bluuebunny/crossref_metadata_2025 huggingface dataset:
Guidance Method with Collision Avoidance Using Guiding Vector Field for Multiple Unmanned Surface Vehicles
Authors: Wei Junbao , Zhang Jianqiang , Li Haiyan , Xia Jiawei , Liu Zhong
Published: January 2025

For the guidance problem of trajectory tracking in multiple unmanned surface vehicles (USVs), a trajectory tracking guidance method with collision avoidance based on a novel guiding vector field is proposed. Firstly, within the framework of the virtual leader–follower method for formation control, a tracking error model for followers is developed based on the motion model of USVs. Secondly, considering the limitations of conventional trajectory tracking guidance methods in addressing various initial error conditions, a novel guiding vector field is developed for the design of the heading guidance law to enhance tracking performance. Then, a multi-USV collision avoidance strategy is proposed for formation navigation safety. The trigger conditions, actions and release conditions for collision avoidance are established in this strategy. USVs could avoid collision in time by following the commands outlined in the strategy, especially in complex situations where multiple USVs are simultaneously at risk of colliding with each other. And the theoretical proof is completed. Furthermore, the heading and velocity guidance laws are designed by combining the guidance vector field and the collision avoidance strategy. It is demonstrated that the tracking errors of the system are uniformly bounded based on Lyapunov stability theory. Finally, the effectiveness of the method is verified through simulation.
Hyperledger Fabric-Based Multi-Channel Structure for Data Exchange in Internet of Vehicles
Authors: Liu Yiluo , Feng Yaokai , Sakurai Kouichi
Published: January 2025

The rapid growth of the Internet of Vehicles (IoV) requires secure, efficient, and reliable data exchanges among multiple stakeholders. Traditional centralized database systems can hardly address the challenges associated with data privacy, integrity, and scalability in this decentralized ecosystem. In this paper, we propose a Hyperledger Fabric-Based Multi-Channel Structure to overcome these limitations. By leveraging the blockchain architecture, the system ensures data confidentiality and integrity by segregating data into exclusive channels and enabling different organizations to collaborate. Cross-channel communication ensures security when data are interacted with. Chaincodes automate transactions and enhance trust between participants. Our functional tests and performance tests by using Hyperledger Caliper verified the effectiveness of the system in real-world scenarios, highlighting its advantages over traditional systems in terms of decentralization, transparency, and security. Future work will focus on enhancing the user experience and integrating the system with edge computing. Eventually, attempts will be made to operationalize it in real-world environments.
Channel Prediction Technology Based on Adaptive Reinforced Reservoir Learning Network for Orthogonal Frequency Division Multiplexing Wireless Communication Systems
Authors: Sui Yongbo , Wu Lingshuang , Gao Hui
Published: January 2025

Channel prediction is an effective technology to support adaptive transmission in wireless communication. To solve the difficulty of accurately predicting channel state information (CSI) due to fast time-varying characteristics, a next-generation reservoir calculation network (NGRCN) is combined with CSI, and a channel prediction method for OFDM wireless communication systems based on an adaptive reinforced reservoir learning network (adaptive RRLN) is proposed. An adaptive elastic network (adaptive EN) is used to estimate the output weight matrix to avoid ill-conditioned solutions. Therefore, the adaptive RRLN has echo and oracle properties. In addition, an adaptive singular spectral analysis (adaptive SSA) method is proposed to improve the local predictability of CSI by decomposing and reconstructing CSI to improve the fitting accuracy of the channel prediction model. In the simulation section, the OFDM wireless communication systems are constructed using IEEE802.11ah and the one-step prediction, the multi-step prediction, and the robustness test are implemented and analyzed. The simulation results show that the prediction accuracy of the adaptive RRLN can reach 3 × 10−5 and 8.36 × 10−6, which offers satisfactory prediction performance and robustness.
The Coordinated Control Strategy of Cascaded Voltage-Source Converter
Authors: Shen Hong , Kuang Qinghua , Chen Kailong , Sun Xiaofeng , Guan Kedi
Published: January 2025

In the field of electric vehicle charging, achieving fast charging at the current mainstream voltage levels of 800 V or 1000 V typically requires the use of a cascaded voltage-source converter to generate higher output voltages. However, traditional cascaded voltage-source converters often rely on two-stage independent control, making them vulnerable to voltage imbalances in the power grid. To address these challenges and simplify control while reducing current distortion and imbalance on the grid side, this paper proposes a coordinated control strategy for cascaded voltage-source converters. The proposed strategy unifies the control of both the front and rear stages, allowing for coordinated adjustment of the grid-side current and output voltage by leveraging the degrees of freedom of both stages. Notably, this approach eliminates the need to sample the DC link voltage, resulting in improved stability and balance in the grid-side current and output voltage. Simulations and experimental results demonstrate the effectiveness and superiority of the proposed control strategy.
Modelling and Transient Simulation of District Heating Networks Based on a Control Theory Approach
Authors: Schojda Dominik , Scheipers Jan , Roes Jürgen , Hoster Harry
Published: January 2025

Heating districts have become one of the key infrastructures to efficiently and sustainably supply heat to consumers. With the current climate change crisis, not only are heating districts of the essence but their efficient and optimized operation as well. To analyze and achieve such an optimization, transient simulations of heating districts are needed. These simulations are a means of upgrading older town networks to smarter energy grids as well as an effective tool for the planning and building of newer heating networks. Therefore, this work presents an easy simulation method for the transient simulation of heating districts based on a control theory approach. The simulation method can calculate multiple-loop networks as well as non-looped networks and correctly predict how a heating network can behave over time. Additionally, this approach allows for the inclusion of new renewable energy sources into existing heating networks and to simulate the resulting network behavior. The method was tested on five different testcases involving a single-loop network, a multiple-loop network, and a real-life non-looped network. In each case, the calculated massflows were validated with the software Epanet (Version 2.2), while the simulated temperatures were compared to the theoretical steady-state values as well as the theoretical times of arrival of each heating network. The simulation results present a good approximation in each testcase. Finally, the limitations of the method are discussed, and a recommendation for the usage of the approach is given.
Model-Based Design and Evaluation of State-of-the-Art Thermal Management Systems for Electrified Trucks
Authors: Johansson Max , Eriksson Lars
Published: January 2025

Electric vehicle thermal management systems have in the last two decades grown to become complex systems. This development has come as a response to the unique challenges faced by electrified powertrains, particularly the driving range reduction in cold climate operation. The rapid increase in complexity makes the systems harder to design, control, and evaluate, and consequently, a need for systematic analysis and design tools has emerged. The key contribution of this work is a model-based simulation tool developed to enable the combined evaluation and control of state-of-the-art thermal management systems. To show how engineers may use the tool to solve industrially relevant problems, two simulation case studies are performed and presented. The first case study compares three thermal management system layouts of increasing complexity and shows how their performance varies as ambient temperature decreases. The second case study concerns the potential benefits of additional cooling radiators for fuel cell trucks under heavy load in hot climates.
Leveraging Machine Learning to Predict and Assess Disparities in Severe Maternal Morbidity in Maryland
Authors: Li Qingfeng , Alfonso Y. Natalia , Wolfson Carrie , Aziz Khyzer B. , Creanga Andreea A.
Published: January 2025

Background: Severe maternal morbidity (SMM) is increasing in the United States. The main objective of this study is to test the use of machine learning (ML) techniques to develop models for predicting SMM during delivery hospitalizations in Maryland. Secondarily, we examine disparities in SMM by key sociodemographic characteristics. Methods: We used the linked State Inpatient Database (SID) and the American Hospital Association (AHA) Annual Survey data from Maryland for 2016–2019 (N = 261,226 delivery hospitalizations). We first estimated relative risks for SMM across key sociodemographic factors (e.g., race, income, insurance, and primary language). Then, we fitted LASSO and, for comparison, Logit models with 75 and 18 features. The selection of SMM features was based on clinical expert opinion, a literature review, statistical significance, and computational resource constraints. Various model performance metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, precision, and recall values were computed to compare predictive performance. Results: During 2016–2019, 76 per 10,000 deliveries (1976 of 261,226) were in patients who experienced an SMM event. The Logit model with a full list of 75 features achieved an AUC of 0.71 in the validation dataset, which marginally decreased to 0.69 in the reduced model with 18 features. The LASSO algorithm with the same 18 features demonstrated slightly superior predictive performance and an AUC of 0.80. We found significant disparities in SMM among patients living in low-income areas, with public insurance, and who were non-Hispanic Black or non-English speakers. Conclusion: Our results demonstrate the feasibility of utilizing ML and administrative hospital discharge data for SMM prediction. The low recall score is a limitation across all models we compared, signifying that the algorithms struggle with identifying all SMM cases. This study identified substantial disparities in SMM across various sociodemographic factors. Addressing these disparities requires multifaceted interventions that include improving access to quality care, enhancing cultural competence among healthcare providers, and implementing policies that help mitigate social determinants of health.
Archaeometric Investigation of Artificial Stone Materials from the Theatrum Marcelli (Rome, Italy)
Authors: Zicarelli Maria Aantonietta , Ricca Michela , Alberghina Maria Francesca , Schiavone Salvatore , La Russa Mauro Francesco , Randazzo Luciana
Published: January 2025

This study illustrates the results of minero-petrographic and microchemical investigations of artificial stone materials (mortars, plasters, and bricks) taken from the Theatrum Marcelli (Rome, Italy). To achieve this objective, the artificial building materials were analysed using Polarized Optical Microscopy (POM) and a Scanning Electron Microscope (SEM) used in backscattered electron (BSE) mode and coupled with an Energy-Dispersive Spectrometer (EDS) after a sampling campaign. The POM was aimed at collecting information on the textural and mineralogical characteristics of the samples (identification of the main minerals constituting the aggregate, grain size and shape, and the evaluation of the binder/aggregate ratio). The data also supported technological assessments through the characterization of the raw materials used for the manufacture of the mortars/plasters. Furthermore, the SEM-EDS investigations revealed the chemical composition of both the aggregate and the binder, which was useful for estimating their hydraulicity index (HI). The diagnostic campaign allowed us to obtain interesting information on the plasters/mortars used in the Theatrum Marcelli, together with their probable production technology. In particular, the raw materials were quite homogeneous, thus confirming the traditional methodology used in Roman times to create natural hydraulic mortars by the addition of pozzolanic volcanic material to aerial lime. The volcanic component of the aggregate seems to be compatible with the ultrapotassic products of the Roman Magmatic Province—likely with the Pozzolane Rosse pyroclastic deposit of the Alban Hills district.
A Panel of Diverse Inflammatory Biomarkers Is Not Associated with BMI-Calibrated Obesity nor with Dyslipidemia or Dysglycemia in Clinically Healthy Adults Aged 20 to 40 Years
Authors: Sater Mai S. , Malalla Zainab H. A. , Ali Muhalab E. , Giha Hayder A.
Published: January 2025

Objectives: Low-grade metabolic inflammation is associated with several chronic metabolic disorders, including obesity. However, no concrete evidence that supports obesity as a direct cause of chronic inflammation. This study aims to identify the association of inflammation with obesity in apparently healthy adults. Methods: In this study, 162 seemingly healthy volunteers, aged between 20 and 40 years, of comparable sex ratio, were recruited and categorized based on their body mass index (BMI) into four obesity scales: normal (N), overweight (OW), obese (OB), and severely obese (SOB). After clinical examination, fasting blood samples were collected from the study subjects for glycemic (fasting blood glucose—FBG, and HbA1c) and lipid (total cholesterol, LDL-C, HDL-C, and triacyl glycerides -TAG) profile analysis. In addition, plasma levels of a panel of diverse inflammatory biomarkers, IL6, IL8, procalcitonin (PCT), TREM1, and uPAR were analyzed by sandwich ELISA. Results: The results showed that LDLC, TAG, FBG, and HbA1c were significantly higher in the obese (OB and SOB) group, compared to the non-obese (N and OW) group, while HDLc was significantly lower. The biomarker levels were not correlated with age or significantly differed between males and females. Importantly, levels of all assessed inflammatory biomarkers were comparable between the obesity classes. Moreover, the assessed biomarkers in subjects with dyslipidemia or dysglycemia were comparable to those with normal profiles. Finally, the biomarker levels were not correlated with the obesity, glycemic, or lipidemic parameters. Conclusions: After correction for age and co-morbidities, our results deny the association of discrete obesity, probably dyslipidemia, and dysglycemia with systemic chronic inflammation. Further studies of local and systemic inflammation in non-elderly, healthy obese subjects are needed.
Pediatric Hepatocellular Carcinoma: A Review of Predisposing Conditions, Molecular Mechanisms, and Clinical Considerations
Authors: Young Elizabeth P. , O’Neill Allison F. , Rangaswami Arun A.
Published: January 2025

Pediatric hepatocellular carcinoma (HCC) is a rare malignant liver tumor affecting children and adolescents and occurring either sporadically or in the context of underlying liver disease. In this review, we detail the epidemiology of pediatric HCC with a focus on predisposing factors including hepatic or systemic disease, genetic disorders, and familial cancer syndromes. We summarize existing research on the pathophysiology of pediatric HCC, including molecular mechanisms of oncogenesis, highlighting unique disease features differentiating pediatric HCC from adult HCC. We then survey the landscape of therapeutic options for pediatric HCC, including novel therapeutics. Lastly, we discuss the pathologic spectrum upon which pediatric HCC is postulated to exist, ranging from hepatoblastoma to HCC and including the hybrid entity hepatocellular neoplasm not otherwise specifed (HCN-NOS). In summary, we highlight the key clinical and molecular features of pediatric HCC that may inform future research and novel approaches to the clinical care of these patients.
Fostering STEM Skills Through Programming and Robotics for Motivation and Cognitive Development in Secondary Education
Authors: Torres Iván , Inga Esteban
Published: January 2025

This paper seeks to identify the impact of learning programming and robotics in the Science, Technology, Engineering, and Mathematics (STEM) educational approach. Studying these areas of knowledge is important to prepare students to face contemporary technological challenges. The approach analyzes how to establish and define the curricular content articulated in developing critical 21st-century skills within the teaching–learning process. A methodological strategy is proposed in the scientific field using the historical-descriptive method to carry out a literature review and a bibliometric study, evaluating scientific articles indexed in Web of Science (WoS) and Scopus from 2020 to 2024. Later, an evaluation is carried out using satisfaction surveys directed to eighth-grade students and teachers of the Unidad Educativa Fiscal Ciudad de Girón. These surveys address various aspects related to the context of learning programming and robotics from the STEM perspective. Consequently, the analytic–synthetic approach revealed that teaching programming and robotics would promote cognitive skills from adolescence, which is crucial for building solid foundations in STEM concepts. The positive impact on the motivation for change in students and teachers is highlighted by facilitating interaction with technologies and applying knowledge in practical projects in the educational process.
Analysis of Descriptors of Concept Drift and Their Impacts
Authors: Costa Albert , Giusti Rafael , dos Santos Eulanda M.
Published: January 2025

Concept drift, a phenomenon that can lead to degradation of classifier performance over time, is commonly addressed in the literature through detection and reaction strategies. However, these strategies often rely on complete classifier retraining without considering the properties of the drift, which can prove inadequate in many scenarios. Limited attention has been given to understanding the nature of drift and its characterization, which are crucial for designing effective reaction strategies. Drift descriptors provide a means to explain how new concepts replace existing ones, offering valuable insights into the nature of drift. In this context, this work examines the impact of four descriptors—severity, recurrence, frequency, and speed—on concept drift through extensive theoretical and experimental analysis. Experiments were conducted on five datasets with 32 descriptor variations, eight drift detectors, and a non-detection context, resulting in 1440 combinations. The findings reveal three key conclusions: (i) reaction strategies must be tailored to different types of drift; (ii) severity, recurrence, and frequency descriptors have the highest impact, whereas speed has minimal influence; and (iii) there is a need to incorporate mechanisms for describing concept drift into the strategies designed to address it.
Four-Dimensional Magnetic Resonance Pulmonary Flow Imaging for Assessing Pulmonary Vasculopathy in Patients with Postcapillary Pulmonary Hypertension
Authors: Nuche Jorge , Ponz Inés , Sánchez Sánchez Violeta , Bóbeda Javier , Gaitán Ángel , López-Linares Karen , García-Cosío María Dolores , Sarnago Cebada Fernando , González Javier Sánchez , Arribas Ynsaurriaga Fernando , Ruíz-Cabello Jesús , Ibáñez Borja , Delgado Juan F.
Published: January 2025

Background: Noninvasive techniques for diagnosing combined postcapillary pulmonary hypertension (CpcPH) are unavailable. Objective: To assess the diagnostic performance of cardiac magnetic resonance (CMR)-based four-dimensional (4D)-flow analysis in identifying CpcPH. Methods: Prospective observational study of heart failure (HF) patients with suspected pulmonary hypertension (PH) who underwent simultaneous CMR and right heart catheterization. The 4D-flow biomarkers were calculated using an automatic pipeline. A predictive model including 4D-flow biomarkers associated with CpcPH with a p-value < 0.20 was built to determine the diagnostic performance of 4D-flow analysis to identify CpcPH. Results: A total of 46 HF patients (55.4 ± 14 years, 63% male) with confirmed PH (19 [41%] isolated postcapillary PH [IpcPH], 27 [59%] CpcPH) were included. No differences were found in baseline characteristics, echocardiography, or CMR anatomical and functional parameters, except for a higher Doppler-estimated systolic pulmonary pressure and larger pulmonary artery in CpcPH patients. The 4D-flow CMR analysis was performed in 31 patients (67%). The maximal peak velocity (67.1 [62.2–77.5] cm/s—IpcPH vs. 58.2 [45.8–66.0] cm/s—CpcPH; p = 0.021) and maximal helicity (339.9 [290.0–391.8]) cm/s2—IpcPH vs. 226.0 (173.5–343.7) cm/s2—CpcPH; p = 0.026) were significantly lower in patients with CpcPH. A maximal multivariable model including sex, maximal average, and peak velocities, Reynolds number, flow rate, and helicity showed fair diagnostic performance (area under the curve: 0.768 [95%-CI: 0.572–0.963]; sensitivity: 100%; specificity: 55%). Conclusions: In HF patients with PH, 4D-flow-derived maximal peak velocity and maximal helicity were significantly lower in CpcPH patients. A multiparametric model including maximal 4D-flow-derived biomarkers showed good diagnostic performance for identifying CpcPH.
Rebound Pain—Management Strategies for Transitional Analgesia: A Narrative Review
Authors: Murphy Kevin J. , O’Donnell Brian
Published: January 2025

Peripheral nerve blocks (PNBs), while effective in reducing postoperative opioid use and side effects, are often associated with rebound pain (RP), a significant clinical issue requiring proactive management. Methods: A systematic search of electronic databases (e.g., PubMed, EMBASE, Cochrane Library) was conducted for studies investigating rebound pain following regional anaesthesia. Recent findings: RP has a high incidence in ambulatory patients and is influenced by patient, surgical, and anaesthetic factors. Preoperative education, multimodal analgesia, continuous nerve blocks, and intravenous dexamethasone may mitigate RP. Although RP does not typically affect overall opioid use, recovery, or patient satisfaction, the majority of patients experiencing RP would still choose PNBs for future surgery.
Numerical Simulation on Hybrid Lifting Operation of Polymetallic Nodules and Rare-Earth Elements-Rich Mud by Air-Lift Pump in Deep Sea around Minamitorishima Island
Authors: Shimizu Yoshiyuki , Sugihara Masatoshi , Fujinaga Koichiro , Nakamura Kentaro , Kato Yasuhiro
Published: January 2025

Polymetallic nodules and REE-rich mud under the seabed of 5500–5700 m water depth around Minamitorishima island are promising and attractive for exploration and development. Following our previous research, numerical analysis was used to investigate the unsteady flow characteristics and the lifting performance of a commercial production system using an air-lift pump for hybrid lifting, lifting both polymetallic nodules and REE-rich mud. Gas–liquid–solid three-phase flow and gas–liquid two-phase flow in the system were analyzed using the one-dimensional drift–flux model. First, the reliability of the schemes and program was verified by comparing the numerical results with the experimental ones. Next, numerical simulations were conducted, in which the model’s dimensions were related to a commercial production system operated in the deep sea around Minamitorishima island, and the conditions fit the expected production rate. The results revealed the unsteady flow characteristics under the operations, such as start-up, shut-down, feed of polymetallic nodules and REE-rich mud, and those associated with disturbances, such as feed rate fluctuations. We demonstrate that the program and the schemes can simulate the unsteady flow characteristics and the lifting performance of a commercial production system with an air-lift pump well, and they can derive useful information and know-how in advance for the safe and continuous operation of the system.
Inlet Passage Hydraulic Performance Optimization of Coastal Drainage Pump System Based on Machine Learning Algorithms
Authors: Jiang Tao , Lu Weigang , Lu Linguang , Xu Lei , Xi Wang , Liu Jianfeng , Zhu Ye
Published: January 2025

The axial-flow pump system has been widely applied to coastal drainage pump stations, but the hydraulic performance optimization based on the contraction angles of the inlet passage has not been studied. This paper combined the computational fluid dynamics (CFD) method, machine learning (ML) algorithms and genetic algorithm (GA) to find the optimal contraction angles of the inlet passage. The 125 sets of comprehensive objective function were obtained by the CFD method. Three contraction angles and comprehensive objective function values were regressed by three ML algorithms. After hyperparameter optimization, the Gaussian process regression (GPR) model had the highest R2 = 0.958 in the test set and had the strongest generalization ability among the three models. The impact degree of the three contraction angles on the objective function of the GPR model was investigated by the Sobol sensitivity analysis method; the results indicated that the order of impact degree from high to low was θ3>θ2>θ1. The optimal objective function values of the GPR model and corresponding contraction angles were searched through GA; the maximum objective function value was 0.963 and corresponding contraction angles were θ1=13.34°, θ2=28.36° and θ3=3.64°, respectively. The results of this study can provide reference for the optimization of inlet passages in coastal drainage pump systems.
System Identification and Navigation of an Underactuated Underwater Vehicle Based on LSTM
Authors: Li Changhao , Hu Zetao , Zhang Desheng , Wang Xin
Published: January 2025

Modeling and system identification are critical for the design, simulation, and navigation of underwater vehicles. This study presents a six degree-of-freedom (DoF) nonlinear model for a finless underactuated underwater vehicle, incorporating port-starboard symmetry and cross-flow terms. Then, hydrodynamic damping parameters are identified using an optimized Extended Kalman Filter (EKF), establishing a steady validation framework for computational fluid dynamics (CFD) simulation coefficients. Additionally, system identification is further enhanced with a Long Short-Term Memory (LSTM) neural network and a comprehensive dataset construction method, enabling time-series predictions of linear and angular velocities. To mitigate position divergence in dead reckoning (DR) caused by LSTM, a Nonlinear Explicit Complementary Filter (NECF) is integrated for attitude estimation, providing accurate yaw computation and reliable localization without dependence on acoustic sensors or machine vision. Finally, validation and evaluation are conducted to demonstrate model accuracy, EKF convergence, and the reliability of LSTM-based navigation.
Quantitative Real-Time Polymerase Chain Reaction (PCR) Assay for Rapid Monitoring of the Harmful Algal Bloom Species Cochlodinium polykrikoides
Authors: Kim Min-Jeong , Kim Hyun-Jung , Park Joon Sang , Kang Donhyug , Cho Sungho , Kim Hansoo , Baek Seung Ho , Park Jordan Jun Chul , Han Jeonghoon , Kim Kang Eun , Jung Seung Won
Published: January 2025

Harmful blooms of the dinoflagellate Cochlodinium polykrikoides (Margalefidinium polykrikoides) had detrimental aquacultural and economic effects globally, and to reduce the damage caused by these blooms, early biomonitoring and quantitative analysis of C. polykrikoides are of the utmost importance. Here, for the detection of C. polykrikoides using quantitative real-time polymerase chain reactions, we developed specific primers targeting the large subunit ribosomal DNA (LSU rDNA) and evaluated their applicability in the field during the occurrence of a C. polykrikoides bloom. The specific primers not only accurately detected C. polykirkoides but also had a detection performance comparable with that obtained using microscopic observations. Accordingly, we developed a system that can be used in the field and applied when red tides occur, with accurate results being obtained more than five times more rapidly than those obtained based on microscopic analysis. Collectively, our findings indicate that the C. polykrikoides bloom detection system developed in this study can be applied to rapidly detect and accurately quantify C. polykrikoides in environmental samples. Data obtained using this system could be used as a basis for developing prompt monitoring and warning systems for the early detection of C. polykrikoides blooms in the field.
The Water–Soil Resource Matching Pattern of Grain Crops in the North China Plain from the Perspective of the Physical Water–Water Footprint
Authors: Xia Wenxue , Zhang Bing , Meng Guangwen , Dong Jiankang
Published: January 2025

The agricultural water–soil matching coefficient is a key factor for reflecting regional grain production status, which can be used to evaluate the reasonableness of water–soil allocation in certain areas. Taking the North China Plain (NCP) as the study area, in this study, we constructed a framework from a “physical water–water footprint” standpoint. The binary matching characteristics of “water–soil–grain” were then analyzed, and the water–soil matching coefficient method was employed to evaluate the pattern of water–soil matching for the years 1984, 1998, 2003, and 2022. Through the perspective of physical water–water footprint coupling, field trials of grain were utilized to calculate the range of water–soil matching coefficients under high yields. The results showed the following: ① From 1949 to 2022, the grain yield and planting areas increased. Wheat, the dominant crop, required substantial irrigation. Precipitation, cultivated land, and irrigation water exhibited spatial mismatches over the last ten years. ② The total water footprint showed an increasing trend, and the blue water footprint accounted for 19.47%. The spatial distribution of the water and land footprints of grain crops largely overlapped, and their values were higher in the central and southern regions, and lower in the north. ③ The current water–soil matching coefficient was in the range of [0.28, 1.75], which fell outside the optimal range of [0.534, 0.724]. The soil–water matching coefficients of wheat and rice were overall higher than those of other crops. We found higher values in the southwestern region and lower values in the northern areas, which aligns with the boundary of the groundwater funnel area. To address the identified challenges, we recommend implementing a tiered regulatory zone system based on the matching coefficient. The government should encourage a reduction in water-intensive crops like wheat and rice in high-value regions by providing subsidies. Additionally, a monitoring mechanism for water and soil compatibility should be established, considering the specific growth requirements of various crops.
Extracting Knowledge from Machine Learning Models to Diagnose Breast Cancer
Authors: Martínez-Ramírez José Manuel , Carmona Cristobal , Ramírez-Expósito María Jesús , Martínez-Martos José Manuel
Published: January 2025

This study explored the application of explainable machine learning models to enhance breast cancer diagnosis using serum biomarkers, contrary to many studies that focus on medical images and demographic data. The primary objective was to develop models that are not only accurate but also provide insights into the factors driving predictions, addressing the need for trustworthy AI in healthcare. Several classification models were evaluated, including OneR, JRIP, the FURIA, J48, the ADTree, and the Random Forest, all of which are known for their explainability. The dataset included a variety of biomarkers, such as electrolytes, metal ions, marker proteins, enzymes, lipid profiles, peptide hormones, steroid hormones, and hormone receptors. The Random Forest model achieved the highest accuracy at 99.401%, followed closely by JRIP, the FURIA, and the ADTree at 98.802%. OneR and J48 achieved 98.204% accuracy. Notably, the models identified oxytocin as a key predictive biomarker, with most models featuring it in their rules. Other significant parameters included GnRH, β-endorphin, vasopressin, IRAP, and APB, as well as factors like iron, cholinesterase, the total protein, progesterone, 5-nucleotidase, and the BMI, which are considered clinically relevant to breast cancer pathogenesis. This study discusses the roles of the identified parameters in cancer development, thus underscoring the potential of explainable machine learning models for enhancing early breast cancer diagnosis by focusing on explainability and the use of serum biomarkers.The combination of both can lead to improved early detection and personalized treatments, emphasizing the potential of these methods in clinical settings. The identified markers also provide additional research and therapeutic targets for breast cancer pathogenesis and a deep understanding of their interactions, advancing personalized approaches to breast cancer management.
Association of the Val66Met Polymorphism of the BDNF Gene with the Depression in a Mexican Population with Multiple Sclerosis
Authors: Rabago-Barajas Brenda Viridiana , Macías-Islas Miguel Ángel , Saldaña-Cruz Ana Miriam , Arana-Yepez Jesús Emmanuel , Olivas-Flores Eva Maria , Aguayo-Arelis Adriana
Published: January 2025

Multiple sclerosis (MS) is a chronic, autoimmune pathology that affects the nervous system. It is characterized by inflammatory lesions that cause axonal damage with neurodegeneration. The signs and symptoms present in this pathology include among others, psychiatric disorders. In MS, depression is the most frequent psychiatric disorder, with prevalence levels of 40 to 60%; to date, the cause is unknown. The brain-derived neurotrophic factor (BDNF) is a neurotrophin related to neuroplasticity. The single-nucleotide polymorphism Val66Met, encoded by the BDNF gene, has been associated with various effects, including the presence of neuropsychiatric disorders. The purpose of our study was to evaluate the association between the BDNF Val66Met polymorphism and depression in MS patients. Methods: Study design, cases, and controls: Mexican mestizo MS patients. Cases: Patients diagnosed with depression. Controls: Patients without depression diagnosis. Measurements: For depression, the Beck Depression Inventory; for polymorphism, real-time PCR. Results: No statistically significant differences were found in sociodemographic and disease variables between the case and control groups. qPCR analysis showed that 68% of the participants were Val/Val wild-type homozygotes, 29% were Val/Met polymorphism heterozygotes, and 3% were Met/Met polymorphism homozygotes. The presence of the BDNF gene rs6265 polymorphism was associated with a 5.6-fold increase in the probability of depression in the cases compared to the controls. Conclusions: The BDNF Val66Met Polymorphism is associated with depression in Mexican mestizo patients diagnosed with MS.
Two-Stage Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization
Authors: Zhang Kai , Zhao Siyuan , Zeng Hui , Chen Junming
Published: January 2025

The core issue in handling constrained multi-objective optimization problems (CMOP) is how to maintain a balance between objectives and constraints. However, existing constrained multi-objective evolutionary algorithms (CMOEAs) often fail to achieve the desired performance when confronted with complex feasible regions. Building upon this theoretical foundation, a two-stage archive-based constrained multi-objective evolutionary algorithm (CMOEA-TA) based on genetic algorithms (GA) is proposed. In CMOEA-TA, First stage: The archive appropriately relaxes constraints based on the proportion of feasible solutions and constraint violations, compelling the population to explore more search space. Second stage: Sharing valuable information between the archive and the population, while embedding constraint dominance principles to enhance the feasibility of solutions. In addition an angle-based selection strategy was used to select more valuable solutions to increase the diversity of the population. To verify its effectiveness, CMOEA-TA was tested on 54 CMOPs in 4 benchmark suites and 7 state-of-the-art algorithms were compared. The experimental results show that it is far superior to seven competitors in inverse generation distance (IGD) and hypervolume (HV) metrics.
Group Constant-Sum Spectrum of Nearly Regular Graphs
Authors: Wang Tao-Ming
Published: January 2025

For an undirected graph G, a zero-sum flow is an assignment of nonzero integer weights to the edges such that each vertex has a zero-sum, namely the sum of all incident edge weights with each vertex is zero. This concept is an undirected analog of nowhere-zero flows for directed graphs. We study a more general one, namely constant-sum A-flows, which gives edge weights using nonzero elements of an additive Abelian group A and requires each vertex to have a constant-sum instead. In particular, we focus on two special cases: A=Zk, the finite cyclic group of integer congruence modulo k, and A=Z, the infinite cyclic group of integers. The constant sum under a constant-sum A-flow is called an index of G for short, and the set of all possible constant sums (indices) of G is called the constant sum spectrum. It is denoted by Ik(G) and I(G) for A=Zk and A=Z, respectively. The zero-sum flows and constant-sum group flows for regular graphs regarding cases Z and Zk have been studied extensively in the literature over the years. In this article, we study the constant sum spectrum of nearly regular graphs such as wheel graphs Wn and fan graphs Fn in particular. We completely determine the constant-sum spectrum of fan graphs and wheel graphs concerning Zk and Z, respectively. Some open problems will be mentioned in the concluding remarks.
Explicit Formulas for Hedging Parameters of Perpetual American Options with General Payoffs: A Mellin Transform Approach
Authors: Zecevic Stefan , Rodrigo Mariano
Published: January 2025

Risk is often the most concerning factor in financial transactions. Option Greeks provide valuable insights into the risks inherent in option trading and serve as tools for risk mitigation. Traditionally, scholars compute option Greeks through extensive calculations. This article introduces an alternative method that bypasses conventional derivative computation using the Mellin transform and its properties. Specifically, we derive Greeks for perpetual American options with general payoffs, represented as piecewise linear functions, and examine higher-order risk metrics for practical implications. Examples are provided to illustrate the effectiveness of our approach, offering a novel perspective on calculating and interpreting option Greeks.
The Irreducibility and Monodromy of Some Families of Linear Series with Imposed Ramifications
Authors: Hu Xiaoyu
Published: January 2025

Suppose that the adjusted Brill–Noether number is zero; we prove that there exists a family of twice-marked smooth projective curves such that the family of linear series with two imposed ramification conditions is irreducible. Moreover, under certain conditions, we show that the monodromy group contains the alternating group. In the case r=1, the monodromy group is the full symmetric group.
Even-Order Pascal Tensors Are Positive-Definite
Authors: Cui Chunfeng , Qi Liqun , Chen Yannan
Published: January 2025

In this paper, we show that even-order Pascal tensors are positive-definite, and odd-order Pascal tensors are strongly completely positive. The significance of these is that our induction proof method also holds for some other families of completely positive tensors, whose construction satisfies certain rules, such that the inherence property holds. We show that for all tensors in such a family, even-order tensors would be positive-definite, and odd-order tensors would be strongly completely positive, as long as the matrices in this family are positive-definite. In particular, we show that even-order generalized Pascal tensors would be positive-definite, and odd-order generalized Pascal tensors would be strongly completely positive, as long as generalized Pascal matrices are positive-definite. We also investigate even-order positive-definiteness and odd-order strong complete positivity for fractional Hadamard power tensors. Furthermore, we study determinants of Pascal tensors. We prove that the determinant of the mth-order two-dimensional symmetric Pascal tensor is equal to the mth power of the factorial of m−1.
On the Integral Representation of Jacobi Polynomials
Authors: De Micheli Enrico
Published: January 2025

In this paper, we present a new integral representation for the Jacobi polynomials that follows from Koornwinder’s representation by introducing a suitable new form of Euler’s formula. From this representation, we obtain a fractional integral formula that expresses the Jacobi polynomials in terms of Gegenbauer polynomials, indicating a general procedure to extend Askey’s scheme of classical polynomials by one step. We can also formulate suitably normalized Fourier–Jacobi spectral coefficients of a function in terms of the Fourier cosine coefficients of a proper Abel-type transform involving a fractional integral of the function itself. This new means of representing the spectral coefficients can be beneficial for the numerical analysis of fractional differential and variational problems. Moreover, the symmetry properties made explicit by this representation lead us to identify the classes of Jacobi polynomials that naturally admit the extension of the definition to negative values of the index. Examples of the application of this representation, aiming to prove the properties of the Fourier–Jacobi spectral coefficients, are finally given.
LLM-Augmented Linear Transformer–CNN for Enhanced Stock Price Prediction
Authors: Zhou Lei , Zhang Yuqi , Yu Jian , Wang Guiling , Liu Zhizhong , Yongchareon Sira , Wang Nancy
Published: January 2025

Accurately predicting stock prices remains a challenging task due to the volatile and complex nature of financial markets. In this study, we propose a novel hybrid deep learning framework that integrates a large language model (LLM), a Linear Transformer (LT), and a Convolutional Neural Network (CNN) to enhance stock price prediction using solely historical market data. The framework leverages the LLM as a professional financial analyst to perform daily technical analysis. The technical indicators, including moving averages (MAs), relative strength index (RSI), and Bollinger Bands (BBs), are calculated directly from historical stock data. These indicators are then analyzed by the LLM, generating descriptive textual summaries. The textual summaries are further transformed into vector representations using FinBERT, a pre-trained financial language model, to enhance the dataset with contextual insights. The FinBERT embeddings are integrated with features from two additional branches: the Linear Transformer branch, which captures long-term dependencies in time-series stock data through a linearized self-attention mechanism, and the CNN branch, which extracts spatial features from visual representations of stock chart data. The combined features from these three modalities are then processed by a Feedforward Neural Network (FNN) for final stock price prediction. Experimental results on the S&P 500 dataset demonstrate that the proposed framework significantly improves stock prediction accuracy by effectively capturing temporal, spatial, and contextual dependencies in the data. This multimodal approach highlights the importance of integrating advanced technical analysis with deep learning architectures for enhanced financial forecasting.
Cyanide Storage on Ferroan Brucite (MgxFe1−x(OH)2): Implications for Prebiotic Chemistry
Authors: Hara Ellie , Templeton Alexis
Published: January 2025

Cyanide is a crucial reagent for the synthesis of biomolecules in prebiotic chemistry. However, effective organic synthesis requires cyanide to be concentrated. One proposed mechanism for cyanide storage and concentration on Early Earth involves the formation of aqueous ferrocyanide complexes. In basic pH conditions, cyanide will spontaneously form ferrocyanide complexes in the presence of aqueous Fe(II). While ferrocyanide aqueous complex formation is well defined, the potential for Fe(II)-bearing minerals to react with cyanide to form ferrocyanide complexes or store cyanide on the mineral surface has yet to be explored under prebiotically relevant conditions. In this study, we demonstrate that when cyanide interacts with ferroan brucite (MgxFe1−x(OH)2), cyanide will both form aqueous and mineral-surface-adsorbed ferrocyanide implying that there are two reservoirs that cyanide will partition into. In addition, we found that cyanide decreased the amount of hydrogen gas produced by the oxidation of ferroan brucite, indicating that cyanide alters the mineral’s redox reactivity. The cyanide adsorbed on brucite can be released by a decrease in pH, which leads to the dissolution of ferroan brucite, thus releasing the adsorbed cyanide. Our findings suggest that iron-bearing minerals may represent an overlooked storage reservoir of cyanide on Hadean Earth, potentially playing a significant role in cyanide availability for prebiotic chemistry.
High-Performance Structures of Biopolymer Gels Activated with Scleroprotein Crosslinkers
Authors: Prochon Miroslawa , Dzeikala Oleksandra , Szczepanik Szymon
Published: January 2025

The study explores innovative crosslinking processes for biopolymer gel materials using amino acids and ion-redox initiators to significantly enhance their structural and functional properties. Advanced analytical techniques, including FTIR, Raman spectroscopy, XRD, TEM, TGA, DSC, ToF-SIMS, SEM/EDS, GPC/SEC, and elemental analysis, were employed for comprehensive material characterization. The synthesized materials show potential applications in packaging and medicine, particularly for single-use products with short life cycles. Two crosslinking strategies were developed. The first combines gelatin with polyvinyl alcohol (PVA); keratin hydrolysate; and amino acids such as cysteine, hydroxyproline, proline, and histidine. The second employs endogenous cysteine, activated by ion-redox initiators, leveraging its trans-sulfuration ability to form highly stable polymer networks with optimized mechanical and thermal properties. Notably, the synergy between cysteine and potassium persulfate redox initiators proved particularly effective, making this approach attractive for industrial applications. This study introduces novel crosslinking methods and highlights the potential of amino acid-based strategies for designing advanced biopolymer gels with enhanced properties.