bluuebunny/crossref_metadata_2025_split huggingface dataset:
Radiological Presentation of Lung Cancer Patients at Khartoum Oncology Hospital: A Descriptive Study
Authors: Yousif Omer Elgaili , Suliman Tawasol , Elgaili Yousif Omer , Alattaya Abdelmoneim , Abdalla Ahmed Omer , Masaad Awadelkareem 
Published: November 2024

Abstract

Lung cancer remains a leading cause of cancer-related mortality globally. Early diagnosis through radiological imaging is crucial for improving outcomes, yet most patients present at advanced stages. This study aims to evaluate the radiological presentation of lung cancer in patients attending Khartoum Oncology Hospital between April 2022 and October 2022. A descriptive cross-sectional study was conducted on 78 lung cancer patients. Data were collected on demographic characteristics, clinical symptoms, and radiological findings, including chest X-ray (CXR) and computed tomography (CT) scan results. Correlation between clinical features and radiological presentations was analyzed using appropriate statistical tests.
Among the 78 patients, 56 (72%) were males, and 22 (28%) were females, with a mean age of 61.2 years. The most common symptoms were cough (87.2%) and dyspnea (55.1%). The duration of symptoms was less than one year in 83.4% of cases. CXR findings revealed mass lesions in 48.7% of patients, pleural effusion in 41%, and atelectasis in 38.5%. CT scans identified mass lesions in 86%, lymph node involvement in 83.3%, and pleural effusion in 59%. Metastatic involvement was detected in 24.4% (liver), 21.8% (bone), and 6.4% (adrenal glands). Centrally located lesions were found in 66.7% of patients, while irregular margins were observed in 64.1% of cases. A statistically significant correlation was found between advanced age and central mass lesions (p < 0.05).
The study revealed that lung cancer in Sudanese patients is predominantly diagnosed in males over 60 years of age, with advanced radiological features such as mass lesions, lymph node involvement, and metastasis. The findings highlight the need for early screening and enhanced diagnostic facilities to improve lung cancer detection and management in Sudan.
"Storm-like" ocean submesoscales enhance submarine melting within Antarctic ice cavities.
Authors: Poinelli Mattia , Siegelman Lia , Nakayama Yoshihiro 
Published: November 2024

Abstract

Thwaites and Pine Island ice shelves are responsible for more than a third of the total ice loss from Antarctica. These ice shelves are experiencing accelerated retreat due to a combination of complex air-sea-ice processes. The ice cavities are particularly vulnerable to warm water intrusions but remain severely understudied due to their remote location and the lack of numerical models capable of resolving fine-scale processes occurring within them. Here we show that ocean submesoscale features (1-10 km-size), reminiscent of storms impacting and damaging the coast, regularly form in the open ocean, propagate toward the ice shelves, intrude the cavity and fragilize the ice from below. A state-of-the-art numerical model at 200m resolution and observations below Thwaites' ice shelf highlight that submesoscale motions are ubiquitous year-round in the Amundsen Sea Embayment. Results show that submesoscales are a key contributor to submarine melting, accounting for a fifth of the total submarine melt variance in the area. Results also highlight a positive feedback loop between submesoscale motions and submarine melting, suggesting these ``storm-like" events will gain increasing importance in a warming climate, with far-reaching implications for ice-shelf stability and global sea level rise.
Correlation between taste and smell alterations and quality of survival in patients treated with radiotherapy for nasopharyngeal carcinoma: a cross-sectional study in China
Authors: Liang Haixin , Yang Huan , Yin Li , Wang Lan , Jiu Enmei , Li Bin , Pang Huarong 
Published: November 2024

Abstract

Purpose This study aims to assess the prevalence of taste and smell alterations in patients undergoing radiotherapy for nasopharyngeal carcinoma and to explore the correlation between these alterations and quality of life.Methods A cross-sectional survey was conducted from June 2023 to March 2024, utilizing the Taste and Smell Survey (TSS) questionnaire administered to 135 patients receiving radiotherapy for nasopharyngeal cancer.Results The mean total score on the TSS scale was 9.37 ± 4.595. Among the participants, 91.1% reported alterations in taste and smell, with only 12 cases (8.9%) showing no changes. The severity of alterations was classified as mild in 9 cases (6.7%), moderate in 45 cases (33.3%), and severe in 69 cases (51.1%). The rates of changes in taste sensitivity for salty, sweet, sour, and bitter flavors were 66.7%, 52.6%, 52.6%, and 53.3%, respectively, while olfactory sensitivity changes were reported in 48.9% of patients. Notably, alterations in taste and smell were significantly correlated with anxiety and overall quality of survival scores (P < 0.05).Conclusion Alterations in taste and smell are prevalent among patients undergoing radiotherapy for nasopharyngeal carcinoma, with the majority experiencing moderate to severe changes. These findings underscore the importance of addressing these sensory alterations and their association with psychological well-being and survival quality in targeted interventions.
WITHDRAWN: Pattern Synthesis in Butler-Matrix-Fed Cylindrical Antenna Array for Monopulse NR-IFF Radar Application
Authors: Fallah Mohsen , Nekounam Majid , Armaki Seyyed Hossein Mohseni 
Published: November 2024

Abstract

The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.
Spontaneous vs Posed: Analysis of Emotion Intensities using Wavelet Coefficient and Support Vector Machine
Authors: Ahmad Asraful Syifaa' , Hassan Rohayanti , Zakaria Noor Hidayah , Sim Hiew Moi , Khairuddin Alif Ridzuan , Shafei Muhammad Luqman Mohd , Kasim Shahreen 
Published: November 2024

Abstract

Emotion detection is a critical aspect of human-computer interaction and various other applications, including real-time video surveillance, psychological studies and social media emotion analysis. Understanding the complexity and nuance of human emotions requires an in-depth look at emotion intensity. Furthermore, occlusion is a common problem in facial emotion detection, leading to low detection accuracy. This paper presents a unique method for utilising Support Vector Machine (SVM) classification with wavelet coefficient analysis to analyse emotion intensities. To assess its effectiveness, we tested our approach on several benchmark datasets, such as the Japanese Female Facial Expression (JAFFE) dataset and the Facial Expression Recognition 2013 (FER2013) dataset. Our method demonstrates scalability and adaptability for application in large datasets, addressing the needs of big data environments. Additionally, we tested our method on occluded sections of the FER2013 dataset to simulate real-world conditions where facial emotions might be partially obscured, such as in video surveillance settings. Results indicate that wavelet coefficient analysis successfully captures nuanced differences in emotional intensity, while SVM performs robustly in classifying emotions, even in challenging, partially occluded conditions. Furthermore, the system’s structure allows for effective implementation in real-time applications and adaptability to the vast data streams typical of social media and large-scale psychological datasets. Our findings suggest that the proposed framework improves detection processes and offers a scalable, adaptable solution for large-scale emotion analysis across varied applications and significant data contexts.
HPLC channel monitoring and analysis based on improved bispectrum adaptive power regulation algorithm model
Authors: Xingliang Cheng , Mingli Zhou , Xiaojun Li , Tongyang Zou , Siyu Men , Lin Chen 
Published: November 2024

Abstract

Aiming at the problems of signal power loss and poor channel equalization in high power line communication (HPLC), an improved bispectrum adaptive power regulation algorithm model is proposed. The algorithm uses bispectral quadratic characteristic model to establish a sparse observation model. By constructing the signal parameter database, the communication signal processing problem is transformed into the estimation of signal motion parameters. At the same time, a new nonlinear adaptive power equalizer is adopted, which has faster convergence speed and lower steady-state mean square error, so that its coefficients can be adjusted and its performance can be adjusted to best adapt to the input signal. The results show that the proposed algorithm can resist noise interference in communication signal processing, and its communication delay is as low as 280ms.
Relationship of Oedaleus asiaticus densities with soil moisture and land surface temperature in Inner Mongolia, China
Authors: Du Bobo , Shan Yanmin , Hai QuanSheng , Yue Yuchao , LiWa Ga , Yun Xiangjun , Han Haibin 
Published: November 2024

Abstract

Oedaleus decorus asiaticus (O-decorus) is a significant pest in the grasslands of Inner Mongolia, posing considerable challenges to the development of animal husbandry. To understand the key factors influencing the population distribution of O-decorus, field surveys were conducted from 2018 to 2020, during which the population count, growth stage, and location information of O-decorus were recorded. Daily soil moisture (SM) data and daily land surface temperature (LST) data were obtained from the National Tibetan Plateau Data Center, and a Generalized Additive Model (GAM) was constructed. Our findings indicate that SM (S8) in August of the previous year is the most critical factor, with an F-value of 27.422, followed by LST (L10) in October of the previous year, LST (L6) in June of the survey year, SM (S9) in September of the previous year, LST (L3) in March of the survey year, and LST (L5) in May of the survey year, with F-values of 7.848, 7.223, 5.823, 4.919, and 3.547, respectively. SM (S8 and S9) can be regarded as vital indicators for predicting and monitoring the occurrence of O-decorus. However, the contributions of S8 and S9 to O-decorus density differ significantly. S8 is negatively correlated with O-decorus density, while S9 values below 0.29 m³/m³ can promote the growth of O-decorus. Higher LST during early overwintering correlates with increased O-decorus density. During the survey year, LST emerged as the primary factor affecting grasshopper density. However, LST plays a more complex role during the incubation period. Additionally, we explored the relationship between the density levels of O-decorus and daily SM and LST, yielding similar results. This study clearly identifies SM and LST as the major factors influencing the occurrence of O-decorus, which will aid in predicting and monitoring its density.
Influence of gear tooth surface wear on nonlinear dynamics of aviation planetary gearbox
Authors: Luo Lan , Cui Kangkang , Jiao Haofeng , Lu Jiamin , Zhao Fei , Wei Yongqiao 
Published: November 2024

Abstract

Aerospace planetary gearboxes undergo wear on the gear tooth backlash after prolonged service cycles. Tooth wear leads to alterations in the gear's backlash and friction force. These factors can result in abnormal vibrations of the planetary gearbox, along with an increase in rotational speed, potentially causing damage to the aerospace electromechanical actuator in which it is installed. To investigate the nonlinear vibration mechanism induced by rotational speed at various wear levels, a torsional dynamics model of a single-row planetary gear system is developed. The tooth friction model and dynamic tooth backlash model are integrated into the torsional dynamics model. Subsequently, the effects of wear level and rotational speed on the vibration characteristics of the planetary gear system are analyzed. The findings indicate that at different wear levels, the system response transitions through quasi-periodic 4 motion - quasi-periodic 2 motion - quasi-periodic motion - chaotic motion.
Exponentially Narrow Delay Interval Technique to Stability and Stabilization for Nonlinear Systems and Its Application to Truck-Trailer System
Authors: Lu Bin , Tian Yufeng , Liu Yutong , Yang Yue , Su Xiaojie , Zhang Linsong 
Published: November 2024

Abstract

This study addresses the stability and stabilization challenges in nonlinear control systems with time-varying delays via fuzzy model theory. An exponentially narrow delay interval technique is introduced, facilitated by a single tunable parameter $p$, to equivalently transform the extensive delay period $[d_0, d_2]$ into a series of variable narrow subintervals, expressed as: $[d_{0},d_{2}]=\cup_{i=1}^{2^l}[d_{\frac{i-1}{2^{l-1}}},d_{\frac{i}{2^{l-1}}}]$. Diverging from traditional delay interval methods, this narrow delay interval approach simplifies the optimization process by characterizing each subinterval with just $d_0$, $d_2$, and $p$. The core advantage of integrating the narrow interval approach with fuzzy systems is its capability to accurately represent and manage the intrinsic uncertainty in time-varying delays, thus facilitating robust control in real-world scenarios. A parameter-type reciprocally convex inequality is introduced to estimate the derivative of the narrow Lyapunov-Krasovskii functional, enhancing the precision over conventional methods. These innovations contribute to the formulation of new criteria for the stability and stabilization of the T-S fuzzy delayed system. The practicality and effectiveness of the proposed strategies are demonstrated through their application to a truck-trailer system.
Automated Diagnosis of Performance Bottolenecks in Lithium-Sulfur Batteries
Authors: Parab Saurabh , Lee Jonathan , Miyagishima Matthew , Miao Qiushi , Bhamwala Bhargav , Liu Alex , Ah Louis , Sreenaraya Bhagath , Ryu Kun , Schmidt Robert , Cai Mei , Dai Fang , Liu Ping , Wang Shen , Meng Ying Shirley 
Published: November 2024

Abstract

Lithium-sulfur (Li-S) batteries offer high energy density and low cost, making them ideal for electric vehicles and aviation. However, the many factors affecting Li-S battery cycling performance complicate researchers' efforts to pinpoint the bottleneck. To address this, a toolkit called High-performance liquid chromatography, Ultraviolet spectroscopy, and Gas chromatography Sequential characterizations (HUGS) was developed using sequential analytical chemistry. Along with this, data analysis software Dr. HUGS© was created to automate the ‘diagnoses’ of the key degradation mechanisms, similar to a doctor assessing a patient. Our analysis reveals that carbon sulfur cathodes suffer capacity loss due to lithium sulfide buildup on the anode. HUGS shows that constant pressure setups in Li-S pouch cells improve compositional uniformity over constant gap setups. Conversely, sulfurized polyacrylonitrile batteries experience non-sulfide solid-electrolyte-interface formation and lithium pulverization—issues mitigated by localized high-concentration electrolytes. This work demonstrates how analytical chemistry techniques and automated data analysis can accelerate the diagnosis of the complexities of electrochemical systems, advancing next-generation, high-performance Li-S batteries.
Neurodynamic Characterization and Prediction of Schizophrenia Using Echo State Networks with Serotonin Modulation: A Temporal and Frequency Band Analysis Approach
Authors: Sowrirajan Anirudh , Sriniva Pranav , Sriniva Sundari Avanthikaa , D Punitha , Dilip Golda 
Published: November 2024

Abstract

Schizophrenia is characterized by significant cognitive dysfunctions, with serotonin playing a crucial role in modulating neural processes. Analyzing the impact of serotonin on EEG patterns can provide important insights into distinguishing schizophrenic patients from healthy individuals. This study integrates serotonin-inspired modulation into Echo State Networks (ESNs) to model the nonlinear dynamics of EEG data from schizophrenic patients, with a focus on key brain regions such as the frontal lobe and medial prefrontal cortex (mPFC). EEG data were preprocessed (0.1–40 Hz), ICA filtered, segmented, and analyzed using ESNs, with serotonin modulation targeting 5 HT1A and 5-HT2A receptors to simulate effects on frontal and mPFC regions. Principal Component Analysis (PCA) and K-means clustering were used to classify schizophrenic and healthy samples. Results showed that EEG patterns in schizophrenia exhibited elevated delta and gamma band activity, with diminished alpha and beta band activity. Serotonin modulation enhanced the performance of ESNs by reducing noise and improving predictive accuracy, particularly in the frontal regions. The incorporation of serotonin modulation into ESNs offers a deeper understanding of the nonlinear dynamics of brain activity in schizophrenia and provides valuable insights into disease-specific EEG features, potentially advancing diagnostic approaches.
Helminth Community Structure of Harlequin Frog, Atelopus Cruciger (Lichtenstein &amp; Martens, 1856) (Anura: Bufonidae)
Authors: Cañizales Israel 
Published: November 2024

Abstract

The harlequin toad (Atelopus cruciger Lichtenstein & Martens, 1856) is an endemic species of Venezuela’s Cordillera de la Costa, found at elevations ranging from 30 to 2,000 m. Currently, only three populations are documented, all on the northern slope of Henri Pittier National Park. Despite the ecological importance of anurans, knowledge of their parasitic fauna in Venezuela remains largely descriptive. This study assessed the helminthological diversity of A. cruciger by examining 97 specimens collected from seven Venezuelan states (Aragua, Carabobo, Cojedes, Distrito Capital, Miranda, Yaracuy, and Vargas) between 1950 and 2009 and housed in three national reference collections. Specimens averaged 29.85 ± 3.30 mm in males and 37.68 ± 5.07 mm in females. A total of 422 helminths were identified, predominantly nematodes (n = 329) belonging to the families Molineidae (Oswaldocruzia), Cosmocercidae (Aplectana, Cosmocerca), and Rhabdiasidae (Rhabdias). Cestodes (n = 93) from the family Nematotaeniidae (Cylindrotaenia) were also present. Parasitization was observed in 80.41% of specimens, with 26.80% exhibiting mixed infections (nematodes and cestodes), 30.93% hosting cestodes exclusively, and 77.32% hosting nematodes exclusively. Epidemiological indices revealed a prevalence of 80.41%, mean intensity of 4.35 parasites, mean abundance of 5.41, and an infection index of 3.50. Parasite prevalence, abundance, and richness were unaffected by host sex or size. Objectives included describing the helminths of A. cruciger, evaluating population-level parasitological parameters (e.g., prevalence, abundance, intensity, and aggregation), analyzing community-level diversity and evenness, exploring parasite-host relationships, and mapping parasite geographic distributions. This study provides new records for both the host and Venezuela, contributing significantly to the understanding of helminth biodiversity in neotropical amphibians. Findings highlight the ecological and conservation value of assessing parasitic communities in endangered amphibians such as A. cruciger.
Mitigating Cognitive Biases in Clinical Decision-Making Through Multi-Agent Conversations Using Large Language Models: Simulation Study
Authors: Ke Yuhe , Yang Rui , Lie Sui An , Lim Taylor Xin Yi , Ning Yilin , Li Irene , Abdullah Hairil Rizal , Ting Daniel Shu Wei , Liu Nan 
Published: November 2024

Background
Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes. Addressing these biases presents a formidable challenge in the medical field.

Objective
This study aimed to explore the role of large language models (LLMs) in mitigating these biases through the use of the multi-agent framework. We simulate the clinical decision-making processes through multi-agent conversation and evaluate its efficacy in improving diagnostic accuracy compared with humans.

Methods
A total of 16 published and unpublished case reports where cognitive biases have resulted in misdiagnoses were identified from the literature. In the multi-agent framework, we leveraged GPT-4 (OpenAI) to facilitate interactions among different simulated agents to replicate clinical team dynamics. Each agent was assigned a distinct role: (1) making the final diagnosis after considering the discussions, (2) acting as a devil’s advocate to correct confirmation and anchoring biases, (3) serving as a field expert in the required medical subspecialty, (4) facilitating discussions to mitigate premature closure bias, and (5) recording and summarizing findings. We tested varying combinations of these agents within the framework to determine which configuration yielded the highest rate of correct final diagnoses. Each scenario was repeated 5 times for consistency. The accuracy of the initial diagnoses and the final differential diagnoses were evaluated, and comparisons with human-generated answers were made using the Fisher exact test.

Results
A total of 240 responses were evaluated (3 different multi-agent frameworks). The initial diagnosis had an accuracy of 0% (0/80). However, following multi-agent discussions, the accuracy for the top 2 differential diagnoses increased to 76% (61/80) for the best-performing multi-agent framework (Framework 4-C). This was significantly higher compared with the accuracy achieved by human evaluators (odds ratio 3.49; P=.002).

Conclusions
The multi-agent framework demonstrated an ability to re-evaluate and correct misconceptions, even in scenarios with misleading initial investigations. In addition, the LLM-driven, multi-agent conversation framework shows promise in enhancing diagnostic accuracy in diagnostically challenging medical scenarios.