bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine Learning based Radiomics from Multiparametric Magnetic Resonance Imaging for Predicting Lymph Node Metastasis in Cervical Cancer
Authors: Liu Jing , Zhu Mingxuan , Li Li , Zang Lele , Luo Lan , Zhu Fei , Zhang Huiqi , Xu Qin 
Published: April 2024

Abstract

Background
Construct and compare multiple machine-learning models to predict lymph node (LN) metastasis in cervical cancer, utilizing radiomic features extracted from preoperative multi-parametric magnetic resonance imaging (MRI).
Methods
This study retrospectively enrolled 407 patients with cervical cancer who were randomly divided into training cohort (n = 284) and validation cohort (n = 123). A total of 4065 radiomic features were extracted from the tumor regions of interest on contrast-enhanced T1-weighted imaging, T2-weighted imaging, and diffusion-weighted imaging for each patient. The Mann-Whitney U test, Spearman correlation analysis, and selection operator Cox regression analysis were employed for radiomic feature selection. The relationship between MRI radiomic features and LN status was analyzed by five machine-learning algorithms. Model performance was evaluated by measuring the area under the receiver-operating characteristic curve (AUC) and accuracy (ACC). Moreover, Kaplan–Meier analysis was used to validate the prognostic value of selected clinical and radiomics characteristics.
Results
LN metastasis was pathologically detected in 24.3% (99/407) of patients. Following three-step feature selection, 18 radiomic features were employed for model construction. The XGBoost model exhibited superior performance compared to other models, achieving an AUC, accuracy, sensitivity, specificity, and F1-score of 0.9268, 0.8969, 0.7419, 0.9891, and 0.8364, respectively, on the validation set. Additionally, Kaplan − Meier curves indicated a significant correlation between radiomic scores and progression-free survival in cervical cancer patients (p < 0.05).
Conclusion
Machine learning-based multi-parametric MRI radiomic analysis demonstrates a promising performance in the preoperative prediction of LN metastasis and clinical prognosis in cervical cancer.
Study on Sliding Surface Characteristics of Expansive Soil Based on Quantitative Evaluation of Microstructure
Authors: Kong Youxing , Li Zhiqing , Qi Zhiyu , Sun Kai , Hu Ruilin 
Published: April 2024

Abstract

Expansive soils are widely distributed and the landslides happened in these area cause significant economic losses. The sliding surface records crucial information about landslides, providing insight into the mechanisms behind their occurrences. This research delves into the microstructural features of landslide surfaces in expansive soils. Various sliding surfaces were created through direct shear tests under different water contents (10%, 20%, 30%) and normal stresses (50 kPa, 100 kPa, 200 kPa, 400 Pa). Scanning electron microscopy (SEM) was used to observe the surfaces and based on their structural morphology and formation mechanisms, we categorized the surfaces’ structure into scratch, tension crack, and residual pore. Four parameters, porosity, average pore area, probability entropy, and area probability distribution index, were calculated and used to quantitatively characterized the surface structures. By correlating field landslide surface features with geological survey data, it was inferred that the triggering moisture content was 25.3–26.8% at least, the sliding surface was situated 10 to 12 meters deep within the slope, and multiple sliding events occurred. The water infiltration from the slope top and precipitation along fractures coupled with rising irrigation water levels that saturated the slope base ultimately leading to fracture layer breakthrough and slope failure.
Integrated Analysis of Seasonal Swells, Wind-seas and associated Wave Energy along the major Indian Ports
Authors: Kumar Prashant , Saha Kamlesh Kumar , Singh Anurag , Bhaskaran Prasad Kumar , Balakrishnan T M , Hisaki Yukiharu , . Rajni 
Published: April 2024

Abstract

An in-depth comprehension and evaluation of the spatio-temporal progression of wind-induced gravity waves encompassing wind-seas and swells in coastal areas are crucial for optimizing the extraction of renewable energy efficiently and identifying ideal locations for planning renewable energy infrastructure. This research offers a thorough examination of the combined potential of average and extreme offshore winds, swells, and wind-seas derived energy along the Indian coastline, utilizing the ERA5 3-hourly reanalysis dataset spanning the past 44 years (1979–2022). The study employs the Generalized Extreme Value (GEV) method for extreme value analysis. Among the six major port locations, the most significant rise in swell and wind-sea wave power is identified at Kandla (1.33 kW/m dec-1) in the Arabian Sea (AS) and Mumbai (0.1 kW/m dec-1) during the June–August (JJA) period, and subsequently during September-November (SON). Likewise, the highest increase in decadal wind energy is observed at Mumbai (2.47 W/m2 dec-1) and Kochi (2.39 W/m2 dec-1). Results indicate that both mean and extreme wave and wind energy exhibit substantial swell wave power at Kandla, averaging around 21.05 kW/m (with peaks up to 66.84 kW/m) during the JJA season. Similarly, the peak mean (extreme) wave power generated from wind-seas per annum is recorded at Kandla and Mumbai, averaging approximately 5.81 kW/m (with peaks reaching 62 kW/m) during JJA. The highest mean (extreme) wind energy is observed at Kandla, averaging about 0.51 kW/m2 (with peaks up to 3.65 kW/m2) during JJA, followed by SON. Across the six principal port locations, the analysis exhibits the JJA season as the prime period for maximum energy production, followed by SON. This scrutiny also underscores the significance of considering seasonal fluctuations and local climatic conditions when developing renewable energy initiatives along the coastal regions of India.
Accelerating Depression Intervention: Identifying Critical Psychological Factors Using MCDM-MOORA Technique for Early Therapy Initiation
Authors: Majumder Pratham , Pal Arkita , Dorai D. Ramya , Gopinathan B. , Mallik Saurav , Ahmad Naim , Badawy Ahmed Said , Changalasetty Suresh Babu 
Published: April 2024

Abstract

Background A thorough psychosocial assessment is time-consuming, often requiring multiple sessions to uncover the psychological factors contributing to mental illness, such as depression. The duration varies depending on the severity of the patient’s condition and how effectively the psychotherapist can establish rapport. However, prolonged assessment periods pose a significant risk of patient deterioration.
Methods The comprehensive psychosocial intervention, led by the Multi-Criteria Decision-Making (MCDM) approach utilizing the Multi-Objective Optimization by Ratio Analysis (MOORA) method, played a pivotal role in identification of the key psychological factors contributing to the depression of the client among the 21 facotrs specified by BDI-II analysis.
Results The integration of the MCDM-MOORA strategy compared to traditional psychotherapy demonstrates a Jaccard similarity coefficient of 0.8, with a minimum error margin of 7% (vulnerability index = 0.57), indicating a significant agreement between the two approaches, both converging towards a similar solution.
Conclusion The implementation of MOORA facilitated the identification and prioritization of key psychosocial intervention strategies, making the process 45.5 times faster compared to traditional methods. This acceleration significantly contributed to the precision and efficacy of the work. Additionally, critical vulnerable factors were identified through ordered statistics and correlation analysis (Pearson (r) = 0.8929 and Spear-man’s rank (ρ) = 0.7551) on the Beck Depression Inventory-II model. These findings were supported by other MCDM schemes such as EDAS and TOPSIS, etc. Moreover, the proposed method demonstrated high stability and robustness in dynamic decision-making environments, maintaining consistency across scenarios adapted by different psychotherapists. Overall, the combined application of MCDM (MOORA) and targeted psychological interventions yielded substantial positive outcomes in enhancing the well-being of individuals with psychological illnesses e.g., depression, cognitive, affective, somatic syndromes.
Chatbot Construction for Business-Oriented Use Cases
Authors: Kováč Jozef , Viktorin Adam , Šenkeřík Roman 
Published: April 2024

Abstract

This paper proposes a reliable way to compensate for the limitations of the current generation of large language models in relation to common business use cases by adapting an automated chat agent with the ability to use self-implemented tools. Using the aforementioned approach, context size limitations were extended using a semantic search database and conversation abilities over a proprietary dataset (as well as a few complementary behavioral patterns) with support for mathematics introduced. The paper clarifies the data extraction and processing methods and the architecture used for agent construction. Lastly, external and implementation-related limitations are mentioned, along with an evaluation of the obtained results.
Eco-friendly sorbents for petroleum and diesel based on macadamia nutshell waste in castor oil-based polyurethane foam for oil spill
Authors: Maia Lana , Neto Thalita da Silva , Perluxo Júlia , Carmo Flávia do , Rosa Derval dos Santos , Mulinari Daniella R. 
Published: April 2024

Abstract

Aquatic environments have experienced profound and severe consequences in recent years due to oil spills and chemical leaks in oceanic and river ecosystems. Because of this, several studies have been conducted to develop sustainable adsorbents with hydrophobic characteristics. Thus, the present research focuses on the development of eco-friendly sorbents from macadamia nutshell waste (MW) in castor oil-based polyurethane foam with different proportions of MW (5, 10, 15, and 20 wt%) and a granulometry of 28 mesh to assess the absorption efficiency for different oil types (crude oil CB, crude oil SB, diesel S10, and diesel S500). The eco-friendly sorbents (biocomposites) were characterized by optical microscopy, scanning electron microscopy, density, and contact angle. The pore morphology of sorbents changed to a partial closed-cell structure with a smaller pore size, and biocomposites exhibited a higher contact angle (119.1º ± 0.4). The oil absorption capacities by biocomposites showed maximum sorption of 7.3, 7.1, 5.1, and 3.9 g.g-1 for crude oils (SB and CB), S10, and S500 diesel, respectively. The sorption results showed that the biocomposites showed good removal of heavy oils (crude oil) compared to light oils (diesel S10 and S500). Among the isotherm models used, the Langmuir model demonstrated the most accurate fit and estimated a maximum adsorption capacity of 5.39, 4.23, 3.24, and 2.69 g.g−1 for crude oil CB, crude oil SB, S10, and S500 diesel respectively, using PU+20% MW. Additionally, PU+ 20 % MW showed excellent reusability during 30, 30, 30, and 10 cycles of sorption-desorption for crude oil CB, crude oil SB, S10, and S500 diesel, respectively.
Histopathological manifestations of liver tissue affected by heated and unheated palm oil in Wistar rats
Authors: Ahookhash Maryam , Moradi Makan , ZHaleh Mohsen 
Published: April 2024

Abstract

Palm oil is the most widely used type of oil in the world, which is oxidized by exposure to heat and leads to damage to body tissues and the creation of free fatty acids. This study aimed to show the harm of palm oil in both heated and non-heated states. For this purpose, twenty-four male Wistar rats were randomly divided into three groups and treated as follows: control (rats treated with their normal chew), palm oil (rats treated with 7.5% w/w of palm oil added to their normal chew), and heated palm oil (rats treated with 7.5% w/w of palm oil into their normal chew, which had been heated repeatedly 10 times at 180°C for 10 min). After experimental treatments, the animals were euthanized. Liver samples were collected for histopathological assessment by hematoxylin and eosin (H&E) staining. In the groups treated with heated and unheated palm oil, signs of non-alcoholic fatty liver disease were observed at different proportions. Histological evidence is an indication that heated and unheated palm oils can cause various degrees of non-alcoholic fatty liver disease. Therefore, the consumption of palm oil, especially when heated, may not be completely safe.
Space Situational Awareness: Conjunction-based Collision Analysis Among Debris and Active Assets in Space
Authors: name Saksha NO family , Shivarajaiah Aishwarya , Ali Tanweer , Vaz Aldrin , Yadav Manish , Hegde Navya 
Published: April 2024

Abstract

In recent years, the escalating concern of space debris and its threats to space assets has driven the need for advanced analysis. This study’s primary objective is to analyze satellite data for collision prediction. Specialized algorithms have been developed to forecast orbital trajectories, assess proximity between objects, conduct conjunction analyses, visualize paths, and compare the conjunction analysis results with theoretical expectations. Extensive satellite data and the PYTHON coding platform were used, to predict orbital paths, distances between objects, and potential collisions within 24 hours. Key PYTHON libraries, including MATPLOTlib, SKYFIELD, PYTZ, Numpy, and Pandas, were utilized. The proposed algorithm accurately predicted orbital paths and distances, with a focus on x, y, and z coordinates. Notably, the algorithms can predict potential collisions within 24 hours. An extended version accommodates thousands of satellite input Two- Line Element (TLE) data, enhancing collision prediction. Rigorous validation compared the built-in to manual calculations based on the orbital elements. The study provides a visualization of predicted satellite collisions, emphasizing the importance of addressing space debris challenges for safer space missions.
Defeating Evasive Malware with Peekaboo: Extracting Authentic Malware Behavior with Dynamic Binary Instrumentation
Authors: Gaber Matthew , Ahmed Mohiuddin , Janicke Helge 
Published: April 2024

Abstract
The accuracy of Artificial Intelligence (AI) in malware detection is dependent on the features it is trained with, where the quality and authenticity of these features is dependent on the dataset and the analysis tool. Evasive malware, that alters its behavior in analysis environments, is challenging to extract authentic features from where widely used static and dynamic analysis tools have several limitations. However, Dynamic Binary Instrumentation (DBI) allows deep and precise control of the malware sample, thereby facilitating the extraction of authentic behavior from evasive malware. Considering the limitations of malware analysis for use with AI, this research had two primary objectives: investigation of the evasive techniques used by modern malware and the creation of Peekaboo, a DBI tool to extract authentic data from live malware samples. Peekaboo instruments and defeats evasive techniques that target analysis tools and virtual environments. A dataset of 20,500 samples was assembled and each sample was run for up to 15 minutes to observe not only the anti-analysis techniques used but also its complete behavior. Peekaboo outperforms other tools on several fronts, it is the only tool to measure start and completion rates, capture the executed Assembly (ASM) instructions, record all network traffic and implements the largest coverage against evasive techniques.
Research on the spatial layout of military defense system in Zhuanglangwei defense zone of Gansu Town in the Ming Dynasty
Authors: Ye Minghui , Miao Lu , Meng Xiangwu , Pei Qiangqiang 
Published: April 2024

Abstract

The Zhuanglang Defense Area is the geographical area of the Zhuanglang Road Defense Area under the jurisdiction of Gansu Town of the Ming Great Wall. It forms a circular defense against external invasion with the northwest Liangzhou Defense, the southwest Xining Defense Area, and the east Lanzhou Defense. Due to the strategic position and geographical advantage of the defense area, it forms a unique spatial structure and layout characteristics of the Great Wall defense system. By using GIS methods such as elevation analysis, neighbor analysis, density analysis, surface cost analysis, and shortest path analysis, this study explores the spatial characteristics of military settlements in the Zhuanglangwei defense area, revealing their spatial structure, geographical location, and influencing factors, providing a theoretical basis for the protection of historical relics in the Zhuanglangwei area.
Binary droplet collisions in bioprinting: Influence of material properties on mixing and repeatability
Authors: Ruiz-Gutierrez Elfego , Hasslberger Josef , Klein Markus , Dalgarno Kenny , Chakraborty Nilanjan 
Published: April 2024

Abstract

The study of suspended binary droplet collisions is an active research topic that has gathered interest due to its complexity and its industrial applications such as bioprinting.

For many techniques that rely on the collision of two droplets, the quality of the outcome depends on the mixing process that begins when the droplets come in contact with each other.

In this work, we study how the difference in material properties of colliding droplets, such as viscosity and surface tension, influence the mixing process.

Employing multiphase direct numerical simulations, the distribution of the impinging liquids and the structure of the flow field have been analysed to find ways of mixing and process repeatability optimisation.

To analyse the effects that differences in viscosity and surface tension have in the mixing process, the flows emerging from the collision are analysed.

The differences in viscosity and surface tension have also been demonstrated to affect the trajectory of the centre of mass and morphology of the droplets upon collision, and thus could have implications from the point of view of process repeatability.
Climatic drivers of chytrid prevalence in the critically endangered Admirable Redbelly Toad
Authors: Pontes Mariana Retuci , Abadie Michelle , Ribeiro Luisa , Augusto-Alves Guilherme , Borges-Martins Márcio , Becker C. Guilherme , Toledo L. Felipe 
Published: April 2024

Abstract

Global warming is driving shifts in rainfall and temperature patterns, and projections indicate an increase in frequency and intensity of climate anomalies. These changes influence wildlife disease dynamics, affecting pathogen development, host behavior, physiology, and disease susceptibility. Understanding the intricate interplay between climatic anomalies and emerging pathogens in amphibian is essential to inform conservation efforts targeted towards this highly threatened vertebrate group. In-situ research is recommended as a conservation action by the International Union for Conservation of Nature (IUCN 2023) for the microendemic and Critically Endangered amphibian Melanophryniscus admirabilis (Admirable Redbelly Toad). We therefore investigated the seasonal climatic fluctuations and climatic anomalies affecting infections by the waterborne chytrid Batrachochytrium dendrobatidis (Bd) in this sole surviving population, which holds significant conservation concern. We found links between high Bd prevalence, monthly low rainfall and rainfall deficit. Additionally, an increase in Bd prevalence was associated with temperatures exceeding historical averages. These findings suggests that climatic anomalies play a crucial role in Bd transmission and infection status among toads, probably due their aggregation behavior in few available pools during drier and warmer periods. Despite the current low prevalence of Bd and infection loads, the projected escalation of climatic anomalies might render M. admirabilis uniquely susceptible to synergistic interactions between Bd and extreme climatic conditions. The insights gained from this study can improve the conservation efforts and underscore the intricate relationship between climatic anomalies and chytrid infection, shedding light on potential vulnerabilities within threatened amphibian populations.
Improving LoRaWAN Localization in Industrial Environments Using Merging Path-Loss Modeling with Extended Kalman Filtering and adopting Map-Matching
Authors: Moradbeikie Azin , Keshavarz Ahmad , lopes sergio 
Published: April 2024

Abstract

In industrial environments, accurate location information is crucial in enabling the seamless operation of technologies. Localization based on the signal features of the implemented network (such as RSSI) is becoming an appropriate substitute to solve its problems, such as low power consumption and cost. LoRaWAN, a contemporary LPWAN technology, can provide long-range coverage, an important requirement for several industrial application domains. RSSI-based localization in LoRaWAN is a widely used low-cost method, but it is susceptible to environmental changes and noise, leading to low performance and accuracy especially in industrial environments. So, applying an adoptable noise-filtering method based on the environment, to make measured RSSI usable in industrial applications such as asset management localization and tracking, is essential. This paper proposes a novel method that merges an Extended Kalman Filter (EKF) with Path-Loss modeling (PLM) for noise filtering in LoRaWAN system. Additionally, we incorporate map considerations to further improve the accuracy of location estimation. For the evaluation step, the proposed method is implemented and tested in a harbor in a highly dynamic and harsh industrial environment. The detailed evaluation demonstrates that the proposed approach leads to an improvement between 15% and 46% compared to normal PLM. In addition, adding map-matching leads to a 36% improvement in location estimation.
The Impact of Social Determinants on Receipt of Adjuvant Radiation Nationally Following Atypical Meningioma Surgery
Authors: Chisango Zvipo M. , Ugochukwu Obiadada , Zhi Xiaojie , Zhong Jim , Eaton Bree , Shu Hui-Kuo , Jahangiri Arman , Bray David , Hoang Kimberly 
Published: April 2024

Abstract

Purpose:
We aimed to identify socioeconomic gaps in the administration of adjuvant radiotherapy for patients with atypical meningioma and secondarily to determine differences in survival between patients receiving radiation and those not receiving radiation at 12 and 60 months.
Methods:
The National Cancer Database was queried for patients receiving atypical meningioma surgery between 2004 and 2019. Statistical analyses were performed to assess the association between receipt of adjuvant radiation and social determinants. Secondarily, KM curves were used to compare overall patient survival between those that received radiation and those that did not.
Results:
Adjuvant radiation was less likely to be administered to patients over 65 (95% CI =0.53- 22 0.77) and more likely to be administered to males (95% CI =1.07-1.38). Compared to the Southern USA, patients were more likely to receive radiotherapy in the Northeastern (95% CI 24 =1.40–2.05), Midwestern (95% CI =1.06-1.54), and Western parts of the USA (95% 25 CI =1.31-2.00). Patients residing furthest from their facility were less likely to receive radiation (95% CI =0.65-0.98). Insured patients were more likely to receive radiation (p = 0.048) than uninsured patients. On multivariate analysis, no differences were found between racial groups regarding adjuvant radiation. For patients unstratified, radiation was shown to improve survival at 12 and 60 months.
Conclusion:
Disparities exist in the administration of adjuvant radiotherapy for atypical meningioma. Patients over 65, women, those residing in the southern USA, those living further from their facilities and uninsured patients are less likely to receive radiation than their counterparts.