bluuebunny/crossref_metadata_2025_split huggingface dataset:
CT-based Radiomics for prediction of response to neoadjuvant immunochemotherapy in patients with esophageal carcinoma
Authors: zhao peng , qiao xianhe , geng yikang , meng ruiqing , yv yaoyi , wang chenyu , li qing , xu shu , wu xiaowei 
Published: October 2024

Abstract

Objectives In order to investigate the value of radiomic features derived from enhanced computed tomography (CT) for assessment of therapeutic efficacy in patients with Esophageal squamous cell carcinoma (ESCC) underwent neoadjuvant immunochemotherapy (NICT).Methods The primary cohort of this study included 120 ESCC patients who received NICT from April 2020 to August 2023, comprising 52 patients with good responders (GR) and 68 patients with non-good responders (non-GR) after NICT, the external validation cohort included 30 patients from another hospital, comprising 14 patients with GR and 16 patients with non-GR after NICT. Features were derived from both the intra-tumoral and peri-tumoral regions of the tumor in the enhanced CT image, and the least absolute shrinkage and selection operator (LASSO) regression was used to establish predictive radiomic models (Rad-Scores) and T-stage model for predicting therapeutic response to NICT.Results The Rad-Score for predicting response to NICT generated the area under the curve (AUC) values of 0.838, 0.831, and 0.769 in the training, internal validation, and external validation cohorts, respectively. For T-stage, corresponding AUC values were 0.809, 0.800, and 0.716 in the same cohorts. Additionally, the nomogram model produced AUC values of 0.867, 0.871, and 0.818 in the training, internal validation, and external validation cohorts, respectively.Conclusions The established models demonstrate promising predictive potential for assessing the efficacy of NICT in ESCC patients, which may assist clinicians in formulating appropriate treatment strategies.Clinical relevance statement: Based on radiomic features derived from enhanced CT scans, can serve as a useful tool for predicting the efficacy of NICT in ESCC patients, particularly in identifying responders among patients who may benefit from NICT.
The long-term mortality rate in older ICU patients compared to the general population
Authors: Dannewitz Anna Aronsson , Svennblad Bodil , Michaëlsson Karl , Lipcsey Miklos , Gedeborg Rolf 
Published: October 2024

Abstract

Background
Understanding how preexisting comorbidities may interact with a critical illness is important for the assessment of long-term survival probability of older patients admitted to the ICU.
Material and methods
The mortality after a first ICU admission in patients ≥55 years old registered in the Swedish Intensive Care Registry was compared to age- and sex-matched individuals from the general population with a landmark after one year. The comparison was adjusted for age, sex, and baseline comorbidity using Cox regression.
Results
The 7-year study period included 140 008 patients, of whom 23% were 80 years or older. Patients surviving the first year remained at an increased risk compared to the general population, but much of this difference was attenuated after adjustment for baseline comorbidity (HR, 1.03; 95% CI, 1.02 to 1.04). Excluding cardio-thoracic ICU admissions, the increased risk remained slightly elevated (adjusted HR, 1.15; 95% CI, 1.13 to 1.16). Also, the subgroup ≥75 years old surviving the first year returned to a mortality rate comparable to the general population (HR, 0.98; 95% CI, 0.96 to 0.99). Stratified by admission diagnosis an increased mortality rate remained beyond the first year for acute-on-chronic respiratory failure (adjusted HR, 1.47; 95% CI 1.36 to 1.58) but not for other respiratory causes (adjusted HR, 1.03; 95% CI 0.99 to 1.07) or admission for septic shock (adjusted HR, 1.04; 95% CI 0.95 to 1.13). No substantial increased mortality rate was notable beyond the first year for other admission diagnoses.
Conclusion
Older ICU patients that survive the first year after an ICU admission return to a mortality rate close to that of the general population having similar baseline comorbidity, but variability is seen depending on the ICU admission diagnosis.
ClinicalTrials.gov ID:
NCT 06234709, date 02/01/2024
Designing and implementation of a mobile application for teaching population oral health needs assessment for dental students
Authors: Ghasemi Hadi , Habibi Amin , Ahmady Soleiman 
Published: October 2024

Abstract

Objective: This study investigates the effectiveness of a newly developed smartphone-based application for teaching population oral health needs assessment to undergraduate dental students.
Methods: Target population in this study consisted of all students of Shahid Beheshti School of dentistry in the 7th and 8th semesters in the year 2023. The intervention group (7th semester) received teaching about population oral health needs assessment based on the book “Oral health surveys; basic methods”, by means of an application, while the control group (8th semester) received the same content through self-learning activity. A questionnaire inquiring about the students’ general aspects of smartphone usage, attitude towards learning based on mobile devices (m-learning), and level of knowledge about the content of the book was used for data collection.
Results: Most dental students used smartphones extensively and expressed positive attitudes toward mobile learning with no significant statistical difference between intervention and control groups. However, both groups demonstrated limited knowledge gain from the book content, with the mobile application showing no superiority to self-learning education.
Conclusion: The present study did not demonstrate superior effectiveness of a smartphone app compared to self-learning, but the students' significant mobile usage and positive attitude towards m-learning suggest potential for further investigation in dental education.
Radiation and photothermal coefficient as major determinants of grain yield and oil content in sunflower under different sowing dates
Authors: Vercellino Roman , Fanna Ignacio , Mortarini Mauro , Polo Alejandro Pérez , Ureta Soledad , Fernández Paola , Pandolfo Claudio , Presotto Alejandro 
Published: October 2024

Abstract

Environmental conditions throughout the sunflower life cycle affect its phenological and physiological aspects, influencing growth and development, and ultimately, grain yield and oil content. While numerous studies have clarified the effects of specific environmental factors on sunflower oil yield and its components, a comprehensive understanding of how combined environmental conditions influence sunflower productivity requires research conducted under real production conditions. Given that different sowing dates expose the crop to varying environmental conditions, this study investigated the effects of three contrasting sowing dates and the underlying environmental conditions on sunflower grain yield, oil content, and oil yield. A broad set of commercial hybrids was evaluated using a randomized complete block design with four replicates across five environments (year or geographic location) in central Argentina. The sowing date was the major source of variation in grain yield, oil content, and oil yield, accounting for 57.2%, 62.9% and 62.6% of the total variation, respectively. Sunflowers sown in early spring exhibited significantly higher grain yield and oil content compared to those sown in late spring or early summer, which resulted in 22.3% and 39.1% lower grain yield and 2.6% and 11.3% lower oil content, respectively. Solar radiation and the photothermal coefficient during grain filling were the major environmental factors influencing sunflower parameters. Surprisingly, genotype and its interactions accounted for less than 2.7% of the variation in sunflower oil yield. This study enhances our understanding of how integrated environmental factors impact sunflower grain yield, oil content, and total oil yield.
Impacts of Medications on Microbiome-mediated Protection against Enteric Pathogens
Authors: Goodman Andrew , Kumar Aman , Sun Ruizheng , Habib Bettina , Barry Natasha , Ivanov Ivaylo , Tamblyn Robyn 
Published: October 2024

Abstract

The majority of people in the U.S. manage health through at least one prescription drug. Drugs classified as non-antibiotics can adversely affect the gut microbiome and disrupt intestinal homeostasis. Here, we identified medications associated with an increased risk of GI infections across a population cohort of more than 1 million individuals monitored over 15 years. Notably, the cardiac glycoside digoxin and other drugs identified in this epidemiological study are sufficient to alter microbiome composition and risk of Salmonella enterica subsp. Typhimurium (S. Tm) infection in mice. The impact of digoxin treatment on S. Tm infection is transmissible via the microbiome, and characterization of this interaction highlights a digoxin-responsive β defensin that alters microbiome composition and consequent immune surveillance of the invading pathogen. Combining epidemiological and experimental approaches thus provides an opportunity to uncover drug-host-microbiome-pathogen interactions that increase infection risk in humans.
Occurrence of Corynespora cassiicola isolates resistant to pyribencarb on eggplant in Kochi Prefecture, Japan
Authors: Shimomoto Yoshifumi , Morizane Yuka , Okada Ryotaro , Okada Tomoyuki 
Published: October 2024

Abstract

Ninety-nine Corynespora cassiicola isolates were collected from eggplant fields in Kochi Prefecture, Japan, in 2020–2023, and their sensitivity to pyribencarb was investigated. Sequence analysis of the cytochrome b gene revealed that 68 strains harbored the G143A substitution mutation. Inoculation tests using potted eggplants showed that pyribencarb had a weaker effect on strains harboring G143A. Sensitivity to pyribencarb was assessed by culturing mycelial homogenates of isolates on PDA medium containing 10 or 100 µg/ml azoxystrobin. The proportion of resistant isolates collected in 2022 and 2023 was significantly higher than that collected in 2020 and 2021.
Security Analysis of Dependency Confusion for Third Party Components: A Risk Assessment Framework Based on Source Code Tree Matching
Authors: Ding Yanwen , Tian Weihan , Cui Baojiang 
Published: October 2024

Abstract

With the widespread use of third-party components in software development, their security has become a major challenge in the field of software security. The implantation of malicious components or the unintentional reference of security weak components by developers may lead to serious security problems.In order to effectively solve the dependency obfuscation problem, this paper proposes a detection framework (Comchecker) based on source code analysis to cope with the dependency obfuscation problem through source code analysis techniques. The framework first accurately identifies and builds the code tree of third-party components and optimizes them through static analysis of component source code. Then, it evaluates the security risk of the components by designing a comprehensive evaluation model which combines various factors such as the source, structural features, and other components. Subsequently, we conduct a series of experiments to evaluate the proposed framework and compare it with state-of-the-art component detection tools.Through extensive experiments with three comparative detection tools, our detection framework achieves 93.51% accuracy for third-party component detection, outperforming existing detection tools in terms of detection accuracy and precision, and 91.04% accuracy for obfuscation-dependent detection.Finally, Comchecker is also well extended to be applied to components used in most mainstream development languages.
Understanding Chinese Undergraduate Students’ Grit and L2 Willingness to Communicate: The Contributions of Foreign Language Enjoyment and Anxiety
Authors: Li Gengchun 
Published: October 2024

Abstract

In recent years, the willingness to communicate among L2 learners (L2WTC) across different cultural and educational contexts have attracted increasing attention from the academic community. A variety of emotional, psychological and situational factors have been scrutinized for their direct and indirect effects on WTC. However, the personal factor of grit in shaping WTC has been insufficiently investigated, and the possible mediating effects of L2 emotions (e.g. foreign language enjoyment and anxiety) have been relatively under-explored. Based on a sample of 301 Chinese undergraduate English majors, this study investigated the influence of the two dimensions of grit, i.e., perseverance of effort (PE) and consistency of interest (CI), on two types of L2 WTC, i.e., meaning-focused and form-focused L2 WTC; and explored the possible mediating effects of foreign language anxiety (FLA) and three sources of foreign language enjoyment (FLE-private, FLE-atmosphere, and FLE-teacher) in their relationship. It is found that: (1) PE significantly positively predicted meaning-focused and form-focused L2 WTC; (2) FLE-private and FLE-atmosphere fully mediated the relationship between PE and meaning-focused and form-focused L2 WTC, and that (3) FLE-atmosphere fully mediated the relationship between CI and meaning-focused and form-focused L2 WTC.
Leveraging Edit Distance to Reveal Hidden Patterns in Sequences of Sets
Authors: Piernik Maciej , Morzy Tadeusz , Susmaga Robert , Szczech Izabela 
Published: October 2024

Abstract

In this paper, we analyze the edit-distance-based approach to classification of sequences of sets.
Our goal is to push the edit distance measure to its limits to see just how weak a signal it can detect when applied to sequences of sets.
It is a thorough experimental study exploring various aspects of the measure in isolation.
To achieve this, we needed precise control over the characteristics of the experimental data.
That is why we also propose a flexible dataset generator capable of controlling the main properties of the sequences of sets model, which we make publicly available as an online tool.
To give our analysis better context, in each experiment we evaluate the edit distance approach against a standard bag of words approach.
Our study uncovers a vast range of findings (from trivial to surprising), which we thoroughly discuss in the paper and, based on them, provide general guidelines on the classification of sequences of sets.
Among others, we find that edit distance is in fact able to successfully capture all the main characteristics of sequences of sets --- even the most subtle ones!
Moreover, the proposed dataset generator proved to be a very powerful tool with much broader applications than the scope of this paper and can be used to create benchmarks for any data processing algorithms involving sequences of sets.
An Application of Process Mining in Organizational Process Redesign
Authors: Asadinia Hamid , Taghavifard Mohammad Taghi , Vanani Iman Raeesi 
Published: October 2024

Abstract

In the competitive business environment, organizations face challenges such as rapid customer needs changes and uncertainty, necessitating continuous adjustments in business processes to maintain competitive advantage. Fundamental changes can lower service costs, reduce time, and ultimately increase profitability. Organizations increasingly leverage information technology to convert traditional processes into web-based systems, enabling detailed data collection that enhances process analysis through a method known as process mining. This study focused on redesigning the logistics process at a private bank, utilizing three process mining techniques: discovery, adaptation, and improvement, with Disco 2.14 software. Data was extracted from the bank’s automation and administrative correspondence systems, allowing for a comprehensive analysis of the software outputs. The study identified process indicators, evaluated the adaptation of existing processes, and extracted values using an optimal process. Finally, the research provided suggestions to improve these processes, highlighting the potential of process mining in enhancing efficiency and effectiveness within organizational frameworks. The findings underscore the importance of adapting business processes to meet evolving market demands and leverage technology for improved operational performance.
Multi-modal Entity Alignment based on Enhanced Relationship Learning and Multi-layer Feature Fusion
Authors: Li Huayu , Wang Cuicui , Chen Xinxin , Zhang Chi 
Published: October 2024

Abstract

Entity alignment is a critical technique for integrating diverse knowledge graphs. Although existing methods have achieved impressive success in traditional entity alignment, they may struggle to handle the complexities arising from interactions and dependencies in multi-modal knowledge. In this paper, we propose a novel multi-modal entity alignment model called ERMF, which leverages distinct modal characteristics of entities to identify equivalent entities across different multi-modal knowledge graphs. Specifically, we first utilize different feature encoders to independently extract features from different modalities. Concurrently, we incorporate visual features and random sampling methods to design a vision-guided negative sample generation strategy based on contrastive learning, guiding the model to learn relationship embeddings. Subsequently, in the feature fusion stage, we propose a multi-layer feature fusion approach that incorporates multiple attention mechanisms to hierarchically process the importance weights and interactions of the modalities, thereby obtaining multi-granularity features of the modalities. Extensive experiments were conducted on two public datasets, and the results demonstrated that ERMF significantly outperforms competitive baseline models, confirming the effectiveness of the proposed model.
Multi-Scale Progressive Dehazing Network with Image Priors and Hybrid Attention Mechanism
Authors: Xing Yiming , Zhang Jindong 
Published: October 2024

Abstract

Currently, most dehazing networks achieve good performance on synthetic images but perform unsatisfactorily when processing real hazy images; moreover, these methods usually ignore the application of prior knowledge about images. To address this issue, we propose a novel Multi-Scale Progressive Dehazing Network (MPDNet). We have found that the residual channel prior of hazy images contains rich structural information; thus, we design a Prior-Guided Block (PGB) and introduce it into the dehazing network. To better apply prior knowledge at different stages, we design an Attention-guided Feature Memory module (AFM) and a Multi-Scale Dehazing Unit (MDU), thereby achieving progressive image dehazing. AFM is used to explore the correlation between the current input and historical information, transferring features across different stages. MDU leverages the impact of dilated convolutions on the receptive field and combines a hybrid attention mechanism to extract more image information for restoring haze-free images. Extensive experimental results demonstrate that our MPDNet performs excellently in handling both homogeneous and non-homogeneous hazy images, comparable to existing state-of-the-art methods.
Adaptive Intelligent Control for Nonlinear Systems Considering Butterfly-Like Hysteresis and Its Application on Dielectric Elastomer Actuated Motion System
Authors: Liu Yuehang , Zhang Xiuyu , Li Zhi , Chen Xinkai , Su Chun-Yi 
Published: October 2024

Abstract

Flexible smart material actuators, i.e., dielectric elastomer actuators(DEAs), have shown great potential applications in the fields of flexiblebionic and rehabilitation robotics due to their high response speed and highenergy conversion efficiency. However, the hysteresis nonlinearities of smartmaterial actuators severely degrade the control performance. To this end, aneural network-based adaptive pseudo-inverse control scheme is proposed for aclass of output-constrained nonlinear time-delay systems actuated by flexiblesmart material actuators. The main features are summarized as follows: 1) anew butterfly-like Krasnoselskii-Pokrovskii (BKP) kernel is derived based onthe traditional Krasnoselskii-Pokrovskii (KP) model, then, by performingweighted superposition of BKP kernel, a new BKP model is proposed to describethe butterfly-like hysteretic loop in the flexible smart material actuators;2) a novel pseudo-inverse control scheme is proposed to overcome thebutterfly-like hysteresis, which implies that the hysteresis inverse modelsare not needed, instead, a temporarily hysteretic controller in which theactual control signal is coupled is designed and a mechanism of extracting theactual control signal from the designed temporarily hysteresis controller isdeveloped, then, the butterfly-like hysteresis is effectively mitigated; 3) toour best knowledge, the output-constrained control problem of thebutterfly-like hysteresis nonlinear systems is solved for the first time bydesigning the barrier-Lyapunov function and the proposed hysteresis looppseudo-inverse control scheme; 4) a DEA-based motion control experimentalplatform is established and the performance of control is verified.
Ransomware Detection Using Convolutional Neural Networks and Isolation Forests in Network Traffic Patterns
Authors: Alzonem Frank , Albrecht Guillermo , Castellanos Dimitrios , Vandermeer Matthias , Stansfield Bernard 
Published: October 2024

Abstract

Ransomware has rapidly become one of the most disruptive cyber threats, targeting critical infrastructures and sensitive data with sophisticated encryption techniques. The proposed approach introduces a novel combination of Convolutional Neural Networks (CNN) and Isolation Forest (iForest) for detecting ransomware in network traffic patterns, providing a significant alternative to traditional signature-based and heuristic methods. The CNN component effectively captures high-level spatial relationships between packet sequences, while iForest isolates anomalous traffic patterns associated with ransomware activity. The hybrid model demonstrates robust performance in distinguishing between benign and ransomware-infected traffic flows, achieving high accuracy and minimizing false positives. Extensive experiments on publicly available datasets highlight the model’s adaptability in detecting previously unseen ransomware variants, offering scalability and computational efficiency suitable for real-time network monitoring. The findings suggest that this approach has the potential to enhance cybersecurity defenses against evolving ransomware threats.
Angiogenesis-Enhanced Biomimetic Nanofiber Dressings: VEGF-Infused Electrospun Membranes for Targeted Wound Healing and Tissue Regeneration
Authors: Lin Xunxun , Liu Haibo , Li Xixi , Ling Yu , Wei Yanlin , Huang Xiazi , Li Hongmian 
Published: October 2024

Abstract

Biomimetic dressings are widely regarded as optimal medical dressings for promoting wound healing. Researchers are endeavoring to develop a new class of dressings that can fend off bacterial infections, accelerate tissue regeneration, and perform specialized tasks to facilitate wound healing and repair. This study presents an electrospinning nanofiber membrane-building approach based on natural biopolymers and the addition of vascular endothelial growth factor (VEGF) to boost angiogenesis and promote wound healing. The composite nanofiber membrane made using the electrospinning technique can retain its physicochemical properties and biological function. It has strong biocompatibility and is appropriate for wound repair because of its improved mechanical and biomedical qualities. The designed nanofiber membrane regulates the release of highly concentrated VEGF to target the wound specifically. It facilitates the interchange of nutrients and oxygen, encourages endothelial cell migration and proliferation, and forms the vascular lumens, all of which help to speed up tissue regeneration and wound healing. As a result, VEGF@PAG nanofiber dressings have the potential to overcome the drawbacks of conventional patches and emerge as the most beneficial alternatives for wound healing and associated biological applications.