bluuebunny/crossref_metadata_2025 huggingface dataset:
Transformers and LLMs as the New Benchmark in Early Cancer Detection
Authors: Kumar Yulia , Huang Kuan , Gordon Zachary , Castro Lais , Okumu Egan , Morreale Patricia , Li J. Jenny
Published: January 2024

The study explores the transformative capabilities of Transformers and Large Language Models (LLMs) in the early detection of Acute Lymphoblastic Leukaemia (ALL). The researchers benchmark Vision Transformers with Deformable Attention (DAT) and Hierarchical Vision Transformers (Swin) against established Convolutional Neural Networks (CNNs) like ResNet-50 and VGG-16. The findings reveal that transformer models exhibit remarkable accuracy in identifying ALL from original images, demonstrating efficiency in image analysis without necessitating labour-intensive segmentation. A thorough bias analysis is conducted to ensure the robustness and fairness of the models. The promising performance of the transformer models indicates a trajectory towards surpassing CNNs in cancer detection, setting new standards for accuracy. In addition, the study explores the capabilities of LLMs in revolutionising early cancer detection and providing comprehensive support to ALL patients. These models assist in symptom analysis, offer preliminary assessments, and guide individuals seeking information, contributing to a more accessible and informed healthcare journey. The integration of these advanced AI technologies holds the potential to enhance early detection, improve patient outcomes, and reduce healthcare disparities, marking a significant advancement in the fight against ALL.
Incentive Mechanism of Online Leaning Based on Blockchain
Authors: Liu Shukun , Liu Zhimin , Zhu Xiaoyu , Pan Xianmin , Chen Baimu
Published: January 2024

Incentive mechanism plays an important role in online education. In order to better play the role of the incentive mechanism, it must rely on contemporary information technology, such as blockchain technology, big data technology and so on. In this paper an incentive mechanism based on blockchain technology is proposed with which can establish a good trust relationship between multiple learning resource nodes and improve data security during the process of online education. A structure of online learning resource chain is proposed in this paper also. And a dynamic optimization framework of learning resource chain by analyzing a series of problems faced in the process of online education is formed. The private chain of learning resource is constructed on the basis of the design of incentive smart contract. The learners and the builders of learning resources can obtain the dynamic rewards by performing a smart method of contract. The experimental results show that the incentive method based on blockchain can well mobilize participants’ enthusiasm for resource construction and dynamic improvement of resource quality, and effectively solve a series of problems in the learning process.
Metric Learning with Sequence-to-sequence Autoencoder for Content-based Music Identification
Authors: Wijesena Pasindu , Jayarathne Lakshman , Wickramasinghe Manjusri , Abeytunge Shakya , Marasinghe Pasindu
Published: January 2024

Content-based music identification is an active research field that involves recognizing the identity of a musical performance embedded within an audio query. This process holds significant relevance in practical applications, such as radio broadcast monitoring for detecting copyright infringement. Various approaches for content-based music identification have been explored in the existing literature, yielding diverse levels of performance. However, despite the considerable attention dedicated to this area, no attempts have been made to leverage the dynamical nature of musical works coupled with the modern advances in machine learning such as metric learning for content-based music identification. In this paper, we propose a novel approach that encodes the dynamic nature of musical performances into the latent space of a sequence-to-sequence auto-encoder network. The learning objective is further enforced with the metric learning for music similarity measurement. The proposed model is extensively evaluated by testing it with 14 distortions of the same musical performance. The experimental results demonstrate a substantial increase of 31.71% in hit-rate over the baseline established using related work found in the literature. These findings highlight the potential of our approach to significantly improve content-based music identification, thereby offering promising applications in various practical scenarios.
Correspondence Learning via Correspondence Embedded and Channel Recalibration Network
Authors: Liu Yizhang , Zhao Shengjie , Deng Hao , Ding Fuqiang
Published: January 2024

Correspondence learning is pivotal to many computer vision-based tasks. Existing methods regard each correspondence equally along the channel dimension, which weakens the feature representation capability of the network. To alleviate this problem, we propose a Correspondence Embedded and Channel Recalibration Network, named CECR-Net, to predict the inlier probability of each correspondence and recover camera poses. The proposed CECR-Net is designed to explore the potential impact of correspondences on the channel dimension, and recalibrate the weight of each channel, so that our CECRNet can capture more exact contextual information. Experiments show that our CECR-Net is effective in outlier removal and camera pose estimation tasks on challenging public datasets.
CatBoost-based Intrusion Detection Method for the Physical Layer of Smart Agriculture
Authors: Wei Zizhong , Ning Fanggang , Jiang Kai , Wang Yang , Bi Zixiang , Duan Qiang , Zhang Jichen , Li Rui
Published: January 2024

Agriculture holds a pivotal role in the progress of human society. The challenges stemming from a burgeoning population, land degradation, water scarcity, and urbanization have intensified the need for more efficient agricultural production. While smart farming brings significant benefits to farmers and agricultural output, it also introduces complex cybersecurity risks to agricultural production. The security of the physical layer in smart agriculture is intricately tied to crop growth and yield, with indirect implications for the security of the network and application layers. This paper introduces a novel intrusion detection scheme based on CatBoost for the physical layer and evaluates its effectiveness using the publicly available ToN_IOT dataset. In binary classification results, the scheme achieves a remarkable recognition accuracy of 99.94%, along with a precision and recall of 99.88%. In multi-classification results, the scheme outperforms other existing solutions across all metrics. The experimental findings clearly illustrate the exceptional recognition accuracy of this implemented method against physical layer attacks within the domain of smart agriculture. Furthermore, the system’s implementation ensures the security of input data for the smart agriculture network layer, cloud, and blockchain applications.
Ensemble learning model on Artificial Neural Network - Backpropagation (ANN-BP) architecture for coal pillar stability classification
Authors: Mendrofa Gabriella Aileen , Handari Bevina Desjwiandra , Hertono Gatot Fatwanto
Published: January 2024

Pillars are important structural units used to ensure mining safety in underground hard rock mines. Unstable pillars can significantly increase worker safety hazards and sudden roof collapse. Therefore, precise predictions regarding the stability of underground pillars are required. One common index that is often used to assess pillar stability is the Safety Factor (SF). Unfortunately, such crisp boundaries in pillar stability assessment using SF are unreliable. This paper presents a novel application of Artificial Neural Network-Backpropagation (ANN-BP) and Deep Ensemble Learning for pillar stability classification. There are three types of ANN-BP used for the classification of pillar stability distinguished by their activation functions: ANN-BP ReLU, ANN-BP ELU, and ANN-BP GELU. These three activation functions were chosen because they can solve the vanishing gradient problem in ANN-BP. In addition, a Deep Ensemble Learning process was carried out on these three types of ANN-BP to reduce the prediction variance and improve the classification results. This study also presents two labeling alternatives for pillar stability by considering its suitability with the SF. Thus, pillar stability is expanded into four categories: failed with a suitable safety factor, intact with a suitable safety factor, failed without a suitable safety factor, and intact without a suitable safety factor. There are five features used for each model: pillar width, mining height, bord width, depth to floor, and ratio. In constructing the model, the initial dataset is divided into training data, validation data, and testing data. In this case, four type of proportions are used. For training-testing division the proportions are: 80 % : 20 %, 70 % : 30 %, for training-validation-testing division the proportions are: 80 % : 10 % : 10 %, 70 % : 15 % : 15 %. Average accuracy,F1-score, andF2-score from 10 trials were used as performance indicators for each model. The results showed that the ANN-BP model with Ensemble Learning could improve ANN-BP performance with an average accuracy 86.48 % and anF2-score 96.35 % for the category of failed with a suitable safety factor.
Calculation of the total number of radiation decay of Radiolabelled-Octreotate using non-linear mixed effect models
Authors: Pratiwi Nining , Riana Ade , Hardiansyah Deni
Published: January 2024

Non-Linear Mixed Effect (NLME) is a method used in the area under the measured time-activity curve (AUC) calculations. The calculation of an accurate AUC is needed for an accurate determination of the radiation absorbed dose. In NLME, the error model might affect the accuracy of the estimation of the AUC. Therefore, the aim of this study was to determine the effect of error models on AUC calculations using NMLE. The data used in this study were from biokinetic data of the 111In-DOTATATE biodistribution in the tumour collected from the literature. The data were fitted using published bi-exponential function $f(t) = {{({{\rm{k}}_e} \times {{\rm{k}}_a})} \over {c({{\rm{k}}_a} - {{\rm{k}}_e})}}\left[ {{e^{ - ({{\rm{k}}_e})t}} - {e^{ - ({{\rm{k}}_a})t}}} \right]$ with several error models, namely constant, proportional, combined and exponential errors. The mean and standard deviation were determined from the AUC for each error model AUC values obtained from constant, proportional, combined, and exponential error were (4.40 ± 1.93) nmol·min, (3.13 ± 2.74) nmol·min, (3.22 ± 2.85) nmol·min and (3.14 ± 2.75) nmol·min, respectively. Based on the research results, the proportional, combined and exponential error were relatively produced better results compared to the constant error model in our dataset.
Granulomatose mit Polyangiitis und mikroskopische Polyangiitis
Authors: Klapa Sebastian , Arnold Sabrina , Lamprecht Peter
Published: January 2024

Die Granulomatose mit Polyangiitis (GPA) und mikroskopische Polyangiitis (MPA) sind zwei zur ANCA-assoziierten Vaskulitis (AAV) zählende Entitäten. Beiden Erkrankungen liegt eine systemische nekrotisierende Kleingefäßvaskulitis zugrunde, die jedes Organ betreffen kann. Bei der GPA ist zusätzlich eine in der Regel den Respirationstrakt betreffende extravaskuläre nekrotisierende granulomatöse Entzündung vorzufinden. Das klinische Bild wird in der Mehrzahl der Fälle durch ein pulmorenales Syndrom mit alveolärer Hämorrhagie und rapid-progressiver Glomerulonephritis sowie weitere Organmanifestationen bestimmt. Bei der GPA imponiert zudem die fast regelhafte Mitbeteiligung des oberen Respirationstrakts. Die GPA ist mit Anti-Neutrophilen zytoplasmatischen Autoantikörpern (ANCA) mit einer Spezifität für die Proteinase 3 (PR3-ANCA) und die MPA für Myeloperoxidase (MPO-ANCA) assoziiert. Die immunsuppressive Therapie richtet sich nach der Krankheitsaktivität und Schwere der Organbeteiligung.
Ultra-wide-angle and broadband metamaterial absorber based on monopole top loading antenna and its reciprocity
Authors: Wang Aixia , Li Lei , Zhang Gaiping , Wang Wenjie , Fu Xinmin , Wang Jiafu
Published: January 2024

In this paper, an ultra-wide-angle and broadband metamaterial absorber with a monopole top loading structure (MA-MTLS) was proposed. The resistive films printed on the dielectric are on the top layer. The dielectric square cylindrical shells with resistive films on the outside are in the middle, and a metal plate is on the bottom layer. The whole structure can be regarded as a top loading monopole antenna that has a good omnidirectional radiation capability. According to reciprocity, when a composite structure is used as an absorber, it will have a good absorption performance. The impedance matching characteristics were demonstrated by Smith chart and equivalent circuit, and then, the absorption performance was optimized. The simulated results show that the absorption of the MA-MTLS is more than 90% under TM polarized waves of 3.6–13.8 GHz as the incident angle changes from 0° to 80°. When the incident angle increases to 83°, the absorption is higher than 80% in the frequency band of 3.7–14.5 GHz. At the stage of experiment, the dielectric substrate was made by three-dimensional printing technology, the resistive films on the top layer were printed using screen printing techniques, and the resistive films on the outside of the square cylindrical shells were completed by pasting. The consistency between the experimental results and simulation data indicates that the design method is feasible and has good application prospects.
Application of conjugate gradient least squares method in solving magnetic moments of magnetic targets based on magnetic dipole array model
Authors: Lu Binjie , Zhang Xiaobing
Published: January 2024

In order to solve the problem of magnetic targets magnetic field modeling, a magnetic dipole array model was established, a modeling inversion equation system was constructed, and the degree of sickness of the solved equation system under different numbers of magnetic dipoles was quantitatively analyzed based on the coefficient matrix condition number. In order to solve the problem of the pathological system of magnetic field modeling equations, a regularization method based on the conjugate gradient least squares method was designed to invert the magnetic moment parameters of the magnetic dipole. In order to analyze the applicability of the regularization method in magnetic field modeling inversion, the magnetic moment solving accuracy, the magnetic moment solving robustness, calculation time, and other metrics are defined. A detailed test of the ship model was designed, and the magnetic field passing characteristics of the two types of ship models at different positive and horizontal conditions at two depths were measured. Under the condition of no interference and interference, the conjugate gradient least squares method is used to invert the magnetic field model, and the numerical test analysis shows that the conjugate gradient least squares method has higher applicability than the generalized inverse solution method and the stepwise regression method. Under the condition of interference, the relative error of magnetic field fitting of the array model with 15 magnetic dipoles is 0.1537, and the relative error of magnetic field extrapolation is 0.0868. The method proposed has the advantages of high accuracy and strong robustness in solving the magnetic moment of the magnetic dipole array model.
Factors influencing wildfire management decisions after the 2009 US federal policy update
Authors: Fillmore Stephen D. , McCaffrey Sarah , Bean Rachel , Evans Alexander M. , Iniguez Jose , Thode Andrea , Smith Alistair M. S. , Thompson Matthew P.
Published: January 2024

Background The decision making process undertaken during wildfire responses is complex and prone to uncertainty. In the US, decisions federal land managers make are influenced by numerous and often competing factors. Aims To assess and validate the presence of decision factors relevant to the wildfire decision making context that were previously known and to identify those that have emerged since the US federal wildfire policy was updated in 2009. Methods Interviews were conducted across the US while wildfires were actively burning to elucidate time-of-fire decision factors. Data were coded and thematically analysed. Key results Most previously known decision factors as well as numerous emergent factors were identified. Conclusions To contextualise decision factors within the decision making process, we offer a Wildfire Decision Framework that has value for policy makers seeking to improve decision making, managers improving their process and wildfire social science researchers. Implications Managers may gain a better understanding of their decision environment and use our framework as a tool to validate their deliberations. Researchers may use these data to help explain the various pressures and influences modern land and wildfire managers experience. Policy makers and agencies may take institutional steps to align the actions of their staff with desired wildfire outcomes.
An analysis of the legal regime for agricultural marketing in India
Authors: Vohra Mehak , Sharma Vijaylaxmi
Published: January 2024

Despite the significant growth in the Indian economy and the expansion of the primary sector, farmers continue to live in extreme poverty and their hopelessness has been on the rise. Improving farmers’ conditions is crucial because there is no way to avoid the agrarian crisis or their dilemma. Over the years, the government has implemented several reforms despite farmers generally opposing these attempts. It goes without saying that the government must continue to develop the industry if it is to ensure the farmers in India continue to profit. The agricultural market system which is predominantly guided by the agricultural produce marketing legislations, has major flaws, which raises significant challenges in achieving a strong position for a farmer. The policies around price assurance have led to acute interventions, which have been attributed to fragmentation. On the one hand, government intervention is necessary, but on the other hand, too much of it will lead to negative effects. It calls for regulatory reform of the whole system using a balanced approach. This study has analysed the present-day structural problems in the marketing sector with relevance to the historical background of the reforms and suggested a few policy changes for a better future for the farmers.
Improved power quality based PVDG system using different optimised MPPT controllers
Authors: Tiwari Anurag
Published: January 2024

The characteristic curve of photovoltaic (PV) modules is nonlinear due to environmental influences. The maximum quantity of energy can only be taken from the nonlinear angle of the solar system at one exact moment. The primary function of an MPPT (maximum power point tracking) controller in a PV system is to extract maximum power point (MPP) from solar panels. In this paper, an enhanced MPPT approach using different optimization techniques is suggested for PV systems. The Fuzzy logic controller (FLC), Whale Optimization Algorithm(WOA), and Modified Grey Wolf Optimization algorithm (MGWO) are presented for the MPPT controller. Among all methods, the MGWO technique is one of the best at determining the MPP in solar systems because of its quick response and low fluctuations. Accurate training data is one of the biggest challenges in developing an effective MGWO. The irradiance and temperature are taken into consideration as input variables, and the optimal voltages are optimized as an output variable using the MGWO algorithm. Matlab/Simulink simulations are run to verify the suggested model’s suggested tracking effectiveness. To ensure accurate results, simulations are run under various climatic conditions. The simulation result shows that the proposed method operates correctly in a variety of climatic conditions efficiently.
A review of novel methods to improve the optical and electrical properties of n-type and p-type sulphides and oxides: leading the frontiers of semiconductor technology
Authors: Madkhali O
Published: January 2024

This review paper focuses on the current advancements in improving the optical and electrical properties of n-type and p-type oxides and sulphide semiconductors. The demand for high-performance semiconductors has grown significantly in recent years due to their wide range of application in electronic and optoelectronic devices. However, the inherent limitations of these materials such as low conductivity, poor optical absorption, and low carrier mobility have hindered their widespread adoption. This paper provides an overview of various techniques that have been employed to improve the optical and electrical properties of n-type and p-type oxides and sulphide semiconductors. These techniques include doping with impurities, defect engineering, surface passivation, and bandgap engineering. The paper also discusses the recent progress in the synthesis of these materials using different methods such as chemical vapor deposition, sol–gel, and hydrothermal methods. Furthermore, this review paper highlights the applications of these improved materials in various fields such as solar cells, light-emitting diodes, photocatalysis, and sensing. Finally, the paper concludes with the prospects of these materials and the challenges that need to be addressed to achieve their full potential. Overall, this review paper provides valuable insights into the current state-of-the-art techniques for improving the optical and electrical properties of n-type and p-type oxides and sulphide semiconductors, which can potentially lead to the development of high-performance devices.
Giant Kerr effect of a p
x
+ ip
y
superconductor engineered by semiconductor nanowires
Authors: Yazdani-Hamid Meghdad
Published: January 2024

Twisted array of proximitized quantum wires is recently proposed as a promising platform to host Majorana zero modes and superconducting phases with broken time reversal symmetry. Thus, this device is expected to have a nonzero polar Kerr effect. We study this signal and the corresponding Hall conductivity for the variety of topological phases in the presence and absence of bulk free carriers. The calculated Kerr angles can easily be measured because of the high characteristic energy scales using existent experimental techniques. Then, the unique optical results of each topological phase may be used as fingerprints of these interesting phases.
Higgs inflation model with non-minimal coupling in hybrid Palatini approach
Authors: Asfour Brahim , Bargach Aatifa , Errahmani Ahmed , Ouali Taoufik
Published: January 2024

In this paper, we propose a hybrid metric Palatini approach in which the Palatini scalar curvature is non minimally coupled to the scalar field. We derive Einstein's field equations, i.e., the equations of motion of the scalar field. Furthermore, the background and perturbative parameters are obtained by means of Friedmann equations in the slow roll regime. The analysis of cosmological perturbations allowed us to obtain the main inflationary parameters, e.g., the scalar spectral index

and tensor to scalar ratio r. From this perspective, as an application of our analysis, we consider the Higgs field with quartic potential, which plays the inflaton role, and show that predictions of Higgs hybrid inflation are in good agreement with recent observational data [Astron. Astrophys. 641, 61 (2020)].
Early prediction of dementia using fMRI data with a graph convolutional network approach
Authors: Han Shuning , Sun Zhe , Zhao Kanhao , Duan Feng , Caiafa Cesar F , Zhang Yu , Solé-Casals Jordi
Published: January 2024

Objective. Alzheimer’s disease is a progressive neurodegenerative dementia that poses a significant global health threat. It is imperative and essential to detect patients in the mild cognitive impairment (MCI) stage or even earlier, enabling effective interventions to prevent further deterioration of dementia. This study focuses on the early prediction of dementia utilizing Magnetic Resonance Imaging (MRI) data, using the proposed Graph Convolutional Networks (GCNs). Approach. Specifically, we developed a functional connectivity (FC) based GCN framework for binary classifications using resting-state fMRI data. We explored different types and processing methods of FC and evaluated the performance on the OASIS-3 dataset. We developed the GCN model for two different purposes: (1) MCI diagnosis: classifying MCI from normal controls (NCs); and (2) dementia risk prediction: classifying NCs from subjects who have the potential for developing MCI but have not been clinically diagnosed as MCI. Main results. The results of the experiments revealed several important findings: First, the proposed GCN outperformed both the baseline GCN and Support Vector Machine (SVM). It achieved the best average accuracy of 80.3% (11.7% higher than the baseline GCN and 23.5% higher than SVM) and the highest accuracy of 91.2%. Secondly, the GCN framework with (absolute) individual FC performed slightly better than that with global FC generally. However, GCN using global graphs with appropriate connectivity can achieve equivalent or superior performance to individual graphs in some cases, which highlights the significance of suitable connectivity for achieving performance. Additionally, the results indicate that the self-network connectivity of specific brain network regions (such as default mode network, visual network, ventral attention network and somatomotor network) may play a more significant role in GCN classification. Significance. Overall, this study offers valuable insights into the application of GCNs in brain analysis and early diagnosis of dementia. This contributes significantly to the understanding of MCI and has substantial potential for clinical applications in early diagnosis and intervention for dementia and other neurodegenerative diseases. Our code for GCN implementation is available at: https://github.com/Shuning-Han/FC-based-GCN.
Monte Carlo study for the thermodynamic and dynamic phase transitions in the spin-S Ising model on Sierpiński carpet
Authors: Jang Hoseung , Azhari Mouhcine , Yu Unjong
Published: January 2024

We study the thermodynamic and dynamic phase transitions (TPT and DPT) of the spin-

1

/

2

and spin-1 Ising models on three graphs constructed on the Sierpiński carpet. This study employs Monte Carlo methods, specifically the Wolff and Metropolis algorithms, in conjunction with finite-size scaling analysis. By calculating the critical temperature and critical exponent ratio

γ

/

ν

associated with the TPT, we demonstrate that the three graphs exhibit an identical critical exponent ratio for both the spin-

1

/

2

and spin-1 Ising models within statistical error. Furthermore, we explore the kinetic Ising model by varying the period of the oscillating external magnetic field and verify the existence of the DPT. We find that the critical exponent ratio

γ

/

ν

for the DPT matches that of the TPT. Our results suggest that the critical exponents or their ratios for continuous phase transitions in equilibrium and non-equilibrium systems with short-range interactions are independent of the graph structure and interaction type, as long as the background space is the same.
Optimal scheduling of park electric and cooling system considering multi-energy synergy
Authors: Jia Yujian , Li Li , Sui Quan , Qin Changlong , Kan Changtao
Published: January 2024

The park’s electric refrigeration system exhibits complex coupling relationships and heterogeneous operational characteristics, making it difficult to achieve optimal operation. Therefore, a strategy for optimizing the park’s electric refrigeration system, considering the potential for mutual assistance from multiple energy sources, is proposed. Firstly, an introduction to the structure of the park’s electric refrigeration system is provided. Subsequently, operational constraints for the park’s units are established through quantitative analysis of the operational characteristics of systems such as the photovoltaic power generation system. Based on this, an optimization scheduling strategy for the park’s electric refrigeration system is proposed to minimize system operating costs. Finally, the model is transformed into a Mixed Integer Linear Programming (MILP) problem by employing linearization techniques. Simulation results demonstrate the effectiveness of the proposed strategy.
A novel ray method for the coking of n-decane in circular tubes with flexible cross-section area
Authors: Zhang Chen , Gao Hui , Zhao Jiajun , Wen Dongsheng
Published: January 2024

Coking of endothermic hydrocarbon fuels significantly deteriorates the efficiency of regenerative cooling for hypersonic airbreathing vehicles. A novel ray method based on dynamic mesh techniques is proposed to describe the coking behavior in a circular tube with a flexible cross-section area. This method can be used to identify metal and deposit coke efficiently, which promotes the applicability of the dynamic mesh method in flexible structures. The cooling performance of n-decane before and after coking is numerically simulated and compared. The deposition brings higher thermal risks by elevating the solid temperature. However, the deposition reduces the channel’s cross-sectional area and increases the flow speed, leading to decreased flow residence time. Because the suppressing effect from lower flow residence time outweighs the promotion effect from increased temperature, the n-decane conversion decreased after coking.
Numerical simulation and experimental application of a 300 MW boiler combustion system
Authors: Liu Huan , Wang Hongwu , Liu Wenjuan , Liu Xiaoguo , Shu Jin , Zhang Yubo , Cao Haojun , Du Jinsong , Ma Yichao , Niu Kun
Published: January 2024

In the actual operation of a 300 MW boiler, there are some problems such as unstable combustion, high combustible material of fly ash and slag, and large thermal deviation on both sides of steam. The Cooper method is used to divide the three parts of the furnace by a hexahedral structured grid, which can reduce the grid number and improve the numerical simulation calculation precision. The Realization k-ε turbulent model is used to simulate the gas-solid two-phase flow in the furnace before and after reconstruction. The boundary of the calculation model adopts the actual operating parameters, and the numerical calculation adopts the three-dimensional steady-state calculation - Simple algorithm. The conclusions are as follows: the amount of pulverized coal powder falling into the cold ash hopper is reduced from 0.077 kg/m3 before the reconstruction to 0.045 kg/m3 after the reconstruction, with a decrease of about 42%. The secondary air concentration arrangement in the lowest two layers strengthened the secondary air supporting capacity. The residence time of pulverized coal particles in the furnace is increased from 30.59 s before the reconstruction to 39.97 s after the reconstruction, and the time is extended by about 9.4 s, which is beneficial to the combustion of pulverized coal particles. The centralized arrangement of primary air in the lowest two layers is beneficial to the initial ignition and stable combustion of pulverized coal gas. The downdip of the tertiary air incidence angle reduces the rotational momentum of the tertiary airflow, and the increase of the rotational momentum of the over-fire air angle is conducive to weakening the residual rotation at the furnace outlet. After the implementation of the reconstruction measures, the boiler can be stably burned at 150 MW, the combustible material of fly ash and slag are reduced to the controllable range, the steam thermal deviation is significantly reduced, and the boiler operating parameters are stable.
Power prediction and operation scheduling strategy of pumped storage power station based on machine learning
Authors: Tian Guang , Chen Chunsheng , Yang Lei , Yang Yang
Published: January 2024

The power of a pumped storage power station depends on the water level of the upstream reservoir and the downstream demand. The change in water level is related to rainfall, evaporation, river inflow, and other factors. Therefore, it is difficult to accurately predict the generating power and its scheduling. A machine learning-based power prediction and operation scheduling strategy for pumped storage power plants is proposed. The relationship between the forgetting gate, input gate, output gate, and memory unit of the long and short-term memory network is redefined. The activation function between input data and output data is constantly updated, which is used as the power prediction function of the pumped storage power station. The power prediction model is built. The operation scheduling process of the pumped storage power station is designed. The operation scheduling of the pumped storage power station is completed by using the daily scheduling time scale. The experimental results show that the research method can accurately predict the power generation of a pumped storage power station. The power generation is more balanced, and the power scheduling effect is better under the application of the research method.
Drilling technology of long horizontal section of shale oil
Authors: Zhang Zengbao , Lv Jianbo , Zhu Xiaoming , Zhu Jing
Published: January 2024

As an important supplementary energy resource, shale oil has attracted worldwide attention due to its huge reserves and rich comprehensive utilization levels. The world is rich in oil shale resources. According to statistics, if the oil shale of 33 countries and regions in the world is converted into shale oil, it will be about 400 billion tons. With the continuous maturity of horizontal well drilling and completion technology in shale oil gas reservoirs and the application of other advanced technologies in horizontal well drilling, mature shale oil gas drilling and completion matching technologies have gradually formed at home and abroad after years of research and drilling practice. In terms of measurement technology, China has been able to produce single-point, multi-point, wired, wireless inclinometers and MWD measuring instruments while drilling and has mastered their usage methods. In terms of control technology, various specifications and types of fixed-angle and ground-adjustable curved shell screw drilling tools have been produced in China. Large deformation analysis and trajectory prediction methods for lower drilling tool assemblies have been completed. Various lower drilling tool assemblies and their usage processes required for wellbore trajectory control have been improved and mastered. In the construction of inclined and horizontal sections, attention should be paid to the response law of drilling tool combinations to drilling pressure in different formations, especially the response law of composite drilling. Reasonable drilling tool combinations should be selected for the control of wellbore trajectory in stable and horizontal sections, especially in the long stable and horizontal sections.
Discoloration of RF antenna coil surface after long-term operation of J-PARC ion source
Authors: Shibata T. , Shinto K. , Nanmo K. , Ohkoshi K. , Ikegami K. , Oguri H. , Ishida M. , Wada M.
Published: January 2024

In J-PARC (Japan Proton Accelerator Research Complex) center, the continuous operation
period of the Radio Frequency (RF) negative hydrogen ion (H−) source equipped with
the internal RF antenna coil is extended step by step in these 6–7 years for the goal to supply
stable beam during the entire period of J-PARC user operation each year. In 2022, the continuous
ion source operation for 4,001 hours (5.5 months) was achieved with 60 mA H− beam
current at the ion source exit, 25 Hz repetition, mainly 500 μs beam pulse width, and 25 kW
RF power injection. As the lifetime of the RF ion source is mainly limited by the deterioration of
the enamel coating on the RF antenna coil, a detailed evaluation of the antenna surface is
required to ensure the feasibility of the further extension of the operation period. In the
present study, surface discoloration on the RF antenna coil observed after 5 months of operation
is investigated by the application of a digital microscope and SEM/EDS analyses. The EDS analyses
show that depositions of the sputtered stainless steel elements and the injected cesium are the
most possible candidates for impurities on the enamel coating. The dimension measurements of the
RF antenna thickness before and after the long-term operation also support the assumption that the
discoloration is due to the deposited elements and hence insulation of the RF antenna coil by
enamel coating is maintained. Possible mechanisms leading to antenna deterioration starting from
element depositions on the antenna coating are also discussed in the summary part of this
manuscript.
T1-weighted MRI of targeting atherosclerotic plaque based on CD40 expression on engulfed USPIO’s cell surface
Authors: Huang Chen , Huang Wentao , Meng Yixuan , Zhou Chengqian , Wang Xiaozhuan , Zhang Chunyu , Tian Yuzhen , Wei Wei , Li Yongsheng , Zhou Quan , Chen Wenli , Tang Yukuan
Published: January 2024

Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of cholesterol within the arterial wall. Its progression can be monitored via magnetic resonance imaging (MRI). Ultrasmall Superparamagnetic Particles of Iron Oxide (USPIO) (<5 nm) have been employed as T1 contrast agents for MRI applications. In this study, we synthesized USPIO with an average surface carboxylation of approximately 5.28 nm and a zeta potential of −47.8 mV. These particles were phagocytosed by mouse aortic endothelial cells (USPIO-MAECs) and endothelial progenitor cells (USPIO-EPCs), suggesting that they can be utilized as potential contrast agent and delivery vehicle for the early detection of atherosclerosis. However, the mechanism by which this contrast agent is delivered to the plaque remains undetermined. Our results demonstrated that with increasing USPIO concentration during 10–100 μg ml−1, consistent change appeared in signal enhancement on T1-weighted MRI. Similarly, T1-weighted MRI of MAECs and EPCs treated with these concentrations exhibited a regular change in signal enhancement. Prussian blue staining of USPIO revealed substantial absorption into MAECs and EPCs after treatment with 50 μg ml−1 USPIO for 24 h. The iron content in USPIO-EPCs was much higher (5 pg Fe/cell) than in USPIO-MAECs (0.8 pg Fe/cell). In order to substantiate our hypothesis that CD40 protein on the cell surface facilitates migration towards inflammatory cells, we utilized AuNPs-PEI (gold nanoparticles-polyethylenimine) carrying siRNACD40 to knockout CD40 expression in MAECs. It has been documented that gold nanoparticle-oligonucleotide complexes could be employed as intracellular gene regulation agents for the control of protein level in cells. Our results confirmed that macrophages are more likely to bind to MAECs treated with AuNPs-PEI-siRNANC (control) for 72 h than to MAECs treated with AuNPs-PEI-siRNACD40 (reduced CD40 expression), thus confirming CD40 targeting at the cellular level. When USPIO-MAECs and MAECs (control) were delivered to mice (high-fat-fed) via tail vein injection respectively, we observed a higher iron accumulation in plaques on blood vessels in high-fat-fed mice treated with USPIO-MAECs. We also demonstrated that USPIO-EPCs, when delivered to high-fat-fed mice via tail vein injection, could indeed label plaques by generating higher T1-weighted MRI signals 72 h post injection compared to controls (PBS, USPIO and EPCs alone). In conclusion, we synthesized a USPIO suitable for T1-weighted MRI. Our results have confirmed separately at the cellular and tissue and in vivo level, that USPIO-MAECs or USPIO-EPCs are more accessible to atherosclerotic plaques in a mouse model. Furthermore, the high expression of CD40 on the cell surface is a key factor for targeting and USPIO-EPCs may have potential therapeutic effects.
Enhancing the therapeutic potential of curcumin: a novel nanoformulation for targeted anticancer therapy to colorectal cancer with reduced miR20a and miR21 expression
Authors: Atwan Qusay S , Al-Ogaidi Israa
Published: January 2024

Curcumin (Cur) possesses remarkable pharmacological properties, including cardioprotective, neuroprotective, antimicrobial, and anticancer activities. However, the utilization of Cur in pharmaceuticals faces constraints owing to its inadequate water solubility and limited bioavailability. To overcome these hurdles, there has been notable focus on exploring innovative formulations, with nanobiotechnology emerging as a promising avenue to enhance the therapeutic effectiveness of these complex compounds. We report a novel safe, effective method for improving the incorporation of anticancer curcumin to induce apoptosis by reducing the expression levels of miR20a and miR21. The established method features three aspects that, to our knowledge, have not been formally verified: (1) use of a novel formula to incorporate curcumin, (2) use of all biocompatible biodegradable materials to produce this formula without leaving harmful residues, and (3) an incorporation process at temperatures of approximately 50 °C. The formula was prepared from lecithin (LE), and chitosan (CH) with an eco-friendly emulsifying agent and olive oil as the curcumin solvent. The formula was converted to nanoscale through ultrasonication and probe sonication at a frequency of 20 kHz. Transmission electron microscopy showed that the nano formula was spherical in shape with sizes ranging between 49.7 nm in diameter and negative zeta potentials ranging from 28 to 34 mV. Primers miR20a and miR21 were designed for molecular studies. Nearly complete curcumin with an encapsulation efficiency of 91.1% was established using a straight-line equation. The nano formula incorporated with curcumin was used to prepare formulations that exhibited anticancer activities. The apoptosis pathway in cancer cells was activated by the minimum inhibitory concentration of the nano formula. These findings suggest the potential of this nanoformulation as an effective and selective cancer treatment that does not affect the normal cells.
Large deviations and phase transitions in spectral linear statistics of Gaussian random matrices
Authors: Valov Alexander , Meerson Baruch , Sasorov Pavel V
Published: January 2024

We evaluate, in the large-N limit, the complete probability distribution

P

(
A
,
m
)

of the values A of the sum

∑

i
=
1

N

|

λ
i

|

m

, where λ

i
(

i
=
1
,
2
,
…
,
N

) are the eigenvalues of a Gaussian random matrix, and m is a positive real number. Combining the Coulomb gas method with numerical simulations using a matrix variant of the Wang–Landau algorithm, we found that, in the limit of

N
→
∞

, the rate function of

P

(
A
,
m
)

exhibits phase transitions of different characters. The phase diagram of the system on the (A, m) plane is surprisingly rich, as it includes three regions: (i) a region with a single-interval support of the optimal spectrum of eigenvalues, (ii) a region emerging for m < 2 where the optimal spectrum splits into two separate intervals, and (iii) a region emerging for m > 2 where the maximum or minimum eigenvalue ‘evaporates’ from the rest of eigenvalues and dominates the statistics of A. The phase transition between regions (i) and (iii) is of second order. Analytical arguments and numerical simulations strongly suggest that the phase transition between regions (i) and (ii) is of (in general) fractional order

p
=
1
+
1

/

|

m
−
1

|

, where

0
<
m
<
2

. The transition becomes of infinite order in the special case of m = 1, where we provide a more complete analytical and numerical description. Remarkably, the transition between regions (i) and (ii) for

m
⩽
1

and the transition between regions (i) and (iii) for m > 2 occur at the ground state of the Coulomb gas which corresponds to the Wigner’s semicircular distribution.
Exploring acoustic properties of banana fiber composites with elastomeric filler through a computational approach
Authors: Jayaraman Prajwal , Pai Anand , Rodriguez-Millan Marcos , Shenoy B Satish , K N Chethan , Hegde Sriharsha
Published: January 2024

In recent years, there has been a significant increase in the use of natural fibers as reinforcements in composites. This trend is mainly driven by their eco-friendly nature and wide availability. The objective of this research is to explore the extensive potential of combining Banana Fiber with Tyre Rubber Particles (TRP) as elastomeric fillers in natural fiber composites, focusing primarily on their applications in the automotive and aeronautical industries, particularly in terms of acoustic response. The composite sequences were named as NFC-I to NFC-V, where NFC represented Natural Fiber Composites. To thoroughly investigate the acoustic properties of these innovative composites, experimental analysis on the impedance tube was conducted along with numerical simulations on the Ansys Harmonic Acoustics® module with Transmission Loss (TL) as the key parameter. An orthotropic material model based on the chopped fiber RVE (Representative Volume Element) was developed on the Ansys 2022 R1® Material Designer Module which could be deemed as the novelty of the current work. From the results, it was seen that within the designated frequency bands, specifically the low (67–400 Hz), medium (400–1900 Hz), and high (1900–6300 Hz) ranges, the NFC-4 (15 wt% of TRP) composite consistently demonstrated the lowest average TL values. Conversely, NFC-3 (27 wt% of TRP) consistently showcased the highest TL values, reaching a peak of 27.13 dB in the low-frequency range and 46.73 dB in the high-frequency range. The results obtained from the impedance tube experiments align remarkably well with the numerical simulation outputs, demonstrating the reliability of the numerical model in estimating the transmission loss of composite laminatesa crucial parameter for evaluating acoustic performance.
Electric field control of magnetization reversal in FeGa/PMN-PT thin films
Authors: Pradhan Gajanan , Celegato Federica , Magni Alessandro , Coisson Marco , Barrera Gabriele , Rizzi Paola , Tiberto Paola
Published: January 2024

Artificial magnetoelectric materials possess huge potential to be utilized in the development of energy efficient spintronic devices. In the past decade, the search for a good ferromagnetic/ferroelectric combination having the ability to create high magnetoelectric coupling, created new insights and also new challenges. In this report, the magnetoelectric effect is studied in the FeGa/PMN-PT(001) multiferroic heterostructures in the presence of electric fields via strain-mediated effects. The formation of magnetic anisotropy in FeGa is observed after changing the polarization of PMN-PT to out-of-plane orientations. The magnetic domain structures forming during the magnetization reversal were studied in compressive, tensile and remanent strained states. The changes in the magnetic properties were reversible after each cycling of the electric field polarity, hence creating a non-volatile system. The control of magnetization switching sustained by an ON–OFF electric field makes our multiferroic heterostructure suitable for application in low-power magnetoelectric based memory applications.