bluuebunny/crossref_metadata_2025 huggingface dataset:
Bibliometric analysis of global research trends and prospects on circulating tumor DNA in colorectal cancer
Authors: Pang Jing , Bai Pengyu , Zhang Yong , Wang Lichun
Published: November 2024

Background

Colorectal cancer (CRC) is one of the most commonly diagnosed advanced-stage malignancies worldwide and places a substantial burden on both the economic and social development of numerous countries.

Objective

This manuscript aims to synthesize the existing evidence and explore potential avenues for future scholarly research on ctDNA in CRC.

Methods

Bibliometric analyses were performed using the bibliometrix package in R, along with CiteSpace and VOSviewer software. The search was restricted to publications up to 31 March 2024, using the following terms: (“ctDNA” OR “circulating tumor DNA”) AND (“colorectal cancer” OR “colorectal tumor”) from the Web of Science Core Collection (WoSCC) database.

Results

Ultimately, we identified 1,310 documents published in 353 journals authored by 7,683 researchers from 2,417 institutions across 66 countries. The USA was the most productive country. The Journal of Clinical Oncology was the most prolific, publishing 111 articles with 3,396 citations. The top five keywords were “colorectal cancer,” “circulating tumor DNA,” “acquired resistance,” “cell-free DNA,” and “plasma.” The top five cluster labels for references were “advanced cancer,” “metastatic colorectal cancer,” “liquid biopsy,” “colorectal cancer,” and “human colorectal cancer xenograft.”

Conclusions

The collaborative networks are primarily composed of highly productive authors, prestigious institutions, and leading countries. Additionally, the advancement of detection technologies, the development of standardized protocols, the exploration of circulating tumor DNA (ctDNA) dynamics in CRC, and the implementation of large-scale clinical trials for ctDNA-guided precision therapy in CRC are expected to become major research priorities in the future.
Advancing EGFR mutation subtypes prediction in NSCLC by combining 3D pretrained ConvNeXt, radiomics, and clinical features
Authors: Hao Peng , Yu Yinghong , Huang Chan-Tao , Zhou Fang , Xu Yi-Kai , Yang Jiancheng , Xu Jun
Published: November 2024

Purpose

The aim of this study was to develop a novel approach for predicting the expression status of Epidermal Growth Factor Receptor (EGFR) and its subtypes in patients with Non-Small Cell Lung Cancer (NSCLC) using a Three-Dimensional Convolutional Neural Network (3D-CNN) ConvNeXt, radiomics features and clinical features.

Materials and methods

A total of 732 NSCLC patients with available CT imaging and EGFR expression data were included in this retrospective study. The region of interest (ROI) was manually segmented, and clinicopathological features were collected. Radiomic and deep learning features were extracted. The instances were randomly divided into training, validation, and test sets. Feature selection was performed, and XGBoost was used to create solo models and combined models to predict the presence of EGFR and subtypes mutations. The effectiveness of the models was assessed using ROC and PRC curves.

Results

We established the following models: ModelCNN, Modelradiomic, Modelclinical, ModelCNN+radiomic, ModelCNN+clinical, Modelradiomic+clinical, and ModelCNN+radiomic+clinical, which were based on deep learning features, radiomic features, clinical data and combinations of these, respectively. In predicting EGFR mutations, ModelCNN+radiomic+clinical demonstrated superior performance compared to other prediction models, achieving an AUC of 0.801. For distinguishing between EGFR subtypes ex19del and L858R, ModelCNN+radiomic reached the highest AUC value of 0.775.

Conclusions

Both deep learning models and radiomic signature-based models offer reasonably accurate non-invasive predictions of EGFR status and its subtypes. Fusion models hold the potential to enhance noninvasive methods for predicting EGFR mutations and subtypes, presenting a more reliable prediction approach.
Population pharmacokinetic study of the effect of polymorphisms in the ABCB1 and CES1 genes on the pharmacokinetics of dabigatran
Authors: Yang Zhuan , Tan Wen Rui , Li Qin , Wang Ying , Liu Shijing , Chen Lu , Zhou Yan , Zeng Chen , Zeng Yan , Xiong Yun , Zhang Qian , Li Na , Du Peng , Liu Lin , Chen Jiyu , He Yan
Published: November 2024

Purpose

The impact of genetic polymorphisms in the ABCB1 and CES1 genes on dabigatran plasma concentrations remains a subject of debate, and the purpose of this study was to quantitatively assess the effects of genetic polymorphisms on dabigatran esters in healthy Chinese subjects employed a population pharmacokinetic (PopPK) approach.

Methods

In total, 1,926 pharmacokinetic (PK) samples from 123 healthy individuals who were given 150 mg of dabigatran orally during a fasting state or postprandially were analyzed using the PopPK model. A two-compartment model with first-order absorption was found to adequately describe the PK data.

Results

The results showed that covariates food intake and ABCB1 SNP rs4148738 were shown to have statistically significant impacts. Specifically, in postprandial administration increased lag time (ALAG) and clearance (CL) by 2.65% and 0.51%, respectively, and decreased absorption rate constant (KA) by 0.24%. Additionally, in subjects with CT genotype ABCB1 (rs4148738), the central ventricular volume of distribution (V2) was increased by 0.38%.

Conclusion

In summary, the PopPK model developed in this study was robust and effectively characterized the pharmacokinetics of dabigatran in healthy Chinese adults, demonstrating that both food and ABCB1 genetic variation significantly influence the absorption and plasma concentration levels of dabigatran.
Effects of school-based high-intensity interval training on health-related fitness in adolescents
Authors: Jovanović Rade , Živković Mladen , Stanković Mima , Zoretić Dajana , Trajković Nebojša
Published: November 2024

Background

High-intensity interval training (HIIT) in school settings has been much less studied in adolescents. The aim of this study was to examine the impact of HIIT on health-related fitness in adolescents.

Methods

The total sample consisted of 60 adolescents (age 16.33 ± 0.62 years) from secondary Grammar school, randomly divided into two groups: the experimental (EG) (30) and the control group (CG) (30). The experimental program (12-weeks; 2 times per week) involved two Tabata sessions during one physical education class lasting 4 min each. Participants were tested for health-related fitness components-cardiorespiratory fitness (The Shuttle Run Test (SRT) and strength, hand grip test, standing long jump (SLJ) and counter movement jump (CMJ).

Results

Both the EG and the CG experienced significant positive changes in SRT (meters) and VO2max values compared to baseline value (p < 0.05), however, the increase in the EG was significantly higher than that in the CG (SRT - ηp2 = 0.111; VO2max - ηp2 = 0.111, p < 0.01). The EG showed significant improvement in SRT (meters) and VO2max values compared to the CG (p < 0.01). Regarding the hand grip test results, a significant time × group interaction was found only for right hand (p < 0.01). Moreover, the improvements in SLJ and CMJ values was greater in EG than that in the CG group (SLJ- ηp2 = 0.182; CMJ- ηp2 = 0.112, p < 0.01).

Conclusion

Findings indicate that HIIT implemented into physical education classes can result in significant improvements in selected health related fitness components in adolescents.
Antibacterial activity of the biogenic volatile organic compounds from three species of bamboo
Authors: Duan Yifan , Lv Bingyang , Zhang Chunlong , Shi Lisha , Li Jingting , Liu Yanjun , Chen Qibing
Published: November 2024

Plant biogenic volatile organic compounds (BVOCs) possess ecological functions in antimicrobial benefits and air purification. The objectives of the study were to determine the differences in antimicrobial capacity of bamboo forests at different sampling sites. Three common bamboo species—Phyllostachys edulis, Bambusa emeiensis, and Phyllostachys violascens—were selected to determinate the antimicrobial activity of bamboo forests as well as under ex vivo conditions. Natural sedimentation method was used to determine the microbe counts in bamboo forests, and the microbe counts in grassland in the same area was measured as control treatment. The results showed that except for the P. violascens in Ya’an, the airborne microbial content of the sampling sites in bamboo forests was significantly lower relative to that of grassland in the same area, and inhibition rate reached 74.14% in the P. violascens forest in Dujiangyan. P. edulis forest and P. violascens forest in Ya'an had significantly lower inhibition rates than the other sampling sites, and there was no significant difference in the inhibition rates among the rest of the bamboo forest. The bacterial inhibition rate of bamboo leaves under ex vivo conditions varied with bamboo species and bacterial strains, with higher antibacterial activity against Gram-negative bacteria overall. Escherichia coli was sensitive to B. emeiensis leaves, while Staphylococcus aureus and Bacillus subtilis were sensitive to P. violascens leaves. Moreover, Candida albicans, S. cremoris, and Shigella Castellani were sensitive to P. edulis leaves. An analysis of the BVOCs composition from P. edulis collected in Changning by SPME-GC/MS revealed that the relative content of ocimene was obviously higher than other components. This study showed that P. edulis BVOCs have strong inhibitory ability to the tested microorganisms, and its main constituent, ocimene, has health benefit. P. edulis has the potential to become a forest recreation bamboo species.
Exploring plant-microbe interactions in adapting to abiotic stress under climate change: a review
Authors: Muhammad Ali , Kong Xiangjun , Zheng Shuaichao , Bai Na , Li Lijie , Khan Muhammad Hafeez Ullah , Fiaz Sajid , Zhang Zhiyong
Published: November 2024

Climatic change and extreme weather events have become a major threat to global agricultural productivity. Plants coexist with microorganisms, which play a significant role in influencing their growth and functional traits. The rhizosphere serves as an ecological niche encompassing plant roots and is a chemically complex environment that supports the growth and development of diverse plant-interactive microbes. Although plant-microbe interactions have been extensively investigated however, limited exploration have been made how abiotic stresses affect the structure and assembly of microbial communities in the rhizosphere. This review highlights climate change influence on plant growth, functional traits, and microbial communities. It explores plant mechanisms for mitigating abiotic stress, such as removing reactive oxygen species (ROS), regulating antioxidant activity and indole-3-acetic acid (IAA) production, and controlling growth-inhibitory ethylene levels through colonization by bacteria producing ACC deaminase. Additionally, we elaborated the systematic communicatory network steered by hormonal crosstalk and root exudation, which can modulate and initiate the dialogues between plants and surrounding microbes. This network ultimately promotes the chemotactic movement of microbes towards the rhizosphere, facilitating their early colonization. Finally, we reviewed the recent advancements for understanding how plant-microbe interactions foster resilience under climate stress.
The relationship between neuroticism as a personality trait and mindfulness skills: a scoping review
Authors: Angarita-Osorio Natalia , Escorihuela Rosa M. , Cañete Toni
Published: November 2024

Introduction

In recent decades, researchers have assessed the relationship between mindfulness and personality traits, including neuroticism, a known target in mental health associated with the development of mental health disorders and physical illnesses. The main aim of mindfulness practice is to help individuals develop the ability to regulate and accept their experiences, emotions, and thoughts. Therefore, it could be suggested that mindfulness may be useful in reducing the expression and negative experience of neuroticism. The aim of our review was to assess the relationship between neuroticism and mindfulness.

Methods

We conducted a scoping review of the literature in December 2023, using the databases PubMed and PsycINFO.

Results

Forty-nine studies were included in the review, with four common themes identified: (i) mental health, (ii) cognitive outcomes, (iii) physiological symptoms, and (iv) mindfulness-based interventions. Across most of the studies, mindfulness negatively correlated with neuroticism, supporting the idea that mindfulness may be useful in reducing neuroticism and its negative effects on mental and physical health.

Discussion

While several limitations were identified, the overall results are promising. Future research in this area should focus on overcoming the current limitations to provide a better understanding of the relationship between mindfulness and neuroticism.
Association between omega-3 index and depersonalization among healthcare workers in a university hospital: a cross-sectional study
Authors: Antao Helena Sofia , Sacadura-Leite Ema , Aguiar Pedro , Gois Carlos , Marques Jessica , Pombo Samuel , Figueira Maria Luisa
Published: November 2024

Introduction

Burnout harms workers physical and mental health due to induced brain changes, autonomous nervous system and hypothalamus-pituitary-adrenal axis excessive activation. Although several correlations and risk factors have been identified, the research around burnout biological correlates remains underdeveloped. The omega-3 index has been proposed in mental health as a contributor to identify high risk patients and monitor disease advancements but the evidence on its relationship with burnout is limited. This study is meant to test the hypothesis that the omega-3 index is inversely associated with burnout levels and to discuss its potential as a biological correlate of burnout.

Methods

It had an observational, cross-sectional design and was carried out at a university hospital center between March 2021 and July 2023. We invited 319 healthcare professionals (doctors and nurses) at the occupational health and emergency departments. The omega- 3 index was determined through a prick finger test. Emotional exhaustion, depersonalization and personal accomplishment were measured by the Maslach Burnout Inventory. Descriptive analyses were conducted to examine the participants’ characteristics and outcome variables. Means, medians, interquartile ranges and standard deviations were calculated for continuous variables. Frequencies and percentages were obtained for categorical variables. We have used the individual dimensions’ scores as continuous data in the evaluation of their relationship with the omega-3 index. The relationship between burnout levels and the omega-3 index was assessed through linear regression analysis.

Results

We surveyed 300 subjects (94% response rate). High emotional exhaustion and depersonalization were reported by 29.9% and 22.1% of participants, respectively; 26.0% reported low personal accomplishment. The mean omega-3 index was 5.75%. The depersonalization score was, on average, 11.132 points higher (95% CI [4.661; 17.603]) in individuals whose omega-3 index was lower than 4%.

Discussion

An omega-3 index below 4% seems to potentially be a biological correlate of depersonalization. Our results contribute to enlarging the knowledge about burnout biological correlates, an area that has been previously signalled as underdeveloped. Omega-3 index should be included in prospective studies that will investigate the evolution of other burnout biological correlates as the syndrome emerges and progresses in subjects at risk.
The staff perspectives of facilitators and barriers to implementing virtual reality for people living with dementia in long-term care
Authors: Wong Joey , Wong Karen Lok Yi , Kan Winnie , Wu Catherine , Upreti Mona , Van Mary , Temirova Alisha , Alfares Hadil , Wen Kayla , Sharma Vaishali , Wallsworth Christine , Mann Jim , Wong Lily , Hung Lillian
Published: November 2024

Introduction

One emerging technology in long-term care (LTC) is virtual reality (VR), an innovative tool that uses head-mounted devices to provide the viewer with an immersive experience. It has been shown that VR has a positive impact on the well-being of residents living with dementia, and staff are essential in the implementation and sustainable use of technology. Currently, there is a lack of inclusion and focus on direct staff perspectives on VR implementation in LTC. This paper aims to report staff perspectives on VR adoption in a 2-year study on a virtual reality program at three Canadian LTC homes.

Methods

Our interdisciplinary team (clinicians, people living with dementia and family partners, trainees, and researchers) explored the facilitators and barriers to implementing VR in LTC, guided by the Consolidated Framework for Implementation Research (CFIR) and intersectionality supplemented CFIR. Twenty-one participants were recruited, including recreation staff, care aides, nurses, screeners, and leadership team members. The team collected data through staff interviews, focus groups, and ethnographic observation field notes. Reflexive thematic analysis was performed to identify themes reporting the facilitators and barriers for VR implementation in LTC from staff perspectives.

Results

The data analysis resulted in three facilitators and four barriers. Facilitators are (1) perceived VR benefits, (2) integrate VR into workflow and routines, and (3) partner with skillful VR champions. Barriers include (1) staff concerns about VR use, (2) financial burden and competing priorities, (3) lack of infrastructure and physical spaces, and (4) staff workload and limited leadership support.

Discussion

This study contributes to the field with staff perspectives on facilitators and barriers to VR implementation. It underscores the rarely discussed aspects of VR implementation, such as funding prioritization and implementation timing. We offer practical strategies to inform future practices and research. Future studies should further explore long-term VR implementation, the involvement of family members as VR facilitators, and the use of VR in LTC.
Late onset pancytopenia post chimeric antigen receptor T cell therapy triggered by a COVID-19 vaccination: a case report
Authors: Maciocia Nicola , Spencer Dean , Maciocia Paul , Childerhouse Anna , O’Reilly Maeve , Payne Elspeth , Roddie Claire
Published: November 2024

Cytopenia is a common side effect after chimeric antigen receptor T cell (CAR-T) therapy but is unusual at late timepoints when it is most commonly due to bone marrow (BM) failure, myelodysplasia (MDS) or other secondary malignancies. In this case report, we describe a patient who developed severe aplastic anaemia 18 months after CD19 CAR-T therapy, and shortly after COVID-19 vaccination. This is the first description of severe aplastic anaemia triggered by COVID-19 vaccination as a cause of post-CAR-T cytopenia. The patient described was treated with a combination of eltrombopag and ciclosporin due to frailty and had a rapid and complete clinical response to this therapy, providing an effective option for the treatment of immune-related cytopenia post-CAR-T in an increasingly elderly and frail population.
Assessing the impact of human activities on the health condition of the Lukosi River catchment using selected physico-chemical parameters as indicators of water quality
Authors: Nyagongo Ahmad A. , Ndibalema Vedasto G. , Lalika Makarius C. S.
Published: November 2024

The Lukosi River catchment plays a crucial role in the economic and ecological framework of the country, serving as a major contributor to the Great Ruaha River (GRR). This study aimed to assess the impact of human activities on the health of the Lukosi River by analyzing key water parameters. Three sampling sites across the catchment were selected, and water samples were collected and analyzed using the American Public Health Association technique. A one-sample t-test was used to measure the significance of the difference between the measured water quality parameters and the water quality criteria established by the Tanzania Bureau of Standards (TBS) and the World Health Organization (WHO). The results revealed mean values for various parameters: pH (8.67 ± 0.57), temperature (24.803 ± 1.361°C), EC (93.30 ± 3.34 mgL−1), BOD (14.85 ± 1.49 mgL−1), TDS (46.95 ± 1.55 mgL−1), total hardness (27.53 ± 1.28 mgL−1), DO (3.4 ± 0.53 mgL−1), turbidity (109.83 ± 40.99 mgL−1), NO2 (79.04 ± 0.73 mgL−1), Cu (6.07 ± 0.60 mgL−1), Ca (7.88 ± 0.36 mgL−1), Mn (0.16 ± 0.07 mgL−1), Cl (7.58 ± 3.97 mgL−1), F (0.12 ± 0.0008 mgL−1), SO4 (20.42 ± 4.12 mgL−1), Mg (1.92 ± 0.13 mgL−1), Zn (0.14 ± 0.11 mgL−1), Fe (3.56 ± 0.15 mgL−1), and NH3 (4.67 ± 0.51 mgL−1). Parameters such as DO, turbidity, NH3, NO3, and BOD significantly (p = 0.05) exceeded the permissible limits set by the TBS and the WHO. Furthermore, elements including Fe and Cu exceeded allowable limits. Moreover, the WQI was used to indicate that the water in the catchment is unsuitable for sustaining aquatic organisms and is unfit for domestic use. Household questionnaires and direct observations identified key human activities in the catchment: gardening (78.3%), agriculture (68%), livestock keeping (50%), sand mining (21%), and brick-making (21%). Less prevalent were car washing (7%) and fishing (4%). These findings underscore the urgent need for conservation efforts and sustainable management practices to safeguard the Lukosi River catchment (LRC).
Effects of early marriage among women married before reaching 18 years old (qualitative study approach)
Authors: Gelchu Adola Shiferaw , Wirtu Dessalegn
Published: November 2024

Background

The consequences of early marriage have become a global concern for young women. The detrimental effects of early marriage disproportionately affect girls. Regarding its effects in poor nations like Ethiopia, there is a dearth of data. As a result, this study offers baseline, first-hand, and updated information for both the nation and the study area.

Objective

The purpose of this study was to investigate the effects of early marriage on Guji women who were married before the age of 18.

Methods

This qualitative study used a phenomenological approach and was carried out from January to April 2024. An in-depth interview technique was employed to collect the data. The interviews were conducted in the Oromo language. A purposive sampling technique was applied to choose the study participants. A total of 25 women who met the eligibility criteria were selected. A thematic data analysis approach was employed to analyze the data in this study.

Results

Three themes, 10 subthemes, and more than hundreds of codes were extracted after the interview data were analyzed. The main themes and subthemes of this study were as follows: 1-Precipitators of early marriage (abduction and arranged marriage); 2-Consequences of early marriage (emotional damage, moral damage, early marriage is a risk factor for maternal and child health, early marriage is a major cause for girls to drop out of school, poverty, gender-based violence and divorce, lack of essential life skills); and 3-Attitudes toward early marriage (early marriage is a harmful cultural practice; girls should be protected).

Conclusion

The study’s findings illustrated the multidimensional effect of early marriage, necessitating the involvement of numerous sectors and stakeholders. The community, religious leaders, the health, education, and legal sectors, as well as the parents of young girls, should all take an active role in delaying early marriage. This indicates the development of policies that permit cooperation amongst all relevant parties. Creating awareness among girls, families, and the community through community-based education is crucial. Early married women should participate in intervention programs to provide their experience to young girls. Women who are victims of early marriage must receive full support to mitigate its negative consequences.
Ultra-relativistic electron flux enhancement under persistent high speed solar wind stream
Authors: Alves L. R. , da Silva L. A. , Deggeroni V. , Marchezi J. P. , Jauer P. R. , Silva G. B. D. , Sibeck D. G.
Published: November 2024

The physical mechanisms usually applied to explain the relativistic electron enhancement have been delved into to elucidate non-adiabatic electron acceleration resulting in the ultra-relativistic electron population observed in the outer radiation belt. We considered multisatellite observations of the solar wind parameters, magnetospheric waves, and particle flux to report an unusual local acceleration of ultra-relativistic electrons under a prolonged high-speed solar wind stream (HSS). A corotating interaction region reaches the Earth’s bowshock on August 3, 2016, causing a minor geomagnetic storm. Following this, the magnetosphere was driven for 72 h by a long-term HSS propagating at 600 km/s. During this period, the magnetosphere sustained both ultra-low frequency (ULF) and very-low frequency (VLF) waves in the outer radiation belt region. Besides the waves, the relativistic and ultra-relativistic electron fluxes were enhanced with different time lags regarding the magnetic storm main phase. The efficiency of wave-particle interaction in enhancing ultrarelativistic electrons is evaluated by the diffusion coefficient rates, considering both ULF and VLF waves together with phase space density analyses. Results show that local acceleration by whistler mode chorus waves can occur in a time scale of 2–4 h, whereas ULF waves take around 10’s of hours and magnetosonic waves take a time scale of days. This result is confirmed by the phase space density analysis. Accordingly, it shows that peaks of local acceleration of 1 MeV electrons are consistent with the observation of the highest chorus wave amplitude at the same L-shell and MLT. Thus, we argue that whistler mode chorus waves interacting with relativistic electrons are the main physical mechanisms leading to ultra-relativistic electron enhancement, while ULF and fast magnetosonic waves are found as secondary physical processes. Lastly, our analysis contributes to understanding how whistler and ULF waves can contribute to ultra-relativistic electrons showing up in the inner magnetosphere under the HSS driver.
Oxidative Stress in Canine Diseases: A Comprehensive Review
Authors: Blanca Perez-Montero , María Luisa Fermín-Rodriguez , Guadalupe Miró , Fátima Cruz-Lopez
Published: November 2024

Oxidative stress (OS), defined as a disruption in redox balance favoring oxidants, has emerged as a major contributor to numerous diseases in human and veterinary medicine. While several reviews have explored the implication of OS in human pathology, an exhaustive review of the canine species is lacking. This comprehensive review aims to summarize the existing literature on the role of OS in canine diseases, highlighting its potentially detrimental effect on various organs and systems. Some inconsistencies among studies exist, likely due to varying biomarkers and sample types. However, there is substantial evidence supporting the involvement of OS in the development or progression of numerous canine disorders, such as cardiovascular, oncologic, endocrine, gastrointestinal, hematologic, renal, neurologic, infectious, and parasitic diseases, among others. Additionally, this review discusses the efficacy of antioxidant and pro-oxidant therapeutic agents for these conditions. Dietary interventions to counteract OS in dogs have gained significant attention in recent years, although further research on the topic is needed. This review aims to serve as a foundational resource for future investigations in this promising field.
LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches
Authors: Lin Liangliang , Wu Junjie , An Ran , Ma Song , Zhao Kun , Ding Han
Published: November 2024

The rise of mobile communication, low-power chips, and the Internet of Things has made smartwatches increasingly popular. Equipped with inertial measurement units (IMUs), these devices can recognize user activities through artificial intelligence (AI) analysis of sensor data. However, most existing AI-based activity recognition algorithms require significant computational power and storage, making them unsuitable for low-power devices like smartwatches. Additionally, discrepancies between training data and real-world data often hinder model generalization and performance. To address these challenges, we propose LIMUNet and its smaller variant LIMUNet-Tiny—lightweight neural networks designed for human activity recognition on smartwatches. LIMUNet utilizes depthwise separable convolutions and residual blocks to reduce computational complexity and parameter count. It also incorporates a dual attention mechanism specifically tailored to smartwatch sensor data, improving feature extraction without sacrificing efficiency. Experiments on the PAMAP2 and LIMU datasets show that the LIMUNet improves recognition accuracy by 2.9% over leading lightweight models while reducing parameters by 88.3% and computational load by 58.4%. Compared to other state-of-the-art models, LIMUNet achieves a 9.6% increase in accuracy, with a 60% reduction in parameters and a 57.8% reduction in computational cost. LIMUNet-Tiny further reduces parameters by 75% and computational load by 80%, making it even more suitable for resource-constrained devices.
DB-Net and DVR-Net: Optimized New Deep Learning Models for Efficient Cardiovascular Disease Prediction
Authors: Javed Aymin , Javaid Nadeem , Alrajeh Nabil , Aslam Muhammad
Published: November 2024

Cardiovascular Disease (CVD) is one of the main causes of death in recent years. To overcome the challenges faced during diagnosing CVD at an early stage, deep learning has been used. With advancements in technology, the clinical practice in the health care industry is likely to transform significantly. To predict CVD, we constructed two models: Dense Belief Network (DB-Net) and Deep Vanilla Recurrent Network (DVR-Net). Proximity Weighted Random Affine Shadow sampling balancing technique is used for balancing the highly imbalanced Heart Disease Health Indicator dataset. SHapley Additive exPlanations exhibits each feature’s contribution. It is used to visualize features contribution to the output of DB-Net and DVR-Net in CVD prediction. Furthermore, 10-Fold Cross Validation is performed for evaluating the proposed models performance. Cross-dataset evaluation is also conducted on proposed models to see how well our proposed models generalize on unseen data. Various evaluation measures are used for assessment of models. The proposed DB-Net outperforms all the base models by achieving an accuracy of 91%, F1-score of 91%, precision of 93%, recall of 89%, and execution time of 1883 s on 30 epochs with batch size 32. The DVR-Net beats the state-of-art models with an accuracy of 90%, F1-score of 90%, precision of 90%, recall of 90%, and execution time of 2853 s on 30 epochs with batch size 32.
Vehicle Collision Analysis of the Reinforced Concrete Barriers Installed on Bridges Using Node-Independent Model
Authors: Kim Jeong J. , Ahn Jae S.
Published: November 2024

This paper focuses on improving vehicle collision simulations using finite element analysis (FEA) to examine interactions between reinforced concrete barriers and bridge decks. Three scenarios are explored: treating the barrier as a rigid body, analyzing reinforcement with a fixed base, and including the bridge deck’s cantilever portion beneath the barrier. Except for the rigid body model, a node-independent approach models the complex interactions between reinforcement and concrete in barriers on the bridge deck. This study evaluates barrier strength, occupant risk, and post-collision vehicle safety. Strength is assessed by examining stress in reinforcement and concrete, while occupant risk is measured using Theoretical Head Impact Velocity (THIV) and Post-impact Head Deceleration (PHD). Vehicle trajectory during collisions is also analyzed for stability. The results show significant differences in stress distribution and failure patterns when the bridge deck is considered compared to scenarios without it. Occupant risk evaluations suggest more flexible responses when the bridge deck is included. However, vehicle trajectory post-collision showed no significant differences across scenarios. These findings indicate that modeling efficiency varies based on evaluation criteria, suggesting a more realistic and effective approach for assessing barriers on bridges.
Research on an Eye Control Method Based on the Fusion of Facial Expression and Gaze Intention Recognition
Authors: Sun Xiangyang , Cai Zihan
Published: November 2024

With the deep integration of psychology and artificial intelligence technology and other related technologies, eye control technology has achieved certain results at the practical application level. However, it is found that the accuracy of the current single-modal eye control technology is still not high, which is mainly caused by the inaccurate eye movement detection caused by the high randomness of eye movements in the process of human–computer interaction. Therefore, this study will propose an intent recognition method that fuses facial expressions and eye movement information and expects to complete an eye control method based on the fusion of facial expression and eye movement information based on the multimodal intent recognition dataset, including facial expressions and eye movement information constructed in this study. Based on the self-attention fusion strategy, the fused features are calculated, and the multi-layer perceptron is used to classify the fused features, so as to realize the mutual attention between different features, and improve the accuracy of intention recognition by enhancing the weight of effective features in a targeted manner. In order to solve the problem of inaccurate eye movement detection, an improved YOLOv5 model was proposed, and the accuracy of the model detection was improved by adding two strategies: a small target layer and a CA attention mechanism. At the same time, the corresponding eye movement behavior discrimination algorithm was combined for each eye movement action to realize the output of eye behavior instructions. Finally, the experimental verification of the eye–computer interaction scheme combining the intention recognition model and the eye movement detection model showed that the accuracy of the eye-controlled manipulator to perform various tasks could reach more than 95 percent based on this scheme.
Study on the Influence Characteristics of Excavation Face Instability of New Tunnels Orthogonally Crossing Existing Tunnels
Authors: Ma Shaokun , Zhou Zhang , Duan Zhibo , Huang Zhen , He Benfu , An Pengtao , Li Jinmei
Published: November 2024

This study aims to solve the problem of stability of the excavation surface of a new tunnel crossing an existing tunnel orthogonally. The relative horizontal spacing between the two tunnels is taken as an influencing factor, and transparent soil model tests are conducted and expanded with numerical simulations. Finally, the active destabilization mechanism and influence characteristics of the excavation surface of the two tunnels at different horizontal spacings, vertical spacings, and tunnel diameter ratios are obtained. The results show that when the excavated face is destabilized, the existing tunnel located in front of and above the excavation surface limits the development of the upper “silo” and the transfer of soil stress in the destabilized area, and the ultimate support pressure is reduced by 17.6% and 8.7%, respectively. This effect increases as the vertical spacing between the two tunnels decreases and the tunnel diameter ratio increases. At this point, the deformation trend of the existing tunnel cross-section is reflected as “elliptical-shaped”. This trend is more apparent when the vertical spacing between the two tunnels and the tunnel diameter ratio are decreased. The protection of the existing tunnel should be strengthened at this time.
Hospital Length-of-Stay Prediction Using Machine Learning Algorithms—A Literature Review
Authors: Almeida Guilherme , Brito Correia Fernanda , Borges Ana Rosa , Bernardino Jorge
Published: November 2024

Predicting hospital length of stay is critical for efficient hospital management, enabling proactive resource allocation, the optimization of bed availability, and optimal patient care. This paper explores the potential of machine learning algorithms to revolutionize hospital length-of-stay predictions, contributing to healthcare efficiency and patient care. The main objective is to identify the most effective machine learning algorithm for building a predictive model capable of predicting hospital length of stay. The bibliographic search of the existing literature on machine learning algorithms applied to hospital length of stay predictions highlighted the most relevant papers within this area of research. The papers were analyzed in terms of model types and metrics that contributed to the considerable impact on healthcare decision making. We also discuss the challenges and limitations of machine learning algorithms for predicting length of stay, and the importance of data quality and ethical considerations.
Elastocaloric Performance of Natural Rubber in Solid State Cooling: Evaluation of the Effect of Crosslinking Density
Authors: Bianchi Marica , Fambri Luca , Fredi Giulia , Pegoretti Alessandro , Dorigato Andrea
Published: November 2024

Elastocaloric cooling is recognized as a promising alternative to modern vapor-compression cooling systems, which often rely on environmentally hazardous refrigerants. Natural rubber (NR), a well-known renewable resource, stands out among elastomers exhibiting elastocaloric behavior due to a peculiar combination of nontoxicity, low cost, softness, long-life fatigue and high caloric power. Despite these properties, research on the refrigeration potential of NR is still in its early stages, and several aspects require attention. This work investigates, for the first time, the effect of crosslinking density on the elastocaloric properties of NR. Samples with three different crosslinking densities (2.9, 4.0 and 5.2 mol·10−4/cm3) were produced by internal compounding and hot pressing, and thermo-mechanically characterized. The assessment of the elastocaloric effect of the produced samples revealed that reducing the crosslinking degree significantly enhanced the elastocaloric properties. To compare the cooling capacity of the samples, a qualitative coefficient of performance (COPmat) was evaluated as the ratio between extracted thermal energy and deformational work per unit volume. The results highlight that the least crosslinked sample achieved the higher COPmat, equal to 2.4. These results underscore the significance of crosslinking density as one of the primary factors to be considered to enhance the refrigeration potential of NR.
A Visco-Elasto-Plastic Constitutive Law for Deformation Prediction of High Concrete Face Rockfill Dams
Authors: Raggi Francesco , Altarejos-García Luis
Published: November 2024

Deformation predictions in high Concrete Face Rockfill Dams tend to underestimate observed settlements due to scale effect and breakage phenomena that cannot be adequately captured by laboratory tests. This paper presents a Visco-Elasto-Perfectly Plastic (VEPP) model for predicting deformations in high Concrete Face Rockfill Dams (CFRDs) that addresses these challenges incorporating explicitly key rockfill parameters like grain size and post-compaction porosity, which influence both the non-linear elastic and plastic behaviors of rockfill. The VEPP model enables deformation prediction while using standard laboratory test results. The model’s effectiveness was demonstrated through its application to the 233 m high Shuibuya Dam, the tallest CFRD in the world. The VEPP model predictions closely align with observed deformations throughout the dam’s construction, impoundment, and early operational stages. By using physically meaningful parameters, the model reduces the uncertainty associated with the empirical assessment of model parameters using back-analysis from similar projects. While the VEPP model offers improved predictive accuracy, particularly during early design phases, further advancements could be achieved by refining the creep formulation and accounting for grain size evolution during construction. This approach has the potential to optimize the design and construction of future high CFRD.
Integration of Fog Computing in a Distributed Manufacturing Execution System Under the RAMI 4.0 Framework
Authors: Oñate William , Sanz Ricardo
Published: November 2024

Technological progress has driven the integration of new technologies in the field of industrial automation, but a structured framework is often lacking to efficiently guide the transition from traditional industries. This article presents the implementation of advanced technologies on FESTO’s (MPS-500) modular production system, using the reference architectural model for Industry 4.0 (RAMI 4.0) as a guide for scaling. It highlights the importance of the synergy between information technologies (ITs), which enables the development of a multi-level processing system. This system performs concurrent tasks, thus managing execution and manufacturing through an MES based on requests from the cloud. On the other hand, at a lower level, a fog computing system was integrated, which relieves the processing load by distributing processes locally. In addition, matrix mapping was performed to map the integrated technologies within the context of a reference model, allowing a clear alignment between the different levels of the system. The results show a significant reduction in waiting times between batches and operations, which directly improves productivity and offers greater flexibility, that is crucial for SMEs during their growth and scaling process towards Industry 4.0.
An Effective Alternative to the Open Trench Method for Mitigating Ground-Borne Environmental Body Waves: Corrugated Cardboard Boxes Reinforced with Balsa Wood
Authors: Subaşı Ayşenur , Çelebi Erkan , Navdar Muhammet Burhan , Kırtel Osman , İstegün Berna
Published: November 2024

This research develops and evaluates a recyclable corrugated cardboard vibration isolation box reinforced with balsa wood as an alternative to traditional open trench methods for mitigating ground-borne environmental body waves. This study includes designing and testing scaled prototypes, laboratory analyses, prototype fabrication, and full-scale field experiments. In soft ground conditions, ensuring slope stability during deep excavations is a key engineering challenge for open trenches. For this purpose, scaled prototypes were subjected to laboratory tests to assess the resistance of the wave barrier’s wall surface. Numerical analyses were also conducted to evaluate the strength of the internal lattice structure under various loads. A prototype was fabricated for on-site experiments simulating real-world conditions. Field experiments evaluated the vibration isolation performance of the proposed barrier. Accelerometer sensors were strategically placed to gather data, analyzing ground surface vibrations for free field motions to assess the vibration shielding efficiency of both the open trench method and the corrugated vibration isolation box, with and without Styrofoam infill. This study concludes that the recyclable corrugated vibration isolation box is a viable alternative, offering comparable or improved vibration isolation efficiency in soft soil conditions while promoting environmental sustainability using recyclable materials.
Prediction of Breakdown Pressure Using a Multi-Layer Neural Network Based on Supercritical CO2 Fracturing Data
Authors: Zhang Xiufeng , Zhang Min , Liu Shuyuan , Liu Heyang
Published: November 2024

Hydraulic fracturing is a widely employed technique for stimulating unconventional shale gas reservoirs. Supercritical CO2 (SC-CO2) has emerged as a promising fracturing fluid due to its unique physicochemical properties. Existing theoretical models for calculating breakdown pressure often fail to accurately predict the outcomes of SC-CO2 fracturing due to the complex, nonlinear interactions among multiple influencing factors. In this study, we conducted fracturing experiments considering parameters such as fluid type, flow rate, temperature, and confining pressure. A fully connected neural network was then employed to predict breakdown pressure, integrating both our experimental data and published datasets. This approach facilitated the identification of key influencing factors and allowed us to quantify their relative importance. The results demonstrate that SC-CO2 significantly reduces breakdown pressure compared to traditional water-based fluids. Additionally, breakdown pressure increases with higher confining pressures and elevated flow rates, while it decreases with increasing temperatures. The multi-layer neural network achieved high predictive accuracy, with R, RMSE, and MAE values of 0.9482 (0.9123), 3.424 (4.421), and 2.283 (3.188) for training (testing) sets, respectively. Sensitivity analysis identified fracturing fluid type and tensile strength as the most influential factors, contributing 28.31% and 21.39%, respectively, followed by flow rate at 12.34%. Our findings provide valuable insights into the optimization of fracturing parameters, offering a promising approach to better predict breakdown pressure in SC-CO2 fracturing operations.
Novel PP2A-Activating Compounds in Neuroblastoma
Authors: Nazam Nazia , Bownes Laura V. , Julson Janet R. , Quinn Colin H. , Erwin Michael H. , Marayati Raoud , Markert Hooper R. , Shirley Sorina , Stewart Jerry E. , Yoon Karina J. , Aye Jamie , Ohlmeyer Michael , Beierle Elizabeth A.
Published: November 2024

Background: Neuroblastoma (NB) remains one of the deadliest pediatric solid tumors. Recent advancements aimed at improving outcomes have been insufficient, and patients with high-risk NB continue to have a poor prognosis. Protein phosphatase 2A (PP2A) is a tumor suppressor protein downregulated in many cancers, including NB. PP2A activation has been shown to affect the malignant phenotype in other solid tumors. The present studies aim to investigate the effects of two novel PP2A activators as a NB therapeutic. Methods: Four established NB cell lines and a patient-derived xenoline were utilized to study the effect on cell viability, proliferation, motility, and in vivo tumor growth using two novel tricyclic sulfonamide PP2A activators, ATUX-3364 and ATUX-8385. Results: ATUX-3364 and ATUX-8385 increased PP2A activity. These PP2A activators led to decreased viability, proliferation, and motility of NB cells. Treatment of animals bearing NB tumors with ATUX-3364 or ATUX-8385 resulted in decreased tumor growth in MYCN-amplified SK-N-BE(2) tumors. At the molecular level, PP2A-based reactivation led to dephosphorylation of MYCN-S62 and decreased MYCN protein expression. Conclusions: PP2A activators decreased NB cell viability, proliferation, and motility. In vivo experiments show that PP2A activators have more significant effects on tumorigenesis in MYCN-amplified tumors. Finally, phosphorylation of MYCN protein was decreased following treatment with novel sulfonamide PP2A activators. These data and mechanistic insights may be useful for developing new PP2A-based therapies that target MYCN for the treatment of NB.