bluuebunny/crossref_metadata_2025 huggingface dataset:
The Bleeding Edge: Managing Coagulation and Bleeding Risk in Patients with Cirrhosis Undergoing Interventional Procedures
Authors: Crăciun Rareș , Grapă Cristiana , Mocan Tudor , Tefas Cristian , Nenu Iuliana , Buliarcă Alina , Ștefănescu Horia , Nemes Andrada , Procopeț Bogdan , Spârchez Zeno
Published: November 2024

This review addresses the peri-procedural bleeding risks in patients with cirrhosis, emphasizing the need for careful coagulation assessment and targeted correction strategies. Liver disease presents a unique hemostatic challenge, where traditional coagulation tests may not accurately predict bleeding risk, complicating the management of procedures like paracentesis, endoscopic therapy, and various interventional procedures. As such, this paper aims to provide a comprehensive analysis of current data, guidelines, and practices for managing coagulation in cirrhotic patients, with a focus on minimizing bleeding risk while avoiding unnecessary correction with blood products. The objectives of this review are threefold: first, to outline the existing evidence on bleeding risks associated with common invasive procedures in cirrhotic patients; second, to evaluate the efficacy and limitations of standard and advanced coagulation tests in predicting procedural bleeding; and third, to examine the role of blood product transfusions and other hemostatic interventions, considering potential risks and benefits in this delicate population. In doing so, this review highlights patient-specific and procedure-specific factors that influence bleeding risk and informs best practices to optimize patient outcomes. This review progresses through key procedures often performed in cirrhotic patients. The discussion begins with paracentesis, a low-risk procedure, followed by endoscopic therapy for varices, and concludes with high-risk interventions requiring advanced hemostatic considerations. Each chapter addresses procedural techniques, bleeding risk assessment, and evidence-based correction approaches. This comprehensive structure aims to guide clinicians in making informed, evidence-backed decisions in managing coagulation in cirrhosis, ultimately reducing procedural complications and improving care quality for this high-risk population.
Agreement Between Resting Energy Expenditure Predictive Formulas and Indirect Calorimetry in Non-Dialysis Dependent Chronic Kidney Disease
Authors: de Oliveira Mariana Cassani , Bufarah Marina Nogueira Berbel , de Oliveira Rodrigo Bueno , de Góes Cassiana Regina , Balbi André Luís
Published: November 2024

Background and Aims: The gold standard method for measuring resting energy expenditure (REE) is indirect calorimetry (IC) using an expensive device that requires specialized training. To overcome the limitations of IC, REE prediction formulas are used in patients with chronic kidney disease (CKD). However, it is still controversial which of these formulas has greater accuracy compared to IC. We aimed to determine the accuracies of REE measured by IC and estimated by formulas in patients with CKD. Methods: Fifty-three patients with stage 4–5 CKD underwent IC and five current REE prediction formulas. Accuracy was measured by Lin’s correlation coefficient. Bland–Altman repeated measures analysis was used to assess the agreement of the formulas’ results with those of IC. Precision was measured by the predicted IC ± 10% and 20%. Systematic bias was assessed by the Student’s t-test, and linear regression was used to assess proportionality bias. Results: Patients had a mean estimated glomerular filtration rate (eGFR) of 12 ± 4 mL/min/1.73 m2, a mean age of 65 years, and 62% were male. The mean REE measured by IC was 1341 ± 37 Kcal/day, and the formula with the lowest mean bias (0.1509 [−653.5121; 398.9056]), best correlation (r = 0.789; p = 0.000), and best accuracy (85%) was the formula developed by Fernandes and Cols (REE (kcal/day) = 854 + (7.4 × body weight) + (179 × sex) − (3.3 × age) + (2.1 × eGFR) + 26 (if diabetes)). Conclusions: The Fernandes and Cols equation had good accuracy and was valuable for estimating energy requirements in the population studied.
A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
Authors: Najafi Hamidreza , Savoji Kimia , Mirzaeibonehkhater Marzieh , Moravvej Seyed Vahid , Alizadehsani Roohallah , Pedrammehr Siamak
Published: November 2024

Background: Lung cancer remains a significant health concern, and the effectiveness of early detection significantly enhances patient survival rates. Identifying lung tumors with high precision is a challenge due to the complex nature of tumor structures and the surrounding lung tissues. Methods: To address these hurdles, this paper presents an innovative three-step approach that leverages Generative Adversarial Networks (GAN), Long Short-Term Memory (LSTM), and VGG16 algorithms for the accurate reconstruction of three-dimensional (3D) lung tumor images. The first challenge we address is the accurate segmentation of lung tissues from CT images, a task complicated by the overwhelming presence of non-lung pixels, which can lead to classifier imbalance. Our solution employs a GAN model trained with a reinforcement learning (RL)-based algorithm to mitigate this imbalance and enhance segmentation accuracy. The second challenge involves precisely detecting tumors within the segmented lung regions. We introduce a second GAN model with a novel loss function that significantly improves tumor detection accuracy. Following successful segmentation and tumor detection, the VGG16 algorithm is utilized for feature extraction, preparing the data for the final 3D reconstruction. These features are then processed through an LSTM network and converted into a format suitable for the reconstructive GAN. This GAN, equipped with dilated convolution layers in its discriminator, captures extensive contextual information, enabling the accurate reconstruction of the tumor’s 3D structure. Results: The effectiveness of our method is demonstrated through rigorous evaluation against established techniques using the LIDC-IDRI dataset and standard performance metrics, showcasing its superior performance and potential for enhancing early lung cancer detection. Conclusions:This study highlights the benefits of combining GANs, LSTM, and VGG16 into a unified framework. This approach significantly improves the accuracy of detecting and reconstructing lung tumors, promising to enhance diagnostic methods and patient results in lung cancer treatment.
Assessment of Inflammatory Scores in Severity Prediction for Elderly Patients with Odontogenic Infections
Authors: Tapalaga Gianina , Nica Luminita Maria , Cirligeriu Laura-Elena , Bumbu Bogdan Andrei , Pricop Marius
Published: November 2024

Background and Objectives: Odontogenic infections (OIs) can lead to severe complications, especially in elderly patients due to age-related physiological changes and comorbidities. This study aims to evaluate the predictive accuracy of inflammatory scores—APACHE II, CURB-65, SOFA, and NEWS2—in determining the severity of odontogenic infections among elderly patients (>70 years) compared to younger patients (<70 years). Materials and Methods: A retrospective cohort study was conducted on patients diagnosed with an OI at the Maxillofacial Surgery Department between January 2018 and January 2024. Patients were divided into two groups: elderly patients (>70 years, n = 49) and younger patients (<70 years, n = 64). The Symptom Severity score (SS) of odontogenic infections was calculated for all patients. Inflammatory scores—APACHE II, CURB-65, SOFA, and NEWS2—were assessed at admission and correlated with infection severity. Additional subgroup analyses were performed based on comorbidities and infection sites. Results: Elderly patients exhibited significantly higher SS scores (mean 12.47 ± 2.93) compared to younger patients (mean 7.82 ± 2.17, p < 0.001). APACHE II, CURB-65, SOFA and NEWS2 scores were significantly elevated in the elderly group (all p < 0.001). The SOFA score demonstrated the highest predictive accuracy for severe OIs in elderly patients, with an area under the curve (AUC) of 0.89 (95% CI: 0.82–0.95). Subgroup analyses revealed that comorbidities such as diabetes mellitus and cardiovascular disease significantly influenced infection severity (p < 0.05). Conclusions: Inflammatory scores, particularly SOFA, are effective in predicting the severity of odontogenic infections in elderly patients. The integration of these scores into clinical practice may enhance early identification of high-risk patients and improve management strategies.
Long-Term Stability in Temporomandibular Joint Replacement: A Review of Related Variables
Authors: Vargas Erick , Ravelo Victor , Rana Majeed , Unibazo Alejandro , Olate Sergio
Published: November 2024

Background: The temporomandibular joint (TMJ) is a key component of the stomatognathic system, playing a major role in maintaining mandibular stability and function. Temporomandibular disorders (TMDs) are a prevalent disease in the world, with surgical treatment being reserved for complex cases or end-stage TMJ disease. A narrative review was conducted to describe the quantitative and qualitative factors that affect TMJ prosthesis stability. Methods: Studies with a sample size equal to or greater than 10 subjects who underwent surgical procedures for joint replacement using stock or customized ATM prostheses were included. This narrative review examined some variables that may influence in terms of the longevity of the TMJ prosthesis, highlighting issues to be considered in future research. Results: The current development of TMJ prostheses is benefiting from technological advances, offering a suitable adaptation to the patient’s anatomy and superior results in functionality and patient satisfaction. However, the biomechanical complexity of the TMJ shows unique challenges compared to other joints in the body, where anatomical, biomechanical, and functional requirements are high. The stability of the TMJ prosthesis is affected by multiple variables, including the selection of biocompatible materials that resist corrosion and wear, the design of the prosthesis, the diagnosis and indication for its use, and the surgeon’s experience. The success of TMJ replacement can be measured by improving the patient’s quality of life, reducing pain, restoring mandibular functionality, and recovering suitable facial morphology for the patient’s conditions. Conclusion: There is a need for training of maxillofacial surgeons in TMJ surgery and replacement, as well as a greater focus on the research and development of systems to simplify surgical design and procedures and to optimize the results of TMJ replacement.
UAV-Mounted RIS-Aided Multi-Target Localization System: An Efficient Sparse-Reconstruction-Based Approach
Authors: Li Jingjing , Wang Jianhui , Cui Weijia , Jian Chunxiao
Published: November 2024

Unmanned Aerial Vehicle (UAV) technology is increasingly gaining attention in localization systems due to its flexibility and mobility. However, traditional localization techniques often fail in complex environments where line-of-sight paths are obstructed. To address this challenge, this paper presents an innovative UAV-assisted high-precision multi-target localization system. The system utilizes UAVs equipped with Reconfigurable Intelligent Surfaces to create a reflective signal path, allowing a receiver sensor to capture these signals, creating favorable conditions for multi-target localization. Exploiting the sparsity of signals, we introduce a direct positioning algorithm that leverages Atomic Norm Minimization (ANM) to estimate the target’s location. To address the high complexity of traditional ANM methods, we propose a novel Coyote-ANM-based direct localization (CADL) approach. This method combines the coyote optimization algorithm with the alternating direction method of multipliers to achieve high-accuracy positioning with reduced computational complexity. Simulation results across various signal-to-noise ratio scenarios demonstrate that the proposed algorithm significantly improves localization accuracy, achieving lower root mean square error values and faster execution times compared to traditional methods.
New Variable-Weight Optical Orthogonal Codes with Weights 3 to 5
Authors: Pak Si-Yeon , Kim Hyo-Won , Ahn DaeHan , Chung Jin-Ho
Published: November 2024

In optical networks, designing optical orthogonal codes (OOCs) with appropriate parameters is essential for enhancing the overall system performance. They are divided into two categories, constant-weight OOCs (CW-OOCs) and variable-weight OOCs (VW-OOCs), based on the number of distinct Hamming weights present in their codewords. This paper introduces a method for constructing VW-OOCs of length kp by using the structure of an integer ring and the Chinese Remainder Theorem. In particular, we present some specific VW-OOCs with weights of 3, 4, or 5. The results demonstrate that certain optimal VW-OOCs can be obtained with parameters that are not covered in the existing literature.
Dynamic Bayesian Networks, Elicitation, and Data Embedding for Secure Environments
Authors: Drury Kieran , Smith Jim Q.
Published: November 2024

Serious crime modelling typically needs to be undertaken securely behind a firewall where police knowledge and capabilities remain undisclosed. Data informing an ongoing incident are often sparse; a large proportion of relevant data only come to light after the incident culminates or after police intervene—by which point it is too late to make use of the data to aid real-time decision-making for the incident in question. Much of the data that are available to the police to support real-time decision-making are highly confidential and cannot be shared with academics, and are therefore missing to them. In this paper, we describe the development of a formal protocol where a graphical model is used as a framework for securely translating a base model designed by an academic team to a fully embellished model for use by a police team. We then show, for the first time, how libraries of these models can be built and used for real-time decision support to circumvent the challenges of data missingness seen in such a secure environment through the ability to match ongoing plots to existing models within the library.The parallel development described by this protocol ensures that any sensitive information collected by police and missing to academics remains secured behind a firewall. The protocol nevertheless guides police so that they are able to combine the typically incomplete data streams that are open source with their more sensitive information in a formal and justifiable way. We illustrate the application of this protocol by describing how a new entry—a suspected vehicle attack—can be embedded into such a police library of criminal plots.
Toward Transparent and Controllable Quantum Generative Models
Authors: Tian Jinkai , Yang Wenjing
Published: November 2024

Quantum generative models have shown promise in fields such as quantum chemistry, materials science, and optimization. However, their practical utility is hindered by a significant challenge: the lack of interpretability. In this work, we introduce model inversion to enhance both the interpretability and controllability of quantum generative models. Model inversion allows for tracing generated quantum states back to their latent variables, revealing the relationship between input parameters and generated outputs. We apply this method to models generating ground states for Hamiltonians, such as the transverse-field Ising model (TFIM) and generalized cluster Hamiltonians, achieving interpretability control without retraining the model. Experimental results demonstrate that our approach can accurately guide the generated quantum states across different quantum phases. This framework bridges the gap between theoretical models and practical applications by providing transparency and fine-tuning capabilities, particularly in high-stakes environments like drug discovery and material design.
Integration of Sense and Control for Uncertain Systems Based on Delayed Feedback Active Inference
Authors: Ji Mingyue , Pan Kunpeng , Zhang Xiaoxuan , Pan Quan , Dai Xiangcheng , Lyu Yang
Published: November 2024

Asa result of the time lag in transmission, the data obtained by the sensor is delayed and does not reflect the state at the current moment. The effects of input delay are often overlooked in active inference (AIF), which may lead to significant deviations in state estimation and increased prediction errors, particularly when the system is subjected to a sudden external stimulus. In this paper, a theoretical framework of delayed feedback active inference (DAIF) is proposed to enhance the applicability of AIF to real systems. The probability model of DAIF is defined by incorporating a control distribution into that of AIF. The free energy of DAIF is defined as the sum of the quadratic state, sense, and control prediction error. A predicted state derived from previous states is defined and introduced as the expectation of the prior distribution of the real-time state. A proportional-integral (PI)-like control based on the predicted state is taken to be the expectation of DAIF preference control, whose gain coefficient is inversely proportional to the measurement accuracy variance. To adaptively compensate for external disturbances, a second-order inverse variance accuracy replaces the fixed sensory accuracy of preference control. The simulation results of the trajectory tracking control of a quadrotor unmanned aerial vehicle (UAV) show that DAIF performs better than AIF in state estimation and disturbance resistance.
Mathematical Modeling of Physical Reality: From Numbers to Fractals, Quantum Mechanics and the Standard Model
Authors: Kupczynski Marian
Published: November 2024

In physics, we construct idealized mathematical models in order to explain various phenomena which we observe or create in our laboratories. In this article, I recall how sophisticated mathematical models evolved from the concept of a number created thousands of years ago, and I discuss some challenges and open questions in quantum foundations and in the Standard Model. We liberated nuclear energy, landed on the Moon and built ‘quantum computers’. Encouraged by these successes, many believe that when we reconcile general relativity with quantum theory we will have the correct theory of everything. Perhaps we should be much humbler. Our perceptions of reality are biased by our senses and by our brain, bending them to meet our priors and expectations. Our abstract mathematical models describe only in an approximate way different layers of physical reality. To describe the motion of a meteorite, we can use a concept of a material point, but the point-like approximation breaks completely when the meteorite hits the Earth. Similarly, thermodynamic, chemical, molecular, atomic, nuclear and elementary particle layers of physical reality are described using specific abstract mathematical models and approximations. In my opinion, the theory of everything does not exist.
Generalized Filter Bank Orthogonal Frequency Division Multiplexing: Low-Complexity Waveform for Ultra-Wide Bandwidth and Flexible Services
Authors: Xin Yu , Hua Jian , Bao Tong , Hao Yaxing , Xiao Ziheng , Nie Xin , Wang Fanggang
Published: November 2024

Terahertz (THz) communication is a crucial technique in sixth generation (6G) mobile networks, which allow for ultra-wide bandwidths to enable ultra-high data rate wireless communication. However, the current subcarrier spacing and the size of fast Fourier transform (FFT) of the orthogonal frequency division multiplexing (OFDM) in 5G NR are insufficient regarding the bandwidth requirements of terahertz scenarios. In this paper, a novel waveform is proposed to address the ultra-wideband issue, namely the generalized filter bank orthogonal frequency division multiplexing (GFB-OFDM) waveform. The main advantages are summarized as follows: (1) The K-point IFFT is implemented by two levels of IFFTs in smaller sizes, i.e, performing M-point IFFT in N times and performing N-point IFFT in M times, where K=N×M. (2) The proposed waveform can accommodate flexible subcarrier spacings and different numbers of the subbands to provide various services in a single GFB-OFDM symbol. (3) Different bandwidths can be supported using a fixed filter since the filtering is performed on each subband. In contrast, the cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) in 4G/5G requires various filters. (4) The existing detection for CP-OFDM can be directly employed as the detector of the proposed waveform. Lastly, the comprehensive simulation results demonstrate that GFB-OFDM outperforms CP-OFDM in terms of the out-of-band leakage, complexity and error performance.
Derangetropy in Probability Distributions and Information Dynamics
Authors: Ataei Masoud , Wang Xiaogang
Published: November 2024

We introduce derangetropy, which is a novel functional measure designed to characterize the dynamics of information within probability distributions. Unlike scalar measures such as Shannon entropy, derangetropy offers a functional representation that captures the dispersion of information across the entire support of a distribution. By incorporating self-referential and periodic properties, it provides insights into information dynamics governed by differential equations and equilibrium states. Through combinatorial justifications and empirical analysis, we demonstrate the utility of derangetropy in depicting distribution behavior and evolution, providing a new tool for analyzing complex and hierarchical systems in information theory.
Inferring About the Average Value of Audit Errors from Sequential Ratio Tests
Authors: Sitek Grzegorz , Pleszczyński Mariusz
Published: November 2024

The book amounts are modeled as values of a random variable, represented by a mixture of distributions of both the correct and error-contaminated amounts. The mixing coefficient represents the proportion of items with non-zero error amounts. This study addresses the problem of determining the sample size needed for testing statistical hypotheses regarding mean accounting errors. The average sample size is estimated using the Sequential Probability Ratio Test (SPRT), applying the Monte Carlo method. Estimating average audit errors is a common challenge in economic research.
Multimodal Food Image Classification with Large Language Models
Authors: Kim Jun-Hwa , Kim Nam-Ho , Jo Donghyeok , Won Chee Sun
Published: November 2024

In this study, we leverage advancements in large language models (LLMs) for fine-grained food image classification. We achieve this by integrating textual features extracted from images using an LLM into a multimodal learning framework. Specifically, semantic textual descriptions generated by the LLM are encoded and combined with image features obtained from a transformer-based architecture to improve food image classification. Our approach employs a cross-attention mechanism to effectively fuse visual and textual modalities, enhancing the model’s ability to extract discriminative features beyond what can be achieved with visual features alone.
Research on Steering-by-Wire System Motor Control Based on an Improved Sparrow Search Proportional–Integral–Derivative Algorithm
Authors: Jin Kai , Xiao Ping , Yang Dongde , Fang Zhanyu , Zhang Rongyun , Yang Aixi
Published: November 2024

To enhance the control performance of a wire-controlled steering system, an improved sparrow search algorithm for fine-tuning the gains of a proportional–integral–derivative (SSA-PID) steering motor control algorithm is proposed. Mathematical models of the steering system and motor were derived based on an analysis of the system’s structure and dynamics. A PID controller was developed with the aim of facilitating the precise control of the steering angle by targeting the angle of the steering motor. The population diversity in the sparrow algorithm was enhanced through the integration of a human learning mechanism along with a Cauchy–Gaussian variation strategy. Furthermore, an adaptive warning strategy was implemented, which employed spiral exploration to modify the ratio of early warning indicators, thereby augmenting the algorithm’s capacity to evade local optima. Following these enhancements, an SSA-PID steering motor control algorithm was developed. Joint simulations were performed using the CarSim software 2019.1 and MATLAB/Simulink R2022a, and subsequent tests were conducted on a wire-controlled steering test rig. The outcomes of the simulations and bench tests demonstrate that the proposed SSA-PID regulation algorithm is capable of adapting effectively to variations and disturbances within the system, facilitating precise motor angle control and enhancing the overall reliability of the steering system.
Guaranteed Cost Control of Singular Fuzzy Time-Delay Systems Based on Proportional Plus Derivative Feedback
Authors: Zhang Huayang , Wang Hebin , Wang Xin
Published: November 2024

This paper explores the guaranteed cost control issue for singular Takagi-Sugeno (T-S) fuzzy systems with time delay. An augmented Lyapunov-Krasovskii functional (LKF) is adopted to analyze the system’s stabilization, and sufficient conditions are established based on Lyapunov stability theory. The method of free weight matrices is employed to provide a systematic approach for determining the controller parameters. Additionally, two compelling examples are presented to demonstrate the viability of the proposed methods.
An Innovative Nonlinear Bounded Component Analysis Algorithm Based on Multivariate Nonlinear Chirp Mode Decomposition
Authors: Tang Mingyang , Wu Yafeng
Published: November 2024

In complex and diverse practical application scenarios, the challenge of blind source separation under underdetermined and nonlinear conditions is often encountered. To address this challenge, this paper proposes an innovative underdetermined nonlinear bounded component analysis method. This method first employs Multivariate Nonlinear Chirp Mode Decomposition (MNCMD) to process and reconstruct the observed signals, transforming the original underdetermined problem into a positive definite problem. Subsequently, Gaussianization techniques are introduced as a means of nonlinear compensation, successfully converting the nonlinear model into an analyzable linear model, laying a solid foundation for subsequent signal separation. Finally, the signal is separated by the bounded component analysis method, which does not require the source signals to be independent of each other. To validate the effectiveness and superiority of the proposed algorithm, detailed simulation experiments were designed and implemented. The experimental results demonstrate that compared to traditional underdetermined blind source separation algorithms, the algorithm presented in this paper exhibits significant advantages in terms of universality, convergence speed, separation accuracy, and robustness. Furthermore, this paper successfully applies the algorithm to the blind extraction of fetal electrocardiogram (FECG) signals from real datasets. The experimental results show that the algorithm can rapidly and effectively extract clearer and more accurate FECG signals, demonstrating its great potential and value in practical applications.
Intelligent Analysis and Prediction of Computer Network Security Logs Based on Deep Learning
Authors: Liu Zhiwei , Li Xiaoyu , Mu Dejun
Published: November 2024

Since the beginning of the 21st century, the development of computer networks has been advancing rapidly, and the world has gradually entered a new era of digital connectivity. While enjoying the convenience brought by digitization, people are also facing increasingly serious threats from network security (NS) issues. Due to the significant shortcomings in accuracy and efficiency of traditional Long Short-Term Memory (LSTM) neural networks (NN), different scholars have conducted research on computer NS situation prediction methods to address the aforementioned issues of traditional LSTM based NS situation prediction algorithms. Although these algorithms can improve the accuracy of NS situation prediction to a certain extent, there are still some limitations, such as low computational efficiency, low accuracy, and high model complexity. To address these issues, new methods and techniques have been proposed, such as using NN and machine learning techniques to improve the accuracy and efficiency of prediction models. This article referred to the Bidirectional Gated Recurrent Unit (BiGRU) improved by Gated Recurrent Unit (GRU), and introduced a multi model NS situation prediction algorithm with attention mechanism. In addition, the improved Particle Swarm Optimization (PSO) algorithm can be utilized to optimize hyperparameters and improve the training efficiency of the GRU NN. The experimental results on the UNSW-NB15 dataset show that the algorithm had an average absolute error of 0.0843 in terms of NS prediction accuracy. The RMSE was 0.0932, which was lower than traditional prediction algorithms LSTM and GRU, and significantly improved prediction accuracy.
Optimization of Reclaiming–Loading Scheduling in Dry Bulk Terminals Based on Knowledge-Driven Memetic Algorithm
Authors: Liu Qiang , Ni Xiaodong , Liu Huashi , Wang Jingjing , Wang Kang
Published: November 2024

Reclaiming–loading operations in dry bulk terminals often face conflicts and delays due to limitations in equipment processing capacity and operational line accessibility, which significantly compromise the safety and efficiency of these operations. This paper aims to optimize the reclaiming–loading schedule for each incoming vessel by considering parallel equipment operations and potential conflicts, with the goal of enhancing both the safety and efficiency of the loading processes. Through a detailed analysis of bulk reclaiming and reclaiming–loading mechanisms, we formulate the dry bulk terminal loading scheduling problem to minimize the total operational time for all loading tasks, taking into account constraints such as parallel reclaiming, collaborative loading, operational conflicts, and line accessibility. In order to obtain a good solution, including task execution sequences and allocation of reclaimers and shiploaders, a knowledge-driven memetic algorithm is developed by integrating knowledge-driven mechanisms with problem-specific operators within a memetic computing framework. Finally, numerical experiments for various scales are conducted using the layout and operational data from the Huanghua Port’s coal port area. The experimental results demonstrate the effectiveness of the proposed optimization algorithm.
A Two-Phase Deep Learning Approach to Link Quality Estimation for Multiple-Beam Transmission
Authors: Kim Mun-Suk
Published: November 2024

In the multi-user multiple-input-multiple-output (MU-MIMO) beamforming (BF) training defined by the 802.11ay standard, since a single initiator transmits a significant number of action frames to multiple responders, inefficient configuration of the transmit antenna arrays when sending these action frames increases the signaling and latency overheads of MU-MIMO BF training. To configure appropriate transmit antenna arrays for transmitting action frames, the initiator needs to accurately estimate the signal to noise ratios (SNRs) measured at the responders for each configuration of the transmit antenna arrays. In this paper, we propose a two-phase deep learning approach to improve the accuracy of SNR estimation for multiple concurrent beams by reducing the measurement errors of the SNRs for individual single beams when each action frame is transmitted through multiple concurrent beams. Through simulations, we demonstrated that our proposed scheme enabled more responders to successfully receive action frames during MU-MIMO BF training compared to existing schemes.
Leveraging Multi-Source Data for the Trustworthy Evaluation of the Vibrancy of Child-Friendly Cities: A Case Study of Tianjin, China
Authors: Zhang Di , Song Kun , Zhao Di
Published: November 2024

The vitality of a city is shaped by its social structure, environmental quality, and spatial form, with child-friendliness being an essential component of urban vitality. While there are numerous qualitative studies on the relationship between child-friendliness and various indicators of urban vitality, quantitative research remains relatively scarce, leading to a lack of sufficient objective and trustworthy data to guide urban planning and the development of child-friendly cities. This paper presents an analytical framework, using Heping District in Tianjin, China, as a case study. It defines four main indicators—social vitality, environmental vitality, spatial vitality, and urban scene perception—for a trustworthy and transparent quantitative evaluation. The study integrates multi-source data, including primary education (POI) data, street view image (SVI) data, spatiotemporal big data, normalized difference vegetation index (NDVI), and large visual language models (LVLMs) for the trustworthy analysis. These data are visualized using corresponding big data and weighted analysis methods, ensuring transparent and accurate assessments of the child-friendliness of urban blocks. This research introduces an innovative and trustworthy method for evaluating the child-friendliness of urban blocks, addressing gaps in the quantitative theory of child-friendliness in urban planning. It also provides a practical and reliable tool for urban planners, offering a solid theoretical foundation to create environments that better meet the needs of children in a trustworthy manner.
Automatic Modulation Recognition Based on Multimodal Information Processing: A New Approach and Application
Authors: Zhang Wenna , Xue Kailiang , Yao Aiqin , Sun Yunqiang
Published: November 2024

Automatic modulation recognition (AMR) has wide applications in the fields of wireless communications, radar systems, and intelligent sensor networks. The existing deep learning-based modulation recognition models often focus on temporal features while overlooking the interrelations and spatio-temporal relationships among different types of signals. To overcome these limitations, a hybrid neural network based on a multimodal parallel structure, called the multimodal parallel hybrid neural network (MPHNN), is proposed to improve the recognition accuracy. The algorithm first preprocesses the data by parallelly processing the multimodal forms of the modulated signals before inputting them into the network. Subsequently, by combining Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU) models, the CNN is used to extract spatial features of the received signals, while the Bi-GRU transmits previous state information of the time series to the current state to capture temporal features. Finally, the Convolutional Block Attention Module (CBAM) and Multi-Head Self-Attention (MHSA) are introduced as two attention mechanisms to handle the temporal and spatial correlations of the signals through an attention fusion mechanism, achieving the calibration of the signal feature maps. The effectiveness of this method is validated using various datasets, with the experimental results demonstrating that the proposed approach can fully utilize the information of multimodal signals. The experimental results show that the recognition accuracy of MPHNN on multiple datasets reaches 93.1%, and it has lower computational complexity and fewer parameters than other models.
On the Lateral Stability System of Four-Wheel Driven Electric Vehicles Based on Phase Plane Method
Authors: Ma Yu-Jie , Chen Chih-Keng , Zhang Xiao-Dong
Published: November 2024

To improve the handling and stability of four-wheel independent drive electric vehicles (FWID EVs), this paper introduces a hierarchical architecture lateral stability control system. The upper-level controller is responsible for generating the additional yaw moment required by the vehicle. This includes a control strategy based on feedforward control and a Linear Quadratic Regulator (LQR) for handling assistance control, an LQR-based stability control, a PID controller-based speed-following control, and a stability assessment method. The lower-level controller uses Quadratic Programming (QP) to optimally distribute the additional yaw moment to the four wheels. A “normalized” method was proposed to determine vehicle stability. After comparing it with the existing double-line method, diamond method, and curved boundary method through the open-loop Sine with Dwell test and the closed-loop Double Lane Change (DLC)test simulation, the results demonstrate that this method is more sensitive and accurate in determining vehicle stability, significantly enhancing vehicle handling and stability.
Unsupervised Anomaly Detection and Explanation in Network Traffic with Transformers
Authors: Kummerow André , Abrha Esrom , Eisenbach Markus , Rösch Dennis
Published: November 2024

Deep learning-based autoencoders represent a promising technology for use in network-based attack detection systems. They offer significant benefits in managing unknown network traces or novel attack signatures. Specifically, in the context of critical infrastructures, such as power supply systems, AI-based intrusion detection systems must meet stringent requirements concerning model accuracy and trustworthiness. For the intrusion response, the activation of suitable countermeasures can greatly benefit from additional transparency information (e.g., attack causes). Transformers represent the state of the art for learning from sequential data and provide important model insights through the widespread use of attention mechanisms. This paper introduces a two-stage transformer-based autoencoder for learning meaningful information from network traffic at the packet and sequence level. Based on this, we present a sequential attention weight perturbation method to explain benign and malicious network packets. We evaluate our method against benchmark models and expert-based explanations using the CIC-IDS-2017 benchmark dataset. The results show promising results in terms of detecting and explaining FTP and SSH brute-force attacks, highly outperforming the results of the benchmark model.
Control Strategy for Disc Coreless Permanent Magnet Synchronous Motor with LC Filter
Authors: Tian Hong , Kang Min
Published: November 2024

The disc coreless permanent magnet synchronous motor has the advantages of a short axial size, high power density, and small volume. Due to the coreless structure, its inductance is very small, which results in a serious current ripple and an unacceptable torque ripple if driven from a conventional inverter. This can be solved by installing an LC filter between the inverter and the motor. However, an undesirable resonance phenomenon is induced by the LC filter. In this paper, a new capacitive current feedback active damping (CCFAD) strategy is proposed. Instead of current sensors in the capacitor branch, a state observer is introduced to estimate the capacitance current. The observer is designed with double sliding mode surfaces, which reduces the order of the system. Compared to conventional capacitive current feedback, no additional current sensors are required, reducing the system cost. Besides the resonant harmonics, the phase current contains obvious fifth and seventh harmonics due to the special plane structure of the rotor. The proportional-integral-resonance (PIR) controller, instead of the traditional PI controller, is designed to suppress lower order harmonics. The experiment results show that current ripples due to resonance and rotor structure are suppressed significantly.
Numerical Investigation of Combustion and Emission Characteristics of the Single-Cylinder Diesel Engine Fueled with Diesel-Ammonia Mixture
Authors: Ali  , Lim Ocktaeck
Published: November 2024

This study proposes a dual-fuel approach combining diesel and ammonia in a single-cylinder compression ignition engine to reduce harmful emissions from internal combustion. Diesel is directly injected into the combustion chamber, while ammonia is introduced through the intake manifold with intake air. In this study, injection timing and the percentage of ammonia energy fraction was varied. A computational fluid dynamics (CFD) model simulates the combustion and emission processes to assess the impact of varying diesel injection timings and ammonia energy contributions. Findings indicate that as ammonia content increases, the engine experiences reductions in peak in-cylinder pressure, temperature, heat release rate, as well as overall efficiency and power output. Emission results suggest that greater ammonia usage leads to a reduction in soot, carbon monoxide, carbon dioxide, and unburned hydrocarbons, though a slight increase in nitrogen oxides emissions is observed. This analysis supports ammonia’s potential as a low-emission alternative fuel in future compression ignition engines.
A Fast Simulation Method for Wind Turbine Blade Icing Integrating Physical Simulation and Statistical Analysis
Authors: Jiang Wei , Wen Renqiang , Qin Ming , Zhao Guohan , Ma Long , Guo Jun , Wu Jinbo
Published: November 2024

Simulating wind turbine blade icing quickly is important for wind farms to issue early warnings and effectively deal with the adverse effects of cold weather. However, current numerical simulation methods suffer from high computational costs and lack straightforward acceleration techniques for practical ice prediction. Here, we developed a fast and simple blade icing simulation method via an integrated physical simulation and statistical analysis method. This method consists of two steps: firstly, numerical simulation with CFD, and secondly, table look-up calculations. Over 10,000 sets of wind turbine blade icing simulations based on FENSAP-ICE and an NACA64-A17 wing were conducted to develop this method and analyze the influences of environmental factors on blade icing. The results show that ice thickness generally increases with an increase in wind speed, a decrease in temperature, and an increase in liquid water content (LWC), but there is a nonlinear relationship between them. For example, ice thickness has a linear relationship with the LWC within a certain range but hardly changes with a LWC beyond that range. The validation results show that the fast simulation method established in this paper has good consistency with the original numerical simulation method. It can greatly improve the computational efficiency of icing simulations while retaining the accuracy of numerical simulations. It takes less than 1 s to complete over 1000 sets of icing simulations, which offers potential for the fast prediction of wind turbine blade icing in the future.
Conceptual View of the Implementation of the Nuclear Energy Program in the Republic of Kazakhstan
Authors: Batyrbekov Erlan , Vityuk Vladimir , Zarva Denis , Sharipov Mazhit
Published: November 2024

This paper presents an analysis of the existing prerequisites and opportunities for the start-up of the Republic of Kazakhstan’s own full-fledged Nuclear Energy Program, a conceptual vision of the development of the Kazakhstani nuclear industry. Recommendations have been formulated for the timely and successful implementation of the program for the introduction of nuclear power plants into the country’s energy generation structure, the development of the necessary related nuclear infrastructure facilities, national legislation, and other issues directly related to nuclear power development.
Exploring the Impact of State of Charge and Aging on the Entropy Coefficient of Silicon–Carbon Anodes
Authors: Böhm Kevin , Zintel Simon , Ganninger Philipp , Jäger Jonas , Markus Torsten , Henriques David
Published: November 2024

Due to its improved capacity compared to graphite, silicon is a promising candidate to handle the demands of high-energy batteries. With the introduction of new materials, further aspects of the battery system must be reconsidered. One of those aspects is the heat generation during the charging and discharging of a cell, which delivers important information for the development of cooling systems, the battery management system and the overall performance of the cell. Since the reversible heat presents an important contribution to the overall heat generation during cycling, the entropy coefficient is the main value that needs to be determined. This study evaluates the entropy coefficient of custom-produced 2032 coin half-cells with lithium counter electrodes, containing 45 wt% nanosilicon and 45 wt% carbon black. The potentiometric method, utilizing VR and self-discharge curves, produced reliable results, yielding entropy coefficient values between 95% SoC and 10% SoC during delithiation. These values of the entropy coefficient are consistently negative. Furthermore, ICA measurements identified two phase transitions during delithiation, with these transitions shifting to lower SoC as SoH decreases, impacting the slope of the entropy coefficient.
Assessing Suitable Areas for PV Power Installation in Remote Agricultural Regions
Authors: Belaid Abdelfetah , Guermoui Mawloud , Khelifi Reski , Arrif Toufik , Chekifi Tawfiq , Rabehi Abdelaziz , El-Kenawy El-Sayed M. , Alhussan Amel Ali
Published: November 2024

Remote agricultural regions in desert areas, such as Ghardaïa in southern Algeria, face significant challenges in energy supply due to their isolated locations and harsh climatic conditions. Harnessing solar energy through photovoltaic (PV) systems offers a sustainable solution to these energy needs. This study aims to identify suitable areas for PV power installations in Ghardaïa, utilizing a geographic information system (GIS) combined with the fuzzy analytical hierarchy process (AHP). Various environmental, economic, and technical factors, such as solar radiation, land use, and proximity to infrastructure, are incorporated into the analysis to create a multi-criteria decision-making framework. The integration of fuzzy logic into AHP enables a more flexible evaluation of these factors. The results revealed the presence of ideal locations for installing photovoltaic stations, with 346,673.30 hectares identified as highly suitable, 977,606.84 hectares as very suitable, and 937,385.97 hectares as suitable. These areas are characterized by high levels of solar radiation and suitable infrastructure availability, contributing to reduced implementation costs and facilitating logistical operations. Additionally, the proximity of these locations to agricultural areas enhances the efficiency of electricity delivery to farmers. The study emphasizes the need for well-considered strategic planning to achieve sustainable development in remote rural areas.
A Detailed Review of Partial Discharge Detection Methods for SiC Power Modules Under Square-Wave Voltage Excitation
Authors: Akbar Ghulam , Di Fatta Alessio , Rizzo Giuseppe , Ala Guido , Romano Pietro , Imburgia Antonino
Published: November 2024

Silicon carbide (SiC) power modules are increasingly being used in high-voltage and high-frequency applications due to their superior electrical and thermal qualities. However, the issue of the partial discharge (PD) phenomenon raises serious reliability difficulties resulting in insulation failure, performance degradation, and potential device collapse. This paper provides a thorough assessment of the current PD detection strategies in SiC power modules. The issues provided by SiC devices’ distinct operational features, such as high switching frequencies and higher voltage stresses, which hinder PD detection and mitigation, are widely investigated. This review compares the effectiveness, benefits, and limitations of various detection methods, emphasizing the need for better strategies to ensure long-term reliability and performance. This study gives an in-depth overview of the numerous forms of PD phenomena that occur in power modules, including internal and surface discharges, as well as how they appear under various detection systems. It examines the performance of several methods for power module technologies such as SiC. To address these PD issues, this article proposes ways to improve reliability and detection accuracy.