bluuebunny/crossref_metadata_2025_split huggingface dataset:
Male Partners` Involvement in Antenatal Care and its Associated Factors: A Community-based Cross-sectional study, Westcentral Ethiopia
Authors: Gessesse Nigusu Ayalew , Gela Getahun Belay , Aweke Amlaku Mulat , Balcha Wondu Feyisa , Chekole Fentahun Alemnew , Beyene Fentahun Yenealem , Kassahun Eden Asmare , Getu Alemwork Abie , Alemayehu Bezawit Abeje , Gezahegn Tigist Wubet , Getie Simegnew Asmer , Adugna Kebede Feyisa 
Published: March 2024

Abstract

Background
Male partner involvement in antenatal care has been recognized as a key factor in reducing maternal and neonatal mortality. However, little attention has been given to male partners’ involvement in maternity care, particularly in developing countries.
Objective
This study aimed to assess male partners’ involvement in antenatal care and its associated factors among male partners whose wives gave birth within the last one-year in Bichena town, Westcentral, Ethiopia, 2019.
Methods
A community-based cross-sectional study design was conducted from March 1 to 15/2019, in Bichena town, among 406 male partners. The data was collected by a simple random sampling technique and analyzed using the Statistical Package of the Social Science 23.0 version. Bivariate and multivariable logistic regression analyses were employed to estimate the crude and adjusted odds ratio with a confidence interval of 95% and a P-value of < 0.05 considered statistically significant. Frequency tables, figures, and descriptive summaries were used to describe the study variables.
Results
In this study, 53.4% [95% CI: 48.3–58.1%] of male partners` were involved in antenatal care. The age group of 20–29 years [AOR = 2.14, 95% CI = 1.04–4.38], having primary, secondary, and diploma and above educational level [AOR = 2.04, 95% CI = 1.02–4.07], [AOR = 3.02, 95% CI = 1.49–6.11] and [AOR = 4.03, 95% CI = 2.06–7.89] respectively, ≤ 10 years marriage length [AOR = 2.92, 95% CI = 1.46–5.87], good knowledge of the services provided in antenatal care visits [AOR = 2.68, 95%, CI = 1.62–4.42], good awareness of the danger signs of pregnancy [AOR = 1.77, 95 CI = 1.10–2.85], favorable attitude towards antenatal care visit [AOR = 2.62, 95% CI = 1.64–4.19], travel less than 15 minutes to reach a nearby health facility [AOR = 3.43, 95% CI = 1.54–7.65] and making decision together to seek care in health facility [AOR = 2.44, 95% CI = 1.19–5.03] were associated with male partners involvement in antenatal care.
Conclusions
Male partners' involvement in antenatal care was encouraging in the study area compared to previous studies done in Ethiopia. We suggest strengthening awareness creation among male partners on their shared responsibility during pregnancy. Male partners should be invited to attend the antenatal care and making the antenatal care visiting times flexible is also needed.
A small neural network deployed on edge devices for detecting citrus Huanglongbing.
Authors: Tang Weijie , Shi Yu , Chen Hang , Liu Haifeng , Zhong Linyi , Chen Wenli 
Published: March 2024

Abstract

Citrus Huanglongbing (HLB) poses a significant threat to the profitability of the citrus industry worldwide. In traditional agricultural practices, manually identifying citrus trees infected with HLB based on certain leaf characteristics is time-consuming, subjective, and inefficient. The initial automatic identification of citrus Huanglongbing (HLB) relies on traditional image processing and machine learning algorithms, exhibiting low accuracy and slow processing speed. In order to enhance both the detection accuracy and speed, researchers have introduced deep learning methods based on neural networks for the identification of citrus HLB. However, the neural network models currently used for citrus leaf HLB identification have large parameter sizes, high deployment costs, and require high computational power, making them unsuitable for deployment on edge devices for field detection. Therefore, in order to promptly detect and address diseased plants, improve farmers' agricultural operational efficiency, ensure the accessibility of deep learning in small-scale agriculture, and address the need for cost-effective measures, there is an urgent need for a low-cost deep learning framework. Therefore, we compared the performance of several commonly used deep convolutional neural networks in industry for citrus Huanglongbing (HLB) detection. We constructed image classification networks based on AlexNet, ResNet, MobileNet-V1, and MobileNet-V3, and evaluated the network models based on model size, parameter count, and classification performance. As a result, we proposed a deep learning-based method for detecting citrus HLB. This method has a small model parameter count, low computational cost, fast detection speed, and high detection accuracy. It can be deployed on edge devices or other embedded devices. This method has a small model parameter count, fast detection speed, and high accuracy. The classification task is achieved by training the overall feature extraction network and the classification network at the network's tail on the constructed training set. The actual detection results show that the detection accuracy for healthy citrus leaves reaches 99.02%, and for HLB-infected leaves, the detection accuracy reaches 99.07%. The overall accuracy is 99.04%. Both recall and precision rates are excellent, meeting the precision requirements for on-site detection.
Convective Dynamics of Swarming Enzymatic Nanomotors
Authors: Sánchez Samuel , Chen Shuqin , Peetroons Xander , Bakenecker Anna , Lezcano Florencia , Aronson Igor 
Published: March 2024

Abstract

Enzymatic nanomotors harvest kinetic energy through the catalysis of chemical fuels. When a group of self-propelled nanomotors is placed in a fuel-rich environment, they assemble into ordered groups and exhibit intriguing swarming behaviors akin to the self-organization observed in bacterial colonies, bioconvection of aerobic microorganismal suspensions, and the coordinated movements of fish, ants, and birds. This swarming behavior presents numerous advantages compared to individual nanomotors, including expanded coverage and prolonged propulsion duration. However, the physical mechanisms underlying the swarming have yet to be fully elucidated. Our study investigates the formation of enzymatic swarms using experimental analysis and computational modeling. We show that the directional movement of enzymatic nanomotor swarms is due to their solutal buoyancy. We investigated various factors that impact the movement of nanomotor swarms, such as particle concentration, fuel concentration, fuel viscosity, and vertical confinement. We examine the effects of these factors on swarm self-organization to gain a deeper understanding. In addition, the urease catalysis reaction produces ammonia and carbon dioxide, accelerating the directional movement of active swarms in urea compared with passive ones in the same conditions. The numerical analysis agrees with the experimental findings. Our findings are crucial for the potential biomedical applications of enzymatic nanomotor swarms, ranging from enhanced diffusion in bio-fluids and targeted delivery to high- efficiency cancer therapy.
A study on urban street garbage detection based onYOLOX
Authors: Li Lingqiao , Zhang Hongwei , Lu Feng , Wang Zhuojian , Lu Huan 
Published: March 2024

Abstract

Urban street garbage detection is a pressing issue for governments at all levels. The traditional manual detection method isbecoming inadequate to keep up with the demands of urban expansion. We must implement a more innovative approach toreplace it. Swift progress in object identification technology has provided a new method for identifying litter on city streets. Theexisting street waste detection is hindered by inadequate datasets and visual features that do not align with the semanteme.There is still a significant gap to cover in utilizing target detection technology for street litter detection. As a result, weproposed a new approach to tackle deficiencies in the dataset for detecting waste in urban streets. It includes a technique forexpanding data that can assist in resolving the problem of an incomplete dataset. In addition, we have introduced a ContextualTransformer (CoT) module based on YOLOX-S, which enables us to harmonize the disparity between visual characteristicsand semantics. By enhancing the aforementioned techniques, our approach achieves a 9% increase in detection accuracycompared to the original YOLOX-S on the identical dataset. Ultimately, it attains a precision of 92.82% in the mean AveragePrecision (mAP) statistic.
Landslide Susceptibility Mapping using Geospatial framework -A Study of Pinder Basin, Chamoli District, Uttarakhand, India
Authors: Naik Deepa D , Palakuzhiyil Yadu 
Published: March 2024

Abstract

The study of the landslides in the Himalayan catchment is crucial due to adverse climatic conditions, increasing anthropogenic activities, and infrastructure management. The Pinder valley falls under Uttarakhand's seismic zone (IV & V). There are many proposed and operational hydropower stations across the catchment, which is increasing the risk of landslides. The widespread digging and tunnelling in the mountain scape make the area vulnerable. As per the Geological Survey of India, villages around Pinder witness many landslides every year. Due to such hydropower projects, landslides have increased (Matu Jansangthan et al., 2016). There are various techniques to study landslides here in this work, a GIS-based spatial multicriteria method and statistical model used to create the Landslide hazard zonation Map (LZM) for the Pinder Basin. This paper considered nine causative elements: slope angle, slope aspect rainfall, lithology, land use land cover and fault, lineament, road, and stream buffer. In this research, considering the historical landslide inventory of Geological survey of India, it is investigated that 20% of the area (GIS-based AHP model) is highly vulnerable and susceptible to landslides. The frequency ratio (Statistical Model) shows around 32% of the area is susceptible to high landslides, which are majorly observed nearer to the mainstream. Studying landslides is essential for stakeholders, policymakers, the landslide hazard zonation map are influential, proactive decision-making tools that prevent economic losses and property damage. Utilizing such models can enhance their resilience and longevity. It also plays a vital role in ecological restoration, sustainable land management, and planning.
Urban Predatory Birds: Unveiling the impact of urban variables on Diversity and Guild Structure in a Neotropical Megacity
Authors: Casas Daniel Augusto Montejo , Parrado-Rosselli Ángela 
Published: March 2024

Abstract

Urbanization poses a considerable threat to biodiversity and ecosystem services, particularly affecting fauna through loss of connectivity, habitat reduction, and limited green spaces. In response, the concept of eco-urbanism has emerged, introducing contrasting urbanization models advocating either a compact city with intensive land use and limited green spaces, coupled with conservation in peri-urban natural areas, or a dispersed city with a large, occupied area, coexisting with natural/green areas. Focusing on predatory birds in Bogotá, a Latin American mega-city, this study evaluates their response to urban variables along a forest (peri-urban)-urban gradient. We evaluated how gray urban variables (e.g. construction index, occupancy index, urban compactness) and green urban variables influence predatory bird diversity and guild structure. Our findings revealed contrasting impacts of gray infrastructure, with construction and occupancy indexes negatively affecting bird communities. Positive influences on diversity and abundance were associated with native plant species richness, herbaceous vegetation, and less impervious surface, particularly in green areas. Interestingly, migratory species exhibited positive associations with gray urban variables, potentially attracted by warmer urban temperatures and increased artificial lighting. The study highlights the contrasting impacts of the "land sparing" and "land sharing" models, indicating that neither inherently supports predatory bird communities. Instead, the effective influence and management of specific variables will be crucial for sustaining predatory bird communities within urban landscapes. This research contributes valuable insights into urban planning practices to support diverse predatory bird communities in megacities, highlighting the significance of studying trophic guilds as sensitive indicators of environmental changes.
Predicting graft survival in paediatric kidney transplant recipients using machine learning
Authors: Aksoy Gulsah Kaya , Akçay Hüseyin Gökhan , Arı Çağlar , Adar Mehtap , Koyun Mustafa , Çomak Elif , Akman Sema 
Published: March 2024

Abstract

Identification of factors that affect graft survival in kidney transplantation can increase graft survival and reduce mortality. Artificial intelligence modelling enables an impartial evaluation of clinician bias. The objective of this study is to examine the factors that affect the survival of grafts in paediatric kidney transplantation through the use of machine learning.
Methods
A retrospective review was conducted on records of paediatric patients who underwent kidney transplantation between 1994 and 2021 and had a post-transplant follow-up of more than 12 months. The nearest neighbour method was used to impute missing fields from a total of 48 variables in the dataset. Models including Naive Bayes, logistic regression, support vector machine (SVM), multi-layer perceptron and XGBoost were trained to predict graft survival. The study used 80% of the patients for training and the remaining 20% for testing. The modelling's success was evaluated based on the accuracy and F1 score metrics.
Results
The study analysed 465 kidney transplant recipients. Of these, 56.7% were male. The mean age at transplantation was 12.08 ± 5.01 years. Of the kidney transplants, 73.1% (n = 339) were from living donors, 34.5% (n = 160) were pre-emptive transplants, and 2.2% (n = 10) were second-time transplants. The machine learning model identified several features associated with graft survival, including antibody-mediated rejection (+ 0.7), acute cellular rejection (+ 0.66), eGFR at 3 years (+ 0.43), eGFR at 5 years (+ 0.34), pre-transplant peritoneal dialysis (+ 0.2), and cadaveric donor (+ 0.2). The successes of the logistic regression and SVM models was similar. The F1 score was 91.9%, and the accuracy was 96.5%.
Discussion
Machine learning can be used to identify factors that affect graft survival in kidney transplant recipients. By expanding similar studies, risk maps can be created prior to transplantation.
Surface Passivation of Carbon Dots for Tunable Biological Performance
Authors: Mikhail Mary M. , Ahmed Hanan B. , Abdallah Amira E. M. , El-Shahat Mahmoud , Emam Hossam E. 
Published: March 2024

Abstract
Recent investigations were shifted this trend toward exploring the biomedical applicability of CDs, relevant to chronic diseases. Herein, a systematic approach is demonstrated for studying tuning of optical character and biological performance for different surface passivated CDs. Alginate and pectin were successfully clustered oxygen-surface passivated CDs, while, chitin was used to nucleate nitrogen-surface passivated CDs. Pectin-treated with base (4.1 ± 1.8 nm) and chitin-treated acid (3.5 ± 1.7 nm) were ingrained the smallest O-surface passivated CDs and N-surface passivated CDs, respectively. However, N-surface passivated CDs were shown with the highest optical activity. CDs colloids prepared from alginate, pectin & chitin, resulted in reduction of tumor cell viability percentage to be 80.8%, 74.0% & 69.0% respectively. O-surface passivated CDs nucleated from alginate showed the highest anti-proliferative effects. Moreover, O-surface passivated CDs (from alginate) showed the supremacy in inhibition of inflammation, while, increasing of its concentration ten times resulted in significant increment in inhibition percent to be 28% & 42%, using 1 μg/mL & 10 μg/mL, respectively. So, O-surface passivated CDs (from alginate) can be applied as concurrent anti-inflammatory/antitumor drug, so as a potential therapeutic for treatment of inflammation, in production of vaccines, immune-therapeutics, and immune-suppressive drugs.
A Novel Cuckoo Search-Based Optimized Deep CNN Model for Phishing Attack Detection in IoT Environment
Authors: Gupta Brij B. , Gaurav Akshat , Attar Razaz Waheeb , Arya Varsha 
Published: March 2024

Abstract
There is a serious risk to information privacy and security in the IoT ecosystem from phishing attacks. This research offers a unique strategy for increasing phishing attack detection by employing the Cuckoo Search algorithm to tune hyperparameters in a deep Convolutional Neural Network (CNN) model. Our suggested approach is very effective, with a 90\% success rate in detecting phishing attacks. To distinguish between legal and fraudulent IoT network activity, we systematically tune hyperparameters using Cuckoo Search to optimise the deep CNN model's performance. This study helps ensure the safety of sensitive information and IoT devices by providing a novel and effective approach for real-time phishing detection.
A comparison of feed forward and recurrent neural networks in time series forecasting
Authors: charef charef Fahima 
Published: March 2024

Abstract
Exchange rate prediction has been treated as a key research matter in international finance because it’s a crucial and an important subject. For this reason, many researchers have sought to develop an accurate technique to forecast series of exchange rates. In this same context, this paper targets at making a comparison between two neural networks architectures, such as RNN( Recurrent Neural Networks) and FFNN (Feed Forward Neural Networks), to forecast the series of exchange rates as USD/NOK, USD/EUR for a daily frequency extending from november 2017 throught november 2022 either 1305 observations. In order to evaluate and compare the quality and performance of these approaches, we have resorted to MSE( Mean Square Error).
The back propagation method is used as the basis for developing algorithms capable of forecasting the series of exchange rates. To our knowledge, this is the only paper that focuses on the comparison between two techniques of neural networks, such as RNN and FFNN for predicting exchange rates.
The results clearly show that the recurrent neural network approach (RNN) yielded the highest prediction accuracy, and it’s more robust and more efficient in forecasting compared to the Feed Forward Neural Network model (FFNN). It’s, therefore, the most recommended.
Exploring the Dimensions of Mental Healthcare Accessibility for Vulnerable Migrant Groups and Actions to Improve Access: A Qualitative Study Conducted in Munich, Germany
Authors: Baierl Sophia , ÖCEK ZELİHA ASLI , Jung-Sievers Caroline , Coenen Michaela 
Published: March 2024

Abstract

Background: The experience of migration is often associated with stressors that affect mental health. Furthermore, migrants face barriers to accessing mental healthcare. This study aims to explore the dimensions influencing mental healthcare access for migrants in Munich, Germany and to develop recommendations for action.
Methods: The study used a two-phase qualitative approach. Phase 1 included individual interviews with 24 migrants from three vulnerable groups (students, refugees, and LGBTQ+ people). Based on the data gathered, seven interviews with health professionals experienced in mental health services for migrants were conducted in Phase 2. The framework from Levesque et al. was applied for analyzing and conceptualization of the dimensions of healthcare access. The health professionals’ proposed actions were grouped based on their respective levels as macro, meso, and micro. Validation was achieved by reviewing the data analysis during a meeting attended by all authors and a professional who was not involved in the interviews.
Results: The dimensions of mental healthcare access encompassed: 1) ability to perceive mental problems, including stigma and knowledge about mental health; 2) ability to seek care, encompassing knowledge about the new healthcare system and social support; 3) acceptability of services, involving provider identity and gender; 4) availability and affordability, including insurance coverage, bureaucratic processes, and capacity and geographical distribution of services; 5) appropriateness including providers’ and patients’ understanding of mental healthcare, and providers’ competence. Language and culture exhibited a strong interplay across all dimensions. The analysis yielded 17 action recommendations. Macro-level recommendations target barriers caused by discrimination and inequality. Meso-level recommendations included increased care capacity and coordination and eliminating language and culture barriers in health services. The micro-level recommendations included activities to promote mental health.
Conclusion: Migrants face numerous barriers to mental healthcare due to health system and providers in Munich. However, culture and language remain the most important access factors, necessitating social support. Actions pertaining to acceptability, accessibility, affordability, and appropriateness of health services are required to ensure that all individuals, including migrants, have access to mental healthcare. However, improving migrant mental health begins with the removal of structural barriers created by discrimination and inequality at the macro level.
Zfp36l1b is a marker of poor prognosis in Hepatocellular carcinoma suppression metastasis
Authors: Chen Jing , Cheng Wenhua , Chen Pinggui , Li Yaoxuan , Liu Ting , Liu Jingmei , Chen Yongjing , Li Gaopeng , Zhang Bo , Gao Zefeng 
Published: March 2024

Abstract
Background & Aims: Given the pivotal role of Zfp36l1b in angiogenesis, proliferation and migration of endothelial cells, we aimed to explore its role in HCC metastasis.
Methods: Zfp36l1b expressions in HCC tissues and cells were assessed by qRT-PCR, western blot and immunohistochemistry. The effect of Zfp36l1b on HCC metastasis was studied in vitroand in vivo.
Results: Zfp36l1b was frequently down-regulated in HCC and its expression level was significantly associated with aggressive clinicopathological features and overall and disease-free survival of HCC patients after hepatectomy. Loss of Zfp36l1b markedly promoted HCC cells invasion and migration in vitro, and facilitated tumor metastasis in vivo.
Conclusions: Zfp36l1b contributes to the development and progression of HCC as an anti-oncogene and may serve as a valuable prognostic marker for HCC patients.
A Novel amino acid metabol-Related Gene Signature to Predict Overall Survival of Esophageal Squamous Cell Carcinoma
Authors: Xu Kebing , Lin Zhizhong , Lin Yijin , Shen Yongshi , Chen Juhui , Chen Yuanmei , Xu Yuanji , Xin Yong 
Published: March 2024

Abstract
Esophageal squamous cell carcinoma (ESCC) is known as low early diagnosis rate and poor prognosis survival. Effective prognostic markers are urgently required to predict outcome for ESCC treatment. Accumulating evidence supports a strong relationship among cancer cell growth and amino acid metabolism. This work focused on the comprehensive evaluation of the association between amino acid metabolism-related gene (AAMRGs) expression profiles and prognosis in ESCC patients based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). 2 AAMRGs (BCAT1 and MMACHC) were selected to develop a novel AAMRGs -related gene predictor for 1-year and 2-years prognostic risk in ESCC patients. Both BCAT1 and MMACHC expression were verified by RT-qPCR. Prognosis nomogram by incorporating clinical factors and BCAT1 and MMACHC gene expression was constructed and the calibration plots illustrated good prognostic performance. In summary, the AAMRGs signature presented in our study is efficient and holds potential for clinical use in forecasting the prognosis of ESCC.