past two decades, however, the field has seen emerge a
prominent line of conversation-analytic (CA) SLA research investigating the procedures and resources L2 speakers use
to accomplish actions in interaction and coordinate these with others, and how these procedures change over time and
proficiency levels. This chapter discusses the key role played by research on French in these developments in terms of
the advancement of methods for longitudinal investigations of L2 interactions, the conceptualization of L2
interactional competence and — relatedly — L2 grammar-for-interaction, and the identification of the basic
developmental trajectory of such competence. It presents the cumulative evidence stemming from developmental studies
of L2 interactional competence and identifies existing French L2 corpora suitable for such studies. The chapter closes
with a discussion of the methodological limits of current research as well as avenues for future investigations.
Remote comparison of two Sr optical lattice clocks through a 58 km fiber link
Authors: Liao Tang-Yin , Liu Hao , Meng Fei , Wang Qiang , Yang Tao , Tian Hao-Chen , Lu Bing-Kun , Zhu Lin , Li Ye , Lin Bai-Ke , Fang Zhan-Jun , Lin Yi-Ge
Published: February 2025

Two 87Sr optical lattice clocks (OLCs) are being developed and operated at the National Institute of Metrology (NIM), located on different campuses that are 40 km apart. In order to compare the optical frequencies of these two Sr OLCs, a 58-km noise canceled fiber link is built to transfer both a 1542-nm transfer laser and a microwave reference from Changping campus to Hepingli campus. Two commercial optical frequency combs (OFCs) with adapted single-branch 698/1542 nm outputs coherently unite the two 698-nm clock lasers and the 1542-nm transfer laser. The fractional instability of the comparison yields 3.1×10-17 at 10000 s averaging time. The measured fractional frequency difference between these two Sr OLCs evaluated to be 1.9 (3.2)×10-17 is within their claimed uncertainties, which shows the consistency of the frequencies of these two Sr OLCs when serving as optical frequency standards. Our remote comparison demonstrates the feasibility of optical clock comparison through long-distance fiber link and contributes to the progress of redefinition of the SI second.
Efficient beam flattening for planar-integrated magneto-optical traps
Authors: Huang Chang-Jiang , Liu Hong-Chen , Chen Liang , Li Chuan-Feng , Guo Guang-Can , Zou Chang-Ling , Xiang Guo-Yong
Published: February 2025

The planar-integrated magneto-optical trap (PIMOT) offers a promising platform for miniaturizing cold atom systems, but its performance and laser-power efficiency are limited by the typically Gaussian profile of the input laser beam. In this work, we present a simplified and cost-effective beam shaping solution to transform the Gaussian input into a flat-top intensity distribution using a compact system of four spherical lenses. The reshaped light field could potentially enhances PIMOT performance by improving the uniformity of the optical radiation pressure and increasing the trap distance from the chip surface. With this approach, we demonstrate a substantial reduction in the optical power required to trap 2.5 × 105
87Rb atoms, down to 30% compared to a standard Gaussian beam input. Our results open the door to more efficient and flexible PIMOT systems for quantum sensing, metrology, and atom-based quantum technology.
Chemical Vapor Deposition of Doped Graphene: Exploring the Role of Solid, Liquid, and Gas Precursors in Tailoring Properties
Authors: Mansoor Majid , Li Luwen , Wang Jiaqi , Shi Qitao , Ullah Sami , Zhang Junjin , Liu Xiangqi , Wang Zhipeng , Zhang Cheng , Yang Xiaoqin , Bachmatiuk Alicja , Martynkova-Simha Grazyna , Rummeli Mark H
Published: February 2025

Graphene is an atomically thin material composed of a single layer of carbon atoms arranged in a hexagonal lattice, which exhibits unique electrical, thermal, and mechanical properties. The intentional introduction of foreign atoms into the structure of graphene by doping is a powerful approach for modifying these properties, making graphene suitable for a range of advanced applications. Among the various synthesis techniques, chemical vapor deposition (CVD) is particularly effective for doping because it allows precise control over the growth conditions and dopant incorporation, outperforming other synthesis strategies in terms of scalability, uniformity, and clean growth. This review examines how solid, liquid, and gaseous precursor types play crucial roles in CVD doping, directly affecting the growth dynamics, doping efficiency, and material quality. By analyzing the mechanisms associated with each precursor form, this review highlights how these strategies address the challenges of achieving consistent and high-quality doped graphene. This discussion provides valuable insight into advancing CVD techniques for producing doped graphene with enhanced properties for cutting-edge applications.
Capacitance Effects of Nanopore Chips on Ionic Current Modulation and Noise Characteristics
Authors: Lin Kabin , Chen Chen , Li Dongxuan , Li Haoyong , Zhou Jinzhu
Published: February 2025

Solid-state nanopores exhibit adjustable pore size, robust chemical and thermal stability, and compatibility with semiconductor fabrication, positioning them as versatile platforms for nanofluidic applications and single-molecule detection. However, their higher noise levels compared to biological nanopores hinder their sensitivity in detecting biomolecules such as DNA and proteins. Enhancing detection sensitivity requires an in-depth understanding of noise sources and strategies for noise reduction. Here, we construct an equivalent circuit model of solid-state nanopores and conduct corresponding experiments to evaluate how chip capacitance, salt concentration, applied voltage, and pore size influence ionic current noise. We find that chip capacitance is the dominant factor affecting ionic current noise, with minimal noise sensitivity to salt concentration below 0.1 M but pronounced increases above this threshold. The pH has little impact on noise, whereas higher applied voltages elevate noise at high salt concentrations. Introducing a SiO2 layer between SiNx and Si significantly reduces chip capacitance; a 1000 nm SiO2 layer reduces capacitance to 7.9 pF, decreasing ionic current noise to 18.7 pA for a 2.2 nm nanopore in 1 M KCl at 40 μm membrane side length and 100 mV and 10 kHz sampling. This reduction in capacitance improves response time and measurement accuracy, marking a critical advancement for high-sensitivity applications of solid-state nanopores.
Optimization assisted probabilistic damage localization of cracks in weld connections of a steel portal frame using electromechanical impedance technique
Authors: Kamal Mayank , Parida Lukesh , Banerjee Sauvik
Published: February 2025

Steel structure members are typically connected through welded connections, that are highly vulnerable to crack development. Therefore, the continuous monitoring of these connections is necessary for early defect detection and to ensure structural stability. Although monitoring approaches such as electromechanical impedance (EMI) have found extensive application in qualitative damage assessment, there is a need for more rigorous EMI-based structural health monitoring (SHM) analysis for sizing and localization of weld damages. In this study a novel methodology is developed that utilizes swarm optimization based on wavelet damage indicators derived from piezo-coupled admittance signatures to experimentally investigate and quantify the amount of crack damages. Initially, a single-story portal steel frame is instrumented with PZTs to gather the admittance signatures in pristine and weld crack damage states. The obtained signatures are then decomposed using discrete wavelet transform (DWT) functions to extract various damage parameters. An objective function has been formulated using the extracted damage parameters like root mean square deviation and DWT parameters such as mean, variance, energy, and entropy. Further, various swarm optimization algorithms like particle swarm optimization (PSO), ant colony optimization, and artificial bee colony optimization (ABC) have been utilized for determining the size and location of the weld crack damage. It has been observed that PSO-based optimization using wavelet coefficients mean achieved 99.904% accuracy, and outperformed other damage parameters in identifying the location of weld crack damages. The proposed methodology demonstrates high efficiency with fewer sensors and has potential real-world uses as a low-cost SHM solution for welded joints in steel structures.
Analysis of breakdown characteristics of GaN-based HEMT by subdividing current components
Authors: Wang Xiao , Lin Zhi-Yu , Zhang Yumin , Wang Jianfeng , Xu Ke
Published: February 2025

In this study, we provided a detailed analysis of HEMTs three-terminal leakage current curves to elucidate the devices breakdown characterization. A comprehensive breakdown curves is collected, and reasonable explanations are offered for phenomena observed in these curves, which challenge conventional assumption regarding the source current IS being buffer leakage current. The leakage current trends of drain current ID, gate current IG and source current IS corresponding to each other under off-state conditions are analyzed in detail within the measurement range. Our findings highlight the significance of gate-side leakage, which cannot be disregarded when investigating the HEMT breakdown mechanism. The characterization method of the breakdown mechanism was optimized by subdividing the leakage current components, and the breakdown mechanism of the HEMT device was revealed by leakage current components.
Artificial electric field algorithm with dynamic neighborhood learning for global optimization
Authors: Lu Qiuli , Cheng Jiatang , Lin Qiuhong
Published: February 2025

Artificial electric field algorithm (AEFA) is a simple yet effective search technique, which has displayed good performance in tackling some optimization problems. However, as the complexity of the problem to be solved increases, AEFA has drawbacks such as premature convergence and insufficient solution accuracy. To mitigate these limitations, a novel variant of AEFA with dynamic neighborhood learning (DNLAEFA) is proposed. In DNLAEFA, a distinctive neighborhood is built for each individual. Then, a dynamic neighborhood learning strategy is adopted to facilitate information exchange between the current individual and its neighbors. Further, a combined parabolic-linear reduction scheme for the electrostatic force is designed based on the observation that calculating the total force consumes a lot of computing resources. Besides, a time-varying attenuation coefficient is employed to regulate the decreasing rate of the Coulomb’s constant, which better balances exploration and exploitation. The performance of DNLAEFA is investigated using 43 benchmark functions with diverse properties and three chaotic time series prediction cases. Simulation results indicate that DNLAEFA is more competitive than six existing AEFAs and 27 well-regarded algorithms.
Rogue waves on the periodic background in the reduced Maxwell-Bloch system
Authors: Zhao YiJie , Zhaqilao Q. , Ao Niqi
Published: February 2025

In this paper, the nonlinearization of the Lax pair and the Darboux transformation method are used to construct the rogue wave on the elliptic function background in the reduced Maxwell-Bloch system, which is described by four components nonlinear evolution equations. On the background of the Jacobian elliptic function, we obtain the admissible eigenvalues and the corresponding non-periodic eigenfunctions of the model spectrum problem. Then, with the help of the one-fold Darboux transformation and two-fold Darboux transformation, the rogue waves on dn-periodic background and cn-periodic background are derived, respectively. Finally, the corresponding complex dynamical properties and evolutions of the four components are illustrated graphically by choosing suitable parameters.
Predicted Stable Two-dimensional Semiconductor Materials TiOS with Promising Photocatalytic Properties: First-Principles Calculations
Authors: Zhang Pan , ShihaiFu  , Pu Chunying , Tang Xin , Zhou Dawei
Published: February 2025

TiO2 is a well-known photocatalyst with a band gap of 3.2 eV, yet its ability to absorb light is limited to the short wavelengths of ultraviolet light. To achieve a more effective photocatalytic material, we have designed two-dimensional semiconductor TiOS materials using swarm intelligence algorithms combined with first-principles calculations. Three stable low-energy structures with space groups of P21/m, P3m1, and P21/c were identified. Among these structures, the Janus P3m1 phase is a direct bandgap semiconductor, while the P21/m and P21/c phases are indirect bandgap semiconductors. Utilizing the accurate hybrid density functional HSE06 method, the band gaps of the three structures were calculated to be 2.34 eV (P21/m), 2.24 eV (P3m1), and 3.22 eV (P21/c), respectively. Optical calculations reveal that TiOS materials exhibit a good light harvesting capability in both visible and ultraviolet spectrum ranges. Moreover, the photocatalytic calculations also indicate that both P21/m and P3m1 TiOS can provide a strong driving force for converting H2O to H2 and O2 in an acidic environment with pH=0. The structural stabilities, mechanical properties, electronic structures and hydrogen evolution reaction activity were also discussed in detail. Our research suggests that two-dimensional TiOS materials have potential applications in both semiconductor devices and photocatalysis.
Development and Research of a Ti/TiN/TiAlN Hard Coating
Authors: Zlatareva Ekaterina , Chitanov Vasiliy , Kolaklieva Lilyana , Kolchev Stefan , Cholakova Tetyana , Pashinski Chavdar , Hribar Tine , Čekada Miha , Kakanakov Roumen
Published: February 2025

The present study is a part of a larger project of developing multicomponent hard, wear-resistant coatings for industrial applications. A Ti/TiN/TiAlN hard coating with improved mechanical properties and adhesion was obtained as a result of the selected technological process.

The coating was deposited via unbalanced magnetron sputtering in closed magnetic field. Its thickness was determined by the ball-cratering method and roughness was measured using a contact profilometer, and a confocal optical microscope. Nanohardness of 32.3 GPa and effective modulus of elasticity of 422 GPa were determined by nanoindentation. No cracks and critical failures were detected during the scratch test up to 30 N. Scratching coefficient of 0.12 was measured against diamond indenter. The morphological examination by SEM showed a dense structure without significant defects. The elemental composition of the main layer determined by SEM/EDS was: N – 42 at. %, Al – 26 at. %, and Ti – 32 at. %.

The coating considered could be used as a basis for the development of multicomponent coatings, including an application as a sublayer in multilayer coatings. As a stand-alone coating, it is applicable for deposition on cutting tools for dry metal processing.
Atlantic multidecadal variability control on European sea surface temperatures is mainly externally forced
Authors: Barghorn L , Börgel F , Gröger M , Meier H. E. M.
Published: February 2025

The semi-enclosed European seas (Baltic Sea, North Sea, Mediterranean Sea, and Black Sea) have been among the fastest warming seas worldwide during the last decades. Projections of future temperature trends can provide essential information for decision makers, but they are subject to a number of uncertainties. One important source of uncertainty is the rather high internal climate variability in that region. Separating internal variability and forced trends is therefore paramount. Particularly powerful tools for this separation are Single-Model Initial-condition Large Ensembles (SMILEs). In such ensembles, the same model is run multiple times with similar forcing but varying initial conditions, resulting in different realizations of internal variability. This study employs two SMILEs to decompose European sea surface temperatures (SSTs) into forced trends and internal variability and to investigate whether and how the latter is affected by the Atlantic Multidecadal Variability (AMV) in the historical period and different future emission scenarios. It is shown that on a decadal scale, the effect of the AMV on the European SSTs will stay important, especially in lower emission scenarios. However, on longer time scales, forced trends will be much higher and in all but the lowest emission scenario considered, more than 2 °C warming compared to 1950 are very likely to be reached until the end of the century in all semi-enclosed European seas.
Interlayer Magnetic Coupling in FePS3 and NiPS3 Stacked Bilayers from First Principles
Authors: Brumme Thomas , Leon Andrea , Heine Thomas , Costa Beatriz
Published: February 2025

Single layers of transition-metal thiophosphates (2D-TMPS$_{3}$) van der Waals magnets are an ideal platform for studying antiferromagnetic interactions in two dimensions. However, the magnetic coupling mechanism between two or more individual layers of these materials remains mostly unexplored.This study presents a density-functional based analysis and analytical models to describe the magnetic configurations of FePS$_{3}$ and NiPS$_{3}$ stacked bilayers. We explore the interplay between magnetic configurations and stacking shift, therefore identifying the mechanisms that result in either ferromagnetic or antiferromagnetic coupling between layers. Our findings indicate that the stacking with the lowest energy is metal-dependent, and the interlayer magnetic configuration (ferromagnetic or antiferromagnetic) varies based on the stacking type and the metal involved. Using an Ising-Hamiltonian model and a tight-binding model based on Wannier functions, we show that interlayer exchange interactions must be considered up to the third nearest neighbor and to elucidate the superexchange mechanism for the NiPS$_{3}$ system.
Reviewing the critical role of coping strategies in enhancing mental health and resilience in disaster survivors
Authors: Olonilua Oluponmile O , Ogbeleakhu Aliu John O
Published: February 2025

In this literature review, the critical role of coping strategies in enhancing mental health and resilience among disaster survivors is examined. Natural disasters, such as hurricanes, earthquakes, wildfires, tsunamis and floods, have been known to inflict significant psychological distress on affected populations. Similarly, man-made disasters, such as terrorist attacks, civil wars and industrial accidents, can lead to lasting mental health challenges for survivors. The focus of this paper is primarily on coping strategies and their impact on mental health and resilience among those affected by disasters. A comprehensive search was conducted using academic databases, including PubMed, PsycINFO, Web of Science, Scopus and Google Scholar, to gather peer-reviewed articles published in recent times. Search terms such as “coping strategies,” “mental health,” “resilience,” “disaster survivors,” “natural disasters” and “man-made disasters” were utilized to refine the search results. A total of 210 articles were initially identified, and 30 articles were retained for further review based on their methodological rigor, relevance to the topic and diversity of coping strategies examined. This study found that problem-focused, emotion-focused, social support and avoidance coping are crucial for understanding how to mitigate psychological distress and promote recovery after disasters. Additionally, it identified several other coping strategies, including mindfulness-based practices, cognitive-behavioral therapy (CBT), cultural coping and proactive coping. The novel contribution of this review lies in its emphasis on the intersection of coping strategies with diverse disaster experiences, offering implications for tailored public health interventions. This study is not only timely but also relevant given the increasing frequency of both natural and man-made disasters, and the growing recognition of the need for effective mental health strategies in disaster response and recovery.