bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Silent Struggle: Nursing Students' Experiences related to barriers to speaking up for Patient Safety: a qualitative study
Authors: Vahidi Maryam , Rahmani Farnaz , Areshtanab Hossein Namdar , Allahyari Zahra 
Published: October 2024

Abstract

Background
Patient safety is a significant concern in healthcare settings, and speaking up for patient safety is essential for promoting quality of care and preventing harm. However, nurses, including nursing students, may face barriers to speaking up, which can hinder their ability to report safety concerns.In this study, we explored the nursing students’ experiences related to the barriers to speaking up for patient safety.
Methods
The study was conducted using a qualitative research design. Data were collected by conducting semi-structured interviews with 19 final-year nursing students from April 2023 and June 2023 in Iran. Data were analyzed by conventional content analysis until data saturation was achieved.
Results
This study has revealed the formation of three distinct themes, namely negative organizational culture,inadequate preparation for speaking up for patients' safety and inconsistencies between learning through theories and learning through practice. These themes are comprised into eight subthemes. The main themes of the study showed the significant barriers to speaking up about patients’ safety. These barriers can lead to critical issues being overlooked, potentially harming patients. When healthcare staff feel unable to speak up, errors and unsafe practices may remain unreported.
Conclusion
Research findings revealed barriers to speaking up in organizational settings, including fear of retaliation, lack of confidence, and perceived futility. These barriers often prevent employees from voicing their concerns or suggestions. By understanding the underlying barriers to speaking up for patient safety, healthcare professionals can develop strategies to promote a culture of safety. They can also empower nursing students to become effective advocates for patient safety. This includes providing training on effective communication and assertiveness skills, encouraging a culture of open dialogue, and building trusting relationships between students and healthcare providers.
Setting a Global Climate Finance Goal to Reflect Economic Realities: a New Equation With Deep Learning
Authors: O'Callaghan Brian 
Published: October 2024

Abstract

Developed countries committed to financially support developing countries under the 1992 United Nations Framework Convention on Climate Change and in 2009 pledged $100 billion per year by 2020. A New Collective Quantified Goal for finance must be agreed by 2025 and potentially revised thereafter. In this study, all submissions to the negotiation process and negotiation transcripts are analysed with deep learning to uncover financial concepts overlooked in discussions, including discounting to recognize the time value of money and a clawback mechanism to recover undelivered finance. A formula for the new commitment is derived and applied to 11,875 scenarios, suggesting a justifiable commitment of $0.3-9.5Tn per year ($1.7Tn median) across scenarios or $0.4-3.9Tn per year ($1.4Tn median) if funding ramps to 2030 and ends in 2040. The findings offer a data-driven foundation to guide future climate finance negotiations, ensuring they are grounded in economic realities and aligned with the pressing need for climate progress.
A Mathematical approach to the role of Chemical reactions and Temperature-Dependent fluid properties of non-Newtonian Ree-Eyring fluid
Authors: Sanil Prathiksha , Gudekote Manjunatha , Choudhari Rajashekhar , Madhavi M R , Vaidya Hanumesh , Prasad Kerehalli Vinayaka 
Published: October 2024

Abstract

The current study highlights a novel exploration of the peristaltic transport of Ree-Eyring fluid within a non-uniform channel. The impact of Temperature-dependent fluid properties and variable liquid characteristics are considered in the analysis. The flow problem is developed mathematically by considering momentum, energy and mass transfer equations. Slip conditions are imposed on the channel walls. Low Reynolds numbers and long-wavelength approximations make the nonlinear governing equations simpler. The nonlinear governing equations are normalised using suitable dimensionless parameters, and a regular perturbation technique is used to acquire the solutions. Using MATLAB R2023a, the impact of critical physical parameters (such as velocity, temperature, concentration, chemical reactions, and streamlines) is graphically displayed.The investigations reveal that the variable liquid properties significantly influence the physical parameters of interest. The study shows that variation in variable viscosity enhances the velocity profiles and diminishes the velocity profiles in the case of variable thermal conductivity. These results shed insight into the understanding of flow characteristics of biological fluids and industry.
Nanoplasmonic SERS on Fidget Spinner for Digital Bacterial Identification
Authors: Cho Yoon-Kyoung , Karmacharya Mamata , Michael Issac , Han Jiyun , Clarissa Elizabeth Maria , Gulenko Oleksandra , Kumar Sumit 
Published: October 2024

Abstract

Raman spectroscopy offers non-destructive and highly sensitive molecular insights into bacterial species, making it a valuable tool for detection, identification, and antibiotic susceptibility testing. However, achieving clinically relevant accuracy, quantitative data, and reproducibility remains challenging due to the dominance of bulk signals and the uncontrollable heterogeneity of analytes. In this study, we introduce an innovative diagnostic tool: a plasmonic fidget spinner (P-FS) integrated with a membrane substrate modified with a nanoplasmonic-enhanced matrix, designed for simultaneous bacterial filtration and detection. We developed a method to fabricate a plasmonic microarray patterned nitrocellulose membrane using photolithography, which is then integrated with a customized fidget spinner. Testing the P-FS device with various bacterial species (E. coli 25922, S. aureus 25923, E. coli MG1655, Lactobacillus brevis, and S. mutans 3065) demonstrated successful identification based on their unique Raman fingerprints. The bacterial interface with nanoplasmonic hotspots on the P-FS significantly enhances sensitivity, allowing for more precise detection, while SERS intensity mappings obtained from the Raman spectrometer are transformed into digital signals using machine learning techniques to identify and quantify bacterial distribution. Given the P-FS's ability to enhance vibrational signatures and its scalable fabrication under routine conditions, we anticipate that nanoplasmonic-enhanced Raman spectroscopy will become the preferred technology for the reliable and ultrasensitive detection of various analytes, including those crucial to human health, with strong potential for transitioning from laboratory research to clinical applications.
Dynamic Normal Stress Determination at the Rigid Sphere-Elastic Half-Space Interface: A Particle Dynamics Approach
Authors: Xie Hong , Zeng Zhiping , Su Miao , Luo Jun , Ge Hao , Dai Gonglian 
Published: October 2024

Abstract

The Hertz theory provides a foundation for understanding the contact behavior between elastic bodies. However, the theory's reliance on static equilibrium assumptions limits its applicability to dynamic scenarios, particularly where the contact interface experiences rapid changes in stress due to impact or vibration. This paper addresses the gap in the literature regarding the dynamic normal stress at the contact interface between a rigid sphere and an elastic half-space based on the theory of particle dynamics. Our approach begins by establishing a system of particle dynamics equations for the interface contact zone. By decoupling these equations based on the lateral and tangential motion characteristics of the particles, we derive a unified normal dynamics equation that describes the relative relationship between the normal strain of each particle and the center point of the interface. Utilizing the dynamic equation of the interface collision center and the motion equation of the small ball, we determine the absolute values of the normal strain of each particle in the interface contact zone. The research results demonstrate that the lateral and tangential strains (forces) prior to collision do not directly affect the sphere motion in the normal direction, while the incremental strains (forces) post-collision significantly do. By analyzing the normal dynamic equations of the contact zone, we obtain the lateral stress increment, tangential stress increment, and the distribution law of normal stress for each particle at the interface. In conclusion, the normal strain values obtained using our proposed method, when compared with those from Hertz theory at the maximum collision depth, are significantly greater. This indicates that our method is more effective in explaining contact interface damage phenomena, which are not adequately addressed by Hertz theory. The findings have implications for understanding and predicting contact interface failure in engineering applications, particularly under dynamic loading conditions.
ChavacanoMT: A Corpus and Evaluation of Neural Machine Translation for Philippine Creole Spanish
Authors: Vicente Aileen Joan , Amamampang Theresse Faith , Lahaylahay Dunn Dexter , Cheng Charibeth 
Published: October 2024

Abstract

Chavacano, formally referred to as Philippine Creole Spanish, is the only Creole spoken in the Philippines. Like many languages, especially Creoles, computational studies on Chavacano are scarce because of the dearth of available corpora. This paper describes the creation of ChavacanoMT, a benchmark corpus for the machine translation study of Philippine Creole Spanish. ChavacanoMT consists of 767,053 parallel sentences between Chavacano and related languages, Spanish, Cebuano, Hiligaynon, Tagalog, and English. It is sourced from scraped bible translations and articles on the Jehovah’s Witness website.
This paper also presents the performance of a multilingual neural machine translation model generated using ChavacanoMT. We report an overall 17 BLEU score on a fine-tuned mT5 model, outperforming an mT5-based model trained from scratch. Our experiments show that ChavacanoMT can generate models on par with a similar system that translates between English and some Philippines languages despite having fewer sentence samples used in training. We also report an improved Chavacano translation to and from its related languages that can be used as benchmark data. In particular, we highlight more than 20 BLEU points of improvement in the translation between Chavacano and English.
The study opens avenues for exploring cross-linguistic interactions of Chavacano and its related languages in its translation that may benefit other low-resource languages.
Wavelet-Based Compressed Sensing of the System Matrix for Magnetic Particle Imaging
Authors: Aydın Vildan Atalay , Möddel Martin , Knopp Tobias 
Published: October 2024

Abstract

Magnetic particle imaging (MPI) is a trending tracer imaging technique relatively newly proposed. In MPI, a time-consuming system matrix calibration scan is required to reconstruct the spatial distribution of superparamagnetic nanoparticles. Compressed sensing (CS) techniques are widely utilized to reduce the time required for system matrix calibration, with Discrete Cosine Transform (DCT) being the state-of-the-art sparsifying transform. This paper proposes a wavelet-based CS method for system matrix reconstruction. To the best of our knowledge, this is the first work to employ wavelets as the sparsifying transform for system matrix recovery in MPI. To this goal, we employ biorthogonal wavelet transform to sparsify the system matrix rows. We propose a variant of the iterative shrinkage/thresholding-based algorithm for system matrix reconstruction with the help of bivariate shrinkage to take advantage of the multiscale nature of wavelets and the dependency between wavelet subband coefficients. The performance of the proposed method is assessed by phantom image reconstructions using the state-of-the-art Kaczmarz algorithm. Experimental results comparing the DCT and the proposed wavelet-based methods for the sparsity levels of several system matrix frequencies and the obtained reconstructions of system matrix and phantom images for different sparsity levels confirm that the proposed method outperforms the state-of-the-art DCT-based compression.
Emotion Recognition on Speech using Hybrid Model CNN and BI-LSTM Techniques
Authors: Rani Rashmi , Ramaiya Manoj Kumar 
Published: October 2024

Abstract

Speech emotion recognition is critical for many applications such as human-computer interactions and psychological analysis. Due to the inability of conventional models to capture the subtle nuance of emotional speech variations, the identification process is less effective. The development of a new hybrid model in this study presents a solution to address this problem through combining the Convolutional Neural Networks and Bidirectional Long Short-Term Memory. The combination of feature extraction and temporal context abilities is a unique value for the model. The study model led to outstanding performance reached 98.48% accuracy, 97.25% precision, 98.29% recall, and an F1-Score of 97.39%. The latter performance surpassed those of other models such as PNN model 95.56%, LSTM model 97.1%, 1-D DCNN model 93.31%, GMM model 74.33%, and Deep Learning Transfer Models 86.54%. The developed hybrid model can accurately detect and classify emotions and speech and can effectively work in real applications.
Chromosomal analysis and short-term outcome of prenatally diagnosed complex congenital heart disease
Authors: Verbeke Marcellino , Hannes Laurens , Devriendt Koen , Bogaert Kris Van den , Cools Bjorn , De Catte Luc , Gewillig Marc , Breckpot Jeroen 
Published: October 2024

Abstract

Congenital structural heart disease (CHD) is the leading cause of infant death from birth defects. Postnatal survival primarily depends on the type and severity of the defect. In addition, worse cardiac prognosis is observed when extra-cardiac anomalies (ECA) are associated. This retrospective chart review was aimed at finding markers for short-term outcome prediction of prenatally-diagnosed complex CHD, focusing in particular on the impact of CHD category, of CHD severity score and of prenatal or postnatal diagnosis of ECA or chromosomal anomalies on 4 primary outcomes: termination of pregnancy (TOP), intrauterine fetal demise, neonatal mortality and 1-year-survival rate. We reviewed medical files from 381 fetuses, presenting at our center between 2018 and 2021 with CHD for which prenatal advice by a pediatric cardiologist was sought. 341 fetuses met the inclusion criteria for the study. Twin pregnancies (7.62%; OR 4.76 (p<0.001)) and pregnancies resulting from assisted reproductive technology (7.33%; OR 2.44 (p<0.001)) were more prevalent compared to the general population. CHD categories and CHD severity scores, ranging from A (extremely high risk) to D (low risk), were assigned to each fetus. Prenatal or postnatal chromosomal microarray results were available for 232 fetuses (68%) and were abnormal in 30 (12.9%). Logistic regression analysis was used to determine significant predictors for the 4 primary outcomes. TOP was carried out significantly more with: prenatal genetic diagnosis (p<0.001), prenatal extracardiac symptoms (p<0.001), severity score A (p=0.045) and B (p=0.036). Survival was significantly lower with: prenatal extracardiac symptoms (p=0.002), prematurity (p=0.046), severity scores A (p=0.009) and B (p=0.004), and with univentricular heart. CHD severity score, chromosomal anomalies and ECA are determining factors for continuation of the pregnancy, for survival and for overall mortality, underscoring the importance of prenatal ultrasound and genotyping for prognostication of fetuses with CHD.
Electrostatic anti-spring-enhanced MEMS accelerometer with auto-tuning capability
Authors: LIU Huafeng , Wang Chen , Yuan Yao , Shojaeian Milad , Sadeghpour Sina , Wang Yuan , Gao Hang , Dou Jin-Hu , Zhang Pan , Martins Rui P. , Mak Pui-In 
Published: October 2024

Abstract

This paper presents a MEMS accelerometer featuring an auto-tuning system based on an electrostatic anti-spring mechanism. The accelerometer's sensitivity can be automatically adjusted to modify its measurement range and bandwidth in response to varying acceleration inputs. Comprehensive theoretical analysis and experimental validation were performed, demonstrating strong agreement between experimental results and theoretical predictions. As the acceleration input increased, the sensitivity of the MEMS accelerometer with the auto-tuning system increased, while its bandwidth decreased. Furthermore, this new design exhibited significantly higher sensitivity for a given acceleration input compared to the conventional design. Specifically, the sensitivity of the new design at 15.4 V actuation voltage was measured at 39.43 V/g at 0 g acceleration, which is 30.80 times greater than that of the conventional design (1.28 V/g). The spring constant was reduced from its original mechanical value of 41.0 N/m to 1.38 N/m at 0 g acceleration. Additionally, the noise floor and dynamic range of the new design are 279 ng/ √Hz at 100 Hz (30.9 times higher than that of the conventional design) and 157 dB (30 dB higher than that of the conventional design), respectively.
Tumour sampling conditions perturb the metabolic landscape of clear cell renal cell carcinoma
Authors: Frezza Christian , Yong Cissy , Schmidt Christina , Yang Ming , von Kriegsheim Alex , Warren Anne , Anand Shubha , Armitage James , Riddick Antony , Mitchell Thomas , Patil Vishal , Saeb-Parsy Kourosh , Vanharanta Sakari , Stewart Grant 
Published: October 2024

Abstract

Human isotopic tracer studies are fast becoming the gold standard model to study cancer metabolism in vivo. Analysed tissues are typically retrieved after surgical resection, which exposes them to a variable extent of warm ischaemia. Although standardised protocols are emerging, the effects of sampling conditions on the tissue metabolome remain understudied. Here, we perform a 13C-glucose study coupled with metabolomic, transcriptomic, and proteomic profiling in patients with clear cell renal cell carcinoma (ccRCC) to assess the metabolic profile of tissues sampled intraoperatively, when blood supply is intact, versus post-surgical resection, which exposes the tissue to ischaemia. We show that ischaemia significantly affects the metabolic landscape of ccRCC, masking some critical features, such as suppressed gluconeogenesis. Furthermore, in orthotopic ccRCC-derived xenografts, we identified that prolonged exposure to ischaemia disrupts the tissue metabolome stability. Therefore, minimising tissue ischaemia is pivotal in accurately profiling cancer metabolism in these important and resource-intense patient studies.
Integrative Learning Design Innovation in Islamic Religious Education Subjects in improving graduate competency through an Independent Curriculum
Authors: Hosaini  , Fitri Agus Zaenul , Badruzaman Abad 
Published: October 2024

Abstract

Objective
This research aims to explore integrative learning design innovations in Islamic Religious Education (PAI) subjects in order to increase graduate competency through the implementation of the Independent Curriculum.
Theoretical Framework:
In this topic, the main concepts and theories underlying the research are presented. The theory used is rooted in the concept of integration of general knowledge and religious values, which is supported by constructivist learning theory and a holistic approach. This approach allows the combination of cognitive, affective and psychomotor skills by prioritizing problem, project and inquiry-based learning experiences that are in line with the principles of Islamic education.
Method
The research method used was classroom action research which was carried out in a number of secondary schools that implemented the Merdeka Curriculum in PAI learning. Data collection techniques include observation, interviews, questionnaires, and document analysis. Data was analyzed qualitatively to identify patterns and impacts of learning design innovation on graduate competencies.
Results and Discussion
The research results show that integrative learning design through the Merdeka Curriculum significantly increases graduate competency, both in terms of understanding religious material, critical thinking skills, creativity and social skills. The discussion highlights the success of this approach in balancing mastery of religious knowledge with the development of 21st century skills needed in the era of globalization
Research Implications:
The implications of this research show that the application of integrative learning design supports the achievement of educational goals that are more relevant to the needs of the times.
Originality/Value:
The originality of the research lies in the application of an integrative learning model in the context of Islamic religious education in Indonesia, which is rarely discussed in previous research, as well as the emphasis on synergy between religious and general competencies through the Merdeka Curriculum.
Energy state guides reward seeking via an extended amygdala to lateral hypothalamus pathway
Authors: Rossi Mark , Shrivastava Kuldeep , Athreya Vikshar , Lu Yi , Han Ashley , Luis-Islas Jorge , Kowalski Tess 
Published: October 2024

Abstract

Impaired regulation of food intake underlies numerous health problems, including obesity and type 2 diabetes, yet how brain systems controlling reward seeking become dysregulated to promote overeating is unknown. Glutamatergic neurons of the lateral hypothalamic area (LHA) are thought to act as a brake on feeding, which is dysregulated during diet-induced obesity. These neurons receive input from the extended amygdala, including the bed nucleus of the stria terminalis (BNST). However, the circuit mechanisms underlying the ability of this pathway to control feeding behavior and how they contribute to dysregulated eating are unclear. Here, we discover that the BNST→LHA pathway promotes reward seeking in an energy state-dependent manner by combining optogenetics, in vivo multiphoton calcium imaging, and electrophysiology. Synaptic strength and neuronal function within the BNST→LHA pathway are dynamically regulated according to energy state to guide reward seeking. These findings suggest that humoral factors modulate the function of the BNST→LHA pathway to align food seeking with current energy needs.
A hybrid Satin Bower Bird Optimization and SVM for Wine Quality Prediction
Authors: P SUNDARAVADIVEL , V UmaRani , J JebamalarTamilselvi , RN Karthika , J Deepa 
Published: October 2024

Abstract

Nowadays, wine is being used extensively, and the industry is rapidly growing. Many wine industries are promoting their products to compete with the wine sector because it generates more revenue than other industries. Every wine industry aspires to make the best possible wines. Industries have started to discover the ideal combination of elements to balance wine quality by using historical and current data. Because of how well machine learning approaches work at revealing insights from both past and current data to improve decision-making in a variety of real-world issues and applications, such as those in the medical, educational, industrial, etc. To produce high-quality wine that will compete in the market, this study investigates how machine learning algorithms can assess data on wine in order to form inferences that will aid decision-makers in selecting the proper combination and amount of ingredients. This research work proposes a new hybrid Satin Bower Bird Optimization and Support Vector Machine (SBO-SVM) for wine quality prediction. Initially the collected data set is balanced by SMOTE ENN and utilize SVM for prediction. The performance of SVM is improved by SBO meta heuristic algorithm. The Experimental result shows that the hybrid SBO-SVM provide 95.8% accuracy which is higher than SVM without meta heuristic optimization.