bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cytological Observation of Distant Hybridization Barrier and Preliminary Investigation of Hybrid Offspring in Tea Plants
Authors: Mo Xiaoli , Wang Yihao , Huang Yahui , Yan Changyu , Zeng Zhen 
Published: November 2024

Abstract

This study focuses on the reproductive barriers of distant hybridization in Camellia sinensis and the characteristics of its hybrid offspring. As a crop with significant economic value, the leaves of Camellia sinensis are not only widely popular for making tea but also have various health benefits. However, although wild tea germplasm resources have unique functional components, their processing quality is often not satisfactory. To improve tea plant varieties, this study employs distant hybridization techniques to break through species boundaries and increase genetic variation, with the aim of cultivating new varieties with superior quality. In this study, utilizing Camellia sinensis cv. Jinxuan as the maternal parent, C. gymnogyna Chang and C. sinensis cv. Yinghong No.9 as the paternal parents, interspecific distant hybridization (DH) and intraspecific hybridization (IH) were conducted. The study found that the pollen tubes of the distant hybridization combinations (DH) grew rapidly in the early stages after pollination, but their growth was hindered upon reaching the base of the style, making it difficult to combine with the ovules to complete fertilization. Despite the barriers, some pollen tubes successfully fertilized, and the early stages of embryo development were normal after fertilization. However, the high rate of fruit drop and low seed germination rate in the distant hybrid offspring limited the success of distant hybridization. A study of the phenotype and biochemical components of 30 offspring obtained from successful distant hybridization revealed that the offspring exhibited rich genetic diversity in both phenotype and biochemical composition. Although distant hybridization has a certain degree of affinity, the problem of fruit drop in the later stages severely affects the success of hybridization. To obtain more distant hybrid offspring, it is recommended to adopt embryo rescue techniques. This study not only provides important theoretical and practical basis for the distant hybridization of tea plants but also offers new ideas and methods for improving tea plant varieties and enhancing the economic benefits of tea production. Through in-depth research on the distant hybrid offspring, it is expected to cultivate new tea varieties with better quality and stronger adaptability.
Enhancing Security in CPS Industry 5.0 Using Lightweight MobileNetV3 with Adaptive Optimization Technique
Authors: Aleisa Mohammed A. 
Published: November 2024

Abstract

The Industrial Revolution of technologies such as the Industrial Internet of Things (IIoT), cloud and Artificial Intelligence (AI) is breaking new frontiers in industrial process automation. Under the Industry 5.0 revolution, AI based manufacturing units are more sophisticated Cyber-Physical Systems (CPS) that allow the interaction of people, objects and machines at any given supply chain level. One of the key advantages of this transformation is that it enables implementing individual-focused and adaptable manufacturing systems. However, this interconnection poses various threats, especially the phenomenon referred to as attacks that are sophisticated in nature sometimes known as distributed denial of service (Ddos) attacks. In the quest to prevent cyber security challenges in the CPS Industry 5.0, this paper proposes a simple and effective System based on Deep Learning architecture. The very first stage concerns data acquisition which entails careful monitoring and collection of raw data from inbuilt sensors for real time performance. In this case, the data is also processed further in order to clean the data, handle missing values within the data set and fix any errors present in the data set, improving it. The next step involves Normalization and feature extraction which takes on the shape of reducing the data into shapes acceptable by the key features; flow-based, time-based and statistical features as well as deep features using ResNet-101. To process the models, MobileNetV3 which are light weight models of deep learning, are predicted to be utilized in the edge devices that are low in resources through quantization and pruning methods. This quantization and pruning is going to reduce the weight of the model or data. The efficient local search method CTPOA is applied for adjustment of parameters, data optimization and performance improvement of the model. Finally, the data in the CPS is safeguarded by the use of AES encryption and Discretionary Access Control policies.
Machine Learning-Based Prediction of Soil Compaction Parameters
Authors: Senturk Muharrem Atakan , Ordu Ertugrul , Tan Rabia Korkmaz 
Published: November 2024

Abstract

This study aims to predict the soil compaction parameters maximum dry density (MDD) and optimum moisture content (OMC) using machine learning models. As a result of this aim, fast and much less costly advanced machine learning models can be used as an alternative to traditional methods such as proctor test, which are time-consuming and costly in geotechnical engineering. The study conducts a comparative analysis of various machine learning models such as decision tree, random forest, gradient boosting and group method of data handling (GMDH) to determine the most accurate and efficient model. As a result of this comparative analysis, the random forest model was the most successful model. According to the 5-fold cross-validation results of the random forest model, the average training coefficient of determination (R²) value is 0.95 and the average testing R² value is 0.71. Surely, if we had a dataset with more data, higher performance results could be achieved with existing machine learning models. The results obtained demonstrate that machine learning models increase project efficiency and reduce costs by reducing reliance on laboratory testing, and offer an approach with the potential to improve soil compaction applications in geotechnical engineering.
Factors Association with Experience of Violence Across Generation Ages: A Gender-based Analysis
Authors: Yomai Korravarn , Charupoonphol Phitaya , Taechaboonsermsak Pimsurang , Fongsatitkul Prayoon , Pitikultang Supachai , Munsawaengsub Chokchai , Sresumatc Vanvisa , Thongworn Sittichai 
Published: November 2024

Abstract

Background Violence is a critical public health issue that impacts the physical, metal, and social well-being of individuals and community, necessitating comprehensive prevention and intervention strategies. a
Methods This cross-sectional survey aims to assess the experience of violence among Thai families and to determine the factors associated with violence across difference generation. A total 6,000 subjects were randomly selected from twelves provinces across six regions of Thailand. Respondents were interviewed using a structured questionnaire administered by well-trained data collectors. Data analysis was conducted using logistic regression to determine the significant predictors of violence within families.
Finding The prevalence of violence was 11.9%, 7.8%, 7.0%, and 5.2% across Generation Z, Generation Y, Generation X, and Baby Boomers, respectively. This study examines factors influencing experiences of violence across generational cohorts. Among Generation Z, non-traditional family structures (OR = 2.30, 95% CI [1.30-4.03]), residing with substance abusers (OR = 1.64, 95% CI [1.13-2.63]), inadequate family income (OR = 1.98, 95% CI [1.26-2.89]), low family strengths (OR = 2.56, 95% CI [1.58-4.16]), and poor violence literacy (OR = 4.78, 95% CI [2.43-9.39]) were significant factors. In Generation Y, being single (OR = 2.20, 95% CI [1.53-3.15]), family income sufficiency (OR = 1.50, 95% CI [1.03-2.19]), moderate (OR = 1.57, 95% CI [1.08-2.28]) and poor family strengths (OR = 3.12, 95% CI [1.77-5.49]), and moderate (OR = 1.52, 95% CI [1.01-2.28]) and poor violence prevention literacy (OR = 3.55, 95% CI [1.53-8.22]) were influential. Generation X was notably affected by residing with substance abusers (OR = 1.46, 95% CI [1.02-2.09]), while Baby Boomers experienced impacts from poor violence prevention literacy (OR = 3.03, 95% CI [1.09-8.41]) and moderate community prevention measures (OR = 1.92, 95% CI [1.03-3.59]). Additionally, across each generation, these factors demonstrate divergent correlations with experiences of violence based on gender.
Discussion This study highlights that experiences of violence differ across generations, shaped significantly by gender norms with each era. Furthermore, that factors influencing these experiences vary distinctly among different age cohort. Each generation exhibits unique attitudes, ideologies, and lifestyles, necessitating customized approaches for violence prevention. Addressing these differences is crucial as societal transitions impact health outcomes and experiences differently across generations.
Dynamic Colormap Visualization Integratedwith Harris Hawks Optimization for EnhancedLung CT Segmentation and Diagnostic Precision
Authors: Dorgham Osama , Ryalat Mohammad , Najdawi Nijad , Alkhawaldeh Rami , AlShaqsi Jamil , Al-Betar Mohammed 
Published: November 2024

Abstract

This study presents a novel method that utilizes Harris Hawks Optimization (HHO) combined with dynamic colormap visualization to enhance the quality of lung CT scan segmentation. The Harris Hawks optimization algorithm is a swarm-based method used to enhance multi-level thresholding for image segmentation, hence facilitating the identification of regions of interest (ROIs) in medical images. An analysis of different colormap schemes, including Accent, Gray, Hot, Inferno and Jet, was conducted to improve the visualization of segmented images.The experimental results show the efficiency of the HHO algorithm from the segmentationaccuracy perspective as compared to the conventional optimization techniques using publicly available datasets from the Cancer Imaging Archive. In particular, the average SSIM was above 98% while the Jaccard Index was more than 90%. The expert evaluation confirms earlier findings that using the HHO algorithm with the Inferno colormap, particularly with four or five thresholds, achieves optimal image clarity and diagnostic value for clinical purposes. In addition, the method provides a promising way to enhance diagnostic precision and treatment strategies for lung diseases, making it highly valuable for pulmonaryhealthcare, particularly in urgent scenarios such as pandemics.
The burden of shift schedules: development and theoretical cross-validation of the Shift Load Index for optimised recovery and emergency department planning
Authors: SCHMIDT Laura , PEREZ Clément , VALERO Bérénice , SCHLATTER Sophie , FRATICELLI Laurie , TAZAROURTE Karim , MAZZA Stéphanie , DOUPLAT Marion 
Published: November 2024

Abstract

Background Healthcare workers in emergency departments work during atypical hours, which can lead to disturbances in the sleep-wake rhythm and provoke a high risk of sleepiness during stressful working hours. We developed and tested an adapted, simplified index to predict the recovery limitations exclusively relying on work schedules.Methods We conducted a retrospective observational multisite study. 140 shifts by 55 healthcare workers across two emergency departments were included. We calculated a Shift Load Index for each day, using the previous two weeks' schedules to assess the recovery limitations of different work patterns. The Shift Load Index rating contains nine items with three levels of risk: half of the maximum score 18 is the cut-off score for a heightened risk. We compared the Shift Load Index ratings with the existing biomathematical model Fatigue Audit InterDyne (FAID)), a commercial tool established in heavy industries, and with the auto-evaluation of instant sleepiness (Karolinska Sleepiness Scale).Results The average Shift Load Index amounted to 6.07 (± 3.14) and varied between 0 to 13, while 21% of shifts were rated at heightened risk and 2% at zero risk. The Shift Load Index did not differ between physicians and nurses. The Shift Load Index was a significant predictor of FAID scores (peak FAID FATIGUE, p < 0.000; peak FAID ALERTNESS, p = 0.047).Conclusions The Shift Load Index offers a promising avenue for assessing workload and recovery limitations in emergency department settings, facilitating targeted planning at both individual and team levels. Its correlation with established models encourages further validation through expanded studies.
Ketoprofen loaded β-Cyclodextrin complexation for enhancing solubility of BCS Class II Drugs.
Authors: Doshi Mitali , Kakade Prajakta , Deshmukh Tejeswini , Gandhi Ravikiran , Mali Kiran 
Published: November 2024

Abstract

Ketoprofen is the most widely used non-steroidal anti-inflammatory drug (NSAID) which comes under BCS Class II, making it effective for relieving pain and inflammation. However, despite its therapeutic benefits, Ketoprofen has low aqueous solubility, which significantly hinders its oral bioavailability. This limitation highlights the need for innovative strategies to improve its absorption in the body. One promising approach involves forming cyclodextrin inclusion complexes, which act as a specialized delivery system for drugs with poor solubility. In our study, we focused on synthesizing Ketoprofen/ β- cyclodextrin inclusion complexes using microwave irradiation, with molar ratios of 1:1 and 1:2. The main goal of our research is to investigate how these β-cyclodextrin complexes affect the solubility and bioavailability of Ketoprofen. To thoroughly evaluate the effectiveness of this novel system, we will examine various parameters, including the percentage of practical yield. This will be carried out using exacting laboratory techniques such as in-vitro drug dissolution investigations, differential scanning calorimetry (DSC), and Fourier-transform infrared spectroscopy (FTIR). We will also assess drug content uniformity to ensure a comprehensive analysis of how these complexes enhance the properties of ketoprofen.
A Mixed-methods Approach to Investigating Chinese English Majors’ Writing Self- Efficacy, Writing Self-Regulated Learning Strategies and Writing Proficiency
Authors: Zhu Peiqi , Sun Xiaohui 
Published: November 2024

Abstract

Writing self-efficacy and writing self-regulated learning (SRL) strategies are pivotal factors influencing writing proficiency from the social cognitive perspective. This study used a mixed-methods design to investigate theirrelationships among English majors in the Chinese English as a Foreign Language (EFL) context. The study was conducted in two stages. In Stage One, quantitative data were collected from 73 English major sophomores at a key university in Beijing through cluster sampling. Participants completed two self-reported questionnaires assessing writing self-efficacy and writing SRL strategies, alongside a timed writing proficiency test. Stage Two involved qualitative data collection, where four students from both low and high proficiency groups were purposefully selected for think-aloud protocols and retrospective semi-structured interviews. The results revealed significant positive correlations between writing self-efficacy, writing SRL strategies and writing proficiency. Notably, there were substantial differences in writing self-efficacy and writing SRL strategy use between high and lowproficiency writers, particularly in the use of English writing self-efficacy and environmental SRL strategies. Moreover, writing self-efficacy accounted for 24% and writing SRL strategies for 9.9% of the variance in writing proficiency. The writers’ perceptions and possible mechanisms were provided as supplementary. Implications for language instructors and EFL learners are discussed in the end.
Profiling current cancer survivorship practices and enhancing survivorship care in public hospitals in Victoria, Australia
Authors: Nash Tegan , Lisy Karolina , Laing Erin , Kelly Helana , Cridland Kate , Jefford Michael 
Published: November 2024

Abstract

Purpose
Comprehensive survivorship care involves cancer surveillance, management of post-treatment effects, health promotion, and coordination between care sectors. This study aimed to understand current survivorship practices, build awareness, and support improved survivorship care in Victoria, Australia.
Methods
This project had three components: (1) a survey of 20 Victorian clinical sites, assessing elements described in the Victorian Quality Cancer Survivorship Framework; (2) educational webinars for oncology health professionals, to increase survivorship knowledge and awareness, (3) implementation of targeted survivorship care quality initiatives in a sample of health services. Survey, evaluation and outcome data were reported descriptively.
Results
All sites responded to the survey (3 hospitals have a common operating model hence supplied a single response). Most (11/18, 60%) rated their survivorship care as ‘developing’ and did not have a clear survivorship care policy (13/18, 72%). Provision of post-treatment information was inconsistent, as was assessment for needs. Most sites do not stratify survivors (< 25% of survivors received stratified care at 13/18 sites, 72%), provide survivorship care plans (< 25% survivors received SCP at 8/18 sites, 44%), and collect limited outcome data. Webinars were well received, with 98% of health professionals reporting improved knowledge and awareness. All seven sites valued targeted implementation support to improve aspects of survivorship care.
Conclusions
Current Victorian survivorship care appears suboptimal. There was good health professional engagement with educational webinars and with improvement activities.
Implications for Cancer Survivors
Results have informed survivorship improvement work, focusing on implementing policy, improving aspects of care delivery and building capability across the state.
The Role of Anesthesia in Decision-making for Robotic/Laparoscopic Urological Surgery: A Propensity Score Matching Analysis
Authors: Wei Mei-Yu , Zhang Ce , Hou Dong-Nan , Wen Li-Jie , Yang Bo 
Published: November 2024

Abstract

Background
Comparative studies between robotic and laparoscopic surgeries primarily focus on surgical aspects, with less emphasis on anesthesia. This study explores the role of the anesthesia team in making complex decisions between robotic-assisted laparoscopic surgery (RALS) and conventional laparoscopic surgery (CLS) by comparing postoperative indicators from Enhanced Recovery After Surgery (ERAS).
Methods: This retrospective analysis included patients who underwent partial nephrectomy and radical prostatectomy for prostate and renal cancer at the Urological Surgery Department of the Second Affiliated Hospital of Dalian Medical University from 2021 to 2023. Propensity score matching (PSM) with a 1:1 ratio was used, considering variables such as age, gender, BMI, operation type, and preoperative comorbidities. Primary outcomes included anesthesia, surgery, and recovery times. Secondary outcomes included resting and activity Visual Analog Scale (VAS) scores, voluntary activities, and the incidence of nausea/vomiting, dizziness, surgical site pain, respiratory depression, and anxiety on postoperative day one (POD1).
Results: A total of 252 cases were assigned to experienced RALS and CLS groups. The anesthesia and surgery times for RALS were 158.52 and 140.15 minutes, respectively, significantly longer than those for CLS (136.56 and 125.34 minutes) (P < 0.05). However, there were no differences in PACU recovery times (24.63 vs. 26.51 minutes) or rest-VAS scores (0.96 vs. 1.06) between the RALS and CLS groups. Activity-VAS scores showed a statistically significant difference between RALS (2.19) and CLS (1.86) (P < 0.05). The occurrence rates of voluntary activity, nausea/vomiting, dizziness, and anxiety were 19.5% vs. 29.1% (P < 0.01), 33% vs. 27.9% (P < 0.05), 25% vs. 18% (P < 0.05), and 12.6% vs. 16.8% (P < 0.05) for the RALS and CLS groups, respectively. No significant differences were observed in pharyngeal pain (6.3% vs. 10.1%), respiratory depression (0.5% vs. 1.7%), or oxygen requirement (5.2% vs. 7.8%) between groups.
Conclusions: From the anesthetist’s perspective, RALS extends surgery and anesthesia durations, increasing the potential risks to respiratory, circulatory, and central nervous systems during the perioperative period. RALS also increases the patient's financial burden. Therefore, it is crucial to consider the patient’s physical condition when selecting the surgical approach, alongside tumor prognosis and economic costs in clinical decision-making.
Identification and evaluation of tumor pyroptosis-associated antigens for design a vaccine candidate against lung cancer
Authors: Nguyen Truc Ly , Kim Heebal 
Published: November 2024

Abstract

Lung cancer is the leading cause of cancer-related mortality worldwide, necessitating the development of new treatment strategies. This study aims to design a novel multi-epitope vaccine targeting pyroptosis-associated proteins (CARD8, NAIP, NLRP1, and NLRP3) using immunoinformatics. T-cell and B-cell epitopes were identified from these proteins and assessed for antigenicity, non-toxicity, and immune-stimulatory potential. Fifteen epitopes with high scores were selected and combined with suitable adjuvants and linkers to form a vaccine construct. The three-dimensional structure of the vaccine was predicted, refined, and validated through molecular modeling techniques. Molecular dynamics simulations confirmed the vaccine's structural stability and flexibility under physiological conditions. Furthermore, in silico immune simulations suggested that the vaccine could elicit strong humoral and cellular immune responses, including high antibody production and robust T-helper and T-cytotoxic cell activation. These findings suggest the vaccine’s potential to generate long-term immunity. While computational predictions are promising, in vitro and in vivo validation is required to confirm the vaccine’s efficacy and safety. This study highlights the potential of targeting pyroptosis pathways in lung cancer and underscores the value of computational tools in accelerating vaccine design. If proven effective in experimental settings, this vaccine could contribute to more effective lung cancer immunotherapies, offering a new avenue for treatment in a disease with limited options.
A Novel Approach of Ransomware Detection with Dynamic Obfuscation Signature Analysis
Authors: Su Leli , Cheng Huapeng , Li Lianlian , Zhang Chicheng , Wang Yuanyuan , Zhao Jia 
Published: November 2024

Abstract

The exponential rise in ransomware attacks has intensified the demand for sophisticated detection methodologies capable of addressing complex evasion tactics. Dynamic Obfuscation Signature Analysis (DOSA) offers an adaptive, multi-layered framework designed to counter ransomware’s polymorphic transformations through a hybrid approach that combines static analysis, dynamic signature mapping, and machine learning-based adaptation. DOSA’s architecture enhances detection accuracy and operational efficiency, with a modular design that supports real-time processing across file-based, network-based, and memory-based detection layers. Experiments demonstrated that DOSA achieved high accuracy rates, maintaining detection efficacy across a diverse array of ransomware variants by continuously evolving signature profiles based on obfuscation patterns. The framework also exhibited substantial resource efficiency, making it suitable for deployment in diverse environments with varied computational constraints. By providing precise and adaptable threat detection, DOSA contributes a significant advancement to the field of ransomware resilience, offering a robust methodology for preemptive ransomware management within modern cybersecurity infrastructures.