bluuebunny/crossref_metadata_2025 huggingface dataset:
Orbital Scale Kuroshio Current Variations of the Northwestern Pacific Ocean in the Early Pleistocene (Marine Isotope Stage 40–36) Based on Calcareous Nannofossil Records
Authors: Kuwano Daisuke , Kameo Koji , Kubota Yoshimi , Utsunomiya Masayuki , Mantoku Kanako , Okada Makoto
Published: February 2025

The Quaternary period was marked by distinct glacial‐interglacial climate cycles driven by orbital forcing, which are closely linked to marine environmental change. In the northwestern Pacific Ocean, the subtropical Kuroshio Current and subarctic Oyashio Current play crucial roles in global climate modulation. Despite their significance, paleoceanographic studies with high temporal resolutions are scarce. Here, we investigated calcareous nannofossil assemblages of the Kazusa Group to reconstruct the sea‐surface conditions around the Japanese Archipelago during the marine isotope stage (MIS) 40–36, providing an important climatic record prior to the mid‐Pleistocene transition. A time‐series analysis of nannofossil floral changes revealed orbital scale Kuroshio Current variability as well as Kuroshio Front migrations, suggesting that the Kuroshio Current variations were linked to the variability of the North Pacific subtropical gyre. Moreover, orbital‐scale southward migrations of the Kuroshio Front during the glacial periods may be correlated with the displacement of the intertropical convergence zone caused by the northern hemisphere climate changes. Finally, spectral and wavelet analyses of calcareous nannofossil abundance showed a periodicity of ∼20 Kyr, indicating that these variabilities in the Kuroshio Current are predominantly controlled by high‐latitude climate forcings. Therefore, the results obtained in this study suggest that Kuroshio Current variations are related to climatic changes from low to high latitudes during the MIS 40–36.
Reduced Penetration of Northern‐Sourced Waters Into the South Atlantic During the Last Interglacial Relative to the Holocene
Authors: Dias B. B. , Chiessi C. M. , Piotrowski A. M. , Campos M. C. , Jena P. S. , Ballalai J. M. , Nascimento R. A. , Santos T. P. , Venancio I. M. , Mashayek A. , Albuquerque A. L. S.
Published: February 2025

Specific previous interglacial periods showed millennial‐scale climate variability that influenced deep water formation and circulation. However, our understanding about the spread of deep waters formed in the North Atlantic throughout the South Atlantic during interglacial periods remains limited. To reconstruct South Atlantic deep water geometry and the Atlantic Meridional Overturning Circulation (AMOC) differences between the Last Interglacial (LIG) and the Holocene, we present new authigenic neodymium isotope (ℇNd) and sortable silt (SS) records from the middepth western South Atlantic. The comparison of our results to other Atlantic ℇNd records reveals notable variations in the penetration of northern‐sourced waters into the South Atlantic between these two periods. Moderate reduction of northern‐sourced waters entrainment in the western South Atlantic was observed during the onset of the LIG (ca. 128–124 ka) compared to the Holocene, with a significant decrease in the eastward spread of this water mass. This reduction may be linked to freshwater inputs into the North Atlantic, which reduced water density. Counterintuitively, when the AMOC was expected to reach its full interglacial mode at the end of the LIG (ca. 120–116 ka), more radiogenic ℇNd values and lower SS values indicated a shallow AMOC. Increased mixing with southern‐sourced waters during the LIG was likely due to decreased deep convection events at the North Atlantic high latitudes due to fresh subsurface water advection. Our findings suggest that the export and mixing ratio between northern‐ and southern‐sourced waters in the South Atlantic are determined by Northern Hemisphere climate during interglacial periods.
Controls of Climate Seasonality and Vegetation Dynamics on the Seasonal Variability of Terrestrial Water Storage Under Diverse Climate Regimes
Authors: Wu Chuanhao , Yeh Pat J.‐F. , Yao Tian , Gong Zhengjie , Niu Jie , Yuan Shanshui
Published: February 2025

Terrestrial water storage change (ΔS) is an important indicator of climate change that can monitor and predict hydrological changes. However, the interactions between ΔS and climate, vegetation, and soil factors add complexity in temporal variability of ΔS, particularly at seasonal scale. Here, we conduct a systematic assessment in the roles that seasonal variabilities of climate and vegetation modulate seasonal variability of ΔS in 769 basins covering a wide range of climate regimes and vegetation types globally. The variance decomposition method of ΔS based on the Budyko framework is used to estimate the contributions of climate factors (precipitation P and potential evapotranspiration PET) and runoff (R) to ΔS variability for different vegetation types. Results indicate that the increased climatic (P, PET) and R seasonal variabilities enhances ΔS seasonal variability under both in‐phase (IP) and out‐of‐phase (OP) seasonal relations between P and PET, with a larger contribution from P than PET and R. However, the P‐PET covariance tends to reduce (enhance) ΔS seasonal variability under the IP (OP) relation, while the P‐R covariance tends to reduce ΔS variability for both IP and OP relations. Climate seasonality influencing ΔS is regulated through vegetation dynamics, mainly via extending plant roots to access deeper soil water under water stress or by seasonally adapting water use efficiency and primary production. The growth of seasonal vegetation under the IP P‐PET relation can cope with limited soil water, while the growth of evergreen vegetation under OP P‐PET relation depends on soil water availability throughout the year.
Efficient Implementation of Tidal Forcing in Simulations of Groundwater Dynamics in Subterranean Estuaries
Authors: Wang Tao , Zhang Chenming , Barry David Andrew , Chen Jiansheng , Wang Yuan , Ren Jie , Li Ling
Published: February 2025

Submarine groundwater discharge from subterranean estuaries is affected by tides, which are represented in computational models as time‐dependent boundary conditions on the seaward boundary. Conventionally, a small time step is used in the numerical model to phase‐resolve the tidal signal so as to ensure accurate results, although at the cost of excessive computation times for long‐term simulations. This study proposes a highly efficient alternative method for modeling the tidal signal, in which a phase‐averaged pressure is assigned to the seawater boundary with a much larger time step. The assigned pressure condition is first determined from an analytical solution of the time‐independent pressure boundary condition. Along with the analytical solution, a single calibration factor is introduced at the beach face to account for the conductance at the beach. This results in good agreement between the results for phase‐averaged and phase‐resolved simulations. The new method is verified by comparison of the results for a wide range of physical cases determined using TOUGHREACT, a model for simulating coupled hydrodynamic, thermodynamic, and geochemical processes. This comparison shows that the phase‐averaged results give good agreement except for a small underestimation of the mixing zone over the saltwater wedge region. These results confirm that the new boundary condition is suitable for efficient, long‐term simulations of coastal aquifers subjected to tidal forcing.
Dual-Locking Strategy with Charge-Delocalizing Ligand for Stable Perovskites
Authors: Aggarwal Pooja , Patel Harsh , Gupta Shivani , De Soumyadeep , Govind Rao Vishal
Published: February 2025

We developed a multifunctional phenyl bis(dithiocarbamate) (PBTC) ligand that enhances perovskite nanocrystal stability under UV exposure and in polar solvents through strong lead chelation and intrinsic UV absorption. Its hole-delocalizing...
The role of defect-modulated HKUST-1 MOF nodes in non-oxidative ethanol dehydrogenation: an observed phenomenon of catalyst transfiguration
Authors: Ganai Anjali , Sarkar Pranab
Published: February 2025

Both defective HKUST-1(H) and HKUST-1(OH) MOF nodes can selectively transform ethanol to acetaldehyde where HKUST-1(OH) undergoes transfiguration to HKUST-1(H) at the end of the first catalytic cycle.
Micromechanical finite element modeling of crystalline lipid-based materials: monoglyceride-based oleogels and their composites
Authors: Grahn Patrick , Lassila Petri , Valoppi Fabio
Published: February 2025

Digital twins based on true 3D microstructures predict the bulk mechanical response of lipid-based materials and composites made thereof.
Activation and electrochemical reduction of carbon dioxide by transition metal atom-doped copper clusters
Authors: Mohanta Manish Kumar , Jena Puru
Published: February 2025

Doping transition metals in Cu13 clusters enhance CO2 reduction by altering electronic and magnetic properties, lowering overpotentials by ∼20%.
Personalised 3D-printed bioactive peek bone plate scaffold for treating femoral defects
Authors: Zhang Wenquan , Shi Dayou , Huang Shirui , Li Shaochuan , Zeng Min , Wei Yanming
Published: February 2025

Personalized bioactive bone plate scaffolds effectively promoted bone regeneration and generated mature bone bridging the defect site.
Evaporation driven buckling of a drop laden with graphene oxide nanosheets
Authors: Prakash Suriya , Krolis Eva , Marin Alvaro , Botto Lorenzo
Published: February 2025

The time-dependent shape of an evaporating spherical water drop containing graphene oxide (GO) nanosheets is measured for varying solid concentration, humidity level, and pH. The drop is sitting on a...
A review of enhancement strategies for pyrocatalysis of perovskite oxides and their applications
Authors: Zhang Qichang , Liu Wenfeng
Published: February 2025

In this review, strategies to enhance the pyrocatalytic performance of perovskite oxides are highlighted. The applications of perovskite oxide pyrocatalysis are then summarized. Finally, the outlook for perovskite oxide pyrocatalysis are presented.
Defect mechanism of the BixSb2−xTe3 alloy catalyst for lithium–oxygen battery applications
Authors: Liu Songyan , Song Xujie , Zhou Jianhua , Gao Jie , Qin Haiqing , Wang Xiaoyang , Chen Jun-Liang , Liu Wenping , Lei Xiaoxu , Miao Lei
Published: February 2025

The concentration of antisite defects and holes is modulated by adjusting the molar ratio of Bi and Sb in the 2D layered BixSb2−xTe3 cathode material, which act as catalytic sites to enhance the electrochemical performance of LOBs.
Efficient CO2 Reduction to Methane on Ru-Based Graphene Catalysts: Insights into Bimetallic Synergies, Thermodynamics, and Kinetics
Authors: Verma Anand Mohan , Kishore Nanda
Published: February 2025

Electrochemical conversion of CO2 into value-added chemicals offers a clean pathway to utilize greenhouse gases along with the production of useful products. In this study, we aim to unravel a...
Detection Limits of Calcium by EELS TEM-EDX and X-ray Absorption Spectroscopy for Biomineralization Studies
Authors: Rez Peter , Houben Lothar , Meng Yu-Feng , Gal Assaf , Kochovski Zdravko , Haas Benedikt
Published: February 2025

To investigate mechanisms of biomineral formation it is necessary to determine Ca concentrations in the vicinity of growing minerals at the highest spatial resolution. All techniques that use ionizing radiation...
Correction: Protective effects of potential probiotic Lactobacillus rhamnosus (MTCC-5897) fermented whey on reinforcement of intestinal epithelial barrier function in a colitis-induced murine model
Authors: Kaur Harpreet , Gupta Taruna , Kapila Suman , Kapila Rajeev
Published: February 2025

Correction for ‘Protective effects of potential probiotic Lactobacillus rhamnosus (MTCC-5897) fermented whey on reinforcement of intestinal epithelial barrier function in a colitis-induced murine model’ by Harpreet Kaur et al., Food Funct., 2021, 12, 6102–6116, https://doi.org/10.1039/D0FO02641G.
Doping Janus MoSSe monolayer with Al/Ga and P/As atoms, and their clusters: effective methods for the band structure and magnetism engineering
Authors: Nguyen Duy Khanh , Ha Chu Viet , Guerrero-Sanchez J. , Hoat D. M.
Published: February 2025

Effective band structure and magnetism engineering of MoSSe monolayer is proposed by doping. Results demonstrate the emergence of feature-rich electronic and magnetic properties that are desirable for optoelectronic and spintronic applications.
Features of the operation of the heat exchanger-condenser of modern passenger aircraft
Authors: Chichindaev Alexander , Khromova Irina , Vyborov Maxim
Published: February 2025

This paper presents a description of the design problems of the heat exchanger-condenser of modern aviation air conditioning systems (ACS) of passenger aircraft. The purpose of the work is to study known design methods and explore an alternative option. The paper presents a description of the problems of operating an ACS heat exchanger-condenser: freezing of the heat transfer surface along the cold and hot paths, as well as known methods of anti-icing systems (AIS) for it. The features of the calculation and design of the design variant under study are described. The results of computer simulation of heat transfer processes for various configuration options of the heat exchanger-condenser are presented. The most effective ways to prevent surface freezing are shown. The work is based on original copyright materials obtained at the Department of Technical Thermophysics of NSTU.
Study of variability phenomena during direct metal deposition of nickel based superalloy on residual porosity and microhardness
Authors: Khaimovich Alexander , Oleynik Maxim , Balyakin Andrey , Zlobin Evgeny , Kudriashova Maria , Nosova Ekaterina
Published: February 2025

The article reveals the influence of the technological regimes of direct laser cultivation, as well as the adverse conditions on the microhardness and the residual porosity of the samples. The speed of movement of the working tool (from 20 mm/s to 30 mm/s, step 5 mm/s), distance between nozzle and surface (9 mm to 13 mm, 2 mm pitch), mass flow (20.5 g/min to 30.7 g/min, 5.1 g/min pitch) and processing pause (32.0 to 48.0, 8.0) were chosen by variable factors. The influence of the mentioned factors on microhardness and residual porosity was determined, a regression model was worked out. The analysis shows that increasing powder flow and process pause reduces the variability of microhardness by the sample’ crosssection and increases its mean value, and the effective focusing distance and the melting speed have an uneven effect. The increase in the focusing distance and the melting speed clearly increase the variability of the residual porosity over the cross-section of the sample. The increase in the values of the technological pause and the powder flow have an opposite effect in the influence.
Experimental investigation of steel fiber dispersed reinforced concrete beams
Authors: Bakhtiyorjon Maksud ugli , Razzakov Sobirjon
Published: February 2025

The article presents the results of research on the deformation characteristics of concrete beams dispersed reinforced with steel fibers. One of the main disadvantages of conventional concrete is that its tensile strength is several times less than that of compressive strength, and the tensile behaviour of concrete in bending elements is not considered. This article takes into account the behaviour of concrete beams in the tensile zone by dispersing reinforcement of concrete beams with steel fibers, and the changes of strains in compressive and tensile concrete and tensile steel reinforcements depending on imposed load are highlighted. Also, by adding steel fibers to the concrete mix with lengths of 10, 20, 30, 40 and 50 mm and 0, 0.5, 1.0, 1.5, 2.0, 2.5 and 3.0% by volume, their optimal amounts were determined by testing cubic and prismatic samples.
Geomagnetic data real-time forecast based on neural networks
Authors: Polozov Yurii
Published: February 2025

Earth magnetosphere has a complex structure and its state is estimated by geomagnetic indexes. The paper suggests an approach to make real-time forecast of SME geomagnetic index variations. The approach is based on the combination of neural networks and wavelets. The features of input data formation for neural network models and forecast results are shown. The obtained neural network models can be applied in geomagnetic state monitoring problems.
Mathematical modeling of environmental management in urban natural-production systems
Authors: Rodionov Stanislav , Michurina Nadezhda , Mosina Lyudmila , Gvozd Varvara , Vasilieva Darya , Kholopov Yuri , Losev Aleksey , Suvorova Anna
Published: February 2025

This article examines the problem of environmental pollution in urbanized territories of natural-social-production systems. A new approach is proposed for applying the fundamentals of control theory in the system of environmental management and environmental monitoring. The paper presents factors influencing the process of human management of the natural environment and ecological processes in urbanized territories of naturalsocial- production systems. The application of control theory principles in geoecology allows for a fresh perspective on the processes of human management of the natural environment, as well as the development of an interdisciplinary terminological apparatus for communication between specialists from different specific fields of knowledge. Approaches for improving environmental management of polluted natural urbanized territories are formulated.
Measurement of hydrodynamic flow velocity by laser Doppler anemometer with different types of photodetectors
Authors: Rakhmanov Vitaly , Kakaulin Sergei , Kashkarova Margarita , Zubanov Kirill
Published: February 2025

The potential applications of three distinct photodetector types in laser Doppler anemometers for the measurement of hydrodynamic flow pulsations have been investigated. Laser Doppler anemometers with a classical vacuum photomultiplier tube, a multipixel silicon photomultiplier and a photomultiplier based on microchannel plates were used in the experiments. The main criteria for evaluating the quality of the Doppler signal were determined and analyzed for measuring the hydrodynamic flow velocity pulsations. The range of flow pulsations was from 1 to 50 Hz. In all experiments, laser Doppler anemometers demonstrated satisfactory performance when measuring pulsations up to 10 Hz. At frequencies from 10 to 50 Hz, the optimal results were achieved with laser Doppler anemometers employing photodetectors based on a silicon multipixel photomultiplier and a vacuum photomultiplier tube.
Determination of mechanical properties of finely-divided protective coating
Authors: Muranov Alexander , Karpov Nikita , Titova Anastasia , Averchenkov Andrey , Ivanov Naur
Published: February 2025

The way of peristaltic pump unit capacity enhancement by the sputtering of protective coating at the most wear off subject parts is represented. Plasma sputtering of protective coating is an efficient way of part wear prevention and also restoration of damaged parts. For this hardening method a developed industrial unit of finely-divided protective coating plasmas sputtering (IU FDPCPS) is given in the article. The development of the unit is a topical issue due to the fact that the existing units either do not implement the function of plasma sputtering or implemented not to the fullest extent. The sequence of operations for the coating of required thickness is described. The objective of the thesis is the quality assessment. For this the mechanical properties of different types of coating were defined. To find a solution, the methods of defining of the mechanical properties of the coated surfaces are given. As a result, it was found out that among latten alloy, titanium and molybdenum, latten alloy is the most suitable material for sputtering to protect the pair from friction and tear off.
Structural features of the roughness profile of hardened steel parts after finishing turning
Authors: Ovsyannikov Victor , Nekrasov Roman , Putilova Ulyana , Gubenko Arseniy
Published: February 2025

This article presents the results of a study of the structural features of the roughness profile of surfaces that are treated with finishing turning. The processing of hardened high-quality carbon steels is considered as initial data. Standard cutting tools with mechanical plate mounting are used. The profile of the treated surface was analyzed using the theory of random processes and fractal geometry methods. As a result of the research, it was found that with a decrease in the arithmetic mean separation of the Ra profile, the proportion of the random component increases. It can be concluded that the role of vibration processes in the formation of the profile is increasing. This factor makes it possible to implement a processing management strategy with the possibility of improving the quality of the surface layer.
Spectral features of structuring fractal objects
Authors: Ryzhikova Yuliya , Ryzhikov Sergey
Published: February 2025

The spectral characteristics of various geometries aperiodic gratings approximants with fractal properties are considered. The equivalence of the Fibonacci structure approximants Fourier spectra formed by the projection and block methods is shown.
The research of the formation of phase portraits of linear second-order systems taking into account the dynamic characteristics of the model
Authors: Kovalev Igor , Kulmurodova Nafisa , Saramud Mikhail , Kalinin Andrey , Borovinsky Dmitry
Published: February 2025

The article discusses the phase space method, widely used in physics and mathematics, from the perspective of studying dynamic systems. A second-order linear system is considered, a vectorized model of the analog equivalent of the system is constructed. A system interface has been developed that allows changing the model coefficients in real time, which simulates the process of generating disturbing influences. The equilibrium positions of the system are determined, such as “stable focus”, “unstable focus”, “center”. In the process of studying the dynamic model of a second-order linear system, phase portraits and graphs of transient characteristics were obtained.
A mathematical model of steel sample stretching: Establishing a unified nature and mechanism of the relationship between deformation stages
Authors: Yurchenko Anatoly S. , Yurchenko Stepan A.
Published: February 2025

The relevance of creating a mathematical model of deformation of various steels is dictated not only by the fact that it can be used to determine their physical and mechanical data used under certain external loads, but also because the mathematical model itself contributes to understanding the processes that occur in the deformed sample itself, allowing us to reveal the essence of the structure of the metal itself. Moreover, the current studies of this issue, despite the fact that they were carried out on the basis of a physical and mathematical approach, could not model the real process of this mechanism due to the fact that they did not find a single essence of this process. However, these studies have significantly helped in identifying the real mechanism of deformation and creating a new mathematical model. Using the data of existing experimental studies on uniaxial tension of steel rods, establish a single relationship between the stages of their deformation, taking into account the discrete structure of the crystal lattice of the metal, based on elastic and plastic shifts of its kinetic units. Based on the results obtained, develop a mathematical model that allows you to determine the necessary loads when using certain steels in specified conditions of their operation.
Uncertainties for symmetric and asymmetric distribution models
Authors: Polosin Vitaly G.
Published: February 2025

The model of the symmetric distribution shows the relationship between both the intervals of entropy uncertainty and the peculiarities of the formation of intervals of parametric uncertainty for symmetric and nonsymmetric distributions. In particular, the proportionality of the entropies of symmetric and non-symmetric distributions is shown. The peculiarity of the formation of entropy features of symmetric and asymmetric distribution is shown. The entropy coefficient of asymmetric distributions is proposed as an independent entropy feature of the shape of asymmetric distributions. The peculiarity of applying the entropy coefficient of a symmetric distribution to the description of the properties of an asymmetric distribution is discussed. In this paper the peculiarity of applying the entropy coefficient of a symmetric distribution to the description of the properties of an asymmetric distribution also is discussed. In particular, it is shown that the use of the entropy feature of a symmetric distribution can be considered as a replacement for an asymmetric distribution near the estimates of the center of random variables by means of its symmetric model intervals of parametric and entropy uncertainty. Such a replacement is possible if the intervals of the models are equal.
Code multiplexing using linear block codes
Authors: Rabin Alexey , Rabin Sergei , Zyulkov Arseniy
Published: February 2025

This paper explores the application of code multiplexing using linear block codes in communication systems. Through a comprehensive analysis of contemporary literature on switching methods and signal modeling, the study investigates the principles of code channel division switching. The research proposes novel approaches for synthesizing orthogonal encoding and decoding matrices, enabling efficient channel division through simplified schemes. The study focuses on the implementation of code division multiplexing using block codes, highlighting its potential for enhancing communication efficiency. Additionally, the paper discusses the dual benefit of this approach, noting its capacity for simultaneous error correction during the channel division process. This feature underscores the method's potential for improving both the reliability and efficiency of data transmission in various communication systems
Convolutional coding for code multiplexing of channels
Authors: Rabin Alexey , Rabin Sergei , Ovchinnikov Andrey
Published: February 2025

This paper explores the application of convolutional codes and their extensions in the context of binary channel code division. A novel method for channel division utilizing convolutional coding is presented, alongside a comparative analysis with block coding techniques. While the approach for implementing convolutional codes in channel division shares similarities with block coding methods, it possesses distinct characteristics. The study introduces the use of elements from the field of formal power Laurent series, with coefficients drawn from a finite field, as components of both the transmitted message and code word. This innovative approach enhances the flexibility and efficiency of the coding process. Furthermore, the research investigates the feasibility and conditions for simultaneous error correction during the code division of channels. This dual functionality has the potential to significantly improve the reliability and performance of communication systems. By examining these advanced coding techniques, the paper contributes to the ongoing development of more robust and efficient channel division methods in digital communications.
Enhancing current sensor sensitivity using faraday effect with multiple reflections in magneto-optical crystals
Authors: Bondarenko Evgeniy , Demin Maksim , Morozova Tatiana
Published: February 2025

This paper develops and studies a sensor element mock-up based on the Faraday effect with multiple reflections. The authors show that as the length of the optical path in a magneto-optical crystal increases, it prompts the increase in the current sensor sensitivity. The paper also establishes the correspondence of the current waveform and the optical response of the sensor mock-up. It is shown that in developed sensor, the output signal magnitude directly correlates with the magnetic field induction.
Magneto-optical effects in the reflection and transmission of light for the case of an equatorially magnetized plate
Authors: Turkmenov Xusan , Ochilov Odil , Malikov Komiljon , Yuldashev Ozodbek
Published: February 2025

Currently, expanding the elemental memory of computers and creating new generations of elemental bases for telecommunication systems are among the most important tasks for the national economy. However, further development of all these directions requires solving problems of a fundamental nature, such as achieving the superparamagnetic limit (which limits recording density), a relatively slow change in the magnetic state under the influence of magnetic field pulses (limiting recording speed), and a small magnitude of magneto-optical effects (limiting their use in integrated photonics). To solve these problems, the development and creation of new hybrid magnetic nanostructures that combine magnetic and non-magnetic materials - dielectrics, semiconductors, and metals - in various combinations is of particular relevance. In such structures, a significant enhancement of non-reciprocal linear and nonlinear optical and magneto-optical phenomena is possible, as well as the manifestation of new effects (for example, the effects of magneto-optical spatial dispersion). This article explores the reflection and transmission of light from a magnetic layer on a transparent (glass) substrate. As a result, formulas were obtained for the equatorial Kerr effect in reflected and transmitted light. These results are necessary for interpreting experimental data on the equatorial Kerr effect.
Surface plastic deformation method for increasing the wear resistance of fire grates in fiber separating machines
Authors: Shin Illarion , Muminov Mansurbek , Yusupov Alo , Iskandarova Nigora
Published: February 2025

The article covers materials on the influence of the wear resistance of cast iron grates of fiber separating machines on the technological clearance between them in the mentioned above grate. It has been shown that the main criterion for the performance of grate bars is their wear resistance, and at the same time, the amount of grate wear should be limited by the required technological clearances in the working part of the grate. It has been revealed that the method of surface plastic deformation can be used to create hardening (strain hardening) of the surface layer in order to increase the wear resistance of cast iron grates. For this purpose, a substantiation for strengthening technology for grate bars in the form of shot-impact treatment with microballs with a diameter of 0.3-0.4 mm, directed at the strengthening surface at an angle of 900 at a compressed air pressure of 0.3-0.4 MPa has been made. Based on data from experimental studies of the wear resistance of grates under production conditions, the values of their one-sided wear for hardened gin and linter grates have been obtained, which indicate a significant reserve of performance compared to grates without treatment.
Evaluation of concentration (Rn222, VOCs, CO2) for dust storm samples in Najaf Governorate, Iraq
Authors: Abed Haneen Talib , Rahi Shaimaa Ali , Hayder Rand A. , Jassim Ali Saeed , Abojassim Ali Abid
Published: February 2025

This study aims to assess the concentrations of (Rn222, vocs, CO2) in models of dust storms in Iraq using the AIRTHINGS method, where the results of (Rn222, vocs, CO2) were respectively. radon gas ranged where they ranged from 5.0±1.63 Bq/m3 to 18.0±3.74 Bq/m3 at The average 10.8±5.10 Bq/m3 on average. the levels of volatile organic chemicals in the models that were looked at ranged from 134 ppb to 672 ppb, with a mean of 374.9 ±327.815 ppb. During dust storms, the levels of carbon dioxide ranged from 415 (ppm) to 1200 (ppm), with a mean of 764.9 ± 369.2 (ppm). This means that most of the results for carbon dioxide, radon gas, and volatile organic chemicals were within what This study says is safe. Except for some samples, it was above the permissible limit.
Changes in the thermal characteristics of fullerenes in a nitrogen atmosphere
Authors: Barbin Nikolay , Yakupova Lydia , Terent’ev Dmitriy
Published: February 2025

In order to study the thermal stability of Cn fullerenes (n = 28, 32, 44, 50, 56, 60, 70, 84, 90, 94) in a nitrogen environment during the transition from the condensed phase to the gas phase, the method of thermodynamic modeling is used. This method consists in the analysis of the system using the TERRA software package. In this work, a comparative study of the thermal stability intervals of Cn fullerenes in the condensed and gas phases was carried out. Thermodynamic characteristics of sublimation reactions were determined: entropy and enthalpy indices, values of coefficients of determination.
Sustainable Material Cutting Optimization Using Deep Q-Networks: A Reinforcement Learning Approach for Resource Efficiency
Authors: Chen Linxuan
Published: February 2025

This paper proposes an innovative approach to the Cutting Stock Problem (CSP) by integrating Graph Neural Networks (GNN) which effectively extract and process graph-structured data and Deep Reinforcement Learning (DRL) which utilizes the data generated by the GNN model to make sequential cutting decisions. The GNN model is embedded with Graph Convolutional Networks (GCN) layers, while the DRL model is structured with Deep Q-network (DQN). In my own study using KTH-TIPS dataset for model training, I have achieved promising experimental outcomes, decreasing loss functions and stabilizing total rewards, which demonstrates the model’s ability to progressively decrease prediction errors in stages and reach the best cutting patterns.
A Survey of Research and Applications of Optimal Path Planning Based on Deep Reinforcement Learning
Authors: Song Zifan
Published: February 2025

This study focuses on the comparison between conventional path planning techniques and Deep Reinforcement Learning-based path planning technology. Traditional algorithms including the A* algorithm, Dijkstra algorithm, and random sampling techniques, which perform well in static contexts, become inefficient in dynamic and complicated high-dimensional settings due to high computing costs. In contrast to DRL, a robot is charged with detecting sensor data linked with optimum path-planning using MDP (Markov Decision Process). Such robots independently study their surroundings while choosing the proper path. This implies it gets better at adapting to new settings and changes. This research looks at the real-time performance of three standard DRL algorithms: Proximal Policy Optimization, Deep Deterministic Policy Gradient, and Deep Q-Network in real-world settings. This work also discusses the drawbacks of DRL in path planning, i.e., high processing requirements, extended training times, weak generalization capabilities, and so on. Future initiatives include creating efficient training algorithms, mechanisms that increase model generalization, and optimizing them using classical techniques.
Optimizing Robotic Arm Control Using Deep Deterministic Policy Gradient: An Exploration of Hyperparameter Tuning
Authors: Ma Yile
Published: February 2025

Robotic arms are essential in a wide range of applications, from industrial automation to medical surgeries, where both accuracy and adaptability are critical. Traditional path-planning methods for robotic arms, such as Rapidly-exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM), often suffer from limitations in dynamic environments. Reinforcement Learning (RL) presents a promising alternative for optimizing robotic arm control by enabling adaptive learning through trial and error. This study focuses on the application of Deep Deterministic Policy Gradient (DDPG), a popular RL algorithm, to control a simulated robotic arm following a mouse pointer. The study investigates the impact of three key hyperparameters—learning rate, batch size, and memory capacity—on the performance of the DDPG model. This paper systematically tested multiple values for each parameter and evaluated the model's success rate and average time per goal. Results showed that the optimal combination of parameters was a learning rate of 0.001, a batch size of 50, and a memory capacity of 30,000, yielding a success rate of 76.00% and an average time per goal of 0.07 seconds. These results emphasize the significance of fine-tuning hyperparameters to achieve optimal performance in robotic control tasks. Future work will focus on exploring adaptive hyperparameter tuning strategies and applying these methods to more complex and dynamic robotic environments to further enhance performance and adaptability.
Optimizing Robotic Arm Learning: Curiosity-Driven Deep Deterministic Policy Gradient
Authors: Liu Jiarun
Published: February 2025

This study explores the application of the Reinforcement Learning (RL) in training robotic arms, particularly using the Deep Deterministic Policy Gradient (DDPG) algorithm enhanced by a curiosity- driven mechanism. Robotic arms have various real-life applications, such as in the surgeries and assistive technologies. However, collecting the large- scale real-world data is costly and impractical, making simulation environments essential for optimization. The DDPG, well-suited for continuous action spaces, was employed to improve the robotic arm’s precision and adaptability. Integrating a curiosity mechanism allowed the system to explore and learn more efficiently, significantly improving the training time and success rate. The results demonstrate a 12% reduction in training time and an 18% increase in the success rate when using curiosity- driven exploration. These findings suggest that the enhanced DDPG algorithm not only accelerates learning but also enables better task execution, offering a promising approach for the real-world robotic applications.
The advancements and applications of deep reinforcement learning in Go
Authors: Zheng Xutao
Published: February 2025

Combining Deep Learning's perceptual skills with Reinforcement Learning's decision-making abilities, Deep Reinforcement Learning (DRL) represents a significant breakthrough in Artificial Intelligence (AI). This paper examines the evolution and uses of Deep Reinforcement Learning (DRL), emphasizing both the theoretical underpinnings and the noteworthy real-world applications—like AlphaGo's triumph over elite Go players—of the technology. DRL systems learn optimal policies through interactions with their environment, maximizing long-term cumulative rewards. DRL has achieved remarkable results in complex decision-making tasks through the combination of deep learning models like Convolutional Neural Networks (CNN) and reinforcement learning techniques. DRL's potential to transform AI applications is demonstrated by its success in a number of industries, including robotics, autonomous driving, and video games. AlphaGo's success, leveraging DRL and Monte Carlo Tree Search (MCTS), exemplifies the impact of this method on game theory and strategic decision-making. This paper aims to explore the key concepts of DRL, its historical evolution, and its future prospects in advanced AI research.
A Comprehensive Investigation of Reinforcement Learning-based Financial Quantitative Analysis: Taking Stock Trading and Risk Control as Examples
Authors: Zeng Yifei
Published: February 2025

Reinforcement learning (RL) has emerged as a transformative approach for addressing complex decision-making challenges, particularly in the financial sector, where its application has garnered substantial interest. This paper offers a comprehensive review of the foundational concepts and classical methods of RL, while providing an in-depth exploration of its advanced applications in crucial domains: stock prediction & quantitative trading, and risk estimation. By analyzing recent advancements over the past decade, the study underscores the expanding role of RL in optimizing financial strategies, improving decision-making processes, and driving innovation in quantitative finance. In addition to reviewing key developments, the paper discusses persistent challenges related to risk management, such as the trade-off between risk and reward, data scarcity, and the need for algorithms to adapt to dynamic and volatile market conditions. Through these insights, this research aims to provide a roadmap for future studies, addressing the limitations. Meanwhile, this study is contributed to guiding the continued evolution of RL applications in finance, ensuring they remain robust, adaptive, and effective in a rapidly changing economic landscape.
Research on Twitter User Tag Preference Prediction Based on Thompson Sampling Algorithm
Authors: Shi Yixuan
Published: February 2025

Twitter's user behaviour data is crucial for studying user patterns and content recommendation. To achieve this goal, the paper first preprocesses a Twitter user dataset obtained from Kaggle. The dataset includes over 40,000 objects in JSON format, focusing on users who tweeted on trending topics and had at least 100 followers and were following at least 100 other accounts. This filtering helps to exclude spam and empty accounts. The study constructs a user-hashtag matrix and applies label encoding technology to convert it into a numerical matrix. The Thompson Sampling algorithm is then applied to predict user hashtag preferences. The experimental results demonstrate the remarkable effectiveness of the Thompson Sampling algorithm in predicting users' preferences for hashtags. By iteratively updating the alpha and beta parameters for each hashtag multiple times, the algorithm can accurately estimate users' preferences and successfully identify hashtags with high recommendation value. There are significant differences in the preference levels of different hashtags among user groups, providing an important basis for subsequent recommendations and push notifications. The findings validate the algorithm's effectiveness and contribute to optimizing social media content recommendation algorithms, ultimately enhancing user experience and benefiting content creators and advertisers. This research contributes to the advancement of social media platforms by improving content recommendation algorithms, enhancing user experience, and fostering a more engaging and personalized user environment.
Performance of Multi-Armed Bandit Algorithms in Dynamic vs. Static Environments: A Comparative Analysis
Authors: Zhao Boxi
Published: February 2025

This paper conducts a comparative analysis of Multi-Armed Bandit (MAB) algorithms, particularly the Upper Confidence Bound (UCB) and Thompson Sampling (TS) algorithms, and focuses on the performance of these algorithms in both static and dynamic environments. Multi-armed bandit algorithms are instrumental in optimizing decision-making problems. While these algorithms have been studied in a static environment where the reward distribution is constant throughout the problem, real-world issues often have an unstable reward distribution, where the reward distribution may change throughout the process. This paper simulates both static and dynamic environments to evaluate the performance of UCB and TS algorithms by using the MovieLens 1M database. The paper demonstrates that the TS algorithm consistently outperforms the UCB algorithm in both static and dynamic environments. However, both algorithm shows a significantly higher cumulative regret in a dynamic environment compared with a static environment, which is due to the challenges of adapting to changing reward distribution over time. These results provide valuable insight into the application of Multi-Armed Bandit algorithms in real-world environments and highlight the need for further advancement in dynamic adaption for algorithms.
Research on the Multi-Armed Bandit Algorithm in Path Planning for Autonomous Vehicles
Authors: Li Jingyu
Published: February 2025

In the technological revolution of the 21st century, autonomous driving technology is rapidly changing transportation modes, and path planning, as a key component, relies heavily on advanced algorithm optimization. The Multi-Arm Bandit (MAB) algorithm may become an efficient decision optimization tool in autonomous driving path planning. Because it can continuously experiment, learn, and quickly determine the optimal strategy to maximize profits under resource constraints. When applied to autonomous driving, the MAB algorithm may be able to demonstrate its advantages. In complex traffic environments, it dynamically adjusts strategies to adapt to constantly changing road conditions, plans safe and efficient driving paths, and quickly responds to unexpected situations to ensure driving safety. Compared with other algorithms, the learning and adaptability of MAB algorithm makes it particularly suitable for the dynamics and unpredictability of real-world driving scenarios. However, the practical application of MAB algorithm in autonomous driving faces challenges, including accurately evaluating path efficiency, efficiently processing large amounts of traffic data, and ensuring the stability and reliability of the algorithm. Further in-depth research and exploration are crucial for fully utilizing the advantages of MAB algorithm in path planning and promoting the sustainable development and enhancement of autonomous driving technology.
Innovative applications and challenges of dobby slot machine algorithms in online gaming
Authors: Yang Lijuehua
Published: February 2025

With the rapid development of the online gaming industry, how to improve user experience, optimise game design and increase business revenue through intelligent algorithms has become a key issue in the industry. The Multi-armed Gambling Machine (MAB) algorithm, as a classic reinforcement learning problem, has shown great potential for decision optimisation in online games. By balancing the strategy of ‘ exploration and development ’ , the MAB algorithm gradually optimises decision making in an uncertain environment, enhancing the personalised gaming experience, optimising the economic system and improving player retention. This paper reviews the innovative application of the MAB algorithm in online games, focuses on its application in game design, personalised recommendation, game economic system and player retention optimisation, discusses the challenges of the MAB algorithm in practical application and proposes future research directions. Through an overview of existing research results, this paper aims to provide a comprehensive perspective for academia and industry to promote the further development and application of the MAB algorithm in online games.
Optimizing Data Filtering in Multi-Armed Bandit Algorithms for Reinforcement Learning
Authors: Zhang Shengshi
Published: February 2025

This study investigates the performance of data filtering algorithms in multi-armed bandit (MAB) problems for reinforcement learning applications. It focuses on five algorithms: Epsilon-Greedy (ε- Greedy), Upper Confidence Bound (UCB), Linear Upper Confidence Bound (LinUCB), Thompson Sampling, and Linear Thompson Sampling (LinTS). The algorithms were evaluated in static and dynamic environments using the MovieLens dataset and transferred to binary rewards to measure performance. Each algorithm was tested in simulations with 1,000 interactions, and compared with cumulative reward, accuracy, and adaptability. The experiments involved multiple arms, each with a unique reward distribution, and simulated static (fixed rewards) and dynamic (periodically changing rewards) environments. Result shows that LinUCB and LinTS achieved the highest cumulative rewards in static settings. In dynamic environments, Thompson Sampling demonstrated superior adaptability by adjusting quickly to changing reward structures. Overall, LinUCB and LinTS maintained an accuracy rate of over 36%. This research provides critical insights into the trade-offs between exploration and exploitation, offering theoretical and experimental support for optimizing MAB algorithms in real-world applications such as recommendation systems.