bluuebunny/crossref_metadata_2025 huggingface dataset:
Overall Staging Prediction for Non-Small Cell Lung Cancer (NSCLC): A Local Pilot Study with Artificial Neural Network Approach
Authors: Cheung Eva Y. W. , Kwong Virginia H. Y. , Ng Kaby C. F. , Lui Matthias K. Y. , Li Vincent T. W. , Lee Ryan S. T. , Ham William K. P. , Chu Ellie S. M.
Published: February 2025

Background: Non-small cell lung cancer (NSCLC) has been the most common cancer globally in the recent decade. CT is the most common imaging modality for the initial diagnosis of NSCLC. The gold standard for definitive diagnosis is the histological evaluation of a biopsy or surgical sample, which usually requires a long processing time for the confirmation of diagnosis. This study aims to develop artificial intelligence models to predict overall staging based on patient demographics and radiomics retrieved from the initial CT images, so as to prioritize later-stage patients for histology evaluation to facilitate cancer diagnosis. Method: Two cohorts of NSCLC patient datasets were utilized for this study. The NSCLC-radiomics dataset from The Cancer Imaging Archive (TCIA) was divided into 70% for the training group and 30% for the internal testing group. Another cohort from a local hospital was collected for the an external testing group. Patient demographics and 107 radiomic features were retrieved from the gross tumor volume delineated by clinical oncologists on CT images. Artificial neural networks were used to build models for NSCLC overall staging (stage I, II, or III) prediction. Four traditional classifiers were also adopted to build models for comparison. Result: The proposed feed-forward neural network (FFNN) model showed good performance in predicting overall staging with an accuracy of 88.84%, 76.67%, and 74.52% in overall accuracies in validation, internal cohort testing, and external cohort testing, respectively. The sensitivity and specificity are balanced in all the stages, with average precision and F1 score in each of the stages. Conclusion: The FFNN demonstrated good performance in overall staging prediction for NSCLC patients. It has the benefit of predicting multiple overall stages in a single model. The software required and the proposed model are simple. It can be operated on a general-purpose computer in the radiology department. The application will eventually be used as a prediction tool to prioritize the biopsy or surgery sample for histological analysis and molecular investigation, thus shortening the time for diagnosis by pathologists, which supports the triage of patients for further testing.
Trends in the Incidence and Mortality of Tobacco-Related Cancers Among Adults in the United States
Authors: Mansingka Nandika , Adekanmbi Victor , Hsu Christine D. , Hoang Thao N. , Baillargeon Jacques G. , Berenson Abbey B. , Guo Fangjian
Published: February 2025

Background: Tobacco use remains a global challenge to public health, accounting for almost eight million deaths per year worldwide, with a significant portion attributable to tobacco-related cancers. Examining the epidemiology of tobacco-related cancers and assessing the trends in the incidence and mortality will allow for more effective prevention, treatment, and targeted strategies. Methods: We assessed the trends in the incidence and mortality of tobacco-related cancers among adults in the United States using data from United States Cancer Statistics (USCS) 2001–2021 and mortality data 1975–2022 from the National Center for Health Statistics (NCHS). The incidence and mortality rates of tobacco-related cancers were calculated as cases per 1,000,000 persons and age-adjusted to the 2000 United States standard population. Results: There was a recent overall decreasing trend in both the incidence (2001–2021) and mortality rate (2001–2022). Among adults 20–49 years old, there was an increasing trend from 2001 to 2021 in the incidence among non-Hispanic American Indians/Alaska Natives (APC 2.6, 95% CI 2.1–3.0) and those in the West (APC 0.2, 95% CI 0.0–0.4); in Hispanics, the incidence rate increased most recently from 2013 to 2021 (APC 1.7, 95% CI 1.0–3.0). The mortality rate first increased from 1975 to 1990 among females 50–64 years old and males 65+ years old and from 1975 to 2000 among females 65+ years old, and then decreased thereafter. Conclusions: The rising incidence in some younger groups highlights the need for targeted public health interventions to address disparities and improve cancer prevention in these vulnerable populations.
Revisiting the Link Between Keratoconus and Mitral Valve Prolapse
Authors: Five Christian K. , Hasselberg Nina E. , Bjerkreim Hilde , Aaserud Linda T. , Castrini Anna Isotta , Bugge Cecilie , Aabel Eivind W. , Helle-Valle Thomas , Dalen Håvard , Kristianslund Olav , Haugaa Kristina H.
Published: February 2025

Keratoconus is a progressive eye disease that results in thinning of the cornea, leading to visual impairment. Mitral valve prolapse (MVP) is a common disorder affecting around 2–4% of the general population. Previous studies have found an overrepresentation of MVP in individuals with keratoconus, with a prevalence of 38–65%, suggesting a shared underlying mechanism. In this case-control study, patients with keratoconus were enrolled from a quality and research registry. They were examined by a 2D echocardiography to identify if they had MVP, billowing or normal mitral leaflets. Controls were matched from the population-based Trøndelag Health Study. Patients and controls underwent a detailed echocardiographic examination to detect abnormal mitral valves. We included 101 patients (age 33 [IQR 29–40], 75% male) with keratoconus and 101 matched individuals. MVP was found in 2 (2%), while billowing was found in 5 (5%) of keratoconus patients. No significant association was found between keratoconus and the prevalence of MVP or billowing compared to the control group. Moreover, no associations were found between severity of keratoconus with presence of MVP nor with billowing of the mitral valves. We could not confirm the previously reported association between keratoconus and MVP, suggesting that routine screening for MVP in keratoconus patients may not be warranted. However, we cannot rule out the possibility of an association in other gender, age and ethnic groups different than ours.
Long Non-Coding RNAs: Key Regulators of Tumor Epithelial/Mesenchymal Plasticity and Cancer Stemness
Authors: Yuan Yuan , Tang Yun , Fang Zeng , Wen Jian , Wicha Max S. , Luo Ming
Published: February 2025

Long non-coding RNAs (lncRNAs) are a class of non-coding RNA molecules with transcripts longer than 200 bp, which were initially thought to be noise from genomic transcription without biological function. However, since the discovery of H19 in 1980 and Xist in 1990, increasing evidence has shown that lncRNAs regulate gene expression at epigenetic, transcriptional, and post-transcriptional levels through specific regulatory actions and are involved in the development of cancer and other diseases. Despite many lncRNAs being expressed at lower levels than those of protein-coding genes with less sequence conservation across species, lncRNAs have become an intense area of RNA research. They exert diverse biological functions such as inducing chromatin remodeling, recruiting transcriptional machinery, acting as competitive endogenous RNAs for microRNAs, and modulating protein–protein interactions. Epithelial–mesenchymal transition (EMT) is a developmental process, associated with embryonic development, wound healing, and cancer progression. In the context of oncogenesis, the EMT program is transiently activated and confers migratory/invasive and cancer stem cell (CSC) properties to tumor cells, which are crucial for malignant progression, metastasis, and therapeutic resistance. Accumulating evidence has revealed that lncRNAs play crucial roles in the regulation of tumor epithelial/mesenchymal plasticity (EMP) and cancer stemness. Here, we summarize the emerging roles and molecular mechanisms of lncRNAs in regulating tumor cell EMP and their effects on tumor initiation and progression through regulation of CSCs. We also discuss the potential of lncRNAs as diagnostic and prognostic biomarkers and therapeutic targets.
The Relations Between Sensory Modulation, Hyper Arousability and Psychopathology in Adolescents with Anxiety Disorders
Authors: Hammud Ginan , Avital-Magen Ayelet , Jabareen Hiba , Adler-Tsafir Reut , Engel-Yeger Batya
Published: February 2025

Background: Sensory modulation may play a significant role in psychiatric conditions, including anxiety, and explain arousability levels, behavioral disorders, and functional deficits. Yet, studies about sensory modulation in adolescents with anxiety disorders are scarce. Purpose: To profile the prevalence of sensory modulation difficulties (SMDs) in adolescents with anxiety and examine their relations to arousability and psychopathology. The study compared adolescents with anxiety disorders to healthy controls using physiological measures and self-reports that reflect daily life scenarios. Then, the study examined the relationship between SMDs, arousability, and psychopathological severity in the study group. Method: Participants were 106 adolescents, aged 10.5–18 years and their parents. The study group included 44 participants diagnosed with anxiety disorder by psychiatrists. The control group included 62 healthy participants matched by age and gender to the study group. Parents completed the demographic questionnaire and the Child Behavior Checklist (CBCL). The adolescents completed The Revised Children’s Manifest Anxiety Scale (RCMAS) and the Adolescent/Adult Sensory Profile (AASP) and underwent the electrodermal activity (EDA) and pulse rate tests while listening to extreme sensory stimuli of auditory startles. Results: Based on AASP, the study group had a higher prevalence of SMDs expressed in lower sensory seeking, difficulties in registering sensory stimuli, and higher sensory sensitivity and avoidance. The study group presented higher arousability while listening to the startles as manifested in higher heart rate and EDA responses. The physiological results correlated with SMD levels measured by the AASP self-reports. SMDs correlated with psychopathological severity. Conclusions: SMDs may characterize adolescents with anxiety disorders and impact their arousability, symptoms severity, and daily functioning. Therefore, sensory modulation should be evaluated using both self-reports (to reflect implications in real life from patients’ own voices) along with objective measures to explain daily behaviors by underlying physiological mechanisms. This may focus intervention towards better health, function, and development.
Optimizing Thermoresponsive and Bioadhesive Systems for Local Application of Erythrosine
Authors: Endrice Igor Alves , Gerarduzzi Sandy Aline Forastieri , de Oliveira Mariana Carla , Bruschi Marcos Luciano , Bassi da Silva Jéssica
Published: February 2025

Photodynamic therapy (PDT) is a light-activated chemical reaction used for the selective destruction of tissue. For this, various colorants may be applied, such as erythrosine (ERI), a dye already approved by the Food and Drug Administration (FDA) for various purposes. Although promising for PDT, ERI has a high hydrophilic profile that impacts its activity. To solve this, the combination of ERI with thermoresponsive and bioadhesive polymers may prove effective. Bio/mucoadhesive and thermoresponsive systems have attracted increasing interest in the development of novel pharmaceutical formulations for topical applications due to their ability to improve adhesion to the mucosa and prolong the residence time at the application site. In this study, systems based on poloxamer 407 (P407) in combination with cellulose derivatives (HPMC and NaCMC) were optimized, aiming at the topical release of ERI for PDT. The results demonstrated that the formulations containing low concentrations of cellulose derivatives exhibited greater adhesiveness and consistency at physiological temperature (37 °C), favoring the maintenance of the system at the application site. Regarding the gelation temperature (Tsol/gel), the formulations displayed values close to body temperature. The formulations with NaCMC showed a slightly higher Tsol/gel compared to HPMC ones, but it was adjustable by the polymer concentration. The addition of ERI influenced the mechanical and adhesive properties of the systems. In formulations containing HPMC, high concentrations of ERI increased bio/mucoadhesiveness, while in systems with NaCMC, the presence of ERI reduced this property. In both cases, the formulations maintained high consistency at 37 °C, contributing to the control of the active release at the application site. Rheological analysis revealed non-Newtonian behavior in all formulations, with greater consistency and elasticity at high temperatures. P407 was mainly responsible for the thermoresponsive transition from sol to gel, conferring desirable characteristics for topical application. Photodynamic activity was relevant in both formulations containing NaCMC and HPMC, which demonstrated greater capacity for degrading uric acid under light exposure. These systems are promising for the controlled release of drugs in photodynamic therapy, providing prolonged retention in the target tissue and maximizing the therapeutic efficacy of ERI.
Design and Implementation of GPU Pass-Through System Based on OpenStack
Authors: Zhao Linchang , Jin Yu , Hu Guoqing , Zhou Wenxi , Wei Hao , Li Ruiping , Zhu Xu , Xu Yongchi , Jin Jiulin , Li Qianbo
Published: February 2025

The paper constructs an OpenStack cloud platform on Inspur servers, including components such as Keystone, Glance, Placement, Nova, Neutron, and Dashboard. It specifically investigates the method of constructing GPU pass-through using Virtual I/O (VFIO) technology. The aim is to directly allocate graphics card resources to cloud hosts to meet high-performance computing needs. VFIO technology can directly assign physical devices (such as graphics cards) to virtual machines, avoiding performance loss and operational complexity associated with traditional virtualization. After passing through the graphics card to the cloud host via VFIO technology, it can directly control and manage the graphics card resources within the cloud host, providing better support for high-performance computing tasks. Test results show that the GPU pass-through design has a memory bandwidth of 107.5 GB/s, a pixel fill rate of 41.6 GPixel/s, a texture fill rate of 53.1 GTexel/s, a base frequency of 1326.9 MHz, and possesses a FP32 (float) floating-point performance of 2.1TFLOPS, with 72 texture units and 48 ROPs raster operations units. Moreover, in high-load computing scenarios, the pass-through graphics card exhibits superior availability and performance.
Developing a Dataset of Audio Features to Classify Emotions in Speech
Authors: Colunga-Rodriguez Alvaro A. , Martínez-Rebollar Alicia , Estrada-Esquivel Hugo , Clemente Eddie , Pliego-Martínez Odette A.
Published: February 2025

Emotion recognition in speech has gained increasing relevance in recent years, enabling more personalized interactions between users and automated systems. This paper presents the development of a dataset of features obtained from RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) to classify emotions in speech. The paper highlights audio processing techniques such as silence removal and framing to extract features from the recordings. The features are extracted from the audio signals using spectral techniques, time-domain analysis, and the discrete wavelet transform. The resulting dataset is used to train a neural network and the support vector machine learning algorithm. Cross-validation is employed for model training. The developed models were optimized using a software package that performs hyperparameter tuning to improve results. Finally, the emotional classification outcomes were compared. The results showed an emotion classification accuracy of 0.654 for the perceptron neural network and 0.724 for the support vector machine algorithm, demonstrating satisfactory performance in emotion classification.
Can ChatGPT Solve Undergraduate Exams from Warehousing Studies? An Investigation
Authors: Franke Sven , Pott Christoph , Rutinowski Jérôme , Pauly Markus , Reining Christopher , Kirchheim Alice
Published: February 2025

The performance of Large Language Models, such as ChatGPT, generally increases with every new model release. In this study, we investigated to what degree different GPT models were able to solve the exams of three different undergraduate courses on warehousing. We contribute to the discussion of ChatGPT’s existing logistics knowledge, particularly in the field of warehousing. Both the free version (GPT-4o mini) and the premium version (GPT-4o) completed three different warehousing exams using three different prompting techniques (with and without role assignments as logistics experts or students). The o1-preview model was also used (without a role assignment) for six runs. The tests were repeated three times. A total of 60 tests were conducted and compared with the in-class results of logistics students. The results show that the GPT models passed a total of 46 tests. The best run solved 93% of the exam correctly. Compared with the students from the respective semester, ChatGPT outperformed the students in one exam. In the other two exams, the students performed better on average than ChatGPT.
Methods for Chin Area Augmentation: Efficacy Evaluation and Prospects for Using Subplatysmal Fat Autograft
Authors: Borisenko Anastasiya S. , Sharobaro Valentin I. , Avdeev Alexey E. , Burkhonova Nigora S. , Ahmed Alsheikh Yousif M. , Cherkesov Igor V.
Published: February 2025

Objective: In this study, we analyzed the existing chin augmentation methods and evaluated the effectiveness of using a subplatysmal fat autograft for augmenting insufficient chin projection. Materials and Methods: From January 2017 to December 2023, we studied 170 patients at the Department of Plastic Surgery, Sechenov First Moscow State Medical University, who presented with insufficient chin projection, localized fat deposits in the cervico-mental area, and age-related changes in the lower third of the face. The patients were divided into two groups: Group 1 (n = 93) consisted of patients who underwent chin augmentation using implants (Silastic, Medpor); Group 2 (n = 77) included patients who underwent medial platysmaplasty and chin augmentation using a fat autograft (subplatysmal autograft). Results: Chin augmentation was performed on all the patients with varying degrees of insufficient chin projection. The mean age was 36.5 ± 2 years. The average follow-up period was 18.9 months (range: 2–45 months). For chin augmentation using implants (Silastic, Medpor), +chin projection increases were observed as follows: 1.1–1.2 cm at 3 months, 0.9–2.3 cm at 6 months, and 0.8–1.7 cm at 12 months. The complications in this group included inflammatory processes in 7 (8%) patients, hematomas in 6 (6%) patients, and contour asymmetry in 13 (14%) patients. In the group that underwent chin augmentation with a subplatysmal fat autograft, the chin projection increased by 0.8–1.2 cm at 3 months, 0.7–1.3 cm at 6 months, and 0.6–1.9 cm at 12 months. The mean age was 38.3 ± 7 years, with an average follow-up of 18.3 months (range: 2–43 months). No infectious–inflammatory complications were observed. Aesthetic complications were noted in 14 (18%) patients. Conclusions: Among the existing methods for chin augmentation, many still consider osseous genioplasty to give more predictable results. However, its traumatic nature and lengthy rehabilitation period prompt surgeons to seek effective yet less invasive alternatives. When selecting a minimally invasive method for chin augmentation, preference is given to fat injections (lipofilling) and intraoperative use of autologous fat grafts. Augmentation using subplatysmal autologous fat grafts is a minimally invasive, safe, predictable, and long-lasting method for chin correction.
A Game of Risk: Human Activities Shape Roe Deer Spatial Behavior in Presence of Wolves in the Southwestern Alps
Authors: Ruco Valentina , Marucco Francesca
Published: February 2025

In human-dominated landscapes, human activities shape prey spatial behavior, creating complex landscapes of risks. We investigated habitat selection of roe deer using resource selection functions in a human-dominated mountain system located in the southwestern Alps, characterized by a high presence of wolves and human disturbance. Our study aimed to assess how the interplay of hunting, presence of infrastructures, and recreational activities in the presence of wolves influenced roe deer spatial responses inside and outside a protected area. We documented that during the hunting period, roe deer increased selection of high-wolf-density areas, with the strongest effect observed during wild boar drive hunts, supporting the risk enhancement hypothesis, where avoiding one predator increases exposure to another, and highlighting the temporary yet significant impact of hunting on predator–prey dynamics. During the period of the wild boar drive hunt, roe deer also showed stronger selection for proximity to buildings, supporting the human shield hypothesis. Protected areas had an increased effect on roe deer avoidance of trails, where hiking and recreational activities are more concentrated. Our findings revealed the complex trade-offs that roe deer face in navigating multiple risks within human-modified landscapes, important for the development of effective conservation and human sustainability strategies.
Prediction of the Cause of Fundus-Obscuring Vitreous Hemorrhage Using Machine Learning
Authors: Kim Jinsoo , Han Bo Sook , Ha Joo Eun , Park Min Seon , Kwon Soonil , Cho Bum-Joo
Published: February 2025

Objectives: This study aimed to predict the unknown etiology of fundus-obscuring vitreous hemorrhage (FOVH) based on preoperative conditions using machine learning (ML) and to identify key preoperative factors. Methods: Medical records of 223 eyes from 204 patients who underwent vitrectomy for FOVH of unknown etiology between January 2012 and July 2022 were retrospectively reviewed. Preoperative data, including demographic information, systemic disease, ophthalmic history, and retinal status of the unaffected eye, were collected. The postoperatively identified etiologies of FOVH were categorized into six groups: proliferative diabetic retinopathy (PDR), retinal vein occlusion (RVO) or rupture of retinal arterial macroaneurysm, neovascular age-related macular degeneration (nAMD), retinal tear, Terson syndrome, and other causes. Four ML algorithms were trained and evaluated using seven-fold cross-validation. Results: The ML algorithms achieved mean accuracies of 76.2% for artificial neural network, 74.5% for XG-Boost, 74.4% for LASSO logistic regression, and 68.5% for decision tree. Key predictive factors commonly selected by the ML algorithms included PDR in the fellow eye, underlying diabetes mellitus, subarachnoid hemorrhage, and a history of retinal tear, RVO, or nAMD in the affected eye. Conclusions: The unknown etiology of FOVH could be predicted preoperatively with considerable accuracy by ML algorithms. Previous ophthalmic conditions in the affected eye and the condition of the fellow eye were important variables for prediction. This approach might assist in determining appropriate treatment plans.
CART-ANOVA-Based Transfer Learning Approach for Seven Distinct Tumor Classification Schemes with Generalization Capability
Authors: Afzal Shiraz , Rauf Muhammad , Ashraf Shahzad , Bin Md Ayob Shahrin , Ahmad Arfeen Zeeshan
Published: February 2025

Background/Objectives: Deep transfer learning, leveraging convolutional neural networks (CNNs), has become a pivotal tool for brain tumor detection. However, key challenges include optimizing hyperparameter selection and enhancing the generalization capabilities of models. This study introduces a novel CART-ANOVA (Cartesian-ANOVA) hyperparameter tuning framework, which differs from traditional optimization methods by systematically integrating statistical significance testing (ANOVA) with the Cartesian product of hyperparameter values. This approach ensures robust and precise parameter tuning by evaluating the interaction effects between hyperparameters, such as batch size and learning rate, rather than relying solely on grid or random search. Additionally, it implements seven distinct classification schemes for brain tumors, aimed at improving diagnostic accuracy and robustness. Methods: The proposed framework employs a ResNet18-based knowledge transfer learning (KTL) model trained on a primary dataset, with 20% allocated for testing. Hyperparameters were optimized using CART-ANOVA analysis, and statistical validation ensured robust parameter selection. The model’s generalization and robustness were evaluated on an independent second dataset. Performance metrics, including precision, accuracy, sensitivity, and F1 score, were compared against other pre-trained CNN models. Results: The framework achieved exceptional testing accuracy of 99.65% for four-class classification and 98.05% for seven-class classification on the source 1 dataset. It also maintained high generalization capabilities, achieving accuracies of 98.77% and 96.77% on the source 2 datasets for the same tasks. The incorporation of seven distinct classification schemes further enhanced variability and diagnostic capability, surpassing the performance of other pre-trained models. Conclusions: The CART-ANOVA hyperparameter tuning framework, combined with a ResNet18-based KTL approach, significantly improves brain tumor classification accuracy, robustness, and generalization. These advancements demonstrate strong potential for enhancing diagnostic precision and informing effective treatment strategies, contributing to advancements in medical imaging and AI-driven healthcare solutions.
Melatonin Pattern: A New Method for Machine Learning-Based Classification of Sleep Deprivation
Authors: Baygin Nursena
Published: February 2025

Background: Pattern recognition and machine learning-based classification approaches are frequently used, especially in the health field. In this research, a new feature extraction model inspired by the melatonin hormone (sleep hormone) and named MelPat (melatonin pattern) has been developed. The developed model has been tested on an open access dataset. Materials and Methods: An open access sleep deprivation electroencephalography (EEG) dataset was tested to evaluate the MelPat method. There are two classes in the dataset. These are (a) sleep deprivation (SD) and (b) healthy control (HC) groups, respectively. In this study, EEG signals were divided into 15 s segments, thus obtaining 1377 SD and 1378 HC samples. In the next phase of the research, a new feature extraction model was proposed, and this model was named MelPat as it was inspired by the melatonin hormone. Additionally, the feature vector was expanded using the statistical moment approach. In the signal decomposition phase of the model, the Tunable Q-Wavelet Transform (TQWT) method was used. Thus, the signal was decomposed into sub-bands, and feature extraction was applied to each band. Neighborhood Component Analysis (NCA) and Chi2 methods were used together to reduce the dimension of the feature vector and select the most significant features. In this phase, the most significant features from both feature selection algorithms were combined, and the final feature vector was obtained. In the classification phase of the model, the Support Vector Machine (SVM) algorithm, which is a shallow classifier, was used. The dataset used in the research has 61 channels. Therefore, after obtaining channel-based results, the iterative majority voting (IMV) algorithm was applied to achieve higher classification performance and generalize the results, and the most accurate results were automatically selected. Results: With the proposed MelPat algorithm, a high classification success of 97.71% was achieved on the open access sleep deprivation dataset. Conclusions: The obtained results show that the MelPat-based new classification approach is highly effective on the dataset collected for SD detection. Moreover, the fact that the proposed method is inspired by the melatonin chemical, which is the sleep hormone, makes the method attractive and ironic.
Morphology of Maxillary Central Incisors in a Mixed Swiss–German Population by Means of Micro-CT
Authors: Wolf Thomas Gerhard , Ottiger Kevin Simon Florian , Donnermeyer David , Schumann Sven , Waber Andrea Lisa
Published: February 2025

Background/Objectives: The objective of this study was to investigate the internal morphology and root canal configurations (RCCs) of maxillary central incisors (MxCIs) in a Swiss–German population by means of micro-computed tomography (µCT). Methods: RCCs, main foramina, and accessory canals of MxCIs were examined using µCT and 3D imaging software. The root canal anatomy was classified according to three classification systems by Vertucci (Ve, 1984), Weine et al. (We, 1969), and Briseño-Marroquín et al. (Br, 2015). Results: The most common RCC observed among a total of 112 investigated single-rooted maxillary central incisors was Br 1-1-1/1 (97.3%, Ve I, We I), with a small percentage showing Br 1-1-1/2 (2.7%). One main foramen existed in 87.5% of the specimens, 8% had one accessory foramen, 3.5% had two, and a rare case had four accessory foramina (0.9%). Accessory root canals were mainly located in the middle and apical regions of the roots. Conclusions: Detailed insights into the root canal morphology of MxCIs in a Swiss–German population are provided. The predominant RCC was a simple root canal (Ve I, Br 1-1-1/1). However, accessory canals were detected in the middle and apical third in over 40% of the teeth examined. These anatomical features should be considered during endodontic treatment planning and execution.
The Information Loss Problem and Hawking Radiation as Tunneling
Authors: Zhang Baocheng , Corda Christian , Cai Qingyu
Published: February 2025

In this paper, we review some methods that have tried to solve the information loss problem. In particular, we revisit the solution based on Hawking radiation as tunneling and provide a detailed statistical interpretation of the black hole entropy in terms of the quantum tunneling probability of Hawking radiation from the black hole. In addition, we show that black hole evaporation is governed by a time-dependent Schrödinger equation that sends pure states into pure states rather than into mixed states (Hawking had originally established that the final result would be mixed states). This is further confirmation of the fact that black hole evaporation is unitary.
A Proposed MIMIC Structural Equation Model for Assessing Factors Affecting Time to Degree—The Case of the Greek Tertiary System
Authors: Kalamaras Dimitrios , Maska Laura , Nasika Fani
Published: February 2025

Using a structural equation modeling approach, this study has attempted to untangle the underlying pathways on how students’ demographics and pre-college characteristics that reflect academic preparation, combined with major factors formulated in the university environment, affect time to degree. It does so by developing and evaluating a conceptual framework whereupon time to degree is associated with specific observed or latent factors. A properly tailored Multiple Indicator Multiple Causes SEM was used for evaluating the hypotheses made on a sample of 1137 graduates which came from a Greek University of Social and Political Science, Athens, Greece. AMOS and LISREL packages were used for the analysis. The results reveal interesting direct and indirect relationships of the various predictor variables with time to degree. In particular, the great contribution of student performance and academic integration to time to graduation has been highlighted. However, the contribution of the pre-university features is also worthy of attention.
Simulation Design of an Electron Gun for Microchannel Plate Scrubbing
Authors: Yi Zengzhou , Xu Yuwei , Zhang Jingjin
Published: February 2025

The microchannel plate (MCP) is susceptible to the adsorption of substantial amounts of gas during its fabrication process. To mitigate this, a uniform electron source is essential for effective electron scrubbing and gas removal. Thermionic emission, a method of electron generation, can be employed to create the electron source. In this study, a flat spiral filament was designed and simulated using the CST Studio Suite electron simulation software to assess the cleaning performance of the electron gun. The impact of variations in electron gun parameters on the uniformity of the electron beam and current density was systematically analysed. The simulation results show that, with filament, grid, focusing sleeve, and anode voltages set to 200 V, 500 V, 250 V, and 300 V, respectively, a uniform electron beam with a diameter exceeding 30 mm can be achieved. In order to obtain the current density (5~50 nA/mm2) required for the MCP, the temperature of the filament should be 1800–2000 K through theoretical calculation. These findings offer valuable insights for designing a more efficient electron gun for MCP scrubbing.
A Novel Approach for Robust Automatic Modulation Recognition Based on Reversible Column Networks
Authors: Jing Dan , Xu Tao , Han Liang , Yin Hongfei , Li Liangchao , Zhang Yan , Li Ming , Pan Mian , Guo Liang
Published: February 2025

Automatic Modulation Recognition (AMR) technology, as a key component of intelligent wireless communication, has significant military and civilian value, and there is an urgent need to research relevant algorithms to quickly and effectively identify the modulation type of signals. However, existing models often suffer from issues such as neglecting the correlation between IQ components of signals, poor feature extraction capability, and difficulty in achieving an effective balance between detection performance and computational resource utilization. To address these issues, this article proposes an automatic modulation classification method based on convolutional neural networks (CNNs)—OD_SERCNET. To prevent feature loss or useful features from being compressed, a reversible column network (REVCOL) is used as the backbone network to ensure that the overall information remains unchanged when features are decoupled. At the same time, a novel IQ channel fusion network is designed to preprocess the input signal, fully exploring the correlation between IQ components of the same signal and improving the network’s feature extraction ability. In addition, to improve the network’s ability to capture global information, we have improved the original reversible fusion module by introducing an effective attention mechanism. Finally, the effectiveness of this method is validated using various datasets, and the simulation results show that the average accuracy of OD_SRCNET improves by 1–10% compared to other SOTA models, and we explore the optimal number of subnetworks, achieving a better balance between accuracy and computational resource utilization.
Optimized Watermelon Scion Leaf Segmentation Model Based on Hungarian Algorithm and Information Theory
Authors: Zhu Yi , Yu Qingcang , Xu Zihao
Published: February 2025

In the fully automated grafting process of watermelon seedlings, it is crucial to ensure that the scion’s cotyledons maintain a perpendicular orientation with the rootstock cotyledons. To achieve precise segmentation of watermelon scion cotyledons and accurately extract parameters, such as cotyledon orientation angles, this study introduces enhancements to the Mask2Former network, aiming to improve segmentation accuracy for watermelon scion cotyledons. Specifically, two innovative modules are designed. Taking Swin-Former as the backbone, an Optimal Feature Re-ranking (OFR) module based on the Hungarian Algorithm is devised to re-rank the feature maps obtained from the feature extraction process. Grounded in information theory, the amount of information in semantic segmentation tasks is quantified as Shannon entropy, enabling the model to perceive the information distribution of the feature maps and dynamically adjust the output features. Experimental results demonstrate that the improved model achieves mIoU, mDice, mPrecision, and mRecall scores of 97.44%, 98.70%, 98.20%, and 99.21%, respectively, greatly outperforming Mask2Former, FCNN, and DeepLabv3. Furthermore, the enhanced network exhibits superior accuracy in low signal-to-noise ratio environments, highlighting its robustness in complex scenarios. This study provides a high-precision solution for agricultural automation in the watermelon industry, contributing to the development of fully automated grafting machines.
Physical-Unclonable-Function-Based Lightweight Anonymous Authentication Protocol for Smart Grid
Authors: Guo Yu , Li Lifeng , Jin Xu , An Chunyan , Wang Chenyu , Huang Hairui
Published: February 2025

In the Internet of Everything era of Web 3.0, smart grid (SG) technology is also developing towards intelligent interconnection of terminal devices. However, in the smart grid scenario, security issues are particularly prominent, especially the openness of wireless sensor networks. Sensor nodes are vulnerable to attacks and other security threats, which makes confirming the legitimacy of access identity and ensuring the secure transmission of data an urgent problem to be solved. At present, although a variety of authentication schemes for smart grid nodes have been proposed, most of them have problems. For example, some cannot achieve forward security. Therefore, this paper aims to solve this problem and proposes a lightweight anonymous authentication protocol based on physical unclonable functions (PUFs), which can implement mutual authentication and session key agreement between gateway nodes and sensor nodes. Compared to five state-of-the-art schemes in security and performance, the proposed scheme achieves all eight of the listed security requirements with lightweight calculation overhead, communication overhead, and storage overhead.
Research on Predictive Maintenance Methods for Current Transformers with Iron Core Structures
Authors: Hu Huan , Xu Kang , Zhang Xianya , Li Fangjing , Zhu Lingling , Xu Rui , Li Deng
Published: February 2025

The reliable operation of power systems is heavily dependent on effective maintenance strategies for critical equipment. Current maintenance methods are typically categorized into corrective, preventive, and predictive approaches. While corrective maintenance often results in significant downtime and preventive maintenance can be inefficient, predictive maintenance emerges as a promising technique for accurately forecasting faults. In this study, we investigated the diagnosis and prediction of fault states, specifically single-phase short circuit (1HCF) and double-phase short circuit (2HCF) faults, using monitoring data from current transformers in 110 kV substations. We proposed a predictive maintenance method for current transformers based on core-type structures, which integrates wavelet transform to extract multi-level frequency domain features, employs feature selection techniques (including the Spearman correlation coefficient and mutual information) to identify key predictive features, and utilizes Random Forest classifiers for fault state prediction. Experimental results demonstrate an overall prediction accuracy of 94%.
A Study on the Factors Influencing Rank Prediction in PlayerUnknown’s Battlegrounds
Authors: Lee Ji-Na , Lee Ji-Yeoun
Published: February 2025

This study analyzes the key factors influencing player rank prediction in PlayerUnknown’s Battlegrounds (PUBG), using machine learning models to evaluate in-game performance. By examining variables such as “walkDistance”, “boosts”, and “weaponsAcquired”, the study identifies these as critical predictors, with “walkDistance” emerging as the most significant across all match types. Utilizing models including random forest (RF), gradient descent (GD), extreme gradient boosting (XGBoost), and feedforward neural network (FNN), the analysis reveals performance variation by match type: XGBoost achieves the highest accuracy in solo matches (88.07%), GD performs best in duo matches (84.75%), and RF records the highest accuracy in squad matches (78.21%). These findings provide valuable insights for game developers in balancing gameplay and offer personalized strategic recommendations for players. Future research may enhance predictive performance by incorporating additional variables and exploring alternative models applicable to PUBG and similar battle royale games.
Socio-Economic Impact Assessment of Hydrogen Injection in the Natural Gas Network
Authors: Kyrimis Spyros , Dimas Petros , Stamopoulos Dimitrios , Tsakanikas Aggelos
Published: February 2025

This study explores the feasibility parameters of a potential investment plan for injecting “green” hydrogen into the existing natural gas supply network in Greece. To this end, a preliminary profitability optimization analysis was conducted through key performance indicators such as the cost of hydrogen and the socio-environmental benefit of carbon savings, followed by break-even and sensitivity analyses. The identification of the major impact drivers of the assessment was based on the examination of a set of operational scenarios of varying hydrogen and natural gas flow rates. The results show that high natural gas capacities with a 5% hydrogen content by volume are the optimal case in terms of socio-economic viability, but the overall profitability is too sensitive to hydrogen pricing, rendering it unfeasible without additional motives, measures and pricing strategies. The results feed into the main challenge of implementing commercial “green” hydrogen infrastructures in the market in a sustainable and feasible manner.
Adaptive Robust Optimal Scheduling of Combined Heat and Power Microgrids Based on Photovoltaic Mechanism/Data Fusion-Driven Power Prediction
Authors: Xu Yueyang , Wang Yibo , Liu Chuang , Xiong Jian , Zhou Mo , Du Yang
Published: February 2025

In order to effectively deal with the adverse effects of the randomness of photovoltaic output on the operation of combined heat and power (CHP) microgrids, this paper proposes an adaptive robust optimal scheduling strategy for CHP microgrids based on photovoltaic mechanism/data fusion-driven power prediction. Firstly, the mechanism of the clear sky radiation model is used to calculate the photovoltaic clear sky limit output and random output, and the latter is reorganized in different periods by using the idea of similar days. Then, the data-driven random prediction results are superimposed with the clear sky limit output, the photovoltaic mechanism/data fusion-driven power prediction model is established, and the fusion-driven power prediction framework is provided. Secondly, the boundary information of uncertain factors is deeply explored, and an adaptive robust uncertainty set considering the confidence interval of predictive error statistical information is constructed. On this basis, a robust optimization model of CHP microgrids with the lowest operating cost is proposed, and the optimization model is solved by column and constraint generation algorithm. Finally, the rationality and effectiveness of the proposed model are verified through simulation examples and analytical calculations.
Importance of High-Concentration Electrolytes for Lithium-Based Batteries
Authors: Krämer Susanna , Weintz Dominik , Winter Martin , Cekic-Laskovic Isidora , Grünebaum Mariano
Published: February 2025

Each battery cell consists of three main components: the anode, the cathode, and the separator soaked with liquid electrolyte, the medium in the battery that allows charged ions to move between the two electrodes. Besides a wide electrochemical stability window and good compatibility with both electrodes, the electrolyte should also be safe, thermally stable and environmentally benign, showing a high ionic conductivity of the charge-carrying Li ions and finally a low price. This unique combination of properties is impossible to achieve with a simple salt–solvent mixture and usually requires a combination of different electrolyte components, i.e., several liquid solvents and additives and one or more conducting salt(s). For lithium-based batteries, which are the most common electrochemical energy storage devices today, a solution based on lithium hexafluorophosphate (LiPF6) in a mixture of organic carbonates as the solvent is used. Usually, the conducting salt concentrations used for lithium-based electrolytes are in the range of ≈1 to 1.2 M, but recently, electrolytes with much higher conducting salt concentrations of 5 M and even over 10 M have been investigated as they offer several benefits ranging from increased safety to a broadened electrochemical stability window, thus enabling cheap and safe solvents, even water.
The Fate of the Cyanotoxin Dihydroanatoxin-a in Drinking Water Treatment Processes
Authors: Dolatimehr Armin , Fastner Jutta , Ruhl Aki Sebastian
Published: February 2025

Only recently has the cyanotoxin dihydroanatoxin-a (dhATX-a) been detected more frequently in different surface waters, some of which are used for supplying drinking water. As data about the fate of dhATX-a in drinking water treatment processes are still scarce, the present study investigated the behavior of dhATX-a in different water treatment steps: slow sand filtration, flocculation, adsorption onto activated carbon, ozonation and chlorination. The almost complete removal (>95%) of dhATX-a was observed in sand columns simulating slow sand filtration without showing a long adaptation phase. The results further indicate that dhATX-a can be removed using powdered activated carbon at dosages of 50 mg/L with removal rates between 75 and 93% and also by using ozonation with dosages above 1 mg/L at a concentration of ca. 4.5 mg/L background organic carbon. In contrast, no elimination of dhATX-a was observed in flocculation and chlorination experiments.
Improving Groundwater Quality Through Biosphere Reserve Management: Insights from the Anaga Reserve, Tenerife
Authors: Rodríguez-Alcántara Joselin S. , Cruz-Pérez Noelia , Rodríguez-Martín Jesica , García-Gil Alejandro , Koritnik Jelena , Santamarta Juan C.
Published: February 2025

The Canary Islands, an outermost Spanish territory in the Atlantic Ocean, are renowned for their subtropical climate and significant tourism. However, substantial areas are designated for environmental protection, notably the Anaga Rural Park in Tenerife, a UNESCO Biosphere Reserve, which is the focus of this study. This research investigates the influence of Biosphere Reserve designation on groundwater quality, a crucial resource for Tenerife’s population. We analysed the physicochemical properties of groundwater within the Anaga region over a decade (2007–2016). Our findings demonstrate that groundwater quality consistently meets regulatory standards, exhibiting no evidence of pollution. This high quality is attributed to several factors, including the low population density, limited tourism impact within the reserve, and crucially, the effective soil protection measures implemented within the Biosphere Reserve. The compact geology of the region further limits infiltration and potential pollution. The sustained high quality of groundwater, even in the absence of detectable pollution, highlights the importance of ongoing monitoring to maintain this valuable resource and support local biodiversity. This case study provides a valuable model for sustainable groundwater management and soil protection strategies in other areas of Tenerife and beyond.